Channel substrate prediction from GIS for habitat estimation in Lake Erie tributaries

Gorman, Ann Marie; Whiting, Peter J.; Neeson, Thomas M.; Koonce, Joseph F.

doi:10.1016/j.jglr.2011.08.008

Cited by 11 publications

(21 citation statements)

References 19 publications

(25 reference statements)

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“…Stream power, which is the rate of potential energy expenditure per channel length, determines sediment transport rate (Bagnold 1980) and influences median substrate particle size (Whiting et al 1999). Stream power is derivable solely from channel slope and drainage area (Whiting et al 1999;Gorman et al 2011), two variables that form a basic ordination for process domains (Montgomery and Foufoula-Georgiou 1993;Brardinoni and Hassan 2006) and process-based channel morphologies (Montgomery and Buffington 1997). We hypothesized, therefore, that stream power might be a particularly useful variable for delineating process domains.…”

Section: Geomorphic Covariatesmentioning

confidence: 99%

“…We estimated stream channel slope from GIS for an 800-m channel segment enclosing the Downloaded by [Selcuk Universitesi] at 03:17 27 December 2014 waypoint, a channel length previously shown to give good correlation with field-measured water surface slopes (Neeson et al 2008). Discharge data for estimating the 2-year flood flow were not available for all sites in our analysis, so we estimated 2-year flood flow by using drainage area (Gorman et al 2011) as follows:…”

Section: Geomorphic Covariatesmentioning

confidence: 99%

“…where a = 6 × 10 −6 and b = 0.8613; these are regional regression coefficients for northeastern Ohio (Gorman et al 2011). After preliminary analysis, we selected the natural logarithm of stream power as our covariate.…”

Section: Geomorphic Covariatesmentioning

confidence: 99%

“…Similarly, tree-identified domains may be used to provide a geomorphic context for other process-based substrate habitat models, such as those based on local stream geometry (Neeson et al 2007) or a combination of channel slope and basin size (Gorman et al 2011;Wilkins and Snyder 2011). A modelaveraging approach may then generate improved predictions or field sampling strategies.…”

Section: Management Applicationsmentioning

confidence: 99%

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Towards a Process Domain‐Sensitive Substrate Habitat Model for Sea Lampreys in Michigan Rivers

2012

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Habitat mapping is a common and often useful tool in the ecological management of rivers. The complex nature of fluvial processes, however, makes it difficult to predict the reach‐scale distribution of substrate habitat from landscape‐scale covariates. An option is to identify and partition a data set on boundaries of geomorphic process domains, within which the globally complex relationships between landscape, climate, and instream habitat may potentially be approximated by a simpler model. In this study, we used regression trees as a machine learning method for partitioning and identifying useful strata in a geographically extensive substrate habitat model for larvae of the sea lamprey Petromyzon marinus, an invasive and economically harmful species in the Laurentian Great Lakes. We used field survey data from over 5,000 substrate habitat transects collected in 43 watersheds of the Lower Peninsula of Michigan, and we created a geographic database of geographical information systems‐derived covariates that represent the principal geomorphic influences on substrate habitat. We created three trees in which tree splits delineated (1) spatially contiguous units, (2) noncontiguous units defined by values of the covariates, and (3) both contiguous and noncontiguous units. The adjusted R2 values of the three trees were 0.30, 0.30, and 0.32, respectively, and all three trees outperformed a single model fitted to the entire data set and a set of models fitted to each watershed individually. The trees identified useful stratifications of Michigan's Lower Peninsula, important geomorphic influences on substrate habitat, and variation in the influence of geomorphic processes on substrate habitat across our study region. Conservation and management applications of our model predictions and tree‐based stratifications include sea lamprey population modeling, habitat survey design, and evaluation of dam removal.

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Section: Geomorphic Covariatesmentioning

confidence: 99%

Section: Geomorphic Covariatesmentioning

confidence: 99%

Section: Geomorphic Covariatesmentioning

confidence: 99%

Section: Management Applicationsmentioning

confidence: 99%

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Towards a Process Domain‐Sensitive Substrate Habitat Model for Sea Lampreys in Michigan Rivers

2012

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“…Its use can better reveal the spatial differentiations between habitat suitability and natural geographical elements and improve the speed and accuracy of habitat evaluation, because of its powerful analytical and computational capabilities. With the use of GIS, more developments and wider spatial and temporal scales have been made (Zhang 2001;Boteva et al 2004;Gorman et al 2011;Yu et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Assessment of habitat suitability in the Upper Reaches of the Min River in China

et al. 2015

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Habitat richness influences and even determines biological diversity. Plant habitat suitability assessment can provide technical guidance and information support for ecological restoration. Thirteen factors in three categories of terrain, meteorology and soil were chosen to build a habitat suitability assessment index framework in the Upper Reaches of the Min River, based on the local natural environment and the actual influencing factors of vegetative growth. Combined with the Analytic Hierarchy Process (AHP) and entropy method, which were used to calculate weights of indexes, habitat suitability was studied by using a multi-objective linear weighting model and geographic information systems (GIS) spatial analysis techniques. The assessment results are as follows: Altitude, soil stability, aspect and slope have more important effects on plant habitat suitability in the Upper Reaches of the Min River, and their weights are 0.311, 0.260, 0.198 and 0.125, respectively. Suitable and sub-suitable habitats cover 4431.80 km 2 and 6171.12 km 2 , respectively; most of which are distributed along both sides of rivers and have higher suitability. Unsuitable habitats cover the largest area (6679.76 km 2 ), accounting for 29.83% of the whole area; and the worst unsuitable habitats are 5107.23 km 2 (22.81%); they account for more than half of the study area. These results indicate that the plant habitat in the Upper Reaches of the Min River is poor and ecological restoration is both urgent and difficult. Therefore, based on the principle of taking measures suitable to the habitat in ecological restoration projects, ecological and engineering measures should be combined to have better effects, while increasing the strength of ecological protection.

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Basin‐scale methods for predicting salmonid spawning habitat via grain size and riffle spacing, tested in a California coastal drainage

Pfeiffer

Finnegan

2016

Earth Surf Processes Landf

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Basin‐scale predictive geomorphic models for river characteristics, particularly grain size, can aid in salmonid habitat identification. However, these basin‐scale methods are largely untested with actual habitat usage data. Here, we develop and test an approach for predicting grain size distributions from high resolution LiDAR (Light Detection and Ranging)‐derived topographic data for a 77 km2 watershed along the central California Coast. This approach improves on previous efforts in that it predicts the full grain size distribution and incorporates an empirically calibrated shear stress partitioning factor. The predicted grain size distributions are used to calculate the fraction of the bed area movable by spawning fish. We then compare the ‘movable fraction’ with 7 years of observed spawning data. We find that predicted movable fraction explains the paucity of spawning in the upper reaches of the study drainage, but does not explain variation along the mainstem. In search of another morphologic characteristic that may help explain the variation within the mainstem, we measure riffle density, a proxy for physical habitat complexity. We find that field surveys of riffle density explain 64% of the variation in spawning in these mainstem reaches, suggesting that within reaches of appropriate sized gravel, spawning density is related to riffle density. Because riffle density varies systematically with channel width, predicting riffle spacing is straightforward with LiDAR data. Taken together, these findings demonstrate the efficacy of basin‐scale spawning habitat predictions made using high‐resolution digital elevation models. Copyright © 2016 John Wiley & Sons, Ltd.

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Channel substrate prediction from GIS for habitat estimation in Lake Erie tributaries

Cited by 11 publications

References 19 publications

Towards a Process Domain‐Sensitive Substrate Habitat Model for Sea Lampreys in Michigan Rivers

Towards a Process Domain‐Sensitive Substrate Habitat Model for Sea Lampreys in Michigan Rivers

Assessment of habitat suitability in the Upper Reaches of the Min River in China

Basin‐scale methods for predicting salmonid spawning habitat via grain size and riffle spacing, tested in a California coastal drainage

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