Community composition as an overlooked driver of spatial population synchrony

Animal populations often display coherent temporal fluctuations in their abundance, with far-ranging implications for species persistence and ecosystem stability. The key mechanisms driving spatial population synchrony include organismal dispersal, spatially correlated environmental dynamics (Moran effect), and concordant consumer-resource dynamics. Disentangling these mechanisms, however, is notoriously difficult in natural systems, and the extent to which the biotic environment (intensity and types of biotic interactions) mediates metapopulation dynamics remains a largely unanswered question. Here, we test the hypothesis that compositional differences among communities (i.e, beta-diversity), used as a proxy of the differences in biotic interactions experienced by separated populations, reduces population synchrony. Using an extensive dataset of fish population abundance time-series across Europe, we provide evidence that higher beta-diversity is associated with reduced spatial populat..., We gathered long-term (>10 years) fish populations time series for 61 basins across Europe – providing the most continuous and consistent data series – from the RivFishTIME database (50). Sampling occurred during low flows, and standard protocols were maintained through time. We included basins with at least eight sites and two species. In addition, only species occurring in more than 80% of sampling events were included for a total of 48 species.
, , # Data from: Community composition as an overlooked driver of spatial population synchrony

[https://doi.org/10.5061/dryad.n2z34tn6x](https://doi.org/10.5061/dryad.n2z34tn6x)

## Description of the data and file structure

The dataset includes estimates of temporal synchrony from stream fish abundance time series.

Raw time-series data are available in the RivFishTime database. 

Additional data available include watercourse distance between sites, mean community beta-diversity, and synchrony in air temperature for each pair of sites

### Files and variables

#### File: synchrony_spp_paper.csv

**Description:** Core dataset used in the paper

##### Variables

* basin_ID: Unique basin ID
* Species: Fish species included
* Synchrony: Estimated synchrony value between fish populations (Spearman correlation [-1;1])
* sYNGEO_ID1: Code of site1
* sYNGEO_ID2: Code of site2
* sitepairs1: unique combination of site-pairs
* dist_ntw: watercourse distance between populations (km)
* Eucl_dist: Eucl..., ,