To watch a recent research seminar given by Daniel Laughlin in the Dept of Forest and Rangeland Resources at CSU in Spring 2019 click here













Here is a list of current funded projects:

1) Applying Trait-Based Models To Achieve Restoration Targets In Rangelands (funded by USDA 2019-2023)

Jen Funk, Daniel Laughlin, Dana Blumenthal

Applying ecological theories to achieve land management objectives can be the strongest test of their generality and relevance to society, yet trait-based models are not being applied to meet restoration targets.  The objective of the proposed work is to test whether a trait-based model can be used to restore valuable ecosystem services by strengthening drought tolerance and invasion resistance in two different rangeland systems.  The research is organized around two objectives.  First, we will determine if trait-based theories can be applied to restore plant communities that are drought-tolerant and invasion-resistant.  Second, we will quantify how communities optimized for drought tolerance, invasion resistance, or functional diversity influence critical ecosystem functions including productivity, nutrient cycling, water use, and plant and arthropod diversity.

2) Integrating vital rates to predict the net effect of functional traits on fitness (funded by NSF 2019-2023)

Daniel Laughlin, Margaret Moore, Jennifer Gremer, Rachel Mitchell, Peter Adler, Brian Barber

One of the greatest challenges facing ecologists is predicting how species will respond to changing environmental conditions. One promising approach to advance prediction in ecology is to determine how traits of species determine their success in a given environment. Traits are attributes of species, such as plant height or body size, that influence their performance (survival and reproduction). In this project the researchers will establish relationships between traits and performance of key plant species to make general predictions about how other species in plant communities will perform. Climate scientists predict that drought may occur more frequently in the southwestern United States. Plants that can resist wilting during dry periods are likely to be winners during long-term drought, so this project will help determine which species will win and lose in a drier climate. This study will train undergraduate students, graduate students, and a secondary school teacher how to quantify plant traits and use them to model population dynamics. By linking physiological traits to plant performance, the results of this study will be used to develop general predictions for plant responses to environmental change.

Theory predicts that functional traits determine fitness differences among species, but direct evidence for this is still lacking for long-lived organisms. Given the inherent difficulty of quantifying fitness, ecologists typically link traits to vital rates rather than to fitness itself. However, analyzing trait-vital rate relationships in isolation can be misleading because of well-documented trade-offs among vital rates. Individual growth rates can be a poor proxy for fitness because the growth-mortality tradeoff can generate variation in growth rates that yield equal fitness. This research will contribute new understanding of the net effects of functional traits on fitness by integrating rates of growth, survival and reproduction across multiple populations of coexisting perennial plant species. This contribution will fill an important gap in knowledge by demonstrating how effects of traits on individual vital rates compare to the net effect of traits on total fitness. Evolutionary models of dynamic fitness landscapes will be confronted with experimental data to test predictions of how traits influence fitness in response to experimental rainfall manipulations. This research will provide an empirical foundation for theories that integrate population ecology, evolutionary biology, and ecophysiology, by developing generalizable trait-based models of population dynamics to predict responses to changing environmental conditions.


3) Modelscape: a Research Collaborative to Develop Highly Predictive Explanatory Models (funded by NSF 2020-2024)


4) Evaluating sagebrush steppe condition, restoration efficacy and community trajectories in Grand Teton National Park. Funded by the National Park Service (2020-2026)