James Grace, Ph.D.
James Grace is a Senior Research Scientist at the USGS Wetland and Aquatic Research Center.
BACKGROUND
2015 - present Senior Research Scientist. U.S. Geological Survey, ST
2002 - 2014 Senior Research Ecologist, U.S. Geological Survey, GS-15
1993 - 2019 Adjunct Professor, Department of Biology, University of Louisiana
2002 – 2005 Affiliate Faculty, School of Renewable Natural Resources, LSU
1992 - 2002 Research Ecologist, U.S. Geological Survey, Biological Division
1990 - 1993 Professor, Department of Botany, Louisiana State University
1985 - 1990 Associate Professor, Department of Botany, Louisiana State Univ.
1989 Visiting Professor, Louisiana Universities Marine Consortium
1986 Visiting Scientist, Div. Wildlife, CSIRO, Darwin, Australia
1980‑1985 Assistant Professor, Dept. Botany and Microbiology, Univ. Arkansas summer
After graduate school, he held faculty positions at the University of Arkansas and Louisiana State University, where he reached the level of Full Professor. In 2000, he received the millennium Meritorious Research Award from the Society of Wetland Scientists and in 2003 received the National Science Excellence Award from the U.S. Geological Survey. He was selected to be a Fellow of the Ecological Society of America and promoted to the Senior Scientist ranks in 2014. Since 2019 he has been designated as a ‘Highly-Cited Researcher’ by the Web of Science in recognition of his scientific impact during the past decade. In 2021 he received the Presidential Rank Award, which is given out by the President of the United States and is the highest performance award given to career senior scientists and administrators. He has published over 200 papers and reports, including 3 books, one on competitive interactions, one on community analysis, and one on structural equation modeling. As of 2020, Grace has given over 200 invited lectures and workshops in 9 countries during his career.
For more information, search 'Jim Grace USGS'.
Education and Certifications
Ph.D., Michigan State University
M.S., Clemson University
B.S., Biology, Presbyterian College
Science and Products
Instrumental variable methods in structural equation models
Biodiversity effects on grape quality depend on variety and management intensity
A graphical causal model for resolving species identity effects and biodiversity–ecosystem function correlations: comment
Hurricane Sandy effects on coastal marsh elevation change
Climate and local environment structure asynchrony and the stability of primary production in grasslands
A 'weight of evidence' approach to evaluating structural equation models
Structural equation modeling
Conceptual frameworks
Scientist’s guide to developing explanatory statistical models using causal analysis principles
The importance of natural versus human factors for ecological conditions of streams and rivers
Climatic controls on the distribution of foundation plant species in coastal wetlands of the conterminous United States: Knowledge gaps and emerging research needs
Discoveries and novel insights in ecology using structural equation modeling
Science and Products
- Science
- Data
- Multimedia
- Publications
Filter Total Items: 180
Instrumental variable methods in structural equation models
Instrumental variable regression (RegIV) provides a means for detecting and correcting parameter bias in causal models. Widely used in economics, recently several papers have highlighted its potential utility for ecological applications. Little attention has thus far been paid to the fact that IV methods can also be implemented within structural equation models (SEMIV). In this paper I present theAuthorsJames GraceBiodiversity effects on grape quality depend on variety and management intensity
Interactions between plants can be beneficial, detrimental or neutral. In agricultural systems, competition between crop and spontaneous vegetation is a major concern. We evaluated the relative support for three non-exclusive ecological hypotheses about interactions between crop and spontaneous plants based on competition, complementarity or facilitation.The study was conducted in Swiss vineyardsAuthorsMagdalena Steiner, James Grace, Sven BacherA graphical causal model for resolving species identity effects and biodiversity–ecosystem function correlations: comment
In a recent paper, Schoolmaster, Zirbel, and Cronin (SZC) (2020) claim “Formal causal analysis show[s] that biodiversity–ecosystem function (BEF) correlations are non-causal associations.” If this conclusion is accepted as true, it suggests a reconsideration of much of our current understanding of how biodiversity relates to the functioning of ecosystems. On the surface, it is easy to spot clear sAuthorsJames B. Grace, Michel Loreau, Bernhard SchmidHurricane Sandy effects on coastal marsh elevation change
High-magnitude storm events such as Hurricane Sandy are powerful agents of geomorphic change in coastal marshes, potentially altering their surface elevation trajectories. But how do a storm’s impacts vary across a large region spanning a variety of wetland settings and storm exposures and intensities. We determined the short-term impacts of Hurricane Sandy at 223 surface elevation table–marker hoAuthorsAlice G. Yeates, James Grace, Jennifer H. Olker, Glenn R. Guntenspergen, Donald Cahoon, Susan C. Adamowicz, Shimon C. Anisfeld, Nels Barrett, Alice Benzecry, Linda K. Blum, Rober T Christian, Joseph Grzyb, Ellen Kracauer Hartig, Kelly Hines Leo, Scott Lerberg, James C. Lynch, Nicole Maher, J Patrick Megonigal, William G. Reay, Drexel Siok, Adam Starke, Vincent Turner, Scott WarrenClimate and local environment structure asynchrony and the stability of primary production in grasslands
Aim Climate variability threatens to destabilize production in many ecosystems. Asynchronous species dynamics may buffer against such variability when a decrease in performance by some species is offset by an increase in performance of others. However, high climatic variability can eliminate species through stochastic extinctions or cause similar stress responses among species that reduce bufferinAuthorsB. Gilbert, A.S. MacDougall, T. Kadoya, M. Akasaka, J. R. Bennett, E.M. Lind, H. Flores-Moreno, J. Firn, Y. Hautier, E.T. Borer, E.W. Seabloom, P.B. Adler, E.E. Cleland, James Grace, W.S. Harpole, E.H. Esch, J.L. Moore, J. Knops, R. McCulley, B. Mortensen, J. Bakker, P.A. FayA 'weight of evidence' approach to evaluating structural equation models
It is possible that model selection has been the most researched and most discussed topic in the history of both statistics and structural equation modeling (SEM). The reason for this is because selecting one model for interpretive use from amongst many possible models is both essential and difficult. The published protocols and advice for model evaluation and selection in SEM studies are complexAuthorsJames GraceStructural equation modeling
This chapter introduces background and historical information on how structural equation modeling (SEM) came to be developed. Then, the main differences between SEM and earlier multivariate methods are explained. The chapter describes three main applications of SEM: path analysis, factor analysis, and hybrid models. Some computer programs are recommended for these applications. The step-by-step seAuthorsMatt Miller, Ivana Tasic, Torrey Lyons, Reid Ewing, James B. GraceConceptual frameworks
The chapter starts by addressing some of the issues that come from not using a conceptual framework. This point is illustrated using an example with causal factors. The chapter then goes on to explain the mechanics of establishing conceptual frameworks. Lastly, it lays out a step-by-step guide on how to create a framework—generating a set of concepts, specifying the relations between concepts, wriAuthorsKeunhyun Park, James B. Grace, Reid EwingScientist’s guide to developing explanatory statistical models using causal analysis principles
Recent discussions of model selection and multimodel inference highlight a general challenge for researchers, which is how to clearly convey the explanatory content of a hypothesized model or set of competing models. The advice from statisticians for scientists employing multimodel inference is to develop a well‐thought‐out set of candidate models for comparison, though precise instructions for hoAuthorsJames B. Grace, Kathryn IrvineThe importance of natural versus human factors for ecological conditions of streams and rivers
Streams are influenced by watershed-scale factors, such as climate, geology, topography, hydrology, and soils, which mostly vary naturally among sites, as well as human factors, agriculture and urban development. Thus, natural factors could complicate assessment of human disturbance. In the present study, we use structural equation modeling and data from the 2008-2009 United States National RiversAuthorsTao Tang, R. Jan Stevenson, James GraceClimatic controls on the distribution of foundation plant species in coastal wetlands of the conterminous United States: Knowledge gaps and emerging research needs
Foundation plant species play a critical role in coastal wetlands, often modifying abiotic conditions that are too stressful for most organisms and providing the primary habitat features that support entire ecological communities. Here, we consider the influence of climatic drivers on the distribution of foundation plant species within coastal wetlands of the conterminous USA. Using region-level sAuthorsMichael Osland, James B. Grace, Glenn Guntenspergen, Karen Thorne, Joel Carr, Laura FeherDiscoveries and novel insights in ecology using structural equation modeling
As we enter the era of data science (Lortie 2018), quantitative analysis methodologies are proliferating rapidly, leaving ecologists with the task of choosing among many alternatives. The use of structural equation modeling (SEM) by ecologists has increased in recent years, prompting us to ask users a number of questions about their experience with the methodology. Responses indicate an enthusiasAuthorsDaniel C. Laughlin, James Grace - News