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Introduction
The cxr package provides diverse functions to handle
empirical datasets, and these need to be in a common format for their
processing. Here we review the structure of the dataset included in the
package, which conforms to the formats accepted by the different
functions of cxr.
The Caracoles dataset
We include a dataset of an annual plant system subjected to spatial
variability in a Mediterranean-type ecosystem of Southern Europe.
Details of the ecosystem and sampling design can be consulted in Lanuza
et al. (2018). The main data file contains, for each focal individual
sampled, its reproductive success and the number of neighbors per plant
species in a 7.5 cm buffer. Note that this format of data is not limited
to plant species. In fact, the package is not taxonomically biased,
meaning that observational data passed to cxr can contain
any information of individual performance as a function of the
interacting species’ relative frequency and density.
You can check the structure of the data in the help file
## [1] "BEMA" "CETE" "CHFU" "CHMI" "HOMA" "LEMA" "MEEL" "MESU" "PAIN" "PLCO"
## [11] "POMA" "POMO" "PUPA" "SASO" "SCLA" "SOAS" "SPRU"## # A tibble: 6 × 19
## obs_ID fitness BEMA CETE CHFU CHMI HOMA LEMA MEEL MESU PAIN PLCO
## <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 116 1 0 0 0 0 0 0 0 0 0
## 2 5 68 0 0 0 0 0 1 0 0 0 0
## 3 9 36 0 0 0 0 0 0 0 0 0 0
## 4 14 64 0 0 0 0 0 5 0 0 0 0
## 5 19 144 2 0 0 0 0 0 0 0 0 0
## 6 27 56 1 0 0 0 0 4 0 0 0 0
## # ℹ 7 more variables: POMA <dbl>, POMO <dbl>, PUPA <dbl>, SASO <dbl>,
## # SCLA <dbl>, SOAS <dbl>, SPRU <dbl>This structure is the one accepted by different cxr
functions, save for the ‘obs_ID’ column. In particular, cxr
accepts a dataframe with a first numeric column named ‘fitness’
(constrained to positive values), and a variable number of numeric
columns with the densities of neighbour taxa. Each row is taken to be an
observation of a focal individual.
Additionally to individual fitness and neighbours, we also recorded the species abundance:
## plot subplot species individuals
## 1 1 A1 BEMA 1
## 2 1 A1 CETE 0
## 3 1 A1 CHFU 8
## 4 1 A1 CHMI 0
## 5 1 A1 HOMA 35
## 6 1 A1 LEMA 4Abundances are stored per plot and subplot, in our spatially explicit
design (see Lanuza et al. 2018 for details). In the
neigh_list dataset, the obs_ID column relates
each observation to the spatial coordinates of the system. The
spatial_sampling dataset is a species list, in which each
element contains the obs_ID of each observation and its
spatial arrangement, i.e. the plot and subplot where it was taken.
## [1] "BEMA" "CETE" "CHFU" "CHMI" "HOMA" "LEMA" "MEEL" "MESU" "PAIN" "PLCO"
## [11] "POMA" "POMO" "PUPA" "SASO" "SCLA" "SOAS" "SPRU"## # A tibble: 6 × 3
## obs_ID plot subplot
## <int> <int> <chr>
## 1 1 1 A1
## 2 5 1 A2
## 3 9 1 A3
## 4 14 1 A4
## 5 19 1 A5
## 6 27 1 B1We also provide seed soil survival and germination rates for each species. These species vital rates have been obtained independently, and are critical to parameterize a model describing the population dynamics of interacting annual plant species. For more information of how to estimate seed soil survival and germination rates see Godoy and Levine (2014). This file also includes the complete scientific name and abbreviation of each species. Such abbreviations are used as species identifier in all analyses and vignettes.
## code species germination.rate seed.survival
## 1 BEMA beta_macrocarpa 0.38 0.43
## 2 CETE centaurium_tenuiflorum 0.53 0.10
## 3 CHFU chamaemelum_fuscatum 0.80 0.38
## 4 CHMI chamaemelum_mixtum 0.76 0.32
## 5 HOMA hordeum_marinum 0.94 0.25
## 6 LEMA Leontodon_maroccanus 0.89 0.33
## 7 MEEL melilotus_elegans 0.59 0.60
## 8 MESU melilotus_sulcatus 0.77 0.63
## 9 PAIN parapholis_incurva 0.33 0.60
## 10 PLCO plantago_coronopus 0.84 0.57
## 11 POMA polypogon_maritimus 0.85 0.46
## 12 POMO polypogon_monspeliensis 0.91 0.49
## 13 PUPA pulicaria_paludosa 0.84 0.55
## 14 SASO salsola_soda 0.52 0.30
## 15 SCLA scorzonera_laciniata 0.69 0.41
## 16 SOAS sonchus_asper 0.60 0.29
## 17 SPRU spergularia_rubra 0.44 0.41The environmental covariate provided for this analysis is soil salinity, measured with a portable Time Domain Reflectometer (TDR). This technology measures the amount of salt dissolved in the soil water that is accessible to plant species. This environmental covariate has been estimated for each sub-plot. There are 36 subplots for each plot, and there are 9 plots in total structured along a micro-topographic gradient:
## [1] "BEMA" "CETE" "CHFU" "CHMI" "HOMA" "LEMA" "MEEL" "MESU" "PAIN" "PLCO"
## [11] "POMA" "POMO" "PUPA" "SASO" "SCLA" "SOAS" "SPRU"## # A tibble: 6 × 2
## obs_ID salinity
## <int> <dbl>
## 1 1 0.986
## 2 5 1.03
## 3 9 1
## 4 14 0.883
## 5 19 0.842
## 6 27 0.912References
Godoy, O., & Levine, J. M. (2014). Phenology effects on invasion success: insights from coupling field experiments to coexistence theory. Ecology, 95(3), 726-736.
Lanuza, J. B., Bartomeus, I., & Godoy, O. (2018). Opposing effects of floral visitors and soil conditions on the determinants of competitive outcomes maintain species diversity in heterogeneous landscapes. Ecology letters, 21(6), 865-874.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.