A theory for species co-occurrence in interaction networks

The study of species co-occurrences has been central in community ecology since the foundation of the discipline. Co-occurrence data are, nevertheless, a neglected source of information to model species distributions and biogeographers are still debating about the impact of biotic interactions on species distributions across geographical scales. We argue that a theory of species co-occurrence in ecological networks is needed to better inform interpretation of co-occurrence data, to formulate hypotheses for different community assembly mechanisms, and to extend the analysis of species distributions currently focused on the relationship between occurrences and abiotic factors. The main objective of this paper is to provide the first building blocks of a general theory for species co-occurrences. We formalize the problem with definitions of the different probabilities that are studied in the context of co-occurrence analyses. We analyze three species interactions modules and conduct multi-species simulations in order to document five principles influencing the associations between species within an ecological network: (i) direct interactions impact pairwise co-occurrence, (ii) indirect interactions impact pairwise co-occurrence, (iii) pairwise co-occurrence rarely are symmetric, (iv) the strength of an association decreases with the length of the shortest path between two species, and (v) the strength of an association decreases with the number of interactions a species is experiencing. Our analyses reveal the difficulty of the interpretation of species interactions from co-occurrence data. We discuss whether the inference of the structure of interaction networks is feasible from co-occurrence data. We also argue that species distributions models could benefit from incorporating conditional probabilities of interactions within the models as an attempt to take into account the contribution of biotic interactions to shaping individual distributions of species.

[1]  Dominique Gravel,et al.  The dissimilarity of species interaction networks. , 2012, Ecology letters.

[2]  J. Bronstein,et al.  How context dependent are species interactions? , 2014, Ecology letters.

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

[4]  Neo D. Martinez,et al.  Simple prediction of interaction strengths in complex food webs , 2009, Proceedings of the National Academy of Sciences.

[5]  Harris David,et al.  Estimating species interactions from observational data with Markov networks , 2015 .

[6]  Joseph A. Veech,et al.  A probabilistic model for analysing species co-occurrence , 2013 .

[7]  P. Yodzis.  The Indeterminacy of Ecological Interactions as Perceived Through Perturbation Experiments , 1988 .

[8]  S. Shen-Orr,et al.  Network motifs: simple building blocks of complex networks. , 2002, Science.

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

[10]  Miguel G. Matias,et al.  Inferring biotic interactions from proxies. , 2015, Trends in ecology & evolution.

[11]  Graham Bell,et al.  THE CO‐DISTRIBUTION OF SPECIES IN RELATION TO THE NEUTRAL THEORY OF COMMUNITY ECOLOGY , 2005 .

[12]  Daniel Simberloff,et al.  The Assembly of Species Communities: Chance or Competition? , 1979 .

[13]  L. Oksanen,et al.  Exploitation Ecosystems in Gradients of Primary Productivity , 1981, The American Naturalist.

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

[15]  Wilfried Thuiller,et al.  A road map for integrating eco-evolutionary processes into biodiversity models. , 2013, Ecology letters.

[16]  David J. Harris.  Inferring species interactions from co-occurrence data with Markov networks , 2015, bioRxiv.

[17]  L. Slobodkin,et al.  Community Structure, Population Control, and Competition , 1960, The American Naturalist.

[18]  Miguel B. Araújo,et al.  Using species co-occurrence networks to assess the impacts of climate change , 2011 .

[19]  Dominique Gravel,et al.  Inferring food web structure from predator–prey body size relationships , 2013 .

[20]  Michael J. Watts,et al.  Adapted conservation measures are required to save the Iberian lynx in a changing climate , 2013 .

[21]  Werner Ulrich,et al.  Pattern detection in null model analysis , 2013 .

[22]  J. Wootton,et al.  The Nature and Consequences of Indirect Effects in Ecological Communities , 1994 .

[23]  Robert D Holt,et al.  Trophic interactions and range limits: the diverse roles of predation , 2009, Proceedings of the Royal Society B: Biological Sciences.

[24]  J. C. de Almeida,et al.  Concluding Remarks , 2015, Clinical practice and epidemiology in mental health : CP & EMH.

[25]  Kai Zhu,et al.  More than the sum of the parts: forest climate response from joint species distribution models. , 2014, Ecological applications : a publication of the Ecological Society of America.

[26]  Christian Mazza,et al.  Modeling Food Webs: Exploring Unexplained Structure Using Latent Traits , 2010, The American Naturalist.

[27]  A. Peterson.  Ecological niche conservatism: a time‐structured review of evidence , 2011 .

[28]  R. Macarthur,et al.  The Theory of Island Biogeography , 1969 .

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

[30]  Daniel B. Stouffer,et al.  Scaling from individuals to networks in food webs , 2010 .

[31]  Dominique Gravel,et al.  Trophic theory of island biogeography. , 2011, Ecology letters.

[32]  S. Shen-Orr,et al.  Networks Network Motifs : Simple Building Blocks of Complex , 2002 .

[33]  Michael E. Gilpin,et al.  Examination of the “null” model of connor and simberloff for species co-occurrences on Islands , 2004, Oecologia.

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

[35]  J. Lawton,et al.  Making mistakes when predicting shifts in species range in response to global warming , 1998, Nature.

[36]  L. Stone,et al.  The checkerboard score and species distributions , 1990, Oecologia.

[37]  Ricard V Solé,et al.  Press perturbations and indirect effects in real food webs. , 2009, Ecology.

[38]  S. Hubbell.  The Unified Neutral Theory of Biodiversity and Biogeography (MPB-32) , 2011 .

[39]  Daniel B. Stouffer,et al.  Evidence for the existence of a robust pattern of prey selection in food webs , 2007, Proceedings of the Royal Society B: Biological Sciences.

[40]  Antoine Guisan,et al.  Combining food web and species distribution models for improved community projections , 2013, Ecology and evolution.

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

[42]  Neo D. Martinez,et al.  Simple rules yield complex food webs , 2000, Nature.

[43]  Jordi Bascompte,et al.  Understanding food-web persistence from local to global scales. , 2010, Ecology letters.

[44]  E. C. Pielou,et al.  Association among species of infrequent occurrence: the insect and spider fauna of Polyporus betulinus (Bulliard) Fries. , 1968, Journal of theoretical biology.

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

[46]  M. Araújo,et al.  Individualistic vs community modelling of species distributions under climate change. , 2009 .

[47]  Gary R. Graves,et al.  Macroecological signals of species interactions in the Danish avifauna , 2010, Proceedings of the National Academy of Sciences.

[48]  Franck Jabot,et al.  Bitrophic interactions shape biodiversity in space , 2012, Proceedings of the National Academy of Sciences.