Co-occurrence is not evidence of ecological interactions.

There is a rich amount of information in co-occurrence (presence-absence) data that could be used to understand community assembly. This proposition first envisioned by Forbes (1907) and then Diamond (1975) prompted the development of numerous modelling approaches (e.g. null model analysis, co-occurrence networks and, more recently, joint species distribution models). Both theory and experimental evidence support the idea that ecological interactions may affect co-occurrence, but it remains unclear to what extent the signal of interaction can be captured in observational data. It is now time to step back from the statistical developments and critically assess whether co-occurrence data are really a proxy for ecological interactions. In this paper, we present a series of arguments based on probability, sampling, food web and coexistence theories supporting that significant spatial associations between species (or lack thereof) is a poor proxy for ecological interactions. We discuss appropriate interpretations of co-occurrence, along with potential avenues to extract as much information as possible from such data.

[1]  Dominique Gravel,et al.  Toward a general theory of metacommunity ecology , 2020 .

[2]  Carlos J. Melián,et al.  Positive associations among rare species and their persistence in ecological assemblages , 2019, Nature Ecology & Evolution.

[3]  T. Garcia,et al.  Testing the link between species interactions and species co‐occurrence in a trophic network , 2019, Ecography.

[4]  Guangliang Chen.  Interval Estimation , 2019, Bayesian Econometric Methods.

[5]  Francis K. C. Hui,et al.  Untangling direct species associations from indirect mediator species effects with graphical models , 2019, Methods in Ecology and Evolution.

[6]  Brandon S. Schamp,et al.  Examining the link between competition and negative co‐occurrence patterns , 2019, Oikos.

[7]  David A. Moeller,et al.  Biotic Interactions Contribute to the Geographic Range Limit of an Annual Plant: Herbivory and Phenology Mediate Fitness beyond a Range Margin , 2019, The American Naturalist.

[8]  Bart Muys,et al.  Biotic predictors complement models of bat and bird responses to climate and tree diversity in European forests , 2019, Proceedings of the Royal Society B.

[9]  S. Gibbons,et al.  The Microbiome Stress Project: Toward a Global Meta-Analysis of Environmental Stressors and Their Effects on Microbial Communities , 2019, Front. Microbiol..

[10]  Antoine Guisan,et al.  Disentangling biotic interactions, environmental filters, and dispersal limitation as drivers of species co‐occurrence , 2018 .

[11]  Oscar Lindberg,et al.  Unravelling changing interspecific interactions across environmental gradients using Markov random fields. , 2018, Ecology.

[12]  Qingsong Yang,et al.  Tree species co‐occurrence patterns change across grains: insights from a subtropical forest , 2018 .

[13]  R. Gutiérrez,et al.  Structure and co-occurrence patterns in microbial communities under acute environmental stress reveal ecological factors fostering resilience , 2018, Scientific Reports.

[14]  B. Menge,et al.  Fundamental contradictions among observational and experimental estimates of non-trophic species interactions. , 2018, Ecology.

[15]  P. Marquet,et al.  Species co-occurrence networks: Can they reveal trophic and non-trophic interactions in ecological communities? , 2018, Ecology.

[16]  David B. Lindenmayer,et al.  Species co‐occurrence networks show reptile community reorganization under agricultural transformation , 2018 .

[17]  Dominique Gravel,et al.  Food-web structure of willow-galling sawflies and their natural enemies across Europe. , 2017, Ecology.

[18]  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.

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

[20]  Avi Bar-Massada,et al.  Non‐stationarity in the co‐occurrence patterns of species across environmental gradients , 2017 .

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

[22]  Janet Franklin,et al.  Effects of biotic interactions on modeled species' distribution can be masked by environmental gradients , 2016, Ecology and evolution.

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

[24]  David B. Lindenmayer,et al.  DYNAMIC SPECIES CO–OCCURRENCE NETWORKS REQUIRE DYNAMIC BIODIVERSITY SURROGATES , 2016 .

[25]  Brian J. McGill,et al.  A network approach for inferring species associations from co-occurrence data , 2016 .

[26]  H. T. Arita,et al.  Species co-occurrence analysis: pairwise versus matrix-level approaches , 2016 .

[27]  Neil Reid,et al.  Modelling the influence of biotic factors on species distribution patterns , 2016 .

[28]  Daniel B. Stouffer,et al.  Knowledge of predator–prey interactions improves predictions of immigration and extinction in island biogeography , 2016 .

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

[30]  David B. Dunson,et al.  Using latent variable models to identify large networks of species‐to‐species associations at different spatial scales , 2016 .

[31]  M. Araújo,et al.  A theory for species co-occurrence in interaction networks , 2016, Theoretical Ecology.

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

[33]  J. Diamond,et al.  The checkered history of checkerboard distributions: comment. , 2015, Ecology.

[34]  Wilfried Thuiller,et al.  From species distributions to meta-communities. , 2015, Ecology letters.

[35]  Sydne Record,et al.  Empirical Evidence for the Scale Dependence of Biotic Interactions , 2015 .

[36]  Mark Vellend,et al.  Elevational shifts, biotic homogenization and time lags in vegetation change during 40 years of climate warming , 2015 .

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

[38]  Peer Bork,et al.  Metabolic dependencies drive species co-occurrence in diverse microbial communities , 2015, Proceedings of the National Academy of Sciences.

[39]  Torsten Hothorn,et al.  Spatio‐phylogenetic multispecies distribution models , 2015 .

[40]  John Alroy,et al.  A new twist on a very old binary similarity coefficient. , 2015, Ecology.

[41]  D. Bolnick,et al.  Mistaking geography for biology: inferring processes from species distributions. , 2014, Trends in ecology & evolution.

[42]  D. Lindenmayer,et al.  Visualization of species pairwise associations: a case study of surrogacy in bird assemblages , 2014, Ecology and evolution.

[43]  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.

[44]  Joseph A. Veech,et al.  The pairwise approach to analysing species co‐occurrence , 2014 .

[45]  Stefanie Widder,et al.  Deciphering microbial interactions and detecting keystone species with co-occurrence networks , 2014, Front. Microbiol..

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

[47]  Dominique Gravel,et al.  Beyond species: why ecological interaction networks vary through space and time , 2014, bioRxiv.

[48]  Daniel Simberloff,et al.  The checkered history of checkerboard distributions. , 2013, Ecology.

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

[50]  E. Borenstein,et al.  Metabolic modeling of species interaction in the human microbiome elucidates community-level assembly rules , 2013, Proceedings of the National Academy of Sciences.

[51]  Miska Luoto,et al.  Horizontal, but not vertical, biotic interactions affect fine-scale plant distribution patterns in a low-energy system. , 2013, Ecology.

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

[53]  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.

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

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

[56]  Jorge Soberón,et al.  The presence–absence matrix reloaded: the use and interpretation of range–diversity plots , 2012 .

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

[58]  M. Cardillo,et al.  Phylogenetic structure of mammal assemblages at large geographical scales: linking phylogenetic community ecology with macroecology , 2011, Philosophical Transactions of the Royal Society B: Biological Sciences.

[59]  D. Caron,et al.  Marine bacterial, archaeal and protistan association networks reveal ecological linkages , 2011, The ISME Journal.

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

[61]  Dirk Husmeier,et al.  Inferring species interaction networks from species abundance data: A comparative evaluation of various statistical and machine learning methods , 2010, Ecol. Informatics.

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

[63]  Erik Meijaard,et al.  Phylogeny and co‐occurrence of mammal species on Southeast Asian islands , 2010 .

[64]  Steven J. Presley,et al.  A comprehensive framework for the evaluation of metacommunity structure , 2010 .

[65]  B. McGill.  Matters of Scale , 2010, Science.

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

[67]  Werner Ulrich,et al.  The empirical Bayes approach as a tool to identify non-random species associations , 2010, Oecologia.

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

[69]  A M Latimer,et al.  Hierarchical models facilitate spatial analysis of large data sets: a case study on invasive plant species in the northeastern United States. , 2009, Ecology letters.

[70]  D. Strayer,et al.  Usefulness of Bioclimatic Models for Studying Climate Change and Invasive Species , 2008, Annals of the New York Academy of Sciences.

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

[72]  B. Menge,et al.  Scale, Environment, and Trophic Status: The Context Dependency of Community Saturation in Rocky Intertidal Communities , 2006, The American Naturalist.

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

[74]  Spyros Sfenthourakis,et al.  Species co‐occurrence: the case of congeneric species and a causal approach to patterns of species association , 2006 .

[75]  Owen L. Petchey,et al.  Interaction strengths in food webs: issues and opportunities , 2004 .

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

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

[78]  Aaron M. Ellison,et al.  Assembly rules for New England ant assemblages , 2002 .

[79]  Nicholas J. Gotelli,et al.  SPECIES CO‐OCCURRENCE: A META‐ANALYSIS OF J. M. DIAMOND'S ASSEMBLY RULES MODEL , 2002 .

[80]  Mathew A. Leibold,et al.  Coherence, species turnover, and boundary clumping: Elements of meta-community structure , 2002 .

[81]  J. Leathwick,et al.  COMPETITIVE INTERACTIONS BETWEEN TREE SPECIES IN NEW ZEALAND'S OLD‐GROWTH INDIGENOUS FORESTS , 2001 .

[82]  Harold A. Mooney,et al.  Review Eradication: What Can Go Wrong Viewing Invasive Species Removal in a Whole-ecosystem Context , 2022 .

[83]  L. Brown,et al.  Interval Estimation for a Binomial Proportion , 2001 .

[84]  P. Chesson.  Mechanisms of Maintenance of Species Diversity , 2000 .

[85]  Uygar Özesmi,et al.  An artificial neural network approach to spatial habitat modelling with interspecific interaction , 1999 .

[86]  N. Gotelli,et al.  NULL MODELS IN ECOLOGY , 1996 .

[87]  J. Teugels.  Some representations of the multivariate Bernoulli and binomial distributions , 1990 .

[88]  Wirt Atmar,et al.  Nested subsets and the structure of insular mammalian faunas and archipelagos , 1986 .

[89]  Ilkka Hanski,et al.  Coexistence of Competitors in Patchy Environment , 1983 .

[90]  Thomas S. Whittam,et al.  Species Interactions and Community Structure in Alaskan Seabird Colonies , 1981 .

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

[92]  R. Levins.  Some Demographic and Genetic Consequences of Environmental Heterogeneity for Biological Control , 1969 .

[93]  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.

[94]  E. C. Pielou,et al.  The detection of different degrees of coexistence. , 1967, Journal of theoretical biology.

[95]  Z. Šidák.  Rectangular Confidence Regions for the Means of Multivariate Normal Distributions , 1967 .

[96]  E. Michael,et al.  Marine Ecology and the Coefficient of Association: A Plea in Behalf of Quantitative Biology , 1920 .

[97]  Stephen Alfred Forbes,et al.  On the Local Distribution of Certain Illinois Fishes: An Essay in Statistical Ecology , 1907 .