Moving beyond species–discharge relationships to a flow-mediated, macroecological theory of fish species richness

Abstract

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.

Using river discharge to model and deconstruct the latitudinal diversity gradient for fishes of the Western Hemisphere

“…This study adds to the growing recognition that Q , as an indicator of the space or habitat available to aquatic organisms, is a key predictor of freshwater community structure and biodiversity (e.g. Oberdorff et al ., ; Poff & Allan, ; Guégan et al ., ; Iwasaki et al ., ; McGarvey, ). Our hemispheric‐scale SDR results were remarkably similar to the global‐scale SDR models of Oberdorff et al .…”

“…A recent study of rivers in the U.S. Pacific Northwest found that native fish richness is more strongly associated with low flow Q than with mean annual Q or high flow Q (McGarvey, ). We observed a similar result in our more extensive study of Western Hemisphere rivers.…”

“…For example, Iwasaki et al . () showed that inter‐annual variability in the frequency of low flow Q events is a particularly strong correlate of global fish richness, while McGarvey () found that discrete periods of low flow Q (7‐day minimum Q ) are more closely associated with fish richness in temperate rivers than mean annual low flow Q . We therefore suggest that Q analyses at finer temporal scales may be a productive area for future research.…”

“…SDR studies most often use mean annual Q (average daily Q within a calendar year) as the predictor variable (e.g. Xenopoulos et al ., ; McGarvey & Ward, ), but theory and empirical data suggest that low or high flow Q may be more ecologically meaningful than annual averages (Rolls et al ., ; McGarvey, ).…”

Why does freshwater fish species richness differ between Pacific and Atlantic drainages of the Americas?

“…Iwasaki et al. () found that their best regression model included both mean discharge and flow variability measures while McGarvey's () best model included only discharge variability measures. Nonetheless, McGarvey and de Freitas Terra () concluded that discharge appeared as a strong predictor of freshwater fish richness in the Americas because it integrated the effects of climate and catchment size, rather than directly determining richness.…”