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

Habitat Habitat ModelingModeling

GoalsGoalsPredict the locations of as-yet undiscovered refuges in the Great Lakes

Develop management protocols to create new unionid habitat

GoalsGoalsPredict 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.

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.

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.

At the local scaleAt the local scaleFocus 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.

At a regional scaleAt a regional scaleUse 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.

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

Ecological Niche ModelEcological Niche Model

Biological ModelBiological Model

0 500 1,000250

Kilometers¯

Biological Model

Value

100%

0%

ZMZebra MusselLocations

Predicted Distribution

0 500 1,000250

Kilometers¯

No Elevation

Value

High : 100

Low : 0

ZMZebra MusselLocations

Predicted Distribution

Biological Model minus Biological Model minus ElevationElevation

Support Vector Data Support Vector Data DescriptionDescriptionThe 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

Support Vector Data Support Vector Data DescriptionDescription

Drake & Bossenbroek, 2009, Theor. Ecol.

Questions? Questions?