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In order to improve understanding of these issues we here propose a conceptual model of socioeconomic biodiversity drivers and pressures.
Making farming compatible with biodiversity conservation requires identifying the biodiversity drivers that operate in agricultural landscapes, while also addressing the role of biodiversity in ecosystem services.
The analysis of the case studies underlines the potential utility of the conceptual model to guide future research into socioeconomic biodiversity drivers and pressures.
Elevation gradients are ideally suited for examining biodiversity drivers, as elevation is correlated with various environmental variables including competition, mutualism, ecotone effects, temperature and rainfall, which determine energy availability and ecosystem productivity (McCain and Grytnes 2010).
Different biotic indicators have been widely used to estimate the state of biodiversity, but we integrate these with biodiversity drivers, i.e. factors driving changes in biodiversity, to track biodiversity response to environmental changes.
Precise information on biodiversity drivers (e.g. past and present management or disturbance regimes, environmental conditions, landscape patterns) has an effective indicator value, but this is often not taken into account in monitoring schemes.
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Also, more recent analysis [28] finds that sea-level changes are a possible biodiversity driver.
Dr Prasad said they used the collated data to see if a better understanding of the impacts of the biodiversity loss driver on seed dispersal networks could be used to develop better management and conservation practices.
The aim of the model is to guide research aimed at improving our understanding of socioeconomic biodiversity pressures and drivers and to serve as a basis for the development of formal, quantitative models in that field.
Our results show that the soil DNA-based earthworm survey at the landscape-scale improves over previous approaches, and opens a way towards large-scale assessment of soil biodiversity and its drivers.
These findings suggest that a multiple scale analysis based on a nested sampling design could significantly improve our knowledge of bathymetric patterns of deep-sea biodiversity and its drivers.
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