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File D source code Chesapeake Fish Passage Prioritization: An Assessment of Dams in the Chesapeake Bay Watershed.
Prioritizing fish passage barriers.
Located in Science and Data / Aquatic Organism Passage I&A and state design guidelines
File Northeast Aquatic Connectivity: An Assessment of Dams on Northeastern Rivers.
A report that describes an approach to strategically reconnect fragmented river, stream, coastal, reservoir, lake and estuarine habitat by removing or bypassing key barriers to fish passage.
Located in Science and Data / Aquatic Organism Passage I&A and state design guidelines
File SEACAP: Southeast Aquatic Connectivity Assessment Project: Assessing the ecological impact of dams on Southeastern rivers.
This report provides details about the data, methods, results, and tools developed for SEACAP.
Located in Science and Data / Aquatic Organism Passage I&A and state design guidelines
File Estimating size-specific brook trout abundance in continuously sampled headwater streams using Bayesian mixed models with zero inflation and overdispersion
We examined habitat factors related to reach-scale brook trout Salvelinus fontinalis counts of four size classes in two headwater stream networks within two contrasting summers in Connecticut, USA. Two study stream networks (7.7 and 4.4 km) were surveyed in a spatially continuous manner in their entirety, and a set of Bayesian generalised linear mixed models was compared. Trout abundance was best described by a zero-inflated overdispersed Poisson model. The effect of habitat covariates was not always consistent among size classes and years. There were nonlinear relationships between trout counts and stream temperature in both years. Colder reaches harboured higher trout counts in the warmer summer of 2008, but this pattern was not observed in the cooler and very wet summer of 2009. Amount of pool habitat was nearly consistently important across size classes and years, and counts of the largest size class were correlated positively with maximum depth and negatively with stream gradient. Spatial mapping of trout distributions showed that reaches with high trout counts may differ among size classes, particularly between the smallest and largest size classes, suggesting that movement may allow the largest trout to exploit spatially patchy habitats in these small headwaters.
Located in Science and Data / Brook Trout Related Publications
File ECMAScript program Predicting Brook Trout Occurrence in Stream Reaches throughout their Native Range in the Eastern United States
The Brook Trout Salvelinus fontinalis is an important species of conservation concern in the eastern USA. We developed a model to predict Brook Trout population status within individual stream reaches throughout the species’ native range in the eastern USA. We utilized hierarchical logistic regression with Bayesian estimation to predict Brook Trout occurrence probability, and we allowed slopes and intercepts to vary among ecological drainage units (EDUs). Model performance was similar for 7,327 training samples and 1,832 validation samples based on the area under the receiver operating curve (»0.78) and Cohen’s kappa statistic (0.44). Predicted water temperature had a strong negative effect on Brook Trout occurrence probability at the stream reach scale and was also negatively associated with the EDU average probability of Brook Trout occurrence (i.e., EDU-specific intercepts). The effect of soil permeability was positive but decreased as EDU mean soil permeability increased. Brook Trout were less likely to occur in stream reaches surrounded by agricultural or developed land cover, and an interaction suggested that agricultural land cover also resulted in an increased sensitivity to water temperature. Our model provides a further understanding of how Brook Trout are shaped by habitat characteristics in the region and yields maps of stream-reach-scale predictions, which together can be used to support ongoing conservation and management efforts. These decision support tools can be used to identify the extent of potentially suitable habitat, estimate historic habitat losses, and prioritize conservation efforts by selecting suitable stream reaches for a given action. Future work could extend the model to account for additional landscape or habitat characteristics, include biotic interactions, or estimate potential Brook Trout responses to climate and land use changes.
Located in Science and Data / Brook Trout Related Publications
File The Importance of Scale: Assessing and Predicting Brook Trout Status in its Southern Native Range
Occupancy models are of increasing interest to managers and natural resource decision makers. Assessment of status and trends, as well as the specific drivers influencing occupancy, both may change as a function of scale, and analyses conducted at multiple scales can help identify important mechanisms leading to changes in distributions. We analyzed extensive fine-scale occupancy data across the southern historic range of the brook trout, Salvelinus fontinalis to determine which landscape metrics and thresholds were useful in predicting brook trout presence across three relevant spatial scales and how brook trout occupancy varied by scale. Percentage occupancy declined markedly with increased spatial resolution, as 52% of watersheds (HUC10) but only 32% of subwatersheds (HUC12) and 14% of catchments (HUC14) were occupied. Across all three scales, habitats which were exclusively occupied by native brook trout (without non-native trout) were rare (<10%). CART models using GIS-derived landscape predictor variables were developed for three classification cases: Case 1:(brook trout; no brook trout), Case 2 (brook trout; non-native trout only; no trout), and Case 3 (brook trout only; brook and non-native trout; non-native trout only and no trout). Model results were sensitive to both scale and the number of classification categories with respect to classification accuracy, variable selection and variable threshold values. Classification accuracy tended to be lowest at the finest (catchment) scale potentially reflecting stochastic population processes and barriers to movement. Classification rates for the overall models were: Case 1: Watershed (80.19%); Subwatershed (85.06%); Catchment (71.13%); Case 2: Watershed (69.31%); Subwatershed (68.72%); Catchment (57.38%); Case 3: Watershed (58.91%); Subwatershed (59.83%); Catchment (47.59%). Our multiscale approach revealed soil permeability (positive) and atmospheric pollution (negative) to be important predictors. The predicted occupancy and observed status of brook trout appear to be influenced by the scale the data are collected and reported.
Located in Science and Data / Brook Trout Related Publications
File Dynamics and regulation of the southern brook trout (Salvelinus fontinalis) population in an Appalachian stream
1. We used information theoretic statistics [Akaike’s Information Criterion (AIC)] and regression analysis in a multiple hypothesis testing approach to assess the processes capable of explaining long-term demographic variation in a lightly exploited brook trout population in Ball Creek, NC. We sampled a 100-m-long second-order site during both spring and autumn 1991–2004, using three-pass electrofishing. 2. Principle component analysis indicated that the site had lower average velocity, greater amounts of depositional substrata and lower amount of erosional substrata during the 1999–2002 drought than in non-drought years. In addition, drought years had lower flows, and lower variation in flows, than non-drought years. 3. Both young-of-the-year (YOY) and adult densities varied by an order of magnitude during the study. AIC analysis conducted on regressions of per capita rate of increase versus various population and habitat parameters for the population, adults and YOY, for both spring and autumn data sets, indicated that simple density dependence almost always was the only interpretable model with Akaike weights (wi) ranging from 0.262 to 0.836. 4. Growth analyses yielded more variable results, with simple density dependence being the only interpretable model for both adult spring data (wi = 0.999) and YOY autumn data (wi = 0.905), and positive density dependence (wi = 0.636) and simple density independence (wi = 0.241) representing interpretable models for spring YOY data. 5. We detected a significant stock–recruitment relationship between both spring and autumn densities of adults in year t and autumn YOY density in year t + 1. Finally, spring YOY density was positively correlated with both autumn YOY density and spring mean YOY standard length (SL), suggesting that processes affecting recruitment show residual effects at least in the first year of life. This population appears to be regulated primarily by density dependent processes, although high flows also negatively affected mean SLs of YOY.
Located in Science and Data / Brook Trout Related Publications
File Population Response to Habitat Fragmentation in a Stream-Dwelling Brook Trout Population
Fragmentation can strongly influence population persistence and expression of life-history strategies in spatially-structured populations. In this study, we directly estimated size-specific dispersal, growth, and survival of stream-dwelling brook trout in a stream network with connected and naturally-isolated tributaries. We used multiple-generation, individual-based data to develop and parameterize a size-class and location-based population projection model, allowing us to test effects of fragmentation on population dynamics at local (i.e., subpopulation) and system-wide (i.e., metapopulation) scales, and to identify demographic rates which influence the persistence of isolated and fragmented populations. In the naturally-isolated tributary, persistence was associated with higher early juvenile survival (,45% greater), shorter generation time (one-half) and strong selection against large body size compared to the open system, resulting in a stage-distribution skewed towards younger, smaller fish. Simulating barriers to upstream migration into two currently-connected tributary populations caused rapid (2–6 generations) local extinction. These local extinctions in turn increased the likelihood of system-wide extinction, as tributaries could no longer function as population sources. Extinction could be prevented in the open system if sufficient immigrants from downstream areas were available, but the influx of individuals necessary to counteract fragmentation effects was high (7–46% of the total population annually). In the absence of sufficient immigration, a demographic change (higher early survival characteristic of the isolated tributary) was also sufficient to rescue the population from fragmentation, suggesting that the observed differences in size distributions between the naturally-isolated and open system may reflect an evolutionary response to isolation. Combined with strong genetic divergence between the isolated tributary and open system, these results suggest that local adaptation can ‘rescue’ isolated populations, particularly in one-dimensional stream networks where both natural and anthropogenically-mediated isolation is common. However, whether rescue will occur before extinction depends critically on the race between adaptation and reduced survival in response to fragmentation.
Located in Science and Data / Brook Trout Related Publications
File Population regulation of brook trout (Salvelinus fontinalis) in Hunt Creek, Michigan: a 50-year study
1. Fisheries models generally are based on the concept that strong density dependence exists in fish populations. Nonetheless, there are few examples of long-term density dependence in fish populations. 2. Using an information theoretical approach (AIC) with regression analyses, we examined the explanatory power of density dependence, flow and water temperature on the per capita rate of change and growth (annual mean total length) for the whole population, adults, 1+ and young-of-the-year (YOY) brook trout (Salvelinus fontinalis) in Hunt Creek, Michigan, USA, between 1951 and 2001. This time series represents one of the longest quantitative population data sets for fishes. 3. Our analysis included four data sets: (i) Pooled (1951–2001), (ii) Fished (1951–65), (iii) Unfished (1966–2001) and (iv) Temperature (1982–2001). 4. Principle component analyses of winter flow data identified a gradient between years with high mean daily winter flows, high daily maximum and minimum flows and frequent high flow events, and years with an opposite set of flow characteristics. Flows were lower during the Fished Period than during the Unfished Period. Winter temperature analyses elucidated a gradient between warm mean, warm minimum and maximum daily stream temperatures and a high number of minimum daily temperatures >6.1 C, and years with the opposite characteristics. Summer temperature analyses contrasted years with warm summer stream temperatures vs years with cool summer stream temperatures. 5. Both YOY and adult densities varied several-fold during the study. Regression analysis did not detect a significant linear or nonlinear stock–recruitment relationship. AIC analysis indicated that density dependence was present in 15 of 16 cases (four population segments · four data sets) for both per capita rate of increase (wi values 0.46–1.00) and growth data (wi values 0.28–0.99). The almost ubiquitous presence of density dependence in both population and growth data is concordant with results from other trout populations and other studies in Michigan.
Located in Science and Data / Brook Trout Related Publications
File application/x-troff-ms What predicts the use by brook trout (Salvelinus fontinalis) of terrestrial invertebrate subsidies in headwater streams?
1. Spatial subsidies are important resources for organisms in receiving habitats, particularly when production in those habitats is low. Terrestrial invertebrates provide a critical subsidy for trout, including eastern brook trout (Salvelinus fontinalis), but we have limited understanding of what causes input and use of these subsidies to vary among streams. 2. We predicted that forest successional stage would be an especially important driver of variation in terrestrial invertebrate subsidies to brook trout in headwater streams due to differences in terrestrial invertebrate biomass in early and late successional habitats. Specifically, we expected biomass of aerial invertebrates, those capable of dispersal to the stream, to be greater in early successional habitat than late successional habitat due to the nutrient-rich, herbaceous vegetation typical of early successional habitat. 3. We measured aerial terrestrial invertebrate biomass in early and late successional habitats, input to streams and use by resident brook trout in 12 first- and second-order catchments in northern New Hampshire, U.S.A. The study catchments represented a range of early successional habitat coverage (0–51.5%). We also measured a suite of reach-scale variables that might influence terrestrial invertebrate input and use by brook trout, including riparian forest conditions and benthic invertebrate biomass. 4. Within study catchments, aerial terrestrial invertebrate biomass and abundance were significantly higher in early successional habitats than late successional habitats. However, terrestrial invertebrate input to streams and use by brook trout were unrelated to per cent early successional habitat in the catchment, and to other catchment and riparian forest characteristics. These results indicate that the management for upland early successional habitat has little effect on terrestrial invertebrate subsidies to headwater streams and fish. 5. Surprisingly, benthic invertebrate biomass was the one significant predictor of per cent terrestrial invertebrates in brook trout diets. Use of terrestrial invertebrate subsidies declined with increasing benthic invertebrate biomass, suggesting that productivity in the aquatic environment influences the degree to which brook trout use terrestrial subsidies. Although subsidy inputs are controlled by the donor system, this study shows that use of these subsidies by consumers can be determined by conditions in the recipient habitat.
Located in Science and Data / Brook Trout Related Publications
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