EFFECTS OF ENVIRONMENT AND GOVERNANCE ON FINANCIAL SUSTAINABILITY 
OF COMMUNAL CONSERVANCIES IN NAMIBIA 

By 

Joseph D. Goergen 

A THESIS 

Submitted to  
Michigan State University 
in partial fulfillment of the requirements 
for the degree of  

Fisheries and Wildlife – Master of Science 

2024 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ABSTRACT 

Ecosystem services from tourism and governance of communal institutions are critical to 

financial sustainability of community-based natural resource management. I evaluated effects of 

large mammal occurrence and landscape attributes on incomes from hunting and photographic 

tourism earned by communal conservancies in Namibia during 1998–2022. I also evaluated 

effects of local management and governance on total income earned by Namibia’s conservancies 

during 2011–2022. I compiled annual incomes and occurrence of ‘Big 5’ species (elephant 

[Loxodonta africana], buffalo [Syncerus caffer], black rhino [Diceros bicornis], lion [Panthera 

leo], and leopard [P. pardus]) using conservancy accounting and wildlife monitoring data. I 

compiled annual conservancy performance scores for natural resource management and 

institutional governance using ‘event book’ monitoring data. Hunting occurred in 70 of 86 

conservancies and generated income almost twice as rapidly as photographic tourism. Hunting 

income increased with conservancy area and number of Big 5 species present but decreased with 

years since establishment and increasing mean elevation, topographic diversity, and distances to 

national parks. Photographic tourism occurred in 39 conservancies and generated 447% greater 

median annual income than hunting for conservancies earning >$0. Photographic income 

increased with years since establishment and higher annual precipitation but decreased with 

higher mean elevation. Large mammals are an important driver of income to Namibia’s 

conservancies and hunting and photographic tourism can provide complementary benefits. 

Conservancies earning >$0 income generated a median annual of $60,518 since 2011. Income 

during 2011–2022 increased with years since establishment, higher management performance, 

presence of nongovernmental organization (NGO) support within conservancies, and annual 

general meeting (AGM) occurrence. Income during 2019–2022 also increased with higher 

governance performance. Median management and governance performances across 

conservancies were only about 50% of their maximum scores, indicating higher income potential 

with improved performance. Conservancies remained financially dependent on NGO support and 

AGMs were important functions for generating income. I recommend Namibia’s conservancies, 

particularly those established more recently and with smaller area or without NGO presence, 

consider inter-conservancy wildlife co-management, collaborating with tourism industries, and 

prioritizing improved local management and governance to develop more sustainable 

community-based natural resource economies. 

 
 
 
This thesis is dedicated to my Mother, the foundation of our family. 

  iii 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ACKNOWLEDGEMENTS 

Thank you to my advisor, Dr. Jerry Belant, for supporting me through this thesis and 

graduate program. I also thank my committee members Drs. Malan Lindeque, Ken Kellner, and 

Gary Roloff for their valuable assistance and manuscript review, and co-author Maxi Louis for 

encouraging me to pursue this research.  

I acknowledge Namibia’s communal conservancies as owners of the accounting, wildlife 

monitoring, and event book data used. Thank you to the Namibian Association of Community-

Based Natural Resource Management Support Organizations and World Wildlife Fund Namibia 

for providing data access including R. Diggle, I. Katjingisiua, K. Dierkes, H. Angula, and R. 

IIleka. Funding was provided by the Boone and Crockett Program in Wildlife Conservation at 

Michigan State University and the Michigan Involvement Committee of Safari Club 

International, Flint Regional Chapter. 

  iv 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
TABLE OF CONTENTS 

INTRODUCTION .......................................................................................................................... 1 

CHAPTER 1: DRIVERS OF HUNTING AND PHOTOGRAPHIC TOURISM INCOME TO 
COMMUNAL CONSERVANCIES IN NAMIBIA ....................................................................... 3 

CHAPTER 2: LOCAL MANAGEMENT AND GOVERNANCE IMPROVE NATURAL 
RESOURCE INCOMES OF COMMUNAL CONSERVANCIES IN NAMIBIA ...................... 19 

REFERENCES ............................................................................................................................. 34 

APPENDIX A: CHAPTER 1 RESIDUAL DIAGNOSTIC PLOTS ............................................ 49 

APPENDIX B: CHAPTER 1 REGRESSION COEFFICIENT TABLES ................................... 51 

APPENDIX C: MANAGEMENT PERFORMANCE INDICATORS ........................................ 52 

APPENDIX D: CHAPTER 2 RESIDUAL DIAGNOSTIC PLOTS ............................................ 54 

APPENDIX E: CHAPTER 2 REGRESSION COEFFICIENT TABLES .................................... 56 

APPENDIX F: DEPENDENCE TESTING FOR INTER-CHAPTER MODEL COVARIATE 
EFFECTS ...................................................................................................................................... 57 

  v 

 
 
 
 
 
 
 
 
 
 
INTRODUCTION 

Integrating socioeconomic dynamics and indigenous peoples’ rights in conservation are 

central to meeting global biodiversity protection goals (e.g., Global Biodiversity Framework; 

Sandbrook et al., 2023). Human dimensions of conservation are especially important in Africa 

where land use policies can cause conflicts among stakeholders (Musavengane & Leonard, 

2022). Agropastoralists coexist with wildlife and depend on ecosystem services, creating 

complex relationships between natural resource management and development (Roe et al., 2013). 

As protected areas fail to support biodiversity (Craigie et al., 2010), effective governance of 

communal lands is needed for conservation (Garnett et al., 2018). Studying rural wildlife-based 

economies therefore includes aspects of biodiversity protection, sustainable development, and 

environmental justice (Snyman et al., 2021).   

A new community-oriented conservation paradigm emerged in southern Africa following 

late 20th century postcolonial reform (Hulme & Murphree, 1999). The concept of community-

based natural resource management (CBNRM) was influenced by common pool resource 

governance (Ostrom, 1990), rights-based decentralization of authority (Campese et al., 2009), 

and neoliberal commercialization of wildlife (Igoe & Brockington, 2007). Colonial policies 

during the 1960s–1970s allowed wildlife ranching on private lands (Barnes & Jager, 1996), 

which was more economically viable than livestock in dry marginal habitats (Child et al., 2012). 

Conditions on communal lands, however, necessitated further decentralization of user rights to 

produce conservation incentives (Child & Barnes, 2010).  

Southern Africa’s progressive policies in the 1980s–1990s developed into national 

CBNRM programs led by Zimbabwe’s Communal Areas Management Program for Indigenous 

Resources Association (Taylor, 2009), community trusts in the Chobe Enclave and northern 

Botswana (Jones, 2002), Administrative Management Design for game management areas in 

Zambia (Marks, 2001), and communal conservancies in Namibia (Jacobsohn & Owen-Smith, 

2003). These programs were designed to remove colonial-era discrimination in land use policy 

by a democratic devolution of rights to local communities, linking wildlife management with 

development opportunities, and providing conservation incentives through socioeconomic 

benefits (Murphree, 2009). Post-reform CBNRM programs increased wildlife abundance on 

communal lands and rapidly generated income (Suich et al., 2009).  

  1 

 
In Namibia, establishment of conservancies in the late 1990s facilitated wildlife recovery 

and species reintroductions (Weaver & Skyer, 2003). Conservancies contributed to 

reclassification of the southwestern black rhino subspecies (Diceros bicornis occidentialis) from 

vulnerable to near threatened by the International Union for Conservation of Nature (Emslie, 

2020). Namibia’s elephant (Loxodonta africana) population increased from about 7,600 to 

23,600 during 1995–2016 (Craig et al., 2021) and geographical ranges of lions (Panthera leo) 

and other large carnivores increased (Stander, 2019). Socioeconomic impact from income 

generation is mixed (Riehl et al., 2015) as conservancies alleviate local poverty (Roe et al., 2013) 

but benefits can be distributed inequitably (Silva & Mosimane, 2013) or insufficient to 

compensate costs of human-wildlife conflicts (Kansky, 2022). Inclusive social organization can 

motivate community participation without income, but non-financial benefits are difficult to 

measure (Silva & Mosimane, 2014). 

A utilitarian approach to conservation assumes that benefits must exceed opportunity 

costs of traditional uses or livestock alternatives, and that CBNRM program success is related to 

the quality of natural resources and governance systems (Murphree, 1993). Despite positive 

social and ecological outcomes from CBNRM (Galvin et al., 2018), there remains a need to 

evaluate performance as programs evolve (Murphree, 2004). An improved understanding of how 

the environment interacts with income generation (Cox, 2010) and for CBNRM programs to 

adopt best governance practices can further conservation (Esmail et al., 2023). Whereas most 

CBNRM research is qualitative and lacks criteria to assess implementation, income is a direct 

indicator of financial sustainability (Murphree, 1993). I identified environmental and governance 

factors influencing income generated by conservancies in Namibia. I evaluated effects of large 

mammal occurrence and landscape attributes on incomes earned by hunting and photographic 

tourism during 1998–2022 in Chapter 1. I then evaluated effects of local management and 

governance performances on total income earned during 2011–2022 in Chapter 2.  

  2 

 
 
 
 
 
 
CHAPTER 1: DRIVERS OF HUNTING AND PHOTOGRAPHIC TOURISM 

INCOME TO COMMUNAL CONSERVANCIES IN NAMIBIA  

1.1 ABSTRACT  

Hunting and photographic tourism provide ecosystem services that can facilitate 

conservation. Understanding factors influencing how tourism industries generate income is 

necessary to ensure sustainable community-based natural resource management. I evaluated 

effects of large mammal occurrence and landscape attributes on incomes from hunting and 

photographic tourism earned by communal conservancies in Namibia during 1998–2022. I 

compiled annual incomes and occurrence of ‘Big 5’ species (elephant [Loxodonta africana], 

buffalo [Syncerus caffer], black rhino [Diceros bicornis], lion [Panthera leo], and leopard [P. 

pardus]) using conservancy accounting and wildlife monitoring data. Hunting occurred in 70 of 

86 conservancies and generated income almost twice as rapidly as photographic tourism (2.9 and 

5.4 years after conservancy establishment, respectively). Hunting income increased with 

conservancy area and number of Big 5 species present but decreased with conservancy age and 

increasing mean elevation, topographic diversity, and distances to national parks. Photographic 

tourism occurred in 39 conservancies and generated 447% greater median annual income than 

hunting for conservancies earning >$0. Big 5 species occurrence increased the probability 

conservancies earned >$0 photographic income but not the amount of photographic income. 

Photographic income increased with conservancy age and higher annual precipitation but 

decreased with higher mean elevation. Large mammals are an important driver of income to 

Namibia’s conservancies and hunting and photographic tourism can provide complementary 

benefits. I recommend Namibia’s conservancies, particularly those established more recently 

with smaller area, consider inter-conservancy wildlife co-management and collaboration with 

tourism industries to improve income potential and develop more sustainable community-based 

natural resource economies.  

1.2 INTRODUCTION  

A utilitarian approach to community-based conservation enables rural people to benefit 

from wildlife management through payment for ecosystem services (Naidoo et al., 2011a). 

Sustainable use of wildlife can accelerate development of community-based natural resource 

management (CBNRM) programs in southern Africa (Frost & Bond, 2008; Weaver et al., 2011). 

  3 

 
Tourist hunting provides localized socioeconomic benefits (Jones, 2009) and creates incentives 

for habitat conservation outside protected areas where there are limited alternative land uses for 

wildlife (Lindsey et al., 2006, 2007). Nature-based photographic tourism can also provide 

income to wildlife economies in some areas (WTO, 2014) with potential to support CBNRM’s 

social and environmental objectives when local people are engaged (Mbaiwa & Kolawole, 

2013).   

Communities that rely on income from hunting and photographic tourism are vulnerable 

to wildlife trade policies and tourist market instability (Nattrass, 2021a). Importation bans on 

hunted wildlife are increasingly proposed and adopted in Euro-American jurisdictions (Lindsey 

et al., 2016). These trade restrictions are often justified morally (Horowitz, 2019) or as solutions 

to biodiversity loss and sustainability issues, but could adversely affect conservation funding (Di 

Minin et al., 2016; Dickman et al., 2019) and livelihoods of local communities (Mbaiwa, 2018). 

Novel zoonotic disease outbreaks also have increased calls to ban wildlife trade (Roe et al., 

2020) and contributed to international travel restrictions which present further challenges to 

tourism that financially jeopardize communities (Hambira et al., 2021; Hulke et al., 2022).  

Tourism benefits were volatile during the coronavirus disease 2019 (COVID-19) 

pandemic, emphasizing a need for improved information on CBNRM economic resiliency and 

income diversification strategies to reduce financial uncertainties (Lendelvo et al., 2020; Lindsey 

et al., 2020). Understanding factors that influence income from ecosystem services and 

implementation of tourism industries are necessary to ensure sustainability of CBNRM (Di 

Minin et al., 2021). Despite the importance of hunting and photographic tourism to conservation, 

few studies have investigated environmental drivers of income or compared relative economic 

performance across large spatial and temporal extents (Suich, 2010; Naidoo et al., 2016).  

In Namibia, communal conservancies function as local wildlife governance institutions in 

a global resource-tourism network (Kalvelage et al., 2020) through leasing tourist concessions 

and forming joint venture partnerships with operators (Jones et al., 2015). Financial benefits 

from CBNRM generally increase with program age (Bandyopadhyay et al., 2009; Brooks, 2017), 

as older conservancies were often established in higher quality wildlife areas and had more time 

to build capacity, attract business partners, and develop tourist infrastructure (Humavindu & 

Stage, 2014). Larger conservancies could also benefit from economies of scale employing 

hunting and photographic tourism operations.  

  4 

 
Large mammals, particularly high-value ‘Big 5’ species (elephant [Loxodonta africana], 

buffalo [Syncerus caffer], black rhino [Diceros bicornis], lion [Panthera leo], and leopard [P. 

pardus]; Di Minin et al., 2013), are an important income source for conservancies (Naidoo et al., 

2011b), especially elephant and buffalo that yield high operator fees and hunting quotas on 

communal lands (Bond, 1994; Arntzen et al., 2003; Naidoo et al., 2016). Hunting fees are critical 

for funding conservancies’ operational expenses (Naidoo et al., 2016) and efficient use of quotas 

optimizes wildlife-based income (Bollig, 2016). However, environmental factors like drought 

can reduce wildlife abundance and subsequent quota allocations (NACSO, 2021a). Black rhino 

and lion presence can increase photographic tourism (Muntifering et al., 2020, 2023a) and 

conservancies have recently received concessions within Namibia’s national parks (MET, 2013), 

proximity to which could also increase wildlife abundance in nearby conservancies. Additional 

environmental attributes including topographic diversity and proximity to tourism infrastructure 

(Nattrass, 2021b; Kalvelage et al., 2021) can improve scenic value and tourist accessibility, 

respectively (Naidoo et al., 2011b).  

I evaluated how incomes from hunting and photographic tourism earned by 

conservancies in Namibia during 1998–2022 were affected by large mammal occurrence and 

landscape attributes. I predicted both income sources would increase with years since 

conservancy establishment and be greater for larger conservancies with more Big 5 species 

present. I predicted that the number of Big 5 species present would have a stronger positive 

correlation with hunting income than with photographic income, while photographic income 

would have a stronger positive correlation with lower and more topographically diverse elevation 

and proximity to national parks and major roads. Finally, although precipitation can positively 

affect wildlife occurrence, I predicted that the mean amount of annual precipitation would not be 

correlated with either income source due to potential resilience of wildlife to drought years and 

competition with wildlife-based income from alternative land uses (e.g., agriculture, livestock) in 

conservancies receiving greater precipitation.   

1.3 STUDY AREA 

Namibia is a large (824,000 km2) arid to semiarid country in southern Africa with desert 

to mixed savanna, shrubland, and woodland vegetation (Atlas of Namibia Team, 2022). Annual 

precipitation ranges from less than 50–650 mm and elevations are 0–2573 m above sea level 

(Atlas of Namibia Team, 2022). Namibia contains the world’s largest free-ranging black rhino 

  5 

 
population (Muntifering et al., 2023b) and increasing elephant (Craig et al., 2021) and lion 

populations (Stander, 2019). Protected areas, communally managed wildlife areas, and private 

freehold lands used for wildlife ranching (Lindsey et al., 2013) combined represent about 46% of 

Namibia’s land area (NACSO, 2021b). Namibia is sparsely populated with 47% of its 2.5 million 

people living in rural areas with high poverty and unemployment (NSA, 2021; WBG, 2023).  

Economic potential for communal areas with high wildlife abundance was recognized in 

the 1990s (Ashley & Barnes, 1997) after commercialization of wildlife ranching on private lands 

(Republic of Namibia, 1975). Following Namibia’s independence in 1990, progressive land use 

policies (e.g., MWCT, 1992; MET, 1995) led to the Nature Conservation Amendment Act 

(Republic of Namibia, 1996) which authorized communities to register customary landholdings 

as conservancies in 1998. Subsequent legislation enabled conservancy ownership and 

management of tourism enterprises (Republic of Namibia, 2002; MET, 2007). Conservancies are 

given conditional property rights and ownership over huntable wildlife and occur primarily in 

northwestern and northeastern Namibia (Jones, 1999). The conservancy program is managed by 

the Namibian Association of Community-Based Natural Resource Management Support 

Organizations (NACSO) and administered by the Ministry of Environment, Forestry and 

Tourism. The 86 registered conservancies represent about 20% (166,179 km2) of Namibia’s land 

area and support more than 230,000 people (Figure 1.1; NACSO, 2022a).  

1.4 METHODS 

I compiled data from NACSO including accounting records collected by conservancies 

during 1998–2022, years since conservancy establishment, and conservancy area. I used 

conservancy-led foot patrols, game counts, and wildlife monitoring data from NACSO to index 

annual occurrence of Big 5 species in each conservancy. I used a digital elevation model (30-m 

resolution) from the Shuttle Radar Topography Mission (RCMRD, 2017), national park 

boundaries and trunk roads (major roads for long distance travel) from the Environmental 

Information Service Namibia eLibrary (EISN, 2020; RAN, 2018), and annual precipitation data 

(5-km resolution) during 1998–2022 from the United States Geological Survey (Funk et al., 

2015). I used conservancy boundaries from NACSO to estimate mean elevation, elevation 

standard deviation (SD), Euclidean distances to nearest national park border and trunk road, and 

annual precipitation for each conservancy in ArcGIS Pro (ESRI, 2022). I spatially resampled 

elevation data to the resolution of annual precipitation data.  

  6 

 
I estimated annual hunting income for each conservancy by summing concession and 

hunting fee payments to respective management committees, salaries earned by conservancy 

members, direct household payments, and in-kind benefits from hunting operators (i.e., non-

financial benefits [e.g., game meat, development projects, donations, training, meals]) included 

in NACSO’s accounting records. I estimated annual photographic income for each conservancy 

by summing concession fees, salaries earned by conservancy members, direct household 

payments, and in-kind benefits from photographic operators (excluding game meat). I used 

values of game meat from hunting operators (excluding conservancy harvest) calculated by 

NACSO using replacement-cost shadow prices applied nationally each year (Naidoo et al., 

2016), which was 27 Namibian dollars (NAD)/kg in 2022 (NACSO, 2023). I standardized all 

income values to 2022 United States dollars (USD) using the geometrically averaged annual 

NAD to USD exchange rate (Bank of Namibia, 2023) and USD consumer price index during 

1998–2022 (USBLS, 2023). I report median income values due to skewed conservancy income 

earned from both tourism sources.  

I used generalized linear mixed models to evaluate how incomes from hunting and 

photographic tourism earned by conservancies in Namibia during 1998–2022 were affected by 

large mammal occurrence and landscape attributes. Distributions for both income sources were 

zero-inflated and skewed. Therefore, I ran two regression analyses for each income source. I 

used logistic regression to model a binary response (0 for conservancies earning no income, 1 for 

conservancies earning >$0 income; Naidoo et al., 2011b) and linear regression to model log-

transformed incomes >$0.  

I tested for dependence between hunting and photographic incomes using Pearson’s 

product-moment correlation r and separate models that included the annual income from the 

other tourism source as a covariate. I identified a weak positive correlation (r = 0.08) between 

income sources, suggesting limited association. Hunting and photographic incomes were not 

correlated (95% confidence intervals overlapped 0) when included as covariates in models 

testing for dependence. I calculated pairwise correlations r between continuous covariates and 

retained only the most relevant in my analyses when |r| > 0.70 (Dormann et al., 2013). 

All final models included fixed-effect covariates for years since conservancy 

establishment, conservancy area (km2), annual number of Big 5 species present, mean elevation 

(m), elevation SD (m), distance to nearest national park (km), distance to nearest major road 

  7 

 
(km), and annual precipitation (mm) (Table 1.1). I included random intercepts by conservancy to 

account for repeated annual measurements of the same conservancies across years in all models. 

I centered and scaled continuous covariates using a standardized z-score normalization (mean = 

0, SD = 1). I tested for statistical significance of regression coefficients using α < 0.05. I 

visualized regression coefficient estimates and uncertainty using dot-whisker plots with 95% 

confidence intervals (CI). I performed all analysis in R version 4.2.2 (R Core Team, 2023) using 

the glmmTMB package for generalized linear mixed models (Brooks et al., 2017) and the 

DHARMa package for residual diagnostics (Hartig, 2022).  

1.5 RESULTS 

Median number of years from conservancy establishment through 2022 was 8, median 

conservancy area was 1167 km2, and median annual number of Big 5 species present was 1 

across conservancies from 1998–2022 (Table 1.1). Leopard had the highest annual mean 

proportional occurrence among Big 5 species recorded by conservancies (𝑝 = 0.64), followed by 

elephant (𝑝 = 0.37), lion (𝑝 = 0.35), buffalo (𝑝 = 0.12), and black rhino (𝑝 = 0.09). Median mean 

elevation across conservancies was 1086 m (Table 1.1). 

Median hunting income was $26,863 for conservancies earning >$0 annual income 

($491,768 maximum, $45,964,012 total; Table 1.2). Hunting occurred in 70 conservancies 

(141,365 km2, 85% of total conservancy area; Figure 1.2) and was the dominant (>70% of 

combined income from both tourism sources) income source in 43 conservancies (78,432 km2, 

47% of total conservancy area; Table 1.2). Median photographic income was $120,081 for 

conservancies earning >$0 annual income ($1,135,747 maximum, $79,203,315 total; Table 1.2). 

Photographic tourism occurred in 39 conservancies (81,797 km2, 49% of total conservancy area; 

Figure 1.2) and was the dominant (>70% of combined income from both tourism sources) 

income source in 16 conservancies (49,161 km2, 30% of total conservancy area; Table 1.2). 

Neither tourism source represented more than 70% of combined income in 12 conservancies, and 

15 conservancies earned no income from either source. Conservancies that earned no income 

from either tourism source had a median area of 1028 km2 (12% less than the median area of all 

conservancies), 6 of which occurred in the northwest Kunene Region. On average, conservancies 

started earning income from hunting and photographic tourism 2.9 and 5.4 years after 

establishment, respectively (Table 1.2).  

  8 

 
Dispersion of observed binary and nonzero quantile-quantile income values deviated 

from expected, except for the probability of earning >$0 hunting income (Appendix A). 

Nonparametric dispersion of the probability of earning >$0 hunting income differed compared to 

simulated distributions (P = 0.032, Appendix A), but other income types did not. Outlier test 

plots suggested no model fit issues for income types from either tourism source.  

Years since conservancy establishment positively affected the probability conservancies 

earned >$0 hunting income (ß = 0.66, CI = 0.46–0.86, Figure 1.3a, Appendix B) but conservancy 

age decreased the amount of hunting income (ß = -0.18, CI = -0.26–-0.10, Figure 1.3b). 

Increasing median years since conservancy establishment by 5 increased the probability 

conservancies earned >$0 hunting income from 46% to 60% but decreased the amount of 

hunting income by $1498 with all other covariates held at median values. The amount of hunting 

income increased with conservancy area (ß = 0.37, CI = 0.02–0.72, Figure 1.3b) and number of 

Big 5 species present (ß = 0.28, CI = 0.17–0.38), which also positively affected the probability 

conservancies earned >$0 hunting income (ß = 0.88, CI = 0.60–1.16, Figure 1.3a). Hunting 

income increased by $3332 when median conservancy area increased by 2000 km2 with all other 

covariates held at median values. Increasing median number of Big 5 species present by 1 and 2 

increased the probability conservancies earned >$0 hunting income from 46% to 62% and 77%, 

respectively, and increased the amount of hunting income by $2399 and $5387, respectively, 

with all other covariates held at median values. The probability conservancies earned >$0 

hunting income and amount of hunting income decreased with increasing mean elevation (ß = -

0.33, -0.63–-0.02, Figure 1.3a; ß = -0.53, CI = -1.03–-0.03, Figure 1.3b; respectively). The 

amount of hunting income also decreased with greater topographic diversity (ß = -0.89, CI = -

1.37–-0.41, Figure 1.3b) and distances to nearest national park (ß = -0.43, CI = -0.71–-0.15).  

Years since conservancy establishment increased the probability conservancies earned 

>$0 photographic income and amount of photographic income (ß = 1.19, CI = 0.92–1.46, Figure 

1.3c; ß = 0.38, CI = 0.27–0.48, Figure 1.3d; respectively, Appendix B). Photographic income 

increased by $5627 when median years since conservancy establishment increased by 5 with all 

other covariates held at median values. Big 5 species occurrence positively affected the 

probability conservancies earned >$0 photographic income (ß = 0.60, CI = 0.28–0.93, Figure 

1.3c) but was not correlated with the amount of photographic income. Increasing median number 

of Big 5 species present by 1 and 2 increased the probability conservancies earned >$0 

  9 

 
 
photographic income from 2% to 3% and 4%, respectively, with all other covariates held at 

median values. The probability conservancies earned >$0 photographic income and amount of 

photographic income decreased with increasing mean elevation (ß = -2.52, CI = -3.54–-1.51, 

Figure 1.3c; ß = -0.83, CI = -1.24–-0.43, Figure 1.3d; respectively). The amount of photographic 

income also increased with greater annual precipitation (ß = 0.25, CI = 0.03–0.48, Figure 1.3d).  

1.6 DISCUSSION 

My predictions on how large mammal occurrence and landscape attributes affected 

incomes from hunting and photographic tourism earned by conservancies in Namibia during 

1998–2022 were partially supported. Conservancy age increased the probability conservancies 

generated >$0 income from both tourism sources. Conservancy age also had a positive effect on 

the amount of photographic income, but a negative effect on the amount of hunting income. 

Larger conservancies generated greater amounts of hunting income, but conservancy area was 

not correlated with photographic income. Annual occurrence of more Big 5 species increased the 

probability conservancies generated >$0 income from both tourism sources and the amount of 

hunting income. That lower mean elevation would be more strongly correlated with 

photographic income than hunting income was supported, but greater topographic diversity and 

proximity to national parks were not correlated with photographic income.  

The amount of hunting income decreased with conservancy age, which could reflect 

drought-related quota reductions (NACSO, 2021a), a limited hunting market, or market declines 

caused by trophy importation issues (Nyamayedenga et al., 2021). That the amount of hunting 

income increased with larger conservancy area indicates that conservancy size is important for 

consumptive land use planning. On average, larger conservancies could have more Big 5 species 

present and potential harvest of other species (e.g., Hartmann’s mountain zebra [Equus zebra 

hartmannae], greater kudu [Tragelaphus strepsiceros]; Naidoo et al., 2016). Inter-conservancy 

co-management of wildlife could increase income, particularly for smaller conservancies with 

less tourism opportunity. For example, the amount of hunting income increased by an equivalent 

12% (i.e., $3332) of median hunting income for conservancies earning >$0 annual income when 

median conservancy area increased by 2000 km2 (i.e., about the median size of 2 conservancies 

earning no income from either tourism source, 6 of which occurred in northwest Kunene 

Region).  

  10 

 
The probability conservancies earned >$0 hunting income and amount of hunting income 

were positively correlated with Big 5 species occurrence, which likely reflected high operator 

fees paid to conservancies from elephant and buffalo (Naidoo et al., 2016). Greater topographic 

diversity likely decreased hunter accessibility. The amount of hunting income decreased with 

greater distances to national parks likely because greater wildlife abundance in national parks 

increases wildlife abundance in nearby conservancies, supporting the idea that conservancies 

serve as conservation buffers around protected areas (Meyer et al., 2021).  

The probability conservancies earned >$0 photographic income and amount of 

photographic income increased with conservancy age, which likely reflected development of 

tourist infrastructure over time and subsequent increases in salaries of conservancy members 

employed by operators (NACSO, 2021a). The amount of photographic income was not 

correlated with Big 5 species occurrence despite the importance of black rhino and lion for 

tourism (Muntifering et al., 2023a; NACSO, 2023). Photographic tourism could offer income 

alternatives for Namibia’s conservancies without Big 5 species present despite international 

tourists’ interest in primarily viewing large mammals (Di Minin et al., 2013).  

Conservancies with lower mean elevation earned more income, particularly from 

photographic tourism, which could reflect that conservancies established earlier occurred in 

lower elevation areas with higher tourist accessibility or habitat quality for Big 5 species. Greater 

topographic diversity was not correlated with photographic income despite possible improved 

scenic value and mountainous terrain on popular tourism circuits (Naidoo et al., 2011b). The 

spatial resolution of elevation SD also could have been too coarse to reflect local variation in 

conservancy elevation. That photographic income was not correlated with proximity to national 

parks (e.g., Etosha National Park) suggests that any recently awarded concessions are not yet 

operational. For example, 23 conservancies shared 19 concessions in national parks in 2020 

(NACSO, 2021a), but photographic tourism started generating income after 5 years on average. I 

was unable to assess income earned by these specific concessions, but they could be expected to 

generate photographic income soon. Conservancies adjacent to national parks earned less income 

from photographic tourism than the median income of conservancies generating income from 

this source.  

Large mammals remain an important driver of ecosystem services to Namibia’s 

conservancies (Naidoo et al., 2011b), although community benefits can be reduced by associated 

  11 

 
human-wildlife conflicts (Carpenter, 2022; Tavolaro et al., 2022). My data for recording annual 

Big 5 species occurrence provides an index of large mammal occurrence including other species 

that could increase tourism (e.g., giraffe [Giraffa camelopardalsi], hippopotamus [Hippopotamus 

amphibius]). Namibia’s conservancy-led monitoring system could be used to encourage 

additional conservation investment by further demonstrating biodiversity occurrence to the 

private sector and donors (Stuart-Hill et al., 2005). This monitoring system is also used to 

establish quotas and manage harvests with the Ministry of Environment, Forestry and Tourism. 

Conservancies used only 37% of their total quota for huntable Big 5 species (excluding black 

rhino) during 2006–2022 (NACSO, unpublished data). More efficient quota use could increase 

hunting income, assuming quotas are sustainable and there is adequate hunting demand.  

Hunting and photographic tourism in Namibia appear to be complementary ecosystem 

services (Naidoo et al., 2016). While income from photographic tourism could equal or exceed 

income from hunting (e.g., 447% greater median annual income for conservancies earning >$0, 

172% greater total income across conservancies during 1998–2022), income from hunting 

occurred in more conservancies and generated income more rapidly, suggesting that hunting 

cannot be easily replaced. Previous analysis simulating a tourist hunting ban revealed negative 

impacts to conservancy operational budgets (Naidoo et al., 2016; NACSO, 2021a). While there 

is unlikely a risk of a tourist hunting ban considering that Namibia’s domestic policy supports 

sustainable use of wildlife (Republic of Namibia, 1990; cf., Botswana during 2014–2019; 

Mbaiwa, 2018), importation restrictions on hunted wildlife in consumer countries can reduce 

community benefits from lower hunter visitation, spending, and quota use (Nyamayedenga et al., 

2021). For example, the United States Fish & Wildlife Service’s elephant import permitting has 

been subject to litigation for economic harm to Namibia’s conservancies (DSC vs. Bernhardt, 

2021). Namibia’s conservancies are especially susceptible to wildlife importation policy in the 

United States given reliance on American hunters targeting high-value species (e.g., elephant; 

MacLaren et al., 2019). Importation restrictions on hunted wildlife could threaten livelihoods and 

habitat conserved by Namibia’s conservancies (MacLaren et al., 2019), along with similar 

CBNRM programs in southern Africa (e.g., Communal Areas Management Program for 

Indigenous Resources Association in Zimbabwe; MECTHI, 2023), unless alternatives to hunting 

are available (White & Belant, 2015). 

  12 

 
Community benefits from tourist hunting also depend on effective international 

regulation of wildlife trade (e.g., Convention on International Trade in Endangered Species of 

Wild Fauna and Flora; Abensperg-Traun, 2009; Carpenter, 2011; Cooney et al., 2021). Hunting 

could be more resilient than photographic tourism to global market dynamics (e.g., COVID-19 

pandemic; MEFT, 2021; 2008 Great Recession; Naidoo et al., 2016) and has widespread support 

from Namibia’s conservancies (Angula et al., 2018), which have more positive attitudes toward 

wildlife when benefiting from hunting (Störmer et al., 2019). Wildlife trade policies that consider 

potential impacts to indigenous people and local communities could improve benefits from 

tourist hunting (Houdt et al., 2021; Clark et al., 2023; Challender et al., 2023). 

Income varied markedly across conservancies in Namibia and is dominated by hunting 

and photographic tourism (estimated 97% of total income, excluding grants, across 

conservancies in 2022; NACSO, unpublished data), compared to other activities available (e.g., 

plant harvesting [e.g., Devil’s claw {Harpophytum spp.}; Lavelle, 2023], craft sales). 

Conservancies’ reliance on hunting and photographic tourism suggest diversification strategies 

could improve income potential by including development of additional tourist markets 

(MacLaren et al, 2019; MEFT, 2021), mixed agriculture production (e.g., small-scale 

horticulture; Hulke et al., 2021), and carbon or biodiversity credits (Smith et al., 2022), which 

are being developed in some of Namibia’s conservancies (NACSO, 2023). Namibia had 46 

community forests and 20 fishery reserves shared by 7 conservancies in 2022 (NACSO, 2023), 

which can generate income from sale of natural plant products (e.g., timber, Devil’s claw) and 

fishing tourism. Wildlife-based income on communal lands could also be improved through 

higher tourist willingness to pay for conservation and community benefits (Fischer et al., 2015; 

Naidoo et al., 2021), direct community participation in the private sector (e.g., negotiating quota 

price; Rigava et al., 2006; marketing; Child & Weaver, 2006; shared business ownership models; 

Hoole, 2009), national investment in sustainable tourism (MET, 2016), and strengthening 

governance (Child & Barnes, 2010; Ullah & Kim, 2020).  

Financial sustainability is critical to building resilient community-based natural resource 

economies. My results support the development of emerging conservancies in Namibia’s 

northeast Zambezi Region that have high tourism potential from Big 5 species presence, low 

elevations, and high annual precipitation (NACSO, 2021a). I recommend that Namibia’s 

conservancies, particularly those established more recently, with smaller area, and adjacent to 

  13 

 
other conservancies (e.g., northwest Kunene Region), consider implementing inter-conservancy 

wildlife co-management or shared land use zonation plans and collaborate with tourism 

industries to improve income potential from hunting and photographic tourism. 

  14 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
TABLES & FIGURES 

Table 1.1: Variables potentially affecting hunting and photographic tourism income earned by 

communal conservancies in Namibia, 1998–2022. 

Variable 
Years since establishment 
Area (km2) 
Annual Big 5 species occurrence 
Mean elevation (m)  
Elevation SD (m) 
Distance to nearest national park (km) 
Distance to nearest major road (km) 
Annual precipitation (mm) 

Median 
8 
1167 
1 

Range 

0–24 
43–9122 
0–5 
1086  600–1624 
0–327 
0–230 
0–320 
31–1037 

70 
38 
83 
241 

Table 1.2: Summary of annual income estimates (2022 United States dollars) from hunting and 

photographic tourism earned by communal conservancies in Namibia, 1998–2022.  

Statistic 
Mean 
Median income >$0 
Maximum  
Total 
Conservancies earning income >$0 
Area (km2) earning income >$0 
Percent total area earning income >$0 
Conservancies by dominant income source (>70% 
of combined income from both tourism sources) 
Area (km2) by dominant income source  
Percent total area by dominant income source 
Average years from conservancy establishment to 
initial income 

Hunting 

$32,622 
$26,863 
$491,768 
$45,964,012 
70 
141,365 
85 
43 

Photographic Tourism 
$56,212 
$120,081 
$1,135,747 
$79,203,315 
39 
81,797 
49 
16 

78,432 
47 
2.9 

49,161 
30 
5.4 

  15 

 
 
 
 
 
 
 
 
Figure 1.1: Communal conservancies (yellow polygons) in Namibia, 2022 (NACSO).

  16 

Earthstar Geographics0100200km´Earthstar Geographics 
 
Figure 1.2: Annual mean income from hunting, photographic tourism, and combined income from both sources earned by communal 

conservancies in Namibia, 1998–2022.

  17 

 
 
 
Figure 1.3: Standardized regression coefficient estimates with 95% confidence intervals (i.e., whiskers) for generalized linear mixed 

models fit to the (a) probability of earning >$0 hunting income, (b) amount of hunting income, (c) probability of earning >$0 

photographic income, and (d) amount of photographic income earned by communal conservancies in Namibia, 1998–2022. 

  18 

 
 
 
CHAPTER 2: LOCAL MANAGEMENT AND GOVERNANCE IMPROVE 

NATURAL RESOURCE INCOMES OF COMMUNAL CONSERVANCIES 

IN NAMIBIA 

2.1 ABSTRACT  

Governance of communal institutions is critical to the financial sustainability of 

community-based natural resource management economies. I evaluated effects of local 

management and governance on income earned by communal conservancies in Namibia during 

2011–2022. I compiled annual income and performance scores for natural resource management 

and institutional governance using conservancy accounting and ‘event book’ monitoring data 

(governance performance collected since 2019). Conservancies earning >$0 income generated a 

median annual of $60,518 since 2011 and $50,283 since 2019 (17% less than during 2011–

2022). Income increased with years since conservancy establishment, higher management 

performance which also positively affected the probability conservancies earned >$0 income, 

presence of nongovernmental organization (NGO) support within conservancies, and annual 

general meeting (AGM) occurrence. Income earned during 2019–2022 also increased with 

higher governance performance. Median management and governance performances across 

conservancies were only about 50% of their maximum scores, indicating higher income potential 

with improved performance. Conservancies remained financially dependent on NGO support and 

AGMs were important functions for generating income. Natural resource management and 

institutional governance facilitated income generation by Namibia’s conservancies, but more 

investment in benefit distribution could increase socioeconomic impact. I recommend that 

Namibia’s conservancies, particularly those established more recently or without NGO presence, 

prioritize improving local management and governance to develop more sustainable community-

based natural resource economies.   

2.2 INTRODUCTION  

Indigenous-led governance of communal lands is considered necessary for effective and 

socially just conservation (Artelle et al., 2019). Community-based natural resource management 

(CBNRM) is a democratic approach to governance based on devolution of land use rights from 

government to local levels (Smith, 2019), decentralized management authority (Ribot, 2002), 

19 

 
 
and shared decision-making (Child, 2019). Benefits generated from ecosystem services (e.g., 

hunting and photographic tourism; Goergen et al., unpublished data), however, are not passively 

converted into conservation outcomes by indigenous peoples or local communities (Child & 

Barnes, 2010). Developing communal institutions (Agrawal & Gibson, 1999) and adaptive 

management (Barnes & Child, 2014) are necessary to administer finances and provide incentives 

for participation in coupled human-natural systems (Ostrom, 2010). Benefit distribution also can 

be a critical governance challenge for CBNRM (Silva & Mosimane, 2013).    

In southern Africa, rapid income generation from sustainable wildlife use (Weaver et al., 

2011) necessitates local financial management with community engagement and leadership 

accountability (Child & Barnes, 2010). Benefits from CBNRM programs (e.g., Communal Areas 

Management Program for Indigenous Resources Association in Zimbabwe; Taylor, 2009) can be 

greater than costs of developing communal institutions when distribution is equitable and 

transparent (Child et al., 2014). While empowerment from inclusive social organization (Silva & 

Mosimane, 2014) can provide conservation incentives without financial benefits (Ashley, 1998), 

income is invested in management capacity and generates community interest in benefit 

distribution which can reduce corruption (Jones et al., 2015). Income potential improves with 

more effective governance (Chidakel & Child, 2022) as community participation increases 

accountability (Child, 2006), conservation practices are accepted (Ostrom, 2000), and 

investments from the private sector (Lapeyre, 2011) or nongovernmental organizations (NGO) 

increase (Lindsey et al., 2014). 

International policies and multilateral environmental agreements (Larson et al., 2022) 

increasingly recognize the importance of indigenous peoples and local communities to 

conservation (e.g., Convention on Biological Diversity; Reyes-García et al., 2022). Despite this 

global support (Garnett et al., 2018), along with concern for socioeconomic impacts of CBNRM 

(Fariss et al., 2022) and programmatic focus on improving governance (NACSO, 2022a), 

quantitative information on performance of communal institutions is limited (Zhang et al., 2023) 

and existing community-led monitoring data is underused (Collomb et al., 2010). Less income 

generated from tourism and social gathering restrictions during the coronavirus disease 2019 

(COVID-19) pandemic (Lindsey et al., 2020) also emphasized need for resilient communal 

institutions (Hulke et al., 2022). 

20 

 
 
Namibia’s communal conservancies monitor natural resource management and 

institutional governance annually to assess programmatic efficacy (NACSO, 2023). Income from 

CBNRM generally increases with program age (Bandyopadhyay et al., 2009), as older 

conservancies have more mature management capacity (Brooks, 2017), implement best 

governance practices, and facilitate private sector or NGO investments (Humavindu & Stage, 

2014). Conservancies employ staff that manage natural resources (e.g., community game guards) 

and are governed by elected committees. Performances of conservancy staff in natural resource 

management and committees in institutional governance are reported separately using indicators 

with categorical ratings (Stuart-Hill et al., 2005). Support NGOs (e.g., Integrated Rural 

Development and Nature Conservation) facilitated development of Namibia’s conservancy 

program in the late 1990s and can assist with natural resource management, private sector 

negotiations, and securing grants (Kalvelage et al., 2020). Annual general meetings (AGM) 

provide opportunities for conservancy members to engage directly in decision-making, 

especially financial management, and with tourism operators or NGOs (Muyengwa, 2015) but 

participation can be limited (Collomb et al., 2010).  

I evaluated effects of local management and governance on income earned by 

conservancies in Namibia during 2011–2022. I predicted that income would increase with years 

since conservancy establishment, higher management performance scores, NGO presence, and 

AGM occurrence. I predicted that income would also increase with higher governance 

performance scores during 2019–2022 despite less income earned and challenges to conservancy 

operations during the COVID-19 pandemic (i.e., 2020–2021) (Lendelvo et al., 2020).  

2.3 STUDY AREA 

Namibia is a sparsely populated country in southern Africa (824,000 km2; Atlas of 

Namibia Team, 2022) with 47% of its 2.5 million people living in rural areas with high poverty 

and unemployment (NSA, 2021; WBG, 2023). Protected areas, communally managed wildlife 

areas, and private freehold lands used for wildlife ranching (Lindsey et al., 2013) combined 

represent about 46% of Namibia’s land area (NACSO, 2021a). Economic potential for 

communal areas with high wildlife abundance was recognized in the 1990s (Ashley & Barnes, 

1997) after commercialization of wildlife ranching on private lands (Republic of Namibia, 1975). 

Following Namibia’s independence in 1990, progressive land use policies (e.g., MWCT, 1992; 

MET, 1995) led to the Nature Conservation Amendment Act (Republic of Namibia, 1996), 

21 

 
 
 
which authorized communities to register customary landholdings as conservancies in 1998. 

Subsequent legislation enabled conservancy ownership and management of tourism enterprises 

(Republic of Namibia, 2002; MET, 2007).  

Conservancies are local natural resource management institutions with conditional 

property rights and reporting requirements (e.g., constitution, zonation, wildlife management and 

benefit distribution plans) to the Ministry of Environment, Forestry and Tourism (MEFT) (Jones, 

1999). Namibia’s conservancies are recognized globally as a leading CBNRM program (Jones, 

2010) enabled by support from NGOs and land use rights legislated by the national government 

(Boudreaux & Nelson, 2011). The conservancy program is managed by the Namibian 

Association of Community-Based Natural Resource Management Support Organizations 

(NACSO) and administered by MEFT. The 86 registered conservancies represent about 20% 

(166,179 km2) of Namibia’s land area and support more than 230,000 people (Figure 2.1; 

NACSO, 2022b).  

2.4 METHODS 

I compiled data from NACSO including accounting records collected by conservancies 

during 1998–2022, years since conservancy establishment, and annual presence of NGO support 

within conservancies. I used conservancies’ annual monitoring data collected during 2011–2022 

from NACSO to compile management performance, governance performance (collected since 

2019), and AGM occurrence for each conservancy that used NACSO’s ‘event book’ monitoring 

tool (Stuart-Hill et al., 2005). 

I estimated total income earned by each conservancy annually during 2011–2022 by 

summing concession and hunting fee payments to respective governing committees, salaries 

earned by conservancy members, direct household payments, and in-kind non-financial benefits 

(e.g., game meat, development projects, training, meals) from tourism operators. I added income 

from small- to medium-sized businesses (e.g., plant harvesting [e.g., Devil’s claw {Harpophytum 

spp.}; Lavelle, 2023], craft sales), game meat from conservancy harvests, grants, donations, bank 

interest, and other miscellaneous sources included in NACSO’s accounting records. I used game 

meat values calculated by NACSO using replacement-cost shadow prices applied nationally each 

year (Naidoo et al., 2016), which was 27 Namibian dollars (NAD)/kg in 2022 (NACSO, 2023). I 

standardized all income values to 2022 United States dollars (USD) using the geometrically 

averaged annual NAD to USD exchange rate (Bank of Namibia, 2023) and USD consumer price 

22 

 
 
index during 2011–2022 (USBLS, 2023). I report median income values due to skewed 

distribution of income across conservancies. 

Performances of conservancy staff in natural resource management and committees in 

institutional governance are reported separately in event books. I compiled annual management 

performance scores for each conservancy during 2011–2022 using categorical ratings of 16 

indicators assessing conservancy commitment to, planning or monitoring of, and benefits from 

natural resource management activities (Appendix C). I excluded other indicators due to missing 

data (i.e., harvest management) or because they did not directly assess performance of 

conservancy staff (i.e., wildlife population trends and status). Categorical ratings were 0–2 to 0–

6 depending on the indicator. Therefore, I scaled all ratings to 1 to treat each indicator equally. I 

summed all ratings to derive an overall management performance score of 16 maximum points 

for each conservancy annually, and I assumed equal importance among indicators as each was 

similarly prioritized by NACSO (Figure 2.2). Data were missing from 15 conservancies that did 

not use event books to report management performance and other conservancies that did not 

report ratings for every indicator each year (Table 2.1).  

I compiled annual governance performance scores for each conservancy during 2019–

2022 using categorical ratings of 6 indicators including member engagement, transparent and 

participatory benefit planning, equitable benefit distribution, committee accountability, external 

stakeholder engagement, and financial management. I excluded an indicator for compliance with 

MEFT requirements due to missing data. Categorical ratings for all indicators ranged from 0 to 5 

(i.e., NA, none, weak, moderate, strong, and exceptional, respectively). I scaled all ratings to 1 to 

treat each indicator equally based on their inclusion as performance metrics. I summed all ratings 

to derive an overall governance performance score of 6 maximum points for each conservancy 

annually, again assuming indicators were equally important as prioritized by NACSO (Figure 

2.2). 

I used generalized linear mixed models to evaluate effects of local management and 

governance on income earned by conservancies in Namibia. I conducted two analyses during 

2011–2022 and 2019–2022 to account for different data domains (governance performance 

collected since 2019, Table 2.1). As distributions of conservancy incomes were zero-inflated and 

skewed, I ran two regression models for each analysis. I used linear regression to model log-

transformed incomes >$0 and logistic regression to model a binary response (0 for conservancies 

23 

 
 
earning no income, 1 for conservancies earning >$0 income; Naidoo et al., 2011b). Both models 

included fixed-effect covariates for years since conservancy establishment, annual management 

performance, annual NGO presence (0 or 1), and AGM occurrence (0 or 1) (Table 2.1). In the 

second analysis, I used the same modeling approach but used only the subset of data during 

2019–2022 when governance performance was collected. Model structures were identical to the 

previous analysis, except I also included a covariate for annual governance performance.  

For all models, I included random intercepts by conservancy to account for repeated 

annual measurements of the same conservancies across years. I calculated pairwise correlations r 

between continuous covariates and retained the most relevant for analyses when |r| > 0.70 

(Dormann et al., 2013). I centered and scaled continuous covariates using a standardized z-score 

normalization (mean = 0, standard deviation = 1). I tested for statistical significance of 

regression coefficients using α < 0.05. I visualized regression coefficient estimates and 

uncertainty using dot-whisker plots with 95% confidence intervals (CI). I performed all analysis 

in R version 4.2.2 (R Core Team, 2023) using the glmmTMB package for generalized linear 

mixed models (Brooks et al., 2017) and the DHARMa package for residual diagnostics (Hartig, 

2022).  

2.5 RESULTS 

Median number of years from conservancy establishment through 2022 was 10 since 

2011 and 15 since 2019 (Table 2.1). Median annual management performance score across 

conservancies was 7.4 during 2011–2022 (Table 2.1) and 4.8 for 19 conservancies without an 

NGO present (maximum of 16). Median annual governance performance score was 3.2 

(maximum of 6; Table 2.1). The proportion of conservancies across years with an NGO present 

was 0.76 and with an AGM was 0.69 during 2011–2022.  

Median annual income for conservancies earning >$0 was $60,518 ($1,368,827 

maximum, $111,928,397 total) during 2011–2022 and $50,283 ($1,022,904 maximum) during 

2019–2022 (Table 2.2). Median annual income for 19 conservancies without an NGO present 

when earning >$0 was $4638 during 2011–2022 and $6808 during 2019–2022 (i.e., 8% and 

14%, respectively, of median annual income for conservancies earning >$0; Table 2.2). All 86 

conservancies earned at least some income since 2011, but 2 conservancies did not earn income 

since 2019 (Table 2.2). Since 1998, conservancies on average generated income 1.8 years after 

establishment. 

24 

 
 
 
Models for 2011–2022, management only  

Residual diagnostics showed no model fit issues except that dispersion of observed 

quantile-quantile values deviated from expected for nonzero incomes during 2011–2022 

(Appendix D). Years since conservancy establishment positively affected the amount of income 

(ß = 0.12, CI = 0.01–0.23, Figure 2.3a, Appendix E) with a median increase of 5 years across 

conservancies resulting in an increase of $4551, holding all other covariates at median values. 

Annual management performance positively affected the amount of income (ß = 0.31, CI = 0.17–

0.45, Figure 2.3a) with a median increase from 7.4 to 16 (i.e., maximum score) across 

conservancies resulting in an increase of $89,581 (i.e., 148% of median annual income for 

conservancies earning >$0), holding all other covariates at median values. Annual management 

performance also positively affected the probability conservancies earned >$0 income (ß = 2.37, 

CI = 1.75–2.99, Figure 2.3b). Annual NGO presence positively affected the amount of income (ß 

= 1.43, CI = 0.43–2.43, Figure 2.3a) with a median decrease to 0 NGO presence across 

conservancies resulting in a decrease of $29,240 (i.e., 48% of median annual income for 

conservancies earning >$0), holding all other covariates at median values. Occurrence of AGMs 

also positively affected the amount of income (ß = 0.31, CI = 0.13–0.49, Figure 2.3a) with a 

median decrease to 0 AGM occurrence across conservancies resulting in a decrease of $10,286, 

holding all other covariates at median values.  

Models for 2019–2022, management and governance  

Years since conservancy establishment positively affected the amount of income (ß = 

0.63, CI = 0.37–0.90, Figure 2.3c, Table E.2) with a median increase of 5 years across 

conservancies resulting in an increase of $43,864, holding all other covariates at median values. 

Annual governance performance positively affected the amount of income (ß = 0.24, CI = 0.10–

0.37, Figure 2.3c) with a median increase from 3.2 to 6 (i.e., maximum score) across 

conservancies resulting in an increase of $86,251 (i.e., 172% of median annual income for 

conservancies earing >$0), holding all other covariates at median values. Annual management 

performance positively affected the probability conservancies earned >$0 income (ß = 1.80, CI = 

0.92–2.68, Figure 2.3d) but not the amount of income. Annual NGO presence positively affected 

the amount of income (ß = 1.13, CI = 0.28–1.97, Figure 2.3c) with a median decrease to 0 NGO 

presence across conservancies resulting in a decrease of $34,515 (i.e., 69% of median annual 

income for conservancies earning >$0), holding all other covariates at median values. 

25 

 
 
Occurrence of AGMs was not correlated with income during 2019–2022 when annual 

governance performance was included.  

2.6 DISCUSSION 

My predictions that income earned by conservancies in Namibia during 2011–2022 and 

2019–2022 was affected by local management and governance were supported. The probability 

conservancies earned >$0 income was positively affected by management performance, but other 

covariates had no effect, likely because most conservancies earned >$0 income annually since 

2011 regardless of NGO presence, AGM occurrence, or age. Conservancy age increased income, 

which likely reflected improved management and governance performance over time. That 

conservancy age increased income could also indicate that older conservancies were established 

in higher quality wildlife areas, which facilitated tourism or NGO investments (Goergen et al., 

unpublished data).  

Management performance positively affected the amount of income and probability 

conservancies earned >$0 income, and governance performance also increased the amount of 

income earned during 2019–2022. That management performance was not correlated with the 

amount of income earned during 2019–2022 indicates that income depended more on 

conservancy age, governance performance, and NGO presence in more recent years or was 

influenced by missing data in management performance. Management performance also could be 

correlated with wildlife-based income potential from ecosystem services (Goergen et al., 

unpublished data), but governance performance was more important for increasing income 

earned from natural resources (Kalvelage et al., 2020). Income increased by an equivalent of 

148% (i.e., $89,581) or 172% (i.e., $86,251) of median annual income for conservancies earning 

>$0 when median annual management or governance performance increased to maximum scores 

since 2011 and 2019, respectively. That median annual management and governance 

performances across conservancies were only about 50% of their maximum scores (30% of 

management performance for conservancies without an NGO present) indicates higher income 

potential with improved performance. 

Presence of an NGO was associated with greater income, which likely reflected natural 

resource management assistance or grants provided by NGOs. Despite potential for financial 

sustainability through ecosystem services (Barnes et al., 2002), conservancies remained 

dependent on NGO support. However, NGOs rely on philanthropic funding that could represent 

26 

 
 
external interests misaligned with conservancy priorities (e.g., wildlife conservation vs. benefit 

distribution; Crosman et al., 2021). More transparent partnerships with conservancies (Buzzard 

et al., 2023) and NGO assistance in institutional development could increase governance 

performance for low scoring indicators (i.e., benefit planning, benefit distribution, financial 

management). I suggest expanding NGO support to other conservancies through new 

partnerships, especially for conservancies earning less income and with lower performance 

scores (e.g., northwest Kunene Region).  

Occurrence of AGMs was associated with greater income earned since 2011 but not since 

2019, which likely reflected less AGMs during the COVID-19 pandemic (i.e., 60% of 

conservancies that reported had no AGM in 2020; NACSO, 2021b). That AGMs positively 

affected the amount of income earned during 2011–2022 indicates that AGMs are important 

functions for conservancies (Muyengwa, 2015). That AGM occurrence recovered quickly after 

the COVID-19 pandemic (i.e., 87% of conservancies that reported had an AGM in 2021; 

NACSO, 2022b) also emphasized their importance to conservancy governance. However, 

conservancy members’ trust in governing committees to administer finances can be low (B. 

Child et al., unpublished data), suggesting that AGMs could be restructured to increase 

participation and governance performance for member engagement and committee 

accountability indicators (Shimansky, 2021). 

Local management and governance facilitate income by Namibia’s conservancies 

(Lapeyre, 2015). Median annual income for conservancies earning >$0 during 2019–2022 was 

17% (i.e., $10,235) less than during 2011–2022, despite an increase in grants (e.g., COVID-19 

pandemic emergency relief funding), but I expect income to recover from the COVID-19 

pandemic as tourism increases (NACSO, 2022b). Number of natural resource-based income 

sources had the lowest median score (i.e., 0.17) among management performance indicators, 

indicating need for diversification (Naidoo et al., 2011a). Income generation can also improve 

with direct participation of indigenous peoples and local communities in tourism industries (e.g., 

shared business ownership models; Hoole, 2009) or international wildlife trade policy (e.g., 

Convention on International Trade in Endangered Species of Wild Fauna and Flora; Roe et al., 

2022). 

Financial sustainability is critical to building CBNRM economies that are resilient to 

climate-changed induced drought (Carpenter, 2022), increasing costs of human-wildlife conflicts 

27 

 
 
(Schnegg & Kiaka, 2018), and tourism market dynamics (e.g., wildlife trade restrictions; 

Nyamayedenga et al., 2021). Benefit distribution had the lowest median score (i.e., 0.4) among 

governance performance indicators but increases participation in CBNRM (Merz et al., 2023). In 

2022, only 7 conservancies met MEFT’s requirement to invest at least 50% of income in 

community development (NACSO, 2023) and compensation for human-wildlife conflicts 

represented 47% of cash benefits to conservancy members (NACSO, 2023). While human-

wildlife conflict compensation is an important social service (Tavolaro et al., 2022), it diverts 

spending from income generating opportunities or more transformative community development 

(B. Child et al., unpublished data). Continued emphasis on equitable benefit distribution and 

more effective conservancy spending could increase the socioeconomic impact of income 

generated (Mosimane & Silva, 2015).  

Namibia’s event books are an effective community-led monitoring system used to assess 

conservancy performance as natural resource management institutions (Stuart-Hill et al., 2005). 

Missing data precluded analysis of factors including conservancy compliance with MEFT 

requirements or gender equality of committees and staff, priorities for NACSO (NACSO, 

2022a). In addition, some NGOs that work in Namibia’s conservancies (e.g., World Wildlife 

Fund Namibia) were not included in the data. Increasing the number of conservancies using 

event books and overall data reporting quality could improve future research. Event books can 

also be used to monitor poaching or human-wildlife conflicts (Wenborn et al., 2022) but some 

data could be socially constructed (Lubilo & Hebinck, 2019) and confirmed by additional data 

collection.  

Namibia’s national level legislation and NGO support enabled effective governance 

(Nelson et al., 2021) relative to CBNRM programs in other countries (Child & Barnes, 2010). 

However, secure land tenure can limit development of CBNRM (Nelson & Agrawal, 2008) and 

its contributions to indigenous peoples’ rights (e.g., Khoi and San peoples; Anaya, 2013). 

Representative committees, while an effective governing structure for Namibia’s conservancies 

(Child et al., 2014), might not be applicable to communal institutions elsewhere in southern 

Africa (Mavah et al., 2022).  

Improvements in natural resource management and institutional governance can greatly 

increase income for CBNRM economies. My results support the development of emerging 

conservancies in Namibia’s northeast Zambezi Region that have high income potential from high 

28 

 
 
management and governance performances and NGO presence (NACSO, 2021b). I recommend 

that Namibia’s conservancies, particularly those established more recently or without NGO 

presence (e.g., northwest Kunene Region), prioritize improving local management and 

governance to develop more resilient communal institutions that are financially sustainable. 

29 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
TABLES & FIGURES 

Table 2.1: Variables potentially affecting incomes earned by communal conservancies in 

Namibia, 2011–2022 and 2019–2022.   

Variable 
Years since establishment 
Annual management performance  
(maximum score of 16)  
Annual governance performance  
(maximum score of 6) 
Annual nongovernmental 
organization presence  
Annual general meeting 
occurrence 

2011–2022 
Range 

0–24 
0.2–11.8 

Median 
10 
7.4 

NAs  Median 
15 
7.8 

- 
227 

2019–2022 
Range 

1–24 
1.1–11.8 

- 

1 

1 

- 

- 

3.2 

1.6–4.6 

0–1 

63 

0–1 

113 

1 

1 

0–1 

0–1 

NAs 
- 
76 

24 

15 

21 

Table 2.2: Summary of annual income estimates (in 2022 United States dollars) earned by 

communal conservancies in Namibia, 2011–2022 and 2019–2022.  

Statistic 
Mean 
Median income >$0 
Maximum 
Total 
Conservancies earning income >$0 
Median income >$0 for conservancies without 
a nongovernmental organization present (19) 

2011–2022 

$114,096 
$60,518 
$1,368,827 

2019–2022 
$104,270 
$50,283 
$1,022,904 
$111,928,397  $35,868,952 
84 
$6808 

86 
$4638 

30 

 
 
 
 
 
Figure 2.1: Communal conservancies (yellow polygons) in Namibia, 2022 (NACSO). 

31 

Earthstar Geographics0100200km´Earthstar Geographics 
 
 
Figure 2.2: Annual mean income, management performance, and governance performance (2019–2022) of communal conservancies 

in Namibia, 2011–2022. 

32 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 2.3: Standardized regression coefficient estimates with 95% confidence intervals (i.e., whiskers) for generalized linear mixed 

models fit to the (a) amount of income earned and (b) probability of earning >$0 income during 2011–2022, and (c) amount of income 

earned and (d) probability of earning >$0 income during 2019–2022 by communal conservancies in Namibia.

33 

 
 
 
 
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48 

 
 
 
 
 
APPENDIX A: CHAPTER 1 RESIDUAL DIAGNOSTIC PLOTS 

Figure A.1: Kolmogorov-Smirnov (left column), nonparametric dispersion (center column), and outlier (right column) test plots for 

generalized linear mixed models fit to the probability of earning >$0 hunting income (top row) and amount of hunting income (bottom 

row) earned by communal conservancies in Namibia, 1998–2022.  

49 

 
 
 
Figure A.2: Kolmogorov-Smirnov (left column), nonparametric dispersion (center column), and outlier (right column) test plots for 

generalized linear mixed models fit to the probability of earning >$0 photographic income (top row) and amount of photographic 

income (bottom row) earned by communal conservancies in Namibia, 1998–2022.  

50 

 
 
 
APPENDIX B: CHAPTER 1 REGRESSION COEFFICIENT TABLES 

Table B.1: Regression coefficient log odds or estimates with 95% confidence intervals (CI) and 

p-values (< 0.05 bolded) for generalized linear mixed models fit to the probability of earning >$0 

hunting income and amount of hunting income earned by communal conservancies in Namibia, 

1998–2022. 

Coefficient 
Intercept 
Years since establishment 
Area (km2) 
Annual Big 5 species occurrence 
Mean elevation (m) 
Elevation SD (m) 
Distance to nearest national park 
(km) 
Distance to nearest major road 
(km) 
Annual precipitation (mm) 

Probability of Earning >$0 
CI 
0.08 – 1.04 
0.46 – 0.86 
-0.31 – 0.75 
0.60 – 1.16 
-1.03 – -0.03 
-0.35 – 1.08 
-0.87 – 0.08 

Log odds 
0.56 
0.66 
0.22 
0.88 
-0.53 
0.37 
-0.40 

p-value 
0.022 
<0.001 
0.417 
<0.001 
0.039 
0.316 
0.104 

Amount of Hunting Income 
CI 
4.59 – 5.22 
-0.26 – -0.10 
0.02 – 0.72 
0.17 – 0.38 
-0.63 – -0.02 
-1.37 – -0.41 
-0.71 – -0.15 

Estimate 
4.91 
-0.18 
0.37 
0.28 
-0.33 
-0.89 
-0.43 

p-value 
<0.001 
<0.001 
0.040 
<0.001 
0.036 
<0.001 
0.003 

-0.39 

-1.04 – 0.27 

0.243 

-0.07 

-0.49 – 0.35 

0.736 

0.35 

-0.01 – 0.71 

0.060 

0.05 

-0.11 – 0.20 

0.539 

Table B.2: Regression coefficient log odds or estimates with 95% confidence intervals (CI) and 

p-values (< 0.05 bolded) for generalized linear mixed models fit to the probability of earning >$0 

photographic income and amount of photographic income earned by communal conservancies in 

Namibia, 1998–2022. 

Coefficient 
Intercept 
Years since establishment 
Area (km2) 
Annual Big 5 species occurrence 
Mean elevation (m) 
Elevation SD (m) 
Distance to nearest national park 
(km) 
Distance to nearest major road 
(km) 
Annual precipitation (mm) 

Probability of Earning >$0 
CI 
-4.04 – -1.97 
0.92 – 1.46 
-1.04 – 0.86 
0.28 – 0.93 
-3.54 – -1.51 
-0.54 – 1.92 
-1.11 – 0.64 

Log odds 
-3.01 
1.19 
-0.09 
0.60 
-2.52 
0.69 
-0.24 

p-value 
<0.001 
<0.001 
0.854 
<0.001 
<0.001 
0.273 
0.594 

Amount of Photographic Income 
p-value 
CI 
Estimate 
<0.001 
5.94 – 6.73 
6.34 
<0.001 
0.27 – 0.48 
0.38 
0.749 
-0.55 – 0.40 
-0.08 
-0.00 – 0.24 
0.054 
0.12 
<0.001 
-1.24 – -0.43 
-0.83 
0.061 
-0.03 – 1.29 
0.63 
0.541 
-0.28 – 0.54 
0.13 

0.29 

-0.85 – 1.42 

0.620 

-0.44 

-0.99 – 0.10 

0.109 

0.06 

-0.37 – 0.50 

0.781 

0.25 

0.03 – 0.48 

0.029 

51 

 
 
  
 
  
 
 
 
APPENDIX C: MANAGEMENT PERFORMANCE INDICATORS 

Figure C.1: Annual natural resource audit report and performance review questionnaire template 

for communal conservancies in Namibia, 2020 (NACSO).  

52 

 
 
 
 
Figure C.1 (cont’d) 

53 

 
 
 
APPENDIX D: CHAPTER 2 RESIDUAL DIAGNOSTIC PLOTS 

Figure D.1: Kolmogorov-Smirnov (left column), nonparametric dispersion (center column), and outlier (right column) test plots for 

generalized linear mixed models fit to the amount of income earned (top row) and probability of earning >$0 income (bottom row) by 

communal conservancies in Namibia, 2011–2022. 

54 

 
 
 
Figure D.2: Kolmogorov-Smirnov (left column), nonparametric dispersion (center column), and outlier (right column) test plots for 

generalized linear mixed models fit to the amount of income (top row) and probability of earning >$0 income (bottom row) by 

communal conservancies in Namibia, 2019–2022.

55 

 
 
 
APPENDIX E: CHAPTER 2 REGRESSION COEFFICIENT TABLES 

Table E.1: Regression coefficient log odds or estimates with 95% confidence intervals (CI) and 

p-values for generalized linear mixed models fit to the amount of income earned and probability 

of earning >$0 income by communal conservancies in Namibia, 2011–2022. 

Coefficient 
Intercept 
Years since establishment 
Annual management performance 
Annual nongovernmental 
organization presence 
Annual general meeting 
occurrence 

Amount of Income 
CI 
-3.57 – -1.73 
0.01 – 0.23 
0.17 – 0.45 
0.43 – 2.43 

Estimate 
-2.65 
0.12 
0.31 
1.43 

p-value  Log odds 
4.34 
<0.001 
0.21 
0.026 
2.37 
<0.001 
-0.26 
0.005 

Probability of Earning >$0 
CI 
3.00 – 5.67 
-0.24 – 0.66 
1.75 – 2.99 
-1.40 – 0.89 

p-value 
<0.001 
0.367 
<0.001 
0.663 

0.31 

0.13 – 0.49 

0.001 

-0.22 

-0.95 – 0.51 

0.555 

Table E.2: Regression coefficient log odds or estimates with 95% confidence intervals (CI) and 

p-values for generalized linear mixed models fit to the amount of income earned and probability 

of earning >$0 income by communal conservancies in Namibia, 2019–2022. 

Coefficient 
Intercept 
Years since establishment 
Annual management performance 
Annual governance performance 
Annual nongovernmental 
organization presence 
Annual general meeting 
occurrence 

Amount of Income 
CI 
-2.85 – -1.28 
0.37 – 0.90 
-0.08 – 0.33 
0.10 – 0.37 
0.28 – 1.97 

Estimate 
-2.07 
0.63 
0.12 
0.24 
1.13 

p-value  Log odds 
4.60 
<0.001 
-0.15 
<0.001 
1.80 
0.241 
0.45 
<0.001 
-0.83 
0.009 

Probability of Earning >$0 
CI 
2.84 – 6.36 
-0.86 – 0.55 
0.92 – 2.68 
-0.21 – 1.10 
-2.21 – 0.55 

p-value 
<0.001 
0.672 
<0.001 
0.181 
0.238 

0.20 

-0.01 – 0.40 

0.067 

-0.73 

-1.89 – 0.42 

0.213 

56 

 
 
  
 
  
 
 
 
 
 
 
 
 
 
 
 
APPENDIX F: DEPENDENCE TESTING FOR INTER-CHAPTER MODEL 

COVARIATE EFFECTS 

I conducted analyses for Chapters 1 and 2 using logical groups of environmental and 

governance factors with different years of data (i.e., 1998–2022 and 2011–2022, respectively) 

and unique predictions for their effects on income sources (i.e., hunting or photographic tourism 

for Chapter 1 and total income for Chapter 2) earned by communal conservancies in Namibia, 

assuming independent model covariate effects across chapters. I tested for dependence between 

inter-chapter model covariate effects separately from analyses in Chapters 1 and 2. I first 

calculated Pearson’s product-moment correlations r between income types (i.e., binary, nonzero) 

from each chapter and continuous covariates from the other chapter. I used generalized linear 

mixed models that included additional fixed-effect covariates from the other chapter if |r| > 0.30 

with the corresponding income type.  

Models fit to the probability of earning >$0 hunting income (Table F.1), amount of 

hunting income (Table F.2), and probability of earning >$0 photographic income (Table F.3) 

included annual management and governance performances as additional fixed-effect covariates, 

excluding 1998–2010 data. Models fit to the probability of earning >$0 total income since 2011 

included annual Big 5 species occurrence (Table F.4) and to the amount of total income during 

2011–2022 also included mean elevation (Table F.5) as additional fixed-effect covariates. All 

models included random intercepts by conservancy that incorporated implicit variation among 

conservancies across chapters. While some results differ from the original reported models in 

Chapters 1 and 2, overall conclusions remained the same.   

Years since conservancy establishment positively affected the probability conservancies 

earned >$0 hunting income during 1998–2022 but had a negative effect since 2011 when annual 

management performance was included (Table F.1). Big 5 species occurrence and mean 

elevation were not correlated with the probability conservancies earned >$0 hunting income 

during 2011–2022, and mean elevation also was not correlated with the amount of hunting 

income during 2011–2022, when annual management and governance performances were 

included (Tables F.1–F.2). Distance to nearest national park positively affected the probability 

conservancies earned >$0 hunting income since 2011 when annual management performance 

was included (Table F.1). Big 5 species occurrence was not correlated with the probability 

conservancies earned >$0 photographic income during 2011–2022 when annual management 

57 

 
 
and governance performances were included, while greater topographic diversity had a positive 

effect since 2019 (Table F.3). Annual management and governance performances positively 

affected the probability conservancies earned >$0 hunting and photographic income and the 

amount of hunting income during 2011–2022 with minimal changes to the significance or effect 

size of original covariates included in Chapter 1 (Tables F.1–F.3). Big 5 species occurrence 

increased the amount of total income since 2019 and lower mean elevation decreased the amount 

of total income during 2011–2022 with minimal changes to the significance or effect size of 

original covariates included in Chapter 2 (Table E.5). 

Table F.1: Regression coefficient log odds or estimates with 95% confidence intervals (CI) and 

p-values (< 0.05 bolded) for generalized linear mixed models fit to the probability of earning >$0 

hunting income by communal conservancies in Namibia, 2011–2022 and 2019–2022, including 

annual management (r = 0.65) and governance (r = 0.52) performances.  

Coefficient 
Intercept 
Years since establishment 
Area (km2) 
Annual Big 5 species 
occurrence 
Mean elevation (m) 
Elevation SD (m) 
Distance to nearest 
national park (km) 
Distance to nearest major 
road (km) 
Annual precipitation (mm) 
Annual management 
performance 
Annual governance 
performance 

Probability of Earning >$0 
Hunting Income 2011–2022 
CI 
1.37 – 2.36 
-0.97 – -0.15 
-0.20 – 0.77 
-0.13 – 0.80 

Log odds 
1.87 
-0.56 
0.28 
0.33 

p-value 
<0.001 
0.007 
0.255 
0.157 

Probability of Earning >$0 
Hunting Income 2019–2022 
CI 
0.79 – 2.28 
-1.35 – 0.38 
-0.59 – 0.84 
-0.31 – 1.24 

Log odds 
1.53 
-0.48 
0.12 
0.46 

p-value 
<0.001 
0.273 
0.732 
0.241 

-0.17 
-0.25 
-0.60 

-0.67 – 0.33 
-0.88 – 0.38 
-1.12 – -0.09 

0.517 
0.432 
0.020 

0.40 
-0.84 
0.01 

-0.35 – 1.14 
-1.83 – 0.14 
-0.72 – 0.73 

0.295 
0.093 
0.985 

-0.56 

-1.15 – 0.02 

0.057 

-0.87 

-1.77 – 0.04 

0.061 

-0.02 
1.91 

-0.55 – 0.52 
1.41 – 2.41 

0.950 
<0.001 

-0.79 
1.59 

-1.72 – 0.14 
0.67 – 2.51 

0.096 
0.001 

- 

- 

- 

1.10 

0.41 – 1.79 

0.002 

58 

 
 
 
  
 
 
 
 
 
 
 
 
Table F.2: Regression coefficient log odds or estimates with 95% confidence intervals (CI) and 

p-values (< 0.05 bolded) for generalized linear mixed models fit to the amount of hunting income 

earned by communal conservancies in Namibia, 2011–2022 and 2019–2022, including annual 

management (r = 0.44 and r = 0.40, respectively) and governance (r = 0.31) performances.  

Coefficient 
Intercept 
Years since establishment 
Area (km2) 
Annual Big 5 species 
occurrence 
Mean elevation (m) 
Elevation SD (m) 
Distance to nearest 
national park (km) 
Distance to nearest major 
road (km) 
Annual precipitation (mm) 
Annual management 
performance 
Annual governance 
performance 

Amount of Hunting Income 
2011–2022 
CI 
4.57 – 5.25 
-0.69 – -0.43 
0.15 – 0.88 
0.02 – 0.32 

Estimate 
4.91 
-0.56 
0.51 
0.17 

p-value 
<0.001 
<0.001 
0.006 
0.026 

Amount of Hunting Income  
2019–2022 
CI 
4.60 – 5.04 
-0.48 – 0.03 
0.29 – 0.76 
0.29 – 0.82 

Estimate 
4.82 
-0.23 
0.53 
0.55 

p-value 
<0.001 
0.082 
<0.001 
<0.001 

-0.32 
-0.89 
-0.48 

-0.69 – 0.05 
-1.41 – -0.37 
-0.80 – -0.16 

0.087 
0.001 
0.003 

-0.18 
-0.72 
-0.15 

-0.44 – 0.08 
-1.12 – -0.32 
-0.41 – 0.10 

0.180 
<0.001 
0.246 

-0.14 

-0.63 – 0.34 

0.570 

0.04 

-0.33 – 0.40 

0.843 

0.03 
0.33 

-0.13 – 0.19 
0.19 – 0.48 

0.739 
<0.001 

0.13 
0.43 

-0.15 – 0.41 
0.16 – 0.70 

0.366 
0.002 

- 

- 

- 

0.28 

0.08 – 0.48 

0.006 

Table F.3: Regression coefficient log odds or estimates with 95% confidence intervals (CI) and 

p-values (< 0.05 bolded) for generalized linear mixed models fit to the probability of earning >$0 

photographic income by communal conservancies in Namibia, 2011–2022 and 2019–2022, 

including annual management (r = 0.47) and governance (r = 0.40) performances.  

Coefficient 
Intercept 
Years since establishment 
Area (km2) 
Annual Big 5 species 
occurrence 
Mean elevation (m) 
Elevation SD (m) 
Distance to nearest 
national park (km) 
Distance to nearest major 
road (km) 
Annual precipitation (mm) 
Annual management 
performance 
Annual governance 
performance 

Probability of Earning >$0 
Photographic Income 2011–2022 
p-value 
Log odds 
CI 
<0.001 
-3.91 – -1.32 
-2.62 
0.001 
0.39 – 1.46 
0.93 
0.479 
-1.62 – 0.76 
-0.43 
0.234 
-0.22 – 0.90 
0.34 

Probability of Earning >$0 
Photographic Income 2019–2022 
p-value 
CI 
Log odds 
0.001 
-4.33 – -1.15 
-2.74 
0.014 
0.39 – 3.44 
1.91 
0.376 
-1.88 – 0.71 
-0.59 
0.584 
-0.89 – 1.58 
0.34 

-2.83 
1.19 
-0.34 

-4.45 – -1.21 
-0.39 – 2.76 
-1.64 – 0.95 

0.001 
0.141 
0.603 

-2.01 
2.23 
0.48 

-3.80 – -0.23 
0.17 – 4.29 
-0.94 – 1.91 

0.027 
0.034 
0.504 

-0.04 

-1.63 – 1.55 

0.961 

-0.78 

-2.76 – 1.21 

0.443 

-0.29 
1.12 

-0.86 – 0.27 
0.41 – 1.83 

0.313 
0.002 

-0.78 
2.02 

-1.86 – 0.29 
0.52 – 3.53 

0.154 
0.009 

- 

- 

- 

1.38 

0.33 – 2.43 

0.010 

59 

 
 
  
 
  
 
Table F.4: Regression coefficient log odds or estimates with 95% confidence intervals (CI) and 

p-values (< 0.05 bolded) for generalized linear mixed models fit to the probability of earning >$0 

total income earned by communal conservancies in Namibia, 2011–2022, including annual Big 5 

species occurrence (r = 0.34). 

Coefficient 
Intercept 
Years since establishment 
Annual management 
performance 
Annual nongovernmental 
organization presence 
Annual general meeting 
occurrence 
Annual Big 5 species 
occurrence 

Probability of Earning >$0  
Total Income 2011–2022 
CI 
3.21 – 4.79 
-0.33 – 0.59 
1.89 – 3.24 

Log odds 
4.00 
0.13 
2.57 

p-value 
<0.001 
0.576 
<0.001 

-0.00 

-0.47 – 0.47 

0.996 

-0.07 

-0.38 – 0.23 

0.642 

-0.53 

-1.16 – 0.10 

0.102 

Table F.5: Regression coefficient log odds or estimates with 95% confidence intervals (CI) and 

p-values (< 0.05 bolded) for generalized linear mixed models fit to the amount of total income 

earned by communal conservancies in Namibia, 2011–2022 and 2019–2022, including annual 

Big 5 species occurrence (r = 0.40 and r = 0.37, respectively) and mean elevation (r = -0.45). 

Coefficient 
Intercept 
Years since establishment 
Annual management 
performance 
Annual governance 
performance 
Annual nongovernmental 
organization presence 
Annual general meeting 
occurrence 
Annual Big 5 species 
occurrence 
Mean elevation (m) 

Amount of Total Income  
2011–2022 
CI 
-1.45 – -0.79 
0.02 – 0.24 
0.16 – 0.45 

Estimate 
-1.12 
0.13 
0.31 

Amount of Total Income 
2019–2022 
CI 
-1.27 – -0.71 
0.26 – 0.77 
-0.10 – 0.31 

p-value 
<0.001 
<0.001 
0.328 

p-value  Estimate 
-0.99 
<0.001 
0.52 
0.024 
0.10 
<0.001 

- 

- 

- 

0.21 

0.08 – 0.34 

0.001 

0.44 

0.12 – 0.75 

0.007 

0.32 

0.01 – 0.63 

0.043 

0.12 

0.05 – 0.19 

0.001 

0.10 

0.01 – 0.18 

0.033 

0.08 

-0.06 – 0.21 

0.258 

0.23 

0.03 – 0.43 

0.025 

-0.84 

-1.20 – -0.49 

<0.001 

-0.57 

-0.88 – -0.25 

<0.001 

60