PAPER Where are the wild things? Why we need better data on species distribution

Aim The effects of ongoing global change are causing increasing concern about the ability of species or biomes to shift or adapt. Tremendous efforts have been made to develop ever more sophisticated species distribution models to provide forecasts for the future of biodiversity. All these models rely on species occurrence data, either for calibration or validation. Here we evaluate (i) whether distribution data diverge among widely used sources, for supposedly well-known taxa, and (ii) to what extent these divergences affect species distribution models. Location Europe (as an example). Methods We compared the distribution maps of 21 of the most common European trees, according to four large-scale, putatively reliable sources of distribution data. For each species, we compared the outputs of correlative species distribution models built using occurrence data from each of these sources of data. We also investigated how discrepancies in large-scale occurrence data affected the validation scores of two process-based tree distribution models. Results Maps of tree occurrence diverged in 8–74% of the forested area, depending on species. These discrepancies affected projections of niche models: for example, 22–75% of the area projected as suitable by at least one model generated using one source of data was not projected as such by all other models. For most species, this proportion increased under scenarios of climate change, whatever the model used. To a lesser extent, uncertainties on current species distributions also affect the validation score of process-based distribution models. Main conclusions Reliable, widely used sources of occurrence data strongly diverge even for well-known taxa – the most common European trees. Scientists and stakeholders should acknowledge this gap in knowledge, since accurate data are a prerequisite to providing stakeholders with robust forecasts on biodiversity. Participatory science programmes and remote sensing techniques are promising tools for rapidly gathering such data.

[1]  Matthew B. Jones,et al.  Challenges and Opportunities of Open Data in Ecology , 2011, Science.

[2]  Alex S. Kutt,et al.  Focus on poleward shifts in species' distribution underestimates the fingerprint of climate change , 2013 .

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

[4]  B. Otto‐Bliesner,et al.  No‐analog climates and shifting realized niches during the late quaternary: implications for 21st‐century predictions by species distribution models , 2012 .

[5]  M. Araújo,et al.  BIOMOD – a platform for ensemble forecasting of species distributions , 2009 .

[6]  Antoine Guisan,et al.  Do pseudo-absence selection strategies influence species distribution models and their predictions? An information-theoretic approach based on simulated data , 2009, BMC Ecology.

[7]  I. Kitching,et al.  Online solutions and the ‘Wallacean shortfall’: what does GBIF contribute to our knowledge of species' ranges? , 2013 .

[8]  Steve Kelling,et al.  Data-intensive science applied to broad-scale citizen science. , 2012, Trends in ecology & evolution.

[9]  C. Dormann.  Promising the future? Global change projections of species distributions , 2007 .

[10]  Michael D. Cramer,et al.  A physiological analogy of the niche for projecting the potential distribution of plants , 2012 .

[11]  J. Hoeting,et al.  FACTORS AFFECTING SPECIES DISTRIBUTION PREDICTIONS: A SIMULATION MODELING EXPERIMENT , 2005 .

[12]  Alberto Jiménez-Valverde,et al.  The uncertain nature of absences and their importance in species distribution modelling , 2010 .

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

[14]  Kamaruzaman Jusoff,et al.  Hyperspectral Remote Sensing for Tropical Rain Forest , 2009 .

[15]  Isabelle Chuine,et al.  Phenology is a major determinant of tree species range , 2001 .

[16]  Wilfried Thuiller,et al.  Accounting for dispersal and biotic interactions to disentangle the drivers of species distributions and their abundances. , 2012, Ecology letters.

[17]  J. Lobo,et al.  Basic questions in biogeography and the (lack of) simplicity of species distributions: Putting species distribution models in the right place , 2012 .

[18]  H. D. Cooper,et al.  Scenarios for Global Biodiversity in the 21st Century , 2010, Science.

[19]  J. Hyyppä,et al.  Tree species classification using airborne LiDAR - effects of stand and tree parameters, downsizing of training set, intensity normalization, and sensor type , 2010 .

[20]  R. Kadmon,et al.  EFFECT OF ROADSIDE BIAS ON THE ACCURACY OF PREDICTIVE MAPS PRODUCED BY BIOCLIMATIC MODELS , 2004 .

[21]  I. Chuine,et al.  Tree species range shifts at a continental scale: new predictive insights from a process‐based model , 2008 .

[22]  Wilfried Thuiller,et al.  Consequences of climate change on the tree of life in Europe , 2011, Nature.

[23]  Hugh P Possingham,et al.  Tradeoffs of different types of species occurrence data for use in systematic conservation planning. , 2006, Ecology letters.

[24]  H. Mooney,et al.  Toward a Global Biodiversity Observing System , 2008, Science.

[25]  Benjamin Smith,et al.  Representation of vegetation dynamics in the modelling of terrestrial ecosystems: comparing two contrasting approaches within European climate space , 2008 .

[26]  A. Márcia Barbosa,et al.  Obtaining Environmental Favourability Functions from Logistic Regression , 2006, Environmental and Ecological Statistics.

[27]  S. Lek,et al.  Uncertainty in ensemble forecasting of species distribution , 2010 .

[28]  Wilfried Thuiller,et al.  Climate change threatens European conservation areas , 2011, Ecology letters.

[29]  David R. B. Stockwell,et al.  Effects of sample size on accuracy of species distribution models , 2002 .

[30]  J. M. Fitzsimmons.  How consistent are trait data between sources? A quantitative assessment , 2013 .

[31]  J. Lobo,et al.  Threshold criteria for conversion of probability of species presence to either–or presence–absence , 2007 .

[32]  J. Lobo.  More complex distribution models or more representative data , 2008 .

[33]  J. Lobo,et al.  The performance of range maps and species distribution models representing the geographic variation of species richness at different resolutions , 2012 .

[34]  Robert P. Anderson,et al.  Ecological Niches and Geographic Distributions , 2011 .

[35]  Isabelle Chuine,et al.  Sensitivity analysis of the tree distribution model Phenofit to climatic input characteristics: implications for climate impact assessment , 2005 .

[36]  Tim Sutton,et al.  How Global Is the Global Biodiversity Information Facility? , 2007, PloS one.

[37]  Thomas Giesecke,et al.  Projecting the future distribution of European potential natural vegetation zones with a generalized, tree species-based dynamic vegetation model , 2012 .

[38]  Steven J. Phillips,et al.  WHAT MATTERS FOR PREDICTING THE OCCURRENCES OF TREES: TECHNIQUES, DATA, OR SPECIES' CHARACTERISTICS? , 2007 .

[39]  R. Real,et al.  AUC: a misleading measure of the performance of predictive distribution models , 2008 .

[40]  C. Körner,et al.  Do the elevational limits of deciduous tree species match their thermal latitudinal limits , 2013 .

[41]  Wilfried Thuiller,et al.  Modelling exploration of the future of European beech (Fagus sylvatica L.) under climate change—Range, abundance, genetic diversity and adaptive response , 2010 .

[42]  Emmanuel S. Gritti,et al.  Estimating consensus and associated uncertainty between inherently different species distribution models , 2013 .

[43]  J A Swets,et al.  Measuring the accuracy of diagnostic systems. , 1988, Science.

[44]  F. Jiguet,et al.  Selecting pseudo‐absences for species distribution models: how, where and how many? , 2012 .

[45]  James S. Clark,et al.  Failure to migrate: lack of tree range expansion in response to climate change , 2012 .

[46]  C. Ricotta,et al.  Accounting for uncertainty when mapping species distributions: The need for maps of ignorance , 2011 .

[47]  Alberto Jiménez-Valverde,et al.  Not as good as they seem: the importance of concepts in species distribution modelling , 2008 .

[48]  M. Zappa,et al.  Climate change and plant distribution: local models predict high‐elevation persistence , 2009 .

[49]  Rick Bonney,et al.  The current state of citizen science as a tool for ecological research and public engagement , 2012 .

[50]  I. C. Prentice,et al.  Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model , 2003 .

[51]  Bette Hileman,et al.  CONSEQUENCES OF CLIMATE CHANGE: Government study finds negative effects on coastlines, water supplies, and health, but little economic impact on crops and forests , 2000 .

[52]  M. Kearney,et al.  Correlation and process in species distribution models: bridging a dichotomy , 2012 .

[53]  D. Warton,et al.  Equivalence of MAXENT and Poisson Point Process Models for Species Distribution Modeling in Ecology , 2013, Biometrics.

[54]  M. Holtrop,et al.  Consequences of climate change. , 1995, Environmental health perspectives.

[55]  Dirk R. Schmatz,et al.  Climate, competition and connectivity affect future migration and ranges of European trees , 2012 .

[56]  Wilfried Thuiller,et al.  Climate change impacts on tree ranges: model intercomparison facilitates understanding and quantification of uncertainty. , 2012, Ecology letters.