RivFishTIME: A global database of fish time‐series to study global change ecology in riverine systems

Abstract

Motivation: We compiled a global database of long-term riverine fish surveys from 46 regional and national monitoring programmes and from individual academic research efforts, with which numerous basic and applied questions in ecology and global change research can be explored. Such spatially and temporally extensive datasets have been lacking for freshwater systems in comparison to terrestrial ones. Main types of variables contained: The database includes 11,386 time-series of riverine fish community catch data, including 646,270 species-specific abundance records, together with metadata related to the geographical location and sampling methodology of each time-series. Spatial location and grain: The database contains 11,072 unique sampling locations (stream reach), spanning 19 countries, five biogeographical realms and 402 hydrographical basins worldwide. Time period and grain: The database encompasses the period 1951-2019. Each timeseries is composed of a minimum of two yearly surveys (mean = 8 years) and represents a minimum time span of 10 years (mean = 19 years). Major taxa and level of measurement: The database includes 944 species of rayfinned fishes (Class Actinopterygii). Software format: csv. Main conclusion: Our collective effort provides the most comprehensive long-term community database of riverine fishes to date. This unique database should interest ecologists who seek to understand the impacts of human activities on riverine fish biodiversity and to model and predict how fish communities will respond to future environmental change. Together, we hope it will promote advances in macroecological research in the freshwater realm.

The geography of metapopulation synchrony in dendritic river networks

“…This provides the theoretical basis of the 'fluvial synchrogram' concept as a graphical exploratory tool. Next, we confront expectations from theoretical synchrograms using empirical synchrony estimates between > 34 000 pairs of fish species populations from an extensive abundance time-series data across Europe (Comte et al 2021). Although not designed to provide an explicit quantification of the different synchrony mechanisms, our analytical framework elucidates the emerging patterns of synchrony manifested from spatial variations in the underlying mechanisms.…”

A new composite abundance metric detects stream fish declines and community homogenization during six decades of invasions

“…Globally, recent efforts suggest a trend towards making existing fish community data more readily available to researchers conducting landscape-level analyses on fish biodiversity. So far, efforts to compile global fish community databases have focused on establishing species lists (occurrence records) for the world's major drainage basins (Tedesco et al, 2017) and isolating data from long-term monitoring sites (stream reaches) subject to routine sampling visits and standardized sampling and data management practices (Comte et al, 2020) Angermeier et al (1991), using an AC-current electric seine, reported consistently higher sampling efficiency across all common fish families than Mahon (1980) reported from the use of a pulsed DC-current method. However, both studies saw variation in efficiency across a range of taxa, wherein percids were least efficiently sampled and catostomids were most efficiently sampled.…”

“…. Consistent, repeatable long-term monitoring, such as what has occurred in those sites included in theComte et al (2020) …”

Partitioning the temporal changes in abundance-based beta diversity into loss and gain components

“…The R scripts for partitioning beta diversity ('ecopart.R'; Ecological COmmunity PARTitioning or Extinction and COlonization PARTitioning) and for extracting the Swedish fish community data from RivFishTIME (Comte et al, 2021) are available at GitHub (https://github.com/communityecologist/ecopart).…”

“…We applied the partitioning method to riverine fish community data retrieved from the Swedish Electrofishing Register database (Sers, 2013) via RivFishTIME (Comte et al, 2021).…”