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

Abstract

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...

Sampling techniques for burbot in a western non‐wadeable river

“…; Sindt et al . ). Depth was measured using a 2‐m sounding pole or a portable electronic depth finder (Marcum LX‐i, Minnetonka, MN).…”

Habitat use of non-native burbot in a western river

“…In-stream habitat characteristics were measured along a diagonal transect from the uppermost point of the channel unit to the lower-most point of the channel unit on the opposite bank. In-stream habitat characteristics (i.e., current velocity, dominant substrate, in-stream cover, and depth) were measured at 20, 40, 50, 60, and 80% of transect width (Flotemersch et al, 2006;Sindt et al, 2012) Orth & Maughan, 1982). Presence or absence of cover (i.e., large woody debris, overhanging vegetation, undercut banks, boulders, macrophytes, and anthropogenic structures) was recorded at each transect point (Fitzpatrick et al, 1998;Neebling & Quist, 2011).…”

“…A Spearman's rank-order correlation coefficient (|q|) was used to assess correlation among covariates (Sindt et al, 2012). If two covariates were highly correlated (|q| [ 0.70), only the most ecologically important and interpretable variable was used in candidate models (Burnham & Anderson, 2002).…”

“…However, one of the primary challenges in any suppression effort is identifying the spatial and temporal distribution of a species. As such, natural resource scientists generally rely on habitat associations to predict species occurrence and distribution (Rieman & McIntyre, 1995;Rich et al, 2003;Sindt et al, 2012). Unfortunately, information on species-specific habitat use is often lacking, requiring further research into the relationship between habitat characteristics and species occurrence and relative abundance.…”

Microhabitat use of native fishes in the Kootenai River: A fine‐scale evaluation of large‐scale habitat rehabilitation efforts

“…Despite the limitations of our sampling gear, over 1,400 individuals representing eight species were sampled following the protocol. This highlights the applicability of the sampling scale in a large river system to some extent but emphasizes the importance of evaluating and monitoring fish populations across multiple spatial scales (Fausch, Torgersen, Baxter, & Li, ; Sindt et al, ).…”