Browsing by Author "Balmford, Andrew"

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Deforestation in an African biodiversity hotspot: extent, variation and the effectiveness of protected areas
(Elsevier, 2013-06) Green, Jonathan M.H.; Larrosa, Cecilia; Burgess, Neil D.; Balmford, Andrew; Johnston, Alison; Mbilinyi, Boniface P.; Platts, Philip J.; Coad, Lauren
The Eastern Arc Mountains of Tanzania show exceptional endemism that is threatened by high anthro- pogenic pressure leading to the loss of natural habitat. Using a novel habitat conversion model, we pres- ent a spatially explicit analysis of the predictors of forest and woodland conversion in the Eastern Arc over 25 years. Our results show that 5% (210 km 2 ) of evergreen forest and 43% (2060 km 2 ) of miombo woodland was lost in the Eastern Arc Mountains between 1975 and 2000. Important predictors of habitat conversion included distance to natural habitat edge, topography and measures of remoteness. The main conservation strategy in these mountains for the past 100 years has been to develop a network of pro- tected areas. These appear to have reduced rates of habitat loss and most remaining evergreen forest is now within protected areas. However, the majority of miombo woodland, an important source of eco- system services, lies outside formal protected areas, where additional conservation strategies may be needed.
Detecting and predicting forest degradation: a comparison of ground surveys and remote sensing in Tanzanian forests
(Plants, People, Planet (PPP), 2021-01-08) Ahrends, Antje; Bulling, Mark T.; Platts, Philip J.; Swetnam, Ruth; Ryan, Casey; Doggart, Nike; Hollingsworth, Peter M.; Marchant, Robert; Balmford, Andrew; Harris, David J.; Gross-Camp, Nicole; Sumbi, Peter; Munishi, Pantaleo; Madoffe, Seif; Mhoro, Boniface; Leonard, Charles; Bracebridge, Claire; Doody, Kathryn; Wilkins, Victoria; Owen, Nisha; Marshall, Andrew R.; Schaafsma, Marije; Pfliegner, Kerstin; Jones, Trevor; Robinson, James; Topp-Jørgensen, Elmer; Brink, Henry; Burgess, Neil D.
Tropical forest degradation is widely recognised as a driver of biodiversity loss and a major source of carbon emissions. However, in contrast to deforestation, more gradual changes from degradation are challenging to detect, quantify and monitor. Here, we present a field protocol for rapid, area-standardised quantifications of forest condition, which can also be implemented by non-specialists. Using the ex- ample of threatened high-biodiversity forests in Tanzania, we analyse and predict degradation based on this method. We also compare the field data to optical and radar remote-sensing datasets, thereby conducting a large-scale, independent test of the ability of these products to map degradation in East Africa from space. • Our field data consist of 551 ‘degradation’ transects collected between 1996 and 2010, covering >600 ha across 86 forests in the Eastern Arc Mountains and coastal forests. • Degradation was widespread, with over one-third of the study forests—mostly protected areas—having more than 10% of their trees cut. Commonly used opti- cal remote-sensing maps of complete tree cover loss only detected severe im- pacts (≥25% of trees cut), that is, a focus on remotely-sensed deforestation would have significantly underestimated carbon emissions and declines in forest quality. Radar-based maps detected even low impacts (<5% of trees cut) in ~90% of cases. The field data additionally differentiated types and drivers of harvesting, with spa- tial patterns suggesting that logging and charcoal production were mainly driven by demand from major cities. • Rapid degradation surveys and radar remote sensing can provide an early warning and guide appropriate conservation and policy responses. This is particularly im- portant in areas where forest degradation is more widespread than deforestation, such as in eastern and southern Africa.
Implementation and opportunity costs of reducing deforestation and forest degradation in Tanzania
(2011 Macmillan Publishers Limited, 2011) Fisher, Brendan; Lewis, Simon L.; Burgess, Neil D.; Malimbwi, Rogers E.; Munishi, Panteleo K.; Swetnam, Ruth D.; Turner, Kerry; Willcock, Simon; Balmford, Andrew
The Cancún Agreements provide strong backing for a REDDC (Reducing Emissions from Deforestation and Forest Degradation) mechanism whereby developed countries pay developing ones for forest conservation1. REDDC has potential to simultaneously deliver cost-effective climate change mitigation and human development2–5. However, most REDDC analysis has used coarse-scale data, overlooked important opportunity costs to tropical forest users4,5 and failed to consider how to best invest funds to limit leakage, that is, merely displacing deforestation6. Here we examine these issues for Tanzania, a REDDCcountry, by comparing district-scale carbon losses from deforestation with the opportunity costs of carbon conservation. Opportunity costs are estimated as rents from both agriculture and charcoal production (the most important proximate causes of regional forest conversion7–9). As an alternativewe also calculate the implementation costs of alleviating the demand for forest conversion—thereby addressing the problem of leakage—by raising agricultural yields on existing cropland and increasing charcoal fuel-use efficiency. The implementation costs exceed the opportunity costs of carbon conservation (medians of US$6.50 versus US$3.90 per Mg CO2), so effective REDDC policies may cost more than simpler estimates suggest. However, even if agricultural yields are doubled, implementation is possible at the competitive price of US$12 per Mg CO2.
Linking science with stakeholders to sustain natural capital
(2007) Mwakalila, Shadrack; Burgess, Neil; Ricketts, Taylor; Olwero, Nasser; Swetnam, Ruth; Mbilinyi, Boniface; Marchant, Rob; Mtalo, Felix; White, Sue; Munishi, Pantaleo; Marshall, Andrew; Malimbwi, Rogers; Smith, Celina; Jambiya, George; Marshall, Andrew; Madoffe, Seif; Fisher, Brendan; Kajembe, George; Morse-Jones, Sian; Kulindwa, Kassim; Green, Jonathan; Balmford, Andrew
Towards regional, error-bounded landscape carbon storage estimates for data-deficient areas of the world
(PLOS ONE, 2012-09-14) Willcock, Simon; Phillips, Oliver L.; Platts, Philip J; Balmford, Andrew; Burgess, Neil D.; Lovett, Jon C.; Ahrends, Antje; Mbilinyi, Boniface; Lewis, Simon L.
Monitoring landscape carbon storage is critical for supporting and validating climate change mitigation policies. These may be aimed at reducing deforestation and degradation, or increasing terrestrial carbon storage at local, regional and global levels. However, due to data-deficiencies, default global carbon storage values for given land cover types such as ‘lowland tropical forest’ are often used, termed ‘Tier 1 type’ analyses by the Intergovernmental Panel on Climate Change (IPCC). Such estimates may be erroneous when used at regional scales. Furthermore uncertainty assessments are rarely provided leading to estimates of land cover change carbon fluxes of unknown precision which may undermine efforts to properly evaluate land cover policies aimed at altering land cover dynamics. Here, we present a repeatable method to estimate carbon storage values and associated 95% confidence intervals (CI) for all five IPCC carbon pools (aboveground live carbon, litter, coarse woody debris, belowground live carbon and soil carbon) for data-deficient regions, using a combination of existing inventory data and systematic literature searches, weighted to ensure the final values are regionally specific. The method meets the IPCC ‘Tier 2’ reporting standard. We use this method to estimate carbon storage over an area of33.9 million hectares of eastern Tanzania, reporting values for 30 land cover types. We estimate that this area stored 6.33 (5.92–6.74) Pg C in the year 2000. Carbon storage estimates for the same study area extracted from five published Africa-wide or global studies show a mean carbon storage value of ,50% of that reported using our regional values, with four of the five studies reporting lower carbon storage values. This suggests that carbon storage may have been underestimated for this region of Africa. Our study demonstrates the importance of obtaining regionally appropriate carbon storage estimates, and shows how such values can be produced for a relatively low investment.