1. MANAGEMENT AND ANALYSIS OF WILDLIFE BIOLOGY DATA Bret A. Collier 1 and T. Wayne Schwertner 2 1 Institute of Renewable Natural Resources, Texas A&M University, College Station, TX 77845, USA 2 Department of Animal Sciences and Wildlife Management, Tarleton State University, Stephenville, TX 76402

2. Introduction  In wildlife biology, data analysis underlies nearly all the research that is conducted  The range of statistical methods available is extensive  Ultimately, good questions, study designs, and analysis are complementary topics

3. First Thoughts  When designing a study: Talk to a professional  No amount of statistical exorcism can fix a bad study design  Methods are rapidly advancing, staying in front is tough  Again: When designing a study: Talk to a professional

4. Study Design  In scientific research, results hinge on study design  Define population of interest  Ecological populations  Inferential populations  Target populations  Sampled populations  Population inference requires data representing population of interest

5. Data Collection  Conceptual framework for ‘how’ to collect  1. Outline study question.  2. Define response variable (e.g., nest survival).  3. Define explanatory and/or descriptive variables that might affect response (e.g., vegetation cover).  4. Define steps for minimizing missing data.  5. Outline data collection approach.  6. Design initial data collection instrument specific to response or explanatory variables.  7. Conduct field test of protocols and data instruments.  8. Evaluate efficiency of data instruments.  9. Repeat steps 2–8 if necessary due to logistical difficulties.  10. Initiate data collection.

6. Data Management  Data types  Qualitative  Quantitative  Data measurement scales  Nominal  Ordinal  Interval  Ratio  Data files  Files containing all data in rows and columns  Commonly put into spreadsheets  More advantageous-database management system

7. Data Presentation  Tables and Graphs  Variety of uses

8. Bar Graphs  Bar Plots

9. Point Graphs  Point Plots

10. Dot Graphs  Dot Plots

11. Scatter Graphs  Scatter Plots

12. Hypothesis Development  Good questions come from good hypotheses about how a process occurs  Statistical models can help evaluate strength, or lack thereof, of how a process occurs  Models should inform the ecological question, not drive the question

13. Hypothesis Development  Good questions come from good hypotheses about how a process occurs  Statistical models can help evaluate strength, or lack thereof, of how a process occurs  Models should inform the ecological question, not drive the question

14. Inference  Descriptive Statistics  Mean  Mode  Median  Variance  Standard Deviation  Standard Error  Confidence Intervals

15. Comparative Analyses  Chi-square tests  T-tests  F-tests ( Analysis of Variance)  Correlation

16. Regression Analyses  Linear Regression  Multiple Regression  Generalized Linear Models

17. Community Analysis  Wildlife research has traditionally focused on the population level.  Some study questions, however, address how wildlife communities:  Respond to management activities or other perturbations  Biodiversity is affected by various activities  Change across space and time

18. Species Richness  Number of species in a community.  Strongly influenced by sample size.  Makes comparisons difficult.

19. Complete Enumeration  Provides the minimum number of species present.  Works for simple communities.  Rarely possible.

20. Richness Indices  Margalef’s index ► Not an estimate. ► Cannot be compared with other indices or richness estimates. ► Strongly influenced by sample size.

21. Richness Estimates  Estimate the actual number of species in the community  Data collected as a single sample ► Rarefaction  Used for standardizing sample sizes, and the resulting estimates of species richness, among samples. ► Chao 1 Method  Especially useful when a sample is dominated by rare species.  Requires species abundance data.  Data collected as a series of samples. ► Chao 2 Method  Modified Chao 1  Can be used with presence-absence data ► Jackknife and Bootstrap estimates  Involve systematically resampling the original dataset.

22. Species Heterogeneity  Measures the degree to which individuals in a community are distributed among the species present. ► Shannon-Weiner Function  Based on information theory  Measures the amount of uncertainty associated with predicting the species of the next individual to be collected. ► Simpson Index  The probability that 2 individuals drawn randomly from a community will be same species.