Ecological Forecasting Initiative 2019 Conference Speaker & Poster Abstracts May 13-15, 2019 AAAS Headquarters 1200 New York Ave, NW Washington, DC 20005 @eco4cast #efi2019 The Ecological Forecasting Initiative (EFI) is a broad, interdisciplinary effort aimed at promoting the use of forecasts to understand, manage, and conserve ecosystems and the services they provide. The EFI 2019 meeting is aimed at bringing together scientists, agencies, industry, and stakeholders to build a community of practice and advance research, applications, and collaboration around near-term (subdaily to decadal) ecological forecasts. EFI2019 is sponsored by the Alfred P. Sloan Foundation, the Frederick S. Pardee Center for the Study of the Longer-Range Future at Boston University, and the National Science Foundation’s Office of International Science and Engineering. @ecoforecast #efi2019 Speaker Abstracts Session 1: Theory and Synthesis Monday, May 13 9:00 am S1.1: Keynote Ecological forecasting: the role of observations in models David Schimel1 [email protected] 1NASA JPL [abstract] 2 @ecoforecast #efi2019 Session 1: Theory and Synthesis Monday, May 13 9:00 am S1.2: Lightning Talk Understanding the uncertainties in estimating post-fire recovery of biomass using the Ecosystem Demography (EDv2.2) model Karun Pandit1, Hamid Dashti1, Nancy F. Glenn1, Alejandro N. Flores1, Kaitlin C. Maguire2, Douglas J. Shinneman2, Gerald N. Flerchinger3, Aaron W. Fellows3 [email protected] 1Boise State University; 2U.S. Geological Survey; 3ARS Frequent wildfires in the sagebrush-steppe ecosystems in the Western United States lead to heavy biodiversity loss, and changes in vegetation composition. It is debated in literature whether fires are beneficial or not for maintaining sagebrush ecosystems, it is essential that we understand the impact of these fires in terms of recovery time and potential changes in vegetation composition. The objective of this study is to estimate post-fire recovery of vegetation in the Ecosystem Demography (EDv2.2) model and quantify uncertainties by comparing with satellite-derived information. This study builds upon previous work in biomass loss and post-fire recovery from the 2015 Soda Fire in the Great Basin, Western United States. We initialized EDv2.2 with the existing vegetation ecosystem using mean metrics based on a field inventory dataset along with fire disturbance from Landsat 8 data and the 2015 fire. We ran the EDv2.2 model for thirty years to observe changes in above ground biomass and PFT distributions in the study area through the years. We also compared overall biomass growth and its spatial patterns projected by EDv2.2 with relevant indices from satellite data from the year 2019. These included NDVI and GPP derived from the Landsat 8 data and PFT classifications based on AVIRIS-NG data from the study area. Preliminary results from EDv2.2 modeling shows spatial patterns of vegetation regrowth being significantly influenced by precipitation and elevation. We observed that the initially grass-dominated areas post-fire are gradually replaced by the shrub PFT in later years. We also found that the volume of biomass regrowth during initial four years post-fire from EDv2.2 matches well with that derived from Landsat 8 images. 3 @ecoforecast #efi2019 Session 1: Theory and Synthesis Monday, May 13 9:00 am S1.3: Lightning Talk Integrating evolutionary history into forecasts of species assembly Amanda Gallinat1, William D. Pearse1 [email protected] 1Utah State University As climate change alters habitats and species distributions, there is a strong need for predictive tools that describe where species will occur and potentially interact under future conditions. Species’ present-day ecological responses are constrained by their evolutionary history, so our understanding and forecasts of those responses may be improved with the use of phylogeny. Species’ phylogeny reflects their shared biogeography, the processes by which functional traits and environmental sensitivities evolved, and how environmentally constrained different groups of species are. With the overarching goal of integrating evolutionary history into forecasts of species assembly, we have been using novel eco-phylogenetic tools to analyze regional species occurrence data for plants, birds, and mammals collected by the National Ecological Observatory Network (NEON), Forest Inventory & Analysis, Breeding Bird Survey, and Thibault et al. 2011. We contrasted the relative importance of species’ traits, phylogeny, and environmental responses for predicting species occurrences in plants, birds, and mammals. Our preliminary results show that environmental tolerances are strongly conserved in all three groups; in other words, related species share similar environmental sensitivities, which could be leveraged to impute sensitivities for species that lack data. And while functional traits also exhibit phylogenetic signal in all taxa, we find that the signal of environmental tolerances is stronger than that of traits alone. By combining information about species’ evolution, functional traits, and present-day environmental responses, we can produce models that make more powerful predictions about the evolution of species’ niches, and their responses to future environmental change. 4 @ecoforecast #efi2019 Session 1: Theory and Synthesis Monday, May 13 9:00 am S1.4: Lightning Talk Estimating Body Temperature and Thermal Performance at fine spatial and temporal scales Francis Choi1, Tarik Gouiher1, Fernando Lima, Gil Rilov2, Rui Seabra, Brian Helmuth1 [email protected] 1Northeastern University; 2Israel Oceanographic & Limnological Institute The rocky intertidal is one of the most dynamic and thermally variable ecosystems, where the joint influences of solar radiation, temperature and topography can lead to differences of up to 20°C over centimeter scales. However, the ecological importance of this variation in the face of climate change remains poorly understood. Here, we present a novel technique for modelling microhabitat heterogeneity and patterns of thermal physiology among interacting organisms. We used drone photogrammetry to re-create virtual topographic maps at a resolution of 400 cm2, which are then fed as inputs to a heat budget model estimating hourly surface temperature. These body temperature layers are then converted to thermal performance layers for organisms using thermal performance curves, creating physiological “landscapes” that display spatially-explicit patterns of “microrefugia”. Our analyses show how nonlinear interactions between these layers lead to distinct predictions about organismal performance and survivorship from those made using any individual layer alone. For instance, thermal performance layers reveal that microrefugia are variable through time and space, showing how mobile species must continue to migrate between microhabitats in order to maintain optimal performance. This approach provides a method for exploring the role of micro-topographic variability in driving organismal vulnerability to environmental change. 5 @ecoforecast #efi2019 Session 1: Theory and Synthesis Monday, May 13 9:00 am S1.5: Lightning Talk Forecasting Population Impacts of Chronic Wasting Disease Alison Ketz1, Daniel J. Storm2, Michael Samuel1, Robin E. Russell3, Daniel P. Walsh3 [email protected] 1University of Wisconsin, Madison; 2Wisconsin Department of Natural Resources; 3U.S. Geological Survey, National Wildlife Health Center Forecasts of wildlife disease typically focus on predictions of geographic spread, temporal dynamics of disease transmission, and the impact of management actions on epidemiological processes. Forecasting the population impacts of an epidemic are further complicated by species- specific demography, inter-specific interactions of host and pathogen, genetics, and spatial- temporal factors influencing patterns of infection, recruitment, and mortality. Chronic wasting disease (CWD) is a contagious prion disease affecting four species of free-ranging and captive cervids in North America. Geographic detection and distribution of CWD noticeably increased after 2002, although the disease has been present in North America since the 1960s. The prolonged course of infection has led to lengthy epizootics that last for decades, making population effects difficult to determine. We are in development of a model to forecast population effects of CWD on white-tailed deer in Wisconsin. As prevalence and spread continue to accelerate, management actions to mitigate CWD impacts will be challenging, costly, and will likely require changes in how we manage cervid populations. 6 @ecoforecast #efi2019 Session 2: Decision Support Monday, May 13 10:45 am S2.1: Keynote Combining Forecasts Yael Grushka-Cockayne1, Jason Merrick2 [email protected] 1Harvard University, 2Virginia Commonwealth University Early research in forecast aggregation focused on Bayesian approaches that account for forecast accuracy and correlation among forecasts. Other research has examined why in practice such theoretically sound methods are often outperformed by simpler techniques such as the average. In recent years, the wisdom of the crowd literature has encouraged the exploration of alternative heuristics that performed better than even the simple average in some cases. In this talk, we will review alternative methods and consider how they are evaluated. We will provide guidance on when various methods perform well, and how one might use forecast aggregation in practice. 7 @ecoforecast