Sources of Fish Decline Habitat disruption Breeding areas Larval development areas Bottom structure

Not all patterns are negative

Population processes: aid to intelligent management? 1830’s – concerns about fluctuations in catch in North Sea Disbelief that humans could cause this C. D. J. Petersen (Denmark) applied science –mark-recapture to estimate population size –collected data on age-dependent reproduction –applied population model to predict connection between fishing mortality & fish populations Criticized as “irrational” (not “profitable”) Tested during WW I and WW II

What do we need to know? Demography: the study of processes affecting populations Processes adding to populations: –births, immigration Processes subtracting from populations: –death, emigration Base number of individuals Whether the processes are constant through time –could vary with season or other scales of time –could vary with the density of individuals, which change over time

What if processes are constant? Population size in the next generation will depend on the base and the difference between births and deaths –assuming we have an isolated group of individuals N t+1 = N t + b – d

Exponential growth

What if processes vary with density? If populations get larger, what do you predict will happen to birth rates? If populations get larger, what do you predict will happen to death rates? How many individuals are added to the population when birth rates and death rates are equal?

“Logistic” growth pattern

Summary of population models The Logistic model of density dependence predicts maximal sustainable yield at ½ K “S-shape” curve of population growth may not be seen when –The response to density lags changes in the environment –For populations with large excesses of births (r>2) and where generations are distinct

Added realism: individuals vary in “b” and “d” Size (or age) influences –Reproductive capacity (# of offspring likely) –The risk of being eaten by a predator –The probability of being captured in a net Age-specific demographic processes –Fecundity –Survivorship

Age-specific parameters Start with a bunch of ♀ individuals newly born (= a cohort) Determine the number of individuals that survive to each successive age (“x”) – S x – The probability of survival from birth to age, “x”: l x The number of ♀ offspring produced per ♀ individual of age “x”: m x

Life Table = collection of data on S x, l x, m x We can then project how each cohort will contribute to the population through its lifetime Some values derived from a life table: –Net Reproductive Rate, R 0 = the number of ♀ progeny expected to accumulate during the entire lifetime of an average ♀ –Intrinsic growth rate, r –Reproductive Value (V x ) = the expected number of future ♀ progeny for a ♀ of age “x” (relative to that of a newborn, = R 0 )

The real world is not a set of simple equations Randomness is a factor –“Deterministic” models always follow the same path given the same conditions –“Stochastic” models include chance How is this done? –Use an average value for a parameter –But for any generation, the value used can deviate somewhat from that average –“Coefficient of Variation” and “distribution” define the limits of deviation

Success of species-based management

What are the connections between food web and demographic approaches? What demographic parameters are influenced? Are models still useful and how?

An alternative to capture fisheries