An Initiative-Wide Study Using Remote Imagery and Matching Methods

Do community-conserved areas in Tanzania achieve conservation goals? An initiative-wide study using remote imagery and matching methods

Anthony Dancer

A thesis submitted in partial fulfilment of the requirements for the degree of Master of Science and the Diploma of Imperial College London

September 2013

I confirm that all this work is my own, except where indicated.

Anthony Dancer

Table of Contents

Abbreviations ...... iii Abstract ...... iv Acknowledgements ...... v I. Introduction ...... 1 1 Problem statement ...... 1 1.1 The need for rigorous evaluation of (area-based) conservation ...... 1 1.2 A brief introduction to WMAs ...... 1 2 Aims and objectives ...... 3 2.1 Aim ...... 3 2.2 Research questions ...... 3 2.3 Objectives ...... 4 II. Background ...... 5 1 Protected Areas & impact evaluation ...... 5 1.1 Effectiveness of PAs in achieving conservation goals ...... 5 1.2 Multiple-use PAs and community-managed areas ...... 5 1.4 Evaluating PA impact & remote assessment ...... 6 2 Bias in PA impact estimates ...... 7 2.1 Establishing counterfactuals & non-random location bias ...... 7 2.2 Controlling for bias through matching methods ...... 8 2.3 Limitations of matching: unobserved or mistakenly ignored covariates ...... 9 3 Community-based natural resource management & Tanzania’s WMAs ...... 10 3.1 Participatory conservation in sub-Saharan Africa ...... 10 3.2 The development of WMAs in Tanzania ...... 11 III. Data and methods...... 14 1 Data ...... 14 1.1 Treatment/control units ...... 14 1.2 Covariates ...... 20 1.3 Outcome variables ...... 24 2 Methods ...... 27 2.1 Data extraction, handling and storage ...... 27 2.2 Matching ...... 28 2.3 Impact analysis ...... 29

IV. Results...... 32 1 Matching ...... 32 1.1 Pre-matching covariate balance ...... 32 1.2 Matching with/without ecoregion ...... 33 1.3 With/without caliper ...... 37 1.4 Summary ...... 37 2 Impact analysis ...... 38 2.1 EVI ...... 38 2.2 Tree cover ...... 41 2.3 Land cover type ...... 46 V. Discussion ...... 50 1 Matching ...... 50 1.1 Covariate balance ...... 50 1.2 Matched samples ...... 51 1.3 Other methods, unobserved covariates and sensitivity analysis ...... 51 1.4 Effects of matching ...... 52 2 Impact analysis ...... 53 2.1 Summary of findings ...... 53 2.2 Similar studies ...... 53 2.3 WMAs aren’t having an effect, or aren’t yet ...... 54 2.4 Effect isn’t discernible via methods used ...... 55 References ...... 58 Appendices ...... 68

Abbreviations

AA Authorized Association

CBNRM Community-based Natural Resource Management

CWM Community-based Wildlife Management

EOP Earth Observation Product

EVI Enhanced Vegetation Index

MODIS Moderate Resolution Imaging Spectroradiometer

NDVI Normalized Difference Vegetation Index

PA Protected Area

SRTM Shuttle Radar Topography Mission

TRMM Tropical Rainfall Monitoring Mission

VCF Vegetation Continuous Fields

WMA Wildlife Management Area

iii

Abstract

Rigorous evaluations of conservation investments are essential to identify which initiatives work and in what contexts, but are often lacking. Protected Area assessments in particular fail to account for a strong source of bias, namely non-random location. Wildlife Management Areas (WMAs) in Tanzania represent a major participatory conservation initiative, for which no environmental impact evaluation has taken place but for which doubts over efficacy exist. This study is the first initiative-wide assessment of WMA environmental impacts, drawing on satellite imagery and using innovative matching methods based on observable characteristics to account for biases in WMA placement. Results indicate that WMAs are not having a discernible effect on conservation outcomes within their boundaries, or any effect is so slight as to be indistinguishable amongst background variation, providing validation for concerns over WMAs and insights into remote assessment of PA impacts. In addition, traditional methods which do not account for non-random placement, or which draw conclusions from simple before/after assessments, would have overstated WMA impacts, demonstrating that rigorous outcome evaluation is feasible and highly warranted.

Word count = 14,194

Acknowledgements

Although this thesis is my own work it would not have been possible, or at least substantially poorer, without the support, insights and data provided by the following people, to whom I am indebted and very thankful (in no particular order): Neil Burgess, Igor Lysenko, Aidan Keane, Erica Rieder, Lindsay Spurrier, Nasser Olwero, Lucas Joppa, Hussein Sosovele, Laura Tarimo, Silvia Ceppi, Marion Pfeifer, Ana Nuno, Andrew Knight and E.J. Milner-Gulland.

I. Introduction

1 Problem statement

1.1 The need for rigorous evaluation of (area-based) conservation

Rigorous evaluations of conservation investments are essential if limited conservation budgets are to be spent in the most effective manner (Ferraro and Pattanayak 2006). Only by assessing how allocation of effort influences conservation outcomes can we learn from our mistakes and improve future practice (McCarthy and Possingham 2007). Protected Areas (PAs), a critical conservation tool, are a case in point: causal links between PA management and outcomes are rarely evaluated in the literature (Geldmann et al. 2013); surprising given considerable uncertainty over the effectiveness of PAs in halting biodiversity and habitat loss (Leverington et al. 2010). The central question many evaluations are trying, but often failing, to address is what would have happened in the absence of conservation initiatives (Ferraro and Pattanayak 2006)? Although PA impact assessments often make comparisons to draw inferences about the effects of protection, until recently the majority failed to take into account a major source of bias, namely that PAs are often positioned on marginal lands so face lower land conversion pressure (Joppa and Pfaff 2010).

Dovetailing debates over the efficacy of PAs and PA evaluations are disagreements over whether traditional PAs or participatory, multiple-use areas might better serve conservation, and in what contexts (Schwartzman et al. 2000). Strict PAs appear to be better at conserving biodiversity, but simultaneously remove incentives for local people, whilst representing a significant social cost to the communities involved (fig. 1; Hutton and Leader-Williams 2003). More likely is that a breadth of area-based initiatives will be necessary to stem biodiversity declines, but it is essential that the conservation community implement rigorous evaluation of activities so that the most effective mix is found.

1.2 A brief introduction to WMAs

All these issues are represented in the context of Tanzania’s Wildlife Management Areas (WMAs). WMAs, community-managed wildlife zones outside of core PAs, were first implemented in Tanzania in the early 2000s in recognition of the fact that the existent PA network was insufficient to accommodate Tanzania’s biologically and economically valuable wildlife migration patterns, leading to increased conflict with a growing rural human population (Wilfred 2010).

The basic theory underlying WMAs is that in return for curtailed habitation and activities (e.g. agriculture/livestock grazing) local communities receive usage rights over wildlife, providing an incentive for enhanced participation in sustainable natural resource management (fig. 1). Villages within WMAs may experience a decrease in wellbeing, for example, in the form of loss of income from agriculture or broken cultural attachments to livestock keeping, but in return they attain overall better living standards through greater and diversified wildlife-related income streams (e.g. hunting quotas or non-consumptive tourism). Wildlife within WMAs may experience offtake in numbers or disturbance, but these are both sustainably managed because of incentives, and in return vital habitat, currently under threat of destruction, is maintained. In theory, therefore, both local communities and wildlife experience net benefits overall.

Figure 1: In theory, ‘Traditional’ PAs curtail human activities, with conservation benefits at expense of local community wellbeing (1a). In practice, strict PAs may remove incentives for sustainable use of natural resources, leading to illegal offtake/unsustainable use and equivocal conservation outcomes (1b). By granting user rights WMAs aim to provide a net benefit to local communities, and thus encourage sustainable use, with net gains for wildlife (2). Red in bar chart indicates loss, green indicates benefit.

However, in order to for the model to work there does have to be a net benefit for both parties: if communities do not gain wellbeing there is no incentive to change destructive practices; and if wildlife continues to experience habitat loss or unsustainable offtake, populations, which provide income, may not persist. At present, it is not clear that WMAs are achieving net benefits for either group: socioeconomic benefits are currently debatable, but are at least being assessed (Sulle et al. 2011); and environmental impact is not being evaluated at all.

A thorough environmental impact assessment would involve field-based ground and aerial monitoring of state and trends in specific WMA values, including wildlife populations, habitat condition and anthropogenic pressures (Leverington et al. 2010). Unfortunately, resources for such an assessment are not available and although a degree of assessment is critical, it is questionable whether limited resources would be best deployed in this fashion. Individual elements of environmental impact of direct relevance to large mammal populations, such as trends in habitat destruction, can however be assessed remotely and at no cost. Furthermore, whilst changes in plant cover and vegetation type are semi-permanent attributes that can reasonably be monitored at large-scales, large mammal attributes are intrinsically more mutable with ephemeral, seasonal and often unpredictable fluctuations (Croze and Gwynne 1975), and thus may not accurately capture the impact of WMAs on ecosystems.

However, it is critical to assess whether WMAs are having an environmental impact, to perceive if they are a viable conservation strategy in their specific context, and to contribute to wider understanding of impacts of community-managed areas in general.

2 Aims and objectives

2.1 Aim

Evaluate the impact of WMA designation on habitat destruction, drawing on freely-available satellite imagery and associated Earth Observation Products, and using matching methods based on observable characteristics to account for biases in their placement, to inform efforts to assess efficacy of WMAs in particular, and multiple-use PAs in general.

2.2 Research questions

Research question 1: Since their inception, have WMAs had a measureable effect on habitat outcomes within their boundaries?

Research question 2: Does matching for relevant, observable covariates alter the degree of difference in outcomes between WMAs and unprotected areas over time?

2.3 Objectives

Objective 1: Identify appropriate and feasibly measurable outcome/s to assess habitat destruction in WMAs, using freely available remote imagery and associated Earth Observation Products (EOPs).

Objective 2: Identify appropriate and feasibly measurable covariates which are likely to influence degree of anthropogenic pressure on land in Tanzania and WMA positioning, and check covariate balance between WMAs and unprotected areas.

Objective 3: Use matching methods to pair land in WMAs with a subset of unprotected areas, based on these covariates, check post-matching covariate balance and, if acceptable, compare habitat destruction over time in WMAs with habitat destruction in subsets.

Objective 4: Assess whether matching altered degree of difference in outcomes between WMAs and unprotected areas by comparing results of matched analyses to naïve comparison of habitat destruction in WMAs against all unprotected areas.

II. Background

1 Protected Areas & impact evaluation

1.1 Effectiveness of PAs in achieving conservation goals

As of the turn of the millennium only 25% of the terrestrial biosphere remained in a natural state, with the remainder transformed by agriculture or human settlement (Ellis et al. 2010). Consequently, habitat is destruction is the foremost, contemporary driver of species extinction (Purvis 2000). An additional 8% of land will be required to accommodate predicted growth in human population and consumption by 2050 (Tilman et al. 2001). Our primary tool for tackling this threat has been the establishment of Protected Areas (PAs), which now cover 12.3% of land outside Antarctica and continue to grow in number and extent (Bertzky et al. 2012).

However, uncertainty exists over the ability of PAs to achieve conservation outcomes. In part, this uncertainty concerns whether PAs provide sufficient biodiversity coverage (Joppa 2009), but more fundamentally there is doubt over the ability of PAs to protect nature within their borders. There may be a substantial range of protection being offered by PAs (Chape et al. 2005). For example, Leverington et al. (2010) conducted a global analysis of 4,000 protected area management effectiveness assessments and found that over half of PAs showed significant or major deficiencies. Consequently, Butchart et al. (2010) document continued declines in the state of global biodiversity despite an increase in policy responses, in which PAs play a significant role.

Successive broad, global quantitative assessments have concluded that the majority of PAs are successful at stopping land clearing within their borders (Bruner et al. 2001; Naughton-Treves et al. 2005; Nagendra 2008), although potentially at the expense of displacement of effects to surrounding areas (Ewers and Rodrigues 2008), leading to increasing isolation of PAs and limiting their ability to maintain species richness. Indeed, global evidence for conservation of species is more equivocal (Geldmann et al. 2013), with both positive (Butchart et al. 2012) and negative (Laurance et al. 2012) results depending on biodiversity context.

1.2 Multiple-use PAs and community-managed areas

Many studies treat PAs as one homogenous category. In reality PAs differ widely in management objective, intensities of use and corresponding breadth of impact (see Dudley 2008 for IUCN categories). Accordingly, Pfeifer et al. (2012) document high variability in PA effectiveness between, and within, PA categories in an assessment of trends in forest coverage in East Africa. The majority of the world’s PAs (>60%) are managed for multiple-use (IUCN categories IV–VI),

with dual conservation and development goals (Gardner et al. 2007). Indeed, Community Conserved Areas (CCAs), biodiverse lands managed by local communities, could double perceived areas under protection (Molnar et al. 2004).

The provenance of PA diversity lies in a growing body of thought that a one-size-fits-all approach to area-based conservation is socially unacceptable and potentially ineffective. Firstly, strict reserves can result in the exclusion of existing, rural populations; precipitating a significant social and economic cost to local communities (West et al. 2006). Given the strong correlation between areas of high poverty and high biodiversity this represents a distinct ethical dilemma for proponents of strong legal protection (Sachs et al. 2009). Secondly, prohibiting multiple-use may in fact have the unintended consequence of removing incentives for conservation by eliminating the potential for local people to extract long-term value from natural resources. As most rural populations will continue to use natural resources despite strict regulations, areas which promote sustainable use may be more effective (Hutton and Leader-Williams 2003).

The hope is that less strict, multiple-use areas can achieve social and conservation goals simultaneously, for mutual benefit. The evidence for their success however is mixed. Global assessments of forest protection have found that multiple-use forest PAs and community- managed forests were equally or more effective in reducing deforestation than strict PAs (Nelson and Chomitz 2011; Porter-Bolland et al. 2012), but evidence for species protection is less convincing: Shahabuddin and Rao (2010) reviewed effectiveness of CCAs globally and found that although species richness in CCAs was not appreciably worse than in strict areas, abundance of species of conservation importance was lower, and declined over time.

1.4 Evaluating PA impact & remote assessment

A significant proportion of variation in PA effectiveness is likely attributable to variation in the nature and quality of evaluation methods. Methods vary widely, in aspects such as data used, scale and analytical technique, but also in criteria assessed. Leverington et al. (2010) detail four distinct assessment criteria: PA coverage; broad scale outcomes; management effectiveness; and detailed monitoring. With the realisation that effectiveness varies, evaluations have moved on to methods for assessing how well PAs are being managed (Chape et al. 2005).

Such methods are now numerous (see Stoll-Kleemann (2010) for a recent review of >40 methodologies) and provide indications of how particular types of management drive effectiveness, and thus recommendations for improving management, but quality is highly variable. Most are reliant upon collection of qualitative data through questionnaires and

interviews and are thus dependent upon assessors having sufficient resources to attain a large and unbiased sample. This is not always the case; for example, Bruner et al. (2001) has been strongly criticised on the basis that surveys of PA managers who have an interest in perceived park performance are likely biased to be more ‘pro-park’ (Vanclay 2001).

Detailed, on-the-ground monitoring is probably the only method that can provide robust trends in biodiversity and threats at fine scales, e.g. in animal populations (Leverington et al. 2010), but to do so require significant resources. However, park managers in developing countries, with high degrees of biodiversity and threat, are likely most limited in financial, human, and political resources, necessitating cheap, but robust, evaluation techniques (Blackman 2013). Accordingly, studies of broad scale outcomes which assess environmental impacts of protection using robust techniques, but at coarser scales, largely using publicly available, remotely sensed satellite imagery, have become common and now dominate the literature (Leverington et al. 2010). For example, DeFries et al. (2005) analysed freely available MODIS satellite imagery (see methods section for discussion of available satellite imagery).

Such studies, whilst limited primarily to assessing deforestation and habitat destruction, have dramatically reduced PA evaluation costs, providing critical and objective information regarding effectiveness for policy makers (Blackman 2013). However, remote assessments have limited in ability to detect change in animal populations, utility outside of forest areas and insights into drivers of effectiveness (Leverington et al. 2010). Consequently, remote sensing should be supplemented with ground-truthing and field studies of social and institutional drivers (Southworth 2006)

2 Bias in PA impact estimates

2.1 Establishing counterfactuals & non-random location bias

Beyond variety in type and focus there remain fundamental analytical issues with some PA impact assessments which cast doubt over the validity of reported effectiveness (Geldmann et al. 2013). This issue is not restricted to PA impact analysis, nor conservation initiatives in general, but critical to program evaluation in all policy arenas: how to establish an appropriate counterfactual, or the outcome that would have occurred in the absence of an intervention (Ferraro and Pattanayak 2006). Only by establishing counterfactuals using adequate analytical techniques can we ensure limited resources are being applied in the most effective manner, however the vast majority of conservation initiative evaluations fall short of this ideal (Ferraro

and Pattanayak 2006). In this regard PAs are no exception with causal links between management and outcomes rarely evaluated (Geldmann et al. 2013).

Joppa and Pfaff (2010) group analytical methods used to assess PA impacts on deforestation into 4 categories: absolute assessments (i.e. non-comparative studies of state or trends in deforestation in PAs); comparisons with all unprotected areas; comparisons with nearby unprotected areas; and comparisons before and after protection. The first method eschews establishing a counterfactual altogether, ignoring background rates of deforestation. The remaining methods, which account for the majority of the literature, attempt to infer causality but fail to account for a strong source of bias, namely the non-random spatial distribution of PAs.

For intelligible reasons, PA networks tend to be geographically biased towards areas which are less attractive to humans for settlement or agriculture (Oldfield et al. 2003). Consequently, most national PA networks are predisposed towards being positioned at high elevations, on steep slopes, and remote from roads and cities (Joppa and Pfaff 2009). Significantly, this non-random placement confers lower land conversion pressures on PAs, without legal protection. Analytical methods for PA impact assessment which fail to take this bias into account are likely to overestimate impacts.

2.2 Controlling for bias through matching methods

Methods for controlling for non-random selection of treatment groups, in this case lands under protection, have been under development since the 1940s and are common in areas such as political science, epidemiology, medicine and economics (Stuart 2010). In this regard conservation scientists are extremely late to the party (Ferraro and Pattanayak 2006). So-called quasi-experimental approaches allow observational studies to attempt to match “like with like” and thus reduce bias due to confounding variables, such as differing land conversion pressure due to non-random placement.

Matching methods attempt to select a subset of untreated units most similar to treated units according to relevant, observable covariates which jointly influence treatment assignment and outcomes, and thus construction of an unbiased counterfactual for outcome comparison (Stuart 2010), and have the potential to significantly improve PA impact estimates (Joppa and Pfaff 2010). Matching encompasses a variety of techniques although all attempt to balance observable covariate distribution among treated and control groups by pruning of dissimilar control units (Stuart 2010). Matching areas based on observable covariates which predict land protection and

conversion, for example, remoteness and agricultural suitability, can improve evaluations of PA impacts.

Accordingly these techniques are now gaining traction in conservation. Andam et al. (2008) used matching methods to assess protected forests in Costa Rica, and found that protection was effective at reducing deforestation: in the absence of protection 10% of PAs would have been deforested, although conventional methods would have overestimated impacts by almost two- thirds. Joppa and Pfaff (2010) extended the technique to PAs worldwide and similarly found that controlling for land characteristics substantially reduced impact estimates; by greater than half in 80% of countries. Although protection was still found to be effective in reducing natural land cover conversion in 75% of countries. A nuanced analysis by Gaveau et al. (2012) demonstrated that even these studies may have overestimated impacts by not accounting for relatively low deforestation rates in unprotected areas sanctioned for forest conversion, at least in Sumatra. Deforestation impacts have also been assessed using matching methods, but with fire occurrence as a proxy for deforestation; providing insights into differing effects between PA categories (Nelson and Chomitz 2011), and demonstrating weakness in the relationship between management effectiveness evaluations and conservation outcomes (Nolte 2013). Most recently, Beresford et al. (2013) estimated that protection reduced rate of loss of natural land-cover in Important Bird Areas in Africa by 58% in comparison to matched points.

Matching methods have also illuminated socioeconomic outcomes of PAs; an area of intense debate. Andam et al. (2010) estimated PA impacts on poverty in Costa Rica and Thailand and found that communities near PAs experienced net poverty alleviation when compared with distant, but similar communities. A naïve analysis would have concluded otherwise, as communities near PAs were substantially poorer than national averages at the outset. These conclusions were echoed in a recent study using similar methods to assess PA impacts on poverty in Bolivia (Canavire-Bacarreza and Hanauer 2012).

2.3 Limitations of matching: unobserved or mistakenly ignored covariates

Matching does have weaknesses, the greatest among which is that it assumes that employed covariates account for all differences between treated and control groups, both observable and unobservable, and thus that treatment assignment is independent of outcome (Stuart 2010). If all relevant, observable covariates are not included in matching, or if the selected characteristics do not adequately represent differences between control and treatment groups, then bias may still

exist in the selection of a control group. In extreme cases ignoring relevant covariates can actually increase bias (Stuart 2010).

One approach to overcome this potential pitfall is to include as many characteristics that might be associated with treatment assignment as feasibly possible, as inclusion of non-associated covariates is less costly in terms of increased bias than ignored covariates (Rubin 2006). In practice this may not be possible, thus selection of covariates based on sound understanding of the drivers of PA placement and land conversion may have to suffice. Lastly, whilst matching attempts to improve covariate balance between treatment and control groups, it does not guarantee that post-match groups will be balanced. Matched group means for each covariate should be compared statistically post-matching, and highly imbalanced matched samples discarded (Stuart 2010).

3 Community-based natural resource management & Tanzania’s WMAs

3.1 Participatory conservation in sub-Saharan Africa

Community-based natural resource management (CBNRM), in which local communities are directly or jointly responsible for managing the use and protection of natural resources, has been practiced in sub-Saharan Africa for over 20 years (Roe et al. 2009). A number of factors, such as constrained resources for conservation and burgeoning rural populations, have driven the development of CBNRM initiatives (Roe et al. 2009). These vary geographically but the majority aim to incentivise and enable local people to use natural resources sustainably.

The prevalence and success of CBNRM initiatives varies by natural resource objective and by political geography. The Communal Areas Management Programme for Indigenous Resources (CAMPFIRE) program in Zimbabwe generated nearly USD$30 million in revenues, mostly from safari hunting, over a 17 year period; although evidence for positive environmental impacts is equivocal (Taylor 2009). In Tanzania, participatory forest management has received considerable attention, from both the research community and from national government, with >3.6 million ha of forest land (4% of land cover) under such protection (Blomley et al. 2008), and one of the most advanced community forestry jurisdictions in Africa (Wily 2000). Case studies of participatory vs. non-participatory forest management in Tanzania suggest that community forest management correlates with improved forest condition (Blomley et al. 2008). Elsewhere impacts are harder to discern, with the majority of reporting focusing on area of land covered rather than effectiveness (Roe et al. 2009).

The success of community-based wildlife management (CWM) initiatives across southern Africa, which explicitly target large mammal populations, has been much more variable; with multiple individual projects failing to meet expectations (Balint 2007). The objective of these schemes, the large grazers, browsers, and predators of Africa's savannas and woodlands, have wide ranges outside of PA networks, leading to human-animal conflict and requiring distinct management mechanisms (Hulme and Murphree 2001). Not all schemes are unsuccessful: communal area conservancies in Namibia contributed to reduction of illegal wildlife use and recovery of game populations (Weaver and Petersen 2008).

A major obstacle to improvement of CBNRM initiatives is the paucity of quantitative data and evaluation of their environmental impacts (Roe et al. 2009). The majority of the literature provide case studies of impacts of individual projects, or explorations of theoretical, legal or institutional underpinnings of initiatives. Almost none provide broad, initiative-wide impact analyses, which take into account variety in scale and context between individual projects, which ultimately appear to determine impact.

3.2 The development of WMAs in Tanzania

A case in point is the unknown impact of WMAs in Tanzania (fig. 2). Villages within WMAs are granted usufruct user rights to wildlife resources in order to enable local communities to derive associated benefits, and in so doing encourage enhanced participation in sustainable natural resource management, and thus enhanced wildlife conservation (Nelson 2007). Local communities set aside land for wildlife conservation, foregoing settlement and agriculture, and in return are granted rights to extract income from wildlife use. For the most part this involves hunting quotas which can be used directly, or sold to tourist hunting operators, but non- consumptive tourism (e.g. photo safaris) has also been identified as economically feasible (Christophersen et al. 2000).

WMAs have received numerous criticisms on the basis of their conception, facilitation and marketing, but the most pressing weaknesses relate to poor legal provisions, which until recently failed to define revenue sharing arrangements and did not include provision of hunting quotas to WMAs (Maliasili Initiatives 2013). Both provisions are critical for management authorities to be able to derive net benefits from WMA designation, and thus provide incentives for conservation. Indeed, although some WMAs are generating revenues there is little evidence thus far that benefits are in excess of income prior to gazettement (Sulle et al. 2011). Suggestions have also been made that apparent flaws in WMA’s legal framework are actually by design and an attempt

by national government to recentralize control of natural resources; exacerbating poverty in WMA villages (Benjaminsen and Hanne-Svarstad 2010; Igoe and Croucher 2007). However, new regulations released by the Tanzanian Ministry of Natural Resources and Tourism in 2012 have addressed both of these provisions (Maliasili Initiatives 2013).

Figure 2: WMAs and PAs of Tanzania (USAID 2013). Named WMAs refer to 14 original WMAs. 3 additional WMAs have achieved full registration since map publication.

The 13 WMAs assessed in this study span 23,200 km2of land, representing a major conservation initiative with impacts on people and wildlife across large swathes of rural Tanzania. However, aside from discussions around the legal basis of WMAs, and studies which have focused on elements of their socioeconomic impact, almost nothing is known about impacts on wildlife or habitats within WMAs, with appropriate evaluation hindered by an almost complete lack of

environmental data (Sulle at al. 2011). A country-wide impact assessment is critical to determine whether WMAs are effective in meeting their objectives, specifically to deliver conservation benefits, to inform management decision-making. However, at present, appropriate data to monitor and evaluate WMA performance are lacking. Importantly, there is nothing in the way of comparative studies; either against counterfactuals or against alternative conservation initiatives.

III. Data and methods

In order to assess whether WMAs have had a measureable effect on habitat outcomes within their boundaries, and whether matching altered the degree of any effect, it was first necessary to establish which data to employ, and secondly to establish which methods to use to analyse those data. This section is structured accordingly.

1 Data

This section begins with a brief description of the study area, the area-based protection regimes of Tanzania, followed by sub-sections detailing identification and handling of: 1) spatial and temporal limits of treatment and control units, or WMA designated and unprotected areas; 2) observable covariates relevant to WMA placement for matching, and; 3) appropriate outcome variables to assess habitat destruction.

1.1 Treatment/control units

1.1.1 Study area

This study focuses exclusively on terrestrial land in Tanzania in Eastern Africa. 27.5% of Tanzania’s over 947,000 km2 of land area is under some form of protection (IUCN and UNEP 2013). The majority of Tanzania’s 635 PAs (607 terrestrial, 28 marine) are designated as Forests Reserves or Village Forest Reserves (549); with relatively small numbers of Game Reserves, National Parks, WMAs and Nature Reserves. Ecologically Tanzania is characterized principally by Miombo woodlands in the west and south, and acacia savannas in the centre and north, with a significant stretch of coastal forests in the east (Olson et al. 2001, table 1).

In order to focus analysis on only the terrestrial lands of Tanzania maps of country boundary and water bodies were required. The former was sourced from the GADM database of Global Administrative Areas (Hijmans 2012). External water bodies would be naturally excluded during covariate calculations (detailed below). A number of maps of internal water bodies were reviewed but found to be consistently inaccurate. Consequently, a map corresponding to internal water bodies was constructed using data from the Terra MODIS Vegetation Continuous Fields (VCF) product (LD DAAC 2013), which includes representations of water at 250 m resolution.

Table 1: Terrestrial biomes and ecoregions of Tanzania, ranked by approximate biome area, then by approximate ecoregion area. Extrapolated from Olson et al. (2001).

Biome Approx. land Proportion Ecoregion area (km2) of land area Tropical and subtropical grasslands, savannas, and shrublands 728,959 80.88% Central Zambezian Miombo woodlands 273,070 30.30% Southern Acacia-Commiphora bushlands and thickets 212,899 23.62% Eastern Miombo woodlands 201,565 22.36% Serengeti volcanic grasslands 17,757 1.97% Victoria Basin forest-savanna mosaic 15,403 1.71% Northern Acacia-Commiphora bushlands and thickets 4,658 0.52% Itigi-Sumbu thicket 3,607 0.40% Tropical and Subtropical Moist Broadleaf Forests 111,644 12.39% Northern Zanzibar-Inhambane coastal forest mosaic 53,450 5.93% Eastern Arc forests 23,500 2.61% Southern Zanzibar-Inhambane coastal forest mosaic 20,500 2.27% East African montane forests 12,221 1.36% Albertine Rift montane forests 1,973 0.22% Flooded Grasslands and Savannas 33,865 3.76% Zambezian flooded grasslands 32,256 3.58% East African halophytics 1,609 0.18% Montane Grasslands and Shrublands 22,100 2.45% Southern Rift montane forest-grassland mosaic 20,875 2.32% East African montane moorlands 1,225 0.14% Mangroves 4,740 0.53% East African mangroves 4,740 0.53%

1.1.2 Time period

To assess effects of protection it was important to ascertain when protection began for each WMA. WMA regulations do not specify at which point in the WMA creation process human habitation or activities are curtailed, for example, when a WMA is gazetted or when wildlife user rights are granted, nor what determines extent of curtailment. In reality, timing and extent of protection vary between WMAs:

“The decision to curtail certain activities in a WMA may start when the area is only being proposed to be a WMA. This can mean villagers may agree not to do anything in that area in as long as a decision to set aside an area as a WMA has been made and accepted at the village level.”“...human settlement and farming are not allowed in WMAs because WMAs are given protection status. Grazing can be allowed but…the decision to allow or not to allow is left to the

villagers based on ecological assessment and in relation to their needs.” (Sosovele, H., 2013, personal communication, 13 June)

As specifics of timing and extent of curtailment for each WMA were unavailable at the time of the study, habitat destruction was assessed from the year in which each WMA’s application process was started, to allow the maximum possible time for curtailment to have an effect, whilst ignoring extent of curtailment as a factor.

1.1.3 WMAs (treated units)

At present (2013) 17 WMAs are fully-registered (WMA gazetted and management authority granted Authorized Association (AA) status by Tanzanian Wildlife Director; see table 2 for key dates). 22 additional WMAs are currently in development.

Table 2: Key dates in creation of 17 WMAs which currently (2013) hold AA status, ranked by year application process started (adapted from data collected by WWF-US/WWF-TZ).

WMA name Year application Year gazetted Year user rights process started granted Burunge 2003 2006 2007 Enduimet 2003 2007 2007 Ikona 2003 2007 2007 Ipole 2003 2006 2007 Liwale 2003 2009 2010 Makame 2003 2009 2012 Mbarang’andu 2003 2006 2010 Ngarambe/Tapika 2003 2006 2007 Pawaga-Idodi 2003 2007 2007 Tunduru 2003 2007 2009 Ukutu 2003 2010 2010 Uyumbu 2003 2006 2007 Wami-Mbiki 2003 2007 2007 Makao 2007 2009 2010 Chingoli 2008 2012 2012 Kimbanda 2008 2012 2012 Kisungule 2008 2012 2012

This study focused on a subset of 13 fully-registered WMAs that began development in 2003 to allow a similar and maximal time period for protection to have an effect, although in reality this probably varied between WMAs. Officially these 13 WMAs account for 23,200 km2 or 2.45% of land area in Tanzania. The shapefiles describing their spatial extent were official maps (sourced

from IUCN and UNEP 2013); although edited to reflect more recent knowledge of incorrect PA overlaps or redundancies by Lindsay Spurrier and Erica Rieder of WWF-US.

The majority (68.0% by area) of WMAs assessed in this study were in the Central & Eastern Miombo Woodland ecoregions (henceforth “Woodlands”, table 3); a landscape dominated by the eponymous, oak-like Miombo tree (Brachystegia spp.), although with a high diversity of other tree species (WWF 2013a). These woodlands are home to a diverse range of large mammals, including large populations of African elephants (Loxodonta africana) and hartebeest (Alcelaphus buselaphus). Although human population density is currently relatively low, woodland clearance, conversion to cropland, resource extraction and grazing will become increasing threats given projected population growth (WWF 2013a). A substantial proportion of remaining WMAs (22.8% by areas) target the grasslands and accompanying Acacia-Commiphora woodlands of central and northern Tanzania (henceforth “Bushlands and thickets”). The region is well known for its ecologically and economically important high concentrations of large, migratory mammals, including wildebeest (Connochaetes taurinus), Thomson’s gazelles (Gazella thomsoni) and plains zebra (Equus burchelli). A large proportion of these lands are well-managed and protected, although outside of PAs the majority of land suitable for agriculture has already been converted, causing conflict with migratory species (WWF 2013b). Northern Zanzibar-Inhambane coastal forest mosaic (henceforth “Coastal forest mosaic”) is an increasingly farmed mixture of tropical dry forests, savannas, grasslands and wetlands (WWF 2013c).

Table 3: 13 study WMAs by target biome/ecoregion, ranked by area. Based on data from Olson et al. (2001).

Approx. Biome land Proportion WMA Partial/fragment ecoregion(s) Sub-category area of WMA Ecoregion (km2) area Tropical and subtropical grasslands, savannas, and shrublands 19,983 90.9% Miombo woodlands 14,961 68.0% Ipole Central & Eastern Miombo Woodlands 3,827 17.4% Liwale Central & Eastern Miombo Woodlands 3,603 16.4% Mbarang’andu Central & Eastern Miombo Woodlands 3,052 13.9% Wami-Mbiki Central & Eastern Miombo Woodlands 2,301 10.5% Tunduru Central & Eastern Miombo Woodlands 1,346 6.1% Uyumbu Central & Eastern Miombo Woodlands 831 3.8% Bushlands and thickets 5,022 22.8% Makame Southern Acacia-Commiphora bushlands and thickets 3,908 17.8% Pawaga-Idodi Southern Acacia-Commiphora bushlands and thickets Eastern Miombo woodlands 732 3.3% Ikona Southern Acacia-Commiphora bushlands and thickets 203 0.9% Burunge Southern Acacia-Commiphora bushlands and thickets Serengeti volcanic grasslands 179 0.8% Tropical and Subtropical Moist Broadleaf Forests 1,269 5.8% Ngarambe/Tapika Northern Zanzibar-Inhambane coastal forest mosaic 712 3.2% Ukutu Northern Zanzibar-Inhambane coastal forest mosaic Eastern Arc forests/Miombo woodlands 557 2.5% Mixed Moist Broadleaf, grasslands, savannas, and shrublands 740 3.4% Northern Acacia-Commiphora bushlands and thickets/ Enduimet Serengeti volcanic grasslands/East African montane forests 740 3.4%

1.1.4 Unprotected areas (control units)

A pool of control units, or areas of unprotected land in Tanzania, for naïve comparison with WMAs and for use as potential control units in matching, were identified by constructing a map of all terrestrial land that excluded known areas under protection (PAs of any designation, including WMAs). The shapefiles describing spatial extent of PAs were official maps (sourced from IUCN and UNEP 2013); although edited by Lindsay Spurrier and Erica Rieder of WWF-US. This process highlighted issues with the mapping of PAs and WMAs in Tanzania, namely substantial overlaps between the two (fig. 3).

Figure 3: Overlaps (red) identified in mapping of PAs (green) and WMAs (blue) in Tanzania. Overlapping areas were excluded from analysis, to remove possibility of inflating the effect of WMAs by comparing land that may already be under protection. Extrapolated from map data (see sections 1.1.3/1.14 for map provenance).

For example, Pawaga-Idodi WMA overlapped in its entirety with Lunda-Mkwabi Game Controlled Area. This may be due to: 1) inaccurate mapping of WMAs, PAs, or both WMAs and PAs; 2) parts of WMA network already under protection as other PA types; or 3) a mixture of 1 and 2. Although it is possible some overlaps may be entirely due to incorrect mapping of PAs, and thus warrant inclusion in the analysis as WMA land, I decided to exclude all PAs, including overlaps, to remove the possibility of inflating the effect of WMAs by comparing land that may already be under protection. Consequently, significant portions of the WMA network, including Pawaga-Idodi WMA, were removed from further analysis.

1.2 Covariates

1.2.1 Drivers of habitat destruction

In order to establish the effect of WMA protection on habitat destruction it was first necessary to assemble a relevant set of observable covariates which are believed to jointly affect the positioning of PAs and habitat destruction in Tanzania, and thence control for differences in these characteristics between WMAs and unprotected areas through matching. Habitat destruction in Tanzania is largely driven by agricultural practices (Msoffe et al. 2011). Drivers which predict suitability for agriculture should thus predict rates of destruction. Conversely, land which is less suitable for agriculture is also more likely to be chosen for protection, as there will be less interest in its use and easier to gazette. Thus, drivers which predict low habitat destruction should also predict WMA placement.

As discussed previously, PA networks globally tend to be biased towards areas which are less attractive to humans for settlement or agriculture e.g. high elevations, steep slopes, remote from roads and cities (Joppa and Pfaff 2009). Olson et al. (2004) highlight factors such as population growth, market access, environmental conditions (including amount and variability of rainfall, prevalence of human, livestock and crop pests and diseases, and soil fertility), changing rural economy and land tenure policy in a review of drivers of habitat destruction in East Africa. More locally, Msoffe et al. (2011), in an analysis of the spatial correlates of land-use change in Tanzania, found that likelihood of agriculture correlated with rainfall (peaking at 800 mm annual precipitation), slope (peaking at 10°) and with proximity to villages and roads.

Ideally, all these factors would be included as covariates. However, to do so requires that associated data are freely available, easily accessible, spatially explicit, and at an appropriate resolution, with clear and authoritative provenance. Only data which fulfilled these criteria were selected, resulting in the following set of covariates (fig. 4): those which describe proximity to

human activities (distance to major towns, distance to road network and population density), land use suitability (elevation, slope and rainfall) and an additional covariate, ecoregion. In particular, efforts were made to locate additional covariates related to agricultural suitability and potential yield, but data meeting these criteria could not be located.

1.2.2 Proximity/remoteness measures

1.2.2.1 Distance to major towns

PAs tend to be positioned far from major towns (Joppa and Pfaff 2009), in accordance with the role of settlements as markets for exchange of goods. Land may have high production value but if far from an appropriate market this will reduce the value of the produce. A raster describing relative Euclidean straight line distance from each cell to closest major town (>50,000 people) was created based on a vector of point data derived from the Africover project database (Alinovi et al. 2000; fig. 4a).

1.2.2.2 Distance to road network

Roads provide access to land/natural resources and markets, thereby reducing transportation costs. Accordingly, roads have been found to promote agricultural land use and deforestation (Chomitz and Gray 1996). As above, a raster describing relative Euclidean straight line distance from each cell to closest major road was created based on a vector of polyline data derived from the Africover project database (Alinovi et al. 2000; fig. 4b).

1.2.2.3 Population density

Population density correlates with land use change, for understandable reasons (Lung et al. 2010). The AfriPop project provided a raster of high-resolution (~100 m) data on population density (Linard et al. 2012; fig. 4c). AfriPop data is created by interpolation of national census data projected forward to 2010 using UN national growth estimates. These data were highly leptokurtic and positively skewed (kurtosis = 3770, skewness = 49; i.e. a few points had very high population density and the majority had very low density), resulting in an error during matching due to perfect correlation with treatment. To enable their use the data were transformed by calculating the fourth root, identified by successive transformations (post-transform: kurtosis = 2.10, skewness = 0.589; reducing the range). Similar high power root transformations have been employed in other ecological examinations (McCune and Grace 2002).

Figure 4: Selected covariates: relative distance from major towns (a) and roads (b), population density (c), elevation (d; metres), annual precipitation (e; millimetres) and slope (f; °).

1.2.3 Land use suitability measures

As discussed by Nelson and Chomitz (2011) shallow slopes and low elevation are more at risk of conversion to agriculture as they are likely to be accessible and productive, in addition to being proxies for agriculturally desirable temperatures and physical soil properties.

1.2.3.1 Elevation

Elevation was extracted from a 250 m resolution raster for the Southeast of the globe, which had been resampled from Shuttle Radar Topography Mission (SRTM) 90 m digital elevation data (Jarvis et al. 2008; fig. 4d).

1.2.3.2 Slope

A raster for slope was constructed by calculating rate of change in degrees from horizontal between each cell and its neighbour, based on elevation data. A z-factor of 0.00000898 (for latitudes° of 0 to 10 (ESRI 2012)) was applied to correct for the change in units from metres to degrees (fig. 4f).

1.2.3.3 Rainfall

Rainfall is a well-documented predictor of land use suitability. In this specific geographic context, long-term annual rainfall has been shown to be a significant driver of forest loss in East Africa (Pfeifer et al. 2012), and agricultural expansion correlates with rainfall In Tanzania (Msoffe et al. 2011). The Tropical Rainfall Measuring Mission 2b31-Based Rainfall Climatology Version 1.0 database provided a raster for total annual rainfall in mm (estimated and interpolated from 1997- 2006 averages) at 1km resolution for a 1000 by 1000 km tile which included Tanzania (Mulligan 2006; fig. 4e).

1.2.4 Ecoregion

An additional covariate, ecoregion, was included to ensure WMA lands were sensibly, matched with areas of similar combinations of soil and landform characteristics. Data were sourced from Olson et al. (2001), who define ecoregion as “relatively large units of land containing a distinct assemblage of natural communities and species, with boundaries that approximate the original extent of natural communities prior to major land-use change” (fig. 5).

Figure 5: An additional covariate, ecoregion, ensured WMA lands were sensibly matched with areas of similar combinations of soil and landform characteristics.

1.3 Outcome variables

1.3.1 Assessing habitat destruction remotely

WMAs were established expressly to preserve wildlife habitat in Tanzania, among other objectives (Wilfred 2010). In order to identify relevant outcomes to assess habitat destruction in WMAs it was first necessary to ascertain what destruction means in the context of WMAs, and how that might be assessed remotely.

The majority of remote assessments have been concerned with monitoring deforestation, either directly, or indirectly using fire occurrence a proxy (Horning et al. 2010). However, WMAs are encompas forested areas, savannas, shrublands and grasslands. Therefore, whilst destruction of forest habitats through deforestation is a relevant outcome to assess for some WMAs, it is not an appropriate outcome to assess habitat destruction in non-forested WMAs.

Despite being a major driver of species loss, habitat destruction, including degradation (diminishment), fragmentation (change in spatial pattern) and outright loss (change in amount), is relatively poorly conceptualised in the literature. In east African rangelands destruction typically involves expansion of agriculture and spread of settlement into rangelands leading to

the loss of habitat for wild herbivores, but can also mean partial modification of habitat quality, rendering it usable, but less suitable (Reid et al. 2004).

Assessing such fine-scale variation in habitat characteristics remotely is inherently challenging given the coarse scale of available remote imagery, but not insurmountable. Recent years have seen the proliferation of spectral vegetation indices (VIs) and earth observation (EO) products with broad applications in biodiversity monitoring (Pfeifer et al. 2012). Land cover products can be used to monitor changes within and between vegetation types, and VIs provide indications of environment-induced changes in vegetation composition, structure and function (Horning et al. 2010).

An array of optical satellite systems, including the Terra and Aqua satellite born Moderate- resolution Imaging Spectroradiometer (MODIS) and the SPOT satellite, provide imagery used to produce a suite of surface reflectance products with differing degrees of processing, spatial resolution and temporal granularity (Jung et al. 2006). For the purposes of this study products were required which were available at no cost, easily accessible, quality controlled, and at a resolution high enough to detect meaningful changes in habitat, but low enough to allow data manipulation in the time available. Three different MODIS level-3 products (level denoting data has been mapped and gridded with defined accuracy/quality assurance) were chosen on this basis: EVI, tree cover and land cover type. (LP DAAC, 2013).

1.3.2 EVI

Vegetation indices are mathematical transformations of two or more wavelengths of satellite- derived surface reflectance to produce quantitative measures of ‘greenness’ (Chen et al. 1998). The positive and strong correlation between the Normalized Difference Vegetation Index (NDVI), a commonly used index, and vegetation productivity is well understood. As vegetation productivity varies predictably with land cover, NDVI can be used to differentiate land cover and detect change, even in low productivity environments. For example, below normal NDVI values predicted deforestation, overgrazing and agricultural land in southern Mauritania (Thiam 2002), and temporal change a useful indicator of landscape destruction in Australia (Holm et al. 2002).

Studies in similar contexts to this analysis have shown that NDVI and primary productivity are important predictors of large mammal parameters. For example, productivity correlated positively with density for the majority of 13 assessed African ungulate species (Pettorelli et al. 2009). NDVI has elucidated wildebeest migration in the Serengeti (Boone et al. 2006), and African

elephant range sizes and diet in Kenya and southern Africa, respectively (Wittemyer et al. 2009, Young et al. 2009).

The Enhanced Vegetation Index (EVI), an ‘optimized’ index, is calculated similarly to NDVI, but with enhanced sensitivity in tropical environments (Huete et al. 2002). MODIS product MOD13Q1 provided a series of 16-day composite EVI layers at 250 m resolution for 2003 to 2012. Data were aggregated into annual averages.

1.3.3 Tree cover

Deforestation may not be a relevant outcome for all WMAs, but a majority do have forested areas. Furthermore, forest loss is a significant and ongoing form of habitat destruction in east Africa (Pfeifer et al. 2012). The MODIS Vegetation continuous fields dataset (VCF) depicts percentage tree cover at 500 m resolution and is able to perceive subtle changes in vegetation density. Indeed DeFries et al. (2005) used the VCF product to demonstrate deforestation in tropical forests. The product has been used in areas with long dry seasons, such as tropical dry forests (Miles et al. 2006), and to assess drivers of tree cover in African savannas (Bucini and Hanan 2007). MODIS product MOD44B provided a series of annual percentage tree cover layers at 250 m resolution for 2003 to 2010.

1.3.4 Land-cover type

Monitoring of change in discrete land-cover types is one of the most commonly applied methods for remote assessment of PA impacts. Different land-cover types are classified on the basis of spectral reflectance (Turner et al. 2003). MODIS product MCD12Q1 provided a series of annual images depicting land cover type classified according to the International Geosphere Biosphere Programme (IGBP) global vegetation classification scheme at 500 m resolution for 2003 to 2010.

2 Methods

Following a brief discussion regarding how data were handled and stored, this section is structured in order of the analyses undertaken: firstly, matching of WMA lands with unprotected areas, and subsequent covariate balance checks; followed by individual sections on impact assessment of each outcome, including naïve (pre-matching) and matched comparisons.

2.1 Data extraction, handling and storage

All spatial data were projected to WGS 84 Geographic Coordinate System and resampled to 250m (if required) and manipulated in ArcGIS 10.1 (ESRI 2012; hereafter ‘ArcGIS’). Covariate and outcome data were extracted into flat file databases for storage. Subsequent matching and outcomes analysis were undertaken in the R programming environment v. 2.15.3 (R Development Core Team 2013; hereafter ‘R’).

2.1.1 Covariate, mapping and matched point data

Covariate/mapping data were manipulated in ArcGIS as follows: 1) covariate layers clipped to Tanzania extent; 2) external water bodies excluded by clipping all layers according to a mask, which was constructed through between-covariate calculations (as a subset excluded external water bodies); 3) all layers clipped to exclude internal water bodies using map detailed previously; 4) then clipped again to exclude all non-WMA PAs, leaving only rasters of covariates corresponding to WMA and unprotected terrestrial land in Tanzania; 6) covariate values, geographic coordinates and protection status were extracted for sampling and matching in R; 7) coordinates of matched points were subsequently used to construct point vectors of treated and untreated points.

2.1.2 Outcome data

R and the ‘MODIS’ package (Mattiuzzi et al. 2013) were employed to automate download of >1000 MODIS files/250GB of data (6 per time period corresponding to tiles encompassing Tanzania), extraction of outcome layers, mosaicking of layers into continuous datasets, subsetting of mosaics to Tanzania extent, conversion to GeoTIFF format and reprojection of output files for manipulation in ArcGIS. Following outcome specific calculations in ArcGIS, data were extracted for impact analysis in R, either for all WMAs and unprotected areas for naïve analysis, or according to matched point vectors.

2.2 Matching

2.2.1 Methodology

Despite matching’s long history of use for causal inference there is still little consensus on the most effective matching method and distance measure to employ (Sekhon 2011). Following Andam et al. (2008), Joppa and Pfaff (2010) and Nelson and Chomitz (2011) I used nearest neighbour matching of continuous covariates (those describing proximity to human activities and land use suitability) using the Mahalanobis distance metric, with replacement. Following Nelson and Chomitz (2011) I combined this approach with exact matching on a categorical covariate, ecoregion. Matching was undertaken in R using the ‘MatchIt’ package (Ho et al. 2011).

For each treated point nearest neighbour matching selects a control point with the smallest distance (measure of similarity) from the treated point (Stuart 2010). The Mahalanobis distance, which is the average distance from each unit to its closest untreated treated, takes into account differing scales in multivariate data and is effective when matching with <8 covariates (Stuart 2010). Exact matching matches treated and control points exactly.

2.2.2 With/without ecoregion

Matching was performed without and with exact matching on ecoregion. Whilst an important covariate to ensure sensible matches, the requirement for exact matching (as a categorical variable) provides the potential to severely restrict potential control units. The effect of its inclusion was assessed post-matching, with regard to number and geographic spread of matched points and impact estimates.

2.2.3 With/without caliper

Matching was also performed with and without a 0.5 standard deviation caliper for each covariate. Nearest neighbour matching has the potential for poor matches if no similar control points are available (Stuart 2010). Imposing a caliper ensures only matches within the caliper are selected, although may lead to few matches. Number and covariate balance was assessed post- matching to identify if a caliper was required and, if so, it’s effect.

2.2.4 Sampling

Spatially explicit variables generate large data sets. For example, Tanzania’s land area at 250 m resolution has >15 million data points. High-performance computing has expanded options for spatial analysis but for this study random sub-sampling was the only feasible option. After Joppa

and Pfaff (2010) treated and control groups were randomly sampled 1 treated unit to 4 control units. Trial and error identified 100,000 treated to 400,000 control points as the maximum matching sample size feasible in the time available.

2.2.5 Covariate balance checks

Matching aims to result in treated and control groups with covariates of equal or similar distribution. An important step is to check balance between groups post-matching, and reject or keep samples accordingly. Following matching balance between treated and control groups was assessed numerically, through calculation of difference in covariate means and percent improvement, and graphically, via comparison of empirical quantile-quantile (Q-Q) plots between unmatched and matched groups. Deviation of points from the 45° line of a Q-Q plot indicate differences in covariate distribution between treated and control groups.

2.3 Impact analysis

2.3.1 Sampling

Prior to impact assessments outcome data for naïve and matched analyses were sampled further to enable manipulation in R in feasible time periods. Treated and control groups were randomly subsampled to 10,000 points or, if either had <10,000 observations the corresponding group was randomly subsampled to the same size. Following sampling methods used to assess both naïve and matched groups were identical according to outcome. Differences in outcomes between WMAs and unprotected areas, and between naïve and matched analyses were highlighted through tables and plots.

2.3.2 EVI

Initial analysis concerned calculation of annual descriptive statistics (mean and standard deviation) for each WMA vs. corresponding control groups (all unmatched or matched unprotected areas), followed by visual examination of differences via plots. In order to answer the question of whether points in treated groups showed greater change in EVI over time than those in control groups, repeated measures ANOVAs (rmANOVA) were employed, with time as the predictor variable observed within-subjects and treatment as the predictor variable observed between-subjects. An rmANOVA is justified as the outcome variable, EVI, is continuous and normally distributed, but observations are longitudinally correlated, as each point is measured at multiple points through time. Analysis was facilitated by the ‘ez’ package for R (Lawrence 2013).

2.3.3 Tree cover

Initial analysis concerned calculation of annual descriptive statistics for each WMA vs. its control group, followed by visual examination of differences via plots. Subsequently two methods were employed:

1) Firstly, a ‘traditional’ before/after treatment methodology, in which change in tree cover was calculated by subtracting proportion tree cover in 2010 from tree cover in 2003, to create a single outcome measure of gain or loss in tree cover (calculated as Δ ± standard deviation). Before/after impact estimates assume a reasonably linear trend in change over time and ignore magnitude of inter-annual variation, such that a perceived increase or decrease may in fact be due to short- term variation. However, following Joppa and Pfaff (2010), it can be useful for discerning large- scale patterns and allows calculation of a simple, single-number impact estimate, which can be compared with existing studies and between unmatched/matched samples.

A Welch’s two-sample t-test was used to compare mean change in tree cover in WMAs and control groups, followed by calculation of Cohen’s d (difference in two group’s mean’s divided by average of their standard deviations) as a measure of effect size. Measures of d >0.2 indicate a small effect size, >0.5 a medium effect size, and 0.8 a large effect size.

Impact estimates are calculated by subtracting change in unprotected areas from change in WMAs, to provide an indication of the amount of change attributable to WMA protection:

ImpactWMA = ΔWMA – ΔControl

2) rmANOVAs were also used to assess finer scale variation in change over time. Proportion data were logit transformed prior to rmANOVAs, following methods outlined by Warton and Hui (2011).

2.3.4 Land cover

The IGBP classification scheme includes 17 land cover types in 7 broad groups (table 4; Friedl et al. 2010). Land cover was assessed at group level, except for ‘Woody savannas’ and ‘Savannas’, which, as the predominant type in Tanzania, were assessed at type level. Analysis involved visualisation of change over time and calculation of pre- and post-matching impact estimates for common types.

Table 4: IGBP land cover classifications and broad groups (Friedl et al. 2010).

Group IGBP land cover type

Evergreen Needleleaf forest Evergreen Broadleaf forest Forest Deciduous Needleleaf forest Deciduous Broadleaf forest Mixed forest Closed shrublands Shrublands Open shrublands Woody savannas Woodlands Savannas Grasslands Grasslands Seasonally or permanently inundated Permanent wetlands Croplands Croplands and mosaics Cropland/Natural vegetation mosaic Urban and built-up Snow and ice Unvegetated Barren or sparsely vegetated Water

IV. Results

Results are reported in order of the analyses undertaken: matching, followed by naïve and matched analyses of each outcome.

1 Matching

1.1 Pre-matching covariate balance

WMAs in Tanzania exhibited markedly different covariate distribution in comparison to unmatched, unprotected areas, across all assessed land characteristics, indicating that matching was justified (table 5). On average WMAs are located in more remote areas: further from major towns and roads, and high human population densities. Furthermore, WMAs appear to be located in locations which differ in terms of land use suitability: at lower altitudes, on less sloped terrain, and with lower annual rainfall.

Table 5: Summary of covariate balance for unmatched, treated (WMA) and control (unprotected) groups. Difference in means highlighted green/red for higher/lower treated means, respectively.

Treated All control Covariate Means Means SD Mean Diff Proximity Distance town 0.865 0.580 0.343 0.285 Distance road 0.122 0.100 0.106 0.022 Population density 0.539 0.747 0.185 -0.208 Land use suitability Elevation (m) 831 1025 476 -195 Slope (°) 1.29 2.85 3.85 -1.56 Precipitation (mm) 723 905 402 -183

1.2 Matching with/without ecoregion

1.2.1 Matched samples

Nearest neighbour matching using the Mahalanobis distance metric resulted in selection of a small subset of matched control points (table 6). Matching with or without exact matching on ecoregion did not substantially alter size of control sample (2.47% vs. 2.18% of all unmatched control points, respectively), indicating that the majority of control points selected by matching without ecoregion were already located in the same ecoregion as treated points, and thus sensible matches.

Table 6: Sample sizes of control and treated groups, for Unmatched and Matched groups

Control Treated group group Unmatched 400,000 100,000 Matched data 9,885 100,000 (w/out ecoregion) Matched data 8,713 100,000 (w/ecoregion)

Visual examination revealed that points matched without ecoregion were often broadly distributed across Tanzania (figure #). This pattern was broadly similar for WMAs in all ecoregion types, for example, Mbarang’andu WMA, located in Miombo woodlands (figure # a/b), Makame WMA, located in Acacia-Commiphora bushlands and thickets (fig. 6 c/d), and Ngarambe/Tapika WMA, located in coastal forest mosaic (fig. 6 e/f). The majority of matched control points using either technique were generally nearby to the corresponding WMA i.e. within 50 km of borders.

Figure 6: Geographic distribution of control points matched with/without ecoregion, respectively, for Mbarang’andu WMA (a/b; woodlands), Makame WMA (c/d; bushlands and thickets) and Ngarambe/Tapika WMA (e/f; coastal forest mosaic). Ecoregion restricts distribution, but with negligible effect on # of matched control points (9,885 vs. 8,713).

1.2.2 Post-matching covariate balance

Matching resulted in substantial covariate balance improvement (table 7): >95% reduction in difference in means between treated and control groups following matching for all covariates, bar >90% reduction for distance to major roads. Matching with/without exact matching on ecoregion had a negligible effect on magnitude of improvement.

Table 7: Summary of covariate balance for matched groups, and balance improvement (change in difference in means following matching)

Matched control Matched control Treated (w/out ecoregion) (w/ecoregion) Covariate Δ Δ Mean Mean Means Means SD Mean Means SD Mean Diff Diff Diff Diff Proximity Town 0.865 0.857 0.260 0.008 97.2% 0.859 0.262 0.006 97.8% Road 0.122 0.120 0.098 0.002 90.9% 0.121 0.098 0.002 92.8% Population 0.539 0.550 0.118 -0.010 95.1% 0.550 0.118 -0.010 95.1% Land-use Elevation 831 835 361 -4.25 97.8% 836 365 -4.86 97.5% Slope 1.29 1.32 1.35 -0.04 97.8% 1.33 1.34 -0.04 97.4% Precipitation 723 729 282 -6.08 96.7% 730 281 -7.51 95.9%

These results were corroborated by visual examination of Q-Q plots, which exhibited points with substantial deviations from the 45° line for unmatched data across covariates, indicating high imbalance between treated and control groups (fig. 7). For four covariates matching resulted in points lying close to the line, indicating similar empirical distributions between treated and control groups post-matching, except slope and distance to road in which a small number of points were imbalanced. There was no discernible difference between groups matched with/without ecoregion.

Figure 7: Q-Q plots of difference in quantiles between treated and control group covariate distributions, for All (unmatched) and Matched groups (with/without ecoregion). Covariates exhibited different distributions between treated and control groups prior to matching. Matching improved balance in all cases, whether with/without ecoregion.

1.3 With/without caliper

Matching with a 0.5 standard deviation caliper for each covariate resulted in selection of an identical group of points as matching without a caliper, indicating that all matched points already fell within 0.5 standard deviations. Thus a caliper was not required, and corroboration that matching had resulted in strong matches.

1.4 Summary

WMAs in Tanzania exhibited markedly different covariate distribution in comparison to unmatched, unprotected areas, indicating that matching was justified. Subsequent matching resulted in selection of a small subset of matched control points with strong covariate balance, which were retained for use in impact assessments accordingly. The majority of matched control points were located within 50 km of WMA borders. Matching with/without ecoregion had little effect on size of control sample or improvement in covariate balance, although matching without ecoregion resulted in a small proportion of points broadly distributed across Tanzania.

2 Impact analysis

2.1 EVI

2.1.1 By WMA

Broadly, mean observed EVI values were highest for WMAs in Coastal forest mosaic (e.g. 3,737 ± 590 SD in Ngarambe/Tapika), lower in Woodlands (e.g. 3,385 ± 364 in Wami-Mbiki) and lowest for WMAs in Bushlands and thickets (e.g. 2,886 ± 208 in Ikona), in comparison to 3,213 ± 732 for all unprotected areas (fig. 8). Mean EVI values in Ipole appeared lower than typical for a WMA in Woodlands (2,450 ± 374), and particularly low in Enduimet (1,694 ± 341), which incorporates mixed forest, woodland, bushland and thicket.

All WMAs displayed a high degree of inter-annual variation. For example, in Ukutu, mean EVI values were lowest at 2, 885 ± 484 in 2003, rose to a peak at 3,860 ± 437 in 2007, before falling again to 3,034 ± 419 in 2009. Discerning patterns was consequently difficult, but in general EVI values appear to rise from the beginning of the period assessed, peak in the middle, then fall to similar starting levels.

Figure 8: Change in mean EVI values from 2003 to 2012 in all unprotected areas and by WMA, according to ecoregion type.

2.1.2 Effect of WMA protection over time

Differences between mean EVI values in treated (WMA protected) and untreated (unprotected) groups over time were highly significant for all WMAs, using unmatched and matched data sets (rmANOVA, p <0.005; see table 12 in Appendices for F-values/d.f. for each interaction). However, plotting of differences revealed that in practical terms any difference was so slight as to be

indiscernible from high background noise. Observed significance of the interaction between protection and time is therefore likely due to large sample sizes, rather than real-world differences (see discussion for details). Whilst small effect sizes were calculable in some cases it was not possible to distinguish the direction of any effect amongst inter-annual fluctuations.

Visual comparison of change in mean EVI vs. unmatched and matched unprotected areas revealed insights (fig. 9). WMAs do differ from all unprotected areas, as might be expected given their wide geographic placement, and in some instances a divergence in trends might be perceived in recent years. For example, whilst in all unprotected areas EVI consistently decreased from 2010 to 2012, in Liwale it increased incrementally each year. Furthermore, whilst EVI values in unprotected areas were generally only slightly lower at the end of the assessed period, values in some WMAs were noticeably higher (e.g. Burunge) or lower (e.g. Ikona), and the majority of WMAs experienced greater decreases than in unprotected areas.

However, in all cases, matching resulted in selection of unprotected areas in which EVI values very closely tracked that of WMAs, sometimes almost perfectly, removing any observed differences. For example, post-matching almost no difference is distinguishable between Liwale and unprotected areas. Matching with ecoregion has a particularly strong effect e.g. in Enduimet. Only for Ipole was change in EVI through time in WMAs not almost exactly mirrored by change in matched areas, although this may be due to low EVI values.

2.1.3 Summary

In summary, it is very difficult to discern the effect of WMA protection on EVI values because any effect it is too small in comparison to existing fluctuations, but visual examination of change in comparison to matched protected areas suggests that the majority of WMAs appear to have had no effect on EVI values within their boundaries in comparison with unprotected areas. The results also suggest that matching WMAs with similar, unprotected areas was appropriate, and substantially reduced any observable differences.

WMA All control Matched control (w/out ecoregion) Matched control (w/ ecoregion)

3900 3700 3500 3300 3100 2900 2700 2500 a) b) c)

3900 3700 3500 3300 3100 2900 2700 2500 d) e) f)

3600 3400 3200 3000 2800 2600 2400 2200 g) h) i)

4500 3300 4300 3000 4100 2700 3900 3700 2400 3500 2100 3300 1800 3100 j) k) 1500 l)

2900 1200

2009 2003 2004 2005 2006 2007 2008 2010 2011 2012

2004 2003 2005 2006 2007 2008 2009 2010 2011 2012

2006 2004 2005 2007 2008 2009 2010 2011 2012 2003

Figure 9: Change in mean EVI values from 2003 to 2012 in Ipole (a), Liwale (b), Mbarang’andu (c), Tunduru (d), Uyumbu (e), Wami-Mbiki (f), Burunge (g), Ikona (h), Makame (i), Ngarambe/Tapika (j), Ukutu k) and Enduimet (l). Year gazetted = open circle, year AA granted user rights = open triangle.

2.2 Tree cover

2.2.1 By WMA

Results from forest cover analyses were broadly similar to those for EVI: mean proportion tree cover was highest in Woodlands (e.g. 21.0 % ± 8.0 SD in Tunduru) and Coastal forest mosaic (e.g. 19.9 ± 16.3 in Ngarambe/Tapika), lower in Bushlands and thickets (e.g. 8.8% ± 2.7 in Ikona), very low in Enduimet’s mixed environment (3.2% ± 3.1), and lower than typical for a Woodland WMA in Ipole (9.2% ± 5.1); all in comparison to 14.1% ± 13.1 mean tree cover in all unprotected areas (fig. 10).

WMAs also displayed a similar degree of inter-annual variation, with an approximately comparable pattern between WMAs over the 8 year period: mean tree cover rising steeply from beginning to the middle of the period, before falling to similar starting levels (e.g. Makame: 5.9% in 2003, peaking at 12.8% in 2006, and falling to 5.8% in 2010). The majority of WMAs appeared to finish lower with notable exceptions in Woodlands and Ngarambe/Tapika. Of note are consistently high ranges of values within years (e.g. 22.8% ± 20.8 SD in Ngarambe/Tapika in 2004).

Figure 10: Change in mean proportion of tree cover from 2003 to 2010 in all unprotected areas and by WMA, according to ecoregion type.

2.2.2 ‘Traditional’ impact estimates

A ‘traditional’ before/after assessment of impact confirmed that mean change in forest cover between 2003 and 2010 varied widely across WMAs, with increases observed in four WMAs and decreases in eight (table 9). The maximum net increase of 9.20% ± 12.5 was observed in Ngarambe/Tapika, and the maximum net decrease of 4.19% ± 6.91 in Uyumbu. Change in cover in each WMA differed significantly from change in all unprotected areas (Welch's t-test, p<0.001), which approached zero (0.40% ± 8.88). Mean change in tree cover in matched unprotected areas however very closely resembled change in WMAs, as might be expected from EVI-based results, removing or reducing significance in the majority of cases (fig. 11).

Table 9: Mean change in % forest cover (Δ ± SD) from 2003 to 2010 in WMAs, and All (unmatched) and Matched (w/out and w/ ecoregion) control areas. Gain or loss in cover highlighted green or red, respectively, shaded according to magnitude.

Matched control Matched control WMA All control (w/out ecoregion) (w/ ecoregion) WMA ± Δ SD Δ ± SD Δ ± SD Δ ± SD Miombo woodlands Liwale 6.28 12.1 0.40 8.88 5.35 11.9 5.29 11.9 Mbarang’andu 4.45 8.04 0.40 8.88 3.09 8.52 4.01 8.78 Tunduru 2.79 8.52 0.40 8.88 2.65 8.95 3.13 9.02 Wami-Mbiki -0.56 7.21 0.40 8.88 0.01 10.5 -0.43 10.4 Ipole -3.26 4.35 0.40 8.88 -3.51 6.98 -3.87 6.58 Uyumbu -4.19 6.91 0.40 8.88 -3.82 7.79 -4.63 8.45 Bushlands/thickets Makame -0.01 4.28 0.40 8.88 -1.14 5.32 -0.99 4.93 Burunge -2.39 3.12 0.40 8.88 -3.23 5.01 -2.55 4.13 Ikona -4.07 3.17 0.40 8.88 -4.48 6.50 -3.56 4.26 Coastal forest mosaic Ngarambe/Tapika 9.20 12.5 0.40 8.88 9.75 13.2 12.55 12.4 Ukutu -2.25 7.13 0.40 8.88 -4.59 9.73 -4.72 9.49 Mixed Enduimet -2.09 2.26 0.40 8.88 -1.40 4.71 -2.44 2.61

Consequently, whilst pre-match differences mirrored change in cover in WMAs e.g. a large effect size for Ngarambe/Tapika (d=0.81), a medium effect size for Uyumbu (d=0.58) and no effect for some WMAs e.g. in Makame (-0.01% ± 4.28, d=0.06), post-matching, only three WMAs still had even a small effect size in comparison with their control groups: change in forest cover was significantly lower in Makame’s matched controls (d=0.23/0.21) and Ukutu (d=0.27/d=0.30), and only higher in Ngarambe/Tapika’s with-ecoregion matched control group (d=0.27).

As a result, whilst pre-match impact estimates indicated that six WMAs experienced net reductions and four net increases in tree cover within their borders in comparison with unprotected areas between 2003 and 2012, post-match impact estimates indicated net increases in only two WMAs, Ukutu (2.34/2.48) and Makame (1.13/0.98), and a net decrease in Ngarambe/Tapika (-3.35, matched with ecoregion only) (fig. 12). In general, the main effect of matching was to reduce impact estimates, such the majority of WMAs had no significant impact or practical effect on tree cover.

Uyumbu ***(M) Ikona ***(M) Ipole ***(M) * Burunge ***(S) Ukutu ***(S) ***(S) ***(S) Enduimet ***(S) *** * Wami-Mbiki *** * Makame *** ***(S) ***(S) Tunduru ***(S) Mbarang’andu ***(S) *** * Liwale ***(M) ** ** Ngarambe/Tapika a) ***(L) b) c) ***(S)

-5.00 5.00 -5.00 5.00 -5.00 5.00

Figure 12: Pre-match (a) and post-match (w/out ecoregion (b) and w/ ecoregion (c)) impact estimates of WMAs on forest cover, ranked by pre-match estimates. Asterisks indicate significance of difference between WMA and control groups (Welch's t-test; p < 0.05 = “*”, < 0.01 = “**”, < 0.001 = “***”) and associated effect size of protection (d; small=”S”, medium=”M”, large=”L”).

2.2.3 Effect of WMA protection over time

Finer scale assessment of differences in tree cover between treated (WMA protected) and untreated (unprotected) groups over time suggested all were highly significant (rmANOVA, p <0.005; see table 13 in Appendices for F-values/df for each interaction), for both unmatched and matched data sets, although plotting revealed that in practical terms most differences were so slight as to be indiscernible from high background noise.

Again, prior to matching in some instances a divergence in trends might be perceived in recent years, reflected in pre-match before/after impact estimates. For example, tree cover in Ikona appeared to reduce in comparison to unprotected areas over time (fig. 13b). However, post- matching change in matched unprotected areas mirrored change in WMAs, removing any observed differences e.g. Ikona’s matched unprotected area tree cover reduced in exactly the same fashion as Ikona’s. Similarities were not always clear cut (fig. 13a), but despite high stochasticity matched unprotected areas experience indistinguishable change over time from WMAs. Furthermore, even small effects suggested by before/after impact estimates were confirmed as the result of high inter-annual fluctuations e.g. in Ukutu (fig. 13c).

2.2.4 Summary

In summary, results indicate that since their inception the majority of WMAs have had little effect on tree cover within their boundaries in comparison with unprotected areas, and any impact indicated by ‘traditional’ before/after impact estimates is likely due to short-term fluctuations. Results also suggest that matching WMAs with similar, unprotected areas was appropriate, and reduced the impact of protection over time on tree cover, although to a lesser degree than differences in impact on EVI values.

30 WMA All control Matched control (w/out ecoregion) Matched control (w/ ecoregion) 28 a) 26 24 22 20 18

16 Mean tree Meantree cover (%) 14 12 10

16 b)

8 Mean tree Meantree cover (%) 6

30 28 c) 26 24 22 20 18

16 Mean tree Meantree cover (%) 14 12 10 2003 2004 2005 2006 2007 2008 2009 2010

Figure 13: Change in mean tree cover (%) from 2003 to 2010 in WMA with highest η2 (vs. all control) from each ecoregion: Wami-Mbiki (a), Ikona (b) and Ukutu (c) (all red) vs. All control (all unprotected areas; yellow) and Matched controls (with/without; blue and green). Prior to matching little discernible difference over time; after matching any difference removed.

2.3 Land cover type

2.3.1 By WMA

Time pressures precluded a full analysis of change in land cover type, but broad patterns were discernible. Issues with data quality necessitated exclusion of Uyumbu from further study. Land cover types varied widely within WMAs and within ecoregion groups (table 10), with Woodland WMAs dominated by Woody Savannas and Savannas, Bushland and thicket WMAs dominated by Savannas with significant components of Grasslands and Croplands/mosaics, Coastal forest mosaic WMAs more variable but still dominated by Savannas, and Enduimet’s mixed ecoregion WMA almost entirely Grassland. These variations explain differences observed in EVI values/tree cover e.g. high grassland components correlating with WMAs with low tree cover (Ipole, Burunge and Enduimet) and low EVI values (Enduimet).

Table 10: Mean proportion land cover in WMAs/All unprotected areas by type/group between 2003 and 2010. Only types/groups with >1% cover in at least one WMA shown.

Woody Croplands Forest Shrublands savannas Savannas Grasslands and mosaics All unprotected 1.6% 1.3% 26.6% 42.8% 10.8% 16.4% Miombo woodlands Ipole 0.2% 0.5% 9.9% 76.1% 8.0% 5.1% Liwale 0.6% 0.2% 14.9% 78.8% 0.1% 5.4% Mbarang’andu 1.1% 0.0% 91.7% 6.9% 0.0% 0.3% Tunduru 0.1% 0.0% 67.3% 31.1% 0.0% 1.5% Wami-Mbiki 0.4% 0.3% 28.2% 48.5% 0.4% 22.1% Bushlands and thickets Burunge 0.0% 2.4% 0.1% 22.0% 42.6% 31.7% Ikona 0.2% 0.1% 0.5% 69.6% 14.9% 14.7% Makame 0.8% 0.4% 12.3% 54.8% 14.2% 17.4% Coastal forest mosaic Ngarambe/Tapika 3.9% 0.1% 6.7% 61.4% 0.1% 27.9% Ukutu 0.4% 0.5% 13.1% 63.4% 0.2% 22.1% Mixed Enduimet 0.0% 12.3% 0.4% 0.4% 84.2% 2.4%

2.3.2 Unprotected areas

Pre-matched unprotected areas had a greater variety of land cover types, in line with their geographic distribution, but were dominated by Savannas, Woody savannas, Croplands and mosaics, and Grasslands (fig. 14). A slight pattern in change of land cover type over time was

discernible, namely an increase in Savannas and Grasslands and a decrease in Woody Savannas and Croplands and mosaics, with implications for impact assessment.

Savannas Woody savannas Croplands and mosaics Grasslands 50% Forest Shrublands

40%

30%

20%

10%

0% 2003 2004 2005 2006 2007 2008 2009 2010

Figure 14: Change in land cover in types in all unprotected areas between 2003 and 2010.

2.3.3 ‘Traditional’ impact estimates

A traditional before/after assessment of change in land cover types in WMAS revealed that ten out of 11 experienced reductions in Croplands/mosaics, over half >2.5%, whilst 7 out of 10 experienced increases in Grasslands, with three >2.5%, and approximately equal numbers experienced increases/decreases in Savannas/Woody Savannas (table 11). Closer examination of year to year variation revealed that in most cases consistent reductions in Croplands/mosaics were accompanied by consistent increases in either Savannas or Grasslands, depending on ecoregion type (fig. 15).

However, examination of change in unprotected areas revealed similarities with change in WMAs. Consequently, calculation of impact estimates based on change in unprotected areas provided much more mixed results: change in Croplands/mosaics and Grasslands approximately equally split between WMAs and generally less extreme changes.

In summary, croplands and mosaics appeared to decrease in the majority of WMAs with corresponding increases in natural land cover types, however taking into account similar change in unprotected areas removed any obvious broad direction of impact of WMAs in a before/after analysis. However, a full analysis of finer scale variation in matched groups over time could find otherwise.

Table 11: Change in cover in WMAs between 2003 and 2010 in most numerous land cover types, ranked by change in Croplands and mosaics, and corresponding impact estimates pre- and post-matching (without ecoregion only). Red = decrease, green = increase; shaded according to magnitude.

Change in cover in WMAs Impact estimate (All control) Impact estimate (Matched control)

Woody Woody Woody

savannas savannas savannas

Savannas Savannas Savannas

Croplands Croplands Croplands Croplands

Grasslands Grasslands Grasslands

mosaics and mosaics and mosaics and Ikona 2.3% -11.4% 0.6% 8.3% 6.6% -13.9% 2.2% 5.7% 14.1% -4.7% 2.5% -14.5% Tunduru -0.1% -0.9% 1.1% 0.0% 4.2% -3.4% 2.7% -2.6% 4.4% 0.5% 2.0% -5.8% Mbarang’andu -0.4% -4.1% 5.3% n/a 3.9% -6.6% 6.9% n/a 1.7% -1.5% 5.3% n/a Makame -1.8% -6.5% 6.8% 1.5% 2.5% -9.0% 8.4% -1.1% 2.7% -13.2% 9.7% 0.5% Liwale -2.3% 11.0% -7.9% 0.0% 1.9% 8.5% -6.4% -2.7% 1.4% -0.5% -0.8% 0.0% Ipole -4.6% 12.0% -5.4% -1.4% -0.3% 9.5% -3.9% -4.1% 3.5% 11.7% -8.5% -5.6% Enduimet -4.8% -1.1% -0.2% 9.3% -0.5% -3.6% 1.3% 6.6% -3.3% -0.6% -1.3% 8.6% Wami-Mbiki -6.4% -8.3% 12.8% -0.1% -2.1% -10.8% 14.4% -2.7% -6.9% -15.0% 17.2% 0.2% Ngarambe/Tapika -9.1% 11.3% -0.3% 0.0% -4.8% 8.8% 1.2% -2.6% -6.7% 6.0% 1.9% 0.1% Ukutu -11.9% 12.4% -1.5% 0.3% -7.6% 9.9% 0.1% -2.3% -4.9% 4.0% 0.5% -0.7% Burunge -31.7% 2.5% -0.2% 26.2% -27.4% 0.0% 1.3% 23.6% n/a -10.8% 9.9% 26.5%

100% a) b) 80% c)

60%

40%

20%

100% d) e) Savannas 80% Woody savannas

60% Croplands and mosaics

40% Grasslands

Forest 20% Shrublands 0%

100% f) g) h) 80%

60%

40%

20%

100% i) j) k) 80%

60%

40%

20%

2006 2004 2005 2007 2008 2009 2010 2003

Figure 15: Change in land cover types between 2003 and 2010 in Ipole (a), Liwale (b), Mbarang’andu (c), Tunduru (d), Wami-Mbiki (e), Burunge (f), Ikona (g), Makame (h), Ngarambe/Tapika (i), Ukutu (j) and Enduimet (k).

V. Discussion

My aim was to evaluate the impact of WMA designation on habitat destruction, using matching methods based on observable characteristics to account for biases in their placement, to inform efforts to assess efficacy of WMAs in particular, and multiple-use PAs in general. Specifically I aimed to answer whether, since their inception, WMAs have had a measureable effect on habitat outcomes within their boundaries, and also whether matching altered the degree of any effect.

To answer these questions first required matching of WMAs with unprotected areas, followed by assessment of each of the outcomes. In the following sections I will discuss the wider implications of the findings from each of these analyses in turn.

1 Matching

1.1 Covariate balance

As expected, WMAs were not randomly distributed across Tanzania’s landscape, as indicated by high covariate imbalance prior to matching. In line with previous studies on PAs (e.g. Joppa and Pfaff 2009) WMAs are generally located in more remote areas: further from major towns and roads, and with lower human population densities. Remote locations are more desirable for protection as they have lower land values, and simultaneously confer lower land conversion pressures, such that naïve comparisons between protected and unprotected areas often overstate the effects of protection (Joppa and Pfaff 2010). Consequently, any future WMA impact assessments will have account to for their non-random placement.

Whilst WMAs differed from unprotected lands in terms of land use suitability, evidence that they are less suitable is unclear. WMAs are located at lower altitudes, on less sloped terrain, and with lower annual rainfall. The literature suggests that lower altitudes and less sloped terrain might be more suitable for agriculture, and lower rainfall less suitable, however the relationships between these attributes and probability of agriculture in Tanzania are not continuous but unimodal, with agriculture peaking at ~800 mm of rainfall and ~10° of slope (Msoffe et al. 2011). Rainfall in WMAs and unprotected areas fall almost equally either side of this peak (723 mm vs. 905 mm), and whilst unprotected areas are located on less sloped terrain than peak agriculture (2.85°), WMAs lands are slightly shallower still (1.29°). Consequently, it is not obvious that WMA lands experience lower than average land conversion pressure. These results suggest that if WMAs do experience lower pressure this is largely due to their remoteness. Consequently, they are particularly at risk if rural populations continue to grow and protection is inadequate.

1.2 Matched samples

Despite input of 500,000 points (one to four treated to control), matching produced a relatively small matched control sample (<10,000 points). Matching prunes control units which fall outside of the distance metric employed (in this case Mahalanobis) to improve balance between covariates. Consequently, increased balance comes at the price of reduced sample size and thus requires a partially subjective opinion on the amount of imbalance or decreased representativeness one is willing to accept. In this case balance was considerably improved (>95% for five of six covariates) at the expense of >97.5% control points. However, close tracking of matched control group outcomes with treated groups indicates that representativeness was not noticeably affected.

The majority of matched control points were located within 50 km of WMA borders. In this respect outcome analyses based on these points are similar to many PA impact assessments, which compare PAs with surrounding ‘buffer zones’ (e.g. DeFries et al. 2005), but the selection of appropriate points in this case is through covariate matching rather than through distance, which could potentially result in selection of close but dissimilar areas and biased outputs.

1.3 Other methods, unobserved covariates and sensitivity analysis

It is possible that alternative matching methods might have resulted in better matches, that all relevant covariates were not included, or there were unobserved variables, biasing results. Regarding relevant covariates there is a strong potential that some other factor of land use suitability not observed confers lower conversion pressure on WMA lands, for example, soil type, nitrogen content, acidity or water capacity. Unprotected areas matched on those covariates may have experienced different change in outcomes than in WMAs. Methods exist for assessing how sensitive treatment effects are to potential unobserved variables (Rosenbaum 2002)), and their implementation is an important step for assessing whether results are robust. In this study the lack of any observable effect negated the need for sensitivity analysis, but should be considered in future impact assessments.

Furthermore, whilst nearest neighbour matching using the Mahalanobis distance metric has been employed multiple times in conservation terms, alternative methods exist which might result in improved balance. Stuart (2010) recommends trying multiple methods and comparing standardised difference of means post-hoc. Alternatively, Diamond and Sekhon (2012) have developed an evolutionary algorithm which searches amongst different distance metrics to

optimise covariate balance. Either way comparison of results of matching methods is an important step in any rigorous matching methodology.

1.4 Effects of matching

Although exact matching with/without ecoregion had little effect on sample size or covariate balance, matching without ecoregion resulted in a small proportion of points broadly distributed across Tanzania. Outcomes in distant points in different environments are unlikely to experience identical non-anthropogenic drivers of change, confounding attempts to discern differences truly related to protection, as evidenced by consistent closer tracking of WMAs with unprotected areas when matched with ecoregion. Consequently, matching within ecoregion is clearly preferable if it does not constrict sample size greatly.

If matching results in majority selection of points near PA borders it could be subject to other criticisms of buffer analysis; specifically that protection can cause spatial spillovers, or leakage, both positive and negative (Joppa and Pfaff 2010). Indeed, forest loss around PAs in East Africa has exceeded background rates (Pfeifer et al. 2012). Thus matched points in buffer zones may not be representative of change in the absence of protection. One solution may be to exclude lands proximal to PAs from matching, although at the expense of sample size.

Nevertheless, matching clearly resulted in selection of unprotected areas with change in outcomes often indiscernible to that in WMAs, and noticeably closer than that in all unprotected areas, from which perspective matching did indeed alter the degree of effect of WMAs and produced a less biased comparison.

In summary, this study represents one of a small number which have successfully employed matching methods to construct an unbiased counterfactual for conservation impact assessment, whilst also indicating that traditional methods would have overstated the impacts of WMAs, demonstrating that rigorous outcome evaluation is both feasible and highly warranted.

2 Impact analysis

2.1 Summary of findings

Pre-matching analyses of change in EVI and tree cover resulted in similar conclusions: if WMAs are having an effect on these outcomes within their boundaries the effect is too small to be distinguishable amongst background variation. However, in the majority of cases matching produced control groups in which change in outcomes very closely mirrored that of WMAs, suggesting that they are not having an effect. A simple before/after assessment of change in tree cover indicated positive effects of two WMAs and negative effects of a third, but this finding is likely to be spurious given inter-annual variation greater than the impacts identified. Analysis of land cover change was incomplete, but initial results suggest that although croplands/mosaics appeared to be reducing in WMAs with the return of natural vegetation, this could be an artefact of background land cover change rather than an effect of WMAs themselves. The answer to the question of whether WMAs have had a measureable effect on habitat outcomes within their boundaries is therefore that for the outcomes assessed with the methods employed a measurable effect was not discernible.

2.2 Similar studies

Comparison of these findings with similar studies is difficult as the aim of this study was to address an explicit shortfall in environmental assessment of WMAs. The closest analyses geographically and contextually reached startlingly different conclusions to each other: Stoner et al. (2007) concluded that stricter protection was better at conserving large mammal species in Tanzania than areas with few or no restrictions. Whilst Gardner et al. (2007) who found that species richness did not decline along a gradient of increasing human activity and decreasing protection between national parks and agricultural land in Tanzania, although they assessed small mammals, butterflies and trees. Outside of Tanzania results are equally ambiguous: Shahabuddin and Rao (2010) reviewed literature on the impact of community-managed areas and concluded that they were better than no protection but insufficient for species of conservation importance.

Comparison with methods used is also difficult, as the majority of remote PA assessments have focused on before/after change, not on serial images. However, results are very similar to other PA impact studies which employed matching in that matching appeared to notably reduce impact estimates, but also unique in that post-matching no effect was found. This is not necessarily

surprising as the majority of these studies are broader in geographic scope, so may capture more variety.

Studies which report no effect of protection, such as in this study and with which to draw comparisons, are relatively rare, although this is unlikely to be because PAs with no effect are uncommon but due to publication bias. For example, Geldmann et al. (2013) report that out of 76 studies of habitat change 62 found positive effects, five no effect and nine negative effects, which if truly indicative of the effectiveness of PAs suggests bimodal peaks in impact. In reality it is likely that a number of unpublished studies do not find an effect, but an analysis of their results and methods would prove enlightening. For the purposes of this study why a measurable effect of WMAs was not discernible is the most important question. The answer to this question is either that WMAs are truly not having any effect or that any effect is not discernible using these methods (or indeed a combination of the two).

2.3 WMAs aren’t having an effect, or aren’t yet

It is entirely feasible that WMAs are not having an effect on habitat destruction within their boundaries. Consequently, these results provide validation of concerns over WMA efficacy and motivation to assess why they are not having an impact and how they can be improved. A key tenet of WMAs is that they create incentives for conservation by providing net benefits to local communities (fig. 1). At present, factors such as limited management capacity amongst AAs, political conflict within villages of WMAs over resource sharing, and an environment which discourages business development (e.g. high taxes) are resulting in some WMAs experiencing lower income than they earned prior to gazettement (Sulle et al. 2011), providing a disincentive. These factors are critical to the success of community-managed areas (Pagdee et al. 2006).

In addition, although protection and lack of protection in this study is treated as a dichotomous treatment variable that starts from the beginning of the WMA application process, in reality the extent and timing of protection likely vary widely according to local political factors. Considerable lags in implementation may exist between official dates and practical change, and even if full curtailment did begin in 2003 that is no guarantee that 8 or 10 years is sufficient for differences in habitat outcomes to take effect. It is also important to clarify exactly what and to what extent is being curtailed in each WMA, as this will directly determine any effect, but at the time of this study this information was not available.

2.4 Effect isn’t discernible via methods used

It is equally possible that even if an effect had existed the methods used in this study were not sensitive enough to detect it. Broadly, sensitivity issues can be categorised as related to remote sensing or high inter-annual variability of outcomes.

2.4.1 Limitations of remote sensing of change in habitat characteristics

With limited resources the majority of imagery is only suitable for broad estimation of change in vegetation. However, habitat destruction can take many forms and differ in extent (Reid et al. 2004), and it is possible that finer scale variation in habitat of relevance to large mammals is being altered within WMAs that the resolution and outcomes employed cannot assess e.g. wildlife corridors. Such destruction may only be properly assessed with on-the-ground studies.

In addition, remote imagery presents statistical challenges which restrict easy analysis of trends. Computation of rmANOVAs highlighted that large sample sizes often show extreme statistical significance despite negligible effect sizes (Lantz 2013), requiring the calculation of an objective measure of magnitude of effect. Whilst not difficult to calculate, effects sizes are rarely reported in the literature and applications to aid analysis are rare in comparison to significance tests (Coe 2002). However, whilst remote imagery resultd in large sample sizes, in truth a large proportion of these points are not independent observations and thus tests overstate significance, highlighting a more serious issue with longitudinal studies of spatial data.

Remote imagery regularises earth’s continuously varying surface into grids of consistent shape/size with attached values. Observed variables thus often correlate with distance between pixels i.e. proximal pixels are more similar than distal, violating assumptions of independence among observations. Accounting for spatial autocorrelation initially involves quantification of autocorrelation, through measures such as Moran’s I and Geary’s c, followed by tests of statistical significance which disaggregate it, specifically modelling relationships using generalised linear models or generalised least squares methods (Dormann et al. 2007). Similarly, temporal autocorrelation violates assumptions of independence as successive observations of phenomena are often serially correlated. Accounting for correlation involves measurement and fitting of models, such as autoregressive integrated moving average models (Ramsey and Schafer 2002). Future assessments need to account for spatial and temporal autocorrelation to infer supportable conclusions.

2.4.2 High inter-annual variation of outcomes

The greatest limitation to detection of WMA effects is the extremely large between-year fluctuations in outcomes, relative to multi-annual change. Discerning an effect of WMAs in such data would either require that the effect was large or that the data were available for many years, neither of which are true. Change in vegetation in savannah ecosystems is inherently highly stochastic (e.g. dynamic with multiple, powerful fluctuating constraints/disturbances and complex interactions (Skarpe 1992)) and any assessment will need to account for this before being able to discern treatment effects.

Vegetative productivity in East Africa is closely associated with rainfall (Nicholson et al. 1990), yet precipitation in the region is persistently variable, with seasonal and multi-annual peaks and droughts driven by broader processes such as the El Nino Southern Oscillation (ENSO) (Indeje et al. 2000). Furthermore, recent changes in ENSO are precipitating unusual rainfall patterns in East Africa (Appelhans and Nauss 2013). The effect of these oscillations is the variable and large fluctuations in vegetation observed in this study’s results.

Consequently, although impact estimates based on before/after assessments may be appropriate in other systems, in this context any results are highly questionable, and finer scale temporal variation which accounts for variability or alternative outcomes with lower variability must be assessed. With regard to the latter the majority of VIs and EO products assess different vegetation attributes, which are also likely to exhibit high variability, necessitating methods which overcome limitations of remote sensing and noise in outcome data simultaneously.

2.4.3 Potential solutions

The main issue of concern is how to disaggregate a small effect from high background noise. The first possible approach is to identify and remove the main determinant of variability in outcome data, namely rainfall. In drylands methods are employed to normalize for the effects of precipitation change by calculating Rain Use Efficiency (RUE), defined as the ratio of above- ground net primary productivity (measured as NDVI) to precipitation (Fensholt et al. 2013). RUE separates out precipitation related impacts on productivity, theoretically leaving other causes behind e.g. anthropogenic degradation. Fensholt et al. (2013) also provide statistical methods for assessing temporal trends in small, noisy data series like NDVI, such as non-parametric Theil–Sen (TS) median slope trend analysis and Mann-Kendall significance tests for assessing significance and direction of trends. The second approach is to disaggregate components which account for variability in the data in order of magnitude and relate these to known drivers of change through

Principal Component Analysis (PCA). Although common in time series analyses of NDVI it has had limited use in separating anthropogenic variations in NDVI from natural variation (Lasaponara 2006), possibly because PCA requires field surveys to help interpret components. Nevertheless the method may prove useful.

2.4.4 Summary

WMAs are a major participatory conservation initiative in a country rich in and reliant on biodiversity, but struggling to accommodate the needs of wildlife and local communities. This study represents the first initiative-wide analysis of the environmental impact of WMAs, with implications for people and biodiversity across 23,200 km2 of rural Tanzania. The study has not been able to discern any effect of WMAs on habitat outcomes. There are good reasons why this may be true, not least a regulatory framework which until recently provided little benefit and thus incentives for local people. However, the methods employed have provided interesting insights into the complexities of assessing the effects of initiatives on potentially fine scale habitat destruction in stochastic systems, with relevance for PA impact assessments across the tropics.

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Appendices

Table 12: Degrees of freedom, F value and significance of interaction between WMA protection and time on EVI values. * indicates p<0.001.

All control Matched (w/out ecoregion) Matched (w/ecoregion) WMA Numerator Denominator Numerator Denominator Numerator Denominator degrees of degrees of degrees of degrees of degrees of degrees of freedom freedom F p freedom freedom F p freedom freedom F p Burunge 9 34,992 465.3 * 9 2,862 14.3 * 9 2,412 10.2 * Enduimet 9 179,982 4,976.7 * 9 13,824 153.9 * 9 6,264 3.0 * Ikona 9 63,756 1,670.1 * 9 3,456 53.4 * 9 2,934 26.9 * Ipole 9 179,982 2,349.7 * 9 10,764 57.5 * 9 8,766 36.4 * Liwale 9 179,982 1,903.7 * 9 20,520 18.4 * 9 20,484 21.3 * Makame 9 179,982 2,009.5 * 9 33,048 117.7 * 9 30,330 81.7 * Mbarang’andu 9 179,982 654.6 * 9 39,330 24.5 * 9 34,956 11.6 * Ngarambe/Tapika 9 179,982 4,279.2 * 9 7,758 25.3 * 9 6,120 12.9 * Tunduru 9 179,982 1,162.0 * 9 17,568 30.1 * 9 16,776 28.0 * Ukutu 9 147,168 4,345.8 * 9 3,600 5.7 * 9 3,186 3.8 * Uyumbu 9 179,982 2,323.1 * 9 9,324 109.0 * 9 7,722 85.7 * Wami-Mbiki 9 179,982 3,359.0 * 9 24,084 170.9 * 9 23,238 189.2 *

Table 13: Degrees of freedom, F value and significance of interaction between WMA protection and time on tree cover. * indicates p<0.001.

All control Matched (w/out ecoregion) Matched (w/ecoregion) WMA Numerator Denominator Numerator Denominator Numerator Denominator degrees of degrees of degrees of degrees of degrees of degrees of freedom freedom F p freedom freedom F p freedom freedom F p Burunge 7 27,216 107.0 * 7 2,226 4.5 * 7 1,876 2.7 * Enduimet 7 139,986 1,425.1 * 7 10,752 54.7 * 7 4,872 9.0 * Ikona 7 49,588 619.0 * 7 2,688 5.2 * 7 2,282 11.7 * Ipole 7 139,986 694.4 * 7 8,372 43.8 * 7 6,818 45.2 * Liwale 7 139,986 768.6 * 7 15,960 8.6 * 7 15,932 7.5 * Makame 7 139,986 650.0 * 7 25,704 87.1 * 7 23,590 60.2 * Mbarang’andu 7 139,986 559.0 * 7 30,590 18.8 * 7 27,188 3.9 * Ngarambe/Tapika 7 139,986 2,801.3 * 7 6,034 38.4 * 7 4,760 20.9 * Tunduru 7 139,986 858.0 * 7 13,664 15.6 * 7 13,048 11.8 * Ukutu 7 114,464 2,392.5 * 7 2,800 15.5 * 7 2,478 8.9 * Uyumbu 7 139,986 1,435.4 * 7 7,252 93.0 * 7 6,006 78.5 * Wami-Mbiki 7 139,986 1,769.5 * 7 18,732 127.2 * 7 18,074 135.9 *