Fish and habitat data were collected from 84 wadeable stream reaches in the Mississippi River drainage of Iowa to predict the occurrences of seven fish species of greatest conservation need and to identify the relative importance of habitat variables measured at small (e.g., depth, velocity, and substrate) and large (e.g., stream order, elevation, and gradient) scales in terms of their influence on species occurrences. Multiple logistic regression analysis was used to predict fish species occurrences, starting with all possible combinations of variables (5 large-scale variables, 13 small-scale variables, and all 18 variables) but limiting the final models to a maximum of five variables. Akaike's information criterion was used to rank candidate models, weight model parameters, and calculate model-averaged predictions. On average, the correct classification rate (CCR = 80%) and Cohen's kappa (κ = 0.59) were greatest for multiple-scale models (i.e., those including both largescale and small-scale variables), intermediate for small-scale models (CCR = 75%; κ = 0.49), and lowest for large-scale models (CCR = 73%; κ = 0.44). The occurrence of each species was associated with a unique combination of large-scale and small-scale variables. Our results support the necessity of understanding factors that constrain the distribution of fishes across spatial scales to ensure that management decisions and actions occur at the appropriate scale. RightsWorks produced by employees of the U.S. Government as part of their official duties are not copyrighted within the U.S. The content of this document is not copyrighted. AbstractFish and habitat data were collected from 84 wadeable stream reaches in the Mississippi River drainage of Iowa to predict the occurrences of seven fish species of greatest conservation need and to identify the relative importance of habitat variables measured at small (e.g., depth, velocity, and substrate) and large (e.g., stream order, elevation, and gradient) scales in terms of their influence on species occurrences. Multiple logistic regression analysis was used to predict fish species occurrences, starting with all possible combinations of variables (5 large-scale variables, 13 small-scale variables, and all 18 variables) but limiting the final models to a maximum of five variables. Akaike's information criterion was used to rank candidate models, weight model parameters, and calculate model-averaged predictions. On average, the correct classification rate (CCR = 80%) and Cohen's kappa (κ = 0.59) were greatest for multiple-scale models (i.e., those including both large-scale and small-scale variables), intermediate for small-scale models (CCR = 75%; κ = 0.49), and lowest for large-scale models (CCR = 73%; κ = 0.44). The occurrence of each species was associated with a unique combination of large-scale and small-scale variables. Our results support the necessity of understanding factors that constrain the distribution of fishes across spatial scales to ensure that management decisions and actio...
Effective conservation of fish species of greatest conservation need (SGCN) requires an understanding of species-habitat relationships and distributional trends. Thus, modeling the distribution of fish species across large spatial scales may be a valuable tool for conservation planning. Our goals were to evaluate the status of 10 fish SGCN in wadeable Iowa streams and to test the effectiveness of IowaAquatic Gap Analysis Project (IAGAP) species distribution models. We sampled fish assemblages from 86 wadeable stream segments in the Mississippi River drainage of Iowa during 2009 and 2010 to provide contemporary, independent fish species presence-absence data. The frequencies of occurrence in stream segments where species were historically documented varied from 0.0% for redfin shiner Lythrurus umbratilis to 100.0% for American brook lamprey Lampetra appendix, with a mean of 53.0%, suggesting that the status of Iowa fish SGCN is highly variable. Cohen's kappa values and other model performance measures were calculated by comparing field-collected presence-absence data with IAGAP model-predicted presences and absences for 12 fish SGCN. Kappa values varied from 0.00 to 0.50, with a mean of 0.15. The models only predicted the occurrences of banded darter Etheostoma zonale, southern redbelly dace Phoxinus erythrogaster, and longnose dace Rhinichthys cataractae more accurately than would be expected by chance. Overall, the accuracy of the twelve models was low, with a mean correct classification rate of 58.3%. Poor model performance probably reflects the difficulties associated with modeling the distribution of rare species and the inability of the large-scale habitat variables used in IAGAP models to explain the variation in fish species occurrences. Our results highlight the importance of quantifying the confidence in species distribution model predictions with an independent data set and the need for long-term monitoring to better understand the distributional trends and habitat associations of fish SGCN. RightsWorks produced by employees of the U.S. Government as part of their official duties are not copyrighted within the U.S. The content of this document is not copyrighted. AbstractEffective conservation of fish species of greatest conservation need (SGCN) requires an understanding of specieshabitat relationships and distributional trends. Thus, modeling the distribution of fish species across large spatial scales may be a valuable tool for conservation planning. Our goals were to evaluate the status of 10 fish SGCN in wadeable Iowa streams and to test the effectiveness of Iowa Aquatic Gap Analysis Project (IAGAP) species distribution models. We sampled fish assemblages from 86 wadeable stream segments in the Mississippi River drainage of Iowa during 2009 and 2010 to provide contemporary, independent fish species presence-absence data. The frequencies of occurrence in stream segments where species were historically documented varied from 0.0% for redfin shiner Lythrurus umbratilis to 100.0% for American...
Fisheries managers desire efficient sampling methods with sufficient precision for monitoring ictalurid catfish populations. For this study, I conducted single unbaited hoop‐net surveys in the Minnesota River to evaluate the efficacy of simultaneously assessing stock‐size (≥280‐mm) Channel Catfish Ictalurus punctatus and quality‐size (≥510‐mm) Flathead Catfish Pylodictis olivaris populations. I compared catch rates and length‐frequency distributions among months (April–September) and used resampling procedures to estimate the number of hoop‐net samples needed to achieve desirable precision of catch rate estimates and adequate statistical power. Catch rates (mean ± SE) were 2.1 ± 0.3 fish/net‐night for Channel Catfish and 0.8 ± 0.1 fish/net‐night for Flathead Catfish. Resampling procedures demonstrated that desirable precision (i.e., relative SE ≤ 25) and adequate statistical power (≥0.80 with α = 0.10) to detect 50% reductions in mean catch rates with two‐tailed t‐tests could be achieved for both species with as few as 68 hoop‐net samples during August. Unbaited hoop nets are effective for simultaneously assessing Channel Catfish and Flathead Catfish populations in the Minnesota River, and conducting surveys during August provides the best balance between catch rate and precision while minimizing perceived environmental and behavioral biases. However, the amount of sampling effort needed to detect small population changes (<50%) and to collect sufficient sample sizes for age and growth analyses may be logistically prohibitive. This study emphasizes the importance of evaluating precision and statistical power of sampling methods so that fisheries managers can identify the most suitable methods for their specific objectives.
Plankton communities have important roles in aquatic ecosystems, but studies of plankton in lotic systems are infrequent. We collected over 100 water, phytoplankton, and zooplankton samples during 2016–2018 to explore spatiotemporal trends in Minnesota River plankton communities and evaluate relationships with physico‐chemical factors. Phytoplankton and zooplankton community structure exhibited temporal patterns but only the zooplankton community differed spatially. Cyanobacteria (M ± SE; 11.27 ± 1.43 mm3/L) and diatoms (8.12 ± 1.08 mm3/L) dominated phytoplankton biovolume with seasonal peaks in Cyanobacteria occurring during July–September and peaks in diatoms occurring during May, August, and September. All phytoplankton taxa except Cryptophyta exhibited a negative relationship with relative discharge. Crustacean zooplankton biomass was greatest at two upstream sites (146.7 ± 32.6 μg/L) where cladocerans and copepods were likely exported from upstream of dams where water residence time is greater. Within the lower free‐flowing reach rotifers dominated the zooplankton community (207.9 ± 40.9 individuals/L and 6.5 ± 1.0 μg/L). Thus, spatial differences in zooplankton community structure were primarily attributed to the influence of dams. Seasonal patterns in zooplankton community structure included peaks in Chydoridae, cyclopoid, immature copepod, and rotifer biomass during May and Bosminidae biomass during October. Excluding the influence of dams on zooplankton, the cumulative effects of month and relative discharge were the most important for explaining variability in plankton community structure. Baseline understanding of plankton community dynamics provides the ability to quantify responses to future perturbations such as climate change and establishment of invasive planktivores.
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