A. Status of the Spectacled Eider - U.S. Fish and Wildlife Service

More documents

Recommendations

Info

initial sample. Then the O~ were re-sampled n 2 times with replacement with weight CL (equation 11) to form the second sample, which represents a random sample from the posterior distribution and is thus used to approximate the posterior distribution (Figure 2). We analyze the three time series both together and separately to illustrate the effects of different quantities and qualities ofdata on the classification process. The seven parameters necessary for the analysis ofall 3 data sets were given prior distributions as follows: (1) N0 (initial population size in 1957, in units ofBreeding Pair Survey) G (23,165, 19,11 12), (2) r [UniformU (-0.095,- 0.045), (3) me--multiplier for additional variance in Breeding Pair Survey U (0.3, 0.8), (4) in0-multiplier for additional variance in Ground Plot Survey U (0.7, 3.5), (5) me--multiplier for additional variance in Coastal Survey U (0.8, 7.5), (6) a0--scaler ofabundance index for Ground Plot to Breeding Pair Survey U (0.35, 2.90), (7) ar--scaler ofCoastal to Breeding Pair Survey U (0.35, 2.50). The prior for N0 is based on the first abundance estimate from the Breeding Pair survey in 1957. The other priors were chosen to be uniform distributions to represent no prior knowledge oftheir value. For practical purposes we bounded the prior distributions because extreme values had nearly zero likelihood. We set these bounds after a few trial runs to ensure that the priors included all possible values ofthe posterior distribution. The classification criterion for endangered (r =-0.05) was based on simulations that included environmental stochasticity. Environmental stochasticity makes the long-term growth rate less than the expected growth rate (r). For example, a population with mean r = 0.00 and some amount ofvariance will not fluctuate around the initial abundance, but rather will decline. The difference between the expected growth rate and the long-term growth rate is because the longterm growth rate is actually the geometric mean ofthe distribution ofgrowth rates, which is always less than the arithmetic mean. To check that our classification decisions are not influenced by the omission ofenvironmental stochasticity, we re-analyzed the full data set using the stochastic population model (equation 1). The prior distribution for the additional parameter ~r, the standard deviation ofthe growth rate, was specified as follows. We used both a uniform distribution and a worst case scenario using only the highest value for variability in r. These values were based on the data available for Common Eiders. Therefore, the prior for s~ was either U (0.07, 0.21) or was fixed at 0.21. This prior distribution for s,. points out another unique advantage ofBayesian methods -- an explicit framework for incorporating prior knowledge into an analysis. It is well known that when fitting a population model to abundance data it is impossible to distinguish between environmental variance and sampling error from the abundance data alone (Hilborn and Walters 1992). This has led researchers to ignore one or the other. Using a Bayesian framework has enabled us to incorporate both, as the sampling error is accounted for by the CV’s and their multipliers, and uncertainty due to environmental variance is incorporated into the prior distribution for s~, using data from Common Eiders. Admittedly the data on environmental variance are not ideal, but they are preferable to either ignoring stochastic population dynamics or assuming a non-informative prior which might over-emphasize the importance ofvariance in r. Appendix II- Page 5
BAYESIAN DECISION ANALYSIS FOR USE IN LISTING AND DELISTING DECISIONS ABOUT SPECTACLED ElDERS Appendix I set thresholds for classification decisions and calculated the probability of different rates ofpopulation growth given the YKD survey data. Before deciding whether or not to classify a population in a certain risk category we must consider the consequences of either under- or over-protecting the species. Appendix I showed that populations declining at higher rates are at a higher risk of extinction. We expect, therefore, that the costs of not classifying a population declining at 10%/year will exceed those of a population declining at 5%/year. Bayesians call the function that relates cost to particular outcomes a “loss function”. Our loss function quantifies the risk of extinction. We expect that the loss caused by incorrectly not classifying a species to a higher risk category will increase as the risk of extinction increases (although once the probability of extinction becomes nearly one, the cost should remain the same for all cases leading to that level of risk). We also expect this loss to become zero when the population is stable or growing because the decision not to classify to a higher risk category is correct. Because the recovery team chose to equalize over- and under-protection errors, the loss function for over-protecting the population is symmetrical to the underprotection loss function and becomes zero at the decision threshold. Figure 9 (Part II: Recovery) shows loss functions for the threatened to endangered classification decision. Figure 11-1 shows the loss functions for the endangered to threatened and threatened to delisted classification decisions. To obtain the loss functions we simulated population trajectories as follows, for rates of decline from r = 0.0 to r = -0.25: 1) choose N 0 with a 50% probability from the 1995 estimate for either the ground plot survey or the coastal survey, 2) choose s~ from U (0.07,0.21), 3) for each year choose r’ from G (r,s~2), 4) project population for 50 years, 5) repeat steps 1-4 10,000 times recording each time the population ended with
Page 2 and 3:
SPECTACLED EIDER RECOVERY PLAN Team
Page 4 and 5:
LITERATURE CITATION U.S. Fish and W
Page 6 and 7:
EXECUTIVE SUMMARY OF THE RECOVERY P
Page 8 and 9:
TABLE OF CONTENTS DISCLAIMER PAGE 1
Page 10 and 11:
LIST OF FIGURES Figure 1. Breeding
Page 12 and 13:
A. Status of the Spectacled Eider R
Page 14 and 15:
) a) j~ 25000 0 2 220000 C ~15000 ~
Page 16 and 17:
B. Causes for Decline and Obstacles
Page 18 and 19:
Increasing interest in avicultural
Page 20 and 21:
Other conservation measures include
Page 22 and 23:
D. Natural History of the Spectacle
Page 24 and 25:
Figure 4. Yukon-Kuskokwim Delta loc
Page 26 and 27:
DEL?A RUSSIA SPECTACLED EIDER RANGE
Page 28 and 29:
helicopter surveys in the Prudhoe B
Page 30 and 31:
geographic populations, because imm
Page 32 and 33:
Figure 8. Study sites and land stat
Page 34 and 35:
their diseases (e.g., Schiller 1954
Page 36 and 37:
E. Distinct Population Segments The
Page 38 and 39:
A final consideration involves info
Page 40 and 41:
Trend data are analyzed to inform w
Page 42 and 43:
Figure 10. A hypothetical posterior
Page 44 and 45:
thresholds are not “magic numbers
Page 46 and 47:
Criteria for Reclassifying from Thr
Page 48 and 49:
Criteria for Delisting from Threate
Page 50 and 51:
The final aspect ofSpectacled Eider
Page 52 and 53:
concordance in their descriptions o
Page 54 and 55:
data on both levels and effects oft
Page 56 and 57:
implicated. In the absence of histo
Page 58 and 59:
. - currently small size of the YKD
Page 60 and 61:
implementation of the Memoranda ofA
Page 62 and 63:
. take of Spectacled Eiders. In add
Page 64 and 65:
. AlS. Produce a handbook summarizi
Page 66 and 67:
. Bl.4. 1.3. Develop visibility cor
Page 68 and 69:
B2. 1.1. Implement YKD satellite tr
Page 70 and 71:
. Appendix Ill). Since epidermal ag
Page 72 and 73:
. feasible if post-fledging staging
Page 74 and 75:
presumed that their activities have
Page 76 and 77:
contaminants within Spectacled Eide
Page 78 and 79:
limited ability to withstand additi
Page 80 and 81:
surface after a year and is no long
Page 82 and 83:
. The reproductive stages (eggs, yo
Page 84 and 85: is inadequate to sustain “normal
Page 86 and 87: ___________ 1967. The geese of the
Page 88 and 89: Dementev, G.P., and N.A. Gladkov (e
Page 90 and 91: ___________ 1 964a. Observations on
Page 92 and 93: Nelson, E.W: 1887. Report upon natu
Page 94 and 95: ________ P.R. Wade, R.A. Stehn, and
Page 96 and 97: Robert M. Platte, Migratory Bird Ma
Page 98 and 99: KEY Task Duration and Costs TBD - T
Page 100 and 101: TASK 324.2 I 12.4.1 .2 PRIOR- — 2
Page 102 and 103: TASK I — [37 E12 EI.3 fTYU —PRI
Page 104 and 105: TASK PRIOR- Dj ITYU — — A12.3 2
Page 106 and 107: — TASK U — 135.2 PRIOR- TASK DE
Page 108 and 109: TASK U PRIOR- ITY U TASK DESCRIPTIO
Page 110 and 111: TASK PRIOR. U — DS.3 3 TASK DESCR
Page 112 and 113: The final risk factor is the effect
Page 114 and 115: This PVA will treat two population
Page 116 and 117: Both aerial surveys are conducted a
Page 118 and 119: 0.2 0.15 -j i 4 0.1 0 -I 0.05 0.08
Page 120 and 121: emaining before the population reac
Page 122 and 123: ) critical population size; and (2)
Page 124 and 125: 40%. The population growth rate cri
Page 126 and 127: Table 1-2. Results of simulation wh
Page 128 and 129: Hilborn, R. and C.J. Walters. 1992.
Page 130 and 131: APPENDIX II TECHNICAL DETAILS OF TH
Page 132 and 133: The likelihood function for the par
Page 136 and 137: LU ~0.8 z -J ~0.6 I-ET1 w107 480 m1
Page 138 and 139: LITERATURE CITED APPENDIX LI De la
Page 140 and 141: where x = age. The fertility rate i
Page 142 and 143: of older females: (1) older females
Page 144 and 145: RESULTS Solutions for single cause
Page 146 and 147: 0.1 0.08 ~0.06 4 0 0.04 0. 0.02 0 0
Page 148 and 149: growth rate. Perhaps this can be mo
Page 150 and 151: decline, by far the fastest way to
Page 152 and 153: LITERATURE CITED APPENDIX In Bailli
Page 154 and 155: APPENDIX IV SPECTACLED EIDER RECOVE
Page 156 and 157: APPENDIX V COMMENTS RECEIVED ON DRA
show all

A. Status of the Spectacled Eider - U.S. Fish and Wildlife Service

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?