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Habitat Modeling

Habitat Modeling. Goals. Predict the locations of as-yet undiscovered refuges in the Great Lakes Develop management protocols to create new unionid habitat. Goals. Predict the locations of as-yet undiscovered refuges in the Great Lakes

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Habitat Modeling

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  1. Habitat Modeling

  2. Goals • Predict the locations of as-yet undiscovered refuges in the Great Lakes • Develop management protocols to create new unionid habitat

  3. Goals • Predict the locations of as-yet undiscovered refuges in the Great Lakes • what habitat parameters are necessary to sustain unionid populations • develop a GIS-based model that will summarize all the important features of the refuges. • Test models predictions • Use an iterative process to refine the model.

  4. Habitat parameters important for unionid protection from zebra mussels may include: • presence of substrates soft enough for unionids to burrow into • large areas of shallow waters (protected bayous) with low flow and warmer temperatures that encourage unionid burrowing • hydrological connection of the bayous to the lake • fish predation of Dreissena attached to unionids • Interactions of all these factors.

  5. Factors that inhibit the establishment of stable dreissenid populations are: • wave action in shallow areas, water level fluctuations, ice scouring • dense reed-beds • remoteness from the source of dreissenid veligers • In addition, there may be other, yet unidentified, mechanisms that promote the long-term coexistence of dreissenids and native mussels.

  6. At the local scale • Focus on areas inhabited by mussels: • substrate type, • depths, • water temperature, • water velocity • location • species richness and abundance. • Use multivariate methods such as multiscaled ordination with CCA (MSO-CCA) to define local scale habitat.

  7. A. A. A. B. B. B. C. C. C.

  8. At a regional scale • Use ecological niche modeling to predict the potential presence or absence of mussel beds. • Lots of options for model types, GARP, SVM, CART, etc. • Use available environmental data • water depth, wind-driven currents, mean, maximum and minimum annual temperature. • Developed GIS data layers • Turbidity, distance to deep-water, bay area and shape, bottom oxygen, distance to rivers, and human-related factors, such as distance to nearest dredging operation and distance to dams in upstream rivers.

  9. Ecological Niche Model • Predicted the potential distribution of zebra mussels. • Based on current distribution of zebra mussels in U.S. • 11 geologic and environmental variables. • Biological model - 6 factors that have plausible explanations for limiting the distribution of zebra mussels. • frost frequency, maximum annual temperature, elevation, slope, bedrock geology, and surface geology. • No Elevation model Drake & Bossenbroek, 2004, Bioscience

  10. Biological Model

  11. Biological Model minus Elevation

  12. Support Vector Data Description • The support vector data description (SVDD) is an SVM for finding the boundary around a set of observations. • This boundary is the simplest boundary in the sense that it represents the smallest possible hyper- volume (a hypersphere) containing a specified fraction of the observations in the projected feature space

  13. Support Vector Data Description Drake & Bossenbroek, 2009, Theor. Ecol.

  14. Questions?

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