1. Species-discharge relationships (SDR) are aquatic analogues of species-area relationships, and are increasingly used in both basic research and conservation planning. SDR studies are often limited, however, by two shortcomings. First, they do not determine whether reported SDRs, which normally use complete drainage basins as sampling units, are scale dependent. Second, they do not account for the effects of habitat diversity within or among samples. 2. We addressed both problems by using discrete fish zones as sampling units in a SDR analysis. To do so, we first tested for longitudinal zonation in three rivers in the southeastern U.S.A. In each river, we detected successive 'lower', 'middle', and 'upper' fish zones, which were characterized by distinct fish assemblages with predictable habitat requirements. Because our analyses combined fish data from multiple sources, we also used rarefaction and Monte Carlo simulation to ensure that our zonation results were robust to spurious sampling effects. 3. Next, we estimated the average discharge within each zone, and plotted these estimates against the respective species richness within each zone (log 10 data). This revealed a significant, linear SDR (r 2 = 0.83; P < 0.01). Notably, this zonal SDR fit the empirical data better than a comparable SDR that did not discriminate among longitudinal zones. We therefore conclude that the southeastern fish SDR is scale dependent, and that accounting for within-basin habitat diversity is an important step in explaining the high diversity of southeastern fishes. 4. We then discuss how our zonal SDR can be used to improve conservation planning. Specifically, we show how the slope of the SDR can be used to forecast potential extinction rates, and how the zonal data can be used to identify species of greatest concern.

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Secondary production, the growth of new heterotrophic biomass, is a key process in aquatic and terrestrial ecosystems that has been carefully measured in many flowing water ecosystems. We combine structural equation modeling with the first worldwide dataset on annual secondary production of stream invertebrate communities to reveal core pathways linking air temperature and precipitation to secondary production. In the United States, where the most extensive set of secondary production estimates and covariate data were available, we show that precipitation-mediated, low–stream flow events have a strong negative effect on secondary production. At larger scales (United States, Europe, Central America, and Pacific), we demonstrate the significance of a positive two-step pathway from air to water temperature to increasing secondary production. Our results provide insights into the potential effects of climate change on secondary production and demonstrate a modeling framework that can be applied across ecosystems.

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Species distribution models (SDMs) in river ecosystems can incorporate climate information by using air temperature and precipitation as surrogate measures of instream conditions or by using independent models of water temperature and hydrology to link climate to instream habitat. The latter approach is preferable but constrained by the logistical burden of developing water temperature and hydrology models. We therefore assessed whether regional scale, freshwater SDM predictions are fundamentally different when climate data versus instream temperature and hydrology are used as covariates. Maximum entropy (MaxEnt) SDMs were built for 15 freshwater fishes using one of two covariate sets: 1) air temperature and precipitation (climate variables) in combination with physical habitat variables; or 2) water temperature, hydrology (instream variables) and physical habitat. Three procedures were then used to compare results from climate vs instream models. First, equivalence tests assessed average pairwise differences (site‐specific comparisons throughout each species’ range) among climate and instream models. Second, ‘congruence’ tests determined how often the same stream segments were assigned high habitat suitability by climate and instream models. Third, Schoener's D and Warren's I niche overlap statistics quantified range‐wide similarity in predicted habitat suitability from climate vs instream models. Equivalence tests revealed small, pairwise differences in habitat suitability between climate and instream models (mean pairwise differences in MaxEnt raw scores for all species < 3 × 10–4). Congruence tests showed a strong tendency for climate and instream models to predict high habitat suitability at the same stream segments (median congruence = 68%). D and I statistics reflected a high margin of overlap among climate and instream models (median D = 0.78, median I = 0.96). Overall, we found little support for the hypothesis that SDM predictions are fundamentally different when climate versus instream covariates are used to model fish species’ distributions at the scale of the Columbia Basin.

Ichthyofaunal zonation occurs when lotic fishes are partitioned into distinct assemblages, usually in response to longitudinally distributed habitats. Several studies have documented zonation within the Rio Grande, but this is the first to quantitatively test the zonation hypothesis along a continuous 2800-km river profile, extending from the Rio Chama headwaters to the Gulf of Mexico. Using a large, multi-source dataset, I detected three ichthyofaunal zones: a high gradient (1.5%) 'upper' zone, a moderate gradient (0.2%) 'middle' zone and a low gradient (<0.1%) 'lower' zone. Species richness was lowest in the upper zone and highest in the lower zone, and all zones contained large numbers of nonnative species. However, species richness did not accumulate in a consistent, downstream manner. Instead, it tracked local-scale changes in mean annual discharge. This demonstrates the strong effect of river regulation and irrigation withdraws on fish diversity in the Rio Grande.

Hydrologic indices can be used in macroecological models of fish species richness. I used fish distributions and river flow records from 89 river basins in the Pacific Northwest (USA) to identify robust associations between aspects of flow and fish species richness. I calculated 148 hydrologic indices for each basin, created single-predictor regression models for each hydrologic index, and tested for significant relationships between native fish species richness and specific components of flow. I used multiple regression to test the significance and fit of models that combined hydrologic indices that were not highly collinear indices. Significant linear relationships ( p ≤ 0.05) were detected for 85 indices. Coefficients of determination (r 2 ) ranged from 0.04 to 0.33 (median = 0.19, coefficient of variation [CV] = 0.55). I identified 2 multiple-regression models, each including 3 hydrologic indices, as best based on information-theoretic model comparisons. One included median large flood rise rate, CV of 1-d maximum flow, and CV of annual flow. The other included mean annual flow, median high flow timing, and CV of small flood timing. Four major themes emerged from the analyses: 1) fish species richness and a variety of indices of flow magnitude were positively related; 2) the strength of the positive association between fish species richness and flow magnitude was inversely related to flow magnitude and the strongest associations were observed during low-flow winter months; 3) flow variability, indicated by the CVs of hydrologic indices, was negatively associated with fish species richness; 4) indicators of episodic high-flow events (e.g., floods and annual maximum flows) were particularly well represented in the best multiple-regression models of fish species richness. These results can provide a baseline for comparison with other rivers and can be used to help formulate a general, flow-mediated theory of fish species richness in lotic ecosystems.

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