Top Banner
MANAGEMENT AND ANALYSIS OF MANAGEMENT AND ANALYSIS OF WILDLIFE BIOLOGY DATA WILDLIFE BIOLOGY DATA Bret A. Collier Bret A. Collier 1 and T. Wayne and T. Wayne Schwertner Schwertner 2 1 Institute of Renewable Natural Institute of Renewable Natural Resources, Texas A&M University, Resources, Texas A&M University, College Station, TX 77845, USA College Station, TX 77845, USA 2 Department of Animal Sciences and Department of Animal Sciences and Wildlife Management, Tarleton State Wildlife Management, Tarleton State University, Stephenville, TX 76402 University, Stephenville, TX 76402
23

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,

Dec 29, 2015

Download

Documents

Cecily White
Welcome message from author
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
Page 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,

MANAGEMENT AND ANALYSIS MANAGEMENT AND ANALYSIS OF WILDLIFE BIOLOGY DATAOF WILDLIFE BIOLOGY DATA

Bret A. CollierBret A. Collier11 and T. Wayne and T. Wayne SchwertnerSchwertner22

11Institute of Renewable Natural Resources, Texas A&M Institute of Renewable Natural Resources, Texas A&M University, College Station, TX 77845, USAUniversity, College Station, TX 77845, USA

22Department of Animal Sciences and Wildlife Department of Animal Sciences and Wildlife Management, Tarleton State University, Stephenville, Management, Tarleton State University, Stephenville,

TX 76402TX 76402

Page 2: 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,

IntroductionIntroduction

In wildlife biology, data analysis underlies nearly all the In wildlife biology, data analysis underlies nearly all the research that is conductedresearch that is conducted

The range of statistical methods available is extensiveThe range of statistical methods available is extensive

Ultimately, good questions, study designs, and analysis Ultimately, good questions, study designs, and analysis are complementary topicsare complementary topics

Page 3: 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,

First ThoughtsFirst Thoughts

When designing a study: Talk to a professionalWhen designing a study: Talk to a professional

No amount of statistical exorcism can fix a bad study No amount of statistical exorcism can fix a bad study designdesign

Methods are rapidly advancing, staying in front is toughMethods are rapidly advancing, staying in front is tough

Again: When designing a study: Talk to a professionalAgain: When designing a study: Talk to a professional

Page 4: 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,

Study DesignStudy Design In scientific research, results hinge on study designIn scientific research, results hinge on study design

Define population of interestDefine population of interest Ecological populationsEcological populations Inferential populationsInferential populations Target populationsTarget populations Sampled populationsSampled populations

Population inference requires data representing Population inference requires data representing population of interestpopulation of interest

Page 5: 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,

Data CollectionData Collection Conceptual framework for ‘how’ to collectConceptual framework for ‘how’ to collect

1. Outline study question.1. Outline study question. 2. Define response variable (e.g., nest survival).2. Define response variable (e.g., nest survival). 3. Define explanatory and/or descriptive variables that might affect 3. Define explanatory and/or descriptive variables that might affect

response (e.g., vegetation cover).response (e.g., vegetation cover). 4. Define steps for minimizing missing data.4. Define steps for minimizing missing data. 5. Outline data collection approach.5. Outline data collection approach. 6. Design initial data collection instrument specific to response or 6. Design initial data collection instrument specific to response or

explanatory variables.explanatory variables. 7. Conduct field test of protocols and data instruments.7. Conduct field test of protocols and data instruments. 8. Evaluate efficiency of data instruments.8. Evaluate efficiency of data instruments. 9. Repeat steps 2–8 if necessary due to logistical difficulties.9. Repeat steps 2–8 if necessary due to logistical difficulties. 10. Initiate data collection.10. Initiate data collection.

Page 6: 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,

Data ManagementData Management Data typesData types

QualitativeQualitative QuantitativeQuantitative

Data measurement scalesData measurement scales NominalNominal OrdinalOrdinal IntervalInterval RatioRatio

Data filesData files Files containing all data in rows and columnsFiles containing all data in rows and columns Commonly put into spreadsheetsCommonly put into spreadsheets More advantageous-database management systemMore advantageous-database management system

Page 7: 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,

Data PresentationData Presentation Tables and GraphsTables and Graphs

Variety of usesVariety of uses

Page 8: 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,

Bar GraphsBar Graphs Bar PlotsBar Plots

Page 9: 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,

Point GraphsPoint Graphs Point PlotsPoint Plots

Page 10: 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,

Dot GraphsDot Graphs Dot PlotsDot Plots

Page 11: 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,

Scatter GraphsScatter Graphs Scatter PlotsScatter Plots

Page 12: 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,

Hypothesis DevelopmentHypothesis Development Good questions come from good hypotheses about how Good questions come from good hypotheses about how

a process occursa process occurs

Statistical models can help evaluate strength, or lack Statistical models can help evaluate strength, or lack thereof, of how a process occursthereof, of how a process occurs

Models should inform the ecological question, not drive Models should inform the ecological question, not drive the questionthe question

Page 13: 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,

Hypothesis DevelopmentHypothesis Development Good questions come from good hypotheses about how Good questions come from good hypotheses about how

a process occursa process occurs

Statistical models can help evaluate strength, or lack Statistical models can help evaluate strength, or lack thereof, of how a process occursthereof, of how a process occurs

Models should inform the ecological question, not drive Models should inform the ecological question, not drive the questionthe question

Page 14: 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,

InferenceInference Descriptive StatisticsDescriptive Statistics

MeanMean

ModeMode

MedianMedian

VarianceVariance

Standard DeviationStandard Deviation Standard ErrorStandard Error

Confidence IntervalsConfidence Intervals

Page 15: 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,

Comparative AnalysesComparative Analyses Chi-square testsChi-square tests

T-testsT-tests

F-tests (F-tests (Analysis of Variance)Analysis of Variance)

CorrelationCorrelation

Page 16: 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,

Regression AnalysesRegression Analyses

Linear RegressionLinear Regression

Multiple RegressionMultiple Regression

Generalized Linear ModelsGeneralized Linear Models

Page 17: 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,

Community AnalysisCommunity Analysis Wildlife research has traditionally focused on the Wildlife research has traditionally focused on the

population level.population level.

Some study questions, however, address how wildlife Some study questions, however, address how wildlife communities:communities: Respond to management activities or other perturbationsRespond to management activities or other perturbations Biodiversity is affected by various activities Biodiversity is affected by various activities Change across space and time Change across space and time

Page 18: 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,

Species RichnessSpecies Richness Number of species in a community.Number of species in a community.

Strongly influenced by sample size.Strongly influenced by sample size. Makes comparisons difficult.Makes comparisons difficult.

Page 19: 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,

Complete EnumerationComplete Enumeration Provides the minimum number of species present.Provides the minimum number of species present.

Works for simple communities.Works for simple communities.

Rarely possible.Rarely possible.

Page 20: 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,

Richness IndicesRichness Indices Margalef’s indexMargalef’s index

►Not an estimate.Not an estimate.

►Cannot be compared with other indices or richness Cannot be compared with other indices or richness estimates.estimates.

►Strongly influenced by sample size.Strongly influenced by sample size.

Page 21: 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,

Richness EstimatesRichness Estimates Estimate the actual number of species in the communityEstimate the actual number of species in the community Data collected as a single sampleData collected as a single sample

►RarefactionRarefaction Used for standardizing sample sizes, and the resulting estimates of species Used for standardizing sample sizes, and the resulting estimates of species

richness, among samples.richness, among samples.

►Chao 1 MethodChao 1 Method Especially useful when a sample is dominated by rare species.Especially useful when a sample is dominated by rare species. Requires species abundance data.Requires species abundance data.

Data collected as a series of samples.Data collected as a series of samples.►Chao 2 MethodChao 2 Method

Modified Chao 1Modified Chao 1 Can be used with presence-absence dataCan be used with presence-absence data

► Jackknife and Bootstrap estimatesJackknife and Bootstrap estimates

Involve systematically resampling the original datasetInvolve systematically resampling the original dataset ..

Page 22: 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,

Species HeterogeneitySpecies Heterogeneity Measures the degree to which individuals in a Measures the degree to which individuals in a

community are distributed among the species present.community are distributed among the species present.

►Shannon-Weiner FunctionShannon-Weiner Function Based on information theoryBased on information theory Measures the amount of uncertainty associated with predicting the Measures the amount of uncertainty associated with predicting the

species of the next individual to be collected.species of the next individual to be collected.

►Simpson IndexSimpson Index The probability that 2 individuals drawn randomly from a community The probability that 2 individuals drawn randomly from a community

will be same species.will be same species.

Page 23: 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,