Testing species assemblage predictions from stacked and joint species distribution models

Predicting the spatial distribution of species assemblages remains an important challenge in biogeography. Recently, it has been proposed to extend correlative species distribution models (SDMs) by taking into account (a) covariance between species occurrences in so‐called joint species distribution models (JSDMs) and (b) ecological assembly rules within the SESAM (spatially explicit species assemblage modelling) framework. Yet, little guidance exists on how these approaches could be combined. We, thus, aim to compare the accuracy of assemblage predictions derived from stacked and from joint SDMs.

[1]  Robert P. Anderson,et al.  Standards for distribution models in biodiversity assessments , 2019, Science Advances.

[2]  Laura J. Pollock,et al.  Do joint species distribution models reliably detect interspecific interactions from co‐occurrence data in homogenous environments? , 2018 .

[3]  Damaris Zurell,et al.  Outstanding Challenges in the Transferability of Ecological Models. , 2018, Trends in ecology & evolution.

[4]  Carsten F. Dormann,et al.  Biotic interactions in species distribution modelling: 10 questions to guide interpretation and avoid false conclusions , 2018, Global Ecology and Biogeography.

[5]  Wilfried Thuiller,et al.  Integrating correlation between traits improves spatial predictions of plant functional composition , 2018 .

[6]  N. Zimmermann,et al.  Frequency and intensity of facilitation reveal opposing patterns along a stress gradient , 2018, Ecology and evolution.

[7]  Damaris Zurell,et al.  Integrating demography, dispersal and interspecific interactions into bird distribution models , 2017 .

[8]  Walter Jetz,et al.  Large conservation gains possible for global biodiversity facets , 2017, Nature.

[9]  Anna Norberg,et al.  How to make more out of community data? A conceptual framework and its implementation as models and software. , 2017, Ecology letters.

[10]  James S. Clark,et al.  Generalized joint attribute modeling for biodiversity analysis: median-zero, multivariate, multifarious data , 2017 .

[11]  C. Rahbek,et al.  Spatial predictions at the community level: from current approaches to future frameworks , 2017, Biological reviews of the Cambridge Philosophical Society.

[12]  Günther Klonner,et al.  Climate change will increase the naturalization risk from garden plants in Europe , 2016, Global ecology and biogeography : a journal of macroecology.

[13]  David J. Harris,et al.  Inferring species interactions from co-occurrence data with Markov networks. , 2016, Ecology.

[14]  N. Zimmermann,et al.  Effects of functional traits on the prediction accuracy of species richness models , 2016 .

[15]  Francis K. C. Hui,et al.  boral – Bayesian Ordination and Regression Analysis of Multivariate Abundance Data in r , 2016 .

[16]  Veronika Braunisch,et al.  Environmental predictors of species richness in forest landscapes: abiotic factors versus vegetation structure , 2016 .

[17]  Francis K. C. Hui,et al.  So Many Variables: Joint Modeling in Community Ecology. , 2015, Trends in ecology & evolution.

[18]  A. Guisan,et al.  Predicting richness and composition in mountain insect communities at high resolution: a new test of the SESAM framework , 2015 .

[19]  Antoine Guisan,et al.  Overcoming limitations of modelling rare species by using ensembles of small models , 2015 .

[20]  Rui F. Fernandes,et al.  Using species richness and functional traits predictions to constrain assemblage predictions from stacked species distribution models , 2015 .

[21]  David J. Harris.  Generating realistic assemblages with a joint species distribution model , 2015 .

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

[23]  D. Bates,et al.  Fitting Linear Mixed-Effects Models Using lme4 , 2014, 1406.5823.

[24]  Laura J. Pollock,et al.  Understanding co‐occurrence by modelling species simultaneously with a Joint Species Distribution Model (JSDM) , 2014 .

[25]  Wilfried Thuiller,et al.  The European functional tree of bird life in the face of global change , 2014, Nature Communications.

[26]  J. Calabrese,et al.  Stacking species distribution models and adjusting bias by linking them to macroecological models , 2014 .

[27]  Veronika Braunisch,et al.  Remotely sensed forest structural complexity predicts multi species occurrence at the landscape scale , 2013 .

[28]  Alejandro F. Rozenfeld,et al.  The geographic scaling of biotic interactions , 2013 .

[29]  W. D. Kissling,et al.  The role of biotic interactions in shaping distributions and realised assemblages of species: implications for species distribution modelling , 2012, Biological reviews of the Cambridge Philosophical Society.

[30]  Antoine Guisan,et al.  The accuracy of plant assemblage prediction from species distribution models varies along environmental gradients , 2013 .

[31]  Damaris Zurell,et al.  Collinearity: a review of methods to deal with it and a simulation study evaluating their performance , 2013 .

[32]  Carsten F. Dormann,et al.  Towards novel approaches to modelling biotic interactions in multispecies assemblages at large spatial extents , 2012 .

[33]  J. HilleRisLambers,et al.  Rethinking Community Assembly through the Lens of Coexistence Theory , 2012 .

[34]  Alexander Brenning,et al.  Spatial cross-validation and bootstrap for the assessment of prediction rules in remote sensing: The R package sperrorest , 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.

[35]  M. Pärtel,et al.  Ecological assembly rules in plant communities—approaches, patterns and prospects , 2012, Biological reviews of the Cambridge Philosophical Society.

[36]  M. Obrist,et al.  Urban arthropod communities: added value or just a blend of surrounding biodiversity? , 2011 .

[37]  A. Guisan,et al.  Predicting spatial patterns of plant species richness: a comparison of direct macroecological and species stacking modelling approaches , 2011 .

[38]  Antoine Guisan,et al.  SESAM – a new framework integrating macroecological and species distribution models for predicting spatio‐temporal patterns of species assemblages , 2011 .

[39]  Niklaus E. Zimmermann,et al.  Co‐occurrence patterns of trees along macro‐climatic gradients and their potential influence on the present and future distribution of Fagus sylvatica L. , 2011 .

[40]  Otso Ovaskainen,et al.  Making more out of sparse data: hierarchical modeling of species communities. , 2011, Ecology.

[41]  S. Wood.  Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models , 2011 .

[42]  C. Graham,et al.  New trends in species distribution modelling , 2010 .

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

[44]  J Elith,et al.  A working guide to boosted regression trees. , 2008, The Journal of animal ecology.

[45]  Steven J. Phillips,et al.  Aligning Conservation Priorities Across Taxa in Madagascar with High-Resolution Planning Tools , 2008, Science.

[46]  G. Moisen,et al.  PresenceAbsence: An R Package for Presence Absence Analysis , 2008 .

[47]  Richard Fox,et al.  Direct and indirect effects of climate and habitat factors on butterfly diversity. , 2007, Ecology.

[48]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[49]  Antoine Guisan,et al.  Spatial modelling of biodiversity at the community level , 2006 .

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

[51]  Greg Ridgeway,et al.  Generalized Boosted Models: A guide to the gbm package , 2006 .

[52]  M. Kéry,et al.  Estimating species richness: calibrating a large avian monitoring programme , 2005 .

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

[54]  André Streilein,et al.  FOR THE VERIFICATION OF COUNTRYWIDE TERRAIN AND SURFACE MODELS , 2003 .

[55]  Vijay P. Singh,et al.  Cross Comparison of Empirical Equations for Calculating Potential Evapotranspiration with Data from Switzerland , 2002 .

[56]  J. Hofierka,et al.  The solar radiation model for Open source GIS: implementation and applications , 2002 .

[57]  Thomas H Meyer,et al.  Terrain Analysis: Principles and Applications , 2001 .

[58]  Heike Lischke,et al.  Swiss National Forest Inventory: Methods and Models of the Second Assessment , 2001 .

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

[60]  Peter E. Thornton,et al.  Generating surfaces of daily meteorological variables over large regions of complex terrain , 1997 .