Printer Friendly

The effects of climate modes on growing-season length and timing of reproduction in the pacific northwest as revealed by biophysical modeling of lizards.


Ongoing climate change is altering natural environments by changing the timing and length of growing seasons (Sparks and Menzel, 2002; Schwartz et al., 2006), the magnitude of daily minimum and maximum temperatures (DeGaetano, 1996; Easterling et al., 1997; DeGaetano and Allen, 2002), the distribution of precipitation (Groisman et al., 1999; Miller and Goodrich, 2007), or a combination of these (Easterling et al., 2000a, b; Meehl et al., 2000). These changes in heat and moisture distribution have already begun to evoke ecological and evolutionary responses as organisms track the environment (see Parmesan, 2006). Beyond merely documenting past change, one of the major challenges facing biologists is the prediction of life's responses to future change given the myriad of possible interactions not only between organisms and their environments but also among organisms (see Travis and Futuyma, 1993). By and large, efforts have focused on modeling blanket changes in the environment such as responses to a warmer world (e.g., Buckley, 2008; Crozier et al., 2008; Li et al., 2009). While there is certainly value in this approach, it may overlook subtle variations in atmospheric and oceanic circulations that give rise to natural dynamical modes, such as the El Nino-Southern Oscillation. However, an increasing number of studies have linked these climate modes to effects on organisms in both aquatic (Jonsson and Jonsson, 2004; Lehodey et al., 2006; Brander, 2007) and terrestrial (Post and Stenseth, 1999; Hallett et al., 2004; Woodward et al., 2008) environments (for reviews see Stenseth et al., 2002; Wang and Schimel, 2003). In this paper, we examine the past impacts of climate modes on a terrestrial ectotherm in the Pacific Northwest of North America in an attempt to understand the effects of future environmental variation.

One well-studied climate mode is the El Nino-Southern Oscillation (ENSO). The ENSO cycle, with a periodicity of 2-7 y, consists of a mass of relatively warm seawater that builds in the eastern tropical Pacific Ocean during positive (El Nino) phases of the oscillation, and relatively cold water during negative (La Nina) phases (Kayano et al., 2005). Due to ocean- atmospheric teleconnections, the ENSO affects both aquatic and terrestrial ecosystems (for reviews see Glynn, 1988; Stenseth et al., 2002; Wang and Schimel, 2003). During strong E1 Nino events, the Pacific Northwest experiences relatively warm conditions, while the opposite is true during a La Nina event (Woodward et al., 2008). Due to its seasonal timing, the ENSO should advance or delay the onset of spring depending on its phase and potentially impact phenological responses of organisms. However, this is not the only climate mode that impacts the Pacific Northwest.

A second important climate mode in the Pacific Northwest is the Pacific Decadal Oscillation (PDO) (for reviews see Francis et al., 1998; Mantua and Hare, 2002; Lehodey et al., 2006). The PDO cycle, with a period of 20-30 y, consists of positive (warm) and negative (cool) phases. A positive PDO leads to a strong Aleutian low-pressure system that shifts southward and pushes the California high-pressure system inland, which increases storminess in the Pacific Northwest (see Fig. 7 of Lehodey et al., 2006). A negative PDO leads to a weak Aleutian low that shifts northward and allows the California high to push offshore, which directs storms north into Canada. Since the PDO is a long-lived phenomenon that affects regional weather, this oscillation could alter the length of the growing season or timing of reproduction depending on its phase. Thus, we use natural variation in climate modes related to the Pacific Northwest to test whether these modes will have an impact on the life history of terrestrial ectotherms, in particular reptiles.

One way to predict the effects of environmental variation on organisms is via biophysical modeling (e.g., Porter et al., 1973; Tracy, 1982; for reviews see Porter and Tracy, 1983; Bakken, 1992). Biophysical modeling has progressed from a focus on individual variation to a focus on life-history variation among populations or species (Adolph and Porter, 1993; Dunham and Overall, 1994). For example, in an attempt to predict the life-history strategies offence lizards (Sceloporus undulatus) in different environments, Niewiarowski (2001) studied the impact of thermal constraints on growth rates among populations. While many biophysical ecology studies used microclimate data, microclimate models and mathematical heat transfer models to elucidate aspects of thermal ecology, another approach is to model operative environmental temperatures ([T.sub.e]) (for review see Bakken, 1992). While Te was proposed as an alternative to earlier biophysical models, computing Te still requires information relating to radiation, conduction, convection, evaporation and metabolism or an operative environmental temperature thermometer (often a taxidermic mount, sculpture or other "model" organism) (Bakken, 1992). For this study we sought to simplify even further to determine if relevant thermally-dependent life-history events could be predicted from assumptions related to organismal biology and environmental temperatures alone. However, we acknowledge that environmental temperatures are generally a poor proxy for body temperatures due to factors such as radiative heating. Thus, a focus on environmental temperature alone may lead to under- or over-estimation of certain parameters, particularly on a localized scale. Yet, we feel this is justified since we were not attempting to predict behavior of lizards on a day-to-day basis, but merely integrate their physiological and behavioral responses over the course a season and compare these responses between years relative to climate modes. We chose to focus on three life-history events (spring emergence from hibernation, seasonal reproductive timing, and termination of seasonal [fall] activity) since these events are likely to be impacted by thermal changes due to climate change and are potentially important for population persistence (Roff, 2002).

In this paper, we present a simple temperature-based biophysical model with the goal of predicting relevant life-history events of ectotherms. We based our model on general findings supported by previous research and used temperature data from meteorological stations as input to allow for general application. The model itself is based on the degree- days concept (see Roff, 2002, p. 384) and is able to determine the onset, termination, and length of the growing season as well as the timing of reproduction from air temperature data. We verified the accuracy of this model by conducting common-garden laboratory experiments on side-blotched lizards, Uta stansburiana (Baird and Girard), in which we controlled thermal environment and then comparing model predictions with empirical observations on seasonal activity and timing of reproduction from a long-term study of this species near the town of Burns in eastern Oregon.

Following construction and verification of the model, we attempted to apply our model to past meteorological data from our eastern Oregon field site. In this sense, "hind-casting" can be used to determine if the onset and termination of the growing season as well as growing season length and the timing of reproduction have changed in response to climate trends. Considering that global average temperatures have been increasing in recent decades (IPCC, 2007), our hypothesis is that emergence and reproduction should be occurring earlier in the spring, while retreat should be occurring later in the fall. However, recognizing that short- and long-term cycles in dynamical climate modes may impact the timing of these events, we tested whether such climate modes have had an impact on the growing and breeding seasons of this population of lizards.


In brief, we based our model on the degree-day concept in which a temperature threshold allows for organism activity and, hence, for development and reproduction to occur. However, we modified our model to take in to account that animal body temperatures may not be linearly related to environmental temperature (sensu Fig. 1 of Adolph and Porter, 1993), but that organisms may regulate body temperature around a set point. For example, lizards actively regulate body temperature to some set point through a variety of physiological and behavioral mechanisms (for reviews see Avery, 1982; Huey, 1982; Shine, 2005). Thus, a strict degree-day model would tend to overestimate the thermal energy necessary to reproduce. Our model then assumes that body temperatures track the environment perfectly unless the organism can thermoregulate, but that thermoregulation requires several conditions be met: that it is day time and that some lower threshold temperature for activity is exceeded.

To create the model we first determined local daily sunrise and sunset (http://www. and local daily high and low air temperatures measured at the nearby (16 km distant) Burns airport ( We calculated the temperature at 15 min intervals by applying a cosine function to the daily high (Tmax) and low (Tmin) air temperatures and assuming that Tmax occurred 3 h after midday (t - 3) using the formula:

Temperature = [(Tmax + Tmin) - (Tmax - Tmin) x cos((t - 3)2[PI]/24)]

For each quarter-hour interval the model determined whether temperature had exceeded the limit for activity and whether it was night or day. If temperature was below the lower limit or if it was night (regardless of temperature) we assumed that body temperature tracked the environmental (=air) temperature. If temperature exceeded the lower limit and it was day, we assumed that animals were active and able to thermoregulate to their set point. For simplicity, we assumed that there was no upper thermal limit to organism activity (or rather, even if there were an upper limit that individuals would still be able to regulate body temperature around the set point). We consider this justified since early spring temperatures (i.e., prior to onset of reproduction) only rarely reach the upper threshold for activity causing animals to be inactive (e.g., Porter and Tracy, 1983). In this case, the model returned the value of temperature (degrees in excess of lower limit) multiplied by the quarter-hour fraction (1 d/96 quarter-hour segments = 0.0104) for each quarter hour that these conditions persisted. For each day, we summed the quarter-hour segments to calculate degree-days (DD).

Assuming that one knows the temperature that actually stimulates and/or maintains activity, one can determine the onset and termination of the growing season as well as its length using this model. Previous research has suggested that both spring emergence (defined as the date on which animals emerge from overwintering sites; equivalent to initiation of growing season) and fall retreat (when animals enter overwintering sites; equivalent to end of growing season) are temperature dependent in lizards (Moberly, 1963; Bishop and Echternacht, 2004), snakes (Vetas, 1951; Jacob and Painter, 1980) and turtles (Grobman, 1990). Regardless of whether the actual cue for activity is absolute temperature (e.g., Lutterschmidt et al., 2006) or thermal gradients in hibernacula (e.g., Viitanen, 1967; Aleksiuk, 1970), many species appear to commence or terminate activity at or near environmental temperatures of 10 C (Vetas, 1951; Crenshaw, 1955; Rickard, 1967; Jacob and Painter, 1980; Blouin-Demers et al., 2000). Thus, we used air temperature (Ta) of 10 C as an estimation of the thermal energy needed to stimulate and maintain activity, and by extension we used this temperature as the lower limit for development in our calculation of DD. However, we recognize that [T.sub.a] measured at a nearby meteorological station is an approximation of actual thermal cues received by individuals at a local level. Therefore, we conservatively constrained our estimation of growing-season onset by assuming that the growing season did not begin on the date of the first non-zero degree-day, but rather a day with at least 6 h of potential activity (i.e., at or above 10 C for 6 h) was required to stimulate activity. Likewise, we assumed that the growing season terminated in the fall on days with fewer than 6 h potential activity. Finally, since temperature has been identified as an important determinant of the reproductive cycles of reptiles (Tinkle and Irwin, 1965; Licht, 1972; Duvall et al., 1982), we used the model to calculate the accumulated DD between the onset of the growing season and the onset of reproduction. This allowed us to estimate the thermal requirements associated with the timing of reproduction.


We attempted to parameterize the model by applying it to a study system for which we have life-history data from multiple years. For the past 7 y we have been studying a population of side-blotched lizards (Uta stansburiana) in eastern Oregon (Zani, 2005, 2008). Lizards were studied at Wrights Point 20 km south of the high-desert town of Burns, OR (1318 m elevation, 43.44[degrees]N Lat., 118.93[degrees]W Long.), which is at the northern edge of the Great Basin desert. In the northern portion of their range, side-blotched lizards are a small (maturation at ~40 mm snout-vent length [SVL]; max SVL = 55 mm), diurnal species. While many lizards can be active year-round at low elevations in the south (Cowles, 1941), Uta activity is restricted to a relatively short favorable growing season in the north from about early Apr. through about Oct. (Nussbaum and Diller, 1976; PAZ pers. obs.).


The lower temperature threshold for activity (10 C) reported for many reptiles appears to apply to Uta as well and multiple lines of evidence support our choice of this temperature. Brattstrom (1965) reported that below body temperature of ~10 C Uta will take shelter in "holes deep in the ground." However, these observations were made in the southern portion of this species' range. A population of Uta in southern Washington were active when maximum air temperature exceeded 16 C in the fall, but inactive below this temperature (Rickard, 1967). However, it should be noted that this study relied on pitfall trap capture data to estimate activity, and that Uta often emerge from refuges (deep rock crevices) on cold days, but do not move far from refuges (PAZ pers. obs.). Thus, pitfall trapping likely overestimates the temperature for activity. Laboratory experiments on animals from Wrights Point in eastern Oregon revealed that winter-acclimated Uta maintained in constant cold, dark conditions remain quiescent (showed no signs of movement) when held at 8 C, but were active and moved about their cages at 10 C (Zani, 2008). These data suggest that the lower thermal limit for voluntary activity is between 10 and 16 C. However, we ultimately relied on direct field observations from multiple years and multiple Pacific Northwest populations in both spring and fall to choose 10 C as the lower limit for activity. Uta are only very rarely active at air temperatures below 10 C regardless of season or location, and even this activity is restricted to basking at the mouths of deep rock crevices (PAZ pets. obs.).

Cowles and Bogert (1944) reported that for Uta stansburiana from coastal California "major activity occurs at 35-36 C." For Uta in Baja California the average body temperature was 35.9 C (Soult, 1963). Brattstrom (1965) reported that for two different subspecies of Uta from coastal southern California and from "the desert" the average body temperature was 33-36.7 C. Based on these previous studies we used 35 C as the body temperature set point during activity. We verified this by collecting 205 measurements over the course of the growing season of air temperatures (Ta) (at 1 m height) and body temperatures ([T.sub.b]) of field-active lizards using a Miller and Weber quick reading cloacal thermometer. Average [T.sub.b] was 34.3 [+ or -] 0.28 C (hereafter: mean [+ or -] 1 S.E.M.). Above 15 C, lizard [T.sub.b] is fairly independent of [T.sub.a] (Fig. 1); that is, animals are able to thermoregulate near their set point. However, below 15 C, lizards appear unable to maintain 35 C Tb, which likely will increase the error of our model by overestimating thermal energy availability (DD accumulated), particularly at the seasonal extremes when these low temperatures are more likely to occur. However, we did not attempt to take this error into account.


Since local environmental variation is expected to increase the error associated with the onset of the breeding season, we verified that thermal energy availability determines the timing of reproduction by rearing 12 female and five male Uta in a common-garden laboratory environment. These animals were collected from Wrights Point in the fall of 2007, given 60 d of simulated winter at 2 C (following protocol of Zani, 2008), and reared under subjective "spring" conditions. Animals were kept as cohorts of three females and one or two males in 1 m diameter circular cages lined with 10 cm of sand. Light and heat were provided via two 40 W fluorescent bulbs and 100 W Powersun UV bulbs (Zoo Med Laboratories, Inc.) suspended 20-25 cm above the surface of one end of the cage. At the onset of "spring" lights were set for a 14:10 light:dark cycle and every 5 d the photoperiod was increased by 15 rain to simulate lengthening days. Initially we provided only one hour of heat lamp per day, but the length of thermoperiod was increased each day by 30 rain until thermoperiod reached 12 h. Thereafter, air temperature (measured at 2 m height in the environmental chamber every 15 min using a Watchdog datalogger [Spectrum Technologies, Inc.]) ranged each day from a high of 23-27 C to a low of 12-15 C. Lizards were fed 2-3 times per day crickets and vestigial wing fruit flies and females were palpated every 5 d to determine reproductive state.

All females reared in a common-garden environment grew and underwent vitellogenesis as normal. Gravid females (those with shelled eggs) were placed in isolation within the environmental chamber until oviposition (3-7 d) (for details see Zani, 2008). First clutches (n = 12) were laid after 48.3 [+ or -] 1.75 d (range: 40-61 d) of subjective spring. We applied our model to the air temperature data from this time period and determined that the average female required 777 [+ or -] 39.7 DD (range: 588-1064 DD) to complete the reproductive cycle. However, consistent with previous results (Zani, 2008) we detected a relationship between the timing of reproduction and body size (measured as SVL: distance from snout to cloaca); oviposition occurred significantly earlier in larger females (F1,10 = 4.94; P = 0.050; [R.sup.2] = 0.330; Fig. 2). Of the 12 females in this experiment, eight produced a second clutch (see Fig. 2) with an inter-clutch interval of 23.3 [+ or -] 2.27 d (range: 18-29 d). The thermal energy accumulated during this inter-clutch interval averaged 561 [+ or -] 38.3 DD (range: 514-655 DD). There was no relationship between inter-clutch interval and body size ([F.sub.1,6] = 0.91; P = 0.376; [R.sup.2] = 0.132) suggesting that the response to lab conditions was consistent for all females. Together, these data indicate that the minimum thermal energy requirements for vitellogenesis in this species are ~550 DD measured either as the earliest onset of reproduction (min = 588 DD) or inter-clutch interval (avg. = 561 DD).


Next, we attempted to verify that lizards in the field begin oviposition when the thermal energy accumulation exceeds the threshold determined in the lab (i.e., ~550 DD). Data pertaining to the timing of reproduction of Uta suggest that the breeding season can vary both among years and among locations (Tinkle, 1961, 1967; Turner et al., 1970; Nussbaum and Diller, 1976; Sinervo and Doughty, 1996), but that the reproductive cycle is temperature dependent (Tinkle and Irwin, 1965). To determine the onset of reproduction of this population in the wild, each spring beginning in early May one or more investigators walked daily transects in suitable habitat to collect lizards as part of a long-term mark-recapture study. For each individual we determined reproductive condition (by abdominal palpation) and body size (SVL), as well as marked each lizard for identification (via unique toe clip). Gravid females were retained in a field lab until oviposition (see Zani, 2008). Upon oviposition females were returned to their site of capture (determined using handheld GPS).

Between 2005 and 2010 we collected 441 clutches of eggs (Fig. 3; Table 1), which allowed us to determine the onset and length of the breeding season for each year. Our results indicate that the timing of reproduction varies from year to year (Fig. 3), but that the general pattern observed in our common-garden experiment (Fig. 2) is consistently observed: larger females begin to lay eggs earlier in the season and second clutches are laid after some interval. We interpret these data to indicate that reproduction is dependent, in part, on the thermal energy accumulated each spring. However, variation between years (due to local weather events) and within years (due to micro-environmental variation and genetic differences among individuals) may make generalization more difficult.

Using our model and local air temperatures, we estimated the degree-days required for the onset of reproduction in each year as follows. First, we extrapolated the relationship between observed lay date and size (Fig. 3) to predict the date of oviposition of the largest females (i.e., 52 mm) and determined the DD accumulated on that day, which we term "first-clutch extrapolations." This has the advantage of not requiring the earliest clutch be known, but assumes that the slope of the lay date-size relationship is known without error. Second, we used timing of the onset of second clutches, which for logistic reasons may be easier to estimate, to determine the onset of first clutches. Previous studies of Uta reported that the inter-clutch interval was about 21 d in Nevada (Turner et al., 1970) or 24 d in California (Sinervo and Doughty, 1996) (note, for our lab population it was 23 d). However, Turner et al. (1970) reported that this interval was shortest later in the breeding season, suggesting a thermal dependence of second clutches. Because temperatures in the northern Great Basin may not be as warm as southern study sites (especially at night), any thermal dependence of reproduction would lead to greater inter-clutch intervals. Based on 41 known second clutches from the field collected from 2005-2010 (i.e., both clutches were laid in the field lab), we were able to estimate the inter-clutch interval at Wrights Point as 32.7 [+ or -] 0.44 d (range: 23-37 d). Thus, we used 33 d to back-calculate from the onset of the second clutch for large (i.e., 52 mm) females to determine the onset of first clutches and determine the DD accumulated on this earlier date, which we term "inter-clutch interval extrapolations." As a further test of our model estimates, we calculated the degree-days that accumulated within 33 d of the onset of the breeding season as determined by inter-clutch extrapolations. If vitellogenesis is temperature-dependent this estimate should approximate our findings from the common-garden experiment (i.e., ~550 DD).

These two different estimates of the thermal energy accumulated at the onset of reproduction reveal substantial variation in the timing of reproduction in terms of both absolute time (JD) (see Fig. 3) and degree-days (Table 2). The average DD for reproduction from first-clutch extrapolations was 820 [+ or -] 53.9 DD (range: 608-953 DD) and for inter-clutch interval extrapolations 635 [+ or -] 57.1 DD (range: 502-876 DD). These two estimates are marginally correlated ([F.sub.1.4] = 7.31; P = 0.054; [R.sup.2] = 0.646), suggesting they are measuring the same aspects of variation in reproductive timing. However, we found that the DD required for the onset of reproduction under lab conditions (~550 DD) was generally less than the DD required in the field, but that the DD that accumulate in the 33 d between first and second clutches matches our expectation of 550 DD quite closely; during the six study years 565 [+ or -] 10.6 DD (range: 537-594 DD) accumulated during the inter-clutch interval. That is, while some of the accumulated thermal energy in nature is un-utilized by lizards for the onset of first clutches, the inter-clutch interval of lizards appears to be strongly temperature dependent. We acknowledge that these results could be interpreted as indicating that 550 DD is an inappropriate estimate of the thermal energy required for reproduction. However, based on our lab results and the thermal energy accumulated during the inter-clutch interval, we feel justified in using 550 DD as a metric of the earliest possible onset of reproduction even if environmental factors make that unlikely to be achieved in nature in some years. In other words, the date on which 550 DD accumulates still informs one as to the nature of the timing of the growing season, which is the hypothesis we set out to test.


To apply the model predictions to past weather data, we obtained values for the ENSO index since 1950 from NOAA's Climate Prediction Center ( products/analysis_monitoring/ensostuff/ensoyears.shtml). Since ENSO is primarily a winter phenomenon with effects that can persist for several months in the spring (Woodward et al., 2008), to create an index for each year we averaged the 3 mo running averages from Nov. through Mar. of the months proceeding the growing season. Although ENSO does not appear to have strong effects in the fall (Woodward et al., 2008), we also computed an ENSO index for the same months following a growing season for completeness.


We obtained values for the PDO index since 1939 from the Joint Institute for the Study of the Atmosphere and Oceans ( Since the PDO is a long-lived phenomenon and has the potential to impact both the beginning and end of a growing season (Mantua and Hare, 2002), we again calculated previous winter and following winter average PDO index by averaging the three-month running averages for Nov. through Mar.

We obtained daily values for high and low temperatures from Burns, OR from NOAA's National Climatic Data Center ( It is worth noting that meteorological stations typically collect data at heights of 2 m or more above the ground and that this may differ from the temperatures experienced by lizards at ground level. Thus, temperatures from such stations are likely cooler than field conditions potentially leading to an underestimation of the thermal energy available for lizards.

We used our degree-day model to calculate the onset of the growing season (defined as days since Jan. 1 [Julian date, JD] with >6 h potential activity), termination of the growing season (JD on which potential activity <6 h) and length of the growing season (termination-onset) as well as the date on which the breeding season began (JD on which cumulative DD > 550) for each of the past 70 y for which meteorological data were available. However, we were only able to test for the effects of climate modes in years since 1950. We tested to see if yearly variation as well as indexes for the prior or following winter's ENSO or PDO could separately predict variation in these traits. To determine which multiple-regression model best predicted each dependent variable we used Akaike Information Criterion (AIC) (Akaike, 1974). Because yearly variation has the potential to be related to (multicolinear with) climate oscillations (especially PDO), we tested for goodness of fit both including and excluding year as an independent variable. We report only the best model fit in both cases. Upon determining the best model fit, we applied a change-in-F-test (Pedhazur, 1982, p. 62) to determine which trait(s) accounted for significant components of variation in the model. All statistical analyses were conducted using JMP V 7.0.1 (SAS, 2007) for Macintosh computer.


We began by examining the characteristics of the growing season (start, end, length) and breeding season across the 70 y (through 2008) for which we had meteorological data from this site. The date on which the growing season began became progressively later with time ([F.sub.1,68] = 6.56; P = 0.013; [R.sup.2] = 0.088; fig. 4A). Conversely, the date on which the growing season ended became earlier with time ([F.sub.1,68] = 38.5; P < 0.001; [R.sup.2] = 0.361; fig. 4B). Similarly, the growing season became shorter more recently ([F.sub.1,68] = 28.6; P < 0.001; [R.sup.2] = 0.296; fig. 4C). Despite these changes, there was no relationship between start of the breeding season (date on which 550 DD accumulated) and time ([F.sub.1,68] = 0.203; P = 0.654; [R.sup.2] = 0.002; fig. 4D).

When predicting the start of the growing season (first day with >6 h potential activity), the model including year, prior ENSO, and following ENSO maximized goodness of fit (AIC = 329.8; [F.sub.3,53] = 3.27; P = 0.028; [R.sup.2] = 0.156; table 3). However, a change-in-F-test indicated that year accounted for most of the variation in this model ([F.sub.1,52] = 9.29; P = 0.004). Because year is not necessarily indicative of climate variation, we recalculated AIC excluding year. In this case the model including prior ENSO, following ENSO and previous PDO provided the best fit, though the model excluding year was not able to predict significant variation in the start of the growing season (AIC = 334.2; [F.sub.3,53] = 1.71; P = 0.177; [R.sup.2] = 0.088; table 3). Of the included variables, the previous PDO explained a significant and positive component of the variance ([F.sub.1,52] = 4.77; P = 0.034); larger positive values of PDO are related to later emergence times.

When predicting the end of the growing season (last day with >6 h potential activity), the model including year, prior ENSO, following ENSO, prior PDO, and following PDO maximized goodness of fit (AIC = 293.25; [F.sub.5,51] = 8.79; P < 0.001; [R.sup.2] = 0.463; table 3). Both year ([F.sub.1,50] = 16.9; P < 0.001) and prior PDO accounted for significant increments in the variance ([F.sub.1,50] = 4.07; P = 0.049). Recalculating AIC without year, prior ENSO, following ENSO, prior PDO and following PDO maximized fit (AIC = 307.6; [F.sub.4,52] = 5.08; P = 0.002; [R.sup.2] = 0.281; Table 3) with previous PDO explaining a significant and negative component of the variance ([F.sub.1,52] = 5.71; P = 0.021; Fig. 5A); larger positive values of PDO are related to earlier retreat times.


When predicting the length of the growing season, the model including year, prior ENSO, and following ENSO maximized goodness of fit (AIC = 364.9; [F.sub.3,53] = 10.2; P < 0.001; [R.sup.2] = 0.366; Table 3), with year accounting for a significant increment in the variance ([F.sub.1,52] = 27.8; P < 0.001). Omitting year revealed that the model including prior ENSO, following ENSO, prior PDO and following PDO maximized fit (AIC = 381.2; [F.sub.4,52] = 2.87; P = 0.032; [R.sup.2] = 0.181; Table 3) with previous PDO explaining a significant and negative component of the variance ([F.sub.1,51] = 4.70; P = 0.035; Fig. 5B) ; larger positive values of PDO are related to shorter growing seasons.

When predicting the onset of the breeding season, we only determined the goodness of fit for models excluding year since there was no relationship between year and breeding-season timing (Fig. 4D). The model including prior ENSO and following ENSO maximized goodness of fit (AIC = 242.8; [F.sub.2,54] = 3.17; P = 0.050; [R.sup.2] = 0.104; Table 3). In this case, the previous ENSO accounted for a significant and negative component of the variance ([F.sub.1,53] = 4.67; P = 0.035; Fig. 5C) ; larger positive values of ENSO are related to earlier reproductive timing.


Natural variation among years appears to be the most important determinant of growing-season characteristics (onset, termination and length). That is, there are strong linear trends in growing season at this site in the Pacific Northwest. However, contrary to our expectations, the growing season at this location in eastern Oregon appears to be starting later, ending earlier and lasting for a shorter time in recent decades (Fig. 4A-C). While several studies have reported that the temperatures in the Pacific Northwest have been warming in recent decades (e.g., Knappenberger et al., 2001; Easterling, 2002; Vose et al., 2005), other studies have indicated that these changes appear to be region-specific or site-specific in their magnitude and direction (e.g., Easterling et al., 1997; Cayan et al., 2001; Mote, 2003). For example, LaDochy et al. (2007) reported that while temperatures in most of California were warming in recent decades, those in the NE interior basins (adjacent to southeastern Oregon) have been cooling. Thus, it is conceivable that the characteristics of the growing season in the interior basins of the Pacific Northwest are changing independent of the average annual temperature and that focus on the average annual temperature may overlook such changes. Regardless, our results indicate that variation among years obscures the influence of long-term climate fluctuations.

When year is omitted from the model predicting growing-season traits, the PDO (particularly the winter prior the growing season) accounts for significant components of the variation in the onset, termination and length of growing season. However, the impacts of PDO on the growing season appear to be strongest in relation to either its termination (28.1% of variation explained) or to its length (18.1% of variation explained), and the model explaining variation in the start of the growing season was not significant when year was excluded. Positive values of the PDO index correlate with earlier termination of the growing season as well as shorter growing-season length (Fig. 5). Since the PDO was in positive mode from 1977 to (at least) the late 1990s (Mantua and Hare, 2002), we conclude that the shortened growing seasons observed in more recent years were at least partly related to this climate mode. If the PDO has shifted recently to a negative (cool) phase, our results indicate that the growing-season length should increase in coming years. Such a shift to a cool PDO phase and the associated lengthening of the growing season could have a major impact on the life history of ectotherms in the interior basins of the Pacific Northwest. For species that are currently limited by the length of the growing season, including northern populations of side-blotched lizards (see Zani, 2005, 2008), a longer growing season would allow for more growth and potentially greater overwinter survival (but see Irwin and Lee, 2003; Zani, 2008). These positive effects on growth and survival could alter population dynamics even in the absence of an effect on reproductive timing.

Unlike traits related to the growing season, the onset of the breeding season as determined by our biophysical model appears to be related to the ENSO index in the previous winter, but not to yearly variation or long-term fluctuations (i.e., PDO). Positive phases of the previous winter's ENSO (El Nino events) correlate with earlier onset of the breeding season (e.g., Fig. 5C). While we tested for the effects of ENSO the following winter, we detected no significant relationship. However, considering that ENSO events typically develop late in Dec. and have effects that may persist for months and that previous research found that warm ENSO events lead to warmer springs (but not falls) in the Pacific Northwest (Woodward et al., 2008), our results are consistent with predictions. Thus, while the PDO appears to affect the length of the growing season (Fig. 5B), particularly via its termination (Fig. 5A), the ENSO appears to affect the shape of the growing season (rate at which warming occurs). The fact that two distinct climate fluctuations affect somewhat independently the length of the growing season and the timing of the breeding season has implications for ectotherm life history. At this elevation and latitude, the breeding season typically commences in mid May and typically ends in early Jul. Following an incubation of -45 d, hatchlings begin to emerge into the population in early Jul. and continuing through Aug. (see Zani, 2008). Thus, juveniles have no more than ~120 d and as few as ~70 d to grow and store energy for the impending 5 mo winter. Yet overwinter survival is size related, with larger juveniles surviving longer than smaller lizards in simulated winter conditions (Zani, 2008). Several studies have indicated recently that climate oscillations, including PDO and ENSO, can act additively to impact temperature and precipitation patterns (Schoennagel et al., 2007; Woodward et al., 2008), which may affect populations in the form of extreme weather conditions (e.g., droughts). Given potential additive effects, it is possible that a combined positive PDO (short growing season) and negative ENSO (late breeding season) could prevent young-of-the-year from growing sufficiently to reach their threshold size to survive the winter. Thus, for species with high annual turnover, such as side-blotched lizards (Tinkle, 1967; Sinervo and Doughty, 1996; Zani, 2005), this combination of climate modes could reduce recruitment to the point of population collapse. While we know of no such events as we describe, a climate-driven scenario related to cloud-forest precipitation in Central America has been proposed for the extinction of the golden toad, Bufo periglenes (Pounds et al., 1995; Pounds, 2001).

Considering that observed changes in temperature or precipitation tend to be specific to regions or sites (Easterling et al., 1997; Mote, 2003; LaDochy et al., 2007), it should come as no surprise that the impacts on aquatic and terrestrial ecosystems are also location-specific (Francis and Hare, 1994; Stewart et al., 2005; Woodward et al., 2008). For example, regional differences in the effects of PDO appear to result in inverse production regimes of Pacific salmon between Alaska and the west coast (Hare et al., 1999). However, it remains to be determined whether the impacts of climate modes such as ENSO and PDO on terrestrial ecosystems are wide spread and consistent among populations. If the impacts of PDO primarily affect northwestern North America, then the strength of those impacts should dissipate across the distribution of a wide-ranging species such as Uta, particularly for populations in the desert Southwest. That is, the importance of climate modes to the life history of ectotherms may be population specific, making generalizations to even single species exceedingly difficult, as pointed out by Travis and Futuyma (1993). Thus, we tend to agree with Parmesan (2006) that indices of climate mode, such as the ENSO, may make projecting future impacts problematic due to their chaotic nature.

In summary, we created a biophysical model based on the degree-days concept to predict relevant life-history events for ectotherms. The model appears able to calculate the timing and length of the growing season as well as the timing of the breeding season for side-blotched lizards, Uta stansburiana, and should be applicable to other ecotherms for which information on activity temperatures, thermoregulatory set-points, and reproduction are available. Using this model, we tested for the effects of short- and long-term fluctuations in climate mode on lizards using indices for the El Nino-Southern Oscillation and the Pacific Decadal Oscillation. Our results indicate that these two oscillations affect different aspects of the life history of this species. The effects of climate modes, either alone or in combination, have the potential to alter not only the timing of relevant life-history events, but also impact population recruitment and even population persistence. Yet, using these climate modes to predict biological outcomes of future climate change may be difficult to accomplish due to their inherent complexity.

Acknowledgments.--We thank: the lab group of W. E. Bradshaw and C. M. Holzapfel at the University of Oregon for useful comments regarding this paper; R. Root for discussion related to our model; S. Schwartz for help testing the model; K. Flanery and D. Ganskopp for logistic support in the field; T. Jones, R, Neuhaus, J. Milgrom, E. Finan, W. Caffry, S. Schwartz, J. Tillman, K. Scoular and J. Thompson for help collecting field data on lizards.




ADOLPH, S. C. AND W. P. PORTER. 1993. Temperature, activity, and lizard life histories. Am. Nat., 142:273-295.

AKAJKE, H. 1974. A new look at the statistical model identification. IEEE Trans. Auto. Cont., 19:716-723.

AVERY, R. A. 1982. Field studies of body temperatures and thermoregulation, p. 93-166. In: C. Gans and F. H. Pough (eds.). Biology of the Reptilia. Academic Press, New York.

BARKEN, G. S. 1992. Measurement and application of operative and standard operative temperatures in ecology. Am. Zool., 32:194-216.

BEAMISH, R.J. AND D. R. BOUILLON. 1993. Pacific salmon production trends in relation to climate Can. J. Fish. and Aquat. Sci., 50:1002-1016.

BISHOP, D. C. AND A. C. ECHTERNACHT. 2004. Emergence behavior and movements of winter-aggregated green anoles (Anolis carolinensis) and the thermal characteristics of their crevices in Tennessee. Herpetologica, 60:168-177.

BRANDER, K. M. 2007. Global fish production and climate change. Proc. Nat. Acad. Sci., USA, 104:19709-19714.

BRATTSTROM, B. H. 1965. Body temperature of reptiles. Am. Midl. Nat., 73:376-422.

BUCKLEY, L. B. 2008. Linking traits to energetics and population dynamics to predict lizard ranges in changing environments. Am. Nat., 171:E1-E19.

CAVAN, D. R., S. A. KAMMERDIENER, M. D. DETTINGER, J. M. CAPRIO AND D. H. PETERSON. 2001. Changes in the onset of spring in the western United States. Bull. Am. Meteor. Soc., 82:399.415.

COWLES, R. B. 1941. Observations on the winter activities of desert reptiles. Ecology, 22:125-140.

-- AND C. M. BOGERT. 1944. A preliminary study of the thermal requirements of desert reptiles. Bull. Am. Mus. Nat. Hist., 83:265-296.

CRENSHAW, J. W., JR. 1955. The life history of the southern spiny lizard, Sceloporus undulatus undulatus Latreille. Am. Midl. Nat., 54:257-298.

CROZIER, L. G., R. W. ZABEL AND A. F. HAMLET. 2008. Predicting differential effects of climate change at the population level with life-cycle models of spring Chinook salmon. Glob. Change Biol., 14:236-249.

DEGAETANO, A. T. 1996. Recent trends in maximum and minimum temperature threshold exceedences in the northeastern United States. J. Clim., 9:1646-1660.

-- AND R. J. ALLEN. 2002. Trends in twentieth-century temperature extremes across the United States. J. Clim., 15:3188-3205.

DUNHAM, A. E. AND K. L. OWRALL. 1994. Population responses to environmental change: life history variation, individual-based models, and the population dynamics of short-lived organisms. Am. Zool., 34:382-396.

EASTERLING, D. R. 2002. Recent changes in frost days and the frost-free season in the United States. Bull. Am. Meteor. Soc., 83:1327-1332.

--, J. L. EVANS, P. Y. GROISMAN, T. R. KARL, K. E. KUNKEL AND P. AMBENJE. 2000a. Observed variability and trend in extreme climate events: a brief review. Bull. Am. Meteor. Soc., 81:417-425.

-- B. HORTON, P. D. JONES, T. C. PETERSON, T. R. KARL, D. E. PARKER, M. J. SALINGER, V. RAZUVAYEV, N. PLUMMER, P. JAMASON AND C. K. FOLLAND. 1997. Maximum and minimum temperature trends for the globe. Science, 277:364-367.

-- G. A. MEEHL, C. PARMESAN, S. A. CHAGNON, T. R. KARL AND L. O. MEARNS. 2000b. Climate extremes: observations, modeling, and impacts. Science, 289:2068-2074.

EVANs, K. J. 1967. Observations on the daily emergence of Coleonyx variegatus and Uta stansburiana. Herpetologiea, 23:217-222.

FRANCIS, R. C. AND S. R. HARE. 1994. Decadal-scale regime shifts in the large marine ecosystems of the Northeast Pacific: a case for historical science. Fish. Oceanogr., 3:279-291.

--, A. B. HOLLOWED AND W. S. WOOSTER. 1998. Effects of interdecadal climate variability on the oceanic ecosystems of the NE Pacific. Fish. Oceanogr., 7:1-21.

GLYNN, P. W. 1988. El Nino-Southern Oscillation 1982-1983: nearshore population, community, and ecosystem responses. Ann. Rev. Ecol. and Syst., 19:309-345.

GROISMAN, P. Y., T. R. KARL, D. R. EASTERLING, R. W. KNIGHT, P. F.JAMASON, K.J. HENNESSY, R. SUPPIAH, C. M. PAGE, J. WIBIG, K. FORTUNIAK, V. N. RAZUVAE, A. DOUGLAS, E. FORLAND AND P.-M. ZHAI. 1999. Changes in the probability of heavy precipitation: important indicators of climatic change. Clim. Change, 42:243-283.

HALLETT, T. B., T. COULSON, J. G. PILKINGTON, H. CLUTTON-BROCK, J. M. PEMBERTON AND B. T. GRENFELL. 2004. Why large-scale climate indices seem to predict ecological processes better than local weather. Nature, 430:71-75.

HARE, S. R., N.J. MANTUA AND R. C. FRANCIS. 1999. Inverse production regimes: Alaska and West Coast Pacific salmon. Fisheries, 24:6-14.

HUEY, R. B. 1982. Temperature, physiology, and the ecology of reptiles, p. 25-91. In: C. Gans and F. H. Pough (eds.). Biology of the Reptilia. Academic Press, New York.

IPCC. 2007. Climate change 2007: the physical science basis. Contribution of working group I to the fourth assessment report of the IPCC, S. Solomon and D. Qin (eds.), Cambridge University Press, Cambridge.

IRWIN, J. T. AND R. E. LEE, JR. 2003. Cold winter microenvironments conserve energy and improve overwintering survival and potential fecundity of the goldenrod gall fly, Eurosta solidaginis. Oikos, 100:71-78.

JACOB, J. S. AND C. W. PAINTER. 1980. Overwinter thermal ecology of Crotalus viridis in the north-central plains of New Mexico. Copeia, 1980:799-805.

JONSSON, N. AND B. JONSSON. 2004. Size and age of maturity of Atlantic salmon correlate with the North Atlantic Oscillation Index (NAOI). J. Fish Biol., 64:241-247.

KAYANO, M. T., V. B. RAO AND R. V. ANDPOLI. 2005. A review of short-term climate variability mechanisms. Adv. Space Res., 35:843-851.

KNAPPENBERGER, P. C., P. J. MIEHAELS AND R. E. DAVIS. 2001. Nature of observed temperature changes across the United States during the 20th century. Clim. Res., 17:45-53.

LADOCHY, S., R. MEDINA AND W. PATZERT. 2007. Recent California climate variability: spatial and temporal patterns in temperature trends. Clim. Res., 33:159-169.

LEHODEY, P., J. ALHEIT, M. BARANGE, T. BAUMGARTNER, G. BEAUGRAND, K. DRINKWATER, J.-M. FROMENTIN, S. R. HARE, G. OTTERSEN, R. I. PERRY, C. ROY, C. D. VAN DER LINGEN AND F. WERNER. 2006. Climate variability, fish, fisheries. J. Clim., 19:5009-5030.

LI, J., D. W. HILBERT, T. PARER AND S. WILLIAMS. 2009. How do species respond to climate change along an elevation gradient? A case study of the grey-headed robin (Heteromyias albispecularis). Glob. Change Biol., 15:255-267.

LUTTERSCHMIDT, D. I., M. P. LEMASTER AND R. T. MASON. 2006. Minimal overwintering temperature of red-sided garter snakes (Thamn0phis sirtalis parietalis): a possible cue for emergence? Can. J. Zool., 84:771-777.

MANTUA, N. J. AND S. R. HARE. 2002. The Pacific Decadal Oscillation. J. Oceanogr., 58:35-44.

MEEHL, G. A., T. KARL, D. R. EASTERLINC., S. CHANGNON, R. PIELKE, JR., D. CHAGNON, J. EVANS, P. Y. GROISMAN, T. R. KNUTSON, K. E. KUNKEL, L. O. MEARNS, C. PARMESAN, R. PULWARTY, T. ROOT, R. T. SYLVES, P. WHETTON AND F. ZWIERS. 2000. An introduction to trends in extreme weather and climate events: observations, socioeconomic impacts, terrestrial ecological impacts, and model projections. Bull. Am. Meteor. Soc., 81:413-416.

MILLER, J. A. AND G. B. GOODRICH. 2007. Regionalization and trends in winter precipitation in the northwestern USA. Clim. Res., 33:215-227.

MOBERLY, W. R. 1963. Hibernation in the desert iguana, Dipsosaurus dorsalis. Physiol. Zool., 36:152-160.

MOTE, P. W. 2003. Trends in temperature and precipitation in the Pacific Northwest during the twentieth century. Northwest Sci., 77:271-282.

NIEWIAROWSKI, P. H. 2001. Energy budgets, growth rates, and thermal constraints: toward an integrative approach to the study of life-history variation. Am. Nat., 157:421-433.

NUSSBAUM, R. A. AND L. V. DILLER. 1976. The life history of the side-blotched lizard, Uta stansburiana Baird and Girard, in north-central Oregon. Northwest Sci., 50:243-260.

PARMESON, C. 2006. Ecological and evolutionary responses to recent climate change. Ann. Rev. Ecol. Evol, and Syst., 37:637-669.

PEDHAZUR, E. J. 1982. Multiple regression in behavioral research 2nd ed. Harcourt Brace Jovanovich, Inc., Fort Worth, Texas.

PORTER, W. P., J. W. MITCHELL, W. A. BECKMAN AND C. B. DEWITT. 1973. Behavioral implications of mechanistic ecology: thermal and behavioral modeling of desert ectotherms and their microenvironment. Oecologia, 13:1-54.

-- AND C. R. TRACY. 1983. Biophysical analyses of energetics, time-space utilization, and distributional limits, p. 55-82. In: R. B. Huey, E. R. Pianka and T. W. Schoener (eds.). Lizard ecology: studies of a model organism. Harvard University Press, Cambridge, Massachusetts.

POST, E. AND N. C. STENSETH. 1999. Climatic variability, plant phenology, and northern ungulates. Ecology, 80:1322-1339.

POUNDS, J. A. 2001. Climate and amphibian declines. Nature, 410:639-640.

--, M. P. L. FOGDEN AND J. H. CAMPBELL. 1995. Biological response to climate change on a tropical mountain. Nature, 398:611-615.

RICHARD, W. H. 1967. Onset of winter dormancy in lizards and beetles. Northwest Sci., 41:92-95.

ROFF, D. A. 2002. Life history evolution. Sinauer Associates, Sunderland, Massachusetts.

SAS. 2007. JMP V 7.0.1. SAS Institute Inc., Pacific Grove, California.

SCHOENNAGEL, T., T. T. VEBLEN, D. KULAKOWSKI AND A. HOLZ. 2007. Multidecadal climate variability and climate interactions affect subalpine fire occurrence, western Colorado (USA). Ecology, 88:2891-2902.

SCHWARTZ, M. D., R. AHAS AND A. AASA. 2006. Onset of spring starting earlier across the Northern Hemisphere. Glob. Change Biol., 12:343-351.

SHINE, R. 2005. Life-history evolution in reptiles. Ann. Rev. Ecol., Evol., and Syst., 36:23-46.

SINERVO, B. AND P. DOUGHTY. 1996. Interactive effects of offspring size and timing of reproduction on offspring reproduction: experimental maternal, and quantitative genetic aspects. Evolution, 50:1314-1327.

SOULE, M. 1963. Aspects of thermoregulation in nine species of lizards from Baja California. Copeia, 1963:107-115.

SPARKS, T. H. AND A. MENZEL. 2002. Observed changes in seasons: an overview. Internat. J. Climatol., 22:1715-1725.

STENSETH, N. C., A. MYSTERUD, G. OTTERSEN, J. W. HURRELL, K.-S. CHAN AND M. LIMA. 2002. Ecological effects of climate fluctuations. Science, 297:1292-1296.

STEWART I. T., D. R. CAYAN AND M. D. DETTINGER. 2005. Changes toward earlier streamflow timing across western North America. J. Clim., 18:1136-1155.

TINKLE, D. W. 1961. Population structure and reproduction in the lizard Uta stansburiana stejnegeri. Am. Midl. Nat., 61:206-234.

-- . 1967. The life and demography of the side-blotched lizard, Uta stansburiana. Misc. Publ. Mus. Zool., Univ. Mich., 132:1-182.

-- AND L. N. IRWIN. 1965. Lizard reproduction: refractory period and response to warmth in Uta stansburiana females. Science, 148:1613-1614.

TRACY, C. R. 1982. Biophysical modeling in reptilian physiology and ecology, p. 275-321. In: C. Gans and F. H. Pough (eds.). Biology of the Reptilia. Academic Press, New York.

TRAVIS, J. AND D.J. FUTUYMA. 1993. Global change: lessons from and for evolutionary biology, p. 234-250. In: P. M. Kareiva, J. G. Kingsolver and R. B. Huey (eds.). Biotic interactions and global change. Sinauer Associates, Sunderland, Massachusetts.

TURNER, F. B., G. A. HODDENBACH, P. A. MEDICA AND J. R. LANNOM. 1970. The demography of the lizard, Uta stansburiana Baird and Girard, in southern Nevada. J. Anim. Ecol., 39:505-519.

VETAS, B. 1951. Temperatures of entrance and emergence. Herpetologica, 7:15-20.

VOSE, R. S., D. R. EASTERLING AND B. GLEASON. 2005. Maximum and minimum temperature trends for the globe: an update through 2004. Geophys. Res. Lett., 32:L23822.

WANG, G. AND D. SCHIMEL. 2003. Climate change, climate modes, and climate impacts. Ann. Rev. Environ. and Res., 28:1-28.

WOODWARD, F. I., M. R. LOMAS AND T. QUAIFE. 2008. Global responses of terrestrial productivity to contemporary climatic oscillations. Phil, Trans. R. Soc. B: Biol. Sci., 363:2779-2785.

ZANI, P. A. 2005. Life-history strategies near the limits of persistence: winter survivorship and spring reproduction in the common side-blotched lizard (Uta stansburiana) in eastern Oregon. J. Herpetol., 39:166-169.

--. 2008. Climate change trade-offs in the side-blotched lizard (Uta stansburiana): effects of growing-season length and mild temperatures on winter survival. Physiol. Biochem. Zool., 81:797-809.


Department of Biology, Gonzaga University, Spokane, Washington 99258



Department of Biology, Lafayette College, Easton, Pennsylvania 18042

(1) Corresponding author present address: Department of Biology, Whitman College, Walla Walla, Washington 99362; e-mail:
TABLE 1.--Reproductive data for Uta stansburiana from eastern
Oregon for 6 y

Year    number    n          Dam SVL (mm)            Dam mass (g)

2005     1st      42    47.5 [+ or -] 2.28 (3)    2.89 [+ or -] 0.387
         2nd      25    49.8 [+ or -] 2.40        3.04 [+ or -] 0.399

2006     1st      43    47.5 [+ or -] 2.66        2.82 [+ or -] 0.484
         2nd      19    49.9 [+ or -] 1.91        2.81 [+ or -] 0.367

2007     1st      33    45.5 [+ or -] 2.06        2.47 [+ or -] 0.315
         2nd      36    48.3 [+ or -] 2.22        2.88 [+ or -] 0.492

2008     1st      53    47.3 [+ or -] 2.05        2.73 [+ or -] 0.450
         2nd      23    49.8 [+ or -] 1.73        2.69 [+ or -] 0.335

2009     1st      47    46.9 [+ or -] 2.52        2.61 [+ or -] 0.549
         2nd      28    50.0 [+ or -] 1.96        3.09 [+ or -] 0.415

2010     1st      59    47.4 [+ or -] 2.70        2.70 [+ or -] 0.526
         2nd      47    48.8 [+ or -] 1.62        2.83 [+ or -] 0.391

        Clutch                                      Relative clutch
Year    number    n          Clutch size               mass (1)

2005     1st      42     4.48 [+ or -] 1.087      0.42 [+ or -] 0.122
         2nd      25     4.52 [+ or -] 1.418      0.38 [+ or -] 0.124

2006     1st      43     4.37 [+ or -] 0.926      0.43 [+ or -] 0.086
         2nd      19     4.95 [+ or -] 0.911      0.50 [+ or -] 0.105

2007     1st      33     3.73 [+ or -] 0.839      0.42 [+ or -] 0.095
         2nd      36     4.33 [+ or -] 0.828      0.43 [+ or -] 0.073

2008     1st      53     4.45 [+ or -] 0.932      0.44 [+ or -] 0.100
         2nd      23     5.00 [+ or -] 0.535      0.46 [+ or -] 0.097

2009     1st      47     3.95 [+ or -] 0.721      0.47 [+ or -] 0.113
         2nd      28     4.50 [+ or -] 1.503      0.46 [+ or -] 0.164

2010     1st      59     4.34 [+ or -] 1.042      0.44 [+ or -] 0.130
         2nd      47     4.64 [+ or -] 0.941      0.48 [+ or -] 0.091

        Clutch               Interclutch
Year    number    n        interval (d) (2)

2005     1st      42      33.4 [+ or -] 6.11
         2nd      25

2006     1st      43      33.0 [+ or -] 2.65
         2nd      19

2007     1st      33      31.3 [+ or -] 2.08
         2nd      36

2008     1st      53      34.5 [+ or -] 4.54
         2nd      23

2009     1st      47      33.3 [+ or -] 1.53
         2nd      28

2010     1st      59      31.0 [+ or -] 4.32
         2nd      47

(1) Clutch Mass Divided by Dam Mass

(2) Animals with Known First and Second Clutches

(3) Mean [+ or -] 1 Standard Deviation

TABLE 2.--Estimates (as days since 1 Jan. [JD] or degree-days
accumulated on that date [DD]) for onset of breeding season
for Uta stansburiana among years

                    Lay date vs. size trend     Inter-clutch interval
Year      Time         extrapolation (1)          extrapolation (2)

2005     JD                   151                        137
         DD (3)               953                        706
2006     JD                   146                        136
         DD                   731                        550
2007     JD                   142                        122
         DD                   830                        536
2008     JD                   145                        138
         DD                   608                        502
2009     JD                   152                        142
         DD                   854                        640
2010     JD                   163                        159
         DD                   944                        876

(1) Estimate is to 52 mm using slope in Fig. 3

(2) Estimate is 33 d prior to initiation of second clutches

(3) Estimates are cumulative for that date

TABLE 3.--Akaike Information Criterion (AIC) goodness-of-fit values
for multiple regression models predicting growing and breeding
season of Uta stansbufiana based on year, prior winter's El
Nino-Southern Oscillation index (pENSO), following winter's ENSO
index (fENSO), prior winter's Pacific Decadal Oscillation index
(pPDO) and following winter's PDO index (fPDO)

                                  First day >1ODD     Last day >10DD
                                     (start of           (end of
AIC Model                         growing season)    growing season)

Year                                 404.5               363.9
Year, pENSO                          331.2               298.0
Year, pENSO, fENSO                   329.8 *             294.0
Year, pPDO, fPDO                     330.6               341.8
Year, pENSO, fENSO, pPDO             330.5               297.9
Year, pENSO, fENSO, fPDO             330.2               295.3
Year, pENSO, fENSO, pPDO, fPDO       330.2               293.3 *
pENSO                                340.1               323.0
fENSO                                341.8               327.3
pENSO, fENSO,                        337.0               319.4
pENSO, fENSO, pPDO                   334.2 **            307.7
pENSO, fENSO, fPDO                   338.9               308.0
pENSO, fENSO, pPDO, fPDO             336.0               307.6 **

                                    Days >1ODD         Day sum DD =
                                     (growing         550 (start of
AIC Model                         season length)     breeding season)

Year                                 446.9                 N/A
Year, pENSO                          368.6                 N/A
Year, pENSO, fENSO                   364.9 *               N/A
Year, pPDO, fPDO                     423.6                 N/A
Year, pENSO, fENSO, pPDO             366.8                 N/A
Year, pENSO, fENSO, fPDO             365.1                 N/A
Year, pENSO, fENSO, pPDO, fPDO       367.0                 N/A
pENSO                                392.2               246.6
fENSO                                393.0               248.6
pENSO, fENSO,                        386.9               242.8 **
pENSO, fENSO, pPDO                   384.1               243.0
pENSO, fENSO, fPDO                   384.3               243.3
pENSO, fENSO, pPDO, fPDO             381.2 **            243.7

* Minimum value for model including year

** Minimum value for model excluding year
COPYRIGHT 2011 University of Notre Dame, Department of Biological Sciences
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2011 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Author:Zani, Peter A.; Rollyson, Mary E.
Publication:The American Midland Naturalist
Article Type:Report
Geographic Code:1USA
Date:Apr 1, 2011
Previous Article:Distribution of Lake sturgeon in New York: 11 years of restoration management.
Next Article:Sex-biased predation on newts of the genus Taricha by a novel predator and its relationship with tetrodotoxin toxicity.

Terms of use | Privacy policy | Copyright © 2021 Farlex, Inc. | Feedback | For webmasters