Quantitative Conservation Biology and Quantitative Fisheries Stock Assessment Flashcards
population viability analysis
suite of population modeling and data-fitting methods for assessing extinction risk and guiding the management of rare or threatened species
-predict likely future status of population
What are the uses of PVA?
- assess extinction risk of single pop
- comparing relative risks of multiple pops
- analyzing and synthesizing monitoring data
- identifying key life stages or demographic processes as management targets
- determine size of reserve to achieve desired level of protection
- setting limits on harvest that is compatible with continued existence
- determining how many and which populations are needed to achieve a desired overall likelihood of species persistence
What are the types of PVA?
- count-based
- demographic (account for differences in contributions and for percentage of each type currently in population. Structured population)
- multi-site (includes 2+ local population patches of suitable haibtat)
- spatially explicit, individually based PVAs
What are factors that can threaten a population?
- life history
- average environmental conditions
- environmental stochasticity
- demographic stochasticity (intrinsic variability caused by small population size)
What are the 3 vital rates?
- birth rate
- death rate
- growth rate
net reproductive rate
average number of female offspring produced by a female over her entire life
lambda
annual population growth rate
When is demographic stochasticity important?
at low population sizes
negative density dependence
- decline in average vital rates as population size increases
- typically due to competition or predation
positive density dependence (Allee effect)
increase in growth rate as population size increase
- improved mating success, group defense, group foraging
- most important at small pop size (decreased growth at low pop size)
quasi-extinction
minimum number of individuals below which the population is likely to be critically and immediately imperiled by Allee effects, loss of genetic diversity, and imbreeding depression
cumulative distribution function
probability of extinction over certain time horizon
-most useful and robust extinction risk estimates
How to incorporate density dependence into count-based PVA?
- can put threshold to pop size (K)
- can use theta logistic model (allows from gradually changing growth rate)
- can use Ricker model (linear growth) often used in fisheries (theta = 1)
How can you determine if there is density dependence in population?
plot population size vs log annual growth rate
observation error
variation in census counts (and thus pop growth rates) caused by our inability to count precisely the number of individuals
structured population
individuals differ in their contributions to population growth
-survival, reproduction
population projection matrix model
primary tool for assessing the viability of structured populations
- divides pop into discrete classes
- uses vital rates (survival, growth/transition rate, fertility rate) at each class
projection matrix
summarizes per capita contributions of all classes at one census to all classes in the next census
How do you get lambda of the projection matrix?
lambda represents the dominant eigenvalue
How do you get the reproductive values of each class in the projection matrix?
dominant left eigenvector
eigenvalue sensitivities
measure of how much changes in a particular matrix element will change the annual growth rate
sensitivity analysis
suite of methods to ask “how sensitive is population growth to particular demographic changes?”
- can help with effective management plans
- change values of vital rates and see how it affects growth rate
elasticities
rescales sensitivities to show proportional change in growth rate resulting from proportional change in vital rate
How is site-based PVA different than count-based or demographic PVA?
requires all info to do other types of PVA but ass data on movement rates between populations, and correlations in vital rates between populations
What is the most important component to include in PVA?
estimates of uncertainty and variability
stock assessment
use of statistical and math calculations to make quantitative predictions about the reactions of fish populations to alternative management choices
What is the purpose of fisheries management?
ensure sustainable production over time from fish stocks while still promoting economic well-being of fishers
overexploited
stock size has been driven to lower levels than would produce the largest annual biological surplus or net economic value
When can we detect overfishing?
when it’s already severe
What are the stages of management decision?
- stock assessment/modelling (for each possible management action, possible outcomes and their probabilities are assessed)
- decide which of the possible actions is best in recognition of the outcomes that it may produce
What are the objectives of fisheries management?
- biological (MSY)
- economic (max net profit)
- recreation
- social (jobs, food)
When are profits maximized?
lower harvest rate (larger stock size) and lower effort than MSY
discounting
value of fish in future is less than value today
-due to uncertainty of tomorrow
population dynamics
birth, death, growth, and movement processes
What are the 4 types of behaviors of exploited populations?
- steady state (steady and sustainable yield, rare)
- cyclical - periods of high catch followed by low catch
- irregular - irregular periods of high abundance
- spasmodic - produced major yields and then collapsed without major recovery
globally stable
will always return to original state regardless of how much it is perturbed
locally stable
will return to original state if pushed to certain limits, but if pushed beyond limits, won’t return
depensatory mortality
predator causes prey mortality to degree that it cannot reproduce at same rate
functional response
relationship between prey abundance and amount consumed by predator
stock
arbitrary collection of pops that is large enough to be self-producing (abundance changes are not dominated by immigration and emigration) with members showing similar patterns of growth, migration, and dispersal
biomass dynamics model
consider only a single indicator of population size (count-based)
surplus production
catch plus net change in biomass over time interval
What are the patterns of fishery development?
- initiation (exploration)
- growth and decline to bionomic equilibrium
- innovation periods and development cycles
catching power
determines how many fish a vessel will catch
-how often they fish, abundance where it fishes, crew skill
How does commercial catch data relate to abundance?
- it’s often not proportional
- fish in high density areas
- may not cover entire distribution
What is CPUE based on?
- spatial pattern of abundance
- spatial pattern of fishing effort
- relationship between abundance and capture success at the site
constant density model
fish maintain a constant density by adjusting the area covered
proportional density model
constant proportion of stock goes to each area so that the density in each area is proportional to abundance
least squares method
parameter estimate minimizes the sum of squared differences between the predicted observations from the model and parameters and the observed data
maximum likelihood method
maximizes the probability that the actual observations would have occurred if the parameters were true
recruitment overfishing
the rate of fishing above which the recruitment to the exploitable stock becomes significantly reduced
How does recruitment related to stock size?
recruitment tends to increase with higher spawning stock size (to a point), but there is greater variation at higher stock size
compensation
relationship between stock size and recruitment limited by resources
-eventually stock-recruit relationship will flatten or decrease at high stock size due to egg survival
overcompensation
when recruitment declines at high stock size
-due to cannibalism, disease
depensation
increase in recruits as spawning stock increases. Due to:
- predation held constant (% eaten decreases as recruits increase)
- Allee effect - inability to find mates at low spawning stock
recruitment
number of individuals still alive at a specified time (i.e. maturity) after the egg stage
The One-Way Trip
fishery data with continuously increasing fishing effort and decreasing CPUE
- k almost never known
- better to have changes in effort to estimate parameters (how abundance relates to CPUE)
- changing effort allows you to estimate catchability and intrinsic growth rate
delay difference models
predicts this year’s biomass directly from the last few years biomass and parameters for survival, growth, and recruitment
virtual population analysis/cohort analysis
calculates stock size based on catches with no underlying statistical assumptions
-calculate number of individuals for each cohort
depletion estimators
examine how measured removals of fish (fishing) influence the relative abundance (CPUE or other abundance index) of fish remaining in total stock
growth overfishing
taking too many fish when they are too small
growth model
usually models relationship between body weight and age
-often use length vs age and weight vs length to predict weight vs age
harvest strategy
plan stating how catch taken from stock will be adjusted from year to year depending upon the stock size, economic/social conditions of the fishery, conditions of other stocks, uncertainty, etc.
F0.1
constant exploitation rate strategy with the fishing mortality (F) set equal to the value of F where the slope of the yield-per-recruit function is 0.1 times the initial slope
Fmax
Fishing mortality (F) set equal to the value of F where the slope of the yield-per-recruit is 0 times the initial slope
How to determine TAC for the year?
estimate stock size, then multiple stock size by desired exploitation rate
When stock size is well know, what is the best harvest strategy?
TAC
When vulnerability to fishing is well know, what is best harvest strategy?
effort limits
optimization methods
used to find best fishing plan given:
- specified quantitative objective
- model of stock dynamics
- specific management alternatives
passive adaptive strategy
uses data to construct single “best guess” and act as though the model were true (or use conservative model) while assuming errors will reveal themselves in future assessments (most fishing stock assessments do this)
trial-and-error adaptive strategy
try a variety of alternative policies at random in hopes of finding best option (usually happens in management)
active adaptive strategy
construct range of alternative models that are consistent with historical evidence and identify a policy that offers some balance between probing for info (experimentation) versus caution about losses in short-term yield and long-term overfishing
