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

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.

Priority conservation areas and a global population estimate for the Critically Endangered Philippine Eagle derived from modelled range metrics using remote sensing habitat characteristics

“…Rectifying the issues for this form of sampling bias with an appropriate data model is currently unknown (Hefley & Hooten 2016). Thus, pooling all the available presence data and then combining with a random sample of background pseudo-absences is justified in this case for a data-poor rare species (Biddle et al . 2021).…”

Comunicação de Ciência e Ciência Cidadã: Estratégias Para o Cidadão Comum

“…This involvement can be developed at different levels of proactivity, designated in the literature as different types of public involvement (Hayes et al, 2012). Concerning the different levels of citizen engagement identified in the literature on CS, many researchers, mainly in the health area, recognize that citizen engagement can occur based on three types of approaches: (a) consultation, (b) collaboration, and (c) coproduction (Biddle et al, 2021;Hayes et al, 2012;Hickey, 2018).…”

“…Finally, the coproduction approach consists of the active participation of citizens as members of the research team in the research's control, direction, and management. In the context of communication, the citizen coproducer can participate as a translator, reviewer, and coauthor of the content that will be shared (Biddle et al, 2021;Hayes et al, 2012;Hickey, 2018).…”

Combining community science and MaxEnt modeling to estimate Wild Turkey ( Meleagris gallopavo ) winter abundance and distribution

“…For instance, some researchers have filtered community data by year (Bradsworth et al 2017) or based on variation around the mean (Supp et al 2015), whereas others have attempted to account for pseudoreplication by removing observations within a predetermined and ecologically informed distance (Razgour et al 2011, Bradsworth et al 2017. Biddle et al (2021), who studied the distribution of an avian species often considered difficult for community members to identify, filtered data based on geographic location and metrics of species identification accuracy, whereas Coxen et al (2017) conducted filtering based on geographic distance between observations, also known as rarefication. Inconsistencies in data cleaning methodology may lead to inaccurate comparisons between studies, even when researchers are studying the same or closely related species.…”