Data for Manuscript
"Systematic and highly resolved modelling of biodiversity in inherently rare groundwater amphipods"
Knüsel, Alther, Locher, Ozgul, Fišer & Altermatt (2024) Journal of Biogeography.
https://doi.org/10.1111/jbi.14975 

Please do not use without citing the original article.

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Description of the data and file structure:


1.- specimen_list.csv: contains a list of identified specimens with corresponding GenBank accession numbers.

index: the row numbers. integer data
sample_id: unique identification number for each sample. integer data
nr_ind: number of individuals included in the corresponding sample. integer data
site: unique identification number for each sampling site. integer data
date: sampling date. format dd.mm.yyyy
sampling_method: collection method of the sample, filternet or kescher (hand net), character data
species: name of the identified species. character data
coi: COI sequence data (only provided if not available from GenBank). character data
genbank_accession: accession number under which the sequence is available on GenBank. character data
remark: additional information, character data


2.- dataset_for_occupancy_model.RData: Dataset used for the occupancy model.

Same format as used in the spOccupancy vignette (https://www.jeffdoser.com/files/spoccupancy-web/articles/modelfitting#example-data-set-foliage-gleaning-birds-at-hubbard-brook).
List, comprising of:
- y: detection-nondetection data. 3-dimensional [species, sites, temporal replicates]
- occ.covs: dataframe with spatial occurrence covariates for each sampling site. Detailed covariate description in the main manuscript.
- det.covs: list of detection covariates (per sampling occasion). Detailed covariate description in the main manuscript.
- coords: Coordinates are not provided, due to sensitivity of drinking water well locations and corresponding data protection laws.


3.- 02_1_run_occupancy_model.R: R script for occupancy model
Please note:
The R code is shared for transparency, however, the code cannot be executed without coordinates.


4.- 02_2_predict_CH_occupancy.R: R script for occupancy model prediction
Please note: The R code is shared for transparency, but cannot be executed independently.