One of the most influential disturbances for stream fish assemblages is large-scale declines in flow caused by periods of drought. Although stream characteristics are known to influence the response of stream fishes to drought, we asked if ecological traits of stream fishes determine, in part, their population level response to drought. To test for ecological trait-based responses to drought in a stream fish assemblage, we quantified species abundances over a period of 5 years that represented a wet to dry period. We sampled stream fishes in Yellow Creek, Wyoming, USA, a high elevation stream dependent on snow-storage for most of its flow. There were five regularly occurring species in the study site: redside shiner (Richardsonius balteatus), northern leatherside chub (Lepidomeda copei), mottled sculpin (Cottus bairdi), speckled dace (Rhinichthys osculus), and mountain sucker (Catostomus platyrhynchus). We used size class, species, and drought measures as predictors of abundance. Mean Palmer drought severity index over the growing season from the previous year (one year lag) provided the best predictor of stream fish abundances. Four of five species showed strong declines in abundance in response to drought conditions (mountain sucker abundance was not affected), but ecological traits of species were not good predictors of the magnitude of response to drought. Northern leatherside chub are most vulnerable to local extirpation during times of severe drought. Overall, juveniles showed a greater decline in abundance than adults in response to drought. Climate models predict that mountain streams will experience changes in flow regime, which may exacerbate effects of drought. Low flow refuge habitat may need to be incorporated into stream restoration designs to help increase recolonization in streams, especially for stream fishes that are most vulnerable to local extirpation and that have low recolonization rates.

Recent research has challenged the long-standing hypothesis that forests in the Upper Midwest of the United States developed during wetter periods and retreated during dry periods. We explored this debate by examining patterns of tree establishment on an oak savanna in east-central Minnesota within the context of variable moisture availability and fire suppression. We used superposed epoch analyses (SEA) to evaluate the mean moisture conditions for a 21-year window surrounding tree establishment dates. Before effective fire suppression (1809–1939), 24 of 42 trees with pith dates (62%) grew to 30-cm height during dry years (Palmer Drought Severity Index < -1), versus only 5 of 42 (12%) that established in wet years (PDSI > 1). Significantly more trees established during dry periods (negative PDSI values) than would be expected with the proportion of wet-to-dry years (x²= 10.738, df = 1, p-value = 0.001). Twenty of the complete sample of 74 trees with pith dates (27%) established during drought in the 1930s. We hypothesize that dry conditions limited plant productivity, which in turn decreased competition between grasses and tree seedlings and reduced rates of accumulation of fine fuels, enabling seedlings to grow tall enough to resist subsequent fires. We recommend SEA as a methodological approach to compare historical climate conditions with the timing of regeneration success in other regions of forest expansion.

Meteorological drought indices are commonly calculated from climatic stations that have long-term historical data and then converted to a regular grid using spatial interpolation methods. The gridded drought indices are mapped to aid decision making by policy makers and the general public. This study analyzes the spatial performance of interpolation methods for meteorological drought indices in the United States based on data from the Co-operative Observer Network (COOP) and United States Historical Climatology Network (USHCN) for different months, climatic regions and years. An error analysis was performed using cross-validation and the results were compared for the 9 climate regions that comprise the United States. Errors are generally higher in regions and months dominated by convective precipitation. Errors are also higher in regions like the western United States that are dominated by mountainous terrain. Higher errors are consistently observed in the southeastern U.S. especially in Florida. Interpolation errors are generally higher in the summer than winter. The accuracy of different drought indices was also compared. The Standardized Precipitation and Evapotranspiration Index (SPEI) tends to have lower errors than Standardized Precipitation Index (SPI) in seasons with significant convective precipitation. This is likely because SPEI uses both precipitation and temperature data in its calculation, whereas SPI is based solely on precipitation. There are also variations in interpolation accuracy based on the network that is used. In general, COOP is more accurate than USHCN because the COOP network has a higher density of stations. USHCN is a subset of the COOP network that is comprised of high quality stations that have a long and complete record. However the difference in accuracy is not as significant as the difference in spatial density between the two networks. For multiscalar SPI, USHCN performs better than COOP because the stations tend to have a longer record. The ordinary kriging method (with optimal function fitting) performed better than Inverse Distance Weighted (IDW) methods (power parameters 2.0 and 2.5) in all cases and therefore it is recommended for interpolating drought indices. However, ordinary kriging only provided a statistically significant improvement in accuracy for the Palmer Drought Severity Index (PDSI) with the COOP network. Therefore it can be concluded that IDW is a reasonable method for interpolating drought indices, but optimal ordinary kriging provides some improvement in accuracy. The most significant factor affecting the spatial accuracy of drought indices is seasonality (precipitation climatology) and this holds true for almost all the regions of U.S. for 1-month SPI and SPEI. The high-quality USHCN network gives better interpolation accuracy with 6-, 9- and 12-month SPI and variation in errors amongst the different SPI time scales is minimal. The difference between networks is also significant for PDSI. Although the absolute magnitude of the differences between interpolation with COOP and USHCN are small, the accuracy of interpolation with COOP is much more spatially variable than with USHCN.