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| fishing down the food web |
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Definition
| switching to less desirable species as originals are depleted |
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| a system consisting of man, fish and the environment |
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| Goal of Fisheries Management |
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| 2. Protection/ Restoration of endangered sp |
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Definition
| Goal of Fisheries Management |
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| 3. Attain Maximum sustainable yield or achieve a reference point |
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| Goal of Fisheries Management |
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| Biological Reference Points (BRP): |
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| Precautionary management strategy |
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| Commercial size and catch limits, Bycatch reduction devices |
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| Holistic Methods use what data? |
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| Catch and Effort Data, Research survey Data |
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| Russel (1931) simple population model |
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| an abstract representation of how the system works |
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| the value of a model is in... |
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| Conceptual, Verbal, Static, Dynamic |
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| A mathematical model is most likely |
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Consists of at least one variable that can be manipulated (eg fishing effort) or is believed to influence at least 1 variable we are interested in (eg catch) |
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| something that can be defined and measured |
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| dictate how variables interact; processes, must be estimated to fit model |
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| Example of a simple model |
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| difference between observed and predicted values |
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| R base console, R studio, R commander |
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| # of organisms per unit area or volume |
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| Total # of organisms in the population of interest |
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| biased, decision to sample where organisms are likely to occur or where they arent |
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| each sample has the same likelihood of selection |
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| population defined by the totality of all possible values of a characteristic for a group |
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| all elements under investigation |
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| some of the elements under investigation |
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| estimators of population parameters |
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| true population statistics |
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| systematic error or biased estimator |
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| an estimator that gives sample statistics when averaged does not give a value close to the parametric value |
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| inversely related to amount of variation between individual estimates of the statistic |
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| inversely related to distance of the estimate of statistic to the actual value (parameter) being estimated |
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| Consistent pattern in inaccuracy |
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| 1. Every member has equal chance of being sampled |
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| 2. a probability based sample |
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| sampling with replacement |
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| each element of the population can be sampled more than once, 2 sample values are independent (Covarience between the two is 0) |
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| sampling without replacement |
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| stratified random sampling |
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| random sample collected from subdivision of population based on some criteria the we believe affects probability of capture. Strata are homogenous |
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| subdivided into sample areas chosen randomly take all random samples from a single area, usually used due to logistical issues |
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Definition
| decide on gap distance ( every 4th fish) |
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| 3 most common sampling designs |
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Definition
| Random, Stratified Random, and Systematic Sampling with random start |
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Definition
| rely on the results of sampling program to direct future sampling |
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| Factors affecting sampling design |
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Definition
| Cost,methods used, experimental design, inherent variablity of data, size/ magnitude of difference you wish to detect, desired power of the test |
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| affected by effort and sampling method, type of information desired and method use to aquire |
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| Gear does not move through the water, depend on animal behavior to catch them |
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| Entangling (gillnet), entrapment (crab trap), Angling (long line) |
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| trawls, plankton nets, sediment coring, harpoons |
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| hypothesis to be tested ( assumed true until evidence shows otherwise) |
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| Statement that parameter differs from value or between populations, ( can never prove alternative only reject the null) |
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| reject null hypothesis when in fact it is true |
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| Accept null hypothesis when in fact it is false |
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anything used for recognition ( external, internal, part of body tissue) ex. fin clip |
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contain specific ID information ex. external tags, pit tags coded wire tags |
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| radio, acoustic, satellite. used for movement and migration studies |
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movement into and out of the system use Jolly Seber |
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no movement into or out of the system use lincoln peterson, schnabel method, schumacher and eschmeyer methods |
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Population estimate (N): N= C*M/R assume (N/M) = (C/R) |
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| Seber (AKA Chapman) is unbiased estimator if R is greater than 7. Sampling WITHOUT replacement. N=[(M+1)(C+1)/(R+1)]-1 |
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| unbiased if R is greater than 7. Sampling WITH replacement, N=M(C+1)/(R+1) |
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