Projected global climate change and impacts on biodiversity

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Chapter 3 : MATERIALS AND METHODS

Introduction

This chapter presents the various methods that were used in the collection, analysis, presentation and interpretation of data in this study. The methods are discussed in line with the aim, specific objectives, research questions and conceptual framework presented in previous chapters. Key aspects discussed include the research paradigm, research design, sampling methods, reconnaissance surveys, field data collection methods and procedures, document analysis. These are discussed in the light of the validity and reliability of methods used.
As the study assesses the response of vegetative species diversity to climate change under semi-arid conditions of Masvingo province in Zimbabwe, the following specific objectives were spelt out: (a) determine the climatic trends in Masvingo province over a 40 year period; (b) examine the relationship between climatic elements and vegetative species diversity (c) assess the relationship between vegetative species diversity and NDVI calculated from remotely sensed biophysical data, and (d) evaluate changes in vegetative species diversity from vegetation indices maps over the 40 year period. To address the stated aim and specific objectives, a multiplicity of research methods were adopted. The methods were deemed adequate in addressing the wider spectrum of key variables to address the posed research questions.

Strategy of inquiry

In this study, a mixed methods design was adopted as the strategy of inquiry. The design uses both quantitative and qualitative methods (Gray, 2011). The quantitative approach is rooted in the positivist paradigm (Collins, 2010) while the qualitative approach is grounded in the phenomenological philosophy (Corbetta, 2003). Morgan (2008) postulates that the mixed methods design emanates from the pragmatic school of thought and is being widely used by researchers from various disciplines. Spradley (1980), Bryman (1988) and Patton (1990) have contributed significantly to the development of this paradigm. The approach is also rooted in the argument that knowledge is generated from activities, circumstances and consequences and not antecedent conditions as in the positivist philosophy (Sango, 2013). The choice of the mixed methods design was based on the sense that it uses the strengths and similarities of both qualitative and quantitative approaches. It absolves the weaknesses of each of the research paradigms by capitalising on the strengths of both. For example in the positivist paradigm, the assumption that all changes can be perceived as a result of the relationship between two variables (e.g. climate and species diversity) could not be accurate as correlation is not always causality. This gap can be filled in by a phenomenological paradigm which tries to understand the views and reactions of the people who have been interacting with the environment over a long period of time concerning the response of vegetative species diversity to climate change under semi-arid conditions.
Punch (2011) reiterates that the mixed method design is highly pragmatic and convenient as it allows the researcher to use quantitative and qualitative techniques either interdependently or independently. Thus, it is vastly flexible and can be used in diverse research projects. While quantitative methods focus on the collection of facts, qualitative methods place prominence on the meanings derived from the facts. Figure 3.1 shows the methodological approach used in this study.
Plano (2010) avers that the choice of the mixed methods approach is dependent on a variety of reasons. These include, inter-alia; to analyse problems from different standpoints to develop and understand the meaning of a singular perspective, to make use of both quantitative and qualitative data to better understand a problem; to develop a complementary picture; to compare, validate, or triangulate results; to provide illustrations of context for trends; or to examine processes/experiences along with outcomes. In this study, the mixed methods approach has been selected to merge quantitative and qualitative data to develop a more complete understanding of the impact of climate change on natural vegetative species diversity. In addition, it is a way of validating the results emanating from image analysis.
Thus, positivist and phenomenological approaches were combined under this study. As a research paradigm, positivism is associated with scientific theories and depends on quantifiable observations that lead to statistical analysis. It has an “atomistic, ontological view of the world as comprising discrete, observable elements and events that interact in an observable, determined and regular manner”, (Collins, 2010). Po sitivists view success of natural science recently as stemming from scientists’ refusal to go beyond what can be supported by empirical evidence, particularly evidence emanating from careful observation of phenomena.
The phenomenological approach seeks to understand, analyse and describe phenomena without emphasis on quantitative measurements and statistics (Dawson, 2007). It focuses on qualitative interpretation of people‘s perceptions and meanings attached to phenomena (Lincoln and Guba, 2000). Contrary to positivism, the phenomenological approach is more subjective than objective. It provides space for interpretation of phenomena and associated changes such as those observed in the assessment of climate change impacts on vegetative species diversity as opposed to strict quantitative measurements. Leedy (1989) claims that the qualitative research methodology is considered “warm” to the central pro blem of research as it investigates issues identified earlier in addition to interpersonal relationships, meanings construction, experiences and associated thoughts or feelings. With this, the researcher attempted to attain rich, deep, real and valid data on climate change experiences and the associated responses of vegetative species diversity in Masvingo province.

Vegetative species documentation and diversity assessment

Individual types of vegetative species were documented and followed by diversity assessments for the whole province. Documentation was done on selected sampling plots, which were deemed representative, initially in August 2013 followed by subsequent seasonal documentations within the same plots to assess changes. Diversity assessments were done using the Shannon weaver and Simpson’s diversity indices.

Plot size determination

The size of sampling plots can influence quality of data collected through affecting representativeness. It determines sampling density, time and resources used in a study. In this study, the species area method was used to determine the size of the sampling plots. This method involves plotting the number of species (Species richness) identified in plots of successively larger size, so that the area enclosed by each one includes the area enclosed by the smaller one. Thus 100 m2, 400 m2, 900 m2, 1600 m2 and 3600 m2 plots were successively constructed to determine the optimum plot size as illustrated in Figure 3.2. Species richness for each plot was recorded. In the 100 m2, 400 m2, 900 m2, 1600 m2 and 3600 m2 plots, 5, 7, 12, 12, 12 species were observed respectively.
The data was plotted in a graph as shown in Figure 3.3 to determine the optimum plot size. The optimum plot size is the one in which the number of species identified will not change with an increase in the size of the plot size. As illustrated in Figure 3.3, the 900m2 plot was identified as the optimum plot size. This falls within the recommended range of 400-2500 m2 as postulated by Sutherland, (1996).

Sampling and data collection

A GIS based nested non-aligned block sampling method was used to sample the study plots from which vegetative species data were collected. The method uses a grid as a basic template, where sampling locations are randomly nested. The grid is a row and/or column that divides space into a unit or units of equal size. This method permits multi-level assessment of variables at varied scales (Chapungu and Yekeye, 2013; Urban and Liu, 2002). It takes into consideration regions of obvious variations, reducing sampling bias as samples will be taken from each area of geographical difference. The sampling was done on the map of Masvingo province using the Integrated Land and Water Information System (ILWIS) software. The process involved 3 main steps.
Step 1: Grids of the same size were overlaid on the map of Masvingo province (Figure 3.4 a). These grids were meant to divide the study area in a way that would allow samples to be obtained from all areas of geographic differences. All grids covering more than 40% of the province were selected. Each grid represents an area from which samples were taken. This makes the samples more representative as they cover all geographic areas throughout the study area. A total of 22 grids were selected.
Step 2: Grids selected in step 1 were further subdivided into smaller grids of same size (Figure 3.4 b) from which 3 were randomly selected using the random point generator in ArcView GIS (ESRI, 1992-1998). Thus, the number of selected grids increased to 66.
Step 3: Grids in step 2 were further subdivided to come up with smaller grids of same size (Figure 3.4 c). Three smaller grids or sub cells were randomly selected from each larger grid established in step 2. Thus, the number of selected grids increased to 198. These were the sampling points from which data was collected. From these selected grids, plots of 30 m x 30 m were established. Figure 3.4 is an illustration of the nested non-aligned block sampling design used in the study.
However, inaccessibility of some randomly selected points was an impediment to collection of data from all the 198 sampling sites. In some cases, the points were located at mountain tops with forest thickets that were difficult to work in. In others, the points fell in the middle of Lake Mutirikwi while in a few isolated cases access was denied at private properties. Figure 3.5 shows the final sampling points in the study area and the points that were inaccessible due to various reasons. It is noted that only 4.04 percent of the sampling points were inaccessible and the final sample constituted of 189 points (See appendix II for the list of points and their coordinates).
The coordinates of the centres of the selected grids were fed into a Hand-held Global Positioning System (GPS) receivers which were then used to navigate to the point locations at approximately 10 meters error. Species counting was conducted within the study units (30 m x 30 m plots). To avoid double counting and skipping of some species, the plots were subdivided using a rope into smaller units that were easy when counting species. Furthermore, within the sampling plots, the Point Centre Quarter Method (PCQM) (Mitchell, 2007) was used to collect various data used in assessing status of vegetative species diversity. The technique involved the observer moving along a transect line in a predetermined direction within the plot, recording data at predetermined intervals. Figure 3.6 shows the plot and transect lines constructed at regular intervals of 6 meters from which measurements were done.
All vegetative species within the confines of the established plots and quadrats were assessed and quantified to determine diversity of species. The rooted frequency approach (Chapungu and Yekeye, 2013; Ludwig and Reynolds, 1988), where only trees, herbs and grasses with roots found within the confines of the plots and quadrats were counted, was used. 30 m transect lines in each selected sampling plot were followed and measurements done at 6 m intervals. At each sampling point the tree nearest to the transect line was recorded together with the distance as illustrated in Figure 3.7.
Species identification and distinction within the plots were done by a plant botanist from the Zimbabwe National Herbarium. The purpose was to guard against attributing particular species to the inaccurate species genera, double counting and skipping of some species. All the species data collected within plots were recorded on data sheets. This data was collected from the same plots four times over a year to cover all seasons (summer –November to March, Post Summer-April to May, winter- June to August and Post winter- September to October) to monitor seasonal changes.

Sampling of graminae and other small species

In this study all vegetation strata were considered. Thus, the assessment criteria for graminae species was different from that of tree species. Within the established plots, a radial arm was designed to facilitate the capture of variations in small, particularly grass, species within the 900 m2 plot. Using the radial arm as the sampling framework, data on small vegetative species were collected from four quadrats: one from the centre, one from the north east, one from the south east and the other from the north-west. The angle between arms was 1200 while the length of the arms was 12.2 m. To construct the radial arm, a campus was used to establish the azimuth of the arms. At the end of each arm and at the centre, a 1 m2 quadrat was designed as shown in Figure 3.8.
Small vegetation species within the 1 m x 1 m quadrats were assessed to determine species richness and evenness. The grasses and other small species were identified and the percentage cover determined with the help of a plant botanist from the national herbarium of Zimbabwe.

Satellite image downloading and processing

The images were acquired from online Landsat archive via GloVis web-link (http://glovis.usgs.gov/). The year for the first imagery was determined by the availability of imagery with bands necessary to calculate NDVI. The selection of years was also determined by the availability of free imagery from the GloVis web link. The study however ensured that the selected images are distributed across specific decades e.g. between 1970 and 1980, 1980 and 1990, 1990 and 2000, 2000 and 2010, 2010 and 2020. It was also considered that there is a gap of more than 8 years between the years. The Landsat images were acquired in digital number (DN) format and calibrated to spectral radiance units (W m–2 sr–1 µm–1). The algorithm developed by Chander et al. (2009) specifically for calibrating Landsat images and the calibration coefficients were provided together with the respective Landsat image files as metadata files as shown in Equation 4.1:
Where L is the quantized calibrated pixel value. Qcal is the calibrated and quantized scaled radiance in units of digital numbers, Lmin is the spectral radiance at QCAL = 0, Lmax is the spectral radiance at QCAL = QCALMAX, and QCALMAX is the range of the rescaled radiance in digital numbers. The conversion from DN to spectral radiance was done by implementing the Chander et al. (2009) algorithm using the Environment for Visualizing Images (ENVI) software.
Landsat 8 Thematic Mapper (TM) imagery with spatial resolution of 30 m was for analysis of NDVI. NDVI is an arithmetical indicator mostly used as a surrogate of plant biomass from remotely sensed data (Rulinda et al., 2010; Kromkamp and Morris, 2006; Tucker, 1979). This index uses the visible red band (0.4–0.7 µm) and near-infrared (NIR) bands (0.75–1.1 µm) of the electromagnetic spectrum (Rulinda et al., 2010; Tucker, 1979) to analyse remotely sensed data.
The relationship between NDVI and species richness was examined to confirm the utility of remote sensing in predicting vegetative diversity. NDVI was calculated using the formula shown in Equation 4.2 (Gao, 1996):
Where and are the reflectances of the near-infrared (NIR, 0.78–0.89 m) and red band (0.6-0.7) regions, respectively.
The greater the difference between the NIR and the Red reflectance the higher the biomass. In this study, the hypothesis is that the higher the biomass the greater the diversity of species. The NDVI values range from −1 through 0 to 1, where neg ative values are a sign that there is water, zero symbolises bare soil while positive values signal healthy vegetation. NDVI was used over other indices because it has low sensitivity to soil differences, it is a function of a ratio, therefore, it is less sensitive to solar elevation, and it is very sensitive to the amount of green vegetation. Tables 3.1 and 3.2 show the paths, row, seasons and dates of the images acquired for NDVI analysis for wet and dry seasons.

Rainfall and Temperature data

Rainfall and temperature data were obtained from weather stations within and near Masvingo province (Figure 3.9) which are run by the Meteorological Services Department (MSD) of Zimbabwe. Specifically, data for Zaka, Masvingo Airport, Chisumbanje, Buffalo Range and Makoholi weather stations were used.
The dataset obtained from the MSDZ was incomplete. The last records from the data were in 2010 yet the research needed data for a period spanning to 2014. Other data was then obtained from the National Climate Data Centre (NCDC) which is managed under National Oceanic and Atmospheric Administration (NOOA) programs for preserving, monitoring and provision of climate and historic weather data (www.ncdc.noaa.gov). The NCDC had records spanning throughout the period under assessment but not for all other districts except Masvingo. To use both sets there was need for validation to assess whether the two systems recorded similar data from the same station. This was done through regression analysis of available data from the two data sources.
Rainfall data validation was performed through regression of data from the meteorological department of Zimbabwe and that obtained from the National Climate data Centre. This was done in order to use both sets of data since no one source had a complete set of bioclimatic data. Spearman rank correlation coefficient analysis revealed a strong positive (r = 0.95) relationship between the two data sets with 0.91 as the coefficient of determination. Figure 3.10 shows the regression results of MSDZ data by NCDC data.
Given the strong positive relationship between the two data sets, the data from the two sources were combined and used for the analysis of climate change in the study.

Questionnaire surveys

Questionnaire surveys were administered in local communities at household level where the natural vegetative species exist. It is generally understood that local communities are aware of the changes that have taken place in their localities over time. Thus, a survey questionnaires was used to gather information on the impact of climate change on vegetative species diversity. This information complemented data obtained through direct observation and remote sensing. Adoption of questionnaire surveys as a tool for data collection was based on its robustness in collecting both quantitative and qualitative data from subjects that have experienced changes over time. Stimpson (1996) opined that questionnaire surveys provide snapshots of existing conditions at specific localities. Mapira (2015) aver that they remain one of the cheapest methods to collect data that can be useful across various disciplines. Furthermore, they are a tool that are cost effective and can be used in large sample sizes based on a structured design where the researcher poses specific questions relevant to the study (Lincoln & Guba, 2000; Ian, 1996).
Questionnaire surveys contain close-ended and open-ended questions. Close ended questions require objective answers selected from a provided list while open ended questions allow respondents to express their views with high flexibility. The surveys used in this study contained both types of questions and managed to capture quantitative data as postulated by Bailey (2007) who observed the aptitude of questionnaires to generate data acquiescent to transformation into quantitative data that can be analysed using statistical procedures. Thus, answers to particular questions can be organised such that parametric and non-parametric tests can be computed.
The sampling criteria followed in the administration of questionnaires was more or less similar to the one adopted for vegetative species assessment. In this case, a household in the final selected plot location was randomly selected to participate in the questionnaire surveys. A total of 198 samples were planned but due to inaccessibility of some plots, a total sample of 189 questionnaires was distributed. The response rate for these surveys was 95.24%. The observed reasons attributed to lack of 100 percent response rate include, inter alia, busy schedules, hesitancy to provide wrong information and general truancy.

Key Informant Interviews

Key informant interviews were conducted to infer data from important institutions and individuals involved in the management of natural vegetative species diversity and climate change related impacts. Thus, the ministry of Environment, Water and Climate was regarded as an important stakeholder in climate change and biodiversity issues. In addition, the Climate change office of Zimbabwe and the Meteorological Services Department (MSD) were regarded important as well with regards to climatic patterns in the region. Furthermore, the Forestry Commission of Zimbabwe and the Environmental Management Agency (EMA) were regarded as important to provide insight on vegetative species diversity dynamics over time. At community level, traditional leaders were regarded as key informants due to their influence as the custodians of natural resources. Members from the above stated institutions and 7 traditional leaders, one from each district, were interviewed as key informants.
Sango (2013) put forward that interviews entail gathering of verbal data from individuals directly or indirectly affected by a phenomenon under investigation. The process involves asking questions whose responses provide answers to the research questions of the ongoing study. Of the different types of interviews available, this study used the semi-structured type in which data collection process was flexible but at the same time maintaining some structure over the concepts being discussed. An interview guide, which is basically a set of pre-conceived questions, was used to guide the interview process. The process enabled the interviewer to have a dialogue with the interviewees as postulated by Fontana and Frey, (1994). Using the semi-structured interviews, the views of the key informants were fully characterised based on their knowledge, expertise and experience with regards to the impacts of climate change on natural vegetative species diversity.

Data analysis methods

The data collected through various collection methods were analysed using different methods and procedures based on the type of data and the objective being addressed. Both exploratory and confirmatory data analysis approaches were used.

Normality tests

Rainfall and temperature data, which constituted the time series data, as well as vegetative species data were tested using the Kolmogorov-Smirnov test to ascertain whether they deviate from normal distribution or not. This helped in determining whether the data satisfy assumptions of parametric or non-parametric statistical analysis methods (Chikodzi and Mutowo, 2014). Parametric tests are applicable when the data assumes a normal distribution; otherwise it is ordinarily sensible to use non-parametric tests (Lettenmaier, 1976; Hirsch et al., 1993). In this study therefore, non-parametric statistical analysis methods were used.

Auto-correlation and Pre-whitening

Prior to trend analysis using the non-parametric Mann-Kendal test, meteorological and vegetative species data was initially tested for autocorrelation to determine the need for pre-whitening. Auto-correlation is the correlation of a time series with its past and future values (Hamed and Rao, 1998). Its detection would require the data to be pre-whitened. Hamed and Rao (1998) noted that geophysical time series are frequently auto-correlated because of inertia or carryover processes in the physical system. This complicates the application of statistical tests by reducing the number of independent observations thereby increasing the chances of detecting significant trends even if they are absent and vice versa.
Pre-whitening is the process of removing undesirable autocorrelations from time series data prior to analysis. Thus, the data was pre-whitened in Paleontological statistics (PAST 3.0) software using the Autoregressive Integrated Moving Average (ARIMA) model (Hamed and Rao, 1998). The ARIMA model performs time series forecasting and smoothening and project the future values of a series based entirely on its inertia. It takes into account trends, seasonality, cycles, errors and non-stationary aspects of a data set when making forecasts. It reduces residuals to white noise in the time series hence removing the possibility of finding a significant trend in the Mann-Kendall test when actually there is no trend (Von Storch, 1995).

Table of contents
Dedication
Declaration
Acknowledgements
Table of contents
List of Figures
List of Tables
List of Appendices
List of Acronyms
Abstract
Key words
Chapter 1 : INTRODUCTION
1.1. Background of the study
1.2 Statement of the problem
1.3 Aim of the study
1.4. Specific objectives
1.5 Research questions
1.6 Significance of study
1.7 Study Area
1.8 Thesis outline
Chapter 2 : LITERATURE REVIEW
2.1 Introduction
2.2 Global climate change: Concepts and trends
2.3 Projected global climate change and impacts on biodiversity
2.4 Climate Change in southern Africa
2.5 Climate change in Zimbabwe
2.6 Climate change in Masvingo province
2.7 Definition of Biodiversity
2.8 Vegetative species diversity
2.9 Global Climate change and biodiversity
2.10 Issues of scale and climate change impact
2.11 Remote sensing and biodiversity assessment
Chapter 3 : MATERIALS AND METHODS
3.1 Introduction
3.2 Strategy of inquiry
3.3 Vegetative species documentation and diversity assessment
3.4 Satellite image downloading and processing
3.5 Rainfall and Temperature data
3.6 Questionnaire surveys
3.7 Key Informant Interviews
3.8 Data analysis methods
3.9 Conclusion
Chapter 4 : RESULTS AND DISCUSSIONS
4.1 Introduction
4.2 Climate change trends in Masvingo province from 1974 to 2014.
4.3 Vegetative species diversity and climate in Masvingo Province
4.4 Relationship between NDVI and Species diversity
4.5 Changes in species diversity between 1974 and 2014
4.6. Discussion
Chapter 5 : CONCLUSIONS AND RECOMMENDATIONS
5.1 Conclusions
5.2 Recommendations
REFERENCES
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