@prefix vivo: . @prefix edm: . @prefix ns0: . @prefix dcterms: . @prefix skos: . vivo:departmentOrSchool "Science, Faculty of"@en, "Earth and Ocean Sciences, Department of"@en ; edm:dataProvider "DSpace"@en ; ns0:degreeCampus "UBCV"@en ; dcterms:creator "Riehl, Brianne"@en ; dcterms:issued "2012-09-15T17:56:21Z"@en, "2012"@en ; dcterms:description "This paper investigates the relationship between high levels of poverty and biodiversity in sub-Saharan Africa (SSA). Using a collection of secondary research, it was found that the link between these two variables is more than geographic, and that biodiversity conservation is a crucial factor in the alleviation of poverty in SSA. A variety of poverty reducing strategies that incorporate biodiversity conservation have been implemented and succeeded elsewhere, implying that the same is possible for this region. Overall, the paper suggests that the biodiversity of SSA will be particularly important for the economic wellbeing of the poor in a future of climate change."@en ; edm:aggregatedCHO "https://circle.library.ubc.ca/rest/handle/2429/43214?expand=metadata"@en ; skos:note """Biodiversity  as  a  Means  of  Poverty  Alleviation  in  Sub-­Saharan  Africa    This  paper  investigates  the  relationship  between  high  levels  of  poverty  and  biodiversity  in  sub-­‐Saharan  Africa  (SSA).  Using  a  collection  of  secondary  research,  it  was  found  that  the  link  between  these  two  variables  is  more  than  geographic,  and  that  biodiversity  conservation  is  a  crucial  factor  in  the  alleviation  of  poverty  in  SSA.  A  variety  of  poverty  reducing  strategies  that  incorporate  biodiversity  conservation  have  been  implemented  and  succeeded  elsewhere,  implying  that  the  same  is  possible  for  this  region.  Overall,  the  paper  suggests  that  the  biodiversity  of  SSA  will  be  particularly  important  for  the  economic  wellbeing  of  the  poor  in  a  future  of  climate  change.       1.  Introduction            Sub-­‐Saharan  Africa  (SSA)  is  defined  as  the  region  of  Africa  that  lies  below  the  Sahara  desert  (Figure  1).  It  includes  47  African  countries,  800  million  people,  and  covers  an  area  of  23.6  million  square  kilometers  (Africa,  2010;  Walker,  2009).  Poverty  in  this  region  is  pervasive,  with  close  to  half  of  the  SSA  population  living  in  absolute  poverty  on  less  than  $1  per  day,  as  defined  by  the  World  Bank  (Fisher  &  Christopher,  2007;  Lufumpa,  2005).  Despite  such  a  simple  definition  in  this  case,  poverty  is  a  complex,  multi-­‐dimensional  material  deprivation  that  involves  the  lack  of  access  to  basic  needs  such  as  education,  health  and  nutrition  (Roe,  2010).  The  poverty  in  SSA  will  only  be  amplified  by  the  region’s  expected  drastic  increase  in  population  to  1.7  billion  people  by  2050,  and  3  billion    Figure  1:  Map  of  sub-­‐Sahara  nations.  From  Buggey  (2007).     by  the  end  of  the  century  (Lufumpa,  2005;  Walker,  2009).  The  majority  of  this  growing  population  lives  in  rural  areas  and  depends,  as  pastoralists  and  cultivators,  on  the  high  levels  of  biodiversity  provided  by  the  broad  range  of  climatic,  geological,  soil  and  landscape  forms  in  the  region  (Darkoh,  2009;  Lufumpa,  2005).  This  biodiversity,  defined  as  species  variability,  encompasses  the  variety  that  occurs  within  living  things,  including  genetic  variation  and  variations  between  species  (Barrett,  Travis,  &  Dasgupta,  2011).  When  measured  in  terms  of  species  richness  and  endemism,  SSA  has  one  of  the  highest  levels  of  biodiversity  globally,  making  it  home  to  7.5%  of  the  world’s  vascular  plant  species,  5.8%  of  mammals,  8%  of  birds,  16%  of  marine  fish,  and  5.5%  of  insects  (Roe,  2010;  Roe,  Walpole,  &  Elliott,  2010).     2.  Understanding  SSA   2.1.  Environmental  Issues            SSA  is  a  region  particularly  vulnerable  to  environmental  degradation  due  to  the  heavy  reliance  of  its  rural  populations  on  the  land  for  their  livelihoods  (Darkoh,  2009).  These  environmental  concerns,  including  deforestation,  desertification,  population  growth,  pollution  and,  most  relevantly,  biodiversity  loss,  are  all  expected  to  be  amplified  by  future  changes  in  climate  (Darkoh,  2009).  The  past  decade  has  been  the  warmest  and  driest  of  the  century,  and  climate  change  is  expected  to  make  the  SSA  climate  more  variable,  bringing  more  frequent  and  severe  weather  events  such  as  droughts  and  floods  (Darkoh,  2009).              The  numerous  environmental  issues  that  exist  in  SSA  are  interlinked,  all  contributing  in  one  way  or  another  to  a  loss  of  biodiversity  in  the  region.  Deforestation  and  desertification  cause  a  loss  of  5.3  million  hectares  of  SSA  forests   and  woodlands  annually  (Darkoh,  2009).  These  losses  are  due  to  unsustainable  land  use  practices  such  as  overgrazing  and  excessive  fertilization,  as  well  as  the  use  of  wood  for  cooking,  heating,  and  lighting    (since  only  approximately  24%  of  the  SSA  population  have  access  to  electricity)  (Darkoh,  2009;  Lufumpa,  2005).  This  high  level  of  land  degradation  poses  serious  threats  to  the  region’s  biodiversity  by  destroying  ecosystems,  natural  habitats,  and  threatening  the  survival  of  many  plant  and  animal  species  (Lufumpa,  2005).  Population  growth  in  this  region  is  extreme,  putting  a  strain  on  environmental  resources  through  a  required  increase  in  production  and  consumption  (Darkoh,  2009).  As  standards  of  living  in  the  region  improve,  the  currently  low  levels  of  air  and  water  pollution  will  likely  be  increased  due  to  demands  for  industrialization  (Darkoh,  2009).  This  increased  stress  on  natural  resources  will  lead  to  further  biodiversity  degradation  (Darkoh,  2009).  Even  civil  conflicts  pose  a  threat  to  the  region’s  diversity,  as  displaced  populations  are  forced  to  pay  little  attention  to  environmental  concerns  (Darkoh,  2009;  Lufumpa,  2005).  These  threats  to  biodiversity  are  a  major  concern  for  a  region  with  such  initially  high  levels  of  diversity  and  such  high  economic  dependence  on  the  land  (Darkoh,  2009).  The  extinction  rate  in  SSA  is  already  high  by  global  standards,  and  the  region’s  plant  and  animal  species  continue  to  be  threatened  daily  (Darkoh,  2009;  Lufumpa,  2005).     2.2.  Poverty          Poverty  in  SSA  is  widespread,  particularly  in  rural  regions,  with  at  least  313  million  of  the  region’s  population  living  on  less  than  $1  a  day  (Munthali,  2007).  Although  not  the  poorest  region  of  the  world,  SSA  is  the  only  region  in  which  poverty  is  anticipated  to  increase  significantly  (by  19%  by  2015),  contrary  to  the  United  Nations  Millennium  development  goal  to  cut  the  number  living  in  poverty  in  half  by  2015  (Lufumpa,  2005;  Munthali,  2007).  SSA  currently  accounts  for  30%  of  the  developing  world’s  population  living  in  poverty  (Figure  2),  compared  to  16%  in  the  1980’s  (Lufumpa,  2005).  This  pervasive  poverty  is  closely  related  to  the  deterioration  of  biodiversity  in  the  region,  as  large  rural  communities  are  forced  to  degrade  the  environment  for  survival  (Lufumpa,  2005).  This  interrelation  is  a  major  concern  for  SSA,  as  these  impoverished  rural  residents  have  a  strong  dependence  on  this  degraded  land  as  their  main  source  of  livelihood,  thereby  creating  a  vicious  cycle  (Lufumpa,  2005).     3.  Link  Between  Biodiversity  and  Poverty   3.1.  Geographical  Link            There  is  a  high  magnitude  of  overlap  between  globally  important  regions  of  biodiversity  and  regions  of  poverty,  and  mounting  evidence  suggests  that  these  two  variables  do  coincide  spatially  (Figure  3)  (Barrett,  Travis,  &  Dasgupta,  2011;  Fisher,    Figure  2:  Population  living  in  poverty  (percentage  below  $1  a  day  of  income).  From  Lufumpa  (2005).     handful of African countries are likely to attain the Millennium Development Goal (MDG) of halving poverty levels by 2015. Close to half of Africa’s population of over 800 million lives in absolute poverty (see Figure 1). While Africa is in absolute terms not the world’s poorest region, it is the only region where the number of poor people is increasing significantly. Though the number of poor people has decreased recently in developing countries, Africa saw a significant increase in the number of its poor. Hence, Africa accounts today for some 30 per cent of the poor in developing countries, compared with about 16 per cent in the mid-1980s. Poverty indicators for Africa show that the majority of the poor live in rural areas, with subsistence agriculture, fishing and hunting as the main sources of livelihood. In both urban and rural areas, women comprise, relative to men, a disproportionately large number of people living in absolute poverty. With regard to the main social indicators, Africa lags behind other developing regions. The crude death rate is about 15.2 per 1,000 people compared with 6.6 in South America and 7.7 for Asia. Though infant mortality in the region has fallen substantially since the 1970s to 80.6 children per 1,000 live births, it still compares unfavourably with the average of 60.9 for all low-income countries (see Figure 2). Further, though most African countries can sustain several harvests a year, malnutrition is still widespread. It is estimated that about 26 per cent of all African children under 5 years of age suffer from severe malnutrition or stunting, while only 62 per ce t f the African popula- tion have access to health services, compared to 80 per cent for develop- ing countries as a whole. Using its human development index (HDI), a measure incorporating aspects such as life expectancy, education and in ome levels to estimate the quality of life, the United Nations Development Program (2005) has Figure 1: Population living in poverty (percentage below $1 a day, 2000) 46.7% 23% 20% 0 10 20 30 40 50 Africa Developing Countries Developed Countries Source: African Development Bank, Statistics Division. 368 C. L. Lufumpa #African Development Bank 2005 &  Christopher  2007;  Roe,  2010).  SSA  is  a  particularly  interesting  case  of  this  overlap  between  biodiversity  and  poverty,  as  it  displays  increasing  poverty  levels  along  with  decreases  in  biodiversity  (Roe,  2010;  Roe,  Walpole,  &  Elliott,  2010).  This  geographical  link  is  important  as  it  is  often  presented  as  rationale  for  pursuing  biodiversity  conservation  and  poverty  reduction  together  (Roe,  Walpole,  &  Elliott,  2010).     3.2.  Misleading  Implication            On  the  surface,  this  strong  overlap  between  high  levels  of  biodiversity  and  high  levels  of  poverty  may  suggest  that  a  healthy  economy  and  diverse  environment  are  mutually  exclusive  occurrences  (Adams  et  al.,  2004).  This  strong  spatial  link  can  lead  to  dangerous  conclusions,  as  it  may  suggest  a  cause-­‐and-­‐effect  relationship  in  which  poverty  is  a  constraint  on  conservation,  or  conservation  is  harmful  to  those  in  poverty  (Adams  et  al.,  2004;  Fisher,  &  Christopher,  2007).  The  more  dangerous  of  these  conclusions  is  that  conservation  efforts  may  be  harmful  to,  and  should  not    Figure  3:  Global  biodiversity  and  poverty  overlap  (Darker  shades  show  the  most  impoverished  of  the  world’s  34  biodiversity  hot  spots).  From  Fisher  &  Christopher  (2007).   By re-aggregating the countries back to the biodiversity hotspots we can get a sense of the hottest areas as based on ecoregion (Table 2). Through this lens we see that 14 hotspots appeared in Table 1 at least three times. Of these only three hotspots made the top 25 five times. They are Eastern Afromontane, Guinean Forests of West Africa, and the Himalaya. Six hotspots appeared in the top 25 four times. They are the Coastal Forests of Eastern Africa, East Melane- sian Islands, Horn of Africa, Indo-Burma, Madagascar and the India Ocean Islands, and Mountains of Central Asia. Of special note are the Coastal Forests of Eastern Africa, Indo- Burma and Madagascar and the India Ocean Islands. These three also appeared in the hottest hotspots list based solely on ecological indicators in the original Myers et al. Nature article. When re-ranked by area affected, we get a different ordering, with the Horn of Africa well above the rest. Again the Indo-Burma, Madagascar and the India Ocean Islands, and the Eastern Afromontane hotspots rank highly. But with this ranking ey are joined by the Tropical Andes and Cerrado hotspots (Table 3). 7. Limitations Our examination of the socio-economic landscape in the countries where CI's hotspots lie has a number of limitations: 1) The biodiversity hotspots are aggregated based on similar ecological characteristics, ignoring political bou daries, while most socio-economic data, including all used in this analysis, are available only for national boundaries. As global datasets improve and become more closely linked with geographical information systems, this analysis could focus dir ctly on hotspots rather than through nati s. At the same time much important initiative funding is channeled and tied to political boundaries i.e. countries (Balmford et al., 2000). 2) With analysis on 125 countries multiple data sources were used. While all attempts were made to standardize the data, deficiencies may still exist. One example is that each country determines its own poverty line, and therefore there are inherent methodological and precision errors. 3) Population density and growth figures are only proxies for human impact on ecological systems (Cincotta et al., 2000). For example, low density slash and burn populations can have large ecological effects. Also, proximity to urban areas may also provide a link to the impact of poverty on ecosystems. 4) The indicators used were picked from available global datasets. Sufficient datasets for additional appropriate socio-economic indicators do not exist. For example, primary fuel source data would be an appropriate indicator for population pressure on local forest resources. Extensive data on the nutritional sources of a country would also be an important indicator to flesh out the human dependence on local resources. On the economic side some figure on national wealth as adjusted by a distribution index (such as the Gini Index) would also be of great value for this analysis. 5) Due to its recent political history there is no data on the state of Western Sahara. This country, which contains part of the West African Forests hotspot, is likely to have poor socio-economic statistics and heref re although it is not included in the analysis both the country and hotspot should be given careful consideration. 6) Myers et al.'s analysis created ecoregion sized biodiversity hotspots, where only 3–30% of their extent would truly be considered a ‘hotspot’. In thi analysis we utilized the entire defined hotspot (ecoregion) for analysis. Fig. 1 –Darker shades show themore imperiled of CI's 34 biodiversity hotspots according to this multifactor assessment, based on aggregate area of hotspot affected by conditions of socio-economic poverty. 98 E C O L O G I C A L E C O N O M I C S 6 2 ( 2 0 0 7 ) 9 3 – 1 0 1 compromise,  poverty  reduction  (Adams  et  al.,  2004;  Fisher,  &  Christopher,  2007).  This  implies  that  poverty  should  not  be  increased  due  to  conservational  efforts,  and  that  the  livelihood  of  the  poor  should  not  be  undermined  in  order  to  conserve  biodiversity  in  the  region  (Adams  et  al.,  2004).  This  is  a  troubling  implication,  as  it  suggests  that  a  choice  needs  to  be  made  between  the  environmental  and  social  wellbeing  of  this  region.              Further  research  has  shown  that  this  implied  causal  link  may  be  too  simplistic  to  describe  the  complex  interconnection  between  these  variables  (Adams  et  al.,  2004;  Fisher,  &  Christopher,  2007;  Roe,  2010).  Although  the  geographical  overlap  should  not  be  ignored,  a  more  in  depth  understanding  of  the  link  between  poverty  and  biodiversity  may  suggest  a  more  accurate  approach  to  this  complex  relationship  (Adams  et  al.,  2004;  Fisher,  &  Christopher,  2007;  Roe,  2010).     4.  Importance  of  Biodiversity  in  Alleviating  Poverty   4.1.  Dependence  on  Biodiversity              The  majority  of  the  poor  in  SSA  live  in  rural  areas  with  a  livelihood  critically  dependent  upon  the  exploitation  of  natural  resources  such  as  water,  arable  land,  and  forest  resources  (Lufumpa,  2005).  This  makes  the  poor  in  this  region  disproportionately  and  directly  dependent  upon  its  biodiversity  (Reid,  &  Swiderska,  2008;  Roe,  Walpole,  &  Elliott,  2010).  The  history  of  civilization  in  SSA  shows  a  remarkable  link  with  biodiversity,  as  pre-­‐colonial  population  centers  were  built  in  areas  with  an  average  of  444.4  species,  as  opposed  to  the  359.6  species  average  in  the  rest  of  the  region  (Fjeldsa,  &  Burgwss,  2011).  Current  population  centers  and  species  richness  also  appear  to  be  strongly  correlated  (Figure  4),  suggesting  that  the    Figure  4:  Scatter  plot  showing  species  richness  and  endemism  against  human  population  density  in  SSA.  From  Fjeldsa  &  Burgwss  (2008).   Discussion Environmental conditions of Africa’s population centres Our assessment confirms the idea of a general large-scale correlation between biodiversity and human population in Africa, suggested by Fjeldså & Lovett (1997) and Balmford et al. (2001). From this, we may infer that the traditional land use in Africa did not erase the natural large-scale biodiversity pattern. Table 1 shows a stronger positive correlation in the past than under the present diachronic regime, where effects of political change and globalization are overlain on the ancient population pattern, and where external demands lead to economic growth and infra- structure development in new areas. The simplest explanations would be that the spatial pattern of population growth in Africa has been gov- erned by environmental factors which also explain the large-scale variation in biological diversity, such as climate (Jetz & Rahbek, 2002). Balmford et al. (2001) demon- strated that the contemporary human population density exhibits a similar (though weaker) hump-shaped rela- tionship with productivity (Net Primary Productivity – NPP), as is seen with species richness. Human population density is particularly high in the transition zone between tropical savannah and rainforest and in tropical highlands and their adjacent foothills. The lowland rainforest, which represents the highest biological production, has never supported many people and also has a moderate species richness (Fig. 2c,d). The main discrepancy is the high biodiversity and moderate population density in southern Cameroon and Gabon. The denser human populations in the northern savannah zones compared with the southern woodland savannahs, especially Zambia, imply that the relation- ship is not simple. The Sahel zone had much higher precipitation in the past, with enormous wetlands in the early Holocene, and generally good rainfall during the early precolonial high cultures (e.g. Street & Grove, 1979). The people of this region took advantage of the Fig 4 Scatter plots of species richness and endemism (range-size rarity score) against human population density across 1805 one-degree grid cells in sub-Saharan Africa. (a) Log vertebrate species richness against log human population density, (b) log range-size rarity against log human population density, (c) log vertebrate species richness against log human infrastructure and (d) log range-size rarity against log human infrastructure 38 J. Fjeldså and N. D. Burgess ! 2008 The Authors. Journal compilation ! 2008 Blackwell Publishing Ltd, Afr. J. Ecol., 46 (Suppl. 1), 33–42 spatial  patterns  of  population  growth  have  been  governed  by  environmental  factors  such  as  biodiversity  (Fjeldsa,  &  Burgwss,  2011;  Roe,  Walpole,  &  Elliott,  2010).  This  implies  that  biodiversity  is  intrinsic  to  the  indigenous  agro-­‐pastoral  systems  of  the  region,  emphasizing  their  dependence  upon  it  (Fjeldsa,  &  Burgwss,  2011;  Roe,  Walpole,  &  Elliott,  2010).              Biodiversity  is  important  in  this  region  as  a  means  of  direct  income,  as  well  as  insurance,  since  the  prevalent  biodiversity  acts  as  a  buffer  against  risks  and  shocks  that  the  region  may  face  (Roe,  2010).  The  direct  economic  benefit  of  biodiversity  comes  from  the  biodiversity-­‐based  resources  used  for  household  income,  production,  and  consumption  (Roe,  2010).  Wild  animals  and  plants  play  an  enormous  role  as  resources  for  the  poor  in  this  region,  and  the  genetic  diversity  in  these  plant  and  animal  resources  is  therefore  vital  for  the  livelihood  of  these  communities  (Roe,  2010).  Table  1  shows  the  dependence  of  different  areas  of  SSA  on  certain  biodiversity  resources,  and  Table  2  shows  how  this  dependence  decreases  for  those  relieved  of  poverty  (The  variability  in  biodiversity  resources  used  as  a  source  of  livelihood  in  these  tables  reflects  the  availability  and  access  to  the  resource  each  area)  (Roe,  2010).  The  biodiversity  in  SSA  is  also  indirectly  relied   upon,  as  it  improves  the  resilience  of  the  regions  ecosystems  and  agricultural  land  (Roe,  2010).  Resilience  in  this  case  refers  to  the  ability  of  the  system  to  absorb  shocks  or  disturbances,  and  return  to  a  reference  state  after  perturbation  (Roe,  2010).  Strong  and  consistent  findings  show  that  by  improving  the  resilience  of  a  system,  biodiversity  has  a  positive  effect  on  mean  crop  yields  and  a  negative  effect  on  the  variability  of  crop  yields  (Roe,  2010).  This  provides  strong  insurance  against  food  security  risks  (Roe,  2010).  High  levels  of  biodiversity  in  SSA  farms  not  only  decrease  the  risk  of  crop  failure,  but  also  increase  soil  fertility,  improve  water  supplies,  and  provide  natural  pest  control  that  allows  for  an  increase  in  productivity  and  a  direct  economic  benefit  (Roe,  2010;  Roe,  Walpole,  &  Elliott,  2010).    Table  1:  Collected  evidence  on  the  dependence  of  different  regions  of  SSA  on  biodiversity  for  income.  From  Roe  (2010).     Source   Region   Evidence   Resource  type  Bene  et  al.  2009   West  Africa     Varies  from  90%(poorest)-­‐29.7%(richest)   Fish  Cavendish  2000   Southern  Africa   35.4%  of  household  income  in  1993-­‐94;  36.9%  in  1996-­‐97   Wild  foods,  wood,  grasses  and  other  environmental  resources  de  Merode  et   al.  2004   West  Africa   24%  of  cash  sales   Wild  foods  Fisher  2004   Southern  Africa     30%  of  household  income   Forests  Kamanga  et  al.  2009   Southern  Africa     15%  of  total  household  income   Forests  Mamo  et  al.  2007   East  Africa     39%  of  total  household  income     Forests           Table  2:  Collected  evidence  on  the  relative  dependence  of  the  poor  in  different  regions  of  SSA  on  biodiversity  resources  (NTFP  means  non-­‐timber  forest  products).  From  Roe  (2010).     Reference     Region   Resource     Relative   Dependence  Babulo  et  al.  2008   East  Africa     Forests     Decreases  with  wealth  Bene  et  al.  2009   West  Africa     Fish   Decreases  with  wealth  Cavendish  2000   Southern  Africa     Multiple     Decreases  with  wealth  de  Merode  et  al.  2004   West  Africa     Wild  plants     Consumption/sale  decreases  with  wealth    Fisher  2004   Southern  Africa     Low  return  forest  activities     Decreases  with  wealth  Kamanga  et  al.  2007   Southern  Africa     Forests   Decreases  with  wealth  Mamo  et  al.  2007   East  Africa     Forests   Decreases  with  wealth  Paumgarten  and  Shackleton  2009   Southern  Africa     NTFP   Sale  decreases  with  wealth  Shackleton  and  Shackleton  2006   Southern  Africa     NTFP   Sale  decreases  with  wealth    Shackleton  and  Shackleton  2006   Southern  Africa     Fuelwood   Consumption  decreases  with  wealth    Shackleton  and  Shackleton  2006   Southern  Africa     Edible  herbs     Consumption  decreases  with  wealth     4.2.  Biodiversity  Conservation  as  a  Means  of  Poverty  Reduction            The  conservation  of  biodiversity  is  a  unique  way  to  provide  direct  and  indirect  services  that  sustain  the  economy  in  SSA  (Turner  et  al.,  2012).  The  labour  of  the  poor  results  in  economic  returns  that  are  directly  dependent  upon  the  quality  and  quantity  of  the  natural  resources  available,  and  these  resources  are,  in  turn,  dependent  upon  the  biodiversity  of  the  region  (Barrett,  Travis,  &  Dasgupta,  2011).  The  high  dependency  of  the  SSA  economy  on  its  biodiversity  suggests  that,  at  a   minimum,  this  biodiversity  acts  as  a  safety  net  to  maintain  the  region’s  current  economy  (Turner  et  al.,  2012;  Roe,  Walpole,  &  Elliott,  2010).  It  also  suggests  that  biodiversity  is  a  crucial  factor  in  any  hope  for  poverty  alleviation  in  the  region  (Turner  et  al.,  2012;  Roe,  Walpole,  &  Elliott,  2010).  Although  ecosystem  services  (sometimes  looked  at  as  natural  capital),  defined  as  the  value  of  services  generated  by  a  habitat,  are  often  taken  for  granted,  underpriced,  and  overexploited,  these  services  are  extremely  valuable  and  essential  in  the  SSA  economy  (Turner  et  al.,  2012;  Roe,  2010).  If  current  payments  for  these  ecosystem  services  made  it  directly  to  the  poor,  there  would  be  an  increase  in  economic  value  of  49.7%,  suggesting  that  management  of  this  natural  capital  could  result  in  poverty  alleviation  in  the  region  (Turner  et  al.,  2012).  An  example  of  the  ability  of  biodiversity  conservation  to  alleviate  poverty  comes  from  the  comparison  of  two  similar  districts  in  both  Costa  Rica  and  Thailand,  one  with  biodiversity  conservation  and  one  without  (Turner  et  al.,  2012).  The  protected  areas  experienced  10%  less  poverty  in  Costa  Rica,  and  30%  less  poverty  in  Thailand,  providing  discrete  examples  of  how  this  relationship  could  provide  possible  benefits  to  the  SSA  economy  as  well  (Turner  et  al.,  2012).  A  variety  of  similar  success  stories  are  available,  emphasizing  the  promising  capability  of  biodiversity  conservation  as  a  means  of  poverty  reduction  (Fisher,  &  Christopher,  2007;  Munthali,  2007;  Roe,  2010).     4.3.  Implementation  of  Findings              The  complex  relationship  between  poverty  and  biodiversity  in  SSA  provides  compelling  reasons  for  its  communities  to  engage  in  conservation,  as  it  can  be  economically,  environmentally,  politically,  socially,  and  culturally  beneficial  (Roe,   Walpole,  &  Elliott,  2010).  Community  appropriate  strategies  and  policies  for  incorporating  biodiversity  conservation  and  poverty  reduction  must  be  designed  in  order  to  take  advantage  of  these  compelling  benefits  (Roe,  Walpole,  &  Elliott,  2010).              There  are  a  variety  of  possibilities  and  previously  implemented  strategies  that  take  advantage  of  the  relationship  between  these  two  variables,  providing  “win-­‐win”  solutions  (Munthali,  2007;  Roe,  2010).  Community  based  natural  resource  management  programs  are  key,  as  they  recognize  the  importance  of  the  participation  of  those  who  live  near  and  are  interconnected  with  the  resources  at  hand  (Munthali,  2007).  A  complex  example  of  such  an  ecosystem  management  initiative  is  Transfrontier  Conservation  Areas  (TFCAs),  which  recognize  that  political  borders  between  countries  are  not  necessarily  ecological  borders  (Munthali,  2007).  This  strategy  aims  to  ensure  that  key  ecological  processes  continue  to  function  where  borders  have  divided  an  ecosystem,  while  also  encouraging  cooperation  between  different  governments  and  communities  in  the  region  (Munthali,  2007).              A  variety  of  other  conservation  mechanisms  provide  strong  evidence  of  contributions  to  reductions  in  poverty  by  conserving  biodiversity  (Roe,  2010).  Examples  include  non-­‐timber  forest  products  (NTFPs)  in  which  products  such  as  honey,  bamboo  and  mushrooms  can  be  cultivated  and  generate  profit  for  the  region,  as  well  as  timber  itself,  when  forests  are  owned  by  communities  and  harvested  sustainably  by  small-­‐scale  wood  processing  to  provide  the  community  with  wealth  that  has  historically  gone  to  national  elites  (Roe,  2010).  The  use  of  these  strategies   in  example  cases  such  as  Mexico,  Bolivia,  and  Vietnam  has  been  successful  in  reducing  poverty  (Roe,  2010).                An  initiative  called  payments  for  environmental  services  (PES),  which  involves  the  selling  of  well-­‐defined  environmental  services  (such  as  watershed  protection  or  carbon  sequestration)  so  that  landowners  are  compensated  for  providing  environmentally  sustainable  ecosystem  services,  has  been  successfully  implemented  in  Costa  Rica  for  forest  protection,  and  in  Ecuador  for  watershed  protection  (Fisher,  &  Christopher,  2007;  Roe,  2010).  These  cases  of  PES  provide  considerable  evidence  of  the  ability  of  this  strategy  to  reduce  poverty,  as  it  now  supplies  more  than  30%  of  the  household  income  for  the  poor  in  both  of  these  regions  (Fisher,  &  Christopher,  2007;  Roe,  2010).              Nature-­‐based  tourism  is  another  possible  option  for  SSA,  as  international  attractions  such  as  eco-­‐lodges  and  safari  operations  provide  direct  and  indirect  benefits  to  the  region  in  which  they  are  implemented  (Roe,  2010).  Direct  benefits  include  the  creation  of  jobs  in  the  tourism  sector  (Roe,  2010).  Indirect  benefits  are  the  infrastructure  and  development  that  come  along  with  tourism,  as  research  has  shown  that  each  dollar  spent  by  a  tourist  leads  to  a  $2-­‐3  national  economic  benefit  (Roe,  2010).              Fish  spillover  is  another  strategy  that  has  been  proven  to  reduce  poverty  in  the  locations  it  is  implemented  (Roe,  2010).  The  protection  of  a  key  area  of  marine  habitat  allows  for  the  fish  stocks  to  replenish  and  overspill  into  adjacent  areas  where  they  can  be  caught  and  benefitted  from  by  the  poor  (Roe,  2010).  The  protected  areas  provide  marine  biodiversity  conservation,  while  the  spillover  areas   generate  income  to  reduce  poverty  in  the  region  (Roe,  2010).  This  strategy  has  lead  to  a  doubling  of  local  incomes  within  five  years  of  its  establishment  in  two  different  Fijian  communities  (Roe,  2010).              It  is  a  combination  of  these  various  strategies  and  policies  that  will  be  necessary  to  establish  any  significant  poverty  alleviation  in  a  region  as  large  and  diverse  as  SSA.  Research  and  experience  have  shown  that  these  strategies  can  contribute  measurably  to  both  the  conservation  of  biodiversity  and  alleviation  of  poverty  if  executed  properly  (Munthali,  2007;  Roe,  2010).  It  is  important  that  the  strategies  implemented  incorporate  sufficient  understanding  of  the  complex  relationship  between  poverty  and  the  environment  in  SSA,  in  order  to  ensure  an  overall  sustainable  outcome  (Lufumpa,  2005).  Challenges  faced  by  such  policy  implementation  include  political  instability  in  the  region,  poor  government  implementation  and  a  disconnection  between  government  policy  and  the  scholarly  research  behind  the  issues  (Munthali,  2007).  A  main  challenge  for  the  region  is  ensuring  that  it  is  the  poor  who  benefit  from  these  policy  implementations,  as  opposed  to  the  elite  capturing  the  benefits  (Roe,  2010).     5.  Conclusions            Biodiversity  loss  and  poverty  reduction  are  global  challenges,  agreed  to  be  of  first  order  importance  in  the  Convention  on  Biological  Diversity  and  in  the  Millennium  Development  Goals  (Barrett,  Travis,  &  Dasgupta,  2011).  The  connection  between  these  two  variables  therefore  holds  profound  possibilities  for  SSA  and,  if  understood  fully,  could  provide  promising  mechanisms  to  combat  poverty  and  the  loss  of  biodiversity  together  (Lufumpa,  2005;  Roe,  2010).  The  close  interrelation  between   these  two  variables  suggests  that  if  not  arrested,  biodiversity  degradation  will  affect  the  regions  economic  growth,  further  worsening  the  situation  of  those  in  poverty  (Lufumpa,  2005).  The  importance  of  this  complex  relationship  must  be  taken  into  account,  and  biodiversity  conservation  should  be  a  priority  in  international  efforts  to  address  poverty  reduction  in  SSA  (Adams  et  al.,  2004;  Roe,  2010).  The  services  provided  by  diverse  ecosystems  and  the  habitats  providing  them  are  vanishing  at  alarming  rates,  and  are  undervalued  in  markets,  businesses,  and  government  decisions  (Turner  et  al.,  2012).  This  is  particularly  true  when  looking  into  a  future  of  climate  change,  where  this  biodiversity  will  be  especially  crucial  (Turner  et  al.,  2012;  Roe,  2010).  Although  SSA  emits  one  of  the  lowest  levels  of  green  house  gases  globally,  research  has  shown  that  this  drought-­‐prone  region  is  most  at  risk  of  climate  change  hazards  (Reid,  &  Swiderska,  2008).  Biodiversity  in  SSA  can  act  as  a  buffer,  ensuring  protection  and  resilience  against  the  adverse  weather  associated  with  climate  change  (Roe,  2010).  Those  in  poverty  have  the  lowest  capacity  to  deal  with  climate  change-­‐related  shocks,  and  the  resilience  provided  by  conserving  the  region’s  biodiversity  will  be  increasingly  important  for  the  economic  wellbeing  of  SSA  communities  in  a  future  of  climate  change  (Reid,  &  Swiderska,  2008; 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