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CSU / Life Science / LIFE 320 / Patterns of distribution suggest species niches. how do we test whethe

Patterns of distribution suggest species niches. how do we test whethe

Patterns of distribution suggest species niches. how do we test whethe

Description

School: Colorado State University
Department: Life Science
Course: Ecology
Professor: Ruth hufbauer
Term: Fall 2015
Tags: modeling and population
Cost: 50
Name: LIFE 320 Midterm 2 Study Guide
Description: Covers lecture notes over chapters 11, 12, and 13.
Uploaded: 03/05/2017
20 Pages 121 Views 5 Unlocks
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Midterm 2 Study Guide


Patterns of distribution suggest species niches. so how do we test whether species are really limited to particular conditions?



Population Distribution Lecture Notes

Chapter 11

2/16/17

Population distribution

-Geographic range: total area

-Species Niche: range of conditions

-Fundamental Niche: whole range a species COULD occupy

-Realized Niche: Range a species ACTUALLY occupies

Patterns of distribution suggest species niches. So how do we test whether species  are really limited to particular conditions?  

2 species of monkey flowers occur at different elevations

-Lewis monkey flower resides in higher elevations

-Scarlet monkey flower resides in lower elevations

Are these two species limited by competition or can they only survive at specific  elevations?


Are these two species limited by competition or can they only survive at specific elevations?



Transplant experiment.

-Scarlet was placed in high elevation

-Lewis was placed in low elevation.  

-Lewis could not survive in low elevation, so it was distribution limited. -Scarlet grew a little at high elevation, so it was limited by competition

It is important to know the fundamental and realized niches of species for  conservation

-Understand the spread of invasive

-Understand that it may be a rare species with already low numbers -Understand how climate change will affect these niches.  

Ecological Niche Modeling predicts where to find species

1. Map locations where individuals have been found (usually use GIS) -Gather data from…


What are the characteristics of population distribution?



Don't forget about the age old question of What is article 19 of the universal declaration of human rights?

-Surveys

-Museums and herbariums

2. Identify ecological envelope

-predicted range of suitable conditions

-what variables are important?  We also discuss several other topics like Where is australia located?

3. Map the locations where these conditions occur.

Characteristics of population distribution

1. Range

2. Abundance

3. Density

4. Dispersion

5. Dispersal  

Population Abundance – number of individuals per sample or ecosystem Methods to estimate abundance

-vegetation quadrats and extrapolate data

-Forest inventory

Size, height, DBH, decomposition

-Line transects

-Mark and recapture

Not great for territorial animals

I-Click question We also discuss several other topics like What is the meaning of codependence?

You collect 100 arthropods. You mark them all. 2 weeks later you capture 200 and  20 were marked. What is the total population?  

200/10=20

100 x 10=1000 is the total population

Population Density – number of individuals per unit area

-May be uneven

-Patchy resources

Populations are most dense near center of geographic range (Kirk Batrick and  Barton)

-limited at edge by environmental tolerance limits

-doesn’t adapt well to edges due to gene flow

-they are best adapted in the middle We also discuss several other topics like What is the symbol for angular velocity (radians/sec)?
If you want to learn more check out What is the cycle of managers?

-more genes in middle that aren’t adapted to edges

I-Click: What would be better adapted to local conditions?

-Individuals at core of range and from regions with high population density

Dispersion: How individuals spread locally

-Random: Null model

-Evenly spaced/ over dispersed Don't forget about the age old question of What is the meaning of static in biometrics?

-measure distance to nearest neighbor and if number is higher than  you expect then it is evenly spaced

-Clustered

-due to social reasons, low dispersal, resources, etc

Dispersal: affects changes in geographical range

Populations may live in distinct habitat patches

Habitat may be patchy at multiple scales

-Patches may vary in quality at small scales

Population densities should be different in patches of different quality

High Quality

Habitat

Benefit

Individual

Low Quality

Habitat 

Dispersal affects the spatial behavior of populations.  

One population: if individuals can disperse freely among patches Subpopulation: fluctuate more independently and dispersal is rare Conceptual models describe spatial structure of sub populations Metapopulation Models

-Levins 1970

-Lots of elaborations on the metapopulation model

-describes behavior of multiple subpopulations

Levins Metapop Model

-Habitat patches

-assume equal quality 

-occasional migration

-look at one species at a time

Simple  

Assumptions

-how do colonization/extinction rates affect patchy occupancy?

Equilibrium occupancy rate

-Assume no differences in patch quality or subpopulation size

p=fraction of occupied patches

e=rate of extinction of occupied patches

c=rate of propagule generation by occupied patches (colonization rate)

Slope tells how quickly p changes in respect to t

-if the slope is zero, then dp/dt is zero

dp/dt increases as new patches are added and decreases when patches go extinct

occupied

dp/dt=cp(1-p)- ep 

If colonies are added or  

subtracte

d

unoccupie d

Extinction  

rates

If you let colonization and extinction happen, you  can reach equilibrium

dp/dt=0

p=1-e/c  equilibrium

Each patch has a 10% probability of going extinct each step. The probability of  being colonized is 20%. What is the equilibrium occupancy rate?

P=1-e/c

p= 1- .1/.2= .5

.5 x 100= 50%

How does Levin’s model predict that you could increase patch occupancy rates? -Justifies use of corridors

-if you increase c, you make it easier for colonization  allowing movement  increases colonization

c>e  size increase

e>c size becomes zero

But in real life, patches aren’t equal

Patchy occupancy is sensitive to quality

Source-Sink Model: differences in patch quality

-Prediction-some patches are more important to population persistence High population growth is more important for x

Sources: send colonists which increases population

Sinks: move in but don’t send colonists out

Implications

-Some patches are important to population persistence

-Variation may be important

-suboptimal patches may act as reserves

-source-sink may fluctuate

Bay Checkerspot butterfly

-during dry conditions, some patches persist while others don’t -during good years, the large patch acts as a sink for the butterfly -during bad years, like drought, large patch acts a source for the butterfly

Landscape model: effects of patch arrangement and surrounding landscapes -isolated and small patches are likely to be unoccupied

-connected and large patches are likely to be occupied

Implication for conservation

-Long term persistence may depend upon maintenance of many potential  habitat patches

-connectivity important but also increases the spread of predators and  pathogens

-patch sources change

Every patch has a risk of extinction

-may need to reintroduce species into several habitats

-Focus on increasing c and decreasing e

Quantifying Population growth Ch12 Lecture Notes

2/21/2017

Is the population stable, increasing, or decreasing?

How do abiotic and or biotic factors affect population growth?

Which demographic rates most influence population growth?

Discrete time

Take measurements at regular time intervals

Continuous time

Models overall population trajectory

Discrete time Equation

Predict N in the future

Population size at time t=N

If per capita birth and death rates are constant, if population growth is  proportional to population size.  

Lambda=Nt+1 /Nt Nt+1= Ntlambda

If N is constant every year, you can estimate population size.

If Lambda = 1, the population size does not change

If lambda is >1, the population increases

If lambda is <1 then population decreases

Continuous time Equations

Time steps shrink to small intervals calculate the slope

dN= change in numbe3r of individuals

dt= per change in time

r=rate of growth

N= number of individuals in a population

dN/dt=rN

Nt=N0ert 

r intrinsic rate of population growth, equals per capita instantaneous birth rate (b) - per capita instantaneous death (d)

lambda= er r=ln(lambda)

Discrete= Nt=N0ert Continuous= Nt=N0lambdat

2/23/2017

Lambda will never be negative

If a population of 1000 mice has an intrinsic rate of increase of .5, how many mice  will be in the population after 6 months?

Nt=N0ert  

1000e(.5)(6)=20117.3

You may want to compare poulation growth between populations Easier to compare slopes of lines than curves

So using a log graph is easier to compare populations

The graphs below are the same graphs (conceptually). The first is an exponential  graph, and the second is a log graph.

Exponential and geometric equations  

Describe density-independent population growth

R and lambda are intrinsic population growth rates, birth rates, and death  rates

Population growth is limited

Density-independent factors

Ex temperature, fire, hurricane, etc

The growth of some populations is determined by density-independent  factors

Population density mostly determined by rain and temperature

Population growth can be limited due to population density

Density-dependent factors

Effect proportional to population size

Negative density dependence: population growth declines ad density  increase. (Pretty common)

Ex diseases, predation, food limitation, nesting sites

Imposes a carrying capacity

Example of negative density dependence

The Northern Gannet

There was data collected on its historic and current colony size

Small colonies grew faster than large colonies because large colonies have a  longer feeding flights in order to find more fish. So the large colonies had an  increase in food scarcity.  

Not everything has a negative effect

Allee effects= cause positive correlation between density and population growth  rates

More density=more chances to meet and mate

Fish

Fish Range

Drop Location

Conceptual example

- Reintroducing a fish species into Lake Eerie  (or any large lake)

Low densities

Difficult to find mates

Failure of group defense

Unorganized Colonies

Cowslip Plant Allee Example

Seeds Per  

Plant

 

Population  

Size

 

The graph shows that as population size increases, the Cowslip plant produces more seeds and as the population size decreases, the plant produces fewer seeds

Allee effects can make recovery difficult

Fisheries when a population crashes, it can’t recover because they need  density to grow.  

Density dependent population growth is modeled using logistic equation Add a term to slow growth ad density increases

As population growth gets slower, N gets larger

dN/dt=rN[(K-N)/(K)]

Max  

Harvest

K

dN/dt N k/ Inflection  

2

Tim

e

pt

k/ K

2

If N>K, it is a negative slope

If the population is small, it is influenced by rN

If the population is large, it is influenced by (K-N)/(K)

As N gets bigger it gets closer to K and the slope gets smaller till it levels off at zero

All these models have assumed demographic equivalence. In real life, populations  differ in age structure and have different demographic rates

In the U.S, the age is fairly even from 0-50. This means that the population size is  stable and won’t change a lot

In Germany, there are more post-reproductive people than pre-reproductive. This  means that there will be fewer people in the future and the population is on the  decline.  

In India, there are more pre-reproductive people than post-reproductive, which  means that the population is increasing.  

Normal age distribution for a species is affected by survivorship patterns

Type I: survival rate high at a young age and most mortality occurring at an old age  (humans)

Type II: Constant rate of death. (Lizards)

Type III: High mortality in earliest age and survivorship increases as they  mature(Sequoia trees)

Survivorsh ip

Ag e

Age structure reflect conservation status

A Juniper in Spain is in trouble because it produces very few seedlings and  juveniles.  

Life Tables

Summarize demographic grates

Usually females only

x=age class

nx= number of individuals in each age class immediately after the population has  produced offspring

sx= survival rate from one age class to next age class

bx= number of offspring produced by each individual

Number of surviving to the next age class= (nx)(sx)

Number of new offspring produced= (nx)(sx)(bx)

Age/Stage based models

Use matrix models

Need to know demographic rates (arrows)

The arrows represent the probability of going onto the next stage or  staying at current stage.

P2 P3 

P2 P3 

P1 

F

1= Small Juvenile

2= Large Juvenile

3= Adults

Building a stage-based model

Build Diagrams

Build equations

Express membership in size class at (t+1) with equation

No subtraction

 n1, t+1= n1P11+n3+F

n2, t+1= n1P21+n2P22

n3, t+1= n2P32+n3P33

2/28/17

Calculate the size of each class for 2 time steps. Start with 100 individuals in every  class

There si a probability rate for every arrow

Put probabilities and starting class sizes into your equations

Calculate nt+1 for every stage class

Total population is sum of all stage class  

(I’m tired of subscripts)

F = .561 P11=.672 P21=.018 P22=.849 P32=.138 P33=.969 t=0 t=1 t=2 t=3

N1 100 123.3 144.9 164

N2 100 86.7 75.8 66.9

N3 100 110.7 119.2 126

N1=(100 x .672)+ (100 x .561)= 123.3

N2= (100 x .018) + (100 x .849)=86.7

N3=(100 x .138) + (100 x .969)=110.7

N1, t+2= (123.3 x .672) + (110.7 x .561)= 144.9603

Projecting a population over time

Iterate until population reaches stable stage distribution and a stable per capita  growth rate (lambda)

Stable Stage Distribution (SSD)

Eventually evens out

Ration of classes stays the same

Determined by arrows

Shows which class will be more at the end

Settle at a constant growth rate

Initial stage structures are not affected by SSD or final intrinsic growth rate lambda SSD and lambda are determined entirely by transition probabilities

To calculate SSD wait until population growth rate stabilizes then calculate the size  of each class a percentage of total population size.  

The population will not grow after a few time steps (lambda=1) How could we increase per capita growth if we want populations to persist? need to change arrows to change lambda

How do you know which arrow to change? Through a perturbation analysis Perturbation Analysis

Reveals how changes in demographic rates would or could affect lambda

Sensitivity

Sensitivity= absolute change in lambda given a small absolute change in  demographic rate (P)

Simple sensitivity equation: change in lambda/ change in P

Can be calculated for every demographic rate, which causes the biggest change Example

If P32 is big, then changing it will affect the growth rate a lot because P32  would be sensitive

Sensitivity is useful in conservation when determining the budget and where to  allocate resources.

Some vital rates are in different units

Changing fecundity by 0.1 is often different than changing survivorship by 0.1 Might want to know the relative effect of a certain proportional change (change in lambda/ lambda)/(change in P/P)

Proportional rather than absolute change

Elasticity

High elasticity value population growth rate is sensitive to changes in that  demographic rate

In cheetahs, which life stage is most important to population growth? Hollow= sensitivity Blue= Elasticity

Because the elasticity is higher in adults, you would want to increase adult  survivorship to affect population survivorship

Youn

g

Adult

3/2/2017

Elasticity Application Considerations

Sensitivity=absolute changes

change in lambda/change in Pji

Elasticity= proportional

-usually calculated in matrix models

(change in lambda/lambda)/(change in P/P)

Population viability Analysis

Use stage-based models to predict risk of extinction

Usually use stochastic models changes in variable stochastically

Run several times (sometimes up to 1,000 times) to see how long it took for a species to go extinct gives an idea how vulnerable populations are

Example of using population viability analysis

Guanaco

Guanaco were reintroduced to Argentina

Their populations where not increasing very much so population modeling  was used

Ecologists created different models with different conditions to compare what management practices would decrease the probability of extinction in 100 years.  

Population Dynamics Chapter 13 Lecture

How populations respond to environmental conditions

Some populations are intrinsically stable, unstable, or fluctuate a lot

Stability is affected by…

Body size and homeostasis How resistant an organism is to environmental  changes

Demographic rates intrinsic properties of the population

Life span, reproduction, etc

Whether it’s a K or r selected species

Example Phytoplankton in Lake Erie

Very stochastic, they are not constant or stable

Plankton are really small which plays a part in their instability

Environmental changes tend to be  

Stochastic, NOT periodic

Populations may track environment challenges. However most population  dynamics are periodic.  

Intrinsic Population Dynamics

Typical patterns

Steady, so no oscillations

Cycling

Chaotic

Some of these patterns can be predictable

Example Gyrfalcons population cycles every 10 years

Why do oscillations occur in population dynamics?

Some density dependent exceed their carrying capacity, which cause a crash. How do they exceed their carrying capacity?

1. There is a delay in their density dependent responses

2. They experience rapid population growth so they approach carrying  capacity very quickly.

A metaphor to explain this is a new golfer. The hole is the carrying capacity, and the new golfer hits the ball so hard it goes past the hole. (The population has now  exceeded their carrying capacity and are at their peak) To compensate going past  the hole, the new golfer hits the ball back to the whole with the same amount of  force. Once again, it goes past the hole (This is when the population crashes)  

What causes delayed density-dependence responses?

Discrete generations, such as ones that have annual reproduction Slow responses to feedback mechanisms

Ex A species of Daphnia stores energy in times of abundance, so they  exceed their carrying capacity when resources are low

Maternal effects

Ex pulse of resources leads to higher quality offspring being produces, so the effects of the resource pulse are not apparent until next generation

Predator-Prey relations

Ex Predators take time to reproduce. This is one reason why when you see cycling of a predator and prey relationship (like snowshoe hare and lynx) the  prey starts to decline before the predator and also increase before the predator

Modeling delayed density dependence

Make logistic equation responsive to population size in the past dN/dt=rN[(K-Nt-tal)/(K)]

tal= time delay in response to population density

The length of the delay (tal) and the rate of increase (r) determine pattern Large tal or r can lead due to oscillations

If you have no delay and/or reached r there will be no oscillations Damped oscillation

overshoot K a little but eventually level off  

-Similar to the metaphor of the beginner golfer, but instead think of an  intermediate golfer.  

If r x tal is small, then there is no oscillation

If r x tal is intermediate, then there is damped oscillation

If r x tal is large, then there is a cycle and chaos

Can you predict risk of outbreaks?

Cycles can lead to periods when N > K (When the population exceeds  carrying capacity)

Populations of parasites and pests may cycle, so it would be useful to predict  and prevent cycles

Interrupting chaos cycles

Ex Flour beetles

r selected species, so it has a large boom and bust cycle

If we were to disrupt the cycle, it could cause the cycle to level out

If individuals were added at the peak of a cycle, it caused the cycle to  level out

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