Fish Assemblage Relationships with Physical Habitat in Wadeable Iowa Streams

Fish assemblages play a key role in stream ecosystems and are influenced by physical habitat. We analyzed fish assemblages and physical habitat at 93 randomly selected sites on second- through fifth-order wadeable Iowa streams to explore fish assemblage relationships with reach-scale physical habitat in this agriculturally dominated landscape. Sites were sampled using DC electrofishing and the wadeable streams physical habitat protocol of the U.S. Environmental Protection Agency\u27s Environmental Monitoring and Assessment Program. In all, 82 species were collected, with species richness at sites averaging 14. Over 80% of the sites had fish assemblages rated as fair (53%) or poor (32%) based on a fish index of biotic integrity (FIBI). Ordination separated sites from the two major river drainages along an axis of impairment, with sites in the Missouri River drainage exhibiting lower FIBI scores than sites in the Mississippi River drainage. Physical habitat at most sites exhibited fine substrates, eroding banks, and low-gradient, nonmeandering channel and was dominated by glides. Thirty physical habitat variables describing channel morphology, channel cross section and bank morphology, fish cover, human disturbance, large woody debris, relative bed stability, residual pool, riparian vegetation, and substrate differed significantly between sites with FIBI scores rated as poor and those with FIBI scores rated as good or excellent. Eighteen physical habitat variables were significant predictors of fish assemblage metrics and FIBI in multiple linear regression models, with adjusted R 2 values ranging from 0.12 to 0.58. Seventy percent of the model coefficients reflected substrate (40%), residual pool (21%), and fish cover (9%) variables. Fish assemblages in wadeable Iowa streams are strongly associated with the quality of physical habitat. Thus, understanding and addressing the determinants of physical habitat are crucial for managing streams in Iowa and other agricultural regions

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

Physical Habitat and Fish Assemblage Relationships with Landscape Variables at Multiple Spatial Scales in Wadeable Iowa Streams

Landscapes in Iowa and other midwestern states have been profoundly altered by conversion of native prairies to agriculture. We analyzed landscape data collected at multiple spatial scales to explore relationships with reach-scale physical habitat and fish assemblage data from 93 randomly selected sites on second- through fifth-order wadeable Iowa streams. Ordination of sites by physical habitat showed significant gradients of channel shape, habitat complexity, substrate composition, and stream size. Several landscape variables were significantly associated with the physical habitat ordination. Row crop land use was associated with fine substrates and steep bank angles, whereas wetland land cover and greater sinuosity and catchment land area were associated with complex channel and bank morphology and greater residual pool volume, woody debris, and canopy cover. Thirteen landscape variables were significant predictors of physical habitat variables in multiple linear regressions, with adjusted R 2 values ranging from 0.07 to 0.74. Inclusion of landscape variables with physical habitat variables in multiple regression models predicting fish assemblage metrics and a fish index of biotic integrity resulted in negligible improvements over models based on only physical habitat variables. Physical habitat in wadeable Iowa streams is strongly associated with landscape characteristics. Results of this study and previous studies suggest that (1) landscape factors directly influence physical habitat, (2) physical habitat directly influences fish assemblages, and (3) the influence of landscape factors on fish assemblages is primarily indirect. Understanding how landscape factors, such as human land use, influence physical habitat and fish assemblages will help managers make more informed decisions for improving Iowa\u27s wadeable streams

Trap array configuration influences estimates and precision of black bear density and abundance

Spatial capture-recapture (SCR) models have advanced our ability to estimate population density for wide ranging animals by explicitly incorporating individual movement. Though these models are more robust to various spatial sampling designs, few studies have empirically tested different large-scale trap configurations using SCR models. We investigated how extent of trap coverage and trap spacing affects precision and accuracy of SCR parameters, implementing models using the R package secr. We tested two trapping scenarios, one spatially extensive and one intensive, using black bear (Ursus americanus) DNA data from hair snare arrays in south-central Missouri, USA. We also examined the influence that adding a second, lower barbed-wire strand to snares had on quantity and spatial distribution of detections. We simulated trapping data to test bias in density estimates of each configuration under a range of density and detection parameter values. Field data showed that using multiple arrays with intensive snare coverage produced more detections of more individuals than extensive coverage. Consequently, density and detection parameters were more precise for the intensive design. Density was estimated as 1.7 bears per 100 km2 and was 5.5 times greater than that under extensive sampling. Abundance was 279 (95% CI = 193-406) bears in the 16,812 km2 study area. Excluding detections from the lower strand resulted in the loss of 35 detections, 14 unique bears, and the largest recorded movement between snares. All simulations showed low bias for density under both configurations. Results demonstrated that in low density populations with non-uniform distribution of population density, optimizing the tradeoff among snare spacing, coverage, and sample size is of critical importance to estimating parameters with high precision and accuracy. With limited resources, allocating available traps to multiple arrays with intensive trap spacing increased the amount of information needed to inform parameters with high precision

Spatiotemporal factors affecting detection of black bears during noninvasive capture-recapture surveys

Accounting for low and heterogeneous detection probabilities in large mammal capture-recapture sampling designs is a persistent challenge. Our objective was to improve understanding of ecological and biological factors driving detection using multiple data sources from an American black bear (Ursus americanus) DNA hair trap study in south-central Missouri. We used Global Positioning System telemetry and remote camera data to examine how a bear\u27s distance to traps, probability of space use, sex-specific behavior, and temporal sampling frame affect detection probability and number of hair samples collected at hair traps. Regression analysis suggested that bear distance to nearest hair trap was the best predictor of detection probability and indicated that detection probability at encounter was 0.15 and declined to \u3c 0.05 at nearest distances \u3e 330 m from hair traps. From remote camera data, number of hair samples increased with number of visits, but the proportion of hair samples from known visits declined 39% from early June to early August. Bears appeared attracted to lured hair traps from close distances and we recommend a hair trap density of 1 trap/2.6 km2 with spatial coverage that encompasses potentially large male home ranges. We recommend sampling during the late spring and early summer molting period to increase hair deposition rates

Influence of drift and admixture on population structure of American black bears (Ursus americanus) in the Central Interior Highlands, USA, 50 years after translocation

Bottlenecks, founder events, and genetic drift often result in decreased genetic diversity and increased population differentiation. These events may follow abundance declines due to natural or anthropogenic perturbations, where translocations may be an effective conservation strategy to increase population size. American black bears (Ursus americanus) were nearly extirpated from the Central Interior Highlands, USA by 1920. In an effort to restore bears, 254 individuals were translocated from Minnesota, USA, and Manitoba, Canada, into the Ouachita and Ozark Mountains from 1958 to 1968. Using 15 microsatellites and mitochondrial haplotypes, we observed contemporary genetic diversity and differentiation between the source and supplemented populations. We inferred four genetic clusters: Source, Ouachitas, Ozarks, and a cluster in Missouri where no individuals were translocated. Coalescent models using approximate Bayesian computation identified an admixture model as having the highest posterior probability (0.942) over models where the translocation was unsuccessful or acted as a founder event. Nuclear genetic diversity was highest in the source (AR = 9.11) and significantly lower in the translocated populations (AR = 7.07-7.34; P = 0.004). The Missouri cluster had the lowest genetic diversity (AR = 5.48) and served as a natural experiment showing the utility of translocations to increase genetic diversity following demographic bottlenecks. Differentiation was greater between the two admixed populations than either compared to the source, suggesting that genetic drift acted strongly over the eight generations since the translocation. The Ouachitas and Missouri were previously hypothesized to be remnant lineages. We observed a pretranslocation remnant signature in Missouri but not in the Ouachitas. © Published 2014. This article is a U.S. Government work and is in the public domain in the USA

Assessing the Ecological Need for Prescribed Fire in Michigan Using GIS-Based Multicriteria Decision Analysis: Igniting Fire Gaps

In fire-suppressed landscapes, managers make difficult decisions about devoting limited resources for prescribed fire. Using GIS-based multicriteria decision analysis, we developed a model assessing ecological need for prescribed fire on Michigan’s state-owned lands, ranging from fire-dependent prairies, savannas, barrens, and oak and pine forests to fire-intolerant mesic forests, and including a diversity of wetlands. The model integrates fine-scale field-collected and broad-scale GIS data to identify where prescribed fire needs are greatest. We describe the model’s development and architecture, present results at multiple scales, introduce the concepts of “fire gaps” and “fire sink”, and rate the fire needs of more than 1.8 million hectares into one of six fire needs classes. Statewide, fire needs increase with decreasing latitude. The highest and lowest needs occur in southwestern Michigan and the Upper Peninsula, respectively, but actual fire application rates for these regions are inverted. The model suggests burn rates should be increased 2.2 to 13.4 times to burn all lands with greater than high fire needs. The model identifies regional patterns; highlights specific sites; and illustrates the disparity of fire needs and fire application. The modeling framework is broadly applicable to other geographies and efforts to prioritize stewardship of biodiversity at multiple scales

Trap array configuration influences estimates and precision of black bear density and abundance.

Spatial capture-recapture (SCR) models have advanced our ability to estimate population density for wide ranging animals by explicitly incorporating individual movement. Though these models are more robust to various spatial sampling designs, few studies have empirically tested different large-scale trap configurations using SCR models. We investigated how extent of trap coverage and trap spacing affects precision and accuracy of SCR parameters, implementing models using the R package secr. We tested two trapping scenarios, one spatially extensive and one intensive, using black bear (Ursus americanus) DNA data from hair snare arrays in south-central Missouri, USA. We also examined the influence that adding a second, lower barbed-wire strand to snares had on quantity and spatial distribution of detections. We simulated trapping data to test bias in density estimates of each configuration under a range of density and detection parameter values. Field data showed that using multiple arrays with intensive snare coverage produced more detections of more individuals than extensive coverage. Consequently, density and detection parameters were more precise for the intensive design. Density was estimated as 1.7 bears per 100 km2 and was 5.5 times greater than that under extensive sampling. Abundance was 279 (95% CI = 193-406) bears in the 16,812 km2 study area. Excluding detections from the lower strand resulted in the loss of 35 detections, 14 unique bears, and the largest recorded movement between snares. All simulations showed low bias for density under both configurations. Results demonstrated that in low density populations with non-uniform distribution of population density, optimizing the tradeoff among snare spacing, coverage, and sample size is of critical importance to estimating parameters with high precision and accuracy. With limited resources, allocating available traps to multiple arrays with intensive trap spacing increased the amount of information needed to inform parameters with high precision

Percent relative bias (%RB) and percent coverage of 95% confidence intervals (%COV) of mean density estimates for simulations of spatial capture recapture models under extensive and intensive trap configurations.

<p>Estimates are based on averages over 100 replicates for each scenario of density (1.0, 2.5 bears per100 km²), g₀ (0.1, 0.2), and σ (5, 10, 15 km).</p><p>Percent relative bias (%RB) and percent coverage of 95% confidence intervals (%COV) of mean density estimates for simulations of spatial capture recapture models under extensive and intensive trap configurations.</p

Model selection results for fitted models ranked by AIC_c with number of parameters (<i>K</i>), log likelihood (LL), and AIC_c weights (<i>w_i</i>) to estimate black bear density in south-central Missouri, USA, for extensive and intensive sampling designs.

<p>We fitted models using the half-normal detection function with baseline capture probability (g₀) and scale parameter (σ). Effects on g₀ and σ included time as a factor (t), global learned response (b), snare-specific learned response (bk), and a snare-specific Markovian response (Bk), and sex. Parameters with “.” indicate no effect.</p><p>Model selection results for fitted models ranked by AIC_c with number of parameters (<i>K</i>), log likelihood (LL), and AIC_c weights (<i>w_i</i>) to estimate black bear density in south-central Missouri, USA, for extensive and intensive sampling designs.</p