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

Louise Mair,Anders Lundström,Philip J Harrison,Tord Snäll,Tomas Lämås,Mari Jönsson,Swantje Löbel,Juha Siitonen,Jenni Nordén

doi:10.1002/ece3.2601

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.

Highlights

Species distribution models (SDMs) have been extensively applied in forecasting species responses to future habitat and climate change (Elith & Leathwick, 2009)
Species distribution models built using citizen science data forecast changes in P. ferrugineofuscus occurrence in response to forest management that were qualitatively congruent with forecasts from a colonization–extinction model built using systematically collected data (Harrison et al in prep)
The five modeling approaches we applied (GLM, PA/PO model, Bayesian occupancy model, MaxEnt random background, and MaxEnt target-group background (TGB)) all projected an increase in probability of occurrence over time in forest set-aside from production

Summary

| INTRODUCTION

Species distribution models (SDMs) have been extensively applied in forecasting species responses to future habitat and climate change (Elith & Leathwick, 2009). Spatial recording bias can be explicitly modeled using a small amount of presence–absence data (Fithian, Elith, Hastie, & Keith, 2015) This reduces the investment required in obtaining presence– absence data while making use of extensive presence-only datasets. This approach performed well on one species group (Fithian et al, 2015), but has yet to be widely tested. All five species distribution models based on CSD utilized presence-only and/or presence–absence data collected by volunteer recorders and were selected to encompass a diverse range of data requirements and assumptions about recording biases. Data from eight recorders were used covering 15,508 grid cells (Appendix S1)

| METHODS

| DISCUSSION

Findings

DATA ACCESSIBILITY