Diabetes Mellitus

SDMs can be constructed in a variety of ways and with a range of outputs. Most models predict species presence or absence, or just presence, to get geographic ranges, based on the environmental data associated with species occurrence records. SDMs can be developed using a variety of algorithms including statistical models (e.g. GLMs (Guisan et al. 1998; Randin et al. 2006), machine learning (Harrison et al. 2006; Phillips et al. 2006)), heuristic models (Beaumont & Hughes 2002) and combinatorial optimization (Fitzpatrick et al. 2007). There are two types of common outputs from these models: binary and continuous. Binary output defines each location as being within or outside the distribution and often requires a threshold to determine this boundary. Continuous results allow a less defined approach to identifying areas of high and low probability of occurrence.

We used the maximum entropy algorithm (MaxEnt) approach (Phillips et al. 2006) because we needed an algorithm that could use both presence/absence data and presence only data, and maximum entropy has consistently been among the top performing algorithms for SDM (Phillips et al. 2006; Elith et al. 2006; Ortega-Huerta & Peterson 2008). Maximum entropy is a machine learning technique that predicts species distributions using detailed geospatial data sets along with species occurrence information, and is conducted using the specialized package MaxEnt (Phillips et al. 2006). MaxEnt was used to develop probability envelopes for the occurrence of the five invasive trees in southeastern forests using distribution-environment relationships derived at the global (GBIF) and regional (FIA) data. Additionally, further analysis was

undertaken at a regional scale using only regional GBIF records. This was to determine whether systematically acquired presence/absence data (FIA) and opportunistically acquired and presence only data (GBIF) from the same region gives different perspectives on distribution-environment relationships or whether the SDMs follow similar patterns. Models were derived using a manual backward selection method, where variables that had little or no impact on the model were removed, this was repeated ten times with different selections of training and test data. The key variables determining the occurrence of each species were identified by their percent

contribution to the final model and with a jack-knife test on gain and influence on the area under the curve (AUC).

Four techniques were used to assess model reliability: the performance of test and training data, the omission rate, AUC and comparison with an external dataset. Occurrence data were randomly split with thirty percent in test and seventy percent in training datasets for the regional

models and run ten times with random selections. It has been shown that the area defined by the absences, pseudo-absences or background can have a major influence on model output (Phillips et al. 2009; Elith et al. 2010). Since GBIF only provides presence data, pseudo-absences were taken as random background points within the maximum latitude (+ 0.5 degrees) recorded in the GBIF data for each of the five species (Table 7-1), limiting the pseudo-absences to areas that the species could potentially invade. FIA data records both presence and absence, thus absence points were used to define the background. For the assessment of models all test data were limited to the southeastern region i.e. the global GBIF model used thirty percent of the data within the southeastern region as test data with all test data outside of the southeastern region dropped from the analysis.

The omission rate is the false negative or the proportion of sites where the species was present but the model predicted absence. To calculate this, a cut-off criterion is required to convert continuous model predictions to binary classifications. We used a threshold value that

maximized the sum of sensitivity and specificity. This has the advantage of giving equal weight to the probability of success for both presence and absence (Manel et al. 2002). This is one of the most appropriate methods to derive a binary variable from continuous probabilities when species presence-absence distribution data are unbalanced (Jiménez-Valverde & Lobo 2006; Liu et al. 2005). This cut-off was also used for mapping of one run of the final selection of variables. AUC provides a single measure of model performance, independent of any particular choice of

threshold but is sensitive to the method in which absences in the evaluation data are selected (Lobo et al. 2008). We used the following classes of AUC to assess model performance: 0.50 to 0.75 = fair, 0.75 to 0.92 = good, 0.92 to 0.97 = very good, and 0.97 to 1.00 = excellent (Hosmer & Lemeshow 2000). AUC is most applicable to data with true absences (Jiménez-Valverde 2011), thus it needs to be used with caution for datasets that do not have absence data. Other studies have shown the strength of using AUC as an assessment tool when predicting species one at a time over the same extent for all models, removing highly correlated variables and

restricting the spatial distribution of all models to a similar environment, as was done in this study (Stohlgren et al. 2010). The proportion of agreement and spatial correlation between models were also assessed, with Pearson’s correlation calculated between FIA model and global GBIF models and FIA models and regional GBIF models.

To address the value added by integrating SMDs from different datasets two approaches were taken; first an ensemble approach in which models were combined, and second by combining the

data before modelling. This was only done at the regional level. With the continuous outputs from the regional GBIF models and FIA models added together to give an ensemble map. This was reclassified to a binary map using the average maximized the sum of sensitivity and

specificity. The combined data modelling approach was undertaken the same as with individual datasets. For comparison BONAP data were used, this is a county level occurrence dataset. All modelled data were converted to presence/absence for each county and omission rates were calculated between this and BONAP data.