Jennifer Pannell Acknowledgements This research is funded by the Tertiary Education Committee and the Bio-Protection Research Centre, New Zealand. Thanks go to Di Carter and the Christchurch City Council for assistance with surveys and providing experimental sites, and to Andrew Tait at NIWA for providing climate grids. I am also grateful to SANBI, GBIF, and the New Zealand National Herbarium Network for providing species occurrence data. My field assistants have been invaluable and am grateful to everyone who has helped me with data collection including: A.McCulla, R.Butters, H.Lim, L.Doherty, K.Murray, K.Perry, R.Murray, M.MacIntosh, D.Dash and many others. References [1] Hijmans, R. J. & Graham, C. H. (2006) The ability of climate envelope models to predict the effect of climate change on species distributions. Global Change Biology 12: , [2] Brook, B. W., Akçakaya, H. R., Keith, D. A., Mace, G. M., Pearson, R. G., Araújo, M. B. (2009) Integrating bioclimate with population models to improve forecasts of species extinctions under climate change. Biology Letters 5(6): [3] Zietsman, P. C. (1998) Pollination biology of Cotyledon orbiculata L. Var. Dactylopsis Toelken (Crassulaceae). Navors. Nas. Mus., Bloemfontein, 14(4):81-96 [4] Global Biodiversity Information Facility (2011) Data portal available at: [5] South African National Biodiversity Institute (2011) The National Herbarium: Overview available at: [6] Phillips, S. J., Anderson, R. P., Schapire, R. E. (2006) Maximum entropy modeling of species geographic distributions. Ecological Modelling 190: [7] NIWA (2011) Climate available at: [8] New Zealand National Herbarium Network (2011) New Zealand National Herbarium Network available at: How does climate affect the performance of Cotyledon orbiculata? Altitude, temperature and precipitation all had significant effects on the performance of C. orbiculata at the study sites (Table 1). Individuals perform better at warmer, drier sites and at lower altitudes, which is consistent with the predictions made by the bioclimate models. Both probability of occurrence and performance can be predicted using these climatic variables with a high degree of accuracy, reinforcing the potential for combining the two modelling approaches. Plant density: Two-way effects of altitude, temperature and precipitation; adjusted r²=0.99, p=0.04 % Flowering: One-way effects of altitude and average temperature; adjusted r²=0.69, p=0.02 Seed output per capita: Two-way effects of altitude and average temperature, one way effect of precipitation; residual =0.31, p=0.29 Conclusions & Ongoing Work Cotyledon orbiculata is mostly confined to the Northern bays of Banks Peninsula, as predicted by the bioclimate model based on the native distribution. Altitude, temperature and precipitation significantly affect the species’ ability to colonise an area and its performance, and using bioclimate and simple linear models we can predict these effects. The next stage of the project will be to design and build a climate-driven population trajectory model by combining the current results with further survey and experimental data. Finally, the demographic and bioclimate models can be combined to predict population growth in a spatially explicit manner. The results presented here indicate that the two techniques will complement each other, creating a powerful tool that will enable us to better understand the species’ potential spread under climate change and inform decisions on its regional management. Figure 1 Cotyledon orbiculata in flower at Pigeon Bay, Banks Peninsula, New Zealand. Methods We collected distribution and demographic data for C. orbiculata, and used this to model its spatial distribution on Banks Peninsula and to quantify how demographic performance varied along climatic gradients. Bioclimate models Data on the native range of C. orbiculata in South Africa was collected from GBIF [4] and SANBI [5]. Environmental layers were the Worldclim bioclimatic layers at 30 second resolution. We chose altitude, minimum temperature of the coldest month, and total annual precipitation for the model, based on the species requirements. Two models were run in Maxent [6]. The first run used the data on native distribution to predict distribution on Banks Peninsula, which were then compared with the observed distribution. The second run used the observed distribution on Banks Peninsula to forecast the potential distribution using Maxent’s inbuilt cross-validation function. Local distribution and performance We surveyed Banks Peninsula recording presence localities for C. orbiculata. In eight populations we measured demographic variables including flower and seed production per plant and measures of plant size. We tested for a response to environmental variables in the per capita seed output, population density and percentage flowering against altitude and climate data obtained from NIWA [7], using multiple linear and mixed effects models. Background Predicting invasive species’ distribution and behaviour in new environments is a key challenge in ecology. Bioclimate and demographic models are two approaches to this problem but they are not often used together to improve forecasts [1,2]. This project uses both techniques to create a spatially explicit model of potential habitat and population growth for the invasive South African succulent Cotyledon orbiculata L. (Crassulaceae) in Banks Peninsula, Canterbury, New Zealand. C. orbiculata was introduced to the area around 100 years ago as an ornamental and has since spread prolifically, with each plant capable of releasing thousands of seeds per year [3]. We present results from the first stages of this project; modelling suitable habitat using native and invaded range data, and quantifying the effect of climate on the demographic performance of individuals. Using bioclimate and demographic models to predict the current distribution and potential spread of South African invasive plant, Cotyledon orbiculata, in New Zealand. Predicting Weed Distributions Under Climate Change: Beyond the Envelope Figure 2 Maxent outputs of predicted distribution of C. orbiculata on Banks Peninsula based on a) native occurrence data from South Africa and b) occurrence data from its invaded range on Banks Peninsula. Figure 3 Confirmed presence localities for C. orbiculata in New Zealand and Banks Peninsula. New Zealand data courtesy of NZ National Herbarium Network [8], Banks Peninsula data collected January-April Results Can we predict local distribution using bioclimate models? Maxent performs well in identifying suitable habitats for C. orbiculata using either the native range or the invaded range data (AUC= and respectively, Fig. 2), and closely matches the observed distribution (Fig. 3). Jackknifing revealed altitude to be the most important variable for the South Africa model, and precipitation for the Banks Peninsula model. Native range data produced more conservative distribution estimates than local data and, despite a higher AUC score, tended to under-predict suitable habitat. Table 1: Linear effects of climatic variables on plant density, percentage flowering and seed output of C. orbiculata. Statistical significance (p value) indicated by asterisks; * = 0.05, ** = Bio-Protection Research Centre Lincoln University Christchurch, New Zealand Bio-Protection Research Centre Lincoln University Christchurch, New Zealand Philip Hulme Richard Duncan