Fish species of greatest conservation need in wadeable Iowa streams: status, habitat associations, and effectiveness of species distribution models

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 may serve as a potentially valuable tool for conservation planning. Our goals were to evaluate the status of fish SGCN in wadeable Iowa streams, test the effectiveness of existing species distribution models, and identify the relative influence and importance of habitat variables measured at multiple spatial scales on fish SGCN occurrences. Fish assemblage and habitat data were collected from 86 wadeable stream segments in the Mississippi River drainage of Iowa during 2009 and 2010. The frequency of occurrence of ten fish SGCN in stream segments where they were historically documented varied from 0.0% to 100.0% with a mean of 53.0% suggesting the status of Iowa fish SGCN is highly variable. The accuracy of existing species distribution models was evaluated with Cohen\u27s kappa values and other model performance measures calculated by comparing field collected presence-absence data to model predicted presences and absences for twelve fish SGCN. Kappa values varied from 0.00 to 0.50 with a mean of 0.15, and indicated that only three models predicted species occurrence more accurately than would be expected by chance. Poor model performance likely reflects the difficulties associated with modeling the distribution of rare species and the inability of large-scale explanatory variables to explain variation in species occurrences. Thus, we developed occurrence models for seven fish SGCN using large-scale habitat variables (e.g., stream order, elevation, gradient), small-scale habitat variables (e.g., depth, velocity, coarse substrate), and habitat variables measured at multiple scales to identify the most influential spatial scale on species occurrences. On average, correct classification rates and Cohen\u27s kappa values were greatest for multiple-scale models, intermediate for small-scale models, and lowest for large-scale models. However, large-scale models predicted the occurrences of two species with greater accuracy than small-scale models. Our results highlight the need for long-term monitoring efforts to better understand distributional trends and habitat associations of fish SGCN, and the necessity of understanding the factors that constrain the distribution of fishes across spatial scales to ensure that management decisions and actions occur at the appropriate scale

Fish Species of Greatest Conservation Need in Wadeable Iowa Streams: Current Status and Effectiveness of Aquatic Gap Program Distribution Models

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

Habitat Associations of Fish Species of Greatest Conservation Need at Multiple Spatial Scales in Wadeable Iowa Streams

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 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 actions occur at the appropriate scale

Evaluation of Four Larval Fish Sampling Methods in a Large Midwestern River

Understanding limitations of larval fish capture gears is critical for developing appropriate sampling protocols and interpreting catch data. We evaluated genera richness, genera diversity, assemblage similarities, abundance indices (i.e., density or catch per unit effort [CPUE]), and sample size requirements between a surface slednet and glow-stick light traps used in 2014 and 2015 and a benthic slednet and light-emitting diode light (LED) traps used in 2015 in the Minnesota River. The surface slednet captured the greatest number of larval fish genera (15) while the LED light trap captured the fewest (1). Similarities of assemblages sampled was highest between surface and benthic slednets (58%) and lowest between the benthic slednet and LED light trap (0%). All evaluated gears had low and variable catch rates; the highest variability was observed for the LED light trap (CV = 800), and the lowest variability was observed for surface slednets (CV = 173). Slednets required less effort to detect a 25% change in total larval fish abundance compared to light traps. Low CPUEs or densities were possibly the result of suspended sediment loads (85.3 ± 8.5 Nephelometric Turbidity Units) that blocked light trap entrance slots and clogged net pores. Further, not targeting habitats critical to adult spawning and larval rearing (e.g., log jams or shallower or inside bends of meanders) may have influenced CPUEs and densities. We recommend modifications to evaluated sampling gears (e.g., nets with larger mesh sizes) or the evaluation of additional larval fish sampling methods (e.g., larval seines or pumps) coupled with a stratified random sampling protocol that incorporates complex habitats for sampling larval fish within the main channel of the Minnesota River or other river systems with similar high turbidity levels