Current and modelled potential distribution of lantana. Data for current global distribution was taken from Global Biodiversity Information Facility 2007. doi:10.1371/journal.pone.0040969.g001 

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... of ecosystem processes. It has successfully invaded diverse habitats due to its tolerance for a wide range of environmental conditions. In Australia, lantana currently covers more than 4 million ha [33] and costs the Australian grazing industry in excess of $121 million per annum in lost production and management costs [34]. We drew on native lantana distribution data from Central and South America [35] as well as its exotic distribution data from South Africa [36] and Asia [37,38,39,40,41] for model parameterization to ensure that the complete range of environmental conditions in which lantana may occur was covered. This model was then used to project lantana’s potential distribution, employing the extensive Australian distribution data for model validation. CLIMEX has been utilized by many researchers involved in estimating invasive species’ potential distributions [30,42,43,44,45,46] and it allows users to model the potential distribution of organisms based primarily on their current distribution. The objective of this study was to conduct a sensitivity analysis to quantify the response of lantana to changes in the temperature, soil moisture and cold stress parameters. The main aim was to identify the parameters that were functionally important and thus provide a better understanding of which aspects of climate have a larger impact on lantana distribution. The results should also provide an indication of the parameters that require detailed data collection to be fitted accurately and others that are relatively insensitive to changes and therefore do not require large investments in research and data collection. The implications of this for management are also discussed. The CLIMEX Version 3 software package [11,47,48] works on the basis of an eco-physiological growth model that assumes that a population experiences a favourable season with positive growth and an unfavourable season that causes negative population growth [48]. Parameters that describe a species’ response to climate are inferred from its geographic range [11,44] and the inferred parameters are then applied to novel climates to project the species potential range in new regions or climate scenarios [49]. The potential for population growth when climatic conditions are favourable is described by an annual growth index (GI A ) while four stress indices (cold, wet, hot and dry) describe the probability that the population can survive unfavourable conditions [48]. The annual growth index is determined from the temperature index (TI) and moisture index (MI) which depict the species’ temperature and soil moisture requirements for growth. Four parameters, minimum, optimum (lower and upper) and maximum limits to temperature and moisture, respectively, describe the temperature and moisture indices (Table 1). These indices are multiplied to give a weekly growth index which are then averaged to give the annual growth index (GI ). Two parameters depict the stress indices, a threshold value and a stress accumulation rate. Stress accumulation during the year is exponential and once the accumulated stress equals 1, the species is not able to persist at the location [48]. Weekly calculations of the growth and stress indices are carried out and combined into an overall annual index of climatic suitability, the ecoclimatic index (EI) which is scaled from 0 to 100. An EI value of zero indicates unsuitable habitat where the species will not be able to survive; marginal habitats are indicated by EI values ranging from 1–10; values ranging from 10–20 can support substantial populations while values above 20 are highly favourable [44]. Growth and stress parameters were fitted using the methodol- ogy described in Sutherst and Maywald [11], Kriticos et al [50] and Chejara et al [51]. A detailed description of the parameters can be found in Sutherst and Maywald [11]. A global meteorological dataset of 0.5 u resolution (approximately 50 km 6 50 km) from the Climate Research Unit (CRU) at Norwich, UK [52] was supplied with CLIMEX. It contained data for a large number of locations across the world and consisted of monthly long-term average maximum and minimum temperatures, rainfall, and relative humidity at 09:00 and 15:00 hours for the period 1961–1990. Initial parameter-fitting was based on this meteorological dataset. Another meteorological dataset for the Australian continent, containing climate data from 1961 to 1990, was utilized for conducting the sensitivity analysis of temperature, soil moisture and cold stress parameters and their impacts on potential lantana distribution in Australia. The Australian dataset included the same five variables as the CRU dataset but at 0.25 u (approximately 25 km 6 25 km) spatial resolution. The Global Biodiversity Information Facility (GBIF) is a database of natural history collections across the world for a variety of species and it is available for download. Information on lantana distribution was downloaded [35] (Figure 1) for parameter fitting. A total of 4126 records were downloaded but many did not have geolocations and were removed, leaving 2753 records. However, many of these records were repeated several times and were also removed. Thus parameter fitting was based upon 1740 records from the GBIF database. Distribution data from South Africa [36] and Asia [37,38,39,40,41] were also obtained to assist in the process of parameter fitting. Seasonal phenology data for the southern states of Brazil were employed to fit growth parameters [53,54]. Although the seasonal phenology observations were restricted to Lantana tiliaefolia and Lantana glutinosa , the ecology of these two species are similar to the weedy taxa of lantana, and thus these data were included in model parameterization. An iterative adjustment of each parameter was performed until a satisfactory agreement was reached between the potential and known distribution of lantana in these areas. A combination of inferential and deductive approaches can be applied in CLIMEX to fit the stress indices [55]. Lantana had a well documented susceptibility to frost [33,56] and thus this information was drawn upon to inform the choice of cold stress parameters. The cold stress parameters derived from the literature agreed with the distribution information and thus it was concluded that the parameters were satisfactory. An inferential approach was used in the case of parameters that did not have a direct observation of lantana’s response to climatic variables. In this instance, the stress parameters were iteratively adjusted, the model was run and the results compared with our known distribution and phenological data. In the lantana model, the stress parameters were set so that stresses restricted the population to the known southern limits in Buenos Aires and northern limits in India, Nepal and China [37,38,39,40,41] while allowing it to survive in Kathmandu (27 u 42 9 N 85 u 18 9 E) [57]. Once the stress parameters were fitted, parameters for the temperature and soil moisture growth indices were adjusted iteratively and model fit was visually assessed until a close match was observed between the projected climate suitability patterns and the observed relative abundance patterns. The objective was to achieve maximum EI values near known vigorous populations and to minimize EI values outside the recorded distribution of lantana. The parameters were checked to ensure that they were biologically reasonable (Table 1). For a detailed explanation of the parameter-fitting procedure, refer to [58]. There is an extensive dataset available from Australia’s Virtual Herbarium (AVH) ( on lantana distribution in Australia and this was treated as an independent dataset for the purposes of model validation. A total of 635 records were downloaded, many of which were not georeferenced and thus were discarded. A number of the remaining records were duplicates and were also removed, leaving a final set of 218 records. The AVH data were collected between 1902 and 2012. Many of the old records were updated between 1996 and 2009. The final map resulting from the CLIMEX baseline model was validated using this herbarium record data (Table 2). As these locations were not employed in model development, they provided independent validation. Sensitivity analysis was carried out to quantify the response of lantana to changes in temperature, soil moisture and cold stress parameters. Incremental models were developed from the baseline model to reflect the possible range of these variables that could occur in Australia. During this procedure, the parameter values of the baseline model were kept constant and only one parameter was altered at a time (Table 3). Soil moisture parameters, SM0, SM1, SM2 and SM3 were adjusted with value changes of 2 0.02, 2 0.01, + 0.01 and + 0.02, respectively, from the baseline simulation (SM0 = 0.1; SM1 = 0.5; SM2 = 1.2; SM3 = 1.6). The change value in this case was set quite low because the baseline value for SM0 was already set fairly low. The temperature parameters, DV0, DV1, DV2 and DV3 were also adjusted with value changes of 2 7, 2 6, 2 5, 2 4, 2 3, 2 2, 2 1, + 1, + 2, + 3, + 4 and + 5 respectively, from the baseline simulation (DV0 = 10 u C; DV1 = 25 u C; DV2 = 30 u C; DV3 = 33 u C). The cold stress temperature threshold (TTCS) was adjusted with changes of 2 1 and + 1 from the baseline value for this parameter (5 u C). The cold stress temperature rate (THCS) was kept constant at the baseline value of 2 0.004 week 2 1 for these two simulations. The minimum degree-day cold stress threshold (DTCS) was varied at 2 1 and + 1 from the baseline model (15 u C) while the degree-day cold stress rate (DHCS) was kept constant at the base model value of 2 0.0022 week 2 1 . The stress rates interact with the thresholds by determining how quickly the species accumulates stress when climatic conditions exceed the stress threshold. The stress rates were kept constant because ...

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... data. In the lantana model, the stress parameters were set so that stresses restricted the population to the known southern limits in Buenos Aires and northern limits in India, Nepal and China [37,38,39,40,41] while allowing it to survive in Kathmandu (27 u 42 9 N 85 u 18 9 E) [57]. Once the stress parameters were fitted, parameters for the temperature and soil moisture growth indices were adjusted iteratively and model fit was visually assessed until a close match was observed between the projected climate suitability patterns and the observed relative abundance patterns. The objective was to achieve maximum EI values near known vigorous populations and to minimize EI values outside the recorded distribution of lantana. The parameters were checked to ensure that they were biologically reasonable (Table 1). For a detailed explanation of the parameter-fitting procedure, refer to [58]. There is an extensive dataset available from Australia’s Virtual Herbarium (AVH) ( on lantana distribution in Australia and this was treated as an independent dataset for the purposes of model validation. A total of 635 records were downloaded, many of which were not georeferenced and thus were discarded. A number of the remaining records were duplicates and were also removed, leaving a final set of 218 records. The AVH data were collected between 1902 and 2012. Many of the old records were updated between 1996 and 2009. The final map resulting from the CLIMEX baseline model was validated using this herbarium record data (Table 2). As these locations were not employed in model development, they provided independent validation. Sensitivity analysis was carried out to quantify the response of lantana to changes in temperature, soil moisture and cold stress parameters. Incremental models were developed from the baseline model to reflect the possible range of these variables that could occur in Australia. During this procedure, the parameter values of the baseline model were kept constant and only one parameter was altered at a time (Table 3). Soil moisture parameters, SM0, SM1, SM2 and SM3 were adjusted with value changes of 2 0.02, 2 0.01, + 0.01 and + 0.02, respectively, from the baseline simulation (SM0 = 0.1; SM1 = 0.5; SM2 = 1.2; SM3 = 1.6). The change value in this case was set quite low because the baseline value for SM0 was already set fairly low. The temperature parameters, DV0, DV1, DV2 and DV3 were also adjusted with value changes of 2 7, 2 6, 2 5, 2 4, 2 3, 2 2, 2 1, + 1, + 2, + 3, + 4 and + 5 respectively, from the baseline simulation (DV0 = 10 u C; DV1 = 25 u C; DV2 = 30 u C; DV3 = 33 u C). The cold stress temperature threshold (TTCS) was adjusted with changes of 2 1 and + 1 from the baseline value for this parameter (5 u C). The cold stress temperature rate (THCS) was kept constant at the baseline value of 2 0.004 week 2 1 for these two simulations. The minimum degree-day cold stress threshold (DTCS) was varied at 2 1 and + 1 from the baseline model (15 u C) while the degree-day cold stress rate (DHCS) was kept constant at the base model value of 2 0.0022 week 2 1 . The stress rates interact with the thresholds by determining how quickly the species accumulates stress when climatic conditions exceed the stress threshold. The stress rates were kept constant because we wanted to assess the effect of changing the stress threshold value on potential distribution. The adjusted models were re-run after each change in parameter value. In all of the incremental CLIMEX models, stress was only applied outside the range of conditions that were suitable for growth [43,48]. Both temperature and moisture parameters were always subject to the constraint DV0 , DV1 , DV2 , DV3 and SM0 , SM1 , SM2 , SM3, respectively. The area, in million square kilometres, falling within the suitable and highly suitable categories, was calculated for the baseline and each adjusted model to assess the sensitivity of different parameters (Table 3). Projections from the incremental models were compared with those from the baseline model by plotting EI values for the baseline model against each incremental model from the sensitivity analysis (for each 25 6 25 km cell) and calculating the R 2 value. If the parameter that was altered in the incremental model was highly sensitive, we expected large changes in the EI value and therefore a lower R 2 value. However, if the parameter was not very sensitive, than we expected the EI values of both the baseline and the incremental models to be similar thereby yielding a high R 2 value (Table 3). The changes in suitability were also assessed by mapping the areas where the suitability had changed in terms of the suitable or highly suitable categories for parameters that showed a high level of sensitivity. Additionally, any changes in the validation data with each parameter change were also assessed by checking any changes in the number of occurrence records that fell within each suitability category (Table 4). Figure 1 shows the current recorded global distribution of lantana and the potential global distribution based on EI values from CLIMEX. According to this projected distribution, much of the tropics and subtropics were shown to have suitable climatic conditions for lantana. Large areas of South and Central America, the southern states of USA, Asia, sub-Saharan Africa, Madagascar and the high volcanic Pacific island groups such as Fiji, Vanuatu, Samoa and New Caledonia, among others, had highly suitable climate for the species. These suitable areas were characterised by EI values of 20 and above. Warm temperate areas such as northern New Zealand and southern Mediterranean Europe including Portugal, Italy and Greece were projected as having marginal climatic conditions with EI values between 1 and 10. Although the model of global climate suitability matched the present global distribution of lantana closely, it did not include occurrence records from Mediterranean Europe and Israel. Lantana is mainly grown as an ornamental plant in this region [59] while irrigation plays an important role in the species’ persistence in parts of Israel [60]. Figure 2 shows similar data for Australia and again the current distribution of lantana was largely consistent with the Ecoclimatic Index. In Australia, the model projected much of the eastern coast from Cape York in northern Queensland to southern New South Wales (NSW) as being climatically suitable (Figure 2) with EI values of above 20. However, no occurrence records were found for Cape York Peninsula because, despite a few isolated infestations in this region, lack of human disturbance limits the rate of spread [61]. Small isolated areas in the Northern Territory were also identified as having suitable climate for lantana. Coastal areas along south-west Western Australia were shown to have suitable climate for lantana and this conformed to the actual distribution since small infestations were reported in these areas [33]. Central Australia was projected as being unsuitable (EI value of zero) to marginal (EI values between 1 and 10), mainly due to dry stress. Table 2 shows a high level of correspondence between the potential distribution and the herbarium specimen records for Australia with 189 (87%) records falling within the suitable and highly suitable categories. The changes to the temperature parameters from the baseline model and their resultant impact on the distribution are illustrated in Figure 3. The baseline model for potential distribution of lantana was very sensitive to changes in DV0, the limiting low temperature, and DV3, the limiting high temperature. When DV0 was set to 4 u C ( 2 6 from the baseline model), suitable and highly suitable areas increased by 0.341 million km 2 and the number of occurrence records that fell within suitable and highly suitable categories changed from 15 to 16 and 174 to 185, respectively, compared to the baseline model. Accordingly, the number of occurrence records in the unsuitable and marginal categories decreased (Table 4). A DV0 value of 4 u C caused a southward shift in distribution with larger areas in coastal Victoria and South Australia becoming suitable or highly suitable (Figure 4). A similar trend could be seen with coastal Tasmania. Previously marginal inland locations on the eastern side of mainland Australia also became suitable while others on the eastern coast became highly suitable. More suitable and highly suitable areas could also be seen on the south west coast of Western Australia. However, when DV0 was adjusted to 15 u C ( + 5 from the baseline model), the area in these two categories was reduced by 0.267 million km 2 . The number of occurrence records in the suitable and highly suitable categories decreased from 15 to 14 and 174 to 88, respectively. A substantial change was seen in the unsuitable and marginal category occurrence records with unsuitable records increasing from 14 to 96 when DV0 was set at 15 u C. This change in suitability was mainly observed along coastal New South Wales (NSW), coastal Queensland and the south west coast of Western Australia where previously suitable and highly suitable areas became marginal or unsuitable (Figure 5). Changes in DV3 had an opposite effect compared to DV0 with a 2 2 (31 u C) adjustment of this parameter yielding a reduction in suitable and highly suitable categories of 0.155 million km 2 . Most of these changes could be observed along inland areas of Queensland and coastal Cape York Peninsula (Figure 6). Occurrence records in the highly suitable category decreased from 174 to 155 but suitable category records increased from 15 to 26. Unsuitable and marginal records showed an increase. An adjustment of + 5 (38 u C) to the baseline DV3 value showed an increase in suitable and highly suitable areas of 0.279 million km 2 . With this parameter change, the number of occurrence records in the highly suitable category increased from 174 to 181 while ...