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WF4133-Fisheries Science

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WF4133-Fisheries Science Introduction to class, fisheries profession, & fisheries science Class 3 Housekeeping • Will be today @ 1pm – TH132 – Everybody attend Where have we been 1. Jobs 2. Understand what a fishery is conceptually 3. Identify different types of fisheries Where are we going Class objectives 1. Understand science 2. Understand science approaches 3. Exposure to some fisheries icons 4. How we start to make scientific inference & make estimates WHAT IS FISHERIES SCIENCE? CONTINUED FROM CLASS 2 Commercial fisheries type is related to technological investment ) tonnes Boat size ( size Boat Technological investment/ man-on-board Industrial fisheries Capital-intensive fisheries using relatively large vessels with a high degree of mechanization and that normally have advanced fish finding and navigational equipment. Such fisheries have a high production capacity and the catch per unit effort is normally relatively high. Modern artisanal & Semi industrial Labor-intensive fisheries using relatively small crafts (if any) and little capital and equipment per person-on-board. Most often family-owned. May be commercial or for subsistence (see below). Usually low fuel consumption. Often equated with artisanal fisheries. Artisanal Typically traditional fisheries involving fishing households (as opposed to commercial companies), using relatively small amount of capital, relatively small fishing vessels, making short fishing trips, close to shore, mainly for local consumption. Commercial fish species in MS Fresh Water • Buffalo • Carp • Paddlefish • Gar • Catfish Commercial fish species in MS Salt Water • Flounder • Red drum • Spotted seatrout • Shrimp • Oysters https://coastal.msstate.edu/oyster-bonnet-carre https://coastal.msstate.edu/oyster-bonnet-carre https://earthobservatory.nasa.gov/images/8738/lake-pontchartrain-and-the- bonnet-carre-spillway-louisiana The muscle that keeps the oyster sealed weakens when oysters live in a threatened environment, said Justin Gremillion, the chief sanitarian for the Louisiana Department of Health's Food and Drug Program. That causes the oysters to gape and die, allowing bacteria to enter in any number of ways as they are transported from the seabed to restaurant tables. WHAT IS SCIENCE? “Science is the acquisition of reliable but not infallible knowledge of the real word, including explanations of the phenomena.” Science is not without error, but the scientific method allows us to learn from our mistakes. Science is based on empirical evidence, NOT value judgments. Supported by evidence! Science should be objective, not subjective. Peer-review process helps maintain scientific integrity. Therefore, fisheries science is the process of obtaining reliable knowledge about fisheries through scientific inquiry. SCIENCE APPROACHES ENCOUNTERED IN FISHERIES There are three approaches to scientific inference: inductive, deductive, strong Inductive Deductive Strong Observation or experiment Generalizations Predictions Multiple Predictions Paradigm or Theory Additive and compensatory mortality are two hypotheses describing how harvest effects population dynamics Compensatory: Harvest will not effect Additive: Harvest will decrease the the population—increased survival will population—harvest is adding to the compensate naturally occurring mortality The inductive approach allows us to collect data, analyze data, draw conclusions 1. Estimate annual survival 2. Harvest the population 3. Estimate post-harvest annual survival 4. Find that annual harvest survival was the same 5. Conclude harvest mortality was compensatory The deductive approach allows us to start with a theory, make predictions, collect data to evaluate predictions 1. Make predictions from harvest theory 1. If mortality is compensatory, annual mortality will remain the same, even with harvest 2. If mortality is additive, annual survival will decrease with increasing harvest 2. Estimate annual survival 3. Harvest the population 4. Estimate post-harvest survival 5. Determine which hypothesis is best supported, do predictions match the data/experiment? Strong inference emphasizes multiple working hypotheses rather than a single hypothesis How do we remain objective? Data Science is implemented by people and they are the most important part of the scientific process. We already had a view of Dr. Ray Hilborn. AN INCOMPLETE GROUP OF FAMOUS FISHERIES SCIENTISTS Ray Beverton (1922-1995) was one of the most influential and respected fish population dynamicist of the century! Pedigree • Forest School • Snaresbrook • Downing College • Joined operations research as part of the war effort • Professor at Bristol, Southampton, University of Wales Beverton is famous for the seminal book Famous for Dynamics of exploited fish populations, yield-per-recruit model, stock recruit function Rn n = 0 t t+1 n 1+ t M Fisheries icon: Dr. John Gulland. Gulland is famous for … Scientific contributions • Virtual population analysis (VPA) • Introduce the F0.1 concept • Developed a number of short cut methods for assessing tropical fish stocks Fisheries icon: Dr. Carl Walters Carl Walters • Retired from University of British Columbia • Ground floor of the IBP program • Colorado State University • Humboldt State University BSc Scientific contributions • Foraging arena theory • Ecopath with Ecosim • Adaptive management! • Just to name a few! Fisheries icon: Bill Ricker Dr. W.E. Ricker • Started out as an entomologist-stoneflies • Published over 400 items! • 1950 Ricker became editor of the Journal of the Fisheries Research Board Scientific contributions • The Ricker Model – Relates stock size to recruitment a r−(1t ) k att+1 =a e Fisheries icon: Dr. Ken Carlander • B.S., M.S., and Ph.D. degrees at the University of Minnesota in 1936, 1938, and 1943 • In 1946, Dr. Carlander began a long career as a member of the faculty of Iowa State University. • Served as leader of the Iowa Cooperative Fishery Research Unit from 1946 to 1965 Claims to fame • Carlander, Kenneth D. 1953. Handbook of freshwater fishery biology, with the first supplement. Wm. C. Brown Company, Dubuque, Iowa, USA. • Carlander, Kenneth D. 1969. Handbook of freshwater fishery biology, Volume 1. Iowa State University Press, Ames, Iowa, USA. • Carlander, Kenneth D. 1977. Handbook of freshwater fishery biology, Volume 2. Iowa State University Press, Ames, Iowa, USA. Fisheries icon: • Don W. Gabelhouse Jr. • University of Nebraska, Lincoln • University of Missouri, Columbia • Kansas Fish and Game Commission • Chief, Nebraska Games and Parks Commission Claims to fame • Structural indices of population – 5 cell PSD system • Slot limits for largemouth bass • Pond management, balanced systems HOW WE START TO MAKE SCIENTIFIC INFERENCE & MAKE ESTIMATES Thinking inside the box Fish Value Habitat Stock aka state variable Something measureable & can stored or lost over time: – Abundance – Biomass Thinking inside the box Fish Value Habitat 10 Fish or 28 Kilograms Quantifying a fish population (aka a state variable) is fundamental to fisheries science How do we figure this nubmer out? 10 Fish or 28 Kilograms “The trouble with fish is that you never get to see the whole population. They’re not like trees, whose numbers can be estimated by flying over a forest. Mostly you see fish only when they’re caught…” Schnute 1987 How many bass? Our “view” of fish populations comes from a variety of sources: anglers, commercial fisheries, and sampling gears. Each has inherent biases, and we rarely have complete information about the fishery of concern. Sampling is designed to provide an unbiased estimate of the sampling frame. Yi ~ Normal (ˆ == 10, 1) 0.4 0.3 0.2 Density 0.1 0.0 0 5 10 15 20 Values Example: A population has 625 individuals and the true mean is 10 and variance is 1 Yi ~ Normal (ˆ == 10, 1) A simple random sample assumes each sampling unit has an equal probability of being selected We know that the mean of the population is 10. 11.0 10.5 10.0 9.5 9.0 If we sample the population many times, again, taking 15 observations, the estimated means should be centered ~10 …. Right? Histogram of y 30 25 20 15 Frequency 10 5 0 9.4 9.6 9.8 10.0 10.2 10.4 10.6 10.8 y Yes, they are centered around 10, the mean using a random sample is unbiased 11.011.0 10.510.5 10.010.0 9.59.5 9.09.0 Yes, they are centered above or below 10, the mean using a random sample is biased 11.0 Over 10.5 Estimate 10.0 Under 9.5 Estimate 9.0 In some cases we want to know something about the total population, like total weight Mean Weight = = 9.68 TotalWeight = N TotalWeight =625 9.68 TotalWei g ht = 6050 Yes, they are centered around 10, the mean using a random sample is unbiased 11.011.0 10.510.5 10.010.0 9.59.5 9.09.0 In some cases we want to know something about the total population, like total weight Mean Weight = = 9.68 TotalWeight = N TotalWeight =625 9.68 TotalWei g ht = 6050 But the estimate of total weight is an estimate, it is not absolutely certain. var(TotalWeight )= N22 / n var(TotalWeight )= 625220.98 /15 var(TotalWeight ) = 3906250.96 /15 var(TotalWeight ) = 25000 From the estimated variance for the total weight estimate we can calculate 95% confidence intervals 2 Estimate 1.96 estimate For the weight example the upper and lower 95% confidence interval can be calculated Upper95% C . I .=+ 6050 1.96 25000 Upper95% C . I .= 6359.9 Lower95% C . I .=− 6050 1.96 25000 Lower95% C . I .= 5740.1 Let’s explore confidence intervals https://mcolvin.shinyapps.io/confidence-intervals/.
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