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This function computes the initial values of richness values for each patch.


  rescale = TRUE



A raster object with the configured landscape


A raster object with values of the distance from the target patch to natural forest patches.


A data frame with three columns: value of land use (integer: 0 = "Other", 1 = "Pine plantation", 2 = "Natural Forests", 3 = "Crop"); lowRich and upRich (lower and upper value of the range of Richness).


density of the pine plantation (integer)


the past land use of the pine plantation (character). One of "Oak", "Shrubland", "Pasture" or "Crop".

elev, rad

elevation and annual radiation of the pine plantation

ftreeden, fdist, fclim

functions used to computed the effect of tree density (ftreeden), and of the distance to natural forest (fdist), and of the climate-proxy variables (fclim) on richness values within pine plantation. See details.

w_dist, w_treeden, w_past, w_clim

weights applied to the functions that correct the plant richness values according to the distance to seed source, the tree density, the past-land use of the pine plantation, and also the climate-proxy variables of the pine plantation.


If "TRUE" the results are rescaled (0 = min and 1 = max)


A raster object with values of initial richness for each pixel.


This function computes the initial plant richness for the land-use categories (e.g. target pine-plantation, surrounding natural forests, shrubland and crops). For each land-use category, the richness value of the pixels in each of the patches is randomly calculated from a range of potential richness values specified by r_range. In diveRpine, richness value for each of the patch classes are calculated considering the range of possible values found on the study area (Sierra Nevada, southern Spain). Specifically:

No Data0.000.00
Pine Plantations12.8213.34
Natural Forests13.7216.11

A richness value is assigned to each pixel. This value will depend on the pixel category.

Pine plantation

The richness values of each pixel of the focal (target) pine patch depends on:

  • Stand Features: tree density, patch size, past land-use, climate-proxy variables

  • Distance to seed source (landscape configuration)

For each pixel j, the initial richness value (\(R_{init,j}\)) is computed as $$Richess \sim Potential\ Richenss \times fc$$ where Potential Richenss is a random value coming from r_range and fc is a correction factor: $$fc = w_{past}\cdot f(\textrm{past~Land~Use}) + w_{dist}\cdot f(\textrm{Seed~source~distance}) + w_{treeden}\cdot f(\textrm{Tree~density}) + w_{clim}\cdot f(\textrm{Climate~proxy})$$

We specified the following weights according to literature (see references):

  • \(w_{past}\) = 0.2

  • \(w_{dist}\) = 0.35

  • \(w_{treeden}\) = 0.25

  • \(w_{clim}\) = 0.2

but different weights can be provided using w_past, w_dist,w_treeden, and w_clim respectively.

Each of the factors affecting the richness within a pine plantation pixel are computed as follows:

Tree density (ftreeden)

Richness and species diversity within pine plantation are strongly conditioned by tree density, which has a negative effect on the plant diversity, and on the total plant species richness. Potential richness is affected as a function of density, as follows: $$\textrm{ftreeden} = \exp \left(-\frac{1}{2} \left( \frac{ \textrm{treeDensity} - 0.22} {1504.1}\right )^2\right )$$

This equation is the used by default, but could be change with ftreeden. Tree density value is specified by treedensity.

Climate-proxy factors

Potential richness are also strongly affected by climate-proxy variables. It has been determined from 19 climatic and topographical variables that the elevation and the annual radiation are the variables that best explaining the variability of potential richness within pine-plantation (they capturing more than 83.3 % of the observed variance). The climate effect on potential richness within pine-plantation has been modeled according to the following equation: $$\textrm{fclim}=\textrm{exp}\left (-\frac{1}{2}\left ( \frac{\textrm{Altitude}- 1557.16}{644.89} \right )^{2} \right ) \times ~\textrm{exp}\left (-\frac{1}{2}\left ( \frac{\textrm{Radiation}}{13.24} \right )^{2} \right )$$

This equation is the used by default, but could be change with fclim. Altitude and radiation values are specified by elev and rad.

Seed source distance (fdist).

Seed dispersal depends on the distance from the seed source. In pine plantations, the presence and abundance of species other than pines is determined, among others, by the distance to the seed source. In Sierra Nevada (southern Spain) natural oak forests are the most influential in terms of distance to the seed source. Oak vegetation has higher plant diversity than pine plantations, especially for herbaceous species. Shorter distances could increase the pool of species in the pine plantations and reduce the evenness of plantation communities. The relationship found between distance to the source and diversity observed in pine plantations is governed by the following equation: $$\textrm{Diversity} = 1.7605 - 0.0932 * \sqrt{\sqrt{\textrm{Distance}}}$$

This equation is the used by default, but could be change with fdist. For each pixel of pine plantation the distances between the centroid of the pixel and the edge of each natural forest patches are computed using the function dist2nf() which generate a distance raster (draster).

Past Land Use

The richness value of a plantation is conditioned by the past land use. For instance, it has been found that regeneration of Quercus in pine plantations depends more on past land-use than on plantation tree density and distance to the seed source. Navarro-González et al. (2013), found that the probability finding regeneration within a plantation varies as a function of past land use. We rescaled the gradient found by Navarro-González et al. (2013) as follow: natural forest (0.9999), Shrubland (0.4982), Cropland (0.0279), and Grassland (0.0001). The value of \(f(\textrm{past Land Use})\) is selected according to the past land use specified in pastUse.

Natural forests and Croplands

The initial richness values of each pixel of natural forest and cropland patches are randomly selected from the value ranges specified on r_range.


Gómez-Aparicio L, Zavala MA, Bonet FJ, Zamora R (2009). “Are pine plantations valid tools for restoring Mediterranean forests? An assessment along abiotic and biotic gradients.” Ecological Applications, 19(8), 2124--2141. doi:10.1890/08-1656.1 .

Mendoza I, Gómez-Aparicio L, Zamora R, Matías L (2009). “Recruitment limitation of forest communities in a degraded Mediterranean landscape.” Journal of Vegetation Science, 20(2), 367--376. doi:10.1111/j.1654-1103.2009.05705.x .

González-Moreno P, Quero JL, Poorter L, Bonet FJ, Zamora R (2011). “Is spatial structure the key to promote plant diversity in Mediterranean forest plantations?” Basic and Applied Ecology, 12(3), 251--259. doi:10.1016/j.baae.2011.02.012 .

Navarro-González I, Pérez-Luque AJ, Bonet FJ, Zamora R (2013). “The weight of the past: land-use legacies and recolonization of pine plantations by oak trees.” Ecological Applications, 23(6), 1267--1276. doi:10.1890/12-0459.1 .

Pérez-Luque AJ, Bonet FJ, Pérez-Pérez R, Aspizua R, Lorite J, Zamora R (2014). “Sinfonevada: Dataset of floristic diversity in Sierra Nevada forests (SE Spain).” PhytoKeys, 35, 1--15. doi:10.3897/phytokeys.35.6363 .


Antonio J Pérez-Luque (