COS 45-6 - Inherent conditions determine collapses: Nutrient regimes define the grazing resilience of macroalgal communities

Wednesday, August 10, 2016: 9:50 AM
209/210, Ft Lauderdale Convention Center
Jordi Boada1, Rohan Arthur1,2, David Alonso1, Jordi Pages1,3, Giulia Ceccherelli4, Luiggi Piazzi4, Albert Pessarrodona1, Silvia Oliva4, Javier Romero5 and Teresa Alcoverro1,2, (1)Center for Advanced Studies (CEAB-CSIC), Spanish Council for Scientific Research, Blanes, Spain, (2)Nature Conservation Foundation, Mysore, India, (3)School of Ocean Sciences, Bangor University, Menai Bridge, United Kingdom, (4)Dipartimento di Scienze della Natura e del Territorio, Università di Sassari, Sassari, Italy, (5)Ecologia, Universidad de Barcelona, Barcelona, Spain

Ecosystems characterized by non-linear dynamics are inherently surprising, making it very difficult to predict where state-changing thresholds lie. Sudden, long-lasting regime shifts have been reported in diverse marine systems around the world. Unpacking the mechanisms underlying these state shifts can help considerably reduce this unpredictability. We examined how differences in nutrient regimes mediated the capacity of temperate macrophyte communities to sustain sea urchin grazing. We explore this question with a combination of field work, controled experiments and a non-linear model.  


We convincingly show that in relatively nutrient-rich conditions, macrophyte systems are considerably more resilient to urchin grazing, shifting to barrens beyond 1800 g/m2 (urchin biomass), more than twice the threshold biomass of more nutrient-poor conditions. The mechanisms driving these differences are linked to how nutrients mediate urchin foraging and algal growth: controlled experiments showed that low nutrient conditions trigger compensatory feeding in urchins and significantly reduce plant growth. These mechanisms act together to halve macrophyte community resilience in relatively oligotrophic conditions. These findings match perfectly the predictions of a consumer-resource mathematical model. Understanding how context-specific conditions modify non-linear ecosystem dynamics can significantly improve our ability to predict where and why ecosystem thresholds occur.