The 2009 American Society of Naturalists Awards
The study of a single purple flower can reveal the evolutionary forces that shape entire ecosystems.
Each year, the American Society of Naturalists (ASN) honors exceptional research that deepens our understanding of evolution and the unifying principles of biology1 . The awards presented in 2009 celebrated work that asked fundamental questions about how life evolves: How do scientists accurately measure an organism's evolutionary fitness? What mathematical frameworks can help predict whether a species will thrive or go extinct?
This article explores the groundbreaking research recognized by the ASN a decade and a half ago, work that provided new tools for decoding the complex narrative of natural selection. We will focus on the award-winning paper that offered a novel method for a fundamental challenge in biology—measuring fitness and population growth in the natural world.
The 2009 ASN Presidential Award was given to a paper that made a significant contribution to the field of evolutionary biology, published in the society's journal, The American Naturalist1 . This award, selected by the sitting ASN President, recognizes the best paper from the previous calendar year1 6 .
by Ruth Shaw, Charles Geyer, Stuart Wagenius, Helen Hangelbroek, and Julie Etterson1 .
This research was celebrated for providing a powerful new statistical framework that allows biologists to more accurately estimate individual fitness and project population growth, crucial metrics for understanding evolutionary success and ecological stability.
In evolutionary biology, fitness is a cornerstone concept, representing an organism's ability to survive, reproduce, and pass its genes to the next generation. However, measuring fitness in wild populations is notoriously complex. Organisms have varied life-histories—some reproduce multiple times, others only once; some have many offspring, others invest heavily in a few.
Prior to this work, analyses of these different life-history strategies often relied on fragmented or incomplete data, making it difficult to draw robust conclusions about long-term population viability. Shaw and colleagues sought to create a unified method that could handle this complexity.
The research conducted by Shaw's team was not a single laboratory experiment but rather the development and demonstration of a novel analytical framework. They combined advanced statistical theory with real-world ecological data to create a tool that could be widely applied across different species.
The researchers' approach can be broken down into several key steps:
The framework incorporated diverse data on an organism's life-history, including age-specific survival rates, reproduction rates, and growth patterns.
The team used a statistical approach called maximum likelihood estimation to find the most probable values for fitness and population growth based on the observed data. This method is particularly powerful for handling the variability and uncertainty inherent in biological field studies.
They linked their analysis to established ecological models known as population projection matrices. This connection allowed them to translate individual life-history data into predictions for the entire population's growth rate (λ).
The authors demonstrated the utility of their framework by applying it to real studies, including research on the Western prairie fringed orchid, a plant subject to complex environmental pressures.
The core result was a robust and flexible statistical method that yielded several key insights:
The research showed that data from various types of life-histories could be analyzed within a single, coherent framework.
A major strength of the method was its ability to formally account for statistical uncertainty.
By applying the method to the prairie fringed orchid, the team illustrated how conservation biologists could more reliably assess the viability of threatened populations.
This work was significant because it provided the scientific community with a powerful toolkit to make more accurate predictions about which individuals or populations are evolutionarily successful and why, thereby illuminating the mechanics of natural selection itself.
The tables below illustrate the types of analyses and results enabled by the award-winning methodology.
This table shows the kind of data a researcher would collect and input into the analytical framework.
| Age Class (years) | Probability of Survival | Average Number of Seeds Produced | Probability of Seed Germination |
|---|---|---|---|
| 1 | 0.60 | 5 | 0.10 |
| 2 | 0.80 | 15 | 0.15 |
| 3 | 0.85 | 25 | 0.20 |
| 4+ | 0.70 | 20 | 0.20 |
This table shows the key outputs the framework provides, including estimates of uncertainty.
| Model Scenario | Estimated Population Growth (λ) | 95% Confidence Interval | Interpretation |
|---|---|---|---|
| Standard Conditions | 1.05 | (1.01 - 1.09) | Population is growing |
| Drought Conditions | 0.95 | (0.90 - 0.99) | Population is declining |
| With Pollinator Support | 1.12 | (1.08 - 1.16) | Strong population growth |
This table details key resources used in this field of research, as exemplified by the award-winning paper.
| Tool or Technique | Function in Research |
|---|---|
| Maximum Likelihood Estimation | A statistical method for finding the most probable values of model parameters (like fitness) from observed data, crucial for dealing with uncertainty. |
| Population Projection Matrices | A mathematical model that uses birth and survival rates to project future population size and structure. |
| Long-Term Field Studies | Multi-year data collection on marked individuals in their natural environment, providing the essential raw data on survival and reproduction. |
| Demographic Data | Quantitative records of survival, reproduction, and development for individuals in a population—the fundamental components of fitness. |
| Mark-Recapture Methods | A technique for tracking individuals in a wild population over time to estimate survival rates and population size. |
The 2009 ASN Presidential Award recognized more than just a single excellent paper; it celebrated a significant methodological advancement. The unified framework developed by Ruth Shaw and her colleagues provided evolutionary biologists and ecologists with a more powerful and precise lens through which to view the processes of natural selection.
By creating a robust bridge between complex life-history data and clear inferences about fitness, this work has undoubtedly aided in countless subsequent studies. It has helped scientists better understand how populations respond to environmental change, how species interactions evolve, and how we might best conserve biodiversity.
The questions explored in 2009 continue to resonate, reminding us that in the intricate details of survival and reproduction lie the universal patterns of life itself.