Orchid Bee Analyses
Asia Kaiser
2023-06-19
Loading Packages
library(vegan)## Loading required package: permute## Loading required package: lattice## This is vegan 2.6-4library(ggplot2)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(tidyr)
library(lme4)## Loading required package: Matrix##
## Attaching package: 'Matrix'## The following objects are masked from 'package:tidyr':
##
## expand, pack, unpacklibrary(lmerTest)##
## Attaching package: 'lmerTest'## The following object is masked from 'package:lme4':
##
## lmer## The following object is masked from 'package:stats':
##
## steplibrary(iNEXT)
library(ggthemes)
library(report)
library(ggiraphExtra)##
## Attaching package: 'ggiraphExtra'## The following object is masked from 'package:ggthemes':
##
## theme_cleanlibrary(sjPlot)## Learn more about sjPlot with 'browseVignettes("sjPlot")'.library(RColorBrewer)
library(MASS)##
## Attaching package: 'MASS'## The following object is masked from 'package:dplyr':
##
## selectReading in the data
bee.data.raw <-read.csv("Orchid Bee Data - Collections.csv")
summary(bee.data.raw)## trap_num treatment date time_start
## Min. :1.000 Length:93 Length:93 Length:93
## 1st Qu.:3.000 Class :character Class :character Class :character
## Median :5.000 Mode :character Mode :character Mode :character
## Mean :4.301
## 3rd Qu.:6.000
## Max. :6.000
## time_end site spec_num order
## Length:93 Length:93 Min. : 1.00 Length:93
## Class :character Class :character 1st Qu.: 4.00 Class :character
## Mode :character Mode :character Median : 8.00 Mode :character
## Mean :10.73
## 3rd Qu.:16.00
## Max. :32.00
## fam genus specie lat
## Length:93 Length:93 Length:93 Min. :10.43
## Class :character Class :character Class :character 1st Qu.:10.43
## Mode :character Mode :character Mode :character Median :10.43
## Mean :10.43
## 3rd Qu.:10.43
## Max. :10.43
## lon elevation canopy Label
## Min. :-84.01 Mode:logical Mode:logical Length:93
## 1st Qu.:-84.01 NA's:93 NA's:93 Class :character
## Median :-84.01 Mode :character
## Mean :-84.01
## 3rd Qu.:-84.01
## Max. :-84.00
## Determination
## Length:93
## Class :character
## Mode :character
##
##
## Data Tidying
#Dataframe with site and treatment data summarized by abundance
bee.data <- bee.data.raw %>%
group_by(treatment,site,date) %>%
summarise(n.obs=n())## `summarise()` has grouped output by 'treatment', 'site'. You can override using
## the `.groups` argument.#A dataframe with grouped days for community analysis
bee.data.grouped <- bee.data.raw %>%
group_by(treatment,site) %>%
summarise(n.obs=n())## `summarise()` has grouped output by 'treatment'. You can override using the
## `.groups` argument.#Visualize new dataframe
bee.data## # A tibble: 12 × 4
## # Groups: treatment, site [6]
## treatment site date n.obs
## <chr> <chr> <chr> <int>
## 1 absent arboleda 6-18-2023 17
## 2 absent arboleda 6-19-2023 5
## 3 absent cafeteria 6-18-2023 4
## 4 absent cafeteria 6-19-2023 3
## 5 absent trail 6-18-2023 9
## 6 absent trail 6-19-2023 2
## 7 present arboleda 6-18-2023 23
## 8 present arboleda 6-19-2023 9
## 9 present cafeteria 6-18-2023 5
## 10 present cafeteria 6-19-2023 4
## 11 present trail 6-18-2023 10
## 12 present trail 6-19-2023 2Tidying and Calculating Bee Diversity Data
#Combining genus and species names into one
#Creating dataframes with bee abundances by species and site
species.data.1 <- bee.data.raw %>%
unite("genus_specie", genus:specie)%>%
unite("treatment_site_date", treatment,site,date) %>%
group_by(treatment_site_date,genus_specie) %>%
summarise(species.obs=n()) ## `summarise()` has grouped output by 'treatment_site_date'. You can override
## using the `.groups` argument.#Pivot dataframes wide to get into correct format for vegan
species.wide <- as.data.frame(pivot_wider(species.data.1, names_from = treatment_site_date, values_from = species.obs, values_fill = 0))
#Create input for iNEXCT
species.iNEXTinput <-species.wide[,-1]
#Running iNEXT to find richness data
Bee_iNEXT <-iNEXT(species.iNEXTinput, q= c(0,1,2), datatype = "abundance")
Bee_iNEXT## Compare 12 assemblages with Hill number order q = 0, 1, 2.
## $class: iNEXT
##
## $DataInfo: basic data information
## Assemblage n S.obs SC f1 f2 f3 f4 f5 f6 f7 f8 f9 f10
## 1 absent_arboleda_6-18-2023 17 6 0.8893 2 1 1 1 0 1 0 0 0 0
## 2 absent_arboleda_6-19-2023 5 3 0.9000 1 2 0 0 0 0 0 0 0 0
## 3 absent_cafeteria_6-18-2023 4 3 0.6250 2 1 0 0 0 0 0 0 0 0
## 4 absent_cafeteria_6-19-2023 3 2 0.8333 1 1 0 0 0 0 0 0 0 0
## 5 absent_trail_6-18-2023 9 4 0.8222 2 0 1 1 0 0 0 0 0 0
## 6 absent_trail_6-19-2023 2 2 0.6667 2 0 0 0 0 0 0 0 0 0
## 7 present_arboleda_6-18-2023 23 3 1.0000 0 1 0 0 0 0 0 0 0 1
## 8 present_arboleda_6-19-2023 9 5 0.8222 2 2 1 0 0 0 0 0 0 0
## 9 present_cafeteria_6-18-2023 5 3 0.7333 2 0 1 0 0 0 0 0 0 0
## 10 present_cafeteria_6-19-2023 4 4 0.1818 4 0 0 0 0 0 0 0 0 0
## 11 present_trail_6-18-2023 10 3 1.0000 0 1 1 0 1 0 0 0 0 0
## 12 present_trail_6-19-2023 2 2 0.6667 2 0 0 0 0 0 0 0 0 0
##
## $iNextEst: diversity estimates with rarefied and extrapolated samples.
## $size_based (LCL and UCL are obtained for fixed size.)
##
## Assemblage m Method Order.q qD qD.LCL
## 1 absent_arboleda_6-18-2023 1 Rarefaction 0 1.000000 1.0000000
## 9 absent_arboleda_6-18-2023 9 Rarefaction 0 4.738914 3.5750907
## 17 absent_arboleda_6-18-2023 17 Observed 0 6.000000 4.2023545
## 26 absent_arboleda_6-18-2023 26 Extrapolation 0 6.791564 4.2926935
## 34 absent_arboleda_6-18-2023 34 Extrapolation 0 7.210755 4.2364074
## 35 absent_arboleda_6-18-2023 1 Rarefaction 1 1.000000 1.0000000
## 43 absent_arboleda_6-18-2023 9 Rarefaction 1 4.127184 2.8954926
## 51 absent_arboleda_6-18-2023 17 Observed 1 4.949177 3.2419351
## 60 absent_arboleda_6-18-2023 26 Extrapolation 1 5.424434 3.4336404
## 68 absent_arboleda_6-18-2023 34 Extrapolation 1 5.696376 3.5358683
## 69 absent_arboleda_6-18-2023 1 Rarefaction 2 1.000000 1.0000000
## 77 absent_arboleda_6-18-2023 9 Rarefaction 2 3.642857 2.4202997
## 85 absent_arboleda_6-18-2023 17 Observed 2 4.313433 2.5637899
## 94 absent_arboleda_6-18-2023 26 Extrapolation 2 4.646518 2.5982697
## 102 absent_arboleda_6-18-2023 34 Extrapolation 2 4.811655 2.6053675
## 103 absent_arboleda_6-19-2023 1 Rarefaction 0 1.000000 1.0000000
## 105 absent_arboleda_6-19-2023 3 Rarefaction 0 2.400000 1.8209993
## 107 absent_arboleda_6-19-2023 5 Observed 0 3.000000 1.7322694
## 110 absent_arboleda_6-19-2023 8 Extrapolation 0 3.175000 0.9727756
## 112 absent_arboleda_6-19-2023 10 Extrapolation 0 3.193750 0.5339315
## 113 absent_arboleda_6-19-2023 1 Rarefaction 1 1.000000 1.0000000
## 115 absent_arboleda_6-19-2023 3 Rarefaction 1 2.273575 1.6569869
## 117 absent_arboleda_6-19-2023 5 Observed 1 2.871746 1.6152277
## 120 absent_arboleda_6-19-2023 8 Extrapolation 1 3.313407 1.2653113
## 122 absent_arboleda_6-19-2023 10 Extrapolation 1 3.473748 0.9984269
## 123 absent_arboleda_6-19-2023 1 Rarefaction 2 1.000000 1.0000000
## 125 absent_arboleda_6-19-2023 3 Rarefaction 2 2.142857 1.5153988
## 127 absent_arboleda_6-19-2023 5 Observed 2 2.777778 1.5472332
## 130 absent_arboleda_6-19-2023 8 Extrapolation 2 3.333333 1.3916019
## 132 absent_arboleda_6-19-2023 10 Extrapolation 2 3.571429 1.2611975
## 133 absent_cafeteria_6-18-2023 1 Rarefaction 0 1.000000 1.0000000
## 134 absent_cafeteria_6-18-2023 2 Rarefaction 0 1.833333 1.4417296
## 136 absent_cafeteria_6-18-2023 4 Observed 0 3.000000 1.5966698
## 138 absent_cafeteria_6-18-2023 6 Extrapolation 0 3.656250 1.3021033
## 140 absent_cafeteria_6-18-2023 8 Extrapolation 0 4.025391 1.0492333
## 141 absent_cafeteria_6-18-2023 1 Rarefaction 1 1.000000 1.0000000
## 142 absent_cafeteria_6-18-2023 2 Rarefaction 1 1.781797 1.3548852
## 144 absent_cafeteria_6-18-2023 4 Observed 1 2.828427 1.3482698
## 146 absent_cafeteria_6-18-2023 6 Extrapolation 1 3.498140 1.0589713
## 148 absent_cafeteria_6-18-2023 8 Extrapolation 1 3.950431 0.7197724
## 149 absent_cafeteria_6-18-2023 1 Rarefaction 2 1.000000 1.0000000
## 150 absent_cafeteria_6-18-2023 2 Rarefaction 2 1.714286 1.2565111
## 152 absent_cafeteria_6-18-2023 4 Observed 2 2.666667 1.1416817
## 154 absent_cafeteria_6-18-2023 6 Extrapolation 2 3.272727 0.6072255
## 156 absent_cafeteria_6-18-2023 8 Extrapolation 2 3.692308 0.0000000
## 157 absent_cafeteria_6-19-2023 1 Rarefaction 0 1.000000 1.0000000
## 158 absent_cafeteria_6-19-2023 2 Rarefaction 0 1.666667 1.0166872
## 159 absent_cafeteria_6-19-2023 3 Observed 0 2.000000 0.6962939
## 161 absent_cafeteria_6-19-2023 5 Extrapolation 0 2.250000 0.1595824
## 162 absent_cafeteria_6-19-2023 6 Extrapolation 0 2.291667 0.0000000
## 163 absent_cafeteria_6-19-2023 1 Rarefaction 1 1.000000 1.0000000
## 164 absent_cafeteria_6-19-2023 2 Rarefaction 1 1.587401 0.9416906
## 165 absent_cafeteria_6-19-2023 3 Observed 1 1.889882 0.5669919
## 167 absent_cafeteria_6-19-2023 5 Extrapolation 1 2.232303 0.0000000
## 168 absent_cafeteria_6-19-2023 6 Extrapolation 1 2.325580 0.0000000
## 169 absent_cafeteria_6-19-2023 1 Rarefaction 2 1.000000 1.0000000
## 170 absent_cafeteria_6-19-2023 2 Rarefaction 2 1.500000 0.8481470
## 171 absent_cafeteria_6-19-2023 3 Observed 2 1.800000 0.4551850
## 173 absent_cafeteria_6-19-2023 5 Extrapolation 2 2.142857 0.0000000
## 174 absent_cafeteria_6-19-2023 6 Extrapolation 2 2.250000 0.0000000
## 175 absent_trail_6-18-2023 1 Rarefaction 0 1.000000 1.0000000
## 179 absent_trail_6-18-2023 5 Rarefaction 0 3.055556 2.0985252
## 183 absent_trail_6-18-2023 9 Observed 0 4.000000 2.4037837
## 188 absent_trail_6-18-2023 14 Extrapolation 0 4.597618 2.3579073
## 192 absent_trail_6-18-2023 18 Extrapolation 0 4.769584 2.2300131
## 193 absent_trail_6-18-2023 1 Rarefaction 1 1.000000 1.0000000
## 197 absent_trail_6-18-2023 5 Rarefaction 1 2.755615 1.8413827
## 201 absent_trail_6-18-2023 9 Observed 1 3.369922 2.0215790
## 206 absent_trail_6-18-2023 14 Extrapolation 1 3.802275 2.1348631
## 210 absent_trail_6-18-2023 18 Extrapolation 1 4.010779 2.1737967
## 211 absent_trail_6-18-2023 1 Rarefaction 2 1.000000 1.0000000
## 215 absent_trail_6-18-2023 5 Rarefaction 2 2.500000 1.6492022
## 219 absent_trail_6-18-2023 9 Observed 2 3.000000 1.7238585
## 224 absent_trail_6-18-2023 14 Extrapolation 2 3.294118 1.7306588
## 228 absent_trail_6-18-2023 18 Extrapolation 2 3.428571 1.7235031
## 229 absent_trail_6-19-2023 1 Rarefaction 0 1.000000 1.0000000
## 230 absent_trail_6-19-2023 2 Observed 0 2.000000 1.1798492
## 231 absent_trail_6-19-2023 3 Extrapolation 0 2.333333 1.2397990
## 232 absent_trail_6-19-2023 4 Extrapolation 0 2.444444 1.2597822
## 233 absent_trail_6-19-2023 1 Rarefaction 1 1.000000 1.0000000
## 234 absent_trail_6-19-2023 2 Observed 1 2.000000 1.1798492
## 235 absent_trail_6-19-2023 3 Extrapolation 1 2.578947 1.2839725
## 236 absent_trail_6-19-2023 4 Extrapolation 1 2.914127 1.3442544
## 237 absent_trail_6-19-2023 1 Rarefaction 2 1.000000 1.0000000
## 238 absent_trail_6-19-2023 2 Observed 2 2.000000 1.1798492
## 239 absent_trail_6-19-2023 3 Extrapolation 2 3.000000 1.3596985
## 240 absent_trail_6-19-2023 4 Extrapolation 2 4.000000 1.5395477
## 241 present_arboleda_6-18-2023 1 Rarefaction 0 1.000000 1.0000000
## 250 present_arboleda_6-18-2023 11 Rarefaction 0 2.739064 2.1481428
## 260 present_arboleda_6-18-2023 23 Observed 0 3.000000 2.3130134
## 270 present_arboleda_6-18-2023 34 Extrapolation 0 3.000000 2.3130134
## 280 present_arboleda_6-18-2023 46 Extrapolation 0 3.000000 2.3130134
## 281 present_arboleda_6-18-2023 1 Rarefaction 1 1.000000 1.0000000
## 290 present_arboleda_6-18-2023 11 Rarefaction 1 2.382571 1.8992774
## 300 present_arboleda_6-18-2023 23 Observed 1 2.527617 1.9934284
## 310 present_arboleda_6-18-2023 34 Extrapolation 1 2.575348 2.0268344
## 320 present_arboleda_6-18-2023 46 Extrapolation 1 2.605772 2.0461512
## 321 present_arboleda_6-18-2023 1 Rarefaction 2 1.000000 1.0000000
## 330 present_arboleda_6-18-2023 11 Rarefaction 2 2.203484 1.7547567
## 340 present_arboleda_6-18-2023 23 Observed 2 2.351111 1.8192245
## 350 present_arboleda_6-18-2023 34 Extrapolation 2 2.398773 1.8387385
## 360 present_arboleda_6-18-2023 46 Extrapolation 2 2.425594 1.8494393
## 361 present_arboleda_6-19-2023 1 Rarefaction 0 1.000000 1.0000000
## 365 present_arboleda_6-19-2023 5 Rarefaction 0 3.730159 2.9492076
## 369 present_arboleda_6-19-2023 9 Observed 0 5.000000 3.6606994
## 374 present_arboleda_6-19-2023 14 Extrapolation 0 5.597618 3.4987498
## 378 present_arboleda_6-19-2023 18 Extrapolation 0 5.769584 3.1388630
## 379 present_arboleda_6-19-2023 1 Rarefaction 1 1.000000 1.0000000
## 383 present_arboleda_6-19-2023 5 Rarefaction 1 3.472604 2.6380940
## 387 present_arboleda_6-19-2023 9 Observed 1 4.585756 3.2061301
## 392 present_arboleda_6-19-2023 14 Extrapolation 1 5.328491 3.4785500
## 396 present_arboleda_6-19-2023 18 Extrapolation 1 5.689727 3.5439458
## 397 present_arboleda_6-19-2023 1 Rarefaction 2 1.000000 1.0000000
## 401 present_arboleda_6-19-2023 5 Rarefaction 2 3.214286 2.3885090
## 405 present_arboleda_6-19-2023 9 Observed 2 4.263158 2.8906893
## 410 present_arboleda_6-19-2023 14 Extrapolation 2 4.990099 3.1969781
## 414 present_arboleda_6-19-2023 18 Extrapolation 2 5.355372 3.3387456
## 415 present_cafeteria_6-18-2023 1 Rarefaction 0 1.000000 1.0000000
## 417 present_cafeteria_6-18-2023 3 Rarefaction 0 2.200000 1.4052781
## 419 present_cafeteria_6-18-2023 5 Observed 0 3.000000 1.6724582
## 422 present_cafeteria_6-18-2023 8 Extrapolation 0 3.562963 1.7044432
## 424 present_cafeteria_6-18-2023 10 Extrapolation 0 3.694650 1.6413339
## 425 present_cafeteria_6-18-2023 1 Rarefaction 1 1.000000 1.0000000
## 427 present_cafeteria_6-18-2023 3 Rarefaction 1 2.037029 1.2773582
## 429 present_cafeteria_6-18-2023 5 Observed 1 2.586409 1.3399521
## 432 present_cafeteria_6-18-2023 8 Extrapolation 1 3.092823 1.3556839
## 434 present_cafeteria_6-18-2023 10 Extrapolation 1 3.294614 1.3260627
## 435 present_cafeteria_6-18-2023 1 Rarefaction 2 1.000000 1.0000000
## 437 present_cafeteria_6-18-2023 3 Rarefaction 2 1.875000 1.1675799
## 439 present_cafeteria_6-18-2023 5 Observed 2 2.272727 1.0838233
## 442 present_cafeteria_6-18-2023 8 Extrapolation 2 2.580645 0.9102764
## 444 present_cafeteria_6-18-2023 10 Extrapolation 2 2.702703 0.8048988
## 445 present_cafeteria_6-19-2023 1 Rarefaction 0 1.000000 1.0000000
## 446 present_cafeteria_6-19-2023 2 Rarefaction 0 2.000000 1.7307905
## 448 present_cafeteria_6-19-2023 4 Observed 0 4.000000 2.6461445
## 450 present_cafeteria_6-19-2023 6 Extrapolation 0 5.487603 3.1024677
## 452 present_cafeteria_6-19-2023 8 Extrapolation 0 6.483437 3.3852076
## 453 present_cafeteria_6-19-2023 1 Rarefaction 1 1.000000 1.0000000
## 454 present_cafeteria_6-19-2023 2 Rarefaction 1 2.000000 1.6746431
## 456 present_cafeteria_6-19-2023 4 Observed 1 4.000000 2.5431722
## 458 present_cafeteria_6-19-2023 6 Extrapolation 1 5.688177 3.1046847
## 460 present_cafeteria_6-19-2023 8 Extrapolation 1 7.031954 3.4947421
## 461 present_cafeteria_6-19-2023 1 Rarefaction 2 1.000000 1.0000000
## 462 present_cafeteria_6-19-2023 2 Rarefaction 2 2.000000 1.6127329
## 464 present_cafeteria_6-19-2023 4 Observed 2 4.000000 2.4498273
## 466 present_cafeteria_6-19-2023 6 Extrapolation 2 6.000000 3.0340598
## 468 present_cafeteria_6-19-2023 8 Extrapolation 2 8.000000 3.4824445
## 469 present_trail_6-18-2023 1 Rarefaction 0 1.000000 1.0000000
## 473 present_trail_6-18-2023 5 Rarefaction 0 2.690476 2.1586081
## 478 present_trail_6-18-2023 10 Observed 0 3.000000 2.4628767
## 483 present_trail_6-18-2023 15 Extrapolation 0 3.000000 2.3279242
## 488 present_trail_6-18-2023 20 Extrapolation 0 3.000000 2.2509734
## 489 present_trail_6-18-2023 1 Rarefaction 1 1.000000 1.0000000
## 493 present_trail_6-18-2023 5 Rarefaction 1 2.433576 1.8540632
## 498 present_trail_6-18-2023 10 Observed 1 2.800094 2.1103019
## 503 present_trail_6-18-2023 15 Extrapolation 1 2.934978 2.2213447
## 508 present_trail_6-18-2023 20 Extrapolation 1 3.012694 2.2846566
## 509 present_trail_6-18-2023 1 Rarefaction 2 1.000000 1.0000000
## 513 present_trail_6-18-2023 5 Rarefaction 2 2.227723 1.6434943
## 518 present_trail_6-18-2023 10 Observed 2 2.631579 1.7835181
## 523 present_trail_6-18-2023 15 Extrapolation 2 2.800830 1.8337284
## 528 present_trail_6-18-2023 20 Extrapolation 2 2.893891 1.8592954
## 529 present_trail_6-19-2023 1 Rarefaction 0 1.000000 1.0000000
## 530 present_trail_6-19-2023 2 Observed 0 2.000000 1.1798492
## 531 present_trail_6-19-2023 3 Extrapolation 0 2.333333 1.2397990
## 532 present_trail_6-19-2023 4 Extrapolation 0 2.444444 1.2597822
## 533 present_trail_6-19-2023 1 Rarefaction 1 1.000000 1.0000000
## 534 present_trail_6-19-2023 2 Observed 1 2.000000 1.1798492
## 535 present_trail_6-19-2023 3 Extrapolation 1 2.578947 1.2839725
## 536 present_trail_6-19-2023 4 Extrapolation 1 2.914127 1.3442544
## 537 present_trail_6-19-2023 1 Rarefaction 2 1.000000 1.0000000
## 538 present_trail_6-19-2023 2 Observed 2 2.000000 1.1798492
## 539 present_trail_6-19-2023 3 Extrapolation 2 3.000000 1.3596985
## 540 present_trail_6-19-2023 4 Extrapolation 2 4.000000 1.5395477
## qD.UCL SC SC.LCL SC.UCL
## 1 1.000000 1.838235e-01 0.03783835 0.3298087
## 9 5.902737 7.835973e-01 0.65862127 0.9085733
## 17 7.797645 8.892734e-01 0.75988458 1.0000000
## 26 9.290435 9.358360e-01 0.84656376 1.0000000
## 34 10.185103 9.604943e-01 0.89625813 1.0000000
## 35 1.000000 1.838235e-01 0.03783835 0.3298087
## 43 5.358875 7.835973e-01 0.65862127 0.9085733
## 51 6.656418 8.892734e-01 0.75988458 1.0000000
## 60 7.415228 9.358360e-01 0.84656376 1.0000000
## 68 7.856884 9.604943e-01 0.89625813 1.0000000
## 69 1.000000 1.838235e-01 0.03783835 0.3298087
## 77 4.865415 7.835973e-01 0.65862127 0.9085733
## 85 6.063076 8.892734e-01 0.75988458 1.0000000
## 94 6.694766 9.358360e-01 0.84656376 1.0000000
## 102 7.017942 9.604943e-01 0.89625813 1.0000000
## 103 1.000000 2.000000e-01 0.00000000 0.4413809
## 105 2.979001 6.000000e-01 0.23149018 0.9685098
## 107 4.267731 9.000000e-01 0.53239072 1.0000000
## 110 5.377224 9.875000e-01 0.73106549 1.0000000
## 112 5.853568 9.968750e-01 0.80164297 1.0000000
## 113 1.000000 2.000000e-01 0.00000000 0.4413809
## 115 2.890163 6.000000e-01 0.23149018 0.9685098
## 117 4.128264 9.000000e-01 0.53239072 1.0000000
## 120 5.361503 9.875000e-01 0.73106549 1.0000000
## 122 5.949070 9.968750e-01 0.80164297 1.0000000
## 123 1.000000 2.000000e-01 0.00000000 0.4413809
## 125 2.770315 6.000000e-01 0.23149018 0.9685098
## 127 4.008322 9.000000e-01 0.53239072 1.0000000
## 130 5.275065 9.875000e-01 0.73106549 1.0000000
## 132 5.881660 9.968750e-01 0.80164297 1.0000000
## 133 1.000000 1.666667e-01 0.00000000 0.5582704
## 134 2.224937 3.333333e-01 0.00000000 0.8466572
## 136 4.403330 6.250000e-01 0.09080367 1.0000000
## 138 6.010397 7.890625e-01 0.44009361 1.0000000
## 140 7.001548 8.813477e-01 0.65033463 1.0000000
## 141 1.000000 1.666667e-01 0.00000000 0.5582704
## 142 2.208710 3.333333e-01 0.00000000 0.8466572
## 144 4.308584 6.250000e-01 0.09080367 1.0000000
## 146 5.937308 7.890625e-01 0.44009361 1.0000000
## 148 7.181089 8.813477e-01 0.65033463 1.0000000
## 149 1.000000 1.666667e-01 0.00000000 0.5582704
## 150 2.172060 3.333333e-01 0.00000000 0.8466572
## 152 4.191652 6.250000e-01 0.09080367 1.0000000
## 154 5.938229 7.890625e-01 0.44009361 1.0000000
## 156 7.545666 8.813477e-01 0.65033463 1.0000000
## 157 1.000000 3.333333e-01 0.00000000 0.9833128
## 158 2.316646 6.666667e-01 0.00000000 1.0000000
## 159 3.303706 8.333333e-01 0.34836802 1.0000000
## 161 4.340418 9.583333e-01 0.72639516 1.0000000
## 162 4.603364 9.791667e-01 0.82058302 1.0000000
## 163 1.000000 3.333333e-01 0.00000000 0.9833128
## 164 2.233112 6.666667e-01 0.00000000 1.0000000
## 165 3.212771 8.333333e-01 0.34836802 1.0000000
## 167 4.603568 9.583333e-01 0.72639516 1.0000000
## 168 5.089778 9.791667e-01 0.82058302 1.0000000
## 169 1.000000 3.333333e-01 0.00000000 0.9833128
## 170 2.151853 6.666667e-01 0.00000000 1.0000000
## 171 3.144815 8.333333e-01 0.34836802 1.0000000
## 173 4.990715 9.583333e-01 0.72639516 1.0000000
## 174 5.887240 9.791667e-01 0.82058302 1.0000000
## 175 1.000000 2.500000e-01 0.00000000 0.5012573
## 179 4.012586 7.341270e-01 0.52411991 0.9441341
## 183 5.596216 8.222222e-01 0.62207958 1.0000000
## 188 6.837328 9.417458e-01 0.83597623 1.0000000
## 192 7.309155 9.761391e-01 0.91002675 1.0000000
## 193 1.000000 2.500000e-01 0.00000000 0.5012573
## 197 3.669848 7.341270e-01 0.52411991 0.9441341
## 201 4.718265 8.222222e-01 0.62207958 1.0000000
## 206 5.469687 9.417458e-01 0.83597623 1.0000000
## 210 5.847762 9.761391e-01 0.91002675 1.0000000
## 211 1.000000 2.500000e-01 0.00000000 0.5012573
## 215 3.350798 7.341270e-01 0.52411991 0.9441341
## 219 4.276141 8.222222e-01 0.62207958 1.0000000
## 224 4.857576 9.417458e-01 0.83597623 1.0000000
## 228 5.133640 9.761391e-01 0.91002675 1.0000000
## 229 1.000000 0.000000e+00 0.00000000 0.8201508
## 230 2.820151 6.666667e-01 0.39328308 0.9400503
## 231 3.426868 8.888889e-01 0.79776103 0.9800168
## 232 3.629107 9.629630e-01 0.93258701 0.9933389
## 233 1.000000 0.000000e+00 0.00000000 0.8201508
## 234 2.820151 6.666667e-01 0.39328308 0.9400503
## 235 3.873922 8.888889e-01 0.79776103 0.9800168
## 236 4.484000 9.629630e-01 0.93258701 0.9933389
## 237 1.000000 0.000000e+00 0.00000000 0.8201508
## 238 2.820151 6.666667e-01 0.39328308 0.9400503
## 239 4.640302 8.888889e-01 0.79776103 0.9800168
## 240 6.460452 9.629630e-01 0.93258701 0.9933389
## 241 1.000000 3.992095e-01 0.28302601 0.5153930
## 250 3.329985 9.564655e-01 0.92389532 0.9890357
## 260 3.686987 1.000000e+00 1.00000000 1.0000000
## 270 3.686987 1.000000e+00 1.00000000 1.0000000
## 280 3.686987 1.000000e+00 1.00000000 1.0000000
## 281 1.000000 3.992095e-01 0.28302601 0.5153930
## 290 2.865864 9.564655e-01 0.92389532 0.9890357
## 300 3.061806 1.000000e+00 1.00000000 1.0000000
## 310 3.123862 1.000000e+00 1.00000000 1.0000000
## 320 3.165394 1.000000e+00 1.00000000 1.0000000
## 321 1.000000 3.992095e-01 0.28302601 0.5153930
## 330 2.652211 9.564655e-01 0.92389532 0.9890357
## 340 2.882998 1.000000e+00 1.00000000 1.0000000
## 350 2.958807 1.000000e+00 1.00000000 1.0000000
## 360 3.001749 1.000000e+00 1.00000000 1.0000000
## 361 1.000000 1.388889e-01 0.00000000 0.3162326
## 365 4.511110 5.753968e-01 0.39330672 0.7574869
## 369 6.339301 8.222222e-01 0.62204431 1.0000000
## 374 7.696486 9.417458e-01 0.78078211 1.0000000
## 378 8.400305 9.761391e-01 0.84987648 1.0000000
## 379 1.000000 1.388889e-01 0.00000000 0.3162326
## 383 4.307115 5.753968e-01 0.39330672 0.7574869
## 387 5.965383 8.222222e-01 0.62204431 1.0000000
## 392 7.178431 9.417458e-01 0.78078211 1.0000000
## 396 7.835508 9.761391e-01 0.84987648 1.0000000
## 397 1.000000 1.388889e-01 0.00000000 0.3162326
## 401 4.040062 5.753968e-01 0.39330672 0.7574869
## 405 5.635627 8.222222e-01 0.62204431 1.0000000
## 410 6.783220 9.417458e-01 0.78078211 1.0000000
## 414 7.371998 9.761391e-01 0.84987648 1.0000000
## 415 1.000000 3.000000e-01 0.00000000 0.7197309
## 417 2.994722 6.000000e-01 0.27344869 0.9265513
## 419 4.327542 7.333333e-01 0.46755776 0.9991089
## 422 5.421483 9.209877e-01 0.78820811 1.0000000
## 424 5.747966 9.648834e-01 0.87178180 1.0000000
## 425 1.000000 3.000000e-01 0.00000000 0.7197309
## 427 2.796699 6.000000e-01 0.27344869 0.9265513
## 429 3.832866 7.333333e-01 0.46755776 0.9991089
## 432 4.829962 9.209877e-01 0.78820811 1.0000000
## 434 5.263165 9.648834e-01 0.87178180 1.0000000
## 435 1.000000 3.000000e-01 0.00000000 0.7197309
## 437 2.582420 6.000000e-01 0.27344869 0.9265513
## 439 3.461631 7.333333e-01 0.46755776 0.9991089
## 442 4.251014 9.209877e-01 0.78820811 1.0000000
## 444 4.600507 9.648834e-01 0.87178180 1.0000000
## 445 1.000000 1.110223e-16 0.00000000 0.2692095
## 446 2.269210 0.000000e+00 0.00000000 0.4696932
## 448 5.353855 1.818182e-01 0.00000000 0.7454926
## 450 7.872739 4.522915e-01 0.06050261 0.8440804
## 452 9.581666 6.333522e-01 0.36120497 0.9054994
## 453 1.000000 1.110223e-16 0.00000000 0.2692095
## 454 2.325357 0.000000e+00 0.00000000 0.4696932
## 456 5.456828 1.818182e-01 0.00000000 0.7454926
## 458 8.271669 4.522915e-01 0.06050261 0.8440804
## 460 10.569166 6.333522e-01 0.36120497 0.9054994
## 461 1.000000 1.110223e-16 0.00000000 0.2692095
## 462 2.387267 0.000000e+00 0.00000000 0.4696932
## 464 5.550173 1.818182e-01 0.00000000 0.7454926
## 466 8.965940 4.522915e-01 0.06050261 0.8440804
## 468 12.517556 6.333522e-01 0.36120497 0.9054994
## 469 1.000000 3.111111e-01 0.10568541 0.5165368
## 473 3.222344 8.571429e-01 0.77355914 0.9407266
## 478 3.537123 1.000000e+00 0.90975031 1.0000000
## 483 3.672076 1.000000e+00 0.96691015 1.0000000
## 488 3.749027 1.000000e+00 0.98786768 1.0000000
## 489 1.000000 3.111111e-01 0.10568541 0.5165368
## 493 3.013090 8.571429e-01 0.77355914 0.9407266
## 498 3.489886 1.000000e+00 0.90975031 1.0000000
## 503 3.648612 1.000000e+00 0.96691015 1.0000000
## 508 3.740732 1.000000e+00 0.98786768 1.0000000
## 509 1.000000 3.111111e-01 0.10568541 0.5165368
## 513 2.811951 8.571429e-01 0.77355914 0.9407266
## 518 3.479640 1.000000e+00 0.90975031 1.0000000
## 523 3.767931 1.000000e+00 0.96691015 1.0000000
## 528 3.928486 1.000000e+00 0.98786768 1.0000000
## 529 1.000000 0.000000e+00 0.00000000 0.8201508
## 530 2.820151 6.666667e-01 0.39328308 0.9400503
## 531 3.426868 8.888889e-01 0.79776103 0.9800168
## 532 3.629107 9.629630e-01 0.93258701 0.9933389
## 533 1.000000 0.000000e+00 0.00000000 0.8201508
## 534 2.820151 6.666667e-01 0.39328308 0.9400503
## 535 3.873922 8.888889e-01 0.79776103 0.9800168
## 536 4.484000 9.629630e-01 0.93258701 0.9933389
## 537 1.000000 0.000000e+00 0.00000000 0.8201508
## 538 2.820151 6.666667e-01 0.39328308 0.9400503
## 539 4.640302 8.888889e-01 0.79776103 0.9800168
## 540 6.460452 9.629630e-01 0.93258701 0.9933389
##
## NOTE: The above output only shows five estimates for each assemblage; call iNEXT.object$iNextEst$size_based to view complete output.
##
## $coverage_based (LCL and UCL are obtained for fixed coverage; interval length is wider due to varying size in bootstraps.)
##
## Assemblage SC m Method Order.q qD
## 1 absent_arboleda_6-18-2023 1.838235e-01 1 Rarefaction 0 1.000000
## 9 absent_arboleda_6-18-2023 7.835974e-01 9 Rarefaction 0 4.738915
## 17 absent_arboleda_6-18-2023 8.892734e-01 17 Observed 0 6.000000
## 26 absent_arboleda_6-18-2023 9.358360e-01 26 Extrapolation 0 6.791564
## 34 absent_arboleda_6-18-2023 9.604943e-01 34 Extrapolation 0 7.210755
## 35 absent_arboleda_6-18-2023 1.838235e-01 1 Rarefaction 1 1.000000
## 43 absent_arboleda_6-18-2023 7.835974e-01 9 Rarefaction 1 4.127185
## 51 absent_arboleda_6-18-2023 8.892734e-01 17 Observed 1 4.949177
## 60 absent_arboleda_6-18-2023 9.358360e-01 26 Extrapolation 1 5.424434
## 68 absent_arboleda_6-18-2023 9.604943e-01 34 Extrapolation 1 5.696376
## 69 absent_arboleda_6-18-2023 1.838235e-01 1 Rarefaction 2 1.000000
## 77 absent_arboleda_6-18-2023 7.835974e-01 9 Rarefaction 2 3.642858
## 85 absent_arboleda_6-18-2023 8.892734e-01 17 Observed 2 4.313433
## 94 absent_arboleda_6-18-2023 9.358360e-01 26 Extrapolation 2 4.646518
## 102 absent_arboleda_6-18-2023 9.604943e-01 34 Extrapolation 2 4.811655
## 103 absent_arboleda_6-19-2023 2.000000e-01 1 Rarefaction 0 1.000000
## 105 absent_arboleda_6-19-2023 8.000000e-01 4 Rarefaction 0 2.632109
## 107 absent_arboleda_6-19-2023 9.000000e-01 5 Observed 0 3.000000
## 110 absent_arboleda_6-19-2023 9.875000e-01 8 Extrapolation 0 3.175000
## 112 absent_arboleda_6-19-2023 9.968750e-01 10 Extrapolation 0 3.193750
## 113 absent_arboleda_6-19-2023 2.000000e-01 1 Rarefaction 1 1.000000
## 115 absent_arboleda_6-19-2023 8.000000e-01 4 Rarefaction 1 2.485630
## 117 absent_arboleda_6-19-2023 9.000000e-01 5 Observed 1 2.871746
## 120 absent_arboleda_6-19-2023 9.875000e-01 8 Extrapolation 1 3.313407
## 122 absent_arboleda_6-19-2023 9.968750e-01 10 Extrapolation 1 3.473748
## 123 absent_arboleda_6-19-2023 2.000000e-01 1 Rarefaction 2 1.000000
## 125 absent_arboleda_6-19-2023 8.000000e-01 4 Rarefaction 2 2.350097
## 127 absent_arboleda_6-19-2023 9.000000e-01 5 Observed 2 2.777778
## 130 absent_arboleda_6-19-2023 9.875000e-01 8 Extrapolation 2 3.333333
## 132 absent_arboleda_6-19-2023 9.968750e-01 10 Extrapolation 2 3.571429
## 133 absent_cafeteria_6-18-2023 1.666667e-01 1 Rarefaction 0 1.000000
## 134 absent_cafeteria_6-18-2023 3.333285e-01 2 Rarefaction 0 1.833309
## 136 absent_cafeteria_6-18-2023 6.250000e-01 4 Observed 0 3.000000
## 138 absent_cafeteria_6-18-2023 7.890625e-01 6 Extrapolation 0 3.656250
## 140 absent_cafeteria_6-18-2023 8.813477e-01 8 Extrapolation 0 4.025391
## 141 absent_cafeteria_6-18-2023 1.666667e-01 1 Rarefaction 1 1.000000
## 142 absent_cafeteria_6-18-2023 3.333285e-01 2 Rarefaction 1 1.781775
## 144 absent_cafeteria_6-18-2023 6.250000e-01 4 Observed 1 2.828427
## 146 absent_cafeteria_6-18-2023 7.890625e-01 6 Extrapolation 1 3.498140
## 148 absent_cafeteria_6-18-2023 8.813477e-01 8 Extrapolation 1 3.950431
## 149 absent_cafeteria_6-18-2023 1.666667e-01 1 Rarefaction 2 1.000000
## 150 absent_cafeteria_6-18-2023 3.333285e-01 2 Rarefaction 2 1.714265
## 152 absent_cafeteria_6-18-2023 6.250000e-01 4 Observed 2 2.666667
## 154 absent_cafeteria_6-18-2023 7.890625e-01 6 Extrapolation 2 3.272727
## 156 absent_cafeteria_6-18-2023 8.813477e-01 8 Extrapolation 2 3.692308
## 157 absent_cafeteria_6-19-2023 6.666667e-01 1 Rarefaction 0 1.097304
## 310 absent_cafeteria_6-19-2023 8.333333e-01 3 Observed 0 2.000000
## 410 absent_cafeteria_6-19-2023 9.166667e-01 4 Extrapolation 0 2.166667
## 510 absent_cafeteria_6-19-2023 9.583333e-01 5 Extrapolation 0 2.250000
## 610 absent_cafeteria_6-19-2023 9.791667e-01 6 Extrapolation 0 2.291667
## 710 absent_cafeteria_6-19-2023 6.666667e-01 1 Rarefaction 1 1.085735
## 910 absent_cafeteria_6-19-2023 8.333333e-01 3 Observed 1 1.889882
## 1010 absent_cafeteria_6-19-2023 9.166667e-01 4 Extrapolation 1 2.094239
## 1110 absent_cafeteria_6-19-2023 9.583333e-01 5 Extrapolation 1 2.232303
## 1210 absent_cafeteria_6-19-2023 9.791667e-01 6 Extrapolation 1 2.325580
## 1310 absent_cafeteria_6-19-2023 6.666667e-01 1 Rarefaction 2 1.072978
## 158 absent_cafeteria_6-19-2023 8.333333e-01 3 Observed 2 1.800000
## 161 absent_cafeteria_6-19-2023 9.166667e-01 4 Extrapolation 2 2.000000
## 171 absent_cafeteria_6-19-2023 9.583333e-01 5 Extrapolation 2 2.142857
## 181 absent_cafeteria_6-19-2023 9.791667e-01 6 Extrapolation 2 2.250000
## 159 absent_trail_6-18-2023 2.500009e-01 1 Rarefaction 0 1.000003
## 511 absent_trail_6-18-2023 7.341234e-01 5 Rarefaction 0 3.055529
## 911 absent_trail_6-18-2023 8.577778e-01 10 Extrapolation 0 4.177778
## 1311 absent_trail_6-18-2023 9.417458e-01 14 Extrapolation 0 4.597618
## 172 absent_trail_6-18-2023 9.761391e-01 18 Extrapolation 0 4.769584
## 182 absent_trail_6-18-2023 2.500009e-01 1 Rarefaction 1 1.000003
## 221 absent_trail_6-18-2023 7.341234e-01 5 Rarefaction 1 2.755595
## 261 absent_trail_6-18-2023 8.577778e-01 10 Extrapolation 1 3.476653
## 301 absent_trail_6-18-2023 9.417458e-01 14 Extrapolation 1 3.802275
## 341 absent_trail_6-18-2023 9.761391e-01 18 Extrapolation 1 4.010779
## 351 absent_trail_6-18-2023 2.500009e-01 1 Rarefaction 2 1.000002
## 391 absent_trail_6-18-2023 7.341234e-01 5 Rarefaction 2 2.499983
## 431 absent_trail_6-18-2023 8.577778e-01 10 Extrapolation 2 3.076923
## 471 absent_trail_6-18-2023 9.417458e-01 14 Extrapolation 2 3.294118
## 512 absent_trail_6-18-2023 9.761391e-01 18 Extrapolation 2 3.428571
## 160 absent_trail_6-19-2023 0.000000e+00 1 Rarefaction 0 1.000000
## 212 absent_trail_6-19-2023 6.666667e-01 2 Observed 0 2.000000
## 313 absent_trail_6-19-2023 8.888889e-01 3 Extrapolation 0 2.333333
## 413 absent_trail_6-19-2023 9.629630e-01 4 Extrapolation 0 2.444444
## 513 absent_trail_6-19-2023 0.000000e+00 1 Rarefaction 1 1.000000
## 612 absent_trail_6-19-2023 6.666667e-01 2 Observed 1 2.000000
## 712 absent_trail_6-19-2023 8.888889e-01 3 Extrapolation 1 2.578947
## 811 absent_trail_6-19-2023 9.629630e-01 4 Extrapolation 1 2.914127
## 912 absent_trail_6-19-2023 0.000000e+00 1 Rarefaction 2 1.000000
## 1012 absent_trail_6-19-2023 6.666667e-01 2 Observed 2 2.000000
## 1112 absent_trail_6-19-2023 8.888889e-01 3 Extrapolation 2 3.000000
## 1212 absent_trail_6-19-2023 9.629630e-01 4 Extrapolation 2 4.000000
## 163 present_arboleda_6-18-2023 3.992122e-01 1 Rarefaction 0 1.000005
## 514 present_arboleda_6-18-2023 8.971732e-01 5 Rarefaction 0 2.333470
## 1013 present_arboleda_6-18-2023 9.564656e-01 11 Rarefaction 0 2.739064
## 1511 present_arboleda_6-18-2023 9.802371e-01 17 Rarefaction 0 2.940711
## 192 present_arboleda_6-18-2023 1.000000e+00 23 Observed 0 3.000000
## 202 present_arboleda_6-18-2023 3.992122e-01 1 Rarefaction 1 1.000005
## 242 present_arboleda_6-18-2023 8.971732e-01 5 Rarefaction 1 2.098868
## 292 present_arboleda_6-18-2023 9.564656e-01 11 Rarefaction 1 2.382571
## 342 present_arboleda_6-18-2023 9.802371e-01 17 Rarefaction 1 2.480874
## 382 present_arboleda_6-18-2023 1.000000e+00 23 Observed 1 2.527617
## 392 present_arboleda_6-18-2023 3.992122e-01 1 Rarefaction 2 1.000004
## 432 present_arboleda_6-18-2023 8.971732e-01 5 Rarefaction 2 1.925417
## 482 present_arboleda_6-18-2023 9.564656e-01 11 Rarefaction 2 2.203484
## 531 present_arboleda_6-18-2023 9.802371e-01 17 Rarefaction 2 2.301230
## 571 present_arboleda_6-18-2023 1.000000e+00 23 Observed 2 2.351111
## 165 present_arboleda_6-19-2023 1.388889e-01 1 Rarefaction 0 1.000000
## 516 present_arboleda_6-19-2023 5.753985e-01 5 Rarefaction 0 3.730167
## 914 present_arboleda_6-19-2023 8.222222e-01 9 Observed 0 5.000000
## 1412 present_arboleda_6-19-2023 9.417458e-01 14 Extrapolation 0 5.597618
## 184 present_arboleda_6-19-2023 9.761391e-01 18 Extrapolation 0 5.769584
## 193 present_arboleda_6-19-2023 1.388889e-01 1 Rarefaction 1 1.000000
## 233 present_arboleda_6-19-2023 5.753985e-01 5 Rarefaction 1 3.472611
## 273 present_arboleda_6-19-2023 8.222222e-01 9 Observed 1 4.585756
## 323 present_arboleda_6-19-2023 9.417458e-01 14 Extrapolation 1 5.328491
## 363 present_arboleda_6-19-2023 9.761391e-01 18 Extrapolation 1 5.689727
## 373 present_arboleda_6-19-2023 1.388889e-01 1 Rarefaction 2 1.000000
## 417 present_arboleda_6-19-2023 5.753985e-01 5 Rarefaction 2 3.214292
## 453 present_arboleda_6-19-2023 8.222222e-01 9 Observed 2 4.263158
## 503 present_arboleda_6-19-2023 9.417458e-01 14 Extrapolation 2 4.990099
## 542 present_arboleda_6-19-2023 9.761391e-01 18 Extrapolation 2 5.355372
## 167 present_cafeteria_6-18-2023 3.000030e-01 1 Rarefaction 0 1.000008
## 318 present_cafeteria_6-18-2023 6.000000e-01 3 Rarefaction 0 2.381950
## 518 present_cafeteria_6-18-2023 8.222222e-01 6 Extrapolation 0 3.266667
## 715 present_cafeteria_6-18-2023 9.209877e-01 8 Extrapolation 0 3.562963
## 915 present_cafeteria_6-18-2023 9.648834e-01 10 Extrapolation 0 3.694650
## 1015 present_cafeteria_6-18-2023 3.000030e-01 1 Rarefaction 1 1.000008
## 1215 present_cafeteria_6-18-2023 6.000000e-01 3 Rarefaction 1 2.173370
## 1413 present_cafeteria_6-18-2023 8.222222e-01 6 Extrapolation 1 2.790300
## 168 present_cafeteria_6-18-2023 9.209877e-01 8 Extrapolation 1 3.092823
## 185 present_cafeteria_6-18-2023 9.648834e-01 10 Extrapolation 1 3.294614
## 194 present_cafeteria_6-18-2023 3.000030e-01 1 Rarefaction 2 1.000006
## 218 present_cafeteria_6-18-2023 6.000000e-01 3 Rarefaction 2 1.979741
## 234 present_cafeteria_6-18-2023 8.222222e-01 6 Extrapolation 2 2.400000
## 254 present_cafeteria_6-18-2023 9.209877e-01 8 Extrapolation 2 2.580645
## 274 present_cafeteria_6-18-2023 9.648834e-01 10 Extrapolation 2 2.702703
## 169 present_cafeteria_6-19-2023 1.110223e-16 2 Rarefaction 0 1.860807
## 219 present_cafeteria_6-19-2023 0.000000e+00 2 Rarefaction 0 2.387816
## 419 present_cafeteria_6-19-2023 3.305785e-01 5 Extrapolation 0 4.818182
## 616 present_cafeteria_6-19-2023 5.518749e-01 7 Extrapolation 0 6.035312
## 716 present_cafeteria_6-19-2023 6.333522e-01 8 Extrapolation 0 6.483437
## 815 present_cafeteria_6-19-2023 1.110223e-16 2 Rarefaction 1 1.860807
## 916 present_cafeteria_6-19-2023 0.000000e+00 2 Rarefaction 1 2.387816
## 1116 present_cafeteria_6-19-2023 3.305785e-01 5 Extrapolation 1 4.892184
## 1315 present_cafeteria_6-19-2023 5.518749e-01 7 Extrapolation 1 6.398349
## 1414 present_cafeteria_6-19-2023 6.333522e-01 8 Extrapolation 1 7.031954
## 1514 present_cafeteria_6-19-2023 1.110223e-16 2 Rarefaction 2 1.860807
## 1610 present_cafeteria_6-19-2023 0.000000e+00 2 Rarefaction 2 2.387816
## 186 present_cafeteria_6-19-2023 3.305785e-01 5 Extrapolation 2 5.000000
## 205 present_cafeteria_6-19-2023 5.518749e-01 7 Extrapolation 2 7.000000
## 2110 present_cafeteria_6-19-2023 6.333522e-01 8 Extrapolation 2 8.000000
## 170 present_trail_6-18-2023 3.111148e-01 1 Rarefaction 0 1.000010
## 320 present_trail_6-18-2023 6.821432e-01 3 Rarefaction 0 2.158334
## 520 present_trail_6-18-2023 8.571416e-01 5 Rarefaction 0 2.690472
## 717 present_trail_6-18-2023 9.472218e-01 7 Rarefaction 0 2.924999
## 917 present_trail_6-18-2023 1.000000e+00 10 Observed 0 3.000000
## 1017 present_trail_6-18-2023 3.111148e-01 1 Rarefaction 1 1.000009
## 1217 present_trail_6-18-2023 6.821432e-01 3 Rarefaction 1 2.001090
## 1415 present_trail_6-18-2023 8.571416e-01 5 Rarefaction 1 2.433573
## 1611 present_trail_6-18-2023 9.472218e-01 7 Rarefaction 1 2.647557
## 187 present_trail_6-18-2023 1.000000e+00 10 Observed 1 2.800094
## 196 present_trail_6-18-2023 3.111148e-01 1 Rarefaction 2 1.000007
## 2111 present_trail_6-18-2023 6.821432e-01 3 Rarefaction 2 1.849316
## 235 present_trail_6-18-2023 8.571416e-01 5 Rarefaction 2 2.227719
## 255 present_trail_6-18-2023 9.472218e-01 7 Rarefaction 2 2.441859
## 275 present_trail_6-18-2023 1.000000e+00 10 Observed 2 2.631579
## 178 present_trail_6-19-2023 0.000000e+00 1 Rarefaction 0 1.000000
## 226 present_trail_6-19-2023 6.666667e-01 2 Observed 0 2.000000
## 324 present_trail_6-19-2023 8.888889e-01 3 Extrapolation 0 2.333333
## 424 present_trail_6-19-2023 9.629630e-01 4 Extrapolation 0 2.444444
## 523 present_trail_6-19-2023 0.000000e+00 1 Rarefaction 1 1.000000
## 618 present_trail_6-19-2023 6.666667e-01 2 Observed 1 2.000000
## 718 present_trail_6-19-2023 8.888889e-01 3 Extrapolation 1 2.578947
## 817 present_trail_6-19-2023 9.629630e-01 4 Extrapolation 1 2.914127
## 918 present_trail_6-19-2023 0.000000e+00 1 Rarefaction 2 1.000000
## 1018 present_trail_6-19-2023 6.666667e-01 2 Observed 2 2.000000
## 1118 present_trail_6-19-2023 8.888889e-01 3 Extrapolation 2 3.000000
## 1218 present_trail_6-19-2023 9.629630e-01 4 Extrapolation 2 4.000000
## qD.LCL qD.UCL
## 1 0.7263815 1.273618
## 9 2.3969779 7.080853
## 17 2.9333951 9.066605
## 26 3.2484676 10.334661
## 34 3.3913059 11.030205
## 35 0.7384997 1.261500
## 43 2.3203503 5.934019
## 51 2.9280967 6.970257
## 60 3.2249142 7.623954
## 68 3.3806640 8.012088
## 69 0.7551780 1.244822
## 77 2.0750345 5.210681
## 85 2.5346339 6.092232
## 94 2.7132312 6.579804
## 102 2.7742447 6.849064
## 103 0.2033566 1.796643
## 105 0.2755771 4.988641
## 107 0.0000000 6.609774
## 110 0.0000000 7.199525
## 112 0.0000000 7.262743
## 113 0.2451367 1.754863
## 115 0.2317404 4.739520
## 117 0.0000000 6.654207
## 120 0.0000000 7.734329
## 122 0.0000000 7.974710
## 123 0.2913163 1.708684
## 125 0.2470022 4.453193
## 127 0.0000000 6.274887
## 130 0.0000000 7.730234
## 132 0.0000000 8.275814
## 133 0.4171855 1.582814
## 134 0.0000000 4.165456
## 136 0.0000000 6.219705
## 138 0.0000000 7.445546
## 140 0.1370795 7.913702
## 141 0.4218005 1.578199
## 142 0.0000000 4.186591
## 144 0.0000000 6.400864
## 146 0.0000000 7.971583
## 148 0.0000000 8.920141
## 149 0.4238499 1.576150
## 150 0.0000000 4.218368
## 152 0.0000000 6.870913
## 154 0.0000000 9.261444
## 156 0.0000000 11.398432
## 157 0.0000000 4.804601
## 310 0.0000000 6.443033
## 410 0.0000000 7.030850
## 510 0.0000000 4.777115
## 610 0.0000000 5.340916
## 710 0.0000000 4.414332
## 910 0.0000000 5.482150
## 1010 0.0000000 6.548490
## 1110 0.0000000 7.326402
## 1210 0.0000000 5.986163
## 1310 0.0000000 5.546826
## 158 0.0000000 6.862507
## 161 0.0000000 5.596130
## 171 0.0000000 6.684095
## 181 0.0000000 7.518139
## 159 0.6998005 1.300206
## 511 1.1226896 4.988369
## 911 1.7648046 6.590751
## 1311 1.8071342 7.388101
## 172 1.7938477 7.745321
## 182 0.7253458 1.274660
## 221 1.3052433 4.205946
## 261 1.7905411 5.162764
## 301 1.8544979 5.750052
## 341 1.8907701 6.130788
## 351 0.7526445 1.247360
## 391 1.2619023 3.738063
## 431 1.6414424 4.512404
## 471 1.6433072 4.944928
## 512 1.6315074 5.225635
## 160 1.0000000 1.000000
## 212 1.1798492 2.820151
## 313 1.2397990 3.426868
## 413 1.2597822 3.629107
## 513 1.0000000 1.000000
## 612 1.1798492 2.820151
## 712 1.2839725 3.873922
## 811 1.3442544 4.484000
## 912 1.0000000 1.000000
## 1012 1.1798492 2.820151
## 1112 1.3596985 4.640302
## 1212 1.5395477 6.460452
## 163 0.9038660 1.096145
## 514 1.7850345 2.881905
## 1013 2.1485064 3.329622
## 1511 2.2903614 3.591061
## 192 2.3130134 3.686987
## 202 0.9155864 1.084423
## 242 1.6372243 2.560512
## 292 1.8965260 2.868616
## 342 1.9573856 3.004362
## 382 1.9934284 3.061806
## 392 0.9283877 1.071620
## 432 1.5186363 2.332197
## 482 1.7472098 2.659758
## 531 1.8024500 2.800011
## 571 1.8192245 2.882998
## 165 0.8373803 1.162620
## 516 2.0304359 5.429898
## 914 1.8029183 8.197082
## 1412 1.5609794 9.634256
## 184 1.4799084 10.059260
## 193 0.8584477 1.141552
## 233 1.9497459 4.995477
## 273 2.0441814 7.127331
## 323 2.1958933 8.461088
## 363 2.3922804 8.987174
## 373 0.8769707 1.123029
## 417 1.8578336 4.570750
## 453 2.3185534 6.207762
## 503 2.7335674 7.246631
## 542 2.9389870 7.771757
## 167 0.1451210 1.854896
## 318 0.8495380 3.914361
## 518 1.0967770 5.436556
## 715 1.0953136 6.030612
## 915 1.0830705 6.306230
## 1015 0.2366312 1.763384
## 1215 0.7333302 3.613410
## 1413 0.7831776 4.797423
## 168 0.6961964 5.489449
## 185 0.6638409 5.925387
## 194 0.3110134 1.689000
## 218 0.6485304 3.310951
## 234 0.6252062 4.174794
## 254 0.4139736 4.747317
## 274 0.2766187 5.128787
## 169 1.0665463 2.655068
## 219 1.1029087 3.672722
## 419 1.7991470 7.837217
## 616 2.0673614 10.003262
## 716 2.7335105 10.233363
## 815 1.0656638 2.655950
## 916 1.1008792 3.674752
## 1116 1.7694890 8.014880
## 1315 2.0350603 10.761638
## 1414 2.6725879 11.391320
## 1514 1.0643112 2.657303
## 1610 1.0986465 3.676985
## 186 1.7323218 8.267678
## 205 2.0072160 11.992784
## 2110 2.6362636 13.363736
## 170 0.5301067 1.469913
## 320 1.4720208 2.844648
## 520 2.0675114 3.313432
## 717 2.2417900 3.608208
## 917 2.2021964 3.797804
## 1017 0.7246930 1.275324
## 1217 1.4052630 2.596917
## 1415 1.8930037 2.974142
## 1611 2.1022904 3.192823
## 187 2.2379962 3.362192
## 196 0.8158835 1.184131
## 2111 1.3227429 2.375889
## 235 1.6730907 2.782348
## 255 1.7988653 3.084853
## 275 1.8831372 3.380021
## 178 1.0000000 1.000000
## 226 1.1798492 2.820151
## 324 1.2397990 3.426868
## 424 1.2597822 3.629107
## 523 1.0000000 1.000000
## 618 1.1798492 2.820151
## 718 1.2839725 3.873922
## 817 1.3442544 4.484000
## 918 1.0000000 1.000000
## 1018 1.1798492 2.820151
## 1118 1.3596985 4.640302
## 1218 1.5395477 6.460452
##
## NOTE: The above output only shows five estimates for each assemblage; call iNEXT.object$iNextEst$coverage_based to view complete output.
##
## $AsyEst: asymptotic diversity estimates along with related statistics.
## Assemblage Diversity Observed Estimator s.e.
## 1 absent_arboleda_6-18-2023 Species richness 6.000000 7.882353 2.4942047
## 2 absent_arboleda_6-18-2023 Shannon diversity 4.949177 6.228502 1.4131704
## 3 absent_arboleda_6-18-2023 Simpson diversity 4.313433 5.440000 1.4633680
## 4 absent_arboleda_6-19-2023 Species richness 3.000000 3.200000 1.7366809
## 5 absent_arboleda_6-19-2023 Shannon diversity 2.871746 3.736207 1.8715485
## 6 absent_arboleda_6-19-2023 Simpson diversity 2.777778 5.000000 2.4604481
## 7 absent_cafeteria_6-18-2023 Species richness 3.000000 4.500000 2.1343747
## 8 absent_cafeteria_6-18-2023 Shannon diversity 2.828427 4.891309 3.2613332
## 9 absent_cafeteria_6-18-2023 Simpson diversity 2.666667 6.000000 NaN
## 10 absent_cafeteria_6-19-2023 Species richness 2.000000 2.333333 1.1758131
## 11 absent_cafeteria_6-19-2023 Shannon diversity 1.889882 2.519842 1.8407499
## 12 absent_cafeteria_6-19-2023 Simpson diversity 1.800000 3.000000 NaN
## 13 absent_trail_6-18-2023 Species richness 4.000000 4.888889 1.3068526
## 14 absent_trail_6-18-2023 Shannon diversity 3.369922 4.381978 0.9967327
## 15 absent_trail_6-18-2023 Simpson diversity 3.000000 4.000000 0.9626926
## 16 absent_trail_6-19-2023 Species richness 2.000000 2.500000 0.7032393
## 17 absent_trail_6-19-2023 Shannon diversity 2.000000 3.375000 1.1134622
## 18 absent_trail_6-19-2023 Simpson diversity 2.000000 Inf NaN
## 19 present_arboleda_6-18-2023 Species richness 3.000000 3.000000 0.3588703
## 20 present_arboleda_6-18-2023 Shannon diversity 2.527617 2.646028 0.2892100
## 21 present_arboleda_6-18-2023 Simpson diversity 2.351111 2.504950 0.2973463
## 22 present_arboleda_6-19-2023 Species richness 5.000000 5.888889 2.2517286
## 23 present_arboleda_6-19-2023 Shannon diversity 4.585756 6.346291 1.8600886
## 24 present_arboleda_6-19-2023 Simpson diversity 4.263158 7.200000 2.3665046
## 25 present_cafeteria_6-18-2023 Species richness 3.000000 3.800000 1.5881111
## 26 present_cafeteria_6-18-2023 Shannon diversity 2.586409 3.698855 1.7305253
## 27 present_cafeteria_6-18-2023 Simpson diversity 2.272727 3.333333 2.2523837
## 28 present_cafeteria_6-19-2023 Species richness 4.000000 8.500000 2.5799146
## 29 present_cafeteria_6-19-2023 Shannon diversity 4.000000 12.275085 4.2689241
## 30 present_cafeteria_6-19-2023 Simpson diversity 4.000000 Inf NaN
## 31 present_trail_6-18-2023 Species richness 3.000000 3.000000 0.3716639
## 32 present_trail_6-18-2023 Shannon diversity 2.800094 3.118342 0.3874074
## 33 present_trail_6-18-2023 Simpson diversity 2.631579 3.214286 0.5969419
## 34 present_trail_6-19-2023 Species richness 2.000000 2.500000 0.7088723
## 35 present_trail_6-19-2023 Shannon diversity 2.000000 3.375000 1.1223812
## 36 present_trail_6-19-2023 Simpson diversity 2.000000 Inf NaN
## LCL UCL
## 1 6.00000000 12.770904
## 2 3.45873909 8.998265
## 3 2.57185134 8.308149
## 4 3.00000000 6.603832
## 5 0.06803958 7.404375
## 6 0.17761039 9.822390
## 7 3.00000000 8.683298
## 8 0.00000000 11.283405
## 9 NaN NaN
## 10 2.00000000 4.637885
## 11 0.00000000 6.127646
## 12 NaN NaN
## 13 4.00000000 7.450273
## 14 2.42841732 6.335538
## 15 2.11315714 5.886843
## 16 2.00000000 3.878324
## 17 1.19265427 5.557346
## 18 NaN NaN
## 19 3.00000000 3.703373
## 20 2.07918684 3.212869
## 21 1.92216253 3.087738
## 22 5.00000000 10.302196
## 23 2.70058399 9.991997
## 24 2.56173617 11.838264
## 25 3.00000000 6.912641
## 26 0.30708742 7.090622
## 27 0.00000000 7.747924
## 28 4.00000000 13.556540
## 29 3.90814780 20.642023
## 30 NaN NaN
## 31 3.00000000 3.728448
## 32 2.35903796 3.877647
## 33 2.04430112 4.384270
## 34 2.00000000 3.889364
## 35 1.17517325 5.574827
## 36 NaN NaNAdding Bee Richness to Site Dataset
Bee_richness <-Bee_iNEXT$AsyEst[Bee_iNEXT$AsyEst$Diversity == "Species richness",c(1,3:4)]
Bee_shannon <-Bee_iNEXT$AsyEst[Bee_iNEXT$AsyEst$Diversity == "Shannon diversity",c(1,3:4)]
Bee_simpson <-Bee_iNEXT$AsyEst[Bee_iNEXT$AsyEst$Diversity == "Simpson diversity",c(1,3:4)]
#Adding to site dataset
bee.data$bee.rare.richness <- Bee_richness$Estimator
bee.data$bee.shannon <-Bee_shannon$Estimator
bee.data$bee.simpson <-Bee_simpson$Estimator
bee.data## # A tibble: 12 × 7
## # Groups: treatment, site [6]
## treatment site date n.obs bee.rare.richness bee.shannon bee.simpson
## <chr> <chr> <chr> <int> <dbl> <dbl> <dbl>
## 1 absent arboleda 6-18-2023 17 7.88 6.23 5.44
## 2 absent arboleda 6-19-2023 5 3.2 3.74 5
## 3 absent cafeteria 6-18-2023 4 4.5 4.89 6
## 4 absent cafeteria 6-19-2023 3 2.33 2.52 3
## 5 absent trail 6-18-2023 9 4.89 4.38 4
## 6 absent trail 6-19-2023 2 2.5 3.38 Inf
## 7 present arboleda 6-18-2023 23 3 2.65 2.50
## 8 present arboleda 6-19-2023 9 5.89 6.35 7.2
## 9 present cafeteria 6-18-2023 5 3.8 3.70 3.33
## 10 present cafeteria 6-19-2023 4 8.5 12.3 Inf
## 11 present trail 6-18-2023 10 3 3.12 3.21
## 12 present trail 6-19-2023 2 2.5 3.38 InfData Exploration
#Histogram of Bee Abundance
hist(bee.data$n.obs,
main = "Histogram of Bee Abundance",
xlab = "Abundance")
#Histogram of Species Richness
hist(bee.data$bee.rare.richness,
main = "Histogram of Species Richness",
xlab = "Richness")
#Histogram of Shannon Diversity
hist(bee.data$bee.shannon,
main = "Histogram of Shannon Diversity",
xlab = "Shannon Diversity")
Analyses
Question 1: How does the presence of a predator impact the likelihood that a bee will forage for floral rewards, and does habitat type mediate this relationship?*
Hypothesis: The abundance of visitors at the traps without predators will be higher than at traps with predators.Canopy cover will mediate bee abundance in traps. Bees will be more abundant in predator traps in habitats with more canopy cover.
q1.mod.1 <- glm(n.obs ~ treatment:site + date, data = bee.data, family = poisson(link = "log"))
summary(q1.mod.1) ##
## Call:
## glm(formula = n.obs ~ treatment:site + date, family = poisson(link = "log"),
## data = bee.data)
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.17181 0.29544 7.351 1.97e-13 ***
## date6-19-2023 -1.00063 0.23389 -4.278 1.88e-05 ***
## treatmentabsent:sitearboleda 0.60614 0.35887 1.689 0.09122 .
## treatmentpresent:sitearboleda 0.98083 0.33850 2.898 0.00376 **
## treatmentabsent:sitecafeteria -0.53900 0.47559 -1.133 0.25708
## treatmentpresent:sitecafeteria -0.28768 0.44096 -0.652 0.51414
## treatmentabsent:sitetrail -0.08701 0.41742 -0.208 0.83488
## treatmentpresent:sitetrail NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 51.3492 on 11 degrees of freedom
## Residual deviance: 3.4896 on 5 degrees of freedom
## AIC: 61.127
##
## Number of Fisher Scoring iterations: 4q1.mod.2 <- glm(n.obs ~ treatment+site + date, data = bee.data, family = poisson)
summary(q1.mod.2) #model 2 with the interaction is a slightly better fit, given by AIC##
## Call:
## glm(formula = n.obs ~ treatment + site + date, family = poisson,
## data = bee.data)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.8322 0.1916 14.780 < 2e-16 ***
## treatmentpresent 0.2814 0.2094 1.344 0.179078
## sitecafeteria -1.2164 0.2846 -4.273 1.92e-05 ***
## sitetrail -0.8535 0.2490 -3.428 0.000609 ***
## date6-19-2023 -1.0006 0.2339 -4.278 1.88e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 51.3492 on 11 degrees of freedom
## Residual deviance: 3.8231 on 7 degrees of freedom
## AIC: 57.461
##
## Number of Fisher Scoring iterations: 4plot(q1.mod.2)
plot_model(q1.mod.2, vline.color = "red",sort.est = TRUE,show.values = TRUE, value.offset = .3,axis.labels = c("Cafeteria Site","Date Two","Trail Site","Predator Present"), title = "The Effect of Predator Presence and Habitat Type on Bee Abundance")+
theme_few()
report(q1.mod.2)## We fitted a poisson model (estimated using ML) to predict n.obs with treatment,
## site and date (formula: n.obs ~ treatment + site + date). The model's
## explanatory power is substantial (Nagelkerke's R2 = 0.99). The model's
## intercept, corresponding to treatment = absent, site = arboleda and date =
## 6-18-2023, is at 2.83 (95% CI [2.44, 3.19], p < .001). Within this model:
##
## - The effect of treatment [present] is statistically non-significant and
## positive (beta = 0.28, 95% CI [-0.13, 0.70], p = 0.179; Std. beta = 0.28, 95%
## CI [-0.13, 0.70])
## - The effect of site [cafeteria] is statistically significant and negative
## (beta = -1.22, 95% CI [-1.81, -0.68], p < .001; Std. beta = -1.22, 95% CI
## [-1.81, -0.68])
## - The effect of site [trail] is statistically significant and negative (beta =
## -0.85, 95% CI [-1.36, -0.38], p < .001; Std. beta = -0.85, 95% CI [-1.36,
## -0.38])
## - The effect of date [6-19-2023] is statistically significant and negative
## (beta = -1.00, 95% CI [-1.48, -0.56], p < .001; Std. beta = -1.00, 95% CI
## [-1.48, -0.56])
##
## Standardized parameters were obtained by fitting the model on a standardized
## version of the dataset. 95% Confidence Intervals (CIs) and p-values were
## computed using a Wald z-distribution approximation.exp(-1.2164)## [1] 0.2962949#Visualizing Q1
palette <-c("#03fce7","#fc0303")
plot1 <- ggplot(data = bee.data, aes(x=site,y=n.obs))+
geom_boxplot(aes(fill=factor(treatment)))+
theme_few()+
labs(y = "bee abundance")+
theme(axis.text.x = element_text(size = 16),
axis.text.y = element_text(size = 16),
axis.title.x = element_text(size = 18),
axis.title.y = element_text(size = 18))+
scale_color_brewer(palette)
plot1 
## Questions 2
Question 2: How does the presence of a predator impact the richness of bee species that forage for floral rewards, and does habitat type mediate this relationship?*
Hypothesis: The richness of visitors at the traps without predators will be higher than at traps with predators. Canopy cover will mediate bee richness in traps. Bees will be more species rich in predator traps in habitats with more canopy cover.
bee.data$log_bee.shannon <-log(bee.data$bee.shannon)
shapiro.test(bee.data$log_bee.shannon)##
## Shapiro-Wilk normality test
##
## data: bee.data$log_bee.shannon
## W = 0.9, p-value = 0.1586bee.data$log_bee.rare.richness <-log(bee.data$bee.rare.richness)
shapiro.test(bee.data$log_bee.rare.richness)##
## Shapiro-Wilk normality test
##
## data: bee.data$log_bee.rare.richness
## W = 0.91336, p-value = 0.2356q2.mod.1 <- lm(log_bee.shannon ~ treatment:site + date, data = bee.data)
summary(q2.mod.1) ##
## Call:
## lm(formula = log_bee.shannon ~ treatment:site + date, data = bee.data)
##
## Residuals:
## 1 2 3 4 5 6 7 8
## 0.31537 -0.31537 0.39147 -0.39147 0.19039 -0.19039 -0.37757 0.37757
## 9 10 11 12
## -0.53994 0.53994 0.02029 -0.02029
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.11701 0.41086 2.719 0.0418 *
## date6-19-2023 0.11967 0.31058 0.385 0.7159
## treatmentabsent:sitearboleda 0.39675 0.53794 0.738 0.4939
## treatmentpresent:sitearboleda 0.23362 0.53794 0.434 0.6822
## treatmentabsent:sitecafeteria 0.07898 0.53794 0.147 0.8890
## treatmentpresent:sitecafeteria 0.73095 0.53794 1.359 0.2323
## treatmentabsent:sitetrail 0.17010 0.53794 0.316 0.7646
## treatmentpresent:sitetrail NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5379 on 5 degrees of freedom
## Multiple R-squared: 0.3388, Adjusted R-squared: -0.4546
## F-statistic: 0.427 on 6 and 5 DF, p-value: 0.8351q2.mod.2 <- lm(log_bee.shannon ~ treatment+site + date, data = bee.data)
summary(q2.mod.2)##
## Call:
## lm(formula = log_bee.shannon ~ treatment + site + date, data = bee.data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.66433 -0.20228 -0.08505 0.26430 0.81280
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.37908 0.33573 4.108 0.00453 **
## treatmentpresent 0.10624 0.30029 0.354 0.73390
## sitecafeteria 0.08978 0.36778 0.244 0.81415
## sitetrail -0.23014 0.36778 -0.626 0.55133
## date6-19-2023 0.11967 0.30029 0.399 0.70213
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5201 on 7 degrees of freedom
## Multiple R-squared: 0.1346, Adjusted R-squared: -0.3598
## F-statistic: 0.2723 on 4 and 7 DF, p-value: 0.8869q2.mod.3 <- lm(log_bee.rare.richness ~ treatment:site + date, data = bee.data)
summary(q2.mod.3) ##
## Call:
## lm(formula = log_bee.rare.richness ~ treatment:site + date, data = bee.data)
##
## Residuals:
## 1 2 3 4 5 6 7 8
## 0.37309 -0.37309 0.25075 -0.25075 0.25769 -0.25769 -0.41487 0.41487
## 9 10 11 12
## -0.48018 0.48018 0.01352 -0.01352
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.0851 0.3958 2.742 0.0407 *
## date6-19-2023 -0.1553 0.2992 -0.519 0.6259
## treatmentabsent:sitearboleda 0.6064 0.5182 1.170 0.2946
## treatmentpresent:sitearboleda 0.4284 0.5182 0.827 0.4461
## treatmentabsent:sitecafeteria 0.1682 0.5182 0.325 0.7586
## treatmentpresent:sitecafeteria 0.7301 0.5182 1.409 0.2179
## treatmentabsent:sitetrail 0.2442 0.5182 0.471 0.6573
## treatmentpresent:sitetrail NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5182 on 5 degrees of freedom
## Multiple R-squared: 0.3839, Adjusted R-squared: -0.3555
## F-statistic: 0.5192 on 6 and 5 DF, p-value: 0.776q2.mod.4 <- lm(log_bee.rare.richness ~ treatment+site + date, data = bee.data)
summary(q2.mod.4)##
## Call:
## lm(formula = log_bee.rare.richness ~ treatment + site + date,
## data = bee.data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.5272 -0.2321 -0.1221 0.3277 0.7378
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.57924 0.32212 4.903 0.00175 **
## treatmentpresent 0.04654 0.28811 0.162 0.87624
## sitecafeteria -0.06825 0.35287 -0.193 0.85212
## sitetrail -0.39532 0.35287 -1.120 0.29953
## date6-19-2023 -0.15529 0.28811 -0.539 0.60661
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.499 on 7 degrees of freedom
## Multiple R-squared: 0.2001, Adjusted R-squared: -0.257
## F-statistic: 0.4378 on 4 and 7 DF, p-value: 0.7783plot(q2.mod.2)
plot_model(q2.mod.2, vline.color = "red",sort.est = TRUE,show.values = TRUE, value.offset = .3,axis.labels = c("Cafeteria Site","Date Two","Trail Site","Predator Present"), title = "The Effect of Predator Presence and Habitat Type on Shannon Diversity")+
theme_few()
report(q1.mod.2)## We fitted a poisson model (estimated using ML) to predict n.obs with treatment,
## site and date (formula: n.obs ~ treatment + site + date). The model's
## explanatory power is substantial (Nagelkerke's R2 = 0.99). The model's
## intercept, corresponding to treatment = absent, site = arboleda and date =
## 6-18-2023, is at 2.83 (95% CI [2.44, 3.19], p < .001). Within this model:
##
## - The effect of treatment [present] is statistically non-significant and
## positive (beta = 0.28, 95% CI [-0.13, 0.70], p = 0.179; Std. beta = 0.28, 95%
## CI [-0.13, 0.70])
## - The effect of site [cafeteria] is statistically significant and negative
## (beta = -1.22, 95% CI [-1.81, -0.68], p < .001; Std. beta = -1.22, 95% CI
## [-1.81, -0.68])
## - The effect of site [trail] is statistically significant and negative (beta =
## -0.85, 95% CI [-1.36, -0.38], p < .001; Std. beta = -0.85, 95% CI [-1.36,
## -0.38])
## - The effect of date [6-19-2023] is statistically significant and negative
## (beta = -1.00, 95% CI [-1.48, -0.56], p < .001; Std. beta = -1.00, 95% CI
## [-1.48, -0.56])
##
## Standardized parameters were obtained by fitting the model on a standardized
## version of the dataset. 95% Confidence Intervals (CIs) and p-values were
## computed using a Wald z-distribution approximation.#Visualizing Q1
palette <-c("#03fce7","#fc0303")
plot2 <- ggplot(data = bee.data, aes(x=site,y=bee.rare.richness))+
geom_boxplot(aes(fill=factor(treatment)))+
theme_few()+
labs(y = "species richness")+
theme(axis.text.x = element_text(size = 16),
axis.text.y = element_text(size = 16),
axis.title.x = element_text(size = 18),
axis.title.y = element_text(size = 18))+
scale_color_brewer(palette)
plot2 
plot3 <- ggplot(data = bee.data, aes(x=site,y=bee.shannon))+
geom_boxplot(aes(fill=factor(treatment)))+
theme_few()+
labs(y = "shannon index")+
theme(axis.text.x = element_text(size = 16),
axis.text.y = element_text(size = 16),
axis.title.x = element_text(size = 18),
axis.title.y = element_text(size = 18))+
scale_color_brewer(palette)
plot3 
Q3: Community Composition
set.seed(1000)
#Creating matrix for metaMDS
BeeNMDSinput <-t(species.iNEXTinput)
Bees.nmds <- metaMDS(BeeNMDSinput, distance = "bray", k=2) #Creating Bee NMDS## Wisconsin double standardization
## Run 0 stress 0.1198121
## Run 1 stress 0.1198121
## ... Procrustes: rmse 0.0001349135 max resid 0.0003039394
## ... Similar to previous best
## Run 2 stress 0.1367906
## Run 3 stress 0.1582009
## Run 4 stress 0.1366986
## Run 5 stress 0.1466947
## Run 6 stress 0.1668436
## Run 7 stress 0.1517568
## Run 8 stress 0.1198121
## ... Procrustes: rmse 0.0001096376 max resid 0.0002307718
## ... Similar to previous best
## Run 9 stress 0.1366998
## Run 10 stress 0.1326014
## Run 11 stress 0.1599083
## Run 12 stress 0.1198121
## ... Procrustes: rmse 0.0001077014 max resid 0.0002516444
## ... Similar to previous best
## Run 13 stress 0.1302663
## Run 14 stress 0.1310474
## Run 15 stress 0.1198121
## ... Procrustes: rmse 0.0001385556 max resid 0.0003357684
## ... Similar to previous best
## Run 16 stress 0.1198121
## ... Procrustes: rmse 0.0001785926 max resid 0.0003417156
## ... Similar to previous best
## Run 17 stress 0.1302665
## Run 18 stress 0.1407991
## Run 19 stress 0.1198122
## ... Procrustes: rmse 0.0002556891 max resid 0.0006268501
## ... Similar to previous best
## Run 20 stress 0.2009723
## *** Best solution repeated 6 times#Creating dataframe of environmental variables of interest
env <-bee.data[,1:3]
#Running envfit function
envfit.results <-envfit(Bees.nmds,env, permutations = 999, na.rm= TRUE)
envfit.results##
## ***FACTORS:
##
## Centroids:
## NMDS1 NMDS2
## treatmentabsent -0.1948 -0.2272
## treatmentpresent 0.1948 0.2272
## sitearboleda -0.1339 0.1554
## sitecafeteria -0.5956 -0.0118
## sitetrail 0.7295 -0.1436
## date6-18-2023 -0.2007 -0.0846
## date6-19-2023 0.2007 0.0846
##
## Goodness of fit:
## r2 Pr(>r)
## treatment 0.1217 0.261
## site 0.4302 0.026 *
## date 0.0644 0.566
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Permutation: free
## Number of permutations: 999genus_specie <-species.wide$genus_specie
#Figure out which species are driving site distribution pattern
sppfit.results <- envfit(Bees.nmds, BeeNMDSinput, permutations = 999)
sppfit.results##
## ***VECTORS
##
## NMDS1 NMDS2 r2 Pr(>r)
## [1,] 0.83301 -0.55326 0.0259 0.877
## [2,] -0.33669 0.94162 0.5401 0.031 *
## [3,] -0.75180 -0.65939 0.0609 0.762
## [4,] -0.60428 0.79677 0.4717 0.059 .
## [5,] -0.95836 0.28556 0.2029 0.352
## [6,] 0.40803 -0.91297 0.0000 1.000
## [7,] 0.36940 0.92927 0.0430 0.806
## [8,] -0.07337 -0.99730 0.1387 0.563
## [9,] 0.78829 0.61530 0.6054 0.082 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Permutation: free
## Number of permutations: 999Bees.nmds$species## MDS1 MDS2
## [1,] 0.2699083 -0.14124510
## [2,] -0.4506024 0.81300402
## [3,] -0.2193381 -0.24766484
## [4,] -0.8639449 0.44592829
## [5,] -0.6888294 -0.35214242
## [6,] 0.7730793 -0.64748363
## [7,] 0.2298522 0.09848662
## [8,] -0.1082257 -0.77726761
## [9,] 1.8513384 0.76351823
## attr(,"shrinkage")
## MDS1 MDS2
## 0.7661137 0.5776601
## attr(,"centre")
## MDS1 MDS2
## 2.862294e-17 1.734723e-17#Extracting scores from NMDS
##Site scores
data.scores <- as.data.frame(scores(Bees.nmds$points))
data.scores$site <-bee.data$site
data.scores$treatment <-bee.data$treatment
##Species scores
species.scores <-as.data.frame(scores(Bees.nmds$species))
species.scores$species <-species.wide$genus_specie
species.scores$pvals <-sppfit.results$vectors$pvals
sig.species.scores <-subset(species.scores, pvals <=0.06)
sig.species.scores$species.clean <-c("Euglossa ignita","Euglossa viridissima")Model Checks
stressplot(Bees.nmds) #Shepard Stressplot. Not too much scatter away from line= good
NMDS.graph <-ggplot(data=data.scores, aes(x=MDS1, y=MDS2, color = site)) +
stat_ellipse()+
geom_point(aes(x=MDS1,y=MDS2),size=4) +
labs(x="NMDS1", y = "NMDS2")+
theme_few()+
theme(axis.text.x = element_text(size = 16, hjust =.75),
axis.text.y = element_text(size = 16),
axis.title.x = element_text(size = 20),
axis.title.y = element_text(size = 20),
legend.title = element_text(size=18),
legend.text = element_text(size=16),
legend.position = "right")
NMDS.graph