How do they get their food?
• Saw earlier in simple way, plants vs meat
• But is it that simple?
• What are some of the considerations in an
individual actually selecting the food it
eats?
• Chap 5 deals with this
How do they get their food?
• Basically “rules” in selecting food and
where to forage for that food.
• Initial thing to keep in mind:
• Foraging animals know what they are
doing!!!
• Why? Their life depends on it!
• Not just balls bouncing around!
“optimal” foraging theory
• IF they know what they are doing, then we
should see some “optimization” occurring.
• What are they optimizing?
• Ratio between costs and benefits.
• Early theory developed with optimization in
mind.
• Lot of critics…Why?
Arguments against optimizing
• Optimizing means eventually leading to
the BEST way of doing it.
• Kind of like Evolution’s “survival of the
fittest”
• Should lead to one “optimal” form.
• Better is: survival of adequate
• And: Adaptive foraging theory.
• Semantics? Reflects individual’s constant
adjustment to changing conditions.
Ok, Adaptive foraging theory
• How do they do it?
• 4 possible areas where they can affect
their energy intake vs costs of getting the
energy.
• 1) What they eat: Diet
• 2) Where they eat: patch choice
• 3) How long they eat: Patch residence
• 4) Where do they go next: Patch movement
Foraging rules: diet selection
• All species have a specific range of food items
they use: Why??
•
•
•
•
Some have very limited diets: specialists
Others have wider diet range: generalists
Why?
“generalists” why do they choose specific foods
at specific times?
• What are the “rules” in selecting a diet
Foraging rules!
• Diet selection: what factors to take into
consideration?
• Caloric value: obviously
• Ease of handling: Pecans vs hickory nuts
deer vs cow
• Risk: of being killed… of being injured.
• Will see later with patch use.
Rules
• “Generalists” rules
• Initial models developed from data on
feeding response to food density
• Type II feeding response
• Forager can only eat so much!
Here
Diet rules
• Basically, should broaden diet when
principle prey 1 drops below a certain
level.
• Considered “optimal” strategy.
General predictions
• Should rank food types relative to
profitability
• Should always include most profitable prey
and only expand to less profitable when 1st
does not meet needs
• Thus decision to switch is based on
abundance of most profitable not less
profitable
• All or none response: either always accept
or never accept them.
Do they hold??
• Most controlled experiments seem to
produce expected results
• Great tits (small bird) Reduced use of
large worms when reduced in number.
Do they hold?
• Works the best for herbivores or predators
of sessile prey
• Not so well with predators and mobile prey
• Also not so well under predation risk.
• Does not include other needs e.g.
minerals, proteins, etc. Not just all Kcal!
• Predators and
prey
• Even at zero
rabbit abundance,
coyotes still had
30% occurrence
in their diet!!
Diet summary
•
•
•
•
•
•
•
Know for sure:
1) all species have specific diets
2) some narrow, some broad
3) even broad diet, have preferred food
Not so sure:
1) why they have the diet they do!
2) rules for selecting
Next is patch selection
• Why “patch” selection?
• Very few habitats uniform, habitat mosaic
is often rule.
• So… if habitat is patchy, so will food
availability.
• Thus, animals have to select among
existing patches: which one to go to.
• Again, trying to balance gains and costs
Balance
• Benefits: How much and quality of food
patch supplies: patch quality.
• Costs: How much energy needed to get to
patch: central place foragers this is biggie
• Costs: How much energy needed to
harvest food that is there. Again, not just
quantity of food resource but how easy it is
to get/handle, etc.
Patch selection
• Criteria?
• 1) Patch quality: resource quantity/quality
Similar to diet selection: “should” pick
“best” patch to feed in.
• What assumes?
• - animal’s knowledge of patch
locations/quality: Reasonable?
• Perfect or imperfect information?
Criteria
• Other factors?
• - patch depletion rate: as patch is used or
has been used, quality goes down.
1) herbivores: renewable/nonrenewable
2) Not so much reduction in # of prey but
“catchability” of prey, will deal with later.
Other criteria
• - patch predation risk: Is it worth going to
high quality patch where you might get
killed? So not just patch quality food wise
• Introduces “predation risk” into foraging
equation as a foraging cost.
• Actually transform risk into potential
energy loss
Predation risk and patch choice
• H = C + P + MOC
• Harvest rate (H), what it can get out of the
patch.
• C = what it needs for metabolism
• P =what it could lose (on average) from
predation
• MOC Missed opportunity costs: energy
used for other things instead of eating:
grooming, displaying, etc.
What does this tell us?
• In patch selection, the patch has to be
higher quality than just supplying
metabolic needs
• This means forager can juggle C and P!
• E.g. could go to patch of lower quality IF
had lower predation risk!
• E.g., could “take the chance” going to high
quality/risk patch but for shorter time
because of higher harvest rate!
What does this mean?
• Much more foraging options than just pure
patch quality.
• Also affects how long they stay in patch,
3rd area!
Patch residency
• Ok, will select a patch based on food
quality that gives good balance when it
enters the patch.
• But patch becomes depleted as it stays in
the patch.
• When should it leave???
Options
• Could stay until all food harvested.
• But…. Diminishing returns, longer it
harvests, less it gains per time/effort!
• Eventually, it would be better (more
profitable) if it moved to another patch of
now higher quality!!
Options
• But when should it do it???
• “leaving rules”: when should an animal
leave a patch?
• Early idea: Charnov 1976 proposed the
“Marginal value theorem” as a leaving rule
Marginal value theorem
• Consider: 10 patches of different value.
• Can calculate average value of these ten
patches (V1 +V2 + V3….+V10/10)
• Some will be above average
• Some below average.
• Assume (based on previous discussion)
animal will first enter an above average
patch.
Marginal value theorem
• As it forages, value of patch approaches
overall average.
• MVT states that when the value of that
patch reaches average of all patches,
animal should leave
• Why? Because there are now other
patches out there that are more valuable!
• Goes to one of these, harvest rate/time
would be higher than if it stays!
Sound pretty simple!
• Is there support for this idea?
• Some, lots of studies looking at ”quitting
harvest rates” and in general animals do
leave BEFORE resource depleted, so
there is some type of leaving rule
• Whether it is the MVT becomes debatable.
• Especially if we add predation risk!!
Predation risk and patch
residence time
• How does the threat of being eaten affects
how long you stay in a patch??
• Again, not just food value of patch but
probability of being killed
Patch safety
• In “safe” patch, animal can afford to stay
longer,= lower quitting harvest rate.
• In “dangerous” patch, longer you stay the
more likely you will be killed!
• Again, a balance between foraging and
safety.
Measuring predation risk
• We know how we can measure food
resources, saw earlier.
• How do we measure predation risk??
• Obviously important in
understanding/predicting patch selection
and residency time.
Giving up densities
• As mentioned, safer areas should have
lower quitting harvest rates: can measure
but difficult. Need to observe animals and
measure how much they are harvesting
just before they leave.
• Brown 1988 demonstrated you can use
not the harvest rate when they leave but
what they leave behind, Giving up
densities (GUDS)
Guds
• What??
• Given any resource, animal will not eat all
of it. Will leave some, gets to point not
worth eating rest:
• What it leaves is its GUD
• More important Brown demonstrated that
how much it leaves is related to predation
risk!
How do you measure GUDs?
• Artificial feed trays/boxes:
• Mix in standard amount of seed/food
• Inedible substrate
They come
• Animals come and search for the seeds
• Give up depending on how dangerous it is!
Will work for deer too!
So what do you get??
500
400
a
Open
Edge
*
*
GUD (g)
• Make
comparisons
between patch
types.
• Identifies safe
vs risky
habitat for
species
NS
300
200
100
0
DF
JU
MM
Patch selection and residence
• All this helps to adjust the patch selection
and residence time rules.
• With these types of studies can adjust for
predation risk.
• Leads to better understanding of how
animals use the habitat they live in.
• Will see more later.
Patch movement rules
• Ok… have idea on why they might select
patch and how long the might stay, but…
• Once you leave a patch, where do you
go?
• This leads to 4th concern, patch movement
rules.
here
•
•
•
•
Patch movement
What are factors to consider?
Quality obviously
Predation risk, obviously,
Travel time is another one: if travel is
energetically costly, distance figures in
• Travel risk: if you have to pass through
dangerous area, becomes important in
your decisions
Dangerous travel, example
• Sheep often have to travel through
wooded areas to get from one open area
to the next.
Summary
• Have now seen that foragers need to
consider the 4 aspects of foraging.
• Have seen that patch quality is dependent
on food value and predation risk
• Result is a pattern of patch use by an
individual
Patch use to habitat use
• Though we talk about patch use relative to
foraging, patches represent habitats
• So… foraging represents or reflects a
large part of the habitat use patterns of a
species.
• Habitat use: spatial landscape use
patterns of an individual related to the
types of dominate vegetation in use
and non-use areas.
What more is important?
• What other considerations relative to
“patch use” (habitat use) are there?
• Move away from getting energy to the
MOC’s
Missed Opportunity Costs
• From a foraging basis, these are all those
things that “get in the way” of eating!
• Obviously important, more important at the
appropriate time than eating!!
• What are they?
MOCs
• One big one is resting/sleeping!
• Although ruminants can combine resting
and eating (re-eating), don’t take in new
food.
• Few can eat and sleep at the same time!
• A large portion of the 24 hours is devoted
to these different stages of rest (doing
nothing with eyes open – sound sleeping
Rest and sleep
• Not concerned with reasons for nor
physiology behind them but how do they
affect habitat use.
• Where to rest/sleep?
• Often not same areas where you eat.
• Comfortable and/or safe (predation again!)
Resting and Sleeping
• Comfort: cool if it is hot, warm if it is cold
• So varies with season/weather
• Most species will have preferred
warm/cold day resting or “loafing” sites.
• Nothing like a sunny rock on a cool day!
• When most likely to rest? After meals of
course!
Resting/sleeping
• When most likely to sleep?
• For many, night-time (a lot of wildlife
species are diurnal). Most birds, squirrels,
primates, etc.
• For them, finding a sleeping spot that
affords protection from night chill.
When do you sleep?
• For many, sleep during the day!
Nocturnal or crepuscular.
• Cre..what! Most active around
sunrise/sunset
• For these, daytime sleeping sites often are
to alleviate daytime heat.
Why sleep in the daytime?
• Temperature extremes: too hot to eat!
• Safer, can see predators better.
• Brings us to predation risk while
resting/sleeping
What is predation risk when
resting/sleeping?
• Should be less than when foraging!
• Can choose “safe” places to rest/sleep
• Nests in trees, in trees, on the water, in
burrows, hidden in shrubby areas, etc.
• Rest at vantage points, provide vista to
see predators
• Disadvantage when asleep, can’t see the
predator coming! Or can they?
Again predation risk!
• Risk of predation also influences where
you rest, sleep and how you sleep!!
Sleeping with one eye open
• Unihemispheric sleep
Sleeping with one side awake!
• Ducks
also: One
foot
keeps
kicking,
turns in
circle.
Resting/sleeping in groups
• Group behavior, watch a herd of
ungulates, some sleeping/usually at least
one with head up.
• So resting/sleeping habitat important
Other MOC?
• Reproduction is a biggie!!!
• Male of many species forgo foraging when
they are reproductively active.
• Can divide into: Courting/mating,
parturition/egg laying, rearing of young
• At each stage, have different habitat
requirements.
Courting
• For many birds, require specific habitat for
courting
• Examples: tops of bushes, ruffed grouse,
woodcock, leks, pronghorn, grebes
Courting
• What is important? Habitat that best
displays your wares!!
• Can lose whole population if conditions for
courting are not right: sage grouse
• Again, predation safety
Courting and predation
•
•
•
•
Males often don’t have a clear focus!!!
Become more susceptible to predation
Bighorn sheep
Often courting habitat has some safety
feature
• Low cover may also be for ease of
predator detection
Birth of young
• Birth/egg laying
• Again special habitat characteristics for
environmental and predator protection of
young.
• Nests, burrows, etc. designed for
protection
• Birthing sites: bighorn sheep
Nest sites
• Bower birds
• Pileated woodpecker
Raising young
• Bed sites: deer/pronghorn
• Foraging shifts
MOC- migration
•
•
•
•
Travel from one place to another
Can be short: deer to winter yards
Can be long: bird migrations
Migratory species needs specific habitats
at either end but also in the middle!
• Staging areas, stop-over sites, etc.
• What does this all have to do with wildlife
ecology and management?
• - Much of management is effort to provide
adequate “habitat” for a species.
• “Adequate” now takes on a new meaning.
• It is just not food but predation risk,
reproduction, climate, migration needs,
etc.
Summary
• So foraging theory provides us with a base
from which to examine a wide varieties of
behaviors important for survival of wildlife.
• Food – Predation – MOC
• Includes all major drivers for habitat use
patterns of a species.
• And all based in behavior of animals!
Summary
• Though in detail look at foraging etc., this
has been a very brief view of animal
behavior as it pertains to wildlife ecology
and management.
• We manage by managing their behavior
• Will have more to say about other
important behaviors later.
Ecology
• Now shift gears somewhat and look at
ecological aspects of wildlife
• Difficult to separate behaviors from
ecology but early ecologists did just that!!
• Reductionist: felt that getting rid of messy
behaviors and treating animals like
inanimate objects would help get at basis
of ecological principles.
Nature through the eyes of an
ecologist
•
•
•
•
Mass action models mostly
Stripped of behavior
Looks mainly at outcomes
Does give us a starting point to which we
can add behaviors.
• Behavioral ecology
here
Where do we start?
• We looked at individual energy capture via
foraging theory
• Now need to look at energy capture by the
species: via reproduction
• How do species gain and loose energy?
• What are overall patterns?
• What are factors that affect this energy
gain?
Energy gain by the species
• First need to define our unit of
consideration: before it was the individual
• Usually we can cluster members of a
species into sometimes discreet,
sometimes artificial groups: Populations
• Definition: group of individuals of a
species living together in the same
place at the same time.
Population
•
•
•
•
•
Sometimes whole species
Sometimes discreetly separated
Often we define the limits to the population
-often political units
-anything we think is reasonable
Populations
• Review: how do populations add new
energy? Reproduction
• Review: How do populations lose energy?
Mortality
• Gains and loses of energy packets is the
key
• Question: What are patterns of energy
gain and lose of populations?
Gains and losses
• Review: know that populations grow
exponentially
• Nt = N0 ert
Express mathematically
•
•
•
•
•
Nt = N0 ert
Nt = population size at time t
N0 = population size at some starting time
e = Natural exponent (2.72)
r = exponential rate of increase (growth
rate)
• t = time
Population Growth
• All have potential to grow exponentially by
a %/cycle …so….
• Need to consider the concept of doubling
time:
• What is it? Time it takes a population to
double its numbers (2-4), 10-12, 1000020000, etc.
Doubling time
• Can do it manually: If % growth is 20%,
• Have 20 animals, year 1 have 20*.2 = 4 or
24. year 2: 28.8, year 3: 33.6, year 4:
40.32, or double of start in 4 years.
• Holds for any starting number 2-2 gazillion
• In approximately 4 years, will have 4 or 4
gazillion.
Doubling times
Doubling times
• Significance of it all?
• Each doubling time, it takes less time to
add the same number as the time
previous.
• Huh??
• Use 20%, start with 1000 individual
Doubling times
• In 3.8 years we have 2000 individuals
• It took 3.8 years to add 1000 individuals
• In 3.8 more years we have 4000
individuals.
• In the same length of time, 3.8 years, we
added twice the number of individuals
(2000) as in the previous time period
(1000)
Doubling times
• That is the significance of exponential
growth:
• The more you have the faster you
grow in the same length of time!
Why is this important??
• The point is that given exponential growth,
a population’s numbers increase more
rapidly, the larger it is (at a given growth
rate/doubling time)
• Increase r, decrease doubling time,
population grows MUCH faster!
120
100
80
60
40
20
% of the maximum deer harvest.
% of the maximum estimate of pumas
0
65
70
75
80
85
Year
90
95
00
120
100
80
60
40
20
% of the maximum deer harvest.
% of the maximum estimate of pumas
0
65
70
75
80
85
Year
90
95
00
What is the mechanism?
• Ok, can grow exponentially but why??
• Basic Math!
• If we start with 2 individuals (one male/one
female), female gives birth to 2
young/year, 50:50 sex ratio.
• After first year: 2 + 2 = 4, 2 females
• Year 2: 2 + 2 + 4 = 8, 4 females
• Year 3: 2 + 2 +4 + 8 = 16, 8 females
Continue?
• Year 3: 2 + 2 + 4 + 8 + 16 = 32, 16
females.
• Start of
exponential
growth!
2D Graph 1
18
16
14
Y Data
12
10
8
6
4
2
0
0.5
1.0
1.5
2.0
2.5
X Data
Col 2 vs Col 4
3.0
3.5
4.0
4.5
• Works for any birthrate: only changes how
fast it goes.
• Works if you remove original parents and
subsequent ones, only slower.
Why?
• The mechanism is that each female
contributes a given quantity of young, of
which 50% are female so next generation
has more females, giving birth to more,
etc. result is exponential growth
• The more females you add, the more
young added to population.
Significance?
• What this means is that females are
important ones to population growth!
• Also, simple concept: all energy enters the
population from bottom!! Natality
• Only source of new energy!
Do they always grow
exponentially
• We know they don’t: aren’t covered knee
deep with any species (except maybe
humans!!)
• Fluctuate between growth and decline.
• Rarely stable
• Important point!
Examples
120
100
80
60
40
20
% of the maximum deer harvest.
0
65
70
75
80
85
Year
90
95
00
What does this mean?
• Exponential growth a built-in mechanism
adapted to CHANGE!
• Good time-bad times.
• View exponential growth as way
population adjusts to change.
Mortality
• So if they don’t always grow exponentially,
why not?? What happens in bad times?
• Mortality happens!!
• Individual: loss of ability to capture energy
• Population: loss of energy packets
Mortality
• How does it occur?
• Where does it occur?
• How does it affect population growth?
How does it occur?
• Can divide mortality sources into various
categories:
• First major one is: prenatal and postnatal
• Prenatal, don’t make it out of womb or egg
• Post natal, from point of birth to life span
Prenatal mortality
•
•
•
•
Can be from:
1) failure to implant
2) re-absorption
3) still-born/addled (die while in shell)
Prenatal mortality
• Various reasons: most nutrition level of
female/ egg – environmental (Happy
Feet!)
• Of importance relative to population
growth
• Less energy entering in the bottom!
• Will see later when we have all together
Postnatal mortality
•
•
•
•
•
Four main ones are:
1) accidents
2) disease
3) starvation
4) predation
• Will look at more in detail but for now….
Postnatal mortality
• Important point is that these mortalities
can happen ANYTIME from birth to life
span.
• Where they happen and to whom they
happen makes a difference!
• Can view mortality as the governor of the
population.
• Working against natality and exponential
growth
General patterns
• N > M: population grows (exponentially)
• N =M : population stable
• N < M: population declining (exponentially)
• The lake model: one river coming in
(natality), many rivers leaving (mortality)
How do we incorporate
mortality?
•
•
•
•
•
•
•
Traditional: Logistic equation
dN/dt = rN(1-N/K)
N number of animals
r is growth rate
t time
K is carrying capacity
(1-N/K) is representation of total mortality
Logistic equation
• Treat mortality (all sources and all ages)
as an increasing function as we get to the
“limits” of the environment: carrying
capacity
• defined as the number of animals an area
can support over time.
• Is good as an initial approach BUT it does
not reflect reality
Logistic equation
• It REALLY does make a difference as to
the sources of mortality and what ages
and sexes it affects.
here
Mortality patterns
• Lets first take where the mortality is
occurring
• Divide: male/female
• Divide: age groups – one is…
• Pre-reproductive
• Reproductive
• Post reproductive
Mortality
• How do we estimate mortality?
• Directly
• Indirectly
Direct methods
• 1) Follow all individuals born one year
(cohort) until last one dies! Can do this
with small pops but difficult with large or
secretive animals.
• 2) Follow a group of marked individuals
represents a sample of the total.
• How do we follow them?
- marked so we can identify them.
Marked individuals
• Capture-recapture: do over time and
record those that show up AND those that
don’t!!
• Aerial surveys: count number of marked
animals. Difference between those you
see and the starting number is number
that died!
• Follow known aged individuals: accounting
e.g. cougar data.
Indirect methods
• If you can’t follow a group of animals…?
• Take a snapshot!
• Live animals
• Dead animals
In both cases
• Use existing age structure as an estimate
of how many are dying each time interval.
• Assumption: no significant change in
mortality rates WITHIN a given age class.
• Assumption: birth rates do not change
• Example: If you have 100- one year olds
but only 50 2-year olds, assume 50 died
from age 1-2.
What does it give you?
• Gives you number of animals that died
during each time interval from birth to
death of last one.
• What do data look like?
Example
• Dall sheep in Alaska: Adolph Murie
1930’s-40’s: 608 sheep skulls
TABLE 6.—Skulls of 608 sheep which died before about 1937, showing number of
diseased and nondiseased animals in annual age classes. Sexes of lambs and yearlings
are combined since usually they are not known.
TABLE 6.—Skulls of 608 sheep which died before about 1937, showing number of diseased and nondiseased animals in annual age classes. Sexes of lambs and yearlings are combined since usually they are not known.
Age in years
Sex, age, and condition
Both sexes, no disease noted [2]
Both sexes, diseased [2]
Ewe, no disease noted [3]
Ewe, diseased [3]
Ram, no disease noted [3]
Ram, diseased [3]
Total
Lam Yearl 2
bs ings
33
-----33
85
3
----88
3
--1
2
4
-7
4
--2
-5
1
7
The 144 were all 9+ years old
5
--2
2
3
-9
--8
4
2
4
18
6
--6
14
3
5
28
7
--9
8
4
8
29
8
--11
7
15
9
9
--14
6
11
16
10
--20
8
33
6
11
--4
1
42
9
12
13
--2
-28
2
----1
--
42 47 67 56 32
1
14 Misc Total
ellan
eous
[1]
--- 118
--3
-- 56 135
-- 26 78
1 49 201
-- 13 73
1 144 608
2Adults.
Sex, age, and condition
Both sexes, no disease noted [1]
Both sexes, diseased [1]
Ewe, no disease noted [2]
Ewe, diseased [2]
Ram, no disease noted [2]
Ram, diseased [2]
Total
Lambs
Yearlings
33
-----33
85
3
----88
2-8
years
--39
37
36
27
139
9 years
and older
--96
41
165
46
364
Total
118
3
135
78
201
73
608.
Lets standardize it
•
•
•
•
•
•
Put it on the basis of
1000 individuals to
start with.
121 died in first year
Or 19.9%
So beginning of next
year 801 left.
We can then depict graphically
What did they learn from this?
• Most mortality occurred in first year of life
and after 8 years.
• Reproductive ages seemed buffered
• Why?
• Peak of health, hard for predators to get
• Predators (wolves) feeding on young and
old.
Impact on energy flow in
population?
• Energy enters in bottom BUT has to be
produced by middle or reproductive
individuals.
• As long as reproductive individuals are
maintained, can loose a high percent of
young and population will remain stable.
• Just need to replace losses in reproductive
ages, which are the smallest!
Is it the same for all species??
• This pattern seems common for many
wildlife species, birds, mammals.
• But is not universal
• Other patterns found:
Variations
Constant mortality rates
• Mud Turtles
High juvenile survival.
Rotifer
• Whitecrowned
Sparrows
Phlox
And so?
• Different energy loss patterns mean
different strategies regarding balance
between losses and gains.
• Becomes important to assess these
patterns for given species
• How? Life tables
Life tables
• Accounting technique to identify where
losses are occurring:
Life table for Dall Sheep in Mount
McKinley National Park, Alaska, based
on age grouping of skulls of animals
dying of natural causes
Age
Interval
(yrs) x
Number
Alive at
Start nx
Number
of Deaths
dx
Annual
Mortality
Rate (%)
qx
Annual
Survival
Rate (%)
sx
0-1
655
41
6.3a
93.7
1-2
614
117
19.1
80.9
2-3
497
10
2.0
98.0
3-4
487
9
1.8
98.2
4-5
478
9
1.9
98.1
5-6
469
23
4.9
95.1
6-7
446
34
7.6
92.4
7-8
412
37
9.0
91.0
8-9
375
48
12.8
87.2
9-10
327
79
24.2
75.8
10-11
248
92
37.1
62.9
11-12
156
88
56.4
43.6
12-13
68
64
94.1
5.9
13-14
4
1
.
.
14-15
3
3
.
.
Total
5239
655
12.5
87.5
What you can do with them
• Since these tables are based on field data,
helps identify losses.
• Helps determine if population is ok or in
trouble
• Helps identify where management of
mortality rates might be needed.
• But life tables are not just death!
• Other statistics can be calculated
• Note above: Survival rate – reciprocal of
mortality rate
• What else?
Typical life table
• dx= percent of total dying
• qx = mortality rate percent that die each
year
• lx = survivorship
• Mentioned that new energy is produced by
middle animals. So it is important to look
at the capability of producing that energy
• Fecundity
• Annual fecundity: Number of young
SUCCESSFULLY raised/yr.
Annual fecundity
• Increases directly with annual adult
mortality
• Short lived species (smaller species) have
high fecundity
• Long lived species (larger species) have
low fecundity.
• Short lived also breed younger.
Table with fecundity
• mx = # young/female
• Note changes with age
Relationships: fecundity/life
expectancy
Why is this all important?
• Age makeup of the population can vary.
• Growth depends on where females in
population are.
• If high in age classes with low fecundity
may not grow fast now but will in future
• If survival rates change for some reason,
will have different affects with different
ages.
Life time fecundity
• Another aspect of fecundity is the number
of successful offspring you raise through
your whole life!
• Related to life span
• Related to annual fecundity
• Question: what should lifetime fecundity
be?
Interspecies comparisons
• Higher annual fecundity in short-lived vs
lower annual for long-lived species: does it
make a difference over lifetime?
• Is one method “better” than another?
Lifetime fecundity
Lifetime fecundity
• Apparently not!
• Makes sense, IF populations are surviving,
then each is doing enough or lifetime
fecundity of females averages around 1!
• Then why all the differences??
• One approach better?? Ideal clutch size,
etc.
• Evolutionary factors
Evolution of reproductive strategies
• No one ideal litter/clutch size/reproductive
strategy
• Each species faces its own suite of
conditions for survival and adjusts
approach
• Resource levels (where you are on food
chain, how much is available,
seasonal/annual abundance of food) have
to be addressed
• Predation: On adults, on young
Summary
•
•
•
•
•
Populations can grow exponentially
Populations can decline exponentially
Seem to do both over time, rarely stable
Simple analogy is like a lake
One inlet (reproduction), many outlets
(mortality)
• More complex, especially mortality.
Summary
• Variety of sources
• Can occur at any age (many young and
old)
• Reproduction: females most important
• Age specific fecundity
• Fecundity patterns differ depending on
evolution of reproductive strategies
• Managing for the gains and control the
loses.
• Ok so that’s the additions and subtractions
to a population.
• One thing to say they die and list the
possible causes, but need more detail on
those causes
• How important are they?
• In a management mode, can they be
managed?