Habitat alterations that often accompany land-use change are one of the major drivers of global biodiversity losses. In Africa, these threats are especially severe, as this continent has the most rapidly growing of all human populations. Inevitably, increasing areas of land are being transformed for agriculture, including drought-prone drylands in southern and central Africa, despite often having poor soils. In Zimbabwe, a land reform programme provided a unique opportunity to study the biodiversity response to abrupt habitat modification in an extensive dryland area of mixed grassland and woodland savannah. Small-scale subsistence farms were created rapidly during 2001-2002 in formerly semi-natural savannah. We measured the changing compositions of bird communities in transformed and untransformed land over an 8-year period, commencing one decade after subsistence farms were established. Over the study period, repeated counts were made along identical transects in order to assess species' population changes that may have resulted from trait-filtering responses to habitat disturbance. We recorded significantly increased abundances in both land-use areas, accompanied by increases in species diversity and functional redundancy. Temporal trends showed increased abundances across all feeding guilds, and in species of virtually all sizes. Influxes of new species did not increase functional traits' diversity, and no species with distinctive traits appear to have been lost as a result of land-use change. Nearly two decades after habitat transformation, the bird communities in the transformed and untransformed areas had become more similar in composition. The broadly benign impact on birds of land conversion into subsistence farms is attributed to the relatively low-level of human activities and disturbance in the transformed land, and the large regional pool of non-specialist bird species.

People’s preferences influence national priorities for economic development and ecological integrity. Often policy makers and development agents base their actions on unclear assumptions about people’s preferences. This paper explores rural citizens’ preferences for economic and ecological development goals and how they differ within and between communities. We collected data from three purposely selected communities representing dominant social-ecological systems in the transboundary Cubango-Okavango River Basin in southern Africa. We used contingent ranking survey experiments, which are a novel methodological advance in policy related research. This included a qualitative experimental design process that provided a broad framing underpinning the research. The contingent ranking itself allowed us to simultaneously assess (i) participants’ ranking priorities for the development goals; and (ii) participants’ preferences for the ordering of those goals. We found relatively strong preference homogeneity within and between communities. Economic development attributes were given high priority across all communities. At the same time, all communities expressed a high preference for a healthy river system providing stable water quality and quantity. This does not mean that our respondents prioritized nature conservation. They showed low preferences for preserving biodiversity and forests which provide less important local benefits than water. This is of high governance relevance. The results point at development domains where policy makers can most likely expect stronger buy-in from citizens. Understanding citizens’ preferences helps to better align national development priorities with what citizens want.

The Earth’s biodiversity and the ecosystem services it provides are declining rapidly because of an increasing use of natural (finite) resources to meet human needs and environmental change affecting biodiversity dynamics. Truly trans-disciplinary solutions are needed for a sustainable development of ecosystem services. Collaboration and knowledge transfer across paleo biology, climatology, global ecology, evolution, biogeography, water sciences, computer science, statistics, and economics has to be mustered for scientists, resources managers and policy makers to counter ecosystem function collapse and ecosystem services loss. We propose a novel framework that closes the implementation gap, in which long-term dynamics of biodiversity, abiotic properties, and ecosystem functions are reconstructed using an unprecedented integration of biochemical and environmental fingerprinting of biological archives spanning centuries. The long-term dynamics obtained from this fingerprinting are then placed in a machine learning pipeline to identify cause-effect relations between environmental change and biodiversity dynamics. Predictive models are tested by hindcasting, and then used to accurately forecast the future of ecosystem services and their socio-economic impact under different climate change scenarios. The framework provides accessible tools to practitioners to translate cutting-edge research into practical solutions for environmental management and practice.