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.

[1]  R. Dennis,et al.  Bias in Butterfly Distribution Maps: The Influence of Hot Spots and Recorder's Home Range , 2000, Journal of Insect Conservation.

[2]  J. Stenlid,et al.  Diverse ecological roles within fungal communities in decomposing logs of Picea abies. , 2015, FEMS microbiology ecology.

[3]  Steven J. Phillips,et al.  Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. , 2009, Ecological applications : a publication of the Ecological Society of America.

[4]  Brendan A. Wintle,et al.  Imperfect detection impacts the performance of species distribution models , 2014 .

[5]  J. Stokland,et al.  The saproxylic database: an emerging overview of the biological diversity in dead wood , 2008, Revue d'Écologie (La Terre et La Vie).

[6]  Jane Elith,et al.  What do we gain from simplicity versus complexity in species distribution models , 2014 .

[7]  J. Silvertown.  A new dawn for citizen science. , 2009, Trends in ecology & evolution.

[8]  Marc Kéry,et al.  Site-occupancy models may offer new opportunities for dragonfly monitoring based on daily species lists , 2010 .

[9]  J. Elith,et al.  Species Distribution Models: Ecological Explanation and Prediction Across Space and Time , 2009 .

[10]  M. Araújo,et al.  Presence-absence versus presence-only modelling methods for predicting bird habitat suitability , 2004 .

[11]  J. Nichols,et al.  To predict the niche, model colonization and extinction. , 2015, Ecology.

[12]  C. Moritz,et al.  Evaluation of species distribution models by resampling of sites surveyed a century ago by Joseph Grinnell , 2013 .

[13]  Tomas J. Bird,et al.  Statistical solutions for error and bias in global citizen science datasets , 2014 .

[14]  R. Julliard,et al.  Predictive ecology in a changing world , 2015 .

[15]  Ljusk Ola Eriksson,et al.  The Heureka Forestry Decision Support System: An Overview , 2011, Math. Comput. For. Nat. Resour. Sci..

[16]  J. Andrew Royle,et al.  Presence‐only modelling using MAXENT: when can we trust the inferences? , 2013 .

[17]  J. Andrew Royle,et al.  Site‐Occupancy Distribution Modeling to Correct Population‐Trend Estimates Derived from Opportunistic Observations , 2010, Conservation biology : the journal of the Society for Conservation Biology.

[18]  Miroslav Dudík,et al.  Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation , 2008 .

[19]  M. Nilsson,et al.  Countrywide Estimates of Forest Variables Using Satellite Data and Field Data from the National Forest Inventory , 2003, Ambio.

[20]  R. Whittaker,et al.  Beyond scarcity: citizen science programmes as useful tools for conservation biogeography , 2010 .

[21]  Harris David,et al.  A statistical explanation of MaxEnt for ecologists , 2013 .

[22]  Robert A. Boria,et al.  Spatial filtering to reduce sampling bias can improve the performance of ecological niche models , 2014 .

[23]  A. Townsend Peterson,et al.  Novel methods improve prediction of species' distributions from occurrence data , 2006 .

[24]  M. Kearney,et al.  Mechanistic niche modelling: combining physiological and spatial data to predict species' ranges. , 2009, Ecology letters.

[25]  J. Andrew Royle,et al.  ESTIMATING SITE OCCUPANCY RATES WHEN DETECTION PROBABILITIES ARE LESS THAN ONE , 2002, Ecology.

[26]  David R. Anderson,et al.  Model selection and multimodel inference : a practical information-theoretic approach , 2003 .

[27]  Marc Kéry,et al.  Metapopulation dynamics in the butterfly Hipparchia semele changed decades before occupancy declined in The Netherlands. , 2011, Ecological applications : a publication of the Ecological Society of America.

[28]  Mevin B. Hooten,et al.  A guide to Bayesian model selection for ecologists , 2015 .

[29]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[30]  David B. Roy,et al.  Statistics for citizen science: extracting signals of change from noisy ecological data , 2014 .

[31]  David N. Bonter,et al.  Citizen Science as an Ecological Research Tool: Challenges and Benefits , 2010 .

[32]  A. Peterson,et al.  No silver bullets in correlative ecological niche modelling: insights from testing among many potential algorithms for niche estimation , 2015 .

[33]  Andrew Thomas,et al.  The BUGS project: Evolution, critique and future directions , 2009, Statistics in medicine.

[34]  T. Landelius,et al.  A high‐resolution regional reanalysis for Europe. Part 2: 2D analysis of surface temperature, precipitation and wind , 2016 .

[35]  R. Julliard,et al.  REVIEW: Predictive ecology in a changing world , 2015 .

[36]  M. Kéry,et al.  Predicting species distributions from checklist data using site‐occupancy models , 2010 .

[37]  Göran Ståhl,et al.  Adapting National Forest Inventories to changing requirements - the case of the Swedish National Forest Inventory at the turn of the 20th century , 2014 .

[38]  Otso Ovaskainen,et al.  Specialist species of wood‐inhabiting fungi struggle while generalists thrive in fragmented boreal forests , 2013 .

[39]  Lynne Boddy,et al.  Climate variation effects on fungal fruiting , 2014 .

[40]  S. Nielsen,et al.  Accounting for spatially biased sampling effort in presence‐only species distribution modelling , 2015 .

[41]  I. Koizumi,et al.  Mapping large‐scale bird distributions using occupancy models and citizen data with spatially biased sampling effort , 2015 .

[42]  B. Jonsson,et al.  Colonization and extinction patterns of wood‐decaying fungi in a boreal old‐growth Picea abies forest , 2008 .

[43]  T. Hastie,et al.  Bias correction in species distribution models: pooling survey and collection data for multiple species , 2014, Methods in ecology and evolution.

[44]  Arco J. van Strien,et al.  Opportunistic citizen science data of animal species produce reliable estimates of distribution trends if analysed with occupancy models , 2013 .

[45]  H. H. Bruun,et al.  The role of demography, intra-species variation, and species distribution models in species' projections under climate change , 2015 .

[46]  Mark New,et al.  Ensemble forecasting of species distributions. , 2007, Trends in ecology & evolution.

[47]  Robert P. Anderson,et al.  Maximum entropy modeling of species geographic distributions , 2006 .

[48]  A. Márcia Barbosa,et al.  Species–people correlations and the need to account for survey effort in biodiversity analyses , 2013 .

[49]  Mathieu Marmion,et al.  Evaluation of consensus methods in predictive species distribution modelling , 2009 .