Conservation

1. Conservation

2. Elongated Elephant

3. Bulte, Van Kooten • CITES • Bans TRADE in endangered species • Reduces Demand • Should be good for elephants, etc • (Same argument for Viagra saving Rhinos)

4. Poaching • Poaching model is the fishing model, but adds enforcement. • P price • E effort (poaching • B enforcement effort • X stock • F fine (punishment)

5. kExp is revenue as in fishing • C(B) E is cost of poaching and increases in B, enforcement. Costs of evading getting caught. • T E (zkEx +p) expected value of punishment • TE is likelihood of getting cauth • Last element is the fine where z is a parameter • Notice that this term has two E’s which drives everything

6. Zero Profits for Long Run • 0 = kExP – C(B) E – TE (zkEx +p) • Zero long run profits • Gives E(P, B, …) • Point is that poaching increases in price and decreases in enforcement. • h = kEx is harvest

7. Look at the Table • CITES goes Along with • DECREASED enforcement • Reminiscent of Kip Viscusi’s idea of a taste for danger. (Gov makes you wear seatbelts, so you drive faster to get in your danger quota.)

9. Social Planner Problem • 1 Elephant = 4.7 cows in terms of forage • D(x) is foregone forage • W(B) costs of enforcement • R(x) are the existence values and tourism values • zTh is the value of the gov’t seized ivory • Q is total sold ivory including legal harvest and illegal

10. maximand • At each time • P(Q) Q +R +zTh –cE –D(x) – w(B) • S.t. dx/dt = G(x) – h – y • Assumes CITES, only a local market

11. With trade • Here P(Q) is world price • Big question is how much local price is below world price, even after otpimization. • Now problem is linear in y, so get most rapid approach

12. Model is really… • Most efficient way to harvest animals • Poach or cull • Right number of animals • Since CITES doesn’t prohibit gov’t from culling, it just reduces price.

13. Bulte and KC • Program this up with Zambia values and they get

14. Payoff Slide

15. So • Elephants are on their way DOWN, Cites or no. • CITES doesn’t do that much. • Underlying reason—strong local market, possibly driven by smuggling.

16. San Joaquin Kit Fox

17. ESA • Endangered Species Act • Listing • Take • Includes annoying • Applies to private land too • Habitat Conservation Plans • Can include a whole county • E.g. each acre of toad habitat you take you have to buy 5 acres and preserve them elsewhere

18. The ESA was not thought to be radical when it was passed. Barely any debate. • Court action and interaction with NEPA made it a very powerful tool • The HCP element allowed negotiation and it is now just another part of doing business

19. ESA • See Gardner Shogren • Most listed animals aren’t going to recover • There is far too little money allocated to recovery plans to make progress • Total value of the animals would need to be improbably high for it to be right for Congress to allocate that much money

20. Who gets listed? • Amy Ando sets up model where listing depends on things like “fur” • And also depends on pressure group activity • She records whether there was comment for or against a listing. That is her measure of pressure.

21. Payoff to a group depends on the other groups actions. The more pressure the other group applies, the more beneficial it is for the group to apply pressure. • Defines a game where the Nash non coop soln is of the form P(i) = a + bP(j) for the two groups i and j.

22. comes down to lobby is a function of furriness and other groups action. • finds that other groups action doesn’t matter • but furriness does.

23. Bollworm

24. Pests • Pests are un elephants. • They are small • We want them dead but • We don’t want to kill ourselves and everything else killing them

25. Pest Control • Cotton, veggies are a big users of pest control • Obvious problem is that pest control materials can • Run off and kill good things • Bio accumulate and kill bigger animals • Like ddt and birds • Some materials cause cancer, reproductive harm and so on. • FERPA regulates these things • Sunding, Zilberman, Siebert worked on costs of regulation in CA

26. Cotton • Livingston, Fackler • Two pests, boll wevil and budworm • Two controls: BT cotton and pyrethroids • Also a refugia • Place where we don’t use control/controls • Problem: Bugs become immune to controls.

27. Biology • Assume single gene for resistance • x,X alleles for resistance/suspectibility for BT • y,Y for pyrethroids • x(t,i) proportion of allele in growing season t and generation i. Multiple generations per season • g is probability of xy etc

28. Since each plant has two (is diploid) alleles there are 9 genotype frequencies. • See paper for a list and their probabilities. • Each plant is two choices from the four possible xy combos with their frequencies g. • This makes a 9 vector of frequencies for a generation

29. Pests spend some time in refugia and some in cotton. • first generation, 95% of pests in non selective environment • then 98% of budworms in cotton • and so on.

30. Different survival rates in refugia vs in sprayed/Bt cotton. • So at end of generation, different percent of alleles in population. • Bigger refugia, higher percent of suspectibles maintained.

31. Problem • Max money • subject to allele dynamics • choose refugia size, how much to spray • findings: use less sprayed refugia and less refugia all together.