Community structure informs species geographic distributions

Understanding what determines species’ geographic distributions is crucial for assessing global change threats to biodiversity. Measuring limits on distributions is usually, and necessarily, done with data at large geographic extents and coarse spatial resolution. However, survival of individuals is determined by processes that happen at small spatial scales. The relative abundance of coexisting species (i.e. ‘community structure’) reflects assembly processes occurring at small scales, and are often available for relatively extensive areas, so could be useful for explaining species distributions. We demonstrate that Bayesian Network Inference (BNI) can overcome several challenges to including community structure into studies of species distributions, despite having been little used to date. We hypothesized that the relative abundance of coexisting species can improve predictions of species distributions. In 1570 assemblages of 68 Mediterranean woody plant species we used BNI to incorporate community structure into Species Distribution Models (SDMs), alongside environmental information. Information on species associations improved SDM predictions of community structure and species distributions moderately, though for some habitat specialists the deviance explained increased by up to 15%. We demonstrate that most species associations (95%) were positive and occurred between species with ecologically similar traits. This suggests that SDM improvement could be because species co-occurrences are a proxy for local ecological processes. Our study shows that Bayesian Networks, when interpreted carefully, can be used to include local conditions into measurements of species’ large-scale distributions, and this information can improve the predictions of species distributions.

[1]  Adrian C. Newton,et al.  Evaluation of Bayesian networks for modelling habitat suitability and management of a protected area , 2014 .

[2]  C. Darwin.  Charles Darwin The Origin of Species by means of Natural Selection or The Preservation of Favoured Races in the Struggle for Life , 2004 .

[3]  G. Powers,et al.  A Description of the Advanced Research WRF Version 3 , 2008 .

[4]  J. Bruno,et al.  Inclusion of facilitation into ecological theory , 2003 .

[5]  R. Guthke,et al.  Novel application of multi-stimuli network inference to synovial fibroblasts of rheumatoid arthritis patients , 2014, BMC Medical Genomics.

[6]  A. Valiente‐Banuet,et al.  Facilitation can increase the phylogenetic diversity of plant communities. , 2007, Ecology letters.

[7]  Takashi Saitoh,et al.  Flood disturbance and predator-prey effects on regional gradients in species diversity. , 2014, Ecology.

[8]  Jordan G. Powers,et al.  A Description of the Advanced Research WRF Version 2 , 2005 .

[9]  J. M. Black,et al.  Predator protection or similar habitat selection in red-breasted goose nesting associations: extremes along a continuum , 2003, Animal Behaviour.

[10]  P. Ja,et al.  Inference in Bayesian Networks , 1999, AI Mag..

[11]  Stefano Allesina,et al.  Beyond pairwise mechanisms of species coexistence in complex communities , 2017, Nature.

[12]  Hong Gu,et al.  BiomeNet: A Bayesian Model for Inference of Metabolic Divergence among Microbial Communities , 2014, PLoS Comput. Biol..

[13]  A. Peterson.  Predicting the Geography of Species’ Invasions via Ecological Niche Modeling , 2003, The Quarterly Review of Biology.

[14]  Wilfried Thuiller,et al.  Predicting extinction risks under climate change: coupling stochastic population models with dynamic bioclimatic habitat models , 2008, Biology Letters.

[15]  Marco Scutari,et al.  Learning Bayesian Networks with the bnlearn R Package , 2009, 0908.3817.

[16]  Gordon C. Tucker.  Hedera helix L. , 2003 .

[17]  H. Shaffer,et al.  Annual review of ecology, evolution, and systematics , 2003 .

[18]  Peter E. Larsen,et al.  Predicting bacterial community assemblages using an artificial neural network approach. , 2012, Methods in molecular biology.

[19]  P. Keddy,et al.  The assembly of experimental wetland plant communities , 1995 .

[20]  M. Pärtel,et al.  The functional assembly of experimental grasslands in relation to fertility and resource heterogeneity , 2014 .

[21]  A. Valiente‐Banuet,et al.  Modern Quaternary plant lineages promote diversity through facilitation of ancient Tertiary lineages , 2006, Proceedings of the National Academy of Sciences.

[22]  Colin M Beale,et al.  Revealing ecological networks using Bayesian network inference algorithms. , 2010, Ecology.

[23]  J. Marshall.  CORYNEPHORUS CANESCENS (L.) P. BEAUV. AS A MODEL FOR THE AMMOPHILA PROBLEM , 1965 .

[24]  Peter E. Larsen,et al.  Predicting bacterial community assemblages using an artificial neural network approach , 2012, Nature Methods.

[25]  A. Valiente‐Banuet,et al.  Plant Facilitation and Phylogenetics , 2013 .

[26]  Emanuel Palamarev,et al.  Paleobotanical evidences of the Tertiary history and origin of the Mediterranean sclerophyll dendroflora , 1989, Plant Systematics and Evolution.

[27]  Bart R. Johnson,et al.  Context-dependent pattern and process: the distribution and competitive dynamics of an invasive grass, Brachypodium sylvaticum , 2010, Biological Invasions.

[28]  A. R. Jonckheere,et al.  A DISTRIBUTION-FREE k-SAMPLE TEST AGAINST ORDERED ALTERNATIVES , 1954 .

[29]  R. Callaway.  Positive interactions and interdependence in plant communities , 2007 .

[30]  D. I. Axelrod.  History of the Mediterranean Ecosystem in California , 1973 .

[31]  C. Parmesan.  Ecological and Evolutionary Responses to Recent Climate Change , 2006 .

[32]  A. Valiente‐Banuet,et al.  Neighborhood phylodiversity affects plant performance. , 2010, Ecology.

[33]  Fernando Boinas,et al.  Use of a Bayesian network model to identify factors associated with the presence of the tick Ornithodoros erraticus on pig farms in southern Portugal. , 2013, Preventive veterinary medicine.

[34]  Paul A. Keddy,et al.  Ecological assembly rules : perspectives, advances, retreats , 1999 .

[35]  M. Araújo,et al.  The importance of biotic interactions for modelling species distributions under climate change , 2007 .

[36]  L. Maiorano,et al.  Climate-based empirical models show biased predictions of butterfly communities along environmental gradients , 2012 .

[37]  J. Raes,et al.  Microbial interactions: from networks to models , 2012, Nature Reviews Microbiology.

[38]  J. R. Koehler,et al.  Modern Applied Statistics with S-Plus. , 1996 .

[39]  Robert Haining,et al.  Statistics for spatial data: by Noel Cressie, 1991, John Wiley & Sons, New York, 900 p., ISBN 0-471-84336-9, US $89.95 , 1993 .

[40]  B. Marcot,et al.  Using Bayesian belief networks to evaluate fish and wildlife population viability under land management alternatives from an environmental impact statement , 2001 .

[41]  Otso Ovaskainen,et al.  Testing the heterospecific attraction hypothesis with time-series data on species co-occurrence , 2010, Proceedings of the Royal Society B: Biological Sciences.

[42]  E. Meineri,et al.  Fine‐grain, large‐domain climate models based on climate station and comprehensive topographic information improve microrefugia detection , 2017 .

[43]  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.

[44]  Radhakrishnan Nagarajan,et al.  Identifying significant edges in graphical models of molecular networks , 2011, Artif. Intell. Medicine.

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

[46]  M. Mayfield,et al.  Opposing effects of competitive exclusion on the phylogenetic structure of communities. , 2010, Ecology letters.

[47]  C. M. Herrera.  Tipos morfológicos y funcionales en plantas del matorral mediterráneo de sur de España , 1984 .

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

[49]  M. Cruzan,et al.  Shifting dispersal modes at an expanding species’ range margin , 2010, Molecular ecology.

[50]  Jean-Baptiste Denis,et al.  Bayesian Networks , 2014 .

[51]  D. Ackerly.  Community Assembly, Niche Conservatism, and Adaptive Evolution in Changing Environments , 2003, International Journal of Plant Sciences.

[52]  R. Tibshirani,et al.  Generalized Additive Models , 1986 .

[53]  A. Nicotra,et al.  The effects of phenotypic plasticity and local adaptation on forecasts of species range shifts under climate change. , 2014, Ecology letters.

[54]  J. Wilson.  Ecological Assembly Rules: Assembly rules in plant communities , 1999 .

[55]  Zalmiyah Zakaria,et al.  A review on the computational approaches for gene regulatory network construction , 2014, Comput. Biol. Medicine.

[56]  William N. Venables,et al.  Modern Applied Statistics with S-Plus. , 1996 .

[57]  Yanli Wang,et al.  Predicting gene ontology annotations of orphan GWAS genes using protein-protein interactions , 2014, Algorithms for Molecular Biology.

[58]  V. V. Krishnan,et al.  NONINTUITIVE CUE USE IN HABITAT SELECTION , 2005 .

[59]  Phillip P. A. Staniczenko,et al.  Linking macroecology and community ecology: refining predictions of species distributions using biotic interaction networks , 2017, Ecology letters.

[60]  Damien A. Fordham,et al.  Dynamics of range margins for metapopulations under climate change , 2009, Proceedings of the Royal Society B: Biological Sciences.

[61]  John-Arvid Grytnes,et al.  Local temperatures inferred from plant communities suggest strong spatial buffering of climate warming across Northern Europe , 2013, Global change biology.

[62]  K. Sachs,et al.  Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data , 2005, Science.

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

[64]  N Jayasuriya,et al.  A Bayesian network model to assess the public health risk associated with wet weather sewer overflows discharging into waterways. , 2012, Water research.

[65]  D. I. Axelrod.  Evolution and Biogeography of Madrean-Tethyan Sclerophyll Vegetation , 1975 .

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

[67]  M. Araújo,et al.  Biotic and abiotic variables show little redundancy in explaining tree species distributions , 2010 .

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

[69]  D. Ackerly,et al.  Plant-pollinator interactions and the assembly of plant communities. , 2008, Trends in ecology & evolution.

[70]  M. Luoto,et al.  Biotic interactions improve prediction of boreal bird distributions at macro‐scales , 2007 .

[71]  D. Ackerly,et al.  A trait-based test for habitat filtering: convex hull volume. , 2006, Ecology.

[72]  Otso Ovaskainen,et al.  Modeling species co-occurrence by multivariate logistic regression generates new hypotheses on fungal interactions. , 2010, Ecology.

[73]  P. Haemig.  Symbiotic nesting of birds with formidable animals: a review with applications to biodiversity conservation , 2001, Biodiversity & Conservation.

[74]  Eric Garnier,et al.  Community assembly along a soil depth gradient: contrasting patterns of plant trait convergence and divergence in a Mediterranean rangeland , 2012 .

[75]  C. Herrera,et al.  Historical Effects and Sorting Processes as Explanations for Contemporary Ecological Patterns: Character Syndromes in Mediterranean Woody Plants , 1992, The American Naturalist.