Patterns of Bird Diversity in Kruger National Park, South Africa: Insights from Distribution Modelling Using Point Count Data in 2009
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Patterns of bird diversity in Kruger National Park, South Africa: insights from distribution modelling using point count data in 2009 Peter Long1,2 & Frazer Higgins1,2 1 Department of Biology and Biochemistry, University of Bath, Claverton Down, Bath BA2 7AY, UK +44 (0)1225 385437 [email protected] 2 Operation Wallacea Ltd. Hope House, Old Bolingbroke, Spilsby, Lincolnshire, PE23 4EX, UK Introduction Kruger National Park in South Africa covers an area of 18,989 square kilometres, making it one of the largest game reserves in Africa. It has become a major tourist attraction, largely due to the biodiversity within the park which comprises Baobab sandveld, Mopane scrub, Lebombo knobthorn-marula bushveld, mixed acacia thicket, Combretum-silver clusterleaf woodland and riverine forest ecosystems. Kruger supports over 500 species of birds, almost 150 species of mammal (including all of the Big Five), over 100 species of reptile and almost 2000 plant species. Operation Wallacea and WEI have worked in partnership with SANparks to collect biodiversity monitoring data in Kruger. Between 4th July and 12th August 2009, teams of volunteers led by experienced guides conducted bird point counts across Kruger National Park, South Africa. Additionally point counts took place in Nzikazi concession of Kruger from 28th April until 12th August. The aim of this study is to use this dataset to explore patterns of bird diversity in the park and develop recommendations for monitoring birds in Kruger. In this study we use a high-quality field monitoring dataset for an assemblage of birds integrated in GIS with readily available environmental data derived from remote sensing in order to model and validate the distributions of 51 bird species in Kruger. Methods Spatial sampling During a preparation visit to Kruger in February 2009 a set of randomly positioned potential sample sites were generated in ArcGIS to satisfy the following criteria. Firstly zones in which sample sites could be placed were generated to satisfy the following criteria: 1. Sample sites had to be in National Park 2. All sites had to be less than 40km from one of the selected camps 3. They could not be in a Wilderness Area 4. They had to be >100m from the road so that when surveys occurred it did not attract attention from passing tourists. 5. They could not be more than 400m from the road to reduce the risks to the surveyors. Secondly, a stratified random set of 460 potential sample sites were generated within these zones, stratified by 17 land types represented in Kruger (in proportion to area). Additionally points had to be at least 500m from their nearest neighbour. The potential sample sites were then visited by Tom Avent and 129 were selected for use in 2009 which were within a Voronoi polygon centred on one of the four selected camps: Nzikazi, Skukuza, Letaba and Berg en Dal and which were deemed to be safe for volunteers to visit. The list of sample sites at which bird point took place in 2009 is given in the appendix. Having finally selected the set of sample sites, the samples were then checked to ensure they adequately represented levels of elephant visitation and density, herbaceous biomass and fire frequency. Bird point counts took place at the centre of a 100m square sampling unit, from which habitat structure data was also collected. Field data collection Team of volunteers led by a guide completed 10 minute point counts on 3 occasions at the central point each of the sample sites. In order to reach the site the teams caused some disturbance in order to avoid an encounter with dangerous game. Upon getting to the site, they therefore waited for a 10 minute period to allow birds to return. On each bird point count occasion the team recorded the site code, the date, the start time, the name of the main observer and whether it was raining or windy. After the settling down period the 10 minute point count began. When a cluster of individual birds was seen or heard the team recorded the species, number of individuals, their approximate distance (in 10m bands to 50m), the method of observation (seen, heard, seen and heard) and the time after the start of the point count. Fig 1. Distribution of 129 bird point count sites used in 2009. Four camps, Nzikazi, Skukuza, Berd-en-Dal and Letaba were used to access the sample sites. Field data was recorded onto paper forms and then entered into a custom Access database on return to camp. The database is designed to reduce data entry errors and streamline analysis by folding together data from several tables (particularly bird species, sample sites, bird point count occasions and bird point count records) to produce queries which contain data prepared for analysis. Environmental data In order to describe the environment of Kruger we prepared seven maps of environmental variables which were used as covariates in species distribution models. All maps were of the same resolution and spatial extent and perfectly geocoded to each other such that they are ‘overlayable’ in subsequent map algebra operations. We used eight Landsat 5 and 7 scenes collected in summer 2008 to characterise landcover in the park (Table 1). The scenes were downloaded from the USGS, and the metadata was used to write macros to first correct all bands of all scenes to at-sensor radiance and then normalise this radiance in all bands of all scenes to top of atmosphere reflectance. These scenes were then overlaid in the sequence given in table 1 to simultaneously gapfill the SLC_off gaps in the Landsat 7 scenes and mosaic all scenes together to produce a complete coverage of Kruger. The six bands produced were then clipped to the outline of Kruger National Park. We then performed a tasselled cap (TC) transformation using TM and ETM coefficients to reduce the dimensionality of this multispectral data set and produce three statistically orthogonal layers which are biologically meaningful: TC greenness represents the amount of healthy green vegetation, TC brightness represents the amount of bare soil, and TC moistness represents soil moisture (Figure 2). We used a digital elevation model derived from topographic mapping to create an elevation map (Figure 2) We used three spatially interpolated climate surfaces (Hijmans et al, 2005), Mean annual temperature, Total annual precipitation and Precipitation in the driest quarter (Figure 2). Table 1. Landsat scenes used to derive environmental variables for Kruger WRS path row Date Satellite Sensor Solar elevation (°) Scene ID Gapfill order WRS-2 p168r077 14 September 2008 Landsat 5 TM 47.39 LT51680772008258MLK00 1 WRS-2 p169r076 05 September 2008 Landsat 5 TM 45.60 LT51690762008249MLK00 2 WRS-2 p169r075 05 September 2008 Landsat 5 TM 46.65 LT51690752008249MLK00 3 WRS-2 p168r076 22 September 2008 Landsat 7 ETM 51.59 LE71680762008266ASN00 4 WRS-2 p168r076 21 August 2008 Landsat 7 ETM 41.66 LE71680762008234ASN00 5 WRS-2 p168r078 21 August 2008 Landsat 7 ETM 39.35 LE71680782008234ASN00 6 WRS-2 p168r078 05 August 2008 Landsat 7 ETM 35.18 LE71680782008218ASN00 7 WRS-2 p168r078 01 May 2008 Landsat 7 ETM 37.83 LE71680782008122ASN00 8 Species distribution modelling In this study we elected to make species distributions using generalised linear models (GLM). We used a bound query from our biodiversity monitoring database which unites all bird records with details of the occasion on which the record was made in order to select the species for which we have presence records in at least 10 unique spatial locations. We then selected only these records for further analysis, rarefied such that there were no spatially duplicated records for any species. We then created a set of randomly placed pseudo-absence points for each species within the boundary of Kruger such that for each species there was an equal number of pseudo-absences and presences. In order to be able to validate our models, we used k-fold partitioning (Fielding & Bell 1997) and split the combined presence and pseudo-absence data for each species into 10 equal sized partitions. All point were then intersected with our seven maps of environmental covariates to create a complete dataframe for analysis. In our study we began by attempting to identify the correct model structure for each species by using the full dataset, we then returned to validate our models using partitioning methods. All statistical analysis was performed in R. All our models were GLMs with a logit link function and binomial error structure since the response variable in this study is a probability of species occurrence, which must be constrained to the range 0-1 and which exhibits non-constant variance across this range. Since some species had as few as 10 presences and thus only 20 cases in the full dataset once pseudo-absences had been generated and since there are seven covariates to analyse, there is potential for numerical problems due to the low case:variable ratio if maximal multivariate models were made for all species. Instead, we elected to first make a set of bivariate GLMs to identify the covariates which were significant for each species. Then we entered only these covariates into a full model for the species. We used a multi-model inference (Burnham & Anderson 2002) approach to simplify stepwise the full models by sequential deletion of the least significant term and compared models at each stage using the Akaike Information Criterion (AIC) to identify the structure of the minimum adequate model (Tables 2 and 7). Figure 2. Environmental covariates used in species distribution models We then created 10 partitioned MAMs for each species using the cases from 9 out of the 10 partitions and compared the predicted probability of occurrence with the 1 or 0 presence or pseudoabsence in the corresponding validation partition.