Interactive Effects of Prey and Weather on Golden Eagle...

Journalof Animal Interactive effects of prey and weather on golden eagle Ecology 1997, 66, 350-362 reproduction

KAREN STEENHOF, MICHAEL N. KOCHERT and TRENT L. MCDONALD* US National Biological Service, Raptor Research and Technical Assistance Center, 970 Lusk Street, Boise, ID

83706, USA; and * Western EcoSystems Technology, 2003 Central Avenue, Cheyenne, Wyoming 82001 USA

Summary

1. The reproduction of the golden eagle Aquila chrysaetos was studied in southwestern Idaho for 23 years, and the relationship between eagle reproduction and jackrabbit Lepus californicus abundance, weather factors, and their interactions, was modelled

using general linear models. Backward elimination procedures were used to arrive at

parsimonious models. 2. The number of golden eagle pairs occupying nesting territories each year showed a significant decline through time that was unrelated to either annual rabbit abundance or winter severity. However, eagle hatching dates were significantly related to both winter severity and jackrabbit abundance. Eagles hatched earlier when jackrabbits were abundant, and they hatched later after severe winters. 3. Jackrabbit abundance influenced the proportion of pairs that laid eggs, the proportion of pairs that were successful, mean brood size at fledging, and the number of

young fledged per pair. Weather interacted with prey to influence eagle reproductive rates. 4. Both jackrabbit abundance and winter severity were important in predicting the

percentage of eagle pairs that laid eggs. Percentage laying was related positively to

jackrabbit abundance and inversely related to winter severity. 5. The variables most useful in predicting percentage of laying pairs successful were rabbit abundance and the number of extremely hot days during brood-rearing. The number of hot days and rabbit abundance were also significant in a model predicting eagle brood size at fledging. Both success and brood size were positively related to

jackrabbit abundance and inversely related to the frequency of hot days in spring. 6. Eagle reproduction was limited by rabbit abundance during approximately two- thirds of the years studied. Weather influenced how severely eagle reproduction declined in those years. 7. This study demonstrates that prey and weather can interact to limit a large raptor population's productivity. Smaller raptors could be affected more strongly, especially in colder or wetter climates.

Key-words: Aquila chrysaetos, black-tailed jackrabbits, Idaho, Lepus californicus, modelling.

Journal of Animal Ecology (1997) 66, 350-362

Introduction breeding areas (Galushin 1974; Hamerstrom 1979; Smith et al. 1981; Korpimaki & Hongell 1986; Kor-

Numerous studies of raptors have emphasized the pimaki, Lagerstr6m & Saurola 1987; Korpimaki & close relationship between raptor reproduction and Norrdahl 1991). In other species, most individuals the densities of principal prey species (e.g. Woffinden remain on nesting territories but do not lay eggs dur- & Murphy 1977; Adamcik, Todd & Keith 1978; Smith ing periods of low prey abundance (Southern 1970; & Murphy 1979; Smith, Murphy & Woffinden 1981; Adamcik et al. 1978; Village 1981). Still other raptors

? 1997 British Korpimaki 1984, 1992). In some species, most indi- lay fewer eggs and/or experience increased egg or nes- Ecological Society viduals respond to prey declines by moving to other tling mortality when prey populations are low (Cave

350

1968; Smith & Murphy 1979; Village 1981; Korpimaki 1984, 1987, 1992).

Few studies have documented weather as a limiting factor for raptor reproduction. Weather can affect

raptors through its effect on prey or on hunting behav-

iour, and adverse weather also can directly affect the survival of both adults and young. Rainfall may influence foraging success and thereby affect either a female's body reserves prior to egg-laying or a male's

ability to deliver prey to an incubating female (Hirons 1982; Village 1986). European kestrel Falco tinnunculus L. laying dates are influenced by winter and

spring precipitation and spring temperatures (Cave 1968), and nesting failures correlate with the amount of spring rainfall (Village 1986). Annual nesting success rates of African black eagles Aquila verreauxi Lesson in Rhodesia (Gargett 1977) and honey buzzards Pernis apivorus L. in Europe (Kostrzewa 1989) are also related inversely to rainfall. Red-tailed hawk Buteo jamaicensis Gmelin nestling mortality in Alberta is related to the biomass of food delivered to the nest as well as the frequency of rain (Adamcik, Todd & Keith 1979). Cold and rain are associated with mortality of small nestlings of both northern

goshawks Accipiter gentilis L. and common buzzards Buteo buteo L. in Europe (Kostrzewa & Kostrzewa

1990). In Wyoming, prairie falcon Falco mexicanus

Schlegel pairs abandoned their nesting attempts dur-

ing a spring snowstorm (Squires, Anderson & Oakleaf

1991). Bald eagle Haliaeetus leucocephalus L. reproductive success is negatively correlated with the sever-

ity of spring weather (Swenson, Alt & Eng 1986). Severe winter weather depleted nesting populations

of common barn-owls (Tyto alba Scopoli) and reduced their productivity the following spring (Marti &

Wagner 1985). In Germany, the density of kestrel

pairs and the percentage of pairs laying eggs are

closely related to winter temperatures and snow cover as well as vole abundance (Kostrzewa & Kostrzewa

1991). Kestrels are more likely to starve during severe

winters, and surviving kestrels are less likely to lay eggs because of their reduced mass. Winter severity, however, does not affect density or laying rates of the larger common buzzards and northern goshawks (Kostrzewa & Kostrzewa 1991), and Tengmalm's owl

Aegolius funereus L. laying dates are unaffected by winter temperatures and snow depth (Korpimaki & Hakkarainen 1991).

Golden eagle Aquila chrysaetos L. reproductive rates are known to fluctuate with prey densities (Smith & Murphy 1979; Tjernberg 1983; Watson & Langslow 1989; Bates & Moretti 1994). Failure of eagles to lay eggs during periods of low prey abundance has been documented in both Sweden (Tjernberg 1983) and the western United States (Smith & Murphy 1979). However, some evidence suggests that this response may be mitigated by mild winter weather (Tjernberg 1983). Spring weather also may affect survival of

golden eagle nestlings. Mosher & White (1976) con-

cluded that golden eagle young are susceptible to ther- mal stress during the first 6 weeks after hatching, particularly between 3 and 6 weeks of age. Beecham & Kochert (1975) found that heat stress was a significant mortality factor for nestling eagles in Idaho.

In this paper we examine 23 years of data on golden eagle reproductive rates in relation to both weather conditions and prey abundance. Golden eagles that nest in southwestern Idaho are year-round residents

(Steenhof, Kochert & Moritsch 1984), and black- tailed jackrabbits Lepus californicus Gray are their

principal prey (Steenhof & Kochert 1988). We predicted that rabbit abundance would influence whether female eagles laid eggs and whether pairs would suc-

cessfully raise young. We also predicted that these effects would be modified by weather. Winter severity should influence the proportion of females that lay eggs, particularly when increased energy demands occur during periods of decreased prey availability. Spring weather should influence nesting success of

pairs that lay eggs: cold, wet weather during incubation and early brood-rearing could result in chilling of eggs or small young; extremely hot weather could cause increased nestling mortality.

Study area

The study area was 243 000 ha of public and private lands within the Snake River Birds of Prey National Conservation Area (NCA) in southwestern Idaho, USA (42?', 115?'W). The principal physiographic fea- ture of the study area is the Snake River Canyon, with basalt cliffs ranging from 2 to 125m in height. Topography above the canyon is generally flat or

slightly rolling with a few isolated buttes. Elevation

ranges from 770 m in the canyon bottom to 1000 m at the rim. Native vegetation in the area is characteristic of a shrub steppe community, with big sagebrush Art- emisia tridentata Nutt., shadscale Atriplex con-

fertifolia [Torr. and Frem.] Wats. and winterfat Cera- toides lanata [Pursh] J.T. Howell vegetation associations (US Department of the Interior 1979). Wildfires destroyed approximately 50% of the shrub cover between 1980 and 1985 (Kochert & Pellant

1986), resulting in large stands of exotic annual grasses

(primarily Bromus tectorum L.) and forbs. Annual

precipitation averages 20cm and occurs mainly in

winter; summers are hot and dry.

Methods

EAGLE NESTING DENSITY AND REPRODUCTION

From 1971 to 1994, the entire NCA was searched for

nesting golden eagles. Nearly all golden eagle nesting territories had been identified by previous research when our surveys began (Beecham 1970; Kochert

1972). We located occupied territories by observing

351 K. Steenhof, M.N. Kochert & T.L. McDonald

? 1997 British Ecological Society Journal of Animal Ecology, 66, 350-362

territorial activity, courtship, brood-rearing activity, eggs, young, or any other conspicuous field sign (e.g. whitewash at a roost). Any area of cliff or power line where nests were found in successive years and where no more than one eagle pair bred at one time was considered a nesting territory (Newton & Marquiss 1982). In each year, we considered the number of

nesting pairs to be equivalent to the number of occu-

pied territories. From 1971 to 1994 we monitored reproductive

activity at all known eagle nesting territories. We sur-

veyed each territory at least twice each year from foot, boat, helicopter or fixed-wing aircraft. Most surveys prior to 1984 were from the ground; most surveys from 1984 to 1994 were by helicopter. We used fixed-

wing aircraft only in 1971. Between 1971 and 1981, we entered some nests as often as every 4 days to assess nestling diet (Steenhof & Kochert 1988). To minimize stress to eggs or young, we did not enter nests during inclement weather. From 1985 to 1994, we conducted at least two helicopter surveys each

year. The first survey in March determined if territories were occupied and if adults were incubating. We rechecked territories with incubating birds during a second flight in late May or early June to assess

productivity. All territories for which the breeding outcome could not be determined from the helicopter were surveyed from the ground.

We categorized eagle pairs that showed no evidence of egg laying after repeated observations or after

climbing into and examining potential nests as 'non-

laying.' We confirmed 'laying' if an occupied territory contained an incubating adult, eggs, young, or any other indication that eggs were laid (e.g. fresh eggshell fragments in fresh nesting material). A pair was 'successful' if it produced > 1 young that reached 80% of normal fledging age [i.e. 51 days (Steenhof 1987)]. A small number of eagle nests (< 10) inspected after

young had fledged were considered successful if: (i) a nest platform decorated the same year was worn flat and contained fresh prey remains; (ii) fresh faecal matter covered the back and extended over the nest's

edge; and (iii) no dead young birds were found within a 50-m radius of the nest (Steenhof & Kochert 1982).

Our analysis of nesting success and productivity excluded nesting attempts where research personnel had treated diseased young, installed shade devices, radioed adults, or transferred young in or out of nests. These activities, which may have influenced the outcome of a nesting attempt (M. Kochert, unpublished data), occurred at fewer than 15% of the nesting attempts over all years. We also excluded nests where

investigators were known to have caused egg or chick mortality (n = 6).

We assigned median hatching dates to all nests where we observed young prior to fledging (n = 873). We used a photographic ageing key (Hoechlin 1976) to age young. We were unable to determine hatching dates at nests where young died or fledged before we

could age them, and we were unable to assign laying dates to pairs that failed during incubation.

We considered the following reproductive par- ameters in our analyses: percentage of territorial pairs laying eggs; percentage of laying pairs that were successful; brood size at fledging; and number of young fledged per territorial pair. Brood size at fledging was based on counts of young in successful nests at or after 80% of fledging age (51 days). It includes information on clutch sizes and egg and nestling survival rates at successful nests, which we did not measure

directly. Number of young fledged per territorial pair was a derived variable that described overall productivity of the population. It was calculated as the

product of percentage of pairs laying, percentage of

laying pairs successful, and brood size at fledging.

PREY ABUNDANCE

Each year from 1977 to 1991, black-tailed jackrabbit abundance was assessed by spotlighting from a vehicle

along line transects in May and June (Smith & Nyd- egger 1985). Jackrabbit densities for the NCA were estimated using the program DISTANCE (Buckland et al. 1993). In 1971, 1973 and 1975-1983 the average number of jackrabbits seen per day from late March to July by a 2-person raptor survey crew was tabulated from field notes. Jackrabbits per crew-day and jackrabbit densities from spotlighting were correlated

(r = 0-86) from 1977 to 1983, and the relationship (y = 0-078 + 0-063x, where y = predicted density andx = jackrabbits seen per crew-day) was used to estimate pre-1977 jackrabbit densities. We used the mean of previous and current spring densities ('midpoint' or estimated winter density) in our analyses. This measure incorporates information about rabbits from both years that could influence eagle reproduction. We assumed that the midpoint density would best approximate rabbit density during egg-laying in late winter and incubation in early spring.

WEATHER

We obtained temperature and precipitation data from US Department of Commerce climatological summaries for three weather stations (Bruneau, Grand View and Swan Falls) in or near the NCA. We obtained data on snow and heating degree days from summaries for the Boise Airport. We analysed five variables that described the severity of winter immedi-

ately preceding the breeding season: number of days when the temperature never rose above 20 ?F (-7 ?C); number of days when the temperature fell below 0 ?F

(-18 C); number of days with snow cover; the last date of continuous snow cover on the ground; and heating degree days. One heating degree day is accumulated for each whole Fahrenheit degree that the mean daily temperature was below 65 ?F (18 3 ?C). We tallied heating degree days for the entire winter

352 Golden eagle reproduction


(November to February) and for the two coldest winter months (December and January). Other variables were summarized for the entire winter as well as for the period just prior to egg-laying (15 January to 15

February). Extreme heat was measured from 15 May to 15 June each year by counting the number of days when temperatures exceeded 32 ?C and 35 ?C. We also summarized the amount of precipitation during incubation (24 February to 10 April), brood-rearing (11 April to 15 June), and the entire nesting period (24 February to 15 June).

ANALYSIS

We undertook a model building process to investigate how prey and weather interact to affect golden eagle abundance, breeding chronology and reproduction. We first examined a SYSTAT (Wilkinson 1989) Pearson correlation matrix of data from all years to reduce the number of variables by eliminating non-independent or highly correlated variables. This reduced the pool of 19 weather and jackrabbit variables to 7. The pool of explanatory variables available in the model build-

ing process for each dependent variable was further reduced for two reasons. First, we did not use a potential explanatory variable if the connection between it and a certain dependent variable did not make sense

biologically. For example, it did not make sense to use May/June temperatures to explain variation in

hatching dates because hatching occurred before May. Secondly, we kept the number of terms low in the models for which only 19-21 data points were available because we did not want to overfit the data for certain dependent variables. As a result, the pool of variables available as explanatory variables differed in each model building and are listed separately for each dependent variable in the subsections below. Dis- tributional assumptions also differed for each variable due to the different nature of dependent variables. All of our modelling fits under the framework of 'general linear modeling.' We did not build a model for one

variable, number fledged per pair, because our best estimator for this variable was a function of three

dependent variables that we modelled, and conse-

quently its distributional properties were unknown.

Nesting abundance and breeding chronology

To assess factors influencing the number of nesting pairs and breeding chronology, we modelled number of nesting pairs and hatching date using regular normal theory multiple regression and performed backward elimination of explanatory variables to reduce the number of terms. Terms in the full model were estimated winter jackrabbit densities per square kilo- metre (JRW), winter heating degree days (HDD), year, and the two-way interactions (JRW:YEAR, JRW:HDD and HDD:YEAR). Terms were eliminated based on the magnitude of the individual term's

F statistic, which for normal theory models is roughly equivalent to the Akaike's information criterion (AIC) used below. Backward elimination stopped when elimination of additional terms drastically reduced the

predictive power of the model, as measured by the residuals and the F statistic.

Reproduction

We modelled the relationships between three dependent variables that characterize reproduction: percentage of pairs laying eggs, percentage of laying pairs successful, and brood size at fledging. The explanatory variables used in the modelling process were estimated winter jackrabbit density (JRW) and the following weather variables: total precipitation (PRECIP), precipitation during brood rearing (PRECIPB), number of days in January and February below-18 ?C (LT18), number of days in May and June above 32 ?C (GT32), number of days with snow cover (SNOW), and winter

heating degree days (HDD). A generalized linear

response surface model was fitted by backwards elimination with a forward look at each step (Weisberg 1985, chapter 8). The backward elimination was car- ried out using the S + (? 1995; MathSoft Inc., version 3-3 for Windows) routine step.glm. At each step in the elimination process, single terms were deleted from the model, and the AIC statistic (Akaike 1973, 1985) of the reduced model was calculated. If deleting a term improved the AIC statistic (i.e. lowered AIC), the term that lowered AIC the most upon deletion was eliminated. The forward look at each step involved

adding single terms eliminated in previous steps. If the AIC statistic was reduced upon addition of a term, the term that reduced AIC the most was added back to the model (Statistical Sciences 1995). Models similar to the final model selected via backwards elimination were investigated to see if any further improve- ment in the model could be achieved. Statistical

significance of terms in the final model was assigned by dropping each in turn and performing a likelihood ratio test using the 'full' and 'reduced' likelihoods.

Percentage of pairs laying eggs. The initial logistic response surface model in the backwards elimination

process for percentage of laying pairs included JRW, HDD, SNOW, JRW2, HDD2, SNOW2, and the two-

way interactions JRW:HDD, JRW:SNOW and HDD:SNOW. The logistic response surface model assumed the number of pairs laying eggs each year to be a binomial random variable with index mk (mk = total pairs in year k) and probability Pk. Under the binomial assumption, the logistic model related logit(pk) = lg(pk/(l-pk)) to a linear function of the

explanatory variables and obtained coefficient estimates by maximizing the binomial likelihood function. All hypotheses about model coefficients were tested using likelihood ratio tests. The predictive power of other explanatory variables, relative to the



final model selected in the backwards elimination process, was investigated. Additional variables considered after backwards elimination were LT18, PRE-

CIP, current year jackrabbit densities, previous year jackrabbit densities, and the interaction of previous year and current year jackrabbit densities. The weather variables of PRECIP and LT18 were investigated by first dropping any weather variables from the final model and then adding PRECIP and LT18 one at a time to assess their predictive power. Current year jackrabbit density, previous year jackrabbit density, and the interaction were investigated by first dropping all jackrabbit variables from the model and then

adding all three to assess their predictive power.

Percentage of laying pairs successful. The initial logistic response surface model in the backward elimination procedure for percentage of laying pairs that

successfully raised young included JRW, GT32, HDD JRW2, GT322, HDD2 and the interactions

JRW:GT32, JRW:HDD and GT32:HDD. The logistic response surface model assumed the number of successful pairs each year to be a binomial random variable with index mk (mk = total pairs in year k) and

probability Pk The logistic model related logit(pk) =

log(p/(l-pk)) to a linear function of the explanatory variables and obtained coefficient estimates by max-

imizing the binomial likelihood function. All hypotheses were tested using likelihood ratio tests. The additional predictive power of other explanatory variables was investigated, relative to the final model selected in the backwards elimination process. Additional variables considered after backwards elimination were

PRECIP, LT18, SNOW, current year jackrabbit den-

sities, previous year jackrabbit densities, and the interaction of previous year and current year jackrabbit densities. The weather variables of PRECIP, LT18 and SNOW were investigated by first dropping any weather variables from the final model and then

adding each in turn to assess their predictive power over current year jackrabbit density, previous year jackrabbit density, and the interaction were inves-

tigated by first dropping all jackrabbit variables from the model and then adding all three to assess their

predictive power.

Brood size at fledging. The initial model in the backwards elimination procedure for brood size at fledging included JRW, HDD, GT32, PRECIPB, JRW2, HDD2, GT322, PRECIPB2, and all two-factor interactions among linear terms. The response surface model used for brood size assumed that the average brood size in each year was a normal random variable with variance proportional to the inverse of sample size in each year. Coefficients in the linear model were estimated through weighted least squares techniques with weights equal to the sample size in each year (Table 1).

Results

GOLDEN EAGLE NESTING POPULATIONS

An average of 31 golden eagle pairs occupied nesting territories in the NCA each year from 1971 to 1994

(range = 28-35, SD = 2-1; Table 1). The percentage of pairs that laid eggs each year (range: 38-100%, mean = 70%, SD = 15 5; Table 1) was relatively more variable than the number of pairs occupying territories (cv = 19-6 vs. 6-6; t = 4-0444, P < 0-001). The

percentage of laying pairs that successfully raised

young also showed considerable variation among years (range: 32-80%, mean = 60%, SD = 13-9, cv = 23-2). Brood size at fledging, in contrast, remained fairly stable across the years (range = 1 00-

2-00, mean = 1-56, SD = 0-22, cv = 14-0). Estimated number of young produced per pair reflected the variation in percentage of pairs laying and percentage of

laying pairs successful (range = 0-16-1-38, mean = 0 79, SD = 0 35, cv = 44 3), as did total number of young produced by the population (range = 5- 41,mean = 25, SD = 10-3, cv = 41-2). Hatching dates

ranged from 17 March to 18 May (mean = 13 April, SD = 10 days). The earliest mean annual hatching date was 5 April, and the latest was 21 April.

JACKRABBIT POPULATIONS

Estimated jackrabbit densities ranged from 1 3 to 54 8

per km2 between 1971 and 1994. Jackrabbit densities were highest in 1971, but they decreased sharply in 1973 and remained low through to 1978 (Fig. 1). Num- bers were high from 1979 to 1981, but the peak reached in 1979 was lower than that attained in 1971. Estimated densities declined again between 1981 and

1982, and the estimated population size between 1984 and 1986 was even lower than in the mid-1970s. In

1987, populations began rebuilding to a third peak in 1992, decreased sharply in 1993, and rebounded

slightly in 1994. Jackrabbit populations had peaked at successively lower densities from 1971, to 1979-81, and 1990-92. Knick & Dyer (1997) distinguished two

jackrabbit population phases (high and low) in the NCA. During our 23-year study, we identified 8 years when jackrabbit populations were at high phases and 15 years when they were in low phases of their population cycle (Fig. 1). Jackrabbit abundance was not correlated with any of the weather variables measured

(r's < 0-4; P's > 0-10).

EAGLE-JACKRABBIT-WEATHER RELATIONSHIPS

Nesting abundance and breeding chronology

Regression analyses showed that the number of eagle pairs occupying nesting territories each year was unrelated to rabbit density and winter severity. Year, how-

ever, was the single significant variable in the final model for the number of nesting pairs. The number of



Table 1. Nesting success and productivity of golden eagles in the NCA, 1971-94. Sample sizes are in parentheses

Year Number of occupied % of pairs nesting territories laying

1971 34 1972 34 1973 35 1974 35 1975 33 1976 35 1977 34 1978 32 1979 30 1980 31 1981 30 1982 30 1983 28 1984 31 1985 32 1986 29 1987 32 1988 32 1989 30 1990 30 1991 29 1992 30 1993 29 1994 30 Mean 31-5 SD 2-1 CV 6-6

100% (31)

65% (34) 67% (33) 75% (32) 68% (34) 82% (34) 75% (32) 97% (30) 87% (31)

100% (30) 87% (30) 96% (27)

38% (32) 55% (29) 78% (32) 74% (31) 80% (30) 87% (30) 90% (29) 86% (29) 90% (29) 63% (30) 79% 15-5 19-6

% of laying pairs Brood size at Number fledged Total successful fledging per pair* fledged

62% (21)

44% (18) 53% (17) 56% (18) 50% (16) 59% (17) 77% (13) 63% (24) 76% (21) 73% (22) 79% (24) 67% (18) 59% (17) 42% (12) 40% (14) 32% (25) 52% (23) 63% (19) 80% (25) 74% (23) 56% (16) 80% (15) 50% (12) 60% 13-9 23-2

1-89 1 64 1-38 1-50 1-45 1-60 1-50 1 70 1 40 1-78 1 74 2-00 1-57 1-56 1 00 1 20 1-38 1-54 1-36 1 65 1-59 1 73 1-71 1-71 1-57 0-22

14-0

(19) 1-17 (11) -

(8) 0.40 (10) 0.53 (11) 0-61 (10) 0.54 (10) 0.73 (10) 0.98 (15) 0-86 (18) 1-18 (19) 1-27 (18) 1-38 (14) 1.01 (9) 0-64t (4) 0-16 (5) 0-26 (8) 0.34 (13) 0-59 (14) 0-69 (20) 1-15 (17) 1-06 (11) 0-83 (14) 1-23 (7) 0-54

0-79 0-35 44.3

40

14 19 20 19 25 31 26 37 38 41 28 20 5 8

11 19 21 34 31 25 36 16 25 10-3 41.2

*Based on percentage of pairs laying, percentage of laying pairs successful, and brood size at fledging. tBased on percentage of pairs successful and brood size at fledging.

60

50 -

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30

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10

Low [ , i I I

i

I I I I I I I I I I I I I I I I 0 I I

i i I i , i i , i i

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71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94

Fig. 1. Black-tailed jackrabbit densities (n km-2) in the Snake River Birds of Prey National Conservation Area, 1971-94. The shaded line separates 'high' and 'low' phases of the population cycle.

eagle pairs in the NCA showed a slight but significant primarily to estimated winter jackrabbit densities decrease during the 24-year study. Year explained (JRW) and also to winter severity, as measured by 57% of the variation in number of pairs (P < 0-001) HDD. Eagles hatched earlier in years when jack-

? 1997 British according to the linear regression: number of pairs rabbits were abundant, and they hatched later after

Ecological Society = 50'4-0-23 x YEAR. severe winters. The final model relating hatching dates Journal of Animal Golden eagle hatching dates, in contrast, showed no to explanatory variables was: hatching date = 95-2- Ecology, 66, 350-362 significant trends over time, and instead were related 0-236 x JRW + 0-002 x HDD. The three-dimen-



v 2 0 ' "

'-7. %J%J 25 @ ;_ , 4000

25 20 3800 JRW 5 3600

Fig. 2. Hatching dates of golden eagles in the Snake River Birds of Prey National Conservation Area in relation to estimated winter black-tailed jackrabbit abundance and winter severity (JRW), as measured by heating degree days (HDD), 1971-94.

sional point cloud and estimated surface appear in

Fig. 2.

Reproduction

Preliminary scatter plots (Fig. 3) and correlation

analyses showed that all four measures of eagle reproduction were strongly related to estimated winter jackrabbit densities but not to the weather variables we measured (r's < 0-5, P's > 0-05). Modelling exercises,

however, revealed that weather interacted with prey to influence eagle reproductive rates.

Percentage of pairs laying eggs. Both estimated winter

jackrabbit density (JRW) and winter severity, as measured by heating degree days (HDD), were important in predicting the percentage of eagle pairs that laid

eggs. The final estimated logistic model relating percentage of pairs laying to explanatory variables was:

100 90

80 0) ., 70

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40 30

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m

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Jackrabbits km-1 Fig. 3. Golden eagle reproduction in relationon to estimated winter black-tailed jackrabbit densities in the Snake River Birds of Prey National Conservation Area, 1971-94.

logit(pk) = 66443-0-3751 x JRW-0-001341 x HDD + 0-00009848 x JRW:HDD.

All three terms in the model were significant (P = 00095 for JRW, P = 0-0006 for HDD, P = 0-0019 for JRW:HDD). Three-dimensional and contour plots of the estimated response surface

(Fig. 4) show that winter severity was most important in predicting laying rates when jackrabbit densities were low. To illustrate the interaction between jackrabbit numbers and heating days, consider the fol-

lowing calculations: if the severity of winter increased from 4500 degree days to 5000 degree days and jackrabbit density was low (5 km-2), the estimated percentage of pairs laying eggs fell from 72% to 63%; if the severity of winter increased from 4500 degree days to 5000 degree days and jackrabbit density was high at a value of 30 per square kilometer, the estimated

percentage of pairs laying eggs was approximately constant, ranging from 93% to 97%.

0.9 0.8

CD 0.7-

0.6 -

n. "^^

Percentage of laying pairs successful. The variables most useful in predicting percentage success were estimated winter jackrabbit densities (JRW) and number of days with temperatures above 32?C

(GT32). The final logistic model relating percentage of laying pairs successful to explanatory variables was: logit(pk) = -042408 + 0.1303 x JRW-0-002501 x JRW2-0-05807 x GT32.

All terms in the final model were significant (P= 00016 for JRW, P= 0-0223 for JRW2, P = 0-0276 for GT32). Three-dimensional and contour plots of the estimated surface (Fig. 5) show how the number of extremely hot days during brood-rear-

ing affects success rates. The quadratic response surface model predicts that to maintain estimated percentage success at 50% when the number of days above 32 C increases from 5 to 10, the density of

jackrabbits must increase by three rabbits km-2; to maintain a higher estimated percentage success of

8

30

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0.867 a

0.867 0.75 ?~~~~~~~~~~~* ~~0.824

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4000 4500 5000 5500

HDD Fig. 4. Three-dimensional and contour plots of the response surface depicting the model describing percentage of golden eagle pairs laying eggs in the Snake River Birds of Prey National Conservation Area, 1971-94.


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0

0

C0) 010

20-

0.8

0.7

GT32 Fig. 5. Three-dimensional and contour plots of the response surface depicting the model describing percentage of laying golden eagle pairs successful in the Snake River Birds of Prey National Conservation Area, 1971-94.

70% when the number of days above 32 ?C increases from 5 to 10, the density of jackrabbits must increase by 8-5 rabbits km-2.

Brood size at fledging. Both winter rabbit densities (JRW) and the number of hot days during brood- rearing (GT32) also were significant (P = 0-024 for JRW, P = 0-029 for GT32) in the final model relating brood size at fledging to explanatory variables: (mean brood size) = 1-587 + 0-008702 x JRW - 0-01939 x GT32.

Three-dimensional and contour plots of the estimated surface are shown in Fig. 6. The magnitude of the estimated coefficients was relatively small, and the practical importance of the model for predicting future changes in brood size should be considered.

Under the least favourable conditions within the range of observed data, jackrabbit density was 1-5 km2, GT32 was 18, and the estimated average brood size was 1-25 chicks per nest. In the most favourable situ- ation within the range of observed data, jackrabbit density was 37 km2, GT32 was 2, and estimated average brood size was 1-87 chicks per nest. All other estimated average brood sizes were between 1-25 and 1-87.

Discussion

Long-term productivity of golden eagles in southwestern Idaho (0-79 young per pair) was similar to that of golden eagle populations in other parts of the world. Watson (1957) reported that golden eagles in



CD

cu lal 0

40

(I) 0 0 la 0 2~

30

20 -

I0--

1-2 ?

5 10 15

GT32

Fig. 6. Three-dimensional and contour plots of the response surfae depicting the model describing brood size at fledging for golden eagles in the Snake River Birds of Prey National Conservation Area, 1971-94.

Scotland produced an average of 0'80 young per pair; Phillips et al. (1990) observed a productivity rate of 0-78 in Montana and Wyoming; and Bates & Moretti

(1994) reported 0-82 young per pair in Utah. As in other studies of golden eagles (Smith & Mur-

phy 1979; Tjernberg 1983; Watson & Langslow 1989; Bates & Moretti 1994), prey abundance was an important factor influencing long-term reproduction in southwestern Idaho. Nesting eagles in southwestern Idaho were tied closely to populations of black-tailed

jackrabbits. Although eagles used alternate prey, they showed strong preferences for jackrabbits even when rabbit populations were low (Steenhof & Kochert

1988). Numbers of territorial pairs were unrelated to

annual rabbit numbers or the weather variables mea-

sured. These findings are consistent with those of Brown & Watson (1964) and Watson & Lansglow (1989), who found stable numbers of golden eagle pairs in Scotland where territorial behaviour appar- ently limits the number of nesting pairs. Our results contrast with Galushin's (1974) prediction that raptors with a specialized and unstable diet should fluctuate in synchrony with prey numbers, but they con- firm Galushin's (1974) observation that such nomadic behaviour is less common in resident, non-migratory populations. Because eagles are relatively long-lived, it is advantageous for them to remain on territory even when prey conditions for breeding are unfavourable (Newton 1979).

In the population we studied, the overall annual

reproductive output was influenced most strongly by


the proportion of pairs that laid eggs. As we predicted, golden eagle laying rates were related to food and weather prior to the nesting season. Both hatching dates and the percentage of pairs laying eggs were related to estimated winter jackrabbit densities and the severity of the winter preceding the nesting season. In Scotland, prey abundance was the most important factor influencing barn owl laying dates (Taylor 1994; p. 128), but owl laying dates also correlated closely with temperatures immediately before breeding. When Idaho jackrabbits were in the low phase of their

population cycle and after cold winters, eagles either laid eggs later or early clutches failed before hatching. Fewer golden eagles laid eggs in Idaho when jackrabbits were scarce, and fewer laid eggs after severe winters. The severity of winter influenced the percentage of pairs laying eggs primarily at low phases of the jackrabbit population cycle. During peaks in the jackrabbit cycle, the percentage of pairs laying eggs was relatively high regardless of how cold the winter was. Well-fed eagles can probably withstand cold better than food-stressed birds (Stalmaster & Gessaman 1984). Our results are consistent with

Tjernberg's (1983) finding that eagle reproduction is related to prey and weather prior to egg laying.

Weather during the nesting season also influenced

eagle reproduction in our study. The number of

extremely hot days during brood-rearing was important in explaining the variability in both percentage of laying pairs successful and mean brood size at

fledging. Both variables were related positively to

jackrabbit abundance and inversely to the number of hot spring days. Tomback & Murphy (1981) found that underfed ferruginous hawk Buteo regalis Gray nestlings were especially vulnerable to heat stress and that the combined effects of inadequate food and high temperatures caused increased nestling mortality in

years when prey populations were low. The number of golden eagle young fledged per pair

was the product of all three independent measures of

reproduction (percentage of pairs laying, percentage of laying pairs successful, and brood size at fledging), and it reflected the patterns described above. Jack- rabbit abundance influenced the number of young fledged per pair in all years. In peak rabbit years, number of young fledged per pair was related inversely to the number of hot days in May and June. During low phases of the jackrabbit population cycle, number

fledged per pair was related primarily (and inversely) to HDD and secondarily (and inversely) to the number of days > 32 ?C in May and June.

Weather can affect raptors directly, and it also can influence them indirectly by affecting prey densities or

prey behaviour. Reproduction of Aquila and other

raptor populations in the southern hemisphere decreased following drought that reduced prey densities (Ridpath & Brooker 1986; Hustler & Howells

1990). However, in our study, both cold and heat affected eagles independently of their effects on the

abundance of the eagle's principal prey. Weather conditions were similar during low and high phases of the

jackrabbit population cycle. There was no evidence that rabbit populations were influenced by winter

severity or spring heat. Instead, rabbit abundance in our study area was unrelated to weather conditions and fluctuated cyclically, as has been documented in other parts of western North America (Gross, Stod- dart & Wagner 1974; Smith 1987). Weather, however, may have affected rabbit behaviour and their vul-

nerability to eagles. Weather also may have affected the availability of alternative prey due to either behaviour or population changes that we did not measure.

Our study demonstrated how prey populations and weather can interact to limit a raptor population's productivity. Other non-migratory populations of

raptors that depend on cyclic prey in arctic and tem-

perate environments are likely to show similar patterns [e.g. kestrels (Kostrzewa & Kostrzewa 1991); snowy owls (Wiklund & Stigh 1986); and barn owls

(Taylor 1994)]. The shrub steppe environment we studied may be unique in that both extreme cold and extreme heat affected nesting populations. Rainfall, however, may be a more important factor for raptors in higher precipitation zones (Cave 1968; Adamcik et al. 1979; Village 1986). Precipitation was not a

significant factor in our southwestern Idaho study area, where rainfall rarely exceeded 5 cm during the

nesting season. Variations in prey and weather should have more serious consequences for smaller, short- lived raptors, because the relative costs of egg for- mation are higher for smaller species (Newton 1979). Kostrzewa & Kostrzewa (1991) found that, unlike

kestrels, northern goshawks and common buzzards are able to withstand severe winters in Germany because of their large masses, their relatively low

energy demands, and their consequent ability to ther-

moregulate effectively. Our results, however, show that even larger raptors can be influenced by cold weather at more southern latitudes.

Our results re-emphasize the importance of long- term studies. Shorter investigations would have been unable to identify the effects of weather on eagles because weather effects were overshadowed by prey effects and were only apparent in some years. Mod-

elling can be an effective approach to understand how weather and prey factors interact. Previous studies had documented short-term responses of raptors to severe weather conditions, but our study is one of the few that has examined how more subtle differences in weather and prey interact over the long term to influence populations. Our results support Wiens' (1977) theory of variable environments in which resources are limiting in some, but not all, years. With golden eagles in southwestern Idaho, years when prey limit

reproduction were approximately twice as frequent as

years when prey were superabundant. Weather influenced how severe the effect of decreased prey would be.



Acknowledgements

This paper is a contribution of the National Biological Service's Raptor Research and Technical Assistance Center (formerly the US Bureau of Land Management [BLM] Snake River Birds of Prey Research Project). The BLM provided funds from 1972 to 1994. From 1984 to 1989 Pacific Power and Light Company provided funds for aerial surveys. The Idaho Army National Guard (IDARNG) assisted with funding from 1990 to 1994 as part of the Cooperative BLM/ IDARNG Research Project.

The Idaho Cooperative Wildlife Research Unit provided the 1971 golden eagle data. J. H. Doremus and A. R. Bammann supervised the collection of nesting data between 1976 and 1983, and J. H. Doremus assisted with data collection from 1983 to 1990. R. N.

Lehman, L. B. Carpenter, and their crews helped to collect data from 1990 to 1994. The study could not have been completed without the assistance of numerous field technicians and dedicated volunteers, especially J.R. Oakley, T.E. Hamer, O.R. Fremming and M. W. Collopy.

Data on jackrabbit abundance were provided by M. L. Wolfe and L. O. Oftedahl, Department of Wildlife

Science, Utah State University (BLM contract 52500-

CT5-1003), G. W. Smith, D. J. Johnson, N. C. Nyd- egger, Department of Biology, University of Idaho

(BLM contract 52500-CT5-1002), M. L. McHenry (Idaho Army National Guard), J.H. Doremus (BLM Bruneau Resource Area), S. T. Knick and S. E. Watts

(National Biological Service). M. W. Collopy, E. Kor-

pimaki, J. M. Marzluff, J. T. Rotenberry, and anony- mous referees critically reviewed drafts of the manu-

script. L. McDonald provided valuable input on statistical modelling. D. M. Parrish tabulated and automated most of the data, and A. Teutsch prepared the graphics.

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Received 11 December 1995; revision received 18 July 1996



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