OOS 7-10 - Towards a theory of ecological catastrophes based on cross-scale interactions: Insights from long-term data

Tuesday, August 8, 2017: 11:10 AM
Portland Blrm 254, Oregon Convention Center
Debra P. C. Peters1, Luis Rodriguez2, D. Scott McVey3, Emile H. Elias4, Angela Pelzel-McCluskey5, Justin D. Derner6, Jin Yao1, Steven J. Pauszek2, T. Scott Schrader7 and Nathan Burruss1, (1)Jornada LTER Program, USDA ARS, Las Cruces, NM, (2)ARS, USDA, Orient Point, NY, (3)ARS, USDA, Manhattan, KS, (4)ARS Jornada Experimental Range, USDA SW Climate Hub, Las Cruces, NM, (5)APHIS, USDA, Fort Collins, CO, (6)USDA-ARS, Rangeland Resources Research Unit, Cheyenne, WY, (7)ARS, USDA, Las Cruces, NM

The availability of long-term environmental data for many variables at multiple scales across large spatial extents provides opportunities for novel questions to be addressed as well as new insights into unresolved questions. In addition, ecologists are being asked to apply their expertise and understanding of these data to questions that have traditionally been the domain of other disciplines. The problem is: Are ecologists ready for this challenge? Will our current theories and models (conceptual, analytical, and numerical) be sufficient? Here we focus on some of the most vexing questions that ecologists are being asked to help address: the ones that involve regional- to continental-scale dynamics that often lead to catastrophes and are driven by interactions among processes occurring at multiple spatial and temporal scales. We argue that these cross-scale interactions cannot be addressed using either top-down or bottom-up scaling approaches. We are developing a strategic framework based on pattern-process integration and interactions across scales with human and machine learning to identify, harmonize, analyze, and interpret big data involving a variety of variables from online and local sources to meet these challenges.


We illustrate our framework with questions related to drivers of spatial and temporal patterns in the invasion by vesicular stomatitis virus (VSV), a vector-borne, zoonotic RNA virus that affected > 1500 livestock premises from 2004-2016 across 10 states in the western US. In addition to incidence and phylogenetic data, we obtained online data for 9 environmental drivers and host density data. For each driver, we selected variables for analysis based on hypothesized relationships with disease processes. The geo-referenced maps of the >50 variables were harmonized in time and space. Multivariate analyses of the resulting data cube showed that the initial incursion of VSV from Mexico into the southwestern US in 2 separate years (2004, 2014) occurred under similar conditions (low surface water in summer and fall, above-average summer vegetation, below-average winter precipitation) that were different from conditions when VSV expanded throughout the 10-state region (2005, 2015: below-average summer temperatures on locations containing soils with high water holding capacity). Our strategic framework that builds on big data technologies to develop testable hypotheses about VSV can be applied to other catastrophic events that are temporally variable and start at small spatial scales, but propagate to influence large spatial extents.