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1.
Article in English | MEDLINE | ID: mdl-27502381

ABSTRACT

Rapidly rising populations and likely increases in incomes in sub-Saharan Africa make tens of millions of hectares of cropland expansion nearly inevitable, even with large increases in crop yields. Much of that expansion is likely to occur in higher rainfall savannas, with substantial costs to biodiversity and carbon storage. Zambia presents an acute example of this challenge, with an expected tripling of population by 2050, good potential to expand maize and soya bean production, and large areas of relatively undisturbed miombo woodland and associated habitat types of high biodiversity value. Here, we present a new model designed to explore the potential for targeting agricultural expansion in ways that achieve quantitatively optimal trade-offs between competing economic and environmental objectives: total converted land area (the reciprocal of potential yield); carbon loss, biodiversity loss and transportation costs. To allow different interests to find potential compromises, users can apply varying weights to examine the effects of their subjective preferences on the spatial allocation of new cropland and its costs. We find that small compromises from the objective to convert the highest yielding areas permit large savings in transportation costs, and the carbon and biodiversity impacts resulting from savannah conversion. For example, transferring just 30% of weight from a yield-maximizing objective equally between carbon and biodiversity protection objectives would increase total cropland area by just 2.7%, but result in avoided costs of 27-47% for carbon, biodiversity and transportation. Compromise solutions tend to focus agricultural expansion along existing transportation corridors and in already disturbed areas. Used appropriately, this type of model could help countries find agricultural expansion alternatives and related infrastructure and land use policies that help achieve production targets while helping to conserve Africa's rapidly transforming savannahs.This article is part of the themed issue 'Tropical grassy biomes: linking ecology, human use and conservation'.


Subject(s)
Agriculture , Biodiversity , Carbon/analysis , Conservation of Natural Resources , Forests , Grassland , Zambia
2.
Conserv Biol ; 29(3): 844-53, 2015 Jun.
Article in English | MEDLINE | ID: mdl-25581070

ABSTRACT

Anecdotal evidence suggests that socioeconomic shocks strongly affect wildlife populations, but quantitative evidence is sparse. The collapse of socialism in Russia in 1991 caused a major socioeconomic shock, including a sharp increase in poverty. We analyzed population trends of 8 large mammals in Russia from 1981 to 2010 (i.e., before and after the collapse). We hypothesized that the collapse would first cause population declines, primarily due to overexploitation, and then population increases due to adaptation of wildlife to new environments following the collapse. The long-term Database of the Russian Federal Agency of Game Mammal Monitoring, consisting of up to 50,000 transects that are monitored annually, provided an exceptional data set for investigating these population trends. Three species showed strong declines in population growth rates in the decade following the collapse, while grey wolf (Canis lupus) increased by more than 150%. After 2000 some trends reversed. For example, roe deer (Capreolus spp.) abundance in 2010 was the highest of any period in our study. Likely reasons for the population declines in the 1990s include poaching and the erosion of wildlife protection enforcement. The rapid increase of the grey wolf populations is likely due to the cessation of governmental population control. In general, the widespread declines in wildlife populations after the collapse of the Soviet Union highlight the magnitude of the effects that socioeconomic shocks can have on wildlife populations and the possible need for special conservation efforts during such times.


Subject(s)
Artiodactyla/physiology , Carnivora/physiology , Conservation of Natural Resources , Animals , Population Dynamics , Russia , USSR
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