research & experimental design why do we do research history of wildlife research descriptive v....

Research & Experimental Design

• Why do we do research• History of wildlife research• Descriptive v. experimental research• Scientific Method• Research considerations• Sampling

Wildlife Ecology ResearchWhy do we do it?

• Understand and explain– Patterns and processes

• What, why, and how

• Predictions

• Monitoring

• Management and conservation

Rigor in Wildlife Ecology

• Historically– Descriptive

• Monitoring

• Natural history observations

• “Conclusions” from associations rather than experimental tests of hypotheses

Rigor in Wildlife Ecology

• Ecologists vs. Physicists & Chemists– Control & controls– Replication– Manipulation

– Cause & effect

• Ecologists have a greater challenge

• Statistics and other quantitative methods– Graphical, observational, information theory,

etc…

Experimental vs. Descriptive Research

• Historically descriptive– monitoring

• Experimental research more powerful

• What’s the difference?

Descriptive Research

• Broad objectives rather than tests of specific hypotheses

• Can provide valuable information– Management & conservation

• Limitations– Best if used in research hypothesis

(conceptual model) formulation, monitoring, and description

Experimental Research

• Tests are made to examine the validity of a hypothesis

• Greater understanding and advancement of knowledge– Can be difficult or impossible

• e.g., weather effects on pheasant abundance

Scientific Method

• Best method of advancing knowledge

• Ideal method

• Used much?

• Hypothetico-deductive Method– Multiple working hypotheses

– Falsification

Scientific MethodIdentify the Research Problem

(#1)• Guides literature review and data

collection

• Applied vs. Basic research

– e.g., the number of pheasant broods seems to change from year to year

Scientific MethodLiterature Review (#2)

• Find possible explanations– e.g., the amount of spring rainfall has

been found to effect broods of numerous species

• Avoid duplication

• Develop methodologies

Scientific MethodIdentify Broad Research Objectives

(#3)• General course of action

• Preliminary plan

– e.g., to identify and understand reasons for inter-annual differences in the number of pheasant broods• Study pheasant broods and some

environmental factors such as rainfall

Scientific MethodCollect Preliminary Data (#4)

• Pilot study– “Mini-study”– e.g., count pheasant broods over several

years

• Literature Review

Scientific MethodExploratory Data Analysis (#5)

• Review and synthesis of data from pilot study or literature review– Describe broad patterns

Scientific MethodSteps 1-5 = Descriptive

Research• Identify the Research Problem

• Literature Review

• Identify Broad Research Objectives

• Collect Preliminary Data

• Exploratory Data Analysis

Scientific MethodFormulate a Research Hypothesis

(Conceptual Model) (#6)

• From observed associations (pilot study or literature)– e.g., we observed more pheasant broods during

years with below-average spring rainfall– Reliability?

• Research hypothesis = Most likely explanation– H1: above-average spring rain reduces survival of

pheasant broods

Scientific MethodFormulate a Research Hypothesis

(Conceptual Model) (#6)

• Alternative Hypotheses– A1: destruction of pheasant nests by

tractors is greatest during years of above-average rain

– A2: above-average spring rainfall results in greater plant growth, which reduces observability of pheasant broods

Scientific MethodFormulate Predictions as Testable

(often Statistical) Hypotheses (#7)

• Research Hypotheses represent theories



• Testable Hypotheses represent predictions from theories– H1: during years of above-average spring

rainfall, broods will have lower daily survival

– A2a: during years of above-average spring rainfall, vegetation will grow more dense

– A2b: in brood habitat, sites with greater vegetation density will reduce the observability of pheasant broods



• Testable form

• Truth and proof– Reject or fail to reject

Scientific MethodDesign Research and Methodology for

each Hypothesis to be Tested (#8)

• Pilot study– Logistics– Methodology problems– Quality of data & samples size



• Research Design Options– Uncertainty vs. applicability (inferential ability &

space)• Manipulative vs. observational research• Lab vs. field research



• Ideal Research Design?– Manipulative field research– Integrated research approach



• For each hypothesis– What data to collect, when, how, how

much, and for how long?

Scientific MethodPrepare a Proposal (#9)

• Describe all aspects of the research– Steps 1-8

Scientific MethodPeer Review and Proposal Revision

(#10)

• We are “to close”• Save time and $

Scientific MethodCollect Data (#11)

• Care in data recording– Avoid personal bias

• “Fun” and boredom

• 20% of process

Scientific MethodData Analysis (#12)

• Take the classes

Scientific MethodEvaluation and Interpretation

(#13)• Avoid expectations of results and

personal bias

• Organize results concisely and clearly in relation to objectives and hypotheses being tested


(#13)• Questions

– Do (statistical) tests support one or more hypotheses?

– Do the results reasonably explain the biology?• e.g., r & P-value in SLR

– Are there alternative explanations?– Are there any problems with the data?

• Small sample size• Unusual variation• Are additional data needed?


(#13)

• Conclusions

• Differentiate between conclusions based on the data and speculation

Scientific MethodSpeculation and New Hypotheses

(#14)

• New directions for better understanding– Limit in publications

Scientific MethodPublication (#15)

• Knowledge is “wasted” without dissemination

• Clear and concise writing– Repeatability

• Traumatic experience– Ego

• Helpful– Researchers are “to close”

• The process

Scientific MethodRepeat the Process (#16)

• New hypotheses

• New design and methods

Research ComponentsPopulations

• Population: a group of interbreeding individuals in the same place at the same time

• 3 types– Biological– Political– Research

• Complete vs. sample• Conclusions based on population studied


• Does the sample represent the research population?

• Does the research population represent the biological population?


• How well does the biological population represent the species?

• Unless all of these can be answered, conclusions from research must be limited to the proper scope

Research Components How good is the data?

(Precision, Bias, & Accuracy)

Research Components Replication

• Sample size: the number of independent sample (experimental) units drawn from the research population (i.e., number of replicates)– Often random

• Subsample: the number of observations in a sampling (experimental) unit


• The precision of a statistic (e.g., mean) is measured by its standard error (SE)

• Standard error depends on the variation in the original measurements (samples) and sample size


• These measurements must be true replicates (i.e., independent sample from the population) or the sample variation will underestimate the actual amount of variation in the population, and the precision of our estimate (e.g., mean) will be over-estimated.

Research Components Replication and Randomization

Burned Unburned

Pseudoreplication Replication

Burned

Unburned

What is “treated” ?


• Why subsample?

Research Components Controls

• What are they?– Control vs. treatment

• Why are they needed?

• Experimental control

Research Components Sample Size

• How many samples are enough?– Depends on variability (precision) of

your data– Power of tests to be employed– Sample size calculations


• How do you estimate variability prior to doing the study?– Pilot study– Literature review


• Why do we need to increase sample size?– Prevent the drawing of erroneous

conclusions• Better describe the population

– Improve the power of our tests

Research Components Power

Research Components Power

• Determinants– Sample size– Type I error (α)

• 0.05– Effect size– Statistical test

• Uses– Sample size– Interpretation of statistical tests

Research Components Sampling Design

Research Components Sampling Design

• Sequential

SamplingDependent and Independent

Samples

Research & Experimental Design

• Study Design• Alternatives to hypothesis testing• Common problems• The research-management connection

Study Design Hypothesis Testing


• Field studies– Mensurative or observational

experiments

• Natural experiments

• Field experiments


• Laboratory experiments– Scale, scope, realism, & generality

• Impact assessment– Before-after/control-impact (BACI)


• Integrated Research Processes– Descriptive studies & field/natural

history observations

– Experiments• Natural, field, & lab

Necessities in Manipulative Experiments

• Specify the research population

• Replication

• Proper use of controls

• (Random) assignment of treatments to experimental units

Thing’s to consider when designing experiments

• What is the hypothesis to be tested?

• What is the response/dependent variable(s) and how should it be measured?

• What is the independent/treatment variable(s) and what levels of the variable(s) will be tested?

• To which population do we want to make inferences?

Thing’s to consider when designing experiments

• What is the experimental unit?

• Which experimental design is best?

• How large should the sample size be?

• Have you consulted a statistician and had your design reviewed?

Single vs. Multifactor Designs

• SLR vs. MLR

• Single-factor vs. multi-factor ANOVA

– Interactions

Dependent Experimental Units

• Lack of independence (Pseudoreplication)– Fix

• Paired designs• Blocks• Repeated measures

Alternatives to Hypothesis Testing

Common Problems

• Sample Size & Power

• Procedural inconsistency

• Non-uniform treatments

• Pseudoreplication*

The Research-Management Connection

• Science-based management decisions

– Problem: a lack of research on program effectiveness

• Adaptive Management

research & experimental design why do we do research history of wildlife research descriptive v....

Documents

destruction of pheasant

data collectionapplied

synthesis of data

average raina2

wildlife ecologyecologists

guides literature review

descriptive researchidentify

pilot studyministudye