ESA presentations 2020

ESA presentations 2020

here are abstracts submitted by the lab to the 2020 annual meeting of the Ecological Society of America;

Capturing emergent interactions that govern food web dynamics with climate change

Tong Qiu1, C. Lane Scher2, Margaret E. Swift1, Jennifer J. Swenson1 and James S. Clark3, (1)Nicholas School of the Environment, Duke University, Durham, NC, (2)University Program in Ecology, Duke University, Durham, NC, (3)Statistical Science, Duke University, Durham, NC

Background/Question/Methods Observational studies have not yet shown that environmental variables can explain pervasive non-linear patterns of species abundance, because those patterns could result from (indirect) interactions with other species (e.g., competition), and models only estimate direct responses. We developed a bio-physical approach to quantify the environment-species interactions (ESI) that govern community change. By embedding dynamic ESI within a time-series framework that admits data for species gathered on different scales, we quantify responses that are induced indirectly through ESI. A hierarchical framework provides probabilistic uncertainty in parameters, model specification, and data. Simulation demonstrates that effects of environment on movement, population growth, and species interactions are needed for accurate interpretation. Applications include published data sets on lake food webs and the breeding bird survey (BBS). For NEON ground beetles, small mammals, and bird data we integrate remote sensing, including lidar and hyperspectral data from the airborne observation platform.

Results/Conclusions Analytical analysis demonstrates how non-linear responses arise even when all direct species responses to environment are linear. Applications to experimental lakes and BBS yield contrasting estimates of ESI. In closed lakes, interactions involving phytoplankton and their zooplankton grazers play a large role. By contrast, ESI are weak in BBS, as expected where year-to-year movement degrades the link between local population growth and previous species abundances. In both cases, non-linear responses are induced by interactions between species. Stability analysis shows stability in the closed-system lakes and instability in BBS. NEON analysis is underway to evaluate how canopy condition and habitat structure mediate food web interactions. The probabilistic framework also has direct application to conservation planning that must weigh risk assessments for entire habitats and communities against competing interests.


Is citizen science a reliable source of big data? Identifying biases in eBird records

C. Lane Scher1,2 and James S. Clark1,2,3, (1)Nicholas School of the Environment, Duke University, Durham, NC, (2)University Program in Ecology, Duke University, Durham, NC, (3)Statistical Science, Duke University, Durham, NC

Background/Question/Methods Understanding how biodiversity is changing requires a capacity to combine information from different sources. Opportunistic citizen science observations are an increasingly important source of data for continent-scale studies of global change and biodiversity. eBird is one of the most successful and popular citizen scientist projects, with over 100 million birds recorded each year by users with varying degrees of experience and species identification skills. By contrast, the Breeding Bird Survey (BBS) is systematic, completed annually at the same times and locations, and is conducted by trained personnel. To determine if these data sources could be integrated, we quantified the differences between them. We compared eBird observations to the more rigorously collected BBS records for 300 species by aggregating eBird checklists spatially near (within 40 km of) BBS routes and at the same time of year. First, we fitted the bird count data from both sources with environmental covariates using a Generalized Joint Attribute Model (GJAM), with data source as a factor covariate and observation effort as observation minutes. The fitted coefficients for the source factor are hereafter termed the “eBird effect”. We then examined the difference between counts per effort from eBird records and from BBS records at each site (“residuals”), and determined the relationship between residuals and observation effort. We used species trait data to determine whether reporting biases might be anticipated from species traits.

Results/Conclusions We found that overall, eBird users underreport most species relative to BBS, but increased observation effort minimizes these effects. Of the 132 species with eBird effect estimates whose credible intervals did not cross zero, 91 (69%) were negative. These estimates were negatively correlated with commonness (R = -0.183), indicating that common species are most underreported. We also found that for 204 species (68%) the differences between the two data sources decreased with increased observation effort. This pattern was also influenced by commonness: there was a positive correlation between commonness and residual-effort relationship (R = 0.162), meaning that for more common species, increased effort of the eBird observation actually increased the difference between the two data sources. Overall, these results indicate that eBird data contain biases, these biases vary by species commonness, and some of these biases can be overcome with increased effort. Because eBird data are often used to evaluate species populations and develop management strategies, particularly for rare species, understanding these biases is crucial.


Understanding the diverse responses of South African savanna communities to climate change

Margaret E. Swift1, Steven I. Higgins2 and James S. Clark1, (1)Nicholas School of the Environment, Duke University, Durham, NC, (2)Dept. of Plant Ecology, University of Bayreuth, Bayreuth, Germany

Background/Question/Methods The Kruger National Park (KNP) in northeast South Africa harbors a unique assemblage of Pleistocene megafauna, increasingly threatened by protracted drought. Managers are concerned that climate change has destabilized this diverse food web by reorganizing competition for surface water resources and shifting ranges. Such destabilization could appear as responses to vegetation and rainfall variation. In particular, locally rare antelope populations (tsessebe Damaliscus lunatus, eland Taurotragus oryx, sable Hippotragus niger, roan Hippotragus equinus) have plummeted over the past six decades, bringing about a decline in the biodiversity that is a cornerstone of tourism and ecosystem resilience. Management of a diverse, destabilized community necessitates an understanding of how species individually and collectively respond to their environment. Such communities susceptible to disturbance are a challenge for traditional species distribution models (SDMs), whose static data lose much of the information present in increasingly available long-term datasets. We utilize decades of census, vegetation, and climate data from the KNP in a dynamic generalized joint attribute model, GJAMTime, extending a traditional Lotka-Volterra framework to include density-independent growth and interspecific density dependence. Our assessment of how changing vegetation, surface water, and biotic interactions have influenced biodiversity in the KNP provides a framework for future adaptive management decisions.

Results/Conclusions Outputs consistent with prior knowledge of biotic and environmental interactions confirm that our model works well in predicting species distribution and behavior. Our model estimated similar responses to environmental variables for species known to respond similarly (i.e. blue wildebeest Connochaetes taurinus and plains zebra Equus quagga). The model attributes little variation in species abundance to movement, which is consistent with almost-full fencing around the park. We found that rare antelope distributions are mostly attributable to density-independent growth. Roan and eland have negative responses to temporal anomalies in grass biomass, and roan and tsessebe respond positively to nutrient-rich clay soils. Sable, however, responded positively to vegetation anomalies and negatively to clays, which suggests an affinity for conditions not shared by the other three. We find that climate change does affect this savanna community, but that consequences for rare antelope are not uniform, indicating a need for more targeted, adaptive management practices.


North American tree migration paced by fecundity and recruitment through contrasting mechanisms east and west

Shubhi Sharma1, Robert Andrus2, Mélaine Aubry-Kientz3, Yves Bergeron4, Michal Bogdziewicz5, Don C. Bragg6, Natalie L. Cleavitt7, Susan Cohen8, Elizabeth E. Crone9, Adrian Das10, Michael C. Dietze11, Timothy J. Fahey12, Istem Fer13, Jerry Franklin14, Catherine A. Gehring15, Greg Gilbert16, Katie Greenberg17, Qinfeng Guo18, Janneke Hille Ris Lambers19, Ines Ibanez20, Jill Johnstone21, Christopher L. Kilner22, Johannes M. H. Knops23, Walter D. Koenig24, Jalene M. LaMontagne25, James A. Lutz26, Jordan Luongo27, Diana S. Macias28, Eliot McEntire29, Yassine Messaoud30, Christopher M. Moore31, Emily V. Moran32, Orrin Myers33, Jonathan A. Myers34, Chase Nunez35, Robert R. Parmenter36, Sam Pease22, Miranda D. Redmond37, Chantal D. Reid38, Kyle Rodman39, C. Lane Scher40, William H. Schlesinger41, Amanda M. Schwantes42, Michael A. Steele43, Nathan L. Stephenson44, Samantha Sutton45, Jennifer J. Swenson46, Margaret Swift47, Thomas T Veblen48, Amy V. Whipple49, T.G. Whitham49, Andreas P. Wion50, Kai Zhu51, Roman I. Zlotin52 and James S. Clark53, (1)Nicholas School of Environment, Duke University, Durham, NC, (2)Colorado University, (3)school of natural sciences, UC Merced, Merced, CA, (4)Forest Research Institute, University of Quebec in Abitibi-Temiscamingue, Rouyn-Noranda, QC, Canada, (5)Department of Systematic Zoology, Adam Mickiewicz University, Poznan, Poland, (6)USDA Forest Service, Southern Research Station, Monticello, AR, (7)Natural Resources, Cornell University, Ithaca, NY, (8)University of North Carolina, (9)Department of Biology, Tufts University, Medford, MA, (10)USGS Western Ecological Research Center, Three Rivers, CA, (11)Earth and Environment, Boston University, Boston, MA, (12)Department of Natural Resources, Cornell University, Ithaca, NY, (13)Finnish Meteorological Institute, Helsinki, MA, Finland, (14)Forest Resources, University of Washington, WA, (15)Northern Arizona University, Flagstaff, AZ, (16)University California Santa Cruz, (17)Bent Creek Experimental Forest, USDA Forest Service, Asheville, NC, (18)Eastern Forest Environmental Threat Assessment Center, USDA Forest Service – Southern Research Station, Asheville, NC, (19)Department of Biology, University of Washington, Seattle, WA, (20)University of Michigan, (21)Biology, University of Saskatchewan, Saskatoon, SK, Canada, (22)Duke University, Durham, NC, (23)School of Biological Sciences, University of Nebraska, Lincoln, Lincoln, NE, (24)Lab of Ornithology, Cornell University, Ithaca, NY, (25)Department of Biological Sciences, DePaul University, Chicago, IL, (26)Department of Wildland Resources, and the Ecology Center, Utah State University, Logan, UT, (27)Duke University, (28)Department of Biology, University of New Mexico, Albuquerque, NM, (29)Pacific Forestry Centre, (30)University of Quebec, (31)Biology, Case Western Reserve University, Cleveland, OH, (32)School of Natural Sciences, UC Merced, Merced, CA, (33)University of New Mexico, Albuquerque, NM, (34)Department of Biology, Washington University in St. Louis, St. Louis, MO, (35)Universität Leipzig, German Centre for Integrative Biodiversity Research (iDiv), (36)Valles Caldera National Preserve, National Park Service, Jemez Springs, NM, (37)Forest and Rangeland Stewardship, Colorado State University, Fort Collins, CO, (38)Nicholas School of the Environment and Earth Sciences, Duke University, Durham, NC, (39)University of Colorado, Boulder, CO, (40)University Program in Ecology, Duke University, Durham, NC, (41)Cary Institute of Ecosystem Studies, Millbrook, NY, (42)Ecology & Evolutionary Biology, University of Toronto, Toronto, ON, Canada, (43)Biology Dept., Wilkes University, Wilkes-Barre, PA, (44)Sequoia and Kings Canyon Field Station, United States Geological Survey, Three Rivers, CA, (45)Biological Sciences, Duke University, Durham, NC, (46)Nicholas School of the Environment, Duke University, Durham, NC, (47)Nicholas School for the Environment, Duke University, Durham, NC, (48)Geography, University of Colorado, CO, (49)Biological Sciences, Northern Arizona University, Flagstaff, AZ, (50)Department of Forest and Rangleland Stewardship, Colorado State University, Fort Collins, CO, (51)Environmental Studies, University of California, Santa Cruz, Santa Cruz, CA, (52)Geography, Indiana University, Bloomington, IN, (53)Statistical Science, Duke University, Durham, NC

Background/Question/Methods Global forest diebacks are the beginnings of change that will be controlled by tree migration, which combines two uncertain processes, tree fecundity and recruitment. Knowledge of how, and how fast, tree migration can proceed is critical for adaptive management of forest resources and conservation efforts. The initial stage of seed production is erratic and poorly observed, with most studies limited to few trees of few species on few sites. At the next stage, tree recruitment is typically too sporadic to characterize at landscape scales. Neither seed production nor seedling recruitment have been quantified or linked to climate and habitat variables at scales needed to evaluate the changes happening now or to anticipate the diversity and structure of 21st century forests. As part of the masting inference and forecasting (MASTIF) project, we synthesized continental-scale data for tree fecundity gathered over the last half century and combined it with forest inventories to connect adult trees (basal area) to i) fecundity (seeds per basal area) and ii) recruitment (recruits per seed). A dynamic model fitted to > 107 tree years of fecundity data provided estimates tree-by-year fecundity. A predictive distribution for the continent combines the fitted model with > 105 trees from Forest Inventory Analysis (FIA), Canadian National Forest Inventory (CNFI) and the National Ecological Observatory Network (NEON).

Results/Conclusions Results show continent-wide tree migration as a balance between regional shifts in fecundity that can diverge from conditions that favour establishment, with clear differences in eastern and western North America. In moist eastern states, the geographic centers for fecundity are most commonly displaced south of tree basal area for the same species. This relationship would be expected if optimal conditions for seed production lie to the south of optimal conditions for growth and survival, despite potential benefits of warming poleward. In the dry west and north-central, fecundity is for most species displaced northwest of tree basal area, as would be expected if the high-rainfall NW is predisposed to lead migration as the continent warms. The east-west contrast diminishes at the transition from fecundity to recruits per seed, which tends to be shifted north in both regions. The net continent-wide migration by contrasting east-west controls highlight interactions, with fecundity primed to lead tree migration in the west, and fecundity slowing progress in the east. The possibility of fecundity limitation offers one explanation for migration lag in species expected to track climate warming by expanding poleward.


Interactions that control the pace of forest change in North America

James S. Clark, Statistical Science, Duke University, Durham, NC, Robert A. Andrus, Department of Geography, University of Colorado, Boulder, CO, Mélaine Aubry-Kientz, school of natural sciences, UC Merced, Merced, CA, Yves Bergeron, Forest Research Institute, University of Quebec in Abitibi-Temiscamingue, Rouyn-Noranda, QC, Canada, Michal Bogdziewicz, Department of Systematic Zoology, Adam Mickiewicz University, Poznan, Poland, Don C. Bragg, USDA Forest Service, Southern Research Station, Monticello, AR, Natalie L. Cleavitt, Natural Resources, Cornell University, Ithaca, NY, Susan Cohen, University of North Carolina, Adrian Das, USGS Western Ecological Research Center, Three Rivers, CA, Michael C. Dietze, Earth and Environment, Boston University, Boston, MA, Timothy J. Fahey, Department of Natural Resources, Cornell University, Ithaca, NY, Istem Fer, Finnish Meteorological Institute, Helsinki, MA, Finland, Jerry F. Franklin, School of Environmental and Forest Sciences, University of Washington, Seattle, WA, Cathrine A. Gehring, Biology, Northern Arizona University, Flagstaff, AZ, Gregory S. Gilbert, Environmental Studies, University of California Santa Cruz, Santa Cruz, CA, Cathryn H. Greenberg, Bent Creek Experimental Forest, USDA Forest Service, Southern Research Station, Asheville, NC, Qinfeng Guo, Eastern Forest Environmental Threat Assessment Center, USDA Forest Service – Southern Research Station, Asheville, NC, Janneke HilleRisLambers, Department of Biology, University of Washington, Seattle, WA, Ines Ibanez, University of Michigan, Jill F. Johnstone, Department of Biology, University of Saskatchewan, Saskatoon, SK, Canada, Christopher L. Kilner, Duke University, Durham, NC, Johannes M. H. Knops, School of Biological Sciences, University of Nebraska, Lincoln, Lincoln, NE, Walter D. Koenig, Lab of Ornithology, Cornell University, Ithaca, NY, Jalene M. LaMontagne, Department of Biological Sciences, DePaul University, Chicago, IL, Jordan Luongo, Duke University, James A. Lutz, Department of Wildland Resources, and the Ecology Center, Utah State University, Logan, UT, Diana S. Macias, Department of Biology, University of New Mexico, Albuquerque, NM, Eliot McEntire, Pacific Forestry Centre, Yassine Messaoud, University of Quebec, Christopher M. Moore, Biology, Case Western Reserve University, Cleveland, OH, Emily V. Moran, School of Natural Sciences, UC Merced, Merced, CA, Jonathan A. Myers, Department of Biology, Washington University in St. Louis, St. Louis, MO, Orrin Myers, University of New Mexico, Albuquerque, NM, Chase Nunez, Universität Leipzig, German Centre for Integrative Biodiversity Research (iDiv), Robert R. Parmenter, Valles Caldera National Preserve, National Park Service, Jemez Springs, NM, Ian S. Pearse, United States Geological Survey, Fort Collins, CO, Miranda D. Redmond, Forest and Rangeland Stewardship, Colorado State University, Fort Collins, CO, Chantal D. Reid, Nicholas School of the Environment and Earth Sciences, Duke University, Durham, NC, Kyle Rodman, University of Colorado, Boulder, CO, C. Lane Scher, University Program in Ecology, Duke University, Durham, NC, William H. Schlesinger, Cary Institute of Ecosystem Studies, Millbrook, NY, Amanda M. Schwantes, Ecology & Evolutionary Biology, University of Toronto, Toronto, ON, Canada, Shubhi Sharma, Nicholas School of Environment, Duke University, Durham, NC, Michael A. Steele, Biology Dept., Wilkes University, Wilkes-Barre, PA, Nathan L. Stephenson, Sequoia and Kings Canyon Field Station, United States Geological Survey, Three Rivers, CA, Samantha Sutton, Biological Sciences, Duke University, Durham, NC, Jennifer J. Swenson, Nicholas School of the Environment, Duke University, Durham, NC, Margaret Swift, Nicholas School for the Environment, Duke University, Durham, NC, Thomas T Veblen, Geography, University of Colorado, CO, Amy V. Whipple, Biological Sciences, Northern Arizona University, Flagstaff, AZ, Thomas G. Whitham, Northern Arizona University, Flagstaff, AZ, Andreas P. Wion, Department of Forest and Rangleland Stewardship, Colorado State University, Fort Collins, CO, Kai Zhu, Environmental Studies, University of California, Santa Cruz, Santa Cruz, CA and Roman I. Zlotin, Geography, Indiana University, Bloomington, IN

Background/Question/Methods: Forests can continue to deliver ecosystem services if tree fecundity keeps pace with 21st century climate change. Not only is fecundity unknown at the continental scale, but so too is its rate of change (ROC). Is seed production responding to climate change and, if so, how? At least two forces could be controlling trends in forest regeneration and the capacity to migrate, i) climate change, and ii) geographic variation in stand growth and structure. Effective management will depend on an understanding of both effects. For example, warming during vulnerable spring months might increase reproductive success. If fecundity is limited by thermal energy that coincides with moisture availability, then warming might benefit reproduction only in regions where moisture remains abundant. At the same time, indirect effects could dominate the response if changing growth moves stands into more or less fecund size classes. We combined an unprecedented synthesis of North American fecundity data from the masting inference and forecasting (MASTIF) collaboration with a novel dynamic bio-physical model to infer continental ROC in fecundity. The biophysical model captures the direct and indirect effects of climate change, including the interactions between them (e.g., size differences matter most in dry climates, or, moisture limitation is most important for small trees).

Results/Conclusions: Fecundity, discussed here as seeds/ha/yr, is highest in the midwest (MW) and west (W) owing to high seeds per basal area in the MW and high basal area of trees in the W. However, fecundity is increasing throughout most of the continent, with highest ROC in the north (N) and northwest (NW). Exceptions include parts of the south (S) and southwest (SW). The full effect of climate change is accelerating ROC in the MW and SW both directly and through effects on growth. Two processes are offsetting a positive effect of warming springs. First, a positive effect on growth moves stands into larger, less fecund size classes in western Canada, the Rockies, and much of the East. However, positive effects on growth do not transfer to increased ROC in much of the W and NE. Despite positive ROC throughout most of the continent, climate change is reducing ROC in the East and NW. Taken together, the full effect of climate change on ROC is primarily positive, despite negative indirect effects. We discuss the implications of species-level differences for migration potential and food web dynamics.


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