Predictive modelling

Species occurrence records were obtained from the South African Frog Atlas Project (Minter et al. 2004) and the Biodiversity Database of Ezemvelo KZN Wildlife. Records with a spatial resolution of between 0 and 250 m were used. In total, 39 presence records were available for use in the modelling. Environmental predictors likely to influence the distribution of the species were collated from the literature (Poynton 1964; Duellman &

Trueb 1994; Bishop 2004b; du Preez & Carruthers 2009; Franklin et al. 2009) and from additional knowledge of the species habitat requirements gathered during the study period (pers. obs.). For the purposes of this model only continuous variables were used, with categorical variables over-layed at a later stage (see below). The continuous variables used were mean minimum and maximum daily temperatures and relative humidity January and July, and mean annual temperature and precipitation for KwaZulu-Natal (Table 4.1). The coverage of these predictors were developed at a scale of 1´ x 1´ using the decimal degree Cape (1880) datum, and were re-projected to the WGS84 datum, transverse mercator lo31 central meridian, and then re-sampled to a 20 m x 20 m (400 m²) grid, based on the Ezemvelo KZN Wildlife 2008 version 2 landcover coverage. No increase in the resolution accuracy of the climatic variables was assumed. A resolution of 400 m² was found to be appropriate for wetlands (B. Escott, pers. comm.). Re-sampling was done to allow the incorporation of finer scale data in the form of wetland and hard transformation (100 % loss of native habitat) coverages. Otherwise, many of the wetlands and associated land transformation would have been lost from the analysis. Minor shifts, of up to 10 m, at mismatched interfaces between the 1´ x 1´ grid and the 20 x 20 m grid in climatic variables will occur. However, due to the initial scale of the mapping of climatic variables and the scale of the research domain, these errors were considered negligible (Elith et al. 2011).

Maxent version 3.3.3e (Philips et al. 2004, 2006; Philips & Dudík 2008; Elith et al. 2011) was used to develop the environmental niche model for H. pickersgilli. Maxent requires predictor variables to be in ASCII format and occurrence points in CSV file format. Five replicates were then run in Maxent using the cross-validate setting. The maximum number of

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iterations was set at 1000 to ensure algorithm convergence and the default settings were used for all other relevant parameters: feature selection = automatic; regularisation multiplier = 1;

convergence threshold = 10^-5 (Jackson & Robertson 2010). Twenty five percent of the occurrence points were randomly selected by the model as testing data for comparison against the model output (Phillips & Dudík 2008). A mask was used to ensure that the background samples were selected from the general region in which the species occurs. This general region was taken to be the Indian Ocean Coastal Belt, which spans altitudes between 0 and 450 m.a.s.l. (Mucina & Rutherford 2006). Hyperolius pickersgilli is a coastal species and has not been recorded above 340 m (Bishop 2004b). Areas of the Province of KwaZulu-Natal above 450 m altitude were therefore masked out of the background selection and also out of the predicted geographical range of the species.

Model accuracy was assessed by the area under the curve (AUC) of the receiver operator characteristic (ROC), which is a measure of discrimination ability (presence from background), where an AUC of 1 = perfect prediction, 0.5 indicates prediction no better than random and AUC values > 0.75 are considered useful predictors of distribution (Fielding &

Bell 1997; Elith et al. 2006). An AUC > 0.9 is considered outstanding (Hosmer & Lemeshow 2000; Van Gils & Kayijamahe 2010). Jack-knife tests performed by the model are used to evaluate the importance of each predictor variable in explaining the observed species distribution (Philips et al. 2006; Monterosso et al. 2009). The response of H. pickersgilli to each variable was evaluated from the response curves generated by the model (Philips et al.

2006; Monterosso et al. 2009).

The resultant Maxent probability map (Figure 4.1) was imported into ArcMap 10.0 (ESRI Inc. 2011), multiplied by 1000 and exported to the Idrisi Geographic Information System (GIS; The Andes Edition, Version 15, 2006; Eastman 1999), in which further overlays were made with the land transformation and wetland coverages for KwaZulu-Natal (Ezemvelo KZN Wildlife) and the ground-truthing data. Wetland types suitable for H. pickersgilli were determined from an overlay of the distribution record locations on the wetland coverage in the Idrisi GIS, and the probability of occurrence of H. pickersgilli in the suitable wetlands obtained from the Maxent probability map through overlay with the suitable wetlands map.

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Hard-transformed land was subtracted from the Maxent probability map to eliminate as many transformed wetlands from the resultant map as possible.

From a previous version of the probability occurrence map, created without the use of a mask and the cross-validate parameter, wetlands with probability of 60 % or greater occurrence for H. pickersgilli were selected and over-layed with 1:50 000 topographical maps (Chief Directorate: National Geo-spatial Information, Mowbray, Cape Town) for the purposes of directing the ground-truthing exercise.

Potential meta-populations of H. pickersgilli were delimited using RAMAS GIS (Akçakaya 2005). The scale and size of the occurrence probability wetland map was adjusted through pixel thinning to a pixel size of 40 m, to enable the GIS program to carry out the required analysis. The resized map was then re-classed to boolean, with wetlands having a probability of occurrence for H. pickersgilli of more than zero being assigned a value of one. Although this may be an overestimate of the extent of occurrence of H. pickersgilli, any other cut-off probability was considered to be arbitrary. The maximum dispersal distance was estimated to be 2 km, as based on the distances to the nearest probable breeding wetland at known sites, and on observations of H. pickersgilli outside of wetlands up to 1.6 km (A. Wilken; J. Harvey pers. comm.).

A friction map for the movement of H. pickersgilli was developed from the KwaZulu-Natal 2008 landcover coverage, according to an order of magnitude scale, and the values for the landcover classes estimated were as follows: 1 – wetlands, grassland / bush clumps mix, grassland; 10 – natural water, irrigated permanent orchards (banana, citrus), commercial sugarcane, emerging farmers‟ sugarcane, forest, dense bush (70-100% canopy cover), bushland (< 70% canopy cover), woodland, forest glade; 100 – golf courses, low density settlement, subsistence (rural), annual commercial crops irrigated, degraded forest, degraded bushland (all types), degraded grassland, old cultivated fields (secondary grassland), old cultivated fields (secondary bushland), smallholdings (grassland), airfields, old plantation (high vegetation), old plantation (low vegetation), rehabilitated mines (high vegetation), rehabilitated mines (low vegetation); 1000 – plantation, mangrove wetlands, dryland permanent orchards (cashew nuts), built-up dense settlement, annual dryland commercial crops, KZN main & district roads, KZN railways, 10 000 – clearfelled plantation, permanent

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dryland pineapples, mines and quarries, bare sand, erosion, bare rock, alpine grass-heath, KZN national roads, dams, estuarine water, sea water, bare coastal sand, outside KZN boundary. The friction map was used to illustrate potential linkages between wetlands in terms of maintaining meta-population dynamics.