population ecology final

hypothesis

gives prediction (null/alternative) and explains why results may be that way

experimental study

control for all factors except variable of interest

strength: proves causation

weakness: done at small scales, simple systems, unnatural conditions

observational study

compares variables along natural gradients

strength: shows real world patterns since it works in natural systems

weakness: can only prove correlation

synthesis/meta analysis study

combines data from previous studies

strength: wide range of data means generally applicable results

weakness: variation in others’ methods and publication bias

deduction

a series of premises leading to a logical conclusion

Aristotle

starts general (major premise) to create a specific conclusion but depends on validity of premises

induction

statements leading to logical conclusion

francis bacon

starts with specific premise to create general inference, suggests what is likely to be true

inductive method

• confirmatory method

• starts with one initial observation and hypothesis

• seeks data to support hypothesis and modifies hypothesis to accommodate data after experiments are conducted to reach accepted truth

hypothetic-deductive method

• seeks to falsify incorrect hypotheses

• seeks data to discriminate between multiple hypotheses

• starts with more than one hypothesis (null/alternative)

• progress is made, truth accepted, by continually raising the bar with new tests of accepted hypotheses

model selection

sets up several working hypotheses or alternative plausible methods

1. fits data to each model

2. measures goodness of fit between models and data

3. applies penalty for model complexity

4. divides weight of evidence

5. accepts best model

systemic sampling

predictable spatial pattern along a transect

spread evenly to get representative of density but could produce biased estimation of population

random sampling

best way to achieve unbiased sample but statistical population and sample units must be defined

if sample is too small, might miss some environment types and sampled patches will be disproportionate to relative areas of actual environment types

stratified random sampling

divide total area into different strata and sample each stratum at random

number of samples in each stratum are proportional to its percentage areas

haphazard sampling

unmeasured “random” sampling that is extremely vulnerable to bias

statistical population

consists of N sample units

sample size

‘n’ units selected from N in which every unit has equal chance of being chosen

‘n’ < N

the more ‘n’ the better, more smaller units are better than less larger ones but the minimum size of quadrats depends on organism size

pseudoreplicates

using stats that assume samples are fully independent of each other wen they are not, degrees of freedom are lower than sample size may imply

factors that influence ability to detect effects

1. sample size (n): the more samples, the higher the confidence/lower standard error

2. variable within treatments: lower variance, higher confidence/lower standard error

3. effect size: difference between 2 or more means, larger effect size, higher confidence/greater significance

factors that make a habitat suitable to sustain a population

1. temperature: summer, max, mean annual temp, winter soil, frost days

2. moisture: precipitation, humidity, soil moisture

intersect in habitats

potential/fundamental niche

intersection of where all basic requirements are met to determine where a species could potentially thrive

climate envelope

adequate abiotic range for survival and reproduction

Argentine Ants abiotic influences on distribution

• native range: 13-18degrees Celsius mean annual temperature (Argentina, Brazil, Uruguay)

• inland california: warmer winter minimums/cooler summer maximums are clearest determinant of present ants

• southern california: ants preferred riparian margins and irrigated plots where there was higher soil moisture and more vegetation for food and shade

• requires 445 degree days above threshold of 15.9 degrees celsius

mechanistic (physiological) models

alternative approach for predicting species’ distribution

works from 1st principles of Eco physiological tolerances, deduce climatic tolerances from ecophysiology

• foraging behavior in relation to temperature

• max soil surface temps for foraging

• min temp requirements for development of brood (degree days)

degree day model

development of brood is more rapid at higher temps

445 days above threshold of 15.9C → half number of days at 2C higher than 15.9C

environmental gradients

show predictable sequence of distribution

• each species restricted to its optimum

• environmental tolerances limit one side, biotic interactions limit the other

realized niche

locations in world where species exists in climate suitable for persistence

facilitation

biotic influence of distribution

any interaction that has positive effect on receiving party

• mutualism (+ve)

• commensalism (nuetral)

• antagonistic (-ve)

examples of facilitation

1. lichens: fungus get sugars from algae and algae gets water and protection

2. nurse crops: suppress grass, protect against frost/wind/sun, draw moisture from soils, provide mycorrhizal partners for restoration (monuka and kohuhu trees facilitate totara)

3. pioneer species: change abiotic env to make favorable for others, stablize loose sand, slow wind, add organic matter (spinifex, pinago, marram grass)

competition/competitive exclusion

biotic influence of distribution

interaction where both species receive negative effects, observable at fine scales, modifies range boundaries

examples of competition

1. semibalanus and chthamalus barancles: semibalanus outcompetes other but when chthamalus is transported north of semibalanus range, it can persist

2. invasive species: kiore rat outcompeted by ship and norway rats in NZ

3. grass outcompetes trees during restoration and trees need to be released from grass growth

facilitation- competition continuum

relationship between species changes over time, may start with facilitation when young and develop into competition

predation/parasitism

biotic influence of distribution

interaction between species that only benefits one species, stronger effects on broad and fine scale

specialist predators and example

cannot exist beyond prey

monophagus herbivores on host plant like Bolaria titania butterfly and polygonum bistorta plant

generalist predator and example

restrict distribution of prey, can prey on multiple species

rats and endemic birds in NZ, birds experience thermal squeeze as they become more rare in warmer regions where rats spread

ecosystem engineer example

rabbits and sheep are grazers who alter abiotic conditions and availability of microsites to facilitate range of silver spotted skipper butterflies

intercontinental scales

• thousands to millions of years

• plate tectonics, biogeographical patterns

regional scale

• multiple generations

• invasive species, metapopulations, colonization

local scale

1-10 generations

habitat associations, local abundance patterns

beech gap in NZ

• gap in south island between northern and southern beech tree population patches

• a result of previous glaciation from ice age that wiped out beech populations and is now being slowly recolonized

diffusive spread

simple model of spread, assumes random movement of inds

random walking vs autocorrelated random walk

individual may follow random walk of constant step length vs successive angles are correlated in their direction, directional bias

dispersal speed

faster moving inds reach edge quicker, mate with nearby inds, and reproduce strains of faster inds at range boundaries

ex: cane toads in australia

seed shadows

heigh is function of seed production and spread from center is function of dispersal mechansims

• animal dispersed seeds fall closer to parent (larger, in fruit, heavier)

• wind dispersed seeds fall further from parent (lighter, smaller)

• higher fecundity trees = further dispersed seeds (lighter seeds)

density and distribution of parent trees

• first determinant of tree recruitment

• low adult density, clumping of adults, or both = seeds do not reach ground and creates patches

• trees whose seeds consistently blanketed floor ensure high colonization indices

seed production

• density and distribution of adults → density and distribution of seeds

• taxa who produce fewer seeds also disperse those seeds short distances → low colonization

• tendency for seed number to trade off with seed size

dispersal

• density and distribution of adults → density and distribution of seeds

• animal vectors are sole means for moving seeds outside perimeters of tree crowns when there is high frequency of adult trees

establishment

• density and distribution of seeds → density and distribution of seedlings

• strongest filter on future distribution of trees

• environmental gradients govern

• germination may depend on suitable microsites that are poorly correlated with the actual parent tree distribution

two phase models

• local diffusion + long distance jump dispersal

• ex: Argentine ants → usual disperse via burrowing with natural spread of 10-40km/year but human mediated transport has spread them globally 40-200km/year

densities of sea rockets

• seaward: births outweigh deaths, emigration outweighs immigration due to wind and water = low density despite being most suitable

• middle: births = deaths, immigration from seaward = highest density

• landward: deaths outweigh deaths, declining immigration = low density

• dispersal (immigration - emigration) can override local (birth - death) rates

gene dispersal of trees

achieved through pollen dispersal, not seeds

migration

• another method of dispersal

• learned (elephants) or innate (monarchs)

• allows access to seasonal resources, avoid harsh environments, mate, give birth, etc

island dispersal

populations on islands may experience selection for reduced dispersal ability

ex: birds on islands have lower flight ability, grow larger