Wildlife Habitat Analysis Laboratory

Department of Wildlife & Fisheries Sciences,
Texas A&M University, College Station, Texas

This Wildlife Habitat Analysis Lab provides support for investigations of wildlife habitat ecology, habitat evaluation, and environmental monitoring. Computing resources facilitate the integration of remotely-sensed imagery and Geographic Information Systems (GIS) and associated databases with spatially-explicit computer simulation models of wildlife populations and their habitats. The Wildlife Nutrition Lab supports comparative studies of animal nutrition and bioenergetics, determinations of the chemical qualities of forages and their influence on digestive processes, and comparisons of morphological and physiological adaptations of wildlife to terrestrial and aquatic environments in support of field research.

Personnel:

Ken L. Risenhoover, Associate Professor and Director
Benamanahally K. Adarsh, Computer Programmer
John H. Malone, Lab Coordinator and Research Assistant
Christine M. Doucet, Graduate Research Assistant
S. Blake Murden, Graduate Research Assistant

Links to Additional Information Related to the Habitat Analysis Laboratory:

Publications and Presentations
Research at the TAES Sonora Research Center
Hearing Sensitivity in White-Tailed Deer
Research Project Descriptions
Current Research
Graduate Students

Research using Individual-based Simulation Models:

Mechanistic Models of Herbivore Foraging Behavior

This rule-based, object-oriented, simulation model is an extension of previous models developed by Risenhoover (BASIC), Roese and Risenhoover (Modula-2, Turbo Pascal). The current version of this model is written in Visual C++ and examines the effects of forage composition and heterogeneity on the feeding behavior and efficiency of white-tailed deer, goats, sheep, or cattle foraging within a 1 hectare-sized fenced area for 800 minutes. The abundance and composition of grass, forb and browse "objects" can be varied to examine how resource heterogeneity influences herbivore movements, diet choices, foraging efficiency, and their spatial patterns of residence time over the landscape. These factors are believed to be the primary mechanisms affecting landscape-tick-host interactions (see below).

Comparison of herbivore spatial responses in high (left panel) and low (right panel)
resource environments during 800 foraging minutes. Line traces represent the
foraging path of the herbivore and the spectral color denotes residence time.

Future research efforts planned for this model include: (1) examining the influence of model-imposed " boundaries" on animal behavior and performance, (2) comparisons of predictions of spatial use patterns created by a continuous data set of animal residence time vs. those created by subsamples of the former dataset. This simulation series will provide some basis for demonstrating the problems of predicting habitat utilization from low frequency point location sampling such as the data derived from telemetry studies. These simulations will span a variety of habitat conditions (abundance and composition) in order to explore the sensitivity of model results to resource distribution patterns, (3) Examine the impacts of intra- and interspecific competitors on foraging behavior. In these simulations we will increase the number and type of herbivores present while maintaining a constant animal density (animals/unit area). This study will allow us to examine the mechanisms of forage competition among ungulates. (4) Examine the appropriateness of various rule-bases ("clever", random, probabilistic) used to determine animal movements in our landscape-level models. Use the mechanistic model to document the effects of landscape resolution (minimum cell size) and neighborhood habitat values on the probability of animals entering a target cell. We hypothesize that as cell size becomes smaller in relation to the perceptive field of the animal object that it will become more efficient (i.e., "clever") at selecting the neighboring cells with the highest habitat value. Alternatively, as cell sizes become larger, we predict that animal movements to neighboring cells will become more random-like. We will conduct a series of simulations to examine cell size, relative habitat value and neighborhood values influence the probability that animals will move to a target cell.

Tick-host-landscape Interactions

This rule-based, object-oriented, simulation model written in C for the SUN Workstation was developed in collaboration with Dr. Pete Teel to examine how landscape composition and structure influence landscape use by populations of deer and cattle at larger ecological scales. Model resolution is relatively high (1 cell = 10 x 10m), and this level of detail provides opportunities to explore how differences in host (deer and cattle) grain response distributes animals and affects the demographics of the tick population. The model tracks use patterns by deer and cattle, and accumulates information on numbers of larvae and adult ticks on individual hosts and throughout the landscape. The specific questions we hope to address with this simulation include: 1) what numbers of deer and cattle are necessary to sustain a tick population on the landscape, and 2) how does the size of one host population influence levels of tick infestation on the other host population present.

The "Deer Management Simulator"

The Deer Management Simulator (DMS) is a general, yet powerful simulation tool designed to assist natural resource specialists attempting to manage problems relating to overabundant ungulate populations. This simulation environment integrates available information on deer population dispersion, productivity and size with GIS databases and predicts population spatial and numerical responses to management scenarios. Simulations assist the user in identifying the set of circumstances and conditions necessary for a particular management strategy to succeed in accomplishing its objectives. The flexibility of the DMS also makes it a ideal research tool for conducting sensitivity analysis on input variables and for evaluating assumptions about the understanding of local deer herd demography and habitat use patterns.