New Insight To Old Hypotheses: Ruffed Grouse Population Cycles

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1 New Insight To Old Hypotheses: Ruffed Grouse Population Cycles Author(s) :Guthrie S. Zimmerman, Rick R. Horton, Daniel R. Dessecker, and R. J. Gutiérrez Source: The Wilson Journal of Ornithology, 120(2): Published By: The Wilson Ornithological Society DOI: URL: BioOne ( is a nonprofit, online aggregation of core research in the biological, ecological, and environmental sciences. BioOne provides a sustainable online platform for over 170 journals and books published by nonprofit societies, associations, museums, institutions, and presses. Your use of this PDF, the BioOne Web site, and all posted and associated content indicates your acceptance of BioOne s Terms of Use, available at Usage of BioOne content is strictly limited to personal, educational, and non-commercial use. Commercial inquiries or rights and permissions requests should be directed to the individual publisher as copyright holder. BioOne sees sustainable scholarly publishing as an inherently collaborative enterprise connecting authors, nonprofit publishers, academic institutions, research libraries, and research funders in the common goal of maximizing access to critical research.

2 Published by the Wilson Ornithological Society VOL. 120, NO. 2 June 2008 PAGES The Wilson Journal of Ornithology 120(2): , 2008 NEW INSIGHT TO OLD HYPOTHESES: RUFFED GROUSE POPULATION CYCLES GUTHRIE S. ZIMMERMAN, 1,2 RICK R. HORTON, 3,4 DANIEL R. DESSECKER, 5 AND R. J. GUTIÉRREZ 1,6 ABSTRACT. We examined factors hypothesized to influence Ruffed Grouse (Bonasa umbellus) population cycles by evaluating 13 a priori models that represented correlations between spring counts of male Ruffed Grouse drumming displays and these factors. We used AIC c to rank the relative ability of these models to fit the data and used variance components analysis to assess the amount of temporal process variation in Ruffed Grouse spring counts explained by the best model. A hypothesis representing an interaction between winter precipitation and winter temperature was the top-ranked model. This model indicated that increased precipitation during cold winters (soft snow cover for roosting) was correlated with higher grouse population indices, but that increased precipitation during warm winters (snow crust effect) was correlated with lower spring counts. The highest ranked model (AIC c weight 0.45), explained only 17% of the temporal process variation. The number of migrating Northern Goshawks (Accipiter gentilis), which has been correlated with grouse cycles in previous studies, does not adequately explain, by itself, the variation in annual population indices of Ruffed Grouse. Other factors not considered in our analysis, such as endogenous mechanisms, parasites, or interactions among factors may also be important, which suggest that mechanisms mediating the Ruffed Grouse cycle still require investigation. Received 17 March Accepted 17 August The Ruffed Grouse (Bonasa umbellus) is a widespread and important game bird in North America (Rusch et al. 2000). Population size 1 Department of Fisheries, Wildlife, and Conservation Biology, University of Minnesota, 200 Hodson Hall, St. Paul, MN 55108, USA. 2 Current address: U.S. Fish and Wildlife Service, Division of Migratory Bird Management, American Holly Drive, Laurel, MD 20708, USA. 3 Ruffed Grouse Society, P. O. Box 657, Grand Rapids, MN 55744, USA. 4 Current address: Minnesota Department of Natural Resources, Grand Rapids, MN 55744, USA. 5 Ruffed Grouse Society, P. O. Box 2, Rice Lake, WI 54868, USA. 6 Corresponding author; gutie012@umn.edu 239 of grouse is known to fluctuate through time and, in some areas, these fluctuations follow a cycle of 10 years (Keith 1963, Rusch et al. 2000). Their population fluctuations and cycles have been studied extensively (Bump et al. 1947, Keith 1963, Gullion and Marshall 1968, Keith and Rusch 1989, Small et al. 1991). These studies have generated many hypotheses about mechanism(s) responsible for grouse cycles. Ideas advanced as mechanisms controlling grouse fluctuations range from the bizarre (e.g., rabbit [Lagomorpha] seasons were too long; Bump et al. [1947]) to the plausible (e.g., predation; Rusch et al. 2000). Most recently, some researchers have presented correlative data that suggest predation by

3 240 THE WILSON JOURNAL OF ORNITHOLOGY Vol. 120, No. 2, June 2008 Northern Goshawks (Accipiter gentilis) and Great Horned Owls (Bubo virginianus) are most likely causing cyclic population fluctuations in Ruffed Grouse (Keith and Rusch 1989, Lauten 1995). However, there have been no studies that simultaneously evaluated competing hypotheses about factors (e.g., predation, weather, competitors) that could potentially influence grouse numbers through their effect on survival and reproduction. We used model selection to compare relative support for multiple hypotheses representing potential factors that could influence Ruffed Grouse population fluctuations. Our objective in this study was to revisit a phenomenon (the 10- year grouse cycle) that has been assumed to be resolved scientifically (i.e., predation causes these cycles, Rusch et al. 2000) using the comparative model approach. We believe this approach can be used to guide future research about Ruffed Grouse population cycles. METHODS Study Area. Our study area encompassed a 20,358-km 2 area around Grand Rapids, Minnesota, USA (47 13 N, E), defined by a circle having a radius of 80.5 km. This was the approximate maximum distance traveled from Grand Rapids by hunters who collected grouse for our study. This region was within a transition zone between the deciduous hardwood and boreal forests. The weather was variable over the study period and was characterized by warm, moist summers and cold, snowy winters. Response Variable. We were interested in factors that might influence Ruffed Grouse cycles and used spring counts of male Ruffed Grouse drumming displays (a gross index to population fluctuations; Rusch and DeStefano 1989, Zimmerman and Gutiérrez 2007) as our response variable. Male Ruffed Grouse use several displays to attract or court females during the spring, one of which is called the drumming display (Rusch et al. 2000:9). This display consists of a male grouse beating its wings in a manner that produces a distinct low frequency nonvocal sound (Rusch et al. 2000). We used data from surveys within the study area that were conducted annually by the Minnesota Department of Natural Resources. Surveys occurred along non-randomly selected, but consistently surveyed, routes that were approximately 14.4 km in length along maintained roads. Wildlife biologists, land managers, and volunteers attempted to survey each route once during the spring (early Apr early May). Volunteers conducted surveys by stopping every 1.6 km along each route for 4-min intervals to listen for drumming displays. The number of drumming displays heard per 4-min interval was recorded at each stop (Minnesota Department of Natural Resources, unpubl. protocol). Grouse were not surveyed on rainy or windy days. We recognize this index may not truly represent population size and may have represented only drumming display activity of male Ruffed Grouse (Anderson 2001). Development of a priori Hypotheses. We evaluated hypotheses (models) about factors that influence Ruffed Grouse population dynamics that exist in the literature (Bump et al. 1947, Gullion and Marshall 1968, Rusch et al. 2000) and our own experience working with grouse. We limited our set of hypotheses to those for which data were available. We considered 13 hypotheses, a priori to analysis, which represented predictions about how goshawk abundance, weather during the breeding season, weather during the previous winter, exploitative competition (forest tent caterpillars, Malacosoma americanum), color phase ratios, mass of male grouse, and age ratios of both genders during the autumn correlated with Ruffed Grouse population indices that were conducted from 1983 to 2004 (Table 1). We limited our analysis to the interval because we did not have individual grouse data prior to 1982; the 1982 data were used to predict the population index for the following spring (i.e., spring 1983 counts). Predation Hypothesis. Ruffed Grouse are killed by many predators (Bump et al. 1947). Correlative evidence supports a substantial, if not controlling, effect of goshawk and Great Horned Owl predation on the Ruffed Grouse cycle (Keith and Rusch 1989). We did not locate relevant data about Great Horned Owl predation or owl abundance on our study area, but we did acquire population indices of goshawks, which are important predators of Ruffed Grouse in Minnesota (Eng and Gullion 1962). We estimated an annual index of goshawk abundance from counts of migrating raptors at Hawk Ridge Nature Center in Du-

4 Zimmerman et al. RUFFED GROUSE POPULATION CYCLES 241 TABLE 1. A priori models representing hypotheses of factors correlating with population cycles in Ruffed Grouse in northern Minnesota, USA, Model Verbal hypothesis Year a Season dates Variables/Predictions b,c 1 Goshawk abundance T Fall Hawk Ridge GH 2 Freezing while female lays eggs t 1 20 Apr 15 May DF l 3 Cold and wet weather during t 1 25 May 25 Jun P h T h (P h *T h ) hatching and brooding 4 Negative effects of snow-less cold T 1 Dec 31 Mar T w P w (T w *P w ) winter 5 Duration of extreme cold events T 1 Dec 31 Mar C w 6 Snow depth T 1 Dec 31 Mar S 7 Duration of adequate snow cover T 1 Dec 31 Mar SC 8 Consecutive years of adequate snow t 1, t 2, t 3 1 Dec 31 Mar SC SC 1 SC 2 9 Tent caterpillars (cover hypothesis) t 1 Summer C 1 10 Tent caterpillars (immediate effect t 1, t 2, t 3 Summer C 1 C 2 C 3 due to loss of buds or production of secondary compounds) 11 Color phase ratios (phenotypic T Fall hunt GR hypothesis) c 12 Mass of males T Fall hunt M m 13 Age ratio of both genders insight into cycles? t 1 Fall hunt A P a t 1 September of previous year to current breeding season. b negative correlation, positive correlation, effect unknown could be positive or negative correlation. c P h amount of precipitation during the hatching period; T h mean minimum daily temperature during the hatching period; P n amount of precipitation during the nesting period; DF 1 number of days below freezing during the laying period; T w mean minimum temperature during the winter season; P w amount of precipitation during the winter season; C w number of nights with min temperature below 15 C; S average daily snow depth during the winter season; SC number of days with 15 cm of snow during previous winter (t); SC 1 number of days with 15 cm of snow during t 1; SC 2 number of days with 15 cm of snow during t 2; GH index to goshawk abundance: number of goshawks counted at Hawk Ridge; C 1 through C 3 percent of area within 80.5 km of Grand Rapids with forest tent caterpillar damage; M m average mass of immature male grouse from RGS hunt data; A P age ratio of both genders (calculated as number of young-of-year birds harvested divided by number of adults harvested) from national hunt; GR proportion of gray-phased birds harvested during the national hunt. luth, Minnesota ( 110 km from Grand Rapids). Raptor counts were conducted by trained observers from late August through November. Volunteers recorded the number of observation hours and used spotting scopes to record counts of migrating raptors by species. We used these data to estimate the average number of goshawks observed per hour per year as our explanatory variable (model 1, Table 1). We recognize these counts may not reflect predation rates on grouse, but they are similar to indices used in previous studies that correlated grouse abundance with goshawk predation (Keith and Rusch 1989, Lauten 1995). Weather Hypotheses. Weather is known to affect both reproduction and survival in gallinaceous birds (Gullion 1970, Moss 1986, Swenson et al. 1994). Thus, we developed seven a priori hypotheses about the potential manner in which weather could affect Ruffed Grouse through its effects on reproduction and survival. We organized the weather data into seasons that were biologically relevant to hypotheses about grouse. The primary egg laying period lasted from 20 April to 15 May, and the hatching/early brood rearing season lasted from 20 May to 20 June. Spring weather could affect reproduction by affecting the physiological condition of females during the breeding season (Moss 1986). When spring weather conditions reduced reproductive success, fewer males should have been available to display during the following spring (models 2 and 3, Table 1). The winter season (Dec Mar) has been shown to be stressful to Ruffed Grouse because of low temperatures and the influence of predators (Gullion 1970, Lauten 1995). Specifically, we predicted that precipitation would benefit grouse during cold winters (i.e., increased snow depth for roosting cover), but would negatively influence grouse during warmer winters (i.e., hard crust forms on snow preventing snow roosting; model 4, Table 1). We also predicted that successive periods of extremely cold weather would negatively influence grouse (model 5, Table 1), whereas snow

5 242 THE WILSON JOURNAL OF ORNITHOLOGY Vol. 120, No. 2, June 2008 depth (regardless of snow quality; model 6, Table 1) and long durations of snow cover (models 7 and 8, Table 1) would positively correlate with grouse indices. Winter conditions may also interact with predation to affect survival (Gullion 1973). However, we did not know which specific weather models (i.e., models 4 8) were most appropriate to include in an interaction prior to analysis. We chose not to include a large number of possible interaction models because we wanted to avoid spurious correlations associated with data dredging (Burnham and Anderson 1998). We used weather data from the Minnesota Climate Working Group (University of Minnesota) for assessing our weather models. Weather data included daily precipitation in cm (as water equivalent when snow fell), snowfall in cm, minimum temperature in C, and snow depth in cm, which were collected by an automated weather station at Grand Rapids. We calculated the average daily minimum temperature ( C), mean daily precipitation (cm), number of days of snow cover that was adequate for snow roosting (snow depth 15 cm), number of days with extreme low temperatures (minimum temperature 15 C), and average daily snow depth (cm) for the winter season. We calculated the number of days with a minimum temperature below freezing during the egg-laying season, and the average daily minimum temperature ( C) and mean daily precipitation (cm) during the hatching/brood rearing season. Competition Hypotheses. Forest tent caterpillar outbreaks may affect Ruffed Grouse population dynamics (Jakubas and Gullion 1991). Tent caterpillars could have influenced grouse directly by reducing foliage cover (model 9, Table 1) or lowering food supply (i.e., defoliated aspen [Populus spp.] trees do not produce catkins, which are the primary winter food for grouse in our study area; model 10, Table 1); or indirectly by causing the defoliated trees to increase production of compounds that discourage defoliation by caterpillars (model 10, Table 1). In the latter case, these compounds either may have made the aspen catkins unpalatable or inhibited digestion of these catkins by grouse. We used data on tent caterpillars collected by the Minnesota Department of Natural Resources Forest Health Unit and GIS (ArcView 3.2, ESRI Inc., Redlands, CA, USA) to estimate the amount of caterpillar defoliation within the study area for each year of our study. These data contained spatially explicit maps with area polygons representing various categories of tent caterpillar defoliation (i.e., heavy, moderate, light, or none) throughout the entire state of Minnesota. We combined these four categories into two groups: tent caterpillar outbreaks (i.e., 20% of an area impacted) and no tent caterpillar outbreaks ( 20% of an area impacted) for each polygon within the annual GIS layers. We then calculated the proportion of our study area that was defoliated (i.e., tent caterpillar outbreaks ) each year. Grouse Hypotheses. Gullion (1970) and Gutiérrez et al. (2003) suggested that survival correlated with color-phases (model 11, Table 1). We also hypothesized that mass of male grouse may correlate with display activity if mass reflected the condition of males entering winter (model 12, Table 1). Finally, we considered whether age ratio of harvested birds reflected overall population momentum (model 13, Table 1). Ruffed Grouse were harvested in the study area by hunters participating in the Ruffed Grouse Society s annual National Grouse and Woodcock Hunt (RGS HUNT) held during the second week of October each year since We collected morphometric data on all grouse registered during the RGS HUNT (n 5,700). We estimated the proportion of gray-phased individuals in the population, the age ratio of both genders (calculated as number of young-of-year harvested divided by number of adults harvested), and the average mass of male grouse from the harvested grouse. We considered these estimates to be characteristics of our study population because the RGS HUNT occurred over the entire area. We assumed the Ruffed Grouse harvest data would not be biased for color phase because we knew of no reason, a priori, why hunters would take one color phase over another. We assumed that hunters would take a greater proportion of juveniles than existed in the overall population due to age-specific vulnerability (Small 1989). However, we did not believe this bias would change among years, and considered the proportion of juveniles as an index to relative proportion of this segment in the population among years. Data Analysis. We used model selection

6 Zimmerman et al. RUFFED GROUSE POPULATION CYCLES 243 (Burnham and Anderson 1998) to rank our a priori hypotheses about factors that correlated with fluctuations in Ruffed Grouse populations. We first expressed our a priori hypotheses as statistical models (Table 1). We then checked for correlations among variables within a model. We used a repeated measures design because grouse were sampled along survey routes each year, and mixed modeling to estimate the parameters for each of our a priori models. We used a global model to identify the best covariance structure for the display data. Modeling covariance using this process allowed us to account for correlations between grouse indices among years (i.e., the autocorrelation associated with time series data). We used Akaike s Information Criterion (AIC c ) to rank the models ability to fit the data, and then estimated the amount of temporal process variation explained by the best (lowest AIC c ) model. We used PROC CORR in SAS (Version 9.1, SAS Institute Inc., Cary, NC, USA) to develop a correlation matrix of all predictor variables included in the a priori hypotheses. If a pair of variables from the same model had a correlation coefficient 0.60, we excluded one variable from the model to avoid multicollinearity. The variable excluded depended upon our subjective assessment of the potential for biological interpretation of each of the two correlated variables. We used multiple linear mixed modeling (PROC MIXED in SAS) to estimate the parameters of our statistical models. We considered year to be a random effect, predictor variables (i.e., weather, goshawk index, tent caterpillar damage) to be fixed effects, and survey routes as the sampling units. We assumed that routes were independent units within years. We used a repeated measures design to analyze the mixed models because, although routes were independent within years, samples from routes were not independent among years. The assumptions of linear regression are that error terms are distributed normally and variances are constant. However, estimation techniques based on normal distributions are robust to departures from normality (White and Bennetts 1996). We modeled the variance-covariance structure because we suspected dependence among repeated samples, non-constant variances among years, and autocorrelation of counts through time. We used restricted maximum likelihood and a global model to estimate five variance-covariance structures described by Littell et al. (1996) including first-order autoregressive (AR1), heterogeneous AR1 (HAR1), compound symmetric, unstructured, and log-linear. The AR1 structure assumes that variances are constant among years and that covariances decline with increasing time between observations. The HAR1 is similar to the AR1 structure, but it allows variance estimates to change among years. The compound symmetric assumes homogeneous variance and covariances. The unstructured model assumes that no pattern in variance and covariances exists. The log-linear structure allows flexibility in modeling variances as a function of experimental conditions (e.g., increased variances during years with higher population indices). The autoregressive covariance structures are particularly applicable to cyclic species because they allow observations closer in time to correlate more than observations further away in time. We used standard maximum likelihood estimation (Littell et al. 1996:498) and AIC c to rank candidate models once we identified the most parsimonious variance-covariance structure. We used the Satterthwaite approximation method for estimating the degrees of freedom for each a priori model. We estimated the amount of variation explained by the AIC c selected model by first estimating an intercept-only model (i.e., a model containing an intercept parameter with no other fixed effects) and a model containing a variable for each year. The difference in residual variation between these two models represents the total temporal process variation in the population index. Next, we estimated the residual variation from the best annual covariate model. We calculated the percent of temporal process variance explained as the difference in residual variation between the intercept-only model and the best covariate model divided by the total temporal process variation. RESULTS Twenty-nine permanent survey routes were sampled within the study area. The number of routes surveyed each year (n 22 years) var-

7 244 THE WILSON JOURNAL OF ORNITHOLOGY Vol. 120, No. 2, June 2008 FIG. 1. Plot of Ruffed Grouse population indices (average drums/stop along male display survey routes) collected by the Minnesota Department of Natural Resources from 1983 to 2004, Grand Rapids area, Minnesota, USA. Error bars represent 95% confidence intervals. ied from 19 to 29 because inclement weather, accessibility of the route, and availability of volunteers to conduct surveys varied by year. The number of times individual routes were surveyed during the entire study varied from 6 to 22. The routes that were surveyed the least were not initiated until , and not all of these newer routes have been surveyed each year since their initiation. The mean number of drums (displays) heard per stop varied from 0.57 in 1993 to 2.27 in Grouse population indices during our study reflected a 10-year cyclic pattern (Fig. 1). Modeling the covariance structure of the Ruffed Grouse population index indicated the data supported a HAR1 structure. This covariance structure included 22 variance estimates (i.e., a variance estimate for each year) and a parameter ( ) that described the correlation between observations separated by 1 year. This structure indicated that years with higher population indices were characterized by greater variance among survey routes and that indices were correlated from 1 year to the next ( 0.74). Our data most strongly supported the interaction between winter temperature and precipitation model (model 4, Tables 1 2, Fig. 2). AIC c weights indicated there was strong support for this model because it was almost three times as likely as the second ranked model. This model indicated that grouse indices were highest during cold snowy and warm dry winters, and lowest following relatively warm snowy and cold dry winters (Fig. 2, Table 3). This interaction model was ranked the highest, but variance components analysis indicated this model only accounted for 17% of the temporal process variation. DISCUSSION The Ruffed Grouse cycle has captured the interest of grouse biologists for many years, and has stimulated many studies to investigate its causative mechanisms (Bump et al. 1947, Keith 1963, Gullion 1984, Keith and Rusch 1989, Balzer 1995, Lauten 1995). Rusch et al. (2000) concluded that avian (particularly goshawk) predation was most closely linked to the grouse cycle and, hence, may be the mech- TABLE 2. Model selection assessing the influence of ecological factors on Ruffed Grouse population indices in northern Minnesota, USA, Model 2 Log likelihood K a n AICc b Delta AICc AICc Weight a K number of fixed effect, covariance, and variance parameters in models. b AICc small sample adjustment of Akaike s Information Criterion.

8 Zimmerman et al. RUFFED GROUSE POPULATION CYCLES 245 FIG. 2. Predicted effects of interaction between precipitation and temperature on Ruffed Grouse population indices in northern Minnesota, anism responsible for these cycles. Predation has been suggested to operate on Ruffed Grouse population cycles in two ways. In the first case, resident predators, particularly raptors, cause grouse cycles when their staple prey (snowshoe hares [Lepus americanus]) decline and they switch to grouse, which precipitates the decline of grouse in Canada and Alaska (Keith and Rusch 1989). In the second case, snowshoe hare declines in northern Canada and Alaska may force goshawks to migrate south (Keith and Rusch 1989). When these migrating raptors reach wintering areas in the northern United States, they may increase predation rates on grouse to the point of initiating the decline phase of the grouse cycle (Keith and Rusch 1989, Lauten 1995). The cycle does not gain upward momentum until either snowshoe hares increase and they become the staple prey of raptors or these raptors no longer invade southern areas. The study by Lauten (1995) is particularly compelling because he radio-marked large numbers of birds and recorded substantial mortality from predation. In addition, he showed a negative correlation between the Ruffed Grouse cycle and counts of migrating goshawks (from Hawk Ridge, Whitefish Point Bird Observatory, Wisconsin Christmas Bird Counts, and the Wisconsin Checklist Project; goshawk indices from all raptor count sources were correlated during their study) and Great Horned Owls (from Wisconsin Christmas Bird Counts and Wisconsin Checklist Project). In addition to the negative correlation between grouse and raptor indices, Lauten (1995) also found a positive correlation between the goshawk index, avian predation, and winter mortality rates of Ruffed Grouse. We did not have data on predation rates during our study, but based on the results of our analysis, we speculate that mortality rates of Ruffed Grouse may be buffered by high quality snow roosts (cold and snowy winters) or less energetically stressful winters (warm and dry winters) even when raptor abundance is high (Gullion 1973). In our analysis, the predation hypothesis was neither a competing model nor explanatory of variation in our grouse population index (drumming display counts). We used goshawk indices similar to those used by Lauten

9 246 THE WILSON JOURNAL OF ORNITHOLOGY Vol. 120, No. 2, June 2008 TABLE 3. Parameter estimates from AIC c best model (effect of winter temperature, precipitation, and their interaction) for assessing changes in Ruffed Grouse population indices in northern Minnesota, USA, Parameter Estimate SE df a 95% Confidence interval Intercept Winter precip Winter min temp Interaction term a Estimated using the Satterthwaite procedure in PROC MIXED. (1995), but found no relationship between grouse and goshawk indices from the previous year, which was used as a surrogate for predation in other studies. Alternatively, the influence of predators may lag due to switching of prey (Tornberg et al. 2005). For example, goshawks may initially prey upon other species (e.g., snowshoe hares) before switching to Ruffed Grouse. We considered this model initially, but did not include it as an a priori model because our initial model set was large and we felt there was more support for the non-lag predation hypothesis. Reviewers suggested including this predator lag model and we estimated this model post hoc to the analysis. This model performed about equally as well as our original predation model (AIC c 1,148.10), but provided no additional explanatory power. In contrast, we found that winter weather explained the most variation among those hypotheses we considered. Winter weather can influence thermoregulation, cover from predators, and condition of females at the start of the breeding season. Our top model supported the hypothesis that cold, snowy winters favored grouse whereas cold, snow free conditions were unfavorable. However, our top model only explained 17% of the variation in grouse population indices, suggesting that other factors must be important in explaining grouse cycles (e.g., interactions among factors, variation in grouse counts). For example, there could be an interaction between the number of goshawks and snow quantity/quality. Such an interaction may explain the different observations between our study and Lauten (1995) because high quality snowroosting may provide protective cover during goshawk invasions. Also, there could be interactions between predators, grouse food quality, and snow conditions (Gullion 1970). For example, when forage quality decreases, individual grouse may need to forage for longer periods to find adequate food, which would expose them to predation for longer periods of time (Jakubas and Gullion 1991). We concluded that goshawk counts (predation hypothesis) by themselves did not explain Ruffed Grouse population cycles within our study area. It was possible that predation was interacting with winter weather conditions in a way that was unknown. Further, we did not evaluate models that hypothesized bottom-up mechanisms for control of cycles. For example, Gullion (1984) hypothesized that food quality changes as a result of intensive foraging on aspen by grouse, which in turn affects either survival or reproduction of grouse. However, Jakubas and Gullion (1991) suggested that food quality alone as manifested by high levels of secondary compounds could not mediate a cycle in Ruffed Grouse. Although weather appeared to correlate with Ruffed Grouse population changes better than some other factors, our study was based on indices of both Ruffed Grouse and goshawk numbers, which could confound our results if the indices did not adequately reflect the population size or ecological interactions (Anderson 2001). In addition, we do not have data on Great Horned Owls, mammalian predators, endogenous factors (Matthiopoulos et al. 2005), or parasites (Mougeot et al. 2005), which may also influence Ruffed Grouse population trends. Neither Northern Goshawk counts nor winter weather appear to explain population indices (relative fluctuations of grouse numbers based on drumming display counts) for our study population based on our simultaneous evaluation of plausible factors affecting grouse numbers. Thus, we believe the mechanism(s) regulating the Ruffed Grouse cycle (at least in northern Minnesota) is still unknown. We suggest that future studies of

10 Zimmerman et al. RUFFED GROUSE POPULATION CYCLES 247 Ruffed Grouse cycles use robust estimators of grouse counts (e.g., Zimmerman and Gutiérrez 2007) and counts of local predators rather than indices to evaluate the influence of predation, weather, habitat and their interactions on Ruffed Grouse population fluctuations. ACKNOWLEDGMENTS We thank Jana Albers, A. E. Elling, and M. A. Larson for providing the tent caterpillar data, weather data, and Minnesota DNR Ruffed Grouse display survey data, respectively. We appreciate the assistance of the Grand Rapids Chapter of the Ruffed Grouse Society. F. J. Nicoletti and the Hawk Mountain Bird Observatory provided goshawk data. L. I. Berkeley, L. G. Erickson, and D. D. Grandmaison provided valuable comments on earlier drafts of this manuscript. J. D. Nichols provided valuable discussion during the development of our modeling approach. C. E. Braun, R. E. Kenward, and Patrik Byholm provided valuable comments that improved the quality and clarity of the manuscript. Support for this research was provided by the University of Minnesota, Minnesota Agriculture Experiment Station, Leigh Perkins Fellowship to G. S. Zimmerman, Donald Rusch Fellowship to G. S. Zimmerman, and the Ruffed Grouse Society. LITERATURE CITED ANDERSON, D. R The need to get the basics right in wildlife field studies. Wildlife Society Bulletin 29: BALZER, C. C Survival, hunting mortality, and natality of Ruffed Grouse in northwestern Wisconsin. Thesis. University of Wisconsin, Madison, USA. BUMP, G., R. W. DARROW, F.C.EDMINSTER, AND W. F. CRISSEY The Ruffed Grouse: life history, propagation, management. New York State Conservation Department, Albany, USA. BURNHAM, K. P. AND D. R. ANDERSON Model selection and inference. A practical informationtheoretic approach. Springer-Verlag, New York, USA. ENG, R. L. AND G. W. GULLION The predation of goshawks upon Ruffed Grouse at the Cloquet Forest Research Center, Minnesota. Wilson Bulletin 74: GULLION, G. W Factors influencing Ruffed Grouse populations. Transactions of the North American Wildlife and Natural Resources Conference 35: GULLION, G. W Grouse cycles: are they real? Minnesota Science 29:6 11. GULLION, G. W Grouse of the North Shore. Willow Creek Press, Oshkosh, Wisconsin, USA. GULLION, G. W. AND W. H. MARSHALL Survival of Ruffed Grouse in a boreal forest. The Living Bird 7: GUTIÉRREZ, R. J., G. S. ZIMMERMAN, AND G. W. GUL- LION Daily survival rates of Ruffed Grouse Bonasa umbellus in northern Minnesota. Wildlife Biology 9: JAKUBAS,W.J.AND G. W. GULLION Use of quaking aspen flower buds by Ruffed Grouse: its relationship to grouse densities and bud chemical composition. Condor 93: KEITH, L. B Wildlife s ten-year cycle. University of Wisconsin Press, Madison, USA. KEITH, L. B. AND D. H. RUSCH Predation s role in the cyclic fluctuations of Ruffed Grouse. International Ornithological Congress 19: LAUTEN, D. J Survival, demography, and behavior of Ruffed Grouse of different color phase during a cyclic decline in northwestern Wisconsin. Thesis. University of Wisconsin, Madison, USA. LITTELL, R. C., G. A. MILLIKEN, W. W. STROUP, AND R. D. WOLFINGER SAS system for mixed models. SAS Institute Inc., Cary, North Carolina, USA. MATTHIOPOULOS, J., J. H. HALLEY, AND R. MOSS Socially induced Red Grouse population cycles need abrupt transitions between tolerance and aggression. Ecology 86: MOUGEOT, F., S. A. EVANS, AND S. M. REDPATH Interactions between population processes in a cyclic species: parasites reduce autumn territorial behaviour of male Red Grouse. Oecologia 144: MOSS, R Rain, breeding success, and distribution of Capercaillie Tetrao urogallus and Black Grouse Tetrao tetrix in Scotland. Ibis 128: RUSCH, D. H. AND S. DESTEFANO To tally the grouse. Pages in Ruffed Grouse (S. Atwater and J. Schnell, Editors). Stackpole Books, Harrisburg, Pennsylvania, USA. RUSCH, D. H., S. DESTEFANO, M. C. REYNOLDS, AND D. LAUTEN Ruffed Grouse. The birds of North America. Number 515. SMALL, R. J Tipping the balance. Pages in Ruffed Grouse (S. Atwater and J. Schnell, Editors). Stackpole Books, Harrisburg, Pennsylvania, USA. SMALL, R. J., J. C. HOLZWART, AND D. H. RUSCH Predation and hunting mortality of Ruffed Grouse in central Wisconsin. Journal of Wildlife Management 55: SWENSON, J. E., L. SAARI, AND Z. BONCZAR Effects of weather on Hazel Grouse reproduction: an allometric perspective. Journal of Avian Biology 25:8 14. TORNBERG, R., E. KORPIMÄKI, S.JUNGELL, AND V. REIF Delayed numerical response of goshawks to population fluctuations of forest grouse. Oikos 111: WHITE, G. C. AND R. E. BENNETTS Analysis of frequency count data using the negative binomial distribution. Ecology 77: ZIMMERMAN, G. S. AND R. J. GUTIÉRREZ The influence of ecological factors on detecting drumming Ruffed Grouse. Journal of Wildlife Management 71: