Analize soils and tree-habitat associations

Installation

# install.packages("remotes")
remotes::install_github("helixcn/fgeo.habitat")

For details on how to install packages from GitHub, see this article.

Example

library(fgeo.habitat)
# For easier data wranging
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, union

Habitat-species associations.

# Example data
habitat <- luquillo_habitat
census <- luquillo_top3_sp

# Pick alive trees, of 10 mm or more
pick <- filter(census, status == "A", dbh >= 10)
# Pick sufficiently abundant trees
pick <- add_count(pick, sp)
pick <- filter(pick, n > 50)

species <- unique(pick$sp)

tt <- tt_test(census, species, habitat)
tt
#> [[1]]
#>        N.Hab.1 Gr.Hab.1 Ls.Hab.1 Eq.Hab.1 Rep.Agg.Neut.1 Obs.Quantile.1
#> CASARB      25     1489      109        2              0       0.930625
#>        N.Hab.2 Gr.Hab.2 Ls.Hab.2 Eq.Hab.2 Rep.Agg.Neut.2 Obs.Quantile.2
#> CASARB      12      168     1431        1              0          0.105
#>        N.Hab.3 Gr.Hab.3 Ls.Hab.3 Eq.Hab.3 Rep.Agg.Neut.3 Obs.Quantile.3
#> CASARB      14      567     1029        4              0       0.354375
#>        N.Hab.4 Gr.Hab.4 Ls.Hab.4 Eq.Hab.4 Rep.Agg.Neut.4 Obs.Quantile.4
#> CASARB      15      934      661        5              0        0.58375
#> 
#> [[2]]
#>        N.Hab.1 Gr.Hab.1 Ls.Hab.1 Eq.Hab.1 Rep.Agg.Neut.1 Obs.Quantile.1
#> PREMON      59      389     1208        3              0       0.243125
#>        N.Hab.2 Gr.Hab.2 Ls.Hab.2 Eq.Hab.2 Rep.Agg.Neut.2 Obs.Quantile.2
#> PREMON      75     1562       37        1              1        0.97625
#>        N.Hab.3 Gr.Hab.3 Ls.Hab.3 Eq.Hab.3 Rep.Agg.Neut.3 Obs.Quantile.3
#> PREMON      56      632      963        5              0          0.395
#>        N.Hab.4 Gr.Hab.4 Ls.Hab.4 Eq.Hab.4 Rep.Agg.Neut.4 Obs.Quantile.4
#> PREMON      44      222     1375        3              0        0.13875
#> 
#> [[3]]
#>        N.Hab.1 Gr.Hab.1 Ls.Hab.1 Eq.Hab.1 Rep.Agg.Neut.1 Obs.Quantile.1
#> SLOBER      14      492     1092       16              0         0.3075
#>        N.Hab.2 Gr.Hab.2 Ls.Hab.2 Eq.Hab.2 Rep.Agg.Neut.2 Obs.Quantile.2
#> SLOBER      16      473     1125        2              0       0.295625
#>        N.Hab.3 Gr.Hab.3 Ls.Hab.3 Eq.Hab.3 Rep.Agg.Neut.3 Obs.Quantile.3
#> SLOBER      19     1181      415        4              0       0.738125
#>        N.Hab.4 Gr.Hab.4 Ls.Hab.4 Eq.Hab.4 Rep.Agg.Neut.4 Obs.Quantile.4
#> SLOBER      17     1151      440        9              0       0.719375
#> 
#> attr(,"class")
#> [1] "tt_lst" "list"

tt_df <- to_df(tt)
# Try also: View(tt_df)
head(tt_df)
#>           metric     sp       value
#> 1        N.Hab.1 CASARB   25.000000
#> 2       Gr.Hab.1 CASARB 1489.000000
#> 3       Ls.Hab.1 CASARB  109.000000
#> 4       Eq.Hab.1 CASARB    2.000000
#> 5 Rep.Agg.Neut.1 CASARB    0.000000
#> 6 Obs.Quantile.1 CASARB    0.930625

tail(tt_df)
#>            metric     sp       value
#> 67        N.Hab.4 SLOBER   17.000000
#> 68       Gr.Hab.4 SLOBER 1151.000000
#> 69       Ls.Hab.4 SLOBER  440.000000
#> 70       Eq.Hab.4 SLOBER    9.000000
#> 71 Rep.Agg.Neut.4 SLOBER    0.000000
#> 72 Obs.Quantile.4 SLOBER    0.719375

Krige soil data.

result <- krig(soil_fake, var = c("c", "p"), quiet = TRUE)
head(to_df(result))
#>   var   x  y        z
#> 1   c  10 10 2.134696
#> 2   c  30 10 2.119651
#> 3   c  50 10 2.104591
#> 4   c  70 10 2.089517
#> 5   c  90 10 2.074427
#> 6   c 110 10 2.059322

tail(to_df(result))
#>      var   x   y        z
#> 2295   p 890 450 5.835048
#> 2296   p 910 450 5.826698
#> 2297   p 930 450 5.819219
#> 2298   p 950 450 5.812612
#> 2299   p 970 450 5.806876
#> 2300   p 990 450 5.802012

summary(result)
#> var: c 
#> df
#> 'data.frame':    1150 obs. of  3 variables:
#>  $ x: num  10 30 50 70 90 110 130 150 170 190 ...
#>  $ y: num  10 10 10 10 10 10 10 10 10 10 ...
#>  $ z: num  2.13 2.12 2.1 2.09 2.07 ...
#> 
#> df.poly
#> 'data.frame':    1150 obs. of  3 variables:
#>  $ gx: num  10 30 50 70 90 110 130 150 170 190 ...
#>  $ gy: num  10 10 10 10 10 10 10 10 10 10 ...
#>  $ z : num  2.13 2.12 2.1 2.09 2.07 ...
#> 
#> lambda
#> 'numeric'
#>  num 1
#> 
#> vg
#> 'variogram'
#> List of 20
#>  $ u               : num [1:9] 60.9 86.5 103 122.7 146.1 ...
#>  $ v               : num [1:9] 0.284 0.422 0.882 0.543 0.211 ...
#>  $ n               : num [1:9] 7 9 10 10 18 19 36 34 38
#>  $ sd              : num [1:9] 0.414 0.48 0.633 0.501 0.405 ...
#>  $ bins.lim        : num [1:31] 1.00e-12 2.00 2.38 2.84 3.38 ...
#>  $ ind.bin         : logi [1:30] FALSE FALSE FALSE FALSE FALSE FALSE ...
#>  $ var.mark        : num 0.317
#>  $ beta.ols        : num 1.36e-09
#>  $ output.type     : chr "bin"
#>  $ max.dist        : num 320
#>  $ estimator.type  : chr "classical"
#>  $ n.data          : int 30
#>  $ lambda          : num 1
#>  $ trend           : chr "cte"
#>  $ pairs.min       : num 5
#>  $ nugget.tolerance: num 1e-12
#>  $ direction       : chr "omnidirectional"
#>  $ tolerance       : chr "none"
#>  $ uvec            : num [1:30] 1 2.19 2.61 3.11 3.7 ...
#>  $ call            : language variog(geodata = geodata, breaks = breaks, trend = trend, pairs.min = 5)
#> 
#> vm
#> 'variomodel', variofit'
#> List of 17
#>  $ nugget               : num 0.352
#>  $ cov.pars             : num [1:2] 0 160
#>  $ cov.model            : chr "exponential"
#>  $ kappa                : num 0.5
#>  $ value                : num 4.64
#>  $ trend                : chr "cte"
#>  $ beta.ols             : num 1.36e-09
#>  $ practicalRange       : num 480
#>  $ max.dist             : num 320
#>  $ minimisation.function: chr "optim"
#>  $ weights              : chr "npairs"
#>  $ method               : chr "WLS"
#>  $ fix.nugget           : logi FALSE
#>  $ fix.kappa            : logi TRUE
#>  $ lambda               : num 1
#>  $ message              : chr "optim convergence code: 0"
#>  $ call                 : language variofit(vario = vg, ini.cov.pars = c(initialVal, startRange), cov.model = varModels[i],      nugget = initialVal)
#> 
#> var: p 
#> df
#> 'data.frame':    1150 obs. of  3 variables:
#>  $ x: num  10 30 50 70 90 110 130 150 170 190 ...
#>  $ y: num  10 10 10 10 10 10 10 10 10 10 ...
#>  $ z: num  6.37 6.35 6.33 6.31 6.29 ...
#> 
#> df.poly
#> 'data.frame':    1150 obs. of  3 variables:
#>  $ gx: num  10 30 50 70 90 110 130 150 170 190 ...
#>  $ gy: num  10 10 10 10 10 10 10 10 10 10 ...
#>  $ z : num  6.37 6.35 6.33 6.31 6.29 ...
#> 
#> lambda
#> 'numeric'
#>  num 1
#> 
#> vg
#> 'variogram'
#> List of 20
#>  $ u               : num [1:9] 60.9 86.5 103 122.7 146.1 ...
#>  $ v               : num [1:9] 0.396 0.4 0.11 0.402 0.385 ...
#>  $ n               : num [1:9] 7 9 10 10 18 19 36 34 38
#>  $ sd              : num [1:9] 0.592 0.414 0.133 0.409 0.488 ...
#>  $ bins.lim        : num [1:31] 1.00e-12 2.00 2.38 2.84 3.38 ...
#>  $ ind.bin         : logi [1:30] FALSE FALSE FALSE FALSE FALSE FALSE ...
#>  $ var.mark        : num 0.267
#>  $ beta.ols        : num -3.14e-09
#>  $ output.type     : chr "bin"
#>  $ max.dist        : num 320
#>  $ estimator.type  : chr "classical"
#>  $ n.data          : int 30
#>  $ lambda          : num 1
#>  $ trend           : chr "cte"
#>  $ pairs.min       : num 5
#>  $ nugget.tolerance: num 1e-12
#>  $ direction       : chr "omnidirectional"
#>  $ tolerance       : chr "none"
#>  $ uvec            : num [1:30] 1 2.19 2.61 3.11 3.7 ...
#>  $ call            : language variog(geodata = geodata, breaks = breaks, trend = trend, pairs.min = 5)
#> 
#> vm
#> 'variomodel', variofit'
#> List of 17
#>  $ nugget               : num 0.305
#>  $ cov.pars             : num [1:2] 0 160
#>  $ cov.model            : chr "exponential"
#>  $ kappa                : num 0.5
#>  $ value                : num 0.818
#>  $ trend                : chr "cte"
#>  $ beta.ols             : num -3.14e-09
#>  $ practicalRange       : num 479
#>  $ max.dist             : num 320
#>  $ minimisation.function: chr "optim"
#>  $ weights              : chr "npairs"
#>  $ method               : chr "WLS"
#>  $ fix.nugget           : logi FALSE
#>  $ fix.kappa            : logi TRUE
#>  $ lambda               : num 1
#>  $ message              : chr "optim convergence code: 0"
#>  $ call                 : language variofit(vario = vg, ini.cov.pars = c(initialVal, startRange), cov.model = varModels[i],      nugget = initialVal)

Information

Acknowledgements

Thanks to all partners of ForestGEO who shared their ideas and code. Functions’ authors include Graham Zemunik, Sabrina Russo, Daniel Zuleta, Matteo Detto, Kyle Harms, Gabriel Arellano.

Name		Name	Last commit message	Last commit date
Latest commit History 222 Commits
.github		.github
R		R
data-raw		data-raw
data		data
docs		docs
inst		inst
man		man
tests		tests
vignettes		vignettes
.Rbuildignore		.Rbuildignore
.gitignore		.gitignore
.travis.yml		.travis.yml
DESCRIPTION		DESCRIPTION
LICENSE.md		LICENSE.md
NAMESPACE		NAMESPACE
NEWS.md		NEWS.md
README.Rmd		README.Rmd
README.md		README.md
_pkgdown.yml		_pkgdown.yml
codecov.yml		codecov.yml
fgeo.habitat.Rproj		fgeo.habitat.Rproj
git.R		git.R
tmp.R		tmp.R

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Analize soils and tree-habitat associations

Installation

Example

Habitat-species associations.

Krige soil data.

Information

Acknowledgements

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Contributors 2

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Analize soils and tree-habitat associations

Installation

Example

Habitat-species associations.

Krige soil data.

Information

Acknowledgements

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