Browsing Environmental Sustainability Research Centre by Authors
Conservation and the botanist effectAhrends, Antje; Rahbek, Carsten; Bulling, Mark T.; Burgess, Neil D.; Platts, Philip J.; Lovett, Jon C.; Kindemba, Victoria Wilkins; Owen, Nisha; Sallu, Albert Ntemi; Marshall, Andrew R. (2013-05-24)Over the last few decades, resources for descriptive taxonomy and biodiversity inventories have substantially declined, and they are also globally unequally distributed. This could result in an overall decline in the quality of biodiversity data as well as geographic biases, reducing the utility and reliability of inventories. We tested this hypothesis with tropical tree records (n = 24,024) collected from the Eastern Arc Mountains, Tanzania, between 1980 and 2007 by 13 botanists, whose collections represent 80% of the total plant records for this region. Our results show that botanists with practical training in tropical plant identification record both more species and more species of conservation concern (20 more species, two more endemic and one more threatened species per 250 specimens) than untrained botanists. Training and the number of person-days in the field explained 96% of the variation in the numbers of species found, and training was the most important predictor for explaining recorded numbers of threatened and endemic species. Data quality was related to available facilities, with good herbarium access significantly reducing the proportions of misidentifications and misspellings. Our analysis suggests that it may be necessary to account for recorder training when comparing diversity across sites, particularly when assessing numbers of rare and endemic species, and for global data portals to provide such information. We also suggest that greater investment in the training of botanists and in the provisioning of good facilities would substantially increase recording efficiency and data reliability, thereby improving conservation planning and implementation on the ground.
Funding begets biodiversityAhrends, Antje; Burgess, Neil D.; Gereau, Roy E.; Marchant, Rob; Bulling, Mark T.; Lovett, Jon C.; Platts, Philip J.; Kindemba, Victoria Wilkins; Owen, Nisha; Fanning, Eibleis; et al. (2013-05-24)Aim Effective conservation of biodiversity relies on an unbiased knowledge of its distribution. Conservation priority assessments are typically based on the levels of species richness, endemism and threat. Areas identified as important receive the majority of conservation investments, often facilitating further research that results in more species discoveries. Here, we test whether there is circularity between funding and perceived biodiversity, which may reinforce the conservation status of areas already perceived to be important while other areas with less initial funding may remain overlooked. Location Eastern Arc Mountains, Tanzania. Methods We analysed time series data (1980–2007) of funding (n = 134 projects) and plant species records (n = 75,631) from a newly compiled database. Perceived plant diversity, over three decades, is regressed against funding and environmental factors, and variances decomposed in partial regressions. Cross-correlations are used to assess whether perceived biodiversity drives funding or vice versa. Results Funding explained 65% of variation in perceived biodiversity patterns – six times more variation than accounted for by 34 candidate environmental factors. Cross-correlation analysis showed that funding is likely to be driving conservation priorities and not vice versa. It was also apparent that investment itself may trigger further investments as a result of reduced start-up costs for new projects in areas where infrastructure already exists. It is therefore difficult to establish whether funding, perceived biodiversity, or both drive further funding. However, in all cases, the results suggest that regional assessments of biodiversity conservation importance may be biased by investment. Funding effects might also confound studies on mechanisms of species richness patterns. Main conclusions Continued biodiversity loss commands urgent conservation action even if our knowledge of its whereabouts is incomplete; however, by concentrating inventory funds in areas already perceived as important in terms of biodiversity and/or where start-up costs are lower, we risk losing other areas of underestimated or unknown value.
Predictable waves of sequential forest degradation and biodiversity loss spreading from an African cityAhrends, Antje; Burgess, Neil D.; Milledge, Simon A. H.; Bulling, Mark T.; Fisher, Brendan; Smart, James C. R.; Clarke, G. P.; Mhoro, Boniface E.; Lewis, Simon L. (2013-05-24)Tropical forest degradation emits carbon at a rate of ~0.5 Pg·y−1, reduces biodiversity, and facilitates forest clearance. Understanding degradation drivers and patterns is therefore crucial to managing forests to mitigate climate change and reduce biodiversity loss. Putative patterns of degradation affecting forest stocks, carbon, and biodiversity have variously been described previously, but these have not been quantitatively assessed together or tested systematically. Economic theory predicts a systematic allocation of land to its highest use value in response to distance from centers of demand. We tested this theory to see if forest exploitation would expand through time and space as concentric waves, with each wave targeting lower value products. We used forest data along a transect from 10 to 220 km from Dar es Salaam (DES), Tanzania, collected at two points in time (1991 and 2005). Our predictions were confirmed: high-value logging expanded 9 km·y−1, and an inner wave of lower value charcoal production 2 km·y−1. This resource utilization is shown to reduce the public goods of carbon storage and species richness, which significantly increased with each kilometer from DES [carbon, 0.2 Mg·ha−1; 0.1 species per sample area (0.4 ha)]. Our study suggests that tropical forest degradation can be modeled and predicted, with its attendant loss of some public goods. In sub-Saharan Africa, an area experiencing the highest rate of urban migration worldwide, coupled with a high dependence on forest-based resources, predicting the spatiotemporal patterns of degradation can inform policies designed to extract resources without unsustainably reducing carbon storage and biodiversity.