*******Application deadline – Friday 29th June 2018*******
Background: Ecosystem services (ES; the goods humans get from nature) such as crop production, carbon capture and livestock grazing are produced by complex interactions among biological species, human activities and the abiotic environment. Primary data characterising ES are rare and the biological component is poorly understood. Thus, ES decision-making often centres on landcover-based assumptions, a methodology with little relevance to biological processes and high levels of uncertainty. This shortcoming impacts several sectors of UK industry (e.g. energy & utilities, engineering & manufacturing, energy & utilities, environment & agriculture, transport & logistics etc) who are required by both UK and EU law to conduct environmental impact assessments on new developments, including understanding the impact on ES.
Aims & Objectives: Species4Services aims to quantify the relationships between species diversity and traits, and ES, providing a step-change in our understanding of how ES are derived from complex landscape-scale systems. We hypothesise that species characteristics provide a better proxy for ES than current landcover-based estimates. We will achieve our aim via the following objectives:
- Collate a pan-continental database of ES data
- Identify which indicator species can provide proxies for specific ES
- Identify which species traits can provide proxies for specific ES
- Contrast our new biologically-driven proxies against existing landcover-based estimates
Using ecological datasets, Species4Services will apply machine-learning techniques to identify novel relationships between ES production (e.g. biomass, grazing) and the species/traits from which these services ultimately derive. We will work closely with environmental consultancies to enhance capacity within two of the four Grand Challenges identified in the UK Industrial Strategy: clean growth (via improved environmental impact assessments), and artificial intelligence, machine-learning and the data-driven economy.
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