knitr::opts_chunk$set(echo = TRUE, fig.width = 5, fig.height = 5)

In addition to the simulation and analysis of spatially-explicit communities,
`mobsim`

provides a function to generate samples from simulated or observed communities.
The combination of simulated data AND simulated sampling is a powerful approach
to test the validity and power of empirical approaches.

Here, we simulate a community and then generate samples with the function
`sample_quadrats`

. By default `sample_quadrats`

distributes a user-defined
number of quadrats with user-defined size in the landscape and provides the
number of individuals for each species in each quadrat.

The function returns two dataframes. The first includes the abundance of every species in every sampling quadrat and the second the positions of the lower left corners of the quadrats.

The community matrix of samples by species can then be analysed using additional
software. For instance the R package vegan
is perfectly suited for the analysis of community data. See the vignette **Introduction
to mobsim** for a worked example.

library(mobsim) sim_com1 <- sim_poisson_community(s_pool = 100, n_sim = 20000)

sample1 <- sample_quadrats(sim_com1) head(sample1$spec_dat[,1:6]) head(sample1$xy_dat)

In `sample_quadrats()`

there is an option to exclude overlapping quadrats from the
random sampling design, which is shown here in two examples with different numbers and sizes
of the quadrats.

sample2 <- sample_quadrats(sim_com1, n_quadrats= 2, quadrat_area = 0.1, avoid_overlap = T)

sample3 <- sample_quadrats(sim_com1, n_quadrats= 20, quadrat_area = 0.001, avoid_overlap = T)

In addition to random designs also transects can be sampled-. This requires specifying a position for the lower left quadrat as well as x and y distances between neighbouring quadrats.

sample4 <- sample_quadrats(sim_com1, n_quadrats= 10, quadrat_area = 0.005, method = "transect", x0 = 0, y0 = 0.5, delta_x = 0.1, delta_y = 0) sample5 <- sample_quadrats(sim_com1, n_quadrats= 10, quadrat_area = 0.005, method = "transect", x0 = 0, y0 = 0, delta_x = 0.1, delta_y = 0.1)

Finally, sampling quadrats can be arranged in a regular lattice. For this design users have to choose distances among the quadrats in x and y dimension as shown in the example.

sample6 <- sample_quadrats(sim_com1, n_quadrats= 25, quadrat_area = 0.005, method = "grid", x0 = 0, y0 = 0, delta_x = 0.1, delta_y = 0.1) sample7 <- sample_quadrats(sim_com1, n_quadrats= 25, quadrat_area = 0.005, method = "grid", x0 = 0.05, y0 = 0.05, delta_x = 0.2, delta_y = 0.2)

By default, `sample_quadrats()`

plots the chosen design. However, the plotting
can be also deactivated for more efficient computations:

sample7a <- sample_quadrats(sim_com1, n_quadrats= 25, quadrat_area = 0.005, method = "grid", x0 = 0.05, y0 = 0.05, delta_x = 0.2, delta_y = 0.2, plot = F) head(sample7a$spec_dat[,1:10])

**Any scripts or data that you put into this service are public.**

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.