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    The value of local community knowledge in species distribution modelling for a threatened Neotropical parrot

    Biddle, R, Solis-Ponce, I, Jones, M ORCID logoORCID: https://orcid.org/0000-0002-2510-8697, Marsden, S ORCID logoORCID: https://orcid.org/0000-0002-0205-960X, Pilgrim, M and Devenish, C ORCID logoORCID: https://orcid.org/0000-0002-5249-0844 (2021) The value of local community knowledge in species distribution modelling for a threatened Neotropical parrot. Biodiversity and Conservation, 30 (6). pp. 1803-1823. ISSN 0960-3115

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    Species distribution models are widely used in conservation planning, but obtaining the necessary occurrence data can be challenging, particularly for rare species. In these cases, citizen science may provide insight into species distributions. To understand the distribution of the newly described and Critically Endangered Amazona lilacina, we collated species observations and reliable eBird records from 2010–2020. We combined these with environmental predictors and either randomly generated background points or absence points generated from eBird checklists, to build distribution models using MaxEnt. We also conducted interviews with people local to the species’ range to gather community-sourced occurrence data. We grouped these data according to perceived expertise of the observer, based on the ability to identify A. lilacina and its distinguishing features, knowledge of its ecology, overall awareness of parrot biodiversity, and the observation type. We evaluated all models using AUC and Tjur R . Field data models built using background points performed better than those using eBird absence points (AUC = 0.80 ± 0.02, Tjur R = 0.46 ± 0.01 compared to AUC = 0.78 ± 0.03, Tjur R = 0.43 ± 0.21). The best performing community data model used presence records from people who were able recognise a photograph of A. lilacina and correctly describe its distinguishing physical or behavioural characteristics (AUC = 0.84 ± 0.05, Tjur R = 0.51± 0.01). There was up to 92% overlap between the field data and community data models, which when combined, predicted 17,772 km of suitable habitat. Use of community knowledge offers a cost-efficient method to obtain data for species distribution modelling; we offer recommendations on how to assess its performance and present a final map of potential distribution for A. lilacina. 2 2 2 2 2

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