Abstract
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 R2. Field data models built using background points performed better than those using eBird absence points (AUC = 0.80 ± 0.02, Tjur R2 = 0.46 ± 0.01 compared to AUC = 0.78 ± 0.03, Tjur R2 = 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 R2 = 0.51± 0.01). There was up to 92% overlap between the field data and community data models, which when combined, predicted 17,772 km2 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.
Highlights
Understanding species distributions is essential for conservation planning (Wilson et al 2005) but for species that are rare, sparsely distributed, or inconspicuous, this information is often lacking
The number of years a participant had lived in the community (Coefficient value: 0.012 ± 0.007, p = 0.14) had no significant effect. We found that both field data and citizen science data in the form of community surveys were able to produce accurate species distribution models and their outputs had an overlap of 92%
We found that models built using background points performed better than those built using absence points generated by eBird checklists, possibly due to the low frequency of eBird records in our study area
Summary
Understanding species distributions is essential for conservation planning (Wilson et al 2005) but for species that are rare, sparsely distributed, or inconspicuous, this information is often lacking. Both presenceabsence and presence-background data methods have limitations; namely that presence data often do not represent an unbiased sample of locations at which the species is present, and that absence data can lead to the inclusion of false absences (Guillera-Arroita et al 2015) These limitations must be considered against the proposed use of model outputs; for instance, presence-background data may be sufficient when outputs are to be used to direct further field investigations, but insufficient if outputs are to directly inform land management for conservation (Lahoz-Monfort et al 2014). It is essential that the effects of imperfect detection are minimised by ensuring a sufficiently large sampling effort at surveyed locations (LahozMonfort et al 2014)
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