Life-history and viability analysis of the endangered Hawaiian stilt

PII: S0006-3207(97)00077-3 ELSEVIER

Biological Conservation 84 (1998) 35--45 Published by Elsevier Science Ltd Printed in Great Britain 0006-3207/98 $19.00+0.00

LIFE-HISTORY A N D VIABILITY ANALYSIS OF THE E N D A N G E R E D HAWAIIAN STILT J. M i c h a e l R e e d , a* Chris S. E l p h i c k b & Lewis W. O r i n g c aBiological Resources Research Center, University of Nevada, Reno, 1000 Valley Rd, Reno, NV 89512 USA bEcology, Evolution, and Conservation Biology Program, University of Nevada, Reno, 1000 Valley Rd, Reno, NV 89512 USA CEcology, Evolution, and Conservation Biology Program, and Department of Environmentaland Resource Sciences, Universityof Nevada, Reno, 1000 Valley Rd, Reno, NV 89512 USA

(Received 15 July 1996; revised version received 24 May 1997; accepted 29 May 1997)

[Hawaiian] stilt ..... A very fine endemic bird which should not be allowed to become extinct or even rare. (Munro, (1946; p.46))

INTRODUCTION

Two primary problems in conservation biology are identifying species at risk of extinction and determining what can be done to reduce that risk. Population viability analysis (PVA) is a tool that can be used to address both problems. Using life-history data and their relationships with environmental factors, PVA is used to estimate persistence probabilities of populations under different conditions (Shaffer, 1981; Salwasser et al., 1984; Gilpin and Soul6, 1986; Marcot et al., 1986; Reed et al., 1988; Woodruff, 1989; see Boyce, 1992 for a review). Data inadequacies and simplifying assumptions can limit confidence in the specific predictions of viability models (Caughley, 1994; Harcourt, 1995; Taylor, 1995). By varying parameter conditions, however, PVA can be used to explore the consequences of different management schemes on model population dynamics (Walsh, 1995), thereby providing insight useful to managers. In this paper, we use PVA to estimate extinction risk and to evaluate management options for an endangered shorebird, the Hawaiian stilt Himantopus mexicanus knudseni. Hawaiian stilts are a subspecies of the black-necked stilt endemic to the Hawaiian islands. They are significantly larger than their North American counterpart (Coleman, 1981), and differ somewhat in plumage characteristics (Wilson and Evans, 1893). Hawaiian stilts are found on all five major islands (Hawai'i, Kaua'i, Maui, Moloka'i, O'ahu) although their presence on the island of Hawai'i might be due to recent recolonization after several decades of absence (Paton et al., 1985; Banko, 1988). Stilts also are found in abundance on Ni'ihau, which shares birds seasonally with Kaua'i, and since 1989 on Lana'i at a newly available water source (Engilis and Pratt, 1993). Hawaiian stilts forage in shallow water and nest on adjacent embankments (Coleman, 1981; Engilis and Reid, 1994). Historic and current population sizes have depended partly on certain agricultural practices that provide breeding and foraging grounds (e.g. taro, sugar cane runoff) (Griffin et al., 1989). Dependence on

Abstract The Hawaiian stilt Himantopus mexicanus knudseni is an endangered, endemic subspecies of black-necked stilt. We present life-history data required to perform population viability analysis (PVA), and the results of a series of PVAs under two scenarios, treating (a) the subspecies as a single population, and (b) six subpopulations as a metapopulation. We performed sensitivity analyses on model parameters and used results to address various management options. Both basic models predicted that stilts would increase to fill available habitat with no chance of a significant decline. Catastrophe, maximum age, and density-dependent reproduction had little effect on population projections. Rapid declines in the probability of stilt populations persisting occurred when clutch failure rate or first-year mortality rate increased above 70%, or when adult mortality rate increased above 30%. Model predictions of mean population size at 200 years tracked changes in carrying capacity. I f current conditions change such that rates of clutch failure or stilt mortafity increase, population declines and eventual extinction becomes more likely. Managers, therefore, should maintain predator control, limit water level fluctuations, and maintain current habitat area. Downlisting is not warranted because wetland management and predator control are necessary for Hawaiian stilts to persist. Published by Elsevier Science Ltd

Keywords: Hawaiian stilt, Himantopus, extinction, PVA, demographic model.

shorebird,

*To whom correspondence should be addressed at: Department of Biology, Tufts University, Medford, MA 02155, USA, Fax: 702-784-4583. 35

36

J. M. Reed, C. S. Elphick, L. W. Oring

agricultural habitats and practices, coupled with habitat conversion for housing and business, has resulted in a fragmented and reduced wetland landscape, particularly in coastal wetlands where stilts are confined (Handy and Handy, 1972; Shallenberger, 1977; Coleman, 1981; Griffin et al., 1989). There are almost no published data on Hawaiian stilt ecology or population biology and only qualitative estimates of population size before the 1940s. Henshaw, (1902) reported that stilts were common on the island of O'ahu in the late 1800s, but by 1900 were very scarce. He attributed the severe decline to overhunting. Although Hawaiian stilt flesh was viewed as being of little value for food, stilts were hunted even before white settlement (Henshaw, 1902; Handy and Handy, 1972). Hunting continued to be legal until 1941 (Schwartz and Schwartz, 1949), and was probably a major factor in keeping population sizes low during the late 1930s (Munro, 1938; Shallenberger, 1977). Munro, (1944) estimated that there were approximately 200 individuals in the early 1940s. Following cessation of hunting, numbers rose rapidly, and by 1947 there were 1000 individuals (Schwartz and Schwartz, 1949). Since then, Hawaiian stilt numbers have increased to their current value of approximately 1200 birds (Reed and Oring, 1993). Little is known of the population structures of Hawaiian waterbirds, but evidence suggests that local stilt populations are connected through dispersal (Teller, 1971, 1972; Pyle, 1978; Teller and Burr, 1978; Engilis and Pratt, 1993; Reed et al., 1994). The Hawaiian stilt population might exist as a metapopulation (Reed et al., 1994), which adds complexity to population processes (e.g. Murphy et al., 1990). Movement among populations affects demographics, population dynamics, and genetics, and is the driving parameter in metapopulation models (e.g. Hastings and Wolin, 1989; Hansson, 1991; Wu et al., 1993). Little is known about Hawaiian stilt dispersal patterns, except that they do move among wetlands and islands (Munro, 1944; Engilis and Pratt, 1993; Reed et al., 1994). We have several goals in this manuscript: (1) we present life-history data required for performing population viability analyses, summarizing data from unpublished studies and supplementing these data with our own research; (2) we used VORTEX (Lacy et al., 1995), a stochastic simulation model, to perform population viability analyses for the endangered Hawaiian stilt. These analyses were done under two population structure scenarios: (a) treating the entire subspecies as a single isolated population, and (b) assuming a metapopulation structure consisting of six islands as interconnected subpopulations; (3) we performed sensitivity analyses on the life- history parameters in the viability analyses. Sensitivity analysis can be used to determine which parameters most influence model output (e.g. Thomas et al., 1990). This information is, perhaps, the primary value of PVA to conservation biology. It identifies crucial life-history stages or processes and allows

conservation efforts to be focused appropriately. For example, Crouse et al. (1987) found juvenile survival limited adult numbers in loggerhead sea turtles Caretta caretta, indicating conservation efforts should focus on juvenile survival rather than on egg production or hatch success. Because the most sensitive variables require the most accurate data, sensitivity analyses also can be used to focus researchers' energies on improving estimates of the most important variables (Reed et al, 1993). We used results of this analysis to address particular management options. Our final goal (4) was to assess population growth potential. State-wide population size in 1947 was estimated to be 1000 (Schwartz and Schwartz, 1949). Only six years earlier Munro, (1944) estimated it to be only 200. It has been supposed that Munro's estimate was low because this rate of growth was viewed as unlikely (e.g., Schwartz and Schwartz, 1949; Fisher, 1951). We used our population model to determine whether or not growth of this magnitude could have occurred.

METHODS Viability criteria and population structures We defined a population as safe from extinction if there was less than a 5% probability of its declining significantly in 200 years. Philosophically, we would have preferred having no biologically si.gnificant decline as our criterion, but we believe this cannot be determined a priori (Reed and Blaustein, 1997). In order to assess significance of an observed decline, we used a onetailed, one-sample t-test. Our null hypothesis was that the population size at the end of 200 years (T = 200) would be equal to, or greater than, the population size at the start (T = 0); our alternative hypothesis was that the population size at T = 200 would be significantly less than that at T = 0. Target times in population projections are arbitrary (cf. Shaffer, 1981), and 200 years was chosen as a reasonable management time frame. Each iteration of a model was treated as a single replicate and 140 iterations were used for each model. This sample size gives a statistical test with a power of 1 - fl = 0.80, when a = 0.05 and the desired effect size is assumed to be small (0.2) (calculated using correction from a two-tailed test; Cohen, 1988 providing a strong, conservative test capable of detecting significant declines. We ran two basic models using VORTEX Version 7 (Lacy et al., 1995): all birds in the state of Hawai'i acting as a single population, and a six-population metapopulation model. Model parameters below follow those required for running VORTEX. Model parameters

Reproduction Hawaiian stilts have a monogamous mating system, typically begin breeding at age two, and have a

Viability analysis of Hawaiian stilt

37

maximum brood size of four (Coleman, 1981; J.M.R., unpublished data). We used data from a wide variety of sources (Teller, 1972; 1974; 1983; 1984; 1985; Ueoka et al., 1976; Ohashi and Telfer, 1977; Dougherty et al., 1978; Ueoka and Telfer, 1980; Telfer et aL, 1981, 1982; US Fish and Wildlife Service, unpublished data) to determine the proportions of females that produced broods of different sizes (n=484 broods, across 15 years, including five sites on three islands) (Fig. 1). Summary data from other unpublished sources, added to these data, give a mean brood size of 2.18 (n = 982 broods) and SD = 1.6 (n = 529), but a very non-normal distribution (Fig. 1). We assumed that there was no density-dependence in reproduction, that the sex-ratio was even, that all adult males (> 2 years old; see below) were in the breeding pool, and that variation in reproductive performance was not correlated with variation in survival. No data exist for these parameters. Current predation problems and flooding events occur with enough regularity that they are incorporated into observed variation in brood sizes. Effects of introducing exotics not currently in Hawai'i, for example, brown tree snakes Boiga irregularis, are unknown but could have catastrophic effects on reproduction and could be incorporated in future models.

mates of first year survival of 0.53 and 0.60 for birds hatched in 1993 and 1994, respectively, and an estimate of 0-81 for second-year survival for birds hatched in 1993. Because these estimates are based on very limited data, we also surveyed the literature for survival information on other large shorebird species to assess how good these estimates are likely to be. Minimum adult survival estimates for eight other species of large shorebirds (Table 1) range between 0-61 and 0-92 (mean=0-76), suggesting that our estimate of 0.81 for Hawaiian stilts is not unreasonable. Measures of variance in annual survival rates (i.e. return rates) exist only for a few species of shorebirds and range from 0.03 to 0.21 (Hildtn, 1978; Safriel et al., 1984; Barter, 1989; Holland and Yalden, 1991; Root et al., 1992; Paton, 1994; Peach et al., 1994; N. Warnock pers. comm.). For our model, we used the mean value (0.12). Finally, we assumed that male and female survival rates were equal, and that birds lived to a maximum age of 15 years. At least two birds banded between 19771980 were alive in 1994 (JMR personal observations). It is unknown whether they were banded as hatch-year or older birds, which creates a range of potential ages from 14 to greater than 18 years of age.

Mortality Few data on the survival rates of Hawaiian stilts exist. We therefore made survival estimates based on our own limited banding of Hawaiian stilts and on published research on other species of large shorebird. Reed and Oring (unpublished data) banded 30 pre-fledging Hawaiian stilts in 1993 and 83 in 1994. This sample of banded birds included individuals from all islands with large stilt populations. Searches for banded birds were conducted weekly on O'ahu and monthly on the other major islands; these censuses covered virtually all available habitat. These resight data provide minimum esti-

Population parameters We used the Hawaii Division of Wildlife's 1995 winter waterbird count population estimate for the initial population size (total n = 1206 stilts). Carrying capacities (K) were calculated as the maximum winter counts for each island, with the K for the single-population model being the sum of these maxima (total K = 1929, Fig. 2); note that maxima could come from different years (Reed and Oring, 1993). VORTEX ws users to model harvesting and supplementing the population. We omitted both parameters from the model because there is no source for supplementation nor legal harvest. We assumed a stable age-distribution.

40-

300

e"

20i _

u.

10-

O

o

0

1

2

3

4

Number of chicks Fig. 1. Distribution of number of chicks hatched from 484 Hawaiian stilt clutches.

Dispersal Birds move between Kaua'i and Ni'ihau seasonally (Engllis and Pratt, 1993), so we treated these two islands as a single population in our metapopulation model. Hatch-year birds move between islands (Reed and Oring, unpublished data). Of 116 chicks banded in 1993/1994, seven individuals were seen to move between islands within 12 months of banding (Fig. 2). Birds are known to have moved between most pairs of adjacent islands, and between O'ahu and Mani. We estimated dispersal rates by dividing the number of birds known to move from one island to another by the number of marked birds on the source island. In our initial rectapopulation model we used known inter-island annual movement rates, which ranged from 0 to 8.3%. We also assumed that adults of any age can disperse between islands and that both sexes disperse at equal rates.

J. M. Reed, C. S. Elphick, L. IV. Oring

38

Table 1. Estimates of the mean minimmn adult survival rate (return rate) for large shorebird species

Species

Minimum survival rate

Eurasian oystercatcher Haematopus ostralegus

0.88

American oystercatcher Haematopus palliatus American avocet Recurvirostra americana Pied avocet Recurvirostra avosetta Eurasian curlew Numenius arquata Whimbrel Numenius phaeopus Black-tailed godwit Limosa limosa Bar-tailed godwit Limosa lapponica Mean

0.85 0.92 0.63 0.79 0-69 0.70 0.61 0-76

2

Reference Boyd (1962), Goss-Custard et al. (1982), Safriel et al. (1984) Nol (1985) Robinson and Oring (unpublished data) Boyd (1962) Boyd (1962), Evans (1991) Boyd (1962) Boyd (1962) Boyd (1962)

3/36

# 1

N95 1 Ni'ihau

Nmax

14

239

2

Kaua'i

229

318

3

O'ahu

706

821

4

Moloka'i

16

70

5

Lana'i

24

24

6

Maui

191

410

7

Hawai'i

28

47

?~

100 k m

Fig. 2. Hawaiian stilt movement patterns among Hawaiian islands, winter population size in 1995 (N95), and maximum population size recorded during winter censuses (Nmax). Solid arrows represent known movements; dashed lines represent suspected colonization; ?s represent unknown source(s). Fractions are number of moves per number of banded individuals. Based on the observation that one bird moved between islands repeatedly over the course of 12 months, we assumed that the cost of dispersal was low and that birds do not die while dispersing. Sensitivity analysis

Sensitivity analysis can be used to assess effects of inaccuracies of parameter estimates on model predictions. To do this, a parameter is varied by a reasonable amount (i.e. within a range of possible or likely values), while keeping other parameter estimates fixed. The model is then rerun, and the effect on population dynamics determined. This procedure is repeated for each parameter of interest. A second reason to do sensitivity analysis is to determine how much change in a parameter value is required for a significant change in population dynamics. This gives information on the most important and effective management strategies by identifying thresholds below which a parameter should not drop, and identifying where management efforts will have the greatest impact on population increase. For

example, does reducing reproductive success (analogous to reducing predator control, in the ease of Hawaiian stilts) have a significant effect on species persistence? If not, then management of predators would be a poor use of limited resources for management. In our sensitivity analysis we altered a number of model parameters (Table 2). We examined the effects of adding catastrophes and density-dependent reproduction to the model. The impact of density-dependent reproduction was tested for using an equation provided in VORTEX:

N) s

P(N) = {P(0) - [(P(0) - P(K)) ~

N

]} N + A

where P ( N ) is the percent of females that breed when the population size is N; P(O) is the percent of females that breed when the population size is close to O; P ( K ) is the percent of females that breed when the population size is at carrying capacity; B describes the shape of the

Viability analysis of Hawaiian stilt

39

Table 2. Parameter c h m l p used in semitivity analyses. For both the single- and metapolmlation models we give rite parameters we varied, the dl~rent va.Jues used, and whether the parameter was included because of potential data inaccuracies or to address management questions

Parameter

Description of analysis

Reason for conducting sensitivity analysis

Probability of occurrence: 0.01 yr-1; impact = complete reproductive failure

Data uncertainty

20 years (a) decrease in reproductive success as the Kis approached. Density-dependence parameter values: B = 1, 2, 8, 16;A = 0. (b) Alice effect. Allee parameter A = 0.5, 1, 2,4;B = 2. Varied 10% to 90%

Data uncertainty Data uncertainty

Mortality (age 0 to 1)

Varied 10% to 90%

Data uncertainty; management

Mortality (age > 1)

Varied 5% to 60%

Data uncertainty

K (carrying capacity)

Halved and doubled

Data uncertainty; management

Standard deviation in K

Varied from 10% of Kto 50% of K

Data uncertainty; management

(a) 0% and 1% annual movement among all islands (b) 1% movement among adjacent islands only, plus between O'ahu and Maui

Data uncertainty

(a) Single-population model Catastrophe (hurricane)

Maximum age Density-dependence

% of females producing no young (clutch failure)

(b) Metapopulation model connectivity

curve relating percent breeding to population size; and A defines the nature of the Allee effect (Lacy et al., 1995). We also varied the maximum breeding age, nest failure rate, mortality rates, and the mean and standard deviation of K. Finally, we varied dispersal rates in our metapopulation model. Parameter changes used to test sensitivity are given in Table 2. Population growth potenliai Finally, we used our single-population model to determine whether or not Munro's, 1944, 1941 population estimate of 200 individuals was unreasonable given Schwartz and Schwartz's, 1949, 1947 estimate of I000 birds. We ran two models to assess this: (1) we used model parameters from our basic model with an initial population size of 200 and ran the model for six years; (2) we assumed good breeding conditions, i.e. every breeding pair produced four chicks, and mortality from age 0 to 1 was decreased to 33% (value in basic model was 43%).

RESULTS The basic single-population model predicted that Hawaiian stilts would increase in numbers to a mean of

Management

1901 (SD = 88.6) individuals in 200 years. This population size is not significantly different from the carrying capacity (t = 0.32, p > 0.25). The probability of a decline in 200 years was 0.0%. When the model was rerun as a metapopulation using observed data on movement among islands, the mean population size at 200 years was slightly smaller (1833, SD = 92.0), but still indistinguishable from hypothesized carrying capacity (K) (t = 1-04, p > 0.1). Despite the occurrence of local extinctions and recolonizations of the Lana'i subpopulation (six of each), the probability of subspecies decline over 200 years remained at 0.0%. Population growth rates for the single-population and metapopulation models, prior to reaching carrying capacity, were r=0.18 (SD = 0.19) and r = 0.19 (SD = 0.10), respectively. Sensitivity analyses indicated that the initial model results were robust to most single- parameter modifications. Incorporating catastrophe and density-dependent reproduction, and increasing the maximum age of birds into the single-population model did not change results significantly. In all cases the final population size increased to a level not significantly different from K (Table 3). Changes in several model parameters exhibited threshold changes in population persistence. As the

J. M. Reed, C. S. Elphick, L. W. Oring

40

Table 3. Comparisom of Hawaiian stilt mean pOlmdationsizes at year 2110( N ~ ) with carrying calmeity (K), and with initial population size (No) when N20o < N~ Polmlation sizes include all iteratiom, imeludiag those that do not last the entire 200 years Parameter

N2oo4- SD

N20o< K

t

N20o< No

p

t

p

Single-population model

1901 4.89

0.31

>0-25

NA b

Catastrophe

1863 4.156

0.42

> 0-25

NA

Maximum age

1875 4.131

0.41

> 0.25

NA

Density dependence~

1845 4.156

0.54

> 0.25

NA

Clutch failure rate 60% 70% 80%

15764.414 5764.604 0.9 4. 8-5

1.39 2.24 226.84

0.05
NA 1.04 141.80

>0.10 < 0.0005

Juvenile mortality rate 70% 80%

14824.441 0-6 4. 3.1

1.01 622-06

>0-10 < 0.0005

NA 388.80

< 0.0005

16374-346 0.8±9.0

0.84 213.30

>0.10 <0-0005

NA 133-30

<0-0005

939 + 78 3766 4- 223

0-34 0.41

> 0.25 > 0.25

NA NA

SD of K 30%/((579) 40%K(772)

1440 4- 639 293 4- 595

0.76 2.75

> 0.10 < 0.005

NA 1.54

Meta-population model

18334-92

1.04

>0.10

NA

Connectivity 0% 1% (all islands) 1% (adjacent)

1866+79 1859+ 109 18784-88

0.80 0.64 0.58

>0.10 >0.25 >0.25

NA NA NA

Adult mortality rate 30% 40% K 50%K(965) 200%K(3858)

0.05
~Of the eight models for density dependence, this is the result with the largest t value. bNA = test not applicable because mean population size increased.

percent of clutches that failed was increased from 70% to 80%, the probability of the subspecies persisting decreased from 0.89 to 0.007 (Fig. 3(a)). For those populations that persisted for 200 years in the model, mean size at year 200 starts to decline at 40% clutch failure. Even though there is a high population persistence at 70% clutch failure, mean final population size is much smaller (Fig. 3(b)). As juvenile mortality (probability o f surviving to age 1) was increased from 70% to 80%, the probability of the subspecies persisting decreased from 1.00 to 0.02 (Fig. 3(c)). Again, mean population size at 200 years begins to decline at lower mortality values (Fig. 3(d)). Finally, as annual adult mortality was increased from 30% to 4 0 0 , the probability of the subspecies persisting decreased from 1.00 to 0.01 (Fig. 3(e)). The size of persisting populations at 200 years also declined rapidly after adult mortality increased above 30% (Fig. 3(1")).

For simulations where the mean population size at 200 years was lower than the initial population size, we calculated one-tailed, single-sample t-tests to determine whether or not the decline was significant. Highly significant declines were found when clutch failure rate was raised to 80%, when juvenile mortality reached 80%, and when adult mortality was 40% (Table 3; Fig. 3). Although population size distributions were skewed by population extinctions, t-tests are robust to non-normality (Miller, 1986) Model predictions of mean population size at 200 years tracked variations in carrying capacity. Specifically, halving K to 965, gave a value o f 939 (SD = 77.6) birds at T20o, while increasing K to 3858 resulted in a mean o f 3766 (SD = 222.7) birds (Table 3). Increasing the standard deviation of K decreased population persistence, decreased mean size o f persisting populations, and increased variance in population size for popula-

Viability analysis of Hawaiian stilt Persistence probability a)

Population size

b) 2ooo

1.0, 0.8,

1500 t

0.6 0.40.2. 0.0 20

40

60

80 100

2()

0

4'0

6'0

8() 100

40

60

80 1(10

% of clutches that fail C)

1.0:

d)

0.8-

20001 15001

0.60.40.2. 0.0 80 100

0

20

% juvenile mortality

0.8-

1500-1

0.6-

1000

0.4. 0.20.0

5001 ,

~ = ; -- .-.. = .7.

O|

2o 40 00 80 lOO

,

o

20 40 e'o do 16o

% adult mortality

Fig. 3. Persistence probability and mean population size at 200 years for varying values of (a)--(b) percent of clutches that fail, (c)--(d) percent juvenile mortality, and (e)-(f) percent adult mortality. Population sizes include only iterations that persist the entire 200 years. 1.01

a) .0

.o

--

0.6-

Q,

0.6-

0e l '

0.40.2.

0 [1.

0.0

1'0

~o

3'0 4'0 s'o

10

20

30

2000-

b) ¢0 p. O ¢1. O

O.

1500 1000 5000 0

40

50

sd of K (as % of K)

Fig. 4. Persistence probability and mean population size at 200 years of iterations lasting the entire 200 years for varying values of the standard deviation of carrying capacity (K), expressed as a percent of K.

41

tions lasting 200 years (Fig. 4). With 40% standard deviation in K, the mean final population size was almost significantly smaller than the starting population size (Table 3); with 50% standard deviation the population failed to persist in all iterations of the model (Fig. 4). This is not surprising, given that a 50% standard deviation would predict K going to 0 frequently. Degree of connectivity among islands in our metapopulation model had little effect on mean metapopulation size (Table 3), and did not affect the probability of metapopulation persistence (100% in all models). Subpopulations went extinct only in the observed-movement and no-dispersal models, In both cases, subpopulation extinctions were infrequent and occurred only on the three islands with small carrying capacities (K< 70 for each). Except for the no-dispersal model, all subpopulation extinctions were followed by recolonization. Finally, we examined the potential for rapid population expansion from relatively low numbers. Using our single-population model and basic parameter values, we found that a population of 200 birds increased with a growth rate of r=0-18 (SD=0.19), to a mean population size of 638 (SD=269.8) in six years. Assuming good breeding conditions (i.e. no clutch failure and reduced chick mortality) a growth rate of r=0-41 (SD=0.11) was achieved, enabling a population of 200 birds to increase to a mean population size of 1857 (SD = 166.2) over six years.

DISCUSSION The results of both our single-population and metapopulation models indicate that, if our parameter values and assumptions are reasonable, Hawaiian stilt populations will not decline over 200 years and should increase to fill available habitat. Available habitat appears to be key to Hawaiian stilts because it limits carrying capacity. Although many larger wetlands are secure in Hawaii, most are unavailable to stilts for various reasons, such as overgrowth by woody vegetation (Engilis and Reid, 1994). Through wetland enhancement and restoration and subsequent management, more habitat would be available to stilts (e.g. Pyle, 1978; Engilis and Pratt, 1993). The high statistical power of our tests of population decline and the results of our sensitivity analyses allow reasonable confidence in this result. Sensitivity analyses indicated that our results are robust to possible single-parameter inaccuracies in parameter estimates. We found that increases in percent of clutches that fail, percent juvenile mortality, percent adult mortality, and standard deviation in carrying capacity (K) can all lead to rapid declines in the probability of population persistence and to smaller population sizes (Figs 3 and 4). We believe it unlikely that our estimates of these parameters are sufficiently inaccurate to warrant concern. Estimates of clutch failure rate are based on a large number of nests from several sites

42

J. M. Reed, C. S. Elphick, L. W. Oring

across many years. Our survival estimates are based on far fewer data; however, differences between the estimates in our initial model and those likely to cause population extinction are large. First-year survival would have to be half of our estimate for significant declines to be likely. If our adult survival estimate were to be more than 10% too high, the chance of a decline would increase. Given the high adult survival rates typical among shorebirds (e.g. Table 1), we believe that our estimate is reasonable. The collection of detailed survival data for Hawaiian stilts to validate this assumption, however, should be a high research priority. Degree of connectivity among islands in our metapopulation model had little effect on mean metapopulation size and did not significantly affect persistence probability. This is contrary to current results regarding metapopulation dynamics (e.g. Wu et al., 1993), and probably resulted from the existence of three sub-populations (Kaua'i-Ni'ihau, Maui, and O'ahu) that were large enough to persist independently and which drove the dynamics of the metapopulation. One should note that our initial model included the assumption that observed movement was equivalent to dispersal. That is, movements were assumed to result in breeding at the destination. There currently are no data to address this assumption; the fates of individuals that move among islands is unknown. Our limited movement data, however, indicate that birds are capable of moving among islands with ease, suggesting that true dispersal is likely. With no dispersal, local subpopulations went extinct, but subpopulation extinctions were infrequent and occurred only on the islands with small carrying capacities (K < 70 for each). Incorporating dispersal into the model allowed these subpopulations to persist. The collection of data on dispersal rates to these islands is, therefore, essential to assessing the likelihood that these subpopulations will persist. We found that when population persistence was high, mean population size was limited by K (1929). This result is similar to Hill and Carter, (1991), who demonstrated that habitat availability limited a small population of pied avocets Recurvirostra avosetta in Britain. Interestingly, our estimate of K suggests that not enough habitat exists in the Hawaiian islands to achieve the arbitrarily selected population size of 2000 birds that the Hawaiian Waterbirds Recovery Plan (Engilis and Reid, 1994) requires for stilts to be removed from protection under the Endangered Species Act (USA). Our modelling results are consistent with recommendations of the Recovery Plan, which emphasizes predator control, protecting key wetlands (in part by purchase-many important wetlands currently are leased), and restoring wetlands both in areas used by stilts and areas that could be used by stilts. Our models also showed that variance in K must be high ( > 30% of K) before it significantly reduces mean population size below K (Table 3). Such high variance is unlikely under current

conditions, where most stilts breed in managed areas. If management control of wetlands were reduced, however, variance in K could increase and cause problems. Based on the results of this model, what should managers do to maintain Hawaiian stilt populations? First, maintain predator control and regulate water level fluctuations. The latter recommendation refers to decreasing flooding. However, we do not recommend static water levels because this would reduce the invertebrate prey base (Griffin et al., 1989; Chang, 1990; Engilis and Pratt, 1993; Engilis and Reid, 1994). Therefore, what might be done is seasonal inundation before the breeding season, with evaporation and subsequent draw down with chick hatch. These recommendations would have to be balanced by the needs of the other endangered Hawaiian waterbirds (Engilis and Reid, 1994). Our estimates of population persistence were based on breeding data from sites with active management to control predators and to limit flooding. Second, maintain current habitat area. Our models suggest that if currently available habitats are maintained through regular management, they are adequate for self-sustaining populations of Hawaiian stilts for 200 years. Given the rate at which alien plants (e.g. California grass Brachiaria mutica) invade Hawaiian wetlands and make them unsuitable for stilts (Griffin et al., 1989), maintaining current habitat area will be an ongoing management problem. Ensuring consistent habitat availability and quality also reduces risk of population failure due to high variance in K. One concern in achieving this goal is that many wetlands important to stilt populations are not owned by management agencies. For example, the US Fish and Wildlife Service does not own any of its three refuges on O'ahu, and their longterm security is uncertain. Finally, we suggest that it should be a research priority to obtain data on adult survival and to determine the extent of actual dispersal among islands, especially to islands with small numbers of birds. Few data existed for these parameters and it is here that errors in our models are most likely to lie. This study demonstrates the difficulties involved in assessing whether endangered organisms should be removed from endangered species lists. Our results suggest that under current conditions the Hawaiian stilt population is likely to increase to fill available habitat and is unlikely to decline significantly over the next 200 years. These observations could be taken as a valid argument for delisting the subspecies. If, however, the stilt were delisted, current conditions would change as funding for management is reduced. Specifically, predator control, water level management, and the area of managed wetlands all would decline. These changes would likely lead to an increase in the number of failed breeding attempts, a decline in chick (and maybe adult) survival, and a reduction in carrying capacity--all factors that are likely to increase the probability of

Viability analysis o f Hawaiian stilt

extinction and result in different model conclusions (Fig. 3). To illustrate this point, we extended our sensitivity analysis to examine the effect of varying values of several parameters simultaneously. We increased the clutch failure rate to 60%, juvenile mortality to 70%, and adult mortality to 30%. These values would not be unreasonable if predator control were reduced. When we examined variables individually in our sensitivity analysis, each of these values resulted in increasing populations and no extinctions (Fig. 3). Under these conditions in combination, however, no model population persisted for 200 years and the mean extinction time was only 31.7 years (SD= 6-9). We argue that for a species to be downlisted (i.e. declared no longer susceptible to extinction), it must be self-sustaining. Our observations suggest that this will not be true for Hawaiian stilts unless non-native predators and invasive wetland plants are removed permanently from the Hawaiian islands. We suspect the same to be true for the other endangered Hawaiian waterbirds (Engilis and Reid, 1994). Hence, if delisting were to occur, it should be contingent on the assurance that current management conditions be maintained. One encouraging result is that our model indicates that Hawaiian stilts are capable of rapid population growth under good conditions. We found that the purported rapid increase in population size in the 1940s (Munro, 1944; Schwartz and Schwartz, 1949) was reasonable, especially if there were a series of good breeding seasons. This suggests that in the event of a catastrophe that is more destructive than that modeled here, stilt populations could recover as long as the cat, astrophe passed.

ACKNOWLEDGEMENTS We thank C, Terry and M. Ueoka of the Hawaii Division of Forestry and Wildlife, the Commander of the Kane'ohe Marine Corps Air Station, J. Beall of the US Fish and Wildlife Service and F. Kaiulani of the National Park Service for access to protected wetlands. D. Lewis, L. McNeil, and H. Wilbanks provided invaluable field work. J. Robinson, M. Silbernagle, C. Terry, and N. Warnock provided us with unpublished data, and D. Delehanty, A. Engilis, J. Dunham, M. Peacock, M. Rubega, N. Waruock, and one anonymous reviewer provided helpful comments on the manuscript. We thank S. Warnock for correcting our feeble attempt at producing the map. J M R and LWO were supported by NSF Grant DEB-9322733, an NSF EPSCoR grant to North Dakota, and a grant from the US Fish and Wildlife Service Endangered Species office in Honolulu. CSE was supported by a fellowship from Ducks Unlimited, Inc. through the Institute for Wetland and Waterfowl Research.

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