Climate ChangeMonitoring Guide_Final

Ochanda D., Pomeroy D., Hole D. and Willis S.G

FUNDED BY:

AuthorsDavid Ochanda (Makerere University, Kampala, Uganda)

Prof. Derek Pomeroy (Makerere University Kampala, Uganda)Dr. Steve G Willis (Durham University, UK)

Dr. Dave G Hole (Conservation International, USA)

Reviews and comments by:Dr. Julius Arinaitwe (BirdLife Africa Partnership Secretariat)

Ken Mwathe (BirdLife Africa Partnership Secretariat)Paul Mugo (BirdLife Africa Partnership Secretariat)

This Publication is funded by The MacArthur Foundation

Preferred citationBirdLife International, 2012. A Guide for Monitoring Climate Change Impacts on Forest Birds in the

Albertine Rift Region. BirdLife International, Africa Partnership Secretariat. Nairobi, Kenya.

Front cover photo:Echuya forest reserve in the Albertine Rift indicating the Muchuya swamp in the middle

and distinctive forest edge boundary demarcated with pine trees.

Back cover photo:An Albertine Rift endemic - the Red throated alethe in Nyungwe National Park:

cover photos by David Ochanda, Makerere University.

Copies available from:BirdLife Africa Partnership Secretariat, BirdLife International Cambridge UK,

Makerere University National Biodiversity Data Bank, Uganda and NatureUganda.

Nairobi, July 2012

A GUIDE FOR MONITORING CLIMATE CHANGE IMPACTS ON FOREST BIRDS IN THE ALBERTINE RIFT REGION

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Contents

Foreword ............................................................................................................................................ iv

Acknowledgements .................................................................................................................................v

Executive summary ................................................................................................................................vi

SECTION ONE: Introduction

1.0 Preamble ...........................................................................................................................1

1.1 The Guide .........................................................................................................................1

1.2 The Albertine Rift ..............................................................................................................2

1.3 Overview of Climate Change and Birds .............................................................................3

SECTION TWO: Methods for Monitoring Cimate Change Impacts on Birds

2.0 Preamble ...........................................................................................................................4

2.1 Ethical Considerations .......................................................................................................4

2.2 Data Collection .................................................................................................................4

2.2.1 Preparations .........................................................................................................................4

2.2.2 Field Requirements ...............................................................................................................5

2.2.3 Rounds of Data Collection ....................................................................................................6

2.2.4 Selection of Points, Transects and Habitat Variables ..............................................................6

2.2.5 Bird Cencus Data .................................................................................................................7

2.2.6 Habitat Data .........................................................................................................................8

2.2.7 Climate Data ........................................................................................................................9

2.3 Data Analysis ..................................................................................................................10

2.3.1 Species Distribution Modelling Techniques .........................................................................10

2.4 Sources of Possible Errors in Methods ..............................................................................12

2.5 Challenges During Data Collection .................................................................................13

2.6 Need for Follow-Up Surveys ...........................................................................................14

2.7 Conclusion ......................................................................................................................14

2.8 References ......................................................................................................................14

SECTION THREE: Appendices

3.1 APPENDIx I: Data collection sheets .............................................................................................16

3.2 APPENDIx II: List of Albertine Rift Endemic species ........................................................19

3.3 APPENDIx III: Transect Information .................................................................................20

3.4 APPENDIx IV: Site maps and Transects ............................................................................21

3.5 APPENDIx V: GPS coordinates for survey points .............................................................25

3.6 APPENDIx VI: Site Information ........................................................................................36

Glossary ...........................................................................................................................................37


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ForewordThe Albertine region is part of the Eastern Afromontane Biodiversity Hotspot, and is renowned for very high rates of endemism across different taxa of animals and plants. Well known examples of these include the Mountain Gorilla, the African Green Broadbill and the pretty Balsams. It has a rich network of mountainous protected areas of diverse habitat types.

This rich biodiversity is matched by high human population, who are mainly dependent on subsistence agriculture and direct exploitation of timber and non-timber forest products. Not surprisingly, encroachment and drainage are leading to loss and degradation of key forest, wetland and grassland habitats, while mining is an appealing alternative livelihood option. These threats are having negative impacts on biodiversity and other ecosystem services. Climate change and its impacts is a recently recognized additional threat, which also exacerbates these existing challenges.

Given that there is growing evidence that climate change could become one of the major drivers of species extinctions in the 21st Century, building the resilience of key sites to facilitate the adaptation of species to climate change is increasingly a high priority for conservation managers.

BirdLife International, in collaboration with national and regional institutions and experts in Rwanda, Burundi and Uganda and abroad undertook a baseline study on the projected impacts of climate change on birds in the Albertine Rift. This study led to the development of a framework for adaptive management planning to enhance the resilience of Important Bird Areas. One of the key components of this framework is a monitoring procedure to assess changes in species distribution and abundance in response to climate change and facilitate planning of appropriate conservation and adaptation measures.

This guide is intended to offer researchers, conservation managers and other practitioners, in particular those concerned with the Albertine Rift, practical guidance on how to respond to the challenges of monitoring for addressing climate change impacts on biodiversity. The guide focuses on birds, but could also be useful for other biodiversity. It builds upon BirdLife International and its partners’ research and experiences in dealing with birds and climate change in the Albertine Rift.

Dr. Julius ArinaitweRegional Director for AfricaBirdLife InternationalNairobi, Kenya


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AcknowledgementsThis guide is a product of BirdLife International, and benefited from the support of the MacArthur Foundation Fund. It was developed as part of the project “Implementing and monitoring an Adaptive Management Framework for Climate Change in the Albertine Rift”, implemented by BirdLife International in collaboration with Durham University UK, and the Albertine Rift Conservation Society (ARCOS).

Preparation of this guide involved the research team under the project that included David Ochanda (Lead Author), Prof. Derek Pomeroy and Dr. Steve Willis (Supervisors and co-authors) and Dr. Dave G Hole (co-author). Data from Bwindi Impenetrable National Park was kindly provided by Dr. Phil Shaw of St. Andrews University, UK.

Substantial valuable comments and inputs were received from Dr. Lincoln Fishpool and Dr. Stuart Butchart (BirdLife International, UK), Mr. Ken Mwathe (BirdLife Africa Secretariat), Dr. Richard Gregory and Dr. Richard Bradbury (Royal Society for Protection of Birds, UK), Dr. Andy Plumptre (Wildlife Conservation Society), Dr. Will Cresswell (University of St. Andrews, UK), Dr. Dave G Hole (Conservation International), Prof. Derek Pomeroy (Makerere University) and Prof. William F Laurance (James Cook University, Australia).

We are greatly indebted to all the project partners: Durham University UK, BirdLife Africa Secretariat and partners; NatureUganda, Association Burundaise pour la protection des Oiseaux (ABO) and Association pour la Conservation de la Nature au Rwanda (ACNR), and the Albertine Rift Conservation Society (ARCOS), and finally collaborators: the Wildlife Conservation Society and Makerere University for the endless effort they offered during the project. Special thanks to Dr. Robert Bagchi of Durham University UK for training the MSc student in Species Distribution Modelling and helping with data analysis, and WCS-Rwanda for hosting our field team and allowing us use their available transects during data collection in Nyungwe.

We are grateful to Rwanda Development Board (RDB) for permitting the research in Nyungwe National Park, Institut National pour l’Environnement et la Conservation de la Nature (INECN-Burundi) for permitting the research in Kibira National Park, National Forestry Authority (NFA) for permitting the research in Echuya forest reserve and Institute of Tropical Forest Conservation (ITFC) for providing a field assistant. We appreciate the excellent expertise of field assistants: Claver Nyotchima (of RDB), Lawrence Tumugabirwe (of ITFC), Jean Paul Ntungane (of ACNR), Charles Rugerinyange and Christian (of ABO).


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Executive summaryThe Albertine rift is a Biodiversity Hotspot and an Endemic Bird Area consisting of a network of mountainous protected areas of diverse habitat types. Climate change, interacting with human drivers such as deforestation and forest fires, are a threat to Africa’s forest ecosystems, with changes in grasslands and freshwater ecosystems also noticeable (Boko et al. 2007). There is growing evidence that climate change will become one of the major drivers of species extinctions in the 21st Century (Foden et al., 2008 and Thomas et al., 2004) with impacts noted such as changes in species’ breeding times and ranges (Doswald et al., 2009), and shifts in distributions (Parmesan & Yohe 2003; Root et al., 2003 and Wilson et al., 2005).

Understanding the likely effects of climate change on human-altered and natural ecosystems and measuring the speed of change and responses to change are vital for African countries to manage their development efficiently and proactively. In the context of biodiversity conservation, effective bio-regional planning will require a much better understanding than we currently have of how (or whether) species will respond to the intense pressures on them by moving through landscapes (e.g. Cowling et al. 2003). This is necessary for conservation planning and also as an essential first step in protecting species as suitable habitats and climatic niches change due to human-induced environmental change.

More is known about birds’ response to climate change to date than for any other animal group, mostly as a result of many species- and location-specific analyses. However, despite the widely voiced concerns about impacts of climate change on birds, and a growing interest in developing adaptation and mitigation plans to enhance resilience of sites to climate change impacts, no specific methods have been articulated to monitor the impacts of climate change on birds in Africa. This guide is intended to be a first step in addressing that gap. The Monitoring guide draws from the methods used in the baseline study of potential impacts of climate change on birds in the Albertine rift under the project “Implementing and monitoring an Adaptive Management Framework for Climate Change in the Albertine Rift”, implemented by BirdLife International between 2010 and 2012 in three forest blocks: Echuya (Uganda), Nyungwe (Rwanda) and Kibira (Burundi). It provides guidance on some key methods and links to tools to monitor a suite of climate change impacts likely to affect forest birds in the Albertine Rift region, focusing mainly on shifts in ranges of birds. It also facilitates future data collection at all baseline sites in the baseline study. Given that climate change is expected to shift ranges of bird species and communities, in addition to other threats, continued research is crucial. Adaptation is gradual and dynamic processes that should be studied continuously over time based on new information. For conservation efforts to meet the climate threat, a major change in approach to bird conservation will be needed for bird species’ diversity, richness and abundance to be maintained.


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SECTION ONEINTRODUCTION

1.0 PREAMBLE

This section outlines the structure of this Guide, describes how it was developed and provides the justification, focus and objectives of the guide. It also provides a brief description of the Albertine rift and an overview of how climate change impacts on birds. In addition, it includes introductory notes on ethical considerations for undertaking research.

1.1 THE GUIDE

Why this guide?

Climate change, interacting with human drivers such as deforestation and forest fires, is a threat to Africa’s forest ecosystems. There is growing evidence that climate change and its impacts may become the dominant direct driver of biodiversity loss and changes in ecosystem services globally. Indeed climate is one of the most important factors in determining birds’ ranges and abundance (Jones et al., 2003). Impacts on birds vary from changes in species migration patterns, breeding times and ranges, to shifts in distributions. Effects of climate change on biodiversity and ecosystems are likely to ultimately pose threats to humans whose livelihoods highly depend on biodiversity resources.

If impacts of climate change on biodiversity and ecosystems are left unaddressed, they stand to exacerbate existing challenges and make it more difficult for countries to achieve sustainable development and reduce poverty. Achieving these goals requires monitoring and adapting to a changing climate.

How was the guide developed?

This Monitoring Guide draws on the methods used in the baseline study of the potential impacts of climate change on birds in the Albertine rift under the project “Implementing and monitoring an Adaptive Management Framework for Climate Change in the Albertine Rift”, implemented by BirdLife International in collaboration with Durham University UK, and the Albertine Rift Conservation Society.

The baseline study was undertaken between 2010 and 2012 in Bwindi Impenetrable National Park and Echuya Forest Reserve in Uganda, Nyungwe National Park in Rwanda and Kibira National Park in Burundi (See appendices IV and VI for site information and maps). The guide therefore provides the methods and links to tools used in the baseline study, which can be used in future to monitor impacts of climate change on forest birds in the baseline study sites and other forest sites in Africa. Future monitoring in the baseline sites would use the same survey points as well as methods (See appendix V for GPS coordinates of survey points at baseline sites).

Objectives and focus of this guide

This Monitoring Guide provides guidance on some methods and links to tools to monitor a suite of climate change impacts likely to affect forest birds in the Albertine Rift region focusing mainly on shifts in ranges of birds. It does not provide methods for monitoring climate variables that meet international standards for climate data records. It is designed for researchers, practitioners and conservation managers interested in impacts of climate change on biodiversity to inform adaptive management and future planning efforts. It is designed to offer continued support for adaptation planning in the Albertine region. It requires some technical expertise in climate change adaptation and monitoring. Specific expertise may be required to best understand climate impacts and bird survey techniques. The Guide may also act as a training tool for students, at college and university, and in forest and wildlife services, and other protected area agencies.

The Guide also aims to encourage researchers to use standardised methods so that survey results can be used to monitor change over time. Long-term monitoring usually involves different surveyors, as people change jobs or move, and each set of new surveyors should use the same methods if the results are to be comparable. While focusing attention on this need for standardised methods, it is understood that methods continue to be improved and different forests, survey team resources, and management questions will all require adaptation of the standard techniques.

Outline of this guide’s structure

Section two, Monitoring methods, provides a description of the recommended methods and links to tools for monitoring climate change impacts on forest birds. It describes data collection and analysis methods, and possible sources of errors and challenges in data collection. In addition, it includes preparations for carrying out surveys, and notes on health and safety. Finally section three, the appendix, provides details on site maps, transects, GPS coordinates and data sheets that


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can be used for monitoring climate change impacts on forest birds in regard to the baseline study. Data from the baseline study can be used for the monitoring purposes and can be accessed from the National Biodiversity Databank (NBDB), Makerere University. More details concerning the baseline study can be obtained from Ochanda, 2012 (unpublished thesis) that can be found at NatureUganda and Makerere University Department of Environmental Management, College of Agriculture and Environmental Sciences.

1.2 THE ALBERTINE RIFT

The Albertine rift region is a Biodiversity Hotspot and an Endemic Bird Area extending 930 km from the northern tip of Lake Albert down to the southern tip of Lake Tanganyika (Figure 1) and is one of Africa’s richest regions in species diversity (Kahindo et al., 2007). The region has more vertebrate species and more endemic and threatened vertebrate

Figure 1. Map showing the approximate boundary of the Albertine Rift (dotted line) (ARCOS)


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species than anywhere else in Africa (Cordeiro et al., 2007, Plumptre et al., 2007, and Burgess et al., 2004). It has been identified as an ‘Endemic Bird Area’ (by BirdLife International; Thirgood and Heath, 1994; Stattersfield et al., 1998), an ‘Ecoregion’ (by the World Wildlife Fund; Olson and Dinerstein, 1998), and as a ‘Biodiversity Hotspot’ (by Conservation International; Myers et al., 2000). As such it is recognised as an area of global importance for conservation.

A total of 1061 species of birds have been recorded in the 33 protected sites (National Parks and Forest Reserves) of the Albertine Rift, over half (52%) of all the bird species of mainland Africa. Forty one of these species are endemic to the Albertine Rift and the associated Eastern Zairean Lowland forests (Kahindo et al., 2007) (See appendix II for Albertine Rift endemic species). In addition, a total of 402 species of mammal (excluding humans), 175 species of reptile, 119 amphibian species, and 5,793 plant species have been recorded within the Albertine Rift.

This area is highly threatened by habitat loss, where there is also a high demand by local people for land and natural resources to support intensive smallholder agriculture (Hartter and Ryan, 2010). Thus, conservation of remaining forested areas is a high priority (Brooks et al., 2001). Protected Area managers and conservation groups are particularly concerned about the impacts of climate variability and change on regional resource availability (Chapman et al., 2006, Malcolm, 2006) given the potential impacts on wildlife (White, 2008) and Chapman, 2005). Much of the attention on climate change in Africa has been focused on the more dramatic impacts in drier areas (Nyong, 2007), shifting attention away from wetter areas, such as the Albertine Rift. The spatial and temporal patterns of rainfall in the Albertine Rift are highly variable (Basalirwa 1995, Stampone, 2011) due to complex topography, large inland water bodies, and the existence of large tracts of forest (Indeje, 2000 and Myers, 1991).

The region has unique ecosystems characterised by some of the highest mountains in Africa, including the Virunga Mountains, Mitumba Mountains, and Rwenzori Range. It has a high diversity of habitats, which include glaciers; lava rock and volcanic hot springs; alpine vegetation, montane forest, savanna, low land forests and woodlands; and papyrus swamps and high altitude swamps. The region represents an excellent opportunity for exploring the potential impacts of climate change on birds and other biodiversity. Its heterogeneous landscapes are extraordinarily rich in biodiversity, and highly variable in rainfall (Stampone et al., 2011), and extensive research has identified and located areas of importance to biodiversity conservation (Plumptre et al., 2007), and human settlement (Burgess et al., 2007).

1.3 OVERVIEW OF CLIMATE CHANGE AND BIRDS

There is growing evidence that climate change will become one of the major drivers of species extinctions in the 21st Century (Foden et al., 2008 and Thomas et al., 2004). Climate has been noted as one of the most important factors in determining birds’ ranges and abundance (Jones et al., 2003). Climate change affects bird species’ behaviour, ranges and population dynamics. In future, climate change will also affect birds indirectly by altering their habitats via sea level rise, changes in fire regimes, and changes in vegetation or land use (Böhning-Gaese et al., 2004).

Shifts in timing of important life cycle events such as breeding and migration times, and shifts in ranges, are two major ways that birds and their ecological communities are already displaying a strong response to climate change. These timing shifts threaten birds when important life cycle events fall out of step with plants and insects they interact with or depend upon. Significant evidence now shows that birds, and other animals and plants, are shifting their ranges in response to climate change, with bird species shifting pole-ward, or to higher altitudes in tropical mountains (Parmesan and Yohe 2003; Root et al., 2003 and Wilson et al., 2005). In future, the extent of such shifts is expected to be considerable; for example, some European birds are expected to undergo range boundary shifts of more than 1,000 km (Huntley et al., 2006). Crucially, range contractions are expected to be more frequent than range expansions (Huntley et al., 2006 and Böhning-Gaese et al., 2004).

However, not all species will respond in the same way to climate change. Susceptibility depends on a variety of biological traits; the species’ life history, ecology, behaviour, physiology and genetics. For example, species with generalised habitat requirements are likely to tolerate a greater level of climate and ecosystem change than specialised species (Warren et al., 2001). Even where such species are able to disperse to new climatically suitable areas, the chances of fulfilment of all their habitat requirements are low (Foden et al., 2008). The Albertine rift is an “Endemic Bird Area” (Thirgood and Heath, 1994; and Stattersfield et al., 1998) and an example of an area with endemic bird species with specific habitat adaptations which will likely render them more prone to the impacts of climate change.

Impacts of climate change on species, particularly the shifts in range and abundance, may also have profound implications for site based conservation approaches such as the Important Bird Areas (IBA) programme which play a significant role in both climate regulation and in helping people and biodiversity to adapt to climate change. Work is therefore needed to strengthen the resilience and connectivity of the IBA network and habitats in general (Hole et al., 2009, Hole et al., 2011 and BirdLife International, 2008). Efforts now to reduce resilience of species and habitat through strengthening populations and addressing existing pressures will help them adapt to climate change in future, which has implications for conservation planning and practice (BirdLife International, 2008). For more information on climate change in Africa, visit the BirdLife Africa climate-exchange website http://www.africa-climate-exchange.org/.


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SECTION TWOMETHODS FOR MONITORING

CLIMATE CHANGE IMPACTS ON BIRDS

2.0 PREAMBLE

This section is devoted to the recommended methods that can be used to monitor climate change impacts on forest birds with particular focus on shifts in species ranges. The section provides links to tools and describes the methods of data collection and analysis, possible sources of errors and challenges during data collection. In addition it includes preparations for carrying out surveys. The methods described are based on a baseline study undertaken on the potential impacts of climate change on birds in the Albertine rift by BirdLife International between 2010 and 2012. The baseline study is here used as a case study in describing the methods, and provides the ability in future to monitor climate change impacts on forest birds in the baseline sites as well as other forest sites. The baseline study had two main objectives: (1) Relate species occurrence to climate, to project how species might respond to future climate change; (2) Relate distribution and abundance of species to habitat traits, to assess effects of habitats on bird populations. However, this guide does not specifically address these objectives but rather provides guidance on some methods and links to tools to monitor a suite of climate change impacts likely to affect forest birds in the Albertine Rift region focusing mainly on shifts in ranges of birds.

A wide range of methods have been used to conduct avian monitoring and habitat data collection, each appropriate for different purposes. This Guide does not address all methods that are available, but focuses on methods that were found to be most appropriate during the baseline study and other studies in assessing potential impacts of climate change on birds. It is important to monitor any changes in the distribution and abundance of birds as a result of change in climate and habitat for better conservation planning and practice. To be able to monitor any change, periodic data on species’ distributions and abundance, and related habitat variables, need to be collected. This facilitates comparing future changes against the baseline study.

2.1 ETHICAL CONSIDERATIONS

Whether field activities are short or long-term surveys, and whether carried out by national or visiting scientists, international standards of ethical and legal practice need to be followed (e.g. Fauna and Flora International, 2000).

These have been compiled by a number of institutions and the reader should refer to the full texts if there is any uncertainty about planned actions. In general, care needs to be taken to:

(a) Ensure that official research permits, including collecting permits and equipment import licences, have been provided, and that a sponsoring national institution has approved and supports the proposed survey work. Also ensure that any products that arise from the work (including reports, books, scientific papers, films, etc.) acknowledge the sponsoring institution, and provide copies to them and other government departments. For example: In Uganda, research permits should be obtained from Uganda National Council for Science and Technology (UNCST), and relevant bodies such as the Uganda Wildlife Authority and National Forestry Authority, in Rwanda, from the Rwanda Development Board and the Institut National pour l’Environnement et la Conservation de la Nature (INECN) in Burundi.

(b) Endeavour to work with and through local institutions, building from their capacity and taking their advice. Wherever possible contribute to building local capacity. When employing local field assistants, ensure that local labour codes are respected.

(c) Collect animal specimens in a humane and ethical manner, with as few specimens collected as necessary to satisfy scientific needs, and with the absolute minimum amount of pain or suffering inflicted upon the animal.

(d) Take account of beliefs, customs and rights of local communities, and guard against the appropriation of their intellectual property.

2.2 DATA COLLECTION

2.2.1 PREPARATIONS

Prior planning and preparations are important for successful field surveys. You must think carefully about the objectives of your survey, as this will determine the data that you need to obtain, and thus the most appropriate methods. It is also important to think how the data will be analysed in order to develop an appropriate sample design.


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It is important to undertake a preliminary survey to assess the feasibility of the study. This enables one to acquaint him/herself to the study sites and test proposed methods. The preliminary survey also facilitates locating and setting out points, identifying transects or habitat variables to be used in the study, and also enables general planning for the fieldwork interms of the logistics and resources required (i.e. human resources, funds, transport, feeding, timing, budgeting, etc.). It is worthwhile spending some time perusing field guides and by speaking to knowledgeable people; it is a good idea to start compiling a species list for the forest sites that will be visited. Bird atlases also provide very useful indications as to the possible occurrence of a species in a particular area (e.g. Bird atlas of Uganda).

As well as being able to identify particular species by both sight and sound, the survey method (Point counts) described here rely on accurate estimation of distances. Distances can be measured using an optical rangefinder. However, use of a rangefinder may not be possible in some places such as dense vegetation areas with visual distractions, hence distances should be estimated visually (naked eye). It is therefore very important to practice distance estimation before you start your work. If you are using a cut-off point of 25 m, for example, go into the forest and estimate this distance, then measure to see how accurate your estimation was. Continue practicing until you can estimate this distance reliably in this habitat. It is important to practice this in a similar vegetation type to the transects you plan on using; distances appear very different in the open when compared to dense forest, and stride lengths tend to become much shorter when hopping over logs or wading through a swamp. It is crucial that all those who are counting are accurate in their distance estimation. To standardise estimations and avoid discrepancies, the same person recording the birds should estimate and record distances.

2.2.2 FIELD REQUIREMENTS

Before heading out to the field to undertake data collection, it is advisable to ensure that the following basic necessities are in place; a pair of gumboots, rain gear, long sleeved shirts, trousers, insect repellants, anti-venom (incase of snake bites), warm clothing, camping equipment, food and beverages (soft drinks, water and snacks). Special materials and equipment are highlighted for the different subsequent surveys. Other requirements include:

(a) Data recording sheets: these should be designed and photocopied in advance. Ensure to have all the study variables included in the data-sheet design to avoid forgetting some records. Have a clipboard to support data sheets when recording. Clipboards have to be held and its best to keep your hands free for binoculars – it is advisable to have a string on the clipboards so you can sling them over your shoulder.

(b) Notebook: these can be handy for taking extra notes in the field for example habitat conditions. Many people prefer to use a loose-leaf binder, so that only the notes for a particular field session are taken to the field. Previous notes can then be kept elsewhere for safety, and photocopied as soon as one returns from the field session, to avoid loss of data. It is advisable to have a strong plastic bag to protect data-sheets and notebooks against rain.

(c) Pencil/pen: propelling pencils, which need no sharpening, are most convenient, or pens with waterproof (India) ink. Ordinary ballpoint pens are NOT recommended for data recording: the ink is not waterproof, and your data sheet or notebook will be a mess if it gets wet, and errors cannot easily be erased with ink.

(d) Field identification guides: Avoid the use of large, cumbersome reference works (which are best consulted back in the office/laboratory), and stick with lightweight, compact field guides. A species checklist for the area (if available) is advisable, or a preliminary list compiled from expected occurrences. Stevenson and Fanshawe (2002) is the recommended bird guide book.

(e) Watch and/or stopwatch: necessary for timing in the field especially for point counts. Should preferably be water-proof.

(f) A compass (in a protective case): essential, not only for making maps and determining survey routes, but also to help teams return to camp if they get lost. Also useful for cutting transects.

(g) A range finder (for estimating distances).

(h) Global Positioning System (GPS): for recording the start and end of transects, or positions of point counts, measuring distances between points and measuring altitude.

(i) Binoculars: these are the most essential piece of equipment for surveys of birds. Binoculars are normally labelled as 7 x 30 or 8 x 40, and so on. The first figure represents magnification, and the second the diameter of the objective lens measured in millimetres. The larger the second figure is, the greater the light-gathering potential of the lens. For forest work, a wide field-of-view and plenty of light-gathering capacity is best. The best magnifications are 7x and 8x; higher magnifications (10x) may allow you to identify birds in the treetops more easily, but will be less effective for more close-up work. The objective lens should be at least x40. A telescope (mounted on a light-weight tripod) is surprisingly useful for identifying treetop birds. Ideally, both binoculars and telescopes should be


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weather-proof; if they are not, then carry strong plastic bags for protection against rain (ziplock bags are useful if available).

(j) Photographic equipment: a good camera is often useful for taking photographs of survey areas, different types of habitats, evidence of human activities, captured or surveyed specimens, etc. A protective bag is recommended, and a digital camera is recommended for quick downloads.

(k) Topographic maps: of the survey area, on as large a scale as available, and map of trails, footpaths, etc. if available (you may have produced your own map from reconnaissance surveys).

(l) Torches: (preferably six-battery) and headlamp for night-time work (spare bulbs and batteries are essential).

(m) First-aid kit.

It is important to have an experienced field assistant with good knowledge of forest surveys to help. It is also advisable to have a laptop while in the field to input data to avoid inputting large sums of data at a later stage. Free time in the afternoons or weekends can be used for this.

2.2.3 ROUNDS OF DATA COLLECTION

Survey results can be strongly influenced by season, time of day and local habitat variation (including elevation). It is essential to minimise bias in your data by taking these sources of variation into account (e.g. by conducting counts at different sites during the same seasons, randomising count order across the day, and stratifying your sample to take habitat and altitudinal variation into account). Replication is important in research to aid precision and accuracy.

As undertaken in the baseline study (Table 1), it is advisable to plan timing of multiple visits across climatic seasons to permit monitoring of any possible seasonal movements of species, and minimise any false absences being recorded at any site. Any other extra data obtained from other sites should also be appropriately incorporated. For example: data for the baseline study were collected for a period of 5 months, subdivided into 3 rounds from October 2010 to February 2011 in Echuya, Nyungwe and Kibira forests with each census point visited three times. Prior to data collection, a preliminary round was undertaken to assess feasibility of the study in September 2010. Data from Bwindi Impenetrable forest were obtained from Dr. Phil Shaw of the University of St. Andrews UK, collected in October 1999 and December 2002 with each census point visited once. Bwindi data were incorporated into data from the other three sites based on the respective rounds (months) of data collection.

If you plan to do any occupancy analyses with the data, it is good to visit more than 3 times – occupancy may be an easier way of analysing the point count data than distance analyses. For monitoring purposes, only one round of data collection may be necessary. This may be carried out annually or bi-annually at the sites.

Table 1. Timing of rounds of data collection in Echuya, Nyungwe and Kibira forests

Round Date Year SeasonPreliminary September 2010 Dry

Round 1 20th October to 23rd November 2010 Dry

Round 2 2nd December to 13th January 2010–2011 Heavily wet

Round 3 23rd January to 21st February 2011 Lightly wet

2.2.4 SELECTION OF POINTS, TRANSECTS AND HABITAT VARIABLES

The preliminary survey facilitates identification and selection of points, transects or habitat variables to be measured in the study before undertaking the actual data collection. Habitat variables such as broad habitat types (e.g. swamp, bamboo or Montane grasslands), and existing transects within each study site should be identified in the pilot survey with the help of local knowledge of the rangers (as recommended by Bibby et al., 1998). It is often desirable to use existing small trails and paths, rather than destroying more vegetation by cutting your own transects. However, where few transects/trails exists for the study, more transects should be created.

At Kibira National Park, few transects existed for the baseline study, so additional transects were established and marked. Four new transects were created and two existing transects (Musigati and Samutuku) were used. In Nyungwe, existing WCS transects were used for the surveys. WCS has an annual biodiversity monitoring programme focused on birds, plants, and large mammals in some forests of the Albertine rift particularly in Nyungwe National Park. The existing transects within Echuya forest reserve are those that were used by WCS during their Albertine rift biodiversity surveys. The Echuya forest transects also act as footpaths for local communities especially crossing between Uganda and Rwanda and


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moving between villages as well as for collection of forest products. Ideally, transects within a site are relatively similar in width, disturbance, etc. – for example, a heavily utilised path may influence the bird community in comparison to a transect cut through pristine forest. Sometimes there’s no option but to use an existing, heavily used trail.

Transects and points should be well separated to avoid re-sampling the same birds. In the baseline survey, it was ensured that transects were at least 2 kilometres apart. By consulting locals and maps, ensure that each transect passes through large portions of each important habitat represented at the site and along different altitudinal gradients. To detect changes resulting from climate change, you need to stratify or sample in different altitudinal ranges and ensure you can detect whether movements are occurring upslope as predicted or not. Although transects do not necessarily have to be straight, it is advisable to ensure that transects are as straight as possible when cutting more transects. This can be done with the help of a compass. Sample as large an area of the study sites as possible; a total of 26 transects with an average length of 2 km and a total of 508 points established at 200 m interval, were undertaken across all four study sites in the baseline survey.

2.2.5 BIRD CENSUS DATA

A wide range of methods have been used to conduct avian monitoring for example; the point count method, mist-netting, timed species counts (TSC) and line transect counts each tailored to meet a different set of objectives in the face of different constraints. This Guide does not address all methods that are available, especially those that are more widely used for research or inventory. The Guide focuses on the point count method because, for the reasons outlined below, point counts were found to be the most appropriate method in the baseline study and a wide range of other studies for assessing impacts of climate change on birds, particularly in forest habitats.

Point counts are often preferred to other methods where the habitat is patchy such as the diverse elevation forests of the Albertine Rift. In species-habitat association studies, point counts are also preferred because habitat data can more easily be associated with the occurrence of individual birds. There is also more time to detect cryptic and difficult birds with point counts as the observer at the point concentrates solely on detecting, locating and identifying birds without the need to traverse what may be difficult terrain (Bibby et al., 2002). Point counts provide a measure of how detectability varies with distance and hence are an important component of distance sampling useful in estimating species densities and abundances. They are also cost effective. For the same effort, point counts might generate five times more independent bird data for analysis as compared to mist-netting that require more time in setting the nets, counts and more labour.

However, given that point counts require high level of observer skill in detecting and identifying bird species, one downside is you need experienced ornithologists who can identify the birds and knows their calls – a rare situation in the Albertine Rift. Certain species are also much harder, if not impossible to detect using point counts – such as small skulking species, that may only be censused through mist-netting. While conducting point counts, it is important to understand the assumptions and ensure that they are met in the field (Bibby et al., 2002). More information on methods for avian surveys and monitoring can be found in Bibby et al., (2002), Bennun et al., (2002), Gregory et al., (2004) and

et al., (2008).

Example of the use of point count method as undertaken in the baseline study

In this study, point count surveys of 10 minutes duration with 2 minutes settling time, and at least 200 metres apart were conducted along altitudinal transects as described by Bibby et al., (2002) and Bennun et al., (2002). Transects were designed to sample diverse habitat types and elevations in the forests. Given that birds are most active in the early morning, and for standardization, transects were walked by starting at dawn (between 06:30 and 12:00). Birds seen or heard were identified using Stevenson and Fanshawe (2002), and with the help of an experienced field assistant, recording individual birds or groups and the group sizes. Distance from the central point to any bird/group was recorded using a rangefinder (or by eye estimation where use of a rangefinder was not possible). Where possible, calls of unidentified birds were recorded using a tape recorder and later identified with the help of a more experienced field ornithologist. Additional information such as sex, age and behaviour were also recorded where possible. The process was repeated at each census point.

Distance between points (at least 200 metres) was measured using a GPS. Bird sound recordings are also available commercially for each major region of Africa and could help in species identification: West (by Claude Chappuis); East (by Brian Finch, published alongside Stevenson and Fanshawe, 2001), and South (by Guy Gibbon and others). Other useful compilations were listed in Pomeroy (1992). It is also important to note down important features of the unidentified bird (e.g. general size/colour, beak colour/shape, eye colour, etc.) or to make a rough sketch. However, do not spend too much time doing this, because the distraction may cause you to miss other sightings.

Note that in this study, distance data was collected but not used for species density estimates in Programme Distance due to limited time that was available for analysis. Density estimates can be used for species distribution modelling to project how populations might respond to future climate change; and can also be used for species-habitat associations to determine how species density may vary with altitude, and other habitat traits.


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2.2.6 HABITAT DATA

Although bioclimatic factors are major determinants of a species’ ecological niche, they are not the only determining factors, particularly at smaller spatial scales; for example, other factors such as altitude, slope, aspect, canopy cover, light availability, or water availability may come into play. In addition, limited dispersal ability, species interactions, and availability of different habitat types such as bamboo, savanna, or swamp may further reduce the range of conditions a species may inhabit, and thus its geographic range as well. Habitat data are therefore necessary for contributing to interpretation of the distribution of birds by relating occurrence of birds to habitat traits. Habitat data should be incorporated in the species-climate models to assess their effects on the distribution of the birds. This also gives you a measure of how bird communities respond to the interaction of change in climate and habitat. A number of habitat variables should be measured depending on the aims of the study. Altitude, habitat types, tree density and canopy estimations were considered appropriate habitat variables during the baseline study as described below. A wide range of methods have been used for measuring tree density and canopy estimations. In the baseline study, the point-centred quarter method (PCQM) was used for estimating tree density while canopy estimations were done visually.

Examples of habitat data collections as undertaken in the baseline study

(a) Altitude and habitat types

Altitude and habitat type at each survey point should be recorded along transects to relate species’ distributions to elevation and habitat. Altitude should be recorded using a GPS device. For example in this study, five distinct habitat types were considered in the baseline survey classified as: Closed-canopy forest, Bamboo, Mixed forest, Swamp and Montane grasslands (Figure 2).

Closed-canopy forest habitats are considered habitats dominated by tall trees with relatively closed/dense canopies (70–100% canopy cover); Bamboo habitats are those with predominantly bamboo plants; Mixed forest habitats are those comprising of both closed-canopy forest and bamboo habitats; Swamp habitat was considered wetlands dominated by aquatic vegetation (shrubs, trees, and reeds) and Montane grasslands are habitats covered predominantly by grass with some interspersed trees and an open canopy.

It is advisable to move points considered to fall at the boundary between two habitats by 50 metres into the subsequent habitat to ensure sampling of distinct habitat types. Such as in this study, high altitude points such as the highest point (2950 m) in Nyungwe – Bigugu – consists of Afro-alpine habitats that could also be distinctively sampled.

(b) Point-centred quarter method

In this study, the Point-centred quarter method (Cottam et al., 1953, and Cottam and Curtis, 1956) was used for estimating tree density at each survey point. Data should be collected in one of the data collection rounds. The area around each survey point along transects is divided into four 90º quadrants, and the distance to the nearest tree is measured in each of the four quadrants to a maximum distance of 10 metres. Thus four point-to-tree distances are generated at each survey point. To be counted as a “tree”, the tree should have a minimum diameter of 10 cm. Material requirements include 50 metre tape, a diameter tape and a notebook.

Figure 2. Illustration of different habitat types in the forests. (�) Closed-canopy forest; (�) Bamboo; (�) Mixed-forest; (�) Montane grasslands; (�) Swamp and (�) Afro-Alpine

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For each survey point record: the quarter number (1, 2, 3, and 4); the distance from the sampling point to the centre of the trunk of the tree; the species of the tree (optional); and the diameter at breast height (DBH) to the nearest cm (Figure 3). It is important to use the same height to measure the diameter or circumference. A standard height of 130 cm is recommended and the notation D130 should be employed rather than DBH to indicate this. Whatever height is used should be explicitly noted in the results. See Table 1c in appendix I for how data should be organised. Repeat this for the other three quarters at this sampling point, and repeat the whole process at all the survey points.

Trees should be identified with the help of a field guide book (e.g. Plants of Nyungwe National Park) or an experienced Botanist. If a tree species cannot be identified, simply record it as A, B, C, etc., and collect and label a sample leaf for comparison purposes at other quarters and later taxonomic identification. For trees with multiple trunks at breast height, record the diameter of each trunk separately and take the average of the trunks. The appropriate unbiased estimate of population density is from Pollard (1971):

Np = Point-quarter estimate of population densityn = Number of survey pointsπ = 3.14159r2

ij = Distance from survey point i to the nearest tree in the quadrant j (j = 1, 2, 3, 4; i = 1, .n)

(c) Canopy estimations

Canopy estimation was used for measures of vegetation cover and performed by eye at different heights above the ground at each survey point as illustrated in Figure 1. The survey point was divided into 4 quarters with a maximum distance of 10 meters from the center of the point. Non-woody vegetation was estimated at heights of 0–1 and 1–3 metres while woody vegetation estimated from 0–1, 1–3, 3–8, and > 8 metres above the ground both at a scale of 0–25 percent in each quarter depending on the denseness of the vegetation. Human activities that may impact on the vegetation and distribution of birds such as fires, tree cutting, bee-keeping, grazing/watering and human presence were also noted at each survey point. See appendix 1b for the data collection sheet.

2.2.7 CLIMATE DATA

Generally, under climate change, species’ geographic ranges are expected to shift upslope in the tropics. Detailed projections of future geographic ranges require detailed information, typically describing the current geographic ranges of species in terms of present-day climate measurements across a number of bioclimatic variables, and combining this information with global circulation model (GCM) projections of future climate, to project future geographic ranges. Essentially, then, we seek to answer two simple questions: What are the climatic conditions under which a species is currently found? And where will those conditions be found in the future? This type of projected geographic range is referred to as a “bioclimatic envelope model”.

Therefore, modeling a species’ current and future geographic range in this way requires three types of data. First, information on the species’ current distribution – (data derived from known point locations at which the species of interest was recorded). Secondly, present climate data, which are freely available from a number of online sources such as the PRISM Climate Group (http://www.prism.oregonstate.edu) and WorldClim (http://www.worldclim.org). And finally, projected future climate data. These data are also available online from, for example, the Intergovernmental Panel on Climate Change (IPCC) website (http://www.ipcc-data.org/sres/gcm_data.html) WorldClim (above) and The Nature Conservancy’s Climate Wizard project website (http://www.climatewizard.org). Future climate data are based

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on future emission scenarios and time slices – see IPCC (2007) for more information. A number of climatic variables exist for both the present climate and future climate projections (GCMs) – see examples of climate variables and general circulation models used in the baseline study, and criteria for their selections in examples below.

It is advisable if possible to use regional or local climate data at finer scales (resolutions) instead of globally available data to be able to make more accurate projections of species distributions. For the Albertine Rift, climate data can be obtained from WCS or through the CHIESA project in Kenya for East-African regional data.

Examples of climate data used in species distribution modeling in the baseline study

Present climate data at a spatial resolution of 1 km2, related to the period 1951–2000 was obtained from the Worldclim database (www.worldclim.org). Four bioclimatic variables were chosen, a priori for their previously demonstrated utility in modelling the climate envelopes of a wide range of bird species in Europe and Africa (Huntley et al., 2006): mean temperature of the coldest month (MTCO); mean temperature of the warmest month (MTWA); an estimate of the ratio of actual to potential evapotranspiration (APET); and wet season intensity (WETSEAS). Wet season was chosen to reflect seasonality in moisture availability (for definitions and derivations see Huntley et al., 2006).

Future climate projections from three General Circulation Model (GCM) predictions of future climate were obtained from the Intergovernmental Panel on Climate Change (IPCC) website (http://www.ipcc-data.org/sres/gcm_data.html): Hadley Centre Global environmental Model (HADGEM) (Gordon et al., 2000), European Centre Hamburg Model (ECHAM5) (Roeckner et al., 2003) and Geophysical Fluid Dynamics Laboratory (GFDL2.1) (Knutson et al., 2004) under future emissions scenario A1B and three time slices; 2025, 2055 and 2085.

The three GCMs were chosen because they project global mean temperature increase close to the mean of the nine GCMs included in the IPCC Third Assessment Report (Cubasch et al., 2001). They are representative of the ‘dry’ (ECHAM5), ‘medium’ (HADGEM) and ‘wet’ (GFDL) groups into which the nine models fall with respect to their projections of change in global precipitation by 2100. Given that precipitation is a key component of climatic patterns in Sub-Saharan Africa, the three GCMs account for differences among GCMs in their projections of future changes in precipitation.

2.3 DATA ANALYSIS

In addition to the various types of data described in the data collection section, a statistical method is also required for exploring species-habitat associations, and to estimate the relationship between the values of the bioclimatic variables and the point locations defining the species’ geographic range (i.e. species distribution modeling). There are many statistical methods and software packages available to do these tasks, but they do not all perform equally well; for example:

The open source statistical package R 2.13.0 (R Core Development Team 2011; (http://www.r-project.org) is a dynamic program with ability to perform various tasks while using the available libraries in the programme. It contains facilities for data manipulation, simulation, calculation and graphical display. It can be used for both species-climate modelling and exploring species-habitat associations. For more information visit: (http://cran.r-project.org/).

The MaxEnt software employs the maximum entropy method for species distribution modelling using species presence-only data. In practice this method is used to find a probability distribution that is consistent with a set of data by maximising the information entropy of the chosen distribution given the constraints of the data. For more information visit: (http://www.cs.princeton.edu/~schapire/maxent).

The open source software Program Distance employs distance-sampling methods commonly used in studies of animal populations to estimate population density. It uses information on observed distances of birds from transects or points of observation to characterise the detection probability of individuals. Under the hypothesis that detection probability is related to the distance between birds and the point of observation, one may obtain an estimate of density that is, in effect, adjusted for non detection bias. For more information see (http://www.ruwpa.st-and.ac.uk/distance/) and Buckland et al. (1993, 2001, and 2004). Species’ densities/abundance obtained in Program Distance can be used to explore species-habitat associations and species-climate modelling.

2.3.1 SPECIES DISTRIBUTION MODELLING TECHNIQUES

Species Distribution Modelling (SDM) is here defined as relating species distribution data (occurrence or distribution at known locations) to environmental or spatial characteristics of those locations (for example climatic conditions). This can be used to provide an understanding of and/or projecting species distributions across a landscape. SDMs have also been called: bioclimatic models, climate envelopes, ecological niche models or habitat models, sometimes with different emphases and meanings.


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The different techniques for species distribution modelling can be classified into two groups based on the species data to be used. For example: Maxent, Biomod and Bioclim can be used for presence-only data; while regression-like models such as Generalised additive models (GAM), generalised linear models (GLM) and Multivariate adaptive regression splines (MARS) can be used for presence-absence and abundance data. Generalised additive models and generalised linear models can also be used for species-habitat association analysis. See example below for species distribution modelling and species-habitat modelling approaches.

Example of species distribution modelling as undertaken in the baseline study

To assess potential impacts of climate change on the birds, generalised additive models were used to relate species occurrence to present climate (i.e. Species distribution models), using presence-absence species data, and the resultant models were applied to potential future climates (of the 21st Century) to project future species altitudinal distributions. Four bioclimatic variables were used: Ratio of actual to potential evapotranspiration (APET), Mean temperature of the coldest month (MTCO), Mean temperature of the warmest month (MTWA) and Wet season intensity (WETSEAS). Future climates were based on three general circulation models (GCMs): European Centre Hamburg Model (ECHAM5), Geophysical Fluid Dynamics Laboratory (GFDL2.1) and Hadley Centre Global Environmental Model (HADGEM) for three future time periods centred on the years 2025, 2055 and 2085 under emission scenario A1B. The GAMs were fitted using a spline smoother, binomial error distribution and a logistic link function using the mgcv package in R statistical program, and probability of occurrence generally calculated using maximum likelihood estimations.

The results suggested that climate change will influence the altitudinal distribution of most birds in the Albertine Rift as illustrated in the example – Figure 5 below for Collared Apalis. APET was the most important explanatory variable in determining species distribution (a significant variable in models for 40 species out of 42 species analysed) while MTCO was the least important variable (significant in only 13 species models) with most of these relationships with both APET and MTCO being negative. Most endemic bird species are projected to move upslope in future. Such shifts could feasibly occur at Nyungwe National Park, which has a large elevation extent and provides climatically suitable areas for most species in future. By contrast, at the three other sites there is limited scope for upward elevation shifts by endemic species, meaning that most are projected to decline in future. Most widespread species were projected to shift their range downslope as more suitable climates were simulated for them at lower altitudes in future at the four study sites.

Example of species-habitat modelling as undertaken in the baseline study

Bird species presence and absence data collected from the surveys were utilised to relate species occurrence to altitude and habitat type. Generalised additive models were used to relate bird occurrence to altitude while generalised linear models were used for relationships with habitat types in the statistical package R 2.13.0. The generalised additive models had splines with a maximum of 4 spline points and assumed a binomial distribution for the errors. For the generalized linear models; an initial model was fitted, habitats were then ranked according to the species’ preferences for each habitat and then fitted a second model where the coefficients for each habitat were compared against those of the best habitat. The best habitat was the top-ranked habitat, while the suitable

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ones were those that were not significantly lower than the best habitat. If the coefficients were not significantly different from the best habitat, those habitats were designated as suitable – otherwise, they were considered less suitable.

Habitat and altitude were found to influence distributions of species as illustrated in Figure 6 below for Collared Apalis and Yellow white eye. Most endemics were found to occur at higher altitudes while most widespread species occurred at mid altitudes. The largest proportions of species were found to be highly associated with closed-canopy and mixed-forest habitats.

GAMs are non-parametric statistical models, are a flexible (Wood, 2006) and automated approach to identifying and describing non-linear relationships between predictors and response with an ability to characterise the nature of the response function (Yee and Mitchell, 1991). Generalised linear models (GLM) are extensions of linear models that can cope with non-normal distributions of the response variable (Venables and Ripley, 1994), and they provide an alternative to transforming the response variable and then applying the linear model (Franklin, 2009).

It is advisable to only consider species that have records of more than 50 individuals in the analyses for satisfactory modelling results as models get better with more data available. In the baseline study, a total 162 bird species were detected and 42 species that had records of more than 50 individuals were analysed. It is also recommended to follow BirdLife International taxonomy (full taxonomy available at http://www.BirdLife.org/datazone/species/downloads/BirdLife Checklist v3 June10.zip, downloaded on 15th March 2011) as it contains an updated species nomenclature.

2.4 SOURCES OF POSSIBLE ERRORS IN METHODS

Understanding errors is of great importance in designing and interpreting a study, and should therefore not be ignored in order to increase precision or accuracy. Errors are here referred to as the difference between our estimate and the true value in a statistical sense rather, than in its common meaning of a mistake. Even if errors are inevitable in almost any realistic bird census method, it should be ensured that errors are minimised as much as possible.

Errors may arise from measuring or sampling errors, for example, surveying only a small part of the total area. If the density of a species varies considerably from place to place, the area sampled may need to be larger in order to include a whole cross section of densities. Sampling most as many areas within each site as possible would be one way of increasing the sample size. Errors may also arise in competence of the observers in identifying the birds. However, in the baseline study, this was minimised by having highly experienced ornithologists, and the help of a field guide book and tape recorder for recording sounds of unidentified birds. The counting methods should also be standardised so that point counts lasts for a fixed period and transects walked in the same time of the day to minimise any bias. It is important to identify or outline any possible sources of errors in your study to be able to minimize them as much as possible for example in the baseline study below:

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Figure 6. Relationship between probability of occurrence of birds with altitude and habitat types (CF- Closed-canopy forest, B- Bamboo, MX- Mixed forest, SW- Swamp, and SV- Montane grasslands). Generalised Additive models were used for bird occurrence relationships with altitude while generalised linear models used for relationships with habitat types. Short black lines above and below represent presence and absence records respectively, indicating where majority of the sample size is in relation to altitude. Shaded polygons indicate the ��% confidence envelopes for the relationship between species occurrence and altitude – wide confidence intervals around the smoothed curve at extreme values indicate very few observations in that range


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Examples of possible sources of errors identified in the baseline study

Some of the areas in the study sites were inaccessible during the surveys due to logistical reasons, and hence the concentration of transects within specific areas especially in Nyungwe and Kibira National Parks (appendix IV). There were also rumours of rebel activities within some parts of Kibira National Park particularly the Teza region that hindered us from sampling this forest section.

Survey points that fell at a boundary between two habitats were moved by 50 metres into the subsequent habitat to ensure sampling of distinct habitat types. However, there were areas where the two habitat types were very narrow (e.g. swamp) that made it a little difficult to sample distinct habitats hence birds recorded at such points were noted in the respective habitats where they were detected. It was sometimes difficult to use the rangefinder to obtain distance of birds from the recorder. This occurred most frequently in very dense habitats (e.g. closed-canopy forest) where there were obstructions from vegetation, in open habitats (e.g. swamp and Montane grasslands) where there was nothing to aim at, and along the hill slope, where there was nothing downslope to aim at and difficult to get the perpendicular distance upslope. In such situations distance estimates were made by visual estimation of accurate distance.

2.5 CHALLENGES DURING DATA COLLECTION

Main challenges in the field may be encountered in using a particular field method, equipment or due to unfavourable weather conditions. It is advisable to as much as possible find solutions to the challenges. Examples of challenges as encountered in the baseline study could include:

(a) Challenges in using equipments: In this study, we encountered difficulty in using a rangefinder in areas with dense vegetation that disrupt measuring

perpendicular distances, and where a bird is on a higher altitude than the observer, or on a lower altitude where there is nothing (space) to aim at so as to easily estimate perpendicular distances. Use of a rangefinder may also be difficult with bird calls especially from far distances. Using a rangefinder delays the process at points with several birds. However, where it is not possible to use a rangefinder, distance should be estimated visually.

When using a GPS, one may also experience unstable readings such as in distance measurements, altitude and compass directions due to poor receptions in the forest. It is recommended to use a strong GPS device such as (Garmin, GPSmap 60Cx) that was used in this baseline study for better reception and minimize errors in unstable readings. Weak GPS devices may not even receive any satellites in some parts of the forest especially in valleys or dense canopy cover areas. It is advisable to ensure that the GPS device accuracy is atleast ≤10 metres for better accuracy or precision.

(b) Logistical challenges: Due to logistical reasons, it may not be possible to sample some areas in the study sites especially those that

are quite far away from the camping base, or areas that are difficult to access due to poor road conditions. For example in this study, bamboo habitat was not sampled in Nyungwe because it was far in the south, with poor roads requiring 4 x 4 vehicles which were not available. It may also be challenging obtaining funds at the required time, which then delays the programmes. Ensure proper planning to overcome logistical challenges.

(c) Insecurity: Due to insecurity, it may not also be possible to sample some areas of the sites. In this study, the Teza region in

Kibira National Park was not sampled due to fears of insecurity in the area, because the area was rumoured to have rebels.

(d) Unfavourable weather conditions: Weather may also be quite unpredictable in the sites. Because of rain, some survey points during the baseline

study were not sampled in a particular data collection round but were later sampled in the subsequent round. You should ensure that no point is missed in the last round as you may not have the resources to sample the missed point.

(e) Knowledge of birds: Knowing and being able to detect all or most birds is a big challenge especially for beginners. It is advisable to

have experienced field assistants and a bird guide book to help with identification. It is also advisable to have a tape recorder to record bird calls/sounds that can be used for future bird identification.

(f) Other challenges may include; challenges in obtaining research permits and field assistance at the needed time, and data handling challenges.


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2.6 NEED FOR FOLLOW-UP SURVEYS

Time series of observed data are a gold mine for developing early warning systems, targeting conservation resources, and ensuring proactive management, in addition to modeling purposes. However, few studies represent a time series across a measure of biodiversity in the developing world implying very little is done about long-term monitoring in the developing world. This is mainly due to lack of financial support for long-term monitoring. There is therefore need for increased funding support to help with monitoring especially in the developing world, and particularly in this case monitoring climate change impacts on birds in the forest sites in the Albertine rift.

2.7 CONCLUSION

This guide has described an array of tools, techniques and resources that organisations and individuals in developing countries can employ in understanding and monitoring climate change impacts on forest birds, while reinforcing the need for capacity building in the field of climate change science. Importantly, this guide has attempted to communicate this information in a way that is practical and useful to site managers and scientists in the Albertine Rift.

2.8 REFERENCES

Bennun L., Davies G., Howell K., Newing H., Linkie M. (2002) African Forest Biodiversity: A field survey manual for vertebrates: 138.

Bibby C.J., Burges N.D., Hill D.A. and Mustoe S.H. (2000) Bird Census Technique. Academic press, London.

Bibby C., Jones M. and Marsden S. (1998). Expedition Field Techniques, BIRD SURVEYS.

Böhning-Gaese K. and Lemoine N. (2004) Importance of Climate Change for the Ranges, Communities and Conservation of Birds. In: Møller, A., Berthold, P. and Fiedler, W. (Eds) Birds and Climate Change, pp. 211. Advances in Ecological Research 35. Elsevier Academic Press.

Buckland S.T., Anderson D.R., Burnham K.P. and Laake J.L. (1993) Distance Sampling: Estimating Abundance of Biological Populations. Chapman & Hall, London, UK.

Malcolm J.R., Liu C., Neilson R.P., Hansen L. and Hannah L. (2006) Global Warming and Extinctions of Endemic Species from Biodiversity Hotspots. Conservation Biology 20: 538–548.

Brooks T., Balmford A., Burgess N., Fjeldsa J., Hansen L.A., et al. (2001) Toward a Blueprint for Conservation in Africa. Bioscience 51: 613–624.

Buckland S.T., Anderson D.R., Burnham K.P., Laake J.L., Borchers D.L. and Thomas, L. (2001) Introduction to Distance Sampling, Oxford: Oxford University Press.

Buckland S.T., Anderson D.R., Burnham K.P., Laake J.L., Borchers D.L. and Thomas L. (eds) (2004) Advanced Distance Sampling, Oxford: Oxford University Press.

Chapman C.A., Lawes M.J. and Eeley H.A.C (2006) What hope for African primate diversity? Afr J Ecol 44: 116–133.

White T.C.R. (2008) The role of food, weather and climate in limiting the abundance of animals. Biol Rev 83: 227–248.

Chapman C.A., Chapman L.J., Struhsaker T.T., Zanne A.E., Clark C.J. et al. (2005) A long-term evaluation of fruit phenology: Importance of climate change. J. Trop. Ecol. 21: 35–45.

Cottam G. and Curtis J.T. The use of distance measures in phytosociological sampling. Ecology, 37(3):451{460, 1956. ISSN 00129658. URL http://www.jstor.org/stable/1930167.

Cottam G., Curtis J.T and Wilde B.H. Some sampling characteristics of a population of randomly

dispersed individuals. Ecology, 34(4):741{757, 1953. ISSN 00129658. URL http://www.jstor.org/stable/1931337.

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�� Tabl

e 1a

. Poi

nt c

ount

dat

a co

llect

ion

shee

t for

bird

cen

sus

Obs

erve

r Nam

e:Tr

anse

ct a

nd P

oint

No:

____

____

_/__

____

_D

ate:

(dd:

mm

:yy)

____

_/__

___/

____

_

Site

Nam

e:

____

____

____

____

____

____

_G

PS P

oint

(UTM

):LN

:___

____

____

____

____

____

____

__

LT:_

____

____

____

____

____

____

___

____

Wea

ther

:C

loud

Rai

n W

ind

Visi

bilit

y__

___

____

___

_ __

___

GPS

Acc

urac

y (m

):Ha

bita

t (e.

g. B

ambo

o):

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

Star

t Tim

e: (h

h:m

m)

End

Tim

e: (h

h:m

m)

___

___

: ___

___

___

___

: ___

___

Elev

atio

n:__

____

____

____

____

___

Slop

e: _

____

____

____

____

___

Asp

ect :

____

____

____

____

____

____

____

____

____

____

____

____

_

Spec

ies

(Com

mon

nam

e)Ra

nge

finde

r (m

)

Dis

tanc

e ba

nds

(m)

Dete

ctio

nI o

r GM

ale/

Fe

mal

eA

or J

No.

of

Bird

sCo

mm

ents

(e.g

. fem

ale

had

nest

m

ater

ial,

in fl

ight

, ter

ritor

ial p

air,

habi

tat c

ondi

tion,

etc

.)0–

1010

–20

20–3

0 30

–50

50–7

5 75

–100

Uni

dent

ified

KEY

: D

etec

tion;

AS

= A

udio

-Sin

ging

, AC

= A

udio

-Cal

ling,

VP

= Vi

sual

-Per

chin

g, V

F =

Visu

al-F

lyin

g; I

= In

divi

dual

, G =

Gro

up, A

= A

dult,

J =

Juv

enile

. LN

= d

egre

es

Long

itude

, LT

= de

gree

s La

titud

e.

SECTION THREE

APPENDICES

3.1 APPENDIx I: Data collection sheets


��Tabl

e 1b

. Dat

a sh

eet

for

Can

opy

esti

mat

ions

Site

Tran

sect

Poin

tN

on-w

oody

Tota

lW

oody

Hum

an A

ctiv

ities

Com

men

ts0–

11–

30–

11–

33–

8>

8Tr

ee c

uttin

gB

ee k

eepi

ngFi

reG

razi

ngPe

ople

Est

imat

ions

don

e at

leve

ls o

f 0–1

, 1–3

, 3–8

and

> 8

met

res

abov

e gr

ound

at a

sca

le o

f 0–2

5, 2

5–50

, 50–

75 a

nd 7

5–10

0 pe

rcen

t dep

endi

ng o

n th

e th

ickn

ess

of th

e ca

nopy

.


��

Table 1c. Data sheet for Point-Centred Quatre method

int Centre Quarter MethodObserver_____________________________ Transect ____________________________ Altitude ___________________Date_______________________________________________________________________________________________

Point No. Quarter No. Species Distance (m) Dbh (cm) Additional observations, e.g. fruiting, flowering

1

1

234

2

1234

3

1234

4

1234

5

1234

TOTALDBH = Diameter at Breast Height, m = metre, cm = centimetre.


��

3.2 APPENDIx II: List of Albertine Rift Endemic species

No Family Species Endemics

1. Musophagidae RWENZORI TURACO Musophaga johnstoni AR

2. Turdidae ARCHER’S ROBIN-CHAT Cossypha archeri AR

3. Turdidae RED-THROATED ALETHE Alethe poliophrys AR

4. Turdidae KIVU GROUND THRUSH Zoothera tanganjicae AR

5. Sylviidae SHORT-TAILED WARBLER Hemitesia neumanni AR

6. Sylviidae GRAUER’S WARBLER Graueria vittata AR

7. Sylviidae REDFACED WOODLAND WARBLER Phylloscopus laetus AR

8. Sylviidae GRAUER’S RUSH WARBLER Bradypterus graueri AR

9. Sylviidae KUNGWE APALIS Apalis agenta AR

10. Sylviidae KABOBO APALIS Apalis kaboboensis AR

11. Sylviidae COLLARED APALIS Apalis ruwenzorii AR

12. Sylviidae MOUNTAIN MASKED APALIS Apalis personata AR

13. Paridae STRIPE-BREASTED TIT Parus fasciiventer AR

14. Nectariniidae BLUE-HEADED SUNBIRD Cyanomitra alinae AR

15. Nectariniidae PURPLE -BREASTED SUNBIRD Nectarinia purpureiventris AR

16. Nectariniidae RWENZORI DOUBLE-COLLARED SUNBIRD Cinnyris stuhlmanni AR

17. Nectariniidae REGAL SUNBIRD Cinnyris regia AR

18. Nectariniidae ROCKEFELLER’S SUNBIRD Cinnyris rockefelleri AR

19. Platysteiridae RWENZORI BATIS Batis diops AR

20. Estrildidae DUSKY CRIMSONWING Cryptospiza jacksoni AR

21. Estrildidae SHELLEY’S CRIMSONWING Cryptospiza shelleyi AR

22. Phasianidae HANDSOME FRANCOLIN Francolinus nobilis AR

23. Strigidae ALBERTINE OWLET Glaucidium albertinum AR

24. Tytonidae CONGO BAY OWL Phodilus prigoginei AR

25. Caprimulgidae ITOMBWE NIGHTJAR Caprimulgus prigoginei AR

26. Caprimulgidae RWENZORI NIGHTJAR Caprimulgus ruwenzorii AR

27. Indicatoridae DWARF HONEYGUIDE Indicator pumilio AR

28. Eurylaimidae AFRICAN GREEN BROADBILL Pseudocalyptomena graueri AR

29. Timaliidae RED-COLLARED MOUNTAIN BABBLER Kupeornis rufocinctus AR

30. Timaliidae CHAPIN’S MOUNTAIN BABBLER Kupeornis chapini AR

31. Campephagidae GRAUER’S CUCKOO SHRIKE Coracina graueri AR

32. Pycnonotidae PRIGOGINE’S GREENBUL Chlorocichla prigoginei AR

33. Muscicapidae YELLOW-EYED BLACK FLYCATCHER Melaenornis ardesiacus AR

34. Prionopidae YELLOW-CRESTED HELMET SHRIKE Prionops alberti AR

35. Ploceidae STRANGE WEAVER Ploceus alienus AR

36. Apodidae SCHOUTEDEN’S SWIFT Schoutedenapus schoutedeni EZL

37. Pycnonotidae SASSI’S OLIVE GREENBUL Phyllastrephus lorenzi EZL

38. Turdidae OBERLANDER’S GROUND THRUSH Zoothera oberlaenderi EZL

39. Monarchidae BEDFORD’S FLYCATCHER Terpsiphone bedfordi EZL

40. Ploceidae GOLDEN-NAPED WEAVER Ploceus aureonucha EZL

41. Ploceidae YELLOW-LEGGED WEAVER Ploceus flavipes EZL

This list combines BirdLife’s endemic bird areas – the Albertine Rift (AR) and Eastern Zairean Lowlands (EZL) because there is overlap in the distribution of some species (Plumptre et al., 2007).


�0

3.3 APPENDIx III: Transect Information

Echuya forest reserve

Transects Points Habitat Length (km) Altitude (m) Condition

T1 13 Bamboo and Closed-forest 2.6 2255–2514 Intact

T2 8 Closed-forest, Mixed-forests and Swamp 1.4 2290–2334 Intact

T3 19 Closed-forest, Mixed-forest, Bamboo and Swamp 3.8 2305–2541 Intact

T5 19 Closed-forest and Swamp 3.8 2308–2429 Intact

T7 16 Closed-forest and Swamp 3.0 2296–2499 Intact

Nyungwe National Park


Bigugu 22 Closed-forest 4.5 2570–2950 Intact

Bururi 22 Closed-forest 4.5 1753–2457 Intact

Uwasenkoko 10 Savannah woodland and Swamp 2.0 2378–2431 Intact

Gasare 10 Swamp and Closed forest 2.0 2284–2366 Intact

Karamba 10 Closed-forest 2.0 1848–1962 Intact

Uwinka 10 Closed-forest 2.0 2129–2451 Intact

Kamiranzovu 3 Swamp 0.5 1951–1958 Intact

T1 10 Closed-forest 2.0 2216–2457 Intact



Kibira National Park


Denga 10 Closed-forest and Bamboo 2.0 2065–2222 Intact

Samutuku 10 Closed-forest and Savannah 2.0 2131–2382 Intact

Mwokora 7 Swamp and Closed-forest 1.5 2165–2230 Intact

Musigati 13 Closed-forest and Bamboo 2.5 2137–2315 Partly degraded

Kamenge 10 Closed-forest 2.0 1798–2134 Partly degraded

Gitaramuka 10 Closed-forest 2.0 2053–2238 Partly degraded

Bwindi Impenetrable National Park

Transects Points Habitat Length (km) Altitude (m) ConditionBuhoma Hill Path 20 Closed-forest 2.9 1480–1880 Partly

degraded

Buhoma Loop 14 Closed-forest 2.0 1560–1780 Partly degraded

Buhoma Track 17 Closed-forest 2.4 1480–1560 Partly

degradedBuhoma Waterfall 19 Closed-forest 2.7 1560–1640 Partly

degradedITFC – Buhoma 29 Closed-forest 3.2 2250–2330 Partly

degradedBamboo Zone path 10 Bamboo 1.4 2440–2520 Partly

degraded

ITFC – Kabale 61 Mixed 9.0 2190–2500 Partly degraded

Mubwindi Swamp Circuit 101 Closed-forest 8.9 2030–2390 Partly

degraded

The Neck 30 Closed-forest 4.4 1480–1560 Partly degraded

Summary information on transects used for the bird surveys in the forest sites. Intact – No trees or vegetation destruction; Partly degraded – a few trees cut down (tree stamps available).


��

3.4 APPENDIx IV: Site maps and Transects

Map of Bwindi Impenetrable National park indicating transects used for bird counts (T�–T�) and localities for bird counts (white circles)


��

Map of Echuya Forest Reserve indicating transects used for bird counts (T�, T�, T�, T� and T�) and localities for bird counts (white circles)


��

Map of Nyungwe National park indicating the WCS transects used for bird counts (T�–T�0) and localities for bird counts (white circles)


��

Map of Kibira National park indicating transects used for bird counts (T�-T�) and localities for bird counts (white circles)


��

3.5 APPENDIx V: GPS coordinates for survey points

YEAR SITE TRANSECT POINT EASTING NORTHING ALTITUDE

2010 Echuya T1 1 812181 9862389 2514

2010 Echuya T1 2 811998 9862298 2500

2010 Echuya T1 3 811795 9862323 2489

2010 Echuya T1 4 811588 9862283 2488

2010 Echuya T1 5 811410 9862194 2487

2010 Echuya T1 6 811318 9862022 2486

2010 Echuya T1 7 811239 9861827 2462

2010 Echuya T1 8 811042 9861817 2397

2010 Echuya T1 9 810833 9861824 2333

2010 Echuya T1 10 810738 9861648 2264

2010 Echuya T1 11 810576 9861738 2287

2010 Echuya T1 12 810417 9861880 2353

2010 Echuya T1 13 810204 9861925 2397

2010 Echuya T2 1 811279 9860960 2307

2010 Echuya T2 2 811307 9860762 2299

2010 Echuya T2 3 811461 9860635 2287

2010 Echuya T2 4 811477 9860436 2287

2010 Echuya T2 5 811362 9860273 2321

2010 Echuya T2 6 811156 9860226 2338

2010 Echuya T2 7 810991 9860339 2364

2010 Echuya T2 8 810815 9860243 2412

2010 Echuya T3 1 814478 9859668 2502

2010 Echuya T3 2 814343 9859815 2515

2010 Echuya T3 3 814232 9859982 2502

2010 Echuya T3 4 814046 9860059 2506

2010 Echuya T3 5 813845 9860042 2536

2010 Echuya T3 6 813661 9859964 2541

2010 Echuya T3 7 813581 9859778 2524

2010 Echuya T3 8 813483 9859599 2474

2010 Echuya T3 9 813365 9859435 2455

2010 Echuya T3 10 813228 9859286 2452

2010 Echuya T3 12 812918 9859086 2374

2010 Echuya T3 13 812766 9858949 2325

2010 Echuya T3 14 812626 9858808 2292

2010 Echuya T3 15 812422 9858816 2302

2010 Echuya T3 16 812275 9858671 2357

2010 Echuya T3 17 811953 9858446 2406

2010 Echuya T3 18 811952 9858446 2439

2010 Echuya T3 19 811795 9858320 2461

2010 Echuya T5 1 815045 9858450 2429

2010 Echuya T5 2 814940 9858280 2384

2010 Echuya T5 3 814844 9858105 2340

2010 Echuya T5 4 814724 9857943 2320

2010 Echuya T5 5 814602 9857726 2309

2010 Echuya T5 6 814542 9857534 2308

2010 Echuya T5 7 814510 9857339 2311


��


2010 Echuya T5 8 814414 9857162 2310

2010 Echuya T5 9 814339 9856950 2313

2010 Echuya T5 10 814193 9856810 2313

2010 Echuya T5 11 814123 9856623 2322

2010 Echuya T5 12 814137 9856424 2324

2010 Echuya T5 13 814087 9856230 2316

2010 Echuya T5 14 813973 9856063 2321

2010 Echuya T5 15 813932 9855867 2321

2010 Echuya T5 16 813894 9855670 2320

2010 Echuya T5 17 813871 9855471 2323

2010 Echuya T5 18 813876 9855271 2340

2010 Echuya T5 19 813911 9855075 2364

2010 Echuya T7 1 814745 9854773 2431

2010 Echuya T7 2 814756 9854979 2407

2010 Echuya T7 3 814855 9855155 2436

2010 Echuya T7 4 814964 9855321 2492

2010 Echuya T7 5 815090 9855485 2499

2010 Echuya T7 6 815249 9855609 2446

2010 Echuya T7 7 815450 9855632 2413

2010 Echuya T7 8 815638 9855712 2369

2010 Echuya T7 9 815785 9855852 2329

2010 Echuya T7 10 815985 9855893 2308

2010 Echuya T7 11 816135 9856023 2302

2010 Echuya T7 12 816481 9856195 2311

2010 Echuya T7 13 816479 9856195 2308

2010 Echuya T7 14 816824 9856405 2345

2010 Echuya T7 15 816829 9856407 2338

2010 Echuya T7 16 816986 9856520 2386

2010 Kibira Mwokora 1 774405 9680805 2172

2010 Kibira Mwokora 2 774251 9680930 2165

2010 Kibira Mwokora 3 774093 9681047 2169

2010 Kibira Mwokora 4 773923 9681151 2172

2010 Kibira Mwokora 5 773737 9681237 2173

2010 Kibira Mwokora 6 773637 9681409 2204

2010 Kibira Mwokora 7 773468 9681518 2230

2010 Kibira Samutuku 1 776527 9676852 2214

2010 Kibira Samutuku 2 776589 9676663 2298

2010 Kibira Samutuku 3 776707 9676504 2382

2010 Kibira Samutuku 4 776871 9676390 2357

2010 Kibira Samutuku 5 777031 9676205 2314

2010 Kibira Samutuku 6 777123 9676077 2225

2010 Kibira Samutuku 7 777252 9675923 2155

2010 Kibira Samutuku 8 777353 9675751 2145

2010 Kibira Samutuku 9 777394 9675556 2131

2010 Kibira Samutuku 10 777573 9675481 2175

2010 Kibira Denga 0 771714 9679489 2065

2010 Kibira Denga 1 771794 9679672 2143

2010 Kibira Denga 2 771937 9679811 2174


��


2010 Kibira Denga 3 772078 9679952 2206

2010 Kibira Denga 4 772099 9680150 2222

2010 Kibira Denga 5 771964 9680294 2198

2010 Kibira Denga 6 771824 9680429 2151

2010 Kibira Denga 7 771910 9680610 2178

2010 Kibira Denga 8 771960 9680804 2237

2010 Kibira Denga 9 771769 9680794 2252

2010 Kibira Denga 10 771599 9680690 2213

2011 Kibira Denga 0 771714 9679489 2065

2011 Kibira Denga 1 771794 9679672 2143

2011 Kibira Denga 2 771937 9679811 2174

2011 Kibira Denga 3 772078 9679952 2206

2011 Kibira Denga 4 772099 9680150 2222

2011 Kibira Denga 5 771964 9680294 2198

2011 Kibira Denga 6 771824 9680429 2151

2011 Kibira Denga 7 771910 9680610 2178

2011 Kibira Denga 8 771960 9680804 2237

2011 Kibira Denga 9 771769 9680794 2252

2011 Kibira Denga 10 771599 9680690 2213

2011 Kibira Denga 10 771599 9680690 2213

2010 Kibira Musigati 1 776984 9673869 2137

2010 Kibira Musigati 2 776789 9673824 2197

2010 Kibira Musigati 3 776587 9673856 2228

2010 Kibira Musigati 4 776390 9673893 2260

2010 Kibira Musigati 5 776190 9673911 2296

2010 Kibira Musigati 6 776087 9673741 2315

2010 Kibira Musigati 7 775900 9673666 2301

2010 Kibira Musigati 8 775858 9673472 2278

2010 Kibira Musigati 9 775862 9673272 2294

2010 Kibira Musigati 10 775833 9673075 2260

2010 Kibira Musigati 11 775792 9672880 2264

2010 Kibira Musigati 12 775639 9672753 2227

2010 Kibira Musigati 13 775448 9672692 2238

2010 Kibira Kamenge 0 747369 9707995 1798

2010 Kibira Kamenge 1 747569 9708005 1881

2010 Kibira Kamenge 2 747768 9708022 1977

2010 Kibira Kamenge 3 747968 9708014 1994

2010 Kibira Kamenge 4 748183 9707967 1951

2010 Kibira Kamenge 5 748360 9707919 2002

2010 Kibira Kamenge 6 748525 9707807 2039

2010 Kibira Kamenge 7 748724 9707786 2002

2010 Kibira Kamenge 8 748916 9707748 2042

2010 Kibira Kamenge 9 749119 9707766 2051

2010 Kibira Kamenge 10 749319 9707770 2134

2010 Kibira Gitaramuka 0 749136 9706974 2071





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2010 Nyungwe Uwinka 0 744606 9725948 2451

2010 Nyungwe Uwinka 1 744552 9726133 2400

2010 Nyungwe Uwinka 2 744446 9726306 2367

2010 Nyungwe Uwinka 3 744372 9726497 2293

2010 Nyungwe Uwinka 4 744402 9726607 2264

2010 Nyungwe Uwinka 5 744375 9726790 2298

2010 Nyungwe Uwinka 6 744405 9726965 2260

2010 Nyungwe Uwinka 7 744403 9727149 2248

2010 Nyungwe Uwinka 8 744421 9727304 2205

2010 Nyungwe Uwinka 9 744440 9727527 2129

2010 Nyungwe Uwinka 10 744445 9727684 2178

2010 Nyungwe Bururi 0 743664 9724564 2426

2010 Nyungwe Bururi 1 743668 9724726 2454

2010 Nyungwe Bururi 2 743658 9724885 2465

2010 Nyungwe Bururi 3 743663 9725058 2442

2010 Nyungwe Bururi 4 743656 9725254 2387

2010 Nyungwe Bururi 5 743642 9725394 2290

2010 Nyungwe Bururi 6 743628 9725616 2224

2010 Nyungwe Bururi 7 743644 9725804 2167

2010 Nyungwe Bururi 8 743607 9725964 2065

2010 Nyungwe Bururi 9 743609 9726138 1992

2010 Nyungwe Bururi 10 743577 9726312 2038

2010 Nyungwe Bururi 11 743569 9726504 2044

2010 Nyungwe Bururi 12 743560 9726704 2089

2010 Nyungwe Bururi 13 743535 9726879 2066

2010 Nyungwe Bururi 14 743447 9727056 2079

2010 Nyungwe Bururi 15 743494 9727225 2013

2010 Nyungwe Bururi 16 743456 9727415 1972

2010 Nyungwe Bururi 17 743354 9727538 1934

2010 Nyungwe Bururi 18 743362 9727713 1917

2010 Nyungwe Bururi 19 743368 9727914 1877

2010 Nyungwe Bururi 20 743366 9728108 1852

2010 Nyungwe Bururi 21 743404 9728271 1794

2010 Nyungwe Bururi 22 743448 9728465 1765

2010 Nyungwe Bigugu 0 749058 9726386 2381

2010 Nyungwe Bigugu 1 749016 9726496 2458

2010 Nyungwe Bigugu 2 749163 9726625 2514

2010 Nyungwe Bigugu 3 749236 9726797 2570

2010 Nyungwe Bigugu 4 749284 9726953 2573

2010 Nyungwe Bigugu 5 749368 9727163 2562

2010 Nyungwe Bigugu 6 749448 9727321 2578


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2010 Nyungwe Bigugu 7 749567 9727495 2554

2010 Nyungwe Bigugu 8 749705 9727623 2554

2010 Nyungwe Bigugu 9 749790 9727798 2601

2010 Nyungwe Bigugu 10 749836 9727981 2651

2010 Nyungwe Bigugu 11 749884 9728188 2626

2010 Nyungwe Bigugu 12 749956 9728341 2694

2010 Nyungwe Bigugu 13 750027 9728507 2740

2010 Nyungwe Bigugu 14 750078 9728651 2783

2010 Nyungwe Bigugu 15 750095 9728845 2785

2010 Nyungwe Bigugu 16 750075 9729021 2836

2010 Nyungwe Bigugu 17 750125 9729153 2854

2010 Nyungwe Bigugu 18 750325 9729201 2891

2010 Nyungwe Bigugu 19 750475 9729376 2897

2010 Nyungwe Bigugu 20 750550 9729509 2911

2010 Nyungwe Bigugu 21 750586 9729724 2914

2010 Nyungwe Bigugu 22 750551 9729902 2937

2010 Nyungwe Karamba 0 734672 9725553 1953

2010 Nyungwe Karamba 1 734657 9725395 1934

2010 Nyungwe Karamba 2 734688 9725199 1962

2010 Nyungwe Karamba 3 734682 9725014 1923

2010 Nyungwe Karamba 4 734717 9724862 1907

2010 Nyungwe Karamba 6 734739 9724451 1886

2010 Nyungwe Karamba 7 734733 9724272 1880

2010 Nyungwe Karamba 8 734758 9724071 1897

2010 Nyungwe Karamba 9 734754 9723890 1848

2010 Nyungwe Karamba 10 734760 9723695 1866

2010 Nyungwe Kamiranzovu 0 739383 9724876 1958



2010 Nyungwe Gasare 0 752740 9724328 2338

2010 Nyungwe Gasare 1 752826 9724150 2324

2010 Nyungwe Gasare 2 752912 9723961 2319

2010 Nyungwe Gasare 3 753000 9723776 2323

2010 Nyungwe Gasare 4 753082 9723597 2348

2010 Nyungwe Gasare 5 753171 9723419 2343

2010 Nyungwe Gasare 6 753227 9723234 2387

2010 Nyungwe Gasare 7 753324 9723072 2334

2010 Nyungwe Gasare 8 753403 9722907 2284

2010 Nyungwe Gasare 9 753494 9722722 2307

2010 Nyungwe Gasare 10 753571 9722533 2321

2010 Nyungwe Uwasenkoko 0 761923 9720225 2378









�0





2010 Nyungwe T1 0 747062 9726002 2404

2010 Nyungwe T1 1 747181 9725868 2358

2010 Nyungwe T1 2 747348 9725757 2331

2010 Nyungwe T1 3 747439 9725616 2353

2010 Nyungwe T1 4 747579 9725499 2379

2010 Nyungwe T1 5 747716 9725362 2274

2010 Nyungwe T1 6 747860 9725259 2232

2010 Nyungwe T2 0 746088 9725553 2488

2010 Nyungwe T2 1 746189 9725415 2538

2010 Nyungwe T2 2 746311 9725280 2541

2010 Nyungwe T2 3 746443 9725138 2450

2010 Nyungwe T2 4 746570 9725019 2440

2010 Nyungwe T2 5 746692 9724898 2379

2010 Nyungwe T2 6 746823 9724753 2347

2010 Nyungwe T2 7 746936 9724623 2257

2010 Nyungwe T2 8 747047 9724506 2212

2010 Nyungwe T2 9 747165 9724365 2180

2010 Nyungwe T2 10 747281 9724206 2174

2010 Nyungwe T3 0 744737 9725591 2462

2010 Nyungwe T3 1 744866 9725418 2465

2010 Nyungwe T3 2 744973 9725306 2406

2010 Nyungwe T3 3 745069 9725172 2403

2010 Nyungwe T3 4 745182 9725048 2325

2010 Nyungwe T3 5 745300 9724899 2289

2010 Nyungwe T3 6 745415 9724756 2212

2010 Nyungwe T3 7 745518 9724601 2209

2010 Nyungwe T3 8 745640 9724498 2202

2010 Nyungwe T3 9 745762 9724344 2229

2010 Nyungwe T3 10 745846 9724236 2239

2011 Nyungwe T3 10 745846 9724236 2239

1999 Bwindi BT 1 808513 9884672 2280

1999 Bwindi BT 2 808382 9884837 2250

1999 Bwindi BT 3 808090 9884834 2280

1999 Bwindi BT 4 807826 9884858 2300

1999 Bwindi BT 5 807707 9885057 2260

1999 Bwindi BT 6 808538 9884614 2330

1999 Bwindi BT 7 808422 9884745 2260

1999 Bwindi BT 8 808348 9884822 2260

1999 Bwindi BT 9 808123 9884800 2250

1999 Bwindi BT 12 807959 9884845 2270

1999 Bwindi BT 15 808895 9884441 2250

1999 Bwindi BT 16 809069 9884294 2300

1999 Bwindi BT 17 809188 9884083 2320

1999 Bwindi BT 18 809196 9883766 2330

1999 Bwindi BT 19 809187 9883469 2350


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1999 Bwindi BT 20 809352 9883331 2360

1999 Bwindi BT 21 809442 9883179 2360

1999 Bwindi BT 22 809466 9883309 2370

1999 Bwindi BT 23 809378 9883397 2320

1999 Bwindi BT 24 809213 9883597 2320

1999 Bwindi BT 25 809186 9884146 2320

1999 Bwindi BT 26 808847 9884362 2250

1999 Bwindi BT 28 808387 9884208 2270

1999 Bwindi BT 29 808302 9884234 2230

1999 Bwindi BT 29 808302 9884234 2230

1999 Bwindi BT 31 808119 9884023 2160

1999 Bwindi BT 32 808063 9884040 2140

1999 Bwindi BT 33 807956 9884022 2120

1999 Bwindi BT 34 807795 9883987 2080

1999 Bwindi BT 35 807737 9883928 2080

1999 Bwindi BT 36 807586 9883934 2070

1999 Bwindi BT 37 807417 9883966 2040

1999 Bwindi BT 38 807334 9883873 2080

1999 Bwindi BT 39 807161 9883819 2100

1999 Bwindi BT 40 807029 9883903 2060

1999 Bwindi BT 42 806898 9883743 2080

1999 Bwindi BT 44 806630 9883597 2120

1999 Bwindi BT 45 806644 9883597 2100

1999 Bwindi BT 46 806586 9883425 2115

1999 Bwindi BT 48 806457 9883261 2200

1999 Bwindi BT 50 809193 9883397 2360

1999 Bwindi BT 51 808985 9883400 2360

1999 Bwindi BT 52 808922 9883272 2320

1999 Bwindi BT 53 808837 9883219 2320

1999 Bwindi BT 54 808760 9883184 2280

1999 Bwindi BT 55 808617 9883139 2280

1999 Bwindi BT 56 808489 9883170 2280

1999 Bwindi BT 57 808443 9883121 2320

1999 Bwindi BT 58 808419 9882975 2360

1999 Bwindi BT 59 808313 9882953 2380

1999 Bwindi BT 60 808201 9882852 2390

1999 Bwindi BT 61 808158 9882727 2380

1999 Bwindi BT 62 808119 9882608 2380

1999 Bwindi BT 63 810028 9882711 2360

1999 Bwindi BT 64 809932 9882976 2360

1999 Bwindi BT 65 810191 9882872 2380

1999 Bwindi BT 66 810004 9882722 2400

1999 Bwindi BT 67 810130 9882647 2390

1999 Bwindi BT 68 810083 9882505 2370

1999 Bwindi BT 69 810158 9882487 2380

1999 Bwindi BT 70 810275 9882276 2380

1999 Bwindi BT 71 810391 9882278 2400

1999 Bwindi BT 72 810556 9882291 2380


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1999 Bwindi BT 73 810538 9882186 2400

1999 Bwindi BT 75 810925 9882023 2400

1999 Bwindi BT 77 811124 9882082 2400

1999 Bwindi BT 78 812520 9880896 2500

1999 Bwindi BT 79 812625 9880726 2480

1999 Bwindi BT 80 812738 9880820 2460

1999 Bwindi BT 81 812879 9880685 2460

1999 Bwindi BT 82 812790 9880369 2460

1999 Bwindi BT 83 812767 9880368 2440

1999 Bwindi BT 84 812590 9880429 2430

1999 Bwindi BT 85 812398 9880285 2450

1999 Bwindi BT 86 812321 9880113 2440

1999 Bwindi BT 87 812421 9879897 2400

1999 Bwindi BT 88 812625 9879870 2410

1999 Bwindi BT 89 811266 9882041 2400

1999 Bwindi BT 90 811480 9882030 2400

1999 Bwindi BT 91 811630 9882011 2410

1999 Bwindi BT 92 811785 9881946 2420

1999 Bwindi BT 93 811683 9881763 2430

1999 Bwindi BT 94 811650 9881598 2430

1999 Bwindi BT 95 811734 9881455 2420

1999 Bwindi BT 96 811916 9881406 2420

1999 Bwindi BT 97 811939 9881182 2440

1999 Bwindi BT 99 812254 9881082 2470

1999 Bwindi BT 100 812312 9880981 2490

1999 Bwindi BT 101 806599 9882031 2040

1999 Bwindi BT 102 806453 9882078 2070

1999 Bwindi BT 104 806363 9882208 2140

1999 Bwindi BT 105 806319 9882185 2120

1999 Bwindi BT 107 806096 9882234 2100

1999 Bwindi BT 108 805994 9882298 2040

1999 Bwindi BT 113 805856 9882601 2180

1999 Bwindi BT 115 806039 9882842 2240

1999 Bwindi BT 116 807992 9881946 2200

1999 Bwindi BT 117 808014 9881592 2180

1999 Bwindi BT 118 808001 9881696 2160

1999 Bwindi BT 119 807940 9881641 2110

1999 Bwindi BT 120 807981 9881470 2100

1999 Bwindi BT 121 807804 9881154 2080

1999 Bwindi BT 122 807565 9881438 2090

1999 Bwindi BT 123 807484 9881322 2070

1999 Bwindi BT 124 812858 9880530 2450

1999 Bwindi BT 125 812826 9880578 2440

1999 Bwindi BT 126 812914 9880492 2450

1999 Bwindi BT 127 812935 9880399 2470

1999 Bwindi BT 128 813041 9880360 2460

1999 Bwindi BT 129 813086 9880272 2510

1999 Bwindi BT 130 813186 9880244 2520


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1999 Bwindi BT 131 813298 9880258 2500

1999 Bwindi BT 132 813388 9880329 2500

1999 Bwindi BT 133 813216 9880288 2490

1999 Bwindi BT 134 812883 9879852 2490

1999 Bwindi BT 135 812693 9879757 2330

1999 Bwindi BT 136 812750 9879578 2330

1999 Bwindi BT 137 812636 9879480 2340

1999 Bwindi BT 138 812466 9879375 2300

1999 Bwindi BT 139 812230 9879425 2280

1999 Bwindi BT 140 812018 9879209 2280

1999 Bwindi BT 141 811920 9879087 2240

1999 Bwindi BT 142 812040 9878960 2220

1999 Bwindi BT 143 812132 9878885 2220

1999 Bwindi BT 144 812300 9878854 2210

1999 Bwindi BT 145 812336 9878886 2190

2002 Bwindi BT 146 807709 9883922 2040

2002 Bwindi BT 147 807446 9883911 2040

2002 Bwindi BT 148 807327 9883866 2040

2002 Bwindi BT 149 807191 9883861 2060

2002 Bwindi BT 150 807061 9883906 2040

2002 Bwindi BT 152 807976 9884036 2080

2002 Bwindi BT 154 807733 9883968 2060

2002 Bwindi BT 155 806955 9883766 2050

2002 Bwindi BT 156 806873 9883679 2070

2002 Bwindi BT 157 806737 9883628 2080

2002 Bwindi BT 158 806666 9883562 2080

2002 Bwindi BT 159 806589 9883470 2080

2002 Bwindi BT 162 806269 9883067 2160

2002 Bwindi BT 163 808027 9882413 2300

2002 Bwindi BT 164 808011 9882274 2280

2002 Bwindi BT 165 808018 9882136 2240

2002 Bwindi BT 168 808013 9881797 2150

2002 Bwindi BT 169 807967 9881629 2120

2002 Bwindi BT 170 808008 9881575 2080

2002 Bwindi BT 172 808460 9884351 2250

2002 Bwindi BT 173 809408 9883409 2380

2002 Bwindi BT 175 808497 9884280 2280

2002 Bwindi BT 176 808433 9884273 2240

2002 Bwindi BT 177 808337 9884196 2210

2002 Bwindi BT 178 808230 9884166 2180

2002 Bwindi BT 179 808128 9884093 2150

2002 Bwindi BT 180 808039 9884052 2120

2002 Bwindi BT 181 808469 9884474 2260

2002 Bwindi BT 182 808381 9884526 2260

2002 Bwindi BT 183 808487 9884615 2280

2002 Bwindi BT 184 808473 9884749 2260

2002 Bwindi BT 185 808330 9884849 2260

2002 Bwindi BT 186 808195 9884845 2260


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2002 Bwindi BT 187 808042 9884884 2280

2002 Bwindi BT 188 807882 9884815 2280

2002 Bwindi BT 189 798829 9892294 1520

2002 Bwindi BT 191 799379 9891162 1500

2002 Bwindi BT 192 799476 9891497 1500

2002 Bwindi BT 193 799429 9891614 1500

2002 Bwindi BT 194 799452 9891747 1500

2002 Bwindi BT 195 799499 9891856 1490

2002 Bwindi BT 196 799495 9891987 1480

2002 Bwindi BT 197 799379 9891927 1500

2002 Bwindi BT 199 799258 9892161 1500

2002 Bwindi BT 200 799152 9892231 1500

2002 Bwindi BT 201 799089 9892238 1500

2002 Bwindi BT 202 799034 9892306 1500

2002 Bwindi BT 203 799030 9892404 1500

2002 Bwindi BT 204 799149 9892502 1500

2002 Bwindi BT 205 799247 9892601 1500

2002 Bwindi BT 206 809081 9883192 2300

2002 Bwindi BT 207 809172 9882906 2320

2002 Bwindi BT 208 809484 9883262 2320

2002 Bwindi BT 209 809468 9883122 2320

2002 Bwindi BT 210 809543 9883006 2320

2002 Bwindi BT 211 809508 9882870 2300

2002 Bwindi BT 212 809584 9882764 2280

2002 Bwindi BT 213 809739 9882651 2290

2002 Bwindi BT 214 809857 9882682 2320

2002 Bwindi BT 215 809009 9883392 2360

2002 Bwindi BT 216 808896 9883268 2330

2002 Bwindi BT 217 808771 9883201 2280

2002 Bwindi BT 218 808620 9883171 2280

2002 Bwindi BT 219 799362 9890922 1560

2002 Bwindi BT 220 798817 9893165 1540

2002 Bwindi BT 221 798946 9893123 1530

2002 Bwindi BT 222 799005 9892997 1510

2002 Bwindi BT 223 798969 9892851 1500

2002 Bwindi BT 224 799066 9892810 1520

2002 Bwindi BT 225 799199 9892798 1500

2002 Bwindi BT 226 799211 9892729 1490

2002 Bwindi BT 227 799250 9892615 1500

2002 Bwindi BT 228 799174 9892509 1520

2002 Bwindi BT 229 799057 9892477 1520

2002 Bwindi BT 230 799064 9892359 1530

2002 Bwindi BT 231 798992 9892284 1540

2002 Bwindi BT 232 790921 9890539 1480

2002 Bwindi BT 233 790926 9890353 1480

2002 Bwindi BT 234 790883 9890127 1500

2002 Bwindi BT 235 790945 9890035 1520

2002 Bwindi BT 236 791015 9889899 1530


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2002 Bwindi BT 237 791067 9889733 1540

2002 Bwindi BT 238 791095 9889593 1560

2002 Bwindi BT 239 791051 9889414 1520

2002 Bwindi BT 240 791206 9889332 1540

2002 Bwindi BT 241 791183 9889116 1560

2002 Bwindi BT 242 791195 9888917 1560

2002 Bwindi BT 244 791380 9888642 1560

2002 Bwindi BT 245 791465 9888528 1560

2002 Bwindi BT 246 791491 9888414 1560

2002 Bwindi BT 247 791494 9888251 1560

2002 Bwindi BT 254 791233 9890771 1480

2002 Bwindi BT 255 791393 9890769 1520

2002 Bwindi BT 256 791395 9890842 1520

2002 Bwindi BT 257 791507 9890807 1560

2002 Bwindi BT 258 791561 9890833 1560

2002 Bwindi BT 259 791610 9890852 1580

2002 Bwindi BT 260 791641 9890860 1620

2002 Bwindi BT 261 791700 9890866 1640

2002 Bwindi BT 262 791742 9890899 1650

2002 Bwindi BT 263 791741 9890899 1680

2002 Bwindi BT 264 791853 9890917 1680

2002 Bwindi BT 265 791943 9890968 1680

2002 Bwindi BT 266 792018 9890967 1710

2002 Bwindi BT 267 792079 9890979 1740

2002 Bwindi BT 268 792069 9890829 1770

2002 Bwindi BT 269 792213 9890833 1770

2002 Bwindi BT 270 792350 9890871 1790

2002 Bwindi BT 271 792373 9890996 1800

2002 Bwindi BT 272 792393 9890951 1840

2002 Bwindi BT 273 792440 9891019 1880

2002 Bwindi BT 278 791594 9889082 1580

2002 Bwindi BT 279 791642 9888985 1580

2002 Bwindi BT 282 791718 9888637 1590

2002 Bwindi BT 286 792030 9888311 1640

2002 Bwindi BT 287 791811 9888208 1580

2002 Bwindi BT 289 790197 9890806 1560

2002 Bwindi BT 294 789545 9890663 1600

2002 Bwindi BT 296 789351 9890692 1680

2002 Bwindi BT 300 788957 9890638 1780

2002 Bwindi BT 301 788930 9890608 1770GPS coordinates for each survey point in the four sites. The year the survey was carried out, transect and altitude at each point is also indicated.


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3.6 Appendix VI: Site Information

Study site Area (km²) Elevation (m) Rainfall (mm) Temp (ºc)

Bwindi 331 1190–2607 1130–2390 7–20

Echuya 40 2100–2400 1000–1500 10–23

Nyungwe 970 1600–2950 1744 10.9–19.6

Kibira 400 1600–2666 1400–2000 17–23

Summary information on study sites used in the baseline study (average/approx).


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Glossary

Climate

The average weather condition over a period of time ranging from months to thousands or millions of years (IPCC, 2007).

Climate change

A change of climate that is attributed directly or indirectly to human activity that alters the composition of the global atmosphere and which is in addition to natural climate variability observed over comparable time periods (UNFCCC, Article 1) (IPCC, 2007).

Climate model

A numerical representation of the climate system based on the physical, chemical and biological properties of its components, their interactions and feedback processes, and accounting for all or some of its known properties (IPCC, 2007).

Climate prediction

A climate prediction or climate forecast is the result of an attempt to produce an estimate of the actual evolution of the climate in the future, for example, at seasonal, inter-annual or long-term time scales. Since the future evolution of the climate system may be highly sensitive to initial conditions, such predictions are usually probabilistic in nature (IPCC, 2007).

Climate projection

A projection of the response of the climate system to emission or concentration scenarios of greenhouse gases and aerosols, or radiative forcing scenarios, often based upon simulations by climate models (IPCC, 2007).

Emission scenario

A plausible representation of the future development of emissions of substances that are potentially radiatively active (e.g., greenhouse gases, aerosols), based on a coherent and internally consistent set of assumptions about driving forces (such as demographic and socioeconomic development, technological change) and their key relationships (IPCC, 2007).

Altitudinal/Elevational range

The distance between the lowest and the highest recorded occurrences for a species (adapted from McCain, 2006).

Endemic

An endemic species is one that is restricted to a particular geographic area (Young, 2007). In this case species restricted to the Albertine Rift. In the context of this study, I therefore defined widespread species as those species whose distributions span beyond the Albertine region. Species with comparatively small ranges constitute an important component of biodiversity and frequently of conservation concern because they are inherently vulnerable to habitat transformation and climate change.

Habitat

An area with the combination of resources (like food, cover, water) and environmental conditions (temperature, precipitation, presence or absence of predators and competitors) that promotes occupancy by individuals of a given species (or population) and allows those individuals to survive and reproduce (Morrison et al. 1992).

Species richness

Species richness in the baseline study is defined as the number of species present at a given point (or a given unit of area).

Glossary References

IPCC (2007) Climate change 2007: The physical science basis. Summary for policymakers – http://www.ipcc.ch.

McCain C.M. (2006) Do elevational range size, abundance, and body size patterns mirror those documented for geographic ranges? A case study using Costa Rican rodents. Evolutionary Ecology Research; 8: 435–454.

Young B.E. (2007) Endemic species distribution on the east slope of the Andes in Peru and Bolivia. NatureServe, Arlington, Virginia, USA.

Morrison M.L., Marcot B.G., Mannan R.W. (1992) Wildlife-habitat relationships: concepts and applications. Univ. Wisconsin Press, Madison: 343.

For more information on this guide, please contact;

David OchandaMakerere University

Department of Environmental ManagementSchool of Forestry, Environmental and Geographical Sciences

Kampala, [email protected]

Climate ChangeMonitoring Guide_Final

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