DOI: 10.5061/dryad.7d7wm37x7
title: Fish assemblages on two continents

description: Aim: Fish assemblages –whether defined by taxonomy or functional
 traits—respond to regional and local habitat variation. We sampled rivers
 of Mongolia and the western United States (US) to determine the scale at
 which habitat could predict fish assemblage variation, classified by
 taxonomy or functional traits. Our hypothesis was that fish assemblages
 could be predicted using valley-scale hydrogeomorphology and reach-scale
 hydrology. We further predicted that if valley-scale variables explained
 high variation in fish assemblages then reach-scale variables would
 explain additional dimensions. Location: Mongolia, United States Methods:
 We evaluated reach- and valley-scale hydrogeomorphology of rivers in the
 US and Mongolia in each of three ecoregions, grassland, forest, and
 endorheic. Fishes were collected using backpack electrofisher following
 standard protocols. Results: Ordinations resulted in distinct assemblage
 patterns that corresponded with habitat variables at both valley- and
 reach-scales. Hydrogeomorphology differed for Mongolia and US rivers and
 likely contributed to different patterns that explained fish assemblage
 variation classified by taxonomy vs. traits. Ecoregions differed in
 factors contributing to fish assemblage patterns, likely a result of
 differences in hydrogeomorphology and historical influences, as well as
 effects of introduced species in the US. Main Conclusions: We found that
 fish assemblages were structured by hydrogeomorphic processes occurring at
 valley- and reach-scales, and that variables predicting fish assemblages
 vary with scale, ecoregion, and continent. We found a common pattern where
 if valley-scale variables provided high explanation of fish assemblages,
 then reach-scale variables frequently explained more ordination dimensions
 than valley-scale variables. This implies that reach-scale hydrology
 variables are always strong predictors of fish assemblage variation, and
 valley-scale geomorphology variables are sometimes strong predictors. We
 found evidence that introduced species or anthropogenic impacts modified
 our analyses predicting fish assemblage variation of Mongolia and US
 mountain steppe rivers. Although anthropogenic impacts were substantially
 higher for western US rivers than for Mongolia rivers, we were unable to
 detect strong differences in our ability to predict fish assemblage
 variation from reach- and valley-scale habitat variables.
description: <strong>2.1 Study area and valley-scale habitat
 assessment</strong> We identified rivers in the
 US and Mongolia in three ecoregions, grassland (G), forest-steppe (F), and
 endorheic (E, Figures 1, 2) (Olson et al., 2001). Unique hydrogeomorphic
 patches were delineated into Functional Process Zones (FPZs, Thorp et al.,
 2006; 2008) using the GIS-based program RESonate (Williams et al., 2013)
 to extract valley-scale hydrogeomorphic and environmental variables from
 existing geospatial data. Maasri et al. (2019; 2021a) described details
 for data extraction using RESonate. We used the ten most influential
 variables for valley-scale hydrogeomorphology to delineate FPZs. These
 variables—which were extracted at 10 km stream intervals because of the
 size of these rivers--included elevation, mean annual precipitation,
 geology, valley width, valley floor (floodplain) width, valley
 width-to-valley width ratio, river channel sinuosity, right valley slope,
 left valley slope, and down valley slope. Data were normalized to a 0 to 1
 scale for each river network, and a dissimilarity matrix was generated
 using a Gower dissimilarity transformation (Gower, 1971). A Gower
 transformation is recommended for non-biological data with
 range-standardization (Thoms & Parsons, 2003). The dissimilarity
 matrix was used in a hierarchical clustering following the Ward linkage
 method, as it resulted in the best partitioning of clusters (Murtagh
 & Legendre, 2014). We then used a Principal Components Analysis
 (PCA) to identify important contributive variables for group partitioning,
 and to describe cluster groups based on the ten variables described above.
 Cluster groups were later mapped to allow identification of sampling
 sites. We performed the clustering of FPZ groups using the
 <em>cluster </em>package (version 2.1.0) (Maechler et al.,
 2018) and the PCA using the <em>FactoMineR</em> package
 (version 1.42) (Lê et al., 2008) in R version 3.6.3 (R Core Team, 2020).
 We mapped the resulting groups using ArcGIS (version 10.5). We examined
 gradients in hydrogeomorphology with PCA using the ten influential
 valley-scale variables (above) in Minitab 18.1 (minitab.com) for all sites
 and by continent and ecoregion. <strong>2.2
 Reach-scale habitat assessment</strong> Each
 selected site was sampled following the Physical Habitat protocols from
 Environmental Monitoring and Assessment Program section 7 (Lazorchak et
 al., 1998) to provide a characterization of hydrogeomorphology at the
 reach-scale. Recorded field measurements were calculated into seven
 different metric sections (channel geometry, bank geometry, substrate,
 fish cover, human influence, riparian cover, flow) representing the
 habitat and dominant processes in the reach (Kaufmann et al., 1999).
 Sampling was conducted over a total reach length of 40 times the average
 wetted width, except where total reach length would have exceeded 5 km,
 where length was halved. Transects were taken at 0.1 intervals of the
 total reach length, while half transects were taken at 0.05 of the total
 reach length. Visual estimates of riparian cover were recorded as the
 amount and type of cover provided in a 10 m by 10 m area on the left and
 right banks centered on each transect. Visual estimates of the amount and
 type of fish cover were recorded representing an area 5 m upstream and 5 m
 downstream in and over the water at a transect. Human influence data were
 collected using a “presence metric” that also indicated closeness to the
 river at a given transect (P- Present &gt; 10m away, C- Present within
 10 m, B- present on the bank, 0- Not Present). Channel geometry data
 included five depth measurements across each transect, and wetted width at
 each transect and half transect. Bank geometry data were collected at each
 transect on both banks and included top-of-bank elevations and distances,
 bankfull elevations and distances, and bank angles. Substrate data were
 collected at the same spot as depth at transects, as well as at half
 transects. Additional reach-scale data were collected
 remotely in ArcGIS using digital elevation models and aerial photography
 to extract slope and sinuosity. In total, 120 characteristics and metrics
 were collected for each sampled site (Appendix 1). These variables were
 reduced by selecting only characteristics that were aggregates of multiple
 similar characteristics (i.e., PCT_FAST sums the percentages of falls
 cascades, rapids, and riffles). FPZ segment data were linked to sampled
 reaches through analysis in GIS, using a spatial join. The spatial join
 was conducted on the most downstream GPS point of a sampled site, joining
 one-to-one with the closest FPZ segment with a search radius to select a
 single FPZ line with a single sampled site. The spatial join was manually
 confirmed that each sampled site had an associated FPZ segment. In cases
 where reaches did not pair with an FPZ segment, manual connections were
 made by identifying the closest FPZ segment downstream. The FPZ dataset,
 originally representing over 4300 valley segments across FPZs (Costello
 et. al, in review), was reduced to 95 segments representing the FPZs that
 were sampled. The most downstream point of a sampled
 reach was used to delineate a contributing watershed area boundary. Using
 the watershed boundary, data were extracted from DEMs (SRTM 30-m Mongolia,
 10-m US), land cover (IGBP Land Cover Classification, Mongolia; National
 Land Cover Dataset, US), and climate (WorldClim, 30 arc-second). Land
 cover characteristics were combined to provide consistent classifications
 between the United States and Mongolia. Land cover characteristics were
 divided by the contributing watershed area to allow for relative
 comparison across differing watershed sizes. A total of 29 characteristics
 were collected for each of the 96 contributing watersheds (Appendix
 1). <strong>2.3 Fish collections and
 traits</strong> We collected fishes from 94 sites
 that were identified as described above. Site distances for fish
 collections were 20 times the mean wetted width. We collected fishes by
 single-pass backpack electrofishing supplemented with angling (Ball State
 University IACUC #126193) following the American Fisheries Society
 standard collection protocols (Bonar et al., 2009). Fish abundances across
 sampled areas were standardized using CPUE fish-per-m. Fishes were
 collected during one-month expeditions in each of the river networks
 during summer or fall seasons from 2017-2019 (Maasri et al., 2021b).
 Species identifications, ecological and biological traits were from
 Mendsaikhan et al. (2017) for Mongolia, and from state fish guides for the
 US. Reproductive traits were reduced to four categories: nonguarder open
 substratum, nonguarder brood hiders, guarders, and viviparous based on
 Balon (1975). <strong>2.4
 Analyses</strong> Valley-scale geomorphology data
 and reach-scale hydrology data were reduced separately into fewer
 variables with minimal collinearity using Principal Components Analysis
 (PCA) in Minitab version 18. We used three PCA axes for the valley-scale
 data and five PCA axes for the reach-scale data. We then evaluated fish
 assemblage responses to valley-scale geomorphology and reach-scale
 hydrology variables using constrained ordinations with forward selection
 of environmental variables in CANOCO 5 software (canoco5.com). CANOCO
 evaluates length of the first ordination axis and recommends either a
 linear method (Redundancy Analysis, RDA) or a nonlinear method (Canonical
 Correspondence Analysis, CCA). RDA is a direct gradient technique for
 multifactorial analysis-of-variance models using ecologically relevant
 distance measures and significance testing of individual variables
 (Legendre & Anderson, 1999). CCA is a direct gradient weighted
 averaging regression technique in CANOCO using environmental predictors
 (Palmer, 1993). PCA axes explaining habitat variation at the reach- and
 valley-scales were used as inputs to RDA or CCA as environmental
 predictors of fish assemblage variation. Both direct gradient analyses
 used 999 Monte Carlo permutations for significance of environmental
 variables as predictors of fish assemblage structure. If RDA or CCA were
 unable to reach a solution for constrained ordinations (usually due to
 number of sites in an ordination were not sufficiently large enough
 compared to the number of environmental predictors; Kent 2006), we used
 unconstrained ordinations with environmental variables projected. For an
 unconstrained ordination, CANOCO uses gradient length to suggests either a
 linear PCA or unimodal (Correspondence Analysis) ordination. All
 multivariate analyses were performed for taxonomic abundances and trait
 abundances with environmental predictors at three scales: two continents
 combined, individual continents, and individual ecoregions. Fish and trait
 abundances were log-transformed by log (X + 1) before analysis to account
 for abundances spanning three orders of magnitude. We summed the variation
 explained in ordinations by continent and ecoregion and compared the
 number of PCA axis predictors at the reach-scale and valley-scale to test
 if more predictors were required at a scale when higher variation was
 explained at the other scale.
description: Please see accompanying manuscript for details.