SPECIES: A platform for the exploration of ecological data

Abstract The modeling of ecological data that include both abiotic and biotic factors is fundamental to our understanding of ecosystems. Repositories of biodiversity data, such as GBIF, iDigBio, Atlas of Living Australia, and SNIB (Mexico's National System of Biodiversity Information), contain a great deal of information that can lead to knowledge discovery about ecosystems. However, there is a lack of tools with which to efficiently extract such knowledge. In this paper, we present SPECIES, an open, web‐based platform designed to extract implicit information contained in large scale sets of ecological data. SPECIES is based on a tested methodology, wherein the correlations of variables of arbitrary type and spatial resolution, both biotic and abiotic, discrete and continuous, may be explored from both niche and network perspectives. In distinction to other modeling systems, SPECIES is a full stack exploratory tool that integrates the three basic components: data (which is incrementally growing), a statistical modeling and analysis engine, and an interactive visualization front end. Combined, these components provide a powerful tool that may guide ecologists toward new insights. SPECIES is optimized to support fast hypothesis prototyping and testing, analyzing thousands of biotic and abiotic variables, and presenting descriptive results to the user at different levels of detail. SPECIES is an open‐access platform available online (http://species.conabio.gob.mx), that is, powerful, flexible, and easy to use. It allows for the exploration and incorporation of ecological data and its subsequent integration into predictive models for both potential ecological niche and geographic distribution. It also provides an ecosystemic, network‐based analysis that may guide the researcher in identifying relations between different biota, such as the relation between disease vectors and potential disease hosts.

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

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

[3]  V. Sánchez‐Cordero,et al.  Can You Judge a Disease Host by the Company It Keeps? Predicting Disease Hosts and Their Relative Importance: A Case Study for Leishmaniasis , 2016, PLoS neglected tropical diseases.

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

[5]  Christopher R. Stephens,et al.  Using Biotic Interaction Networks for Prediction in Biodiversity and Emerging Diseases , 2008, PloS one.

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

[7]  Werner Ulrich,et al.  A comprehensive framework for the study of species co‐occurrences, nestedness and turnover , 2017 .

[8]  Christopher R. Stephens,et al.  Bayesian Inference of Ecological Interactions from Spatial Data , 2017, Entropy.

[9]  C. González-Salazar,et al.  Constructing Ecological Networks: A Tool to Infer Risk of Transmission and Dispersal of Leishmaniasis , 2012, Zoonoses and public health.

[10]  Luis A. Bojórquez-Tapia,et al.  Identifying Conservation Priorities in Mexico Through Geographic Information Systems and Modeling , 1995 .

[11]  Raúl Sierra-Alcocer,et al.  Exploratory analysis of the interrelations between co-located boolean spatial features using network graphs , 2012, Int. J. Geogr. Inf. Sci..

[12]  Pedro Jordano,et al.  Interaction frequency as a surrogate for the total effect of animal mutualists on plants , 2005 .

[13]  E. Connor,et al.  Interspecific competition and species co - occurrence patterns on islands: null models and the evalu , 1983 .

[14]  Jorge Soberón,et al.  The use of specimen-label databases for conservation purposes: an example using Mexican Papilionid and Pierid butterflies , 2000, Biodiversity & Conservation.

[15]  Tim Sutton,et al.  How Global Is the Global Biodiversity Information Facility? , 2007, PloS one.

[16]  F. Palomares,et al.  Jaguar (Panthera onca) and puma (Puma concolor) diets in Quintana Roo, Mexico , 2018 .

[17]  Werner Ulrich,et al.  A consumer's guide to nestedness analysis , 2009 .

[18]  Kevin J. Gaston,et al.  Ecogeographical rules: elements of a synthesis , 2008 .

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

[20]  Jorge Soberón Grinnellian and Eltonian niches and geographic distributions of species. , 2007, Ecology letters.

[21]  José Sarukhán,et al.  Generating intelligence for decision making and sustainable use of natural capital in Mexico , 2016 .

[22]  D. Boakye,et al.  40 Years of the APOC Partnership , 2015, PLoS neglected tropical diseases.

[23]  J. Nabout,et al.  Trends and biases in global scientific literature about ecological niche models. , 2015, Brazilian journal of biology = Revista brasleira de biologia.

[24]  J. Morrone,et al.  Understanding transmissibility patterns of Chagas disease through complex vector–host networks , 2017, Parasitology.

[25]  V. Sánchez‐Cordero,et al.  Leishmania (L.) mexicana Infected Bats in Mexico: Novel Potential Reservoirs , 2015, PLoS neglected tropical diseases.

[26]  A. Peterson,et al.  INTERPRETATION OF MODELS OF FUNDAMENTAL ECOLOGICAL NICHES AND SPECIES' DISTRIBUTIONAL AREAS , 2005 .

[27]  Joel E. Cohen,et al.  Food web patterns and their consequences , 1991, Nature.

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

[29]  Inferring Competition from Biogeographic Data: A Reply to Wright and Biehl , 1984, The American Naturalist.

[30]  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.

[31]  Earl D. McCoy,et al.  Some Observations on the Use of Taxonomic Similarity in Large-Scale Biogeography , 1987 .

[32]  Christopher R. Stephens,et al.  When is the Naive Bayes approximation not so naive? , 2018, Machine Learning.

[33]  Michael E. Gilpin,et al.  Factors contributing to non-randomness in species Co-occurrences on Islands , 2004, Oecologia.

[34]  P. Marquet,et al.  Comparing the relative contributions of biotic and abiotic factors as mediators of species’ distributions , 2013 .

[35]  Thiago F. Rangel,et al.  Labeling Ecological Niche Models , 2012 .

[36]  Harry Zhang,et al.  Naive Bayes for optimal ranking , 2008, J. Exp. Theor. Artif. Intell..

[37]  P. Zwartjes,et al.  PATTERNS OF CAVITY-ENTRANCE ORIENTATION BY GILDED FLICKERS (COLAPTES CHRYSOIDES) IN CARDON CACTUS , 1998 .

[38]  M. Araújo,et al.  Five (or so) challenges for species distribution modelling , 2006 .

[39]  A. Peterson,et al.  No silver bullets in correlative ecological niche modelling: insights from testing among many potential algorithms for niche estimation , 2015 .

[40]  S. Carpenter,et al.  Stability and Diversity of Ecosystems , 2007, Science.