BEFdata | Similarity weights of German Raster Quadrants

Title:

Similarity weights of German Raster Quadrants

Access rights:

Free for public

Usage rights:

No information available

Published:

Eichenberg, D., Bowler, D., Bonn, A., Bruelheide, H., Grescho, V., Harter, D., … Jansen, F. (2020). Widespread decline in plant diversity across six decades. BioRxiv. https://doi.org/10.1101/2020.08.31.275461

Abstract:

Ecological similarity weights of German Raster quadrants according to 76 different variables. This dataset is a companion to the Analyses presented in Eichenberg et al. (2020). It is used to weight the Occurrence probabilities of a species A in grid-cell (MTB_Q) j in Germany as described in Hill (2012). Here, the 100 most similar neighboring grid cells out of 200 adjacent cells to target grid-cell j is presented. The similarity weights were generated from a set of external variables (described in more detail in dataset "Data to compute ecological similarity of German Raster Quadrants") using the function createWeights() - the code is given as a freeformat file attached to this dataset - available from the R library "sparta" (August et al, 2020, https://github.com/BiologicalRecordsCentre/sparta). NOTE: the downloadable Excel-sheet is only part (150 randomly selected target cells) of the full dataset of ecological weight! See freefromat files attached to this dataset (similarity_weights_100_of_200.rds) to get the full list of 120200 entries.

Design:

see Hill, M. O. (2012). Local frequency as a key to interpreting species occurrence data when recording effort is not known. Methods in Ecology and Evolution, 3(1), 195–205. https://doi.org/10.1111/j.2041-210X.2011.00146.x

Spatial extent:

All 12024 MTB_Q grid-cells of Germany (in the freeformat file attached).

Temporal extent:

The values here are based on mean values of available data (see "Data to compute ecological similarity of German Raster Quadrants") between 1960 and 2017.

Taxonomic extent:

None

Measurement cirumstances:

No information available

Data analysis:

See Eichenberg, D., Bowler, D., Bonn, A., Bruelheide, H., Grescho, V., Harter, D., … Jansen, F. (2020). Widespread decline in plant diversity across six decades. BioRxiv. https://doi.org/10.1101/2020.08.31.275461 - Supporting Information: Technical Details

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Dataset column

Name:

TARGET

Definition:

TARGET quadrant of the Frescalo algorithm (Hill 2012). This is the central grid-cell (MTB_Q) in a neighborhood of (here 100) neighboring grid cells. NOTE: It is a variable, composed by "S_" and then the 5-digit code of the German raster quadrants. The reason why not the 5-digit codes for raster quadrants were used directly is, that some of the 5-digits start with 0, which in R is internally deleted and thus turned into a 4-digit identifier. Thus, in your file used to characterize the observations, your raster quadrants MUST also start with "S_" followed by the 5-digit code of the raster quadrant.

Unit:

No information available

Datagroup:

Raster Quadrants (Messtischblatt Quadranten)

Keywords:

Target gid-cell

Values:

S_21484
S_20511
S_18323
S_20202
S_22262

Contributors:

No information available

Dataset column

Name:

NEIGHBOUR

Definition:

NEIGHBOURhood quadrants of the Frescalo algorithm (Hill 2012). This is the neighboring grid-cell (MTB_Q) of a target grid cell of the Frescalo algorithm (Hill 2012). NOTE: It is a variable, composed by "S_" and then the 5-digit code of the German raster quadrants. The reason why not the 5-digit codes for raster quadrants were used directly is, that some of the 5-digits start with 0, which in R is internally deleted and thus turned into a 4-digit identifier. Thus, in your file used to characterize the observations, your raster quadrants MUST also start with "S_" followed by the 5-digit code of the raster quadrant.

Unit:

No information available

Datagroup:

Raster Quadrants (Messtischblatt Quadranten)

Keywords:

Neighborhood grid-cell

Values:

S_21472
S_18494
S_19264
S_20214
S_21324

Contributors:

No information available

Dataset column

Name:

WEIGHT

Definition:

WEIGHT is the ecological weight that was computed from 76 different ecological variables (including edaphic, climatic and geographic). In addition, this ecological similarity is weighted by the geographic distance between the TARGET and the NEIGHBOUR gird-cell, based on the distance between their central coordinates (in km).

Unit:

No information available

Datagroup:

Ecological similarity weight

Keywords:

No information available

Values: