Application of consensus theory to formalize expert evaluations of plant species distribution models

Aim Application of environmental envelope modelling (EEM) for conservation planning requires careful validation. Opinions of experts who have worked with species of interest in the field can be a valuable and independent information source to validate EEM because of their first-hand experience with species occurrence and absence. However, their use in model validation is limited because of the subjectivity of their feedback. In this study, we present a method on the basis of cultural consensus theory to formalize expert model evaluations. Methods We developed, for five tree species, distribution models with nine different variable combinations and Maxent EEM software. Species specialists validated the generated distribution maps through an online Google Earth interface with the scores from Invalid to Excellent. Experts were also asked about the commission and omission errors of the distribution models they evaluated. We weighted expert scores according to consensus theory. These values were used to obtain a final average expert score for each of the produced distribution models. The consensus-weighted expert scores were compared with un-weighted scores and correlated to four conventional model performance parameters after cross-validation with test data: Area Under Curve (AUC), maximum Kappa, commission error and omission error. Results The median consensus-weighted expert score of all species–variable combinations was close to Fair. In general, experts that reached more consensus with peers were more positive about the EEM outcomes, compared to those that had more opposite judgements. Both consensus-weighted and un-weighted scores were significantly correlated to corresponding AUC, maximum Kappa and commission error values, but not to omission errors. More than half of the experts indicated that the distribution model they considered best included areas where the species is known to be absent. One third also indicated areas of species presence that were omitted by the model. Conclusions Our results indicate that experts are fairly positive about EEM outcomes. This is encouraging, but EEM application for conservation actions remains limited according to them. Methods to formalize expert knowledge allow a wider use of this information in model validation and improvement, and they complement conventional validation methods of presence-only modelling. Online GIS and survey applications facilitate the consultation of experts.

[1]  B. Bradley,et al.  Using Expert Knowledge to Satisfy Data Needs: Mapping Invasive Plant Distributions in the Western United States , 2011 .

[2]  L. Willemen,et al.  Distribution, diversity and environmental adaptation of highland papayas (Vasconcellea spp.) in tropical and subtropical America , 2007, Biodiversity and Conservation.

[3]  Mark S. Boyce,et al.  Modelling distribution and abundance with presence‐only data , 2006 .

[4]  Patrick Van Damme,et al.  Horticultural potential of Andean fruit crops exploring their centre of origin , 2003 .

[5]  R. Whittaker,et al.  Species Diversity--Scale Matters , 2002, Science.

[6]  Allison J. Miller,et al.  GIS-based characterization of the geographic distributions of wild and cultivated populations of the Mesoamerican fruit tree Spondias purpurea (Anacardiaceae). , 2006, American journal of botany.

[7]  V. Ramanatha Rao,et al.  Managing Plant Genetic Diversity , 2002 .

[8]  Eloy Solano,et al.  Ecological niche modeling and geographic distribution of the genus Polianthes L. (Agavaceae) in Mexico: using niche modeling to improve assessments of risk status , 2007, Biodiversity and Conservation.

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

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

[11]  W. Thuiller.  BIOMOD – optimizing predictions of species distributions and projecting potential future shifts under global change , 2003 .

[12]  J. Oldeland,et al.  Modelling potential distribution of the threatened tree species Juniperus oxycedrus: how to evaluate the predictions of different modelling approaches? , 2011 .

[13]  R. Hijmans,et al.  Cross-validation of species distribution models: removing spatial sorting bias and calibration with a null model. , 2012, Ecology.

[14]  J. L. Parra,et al.  Very high resolution interpolated climate surfaces for global land areas , 2005 .

[15]  Jens-Christian Svenning,et al.  Determinants of palm species distributions across Africa: the relative roles of climate, non‐climatic environmental factors, and spatial constraints , 2010 .

[16]  M. Araújo,et al.  Uses and misuses of bioclimatic envelope modeling. , 2012, Ecology.

[17]  Bruce D. Smith,et al.  The origins of agriculture : an international perspective , 1993 .

[18]  Jane Elith,et al.  Error and uncertainty in habitat models , 2006 .

[19]  C. Gouveia.  Predicting the impacts of climate change on the distribution and conservation of endemic forest land snails of Madeira Island , 2014 .

[20]  J. Lobo,et al.  An evaluation of methods for modelling distribution of Patagonian insects , 2009 .

[21]  Luigi Guarino,et al.  Computer tools for spatial analysis of plant genetic resources data: 1. DIVA-GIS , 2001 .

[22]  Bette A. Loiselle,et al.  Avoiding Pitfalls of Using Species Distribution Models in Conservation Planning , 2003 .

[23]  Géo Coppens D'Eeckenbrugge,et al.  Origin and domestication of native Amazonian crops. , 2010 .

[24]  S. Saatchi,et al.  Predicting geographical distribution models of high-value timber trees in the Amazon Basin using remotely sensed data , 2008 .

[25]  Sarah Minty.  Working paper 7 , 2014 .

[26]  Flemming Skov,et al.  Could the tree diversity pattern in Europe be generated by postglacial dispersal limitation? , 2007, Ecology letters.

[27]  S. Brush,et al.  Tzeltal and Tzotzil Farmer Knowledge and Maize Diversity in Chiapas, Mexico , 2007, Current Anthropology.

[28]  M. Haine,et al.  Van Damme A. , 1986 .

[29]  Luigi Guarino,et al.  Computer tools for spatial analysis of plant genetic resources data: 2. FloraMap , 2002 .

[30]  John Doe,et al.  Soil Map of the World , 1962 .

[31]  Glenn Marion,et al.  Bayesian image restoration models for combining expert knowledge on recording activity with species distribution data , 2010 .

[32]  T. Bernhardsen.  Geographic Information Systems: An Introduction , 1999 .

[33]  A. Hernández-Vásquez,et al.  [Geographic information systems]. , 2016, Revista peruana de medicina experimental y salud publica.

[34]  T. Dawson,et al.  Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? , 2003 .

[35]  Jacob van Etten,et al.  Changes in farmers' knowledge of maize diversity in highland Guatemala, 1927/37-2004 , 2006 .

[36]  C Hilton-Taylor,et al.  Measuring the fate of plant diversity: towards a foundation for future monitoring and opportunities for urgent action , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.

[37]  N. Batjes.  SOTER-based soil parameter estimates for Latin America and the Caribbean (ver. 1.0) , 2005 .

[38]  M. Silman,et al.  Keep collecting: accurate species distribution modelling requires more collections than previously thought , 2011 .

[39]  Andy Jarvis,et al.  Climate change impact predictions on Pinus patula and Pinus tecunumanii populations in Mexico and Central America , 2009 .

[40]  Richard Field,et al.  Spatial species‐richness gradients across scales: a meta‐analysis , 2009 .

[41]  Martijn Gough.  Climate change , 2009, Canadian Medical Association Journal.

[42]  S. Romaine,et al.  Co-occurrence of linguistic and biological diversity in biodiversity hotspots and high biodiversity wilderness areas , 2012, Proceedings of the National Academy of Sciences.

[43]  M. Austin.  Species distribution models and ecological theory: A critical assessment and some possible new approaches , 2007 .

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

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

[46]  R. G. Davies,et al.  Methods to account for spatial autocorrelation in the analysis of species distributional data : a review , 2007 .

[47]  C. Piedallu,et al.  Soil nutritional factors improve models of plant species distribution: an illustration with Acer campestre (L.) in France , 2006 .

[48]  A. Newton,et al.  Red Listing the world’s tree species: a review of recent progress , 2008 .

[49]  Richard M Cowling,et al.  Knowing But Not Doing: Selecting Priority Conservation Areas and the Research–Implementation Gap , 2008, Conservation biology : the journal of the Society for Conservation Biology.

[50]  P. Hernandez,et al.  The effect of sample size and species characteristics on performance of different species distribution modeling methods , 2006 .

[51]  W. Batchelder,et al.  Culture as Consensus: A Theory of Culture and Informant Accuracy , 1986 .

[52]  W. Thuiller,et al.  Predicting species distribution: offering more than simple habitat models. , 2005, Ecology letters.

[53]  B. Minasny,et al.  Digital Soil Map of the World , 2009, Science.

[54]  Eve McDonald-Madden,et al.  Predicting species distributions for conservation decisions , 2013, Ecology letters.

[55]  Antoine Guisan,et al.  Predictive habitat distribution models in ecology , 2000 .

[56]  Walter Jetz,et al.  Species richness, hotspots, and the scale dependence of range maps in ecology and conservation , 2007, Proceedings of the National Academy of Sciences.

[57]  Patrick Crist,et al.  GAP Analysis , 2000 .

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

[59]  Catherine H. Graham,et al.  A comparison of methods for mapping species ranges and species richness , 2006 .

[60]  Juliana Schietti,et al.  Historical Human Footprint on Modern Tree Species Composition in the Purus-Madeira Interfluve, Central Amazonia , 2012, PloS one.

[61]  John Bell,et al.  A review of methods for the assessment of prediction errors in conservation presence/absence models , 1997, Environmental Conservation.

[62]  X. Scheldeman,et al.  Training manual on spatial analysis of plant diversity and distribution. , 2010 .

[63]  A. Jarvis,et al.  A Gap Analysis Methodology for Collecting Crop Genepools: A Case Study with Phaseolus Beans , 2010, PloS one.

[64]  Robert P. Anderson,et al.  Evaluating predictive models of species’ distributions: criteria for selecting optimal models , 2003 .

[65]  C. Margules,et al.  Nature Conservation: Cost Effective Biological Surveys and Data Analysis , 1990 .

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

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

[68]  Trevor Hastie,et al.  A statistical explanation of MaxEnt for ecologists , 2011 .

[69]  J. Busby.  BIOCLIM - a bioclimate analysis and prediction system , 1991 .

[70]  S C Weller,et al.  Assessing Rater Performance without a "Gold Standard" Using Consensus Theory , 1997, Medical decision making : an international journal of the Society for Medical Decision Making.