Data from a flexible framework to assess patterns and drivers of beta diversity across spatial scales
Cite this dataset
He, Siwen; Qin, Chunyan; Soininen, Janne (2023). Data from a flexible framework to assess patterns and drivers of beta diversity across spatial scales [Dataset]. Dryad. https://doi.org/10.5061/dryad.0zpc8672w
Abstract
The patterns and underlying ecological (e.g., environmental filtering) and historical (e.g., priority effects) drivers of beta diversity are scale-dependent but generally difficult to distinguish and rarely explored with a sufficiently broad range of spatial scales. We propose a general scale-explicit framework to assess and contrast the patterns and drivers of beta diversity across hierarchical spatial scales ranging from within fine-scale ecoregion-scale to among broad-scale ecoregion-scale. By applying this framework to aquatic macroinvertebrate datasets, we show that beta diversity generally increases with spatial extent. With an increasing spatial extent, beta diversity shifts from being more influenced by environmental filtering to being more influenced by recent historical factors (i.e., past beta diversity). Such recent historical effects may result from past environmental variation rather than priority effects. We also found that the small-scale and large-scale environmental drivers act differently on beta diversity across spatial extents. Our research reveals a complex spatial-scale dependence in beta diversity patterns and their drivers and provides a more holistic understanding of beta diversity dynamics. Our framework represents a flexible way to unravel the internal structure of beta diversity across scales by partitioning of entire beta diversity variation into scale-specific differences and may have broad application in community ecology, landscape planning and biodiversity conservation.
README: Macroinvertebrate taxon dataset assembled two different times (past and contemporary) and explanatory data
Authors: Siwen He, Chunyan Qin, Janne Soininen; A flexible framework to assess patterns and drivers of beta diversity across spatial scales
Correspondence: Siwen He; siwenhe@cqu.edu.cn
Description of files:
- nrsa0304_past_communities.csv: containing the following information: Presence-absence macroinvertebrate taxon dataset in previous (2000-2004) survey
- nrsa0809_contemporary_communities.csv: containing the following information: Presence-absence macroinvertebrate taxon dataset in the following (2008-2009) survey
- Explanatory_variables.csv: containing nine physicochemical variables, and four landscape variables, and five spatial network variables, and four bioclimatic variables
Physicochemical variables:
- NH4 (mg/L) ammonia nitrogen
- total.P (μg/L) total phosphorus
- NO3 (mg N/L) nitrate
- pH.lab pH
- DOC (mg/L) dissolved organic carbon
- LWD.reach (volume (m3) per 100m channel) large woody debris cover
- Cond (μS/cm) conductivity
- NAT.cover (proportion of areal cover) natural cover
- ALG.cover (proportion of areal cover) algal cover
Landscape variables:
- pct.for (% of basin area) forested land cover
- pct.ag (% of basin area) agricultural land cover
- pct.urb (% of basin area) urban land cover
- pct.ISC (% of basin area) impervious surface cover
Spatial network variables:
- site.lat (GPS decimal degrees) latitude
- site.long (GPS decimal degrees) longitude
- basin.area (square kilometers) basin area
- mean.annual.flow (cubic feet per second (cfs) at bottom of flowline) mean annual flow
- site.centrality (distance to centroid in km) network centrality
Bioclimatic variables:
- BIO1 (degrees C, times 10) annual temperature
- BIO4 (degrees C, times 100) temperature seasonality
- BIO12 (mm) annual precipitation
- BIO15 (mm) seasonality of precipitation, coefficient of variation
Usage notes
Data are compiled in different files containing the following information: 1) Presence-absence macroinvertebrate taxon dataset in the previous survey, 2) Presence-absence macroinvertebrate taxon dataset in the following survey, 3) dataset of explanatory variables.
Funding
National Natural Science Foundation of China, Award: 32101270