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Niches for Species, a multi-species model to guide woodland management: An example based on Scotland’s native woodlands
- Broome, A., Bellamy, C., Rattey, A., Ray, D., Quine, C.P., Park, K.J.
- Ecological indicators 2019 v.103 pp. 410-424
- Bryophyta, biodiversity, birds, data collection, decision making, environmental indicators, expert opinion, fungi, invertebrates, issues and policy, landscapes, lichens, mammals, microhabitats, models, niches, prediction, protected species, rare species, stand structure, threatened species, vascular plants, woodlands, Scotland
- Designating and managing areas with the aim of protecting biodiversity requires information on species distributions and habitat associations, but a lack of reliable occurrence records for rare and threatened species precludes robust empirical modelling. Managers of Scotland’s native woodlands are obliged to consider 208 protected species, which each have their own, narrow niche requirements. To support decision-making, we developed Niches for Species (N4S), a model that uses expert knowledge to predict the potential occurrence of 179 woodland protected species representing a range of taxa: mammals, birds, invertebrates, fungi, bryophytes, lichens and vascular plants. Few existing knowledge-based models have attempted to include so many species. We collated knowledge to define each species’ suitable habitat according to a hierarchical habitat classification: woodland type, stand structure and microhabitat. Various spatial environmental datasets were used singly or in combination to classify and map Scotland’s native woodlands accordingly, thus allowing predictive mapping of each species’ potential niche. We illustrate how the outputs can inform individual species management, or can be summarised across species and regions to provide an indicator of woodland biodiversity potential for landscape scale decisions. We tested the model for ten species using available occurrence records. Although concordance between predicted and observed distributions was indicated for nine of these species, this relationship was statistically significant in only five cases. We discuss the difficulties in reliably testing predictions when the records available for rare species are typically low in number, patchy and biased, and suggest future model improvements. Finally, we demonstrate how using N4S to synthesise complex, multi-species information into an easily digestible format can help policy makers and practitioners consider large numbers of species and their conservation needs.