Week 1 - Principles of Ecology

Intro to principles

Biology is organized

Hierarchically based on complexity

Ecology largely covers

“Organism-and-up” biology (Hierarchy)

Ecology is the study of interactions between organisms and their?

Environments

Ecology is the study of the interactions that determine the

distribution and abundance of organisms

Individual

• Response of individulas to environmet

• Behavior and physiology

Populations

• All individuals of a species in a given area

• Abundance, change over time

Communities

• Mixture of populations of different species

• Processes determining structure, function

• Who is present?

Ecosystems

• Biotic community in conjunction with physical environment (abiotic)

• Nutrient availability, food webs, energy flows

Methods: Observation

• Life history

• Behaviors

• Patterns

• Central to Darwin & Wallace’s theory of evolution by natural selection

• Oldest techniques in ecology

Methods: Null Hypothesis Testing - TREATMENTS

Manipulated by experimenter

Ecology must avoid?

“Just-so” stories

Methods: Null Hypothesis Testing - CONTROLS

Unmanipulated

• Or manipulated to be constant

Null hypothesis (H₀):

Focal factors have no effect; no relationship exists

Alternative hypothesis (H₁ or H_A):

Focal factors do have some effect

• Possibly specified in which direction

(Null Hypothesis Testing) This method does not:

PROVE hypotheses. We DISPROVE them

Methods: Multiple Hypothesis Testing — Often unable to perform experiments

Large systems, long timescales, etc.

Methods: Multiple Hypothesis Testing — Often many

Variables of interest

Methods: Multiple Hypothesis Testing — Compare real-world data to

Data predicted by different hypotheses

Ecological Modeling

All models are wrong, some models are useful!!

Conceptual models

Relationships between components

•Important for ANY research question

Methods: Ecological Modeling — Analytical models

•simple enough to solve the equations

(dN_1)/dt=r_1 N_1 ((K_1  -N_1-αN_2)/K_1 )

Methods: Ecological Modeling — Simulation models

Complex, require running multiple simulations and looking at range of outcomes

Methods: Ecological Modeling — Realism is a trade-off (REALISTIC)

• More realistic models are complex, more system-specific

• Require more field data to verify

Methods: Ecological Modeling — Realism is a trade-off (SIMPLER)

Simpler models may be more generalized

•But, may: miss important local context in some systems

Anthropocene

A geological epoch defined by human (anthropogenic) influence

Rapid acceleration of human impacts on environment

Starting from the 1800s industrial revolution (MID 20TH CENTURY)

Study of ecology cannot avoid?

Human impact

Quantitative thinking is?

Anything numerical (count or measure)

Quantitative thinking is important for ecologists:

All of the above

Building models EX: Signal Crayfish - what could be measured (quantitative thinking)

All of the above

Variables do what?

Change

Parameters are?

Fixed (or have specific range)

Parameters will not?

Change across iterations of the model

Parameters are represented how in a model?

Lowercase Greek or Roman (a,b,c,α, β, γ)

Variables will?

Change across iterations within a model

Variations are represented how?

Capital letters (X,Y,N, etc.)

Population size would be a?

Variable (N = abundance)

Average birth rate per individual would be a?

Parameter

Average death rate would be a?

Parameter

Measuring out change is?

A variable

Deterministic models

Always produce the same result if we begin with the same values

• Same initial conditions, same outcome

Simulation approach

Run a model several different times with slightly different initial conditions representing possible real-world conditions

What do stochastic models do?

Explore range AND frequency of outcomes

What is a stochastic model (random)

The simulation approach

Deterministic model EX:

Average egg production is exactly 300/individual

Stochastic model EX:

Each run, vary average egg production from 200-400/individual

(Want a wider range)

Types of data: Categorical

Nominal and Ordinal

Types of data: Numerical

Continuous and Discrete

Nominal Categorical data has no?

Inherent numerical value

Nominal Categorical data common examples?

Qualitative data

Nominal Categorical data what to record?

• Color

• Behavior

• Sex / mating type

• Alive / Dead

• Species / Taxon

Ordinal Categorical data is not?

Numerical, but does have an inherent order

Ordinal Categorical data common examples?

• Age / life stage

  • Egg, juvenile, adult

  • Egg, larva, pupil, adult)

• Size or count

  • Small, medium, large

  • None, few, many (counting)

You cannot average?

Ordinal variables (DON’T treat them like real numbers)

Continuous Numerical data can take any?

Value, including fractions and decimals

Continuous Numerical data examples?

All of the above

Discrete Numerical data (Integer data) only come in?

Whole numbers

Discrete Numerical data (Integer data) examples?

• Age (measuring yrs)

• Count data (can’t count 3.7 wolves)

What data type can be tricky?

Discrete Numerical data

Explanatory Variables

What we think is affecting, changing, or causing something about the response variable

What variable goes on the x-axis in figures?

Explanatory Variables

What variable goes on the y-axis in figures?

Response Variables

Response Variables

Usually the thing we are most interested in

• Often trying to determine what drives change

What are the variables?

Response and Explanatory

Population (statistical definition)

The entire group of interest

Sample definition

What we measured

A good sample will be?

Unbiased

What are common sampling biases?

All of the above

When two variables (in data) are correlated, there are always?

Four possible explinations

What is variable A?

X causes Y

What is variable B?

Y causes X

What is variable C?

Z, affects both X and Y

What is variable D?

X and Y are completely unrelated

Small data samples produce?

Weird results

Mean definition

Sum the values and divide by the number of observations

Median definition

Order the values from smallest to greatest and find the middle

Mode definition

Find the value that occurs most often

Statistics and science are ultimately

Interpretative

The y-axis is always?

The frequency

Standard deviation

A measure of the average distance that points lie from the mean

What is a histogram?

Plot of frequency distribution

Distribution represents?

All possible values in the data and how often each value occurs

Scientific hypothesis

Proposed explanation for some observations

Statistical hypothesis

Tests the pattern we predicted / tests the prediction

We use statistics to?

Analyze patterns in data to then test the pattern’s likelihood of happening to be able to test the results of our scientific hypothesis

Null hypothesis

• There is no pattern

• Differences between groups are no greater than we would expect due to random variation

Alternative hypothesis

There is a pattern

What are the STATISTICAL hypothesis?

Alternative and Null hypothesis

P-value

The probability of observing a test statistic that is at least as extreme as your tests statistic, assuming the null hypothesis is true

Error rate is not an?

Error rate

Statistical significance is NOT equal to?

Biological significance

What affects a p-value?

All of the above