Fish-habitat modeling for gap analysis to conserve the endangered Topeka shiner (<i>Notropis topeka</i>)

Wall, Steven S.; Berry, Charles R.; M, Blausey C; Jenks, Jonathan A.; Kopplin, C. J.

doi:10.1139/f04-017

Cited by 42 publications

(41 citation statements)

References 35 publications

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“…Filipe et al (2004) used species distribution models, the conservation status of fish, and a GIS to identify the conservation priority of catchments in the Guadiana River basin, Portugal. Others have used predicted species occurrences to identify stream segments and catchments that should be given priority for the conservation of particular species (Wall et al, 2004) or conservation of freshwater biodiversity (Argent et al, 2003;Sowa et al, 2007). Thus, spatially explicit information and GIS are valuable tools for managers who need to identify areas that are to have a fish conservation emphasis (Fisher and Rahel, 2004).…”

Section: Discussionmentioning

confidence: 99%

“…Such models also make it is easier to identify large areas with suitable habitat that then can be targeted for conservation or restoration efforts (Rodrı´guez et al, 2007). For example, Wall et al (2004) modelled the distribution of Topeka shiner (Notropis topeka) by using large-scale geospatial data, and combined its predicted distribution with land protection information to identify areas that should be given high conservation priority. Brewer et al (2007) used their models to predict the distribution of smallmouth bass throughout Missouri, and suggested that predictions could be used to identify stream segments of management interest where smallmouth bass populations were not meeting their natural potential.…”

Section: Introductionmentioning

confidence: 99%

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Distribution modelling to guide stream fish conservation: an example using the mountain sucker in the Black Hills National Forest, USA

Dauwalter

Rahel

2008

Aquatic Conservation

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ABSTRACT1. Conservation biologists need tools that can utilize existing data to identify areas with the appropriate habitat for species of conservation concern. Regression models that predict suitable habitat from geospatial data are such a tool. Multiple logistic regression models developed from existing geospatial data were used to identify largescale stream characteristics associated with the occurrence of mountain suckers (Catostomus platyrhynchus), a species of conservation concern, in the Black Hills National Forest, South Dakota and Wyoming, USA.2. Stream permanence, stream slope, stream order, and elevation interacted in complex ways to influence the occurrence of mountain suckers. Mountain suckers were more likely to be present in perennial streams, and in larger, higher gradient streams at higher elevations but in smaller, lower gradient streams at lower elevations.3. Applying the logistic regression model to all streams provided a way to identify streams in the Black Hills National Forest most likely to have mountain suckers present. These types of models and predictions can be used to prioritize areas that should be surveyed to locate additional populations, identify stream segments within catchments for population monitoring, aid managers in assessing whether proposed forest management will potentially have impacts on fish populations, and identify streams most suitable for stream rehabilitation and conservation or translocation efforts.4. When the effect of large brown trout (Salmo trutta) was added to the best model of abiotic factors, it had a negative effect on the occurrence of mountain suckers. Negative effects of brown trout on the mountain sucker suggest that management of recreational trout fisheries needs to be balanced with mountain sucker conservation in the Black Hills. However, more spatially explicit information on brown trout abundance would allow managers to understand where the two species interact and where recreational fisheries need to be balanced with fish conservation.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Distribution modelling to guide stream fish conservation: an example using the mountain sucker in the Black Hills National Forest, USA

Dauwalter

Rahel

2008

Aquatic Conservation

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“…Furthermore, an understanding of species-habitat relationships can provide insight into the effects of land use practices, habitat alterations, and climate change on species distributions (Wang et al 2003;Wall et al 2004;Lyons et al 2010). Modeling of species distributions is an important tool for addressing many issues in conservation (Guisan and Thuiller 2005), and the use of predictive occurrence models to further the understanding of fish species' relationships with habitat features in freshwater systems is common (e.g., Olden and Jackson 2001;Rich et al 2003;Steen et al 2008).…”

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confidence: 99%

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

Sindt

Quist

Pierce

2012

N American J Fish Manag

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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...

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“…Fish, and Notropis spp. specifically, have been shown to exhibit habitat-specific choice tendencies (Aadland 1993, Wall et al 2004). …”

Section: Discussionmentioning

confidence: 99%

Life History Traits of the Mirror Shiner,Notropis spectrunculus, in Western North Carolina

Olson

2016

Southeastern Naturalist

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Fish-habitat modeling for gap analysis to conserve the endangered Topeka shiner (Notropis topeka)

Cited by 42 publications

References 35 publications

Distribution modelling to guide stream fish conservation: an example using the mountain sucker in the Black Hills National Forest, USA

Distribution modelling to guide stream fish conservation: an example using the mountain sucker in the Black Hills National Forest, USA

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

Life History Traits of the Mirror Shiner,Notropis spectrunculus, in Western North Carolina

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