Dataset Information

Evaluating citizen science data for forecasting species responses to national forest management.

ABSTRACT: The extensive spatial and temporal coverage of many citizen science datasets (CSD) makes them appealing for use in species distribution modeling and forecasting. However, a frequent limitation is the inability to validate results. Here, we aim to assess the reliability of CSD for forecasting species occurrence in response to national forest management projections (representing 160,366 km²) by comparison against forecasts from a model based on systematically collected colonization-extinction data. We fitted species distribution models using citizen science observations of an old-forest indicator fungus Phellinus ferrugineofuscus. We applied five modeling approaches (generalized linear model, Poisson process model, Bayesian occupancy model, and two MaxEnt models). Models were used to forecast changes in occurrence in response to national forest management for 2020-2110. Forecasts of species occurrence from models based on CSD were congruent with forecasts made using the colonization-extinction model based on systematically collected data, although different modeling methods indicated different levels of change. All models projected increased occurrence in set-aside forest from 2020 to 2110: the projected increase varied between 125% and 195% among models based on CSD, in comparison with an increase of 129% according to the colonization-extinction model. All but one model based on CSD projected a decline in production forest, which varied between 11% and 49%, compared to a decline of 41% using the colonization-extinction model. All models thus highlighted the importance of protected old forest for P. ferrugineofuscus persistence. We conclude that models based on CSD can reproduce forecasts from models based on systematically collected colonization-extinction data and so lead to the same forest management conclusions. Our results show that the use of a suite of models allows CSD to be reliably applied to land management and conservation decision making, demonstrating that widely available CSD can be a valuable forecasting resource.

SUBMITTER: Mair L

PROVIDER: S-EPMC5216679 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

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Evaluating citizen science data for forecasting species responses to national forest management.

Mair Louise L Harrison Philip J PJ Jönsson Mari M Löbel Swantje S Nordén Jenni J Siitonen Juha J Lämås Tomas T Lundström Anders A Snäll Tord T

Ecology and evolution 20161220 1

The extensive spatial and temporal coverage of many citizen science datasets (CSD) makes them appealing for use in species distribution modeling and forecasting. However, a frequent limitation is the inability to validate results. Here, we aim to assess the reliability of CSD for forecasting species occurrence in response to national forest management projections (representing 160,366 km<sup>2</sup>) by comparison against forecasts from a model based on systematically collected colonization-exti ...[more]

PMID: 28070299

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Project description:Abstract The GLOBE Program's GLOBE Observer application is a free citizen science mobile data collection and visualization tool compatible with iOS and Android operating systems. Citizen scientists armed with the app can report the mosquito larval habitats they identify using the GLOBE Mosquito Habitat Mapper tool. This data can complement the climate, weather, and land cover data obtained from satellite measurements by scientists who develop risk models for mosquito‐borne diseases. Public participation in mosquito surveillance research provides the opportunity to obtain the volume, velocity and variety of data needed to fight the threat of vector‐borne diseases, especially in under‐resourced communities with minimal to no municipal surveillance and mitigation services. GLOBE Mosquito Habitat Mappers document and describe potential and active mosquito larval habitats in and around their homes and communities. An easy‐to‐use pictorial interface enables users to geolocate and describe oviposition sites encountered, count and identify mosquito larvae, and when appropriate, eliminate the larval habitats. During Mosquito Habitat Mapper's first 3 years of use, over 24,000 data observations have been reported throughout the world. This technical report summarizes GLOBE Mosquito Habitat Mapper data reported by GLOBE citizen scientists from three regions: Africa, Asia and the Pacific Islands, and Latin America and the Caribbean. Localized mosquito larvae distribution patterns were examined by comparing data collected in three cities in Senegal–Dakar, Touba, and Thilmakha. The Senegal data show habitat and genera differences among mosquitoes identified by citizen scientists in the cities and illustrates the potential of the app for community‐based surveillance and research. Key Points GLOBE Observer is The GLOBE Program's mobile app. The GLOBE Mosquito Habitat Mapper on this platform enables citizen scientists to identify, describe and report mosquito larvae and habitats GLOBE Mosquito Habitat Mappers not only collect data, but participate in source reduction and reduce risk of disease in their community GLOBE Mosquito Habitat Mapper data has practical applications in municipal surveillance as well as in mosquito vector borne disease research

Dataset Information

Evaluating citizen science data for forecasting species responses to national forest management.

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Evaluating citizen science data for forecasting species responses to national forest management.

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