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Appendix S1. Details of Species Distribution Modeling and Population Modelling Methods Species Distribution Modeling For both species, we used spatially explicit data on occurrences (“presence”) from the San Diego Natural History Museum as well as a database of vegetation plots (Hannah 2008). To base the species distribution models on as many presence locations as possible, we did not set aside a fraction of the data for testing accuracy because our objective was to create predictive maps, as opposed to explaining the most important drivers of the species’ distribution. Model fit was assessed using the area under the curve (AUC) of the receiver operating characteristic (ROC). The AUC represents the probability that, for a randomly selected set of observations, the model prediction is higher for a presence observation than an absence observation. The species distribution model of current habitat for C. verrucosus had a training accuracy of 0.99, as calculated by the area under the curve (AUC) of the receiver operating characteristic (ROC). The AUC for C. greggii was 0.93. This resubstitution accuracy, based on the training data, represents a slightly higher performance measure than would likely be found with independent assessment data (Franklin, 2002). Both species showed “ecologically sensible” unimodal responses to climate variables (Austin, 2002) (Figure S1). Climate variables were among the most important predictors in both models (Table S1). Figure S1. Marginal response curve for climate predictors estimated using MaxEnt Table S1. A heuristic estimate of relative contributions of the climate, soil and terrain variables used in the Maxent model for each species, presented in the order of relative importance for C. greggii. In each iteration of the training algorithm, the increase in regularized gain is added to the contribution of the corresponding variable (or subtracted from it if the change to the absolute value of regularized gain is negative) to determine the estimate. Variable Source Absolute minimum January temperature (averaged over 30 yr) Annual precipitation (averaged over 30 yr) Absolute maximum July temperature (averaged over 30 yr) Soil depth (m) Soil order Soil available water capacity (cm/cm) potential winter solstice solar insolation (Watt hr /m 2) Slope angle (degrees) Topographic moisture index (unitless) potential summer solstice solar insolation (Watt hr /m 2) Soil pH PRISM1 Ceanothus greggii 53.8 Ceanothus verrucosus 48.7 PRISM1 27.0 3.2 PRISM1 5.1 19.6 STATSGO2 County soil series maps aggregated to order, 13 categories STATSGO2 3.3 2.5 0.6 4.5 2.4 1.2 from DEM using Solar Analyst USGS 30-m DEM From USGS 30-m DEM 1.8 0.4 1.5 1.4 6.0 0.8 from DEM using Solar Analyst STATSGO2 1.1 0.6 0.3 14.5 1 PRISM: http://www.prism.oregonstate.edu/ 2 STATSGO: State Soil Geographic data base for California, U.S. Department of Agriculture Natural Resources Conservation Service. [WWW document]. URL http://gis.ca.gov/catalog/BrowseRecord.epl?id=21237. DEM: Digital Elevation Model; USGS: U.S. Geological Survey; Solar Analyst: an ArcView extension for modeling solar radiation at landscape scales Preparing habitat suitability maps for input to population models For C. greggii, we used a regional vegetation map (http://www.fs.fed.us/r5/rsl/projects/classification/system.shtml) to select all polygons classified as vegetation types in which C .greggii is known to occur. We overlaid the C. greggii presence points on these polygons and selected those that intersected a point to represent occupied habitat patches. A threshold discriminates between suitable and unsuitable habitat so that areas with a predicted probability higher than the threshold value are considered suitable and those below the value are considered unsuitable. To be consistent for both species, we used “equal training sensitivity and specificity” as a threshold criterion because it provided a balance between errors of omission and commission (based on “availability” data of MaxEnt). This threshold was 0.301 for C. verrucosus and 0.397 for C. greggii. All areas with probabilities of occurrence lower than the threshold value were assigned a habitat suitability value of 0. Because this results in some very large patches for C. greggii, greater than the average size of fires in the regions, we further split large patches into smaller patches using the incidence of roads to ensure that fires occurred at a realistic spatial scale. After applying the thresholds to the habitat maps, we also developed criteria for distinguishing between which patches were initially occupied for the population models and which patches represented suitable but unoccupied habitat. For C. verrucosus, we obtained a map that outlined all areas where the species is currently located (D. Lawson, personal communication) to designate as occupied in our habitat maps. We assigned an initial habitat suitability of 1.0 to all occupied habitat patches and maintained a continuous distribution of predicted probabilities (i.e., between the threshold probability and 1.0) for unoccupied, suitable patches to serve as indicators of relative habitat quality, which is related to carrying capacity in the population models. We also filtered out patches smaller than 25 ha because these would contain few individuals relative to the entire population and would considerably slow processing time. For maps of predicted distribution in the future climate scenarios, we applied the same thresholding and patch size filtering rules. Population Modelling We used RAMAS® GIS (Akçakaya et al., 2005) to link the dynamic spatial distributions, the population model and fire risk functions and to run stochastic simulations of the resulting population dynamics. All survival rates and fecundities for C. greggii were particular to the species, whereas early survival rates for C. verrucosus were based on rates of the congeneric species C. impressus, C. ramulosus, C. cuneatus, and C. megacarpus and survival rates for older age classes were the same as for C. greggii. Fecundities for C. verrucosus were scaled from those for C. greggii based on relative seed size. The average first-year survival rate for C. greggii range was estimated to be 0.56 (Schmalbach, 2005, Keeley et al., 2006), whereas the average first year survival rate for C. verrucosus was estimated to be 0.47 (Tyler & D’Antonio, 1995; Thomas & Davis, 1989; Frazer & Davis, 1988; Keeley et al., 2006). Age 1 to 5 survival rates were considerably higher for both species (Table 1): 0.95 and 0.99 for age 1 and ages 2-5, respectively, for C. greggii, and 0.707 and 0.718 for ages 1-2 and ages 3-5, respectively, for C. verrucosus (standard deviations in Table 1). Survivorship data (i.e. the proportion of individuals surviving germination to specified age) at ages 6, 13, 32, 57, and 82 years for north- and south-facing slopes from Zammit & Zedler, (1993) were used to calculate average survival rates of shrubs 6-81 years (Table 1). Standard deviations were assumed to be the maximum absolute difference between the estimated mean and the highest and lowest survival rate across north- and south-facing slopes. The survival rate for age classes 82-97+ years was estimated by assuming a 100 year longevity for 95% of plants in the age class, with 5% reaching older ages (Keeley et al., 2006). Survival rates were assumed to be uncorrelated across patches; environmental correlation in survival rates across space was shown to have negligible effects for C. greggii (Regan et al. 2010). The seed production function of best fit was the polynomial f s x 0.3422x 2 33.886x 1030.1 where x ≥ 6 years is the age of mature plants in the stand. Seed production is highly variable with an estimated coefficient of variation of 200% (Zammit & Zedler 1993, Keeley, 1977). A weighted average (weighted by sample size) of seed predation rates for C. greggii in Davey (1982) (91% predation) and Zammit &Zedler (1993) (34% predation) was used to estimate a predation rate of 74.8% and seed viability was estimated as 50% (Keeley 1977). A weighted average (weighted by sample size) of seed predation rates for C. greggii in Davey (1982) (91% predation) and Zammit &Zedler (1993) (34% predation) was used to estimate a predation rates of 74.8%. Ceanothus has ballistic dispersal and seeds explode out of the pods, dropping and rolling for 1-3 m, then join the soil seed bank and wait for a fire. There is no long distance dispersal on time scales of centuries except the slow creep at 1-3 m steps. Dispersers such as harvester ants do not move the seeds more than a few meters. While sedds have obviously dispersed on geological time scales, the probability of opportunistic dispersal via floods, for instance, is thought to be negligible on the century time scale of the simulations. Two sets of parameters are required to calculate K across the landscape: the per hectare maximum density of plants in the largest sized age class and the relative differences in K across age classes. To estimate per hectare maximum density of plants in the largest sized age class, a least squares line of best fit was applied to the maximum observed densities for each stand age, from which the maximum density per meter squared for C. greggii at age 60+ was estimated. However, these densities relate to a local scale smaller than 1 ha; in order to scale up to 1 ha plots, this estimate was reduced by the abundance of Ceanothus across the landscape. C. greggii was observed to be present in 12% of chaparral plots, with an average cover of 18% (J. Franklin, unpubl. data), resulting in a carrying capacity of 150 plants per ha for 60+ year old stands. For C. verrucosus, average cover in southern California is 17% (J. Franklin, unpubl. data) and we assumed that this applied to all occupied patches (i.e. no further reduction was applied because the predicted patch sizes for C. verrucosus were small relative to C. greggii patches). This leads to a carrying capacity estimate of 1,173 per ha for age 60+ shrubs. While these values may underestimate the actual but theoretical ceiling possible, they provide a consistent upper bound that can be used across all scenarios for ranking and comparison of outcomes. Density dependence was implemented by reducing rates of survival and growth (due to intraspecific competition) such that abundance declined faster than the self-thinning function, W(x), as plant age increased whenever a population exceeded the carrying capacity of its habitat patch. This is realistic; if there are more individuals in the patch than the habitat suitability can accommodate then populations would be expected to decline faster (or at least as fast) due to intra-specific density dependence than background survival rates or self-thinning relationships would indicate. Fires were assumed to be uncorrelated across patches. In the model a fire burns the size of the relevant patch. For C. greggii this results in a mixture of large and small fires across the landscape through time. For C. verrucosus, which occurs in smaller patches across the landscape, this results in small patchy fires. This is realistic because we do not expect fires to be correlated across C. verrucosus habitat because of urbanization. This species is already restricted to isolated canyons in urban areas and fires tend to be highly uncorrelated (because fires in urban areas tend to be effectively suppressed). Since our initial populations began with age 16 individuals, the age 60 initial abundance was multiplied by the age 16 weight, W(16), and thus initial abundance was set at 207 age 16 individuals/ha for C. greggii and 1,623 age 16 individuals/ha for C. verrucosus. We calculated expected minimum abundance (EMA) with and without the first 20 years of the population trajectories to determine if initiating the population as an even-aged aged cohort versus allowing a burn-in period (which would result in stands of different ages across the landscape) had any effect on results and determined that it did not. Therefore, results are reported here with the full time series contributing to EMA calculations. References Akcakaya, H.R. (2005) Linking Spatial Data with Population Viability Analysis (version 5). Applied Biomathematics, Setauket, New York. Austin, M.P. (2002) Spatial prediction of species distribution: an interface between ecological theory and statistical modelling. Ecological Modelling, 157, 101-118. Davey, J.R. (1982) Stand replacement in Ceanothus crassifolius, California State Polytechnic University, Pomona. Franklin, J. (2002) Enhancing a regional vegetation map with predictive models of dominant plant species in chaparral. Applied Vegetation Science, 5, 135-146. Frazer, J.M. & Davis, S.D. (1988) Differential survival of chaparral seedlings during the first summer drought after wildfire. Oecologia, 76, 215-221. Hannah L., Midgley G., Davies I. et al. (2008) BioMove-Improvement and Parameterization of a Hybrid Model for the Assessment of Climate Change Impacts on the Vegetation of California. 91 pp, California Energy Commission, Public Interest Energy Research Program CEC-500-02-004 Keeley, J.E. (1977) Seed production, seed populations in soil, and seedling production after fire for two congeneric pairs of sprouting and non-sprouting chaparral shrubs. Ecology, 58, 820-829. Keeley, J.E., Fotheringham, C.J. & Baer-Keeley, M. (2006) Demographic patterns of postfire regeneration in Mediterranean-climate shrublands of California. Ecological Monographs, 76, 235-255. Regan, H.M., Crookston, J.B., Swab, R., Franklin, J. & Lawson, D.M. (2010) Habitat fragmentation and altered fire regime create trade-offs for an obligate seeding shrub. Ecology, 91, 1114-1123. Thomas, C.M. & Davis, S.D. (1989) Recovery patterns of three chaparral shrub species after wildfire. Oecologia, 80, 309-320. Tyler, C.M. & D’ Antonio, C.M. (1995) The effects of neighbors on the growth and survival of shrub seedlings following fire. Oecologia, 102, 255-264. Zammit, C.A. & Zedler, P.H. (1992) Size structure and seed production in even-aged populations of Ceanothus greggii in mixed chaparral. Journal of Ecology, 81, 499511.