1. Kristin Vanderbilt and Karen Wetherill Flowering Phenology of Blue and Black Grama (Bouteloua gracilis and Bouteloua eriopoda) Where Their Ranges Meet at the Sevilleta LTER Abstract We used multiple logistic regression to look at abiotic environmental triggers for flowering phenologies of 79 species at two types of grassland at the Sevilleta LTER site. For this poster, we focus on the results of blue and black grama (Bouteloua gracilis and B. eriopoda), the dominants of each grassland site. We have six years of data for the Black Grama Site and five years for the Blue Grama Site. Sta ble logistic regression models were developed for the Blue Grama Site, but not for the Black Grama Site. We believe that the methods used for this analysis are suitable for the development of predictive models, but that the sampling time of five and six years is inadequate to remove spurious correlations from the models. Our current results suggest that both blue and black grama increase flowering in response to inputs of precipitation in the current month and previous month. Introduction Blue grama (Bouteloua gracilis) is the dominant grass species of the steppes from central and eastern New Mexico north into Canada. Blue grama plants are resilient after fire and grazing disturbance, but sensitive to drought (Lauenroth et al. 1994). Black grama (Bouteloua eriopoda) dominates arid grasslands from central New Mexico southward into Mexico. Relative to B. gracilis, B. eriopoda is more susceptible to fire and grazing, but less susceptible to drought (Wright and VanDyne 1976). For this poster, we compare the flowering phenology of blue and black grama at two sites where their ranges overlap at the Sevilleta LTER site. The Blue Grama Site is dominated by blue grama with black grama interspersed. The Black Grama Site is dominated by black grama and has swaths of blue grama running through it. Methods Plant phenology data is collected on the last day of every month, from February through October at two sites on the Sevilleta National Wildlife Refuge. Four, 200 meter transects are walked from north to south at each site. The first ten individuals of each species encountered along the transect are observed for new green growth, old green leaves, brown senescent leaves, flower buds, flowers and fruit. This provides a maximum of 40 individuals of each species per site. For this analysis, we used the number of flowering individuals divided by the total number of individuals recorded as a measure of percent flower of each species at each site. All species that had less than ten flowering observations in the duration of the study or that the species was recorded in less than five out of the six or four out of the five years were deleted. Meteorology data is collected on a daily basis at each site. We used current monthly precipitation, monthly precipitation at lags of one, two, and three months, and average maximum and minimum temperature in the model. We also included monthly precipitation of each month of the year to see if plants respond to precipitation events in certain months, but not in other months. All precipitation variables were divided by five to create a one unit increase of five mm. Logistic regression is appropriate for zero-heavy counted proportion data (Ramsey and Schafer 2002), such as the data from this study. This method has been used by others exploring the effects of precipitation on flowering in arid ecosystems (Friedel et. al 1993). In our analysis, we first ran each variable by itself in a simple logistic regression. Only those variables with significant p-values were included in the multiple logistic regression model. Stepwise selection was used and the final models were evaluated using the Hosmer-Lemeshow goodness of fit criterion. If the goodness of fit criterion was not met, then the model was rejected. Figure 1. Percent flowering of blue and black grama and precipitation vs. month at a blue grama dominated site (2001-2005) and a black grama dominated site (2000-2005). Results Blue and black grama responded in a very similar pattern at each of the two sites, but very differently from one site to the other (Figure 1). The logistic regression models for blue and black grama at the Blue Grama Site are in Table 1. Both models had a non-significant Hosmer-Lemeshow test and are therefore appropriately modeled using logistic regression. An odds ratio greater than one indicates that that an event is more likely to occur after a one unit increase in the explanatory variable. For instance, the odds that a black grama plant will be flowering after a 5mm increase in rainfall in the current month are 1.273 times greater than the odds that a plant will not be flowering after such an event, after all other variables in the model are accounted for. An odds ratio of less than one suggests that, after all other variables are taken into account, a plant is less likely to flower after a one unit increase in that variable. The regression models for blue and black grama at the Black Grama Site both had significant (p<0.05) Hosmer-Lemeshow tests, suggesting that logistic regression is not an appropriate model for this analysis. Table 1. Odds ratios resulting from logistic regression models for Blue and Black Grama at the Blue Grama Site. PPT = Current month precipitation; LAG1 = Previous month precipitation; APR = Precipitation during April; DEC = Precipitation during December Discussion Although blue and black grama are adapted to different climate and disturbance regimes, our results suggest that both are capable of quickly responding reproductively to precipitation inputs from late spring into the fall. The two species responded similarly within each site, but differently between sites (Figure 1), indicating that local abiotic factors are more important in determining bloom than the inherent differences between the two species. The consistent logistic regression models for both species at the Blue Grama Site support the importance of immediate local rainfall as triggers of flowering. However, the significance of the December precipitation to flowering, as detected by the model for black grama, is hard to explain and may be a spurious result. Poor fit of the logistic regression model at the Black Grama Site suggests that the time series is not yet long enough to discern between spurious and real environmental triggers on flowering. References Cited: Friedel, M.H., Nelson, D.J., Sparrow, A.D., Kinloch, J.E., and Maconochie, J.R. 1993. What induces Central Australian Arid Zone trees and shrubs to flower and fruit? Aust. J. Bot. 41:307-319. Hosmer, D.W. and Lemeshow, S. 2000. Applied Logistic Regression. John Wiley and Sons, New York. 373 p. Lauenroth, W.K., Sala, O.E., Coffin, D.P., and Kirchner, T.B. 1994. The importance of soil water in the recruitment of Bouteloua gracilia in the shortgrass steppe. Ecol. App. 4:741-749. Ramsey, F. and Schafer, D. 2002. Statistical Sleuth. Duxbury Press, 768 p. Wright, R.G. and VanDyne, G.M. 1976. Environmental factors influencing semidesert grassland and perennial grass demography. The Southwestern Naturalist. 21:259-274. Blue Grama Site Black Grama Site