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Computational Ecology Introduction to Ecological Science Sonny Bleicher Ph.D.

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Page 1: Computational Ecology Intro. to Ecology Ecology Introduction to Ecological ... (ex. The Maasai Mara ) ... GUD (g) No Owl Coefficients R2

Computational Ecology Introduction to Ecological

Science Sonny Bleicher Ph.D.

Page 2: Computational Ecology Intro. to Ecology Ecology Introduction to Ecological ... (ex. The Maasai Mara ) ... GUD (g) No Owl Coefficients R2

Ecos Logos

Page 3: Computational Ecology Intro. to Ecology Ecology Introduction to Ecological ... (ex. The Maasai Mara ) ... GUD (g) No Owl Coefficients R2

Defining Ecology

• Interactions: • Organisms:

• Plants

• Animals:• Bacteria

• Fungi

• Invertebrates

• Vertebrates

• The physical environment:• Air:

• Gasses

• Water Vapor

• Water: • H2O

• Ions

• Earth:• Minerals

• Ground water

• Dissolved organic matter

Page 4: Computational Ecology Intro. to Ecology Ecology Introduction to Ecological ... (ex. The Maasai Mara ) ... GUD (g) No Owl Coefficients R2

But first what is a living organism?

• Reproduction

• Growth

• Metabolism

• Death

Page 5: Computational Ecology Intro. to Ecology Ecology Introduction to Ecological ... (ex. The Maasai Mara ) ... GUD (g) No Owl Coefficients R2

Scales of Study

From the individual to the biome

• The individual

Not a traditional, however at the forefront of ecological research

• Micro-scale genetic makeup and epigenetics affecting personality and choices of individuals:• Should an individual take risk (be

bold) or avoid risk? • Is any mate a good mate for any

individual? • Should an individual seed germinate

now, or wait for better conditions?

Page 6: Computational Ecology Intro. to Ecology Ecology Introduction to Ecological ... (ex. The Maasai Mara ) ... GUD (g) No Owl Coefficients R2

The population

A group of individuals of the same species that inhabit the same space.

• How many individuals cans the space(and resources) sustain?

• Demographics (distribution of individuals between the sexes, age classes).

• Life tradeoffs (where to invest energy in reproductions (cost of offspring, parental care, and time of reproduction)

Page 7: Computational Ecology Intro. to Ecology Ecology Introduction to Ecological ... (ex. The Maasai Mara ) ... GUD (g) No Owl Coefficients R2

The Community

A number of populations of difference species interacting in the same space

• Interaction types :• Predation (+, -) (and parasitism)

• Competition (-,-)

• Neutralism (0,0) (not fully an interaction)

• Commensalism (+,0)

• Amesalism (-,0)

• Mutualism (+,+) (can also be referred to as symbiosis if persistent over long time)

Page 8: Computational Ecology Intro. to Ecology Ecology Introduction to Ecological ... (ex. The Maasai Mara ) ... GUD (g) No Owl Coefficients R2

The ecosystem

A community of organisms interacting with each other and with their environment such that energy is exchanged and system-level processes, such as the cycling of elements, emerge.

• Focus predominantly on the movement of the non-biological elements, needed to sustain life, movement in the environment:• Water

• Energy

• Carbon

• Nitrogen

• Phosphorus

Page 9: Computational Ecology Intro. to Ecology Ecology Introduction to Ecological ... (ex. The Maasai Mara ) ... GUD (g) No Owl Coefficients R2

Ecosystem (continued)

Ecosystem Services

• Ecosystem ecologists measure system health using measures of biological output, usually translated into human economic value:• Biomass (lumber, crops, food).

• Gas production, and sequestration (oxygenation of air, carbon sequestration and fixing).

• System regeneration (water filtration, pollutant sequestration and absorbent).

• Climate regulation

Page 10: Computational Ecology Intro. to Ecology Ecology Introduction to Ecological ... (ex. The Maasai Mara ) ... GUD (g) No Owl Coefficients R2

Biomes:

Page 11: Computational Ecology Intro. to Ecology Ecology Introduction to Ecological ... (ex. The Maasai Mara ) ... GUD (g) No Owl Coefficients R2

The Biome: Macro-Ecology

• Study of the effects of climatic conditions on biological communities and ecosystems.

• A study of convergent systems on a global scale.

Page 12: Computational Ecology Intro. to Ecology Ecology Introduction to Ecological ... (ex. The Maasai Mara ) ... GUD (g) No Owl Coefficients R2

Temporal Scales

Scale Times

Individuals Minutes-Days

Populations Years-Decades (occasionally days )

Communities Years – Centuries

Ecosystems-Biomes Centuries-Millenia

Page 13: Computational Ecology Intro. to Ecology Ecology Introduction to Ecological ... (ex. The Maasai Mara ) ... GUD (g) No Owl Coefficients R2

Spatial Scale

Micro-Habitat Habitat

Page 14: Computational Ecology Intro. to Ecology Ecology Introduction to Ecological ... (ex. The Maasai Mara ) ... GUD (g) No Owl Coefficients R2

Ecological Niche Biome

Page 15: Computational Ecology Intro. to Ecology Ecology Introduction to Ecological ... (ex. The Maasai Mara ) ... GUD (g) No Owl Coefficients R2

Virtual Scale?

Page 16: Computational Ecology Intro. to Ecology Ecology Introduction to Ecological ... (ex. The Maasai Mara ) ... GUD (g) No Owl Coefficients R2

The Three Ecologists

Page 17: Computational Ecology Intro. to Ecology Ecology Introduction to Ecological ... (ex. The Maasai Mara ) ... GUD (g) No Owl Coefficients R2

The Theoretician – Modeler

• Using observations and computations to distill the laws by which ecological interactions occur.

• Based on the derived models, making predictions and designing management plans for resources

The Empirical Ecologist

• Using the natural conditions in the field to test the modeler’s laws and make observations.

Page 18: Computational Ecology Intro. to Ecology Ecology Introduction to Ecological ... (ex. The Maasai Mara ) ... GUD (g) No Owl Coefficients R2

The Conservation Ecologist

• Using the theories and management plans, together with the field experiment of the empiricist to manage the biological resources and diversity.

Page 19: Computational Ecology Intro. to Ecology Ecology Introduction to Ecological ... (ex. The Maasai Mara ) ... GUD (g) No Owl Coefficients R2

Approaching Science:World Views (Research Programs, Lakatos)

Page 20: Computational Ecology Intro. to Ecology Ecology Introduction to Ecological ... (ex. The Maasai Mara ) ... GUD (g) No Owl Coefficients R2

Ecological Research Programs

History of Life

• All organisms evolved from common ancestors.

• Tracing back the ancestors, and identifying relative relatedness can shed light on how species interact and what are the conservation needs of a species.

Tools:

• Cladistics

• Phylogenies

• Genetic analysis

Page 21: Computational Ecology Intro. to Ecology Ecology Introduction to Ecological ... (ex. The Maasai Mara ) ... GUD (g) No Owl Coefficients R2

Ecological Research Programs

Diversity

• Systems with richer diversity are more stable.

• Higher diversity means system stability.

• High species richness allows for less invasion by alien species.

Tools

• Taxonomy

• Genetic diversity testing (microbial)

• Diversity indices

• Diversity extrapolation and estimation models

Page 22: Computational Ecology Intro. to Ecology Ecology Introduction to Ecological ... (ex. The Maasai Mara ) ... GUD (g) No Owl Coefficients R2

Ecological Research Programs

Optimization • Competition for resources (energy, safety,

mates) drives all interactions in nature.

• All interacting species are in a constant armament race against each other, the losers go extinct.

• Thus, every physical and behavioural trait must have (or have had) biological benefit, and the cost of it must not be grater than that of the benefits to the current living organisms

The Red Queen responds: "Now, here, you see, it takes all the running you can do to keep in the same place"

Page 23: Computational Ecology Intro. to Ecology Ecology Introduction to Ecological ... (ex. The Maasai Mara ) ... GUD (g) No Owl Coefficients R2

Ecological Research Programs

Tools

• Mathematical models

• Manipulative field experiments

Page 24: Computational Ecology Intro. to Ecology Ecology Introduction to Ecological ... (ex. The Maasai Mara ) ... GUD (g) No Owl Coefficients R2

The greater ecological questions:

• Distribution: Where do we find species? And what are the resources they need?

• Abundance: How many individuals can an area support ?

• Procession of life: What should an individual do at what age ?

• Fit of form and function: How do species use the resources in the environment?

Page 25: Computational Ecology Intro. to Ecology Ecology Introduction to Ecological ... (ex. The Maasai Mara ) ... GUD (g) No Owl Coefficients R2

Why does this matter at all?

• The gene containment unit.

• Biological beings as computer algorithms

• Survival of the code, not the being.

Page 26: Computational Ecology Intro. to Ecology Ecology Introduction to Ecological ... (ex. The Maasai Mara ) ... GUD (g) No Owl Coefficients R2

How does that actually work?

• From the will to change

• To the forced constraints of the environment.

Page 27: Computational Ecology Intro. to Ecology Ecology Introduction to Ecological ... (ex. The Maasai Mara ) ... GUD (g) No Owl Coefficients R2

Measuring Biological Success The Jewish Mother Phenomenon

Fitness

1

𝑁

∆𝑁

∆𝑡

Factors impacting fitness

• Energy

• Mate Quality

• Offspring survivorship

• (the all encompassing power)

Page 28: Computational Ecology Intro. to Ecology Ecology Introduction to Ecological ... (ex. The Maasai Mara ) ... GUD (g) No Owl Coefficients R2

From Fitness, through Evolution to Ecological Systems• Species in a community compete for resources

• Each resource can sustain multiple species as long the strategies, extraction methods, they use do not overlap.

• Species constantly change their strategies, however balance on the strategy where any change would result in lower fitness, a point called an evolutionary stable strategy (Maynard-Smith and Price, 1973).

Page 29: Computational Ecology Intro. to Ecology Ecology Introduction to Ecological ... (ex. The Maasai Mara ) ... GUD (g) No Owl Coefficients R2

Individual 2

Aggressive Submissive

Individual 1 Aggressive (-1, -1) (2,0)

Submissive (0,2) (1,1)

Page 30: Computational Ecology Intro. to Ecology Ecology Introduction to Ecological ... (ex. The Maasai Mara ) ... GUD (g) No Owl Coefficients R2

Individual 2

Aggressive Submissive

Individual 1 Aggressive (-1, -1) (2,0)

Submissive (0,2) (1,1)

Page 31: Computational Ecology Intro. to Ecology Ecology Introduction to Ecological ... (ex. The Maasai Mara ) ... GUD (g) No Owl Coefficients R2

Some Basic Concepts In Ecology

In the time we have left

Page 32: Computational Ecology Intro. to Ecology Ecology Introduction to Ecological ... (ex. The Maasai Mara ) ... GUD (g) No Owl Coefficients R2

Population Dynamics – First and second laws of ecology 1. Every population has the intrinsic potential to grow exponentially.

2. No population can grow exponentially without resource limitations.

Page 33: Computational Ecology Intro. to Ecology Ecology Introduction to Ecological ... (ex. The Maasai Mara ) ... GUD (g) No Owl Coefficients R2

Lotka-Volterra Competition Equations

Page 34: Computational Ecology Intro. to Ecology Ecology Introduction to Ecological ... (ex. The Maasai Mara ) ... GUD (g) No Owl Coefficients R2

Predator-Prey Limiting Cycles

Page 35: Computational Ecology Intro. to Ecology Ecology Introduction to Ecological ... (ex. The Maasai Mara ) ... GUD (g) No Owl Coefficients R2

Diversity

• Species Richness – The Number of species that are found in a system

• Species Diversity – A variable of the number of species in a system with a relative abundance of that species out of the total number of individuals measured.

Page 36: Computational Ecology Intro. to Ecology Ecology Introduction to Ecological ... (ex. The Maasai Mara ) ... GUD (g) No Owl Coefficients R2

Whittaker’s Diversities

• α–diversity: Local scale (in a specific plot)

• β-diversity: Between plots (relative)

• ϒ-diversity: Overall diversity in the landscape = α*β

Page 37: Computational Ecology Intro. to Ecology Ecology Introduction to Ecological ... (ex. The Maasai Mara ) ... GUD (g) No Owl Coefficients R2

Island Biogeography (McArthur and Wilson, 1967)

Page 38: Computational Ecology Intro. to Ecology Ecology Introduction to Ecological ... (ex. The Maasai Mara ) ... GUD (g) No Owl Coefficients R2

SLOSS – Debate (Jared Diamond, 1975)

• Do we make on large nature preserve (ex. The Maasai Mara )

• Or do we have many smaller reserves that protect smaller resource hot spots.

Page 39: Computational Ecology Intro. to Ecology Ecology Introduction to Ecological ... (ex. The Maasai Mara ) ... GUD (g) No Owl Coefficients R2

Intermediate Disturbance Hypothesis (Wilkinson 1999)

Page 40: Computational Ecology Intro. to Ecology Ecology Introduction to Ecological ... (ex. The Maasai Mara ) ... GUD (g) No Owl Coefficients R2

Holling’s Panarchy (2001)

Page 41: Computational Ecology Intro. to Ecology Ecology Introduction to Ecological ... (ex. The Maasai Mara ) ... GUD (g) No Owl Coefficients R2

Let’s test our understanding with actual examples • What research program would the research question fit in?

• How would results of such studies look like?

Page 42: Computational Ecology Intro. to Ecology Ecology Introduction to Ecological ... (ex. The Maasai Mara ) ... GUD (g) No Owl Coefficients R2

A couple of examples of ecological research

Page 43: Computational Ecology Intro. to Ecology Ecology Introduction to Ecological ... (ex. The Maasai Mara ) ... GUD (g) No Owl Coefficients R2

Effects of land management on ant assemblages

Page 44: Computational Ecology Intro. to Ecology Ecology Introduction to Ecological ... (ex. The Maasai Mara ) ... GUD (g) No Owl Coefficients R2

0

5

10

15

20

25

30

35

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

Spe

cie

s R

ich

ne

ss

Samples

Grazing+Logging

Grazing

Logging

Control

Page 45: Computational Ecology Intro. to Ecology Ecology Introduction to Ecological ... (ex. The Maasai Mara ) ... GUD (g) No Owl Coefficients R2

Effects of predation risk by vipers and owls on gerbils and heteromyids • Is the effect of multiple predators cumulative on desert rodents or do

rodents respond to the risk posed only by the greater feared predator?

Page 46: Computational Ecology Intro. to Ecology Ecology Introduction to Ecological ... (ex. The Maasai Mara ) ... GUD (g) No Owl Coefficients R2

The Model (optimal patch use theory (Brown 1988))

𝐻 = 𝐶 + 𝑃 +𝑀𝑂𝐶

Page 47: Computational Ecology Intro. to Ecology Ecology Introduction to Ecological ... (ex. The Maasai Mara ) ... GUD (g) No Owl Coefficients R2

Divergent Behaviours

0

0.5

1

1.5

2

2.5

3

0 0.5 1 1.5 2 2.5 3

GU

D (

g) O

wl

GUD (g) No Owl

Coefficients R2

G. andersoni allenbyi 0.113 0.0197

G. Pyramidum 0.4794 0.258

C. Penicillatus 0.6833 0.619

D. Merriami 1.0262 0.771

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How and when did Chameleons reach the Seychelles Islands?

Page 51: Computational Ecology Intro. to Ecology Ecology Introduction to Ecological ... (ex. The Maasai Mara ) ... GUD (g) No Owl Coefficients R2