EcoNiche workflow

Input data

EcoNiche expects a taxon-by-sample abundance matrix and a sample-by-variable environmental table. The sample IDs in colnames(plant_otu) match rownames(plant_env) and names(plant_group).

data("plant_otu")
data("plant_env")
data("plant_group")

dim(plant_otu)

## [1] 1038  290

dim(plant_env)

## [1] 290  11

table(plant_group)[1:5]

## plant_group
## BDGS_BDGS   BTM_BTM   BYS_BYS   DHY_DHY   DMS_DMS 
##        10        10        10        10         4

For fast examples, we retain species observed in at least 15 samples.

otu_ex <- plant_otu[rowSums(plant_otu > 0) >= 15, ]
dim(otu_ex)

## [1] 200 290

CCA / partial-CCA workflow

The CCA workflow uses multiple environmental variables to define the focal composite gradient and annual mean temperature as the covariate and plotting gradient.

cca_res <- cca_workflow_gradient(
  otu = otu_ex,
  env = plant_env,
  sel = c("AMP", "Moisture", "AP", "pH"),
  covariates = "AMT",
  var = "AMT",
  method = "loess",
  galaxy_colnum = FALSE,
  make_plot = FALSE,
  top_node = 20
)

names(cca_res)

## [1] "step1" "step2" "step3" "step4"

head(cca_res$step3$species)

##                          NichePosition NicheWidth
## Zanthoxylum.echinocarpum     0.1697882     4.7420
## Xanthophyllum.hainanense     1.7444806     5.3941
## Vitis.amurensis             -1.0263057     5.6119
## Viburnum.erosum             -0.5273252    10.2469
## Viburnum.dilatatum          -0.5462778     8.7614
## Viburnum.betulifolium       -0.3724929     5.2418

Group-level workflow

group_res <- cca_workflow_group(
  otu = otu_ex,
  env = plant_env,
  group = plant_group,
  sel = c("AMP", "Moisture", "AP", "pH"),
  covariates = "AMT",
  var = "AMT",
  choice = "all",
  method = "loess",
  plot_type = "sample",
  galaxy_colnum = FALSE,
  make_plot = FALSE
)

names(group_res)

## [1] "step1" "step2" "step5" "step6"

head(group_res$step6$data)

##          ENV NicheWidth NicheWdithSD
## DQS_DQS1 1.5   5.815143     2.058778
## DQS_DQS2 1.5   5.657917     2.208762
## DQS_DQS3 1.5   5.657917     2.208762
## DQS_DQS4 1.5   5.307614     2.219123
## DQS_DQS5 1.5   6.118060     2.123746
## DQS_DQS6 1.5   5.620067     2.233015

GAM response analysis

GAMs can be used to estimate taxon optima and response breadth along a single gradient.

otu_gam <- plant_otu[rowSums(plant_otu > 0) >= 20, ]

gam_res <- gam_fit_model(
  otu = otu_gam,
  env = plant_env,
  env_var = "AMT",
  data_type = "count",
  count_family = "nb",
  use_offset = TRUE,
  min_prev = 0.10,
  min_total = 20,
  k_spline = 5,
  n_grid = 100,
  verbose = FALSE
)

head(gam_res)

##                         OTU n_nonzero prevalence total_counts r2  dev_expl
## 7         Spiraea.pubescens        57  0.1965517          978 NA 0.8008558
## 12      Rhododendron.simsii        38  0.1310345          528 NA 0.7288264
## 14     Quercus.wutaishanica        64  0.2206897         1586 NA 0.8141505
## 18           Pinus.armandii        31  0.1068966          401 NA 0.8160050
## 30 Daphniphyllum.macropodum        41  0.1413793          184 NA 0.6504727
## 39         Carpinus.viminea        52  0.1793103          396 NA 0.6238261
##         edf   F_stat p_value optimum_env env50_min env50_max breadth50
## 7  2.876958 73.23198       0    1.500000  1.500000  2.663636 1.1636364
## 12 3.857311 51.25146       0   10.421212 10.033333 10.809091 0.7757576
## 14 2.842716 84.33932       0    5.572727  2.857576  6.930303 4.0727273
## 18 3.762329 76.05598       0    6.736364  6.348485  7.318182 0.9696970
## 30 2.638485 40.99768       0   11.778788 10.809091 12.748485 1.9393939
## 39 3.924044 41.33183       0   10.033333  9.645455 10.421212 0.7757576

Levins breadth along a binned gradient

levins_res <- levins_calc_binned(
  otu = otu_ex,
  env = plant_env,
  axis_var = "AMT",
  nbin = 8,
  bin_method = "equal_freq",
  min_occ = 3,
  min_abund = 5
)

head(levins_res)

##                       OTU axis n_states levins_B levins_Bstd n_samples
## 1       Spiraea.pubescens  AMT        8 1.600861  0.08583722        57
## 2    Quercus.wutaishanica  AMT        8 1.699995  0.09999926        64
## 3          Quercus.aliena  AMT        8 1.838842  0.11983455        25
## 4      Quercus.acutissima  AMT        8 1.748160  0.10688006        31
## 5       Lespedeza.bicolor  AMT        8 3.792412  0.39891596        42
## 6 Indosasa.shibataeaoides  AMT        8 1.246672  0.03523889        18
##   total_abund
## 1    8.128477
## 2   15.323397
## 3    5.140355
## 4    6.668544
## 5    5.226307
## 6   10.465862