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.

Biological traits explain bryophyte species distributions and responses to forest fragmentation and climatic variation

“…We used the estimates on living tree volumes and forest age to calculate different connectivity measures as in Mair et al. (, see Appendix b). We tested three different dispersal parameters representing mean dispersal distances of 1 km, 5 km and 10 km and included the connectivity variable which best explained the occurrence of a focal species.…”

Land use changes could modify future negative effects of climate change on old‐growth forest indicator species

“…Data were for the period 2000–2013 at the 100 m grid cell resolution. The observation data were presence‐only (PO; number of observations given in Table ); however, we established presence–absence (PA) data based on observation records from eight methodical recorders (see Mair et al., for details). We also established repeat‐visit detection/non‐detection data based on presence‐only records of 35 old‐forest indicator species of dead wood‐dependant fungi.…”

Large Extent Volunteer Roadkill and Wildlife Observation Systems as Sources of Reliable Data

“…It will also be important to encourage integration and compatibility among taxonomically and/or geographically overlapping systems in order to encourage the formation of a "wildlife observation network" that provides scientists, managers, and the public with information about wildlife. These federated, standardized systems of wildlife observations will be instrumental in understanding and monitoring large-scale ecological characteristics and processes, such as extensive, taxonomically-focused monitoring (e.g., Gardiner et al, 2012), conservation success (Homayoun and Blair, 2016;Miller et al, 2017), changes in species distributions (Mair et al, 2016), mammalian invasions (Maistrello et al, 2016;Courchamp et al, 2017), and changes in species occurrence and abundance in response to climate change.…”

“…Involving society directly in scientific investigation can transform science from an exclusive process, remote from peoples' dayto-day experience, to one that includes millions of new environmental data collectors (Goodchild, 2007) and is participatory and has immediate relevance and value (Ceccaroni et al, 2016). Projects involving citizen/volunteer scientists have grown considerably in recent years (Silvertown, 2009;Conrad and Hilchey, 2010;Roy et al, 2012), providing data collection at large geographic scales (Devictor et al, 2010), that are often of high-quality (e.g., Ratnieks et al, 2016), have been found to be useful for species-distribution modeling (e.g., Mair et al, 2016), and help connect people to nature and conservation problems (Cooper et al, 2007;Devictor et al, 2010). Sub-national and national governments including transportation organizations (e.g., Harris et al, 2016), increasingly are recognizing the importance of volunteer-collected information (e.g., Bowser and Shanley, 2013 and the Federal Crowdsourcing and Citizen Science Toolkit, https://crowdsourcing-toolkit.sites.usa.gov/).…”

“…Volunteer scientists play an important role in understanding the changing distributions of biodiversity (Mair et al, 2016). Their role as sensors can multiply the geographic extent of observations many-fold compared to academic or governmental scientific investigations, while providing data of comparable quality (Ryder et al, 2010;Haklay, 2013).…”