Girdled Lizards Research: Namazounorus Pustultus And Karusasaurus Jordain Girdled Lizards

Introduction

Background

Across the globe, conservation and the protection action for species that are being endangered or at the edge of facing a risk of getting extinct is being put in place. The main issue with the ecology and conservation biology is the determination of how the species are distributed in space, including identifying the factors limiting their distribution (Fourcade, Engler, Rödder, & Secondi, 2014). There have been several method that have been used for species modelling methods (SMM), also known as the ecological niche modelling (ENM), these were developed during the early 1980’s (Boyce & McDonald, 1999; Ripley, 1996). This was used to relate the known localities of the species with the environment variables. To get the end result, that predicted the potential geographical distribution range in which the species could be found. According to (Kumar & Stohlgren, 2009) the prediction and mapping of suitable potential habitat for both the threatened and endangered species is very critical for monitoring and restore their declining natural populations in their natural habitat. Also using the spatial distribution modeling tools to estimate a set of functions that relate environmental variables and habitat suitability in order to approximate the species’ niche and potential geographic distribution(Warren & Seifert, 2011).

However, distribution data on this endangered species are often scarce and clustered making commonly used habitat modeling methods difficult. With the Species distribution modeling tools or the habitat distribution modeling, which has been widely being used for many ecological applications and establishing a relationships between the occurrences of species and environmental conditions within an area(Morales, Fernández, & Baca-González, 2017). There are many methods that have been used for to model the distribution of species, such as the Generalized Linear Models, Reference Selection Function, including the Maximum Entropy Maxent, which in the recent years became increasingly popular (Fourcade et al. , 2014). This is due to is ability to perform better than other modelling technique and being efficient yet not too sensitive to small sample sizes and its simplicity to use. Also being used as a Habitat suitability model, to describe the habitat of rare species (Barazowski, 2016).

In Africa,the southern part of the continent has the richest reptile diversity than any place, with the southern African reptile fauna displayng a high endemicity, particulary in lizards, which plays a major role in the ecologicsl food web. The “Girdled lizards” are predominantly found in Southern Africa, they are small- to meduim bodied and are diurnally active and spend most of their time in the crevices of the rocks. Like all the flat lizards, majority of this species is found in a habitat that is rocky and depend on rocks mainly granite,sandstone etc. , of which they take refug in the narrow crevices where they escape the temperature and sub-optimal preditors. They are known as preditors that’s sit and wait for theyie prey and are often seen basking in the sun, near the entrances of the rocks crevices,in which they retreat when they feel threatened and use their spiny tail, legs and body to hinder any sort of extraction (Stanley, Bauer, Jackman, Branch, & Le Fras N Mouton, 2011).

This study closely looks at two girdled species, that are endemic toNamibia and only found in Namibia in the world; namely the Namazounorus pustultus (known as the Herero)and Karusasaurus Jordani girdled lizards which is known to befound in the central part of the country,Windhoek. Which are also listed on the red list of the protected species – as a “level 3 protection” known as CITES(the Convention on International Trade in Endangered Species of Wild Fauna and Flora) legislation which regulates the international trade in these animals,and are highly valued in the illegal trade of pouching-money making on the black market.

Aim and Objectives

This Honours thesis aims to build a suitable Habitat model, to predict the distribution for threatened and endangered girdled lizards, which will display the ecological distribution of the two species closely being studied on namely the Namazounorus pustultus (known as the Herero)and Karusasaurus Jordani girdled lizards entirely in Namibia. The objectives that this study aims to achieve are the following:

• To build a species distribution model.
• To identify and analyse the probability distribution of the species, by predicting the suitability conditions for each species within each cell grid.

Study Area

The proposed area for this research study is the entire area of Namibia. It is located in the Southern part of Africa, extending from 17°S to 25°S latitude and stretching out at 825,615km2. it is known to be the thirty-fourth largest country in the world, laying between Namib and Kalahari desert. Its landscape is also made up of other three geographical areas: the Bushveld that lies in the north-astern part of Caprivi Strip and Angola, the Central Plateau that runs from the northern to southern part of the country and the Great Escarpment. Amongst other Sub-Saharan African counties, it receives the minimum rainfall. It has a climate that is made up of arid, sub-humid to semi-arid t hyper-arid coastal plains, with maximum temperatures being limitd by the entire region’s overall elevation.

Literature Review

The following literature reviews demonstrates and supports the usage of Maxent software for species distribution modelling: A research study by (Mooney, 2010) was carried out, on the prediction of the occurrence of the hydromates shastae (salamanders) in the Shasta County, California, using Maxent software for species distribution modelling. Two different data inputs were used to feed the model, the species occurrence data and the environment variables, using the collerative spatial distribution modelling approach because of the availability of the biological and environment data. This was done in order to estimate the predicted suitable habitat and the species habitat relationship. Data preparation was done using Esri’s Arcmap for all the 63 occurrence records, which was then converted into comma separated values (. csv) files (biological files) using the Microsoft excel to meet the input requirements of Maxent (Jueterbock, 2016,p2).

While the environment variables were converted to 10m x 10m grid cells, using Esri’s ArcGIS Desktop spatial analyst extension. This were then converted to ASCII format (America Standard Code for Information Interchange) using ArcToolbox, this is carried out, in order to evaluate the model fit in Maxent. All this was used an input data, used to generate the prediction model and an (area under the curve) AUC evaluation model. With the final results producing a model with accurate occurrence prediction, Mooney (2010) came to conclusion that the Maxent has the ability to work successfully when it comes to modelling rare and endemic species,yet using these analysis to support biological conservation and restoration. However, Cao, Bai, Zhang, Li, & Mao (2016) argue that by only using Maxent modelling,as a stand-alone to predict the potential geographical distribution of species, will lead to getting biased results. With this, producing highly suitable areas that may not be the ideal distributional region. The solution to this contradiction is to join Maxent modelling with the fuzzy logics (a mathematical evaluation algorithm) to predict the accurate potential suitable distribution areas. (explain more why on fuzzy)

Research type and Design

• Deductive Research

Deductive (read more – since u have a model and localities)

• Exploratory Research

The study being carried out for the thesis research is an exploratory research is defined as “research conducted for a problem that has not been studied more clearly, intended to establish priorities, develop operational definitions and improve the final research design”. It helps lay a foundation for to future studies determine the best research design, data-collection method and selection of subjects.

Background of Tools

This study will be using the following two software packages to process, analyse and produce the final output results:

• ArcMap

As the course made use of a wide range of software, it will be beneficial to undertake the rest that fit to be used. Such that this will be used to carry out the data processing and analysis, using the available data from different sources. Then be used to create the mas and graphs that will be used to display the findings and results.

• Maxent

This software will be used to build the habitat model. It is used for modeling species niches and distributions by applying a machine-learning technique called maximum entropy modeling.

Methods

A sequence of working steps with detailed decsription of the logical sequence, will be implemented throughout this study, as shown below in Table 1. This will be followed and carried out, in order to meet the goal and objectives of this study. During the definite implementation period of the study, steps may change to accommodate any additional changes. As shown in Table 1, the first column represents the activates or tasks that will carried out, the second column describes the methods in detail, the third column gives a description of the methods and finally the fourth column shows the expected results after the methods are carried out.

The following activities are to be conducted:

• Create two geographic databases using Catalog feature in ArcMap/ArcCatalogue
• Import unprocessed/original data in the first DB and the other is reserved for processed data.
• Sort out all attributes, then identify and eliminate any duplication within all the datasets, including the lizards’ localities.
• Projecting both vector and raster data respectively, the same projected coordinate system (UTM '84 projection )will be applied to all the dataset

Work step 5: Data AnalysisThis working step looks at the potential data processing techniques and data analysis that will be used in this study, to achieve its expected outcomes. Furthermore, it is used to analyse the spatial data looking at trends patterns and its relationships. This involves merging, resampling of the datasets, masking, conversion of Raster to ASCII (America Standard Code for Information Interchange) and Species distribution modeling. This will be achieved through the following methods: --Finish up with the descriptions, get into details and add the citations-

• Merging Data Management Tool It allows combining multiple input datasets, into a single data of the same data type, into a new feature class for analysis.
• Resample Data Management ToolChanges the cell size by modifying the raster dataset( for this project all layers resampled to 250m resolution)
• Masking/Clipping Using the Clip Data Management Tool, to clips layers to an extent or area – all layers are to be clipped on the study area’s boundary.
• Raster to ASCII (Conversion) ToolAllows converting raster datasets to an ASCII text file (. ascii) representing raster data. In this project, this will be used in Maxent software. Species distribution modeling:
• Maximum entropy modeling techniqueA set of two inputs, the georeferenced occurrence areas and the e environmental grids such as climate are fed into the model, results produces a probability distribution model of suitable conditions within each grid cell for the species being studied, based on the input data.

Work step 6: Report Compilation & Results

Based on the conditions and accurate input fed into the Maxent application, it should be able to produce outputs. Outputs showing potential distribution of the species. In this working step, a report compiled and documented based on the methodological steps will be used, and results obtained. A species distribution model will be generated - a Habitat model and generated output maps and graphs will be created. After numerous numbers of compilations of drafts done, and then viewed and quality of document credited by a supervisor, a final report is then compiled. A flowchart chart in Figure 3 shows a Mindmap for this research study, with detailed description of the logical sequence of the working steps to be carried out in order to reach the goal of the project.