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Data from: Trends in plant cover derived from vegetation-plot data using ordinal zero-augmented beta regression

Cite this dataset

Retel, Cas (2024). Data from: Trends in plant cover derived from vegetation-plot data using ordinal zero-augmented beta regression [Dataset]. Dryad. https://doi.org/10.5061/dryad.4xgxd25g4

Abstract

Questions. Plant cover values in vegetation-plot data are bounded between 0 and 1, and cover is typically recorded in discrete classes with non-equal intervals. Consequently, cover data are skewed and heteroskedastic, which hampers the application of conventional regression methods. Recently developed ordinal beta regression models consider these statistical difficulties. Our primary question is if we can detect species trends in vegetation-plot time series data with this modelling approach. A second question is whether trends in cover have additional value compared to trends in occurrence, which are easier to assess for practitioners.

Location. The Netherlands, Western Europe.

Methods. We used vegetation-plot data collected from 10.000 fixed plots which were surveyed once every four years during 1999-2022. We used the ordinal zero-augmented beta regression (OZAB) model, a hierarchical model consisting of a logistic regression for presence and an ordinal beta regression for cover. We adapted the OZAB model for longitudinal data and produced estimates of cover and occurrence for each four-year period. Thereafter we assessed trends in cover and in occurrence across all periods.

Results. We found evidence of a trend in cover in 318 out of the 721 species (44%) with sufficient data. Most species showed similar directional trends in occurrence and percent cover. No trend in occurrence was detected for 64 species that had evidence of a trend in cover. Declining species had stronger relative changes in cover than in occurrence.

Conclusions. Our model enables researchers to detect trends in cover using longitudinal vegetation-plot data. Cover trends often corroborated trends in occurrence, but we also regularly found trends in cover even in the absence of evidence for trends in occurrence. Our approach thus contributes to a more complete picture of (changes in) vegetation composition based on large monitoring datasets.

README: Data from: Trends in plant cover derived from vegetation-plot data using ordinal zero-augmented beta regression

All data files with the extension '.csv' are colon-separated text files.

Visits table: one row per visit

  • Site_id: key variable identifying location
  • Visit_id: key variable identifying a location-date combination
  • Period: four-year visiting period

Observations table: one row per observation (species seen at a specific time at a specific place)

  • Site_id: key variable identifying location
  • Visit_id: key variable identifying a location-date combination
  • Species_id: key variable identifying species
  • Cover: plant cover percentage (calculated as described in the publication)
  • Cover_class: plant cover class (as documented in the field)

Species table: one row per species

  • Species_id: key variable identifying species
  • Scientific_name: scientific name

Methods

The national vegetation monitoring scheme consists of more than 10.000 fixed plots in the Netherlands. The program monitors all major terrestrial habitat types except urban areas. Fieldwork is organised on the level of provinces and conducted by professional field workers. These field workers selected the plots and collected vegetation data, using field guidelines to ensure proper selection of plot location and standardisation of data collection at the national level. All vascular plants were visually recorded using the extended Braun-Blanquet cover scale (Van der Maarel, 2007). This data package contains all observations of the species Acer pseudoplantanus, Senecio inaequidens and Caltha palustris from the period 1999 - 2022 recorded in this monitoring scheme.

Usage notes

All data tables are colon-separated text files with extension '.csv'.