Data from: Non-native species affect the long-term dynamics of native stream fish assemblages
Data files
Apr 03, 2025 version files 30.38 KB
-
Czegledi_et_al_oikos.zip
28.39 KB
-
README.md
1.99 KB
Abstract
Characterizing the temporal changes of biotic communities and disentangling the importance of their driving mechanisms are central themes in ecology and environmental management. Non-native species have multiple adverse impacts on native communities and species. However, there is a dearth of information on how non-natives influence the long-term dynamics of native communities. In this study, we compared the role of non-native species alongside various local and regional factors in the long-term dynamics of stream fish assemblages in the catchment area of Lake Balaton, Hungary, the largest lake in Central-Europe. Although, we found no consistent trend in species re-ordering between native and non-native species, native fish assemblages indicated significantly lower temporal stability with increasing relative abundance of co-occurring non-natives. Structural equation modelling revealed that assemblage dynamic patterns were also determined by a habitat gradient influenced by both natural and human-induced processes. Moreover, habitat degradation and the presence of fishponds increased the relative abundance of non-native species, further affecting native assemblages through indirect interactions. Fish assemblage dynamics also exhibited a certain degree of finer-scale spatial structure highlighting the need for tailored management strategies for each stream. Non-metric multidimensional scaling reinforced the destabilizing effect of non-native species but highlighted that native fish assemblages could generally be described by non-directional gradual or saltatory changes over time, regardless of the relative abundance of non-natives. Our results thus revealed that the studied native fish assemblages may possess some degree of resilience against biological invasions. However, increasing temporal variability induced by non-natives makes native assemblages more vulnerable to environmental stochasticity threatening their long-term persistence. This is especially worrying, since the frequency and intensity of environmental disturbances are expected to rise in the future due to climate change and increasing anthropogenic pressure on aquatic ecosystems.
Dataset DOI: 10.5061/dryad.1vhhmgr42
Description of the data and file structure
This dataset includes the collected raw biotic data (i.e. fish abundance from 2006-2023), environmental data and spatial variables for the study “Non-native species affect the long-term dynamics of native stream fish assemblages”.
Files and variables
Descripton:
1. Fish.csv
-
Sites: Site ID
-
Year: Sampling year
-
The additional colums contain the scientific names of the species with the raw abundance data
2. Environment.csv
-
Sites: Site ID
-
The additional columns contain the mean values of the environmental variables among sampling years:
- Artificial_surfaces (%) - Agricultural_areas (%)- Forests (%) - Non_forest_vegetation (%) - Wetlands (%) - Water bodies (%): % of land use types of each stream subcatchment
- Elevation(m): elevation
- Width(m): stream width
- Depth(cm): water depth
- Water velocity (cm/s): water velocity
- Riparian_herbaceous (%) - Riparian_arboreal (%) - Riparian_concrete (%): % of habitat structure of the stream margin along a ~10 m wide strip in both sides
- Substratum_Silt/silty-sand (%) - Substratum_sand (%) - Substratum_Gravel (%) - Substratum_Stone (%) - Substratum_Rock (%) - Substratum_Concrete (%): % of substraum types
- Hydrophyte_emergent (%) - Hydrophyte_submerged (%) - Hydrophyte_floating-leaved (%) - Hydrophyte_filamentous algae (%) - Hydrophyte_plant free area (%): % of aquatic vegetation
- Dissolved oxygen (mg/l) - Conductivity_SPC (µS) - Total Dissolved Solids - pH - Nitrate N (mg/l) - Nitrite N (mg/l) - Ammonia N (mg/l) - Ortho-phosphate P (µg/l): chemical variables
3. Spatial.csv:
-
Site_ID: Site ID
-
PCNM 1-31: spatial variables resulted from a principal coordinates of neighbour matrix analysis (PCNM) based on Euclidean watercourse distance among the sampled sites