Utilisateur
random, systematic, combination of the 2
many small/med samples
a hetergenous area requires more sampling
type of area and distribution of population within the area
clumped populations require more sampling
the cumulative mean value of a trait agaisnt number of samples taken
sum of total # objects occured divided by total number of samples at that point
(5+15) /2= 10
not the first few samples but as more samples are collected, the curve flattens out
the change in mean becomes very small with addition of another sample, additonal sampling is not neccesary
the quadrat method and the mark/recapture method
small, sessile (non motile) or relatively sedentary organisms
relatively large and/or mobile organisms
square usually of a known defined size
count all individuals within a number of known size sample areas, then extrapolate the average density of your samples to the entire area
n/a= N/A
n= avg # of sampled in the quadrats (avg # in the total area)
a= size of single quadrat
N= total pop size
A= total area of all quadrats together
it has to be a whole number, RAISE decimals to the next whole number no matter if its 15.2 or 15.9
800 spruce trees in the entire forest
1. # of individuals in each quadrat must be known exactly
2. the size of the quadrats must be known
3. the quadrats must be representative of the study area as a whole
observation- question- hypothesis -prediction-data collection- stat test
if hyopthesis is supported: make a new prediction or another observation
if hypothesis is not supported: make a new hypothesis
1: the tree layer
2: layer of woody shrubs o fna intermediate height
2: a layer of forbs (smaller non-woody plant-grasses/small herbs)
extract the core sample from soil, so soil layers can can be observed. corkscrew motion
vernier calipers
compass/clinometer
DBH tape
1: measure distance from partner to the base of the tree using a field tape
2: meausre the angle from ground to top of tree using clinometer
3: measure height of the base of the tree to the person eye level (hPE)
tan a= opposite/adjacent= y/d, rearranging
- y= d*tana and add hPE (all in cm)
1232.95cm
by meausring different variable and looking for patterns in the data
through a manipulative experiement by intentionally manipulating the factor that you think is driving the observations you made while controling the other competing factors
entire range of conditions in which a plant can survive
optimal performance (g and r), growth only but no reproduction (sub optimal-dec energy can only perform one), survival but no g and r (edges of range, undert stress)
no
make morphological or phenotypic adjustments
phenotypic plasticity
1. capture (sample), a number of individuals
2. mark them
3. release marked individuals back into regular pop and allow them to disperse
4. recapture a random sample of individuals, note number of marked and unmarked
1. marks must not be lost/overlooked
2. change in the ratio of unmarked and marked in the pop happens bc of death,birth,immigration,emigration
3. marked and unmarked are similar in all aspects
4. probability of recapture is equal for marked and unmarked
M/N=m/n
M= total # marked in the pop
N=total pop size
m= # marked in the sample
n= total # in the sample
t-test
x= means
N= the sample size
s2= variability
chi-sq goodness of fit test: count data (discrete)
t test: continous data (comparing means)
(t= x.00, df= y, p= x.0000z)
the relationship between host and disease prevalence
susceptible host
disease
conductive environment pathogen
- how diseases arise
- how they move between hosts (within & between populations)
-predict how the disease will spread locally and globally
bc the pathogen pop size will be affected by factors WITHIN hosts and factors that affect how they move BETWEEN hosts
- the availibilty of resources needed for growth
-competition of those resources with other diseases
- the hosts own immune response
-rate of transmission (determined by life history of the pathogen)
- ecology and pop bio of the host
-life history
-pathogenicity
- transmittion rate
-host density
hantavirus an RNA virus vectored by species of mice and rate, the virus can be fatal to humans but rodent individual remain asymoptomatic to the virus.
- more likely to live in rural areas (farmers/forest people more likely to contract disease)
-postive correlation of disease to larger mouse pop size
-rodent pop size linked to food availability
-rodents widley herbivorous-- food availability linked to environmental condition that promote plant growth (precipitation)
- both variables are responding to a common cause (not directly causative to each other)
- one variable causes direct changes to the other (direct cause-and-effect)
after we have established a significant correlation between two variables but still correlation shouldn't be confused causation
- correlation: conducted correlation stat analysis
-relationship: results when using a linear regression
when a cause and effect is already suspected
the line of best fit that minimizes the distance of data points, this line is represented by an equation that describes relationship of x on y
y= a +bx
y= dependent varaible
x= independent variable
a= y intercept
b = slope
strength of linear relationship and reliability of the equation
the coefficient of determination (r 2)
0= weak relationship
+1= strong linear relationship
p value (probability of obtaining this linear relationship if the null hypothesis were true)
( r2= 0.xxx, df= 5, p=0.xx)
one- way analysis of variance (ANOVA)
ratio of variation between a group of means relative to the variation within the groups
if atleast one of the means is significantly different
which groups are different from which
a post-hoc test like the tukey honest significant difference post hoc test
- preforms pairwise comparisons of the means
( F between group df, within group df (subscript)= 10.xx, p= 0.00024)
the same letter for no difference unique letter for difference
- make sure caption says that letters represent significance grouping
through photosynthesis and is converyed into organic compounds like simple sugars or more complex carbs
respiration, when plants are eaten, die, decompose carbs are broken down
- rock (sedimentary (primarily), fossil fuels ( oil and coal)
- the atmosphere (CO2, methane, water vapor)
- the ocean (dissolved carbon)
- all life ( both alive and dead, and broken down matter in soil)
soil respiration and plant respiration
use of oxygen and/or release of CO2 by living organisms in the soil .
happens when microrganisms in the soil decompose detritus (esp lead litter)
1) LEACHING (of water soluble minerals/simple sugars (like glucose) from the material
2) FRAGMENTATION (of the litter into smaller pieces mechanically or biologically)
3) MINERALIZATION ( the conversion of larger organic compounds to simpler inorganic forms)
oxygen: neccesary for respiration (direct effect)
temp: inc temp inc rate of metabolism of microorganisms
water availability
composition : dif types of carbon compounds break down at different rates
glucose, cellulose, lignin
organisms to produce specific cellulose degrading enzymes to reduce it to simpler compound that microbes can metabolize
extracellulary when enzymes are secreted to depolymerize the cellulose
-lignin is a complex plant polymer which porvides structural support in woody plants
-only broken down by fungi through extracellular decomposition
- long period of time
they are small and soluble in water, quickly lost by leaching or are immediately consumed by microbes
C:N
neccesary source of nutrient for decomposers
high in Nitorgen so higher in nutrients and softer, so faster to decompose where as high C:N means high in C so tough and slower to decompose
CO2 in the atmosphere acts as an insulating blanket trapping solar heat in the earths atmosphere
CO2 in atmosphere was much higher than it is today , the greenhouse effect caused the polar region s of the earth to even have a tropical climate but as plants evovled there was a dramatic dec of atmospheric CO2
since the industrial revolution in the mid 1800s, from burning coal,oil,gas and wood to keep up with the demand for energy as our pop grows
- annual temp has inc as well with the last 3 decades being the warmest in 400 years, rate of warming also inc
- climate change is not happening uniforming, the rate of warming affecting the artic at double the global average this is causing the artic permafrost to thaw at an inc rate which accelarates carbon transfer to the atmosphere by releasing trapped methane gas and making previously frozen organic material avaiblbe to decomposers
drought, flooding,salinity, alkalization and changes in nutrient avaibility
with inc temp comes a risk of soils being more saline as evaporation cause salts left behind. the ph of soil is predicted to change with inc temp
- depends on depth of soil, type of region, warming combined with precipation
increased fertilizer run off as farmed combat food insecurity and decreases yield
(ppm CO2/g/min)
multifactor ANOVA
the interaction between the two manipulated variables and their combined effects on the responding variables
(F df, within df = , p=)
1 for each independent variable (main effects)
1 for the interaction effect
the effect of 1 independent variable on the dependent variable changes depending on the level of another independent variable
the directionality of any of the effects, you need to graph the means to see this
cant reliabily interpret main effects because the interaction is affecting them
group of individuals from the same species that co-occur in space and time
individual ecology (how a single organism interacts with its environment) and community ecology ( how multiple different species of organisms interact between themselves and their environment)
in resource based economies like forestry or fishiers, dyanimcs of pop important to know for the success and protection of the resources
BIDE equation
Nt (# indivs at time t)
B (born)
D (died)
I (immigrated)
E (emigrated)
humans. few numbers of offspring, invest in high care of offspring, high survival rate of offspring
birds. have an equal death rate throughout all stages of their life
many marine fish. produce high # of offspring, invest little in securing their survivability , high mortality rate in young offspring
x axis: age
y axis: number of survivors (logarithmic)
group of individuals that are born around the same time and move through life within the same period
follow each member of the cohort through their entire lifetime and track the age at which each individual dies
- create an age distribution or a static life table
the age at a time of death is recorded for a group of individuals that were born at different times
x= age
nx= # of indivs surviving to day x
lx= proportion of indivs surving until day x
when pop size is small or when there is little to no competition for resources
rate of pop growth (dN/dt) under exponential conditons
r= per capita rate of inc
N= pop size
Nt= # of indivs at a given time (t)
N0= intial number of indivs
r= per capita rate of inc
t= # of time intervals
e= constant
r= per capita rate of inc remains constant
Nt= gets successively larger
exponential growth: time vs pop size, continues to inc curve
logisitic growth: has a K= carrying capacity value which is an upper limit to pop growth
factors like resource availability, space and inter or intra specific comp can put a limit of the max size of a pop
density depndent becuase their effect depends on the density of the population
the theoretical max size of the pop at particular location
- happens where the logistic curve flattens out
the logistic population growth
- theres a max on the per capita rate of increase under ideal conditons and the term (1-N/K) which accounts for limiting resources
factors in the effect of K and rmax on pop growth
groups, data/units, sample size, error bars
background info, objective, hypothesis and prediction
patterns,trends, supporting stats, signifcance and directionality
caption above for tables
methods and results section
tertiary literature
mean calculation
xi: sum of indiv observations
n= # of osbervations
the standard deviation
bell curved
no
size and variabiltiy of sample
the sample mean is close to the actual pop mean, smaller confidence interval
less plausible locations for the true population mean
higher than 0.05 is not signifcant
decimals, measurements, means/averages
counted values/whole numbers
observational or manipulative
randomization, replication, reducing noise
randomly allocate treatments to experimental units, helps enutralize any other effects that are happening not due to your treatment. most stat tests assume this
repititon of treatments, quantifies the natural variation between experiemntal units, helps with accuracy.
control other conditions as much as possible
variable that will cause a change in the variable we are meausing , goes on x axis
what we suspect will have changes, y axis
getting data for all data points, some records don't indicate age/birth year, getting data into a form that is useable
cohort, almost impossible to follow a whole cohort of people from birth to death
chosen a pop that was biased in some way (disease, WW2 etc)
pop size inc
lag less before pop growth explodes
both low for a flat curve
intermediate r and low N0 for a slow but accerlating curve
both high r and N0 for extreme rapid growth
no, because it predicts that there is no upper limit on growth
habitat degradtion, food limitation, hunting/poaching, disease, pollution, natural disaster
as r inc the time it takes to reach K decreasezs
predators, resource availability, disease, competion NOT N0 or growth rate
age distribution, sex distribution, reproductive potential
that density of plants is the msot optimal anymore won't add any benefit