SIMPLE MODELS THAT CAPTURE THE COMPLEXITY OF MULTI-SPECIES COEXISTENCE

UNDERSTANDING BIODIVERSITY MAINTENANCE IS CENTRAL TO ECOLOGY, ESPECIALLY ON THE FACE OF HUMAN-INDUCED ENVIRONMENTAL CHANGE AND THE ALARMING RATES OF BIODIVERSITY LOSS. WE HAVE MADE GREAT PROGRESS IN BUILDING SOLID MATHEMATICAL MODELS ABLE TO PREDICT COEXISTENCE AMONG INTERACTING SPECIES ACROSS TROPHIC LEVELS. THESE ADVANCES INCLUDE RECENT CONCEPTUAL AND MATHEMATICAL TOOLBOXES DEVELOPED BY OUR GROUP ALLOWING THE SIMULTANEOUS ASSESSMENT OF COEXISTENCE ON COMPLETE COMMUNITIES COMPOSED BY SEVERAL TROPHIC LEVELS, FOR EXAMPLE BETWEEN PLANTS, POLLINATORS, AND HERBIVORES. HOWEVER, THE EMPIRICAL EVALUATION OF THIS THEORETICAL FRAMEWORK HAS PROVED TO BE MORE CHALLENGING THAN EXPECTED FOR TWO REASONS. FIRST, THERE IS A PAUCITY OF DATASETS MEASURING MULTITROPHIC INTERACTIONS FOR COMPLETE COMMUNITIES INTEGRATED BY SEVERAL TYPES OF INTERACTIONS (E.G INCLUDING COMPETITION, PREDATION, POLLINATION OR PARASITISM). SECOND, THE CURRENT COEXISTENCE MODELS ARE COMPLEX AND THE NUMBER OF PARAMETERS TO ESTIMATE GROWS EXPONENTIALLY WITH THE NUMBER OF SPECIES IN THE COMMUNITY, MAKING THEM IMPRACTICAL FOR REAL-WORLD COMMUNITIES. TO SOLVE THIS CONUNDRUM, WE NEED TO FIND NEW WAYS TO RECONCILE THE POWER OF LARGE DATASETS WITH MODELS ROOTED IN SOLID THEORY. THE USE OF MACHINE LEARNING TECHNIQUES HAS REVOLUTIONIZED THE PREDICTIVE ABILITY OF SEVERAL COMPLEX PROBLEMS BY LEARNING PATTERNS FROM DATA, BUT MACHINE LEARNING ALGORITHMS ARE TRADITIONALLY NON-INTERPRETABLE, AND HENCE DISCONNECTED FROM THEORY. HERE WE PROPOSE TO USE IN-DEVELOPMENT RULE-BASED ALGORITHMS TO SIMPLIFY PARAMETER ESTIMATION WITHOUT LOSING THE INTERPRETABILITY. IN ADDITION, WE WILL COMPLETE TWO UNIQUE HIGHLY RESOLVED EMPIRICAL MULTI-TROPHIC DATASETS COMPRISING COMPLETE COMMUNITIES IN SPAIN AND CANADA. TO TIGHT TOGETHER DATA AND MODELS, WE CHOOSE A KEY QUESTION AT THE FOREFRONT OF COEXISTENCE THEORY: CAN COMPUTER TECHNIQUES HELP PREDICTING THE SPECIES INTERACTION STRUCTURE THAT ENHANCES MULTI-SPECIES COEXISTENCE?