DATASET: TESTING NICHE EQUIVALENCE IN AMPHIDROMOUS FISH POPULATIONS

Presence records of Galaxias maculatus were collected from 12 locations across five river basins in central-southern Chile during March, May, August, and November of 2019 as part of a study on fish sampling and processing (Ramírez-Álvarez et al. 2022. doi.org/10.1038/s41598-022-06936-8)

Isotopic niches defined using a standard ellipse area (SEAc) in isotopic space, represented by a 2D ellipsoidal space (δ13C - δ15N) (see Supporting Information: Standard ellipse area functions - Ramírez-Álvarez et al. 2024. doi:10.1007/s10750-024-05738-5) - Empirical Bayesian Kriging

Database of varibles used for niche modelling: isotopic niche and seven abiotic variables selected from 23 predictor variables: (1) 19 climate variables representing 1950–2000 climate averages from WorldClim (http://www.worldclim.org/). (2) Four spatially continuous topographic and hydrological variables from the EarthEnv Project adjusted to the HydroSHEDS river network (http://www.earthenv.org/) (Domisch et al. 2015). Selection of variables was accomplished by analysis of covariance and multicollinearity, using the ENMeval R package (Muscarella et al. 2014): (1) principal component analysis (PCA) to explore relationships among all predictors, evaluating the composition of components (component variables) that accounted for ≥65% of variance explained, (2) pairwise comparisons  to detect pairs of variables with strong correlations (Pearson correlation coefficients <0.8), groups with a correlation of less than 0.8 were considered independent, and (3) variance inflation factor (VIF) <10, to reduce the effect of collinearity between predictors (Listed below). A VIF greater than 10 indicates collinearity problems in the model. The vifcor and vifstep functions were employed by calculating two different strategies to exclude highly collinear variables using a stepwise procedure (Muscarella et al. 2014).

Variable description and ecological question associated, selected variables are marked in bold.

Series 1: Temperature, temperature variations and interaction with precipitation.

bio1: Annual Mean Temperature; Is the temperature usually suitable? 

bio2: Mean Diurnal Range; Are the days too warm or too cold?

bio3: Isothermality; Do temperatures fluctuate greatly over the course of a month? 

bio4: Temperature Seasonality (standard deviation); Do temperatures fluctuate greatly over the course of a year?

bio5: Min Temperature of Coldest Month; Is the maximum temperature too high?

bio6: Min Temperature of Coldest Month; Is the temperature constantly too high?

bio7: Temperature Annual Range; Do temperatures fluctuate greatly over the course of a year?

bio8: Mean Temperature of Wettest Quarter; Is it too cold or too warm during the rainy season?

bio9: Mean Temperature of Driest Quarter; Is it too cold or too warm during the dry season?

bio10: Mean Temperature of Warmest Quarter; Are the warmer months too cold?

bio11: Mean Temperature of Coldest Quarter; Are the colder months too warm?

Series 2: Precipitation and Rainfall Patterns

bio12: Annual Precipitation; Does it rain enough in a year?

bio13: Precipitation of Wettest Month; Does it rain a lot during the wettest month?

bio14: Precipitation of Driest Month; Does it rain poorly during the driest month?

bio15: Precipitation Seasonality (Coefficient of Variation); Would rainfall fluctuate much between seasons?

bio16: Precipitation of Wettest Quarter; Does it rain a lot in the rainy season?

bio17: Precipitation of Driest Quarter; Is rainfall low during dry seasons?

bio18: Precipitation of Warmest Quarter; Does it rain enough during the warmer months?

bio19: Precipitation of Coldest Quarter; Does it rain enough during the colder months?

Series 3: Topology and hydrology

dem: Average elevation; Does altitude play a role as a topological factor?

slope_av: Average slope; Is the slope suitable for the accumulation of small ponds?

flow_ac_ac: Accumulation flow; Does enough water accumulate or does it drain too quickly?

flow_ac_le: Accumulation flow direction; Does the flow direction support the formation of small ponds?

Domisch, S., G. Amatulli & W. Jetz, 2015. Near-global freshwater-specific environmental variables for biodiversity analyses in 1 km resolution. Scientific Data 2(1):150073 doi:10.1038/sdata.2015.73.

Muscarella, R., P. J. Galante, M. Soley‐Guardia, R. A. Boria, J. M. Kass, M. Uriarte & R. P. Anderson, 2014. ENM eval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models. Methods in ecology and evolution 5(11):1198-1205