Influences of Temperature and Precipitation on Wood Frog (Rana sylvatica) Breeding Phenology with Predictions of Climate Change Impacts

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1 Influences of Temperature and Precipitation on Wood Frog (Rana sylvatica) Breeding Phenology with Predictions of Climate Change Impacts Caitlin Leach, Environmental Science Jennifer Olker, Natural Resources Research Institute, University of Minnesota Duluth ABSTRACT Climate change has been shown to alter seasonal timing in animals and plants. Amphibian breeding phenology may be especially sensitive to predicted climate changes due to their dependence on temperature and aquatic habitat. In this study, we investigated the influences of temperature and precipitation on the breeding phenology of a North American amphibian species, the wood frog (Rana sylvatica), to identify correlations with annual weather conditions and make predictions about potential effects of climate change. We analyzed calling survey data from vernal pools in the upper Midwest U.S. to see whether there was a relationship between weather conditions and breeding times for the wood frog. Specifically, date of maximum calling, date of first calling, and spawning date were compared to precipitation and temperature data in the four months immediately preceding wood frog breeding. Calling and spawning dates were strongly correlated with average temperature in March. Using this linear relationship in combination with observed trends in March temperatures over the last 60 years, we assessed the potential effects of climate change on the wood frog breeding phenology. We predicted that spawning in the next 40 years will be 6 days earlier than present, and 10 days earlier than predicted for Introduction There is increasing evidence of climate change that has the potential to negatively affect ecosystems around the world (Walther et al., 2010). Climate change in general has been a natural ongoing process; however changes over the last century have been greater than the previous 100 years before it. Specifically in the Great Lakes region the temperature has increased 0.6 degrees Celsius over the last century (Magnuson et al., 1997; Walther et al., 2002). In the Midwest U.S., including the Great Lakes watershed, temperature is predicted to increase an average of 2 degrees Celsius over the next 100 years (IPCC, 2007). Precipitation is predicted to increase during the next 100 years, with the majority of the increase occurring during winter months (Magnuson et al., 1997). Annual precipitation averages are predicted to not vary dramatically, while high and low precipitation events will become greater (IPCC, 2007). With the projected increase in evaporation and temperature, the most dramatic effect of climate change will be the total evaporation of small lakes as well as temporary and seasonal wetlands (Manguson et al., 1997). The effects of climate change on seasonal timing in animals and plants have been well documented (Walther et al., 2010). As many animals rely on seasonal cues for timing of spring activities (e.g. breeding, migration), changes in temperature and precipitation play an important role in wildlife life history (Walther et al., 2002; Inouye, Armitage & Inouye, 2000). In a study in the high altitude of the Rocky Mountains, marmots were found to start migrating significantly earlier from 1976 to 1999, with a strong correlation with the increase of temperature over that time period (Inouye et al. 2000). As cited in the review paper by Organisms may respond to other environmental factors, such as photoperiod, as seasonal cues for breeding or migration, in addition to climate (Walther et al. 2010), It is often unknown if a species responds solely to photoperiod, temperature, or another natural cue, or to a combination of cues (Eilersten, Sandberg & Tollefsen, 1995). The repercussions of an altered phenology, including earlier migration or emergence from hibernation, could greatly affect the survival and success of an individual or population. For example, there might not be as much food available or there may be novel or increased competition with other species (Walther et al., 2002; Inouye et al., 2000). The effects of 1

2 climate change on amphibians may be easily detected because these organisms are very sensitive to external forces (Raffel, Rohr, Kiesecker & Hudson, 2006; Tryjanowski, Rybacki & Sparks, 2003). Trends in altered amphibian breeding phenology have been documented in many areas of the world, with most long term studies coming from Europe (BeeBee, 1995; Reading, 1998). BeeBee (1995) examined three native anuran species over a 17-year period and found that earlier breeding and arrival time at breeding ponds was strongly correlated with increased temperature over that time period, with increased winter and spring average temperatures as predictive variables. In another long term study, Reading (1998) found that 40-day running average temperature of the days preceding spawning were highly correlated with spawning date. Most studies on amphibian breeding phenology and climate change have found trends towards earlier arrival times and spawning dates with increasing average temperatures (Carroll et al., 2008; Todd et al., 2010; Kusano & Inoue, 2008). In this study, we investigated the influences of temperature and precipitation on the breeding phenology of a North American species, the wood frog (Rana sylvatica) to identify correlations with annual weather conditions and make predictions about potential effects of predicted climate change. Methods Amphibian surveys Amphibian surveys are conducted in Hartley Park in Duluth, MN every year from (citation? Or personal communication). Hartley Park is 660 acre park located within the City of Duluth, MN in an area surrounded by residential development. Within this forested park, there are many vernal pools and ponds (Figure 1). Located within the park, the Hartley Nature Center is a non-profit environmental education corporation that is completely funded by memberships and donations. Hartley Nature Center provides many educational services for the public, including school field trips, day camps, homeschooling events and a variety of adult programs (Hartley Nature Center, 2011). Figure 1. Map of study area showing the location of all ponds with frog and toad calling surveys in Hartley Park (the four ponds with complete records are circled) and the location of Hartley Park in Duluth, Minnesota. Nighttime calling surveys were completed by both Hartley Nature Center staff and volunteers with standardized methods according to the North American Amphibian Monitoring Protocol (NAAMP). Volunteers trained by nature center staff conducted three surveys per year. In the years of 2008, 2009 and 2

3 2010, additional data was collected by staff and volunteers outside of the standardized monitoring periods and methods. Such data included observations of amphibians and calling heard during causal visits to the wetlands and presence of egg masses. Survey data for all years was combined into a common database and summarized for the wood frog. The wood frog was used because it may be especially sensitive to climate change as it is an explosive early spring breeder that breeds primarily in temporary pools (Gibbs et al., 2001). Data used for analyses were: 1) the earliest calling dates (first date any wood frog heard for each year and/or pond), 2) dates of maximum calling (first date wood frogs heard at calling index of 2 or 3, based on NAAMP methods), and 3) spawning dates (when available). Spawning was observed in Hartley Park in 2008, 2009, 2010 and 2011 with additional spawning data collected outside the park at vernal pools 2.5 km from Hartley Nature Center (2005, 2006, 2009, 2010, 2011). Dates were converted to Julian dates for statistical analyses. Weather Data Daily weather data, including surface temperatures and precipitation, was compiled from the NOAA Online Climate Data Directory ( assessed on April 12, 2011). Weather data from the Duluth International Airport station from was summarized and used for all analyses. These data were considered representative of conditions at Hartley Park based on the strong correlations and statistically signification linear relationship with data collected at Hartley Nature Center from 2008 to 2011 (linear regression, slope ~ 1 and intercept ~1, p<0.001, R 2 = 0.98, 0.95, 0.80 for daily maximum temperature, minimum temperature and average monthly precipitation, respectively). Temperature (degrees F) and precipitation (inches per day) data was averaged monthly for January through April as well as for the entire period from January through April. Total monthly precipitation and total precipitation from January through April was also used. Where did the 60 year weather record data come from? Where did the climate projections into the future come from? Statistical Analysis Wood frog breeding phenology was compared to annual weather conditions (temperature and precipitation) with Pearson s correlations and linear regression analysis using Statistica 7.1 (StatSoft, 2005). Of the multiple wetlands that were surveyed in Hartley Park over a time span of 13 years ( ), four ponds have been continuously surveyed from ; these ponds were used to evaluate the with-in year variation in neighboring ponds. Data and analyses from all the ponds were compared with the data and analyses from these four specific ponds. Wood frog breeding phenology (calling and spawning) was predicted for projected future climate using the linear regression results from and climate trends for the past 60 years. Results During the nine years of amphibian surveys ( ), there were no trends in mean monthly temperatures from months January through April, mean monthly precipitation, or total precipitation for each month. Nor were there weather trends with precipitation or temperature collectively from the months of January through April. Earliest Calling Date and Temperature When all pond were included in the analysis, average March temperature, average April temperature, and mean temperature from January to April were all negatively correlated with earliest calling date ( r > 0.5), with moderate strength of the linear regressions. Each individual pond also had strong negative correlations between average March temperature and earliest calling date, however there was variation in response to the other weather variables. For example, ponds Carolyn and Cut Across showed strong correlations between earliest calling date and average temperature from January April, while Fairmont and Tamarack did not. 3

4 Table 1. Pearson correlation and linear regression results for first calling date versus temperature. Correlations with r > 0.5 included, with significant values for correlation and linear regression highlighted (α=0.1). Mean Temperature Pearson Linear regression R 2 for: correlation (r) All Ponds March April January - April Carolyn March April January - April Cut Across March January - April Fairmont March Tamarack March Date of Maximum Calling and Temperature When all ponds were analyzed, March and April average temperatures showed the strongest correlations with date of maximum calling (for both calling index 2 and 3), along with the averaged temperatures from January to April ( r >0.5). For calling index 2, Carolyn had the most significant correlations when compared to the other ponds and all ponds, while Cut Across and Tamarack did not have enough data to be analyzed (Table 2). For calling index 3, all ponds showed strong negative correlations with average March, average April, and average January-April temperature (Table 3). Individually, the ponds showed strong relationships with the average April temperatures with some discrepancies in strength and direction of relationships across ponds. Most of the ponds showed strong relationships with the months January, March, April and January-April. 4

5 Table 2. Pearson correlation and linear regression results for date of maximum calling (Index 2) versus temperature. Correlations with r > 0.5 included, with significant values for correlation and linear regression highlighted (α=0.1). Index 2 Mean Temperature Pearson correlation (r ) Linear Regression R 2 All March April January April Carolyn January March April January April Cut Across Not enough data NA NA Fairmont March April Tamarack Not enough data NA NA NA = not enough data (< 4 records for these ponds) Table 3. Pearson correlation and linear regression results for maximum calling date (Index 3) versus temperature. Correlations with r > 0.5 included, with significant values for correlation and linear regression highlighted (α=0.1). All Index 3 Mean Temperatures Pearson correlation (r ) Linear Regression R 2 5

6 Figure 2. Mean March temperature ( F) versus wood frog breeding phenology for all ponds in Hartley Park: A) date of first calling, B) first day of maximum calling, index 2, and C) first day of max calling, index 3. Spawning Date versus Temperature Spawning data from Hartley Park was combined with data from nearby vernal pools for analyses in order to increase the number of years with spawning date recorded. Average March temperature was the only weather parameter significantly correlated with wood frog spawning date (Figure 3). 6

7 Figure 3. Wood frog spawning day of year versus mean March temperature ( F) for vernal pools in Hartley Park and surrounding vernal pools. Precipitation Data There was no dominant trend in the relationship between average monthly precipitation and wood frog breeding phenology in all ponds combined or in individual ponds. First calling date was not highly correlated with any precipitation parameter ( r < 0.5 in all cases). For all ponds, total and average precipitation from January to April was highly negatively correlated with date of maximum calling (for index 3). 7

8 Table 4. Pearson s correlation coefficients for calling indices versus precipitation data. Correlations with r > 0.5 included, with significant values highlighted (α=0.1). Mean Precipitation Precipitation total Jan - Apr Date of: Jan Feb Mar Apr Jan Feb Mar Apr total mean All Max calling NA NA NA NA NA NA NA NA (index 2) Max calling NA NA (index 3) Carolyn Max calling NA NA NA NA (index 2) Max calling 0.8 NA 0.6 NA 0.8 NA 0.6 NA NA NA (index 3) Cut Max calling Not enough data Across (index 2) Max calling -0.8 NA NA NA -0.8 NA NA NA (index 3) Fairmon Max calling NA NA t (index 2) Max calling Not enough data (index 3) Tamarac Max calling Not enough data k (index 2) Max calling 0.75 NA NA NA 0.75 NA NA NA (index 3) NA = low correlation r < 0.5 Discussion Understanding the relationship of amphibian breeding phenology with annual weather conditions leads to an increased ability to predict the potential effects of climate change. With volunteer-collected anuran calling surveys we were able to detect significant relationships of temperature data from January to April with calling and spawning in wood frogs. Spawning date was strongly negatively correlated with average temperature for March and April (Figure 1, Table 4); these correlations suggest that spawning is dependent on the temperature in the previous months (Menzel et al., 2006). Here we show that the average temperature in the months of April and March had higher correlation with spawning date than the months of January and February, which supports the results in Tryjanowski et al. (2003). Only months from January to April were used because these are considered the most regionally-relevant portion of the 8 critical months where the process of wintering, emergence, courtship and spawning take place (Gibbs et al. 2001). Of these months, our results show that March weather, specifically temperature, may be the most influential on wood frog breeding phenology with warmer weather in March leading to earlier calling and breeding. Precipitation was not as strongly correlated with the wood frogs calling and spawning when compared to temperature (Table 4). These results may show that precipitation is not a dominant driver of wood frog spring phenology. There have been many studies confirming that precipitation in the preceding months have little or no affect on the spawning time of seasonally dependent animals (Inouye et al., 2000; Reading 1998, Tryjanowski et al., 2003). Correlation with total precipitation January to April and maximum calling date could be due to snowpack, which influences the date that ponds open up. The variation in the four ponds provide support for this conclusion, as Cut-Across is a heavily forested 8

9 wetland that remains ice-covered longer than more open ponds in Hartley Park, such as Fairmont, Tamarack, and Carolyn. The comparison of all ponds with the four ponds with complete records showed that breeding is fairly synchronous within the area, as was expected for this species. There were, however, differences in correlation directions and strengths, primarily due to differing amounts of data at each pond. These variations were expected given that data was volunteer-collected, and this result suggests that combining data from multiple nearby sites may be more reliable for analyses with this type of data. Overall, the amphibian survey data appeared robust despite potential errors due to the fact that it was largely volunteer-collected with variable training across years. Additionally, the inclusion of data collected outside of the standard calling surveys ( non-monitoring data) did not seem to produce outliers in the data. The work done by Reading (1998) suggests that closer correlations of breeding phenology with annual weather conditions might have been obtained by using a 40-day running average. However, in the amphibian survey data from Hartley, the 40-day running averages were only significantly correlated with date of first calling. The lack of correlation with maximum calling date and spawning date may be because late winter cues are more influential than the weather on the days immediately preceding breeding. This is supported by the strong correlations of mean March temperature with first calling, maximum calling, and spawning dates. Monthly averages were used for this study in order to allow comparison to climate trends from the last 100 years and predicted future trends to see the possible effects of climate change on spawning of the wood frogs in the upper Midwest U.S. Significant trends in increased average temperatures and increased precipitation have been observed over the last century (IPCC, 2007). Although there were no observed trends in the average temperatures or precipitation data over the ten year time period of the amphibian surveys, a significant positive trend was observed in average March temperature and average temperature from January to April over the last 60 years (Fig. 4, linear regression, p=0.005). During the period from 1950 to 2010, the average March temperature increased one degree F every 10.3 years, and the average temperature from January to April increased one degree F every years. This corresponds to increases of 3.4 degrees F (1.9 C) from the period to present ( ) and 7.5 degrees F (4.2 C) from the period to projected temperatures (based on trend line shown in Fig. 4). Although the data set at Hartley Park is not long enough to detect a trend in wood frog breeding phenology, the linear relationships between temperature and calling/spawning can be used to predict calling and spawning historically as well as under projected climate change (Fig. 4). 9

10 Julian Date Temp (F) y = x, p=0.005 A year Spawning Date Max Calling (index 3) Obs Spawning Obs Max Calling B year Figure 4. Predicting potential effects of climate change on wood frog breeding phenology with: A) historical March mean temperature with trend line; and B) maximum calling and spawning dates for historical and predicted temperatures under climate change (following trend from A, which is approximately +2 C from present to 2050). Overall from this study, wood frog phenology was highly correlated with temperature in the months preceeding breeding, and less correlated with total precipitation in these months. Using historical data from the last 60 years and the relationship between temperature and spawning identified in this paper, we predict that wood frog spawning in northern Minnesota over the next 40 years will be ten days earlier than predicted spawning dates for , and six days earlier than observed from 2005 through Earlier breeding has its benefits and setbacks. When the breeding season is extended, there 10

11 is longer growing seasons for the frogs, however because of the temperament of spring weather many amphibians may struggle with spring freezes and changing resources, such as availability of open water and food sources (Carroll et al., 2009). There are many other potential impacts that need to be considered when evaluating the effect of climate change on amphibian populations. Altered breeding phenology may reduce or increase exposure to these risks from climate change. Some of the most recognized climate change impacts are reduced availability of aquatic habitat (ponds and wetlands drying up) and increased stress. The availability of aquatic habitat is very important to many amphibians for breeding and larval development, as well as during other life stages. Climate change can alter aquatic habitat through increased temperature as well as evaporation (Magnuson et al., 1997), which could lead to unsuccessful breeding or the loss of species in these warmer, dryer conditions (Reaser et al., 2005). Climate change and increased temperatures in both aquatic and terrestrial ecosystems may increase stress for amphibians and reduce their health. Because they are ectotherms, amphibian immune systems are very temperature dependant (Raffel et al., 2006). Amphibian immunity might be lower or they might be more at risk to diseases as the temperature ranges increase (Fisher, 2007; Rohr et al. 2010). How these factors are ameliorated or exacerbated by altered breeding phenology must be evaluated for prediction of climate change impacts on amphibians.. 11

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