Comparative Determination of the Numbers of the House

COMPARATIVE DETERMINATION OF THE NUMBERS OF THE HOUSE SPARROW, PASSER DOMESTICUS, THE CAPE GLOSSY STARLING, LAMPROTORNIS NITENS, THE CAPE TURTLE DOVE, STREPTOPELIA CAPICOLA AND THE LAUGHING DOVE, STREPTOPELIA SENEGALENSIS IN THE JOHANNESBURG AND VAALWATER AREA, WITH STUDY INTO THE POSSIBLE CAUSES OF EXPECTED DECLINES.

LINDI STEYN

DISSERTATION

Submitted in fulfilment

of the requirements for the degree

MASTER OF SCIENCE

BIODIVERSITY AND CONSERVATION

in the

FACULTY OF SCIENCE

at the

UNIVERSITY OF JOHANNESBURG

SUPERVISOR: PROF. J.N. MAINA

DECEMBER 2013

ACKNOLEDGEMENTS

My deepest thanks and gratitude to the following

 God for the abilities He has given me, and for the guidance during the execution of this project.

 Prof. J.N. Maina for his continual support, guidance and never ending encouragement.

 My parents and sister, Gert, Juset and Elna, for their love, patience and support at the times when it was most needed.

 Aunt Jurieka, Uncle Cyril, family and friends for their support and willingness to help even at very early hours of the day, it is much appreciated.

 Mr. Beric Gilbert for all his advice and assistance through the course of this degree, you are a true friend.

 The Department of Zoology at the UJ for the use of their equipment and facilities.

 All the farmers, who willingly allowed me on their farms.

 The ADU (Avian Demographic Unit) and the SAWS (South African Weather Services) for making the data available to me.

 STATKON, for assistance in statistical analyses of data.

 The University of Johannesburg for financial support and the use of their facilities.

 Financial assistance of the National Research Foundation (NRF) towards this research is hereby acknowledged. Opinions expressed and conclusions arrived at are those of the author and are not necessarily to be attributed to the NRF.

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ABSTRACT

Of the ~9,920 extant bird species, 1,253 of them are threatened. The decline of birds, particularly the House Sparrow and the starlings, over about the last three decades have been well-documented. In Great Britain, the numbers of House Sparrows have declined by as much as 60%, the starlings by 41% and the Turtle Doves by 71 %. In South Africa, the numbers of Cape Turtle Dove and the Laughing Dove have declined by 10%. A difference also exists between the numbers decline in the urban and the rural areas in other parts of the world, with the overall declines being greater in the rural areas.

Bird counts were performed for two weeks in the winter and two weeks in the summer of two consecutive years (2012 and 2013). These counts (using a point count method) were performed in an urban area (Auckland Park) and a rural area (Vaalwater). These two areas fell in two provinces namely Gauteng and Limpopo. In this study, the density indices of four bird species were determined in Gauteng (urban-) and Limpopo (rural) Provinces. Data obtained from the Avian Demographic Unit (ADU) of the University of Cape Town (UCT) for two national counts, SABAP1 (1987-1992) and SABAP2 (2007-2012), were statistically analysed. The numbers were compared for each species between the two provinces and an assessment of whether the overall indices of density had declined in the last 26 years made. After determining the declines, three reasons were investigated. These are: interspecific competition through a behavioural study, climate changes variations by weather service data analysis, and air pollutions by macrophage enumeration. The behavioural studies were performed in the same areas where the counts were done and the survey observed the relationship between the birds of interest and other invasive species like the Indian Mynas. These observations were done for 30 minutes daily at various sites, on the Auckland Park Campus of the University of Johannesburg and on the farms in the Vaalwater area. Temperature, rainfall and humidity data were obtained from South African Weather Service for the time that corresponds to the two bird counting (atlas) projects. By performing descriptive statistics on the climate data for the two provinces, a deduction could be drawn as to whether or not the weather patterns influenced the indices of density of the birds. The macrophage enumeration was conducted after the birds were captured with the aid of mist nets, lavage on the lung-air sac system performed and the cells stained.

The numbers of macrophages in the urban birds were compared to the numbers in the rural ones. Comparison was made between the different species to determine which species were most affected by high levels of air pollution.

Bird counts showed that higher numbers of Laughing Doves, Cape Turtle Doves and Cape Glossy Starlings occurred in the Vaalwater area compared to Auckland Park, while the House Sparrows were the only species that occurred in higher numbers in the urban area. The long term data analysis indicated that the House Sparrows and Laughing Doves declined between the two atlas projects (SABAP1 and SABAP2) in both provinces while the Cape Glossy Starlings increased in both areas. The Cape Turtle Doves increased in Gauteng and decreased in Limpopo between the two bird counting projects. The behavioural study showed that competition between native and invasive species like the Indian Myna, Hadida and Egyptian Goose over food resources possibly contributed to the declines in the numbers of birds. Even though the House Sparrows are also an invasive species, no aggressive behavior was noticed from them towards the other species. Weather might be an influential factor as the temperatures in Gauteng increase with 0.1°C and in Limpopo it increased by 0.45°C when the conditions were compared for the atlas project timeframes; 1987- 1992 and 2007-2012. The rainfall decreased in these timeframes in both provinces, (Gauteng from 497.3mm to 441.7mm and Limpopo from 799.9mm to 451.3mm) while the humidity was higher during the second timeframe (69.1kg/mᵌ) than the first (67.5 kg/mᵌ) in the Gauteng Province and the opposite was true for the humidity levels in Limpopo (73.8 kg/mᵌ against 71.4kg/mᵌ). Under such circumstances plant growth and ultimately the food source of the birds should have decreased. The macrophage study indicated that air pollution might not be a major factor in the declines as the numbers of macrophages normalized with body mass for all the species in the urban areas were significantly higher than the rural birds’ macrophage numbers. However, as higher number of birds were present in the urban area (according to the atlas data analysis), air pollution cannot be the foremost reason for the bird number declines. It can, however, be a contributing factor along for example with parasites. The specific cause of the bird number declines is still uncertain. A combination of factors appears to be involved.

KEY WORDS:

Passer domesticus

Lamprotornis nitens

Streptopelia senegalensis

Streptopelia capicola

Bird declines

Bird index of density

Free macrophages

TABLE OF CONTENT

CHAPTER 1: Introduction……………………………………………………………...…1

1.1 Background……………………………………………………………………………...2

1.2 Species studied....………………………………………………………………………5

1.2.1 House Sparrow (Passer domesticus)………………………………………5

1.2.2 Cape Glossy Starling (Lamprotornis nitens)……………………………....7

1.2.3 Cape Turtle Dove (Streptopelia capicola)……………………………..…..8

1.2.4 Laughing Dove (Streptopelia senegalensis)……………………………...11

1.2.5 Relationship between the three bird families……………………………..12

1.3 Suggested causes of decline of the numbers of birds…………………………….12

1.3.1 Predation………………………………………………………………….….13

1.3.2 Competition………………………………………………………………..…14

1.3.3 Disease……………………………………………………………………....15

1.3.4 Breeding success……………………………………………………………15

1.3.5 Availability of food …………………………………………………………..16

1.3.6 Changes in the agricultural practices……………………………………..17

1.3.7 Pollution……………………………………………………………………...18

1.3.8 Architectural design of buildings……………………………………….….19

1.3.9 Climate change……………………………………………………………...19

1.3.10 Electromagnetic fields…………………………………………………….22

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1.3.11 Multiple factors……………………………………………………………..23

1.4 The avian respiratory system………………………………………………………...24

1.4.1 Morphology of the lungs………………………………………………...….24

1.4.2 Airway system…………………………………………………………...…..27

1.4.2.1 Primary bronchus………………………………………………….27

1.4.2.2 Secondary bronchus…………………………………………..….27

1.4.2.3 Parabronchi (Tertiary bronchi)...………………………………...27

1.4.3 Morphology of the air sacs……………………………………………..….28

1.4.4 Ostia…………………………………………………………………..…...... 30

1.4.5 Morphometry of the avian respiratory system………………………..….31

1.4.6 Blood-gas barrier…………………………………………………………....31

1.4.7 Function of the avian respiratory system………………………….…..….32

1.4.8 Pulmonary cellular defences…………………………………………...... 33

1.4.9 Air pollution……………………………………………………………..…....35

1.5 Behaviour……………………………………………………………………………....35

1.5.1 Causes of behavioural differences………………………………………..36

1.5.2 Adaptive behaviours for survival…………………………………………..37

1.6 Hypothesis, aims and research questions……………………………………….…40

1.7 Outline of the dissertation………………………………………………………….…43

1.7.1 Oral presentations…………………………………………………………..44

1.7.2 Special awards received during the study…………………………….….45

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CHAPTER 2: The study areas……………………………………………………….....46

2.1 General information………………………………………………………………...…47

2.2 The sites………………………………………………………………………………..48

2.2.1 Gauteng: Auckland Park (Kingsway) Campus (urban).…………..……48

2.2.1.1 Location and site description…………………………………….48

2.2.1.2 Demographics of the study area………………………………...52

2.2.1.3 Meteorology…………………………………….………………….52

2.2.2 Limpopo: Vaalwater (rural)….…………………………………………...…52

2.2.2.1 Location and site description…………………………………….52

2.2.2.2 Demographics of the study area………………………………...56

2.2.2.3 Meteorology………………………………………………………..56

CHAPTER 3: Biological study procedures and techniques………………………57

3.1 Bird counts…………………………………………………………………………..…58

3.2 Atlas (bird counts) data.……………………………………………………………....59

3.3 Weather service data…………………………………………………………….……62

3.5 Behaviour ……………………………………………………………………………...63

3.4 Macrophages ……………………………………………………………………….....63

CHAPTER 4: Results………………………………………………………………….....66

4.1 Bird counts……………………………………………………………………………..67

4.2 Atlas and weather service data……………………………………………………...88

4.2.1 Comparison of the indices of density for the different timeframes in each region using listwise deletion of missing values..….…………..88

4.2.2 Comparison of bird indices of the regions using pairwise deletion for missing values…………………………………………………………..….92

4.2.3 Comparison between the two regions with all the indices of the bird species combined to determine the health of the environments……..99

4. 3 Weather service……………………………………………………………………....99

4.3.1 Temperature……………………………………………………………..…..99

4.3.2 Rainfall………………………………………………………………….…..101

4.4.3 Humidity………………………………………………………………….…102

4.4 Behaviour…………………………………………………………………………..…104

4.5 Macrophages……………………………………………………………………...….108

4.5.1 Urban (Auckland Park, Gauteng Province)……………………………..108

4.5.1.1 Correlation analysis……………………………………………..108

4.5.1.2 Regression analysis………………………………………...…..112

4.5.2 Rural (Vaalwater, Limpopo Province)……………………………………113

4.5.2.1 Correlation analysis……………………………………………..113

4.5.2.2 Regression………………………………………………….……117

4.5.3 Comparison between the rural and urban area………………..118

CHAPTER 5: Discussion and conclusion……………………………………….....124

5.1 General comments…………………………………………………………………..125

5.2 Bird counts data analysis………….………………………………………………...126

5.3 Atlas data analysis………………………………………………………………...…130

5.4 Weather service……………………………………………………………………...134

5.5 Behaviour…………………………………………………………………………..…136

5.6 Macrophages…………………………………………………….………………..….136

5.7 Conclusion and recommendations ……………………………….…………....….140

CHAPTER 6: References………………………………………………….……..…….144

CHAPTER 7: Appendix…………………………………………………………………164

LIST OF TABLES

Table 4.1: The number of birds counted and the levels of statistical significance of the differences between the two years of the study.……………………69

Table 4.2: The level of statistical significant values of the differences between counts of the rural and urban bird numbers……..……….…………………..……70

Table 4.3: The statistical differences between the two seasons of each year, with the two areas combined….……………………………..…………………....….72 Table 4.4: The significant/non-significant values of the differences between SABAP1 and SABAP2 of each region, determined by using a paired sample t-test...... 91

Table 4.5: The significance/ non-significance values of the differences between the indices of density (of the various species) in the two counting regions, determined by using an independent sample t-test……………..……….98

Table 4.6: The statistical significance levels of the differences between the weather patterns over the timeframes of the Atlas Data Project bird counts.....103

Table 4.7: The behavioural traits of the four species of interest………………...…..104

Table 4.8: The Spearman correlation coefficient of the correlation between the body mass and the number of macrophages normalized with body mass in the urban area……………………………………………………..…………….111

Table 4.9: The Spearman correlation coefficient of the correlation between the body mass and the number of macrophages normalized with body mass in the rural area…………………………..………………………………………...116

Table 4.10: The differences between the number of macrophages normalized with body mass for urban and rural both areas……………………….……....120

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Table 7.1: The weather conditions for the years that correspond to the Bird Atlas Data project………………………………….……………………………...177

Table 7.2: Complete count data set for the urban area in 2012 and 2013………...178

Table 7.3: Complete count data set for the rural area in 2012 and 2013………….194

LIST OF FIGURES

Chapter 1

Figure 1.1: Male (A) and Female (B) House Sparrow Passer domesticus...... 7

Figure 1.2: Adult Cape Glossy Starling Lamprotornis nitens………………………..….9

Figure 1.3: Adult Cape Turtle Dove Streptopelia capicola …………………..………..10

Figure 1.4: Adult Laughing Dove Streptopelia senegalensis...... 12

Figure 1.5: Dorsal view of the lungs of the ostrich (Struthio camelus).....……………24

Figure 1.6: Schematic diagram of the lung of the domestic fowl drawn as if transparent to show the airways………………………………....….…..…26

Figure 1.7: Latex casts showing the lateral- (A) and dorsal (B) views of the lung and air sacs of the domestic fowl, Gallus gallus variant domesticus………..28

Figure 1.8: Avian respiratory system illustrating the cranial- (0) and caudal groups (#) of air sacs..……………….. ………………………………………………… 29

Figure 1.9: Schematic illustration of the respiratory cycle of the avian lung-air sac system..….…………………………………………………………………….33

Chapter 2

Figure 2.1: The nine provinces of South Africa………………………...... 47

Figure 2.2: The location of the two study sites …..…………………………………….48

Figure 2.3: The University of Johannesburgs Kingsway Campus grounds...……....49

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Figure 2.4: Site one: the parking area and the rows of Searsia trees………………..50

Figure 2.5: Site two: consisting of half concrete and half natural vegetation..……...50

Figure 2.6: Site three: consisting of grass fields bordered with indigenous trees...... 51

Figure 2.7: Site four: illustrating the open grass field………..………………………...51

Figure 2.8: The sites around the town of Vaalwater……………….…………………..53

Figure 2.9: Summer (A) and winter (B) plant growth on the Groenfontein site……...54

Figure 2.10: Summer (A) and winter (B) plant growth on the Goedehoop site……...54

Figure 2.11: Summer (A) and winter (B) plant growth on the Olifantsbeen site….....55

Figure 2.12: Summer (A) and winter (B) plant growth on the Leeudrift site…………55

Figure 2.13: Summer (A) and winter (B) plant growth on the Slypsteendrift site……56

Chapter 3

Figure 3.1: The grid division of the Gauteng Province………………………………...60

Figure 3.2: The grid division of the Limpopo Province………………………………...61

Figure 3.3: The mist net construction used to catch the birds (A), and an illustration of how the bird get caught in the nets (B)…………………..…….……....64

Chapter 4

Figure 4.1: Comparisons between the two years counts of House Sparrows for both the urban- and the rural areas………………………..…………………….67

Figure 4.2: Comparisons between the two years counts of Cape Glossy Starling for both the urban- and the rural areas……………………………….………68

Figure 4.3: Comparison between the two years counts of Cape Turtle Doves for both the urban and the rural areas………………...………………….……...... 68

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Figure 4.4: A combination boxplot illustrating the difference between the summer and the winter bird counts of 2012...... …………………….71

Figure 4.5: A combination boxplot showing the difference between the summer and the winter bird counts of 2013…………..………………………………..…71

Figure 4.6: Comparison between the number of House Sparrows counted and the temperature recorded during the summer and winter seasons of 2012…………………………………………………………………………...74

Figure 4.7: Comparison between the number of House Sparrows counted and the temperature recorded during the summer and winter seasons of 2012.…………………………………………………………………………..74

Figure 4.8: Comparison between the number of Cape Turtle Doves counted and the temperature recorded during the summer and winter seasons of 2012..………………………………………………………………………….78

Figure 4.9: Comparison between the number of Cape Turtle Doves counted and the temperature recorded duing each count for 2013. The seasonal differences is also shown…..………………………………………………………………..80

Figure 4.10: Comparison between the number of Cape Glossy Starlings counted and the temperature recorded during the summer and winter seasons of 2012...... 82

Figure 4.11: Comparison between the number of Cape Glossy Starlings counted and the temperature recorded during the summer and winter seasons of 2013…………………………………………………………………...….…...84

Figure 4.12: Comparison between the number of Laughing Doves counted and the temperature recorded during the summer and winter seasons of 2013.………………………………………………………………………...... 86

Figure 4.13: Comparison between the indices of density of the House Sparrows for SABAP1 and SABAP2 in Gauteng- and Limpopo Provinces………..….88

Figure 4.14: Comparison between the indices of density of the Cape Glossy Starlings for SABAP1 and SABAP2 in Gauteng- and Limpopo Provinces……………………………………………………………………...89

Figure 4.15: Comparison between the indices of density of the Laughing Doves for SABAP1 and SABAP2 in Gauteng and Limpopo…….…………………..89

Figure 4.16: Comparison between the indices of density of the Cape Turtle Doves for SABAP1 and SABAP2 in Gauteng and Limpopo……………….………..90

Figure 4.17: Comparison of the indices of density of the House Sparrows for Gauteng- and Limpopo Provinces during the SABAP1 count………….92

Figure 4.18: Comparison of the indices of density of the House Sparrows for Gauteng- and Limpopo Provinces during the SABAP2 count...….…….93

Figure 4.19: Comparison of the index of density of the Cape Glossy Starlings for Gauteng- and Limpopo Provinces during the SABAP1 count ….…...…94

Figure 4.20: Comparison of the indices of density of the Cape Glossy Starlings for Gauteng- and Limpopo Province during the SABAP2 count……..…….94

Figure 4.21: Comparison of the indices of density of the Laughing Doves for Gauteng- and Limpopo Provinces during the SABAP1 count………….95

Figure 4.22: Comparison of the indices of density of the Laughing Doves for Gauteng- and Limpopo Provinces during the SABAP2 counts………...96

Figure 4.23: Comparison of the indices of density of the Cape Turtle Doves for Gauteng- and Limpopo Provinces during the SABAP1 count………….97

Figure 4.24: Comparison of the indices of density of the Cape Turtle Doves for Gauteng- and Limpopo Provinces during the SABAP2 count………….97

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Figure 4.25: Comparisons between the grouped indices of density of all the species or the two provinces during the two counts (SABAP1 & SABAP2)……99

Figure 4.26: Comparison between the average temperature in Gauteng Province of the two periods of the Atlas projects (SABAP1 & SABAP2)……...……100

Figure 4.27: Comparison between the average temperature in Limpopo Province of the two periods of the Atlas projects (SABAP1 & SABAP2)…………...100

Figure 4.28: Comparison between the average rainfall in Gauteng Province of the two periods of the Atlas projects (SABAP1 & SABAP2)………….…....101

Figure 4.29: Comparison between the average rainfall in Limpopo Province of the two periods of the Atlas projects (SABAP1 & SABAP2) ……………....101

Figure 4.30: Comparison between the average humidity in Gauteng Province of the two periods of the Atlas projects (SABAP1 & SABAP2) ………………102

Figure 4.31: Comparison between the average humidity in Limpopo Province of the two periods of the Atlas projects (SABAP1 & SABAP2)…………..…...102

Figure 4.32: RDA (Redundancy analysis) tri-plot illustrating the similarities between the various sites and the climate variables……………………………....104

Figure 4.33: The relationship between the body mass and numbers of free macrophages normalized with body mass of House Sparrows in the urban area…….………………………………….……...………………….108

Figure 4.34: The relationship between the body mass and numbers of free macrophages normalized with body mass of Cape Glossy Starlings in the urban area…………….……………………………………….………...109

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Figure 4.35: The relationship between the body mass and numbers of free macrophages normalized with body mass of Laughing Doves in the urban area…………………………………………….………….…….…..110

Figure 4.36: The differences in body mass between the three species studied in the urban area. ……………..………………….……………...……………….111

Figure 4.37: Comparison of the numbers of free macrophages normalized with body mass for the three species studied……………….………….……..…...112

Figure 4.38: The relationship between the body mass and numbers of free macrophages normalized with body mass of House Sparrows in the rural area…………………………………………..…………………………..….113

Figure 4.39: The relationship between the body mass and numbers of free macrophages normalized with body mass of Cape Glossy Starlings in the rural area…………………………………………………………………….114

Figure 4.40: The relationship between the body mass and number of macrophages normalized with body mass in the Laughing Doves of the rural area…………………………………………..………..……………………..115

Figure 4.41: Comparison of the body masses of the three bird species studied in rural area……….…..…...………………………………………………….116

Figure 4.42: The number of free macrophages normalized with the body mass of the three species studied in the rural area………………………….……..…117

Figure 4.43: Comparison between the number of free macrophages normalized with body mass of House Sparrows in the two study areas….………….….118

Figure 4.44: Comparison between the numbers of free macrophages normalized with body mass of Cape Glossy Starlings in the two study areas…..……...119

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Figure 4.45: Comparison between the number of macrophages normalized with body mass of Laughing Doves in the two study areas………………....….….120

Figure 4.46: The body mass comparisons of the House Sparrows…………………121

Figure 4.47: The body mass comparisons of the Cape Glossy Starlings…….…….121

Figure 4.48: The body mass comparisons of the Laughing Doves…………………122

Figure 4.49: Photomicrographs of the free macrophages from the Laughing Doves from the urban areas. ……………………....………………………….….123

Chapter 7

Figure 7.1: Differences between the numbers of House Sparrows found in 2012 in the summer on each farm in the Vaalwater (rural) area of the Limpopo Province...... 165

Figure 7.2: Differences between the numbers of House Sparrows found in 2013 in the summer on each farm in the Vaalwater (rural) area of the Limpopo Province……………………………..……………………………………….165

Figure 7.3: Differences between the numbers of Cape Glossy Starlings found in 2012 in the summer on each farm in the Vaalwater (rural) area of the Limpopo Province…………………………………………………………..166

Figure 7.4: Differences between the numbers of Cape Glossy Starlings found in 2013 in the summer on each farm in the Vaalwater (rural) area of the Limpopo Province…………………………………………………………..166

Figure 7.5: Differences between the numbers of Cape Turtle Doves found in 2012 in the summer on each farm in the Vaalwater (rural) area of the Limpopo Province ……………………………...………..……………………………167

Figure 7.6: Differences between the numbers of Cape Turtle Doves found in 2013 in the summer on each farm in the Vaalwater (rural) area of the Limpopo Province ………………………………………………………………….....167

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Figure 7.7: Differences between the numbers of Laughing Doves found in 2013 in the summer on each farm in the Vaalwater (rural) area of the Limpopo Province …………..……………………………………………….……….168

Figure 7.8: Differences between the numbers of House Sparrows found in 2012 in the winter on each farm in the Vaalwater (rural) area of the Limpopo Province …………… ……………………………………..………...…….169

Figure 7.9: Differences between the numbers of House Sparrows found in 2013 in the winter on each farm in the Vaalwater (rural) area of the Limpopo Province ……………………………………………………………….…..169

Figure 7.10: Differences between the numbers of Cape Glossy Starling found in 2012 in the winter on each farm in the Vaalwater (rural) area of the Limpopo Province ……….……………………………………………..…170

Figure 7.11: Differences between the numbers of Cape Glossy Starling found in 2013 in the winter on each farm in the Vaalwater (rural) area of the Limpopo Province ………………………….…………………………..…170

Figure 7.12: Differences between the numbers of Cape Turtle Dove found in 2012 in the winter on each farm in the Vaalwater (rural) area of the Limpopo Province …………………………..……………..……..…………………..171

Figure 7.13: Differences between the numbers of Cape Turtle Doves found in 2013 in the winter on each farm in the Vaalwater (rural) area of the Limpopo Province ………………………………………………………….………….171

Figure 7.14: Differences between the numbers of Laughing Doves found in 2013 in the winter on each farm in the Vaalwater (rural) area of the Limpopo Province …………………………………...….…………..………………...172

Figure 7.15: The reporting rate comparisons of the House Sparrows during SABAP1 and SABAP2 in South Africa…………………………………..….……….173

Figure 7.16: The reporting rate comparisons of the Cape Glossy Starlings during SABAP1 and SABAP2 in South Africa ………………………………….174

Figure 7.17: The reporting rate comparisons of the Cape Turtle Doves during SABAP1 and SABAP2 in South Africa ………….………………………175

Figure 7.18: The reporting rate comparisons of the Laughing Doves during SABAP1 and SABAP2 in South Africa ……………………….………………….....176

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Chapter 1:

Introduction and objectives.

1.1 Background

Planet earth has been transformed by humans for centuries, with natural habitats being replaced by industries, housing, farming and recreational purposes (Sodhi & Sharpe, 2006). This has created a rift in the natural balance between humans and nature (Sodhi & Sharpe, 2006). While generally such imbalance has had harmful effects on the flora and fauna, in some cases such changes have led to the spread and subsequent colonisation of areas by exotic birds (Crick et al., 2002). The introduction of exotic species influences the natural biota at different ecological levels and can be linked to the modification of population dynamics and the community structures (MacGregor-Fors et al., 2010). It has been suggested that human-induced environmental changes may lead to alterations in the range of species and that these deviations should be anticipated in order to prevent the loss of biodiversity (Mehlman, 1997).

The term biodiversity refers to the total number of species, ecosystems and genetic diversity in a geographical area (e.g. Wormworth & Mallon, 2006). Genetic diversity is the differentiation of the genotype of species and is crucial to maintaining a healthy breeding population and so avoiding extinction (Gaston & Spicer, 2004). Genetic diversity also influences the adaptability of the organism, increasing its chance of survival. Therefore the greater the degree of genetic variation that exists within a population, the lower the risk of disease and infection (Gaston & Spicer, 2004). Species diversity is defined as the number of species present in a particular area or region (Hamilton, 2004). A species is described as a group of organisms with the ability to interbreed and produce fertile offspring (Gaston & Spicer, 2004). This type of diversity can differ substantially between ecosystems. Ecosystem diversity in turn refers to the assortment of habitats found in a specific area which can be divided into natural and modified systems (Rahbek & Graves, 2001). Natural systems are those currently undisturbed by anthropogenic activities while modified systems are those that have experienced development as a result of human activities (Gaston & Spicer, 2004).

The pressure on biodiversity is mainly linked to habitat change (for example, modifications of river systems and changes in land use) (Reidsma et al., 2006). The drivers behind such pressure are economic, demographic, socio-political, cultural

and technological changes. However natural causes may also play a role, for example, climate changes (Omann et al., 2009). In Sub-Saharan Africa, biodiversity is threatened by increasing human populations, growing poverty, decrease in agricultural productivity and desertification (PFIOA, 1997). According to Birdlife International (BLI) (2000) by that time South Africa had lost 2 bird species, 59 were threatened and 64 were near threatened.

In comparison to invertebrates, birds are organisms that humans can relate to: birds are good bio-indicators of pollution (Paoletti, 1999). Bio-indicators can be used to identify habitat conservation objectives, demonstrate differences between habitat types and show anthropogenic effects on target species (Hvenengaard, 2011).

Apart from the above mentioned reason, birds also make good bio-indicators because:

A) They are easily identifiable (Francl & Schnell, 2002).

B) Long term data are available on them regarding their numbers (Francl & Schnell, 2002).

C) They are taxonomically stable (Hvenengaard, 2011).

D) Their ecology and behaviour have been well studied (Genghini et al., 2006).

E) They occupy higher trophic status and can be incorporated into bio- monitoring studies within a particular ecosystem (Genghini et al., 2006).

F) They mostly occupy small habitats (Schilderman et al., 1997).

G) Energetically active, they have a unique respiratory system, a large tidal volume and are therefore ideal for use in the determination of air pollution (Brown et al., 1997; Schilderman et al., 1997).

H) Their populations, community numbers, behaviour and reproductive success reflect the stability of an ecosystem (Sudlow, 2004).

Worldwide, currently 1313 bird species are in danger of extinction (critically endangered, endangered or vulnerable) and this number will continue to rise (BLI, 2012a). According to Birdlife International (2012a), this is about 13% of the total number of bird species existing today. The decline of birds, specifically House Sparrows and starlings, over the last three decades has been well-documented (Summers-Smith, 1999; Crick et al., 2002; Dandapat et al., 2010). House Sparrows populations in Great Britain have declined by as much as 60% (Summer-Smith & Thomas, 2002) while starlings have declined by 41% (Crick et al., 2002). In comparison with the House Sparrow and the Cape Glossy Starling, the numbers of Cape Turtle Doves have only declined by 10% in the last two decades (IUCN, 2009). The Turtle Doves have, however, declined by 71% in the UK (Sheehan, 2011). Laughing Doves (like the Cape Turtle Doves) have declined very little, their decline is estimated to be <10% over a ten year period (BLI, 2012b). In most cases there is a difference in the declining numbers between rural and urban areas.

Generally, greater declines in bird populations are found within urban areas as opposed to rural areas (Crick et al., 2002). Crick et al. (2002) showed that in the United Kingdom (UK) there has been a decline in starling numbers in rural and urban areas. The investigators demonstrated that starling populations have experienced a 66% decline in rural areas since 1962 in comparison to a very high decline of 92% of urban ones. The same urban to rural differences have been found for House Sparrows in Britain where a decline of 47% was observed in rural areas in relation to the 60% decline in urban settings (Robinson et al., 2005). Currently all these species are on the Red List of species of conservation concern in the UK (Chamberlain et al., 2007; Macleod et al., 2008; Moran, 2011).

Worldwide, there are approximately 10, 000 bird species, ranging from the smallest, a humming bird of 2.25g, to the largest, being the ostrich weighing about 130kg (Carnaby, 2010). These species can roughly be divided into two groups, namely the passerines (~6,000 species) and non-passerines (~4,000 species) (Carnaby, 2010). Passerines are also known as perching birds or songbirds and comprise the most species (Raikow & Bledsoe, 2002). This is not always the most accurate way of referring to them as most birds can perch and for many perching birds their vocalizations cannot be described as a song. An example of such a bird is the crow which is largest of the passerines. More specifically, the morphology of the foot of

passerines is better developed and equipped for perching than most other birds: it consists of four unwebbed toes, three forward facing toes and one backward facing toe without any specialized muscles which are level and not joined in any way (Carnaby, 2010). An exception to this is that of the Eurylaimidae (primitive Broadbills).

The passerines also have several other anatomical and spermatozoal similarities. The plumage of passerine birds generally consists of nine or ten primary flight feathers (remiges) and twelve tail feathers (rectrices). On hatching, all the chicks are altricial, meaning that they are born naked, blind and totally helpless (Ehrlich et al., 1988). In all passerine birds both the males and females are approximately the same size or in some cases the males maybe slightly bigger than their female counterparts (e.g. Cape Glossy Starlings) (Sinclair et al., 2002; Carnaby, 2010). However, there are exceptions to this rule, where the female is not only bigger than the male but also heavier, for example, members of the family Zosteropidae (Carnaby, 2010). Passerines also have a higher metabolic rate which contributes to these birds having higher average body temperature (40-42°C) (Lasiewski & Dawson, 1967; Sabat et al., 2010). Pulmonary morphometric specialisations have been related to their active lifestyles (Maina, 2005).

The non-passerines consist of a very diverse group of birds. Overall there are fewer species than the passerines. They are grouped together since they have very little in common with each other and with the passerines (Carnaby, 2010). They do, however, have a lower body temperature (38°C) as well as lower metabolic rates than the passerines (Lasiewski & Dawson, 1967).

The starlings as well as the sparrows are classed as passerines, while the doves fall under the group non-passerines.

1.2 Species studied

1.2.1 House Sparrow (Passer domesticus)

Five local sparrow species are found in southern Africa. These are the House Sparrow (Passer domesticus), the Great Sparrow (Passer motitensis), the Cape

Sparrow (Passer melanarus), the Northern Grey-headed Sparrow (Passer griseus) and the Southern Grey-headed Sparrow (Passer diffuses) (Sinclair et al., 2002; Carnaby, 2010). Passer domesticus is one of the largest members of the Passeridae family with a wingspan of 210–255 mm and a length of 160–165 mm (Vincent, 2005). House Sparrows were originally native to Europe but today they are distributed across two thirds of the world (From North and South America to Africa and Australasia). This global spread of P. domesticus is linked to human migrations (Summers-Smith, 1990). House Sparrows are one of the most successful invasive species to the new world (Crick et al., 2002). They are very sedentary and will rarely move more than 1 or 2 km from their nesting colony (Summers-Smith, 1988, Summers-Smith & Thomas, 2002). These birds are known to live in close association with human habitation in both urban and rural settings. They are sexually dimorphic; with the males being boldly patterned while females are duller in appearance (Vincent, 2005). The males are brown above with a light grey crown and nape. Their ventral region (under parts) and cheeks are grey-white; they have black lores and a black eye region as can be seen in figure 1.1A. Their bib is black and increases in size with age and sexual dominance (Møller, 1989; Vincent, 2005). The females (Fig. 1.1B), which are lighter brown than the males, on the other hand have a grey-brown crown, unmarked breast and throat area, and two wing bars of a pale brown colour (Vincent, 2005). The colours of the juvenile plumage are very similar to those of the female although it may be lighter (Crick et al., 2002).

House Sparrows have a stocky body shape with a thick beak which is specifically adapted for their seed-eating habits (Crick et al., 2002). Their granivorous diets are known to be supplemented with human scraps. Nestlings are raised on invertebrates, with aphids making up the main part of their diet (Vincent, 2005). Body condition has been cited as a prominent difference between the rural and the urban sparrow populations and has further been linked to differences in their diets. Urban sparrows are smaller and leaner than their rural counterparts (Bókony et al., 2009). An additional factor that influences body condition is reproductive behaviour. House Sparrows can breed up to four times a year, with peaks in breeding frequency taking place in the spring and early summer months (Oatley, 2003). House Sparrows form monogamous breeding pairs but extra pair copulation does occur to strengthen their survival and expand their gene pool (Cordero et al., 1999). Anderson (1995) noted

that there is a correlation between the weight of the females and the size of the clutch. The higher the fat content of the female, the higher the number of eggs she will produce. This is because the process of egg formation as well as the laying is very taxing on the female (Anderson, 1995).

House Sparrows are very vocal birds, with different types of calls. The classic sparrow chirp is normally heard for long periods and is mostly performed by males calling females. Lone sparrows may start up a chirp to attract other sparrows (Elgar, 1986). ‘Chattering’ sounds are common when the birds are congregated together; the use of this sound is thought to be a warning system for predators and an indicator of foods’ location (Elgar, 1986). Low ‘churr’ sounds are not frequently heard and may occur when sparrows are feeding in close proximity to one another (Del Hoyo et al., 2009).

A B 3cm 3cm

Figure 1.1: Male (A) (www.pbase.com) and Female (B) House Sparrow Passer domesticus (www.biodiversityexplorer.org).

1.2.2 Cape Glossy Starling (Lamprotornis nitens)

Starlings belong to the family Sturnidae, which is a diverse clade with 63 species present in Asia and 45 species in Africa and only a single species in Europe (Craig & Feare, 2009). In southern Africa there are 8 genera of the Sturnidae family, consisting of 6 species (Sinclair et al., 2002). These include the starlings, the Indian

Myna (Acridotheres tristis) and the oxpeckers (Buphagus sp.) (Sinclair et al., 2002; Carnaby, 2010).

The Cape Glossy Starling (Lamprotornis nitens) is found in a variety of habitats ranging from wooded savannas to forest edges (Hockey et al., 2005). They have a home range of approximately 2.8km² in non-breeding seasons which expands to approximately 3.2km² during the breeding season when nestlings need to be fed and adults travel greater distances from the nest in search of food (Tobler & Smith, 2004). Lamprotornis nitens are gregarious by nature; they are often found in small flocks or in pairs (Rob, 2011).

There is no sexual dimorphism in their colour, although with regard to size female starlings are slightly smaller than the males. Adult birds are approximately 25 cm in length, with blue-green ventral region, wings and tails. They have bronzy-purple epaulets, a blue throat and upper breast area. In addition they are characterised by highly distinguishable orange eyes, black bills and black legs. Such features can be seen in figure 1.2 which illustrates an adult Cape Glossy Starling. Overall they have an iridescent appearance, from which their name, Lamprotornis nitens derives: Lamprotornis is a Greek word for ‘bright’ and nitens is Latin for ‘shining’ (Hockey et al., 2005; BLI, 2010a). Juvenile plumage colours are duller with matt black ventral region (underparts). Insects, fruit and nectar make up the main part of the Cape Glossy Starling diet, but they also feed on human scraps (Sinclair et al., 2002). Lamprotornis nitens are known for their local migration patterns looking for food (Sinclair et al., 2002). Cape Glossy Starlings are monogamous, they can pair up for several seasons (Rob, 2011). These birds build their nest in cavities in trees which can either be natural or made by other birds, such as woodpeckers. Cape Glossy Starlings often use the same nest for multiple breeding seasons. Breeding takes place between the months of September to February (Hockey et al., 2005). These co-operative breeders can be assisted by up to 6 family members (Spottiswoode, 2008).

The call of the Cape Glossy Starling can be sustained for long periods of time and are known for their uncanny ability to mimic sounds from their environment. Their call is a loud “turreeu”, after which their Afrikaans common name ‘the spreeu’ has been given (BLSA, 2011).

3.6 cm

Figure 1.2: Adult Cape Glossy Starling Lamprotornis nitens (www.capebirdclub.org.za)

1.2.3 Cape Turtle Dove (Streptopelia capicola)

Streptopelia capicola (Cape Turtle Dove), also known as the Ring-necked Dove, belongs to the family Columbidae. Globally this family comprises of 311 species, including pigeons and doves. In southern Africa there are six genera consisting of 15 species (Sinclair et al., 2002). Several differences are used to distinguish between pigeons and doves, the most prominent of these is their shape and size. Doves are smaller than pigeons, with a shorter, rounded tail and are mainly terrestrial seed- eaters. Pigeons on the other hand are larger with a long squared-off tail and are arboreal seed eaters (Carnaby, 2010).

According to Oatley (1999) the Cape Turtle Dove is the most abundant and widespread dove species in southern Africa. They may occur in single, pairs or in flocks. Large numbers are known to congregate around areas where food and water exist. They have a life expectancy of about 28 months, which is mostly attributable to their habit of feeding on a variety of grains next to road sides (Oatley, 1999).

Their diet consists mostly of seeds that they gather from the ground but they will also feed on insects. When foraging they are known to walk rather than hop, as is

characteristic in the sparrows for example. The diet of S. capicola contains little water and as a result they have to drink water daily. They do so by submerging their beaks in water, sucking up the liquid and swallow without tilting their heads back (Carnaby, 2010).

Sexual dimorphism in Cape Turtle Doves is absent, with both males and females being a pale grey-brown colour. The wings are a darker shade than the pale grey ventral region. They have a very recognisable black ring around their necks and a black eye as can be seen in figure 1.3. Their tail feathers have white tips and are especially noticeable when the bird is in flight. Juveniles are paler than the adults and lack the characteristic black collar (Sinclair et al., 2002). Cape Turtle Dove pairs are known to remain together once they have mated. In the breeding season males attract the attention of females with towering flight displays and courtship dances (Carnaby, 2010). They normally breed in the spring and summer months with both parents being actively involved in the brooding of eggs. The “Cook-KooRRooo” call of the Cape Turtle Dove is often heard in the savannah regions of southern Africa one of their favourite regions (Oatley, 1999).

2.9cm

Figure 1.3: Adult Cape Turtle Dove Streptopelia capicola (www.biodiversityexplorer.org).

1.2.4 Laughing Dove (Streptopelia senegalensis)

The Laughing Dove (Streptopelia senegalensis) like the Cape Turtle Dove (S. capicola) belongs to the family Columbidae. Streptopelia is just one of the many genera in this family. This genus consists of 20 species, which includes the Laughing and the turtle doves (Carnaby, 2010). The Streptopelia species originally had an African and Eurasia distribution but was later introduced to the Americas and Australia (Johnson et al., 2001).

The Laughing Doves’ common name is adopted from their call that sounds like human laughter (Rowan, 1983). This smallish (25cm in length) dove has a pink- brown head and back as can be seen in figure 1.4. Their wings are blue-grey and their bellies are white. Their distinguishing colouration is on the front of their necks where black spots can be found. The sexes are very similar with the only distinguishing feature being that the females are slightly paler than the males. The juveniles, however, lack the distinctive black spots on the neck (Pizzey & Knight, 1997).

These birds are monogamous, returning to the same nest sites for a number of consecutive years (BLI, 2010b). The male Laughing Doves are known for their spectacular courting behaviour, which varies from flight displays to continuous head bobbing (Rowan, 1983). The pairs generally feed together on a diet that consists mostly of grains and seeds but may also include bread scraps (Pizzey & Knight, 1997). They have an enormous range, collectively they occur in more than 20 000 km ² (BLI, 2012a).

3.2cm Figure 1.4: Adult Laughing Dove Streptopelia senegalensis (www.andredutoitsafaris.co.za).

1.2.5 Relationship between the three bird families.

As outlined above, the birds described above belong to three different families, namely Passeridae, Sturnidae and Columbidae. Birds of these families are found in similar habitats throughout the world and it has been further suggested that such similarity in their distribution is due to their comparable feeding habits (Crick et al., 2002; Carnaby, 2010). All four species are known to co-exist with other birds. They forage together to reduce their predation risk and to increase the time spent foraging by being able to decrease the amount of time spent on being alert (Carnaby, 2010). In addition, all four species are closely associated with humans (Hockey et al., 2005). They are rather small bird species, with a high metabolic turnover, a high respiratory rate and occupy rather small habitats (e.g. Koteja, 1991; Sinclair et al., 2002; Carnaby, 2010). This makes them ideal candidates for testing the effect of environmental pollution and other factors on birds (Schilderman et al., 1997).

1.3 Suggested causes of decline of the numbers of birds

A number of hypotheses have been suggested as possible reasons for the decline of birds in both urban and rural areas. These include predation, competition, lack of nest sites, diseases, changes in the agricultural activities, food availability, changes

in the architectural designs of buildings, pollution, climate changes and electromagnetic fields (Summers-Smith, 1999; Crick et al., 2002; Vincent, 2005).

1.3.1 Predation

Predators affect various bird species differently. For some species (e.g. Laughing Dove), if a few individuals are removed from the ecosystem, it does not have a noteworthy effect on the breeding numbers and therefore the overall population (Carnaby, 2010). However, for other species, numbers are essential for their survival (Vincent, 2005). An example illustrating this is that of the Cape Glossy Starling, where the size of the group influences their co-operative breeding success (Carnaby, 2010).

A range of predators have been reported to affect the density of bird populations, especially in urban areas (Summers-Smith, 1999). Examples of such predators include feral house cats, predatory birds and snakes to name a few (Crick et al., 2002; Vincent, 2005). Cats, in particular, are popular pets and depending on their feeding patterns may need to supplement their diet with wild caught prey. Within urban areas, an increase in both the domestic and feral cat populations have been correlated with decreases in the population densities of many bird species in a particular area (Churcher & Lawton, 2009). They are very successful predators and according to Summers-Smith (1999) the number of house cats has doubled in the last 30 years in the UK. This means that the cull rate of garden birds, in particular, in urban areas over this time may have been higher than was once thought (Vincent, 2005).

Predatory birds are mostly generalists, with only a few specialising in a specific food source (Vincent, 2005). An example of a predatory bird is the Sparrowhawk which is well-known for its impact on the numbers of House Sparrows in European countries (Crick et al., 2002). If the prey numbers and variety are high in an area, it may attract other predators, resulting in one predatory bird species becoming more specialised than normal. This is the case with Sparrowhawks which have become specialised to their prey, the House Sparrow (Vincent, 2005).

Within rural areas, snakes are believed to be a bigger threat to native bird species than in urban areas. The impact that snakes have on bird numbers has been found

to correlate strongly with reduction in nesting success. This has been linked to the fact that many snake species tend to feed on nestlings, eggs and brooding adults (Branch, 1998).

Birds preyed upon by raptors and other predators are able to avoid predation or decrease the risk of being preyed on by decreasing their body weight (Yom-Tov, 2001). Small birds are more agile and for this reason are able to increase their chance of escape and thus their survival rate (Macleod et al., 2008).

1.3.2 Competition

Competition impacts the community composition and habitat choices of most organisms (Cooper et al., 2007). Competition is mainly caused by an overlap in resources, for example, habitat, food, water and nesting sites, utilized by a number of different related and/or unrelated organisms (Wootton, 1987). Competition can be between members of the same species (intraspecific) or between different species (interspecific) (Carnaby, 2010). Invasive species are keen competitors for resources; due to their naturally aggressive nature and they often succeed in out competing native species (Cooper et al., 2007; Carnaby, 2010). Wootton (1987) demonstrated that in North America, the House Finches (Carpodacus mexicanus) out competed the House Sparrows (Passer domesticus) for space and resources, resulting in a decline in the numbers of House Sparrows.

There are two main types of competition, namely exploitation and interference competition (Vincent, 2005). Exploitation competition is defined as the competition over resources that are available to all species in a habitat but as one species dominates the use of the resources, it decreases their availability to other species. Interference competition however, is when one species is denied access to a resource by the aggressive behaviour of another. In extreme cases, dominant species may take over food resources to such an extent that the other species could starve (Vincent, 2005). This was noted by Furness (1992) between Northern Gannets (Morus bassanus) and Herring Gulls (Larus argentatus) at fishing ship yards: the feeding success of the Herring Gulls declined in direct proportion to the numbers of the Northern Gannets (Furness, 1992).

In addition, competition levels can be influenced by factors such as disease. If an organism becomes infected by parasites or other disease causing pathogens its energy levels may decrease to the extent that it becomes easily outcompeted by others (Cooper et al., 2007).

1.3.3 Disease

Disease is the impairment of the health of an organism. It is caused by pathogens such as fungi, viruses, parasites and bacteria (Clifford et al., 2011). In severe circumstances, the end result of disease may be death. In general, disease simply increases the organisms’ vulnerability and decreases their energy levels (Clifford et al., 2011).

In both rural and urban areas, many birds serve as reservoirs for pathogens such as parasites, bacteria and viruses (Clifford et al., 2011). A number of pathogens utilize birds as either intermediate or final hosts. Examples of such pathogens include Salmonella, Campylobacter, Chlamydia psittaci (bacteria), Cryptosporidium (parasite), Crimean Congo haemorrhagic fever, SARS virus, West Nile virus and Influenza (viruses) (Clifford et al., 2011). A bird’s vulnerability to infections is dependent on factors such as their overall condition of health, the environmental conditions in their habitat, the number of pathogens they are exposed to, a combination of pathogens which may break down the immune system, and weather conditions (Lee et al., 2005; Carnaby, 2010; Clifford et al., 2011). The immune response of birds, native to a particular area, may become challenged by new diseases brought into that area by invasive species (Lee et al., 2005). Birds that occur in large congregations or are communal breeders, like the Cape Glossy Starling, are known to have a higher infection rate (Sinclair et al., 2002; Vincent, 2005).

1.3.4 Breeding success

To achieve breeding success, adult pairs must work well together and they must demonstrate a high dedication to the young (Crick et al., 2002). Breeding must take place in the right season and must coincide with the time when invertebrates are most abundant (Freeman et al., 2007). Most nestling birds depend on insects as a food source for the early part of their lives. If the insect abundance levels are too low

the death rate is increased as a result of starvation (Summers-Smith, 1999). The choice of nest site also plays a role, the number of nesting locations may be limited by urbanisation and changes in natural habitats by human development (Vincent, 2005).

Breeding success can be lowered by environmental stresses (e.g. El Niño’s) on first- winter fledglings (Vincent, 2005). As they lack experience in both foraging and knowledge pertaining to their habitat, they will be driven from prime spots by dominant birds. This increases their risk of starvation as well as predation (Freeman et al., 2007). The reproductive success of birds may be limited by food shortages, as the energy levels of the adult females can be too low to produce viable eggs (Vincent, 2005).

1.3.5 Availability of food

Food shortage is believed to be the main cause of declines, as this does not only influence a bird species directly but also indirectly (Vincent, 2005). The result of food shortage is starvation, failure in breeding success and malnutrition of nestlings which leads to growth abnormalities (Freeman et al., 2007). If the amount of food needed is too little or its nutritional value is low, then egg production may be avoided and very small clutches are a common result (Vincent, 2005). The availability of food is dependent on the conditions of the environment and the habitat in which birds live. If the area is experiencing a drought, the invertebrate numbers will be lower, especially the soil invertebrate (Freeman et al., 2007). This leads to a lower body mass of both adult birds and chicks of species feeding on invertebrates. The lack of proper nourishment may then ultimately result in higher susceptibility to diseases and increased predation risk (Crick et al., 2002).

The substitution of indigenous vegetation with exotic plants decreases the amount of seeds available for seed eating birds (granivores). In addition, changes in garden designs may also lead to a reduction in food availability (Crick et al., 2002). Therefore it can be speculated that well-groomed gardens will be advantageous to one species but on the other hand detrimental to another species. For example, starlings have specialised mandibular apparatus that allows for successful foraging in short grass low in seed, in comparison to the Cape Turtle Doves and Laughing

Doves which need to find new foraging areas as they feed predominantly on seeds (Dean, 1979; Crick et al., 2002).

1.3.6 Changes in the agricultural practices

Agricultural practices have changed substantially over the last three decades (Summers-Smith, 1999; Brichetti, 2008; Dandapat et al., 2010). Determining if agricultural practices can be linked to declines in bird populations is problematic. This is due to the fact that no single process can be studied in isolation but rather a number of different processes must be considered together when performing such an analysis (Newton, 2004).

When a new farm is developed or a farm is being rested, vegetation is removed which inadvertently reduces potential nesting sites for native bird species (Newton, 2004). Furthermore, agro-chemicals are used to keep pests and natural vegetation from re-growing on a site. This reduces the number of weeds which provide food for both seed eater and some insect eating species (Crick et al., 2002).

In addition, changes in growth patterns of crops, time of planting, fire management and drainage patterns all play a role in the decline of birds. The planting of dense monocultures not only alters the diversity of seeds but also decreases the availability of nest sites (Mason & MacDonnald, 1999; Wrettenberg et al., 2006). In general if the sowing of cereals changes from spring to late summer/early autumn, it decreases the amount of spilled seeds that previously were available in the winter (Summers-Smith, 1999). The earlier harvest time also poses a threat to the destruction of egg clutches (Newton, 2004). Changes in fire management regimes can have a permanent effect on the vegetation. Such an action can permanently reduce vegetation, especially if the time a burn is done in the wrong season (Mucina & Rutherford, 2006). Water management can be seen as a two sided coin, having both negative and positive effects on the natural environment. Negative effects includes changes in land drainage resulting in the drying up of a previously wet area, a reduction in both vegetation and the numbers of invertebrates. Building of dams can also alter the flow of a river system ultimately resulting in an overall transformation of the natural biota (Poff et al., 1997). On the positive side, water may be brought to dry areas during drought periods to sustain plants like citrus (Newton, 2004).

1.3.7 Pollution

Pollution is responsible for numerous declines in bird populations (Crick et al., 2002). Pollution of the natural habitats is mostly attributed to many different anthropogenic impacts (Vincent, 2005). Such impacts include the release and introduction of toxicants (e.g. heavy metals, insecticides, herbicides, fertilizers, etc.) into the ecosystem. Such introduced chemicals have been shown to result in alterations in the ecosystem processes: habitat, food, predation, competition and diseases all are impacted. The biggest impact of pollution may, however, be on the reproductive success of birds (Vincent, 2005).

In the UK according to Crick et al. (2002), the use of pesticides increased 10 times since 1970. The use of anthelminthic drugs, such as ivermectin®, in the control of parasites in livestock practices can be correlated to declines in bird populations (Crick et al., 2002). Crick et al. (2002) related such a finding to declines in the numbers of starlings in the UK, which were found to feed on ectoparasites of livestock, which were affected by the ivermectin® drug administered. Suppression of the immune system is another effect resulting from exposure to pesticides and pollutants, which subsequently leads to an increased susceptibility to diseases (Martin et al., 2010). Chemicals can therefore change the relationship between hosts and their parasites by decreasing the effectiveness of the birds’ immune systems (Eeva et al., 1997).

Other effects that exposure to pollutants can cause include: nervous system impairments, cell and tissue deaths, alterations in leukocyte production, antibody modification and a reduction in cytokine production (Martin et al., 2010). These effects are mainly caused by heavy metals that are leached into the ecosystem from agricultural practices: they increase the birds’ predation risk (Martin et al., 2010). Metals are also responsible for a decrease in food availability, change in behaviour of adults, a decrease in the calcium metabolism of chicks resulting in rickets (soft bent legs), thinning in egg shells as well as overall decrease in growth rate at all stages of development (Eeva et al., 1997; Vincent, 2005). Insecticides have a marked effect on body weight of many nestlings which has been correlated to decreases in food supply (Howe et al., 1996). Such growth declines have been seen especially with regard to the wing and tarsus lengths (Howe et al., 1996). Certain

agricultural products contains high levels of dicofol metabolites such as dichlorobenzophenone (DCBP), which is known to not only cause egg shell thinning but also accumulate in the yolk resulting in the death of the embryo (Schwarzbach, 1991).

A change in transport from horses to internal combustion engines took place in the 1920s (Summers-Smith, 1999). Not only did potential food sources in the form of droppings disappear but the use of petrol and diesel may also have contributed to a decline in various species’ nesting success (De Laet & Summers-Smith, 2007). Vehicle emissions include nitrogen oxides, volatile organic compounds, polycyclic aromatic hydrocarbons, metals and particles all of which have an impact on birds and their life cycles (Summers-Smith, 1999; Peach et al., 2008). In many cases pollutant molecules may be ineffective on their own. However, when in combination with other chemicals, they can elicit detrimental effects on the population dynamics of many bird species (Schwarzbach, 1991).

1.3.8 Architectural design of buildings

Architectural designs have changed from older buildings with roof overhangs to modern designs with very little resting and nesting place for birds under the eaves (Robinson et al., 2005). In general, older buildings tend to be broken down and tall office buildings are constructed in their place. These newer buildings have improved rooftop insulation, preventing birds from nesting on top of them (Brichetti et al., 2008). Lofts are insulated with the aid of fiberglass, which damages the respiratory system of most birds (Robinson et al., 2005).

1.3.9 Climate change

Climate changes manifest as unusual fluctuations in long-term rainfall patterns, temperatures and humidity levels (Chase et al., 2005). Over the last 100 years the Earths’ climate has become warmer and precipitation patterns have changed (Araújo & Rahbek, 2006). The United Nations Intergovernmental Panel on Climate Change (IPCC) has predicted that by 2100 the global temperatures will be between 2–6°C warmer than present. They ascribe this change to the rise in the total amount of carbon dioxide in the atmosphere, which has increased by 30% since the start of the

industrial revolution (McKechnie, 2005). In this century the global surface temperature has risen by 0.76°C (ICPP, 2007; Mustin et al., 2007).

Africa is the continent most at risk to the adverse effects of climate change due to its large dry regions. It is predicted to become dryer, have higher temperatures, more frequent El Niños as well as wild fires and experience a range of weather abnormalities (Wormworth & Mallon, 2006). Climate change is emerging as one of the greatest threats to natural ecosystems and communities (Thomas et al., 2004; Malcolm et al., 2006; Wormworth & Mallon, 2006). According to the World Wildlife Foundation (WWF) (2000), continued change in the global climate will result in a loss of 35% of the Earth’s habitats.

Climate change affects birds’ from the population level to the biochemical level (e.g. WWF, 2000; Chase et al., 2005; Araújo & Rahbek, 2006). Studies have shown that a change in the climatic conditions can affect a bird’s behaviour, distribution, population dynamics, breeding success and survival (WWF, 2000; Mustin et al., 2007). At the population level, disruptions in the predator-prey relationship as well as host-parasite relationship have been shown to become altered with changes in climate (Omann et al., 2009). Such changes can be expressed in a number of ways, namely a shift in timing and range and ecological community disruptions. The scale of climate change impacts on birds and humans in different ways. These are outlined below.

A) Shift in timing

Seasonal events such as the synchrony of egg-laying and migration can become disrupted with differences in the abundance of plants and insects at different times of the year. Studies have shown that egg-laying has advanced by 6 days per decade (WWF, 2006). Migration patterns may also be influenced when the migration time stays the same but the time when insect numbers peak is earlier. According to Price and Glick (2002) some birds migrate earlier in spring and some even fail to migrate at all as a result of alterations in the temperature cycles. Climate change is responsible for disrupting the link between predictive environmental cues and spring phenology which leads to the inability of females to time their egg laying correctly (BLI, 2004; Scharper et al., 2012).

B) Shift in range

Range shifts are mainly due to a change in precipitation and temperature in a particular area (Omann et al., 2009). Range changes may occur too fast, exceeding the ability of birds to adapt, resulting in extinction of sensitive species. In southern Africa the range expands westwards. Some species (e.g. Cape Longclaws, Macronyx capensis) have, however, moved their ranges south and to the east. Hockey (2003) suggested that this may be due to the borders of the arid regions changing. Another challenge that range shift presents is that if the area is surrounded by human habitation and development, it may prevent birds from expanding their ranges. Natural, physical barriers such as water bodies and/or mountains prevent migration and result in extinction (Wormworth & Mallon, 2006). In South Africa, it has been estimated that the average bird range has undergone contraction by as much as 78% (WWF, 2006). This finding has been linked to the prediction by Whichmann et al. (2003) that the range of Tawny Eagles would likely contract to such an extent that they may go extinct in the southern Kalahari. The expansion of a range, generally results in the reorganisation of natural communities, as such communities will be exposed to new predators, prey, competitors and unfamiliar parasites (Wormworth & Mallon, 2006).

C) Ecological community disruptions

All the aspects of a community need to work together to achieve a stable environment. However, various factors have been shown to impact on environmental stability (WWF, 2006). Such instabilities can be seen as the lack of nesting material and food, and changes in the numbers and species of prey species. A reduction in food availability of a replacement food source can be less palatable than the original (Mustin et al., 2007). Weather not only influences food supply but also the reproductive success of birds, as with warmer weather and higher rainfall nest predators are more active, increasing the nest predation rate (Chase et al., 2005). Rainfall can also influence the time spent incubating eggs and can destroy nests by saturating them with water (Murphy, 1978). Conversely, drought reduces the amount of vegetation available to build nests and decreases the food supply (Chase et al., 2005). Pathogens increase their transmission rate as the hosts may be more susceptible (Sekercioglu et al., 2011). Parasites’ ability to adapt is a lot higher than

that of their hosts; their distribution patterns can change drastically (Mustin et al., 2007).

D) Scale of climate change impacts

The responses to and influences of climate change on birds has been shown to be highly variable (WWF, 2006). Specialist bird species are at higher risk than generalist species (Mustin et al., 2007). An example of a specialist bird that will be affected by climate changes impacts is the Cape Sugarbird (Promerops cafer) as they specifically feed on the nectar of the Proteaceae family (Butchart & Ekstrom, 2008). The extinction risk of non-migratory species; birds with low population numbers, poor dispersal abilities, poor conservation status and restricted habitats are high (Wormworth & Mallon, 2006; Sekercioglu et al., 2011).

E) Human impacts

Climate change influences human behaviour. The more moderate previously cold areas have become more popular tourist locations. This results in an increase in development and a decrease in the natural environment (Mustin et al., 2007).

1.3.10 Electromagnetic fields

Electromagnetic fields are created in a number of ways. The most common one is through the generation, transmission or usage of electricity (Hanowski et al., 1996). Magnetic fields are also created by any telecommunication devices which include 3G wireless phones, wireless local and personal area networks, bluetooth devices as well as mobile phones. They all make use of radiofrequency radiation signals (Balmori, 2009). Electromagnetic radiation is suggested as the main cause of the declines of sparrow populations in London, as the declines coincide with the time when mobile phones were introduced (Balmori, 2009).

Electromagnetic radiation impacts the following aspects of birds: their behaviour, reproductive success, growth and development, physiology, oxidative stress as well as their endocrine system (Hanowski et al., 1996; Doherty & Grubb, 1998; Balmori, 2009). In a study by Balmori (2009) it was shown that sparrows became more aggressive after long term exposure to field strength higher than 2 V/m. In addition to this, they also suffered from difficulties concerning nest building, males had lowered

sperm counts and egg sizes increased. Such exposures were ultimately linked to changes in the hatching success of chicks as bigger eggs are harder to incubate for small birds (Balmori, 2009). Growth problems could start with the embryo where the heart and nervous system development is disrupted. If the chick does manage to hatch, its bone growth is stunted and the bones may be brittle (Saunders, 2005; Balmori, 2009). The physiology of the birds can also be affected by electromagnetic radiation as the blood pressure and heart rate is influenced negatively. The endocrine system of birds may also be impacted by exposure to radiation (Balmori, 2009). This has been seen for example with regard to changes in the circadian rhythms (sleep-wake cycle) through the change in the hormones and the serotonin secretions from the pineal gland (Doherty & Grubb, 1998; Balmori, 2009). Most concerning though is the fact that electromagnetic fields affects the DNA of birds (Joris & Dirk, 2007). This effect is species specific and varies between individuals, with some being more affected than others. For example, insectivores, like starlings, will be more affected as their food source will also be exposed to continuous radiation (Doherty & Grubb, 1998).

1.3.11 Multiple factors

The greatest effect on a population is exhibited by exposure to multiple or combined impacts. A single factor might play a larger role in the decline of a species but it will always be strengthened by another (Vincent, 2005). Added pressures on bird numbers include an increase in human populations, hunting and tourism, roads and their construction and mining development (Thiollay, 2006).

An influx of people into a previously undeveloped area results in an increase in noise pollution, an increase in the trade of rare species (e.g. Ground Hornbills, Bucorvus leadbeateri) and an increase in poaching (Williams et al., 2013).

Declines can be divided into ones that will have a bigger effect on rural birds and those that will largely affect urban birds (Summers-Smith, 1999). Rural declines tend to be caused mainly by an increase in predatory birds and snakes, competition, food availability, diseases, agricultural changes, pesticide and insecticide pollution and climate change. Urban declines are generally due to domestic cat predation, competition, loss of nest sites, food availability, pollution of vehicles emissions, architectural design changes, climate change and electromagnetic fields.

1.4 The avian respiratory system

In the Animal Kingdom, the avian respiratory system, the lung-air sac system, is structurally and functionally unique (e.g. King & McLelland, 1989; Maina 2002). Unlike that of the mammals, it comprises of a rigid lung that is connected to a series of air sacs (Fig. 1.5) (Maina, 2005). Minor variation occurs between the morphologies of the lung-air sac system of bird species (King & McLelland, 1989). The description given below is based on that of the domestic fowl, as it is the most common bird species in the world (King & Molony, 1971; Powell & Scheid, 1989; Fedde, 1998; Maina, 2002).

1.4.1 Morphology of the lungs

The avian lung is dorsally located and unlike in mammals, the lung does not surround the heart: because a diaphragm is lacking, it is the liver that surrounds it (heart) (King & McLelland, 1984). Bird lungs are generally quadrilateral, flattened and small. They have three surfaces namely, the costal-, the ventral- and the septal surfaces (Fig. 1.5) (Maina, 2005). The costal surface is also known as the dorsolateral surface and is in contact with the ribs. The vertebral surface (dorsomedial surface) touches the vertebrae and the septal surface (ventromedial surface) is in contact with the tissue of the horizontal septum (Fedde, 1998).

Figure 1.5: Dorsal view of the lungs of the ostrich (Struthio camelus). Tr, trachea; EPPB, extrapulmonary primary bronchus; S, costal sulci. Scale bar 1cm. From Maina & Nathaniel (2001). Reproduced with permission from authors.

The avian lung is not lobulated and is almost totally rigid and inflexible (McLelland, 1989; Maina, 2005). The left- and right lungs are equal in their volume (Maina, 2013). The trachea divides into the left and right extrapulmonary primary bronchi at the syrinx (Fig. 1.5) (King & McLelland, 1984). After the extrapulmonary primary bronchi enter the lungs they are known as the intrapulmonary primary bronchi. The intrapulmonary primary bronchi pass through the lung in a craniocaudal direction, giving rise to four sets of secondary bronchi. These are the medioventral secondary bronchi, the mediodorsal secondary bronchi, the laterodorsal secondary bronchi and the lateroventral secondary bronchi (McLelland, 1989; Maina, 2005). The secondary bronchi give rise to the parabronchi, the tertiary bronchi (Fig. 1.6) (King & McLelland 1984).

The lung-air sac system is divided into two compartments; the lung which functions as the gas exchanger and the air sacs which function as mechanical ventilators of the lungs (Duncker, 1974).

PB PB PB PB

MVSB MDSB EPPB IPPB

PB O LVSB PB PB O O O

Figure 1.6: Schematic diagram of the lung of the domestic fowl drawn as if transparent to show the airways. These are the extrapulmonary primary bronchi (EPPB) that becomes the intrapulmonary primary bronchi (IPPB) that gives rise to three sets of secondary bronchi: the medioventral secondary bronchi (MVSB), the mediodorsal secondary bronchi (MDSB), the lateroventral secondary bronchi (LVSB). The laterodorsal secondary bronchi (LDSB) which originate from the MDSB but on the lateral side of the IPPB are not shown here to keep the figure simple. The parabronchi (PB) branch off from the secondary bronchi. The ostia (O) are the sites where the air sacs connect to the lung. Reproduced with permission from the author, Maina (2013).

1.4.2 Airway system

1.4.2.1 Primary bronchus

The left and right extrapulmonary primary bronchi enter the lung and continue as the intrapulmonary bronchi to the caudal margin of the lung where they enter the abdominal air sacs (King & McLelland, 1984). As the intrapulmonary primary bronchi transits the lung, their diameter decreases gradually (Fig. 1.6). The walls of the extra- and intrapulmonary primary bronchi are supported by cartilages which later ossify (McLelland, 1989). Pseudostratified ciliated columnar epithelium with goblet cells lines the primary bronchi (McLelland, 1989).

1.4.2.2 Secondary bronchus

The four sets of secondary bronchi that originate from the primary bronchi are named according to the regions of the lung which they supply air to (King & McLelland, 1984). If the intrapulmonary primary bronchi are divided into thirds, the medioventral secondary bronchi originate from the first third, while the mediodorsal-, laterodorsal- and lateroventral secondary bronchi stem from the caudal two thirds (Duncker, 1974) (Fig. 1.6). There are great variation between the numbers and diameters of the secondary bronchi (King & McLelland, 1984; Maina, 2006). The lining of the secondary bronchi also consists of pseudostratified ciliated epithelium, similar to that which lines the intrapulmonary primary bronchi. The epithelium, however, lacks the mucus secreting goblet cells (McLelland, 1989).

1.4.2.3 Parabronchi (Tertiary bronchi)

The first parabronchi is found very close to the origin of the secondary bronchi (Maina, 2005). In some birds like the galliformes (e.g. the Guinea Fowl), the parabronchi are separated by intraparabranchial septa (a band of connective tissue). This is, however, absent in both the passeriformes and the columbiformes. Passeriformes are one of the bird groups which have the smallest parabronchi, with the highest proportion of exchange tissue (Maina, 2013).

The parabronchi (uniform calibre tubes) in the cranial and dorsomedial parts of the lung form hooplike structures that connect the medioventral- and mediodorsal secondary bronchi (King & McLelland, 1984). Together these secondary bronchi and

their parabronchi form the paleopulmonic part of the lung (Maina, 2002, 2005). In contrast, the lateroventral- and laterodorsal secondary bronchi, with their associated parabronchi, form the neopulmonic section of the lung (Maina, 2002, 2005).

1.4.3 Morphology of the air sacs

Air sacs are voluminous, translucent structures that are attached to the avian lung (Fig. 1.7) (Dunker, 1974). The position, size and diverticulae of the air sacs are different amongst species (Fedde, 1980). In the embryo of the Domestic Fowl, six pairs of air sacs exist. These are the cervical-, the lateral clavicular-, the medial clavicular-, the cranial thoracic-, the caudal thoracic- and the abdominal sacs (McLelland, 1989). However, in adults the number is reduced as the clavicular- as well as the cervical sacs fuse to form larger median chambers (McLelland, 1989).

1 L 5 4 2 TR 3

1cm

3 4 1 2 L 5 TR

L 5 3 4 B 1cm

Figure 1.7: Latex casts showing the lateral (A) and dorsal (B) views of the lung and air sacs of the domestic fowl, Gallus gallus variant domesticus. TR, Trachea; L, Lungs; arrow costal sulci; circles ostia; 1, cervical air sac; 2, interclavicular air sac; 3,

craniothoracic air sac; 4, caudothoracic air sac; 5, abdominal air sac. Scale bar 1cm. A is from Maina (2002) and B from Maina and Africa (2000). Reproduced with permission from authors.

Functionally, the air sacs can be divided into two groups, namely the cranial- and caudal groups (Dunker, 1974). The cranial group includes the cervical-, the interclavicular- and the cranial thoracic sacs while the caudal group consists of the caudal thoracic and abdominal air sacs (Fig. 1.8) (McLelland, 1989).

The cervical air sac can be found at the base of the neck, where it occupies the cranial dorsal part of the thoracic cavity (Fig. 1.8) (Dunker, 1974). This small paired structure consists of a main chamber and several diverticulae which are located close to the cervical vertebrae and pneumatize the vertebrae (McLelland, 1989).

Lung Abdominal air sacs (#)

Trachea

Cervical air sacs (0)

Interclavicular Cranial thoracic air Caudal thoracic air air sacs (0) sacs (0) sacs (#)

Figure 1.8: Avian respiratory system illustrating the cranial- (0) and caudal (#) groups of air sacs (hd = humeral diverticulum of the clavicular air sac; Redrawn from Sereno et al. (2008).

The clavicular air sac is a large unpaired structure which is found at the cranioventral part of the thorax, at the base of the neck and in a large part of the right and left axillary spaces (Fig. 1.8) (King & Molony, 1971). Its median chamber stretches from the clavicles to the heart and demonstrates its size. Its dorsal wall is indented by the ventral part of the oesophagus as well as the trachea, syrinx and primary bronchi. The median chamber is in close proximity with the syrinx and it is this association that assists in the diverse vocal repertoire of birds (Duncker, 1974).

The cranial- and caudal thoracic air sacs are paired structures and are found in the subpulmonary cavity (the space ventral to the lung and the horizontal septum). The caudal thoracic air sacs are generally one quarter to one-third bigger than the cranial thoracic sacs (McLelland, 1989). The abdominal air sacs are also paired structures which invaginate into the intestinal peritoneal cavity (McLelland, 1989).

1.4.4 Ostia

The ostia are the areas where the air sacs connect to the lung (Figs. 1.6, 1.7). They can be divided into two types of connections, namely, the direct and the indirect ones. A direct connection occurs where an air sac opens to the primary or secondary bronchi. An indirect connection refers to the fusion of the parabronchus with an air sac (McLelland, 1989). All the air sacs, with the exception of the abdominal air sacs, have direct ostia which are connected directly to the secondary bronchi. The caudal thoracic sac is connected to one of the lateroventral secondary bronchi (Duncker, 1974): the cranial air sacs are connected to the medioventral secondary bronchi (Duncker, 1974). The abdominal air sac joins directly with the posterior end of the primary bronchus (King & McLelland, 1984). All air sacs, except the cervical sac, possess indirect connections. Air sacs with an indirect connection are associated with the parabronchi of the lung. Such ostia may be located in as many as 5 different regions. The indirect connections are smaller in diameter than the direct ones; this is true for all except the abdominal sac which has a very extensive and large indirect connection. Each air sac contains several indirect connections which make the total cross-sectional area more than that of the direct connections (King & Molony, 1971).

1.4.5 Morphometry of the avian respiratory system

Small aerobic bird species have highly morphometrically specialized lungs (Maina, 2002). The volume of a bird lung is one third that of a mammal of similar body size (Maina et al., 1989). The mass specific lung volume of the House Sparrow is 0.03cmᵌ/g while for the Sturnus vulgaris (the starling species closest to the size of L. nitens) the value is 0.028cmᵌ/g. The Collared Turtle Doves (the dove species with the closest size to the Cape Turtle Dove) and the Laughing Doves had lower (volumes/bodymass) values of 0.0034cmᵌ.g⁻¹ and 0.0031cmᵌ.g⁻¹ respectively (Maina, 2005). The gas exchange tissue of the avian lung constitutes ~ 48% of the lung volume (Maina, 2005); the respiratory surface area, i.e., the surface area of the blood-gas barrier exceeds that of mammals by ~ 15% (Maina, 2005). The total morphometric diffusing capacity (diffusing capacity of the blood gas barrier, the plasma layer and the red blood cell) of the House Sparrow is 0.0001ml.O₂.s⁻¹. mbar⁻¹.g⁻¹ and for the starlings it is 0.0006ml.O₂. s⁻¹. mbar⁻¹.g⁻¹. The Cape Turtle Dove has a diffusing capacity of 0.0006ml.O₂. s⁻¹. mbar⁻¹.g⁻¹ and the Laughing Doves in turn have a diffusing capacity of 0.00008ml.O₂. s⁻¹. mbar⁻¹.g⁻¹ (Maina, 2005). These factors illustrate the variation in the morphometry of the lung-air sac systems of the different species.

1.4.6 Blood-gas barrier

The blood-gas barrier of the avian lung is very thin (e.g. Maina, 2000a, b; West, 2009). In its thinnest parts, it consists of a three-ply design, of endothelial cells, an intermediate matrix layer (a basement membrane) and an epithelial cell layer (Maina & West, 2005). The thinness of the blood-gas barrier is due to the lack of an interstitial space between the individual basal laminae of the endothelial and epithelial cells (Maina, 2005). The harmonic mean thickness of the blood-gas barrier ranges from 0.09µm in the Violet-eared Hummingbird (Colibri coruscans) and the African Rock Martin (Hirundo Fuligula) to 0.56µm in ostriches respectively (Maina, 2000b; 2005). In comparison with non-flying mammals and bats, the avian lungs’ blood-gas barrier is 56-67% thinner (Maina, 2000b; 2005).

1.4.7 Function of the avian respiratory system

In the paleopulmonic parabronchi, the air flows in the paleopulmo is continuously and unidirectionally during both inspiration and expiration; the air moves from the mediodorsal secondary bronchi, through the parabronchi, to the medioventral secondary bronchi (Scheid & Piiper, 1989). Contrary to this, air in the neopuolmo changes direction between the phases of respiration, i.e., it is bidirectional (Scheid & Piiper, 1989). The path followed by a single volume of air is as follows; during the first inspiration phase air moves from outside then disperse in the lung, therafter moves by way of the intrapulmonary primary bronchus to the caudal air sacs. A part of the air also passes through the neopulmonic parabronchi (Fig. 1.9: A) (King & McLelland, 1984). During the first expiration phase the air in the caudal air sacs moves through the last part of the intrapulmonary bronchus as well as through the neopulmonic parabronchi into the mediodorsal secondary bronchi and then flows into the paleopulmonic parabronchi (Fig. 1.9: B) (Scheid & Piiper, 1989). In the second inspiration phase the air flows from the paleopulmonic parabronchi into the cranial air sacs (Fig. 1.9: C) (King & McLelland, 1984). Lastly during the second expiration phase air in the cranial air sac flows out through the intrapulmonary primary bronchus via the extrapulmonary primary bronchi to the outside (Fig. 1.9: D) (King & McLelland, 1984). The inspired air thus completes a full cycle of the respiratory system in two breaths (Fedde, 1998).

A B

C D

Figure 1.9: Illustration of the respiratory cycle of the avian lung-air sac system; the blue indicates the movement of the air firstly into the caudal air sacs during first inspiration (A). Movement of air into the lung during the first expiratory cycle (B), proceeding into the cranial air sacs during the second inspiration (C). Finally being expelled during the second expiration (D) Maina (2013), reproduced with permission from the author.

1.4.8 Pulmonary cellular defences

At the gas exchange level, the first line of protection of the avian lung is the free (surface) macrophages (FMs) (Nganpiep & Maina, 2002). These cells engulf the foreign agents (biological and particulate) that enter the lung and destroy or sequester them (Nganpiep & Maina, 2002). Macrophages are large leukocytic cells (8-15µm in diameter) which segregate into organ-specific subpopulations (Nganpiep & Maina, 2002; Maina, 2012). In the cytoplasm of the FMs, membrane enclosed

organelles (~0.5µm) are found: they include lysosomes, phagosomes, vacuoles and lipid droplets (Maina, 2012). The vacuoles contain a high concentration of hydrolytic enzymes which originated from the rough endoplasmic reticulum present in the nucleus of the FMs where they form the primary lysosomes (Bowden, 1984). FMs form part of the immune system’s cell mediated mechanisms, which essentially consists of a number of phagocytic cells along with neutrophils and dendritic cells (Reese et al. 2006). Free macrophages become attracted to the foreign particles through chemical stimuli. They are mobile (move by means of pseudopodia) and remain so until they reach a critical particle loading (Kiama et al., 2008). Primary lysosomes combine with the phagosomes, forming secondary lysosomes. They are responsible for breaking down foreign particles/pathogens (Hickman et al., 2008). If a large number of foreign particles are present and they cannot be successfully phagocytised by individual cells, multinucleated cells are formed. Such cell clusters form part of a nodular inflammatory lesion that is known as granuloma (Maina, 2012).

The lack and scarceness of the FMs have been noticed by Klika et al. (1996), Lorz & Lopez (1997), Ficken et al. (1986) and Maina & Cowley (1998). The scarcity of FMs has been taken to shows high susceptibility of birds to respiratory pathogenic conditions. This was, however, based on largely circumstantial circumstances (Toth & Siegel, 1986). Without empirical evidence, low numbers of FMs should not be seen as a weak or compromised cellular defence system (Nganpiep & Maina, 2002). As the defence system of the avian respiratory system includes phagocytic bronchial epithelium cells, free (surface) macrophages, pulmonary intravascular macrophages, subepithelium macrophages as well as red blood cells that are phagocytic (Maina & Cowley, 1998; Nganpiep & Maina, 2002); the presence of a large resident population of FMs may therefore not be essential (Nganpiep & Maina, 2002). Even with such a proficient defence system, respiratory disease is one of the main causes of mortalities in caged birds (Spira, 1996).

A correlation exists between the number of FMs and the level of environmental pollution in mammals (Brain, 1987). A similar correlation can be expected in birds.

1.4.9 Air pollution

Air pollution is defined as the presence of foreign particles in the atmosphere (Kampa & Castanas, 2008). These may include chemicals, particulate matter or biological materials (Van Loon & Duffy, 2005). In South Africa, the burning of coal, oil, fossil fuels and natural gases as an anthropogenic practice has been cited as a major contributory factor to air pollution (Matooane et al., 2004). The areas that are most affected in South Africa are the Vaal Triangle and the South Durban Industrial Basin (Matooane et al., 2004). The air pollution in these areas sometimes exceeds the recommendations set out by the World Health Organisation (WHO) and the South African guidelines for permissible levels of atmospheric pollution (Matooane et al., 2004). According to Matooane et al. (2004), the most common air pollutants in South African include sulphur dioxide (SO₂), nitrogen oxides (NOᵪ), particulate matter (PM), ozone (O₂), carbon dioxide (CO₂) and organic compounds.

Air pollution may have either a direct or an indirect effect on reproduction of birds (Morrison, 1986; Furness & Greenwood, 1993). An indirect effect refers to the deterioration of a habitat, a decrease in the number of nest sites or reduction in food availability (Martin, 1987). Conversely a direct effect refers to an overall decline in the health of the birds (Furness and Greenwood, 1993). That is, an effect that directly impacts on a population or individual within a population in the environment. In the environment, air borne particles and toxic gasses can have an incapacitating effect on a bird’s respiratory system (Brown et al., 1997). According to Brown et al. (1997) the level of ammonia occurring in enclosed poultry houses (~20ppm) is responsible for major and minor damage to the cilia of the epithelium lining the respiratory system, resulting in a higher susceptibility to diseases.

1.5 Behaviour

Behaviour refers to a range of actions or mannerisms performed by an organism in response to a specific stimulus or situation (Hickman et al., 2008). Behaviour can either be innate or learned and the complexity of this is thought to be directly correlated to the intricacy of an organisms’ nervous system (Dusenbery, 2009).

1.5.1 Causes of behavioural differences

Differences in the behaviour of an organism are driven by factors such as the genetics, family unit and the environment (McGue & Bouchard, 1998). For instance the behaviour of nestlings is determined by the individuals genetics (50%), by a shared environment (the habitat that whole family occurs in and is exposed to) (20– 30%) and the rest by a non-shared environment (exposure to newly unexplored environments) (McGue & Bouchard, 1998).

The genetic make up of an individual mostly affects behaviour when there are few environmental differences between generations (Breed & Sanchez, 2010). By influencing the morphology and physiology, genes bring about a framework within which the environment can influence a birds’ behaviour (Breed & Sanchez, 2010). Chicks inherit traits from their parents. Communication signals are some of the traits that are passed from generation to generation and are therefore influenced by the genotype of the individual. For example, brood parasite young can perform their own species call without ever hearing it. However, this is not the case for all birds as some learn these signals from their parents (Breed & Sanchez, 2010; Minderman et al., 2010).

The environment plays a significant role in the development of individual behavioural traits (Hickman et al., 2008). Environmental factors such as the climate, the condition of the habitat, the amount of pollution and family structures all influence the behaviour of young (Hickman et al., 2008). An example of this was illustrated by Guez and Allen (2000): if young are exposed to toxic chemicals, their behaviour can alter as brain damage may occur from the exposure. The family structure is another aspect. For example, if one chick is fed more than the other it may lead to the better- fed chick becoming unnaturally aggressive, resulting in siblingcide (Carnaby, 2010). Behaviour can also differ between individuals and families (Guez & Allen, 2000). It is therefore dependent on the habitat to which organisms are exposed (McGue & Bouchard, 1998).

1.5.2 Adaptive behaviours for survival

A) Surviving the elements

Birds are endotherms and regulate their body temperature around a constant point, by relying on a variety of physiological and behavioural adaptations (Carnaby, 2010). An example of a physiological adaptation is an increase in the metabolic rate resulting in an increase in energy and subsequently temperature. This is a primary mechanism for raising the body temperature (Ohmart & Lasiewski, 1971).

Behaviour also plays a major role in controlling of body temperature. By orienting their bodies to the sun, birds effectively expose a larger surface area of their body to the sun and in this way they are able to increase their body temperature. This behaviour is known as “sunning” (Ohmart & Lasiewski, 1971). “Huddling” is another behavioural trait employed by birds to preserve body heat when it is cold (Vickery & Millar, 1984; Carnaby, 2010). Individuals position themselves close together and in this way reduce the surface area of their bodies that are exposed to cold air. This is a mechanism employed to preserve as well as gain heat from a neighbouring bird and effectively reduce the amount of energy that would be required to warm the body temperature (Vickery & Millar, 1984). Birds are also known to seek shelter in their nests or under overhangs as a means of conserving heat. Such behavioural strategies have been shown to result in an increase in temperature of 5–10◦C in comparison to the external environment (Carnaby, 2010). A third behavioural adaptation is referred to as the “fluffing” of feathers (Ferns, 1992; Carnaby, 2010). Birds effectively raise their feathers so as to trap a layer of air close to their body (Carnaby, 2010). In this way the layer of air warms and aids in maintaining a constant body temperature (Carnaby, 2010).

Unlike behavioural adaptations, physiological mechanisms only set in once the birds’ core temperature has decreased to the point that the individual is at risk of hypothermia. This temperature generally varies between body sizes, the smaller the bird the higher the core temperature. At this point, the birds’ metabolic rate will increase and produce more heat. In addition, it will start to shiver when the muscles contract at an accelerated rate so as to increase body temperature (Carnaby, 2010). This will, however, reduce the bird’s energy level and result in it having to feed sooner and more frequently (Kirkley & Gessaman, 1990).

To prevent the body from overheating, birds have also developed mechanisms and behaviours to decrease their body temperature. One such mechanism is to increase the consumption of water, by doing so they can use evaporation processes without the risk of suffering from dehydration (Kirkley & Gessaman, 1990; Ferns, 1992). By drooping their wings they are increasing the surface area of their bodies that are exposed to the cooler environmental temperatures. By opening their beaks and actively panting they are subjecting blood in the epithelium of buccal cavity to cooler air. This then cools the blood returning to the deeper tissues of the body (Ferns, 1992).

The use of shade is a behavioural adaptation whereby birds actively seek out cooler areas in their environment during the warmest times of the day. This may be done by standing on the ground or perching in the branches of trees. By standing tall without bending their knees, birds can raise their bodies approximately 1 cm up from the ground and in this way are able to reduce their body temperature by 1°C (Carnaby, 2010). Physiological methods will start when there is concern of overheating. The above mentioned factors are for changes in short term temperature conditions. In the case of long term periods of cold temperatures, some birds have developed migratory behaviours whereby they actively escape cold conditions, only to return when the temperatures in an area begin to rise again (Wormworth & Mallon, 2006).

B) Adaptations to survive nutritional stress

Birds have developed new behavioural patterns in the quest of finding food. This can range from changing from one food source to another or developing new techniques to access food (Overington et al., 2011). Birds choose where to feed based on food availability, food accessibility and predation risk (Buckingham & Peach, 2005). According to their foraging strategies, ground feeding birds can be divided into three groups. Tactile hunting species are known to forage as they walk, probing continuously. Pause-travel species scan the area in front of them and then only peck at prey once it is detected. Visual feeding species continually forage, pecking at items that are seen on the surface even though they may not always be edible. Some foraging tactics may be more advantageous than others (Barbosa, 1995). For example, birds that make use of the visual hunting technique have a higher vigilance rate; as a result they have a lower predation rate. Visual species (pause-travel or

continuous) are mostly solitary birds or in some instances are found in small flocks, while probing feeders tend to travel and forage in large flocks. This explains how it is possible for them to forage for longer periods as there is safety in numbers (Barbosa, 1995).

Malnutrition is life threatening to both young and adult birds. As a result nestlings are mostly fed by both parents, increasing their food intake and elevating their chance of survival (Carnaby, 2010).

C) Adaptations to ensure reproductive success

The reproductive success of species determines how successful the species will be in surviving extreme environmental changes (Summers-Smith, 1999). Breeding behaviour differs extensively from species to species but the norm is that when it comes to time for finding a mate the onus rests with the males. Males attract mates through a variety of displays, song patterns and adornments as well as their nest building skills (Carnaby, 2010). Males will defend their territories with vigour from intruding rivals. This territorial behaviour can be in the form of song or as an aggressive attack on the intruder (Crick et al., 2002). Nestlings have very little defence mechanisms and depend mostly on the adults for protection. There are a few species of birds whose nestlings sham death (play dead and go limp) to survive. Others secrete a foul-smelling, noxious fluid which they squirt at the attacker along with fluffing themselves up and swaying. This has proven to be a very effective measure in deterring predators of about the same size as themselves (Carnaby, 2010). The adults’ nest protection behaviour includes broken wing and open wing displays by which the parents attempt to draw the predator away from the nest. A change in the anti-predator behaviour indicates a change in the parental investment (Burger et al., 1989).

D) Adaptations to survive predators

Predation is one of the principle pressures that have driven birds to flock together. Flocking behaviour decreases the amount of time spent being vigilant (Barbosa, 1995). The other advantage to feeding in flocks is an increase in the foraging efficiency, as the food intake rate of individual birds is increased (Barbosa, 1995). Since adult birds are very agile and mobile they can perform a variety of behavioural

displays. Mobbing attacks and dive bombing are some of the best known antipredator displays. Mobbing involves smaller birds in an area coming together and badgering the predator until it is driven away from the area. Dive bombing entails diving from a height and hitting the intruder on the head (Carnaby, 2010). Most birds, however, will rather choose a flight response as aggressive attacks waste energy and always carry the possibility of incurring injuries (Carnaby, 2010). Behaviour may differ not only between species but also between habitats (Burger et al., 1989).

1.6 Hypothesis, aims and research questions

Bird count and Atlas Data Project:

Problem:

There is evidence indicating that there are major global declines in the numbers of sparrows, starlings and doves, but no specific causes can be pinpointed (e.g. Yom- Tov, 2001; Crick et al., 2002; De Laet & Summers-Smith, 2007; Brichetti et al., 2008; Dandapat et al., 2010). There is also a basic lack of information on the status of numbers of House Sparrows, Cape Glossy Starlings, Laughing Doves and Cape Turtle Dove populations in South Africa.

Research Question:

Have the changes in the South African climate and human activities over the last 26 years (period over which accurate records of bird numbers and distribution have been kept in South Africa) influenced the bird numbers and is the effect the same in rural and urban areas?

Hypothesis:

 Following the global trend, there has been a decline in the numbers of garden birds in South Africa.

 The decline has been greater in the urban- compared to rural areas.

Aims of this section:

 To determine the numbers of House Sparrows, Cape Glossy Starlings, Laughing Doves and Cape Turtle Doves around the University of Johannesburg Auckland Park Campus (urban area), as well as from the Vaalwater area (rural area).

 Analyze the data from the Avian Demographic Unit (ADU) of the University of Cape Town for the Gauteng- and Limpopo provinces.

This aims was met through the following assessments:

 Multiple counts to illustrate spatial (urban- and rural counts) and temporal (seasonal counts) variation.

 Analyzing data obtained from the ADU to help draw a conclusion regarding the overall numbers of these four bird species over the last few decades.

Behavioural study:

Hypothesis:

 The behavioural differences between the species will affect their numbers.

Aim of this section:

 To observe certain behavioural differences of the House Sparrow, the Cape Turtle Dove, the Laughing Doves and the Cape Glossy Starling.

The aim was met through:

 By conducting behavioural studies of these birds in their natural setting, to detect direct competition for food as well as selection of nesting sites.

Weather analysis:

Hypothesis:

 Global warming in general has had an effect on the demise of birds.

Aim for this section:

 Obtain rainfall, temperature and humidity data for Limpopo Province and Gauteng Province from the South African Weather Services.

The aim for this section was met through:

 Analysing the weather data for the timeframes that correspond to that of the ADU counts, to establish if weather changes influenced the bird numbers.

Study of free macrophages:

Problem:

There is currently very little research done on the numbers of free (surface) avian respiratory macrophages in the lung-air sac system of the House Sparrow, Laughing Dove and Cape Glossy Starling and their correlation with environmental pollution.

Hypothesis:

 Birds are good bio-indicators of environmental pollution.

 The number of FMs is a direct indicator of environmental pollution, especially with regards to inhalant particulates.

The aim of this part of the study:

 To determine the correlation, if any, between the level of air pollution and cellular pulmonary defenses (as reflected by the numbers of FMs) in the lungs of these avian species.

This aim was met through:

 Counting the numbers of free surface macrophages in the lung-air system after lavage and observing their general morphologies.

1.7 Outline of the dissertation

Based on the objectives and the hypothesis listed above the study was planned and is presented in the following order.

Chapter 1: Introduction and objectives: General introduction to introduce the species studied, their general biology, the structure and function of the avian respiratory system and the environmental and human challenges they face from the humans and contemporaneous animals.

Chapter 2: The study areas: The two areas are described and separated into smaller study sites.

Chapter 3: Biological study procedures and techniques: All the methodology is described

Chapter 4: Results: The results are presented and analyzed.

Chapter 5: Discussion: Presents a general discussion where all the observations of the preceding chapter are compared and related with one another.

Chapter 6: References: The references and works cited are set out in alphabetical and chronological order following the Harvard style of citation.

Chapter 7: Appendices: The larger tables and figures are given in the appendix section.

From the findings of this study the following outputs have been delivered:

1.7.1 Oral presentations

1. Steyn, L. Comparative determination of the numbers of the House Sparrow, Passer domesticus, the Cape Glossy Starling, Lamprotornis nitens, the Cape Turtle Dove, Streptopelia capicola and the Laughing Dove, Streptopelia senegalensis in the Johannesburg and Vaalwater areas, with study into possible causes of expected declines. Postgraduate proposal symposium, Department of Zoology, University of Johannesburg. 10th June 2012

2. Steyn, L. & Maina, J.N. Comparison of the numbers of free macrophages in the lungs of Passer domesticus and Lamprotornis nitens from different habitats. Biodiversity within and beyond protected areas symposium, SAMWA, Kruger National Park. 16th September 2013

3. Steyn, L. & Maina, J.N. Comparative determination of the numbers of four bird species, House Sparrow, Cape Glossy Starling, Laughing Doves and Cape Turtle Dove in the Gauteng and Limpopo Provinces. Biologiese wetenskappe jaarkongres. University of Pretoria. 16th October 2013.

4. Steyn, L. Comparative determination of the numbers of the House Sparrow, the Cape Glossy Starling, the Cape Turtle Dove and the Laughing Dove in the Johannesburg and Vaalwater areas, with study into possible causes of expected declines. Postgraduate result symposium, Department of Zoology, University of Johannesburg. 1st November 2013

5. Steyn, L. & Maina, J.N. Changes in the numbers of four species of birds, the House Sparrow, Passer domesticus, the Cape Glossy Starling, Lamprotornis nitens, the Laughing Dove, Streptopelia senegalensis and the Cape Turtle Dove, Streptopelia capicola in two provinces of South Africa. International Ornithological Congress, Tokyo, Japan. August 2014 (to be presented).

1.7.2 Special awards received during the study

1. University of Johannesburg merit bursary received on the basis of BSc. Hons. Marks

2. Recipient of the Masters Block Grant Scholarship supplied by the National Research Foundation (NRF).

Date of first registration 06 February 2012.

Chapter 2:

The study areas.

2.1 General information

In South Africa there are seven biomes. This study took place in two of these areas namely the Grassland (Johannesburg) and the Savanna (Bushveld) biomes which are situated in the northern parts of South Africa (DEAT, 2005). The two locations were chosen for comparison between natural and urban areas as well as between polluted and unpolluted environments. Geographically the two sites are located in two provinces, the Gauteng- and the Limpopo Provinces (Fig. 2.1). For Gauteng, the sampling sites were more specifically found on the Auckland Park Campus of the University of Johannesburg (Fig. 2.1). This is surrounded by mines and various industries and factories, which led to high levels of air pollution and few open land spaces. The Gauteng site (Johannesburg) is situated in the one of the highest polluted areas in South Africa (Liebenberg, 1999). In the Bushveld, the farm sites were situated around the town Vaalwater (Fig. 2.2). This is mainly a game and cattle farming community with very few to no factories and industries.

Figure 2.1: The nine provinces of South Africa, with the two countries that are surrounded by SA in white (Lesotho and Swaziland). The red squares indicate the two provinces in which this study took place (www.sacarrental.com).

LIMPOPO PROVINCE

Vaalwater

GAUTENG

Johannesburg

100km

Figure 2.2: The location of the two study sites indicated by the red dots in relation to the locality of the provinces (www.bing.com).

2.2 The sites

2.2.1 Gauteng: Auckland Park (Kingsway) Campus (Urban)

2.2.1.1 Location and site description

This urban study area was in the heart of Johannesburg with an altitude of 1400- 1800m above sea level. The Highveld sites centre was found at 26°11’45.10”S and 27°57’41.37”E. On the campus grounds, four sites were chosen randomly (Fig. 2.3).

Figure 2.3: The University of Johannesburgs Kingsway Campus grounds, with the sites indicated (www.google.com).

Site one was situated on the western side of the campus (in the D parking area); here the relation between the birds, humans and traffic could be observed. The vegetation growth in this site was restricted to trees with the majority of them belonging to the Searsia family (Fig. 2.4).

Figure 2.4: Site one showing the parking area and the rows of Searsia trees.

Site two was situated on the southern side of the campus adjacent to a main road (Ripley Road). The site consisted of both concrete and grass (illustrated in Fig. 2.5). The amount and variety of trees were a lot higher than those on site one. This site allowed for the observation of the relationship between the birds and traffic.

Figure 2.5: Site two consisting of half concrete and half natural vegetation.

Site three was in the eastern corner of the university grounds, this site was completely isolated, with the least car and human movement. The area was bordered by a row of indigenous trees, increasing its isolation (Fig. 2.6).

Figure 2.6: Site three consisted of grass fields bordered with indigenous trees.

Site four was situated behind the student centre (northern side of campus) on the large open field (Fig. 2.7). There was a large amount of activity especially during midday. A large amount of human scraps added to the type of bird species found here. The three dominant species were the Hadidas, House Sparrows and Indian Mynas.

Figure 2.7: Site four illustrating the open grass field

2.2.1.2 Demographics of the study area

The Kingsway Campus is surrounded by a number of major roads that carries high traffic especially during peak traffic times, i.e. mornings and evenings. Since this area is relatively close to the industrial parts of Johannesburg, the number of particle matter (PM) in air will be high (Liebenberg, 1999).

2.2.1.3 Meteorology

The weather of the Johannesburg area is cool with maximum temperatures ranging from 21°C to 24 °C and the minimums from 3°C to 6°C. The weather can sometimes reach 38°C in summer and can fall as low as -11°C in winter. The mean annual rainfall is between 400-900mm, falling predominantly in the summer months (Burgess et al., 2004).

2.2.2 Limpopo: Vaalwater (Rural)

2.2.2.1 Location and site description

This rural area was in the Bushveld surrounded by the Waterberg Mountains. The elevation is between 1100 and 1500m. The town of Vaalwater is 24°17’0”S and 28°6’0”E. This areas as the name of the mountains and the town state, is a very water saturated area, with a number of rivers and dams (Fig. 2.8).

Figure 2.8: The sites around the town of Vaalwater (www.googlemaps.com).

Site 1: Groenfontein

This site was located on the southern side of the town Vaalwater. This farm is mainly used as horse stables and a stud farm. There was lots of vegetation between and around the stables. The trees around the stables were a combination of bushveld and savanna trees as can be seen in figure 2.9. A large amount of scrap and unutilized implements were also present on this farm. The site was very close to the Magol River and its bank vegetation.

1m A 1m B Figure 2.9: Summer (A) and winter (B) plant growth on the Groenfontein site.

Site 2: Goedehoop

This site was furthers away from the town, being about 30km North-West from the centre of the town. This farm is a mixed farm with the majority of it functioning as a game farm, the site however was half in the cattle kraal and the other half in the natural vegetation (Fig. 2.10). The two types of farming were interlinked, so the site was not divided into two different sections by human made structures. The natural part was completely undisturbed which lend itself to some challenges with regards to counting and for that reason to increase the accessibility of the site it was done as half and half. The small Vaalwater river ran past the site. There was limited human movement close to the site.

1m A 1m B

Figure 2.10: Summer (A) and winter (B) plant growth on the Goedehoop site.

Site 3: Olifantsbeen

This farm was situated North-North West from the town Vaalwater. This farm consists of old fields which were in the process of being rehabilitated. This farm functions as a feeds farm and storage facility (Fig. 2.11). This site was very busy with a lot of both human and vehicle activity.

A B 1m 1m Figure 2.11: Summer (A) and winter (B) plant growth on the Olifantsbeen site.

Site 4: Leeudrift

This farm was situated close to the town with it being just on the northern border of the town. It functions as a small chicken farm, therefore the natural competition between poultry and wild birds could be observed (Fig. 2.12). There was limited human activity.

A B 1m 1m Figure 2.12: Summer (A) and winter (B) plant growth on the Leeudrift site.

Site 5: Slypsteendrift

This site was situated on the eastern side of the town about 8 km from the centre of the town. It was surrounded by natural vegetation and very limited human activity could be found at this site (Fig. 2.13). This farm was being rested and therefore no farming took place. The main water source was a dam that was close to the site.

1m A 1m B B Figure 2.13: Summer (A) and winter (B) plant growth on the Slypsteendrift site.

2.2.2.2 Demographics of the study area

Vaalwater is a very small town with two main roads. The general area around the town is mostly used for farming of some kind, with the majority functioning as game farms. Therefore, this area is the ideal control area for determining the impact of air pollution on birds.

2.2.2.3 Meteorology

This part of the Bushveld (rural) in general is warmer than Johannesburg (urban), with the temperatures ranging between -3°C and 40°C. In summer an average annual rainfall of 650- 900mm can be expected (Burgess et al., 2004).

Chapter 3:

Biological study procedures and techniques.

3.1 Bird counts

The population size and the population index were determined. The population size refers to the specific number of organisms that are found within a certain area, while the population index refers to whether the numbers have decreased, increased or remained stable (Hofmeyr, 2012). This study took place in two areas. The urban sites were on the Auckland Park Campus (Gauteng Province) while the rural sites were in the surrounding areas of the town Vaalwater (Limpopo Province). Bird census was done by using the point-count method (Gregory et al., 2004). The point- count works on the principle that the surveyor stands in the middle of a predetermined point and counts all four species of the birds of interest at the site. The counts were done by one surveyor to minimize variations between the counts. The surveyor stood in the middle of a 200m Х 200m radius block and recorded the numbers of the birds in the air as well as on the ground. The points were determined after dividing the map of the Auckland Park Campus (urban) into grids and each block was then allocated a number. With the aid of a random number generator the sites to be studied were picked. This was done to eliminate bias in identifying a test area.

The farms in the Vaalwater area (rural) were chosen after performing a preliminary study to assess the accessibility of the farms as well as to establish the type of farming done on them. The time for the counts in both the urban and the rural areas was 5 minutes with a 1 minute settling time at each point within the study area. The settling time allowed the birds to be undisturbed by the surveyor’s presence. The short count time period eliminated or reduced the likelihood of double counts. Point- counts are ideal to use for determining both the bird density and the species abundance (Gregory et al., 2004). The surveyor returned to the same points for a duration of two weeks (14 days) in the winter and summer months for two consecutive years. The counts were performed early in the morning (6:00 in summer, 7:00 in winter), at midday (12:00) and late afternoon (18:00 in summer, 17:00 in winter). By increasing the number of counts performed, it minimized the impact of human movement and the impact of short term temperature fluctuations could be studied. The counts were done in the drier summer months (March, April); as rain disrupts birds’ normal feeding patterns which would reduce the reliability of

the counts. The differences in the times when the counts were performed were dependant on the light visibility.

The choice of the survey area was dependant on the vegetation type present in the area. The more overgrown sites lend them self to a set of challenges, specifically when accessing them. Open areas are in general more suited for all four of the bird species of interest, as they mainly forage on the ground. Open areas allows for easier escape when birds are attacked (Carnaby, 2010). The advantages of the point-counts: a) they were well-suited for most habitats, b) they were right for shy birds, and c) it was apt for populations with higher densities. The disadvantages were: a) time was lost moving between the points, b) the birds might also be attracted to the surveyor and c) the birds might be wrongly identified (Gregory et al., 2004). The chance of faulty identification was decreased by practicing and using a pair of 50 Х 10 Ultratec binoculars.

Statistics:

Statistical analyses were performed by using the IBM SPSS Statistics 21 program. An independent sample t-test compared the differences between the means and the significance of those differences. This was tabulated along with the means and standard deviations of the various sections. Boxplots were used to illustrate the differences between the numbers of birds counted per species. Scatterplots demonstrated the effect that temperature had on the number of birds recorded during the three times per day counts. A boxplot gives a lot of information here is a short summary on all the important factors; the distribution of the scores is illustrated as a box with protruding lines (whiskers). The length of the box represents the variables interquartile range and express 50 present of the cases. The whiskers ends illustrate the highest and the lowest values. The median is indicated by the solid line running across the box. The circles and stars indicates the outliers and the extreme values respectively. The numbers found next to the outliers and extreme values does not represent a value, but rather the specific data point in the data set.

3.2 Atlas (bird count) data

Another type of bird monitoring is bird atlasing. This is based on the concept where checklists are compiled for a certain area (Hofmeyr, 2012). In South Africa the first atlasing project (SABAP1) started in 1987 and lasted until 1992. SABAP2 began in 2007 and is still ongoing, although the data used for this project was only until 2012. There is a 15 year gap between the two projects, this allows for comparison between two distinct data sets. The atlas projects were performed in the whole of South Africa, this study, however, only focussed on two provinces namely, Limpopo- and Gauteng Provinces. The complete protocol for SABAP1 and SABAP2 can be found in Harrison and Underhill (1997), and on sabab2.adu.org.za, respectively. A brief summary follows to aid with understanding the main characteristics of the two projects. The presence of a number of birds species were recorded by volunteers in a grid system for a specified time frame. The grid cells were marked out along longitudinal and latitudinal lines (Fig. 3.1 and Fig. 3.2).

28 km

Figure 3.1: The grid division of the Gauteng Province, used in the SABAP1 and SABAP2 counts.

Figure 3.2: The grid division of the Limpopo Province, used in the SABAP1 and SABAP2 counts.

The most important difference between the two Atlas Projects were the sizes of the grid cells. The quarter degree grid cells (QDGC) in SABAP1 were 15ʹ by 15ʹ, compared to the pendants of 5ʹ by 5ʹ in SABAP2. The QDGC of SABAP2 thus consisted of nine pendants. Another difference in the protocol was the time period over which a list could be compiled. For SABAP1, counts had to be done within a 30 day time span in contrast to that for SABAP2 which had to be done in 5 days. The observers of SABAP2 had to spend a minimum of 2 hours in a pendant and had to cover all the habitats in that pendant, while there were no such requirements for SABAP1. These differences do offer a number of challenges for statistical analyses. However, the lengths of the two checklists were nearly the same. A total of 50 species were on the SABAP1 list and 53 on the SABAP2 list. This proves that the average reporting rate across the board was similar for all species. If a substantial difference was present, then a factor would have been needed to adjust the reporting rate but this was unnecessary.

The key aim of the Atlas Projects was to collect presence/absence data on bird species in South Africa. As reporting rate is related to abundance in a non-linear manner, the changes in reporting rates can be inferred from the changes in

abundance. Griffioen (2001) indicated that the densities of majority of bird species are well-modelled by their reporting rates. This was illustrated through the following formula:

̂ ( ) [1]

where ̂ is the estimated population density, represents the reporting rate and ln is the natural logarithm with a transcendental base. The 훂 and 훃 are parameters that need to be estimated. The last mentioned parameters (훃) were determined for the vast majority of Australian birds by Griffioen (2001) and the values for a large number of the species were nearly unity. The values used for the species that were studied were similar to those studied by Griffioen (2001). This means that the effect

of ⁄ was negligible. Since the aim was to calculate the index of density and not the actual density (as some data needed for this was unavailable) the value of 훂 can also be excluded from the equation. The index of density can thus be expressed through the following equation:

̂ [2]

Equation 2; the reporting rate as mentioned earlier, was applied to all the reporting rate data of the SABAP1 and SABAP2 projects for the four species of interest. On the indices of density values, various statistical analyses were performed.

Statistics: Numerous boxplots were constructed to compare for example the index of density in Limpopo with that of Gauteng for all of the species of interest. A paired sample t-test was used to determine if there were a significant difference in the index of density between SABAP1 and SABAP2. A paired sample t-test was also performed for all the species’ index of densities to amplify a difference in the number of birds per species found in the two provinces.

3.3 Weather service data

The temperature, rainfall and humidity for the same time periods as SABAP1 (1987- 1992) and SABAP2 (2007-2012) was acquired from the South African Weather Services. The data from various weather stations in Gauteng- and Limpopo Province were averaged per year. Descriptive analyses were performed with this data, as the data of the SAPAP1 and SABAP2 counts were an average of the 6 years and

therefore the two data sets could not be directly compared. A RDA-plot (Redundancy analysis) was drawn, by using Canoco version 4.5, between the averages of the indices of density and the climatic conditions. The RDA-plot allowed for a comparison between these two data points and was done to determine if weather might have significantly impacted the density numbers.

3.4 Behaviour

The behaviour was studied simultaneously with the field point-count. The behaviour was observed for residency time at a single spot. At the most active spot, the surveyor sat down and observed the birds for a 30 minute period using Ultratec binoculars. A baseline was established of normal behaviour for the four species and the observations were supported by accounts in the literature. Thereafter special attention was paid to behavioural altercations between the four species as well as interspecific competition. Such behavioural observations might have been imprecise as it was assumed the bird was intimidated by another species, when it moved out of the line of sight: a bird might have simply moved to some vegetation.

3.5 Macrophages

A total of ten birds per species (House Sparrows, Cape Glossy Starlings and Laughing Doves) per area were captured in the study sites. The Cape Turtle Doves was excluded from this section of the project as they proved to be very difficult to catch. Catching was done with the aid of mist nets of 4m Х 8m in size. Nets were set up in the most accessible place with a back drop to make the catching more successful (Fig. 3.3A). The nets were manned the whole time to prevent the birds from strangling themselves (Fig. 3.3B): the birds were removed as soon as they were caught and put in a small cage for transport to the laboratory.

0.88m A 3cm B

Figure 3.3: The mist net constrution to used catch the birds (A), and an illustration of how the bird get caught in the nets (B).

The birds were sacrificed with an intramuscular injection of Euthanase® (pentobaritone sodium, 200mg.mlˉ¹) and syringes of 5ml were used with needles of 25G. In larger birds 1ml and 0.5ml in smaller birds were injected after the birds’ wings were extended and the feathers were removed with the aid of absolute ethanol (99%). This was done only after obtaining the necessary permission from the University of Johannesburg’s Animal Ethical Board and permits from the Nature Conservation Departments of the two provinces. The birds were weighed using an AND GX- 12K scale immediately after being euthanized.

Lavage of the respiratory system was performed by placing the bird in a supine position at room temperature (25ºC). A cannula of tygon tubing of 1 mm in diameter in smaller birds and 3mm in diameter in larger birds was placed in the trachea and tied in place with cotton string. The respiratory system was filled with a known amount of 40ºC phosphate buffer saline (PBS) at a pressure of 30 cmH₂O (3 kPa) by using a funnel attached to the cannula. The PBS (pH 7.4) was left in the lung-air sac system for 10 minutes. Afterwards it was extracted with the aid of a 50ml syringe. The extracted volume was measured.

Ten milliliters of the extracted fluid was spun at 500 G using a MVC AC centrifuge. The supernatant was removed and the pellet resuspended in 0.5ml of PBS, making

a dilution. A portion was then stained with 0.1ml 0.4% trypan blue. The trypan blue is phagocytized by the free (surface) macrophages (FMs), making them more visible and assisting in identification and counting. A Neubauer brightline hemocytometer was used along with a coverslip to count the cells in suspension. The cells were counted with the aid of a light microscope. The cells in the four 1mm corner squares and the middle one was counted. The total number of FMs was calculated. The cells were then fixed using glutaraldehyde (2.5%) in a 1:1 ratio with the cells in the PBS suspension, along with heated (40°C) 100% ethanol in a ratio of 2:1. Light microscope photographs were taken using a Zeiss Ax10 microscope with an AxioCam ERc5s camera.

Statistics:

The number of macrophages counted was determined by using the following procedure; the 5 squares counted on the hemocytometer each had a volume of 0.1mm². Three counts were performed on every sample. The number of cells was thus divided by 1.5 (5 Х 0.1 Х 3) to give the total number of macrophages in 1mm³. This value was then multiplied by a 1000 to give the number of macrophages in one cubic centimeter (or 1ml) of liquid. To account for the 0.5 ml dilution factor the value was multiplied by 5. Finally the value was multiplied by the volume of the extracted liquid to arrive at the number of cells/ml in the extracted volume. This number was then normalized with the body mass, by dividing the number of FMs from a bird by its body mass, before any statistical analysis was performed.

An independent sample t-test was used to compare the differences between the means of the numbers of macrophages present in the various species in their individual provinces and the significance of those differences. Spearman rank order (rho) correlation analysis was done to test if there were linear relationships between the body mass and the number of macrophages normalized with body mass of the three species for both areas. To determine whether or not there is a relation between the number of macrophages and the body mass, an ANOVA test was used. Both parametric and non-parametric tests were performed to support the outcomes of the other tests and to reduce any effects which might have arisen from the data being slightly skewed, i.e., deviating from normal (Poisson) distribution.

Chapter 4:

Results.

4.1 Bird counts

Bird counts were performed during the years 2012 and 2013 in the urban (Auckland Park) and rural (Vaalwater) areas. The number of House Sparrows observed decreases from 2012 to 2013, in both the urban and the rural areas (Fig. 4.1). The median for the number of P. domesticus in the urban area was 125 during 2012 and 94 during 2013. In the rural area the medians were lower at 19 and 9 for 2012 and 2013 respectively. The numbers found next to the outliers and extreme values does not represent a value, but rather a specific data point in the data set.

Figure 4.1: Comparisons between the two years counts of House Sparrows for both the urban- and the rural areas.

For the number of L. nitens the median was 3 in 2012 and 0 in 2013 (Fig.4.2). These low medians show that during most of the counts few to no Cape Glossy Starlings were observed in the urban area. In the rural area the median decreased from 10 to 6 between the two years.

Figure 4.2: Comparisons between the two years counts of Cape Glossy Starling for both the urban- and the rural areas.

S. capicola had a median below 10 in both the urban and rural area (Fig. 4.3). In the urban area the median was 8 in the year 2012 and 2 in 2013. In the rural area the median changed form 7 during 2012 to 4 in 2013.

Figure 4.3: Comparison between the two years counts of Cape Turtle Doves for both the urban- and the rural areas.

The Laughing Doves (S. senegalensis) were only included in the study at the end of 2012, this was done because the Cape Turtle Doves proved to be difficult to catch and for consistency throughout the project it was included in all other sections (i.e. Atlas Data analysis, behavioural study and macrophage enumeration) of the project.

There was a significant difference between the number of House Sparrows and Cape Turtle Doves counted during 2012 and 2013 (Table 4.1). The Cape Glossy Starlings in the urban area showed an increase in the numbers recorded during the two years. In the rural area L. nitens showed a decline, however, it was not significant.

Table 4.1: The number of birds counted and the level of statistical significance of the differences between the two years of the study.

Comment Region Standard Species Year Mean t-value Sig. (P) deviation

2012 169.82 92.967 Urban 3.517 0.001 S House 2013 101.00 45.563 Sparrows 2012 58.46 63.356 Rural 3.846 0.001 S 2013 11.93 9.241 2012 3.21 2.672 Cape Urban -0.590 0.559 NS 2013 3.93 5.824 Glossy 2012 16.50 16.128 Starlings Rural 2.216 0.032 S 2013 8.82 8.731 2012 unknown* unknown* Urban unknown* unknown* / Laughing 2013 15.96 6.197 Doves 2012 unknown* unknown* Rural unknown* unknown* / 2013 203.04 85.796 2012 9.89 7.941 Cape Urban 5.241 0.0001 S 2013 1.89 1.474 Turtle 2012 12.82 11.842 Doves Rural 4.031 0.0001 S 2013 8.82 2.887

*The Laughing Doves were only included during the second year therefore the values for 2012 is unknown (Table. 4.1). S, Significant; NS, non significant.

Lower numbers of birds were recorded in the urban area than in the rural one. This was true for all but one species, the House Sparrows (Table 4.2).

Table 4.2: The level of statistical significance of the differences between counts of the rural and urban bird numbers. Comment Standard Species Region Mean t-value Sig. (P) deviation

House Urban 135.41 80.42 82.68 0.0001 S Sparrows Rural 35.20 50.63

Cape Urban 3.57 4.50 Glossy 67.23 0.0001 S Starlings Rural 12.66 13.42

Laughing Urban 15.96 6.19 27.28 0.0001 S Doves Rural 203.04 85.76

Cape Urban 5.89 6.95 Turtle 110.0 0.156 NS Doves Rural 8.18 9.74

S, significant; NS, non significant.

During the 2012 counts P. domesticus had the highest presence while S. capicola had the lowest (Fig. 4.4). This supports the decision to include S. senegalensis during the 2013 counts. Comparing the combined winter and summer count results of 2012, higher numbers of birds were recorded during the winters than the summer (Fig. 4.4). A similar pattern was found in 2013, with higher numbers being found in the winter than the summer (Fig. 4.5). During the second year (2013), S. senegalensis was the most abundant followed by P. domesticus.

counted Numbers

Figure 4.4: The difference between the summer and the winter bird counts of 2012.

counted Numbers

Figure 4.5: The difference between the summer and the winter bird counts of 2013. There were significant differences between the summer and winter counts for all but the Cape Turtle Doves in 2012 (Table 4.3).

Table 4.3: The statistical differences between the two seasons of each year, with the two areas combined. Comment Standard Species Year Season Mean t-value Sig. (P) deviation

Summer 58.93 51.004 S 2012 -5.238 0.001 House Winter 169.36 100.808 Sparrows Summer 41.00 35.360 S 2013 -10.138 0.001 Winter 71.93 67.297 Summer 4.50 2.769 S Cape 2012 -4.300 0.001 Winter 15.21 17.076 Glossy Summer 1.93 2.892 S Starlings 2013 -2.467 0.017 Winter 10.82 8.555 Summer unknown* unknown* / 2012 unknown* unknown* Laughing Winter unknown* unknown* Doves Summer 123.00 129.469 S 2013 -11.508 0.001 Winter 96.00 91.680 Summer 3.21 2.217 NS Cape 2012 -1.087 0.283 Winter 19.50 8.081 Turtle Summer 2.11 2.200 S Doves 2013 -2.682 0.011 Winter 3.32 2.510

*value unknown due to exclusion from the study in 2012. S, Significant; NS, Non-significant.

Counts were performed for 14 consecutive days during the summer- and winter seasons in both years (Appendix 1; Table 7.2 and 7.3). During the summer of 2012 relatively high numbers of House Sparrows were recorded throughout the day (Fig. 4.6: A,B) with the highest numbers present in the evenings (Fig. 4.6; C). Temperatures played an insignificant role in the number of House Sparrows recorded during the morning (A), midday (B) and evening counts (C). In the winter (Fig. 4.6; D, E, F) more birds were recorded than during the summer counts (Fig.

4.6; A,B,C). In the mornings (D) the lower the temperature the fewer birds were seen. The temperature in the rural area was higher than the urban area. The numbers of House Sparrows recorded in the rural area (Fig. 4.6; G-L) were lower than that of the urban area (Fig. 4.6; A,B,C) during the summer period. In 2013 the House Sparrows were mostle active during the mornings (Fig.4.7; A, D,G,J) and in the evenings (Fig. 4.7; C,F,I,L), even though fewer birds were active during the midday (Fig. 4.7; B,E,H,K) counts, it was not as low as can be seen in some of the other species. Overall the numbers for the rural area was lower in both the summer (Fig. 4.7; G,H,I) and the winter (Fig. 4.7; J,K,L) periods than the numbers that were observed in the urban area. The Cape Turtle Dove numbers in 2012 (Fig.4.8), in both areas were very low. During the summer in the urban area the highest numbers were recorded in the mornings (Fig. 4.8; A), during the midday counts the birds were mostly absent. In the winter (urban) (Fig. 4.8; D,E,F) higher numbers were observed than in the summer (Fig. 4.8; A,B,C). In Vaalwater (rural) the lowest numbers were recorded during the midday counts (Fig. 4.8; H,K), this was for both the summer and the winter counts. The numbers of Cape Turtle Doves recorded in 2013 was just as low as during 2012. These low numbers were recorded in the urban (Fig. 4.9; A-F) as well as the rural areas (Fig. 4.9; G-L). The numbers were variable and a pattern could not be detected. The Cape Glossy Starlings (Fig. 4.10) were the most active during the morning counts. In the urban areano noticeable pattern could be observed during the three counts in the summer. The winter counts (Fig. 4.10; D,E,F) in in the urban area showed low numbers during the midday times (Fig. 4.10; E). In the rural areain the winter the lowest number of birds were observed in the evenings (Fig. 4.10,C) and the highest in the mornings (Fig. 4.10; J). During the urban counts of 2013 no Cape Glossy Starlings were recorded (Fig. 4.11; A, B,C). Lower numbers were also observed in the summer times (Fig. 4.11, G, H, I) compared to the winter counts, in the rural area (Fig. 4.11; J,K,L). The Laughing Doves showed higher numbers in the rural areas compared to the urban areas, with the lowest numbers being present during midday counts (Fig. 4.12; H,K) and the highest during the early mornings (Fig. 4.12; G,J).

A B C

F D E

Figure 4.6; A-F: Comparison between the number of House Sparrows counted and the temperature recorded during the summer and winter seasons of 2012.

G H I

J K L

Figure 4.6; G-L: Comparison between the number of House Sparrows counted and the temperature recorded during the summenr and winter seasons of 2012.

A B C

D E F

Figure 4.7; A-F: Comparison between the number of House Sparrows counted and the temperature recorded during the summer and winter seasons of 2013.

G H I

J K L

Figure 4.7; G-L: Comparison between the number of House Sparrows counted and the temperature recorded during the summer and winter seasons of 2013.

A B C

E F D

Figure 4.8; A-F: Comparison between the number of Cape Turtle Doves counted and the temperature recorded during the summer and winter seasons of 2012.

G H I

J K L

Figure 4.8; G-L: Comparison between the number of Cape Turtle Doves counted and the temperature recorded during the summer and winter seasons of 2012.

A B C

D E F

Figure 4.9; A-F: Comparison between the number of Cape Turtle Doves counted and the temperature recorded during the summer and winter seasons of 2013.

G H I

J K L

Figure 4.9; G-L: Comparison between the number of Cape Turtle Doves counted and the temperature recorded during the summer and winter seasons of 2013.

A B C

D E F

Figure 4.10; A-F: Comparison between the number of Cape Glossy Starlings counted and the temperature recorded during the summer and winter seasons of 2012.

G H I

K L J

Figure 4.10; G-L: Comparison between the number of Cape Glossy Starlings counted and the temperature recorded during the summer and winter seasons of 2012.

A B C

E F D

Figure 4.11; A-F: Comparison between the number of Cape Glossy Starlings counted and the temperature recorded during the summer and winter seasons of 2013.

G H I

J K L

Figure 4.11; G-L: Comparison between the number of Cape Glossy Starlings counted and the temperature recorded during the summer and winter seasons of 2013.

A B C

D E F

Figure 4.12; A-F: Comparison between the number of Laughing Doves counted and the temperature recorded during the summer and winter seasons of 2013.

G H I

J K L

Figure 4.12; G-L: Comparison between the number of Laughing Doves counted and the temperature recorded during the summer and winter seasons of 2013.

4.2 Atlas and weather service data

4.2.1 Comparison of the indices of density for the different timeframes in each region using listwise deletion of missing values.

Data obtained from the ADU (Avian Demographic Unit) of the University of Cape Town for the two provinces (Gauteng and Limpopo) were analysed. The main reason as to why only these two provinces were used in this analysis was the magnitude of the data extraction process; it was thus decided to use the two provinces which coincide with the rest of the work. Listwise deletions were used because they include only cases that have a full set of variables. Thus, if the one data point is unavailable, the other data point will be excluded from all the analysis run. This was done to ensure that the results obtained between the readings of SABAP1 and SABAP2 were not skewed.

The indices of density decreased between SABAP1 and SABAP2 for both provinces (Fig. 4.13). The medians were 0.7963 (SABAP1) and 0.5084 (SABAP2) for Gauteng. For Limpopo it was 0.5176 and 0.2231 for SABAP1 and SABAP2 respectively. The outliers and extreme values illustrate the data points, they correspond to the grid blocks on the maps of the two Atlas Data Projects.

Index of density Index Index of density Index

Figure 4.13: Comparison between the indices of density of the House Sparrows for SABAP1 and SABAP2 in Gauteng- and Limpopo Provinces.

In Gauteng (Fig. 4.14) the medians of the index of density of the Cape Glossy Starlings increase slightly from 0.5972 to 0.7349 (Fig. 4.14), while in Limpopo the medians of the indices of density showed a not significant difference of 1.1301 for SABAP1 and 1.1002 for SABAP2.

Index of density Index Index of density Index

Figure 4.14: Comparison between the indices of density of the Cape Glossy Starlings for SABAP1 and SABAP2 in Gauteng- and Limpopo Provinces.

The index of densities (Fig. 4.15) had a moderate decline, the median of SABAP1 (2.8035) were higher than the median of SABAP2 (2.6395) for Gauteng. In Limpopo the medians were very similar for SABAP1 and SABAP2.

Index of density Index Index of density Index

Index ofIndex density

SABAP1 SABAP2

Figure 4.15: Comparison between the indices of density of the Laughing Doves for SABAP1 and SABAP2 in Gauteng- and Limpopo Provinces.

For the Cape Turtle Doves, the median of the indices of density increased from 1.7277 to 1.9610 in Gauteng while in Limpopo the medians were similar (1.5041) (Fig. 4.16).

Index ofIndex density ofIndex density

Figure 4.16: Comparison between the indices of density of the Cape Turtle Doves for SABAP1 and SABAP2 in Gauteng- and Limpopo Provinces.

A decline between the index of density for the timeframes of SABAP1 (1987-1992) and SABAP2 (2007-2012) was evident (Table 4.4). The means vary from the medians but overall the trend stayed the same with the House Sparrows, Laughing Doves and Cape Turtle Doves (Limpopo), showing a numerical decline. The Cape Glossy Starlings (in both provinces) and the Cape Turtle Doves (Gauteng) showed an increase. A paired sample t-test (Table 4.4) was conducted to evaluate whether or not a decline was present and the extent of it between the index of densities of SABAP1 and SABAP2. The House Sparrows had a significant decrease in the index of densities between SABAP1 and SABAP2 (t= 4.888; P< 0.0001) for Gauteng. For Limpopo, the House Sparrows also had a significant decline (t= 5.704; P<0.0001) similar to the Gauteng birds. The Cape Glossy Starlings (Table 4.4) had an increase in the index of densities, with the t-value for Gauteng Province being significant (P< 0.019) while the t-value for Limpopo Province was not significant (P>0.628). The Laughing Doves (Table 4.4) for the two provinces showed a similar trend with t- values of 1.029 and 0.836, with levels that were not significant (P> 0.309; P>0.404). The Cape Turtle Doves (Table 4.4) were the only birds that showed a difference between the two provinces, where an increase was noticed in the index of density for Gauteng and a decrease was present for Limpopo. However, the t-value for Gauteng was significant (P< 0.027) while the value for Limpopo was not significant (P>0.495).

Table 4.4: The significant/non-significant values of the differences between SABAP1 and SABAP2 of each region, determined by using a paired sample t-test Comment Standard Sig. Species SABAP Region n Mean t-value deviation (P)

1 Gauteng 43 0.877 0.515 4.888 0.0001 S 2 Gauteng 43 0.534 0.344 House 1 Limpopo 0.641 0.536 Sparrows 174 5.704 0.0001 S 2 Limpopo 119 0.350 0.374

1 Gauteng 0.706 0.623 42 -2.451 0.019 S 2 Gauteng 0.880 0.487 Cape 43

Glossy 1 Limpopo 183 1.187 0.735 Starling -0.486 0.628 NS 2 Limpopo 160 1.220 0.715

1 Gauteng 43 2.845 0.777 1.029 0.309 NS 2 Gauteng 43 2.701 0.731 Laughing 1 Limpopo 1.728 0.857 Dove 167 0.836 0.404 NS 2 Limpopo 153 1.662 0.865

1 Gauteng 1.720 0.721 43 -2.293 0.027 S 2 Gauteng 1.980 0.725 Cape 43

Turtle 1 Limpopo 186 1.555 0.739 Dove 0.685 0.495 NS 2 Limpopo 138 1.504 0.850

* S, Significant difference; NS, Non-significant differences.

4.2.2 Comparison of bird indices of the regions using pairwise deletion for missing values.

Pairwise deletion excluded the cases only if data required for a specific analysis was missing. This was done because during SABAP1 the numbers of surveyors were less and therefore all the quadrates in each province were not covered to the same extent as in SABAP2. This statistical tool, however, made comparisons possible.

Higher indices of density of the House Sparrows were present in Gauteng compared to Limpopo for SABAP1 (1987-1992). The median in Gauteng for SABAP1 was 0.7969 compared to 0.3706 in Limpopo (Fig. 4.17). For SABAP2 the medians were 0.5085 and 0.2231 for Gauteng- and Limpopo Province respectively (Fig. 4.18).

Figure 4.17: Comparison of the indices of density of the House Sparrows for Limpopo- and Gauteng Provinces during the SABAP1 counts.

Figure 4.18: Comparison of the indices of density of the House Sparrows for Limpopo- and Gauteng Provinces during the SABAP2 count.

The Cape Glossy Starlings had a substantially higher indices of density in Limpopo compared to Gauteng during both SABAP1 (Fig. 4.19) and SABAP2 (Fig. 4.20). The medians of the indices of density for SABAP1 were 0.5972 (Gauteng) and 1.0986 (Limpopo). Compared to that the medians for SABAP2 were 0.7216 (Gauteng) and 1.0986 (Limpopo) respectively. In both cases the indices of density almost doubled in Limpopo.

Figure 4.19: Comparison of the index of density of the Cape Glossy Starlings for Limpopo- and Gauteng Provinces during the SABAP1 count.

Figure 4.20: Comparison of the indices of density of the Cape Glossy Starlings for Limpopo- and Gauteng Provinces during the SABAP2 count.

The Laughing Doves showed a higher indices of density in Gauteng compared to Limpopo for both SABAP1 (Fig. 4.21) and SABAP2 (Fig. 4.22). The medians for the indices of density of the Laughing Doves for SABAP1 in Gauteng were 2.8035 compared to Limpopo’s 1.7098, for SABAP2 it was 2.6395 in Gauteng and 1.7271 in Limpopo.

Figure 4.21: Comparison of the indices of density of the Laughing Doves for Limpopo- and Gauteng Provinces during the SABAP 1 counts.

Figure 4.22: Comparison of the indices of density of the Laughing Doves for Limpopo- and Gauteng Provinces during the SABAP2 count.

The Cape Turtle Doves’ (Fig. 4.23, 4.24) indices of density followed the same trend as the House Sparrows’ with a lower index of density value present in Limpopo compared to Gauteng for both SABAP1 (P> 0.104) and SABAP2 (P< 0.001). The medians were 1.7160 (Gauteng) and 1.4795 (Limpopo) respectively for SABAP1 and 0.5084 (Gauteng) and 0.2231 (Limpopo) for SABAP2.

Figure 4.23: Comparison of the indices of density of the Cape Turtle Doves for Limpopo- and Gauteng Provinces during the SABAP1 count.

Figure 4.24: Comparison of the indices of density of the Cape Turtle Doves for Limpopo- and Gauteng Provinces during the SABAP2 count.

An independent sample t-test (Table 4.5) was conducted to compare the difference in the indices of density between the two regions, for both the SABAP bird count time periods. For the House Sparrows there was a significant difference between the indices of density for the two provinces during SABAP1 (P< 0.001) and SABAP2 (P< 0.007). The Cape Glossy Starlings (Table 4.5) showed a similar trend with a significant difference (P< 0.001, P< 0.0002) in the indices of density of the two regions. The Laughing Doves (Table 4.5) had the highest t-values for SABAP1 and SABAP2 both of which was significant (P< 0.0001) The Cape Turtle Doves (Table 4.5) showed different results than the other species with their T-value for SABAP1 being not significant (P> 104) but for SABAP2 it was significant (P< 0.001).

Comment

SABAP Standard Species Region Mean t-value Sig. deviation

(P) 1 Gauteng 0.877 0.515 3.524 0.001 S 1 Limpopo 0.556 0.540 House 2 Gauteng 0.534 0.343 -3.954 Sparrows 0.007 S 2 Limpopo 0.353 0.382 1 Gauteng 0.706 0.622 -3.826 0.0001 S Cape 1 Limpopo 1.184 0.724 Glossy 2 Gauteng 0.863 0.494 -3.117 0.0002 S Starlings 2 Limpopo 1.223 0.712 1 Gauteng 2.845 0.776 8.210 0.0001 S 1 Limpopo 1.740 0.824 Laughing 2 Gauteng 2.701 0.731 7.163 Doves 0.0001 S 2 Limpopo 1.671 0.859 1 Gauteng 1.720 0.720 1.631 0.104 NS Cape 1 Limpopo 1.513 0.758 Turtle 2 Gauteng 1.980 0.725 3.612 0.001 S Doves 2 Limpopo 1.505 0.839

4.2.3 Comparison between the two regions with all the indices of the bird species combined to determine the health of the environments.

The medians of the bird indices for both provinces declined from SABAP1 to SABAP2 (Fig. 4.25). In Gauteng this decline was from 1.3643 to 1.1787 and in Limpopo it declined from 1.3845 to 1.0987. This shows an overall decline of birds in both provinces, when just taking a grouping of these four species into consideration. The difference between the two projects in Gauteng- and in Limpopo Province was significant (P>0.01; P> 0.002).

Figure 4.25: Comparisons between the grouped indices of density of all the species for the two provinces during the two counts (SABAP1 and SABAP2).

The variations between the two Atlas Data Projects, is also illustrated in the maps found in Appendix 1 (Fig. 7.15- 7.18).

4. 3 Weather service

4.3.1 Temperature

The average temperature in Gauteng (Fig. 4.26) was the same for both of the time frame of the Bird Atlas Data project; SABAP1 (1987-1992) and SABAP2 (2007- 2012). In Limpopo the average temperature was lower during the SABAP1 project than during the SABAP2 project (Fig. 4.27).

18.00

16.00 Average temperatures 14.00 in Gauteng SABAP1

° 12.00 Average temperatures 10.00 in Gauteng SABAP2 8.00 6.00 LinearBest fit(Average line Temperature( temperaturesSABAP1 in 4.00 Gauteng SABAP1) 2.00 LinearBest (Averagefit line 0.00 temperaturesSABAP2 in 1 2 3 4 5 6 Gauteng SABAP2) Years

Figure 4.26: Comparison between the average temperatures in Gauteng Province of the two periods of the Atlas projects (SABAP1 & SABAP2).

20 Average temperature 19 Limpopo SABAP1

° 19 Average temperature 18 Limpopo SABAP2 18 Trendline SABAP1 17 Best fit line

Temperature ( Temperature SABAP1 17 Best fit line 16 Trendline SABAP2 1 2 3 4 5 6 SABAP2 Years

Figure 4.27: Comparison between the average temperatures in Limpopo Province of the two periods of the Atlas projects (SABAP1 & SABAP2).

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4.3.2 Rainfall

The average rainfall of Gauteng (Fig. 4.28) and Limpopo (Fig. 4.29) were lower during the second time periods of counts, Limpopo’s was more distinguishable.

1000 900 Gauteng average rainfall SABAP1 800 700 Gauteng average 600 rainfall SABAP2

500 400 TrendlineBest fit lineSABAP1 SABAP1 (mm)Rainfall 300 200 TrendlineBest fit SABAP2line 100 SABAP2 0 1 2 3 4 5 6 Years

Figure 4.28: Comparison between the average rainfall in Gauteng Province of the two periods of the Atlas projects (SABAP1 & SABAP2).

1200 Limpopo average 1000 rainfall SABAP1

Limpopo Limpopo average 800 average rainfall rainfall SABAP1 SABAP2 600 TrendlineBest fit line SABAP1 400 SABAP1 Rainfall (mm) Rainfall

200 TrendlineBest fit line SABAP2 SABAP2 0 1 2 3 4 5 6 Years

Figure 4.29: Comparison between the average rainfall in Limpopo Province of the two periods of the Atlas projects (SABAP1 & SABAP2).

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4.3.3 Humidity

The humidity levels in Gauteng were higher during the second bird count time period (2007-2012) than the first time period (1987-1992), this was also true for the Limpopo Province.

74 72 Average humidity Gauteng SABAP1 70

Average humidity 68 Average humidity Gauteng SABAP2 Gauteng SABAP1 66

Humidity 64 TrendlineBest fit SABAP1line 62 SABAP1 60 TrendlineBest fit SABAP2line 58 SABAP2 1 2 3 4 5 6 Years

Figure 4.30: Comparison between the average humidity in Gauteng Province of the two periods of the Atlas projects (SABAP1 & SABAP2).

90 Average humidity 80 Limpopo SABAP1

70 Average humidity 60 Average humidity LimpopoLimpopo SABAP1 SABAP2 50 40

Humidity TrendlineBest fit lineSABAP1 30 SABAP1 20

10 TrendlineBest fit lineSABAP2

0 SABAP2 1 2 3 4 5 6 Years

Figure 4.31: Comparison between the average humidity in Limpopo Province of the two periods of the Atlas projects (SABAP1 & SABAP2).

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The raw data for Fig. 4.28- 4.31 is in Appendix 1 (Table 7.1).

A significant difference was only present between the rainfall patterns of SABAP1 (1987-1992) and SABAP2 (2007-2012) of the Limpopo Province and between the humidity levels in Gauteng Province. All the other differences were not significant according to the independent sample t-test (Table 4.6).

Table 4.6: The statistical significance level of the differences between the weather patterns over the timeframes of the Atlas Data Project bird counts.

Weather Area Count t-value Sig.(P) Comment condition SABAP1 Gauteng -0.103 0.921 NS SABAP2 Temperature SABAP1 Limpopo -0.972 0.360 NS SABAP2 SABAP1 Gauteng -0.194 0.851 NS SABAP2 Rainfall SABAP1 Limpopo 4.176 0.003 S SABAP2 SABAP1 Gauteng -2.818 0.023 S SABAP2 Humidity SABAP1 Limpopo -0.909 0.351 NS SABAP2

The RDA (redundancy analysis) tri-plot (Fig. 4.32) indicates 88% of the variation were describes on the first axis and 11% on the second axis. There was a division between the provinces and that three of the four bird species had a higher index of density in the Gauteng Province, with only the Cape Glossy Starling being present in higher numbers in the Limpopo Province. The Cape Glossy Starlings were more influences by the weather conditions that the other species. The higher temperatures and lower rainfall levels had the greatest impact on L. nitens. A division was also

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formed between the two different counts in Limpopo Province as well as in Gauteng Province.

0.6 Cape Turtle Dove

Laughing Dove

Cape Glossy Starling

Humidity Temperature

Rainfall 0.4 -

House Sparrow

-1.5 1.5

Figure 4.32: RDA (redundancy analysis) tri-plot illustrating the similarities between the various sites and the climate variables.

4.4 Behaviour

Table 4.7: The behavioural traits of the four species of interest.

Trait House Cape Turtle Cape Glossy Laughing Dove Sparrow Dove Starling Average group 2-130 1-2 1-20 1-110 size Dependency Very group Single or in Strong relation Singletons or on group dependant pairs with other in pairs (Swarm) members of the Sturnidae family Habitation Mostly in small Mostly on the Mostly in tree Mostly on the behaviour shrubs or on ground tops or on the ground the ground ground

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Reaction to Large group fly Fly towards Fly up to tree Fly into the predators up at once shelter tops trees causing confusion, seek shelter in trees Reaction to Move closer to Less active at No noticeable Less active. strong wind the buildings times when reaction wind were at its strongest Reaction to Huddle ‘puff’ ‘puff’ ‘puff’ cold weather together and themselves up themselves up themselves up ‘puff’ but not as themselves up, noticeable as move closer to with the Cape buildings Turtle Doves Reaction to Not as effected Sit in trees Become very Sit in tree, or warm weather by warm inactive forage in the weather, shade although when it was very hot they stayed in the shade Foraging Tactile feeding Visual feeding Pause travel Visual feeding techniques technique feeding (Barbosa, technique 1995) Reaction to Depending on More tolerant Very nervous, Dependng on humans amount of to vehicles will fly off their numbers, exposure to than to quickly but generally humans, lots of humans very docile expose increases their

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tameness Reaction to Easily Less Intimidated, by Easily other birds intimidated by intimidated, other bird intimidated, but (intimidation) larger birds as intimidated by specie except not as much well as large numbers rather for the as the House numbers of than species Sturnidae Sparrows other species family members Species most Egyptian Hadida, large Hadidas, Indian Mynas, intimidated by Geese, number of Egyptian Hadidas, Hadida, Feral Sparrows and geese and Egyptian pigeons, Indian Indian Mynas Pied Crows geese and Mynas and Pied Crows Pied crows Species most Weavers Laughing Indian Myna, Cape Turtle associated Doves, Red- Red-wing Doves with eyed Dove Starling, Wattled Starling

Observations were made throughout the course of the study (Table 4.7). The behaviour of the birds was observed at all the counting stations as well as on all the farms (Appendix 1; Fig. 7.1-7.13). In the urban area attention was paid to how the birds respond to humans, traffic and invasive species, as well as if there were any noteworthy behavioural associations between the four species that were studied. In the rural area attention was paid to how the birds fair on the various farms and if the type of farming not only influences their numbers but also their behaviour. It was also noted how the birds respond to humans in the natural environment and if this impacts their behaviour. Specifically attention was paid as to whether or not there were behavioural differences between the two areas and if so what would be the reasons for it. In the urban area at station one the only behaviour that was noticed on this concrete dominated site was that the Cape Glossy Starlings fed alongside the Indian Mynas without any altercations. Birds were noted to feed on the seeds of the

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Searsia trees during winter. In the summer very few birds were observed at this site. Site two showed the relationship between the birds and traffic, here the birds will move out of the way of a car, but only to such an extent that they are able to quickly return to what they were feeding on as soon as the car passes, it appears as if they are not intimidated by motor vehicles. Site three was the quietest site with regards to human movement, and was the only site where the Cape Turtle Doves were often observed. However, when either the Egyptian Geese or the Hadidas were present at this site no other birds were observed, their size and aggressive behaviour intimidated the other bird species. Site four had very high numbers of House Sparrows, this was probably due to the large amount of food scraps that could be found at this site. If the House Sparrows had the number advantage (present in higher numbers) they were often not intimidated by other species like the Indian Mynas, however, if only a few birds were present they were observed to move away.

In the rural area on Groenfontein farm, horse stables were noted to be present but very few birds were observed. Such a finding is fairly curious as one would expect to find high sparrow densities because of the feed as well as the droppings of the horses which are high in seeds and grains, on which birds feed. Therefore, the relationship between the horses and the birds could not be observed. At Goedehoop the bird most often observed was the Cape Glossy Starlings, the numbers reflected in appendix though shows that it was not the farm with the highest density of these birds. At Olifantsbeen the feeds storage facilities attracted a high number of birds, the high numbers of Laughing Doves did not cause other birds to avoid the area, they also did not appear to be aggressive. The other species might have been present in lower number on this farm (Olifantsbeen). At Leeudrift the relationship between the chickens and the wild birds were observed. The House Sparrows easily took advantage of the food availability inside the chicken coops, while doves and starlings were only found outside. This, however, might have been because of accessibility and P. domesticus could easily enter the coops due to their smaller stature. The birds on Slypsteendrift were predominantly found around the farm house or at the dam, very few birds of these four species were observed in the veld.

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4.5 Macrophages

4.5.1 Urban: (Auckland Park, Gauteng Province):

4.5.1.1 Correlation analysis A negative correlation was present between the body mass and number of free macrophages (FMs) normalized with body mass in the House Sparrows of the urban area Fig. 4.33).

mass

with bodynormalized

FMs

Number of

Figure 4.33: The relationship between the body mass and number of free macrophages normalized with body mass of House Sparrows in the urban area.

A positive correlation between the number of macrophages normalized with the body mass and the body mass was found for the urban Cape Glossy Starlings (Fig. 4.34).

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mass

normalized with bodynormalized

FMs

Number of

Figure 4.34: The relationship between the body mass and number of free macrophages (FMs) normalized with body mass of Cape Glossy Starlings in the urban area.

A negative correlation was observed between the numbers of macrophages normalized with body mass against body mass of the urban Laughing Doves (Fig.4.35).

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mass body with normalized FMs

ofNumber

Figure 4.35: The relationship between the body mass and number of free macrophages (FMs) normalized with body mass of Laughing Doves in the urban area.

 House Sparrow: according to the Spearman rank order correlation (rho) (Table 4.8), there was a moderately weak negative correlation (-0.382) between the weight and the number of macrophages in the House Sparrows (P> 0.276) it might be unlikely. The negative correlation illustrate the heavier the House Sparrows the fewer macrophages were present.

 Cape Glossy Starling: they (Table 4.8) had a moderate correlation of 0.479 and a significance level of 0.162. The positive correlation indicates that the heavier the doves the higher the number of macrophages.

 Laughing Doves: a moderately high correlation with a Spearman rank order (rho) correlation coefficient of -0.624 (Table 4.8) and a significance level of 0.054 were found in these urban birds. Thus the heavier the Laughing Dove the lower the number of macrophages.

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Table 4.8: The Spearman correlation coefficients of the correlation between the body mass and the number of macrophages normalized with body mass in the urban area. Cape Glossy House Sparrows Laughing Doves Starlings Spearman (rho) -0.382 0.479 -0.624 correlation Sig. (P) 0.276 0.162 0.054

The House Sparrows were significantly (P> 0.01) smaller than the Cape Glossy Starlings and the Laughing Doves. The two last mentioned species had very similar body masses. The House Sparrows had a mean body mass of 23.746 with an standard deviation of 2.524. The Cape Glossy Starlings had a mean and a standard deviation of their body mass of 78.17 and 7.245, respectively. The Laughing Doves had a mean body mass of 82.070 and a standard deviation of 15.213 (Fig. 4.36). A slight variation is present between the mean and median values.

Figure 4.36: The differences in body mass between the three species of birds studied in the urban area.

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The House Sparrows had the least number of FMs followed by the Cape Glossy Starlings and the Laughing Doves having the most FMs (Fig. 4.37).

Figure 4.37: Comparison of the numbers of free macrophages normalized with body mass for all three species studied.

4.5.1.2 Regression analysis To determine whether or not there was a relation between the number of macrophages and the body mass, an ANOVA test was used. This test indicated that only the Laughing Doves had a strong correlation, in this case 43% variability in the macrophage count was due to the body mass from the Laughing Doves of the urban area. The variability of the House Sparrows and the Cape Glossy Starlings were 23.8% and 13%, respectively.

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4.5.2 Rural (Vaalwater, Limpopo Province):

4.5.2.1 Correlation analysis

A negative correlation was found between the body mass and the number of macrophages normalized with body mass in the House Sparrows of the rural area (Fig. 4.38).

FMs normalized with body massnormalized FMs

Number of

Figure 4.38: The relationship between the body mass and number of free macrophages normalized with body mass of House Sparrows in the rural area.

A negative correlation was present between the number of macrophages normalized with body mass and the body mass of the Cape Glossy Starlings in the rural area (Fig. 4.39).

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with body massnormalized FMs

Number of

Figure 4.39: The relationship between the body mass and numbers of free macrophages normalized with body mass of Cape Glossy Starlings in the rural area.

A weak correlation was present between the body mass and number of macrophages normalized with body mass in the Laughing Doves (Fig. 4. 40).

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FMs normalized with body massnormalized FMs

Number of

Figure 4.40: The relationship between the body mass and numbers of free macrophages normalized with body mass in the Laughing Doves of the rural area.

 House Sparrow: a moderately weak negative correlation (-0.321) was present between the body mass and the number of macrophages in the rural House Sparrows (Table 4.9). The negative correlation illustrate that the heavier the House Sparrows the fewer macrophages were present.

 Cape Glossy Starling: illustrated a moderately strong correlation of -0.612 with a significance level of 0.060 (Table 4.9). The negative correlation indicates that the heavier the starlings the lower the number of macrophages.

 Laughing Doves: had a Spearman (rho) correlation coefficient of -0.176, this was a weak correlation that was supported by the significance level of 0.627. Thus the heavier the Laughing Dove the lower the number of macrophages (Table 4.9).

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Table 4.9: The Spearman correlation coefficient of the correlation between the body mass and the number of macrophages normalized with body mass in the rural area.

Cape Glossy House Sparrows Laughing Doves Starlings Spearman (rho) -0.321 -0.612 -0.176 correlation Sig. (P) 0.365 0.060 0.627

The urban House Sparrows (Fig. 4.41) were smaller than the other two species, as was the situation of the rural birds. These House Sparrows had a mean body mass of 21.863 with an average standard deviation of 1.28. The Cape Glossy Starlings had a mean and a standard deviation in their body mass of 80.726 and 9.73 respectively. The Laughing Doves had a mean body mass of 97.047 and a standard deviation of 4.69.

Figure 4.41: Comparison of the body masses of the three bird species studied in the rural area.

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The House Sparrows (Fig. 4.42) had a mean number of macrophages (after normalization with the body mass) of 31305.90 with a standard deviation of 7991.28. The Cape Glossy Starlings had a mean of 43082.00 with a standard deviation of 11782.97 while the Laughing Doves had a mean of 75743.6 with a standard deviation of 10576.12. A variation is present between the median and mean values.

Figure 4.42: The number of free macrophages normalized with the body mass of the three species of interest studied in the rural area.

4.5.2.2 Regression

The ANOVA test indicated that only the Laughing Doves had a strong correlation; 58% variability in the macrophage count was due to the body mass of the Laughing Doves from the rural area. The variability of the House Sparrows and the Cape Glossy Starlings were 11.4% and 0.05% respectively.

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4.5.3 Comparison between the urban and rural area:

The mean number of macrophages present in the lung-air sac systems of the urban House Sparrows (Fig. 4.43) was 50516.20 (std dev: 15841.85), compared to the mean number of macrophages (31305.90) present in the lung-air sac systems of the macrophages macrophages rural House Sparrows (std dev: 7991.237).

Number of Number normalized with bodyweight with bodyweight normalized

Urban Rural Area

Figure 4.43: Comparison between the numbers of free macrophages normalized with body mass of House Sparrows for the two study areas.

The mean number of macrophages in the lung-air sac system of the urban Cape Glossy Starlings (Fig. 4.44) was 90883.10 (std dev: 32670.67), while the rural birds had a mean number of macrophages of 43082.00 with a standard deviation of 11782.97.

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Urban Rural Area

Figure 4.44: Comparison between the numbers of free macrophages normalized with body mass of Cape Glossy Starlings of the two study areas.

The urban Laughing Doves had a mean number of 97942.30 macrophages in their lung-air sac systems with a standard deviation of 24055.29 (Fig. 4.45). In turn, the rural doves had a mean macrophage number of 75743.60 (std dev: 10576.12).

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Urban Rural Area

Figure 4.45: Comparison between the numbers of free macrophages normalized with body mass of Laughing Doves in the two study areas.

A significant difference was found in the number of macrophages present in the lung- air sac systems of all three species in the urban and the rural areas (Figs. 4.43, 4.44, 4.45). Higher numbers of macrophages were present in all the urban birds compared to the rural bird ones (Table 4.10).

Table 4.10: The differences between the number of macrophages normalized with body mass for the urban and rural areas.

House Cape Glossy Laughing Sparrows Starlings Doves T test T-value 3.424 4.352 2.671 Sig(p) 0.003 0.001 0.016

The urban House Sparrows were heavier than the rural birds (Fig. 4.46). The opposite was true for the Cape Glossy Starlings (Fig. 4.47) and the Laughing Doves (Fig. 4.48) where they were heavier in the rural area compared to the urban area.

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Urban Rural Area

Figure 4.46: The body mass comparisons of the House Sparrows.

Urban Rural Area Figure 4.47: The body mass comparisons of the Cape Glossy Starling

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Urban Rural Area

Figure 4.48: The body mass comparisons of the Laughing Doves

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The photomicrographs of the macrophages (Fig. 4.49), no distinctions were observed between the macrophages of the different species or from the different areas. The cells were heavily endowed with lysosomes.

10µm C A 10µm B

Figure 4.49: Photomicrographs of the free macrophages from the Laughing Doves form the urban area. Figures A, B & D are light microscope photos and C is a fluorescence light micrograph. N, nucleus; arrows, lysosomes. 10µm D 10µm E

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Chapter 5:

Discussion and conclusion.

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5.1 General comments

To briefly recap, the current work set out to prove the following: a) the numbers of the four bird species of interest (for the purpose of the discussion they will only be referred to as birds) changed in South Africa over the last 26 years and b) the changes in numbers were a result of air pollution, weather changes, human influences and behavioural differences between the species. From this the following hypotheses were advanced a) following the global trend there has been a decline in the numbers of the garden bird species in South Africa, b) the decline has been greater in the urban (Gauteng) compared to the rural (Limpopo) area, c) behavioural differences between the species will affect their numbers, d) global warming has had an effect on the demise of birds, e) birds in general are good bio-indicators of environmental pollution and f) the number of pulmonary free macrophages (FMs) is a direct indicator of environmental pollution, especially with regards to inhaled particulates. To test these hypotheses the following aims and objectives were set; a) the number of House Sparrows, Cape Glossy Starlings, Laughing Doves and Cape Turtle Doves around the University of Johannesburg’s Auckland Park Campus (urban area) and around the Vaalwater area (rural area) were determined: these multiple counts illustrated spatial and temporal variations at the sites between seasons, B) analyze data obtained from the Avian Demographic Unit (ADU) of the University of Cape Town to help draw a conclusion regarding the overall numbers of these four bird species over the last few decades, c) conduct behavioural studies of these birds in their natural setting to identify direct competition for food as well as selection of nesting sites, d) compare the rainfall, temperature and humidity conditions (for the two timeframes of the Bird Atlas Projects): by doing this, the extent of the weather conditions on the bird numbers could be estimated and e) determine the correlation, if any, between the level of air pollution and cellular pulmonary defenses [as reflected by the numbers of free macrophages (FMs)] in the lungs of these species of birds, by counting the numbers of FMs in the lung-air system after lavage. In this chapter the hypotheses set will be discussed and the significance of the findings will be addressed. This chapter follows the same trend as the previous chapters, where the declines in the bird numbers are examined followed by the possible reasons for the declines.

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5.2 Bird counts data analysis

Bird counts were performed to understand the differences in habitats (urban and rural areas), the impact of daily weather variations as well as seasonal climate changes. The counts were done in the summer and winter months of 2012 and 2013. The 2012 counts excluded the Laughing Doves. This was because S. senegalensis were only included in the study at the end of 2012, after all efforts to capture S. capicola proved futile. The counts performed in the second year (2013) included all the species (House Sparrows, Cape Glossy Starlings, Cape Turtle Doves and Laughing Doves). This allowed for comparisons between S. senegalensis and the other species in the various sections (e.g. behavioural study) of this study. The observed decline in the numbers of birds between the two years may not indicate that the number of birds decreased but may rather illustrate yearly variations which are natural to wild bird populations (Hofmeyr, 2012). The observed variations might be ascribed to birds moving out of an area, either temporarily or on a permanent basis because of increased challenges in the area, which can be natural or man-made (WWF, 2000).

Three of the four species showed higher numbers in rural areas compared to urban areas, with only the House Sparrows being present in higher numbers in urban areas. Such a finding has previously been related to the fact that since 1920 (Summers-Smith, 1988) this species of bird has been known for its close association with humans (Newton, 2004). The variation between the numbers of all four species of birds observed in the urban area and the rural area is attributed to the habitat in which the counts were done. The specific rural area used in the study was selected based on the nature of the environment. This site was specifically chosen due to its natural and undeveloped conditions. In comparison the specific urban area employed was chosen as it is present in a highly polluted area (Molewa, 2012). The selected study areas were opposites of each other. If areas were chosen that were not as extreme, it can be speculated that the results might have been different; the numbers of the different areas could have been closer to one another and the species might also have preferred a different habitat. In the rural area, the type of farm plays an important role in the number of birds that were observed. Rintala and Tiainen (2007) found that on dairy farms, the numbers of starlings recorded was significantly higher than on other farms, e.g. crop farms. During the selection process

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of the farms, this was one of the considerations and five different farms were selected for that specific reason. When carrying out the bird counts on the different farms it was found that in this study the type of farming did not have a substantial impact on the numbers observed (Appendix 1), contrary to what was observed by Rintala and Tiainen (2007). The overall environmental condition of the farms on the other hand was a greater driving factor determining the distribution of bird species. Some farms like Olifantsbeen and Leeudrift had higher numbers of birds because of their high levels of grains present at the grain storage facilities and at the chicken coops. The lowest numbers of birds were recorded at Groenfontein. This can be ascribed to the condition of the last mentioned farm (Groenfontein). It appeared to be neglected, with a large number of scattered scrap and unutilized implements. The number of birds recorded on Slypsteendrift and Goedehoop was what can be expected on farms that function mostly as wild environments, with limited human influence. The numbers of buildings were also limited on Slypsteendrift- and Goedehoop farms. It further explains the lower numbers of birds that were present on these farms. In the urban area, the level of urbanization influences the number of birds observed (Reis et al., 2012). According to Reis et al. (2012), a higher number of birds are present in areas which are not completely built over, compared to commercial blocks that are covered with concrete and pavements. The Auckland Park Campus of the University of Johannesburg has large lawns and dense vegetation between and around the buildings. The area is also surrounded by a number of residential areas which contribute to the high amount of vegetation. The study of the influence of the effects of urbanization on bird populations was thus conducted in an area demonstrating a decreased level of urbanization, but was markedly greater than the rural areas chosen. Another consideration that can impact on the numbers of birds found in an area is the height and the type of trees that are present in the area. Although a large number of native trees were present in the urban area, exotic species like the Blue gum (Eucalyptus globulus) could also be found. Reis et al. (2012) demonstrated that the presence of exotic plant species influences the bird species richness of an urbanized area. This may similarly apply to the rural area, although fewer non-native plants were noted in this area.

This study further showed that bird numbers varied greatly between season, sites and the year in which the study was done. Overall all the species studied were

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reported in numbers that would not raise concern. The only species that were particularly observed in concerning low numbers or were even completely absent during periods of the study was the Cape Turtle Dove. This was paradoxical as S. capicola are seen as the most common and - abundant dove species in South Africa (Colhan & Harrison, 1996; Oatley, 1999). They were expected to exist in higher numbers than what was observed in the present study. A possible reason why S. capicola could be displaced from the areas is the presence of high numbers of S. senegalensis. Interspecific competition for, e.g., resources and nesting areas would be intense. This, however, would only explain the scenario in the rural area, where the numbers of Laughing Doves were high. The exact reason for the low numbers in the urban area is unclear.

During the 2012 and 2013 surveys the number of birds recorded was higher in the winter months compared to the summer ones. The reason for this may be due to seasonal changes of the vegetation density, which becomes diminished during winter. Such an influence has been noted to have an effect on the visibility of birds which increases with a decrease of vegetation cover (Gregory et al., 2004). This allows for the birds to become more conspicuous and thus easier to identify. Spearpoint et al. (1988) and Summers et al. (1984) also found higher numbers of birds in winter compared to summer and attributed this to species being more reliably counted than others in the winter. A bird’s lifecycle and behaviour is closely associated to seasonal changes (WWF, 2000). Seasonal differences include temperatures, precipitation variations as well as seed and insect availability (Reis et al., 2012). Food availability can thus be one of the main reasons for the higher presence of birds in winter compared to summer. Some of the species of interest are also known to form groups/large flocks in winter to increase their chance of survival. The challenges of winter decrease their body condition, chance of escaping a predator and feeding success (Barbosa, 1995; Crick et al., 2002; Carnaby, 2010). Higher numbers of Cape Glossy Starlings were recorded during the winter, while during summers in Gauteng very few to none were observed. This is because these starlings migrate locally to the ideal feeding and nesting place (Sinclair et al., 2002). The local migratory patterns of the Cape Glossy Starlings were unknown when the species was chosen. According to Tobler and Smith (2004), the migratory area of the starlings is approximately 26km². This would still place them in the surrounding areas

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of Johannesburg and therefore might influence the count results but it would not influence the results of the rest of the study.

The period of study (2012 and 2013) was not long enough to allow one to conclude if an actual decline in the bird populations took place. This, however, allows for a brief examination of the effect of short term weather variations on bird numbers. As shown in chapter 3 measurements of the daily air temperature was recorded three times per day during each survey and compared to the number of bird species recorded. This was done to determine whether one species demonstrated greater sensitivity than another to temperature fluctuations. The temperature was also measured to determine if these short term fluctuations influences the behaviour of the birds, as well as to identify if there were any variation regarding these differences between the areas and seasons. Temperature did not seem to influence the times when P. domesticus were the most active. This was different from the results that Beer (1961) found, where the House Sparrows were predominantly active in the mornings. The reason for this difference is unclear. It can, however, be speculated that food availability might be the driving force, as they feed on seeds and human scraps. Food scraps would not be thrown out at a set time in the day and is an important contributor to P. domesticus’ diet (Sinclair et al., 2002). Their feeding times would thus change according to the availability of food. The Cape Turtle Doves and Laughing Doves were generally active during the morning and were absent or found in lower numbers at midday. The Cape Glossy Starling followed the same pattern as the doves which make their activity times likely to be due to behavioural traits. Sudlow (2004) found that majority of birds are most active early in the mornings. Janicke and Chakarov (2007) tested the effect of daily temperatures, wind speed and precipitation on the activity times of birds and they found no significant effect: the birds did not shorten or extend their activity times under such circumstances. According to Sudlow (2004) birds in warmer climates generally seek food from sunrise until about midmorning: when the ambient temperature starts to increase, the birds start to look for shelter. Thus, birds’ activity level decreases with an increase in the ambient temperature. This was true for three of the four species examined in this study, i.e., S. senegalensis, S. capicola and L. nitens.

The high variation in the bird counts may be the result of inaccuracies in the method employed in the current work. The various aspects that were discussed by Gregory

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et al. (2004) were considered when the counting method and its particulars were developed for this study. Among others a random number generator was used to indicate the blocks in which the counts in the urban area were made. Furthermore, the counts were conducted three times a day to ensure that none of the species were more active during a particular part of the day: such species numbers would have been misrepresented if the counts were only conducted in one part of the day. A standard protocol was formulated and the surveyor kept to that. However, as the observer was not an expert in bird counting, fewer birds might have been recorded than what were actually present. Summers et al. (1984) noted that the less experienced observers undercounted the number of birds.

The hypotheses that states that the numbers would be higher in the rural area compared to the urban area is thus accepted for this short term study. The counts results are, however, different from that determined in the Atlas Project and in certain instances it is a direct opposite. This might be explained by the fact that this is a short term study and the atlas project was a long term one. The conclusions drawn above might be relevant to the short term results only.

5.3 Atlas Data Analysis

Reporting rates of birds reflect abundance, but varying from species to species, the relationship is generally curvilinear (Reis et al., 2012). At levels of high abundance, the smallest variations in the reporting rate represent large differences in the abundance (Hockey et al., 1989; Allan, 1997). The decrease between the number of House Sparrows in both the urban and rural area amongst the two projects might be ascribed to their granivorous diet. House Sparrows feed predominantly on seeds (Vincent, 2005): the only part of their lifecycle when they are dependent on insects is when they are chicks and are fed predominantly on aphids. De Laet and Summers- Smith (2007) postulated that the decline of House Sparrows is due to lack of food during the breeding season. The Cape Glossy Starlings showed an increase of the index density amongst the two projects in both areas. This might be because they are not intimidated by one of South Africa’s main invasive species, the Indian Myna (Peacock et al., 2007). Compared to other species like the Laughing Doves which will avoid these aggressive invaders (Peacock et al., 2007), the Cape Glossy

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Starlings will easily co-exist with their family member (Lim et al., 2003). Overall the increase of the Cape Glossy Starling might have been because of their breeding strategy: they are successful co-operative breeders and up to six members help raise a clutch (Sinclair et al., 2002). Thus, if one member of the group is removed it does not influence the breeding success as much as it would if it was just a male- female breeding pair (Crick et al., 2002). The changes in the density indices and areas showed that the Cape Turtle Doves had an increase in the density index in the urban area (Gauteng) and a decrease in the rural environment (Limpopo). The Laughing Doves on the other hand showed declines between the two atlas projects. This was, however, not significant. As S. senegalensis, occur in higher numbers, the population will have to be severely affected with a higher mortality rate to show significant declines. Robinson and Sutherland (2002) found that the decline of farmland birds that feed on seeds are much greater than other farmland birds. This corresponds with the difference between the decline in the numbers of Cape Glossy Starlings and Cape Turtle Doves observed in this study. It can be speculated that the numbers of some of the species might be increasing in the urban area, but overall the bird species richness might be decreasing. Zhou et al. (2012) observed a corresponding pattern (increase in abundance but decrease in species richness) in and around Hong Kong.

The numbers of birds were higher in Gauteng- (urban) than in Limpopo Province (rural). This was true for all but one species- the Cape Glossy Starling (although it was only the increase in Gauteng (urban) that was significant). It was expected that the numbers might be lower in the urban area compared to the rural area as the challenges are greater in the urban area for birds. This was to be the case found by Heij (1985) and Robinson et al. (2005) for the House Sparrows. Heij (1985) and Robinson et al. (2005) also found that the level of urbanization, i.e., the number of buildings, their proximity to one another, the number of gardens as well as the type of species of birds all influence the density of the birds. Urban birds are also known to congregate more due to space constraints (Crick et al., 2002), this might have led to higher numbers being recorded. The numerical densities of these species (Laughing Doves, Cape Turtle Doves and House Sparrows) were higher in the urban area compared to the rural area and might be because they were specifically chosen for their close association with humans. In that regard, they can be less sensitive to

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urbanization. The decline in the rural area is dependent on the type of farmlands that the birds are exposed to (Crick et al., 2002). Livestock and grain farms have decreased over time in the Limpopo Province and were converted into game farms (DAFF, 2012). Characteristically game farms have a lower food supply than commercial farms. They lack large quantities of spilt grains and/or supplementary food except for short periods in winter when wild animal diets are supplemented (Fourie, 2008). Additionally, climatic variations between the two provinces can also be a reason for the higher numbers of the House Sparrows, Cape Turtle Doves and Laughing Doves in the urban areas. Gauteng Province has lower rainfall and lower average temperatures than Limpopo Province (SAWS, 2012). These species of birds might thus be better adapted to such conditions while the Cape Glossy Starling thrives in an area with higher average temperatures and rainfall. The supplemented feeding of the birds in the urban gardens can contribute to their higher numbers, especially in winter when their food sources would not be diminished as much as the rural birds’ (Vincent, 2005). The hypothesis that stated that the declines will follow the worlds’ trend cannot be fully accepted, but it cannot be rejected either as some of the birds’ numbers increased and some decreased. The hypothesis regarding higher number declines in the urban area compared to the rural area is not accepted either as higher numbers of birds were recorded in the urban area compared to the rural area. This is postulated to be the case because the time period of the study was not long enough or because the two provinces in which this study were conducted do not illustrate the situation of the whole of South Africa’s urban and rural areas.

The overall health of the environments at each location was evaluated by combining the indices of density of all four species in each province. This formed part of a baseline study where by the overall decline in numbers of birds in each province was determined between the two Atlas Projects. The results of this study merely served to illustrate what happened in the environments of these four generalist species. The lack of data hindered the formulation of a complete picture of the environmental conditions. This may be rectified by increasing the collection of bird species used to provide a more comprehensive picture of the situation. However, the combined numbers gave a brief overview of what changed and if any environmental factors might have influenced the numbers of birds. The numbers declined from SABAP1 to SABAP2, demonstrating that there was an alteration in the environments. This was

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found to be true for all bird population in the two provinces studied. Changes in the environment can either be the result of human interference or by natural events. Human interference can be anything from building new structures to moving into an area and ultimately changing the natural environment by either removing the vegetation or polluting it (Vincent, 2005). The natural events will include changes in the climate, bush fires, and invasive species that move into an area. Allan (2005) suggested that natural habitat loss contributes to the decline of the numbers of birds, resulting in the decrease of the carrying capacity of an area and causing the environment to change (Vincent, 2005; Hofmeyr, 2012). Populations of short-lived (i.e. short lifespan) birds like the species in this study, are sensitive to changes in food availability. This can be a reason for the change in the overall environmental condition (Geiser et al., 2008).

Weather patterns are expected to influence the reporting rate outcomes as the birds are less visible and less active during rain and cold misty conditions (Hofmeyr, 2012). Gregory et al. (2004) advised that counting or surveying of birds should be discontinued during rainy conditions. This, however, is not always possible especially in areas where it (rain) lasts for 5 consecutive days, the time frame in which the SABAP2 counts has to be completed. Wind might also influence the accuracy of the counts as the feathers and surroundings of the birds may move, making identification of the bird challenging. This is particularly a problem with birds that are very similar in colouration. Changes in the numbers and with it the experience of the observers between the two Atlas Projects may possibly have influenced or contributed to inaccuracy in the counts. However, it is highly unlikely that the combination of these effects could have resulted in the significantly lower numbers of birds detected during these observations. This is because the number of observers increased during the second project. A factor that could have influenced the results was the extent to which the grid blocks were surveyed: a large number of the blocks in Limpopo were not surveyed. Other factors that should be considered regarding the surveying and reporting accuracy include, the accessibility of the areas. The likelihood that protected areas compared to the rural homesteads are surveyed is a lot higher. As safety is a problem in the rural area, lower reporting rate can thus be expected in secluded areas. SABAP2 made requirements for the participants to try

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and survey all the habitats in the 5’ by 5’ pendants, while SABAP1 made no such stipulations for the 15’ by 15’ pendants.

The reporting rate across the species cannot be compared as these species differ substantially in their behaviour, it is possible that one species will have a higher reporting rate than another. For example, the House Sparrows and Cape Turtle Doves. P. domesticus have higher reporting rates due to their vocal nature and S. capicola are less vocal and are mainly detected visually (Hofmeyr, 2012). Another important consideration is that in the 15 year interim of the two projects (SABAP1 and SABAP2), the numbers could have undergone dramatic alterations in the population density. As such it can only be speculated as to what factors (if any) have impacted on the bird populations during the period of time. A final consideration which one should be mindful of when dealing with the data presented in this section pertains to the statistical analysis of this section. The data that were obtained for the bird Atlas Data Projects were a combination of data spanning a 6 year period (SABAP1 and SABAP2) and it was therefore not possible to compare the index of density data to any of the other data sets (i.e. weather service data) for example to do regression analysis. This makes deductions of the reasons for the declines mostly speculative.

5.4 Weather service

Birds’ responses to climate change are well known (e.g. WWF, 2000, 2006; Menendez et al., 2006). Different species respond differently to climate change, Whichman et al. (2003) noted that even a 10% decrease in the annual precipitation may result in a marked decrease or even extinction of Tawny Eagles (Aquila rapax) in southern Africa. It is still unclear how the four bird species of interest (i.e. those studied here) respond to the changes in the climatic conditions in South Africa. The impact of climate change is species specific; their behaviour, genetics, environment, as well as other contributing factors will all influence how these bird species are affected. The fact that only three variables (temperature, humidity and rainfall) were included in this study underestimate the influence that climate change might have on for example the habitat constriction of birds (Pounds & Puschendorf, 2004). The increase in the temperature and decrease in the rainfall between the two time

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periods of the atlas projects in both provinces can result in a decrease in the plant growth of an area. This not only decreases the food sources for the granivorous species but also leads to a decline of suitable habitat and cover; a shortage of cover can increase the predatory risk (Hofmeyr, 2012). According to Williams et al. (2003) periods of low rainfall are often associated with a reduction in the bird population size since the decrease in the moisture levels result in lower food availability (Williams et al., 2003). The impact of the weather conditions in a 6 year period might not provide a conclusive picture. However, as the total time from the beginning of the first SABAP project to the end to the second SABAP project was 26 years the time period is extremely significant. Any major impacts are expected to have most certainly shown in that time.

As illustrated by the RDA plot, three of the four species occur in higher numbers in Gauteng Province (urban) compared to Limpopo Province (rural). This was supported by the Atlas Project data which found similar results. L. nitens was the only species that was present at higher numbers in the Limpopo Province. This could possibly be attributed to their ability to adapt to areas with higher rainfall, humidity and temperature conditions. The rainfall had the lowest impact on the number of starlings and the temperature the greatest influence. It was expected that the humidity and rainfall would have had similar impacts as these climate variables are closely related; the exact reason for this discrepancy is unknown. The descriptive statistics where it was found that the rainfall decreased and the humidity increased support these findings. The reasons for the Cape Glossy Starlings’ ability to adapt to higher conditions cannot be due to physiological conditions as the physiology of both the Passeriformes (L. nitens and P. domesticus) would be very similar. The RDA plot also showed that the three other species adapt better to an area with lower climate conditions. The Laughing Doves and Cape Turtle Doves showed a closer relationship. This, that different dove species generally have a close association with one another was similarly observed by Irwin (1981). The reason for the Cape Glossy Starling being better adapted to higher temperature is unknown. It can, however, be speculated that over time, their behaviour and feeding habits allow them to become better adapted to hotter and drier macro environments, like the Limpopo Province (rural).

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As birds are highly sensitive to climate change, they make very good bio-indicators of weather variations (Berthold et al., 2004). The most severe impacts of climate change are probably due to a combination of threats rather than from climate change only (Thomas et al., 2004). Being generalists, these four species would most likely be affected by a loss of their food sources. This arose from climate change which compels them to extend their home ranges. The hypotheses that climate change would have an impact on the numbers of the species studied cannot be rejected or accepted. Further studies will be needed to determine this.

5.5 Behaviour

Different behaviours of the birds were observed in both the urban and rural localities. The behaviour of the birds determine how they are influenced by a number of factors and how they respond to them (Carnaby, 2010). The House Sparrow is an invasive species in a large number of countries in the world. According to Brooke (1996) they are not a major threat to indigenous species: they might influence the food sources to a certain extent but as they supplement their diet with human food scraps they are not seen as a direct threat. Invasive birds are known for their aggressive behaviour; aggression and boldness in birds can be due to genetic variations or because of pollutant and toxicants (Breed & Sanchez, 2010). However, no noteworthy aggressive behaviour by the House Sparrows in the urban and the rural areas was noticed. The size of the invasive species as well as their numbers influences how native species respond to them (Carnaby, 2010). This was seen for the Hadidas and Egyptian Geese that intimidated all the studied species.

In the rural area the type of farm did not influence the birds numbers and behaviours as much as the environmental conditions of the farm. The reason for this can be speculated to be because of the type of species; the fact that they are generalist, associated with humans and appear to be easily adaptable.

5.6 Macrophages

Environmental factors such as the level of pollution the organisms are exposed to as well as their lifestyle play an important role in the numbers of macrophages that can be found in the lung-air sac system of birds (Maina & Cowley, 1998). Only three

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species, namely the House Sparrows, the Cape Glossy Starlings and the Laughing Doves were studied here. As mentioned before, it was done because of the difficulty to capture S. capicola. Macrophages make very good bio-indicators of air-pollution as the birds move through the whole of their environment sampling the particles in the air (Brown et al., 1997). Larger inhaled particles (3.7-7µm in diameter) are removed from the air in the nasal cavity and in the proximal part of the trachea (Hayter & Besch, 1974); midsize (1.1µm) and small (<0.091µm) particles are trapped in the lungs and the air sacs where they are removed predominantly by the free macrophages (FMs) (Hayter & Besch, 1974). As these particles are trapped by the FM’s, the cells serve as good indicators of the air quality.

In this section, the reasons why bird lungs and their associated macrophages are ideal organs for studying the effects of air pollution are discussed. Compared to mammals, the respiratory surface area of a bird lung is 15% greater (Maina et al., 1989) and the blood gas barrier is 62% thinner (Maina et al., 1989). Furthermore, the lung are very efficiently ventilated (Scheid, 1979). These factors allow the bird lungs to be more greatly exposed to the external environment (Brown et al., 1997; Kiama et al., 2008). The reasons why this study focussed on the macrophages was because FMs are known to remove small particles from the air on the way to the vulnerable gas exchange area (Nganpiep & Maina, 2002). They engulf particles smaller than 0.1µm. In the natural environment, these size particles are commonly found in vehicle emissions (Schaerfer, 1969; Liebenberg, 1999). Natural organic dust is often characterised by high levels of fungal spores, bacteria and viruses (Brain, 1977; Castranova et al., 1996) and might also be responsible for stimulating production of macrophages. These cells will not only be confined to birds present in the urban environment but will also occur in the rural ones. The numbers of macrophages are thus expected to be variable at each site. The hypothesis of this part of the study is that the number of FMs is a direct indicator of environmental pollution, especially with regards to inhaled particulates. A direct measure of the level of particulates and gaseous pollution in the study sites were not measured. It can be speculated that the differences in the numbers of FMs in the birds from the urban and rural areas reflect the air quality. The first hypothesis is therefore accepted. The second hypothesis stated that birds are good bio-indicators of environmental pollution. As a direct comparison between the numbers of FMs in the

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lungs of mammals were not compared to the results of this study, it is speculated on the basis of the number of FMs present in the respiratory systems of the birds that they are good bio-indicators of pollution.

Higher numbers of macrophages were present in the lungs of the urban birds compared to their rural counterparts. All three species showed significant differences between the number of body mass normalized macrophages present in urban and rural birds. This can be ascribed to the higher levels of air pollution in the urban area especially because of car exhaust fumes and the metal catalysts present in the exhaust fumes (Schildeman et al., 1997). The correlation between the two areas differed substantially: in the urban area, a negative correlation was present between the number of macrophages normalized with body mass and the body mass. This illustrated that the heavier the bird the lower the numbers of macrophages. This pattern was found for the House Sparrow as well as for the Laughing Doves. The reason for the negative correlation might have been because as they were younger birds and therefore had a higher respiratory rate and took up more particles (McNab, 1988; Steinman et al., 2003). In the urban area, the Cape Glossy Starlings showed a positive correlation between the number of macrophages normalized with body mass and the body mass. This relationship was opposite to that of the House Sparrow; the heavier the bird the higher the number of macrophages present. Toth and Siegel (1986) found a similar pattern in chickens where the heavier the birds, the higher the number of macrophages present in the respiratory system. This may possibly result from the fact that bigger birds have a higher exposure level: they have a larger tidal volume by virtue of their size (Maina, 2005). The results from the rural birds were different from those of the urban ones but the explanations given above are also applicable to the rural birds. The lack of correlation between the number of macrophage and body mass in the rural S. senegalensis showed that there was no distinction between the number of macrophages found in birds of different size or ages. As macrophages only have an expected half-life of 10-30 days (Murphy et al., 2008), the age of the bird would not play a role in the number of particulates accumulated. A bird with a longer life-span would for example accumulate more of this organic substance than a bird with shorter life-spans when exposed to levels of DDT (Ehrlich, et al., 1988). Since the half-life of a macrophage is short, this argument does not suffice here. The age of the birds might, however, play a role in

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the immune system response of the birds. The longer the time birds are exposed to pathogens or particulates, the quicker and more efficiently their immune response will be (Reese et al., 2006).

The number of macrophages per unit body mass present in the three species, illustrated that the Laughing Doves had the highest number of macrophages followed by the Cape Glossy Starlings and the House Sparrows. The order of the birds with the most and least number of FMs was true for both study areas; urban and rural. The differences in the numbers of FMs found are difficult to explain. The answer cannot be found in the morphometrics of the respiratory system, as the total pulmonary morphometric diffusing capacity (a comprehensive indicator of pulmonary structural specialization) of the sparrows lungs is ~ 1.7 times that of starling and ~3.2 times that of the doves (Maina, 2005) while the surface area of the lung of the sparrows is ~1.2 times that of starlings and ~2.2 times that of the doves (Maina, 2005). The difference might be explained as follows: Cape Glossy Starlings and Laughing Doves have larger home ranges than sparrows and may therefore be exposed to a great amount or a wider variety of environmental pollution (Crick et al., 2002; Vincent, 2005). Another consideration may be that omnivorous species have a higher chance of exposure to pathogens than granivorous species. This, however, does not explain why the Laughing Doves had more macrophages than L. nitens since S. senegalensis are granivorous species. The differences between the number of FMs found in the studied species is paradoxical but it can be speculated that it might be because of environmental factors or because of the behavioural differences between the bird species. Schildeman et al. (1997) postulated that the amount of particulates in the air is dependent on factors such as the humidity, temperature and wind speed. These factors might have directly influenced the number of FMs found in the lungs of birds.

Changes in the natural environment caused by pollutants have led to a number of evolutionary adaptations in grasses and forbs (Bradshaw & McNeilly, 1981). The speed and extent of such adaptations by the organisms is dependent on the pressure exerted by the pollutants (i.e. pollutant level Х genotype susceptibility), the magnitude and nature of the gene flow between generations as well as between populations and the timing of the exposure to air pollution in relation to the stage of the plants development (Barnes et al., 1999). Natural selection might thus have

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made birds adapt to their environment and the level of pollution in both the urban (mostly anthropogenic) and the rural (natural) areas. As mentioned above, the timeframe between generations needs to be taken into account as well as when such a change needed to occur. In this case the change in the urban area would have begun after the industrial revolution which only started in the Johannesburg area from 1886 after gold was discovered (Oakes & Saunders, 1992). As the data regarding the bird numbers in South Africa are only available for the last 26 years, it can only be speculated whether change in the birds natural environment might have led to better adapted birds with higher resistance in their respiratory systems.

Two of the species (Laughing Dove and Cape Glossy Starling) weight more in the rural area than the urban area, with only the House Sparrow weighing more in the urban area. Seress et al. (2011) found similar results where the urban House Sparrows weighed less than the rural birds. They ascribed this to the high numbers of predators in the urban area. This might not be the reason for the differences in the birds from Gauteng Province (urban) as the numbers of predators in this urban area were not that high. Another reason might be because birds forage less in polluted areas, and will thus weigh less in the urban area (Eeva et al., 1997). Smaller birds have a lower predation risk as they can escape easily (Eeva et al., 1997). Thus the predation risk combined with the environmental stresses influences the weight of the birds. If the predation risk was a main influence in the rural area, it would be expected that all the species would have a lower body mass. It can thus be assumed that as the birds (in the two study areas) were captured in the winter, they would have experienced stress with regards to food availability and maintenance of body temperature.

The species were caught on the same farm to exclude variation that might be caused by other external factors. The reason why different sexes were not caught and studied separately was because of number restrictions from the Ethics Committee at the University of Johannesburg, which regulates use and handling of research animals. Here only 10 birds per species were approved. The first 10 birds were used to not show preference to a specific sex. Another factor that had to be considered in this regard was that two of the species (Cape Glossy Starlings and the Laughing Doves) did not show external colour variations between the sexes. That would have led to sex determination only once dissection of the birds was completed

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and gonads identified. Fulton et al. (1990) found that there is no statistical significant difference between the numbers of macrophages found in the lungs of different sexes of birds. It can be assumed that the same would have occurred in this study if the numbers of macrophages in the different sexes were compared. Air samples were not tested for presence and concentration of pathogenic and particulate matter in the study area, as the facilities were not available. According to Molewa (2012) and SAWS (2012) differences occur between the two sites. The Vaalwater area falls within the Waterberg-Bojanala area which in June 2012 was declared to be a pristine area with regards to its air quality (Molewa, 2012). On the other hand in 2003 the Johannesburg area’s air was classified as highly polluted (SAWS, 2012). It explains why the number of particles will be significantly higher in animals and people residing in Johannesburg (urban) compared to those inhabiting Vaalwater (rural) Area.

5.7 Conclusion and Recommendations:

If an overview is considered of all the results it shows that; three of the four species studied here declined during the two time frames (SABAP1: 1987-1992 and SABAP2: 2007-2012) of the Atlas Projects. The number of birds was lower in the rural area compared to the urban area. The rates of decline of the birds were higher in the Limpopo Province (rural) compared to the Gauteng Province (urban). The number of macrophages was higher in birds from the urban area compared to the rural area. This suggests a higher level of air pollution in Johannesburg compared to Vaalwater. Indicating that FMs in bird lung-air sac systems may act as useful bio- indicators of air pollution. However, this finding does not support the hypothesis that air pollution was one of the reasons for the decline of the birds as the urban birds had higher numbers FMs than rural ones. During the study period, weather patterns appeared to have influenced three of the four bird species (S. senegalensis, L. nitens and S. capicola) activity times, while P. domesticus does not appear to be influenced by the changes. The long term effects of climate conditions on the bird numbers can only be speculated on, but it might influence the feeding rate and success of the bird.

Birds make very good indicators of the quality/condition of their environment (e.g. Sudlow, 2004). Because they are so closely associated with humans, they can help draw a picture of how some environmental changes affect humans. By looking at the reasons that were discussed in the introduction (Chapter 1) as possible causes for

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the declines, a few of these might also have affected the birds in this study. The reasons included predation, competition, lack of nest sites, diseases, changes in the agricultural activities, food availability, changes in the architectural designs of buildings, pollution, climate changes and electromagnetic fields (Summers-Smith, 1999; Crick et al., 2002; Vincent, 2005). A number of cats were observed in both the urban as well as the rural area but these were not counted. Therefore a comparison between the index of density of the birds and the number of cats could not be made. A number of predatory birds were observed in both the urban- and the rural areas. A combination of predators in the natural environment appeared to be higher than the urban areas. Competition especially over food resources is possibly a principle factor that contributed to the declines in numbers observed in the present study. As the architecture in both provinces is still predominantly older buildings with only a few newer designs, for the species in this study it is highly unlikely that the design of the buildings would be the cause of the declines in the numbers of birds. Diseases might be a contributing factor as nematodes were found in two of the species (P. domesticus and L. nitens) in the rural area. The extent of the infection in the two birds was high but overall the external appearance and overall health of these birds did not seem to be influenced by this infection in any way. Although cellular and physiological studies were not performed, this conclusion may not be correct and further studies are needed. Agricultural changes and food availability can be combined in this study as the urban-rural comparison allows for that. Substantial declines in the number of farms was observed which may contribute to the number declines. The influence of electromagnetic fields is hard to determine. However, as South Africa is a developing country and the usage of these instruments are on the rise, the likelihood that this will have detrimental effect on the numbers of birds is high. The effect of air pollution did not seem to have a significant impact on bird populations as the numbers of birds were higher in the urban area and they also had the highest number of macrophages. However, as these are short term studies, the effect might not be as marked as it would have been in long standing studies. Climate change might influence these four species in a number of ways. To determine the extent of these influences, long term studies are needed. The exact reason explaining the observed declines of the birds are still largely unclear but it appears to be caused by a combination of factors.

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Recommendations:

 It is recommended that the actual bird numbers are reported. This will allow more meaningful comparisons between the influential factors (i.e. climate change, pollution, urbanization etc.) and the bird numbers.

 Habitats need to be taken into account when trying to determine if one environment or anthropogenic factor will have a larger impact than another.

 By determining the species diversity on the farms, the ecological state of the farm should be determined. This can thus help identify to what extend a specific species is influenced by the condition of its environment.

 A pilot study is needed before deciding upon a specific species to study. This should be done after thoroughly reviewing the literature.

 Air-sampling analysis should be performed. It will allow for a better comparison between the levels of air-pollution and the numbers of FMs counted.

Future projects:

 Expanding the scope of the project to the rest of South Africa to say with conviction that the numbers have changed in the whole of South Africa and not just in two of the nine provinces.

 Extending the study of the lungs, by including a histological study to determine if lesions might have formed in the birds’ lungs that were exposed to higher levels of air pollution.

 Do more in-depth studies of other reasons for the declines, like predatory counts and electromagnetic radiation monitoring around nest sites, etc.

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Chapter 7:

Appendix

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The figures illustrate the number differences of each bird species on all the farms. Farm 1 was Leeudrift; Farm 2 was Groenfontein; Farm 3 was Goedehoop; Farm 4 was Slypsteendrift; and Farm 5 was Olifantsbeen.

Figure 7.1: Differences between the numbers of House Sparrows found in 2012 in the summer on each farm in the Vaalwater (rural) area of the Limpopo Province.

Figure 7.2: Differences between the numbers of House Sparrows found in 2013 in the summer on each farm in the Vaalwater (rural) area of the Limpopo Province.

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Figure 7.3: Differences between the numbers of Cape Glossy Starlings found in 2012 in the summer on each farm in the Vaalwater (rural) area of the Limpopo Province.

Figure 7.4: Differences between the numbers of Cape Glossy Starlings found in 2013 in the summer on each farm in the Vaalwater (rural) area of the Limpopo Province.

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Figure 7.5: Differences between the numbers of Cape Turtle Doves found in 2012 in the summer on each farm in the Vaalwater (rural) area of the Limpopo Province.

Figure 7.6: Differences between the numbers of Cape Turtle Doves found in 2013 in the summer on each farm in the Vaalwater (rural) area of the Limpopo Province.

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Figure 7.7: Differences between the numbers of Laughing Doves found in 2013 in the summer on each farm in the Vaalwater (rural) area of the Limpopo Province.

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Figure 7.8: Differences between the numbers of House Sparrows found in 2012 in the winter on each farm in the Vaalwater (rural) area of the Limpopo Province.

Figure 7.9: Differences between the numbers of House Sparrows found in 2013 in the winter on each farm in the Vaalwater (rural) area of the Limpopo Province.

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Figure 7.10: Differences between the numbers of Cape Glossy Starling found in 2012 in the winter on each farm in the Vaalwater (rural) area of the Limpopo Province.

Figure 7.11: Differences between the numbers of Cape Glossy Starling found in 2013 in the winter on each farm in the Vaalwater (rural) area of the Limpopo Province.

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Figure 7.12: Differences between the numbers of Cape Turtle Dove found in 2012 in the winter on each farm in the Vaalwater (rural) area of the Limpopo Province.

Figure 7.13: Differences between the numbers of Cape Turtle Doves found in 2013 in the winter on each farm in the Vaalwater (rural) area of the Limpopo Province.

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Figure 7.14: Differences between the numbers of Laughing Doves found in 2013 in the winter on each farm in the Vaalwater (rural) area of the Limpopo Province.

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Limpopo Province

Gauteng Province

Figure 7.15: The reporting rate comparisons of the House Sparrows during SABAP1 and SABAP2 in South Africa.

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Limpopo Province

Gauteng Province

Figure 7.16: The reporting rate comparisons of the Cape Glossy Starlings during SABAP1 and SABAP2 in South Africa.

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Limpopo Province

Gauteng Province

Figure 7.17: The reporting rate comparisons of the Cape Turtle Doves during SABAP1 and SABAP2 in South Africa.

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Limpopo Province Figure 7.18: The reporting rate comparisons of the Laughing Doves during SABAP1 and SABAP2 in South Africa.

Gauteng Province

Figure 7.18: Comparisons of the reporting rate of Laughing Doves during SABAP1 and SABAP2 in South Africa.

176

Table 7.1: The weather conditions for the years that correspond to the Bird Atlas Data project.

Gauteng Limpopo Gauteng Limpopo Gauteng Limpopo Year Time period average average average average Average average rainfall rainfall temperatures temperature humidity humidity 1987 1 219.10 1077.2 14.93 17.90 66.85 79.92

1988 1 209.60 1109 13.67 17.38 69.53 76.75

1989 1 934.60 672.4 13.82 19.28 67.66 66.92

1990 1 634.10 626.2 13.18 17.61 64.25 80.58

1991 1 522.40 877.5 16.96 17.77 69.99 72.67

1992 1 464.10 437.45 14.43 17.19 66.83 65.85

2007 2 265.15 569.68 14.10 18.00 68.15 72.89

2008 2 554.18 390.95 14.72 18.06 68.15 72.89

2009 2 490.66 405.40 14.41 18.52 72.88 69.58

2010 2 509.44 397.93 14.38 18.96 70.33 71.76

2011 2 455.92 576.12 13.60 17.85 70.24 75.70

2012 2 374.60 367.77 15.84 18.43 67.46 70.75

177

Table 7.2: Complete count data set for the urban area in 2012 and 2013.

House House House House Sparrows Sparrows Sparrows Sparrows Temp Year Season Day Time Site 1 Site 2 Site 3 Site 4 (°C) 2012 Summer 1 06:00 9 0 4 0 61 2012 Summer 13:00 11 0 4 0 16

2012 Summer 17:00 13 0 24 12 2

2012 Summer 2 06:00 6 0 6 0 63 2012 Summer 13:00 18 0 12 0 4

2012 Summer 17:00 20 0 15 2 25

2012 Summer 3 06:00 5 2 10 0 34 2012 Summer 13:00 17 2 6 19 7

2012 Summer 17:00 18 0 6 2 0

2012 Summer 4 06:00 12 0 8 2 45 2012 Summer 13:00 21 0 4 6 2

2012 Summer 17:00 24 0 9 31 1

2012 Summer 5 06:00 16 2 8 2 34 2012 Summer 13:00 23 0 2 12 0

2012 Summer 17:00 25 0 2 14 5

2012 Summer 6 06:00 12 0 9 2 33 2012 Summer 13:00 24 2 0 15 0

2012 Summer 17:00 26 2 8 2 17

2012 Summer 7 06:00 14 3 8 2 15 2012 Summer 13:00 24 0 4 3 0

2012 Summer 17:00 26 2 6 15 46

2012 Summer 8 06:00 12 0 6 2 12 2012 Summer 13:00 23 4 10 9 8

2012 Summer 17:00 22 0 4 2 7

2012 Summer 9 06:00 10 0 7 0 35 2012 Summer 13:00 28 2 3 2 14

2012 Summer 17:00 26 2 6 0 34

2012 Summer 10 06:00 10 0 8 4 36 2012 Summer 13:00 24 2 2 2 9

2012 Summer 17:00 22 4 4 34 74

2012 Summer 11 06:00 8 0 8 0 34 2012 Summer 13:00 21 2 4 18 10

2012 Summer 17:00 22 2 0 0 7

2012 Summer 12 06:00 10 4 6 0 37 2012 Summer 13:00 21 2 4 12 6

2012 Summer 17:00 24 0 5 29 13

2012 Summer 13 06:00 14 0 4 4 34 2012 Summer 13:00 25 0 4 2 3

178

2012 Summer 17:00 25 0 4 43 0

2012 Summer 14 06:00 15 0 5 14 6 2012 Summer 13:00 26 2 7 0 36

2012 Summer 17:00 25 2 5 9 18

2012 Winter 1 07:00 2 0 3 3 42 2012 Winter 13:00 13 4 25 37 4

2012 Winter 17:00 13 3 5 3 0

2012 Winter 2 07:00 4 2 3 4 17 2012 Winter 13:00 16 3 9 3 0

2012 Winter 17:00 18 3 18 0 5

2012 Winter 3 07:00 6 3 5 4 75 2012 Winter 13:00 19 0 34 32 5

2012 Winter 17:00 17 0 3 2 14

2012 Winter 4 07:00 4 0 5 3 43 2012 Winter 13:00 13 0 37 24 5

2012 Winter 17:00 14 0 3 27 15

2012 Winter 5 07:00 4 0 3 6 85 2012 Winter 13:00 12 0 33 0 7

2012 Winter 17:00 14 0 3 2 3

2012 Winter 6 07:00 6 0 3 0 130 2012 Winter 13:00 8 0 22 10 73

2012 Winter 17:00 7 0 3 3 5

2012 Winter 7 07:00 6 0 5 6 105 2012 Winter 13:00 14 0 30 31 130

2012 Winter 17:00 12 3 3 3 0

2012 Winter 8 07:00 6 0 5 5 45 2012 Winter 13:00 17 3 36 5 17

2012 Winter 17:00 16 2 5 33 150

2012 Winter 9 07:00 9 0 6 3 133 2012 Winter 13:00 17 0 13 22 0

2012 Winter 17:00 16 0 3 28 93

2012 Winter 10 07:00 9 0 3 2 104 2012 Winter 13:00 18 0 3 23 8

2012 Winter 17:00 18 0 3 17 84

2012 Winter 11 07:00 7 3 10 5 99 2012 Winter 13:00 14 0 9 2 12

2012 Winter 17:00 12 0 3 41 101

2012 Winter 12 07:00 3 0 2 0 140 2012 Winter 13:00 6 0 5 4 4

2012 Winter 17:00 10 0 2 2 0

2012 Winter 13 07:00 8 0 5 2 3 2012 Winter 13:00 12 0 30 9 130

2012 Winter 17:00 14 0 3 23 28

2012 Winter 14 07:00 0 0 9 0 7 2012 Winter 13:00 10 0 3 18 6

179

2012 Winter 17:00 12 2 3 0 4

2013 Summer 1 07:00 16 2 2 15 24 2013 Summer 13:00 27 2 0 16 2

2013 Summer 17:00 25 0 0 2 8

2013 Summer 2 07:00 18 0 2 13 17 2013 Summer 13:00 30 0 0 25 3

2013 Summer 17:00 24 0 0 66 3

2013 Summer 3 07:00 17 3 0 14 17 2013 Summer 13:00 22 0 0 7 8

2013 Summer 17:00 25 0 0 4 0

2013 Summer 4 07:00 17 0 0 8 5 2013 Summer 13:00 21 0 0 9 4

2013 Summer 17:00 22 0 0 13 0

2013 Summer 5 07:00 17 0 4 0 13 2013 Summer 13:00 29 0 0 17 9

2013 Summer 17:00 29 2 3 7 21

2013 Summer 6 07:00 13 2 3 15 18 2013 Summer 13:00 23 0 2 5 0

2013 Summer 17:00 23 0 0 15 21

2013 Summer 7 07:00 16 0 2 0 5 2013 Summer 13:00 19 0 0 7 0

2013 Summer 17:00 23 2 3 21 0

2013 Summer 8 07:00 12 0 2 7 5 2013 Summer 13:00 25 0 2 0 0

2013 Summer 17:00 21 2 4 15 5

2013 Summer 9 07:00 13 0 2 7 13 2013 Summer 13:00 25 0 0 3 0

2013 Summer 17:00 24 0 3 9 18

2013 Summer 10 07:00 13 0 3 11 21 2013 Summer 13:00 21 0 0 2 0

2013 Summer 17:00 20 0 0 10 22

2013 Summer 11 07:00 13 0 3 9 21 2013 Summer 13:00 27 0 0 0 5

2013 Summer 17:00 25 0 0 10 19

2013 Summer 12 07:00 14 0 0 12 21 2013 Summer 13:00 23 0 3 9 10

2013 Summer 17:00 23 0 0 43 2

2013 Summer 13 07:00 13 0 2 21 12 2013 Summer 13:00 28 0 0 9 5

2013 Summer 17:00 30 0 2 19 21

2013 Summer 14 07:00 15 2 2 24 22 2013 Summer 13:00 27 0 0 0 0

2013 Summer 17:00 26 0 3 11 14

2013 Winter 1 07:00 4 0 0 2 42 2013 Winter 13:00 18 0 0 4 16

180

2013 Winter 17:00 18 0 0 5 11

2013 Winter 2 07:00 4 0 14 0 27 2013 Winter 13:00 18 2 15 18 37

2013 Winter 17:00 15 0 0 0 0

2013 Winter 3 07:00 5 0 5 0 47 2013 Winter 13:00 11 0 2 0 0

2013 Winter 17:00 10 0 2 57 0

2013 Winter 4 07:00 2 0 5 0 39 2013 Winter 13:00 14 0 0 2 0

2013 Winter 17:00 15 0 5 49 0

2013 Winter 5 07:00 6 0 5 2 10 2013 Winter 13:00 16 0 0 5 43

2013 Winter 17:00 18 0 0 10 63

2013 Winter 6 07:00 7 0 0 0 92 2013 Winter 13:00 19 0 0 57 0

2013 Winter 17:00 18 0 0 0 82

2013 Winter 7 07:00 8 0 0 0 0 2013 Winter 13:00 2 0 5 59 0

2013 Winter 17:00 20 0 5 0 83

2013 Winter 8 07:00 9 0 0 0 0 2013 Winter 13:00 22 0 11 0 55

2013 Winter 17:00 22 0 0 15 17

2013 Winter 9 07:00 5 0 0 0 10 2013 Winter 13:00 17 0 0 11 24

2013 Winter 17:00 17 0 0 4 48

2013 Winter 10 07:00 4 0 0 0 0 2013 Winter 13:00 16 2 0 32 0

2013 Winter 17:00 16 0 0 0 110

2013 Winter 11 07:00 3 0 0 10 0 2013 Winter 13:00 13 0 0 0 87

2013 Winter 17:00 16 0 5 0 50

2013 Winter 12 07:00 3 0 0 5 50 2013 Winter 13:00 16 0 0 10 0

2013 Winter 17:00 15 0 0 18 0

2013 Winter 13 07:00 2 0 5 15 83 2013 Winter 13:00 17 0 0 0 0

2013 Winter 17:00 17 0 55 0 15

2013 Winter 14 07:00 3 0 5 0 84 2013 Winter 13:00 17 0 0 18 0

2013 Winter 17:00 17 0 54 0 0

Cape Cape Cape Cape Turtle Turtle Turtle Turtle

Doves Doves Doves Doves Temp Year Season Day Time Site 1 Site 2 Site 3 Site 4 (°C)

181

2012 Summer 1 06:00 9 0 0 0 0 2012 Summer 13:00 11 0 0 0 0

2012 Summer 17:00 13 0 0 0 0

2012 Summer 2 06:00 6 0 0 2 0 2012 Summer 13:00 18 0 0 0 0

2012 Summer 17:00 20 0 0 0 0

2012 Summer 3 06:00 5 1 0 4 0 2012 Summer 13:00 17 0 0 0 0

2012 Summer 17:00 18 0 0 1 0

2012 Summer 4 06:00 12 0 0 1 0 2012 Summer 13:00 21 0 0 0 0

2012 Summer 17:00 24 0 0 0 0

2012 Summer 5 06:00 16 0 0 0 1 2012 Summer 13:00 23 0 0 0 0

2012 Summer 17:00 25 0 0 0 0

2012 Summer 6 06:00 12 0 1 2 0 2012 Summer 13:00 24 0 0 0 0

2012 Summer 17:00 26 0 0 0 0

2012 Summer 7 06:00 14 1 1 1 0 2012 Summer 13:00 24 0 0 0 0

2012 Summer 17:00 26 1 0 1 0

2012 Summer 8 06:00 12 3 0 3 0 2012 Summer 13:00 23 0 0 0 0

2012 Summer 17:00 22 0 0 0 0

2012 Summer 9 06:00 10 1 1 0 0 2012 Summer 13:00 28 0 0 0 0

2012 Summer 17:00 26 0 0 2 0

2012 Summer 10 06:00 10 1 0 1 0 2012 Summer 13:00 24 0 0 0 0

2012 Summer 17:00 22 1 0 1 0

2012 Summer 11 06:00 8 3 0 1 0 2012 Summer 13:00 21 0 0 0 0

2012 Summer 17:00 22 0 0 3 0

2012 Summer 12 06:00 10 2 0 1 0 2012 Summer 13:00 21 1 0 0 0

2012 Summer 17:00 24 0 0 0 0

2012 Summer 13 06:00 14 1 0 0 0 2012 Summer 13:00 25 0 0 0 0

2012 Summer 17:00 25 0 0 0 0

2012 Summer 14 06:00 15 0 0 0 0 2012 Summer 13:00 26 0 0 0 0

2012 Summer 17:00 25 0 0 1 0

2012 Winter 1 07:00 2 1 0 1 2 2012 Winter 13:00 13 0 0 4 0

2012 Winter 17:00 13 0 1 0 0

182

2012 Winter 2 07:00 4 0 0 2 2 2012 Winter 13:00 16 2 0 0 0

2012 Winter 17:00 18 0 0 2 2

2012 Winter 3 07:00 6 2 0 4 2 2012 Winter 13:00 19 0 0 0 3

2012 Winter 17:00 17 1 0 2 0

2012 Winter 4 07:00 4 0 3 2 3 2012 Winter 13:00 13 0 1 1 2

2012 Winter 17:00 14 4 0 2 2

2012 Winter 5 07:00 4 3 2 4 2 2012 Winter 13:00 12 0 0 3 2

2012 Winter 17:00 14 3 0 0 0

2012 Winter 6 07:00 6 1 1 5 2 2012 Winter 13:00 8 2 4 2 3

2012 Winter 17:00 7 2 0 1 2

2012 Winter 7 07:00 6 2 0 3 2 2012 Winter 13:00 14 2 0 2 0

2012 Winter 17:00 12 3 0 4 2

2012 Winter 8 07:00 6 1 1 3 0 2012 Winter 13:00 17 2 0 0 2

2012 Winter 17:00 16 2 0 2 2

2012 Winter 9 07:00 9 0 0 2 1 2012 Winter 13:00 17 0 0 0 0

2012 Winter 17:00 16 4 2 2 0

2012 Winter 10 07:00 9 5 2 3 3 2012 Winter 13:00 18 0 0 1 4

2012 Winter 17:00 18 0 2 2 4

2012 Winter 11 07:00 7 0 3 2 2 2012 Winter 13:00 14 2 0 0 4

2012 Winter 17:00 12 0 0 4 2

2012 Winter 12 07:00 3 0 1 5 2 2012 Winter 13:00 6 0 2 0 0

2012 Winter 17:00 10 2 0 3 4

2012 Winter 13 07:00 8 0 0 2 0 2012 Winter 13:00 12 2 0 0 5

2012 Winter 17:00 14 2 0 3 1

2012 Winter 14 07:00 0 0 0 0 0 2012 Winter 13:00 10 2 0 0 0

2012 Winter 17:00 12 2 2 2 0

2013 Summer 1 07:00 16 0 0 2 0 2013 Summer 13:00 27 0 0 0 0

2013 Summer 17:00 25 0 0 0 0

2013 Summer 2 07:00 18 0 0 1 0 2013 Summer 13:00 30 0 0 0 0

2013 Summer 17:00 24 0 0 0 0

183

2013 Summer 3 07:00 17 0 0 1 0 2013 Summer 13:00 22 0 0 0 0

2013 Summer 17:00 25 0 0 0 0

2013 Summer 4 07:00 17 0 0 1 0 2013 Summer 13:00 21 0 0 0 0

2013 Summer 17:00 22 0 0 0 0

2013 Summer 5 07:00 17 0 0 0 0 2013 Summer 13:00 29 0 0 1 0

2013 Summer 17:00 29 0 0 0 0

2013 Summer 6 07:00 13 0 0 1 0 2013 Summer 13:00 23 0 0 1 0

2013 Summer 17:00 23 0 0 0 0

2013 Summer 7 07:00 16 0 0 0 0 2013 Summer 13:00 19 0 0 1 0

2013 Summer 17:00 23 0 0 1 0

2013 Summer 8 07:00 12 0 0 0 0 2013 Summer 13:00 25 0 0 0 0

2013 Summer 17:00 21 0 0 2 0

2013 Summer 9 07:00 13 0 0 2 0 2013 Summer 13:00 25 0 0 2 0

2013 Summer 17:00 24 0 0 0 0

2013 Summer 10 07:00 13 0 0 0 0 2013 Summer 13:00 21 0 0 0 0

2013 Summer 17:00 20 0 0 0 0

2013 Summer 11 07:00 13 0 0 0 0 2013 Summer 13:00 27 0 0 0 0

2013 Summer 17:00 25 0 0 0 0

2013 Summer 12 07:00 14 0 0 1 0 2013 Summer 13:00 23 0 0 0 0

2013 Summer 17:00 23 0 0 1 0

2013 Summer 13 07:00 13 0 0 0 0 2013 Summer 13:00 28 0 0 0 0

2013 Summer 17:00 30 0 0 2 0

2013 Summer 14 07:00 15 0 0 0 0 2013 Summer 13:00 27 0 0 1 0

2013 Summer 17:00 26 0 0 1 0

2013 Winter 1 07:00 4 0 0 0 0 2013 Winter 13:00 18 0 0 0 0

2013 Winter 17:00 18 0 0 0 0

2013 Winter 2 07:00 4 0 0 0 0 2013 Winter 13:00 18 0 0 0 2

2013 Winter 17:00 15 0 0 0 0

2013 Winter 3 07:00 5 0 0 0 0 2013 Winter 13:00 11 0 0 0 0

2013 Winter 17:00 10 0 0 0 0

184

2013 Winter 4 07:00 2 2 0 0 0 2013 Winter 13:00 14 0 0 0 0

2013 Winter 17:00 15 0 1 0 0

2013 Winter 5 07:00 6 0 0 1 0 2013 Winter 13:00 16 0 0 2 0

2013 Winter 17:00 18 2 0 0 0

2013 Winter 6 07:00 7 0 0 0 0 2013 Winter 13:00 19 0 0 2 0

2013 Winter 17:00 18 0 0 0 0

2013 Winter 7 07:00 8 0 0 2 0 2013 Winter 13:00 2 0 0 0 0

2013 Winter 17:00 20 0 0 2 0

2013 Winter 8 07:00 9 0 0 0 0 2013 Winter 13:00 22 0 0 0 0

2013 Winter 17:00 22 0 0 2 0

2013 Winter 9 07:00 5 0 0 0 2 2013 Winter 13:00 17 0 0 0 0

2013 Winter 17:00 17 0 0 2 0

2013 Winter 10 07:00 4 0 0 2 0 2013 Winter 13:00 16 0 0 0 0

2013 Winter 17:00 16 0 0 2 0

2013 Winter 11 07:00 3 0 0 0 0 2013 Winter 13:00 13 0 0 0 0

2013 Winter 17:00 16 0 0 2 0

2013 Winter 12 07:00 3 0 0 0 0 2013 Winter 13:00 16 0 0 0 0

2013 Winter 17:00 15 0 0 0 0

2013 Winter 13 07:00 2 0 0 2 0 2013 Winter 13:00 17 0 0 0 0

2013 Winter 17:00 17 0 0 2 0

2013 Winter 14 07:00 3 0 0 0 0 2013 Winter 13:00 17 0 0 0 0

2013 Winter 17:00 17 0 2 2 0

Cape Cape Cape Cape Glossy Glossy Glossy Glossy

Starlings Starlings Starlings Starlings Temp Year Season Day Time Site 1 Site 2 Site 3 Site 4 (°C) 2012 Summer 1 06:00 9 0 0 0 0 2012 Summer 13:00 11 0 0 0 0

2012 Summer 17:00 13 0 3 0 0

2012 Summer 2 06:00 6 0 0 0 0 2012 Summer 13:00 18 0 3 0 0

2012 Summer 17:00 20 0 0 0 0

2012 Summer 3 06:00 5 0 0 0 0

185

2012 Summer 13:00 17 0 0 0 0

2012 Summer 17:00 18 0 0 4 0

2012 Summer 4 06:00 12 1 0 0 0 2012 Summer 13:00 21 0 0 3 0

2012 Summer 17:00 24 0 0 5 0

2012 Summer 5 06:00 16 0 0 2 0 2012 Summer 13:00 23 0 0 3 0

2012 Summer 17:00 25 0 0 2 0

2012 Summer 6 06:00 12 0 0 0 0 2012 Summer 13:00 24 0 0 0 0

2012 Summer 17:00 26 0 0 5 0

2012 Summer 7 06:00 14 1 0 0 0 2012 Summer 13:00 24 0 0 0 0

2012 Summer 17:00 26 0 0 0 0

2012 Summer 8 06:00 12 0 0 0 0 2012 Summer 13:00 23 0 0 0 0

2012 Summer 17:00 22 0 0 0 2

2012 Summer 9 06:00 10 0 0 0 0 2012 Summer 13:00 28 2 0 0 0

2012 Summer 17:00 26 1 0 0 0

2012 Summer 10 06:00 10 0 0 4 0 2012 Summer 13:00 24 0 0 2 0

2012 Summer 17:00 22 0 1 3 0

2012 Summer 11 06:00 8 0 0 0 0 2012 Summer 13:00 21 0 0 1 0

2012 Summer 17:00 22 0 0 0 0

2012 Summer 12 06:00 10 0 0 0 0 2012 Summer 13:00 21 0 0 0 0

2012 Summer 17:00 24 0 0 3 0

2012 Summer 13 06:00 14 0 1 1 0 2012 Summer 13:00 25 0 0 0 0

2012 Summer 17:00 25 0 1 4 0

2012 Summer 14 06:00 15 0 0 1 0 2012 Summer 13:00 26 0 0 0 0

2012 Summer 17:00 25 0 0 2 2

2012 Winter 1 07:00 2 0 0 0 0 2012 Winter 13:00 13 0 0 0 0

2012 Winter 17:00 13 0 0 0 0

2012 Winter 2 07:00 4 0 1 0 0 2012 Winter 13:00 16 0 0 0 0

2012 Winter 17:00 18 2 0 0 0

2012 Winter 3 07:00 6 0 0 0 0 2012 Winter 13:00 19 0 0 0 0

2012 Winter 17:00 17 0 0 0 0

2012 Winter 4 07:00 4 0 0 0 0

186

2012 Winter 13:00 13 0 0 0 0

2012 Winter 17:00 14 0 0 0 0

2012 Winter 5 07:00 4 0 0 0 0 2012 Winter 13:00 12 0 0 0 0

2012 Winter 17:00 14 0 0 0 0

2012 Winter 6 07:00 6 1 0 0 0 2012 Winter 13:00 8 0 0 0 0

2012 Winter 17:00 7 0 0 0 0

2012 Winter 7 07:00 6 2 0 0 0 2012 Winter 13:00 14 0 0 0 0

2012 Winter 17:00 12 0 0 0 0

2012 Winter 8 07:00 6 0 0 0 0 2012 Winter 13:00 17 0 0 0 0

2012 Winter 17:00 16 2 0 0 0

2012 Winter 9 07:00 9 0 0 0 0 2012 Winter 13:00 17 0 0 0 0

2012 Winter 17:00 16 0 0 0 0

2012 Winter 10 07:00 9 4 0 0 0 2012 Winter 13:00 18 0 0 0 0

2012 Winter 17:00 18 0 0 0 0

2012 Winter 11 07:00 7 5 0 0 0 2012 Winter 13:00 14 0 0 0 0

2012 Winter 17:00 12 0 0 0 0

2012 Winter 12 07:00 3 0 0 0 0 2012 Winter 13:00 6 0 0 0 1

2012 Winter 17:00 10 4 0 0 0

2012 Winter 13 07:00 8 0 0 0 0 2012 Winter 13:00 12 0 1 0 2

2012 Winter 17:00 14 0 0 0 1

2012 Winter 14 07:00 0 0 0 0 0 2012 Winter 13:00 10 0 0 0 0

2012 Winter 17:00 12 0 2 0 0

2013 Summer 1 07:00 16 0 0 0 0 2013 Summer 13:00 27 0 0 0 0

2013 Summer 17:00 25 0 0 0 0

2013 Summer 2 07:00 18 0 0 0 0 2013 Summer 13:00 30 0 0 0 0

2013 Summer 17:00 24 0 0 0 0

2013 Summer 3 07:00 17 0 0 0 0 2013 Summer 13:00 22 0 0 0 0

2013 Summer 17:00 25 0 0 0 0

2013 Summer 4 07:00 17 0 0 0 0 2013 Summer 13:00 21 0 0 0 0

2013 Summer 17:00 22 0 0 0 0

2013 Summer 5 07:00 17 0 0 0 0

187

2013 Summer 13:00 29 0 0 0 0

2013 Summer 17:00 29 0 0 0 0

2013 Summer 6 07:00 13 0 0 0 0 2013 Summer 13:00 23 0 0 0 0

2013 Summer 17:00 23 0 0 0 0

2013 Summer 7 07:00 16 0 0 0 0 2013 Summer 13:00 19 0 0 0 0

2013 Summer 17:00 23 0 0 0 0

2013 Summer 8 07:00 12 0 0 0 0 2013 Summer 13:00 25 0 0 0 0

2013 Summer 17:00 21 0 0 0 0

2013 Summer 9 07:00 13 0 0 0 0 2013 Summer 13:00 25 0 0 0 0

2013 Summer 17:00 24 0 0 0 0

2013 Summer 10 07:00 13 0 0 0 0 2013 Summer 13:00 21 0 0 0 0

2013 Summer 17:00 20 0 0 0 0

2013 Summer 11 07:00 13 0 0 0 0 2013 Summer 13:00 27 0 0 0 0

2013 Summer 17:00 25 0 0 0 0

2013 Summer 12 07:00 14 0 0 0 0 2013 Summer 13:00 23 0 0 0 0

2013 Summer 17:00 23 0 0 0 0

2013 Summer 13 07:00 13 0 0 0 0 2013 Summer 13:00 28 0 0 0 0

2013 Summer 17:00 30 0 0 0 0

2013 Summer 14 07:00 15 0 0 0 0 2013 Summer 13:00 27 0 0 0 0

2013 Summer 17:00 26 0 0 0 0

2013 Winter 1 07:00 4 11 0 0 0 2013 Winter 13:00 18 0 0 0 0

2013 Winter 17:00 18 0 0 0 0

2013 Winter 2 07:00 4 10 0 0 0 2013 Winter 13:00 18 7 0 0 0

2013 Winter 17:00 15 0 0 0 0

2013 Winter 3 07:00 5 10 0 1 0 2013 Winter 13:00 11 0 0 0 0

2013 Winter 17:00 10 0 0 0 0

2013 Winter 4 07:00 2 0 0 0 0 2013 Winter 13:00 14 12 0 0 0

2013 Winter 17:00 15 0 0 0 0

2013 Winter 5 07:00 6 10 0 2 0 2013 Winter 13:00 16 0 2 0 0

2013 Winter 17:00 18 0 2 0 0

2013 Winter 6 07:00 7 9 2 0 0

188

2013 Winter 13:00 19 0 2 0 0

2013 Winter 17:00 18 0 0 2 0

2013 Winter 7 07:00 8 12 0 0 0 2013 Winter 13:00 2 0 0 0 0

2013 Winter 17:00 20 0 0 0 0

2013 Winter 8 07:00 9 0 0 0 0 2013 Winter 13:00 22 0 0 0 0

2013 Winter 17:00 22 2 2 0 0

2013 Winter 9 07:00 5 0 0 0 0 2013 Winter 13:00 17 0 0 0 0

2013 Winter 17:00 17 0 2 0 0

2013 Winter 10 07:00 4 0 0 0 4 2013 Winter 13:00 16 0 0 0 0

2013 Winter 17:00 16 0 0 0 0

2013 Winter 11 07:00 3 0 0 2 0 2013 Winter 13:00 13 0 0 0 0

2013 Winter 17:00 16 0 0 0 0

2013 Winter 12 07:00 3 0 0 0 0 2013 Winter 13:00 16 0 0 2 0

2013 Winter 17:00 15 0 0 0 0

2013 Winter 13 07:00 2 0 0 0 0 2013 Winter 13:00 17 0 0 0 0

2013 Winter 17:00 17 0 0 0 0

2013 Winter 14 07:00 3 0 0 0 0 2013 Winter 13:00 17 0 0 0 0

2013 Winter 17:00 17 0 0 2 0

Laughing Laughing Laughing Laughing Doves Doves Doves Doves Temp Year Season Day Time Site 1 Site 2 Site 3 Site 4 (°C) 2012 Summer 1 06:00 9 Unknown Unknown Unknown Unknown 2012 Summer 13:00 11 Unknown Unknown Unknown Unknown

2012 Summer 17:00 13 Unknown Unknown Unknown Unknown

2012 Summer 2 06:00 6 Unknown Unknown Unknown Unknown 2012 Summer 13:00 18 Unknown Unknown Unknown Unknown

2012 Summer 17:00 20 Unknown Unknown Unknown Unknown

2012 Summer 3 06:00 5 Unknown Unknown Unknown Unknown 2012 Summer 13:00 17 Unknown Unknown Unknown Unknown

2012 Summer 17:00 18 Unknown Unknown Unknown Unknown

2012 Summer 4 06:00 12 Unknown Unknown Unknown Unknown 2012 Summer 13:00 21 Unknown Unknown Unknown Unknown

2012 Summer 17:00 24 Unknown Unknown Unknown Unknown

2012 Summer 5 06:00 16 Unknown Unknown Unknown Unknown 2012 Summer 13:00 23 Unknown Unknown Unknown Unknown

2012 Summer 17:00 25 Unknown Unknown Unknown Unknown

189

2012 Summer 6 06:00 12 Unknown Unknown Unknown Unknown 2012 Summer 13:00 24 Unknown Unknown Unknown Unknown

2012 Summer 17:00 26 Unknown Unknown Unknown Unknown

2012 Summer 7 06:00 14 Unknown Unknown Unknown Unknown 2012 Summer 13:00 24 Unknown Unknown Unknown Unknown

2012 Summer 17:00 26 Unknown Unknown Unknown Unknown

2012 Summer 8 06:00 12 Unknown Unknown Unknown Unknown 2012 Summer 13:00 23 Unknown Unknown Unknown Unknown

2012 Summer 17:00 22 Unknown Unknown Unknown Unknown

2012 Summer 9 06:00 10 Unknown Unknown Unknown Unknown 2012 Summer 13:00 28 Unknown Unknown Unknown Unknown

2012 Summer 17:00 26 Unknown Unknown Unknown Unknown

2012 Summer 10 06:00 10 Unknown Unknown Unknown Unknown 2012 Summer 13:00 24 Unknown Unknown Unknown Unknown

2012 Summer 17:00 22 Unknown Unknown Unknown Unknown

2012 Summer 11 06:00 8 Unknown Unknown Unknown Unknown 2012 Summer 13:00 21 Unknown Unknown Unknown Unknown

2012 Summer 17:00 22 Unknown Unknown Unknown Unknown

2012 Summer 12 06:00 10 Unknown Unknown Unknown Unknown 2012 Summer 13:00 21 Unknown Unknown Unknown Unknown

2012 Summer 17:00 24 Unknown Unknown Unknown Unknown

2012 Summer 13 06:00 14 Unknown Unknown Unknown Unknown 2012 Summer 13:00 25 Unknown Unknown Unknown Unknown

2012 Summer 17:00 25 Unknown Unknown Unknown Unknown

2012 Summer 14 06:00 15 Unknown Unknown Unknown Unknown 2012 Summer 13:00 26 Unknown Unknown Unknown Unknown

2012 Summer 17:00 25 Unknown Unknown Unknown Unknown

2012 Winter 1 07:00 2 Unknown Unknown Unknown Unknown 2012 Winter 13:00 13 Unknown Unknown Unknown Unknown

2012 Winter 17:00 13 Unknown Unknown Unknown Unknown

2012 Winter 2 07:00 4 Unknown Unknown Unknown Unknown 2012 Winter 13:00 16 Unknown Unknown Unknown Unknown

2012 Winter 17:00 18 Unknown Unknown Unknown Unknown

2012 Winter 3 07:00 6 Unknown Unknown Unknown Unknown 2012 Winter 13:00 19 Unknown Unknown Unknown Unknown

2012 Winter 17:00 17 Unknown Unknown Unknown Unknown

2012 Winter 4 07:00 4 Unknown Unknown Unknown Unknown 2012 Winter 13:00 13 Unknown Unknown Unknown Unknown

2012 Winter 17:00 14 Unknown Unknown Unknown Unknown

2012 Winter 5 07:00 4 Unknown Unknown Unknown Unknown 2012 Winter 13:00 12 Unknown Unknown Unknown Unknown

2012 Winter 17:00 14 Unknown Unknown Unknown Unknown

2012 Winter 6 07:00 6 Unknown Unknown Unknown Unknown 2012 Winter 13:00 8 Unknown Unknown Unknown Unknown

2012 Winter 17:00 7 Unknown Unknown Unknown Unknown

190

2012 Winter 7 07:00 6 Unknown Unknown Unknown Unknown 2012 Winter 13:00 14 Unknown Unknown Unknown Unknown

2012 Winter 17:00 12 Unknown Unknown Unknown Unknown

2012 Winter 8 07:00 6 Unknown Unknown Unknown Unknown 2012 Winter 13:00 17 Unknown Unknown Unknown Unknown

2012 Winter 17:00 16 Unknown Unknown Unknown Unknown

2012 Winter 9 07:00 9 Unknown Unknown Unknown Unknown 2012 Winter 13:00 17 Unknown Unknown Unknown Unknown

2012 Winter 17:00 16 Unknown Unknown Unknown Unknown

2012 Winter 10 07:00 9 Unknown Unknown Unknown Unknown 2012 Winter 13:00 18 Unknown Unknown Unknown Unknown

2012 Winter 17:00 18 Unknown Unknown Unknown Unknown

2012 Winter 11 07:00 7 Unknown Unknown Unknown Unknown 2012 Winter 13:00 14 Unknown Unknown Unknown Unknown

2012 Winter 17:00 12 Unknown Unknown Unknown Unknown

2012 Winter 12 07:00 3 Unknown Unknown Unknown Unknown 2012 Winter 13:00 6 Unknown Unknown Unknown Unknown

2012 Winter 17:00 10 Unknown Unknown Unknown Unknown

2012 Winter 13 07:00 8 Unknown Unknown Unknown Unknown 2012 Winter 13:00 12 Unknown Unknown Unknown Unknown

2012 Winter 17:00 14 Unknown Unknown Unknown Unknown

2012 Winter 14 07:00 0 Unknown Unknown Unknown Unknown 2012 Winter 13:00 10 Unknown Unknown Unknown Unknown

2012 Winter 17:00 12 Unknown Unknown Unknown Unknown

2013 Summer 1 07:00 16 2 2 4 0 2013 Summer 13:00 27 1 0 0 0

2013 Summer 17:00 25 4 0 0 2

2013 Summer 2 07:00 18 3 2 9 0 2013 Summer 13:00 30 0 0 2 0

2013 Summer 17:00 24 1 0 4 0

2013 Summer 3 07:00 17 1 2 2 0 2013 Summer 13:00 22 0 0 2 0

2013 Summer 17:00 25 0 2 2 0

2013 Summer 4 07:00 17 0 2 2 2 2013 Summer 13:00 21 0 1 0 2

2013 Summer 17:00 22 2 0 4 0

2013 Summer 5 07:00 17 0 0 2 2 2013 Summer 13:00 29 0 0 2 2

2013 Summer 17:00 29 1 0 2 4

2013 Summer 6 07:00 13 2 3 2 0 2013 Summer 13:00 23 0 4 0 0

2013 Summer 17:00 23 0 0 0 5

2013 Summer 7 07:00 16 0 0 0 0 2013 Summer 13:00 19 0 0 2 0

2013 Summer 17:00 23 1 2 4 0

191

2013 Summer 8 07:00 12 0 2 0 0 2013 Summer 13:00 25 0 0 1 0

2013 Summer 17:00 21 2 0 2 0

2013 Summer 9 07:00 13 2 0 4 1 2013 Summer 13:00 25 0 0 0 0

2013 Summer 17:00 24 2 2 0 4

2013 Summer 10 07:00 13 0 0 2 4 2013 Summer 13:00 21 0 0 1 0

2013 Summer 17:00 20 0 0 0 2

2013 Summer 11 07:00 13 0 0 4 1 2013 Summer 13:00 27 0 0 2 0

2013 Summer 17:00 25 0 0 2 0

2013 Summer 12 07:00 14 0 0 0 2 2013 Summer 13:00 23 2 0 2 4

2013 Summer 17:00 23 0 0 9 0

2013 Summer 13 07:00 13 0 0 0 1 2013 Summer 13:00 28 0 0 2 2

2013 Summer 17:00 30 0 0 4 2

2013 Summer 14 07:00 15 2 2 0 5 2013 Summer 13:00 27 0 0 0 0

2013 Summer 17:00 26 2 0 4 5

2013 Winter 1 07:00 4 2 2 0 15 2013 Winter 13:00 18 0 0 0 1

2013 Winter 17:00 18 2 2 0 0

2013 Winter 2 07:00 4 2 0 0 11 2013 Winter 13:00 18 2 0 2 11

2013 Winter 17:00 15 0 0 5 0

2013 Winter 3 07:00 5 0 0 0 5 2013 Winter 13:00 11 0 0 0 2

2013 Winter 17:00 10 0 0 1 0

2013 Winter 4 07:00 2 0 0 1 4 2013 Winter 13:00 14 0 0 0 0

2013 Winter 17:00 15 2 0 2 0

2013 Winter 5 07:00 6 0 0 2 2 2013 Winter 13:00 16 2 2 0 0

2013 Winter 17:00 18 0 0 0 10

2013 Winter 6 07:00 7 0 0 0 0 2013 Winter 13:00 19 0 0 0 11

2013 Winter 17:00 18 2 2 0 0

2013 Winter 7 07:00 8 2 0 2 10 2013 Winter 13:00 2 0 0 0 0

2013 Winter 17:00 20 2 2 2 0

2013 Winter 8 07:00 9 0 2 0 11 2013 Winter 13:00 22 0 0 0 0

2013 Winter 17:00 22 0 4 0 11

192

2013 Winter 9 07:00 5 0 2 2 6 2013 Winter 13:00 17 0 0 0 0

2013 Winter 17:00 17 0 2 0 0

2013 Winter 10 07:00 4 0 0 2 10 2013 Winter 13:00 16 1 0 0 0

2013 Winter 17:00 16 2 2 0 0

2013 Winter 11 07:00 3 0 2 0 12 2013 Winter 13:00 13 0 0 4 0

2013 Winter 17:00 16 0 0 0 5

2013 Winter 12 07:00 3 0 2 0 5 2013 Winter 13:00 16 0 0 4 0

2013 Winter 17:00 15 0 0 0 5

2013 Winter 13 07:00 2 0 2 2 10 2013 Winter 13:00 17 0 0 0 0

2013 Winter 17:00 17 0 2 2 0

2013 Winter 14 07:00 3 0 0 0 10 2013 Winter 13:00 17 0 0 0 0

2013 Winter 17:00 17 0 2 2 0

*Site 1: D parking (western side of campus grounds)

Site 2: Southern side of the campus

Site 3: Eastern corner of the campus grounds

Site 4: behind the student centre (northern side of the campus)

Unknown: The Laughing Doves was only included in the study in 2013

193

Table 7.3: Complete count data set for the rural area in 2012 and 2013.

House House House House House

Sparrows Sparrows Sparrows Sparrows Sparrows Temp Groen Goede Slypsteen Olifants Year Season Day Time Leeudrift (°C) fontein hoop drift been 2012 Summer 1 06:00 18 0 0 0 0 0 2012 Summer 13:00 28 2 0 0 0 0 2012 Summer 17:00 25 1 0 0 0 2 2012 Summer 2 06:00 14 0 0 0 0 0 2012 Summer 13:00 21 0 0 0 0 0 2012 Summer 17:00 28 0 0 0 0 2 2012 Summer 3 06:00 16 0 0 0 0 3 2012 Summer 13:00 23 0 0 2 0 6 2012 Summer 17:00 25 2 0 0 0 2 2012 Summer 4 06:00 16 2 0 0 0 0 2012 Summer 13:00 26 2 2 0 0 4 2012 Summer 17:00 23 2 0 0 0 0 2012 Summer 5 06:00 17 1 0 0 0 5 2012 Summer 13:00 32 2 0 0 0 5 2012 Summer 17:00 30 2 0 0 0 4 2012 Summer 6 06:00 18 0 0 0 0 5 2012 Summer 13:00 27 0 0 0 0 7 2012 Summer 17:00 25 2 0 0 0 3 2012 Summer 7 06:00 16 0 0 0 0 2 2012 Summer 13:00 25 2 0 0 0 2 2012 Summer 17:00 23 1 0 0 0 1 2012 Summer 8 06:00 14 2 0 0 0 5 2012 Summer 13:00 27 0 0 0 0 4 2012 Summer 17:00 25 2 0 0 0 5 2012 Summer 9 06:00 16 2 3 0 0 5 2012 Summer 13:00 33 0 0 0 0 3 2012 Summer 17:00 30 2 0 0 0 1 2012 Summer 10 06:00 18 2 0 2 0 4 2012 Summer 13:00 37 0 0 0 0 0 2012 Summer 17:00 35 0 0 0 0 2 2012 Summer 11 06:00 18 0 2 0 0 4 2012 Summer 13:00 39 0 0 0 0 2 2012 Summer 17:00 36 2 3 0 0 6 2012 Summer 12 06:00 18 0 0 0 0 2 2012 Summer 13:00 38 0 0 0 0 2 2012 Summer 17:00 35 0 0 0 0 3 2012 Summer 13 06:00 14 2 0 0 0 4 2012 Summer 13:00 32 0 0 0 0 4 2012 Summer 17:00 29 2 0 0 0 3 2012 Summer 14 06:00 13 0 0 0 0 5

194

2012 Summer 13:00 28 0 0 0 0 4 2012 Summer 17:00 27 2 0 0 0 2 2012 Winter 1 07:00 5 7 10 0 0 16 2012 Winter 13:00 25 6 3 0 0 27 2012 Winter 17:00 25 3 2 0 0 21 2012 Winter 2 07:00 8 2 3 0 0 0 2012 Winter 13:00 24 3 0 0 0 9 2012 Winter 17:00 26 1 2 0 0 4 2012 Winter 3 07:00 5 4 3 2 0 33 2012 Winter 13:00 25 3 5 0 0 17 2012 Winter 17:00 27 2 1 2 0 23 2012 Winter 4 07:00 6 3 0 1 0 29 2012 Winter 13:00 25 0 3 2 0 0 2012 Winter 17:00 25 0 6 0 0 20 2012 Winter 5 07:00 9 0 0 0 0 96 2012 Winter 13:00 23 7 5 0 0 1 2012 Winter 17:00 25 0 2 2 0 38 2012 Winter 6 07:00 6 0 0 0 0 3 2012 Winter 13:00 24 3 3 0 0 0 2012 Winter 17:00 27 0 0 0 0 39 2012 Winter 7 07:00 5 3 1 0 0 27 2012 Winter 13:00 26 6 0 0 0 4 2012 Winter 17:00 26 9 0 0 0 27 2012 Winter 8 07:00 5 4 0 2 0 31 2012 Winter 13:00 23 9 0 0 0 35 2012 Winter 17:00 27 0 0 0 0 22 2012 Winter 9 07:00 4 2 0 2 0 39 2012 Winter 13:00 25 9 0 0 0 0 2012 Winter 17:00 23 9 0 0 0 66 2012 Winter 10 07:00 4 1 2 0 0 18 2012 Winter 13:00 22 10 9 0 0 120 2012 Winter 17:00 22 2 2 0 0 20 2012 Winter 11 07:00 4 5 9 0 0 74 2012 Winter 13:00 24 11 0 0 0 0 2012 Winter 17:00 23 0 2 0 0 23 2012 Winter 12 07:00 3 1 3 0 0 29 2012 Winter 13:00 21 12 5 0 0 0 2012 Winter 17:00 20 0 0 2 0 13 2012 Winter 13 07:00 2 11 0 0 0 5 2012 Winter 13:00 22 12 0 0 0 7 2012 Winter 17:00 23 0 0 0 0 5 2012 Winter 14 07:00 4 6 4 0 0 63 2012 Winter 13:00 24 9 0 0 0 0 2012 Winter 17:00 25 2 0 0 0 25 2013 Summer 1 06:00 16 0 0 0 0 5

195

2013 Summer 13:00 32 0 0 0 0 0 2013 Summer 17:00 30 0 0 0 0 0 2013 Summer 2 06:00 19 0 0 0 0 9 2013 Summer 13:00 27 0 0 0 0 0 2013 Summer 17:00 25 0 0 0 0 6 2013 Summer 3 06:00 13 0 0 0 0 6 2013 Summer 13:00 27 0 2 0 0 4 2013 Summer 17:00 25 0 0 0 0 8 2013 Summer 4 06:00 19 0 0 0 0 8 2013 Summer 13:00 24 0 0 0 3 6 2013 Summer 17:00 23 0 0 0 3 0 2013 Summer 5 06:00 18 0 0 0 0 5 2013 Summer 13:00 25 0 0 0 0 4 2013 Summer 17:00 25 0 0 0 0 8 2013 Summer 6 06:00 20 0 0 0 0 6 2013 Summer 13:00 26 0 0 0 0 8 2013 Summer 17:00 25 0 0 0 0 0 2013 Summer 7 06:00 20 0 0 0 0 6 2013 Summer 13:00 24 0 0 0 0 8 2013 Summer 17:00 25 0 0 0 0 2 2013 Summer 8 06:00 20 0 0 0 0 0 2013 Summer 13:00 26 0 0 0 0 0 2013 Summer 17:00 25 0 0 0 0 8 2013 Summer 9 06:00 18 0 0 0 0 0 2013 Summer 13:00 26 0 0 0 0 5 2013 Summer 17:00 27 0 0 0 0 0 2013 Summer 10 06:00 21 0 0 0 0 0 2013 Summer 13:00 29 0 0 0 0 8 2013 Summer 17:00 29 0 0 0 0 0 2013 Summer 11 06:00 22 0 0 0 0 5 2013 Summer 13:00 25 0 0 0 0 0 2013 Summer 17:00 24 0 0 0 0 6 2013 Summer 12 06:00 20 0 0 0 0 0 2013 Summer 13:00 27 0 0 0 0 0 2013 Summer 17:00 26 0 0 0 0 2 2013 Summer 13 06:00 18 0 0 0 0 0 2013 Summer 13:00 27 0 0 0 0 4 2013 Summer 17:00 27 0 0 0 0 0 2013 Summer 14 06:00 20 5 0 0 0 5 2013 Summer 13:00 29 0 0 0 0 0 2013 Summer 17:00 29 0 0 0 0 0 2013 Winter 1 07:00 11 0 0 0 0 0 2013 Winter 13:00 26 0 0 0 0 0 2013 Winter 17:00 28 0 0 0 0 0 2013 Winter 2 07:00 11 0 0 0 0 5

196

2013 Winter 13:00 24 0 0 0 0 5 2013 Winter 17:00 26 0 0 0 0 3 2013 Winter 3 07:00 8 11 0 0 0 3 2013 Winter 13:00 25 0 0 0 0 0 2013 Winter 17:00 25 0 0 0 0 2 2013 Winter 4 07:00 9 0 0 0 0 5 2013 Winter 13:00 25 0 0 0 0 0 2013 Winter 17:00 22 0 0 0 0 0 2013 Winter 5 07:00 7 0 0 0 0 0 2013 Winter 13:00 26 0 0 0 0 0 2013 Winter 17:00 21 0 0 0 0 5 2013 Winter 6 07:00 9 0 0 0 0 0 2013 Winter 13:00 24 0 0 0 0 0 2013 Winter 17:00 24 0 0 0 0 0 2013 Winter 7 07:00 12 11 0 0 0 3 2013 Winter 13:00 19 11 0 0 0 0 2013 Winter 17:00 21 0 0 0 0 0 2013 Winter 8 07:00 10 0 0 0 0 5 2013 Winter 13:00 19 0 0 0 0 0 2013 Winter 17:00 21 0 0 0 0 0 2013 Winter 9 07:00 9 11 0 0 0 13 2013 Winter 13:00 21 0 0 0 0 9 2013 Winter 17:00 22 0 0 0 0 0 2013 Winter 10 07:00 7 0 0 0 0 0 2013 Winter 13:00 17 0 0 0 0 0 2013 Winter 17:00 19 0 0 0 0 5 2013 Winter 11 07:00 6 0 0 0 0 0 2013 Winter 13:00 17 0 0 0 0 0 2013 Winter 17:00 21 10 0 0 0 15 2013 Winter 12 07:00 6 2 0 0 0 5 2013 Winter 13:00 18 0 0 0 0 0 2013 Winter 17:00 19 0 0 0 0 0 2013 Winter 13 07:00 6 6 0 0 0 0 2013 Winter 13:00 24 0 0 0 0 0 2013 Winter 17:00 24 0 0 0 0 0 2013 Winter 14 07:00 7 5 0 0 0 18 2013 Winter 13:00 23 0 0 0 0 0 2013 Winter 17:00 23 6 0 0 0 5 Cape Cape Cape Cape Cape Turtle Turtle Turtle Turtle Turtle Doves Doves Doves Doves Doves Temp Groen Goede Slypsteen Olifants Year Season Day Time Leeudrift (°C) fontein hoop drift been 2012 Summer 1 06:00 18 0 2 0 2 3 2012 Summer 13:00 28 0 0 0 0 0 2012 Summer 17:00 25 0 0 0 2 1

197

2012 Summer 2 06:00 14 0 2 0 0 0 2012 Summer 13:00 21 0 1 0 0 0 2012 Summer 17:00 28 0 1 1 2 2 2012 Summer 3 06:00 16 0 0 2 0 1 2012 Summer 13:00 23 0 0 0 0 0 2012 Summer 17:00 25 0 1 0 2 0 2012 Summer 4 06:00 16 0 1 1 2 2 2012 Summer 13:00 26 0 0 0 0 2 2012 Summer 17:00 23 0 1 0 2 0 2012 Summer 5 06:00 17 0 0 0 2 0 2012 Summer 13:00 32 0 0 0 0 0 2012 Summer 17:00 30 0 0 1 0 0 2012 Summer 6 06:00 18 0 0 1 2 3 2012 Summer 13:00 27 0 0 0 0 0 2012 Summer 17:00 25 0 0 0 0 0 2012 Summer 7 06:00 16 0 0 0 2 0 2012 Summer 13:00 25 1 0 1 0 0 2012 Summer 17:00 23 0 0 2 2 1 2012 Summer 8 06:00 14 1 0 1 2 1 2012 Summer 13:00 27 0 0 0 0 0 2012 Summer 17:00 25 0 0 1 1 0 2012 Summer 9 06:00 16 0 0 0 1 2 2012 Summer 13:00 33 0 0 1 0 0 2012 Summer 17:00 30 0 0 1 2 2 2012 Summer 10 06:00 18 1 1 0 0 0 2012 Summer 13:00 37 0 0 0 0 0 2012 Summer 17:00 35 0 0 4 2 2 2012 Summer 11 06:00 18 1 0 1 1 0 2012 Summer 13:00 39 0 0 0 0 0 2012 Summer 17:00 36 2 0 0 1 2 2012 Summer 12 06:00 18 0 0 1 1 0 2012 Summer 13:00 38 0 0 0 0 0 2012 Summer 17:00 35 1 0 1 2 1 2012 Summer 13 06:00 14 0 0 2 0 2 2012 Summer 13:00 32 0 0 0 0 0 2012 Summer 17:00 29 0 0 0 0 1 2012 Summer 14 06:00 13 0 0 0 0 0 2012 Summer 13:00 28 0 0 0 2 1 2012 Summer 17:00 27 0 0 0 0 0 2012 Winter 1 07:00 5 2 2 2 4 4 2012 Winter 13:00 25 2 2 2 2 0 2012 Winter 17:00 25 2 3 4 2 2 2012 Winter 2 07:00 8 4 2 5 2 2 2012 Winter 13:00 24 0 1 6 0 2 2012 Winter 17:00 26 4 4 2 3 0

198

2012 Winter 3 07:00 5 5 1 5 2 1 2012 Winter 13:00 25 1 0 5 0 2 2012 Winter 17:00 27 1 2 7 2 1 2012 Winter 4 07:00 6 0 2 2 2 0 2012 Winter 13:00 25 0 1 3 0 3 2012 Winter 17:00 25 0 0 3 2 0 2012 Winter 5 07:00 9 1 4 2 2 0 2012 Winter 13:00 23 0 0 2 0 1 2012 Winter 17:00 25 2 2 3 2 0 2012 Winter 6 07:00 6 0 2 5 0 2 2012 Winter 13:00 24 2 4 0 2 5 2012 Winter 17:00 27 0 0 4 0 2 2012 Winter 7 07:00 5 0 2 2 4 0 2012 Winter 13:00 26 0 0 2 0 2 2012 Winter 17:00 26 2 4 2 0 2 2012 Winter 8 07:00 5 0 1 2 2 2 2012 Winter 13:00 23 2 0 0 0 0 2012 Winter 17:00 27 0 2 2 4 2 2012 Winter 9 07:00 4 0 1 2 1 1 2012 Winter 13:00 25 0 0 0 0 0 2012 Winter 17:00 23 0 1 0 0 0 2012 Winter 10 07:00 4 0 2 0 4 2 2012 Winter 13:00 22 0 1 1 0 4 2012 Winter 17:00 22 1 1 2 5 7 2012 Winter 11 07:00 4 0 2 1 3 7 2012 Winter 13:00 24 0 0 0 0 0 2012 Winter 17:00 23 0 1 0 2 4 2012 Winter 12 07:00 3 0 1 2 3 1 2012 Winter 13:00 21 0 0 0 0 2 2012 Winter 17:00 20 0 1 0 2 0 2012 Winter 13 07:00 2 0 3 0 4 4 2012 Winter 13:00 22 0 0 0 0 2 2012 Winter 17:00 23 0 2 0 4 0 2012 Winter 14 07:00 4 2 2 0 3 0 2012 Winter 13:00 24 0 0 0 0 0 2012 Winter 17:00 25 0 0 0 4 3 2013 Summer 1 06:00 16 0 0 0 0 0 2013 Summer 13:00 32 0 0 0 0 0 2013 Summer 17:00 30 0 0 0 0 0 2013 Summer 2 06:00 19 0 0 0 0 0 2013 Summer 13:00 27 0 0 0 0 0 2013 Summer 17:00 25 0 0 0 0 0 2013 Summer 3 06:00 13 0 0 0 0 0 2013 Summer 13:00 27 0 0 0 0 0 2013 Summer 17:00 25 0 0 0 0 0

199

2013 Summer 4 06:00 19 0 0 0 0 0 2013 Summer 13:00 24 0 0 0 0 0 2013 Summer 17:00 23 0 0 0 0 0 2013 Summer 5 06:00 18 0 0 0 0 0 2013 Summer 13:00 25 0 0 0 2 0 2013 Summer 17:00 25 0 0 0 2 0 2013 Summer 6 06:00 20 0 0 0 0 0 2013 Summer 13:00 26 0 0 0 0 0 2013 Summer 17:00 25 0 0 0 0 0 2013 Summer 7 06:00 20 0 0 0 0 1 2013 Summer 13:00 24 0 0 2 0 0 2013 Summer 17:00 25 0 0 0 0 0 2013 Summer 8 06:00 20 0 0 0 0 2 2013 Summer 13:00 26 0 0 0 0 2 2013 Summer 17:00 25 0 0 0 2 0 2013 Summer 9 06:00 18 0 0 0 0 2 2013 Summer 13:00 26 0 0 0 0 2 2013 Summer 17:00 27 0 0 0 0 0 2013 Summer 10 06:00 21 0 0 1 0 0 2013 Summer 13:00 29 0 0 0 2 2 2013 Summer 17:00 29 0 0 0 0 2 2013 Summer 11 06:00 22 0 0 0 0 4 2013 Summer 13:00 25 0 0 0 0 2 2013 Summer 17:00 24 0 0 0 2 0 2013 Summer 12 06:00 20 0 0 0 0 0 2013 Summer 13:00 27 0 0 0 0 0 2013 Summer 17:00 26 0 0 0 0 0 2013 Summer 13 06:00 18 0 0 0 0 4 2013 Summer 13:00 27 0 0 2 0 0 2013 Summer 17:00 27 0 0 0 0 0 2013 Summer 14 06:00 20 0 0 2 0 0 2013 Summer 13:00 29 0 0 0 0 0 2013 Summer 17:00 29 0 0 0 0 0 2013 Winter 1 07:00 11 0 0 0 0 2 2013 Winter 13:00 26 0 0 0 0 0 2013 Winter 17:00 28 0 0 0 0 0 2013 Winter 2 07:00 11 0 0 0 0 2 2013 Winter 13:00 24 0 0 0 2 0 2013 Winter 17:00 26 0 0 0 0 0 2013 Winter 3 07:00 8 0 0 0 0 2 2013 Winter 13:00 25 0 0 0 0 0 2013 Winter 17:00 25 0 0 0 2 0 2013 Winter 4 07:00 9 0 0 0 0 0 2013 Winter 13:00 25 0 0 0 0 2 2013 Winter 17:00 22 0 0 2 0 2

200

2013 Winter 5 07:00 7 0 0 0 2 2 2013 Winter 13:00 26 0 0 0 0 0 2013 Winter 17:00 21 0 0 0 0 0 2013 Winter 6 07:00 9 0 0 0 0 0 2013 Winter 13:00 24 0 0 0 0 0 2013 Winter 17:00 24 0 0 0 2 0 2013 Winter 7 07:00 12 0 0 0 0 2 2013 Winter 13:00 19 0 0 0 0 0 2013 Winter 17:00 21 0 0 2 0 0 2013 Winter 8 07:00 10 0 0 0 0 0 2013 Winter 13:00 19 0 0 0 0 0 2013 Winter 17:00 21 0 0 0 2 0 2013 Winter 9 07:00 9 0 0 0 0 0 2013 Winter 13:00 21 0 0 2 2 0 2013 Winter 17:00 22 0 0 0 0 0 2013 Winter 10 07:00 7 0 1 0 2 3 2013 Winter 13:00 17 0 0 0 2 2 2013 Winter 17:00 19 1 0 0 0 0 2013 Winter 11 07:00 6 0 0 0 0 0 2013 Winter 13:00 17 0 0 2 0 0 2013 Winter 17:00 21 0 0 2 0 2 2013 Winter 12 07:00 6 2 0 0 0 0 2013 Winter 13:00 18 0 0 0 0 0 2013 Winter 17:00 19 2 0 2 0 2 2013 Winter 13 07:00 6 0 0 0 0 0 2013 Winter 13:00 24 0 0 0 0 0 2013 Winter 17:00 24 0 0 0 0 0 2013 Winter 14 07:00 7 0 0 0 0 0 2013 Winter 13:00 23 0 0 2 0 2 2013 Winter 17:00 23 2 0 0 0 2 Cape Cape Cape Cape Cape Glossy Glossy Glossy Glossy Glossy Starlings Starlings Starlings Starlings Starlings Temp Groen Goede Slypsteen Olifants Year Season Day Time Leeudrift (°C) been been fontein hoop drift 2012 Summer 1 06:00 18 2 0 0 0 0 2012 Summer 13:00 28 1 0 0 0 0 2012 Summer 17:00 25 0 0 0 2 0 2012 Summer 2 06:00 14 0 1 0 2 0 2012 Summer 13:00 21 1 0 2 2 0 2012 Summer 17:00 28 1 0 0 4 0 2012 Summer 3 06:00 16 2 0 2 2 0 2012 Summer 13:00 23 0 0 1 0 0 2012 Summer 17:00 25 2 0 2 4 0 2012 Summer 4 06:00 16 2 0 1 2 0 2012 Summer 13:00 26 2 0 0 0 0

201

2012 Summer 17:00 23 0 0 2 2 2 2012 Summer 5 06:00 17 1 0 0 4 0 2012 Summer 13:00 32 1 0 0 0 0 2012 Summer 17:00 30 2 0 1 0 1 2012 Summer 6 06:00 18 2 0 7 2 0 2012 Summer 13:00 27 2 0 0 0 2 2012 Summer 17:00 25 0 0 0 4 0 2012 Summer 7 06:00 16 2 0 0 0 1 2012 Summer 13:00 25 2 0 1 0 0 2012 Summer 17:00 23 1 0 2 4 0 2012 Summer 8 06:00 14 1 0 1 2 0 2012 Summer 13:00 27 2 0 0 0 1 2012 Summer 17:00 25 1 0 1 1 0 2012 Summer 9 06:00 16 2 0 0 2 0 2012 Summer 13:00 33 2 0 1 0 1 2012 Summer 17:00 30 1 0 1 2 1 2012 Summer 10 06:00 18 2 0 1 0 0 2012 Summer 13:00 37 1 0 0 0 0 2012 Summer 17:00 35 1 0 3 4 0 2012 Summer 11 06:00 18 1 0 1 2 0 2012 Summer 13:00 39 1 0 2 0 0 2012 Summer 17:00 36 1 0 1 1 0 2012 Summer 12 06:00 18 1 0 2 1 0 2012 Summer 13:00 38 1 0 1 0 0 2012 Summer 17:00 35 2 0 1 4 0 2012 Summer 13 06:00 14 0 0 0 0 1 2012 Summer 13:00 32 2 0 2 2 0 2012 Summer 17:00 29 1 0 1 0 0 2012 Summer 14 06:00 13 1 0 0 0 0 2012 Summer 13:00 28 0 0 0 4 0 2012 Summer 17:00 27 1 0 0 0 0 2012 Winter 1 07:00 5 0 0 0 2 17 2012 Winter 13:00 25 2 0 1 0 14 2012 Winter 17:00 25 0 0 0 0 14 2012 Winter 2 07:00 8 0 0 1 0 5 2012 Winter 13:00 24 0 0 2 2 3 2012 Winter 17:00 26 0 0 0 2 2 2012 Winter 3 07:00 5 2 0 4 1 2 2012 Winter 13:00 25 0 0 3 0 0 2012 Winter 17:00 27 0 0 3 2 0 2012 Winter 4 07:00 6 0 0 0 0 3 2012 Winter 13:00 25 0 0 1 2 2 2012 Winter 17:00 25 0 0 0 2 0 2012 Winter 5 07:00 9 0 0 4 2 18 2012 Winter 13:00 23 0 0 0 0 11

202

2012 Winter 17:00 25 0 0 2 2 1 2012 Winter 6 07:00 6 0 0 1 0 3 2012 Winter 13:00 24 0 0 2 2 2 2012 Winter 17:00 27 0 0 2 2 2 2012 Winter 7 07:00 5 0 0 2 2 9 2012 Winter 13:00 26 0 0 2 1 3 2012 Winter 17:00 26 0 0 1 2 0 2012 Winter 8 07:00 5 0 0 1 2 9 2012 Winter 13:00 23 0 0 0 0 4 2012 Winter 17:00 27 0 0 0 2 4 2012 Winter 9 07:00 4 0 0 1 0 1 2012 Winter 13:00 25 1 0 0 0 6 2012 Winter 17:00 23 0 0 0 0 7 2012 Winter 10 07:00 4 0 0 1 2 11 2012 Winter 13:00 22 0 0 1 1 2 2012 Winter 17:00 22 0 0 0 2 0 2012 Winter 11 07:00 4 0 0 1 2 29 2012 Winter 13:00 24 0 0 0 0 13 2012 Winter 17:00 23 0 0 0 2 13 2012 Winter 12 07:00 3 0 0 1 1 9 2012 Winter 13:00 21 0 0 0 0 11 2012 Winter 17:00 20 0 0 1 2 11 2012 Winter 13 07:00 2 0 0 2 0 3 2012 Winter 13:00 22 0 0 2 0 13 2012 Winter 17:00 23 0 0 1 0 11 2012 Winter 14 07:00 4 2 0 2 2 15 2012 Winter 13:00 24 0 0 0 0 9 2012 Winter 17:00 25 0 0 0 2 9 2013 Summer 1 06:00 16 0 0 4 0 0 2013 Summer 13:00 32 0 0 0 0 0 2013 Summer 17:00 30 0 0 1 0 0 2013 Summer 2 06:00 19 0 0 0 0 0 2013 Summer 13:00 27 0 0 0 0 0 2013 Summer 17:00 25 0 0 0 0 0 2013 Summer 3 06:00 13 0 0 0 0 0 2013 Summer 13:00 27 1 0 0 0 0 2013 Summer 17:00 25 0 0 0 0 0 2013 Summer 4 06:00 19 6 0 0 0 0 2013 Summer 13:00 24 0 0 0 0 0 2013 Summer 17:00 23 2 0 0 0 1 2013 Summer 5 06:00 18 0 0 0 0 0 2013 Summer 13:00 25 0 0 0 0 0 2013 Summer 17:00 25 0 0 2 2 0 2013 Summer 6 06:00 20 0 0 0 0 0 2013 Summer 13:00 26 0 0 0 0 0

203

2013 Summer 17:00 25 0 0 0 4 0 2013 Summer 7 06:00 20 0 0 0 0 0 2013 Summer 13:00 24 0 0 0 0 0 2013 Summer 17:00 25 0 0 0 0 0 2013 Summer 8 06:00 20 0 0 2 0 0 2013 Summer 13:00 26 0 0 0 0 0 2013 Summer 17:00 25 0 0 1 0 0 2013 Summer 9 06:00 18 0 0 0 0 2 2013 Summer 13:00 26 0 0 0 0 1 2013 Summer 17:00 27 0 0 1 2 0 2013 Summer 10 06:00 21 0 0 0 0 0 2013 Summer 13:00 29 0 0 0 0 0 2013 Summer 17:00 29 0 0 0 0 0 2013 Summer 11 06:00 22 0 0 0 0 2 2013 Summer 13:00 25 0 0 1 0 0 2013 Summer 17:00 24 0 0 1 2 0 2013 Summer 12 06:00 20 0 0 0 0 0 2013 Summer 13:00 27 0 0 2 0 0 2013 Summer 17:00 26 0 0 0 0 0 2013 Summer 13 06:00 18 0 0 0 0 3 2013 Summer 13:00 27 0 0 2 2 0 2013 Summer 17:00 27 0 0 0 2 0 2013 Summer 14 06:00 20 0 0 0 0 0 2013 Summer 13:00 29 0 0 0 0 1 2013 Summer 17:00 29 2 0 2 0 0 2013 Winter 1 07:00 11 0 0 0 0 0 2013 Winter 13:00 26 0 0 0 0 1 2013 Winter 17:00 28 0 0 0 2 0 2013 Winter 2 07:00 11 0 0 0 2 8 2013 Winter 13:00 24 0 0 0 2 0 2013 Winter 17:00 26 0 0 0 0 0 2013 Winter 3 07:00 8 2 0 0 2 0 2013 Winter 13:00 25 0 0 0 0 0 2013 Winter 17:00 25 0 0 0 0 0 2013 Winter 4 07:00 9 0 0 0 0 0 2013 Winter 13:00 25 0 0 0 0 2 2013 Winter 17:00 22 0 0 0 0 0 2013 Winter 5 07:00 7 0 0 0 0 0 2013 Winter 13:00 26 0 0 0 0 0 2013 Winter 17:00 21 0 0 0 0 7 2013 Winter 6 07:00 9 0 0 0 0 0 2013 Winter 13:00 24 0 0 0 0 0 2013 Winter 17:00 24 0 0 2 0 8 2013 Winter 7 07:00 12 0 0 0 0 15 2013 Winter 13:00 19 0 0 0 2 0

204

2013 Winter 17:00 21 0 0 2 2 0 2013 Winter 8 07:00 10 0 0 0 2 5 2013 Winter 13:00 19 2 0 0 2 0 2013 Winter 17:00 21 0 0 0 2 0 2013 Winter 9 07:00 9 2 0 0 2 0 2013 Winter 13:00 21 0 0 0 2 5 2013 Winter 17:00 22 0 0 0 0 0 2013 Winter 10 07:00 7 0 0 0 2 0 2013 Winter 13:00 17 2 0 2 0 0 2013 Winter 17:00 19 0 0 2 2 0 2013 Winter 11 07:00 6 3 0 0 0 0 2013 Winter 13:00 17 2 0 0 2 0 2013 Winter 17:00 21 2 0 0 2 8 2013 Winter 12 07:00 6 0 0 0 0 0 2013 Winter 13:00 18 0 0 2 2 8 2013 Winter 17:00 19 0 0 0 2 2 2013 Winter 13 07:00 6 2 0 2 2 2 2013 Winter 13:00 24 0 0 0 2 13 2013 Winter 17:00 24 0 0 0 5 8 2013 Winter 14 07:00 7 2 0 0 5 8 2013 Winter 13:00 23 0 0 0 0 0 2013 Winter 17:00 23 2 0 2 2 8 Laughing Laughing Laughing Laughing Laughing

Doves Doves Doves Doves Doves Temp Groen Goede Slypsteen Olifants Year Season Day Time Leeudrift (°C) fontein hoop drift been 2012 Summer 1 06:00 18 Unknown Unknown Unknown Unknown Unknown 2012 Summer 13:00 28 Unknown Unknown Unknown Unknown Unknown 2012 Summer 17:00 25 Unknown Unknown Unknown Unknown Unknown 2012 Summer 2 06:00 14 Unknown Unknown Unknown Unknown Unknown 2012 Summer 13:00 21 Unknown Unknown Unknown Unknown Unknown 2012 Summer 17:00 28 Unknown Unknown Unknown Unknown Unknown 2012 Summer 3 06:00 16 Unknown Unknown Unknown Unknown Unknown 2012 Summer 13:00 23 Unknown Unknown Unknown Unknown Unknown 2012 Summer 17:00 25 Unknown Unknown Unknown Unknown Unknown 2012 Summer 4 06:00 16 Unknown Unknown Unknown Unknown Unknown 2012 Summer 13:00 26 Unknown Unknown Unknown Unknown Unknown 2012 Summer 17:00 23 Unknown Unknown Unknown Unknown Unknown 2012 Summer 5 06:00 17 Unknown Unknown Unknown Unknown Unknown 2012 Summer 13:00 32 Unknown Unknown Unknown Unknown Unknown 2012 Summer 17:00 30 Unknown Unknown Unknown Unknown Unknown 2012 Summer 6 06:00 18 Unknown Unknown Unknown Unknown Unknown 2012 Summer 13:00 27 Unknown Unknown Unknown Unknown Unknown 2012 Summer 17:00 25 Unknown Unknown Unknown Unknown Unknown 2012 Summer 7 06:00 16 Unknown Unknown Unknown Unknown Unknown

205

2012 Summer 13:00 25 Unknown Unknown Unknown Unknown Unknown 2012 Summer 17:00 23 Unknown Unknown Unknown Unknown Unknown 2012 Summer 8 06:00 14 Unknown Unknown Unknown Unknown Unknown 2012 Summer 13:00 27 Unknown Unknown Unknown Unknown Unknown 2012 Summer 17:00 25 Unknown Unknown Unknown Unknown Unknown 2012 Summer 9 06:00 16 Unknown Unknown Unknown Unknown Unknown 2012 Summer 13:00 33 Unknown Unknown Unknown Unknown Unknown 2012 Summer 17:00 30 Unknown Unknown Unknown Unknown Unknown 2012 Summer 10 06:00 18 Unknown Unknown Unknown Unknown Unknown 2012 Summer 13:00 37 Unknown Unknown Unknown Unknown Unknown 2012 Summer 17:00 35 Unknown Unknown Unknown Unknown Unknown 2012 Summer 11 06:00 18 Unknown Unknown Unknown Unknown Unknown 2012 Summer 13:00 39 Unknown Unknown Unknown Unknown Unknown 2012 Summer 17:00 36 Unknown Unknown Unknown Unknown Unknown 2012 Summer 12 06:00 18 Unknown Unknown Unknown Unknown Unknown 2012 Summer 13:00 38 Unknown Unknown Unknown Unknown Unknown 2012 Summer 17:00 35 Unknown Unknown Unknown Unknown Unknown 2012 Summer 13 06:00 14 Unknown Unknown Unknown Unknown Unknown 2012 Summer 13:00 32 Unknown Unknown Unknown Unknown Unknown 2012 Summer 17:00 29 Unknown Unknown Unknown Unknown Unknown 2012 Summer 14 06:00 13 Unknown Unknown Unknown Unknown Unknown 2012 Summer 13:00 28 Unknown Unknown Unknown Unknown Unknown 2012 Summer 17:00 27 Unknown Unknown Unknown Unknown Unknown 2012 Winter 1 07:00 5 Unknown Unknown Unknown Unknown Unknown 2012 Winter 13:00 25 Unknown Unknown Unknown Unknown Unknown 2012 Winter 17:00 25 Unknown Unknown Unknown Unknown Unknown 2012 Winter 2 07:00 8 Unknown Unknown Unknown Unknown Unknown 2012 Winter 13:00 24 Unknown Unknown Unknown Unknown Unknown 2012 Winter 17:00 26 Unknown Unknown Unknown Unknown Unknown 2012 Winter 3 07:00 5 Unknown Unknown Unknown Unknown Unknown 2012 Winter 13:00 25 Unknown Unknown Unknown Unknown Unknown 2012 Winter 17:00 27 Unknown Unknown Unknown Unknown Unknown 2012 Winter 4 07:00 6 Unknown Unknown Unknown Unknown Unknown 2012 Winter 13:00 25 Unknown Unknown Unknown Unknown Unknown 2012 Winter 17:00 25 Unknown Unknown Unknown Unknown Unknown 2012 Winter 5 07:00 9 Unknown Unknown Unknown Unknown Unknown 2012 Winter 13:00 23 Unknown Unknown Unknown Unknown Unknown 2012 Winter 17:00 25 Unknown Unknown Unknown Unknown Unknown 2012 Winter 6 07:00 6 Unknown Unknown Unknown Unknown Unknown 2012 Winter 13:00 24 Unknown Unknown Unknown Unknown Unknown 2012 Winter 17:00 27 Unknown Unknown Unknown Unknown Unknown 2012 Winter 7 07:00 5 Unknown Unknown Unknown Unknown Unknown 2012 Winter 13:00 26 Unknown Unknown Unknown Unknown Unknown 2012 Winter 17:00 26 Unknown Unknown Unknown Unknown Unknown 2012 Winter 8 07:00 5 Unknown Unknown Unknown Unknown Unknown

206

2012 Winter 13:00 23 Unknown Unknown Unknown Unknown Unknown 2012 Winter 17:00 27 Unknown Unknown Unknown Unknown Unknown 2012 Winter 9 07:00 4 Unknown Unknown Unknown Unknown Unknown 2012 Winter 13:00 25 Unknown Unknown Unknown Unknown Unknown 2012 Winter 17:00 23 Unknown Unknown Unknown Unknown Unknown 2012 Winter 10 07:00 4 Unknown Unknown Unknown Unknown Unknown 2012 Winter 13:00 22 Unknown Unknown Unknown Unknown Unknown 2012 Winter 17:00 22 Unknown Unknown Unknown Unknown Unknown 2012 Winter 11 07:00 4 Unknown Unknown Unknown Unknown Unknown 2012 Winter 13:00 24 Unknown Unknown Unknown Unknown Unknown 2012 Winter 17:00 23 Unknown Unknown Unknown Unknown Unknown 2012 Winter 12 07:00 3 Unknown Unknown Unknown Unknown Unknown 2012 Winter 13:00 21 Unknown Unknown Unknown Unknown Unknown 2012 Winter 17:00 20 Unknown Unknown Unknown Unknown Unknown 2012 Winter 13 07:00 2 Unknown Unknown Unknown Unknown Unknown 2012 Winter 13:00 22 Unknown Unknown Unknown Unknown Unknown 2012 Winter 17:00 23 Unknown Unknown Unknown Unknown Unknown 2012 Winter 14 07:00 4 Unknown Unknown Unknown Unknown Unknown 2012 Winter 13:00 24 Unknown Unknown Unknown Unknown Unknown 2012 Winter 17:00 25 Unknown Unknown Unknown Unknown Unknown 2013 Summer 1 06:00 16 0 1 3 2 8 2013 Summer 13:00 32 2 0 0 2 15 2013 Summer 17:00 30 0 0 0 4 64 2013 Summer 2 06:00 19 1 2 0 2 196 2013 Summer 13:00 27 0 0 0 0 8 2013 Summer 17:00 25 2 2 2 2 74 2013 Summer 3 06:00 13 0 0 0 0 27 2013 Summer 13:00 27 1 1 0 2 104 2013 Summer 17:00 25 0 0 0 2 96 2013 Summer 4 06:00 19 0 2 1 0 172 2013 Summer 13:00 24 0 0 0 0 74 2013 Summer 17:00 23 1 0 0 0 114 2013 Summer 5 06:00 18 1 0 0 0 220 2013 Summer 13:00 25 0 1 0 2 97 2013 Summer 17:00 25 1 0 1 2 74 2013 Summer 6 06:00 20 2 0 0 0 182 2013 Summer 13:00 26 1 0 2 2 29 2013 Summer 17:00 25 1 0 2 0 127 2013 Summer 7 06:00 20 2 1 0 0 130 2013 Summer 13:00 24 2 0 2 0 87 2013 Summer 17:00 25 0 0 0 2 87 2013 Summer 8 06:00 20 0 1 1 2 37 2013 Summer 13:00 26 2 1 0 0 84 2013 Summer 17:00 25 2 0 0 2 89 2013 Summer 9 06:00 18 1 0 1 0 85

207

2013 Summer 13:00 26 0 0 2 0 19 2013 Summer 17:00 27 0 0 2 2 27 2013 Summer 10 06:00 21 0 2 2 2 38 2013 Summer 13:00 29 1 2 2 0 11 2013 Summer 17:00 29 0 0 1 0 68 2013 Summer 11 06:00 22 0 0 0 0 78 2013 Summer 13:00 25 0 1 0 0 24 2013 Summer 17:00 24 1 0 0 4 47 2013 Summer 12 06:00 20 2 2 2 2 55 2013 Summer 13:00 27 0 0 0 0 56 2013 Summer 17:00 26 0 0 0 2 58 2013 Summer 13 06:00 18 2 0 0 0 96 2013 Summer 13:00 27 1 2 2 2 24 2013 Summer 17:00 27 1 2 0 0 46 2013 Summer 14 06:00 20 2 2 2 2 66 2013 Summer 13:00 29 0 0 2 2 68 2013 Summer 17:00 29 0 0 0 2 54 2013 Winter 1 07:00 11 0 0 0 2 5 2013 Winter 13:00 26 0 4 0 0 27 2013 Winter 17:00 28 1 4 2 2 47 2013 Winter 2 07:00 11 1 0 4 2 17 2013 Winter 13:00 24 0 0 0 2 21 2013 Winter 17:00 26 0 0 9 0 62 2013 Winter 3 07:00 8 2 3 2 2 29 2013 Winter 13:00 25 2 2 2 2 66 2013 Winter 17:00 25 0 0 3 2 111 2013 Winter 4 07:00 9 0 2 9 4 25 2013 Winter 13:00 25 0 0 7 0 19 2013 Winter 17:00 22 0 3 3 0 57 2013 Winter 5 07:00 7 0 6 0 5 154 2013 Winter 13:00 26 0 0 0 0 55 2013 Winter 17:00 21 0 2 2 0 121 2013 Winter 6 07:00 9 0 0 2 2 37 2013 Winter 13:00 24 0 0 0 0 55 2013 Winter 17:00 24 0 0 0 2 57 2013 Winter 7 07:00 12 0 0 0 3 74 2013 Winter 13:00 19 0 2 2 2 40 2013 Winter 17:00 21 2 0 2 2 63 2013 Winter 8 07:00 10 0 0 0 2 17 2013 Winter 13:00 19 0 2 0 2 19 2013 Winter 17:00 21 0 0 22 2 51 2013 Winter 9 07:00 9 2 0 0 2 43 2013 Winter 13:00 21 0 0 2 2 55 2013 Winter 17:00 22 0 0 0 0 55 2013 Winter 10 07:00 7 0 0 3 2 47

208

2013 Winter 13:00 17 0 0 4 2 82 2013 Winter 17:00 19 0 0 6 2 86 2013 Winter 11 07:00 6 0 0 4 0 21 2013 Winter 13:00 17 0 0 2 0 58 2013 Winter 17:00 21 0 0 4 2 83 2013 Winter 12 07:00 6 2 0 0 0 58 2013 Winter 13:00 18 0 0 5 2 64 2013 Winter 17:00 19 0 2 2 2 72 2013 Winter 13 07:00 6 0 0 6 0 61 2013 Winter 13:00 24 0 0 0 1 24 2013 Winter 17:00 24 0 0 5 2 75 2013 Winter 14 07:00 7 0 0 0 5 27 2013 Winter 13:00 23 0 0 4 2 29 2013 Winter 17:00 23 2 2 2 4 76

*unknown: the Laughing Doves were only included in the study in 2013

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