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publications
Ten simple rules for training yourself in an emerging field
Published in PLoS Computational Biology, 2021
This paper provides recommendations for graduate students in emerging disciplines, from a group of early career scientists in the emerging field of ecological forecasting.
Recommended citation: Woelmer, W. M., Bradley, L. M., Haber, L. T., Klinges, D. H., Lewis, A. S. L., Mohr, E. J., Torrens, C. L., Wheeler, K. I., & Willson, A. M. (2021). "Ten simple rules for training yourself in an emerging field." PLoS Comput. Biol. 17:e1009440. https://doi.org/10.1371/journal.pcbi.1009440
Climate and hydraulic traits interact to set thresholds for liana viability
Published in Nature Communications, 2022
Tropical trees and lianas are functionally differentiated by hydraulic traits, particularly the rate of water conductivity through the xylem. The difference in hydraulic traits explains difference in GPP at the individual plant level. Despite observations that lianas are most prevalent under drier hydroclimatic conditions at present, we show that liana GPP is more sensitive to projected drying hydroclimate in the future as a result of more acquisitive and vulnerable hydraulic functional traits.
Recommended citation: Willson, A. M., Trugman, A. T., Powers, J. S., Smith-Martin, C. S. & Medvigy, D. (2022). "Climate and hydraulic traits interact to set thresholds for liana viability." Nat. Commun. 13:3332. https://doi.org/10.1038/s41467-022-30993-2
The NEON Ecological Forecasting Challenge
Published in Frontiers in Ecology and the Environment, 2023
The NEON Forecasting Challenge is a joint effort by NEON and the Ecological Forecasting Initiative to solicit near-term forecasts of NEON data products from the broader scientific community to address questions related to predictability.
Recommended citation: Thomas, R. Q., Boettiger, C., Carey, C. C., Bietze, M. C., Johnson, L. R., Kenney, M. A., McLachlan, J. S., Peters, J. A., Sokol, E. R., Weltzin, J. F., Willson, A. M., Woelmer, W. M. & Challenge Contributors†. (2023). "The NEON Ecological Forecasting Challenge." Front. Ecol. Environ. 21:112-113. https://doi.org/10.1002/fee.2616
Assessing opportunities and inequities in undergraduate ecological forecasting education
Published in Ecology and Evolution, 2023
We collate resources for learning ecological forecasting at the undergraduate level and assess opportunities and inequities at three levels: online resources, US university courses on ecological forecasting, and US university courses on topics related to ecological forecasting. Finally, we provide recommendations for ways to move the discipline towards greater equity and inclusion in educational efforts.
Recommended citation: Willson, A. M., Gallo, H.†, Peters, J. A., Abeyta, A., Bueno Watts, N., Carey, C. C., Moore, T. N., Smies, G., Thomas, R. Q., Woelmer, W. M. & McLachlan, J. S. (2023). "Assessing opportunities and inequities in undergraduate ecological forecasting education." Ecol. Evol. 13:e10001. https://doi.org/10.1002/ece3.10001
Defining model complexity: An ecological perspective
Published in Meteorological Applications, 2024
Model complexity is often used as an umbrella term when comparing model performance. Here, we offer a framework where the concept of model complexity is divided into multiple facets of complexity. We urge scientists to consider describing and reporting the complexity of their models using our more detailed facets to improve communication and interoperability of modeling efforts.
Recommended citation: Malmborg, C. A., Willson, A. M., Bradley, L. M., Beatty, M. A., Klinges, D. H., Koren, G., Lewis, A. S. L., Oshinubi, K., Woelmer, W. M. (2024). "Defining model complexity: An ecological perspective." Meteor. Appl. https://doi.org/10.1002/met.2202
Near-term ecological forecasting for climate change action
Published in Nature Climate Change, 2024
We highlight the last few years of progress in the field of ecological forecasting and provide recommendations for future research directions.
Recommended citation: Dietze, M., White, E. P., Abeyta, A., Boettiger, C., Bueno Watts, N., Carey, C. C., Chaplin-Kramer, R., Emanuel, R. E., Ernest, S. K. M., Figueiredo, R., Gerst, M. D., Johnson, J. R., Kenney, M. A., McLachlan, J. S., Paschalidis, I. C., Peters, J. A., Rollinson, C. R., Simonis, J., Sullivan-Wiley, K., Thomas, R. Q., Wardle, M., Willson, A. M., Zwart, J. (2024). "Near-term ecological forecasting for climate change action." Nat. Clim. Change. https://doi.org/10.1038/s41558-024-02182-0
talks
Little Grand Canyon parcel management plan
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Invasive species at Aquinas College as a representation of invasive species in the Grand Rapids area
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Abiotic factors affecting the prevalence of Rosa multiflora (Rosaceae) populations at Pierce Cedar Creek Institute (Hastings, Michigan, USA)
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Rosa multiflora (multiflora rose) is a non-native shrub that has invaded many North American natural areas, resulting in negative impacts on native flora and fauna. In order to prevent further spread of R. multiflora, it is important to understand the abiotic habitat associations that characterize R. multiflora prevalence. Here, we examine how distance from the nearest trail, soil moisture, soil pH, relative sunlight availability, and dominant overstory composition are associated with R. multiflora presence and abundance at a preserve in southwest Michigan. We found that R. multiflora presence is associated with high sunlight availability and Red Maple-dominated forests. Additionally, R. multiflora abundance is associated with low soil moisture and Black Oak-dominated forests. The purpose of this research was to inform land management in determining the uninvaded forests that are most susceptible to R. multiflora invasion. Based on our results, we recommend that land managers focus on areas of high light availability (along forest edges and within open canopy areas) and low soil moisture in an effort to curtail R. multiflora invasion.
Climate and hydrualic traits interact to set thresholds for liana viability
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Trees and lianas dominate the canopy of tropical forests, competing for light and water. Their competition influences forest community structure and composition, with consequences for ecosystem services and forest management. These growth forms respond differently to variation in climate and resources. However, our understanding of the relevant mechanisms is limited and lianas have historically been left out of predictive ecosystem models. One factor limiting the inclusion of lianas in ecosystem models is the lack of compiled data on liana functional traits to understand how lianas differ from other plant functional types. We conducted a meta-analysis of liana functional traits, including traits related to leaves, stems, roots, and hydraulic architecture, that represent fundamental trade-offs in allocation and life history strategy. We compared liana and tree trait distributions to identify traits that differ between growth forms. We then developed a liana-tree competition model and parameterized the hydraulic traits using our meta-analysis. We used our model to simulate the hydraulic conductivity required to maintain positive annual net primary production (Kreq) for both lianas and trees under hydroclimate and competition scenarios representative of American tropical moist and tropical dry forests in present day and under projected end-of-century hydroclimate scenarios.
Paving the way for a more diverse next generation of ecological forecasters
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Ecological forecasters, as participants in a quantitatively advanced field, have a responsibility to actively work towards diversifying participation. As participants in an emerging field, we at the Ecological Forecasting Initiative have the unique opportunity to establish a culture of inclusivity as the discipline grows. Our ultimate goal is to improve equity in the access to and quality of ecological forecasting education. We detail progress on our current initiatives, including collaborations with students and faculty at minority serving institutions to identify areas where current educational resources do not meet student needs, and leveraging the NEON Forecasting Challenge to develop forecasting curricula.
Assessing opportunities and inequities in undergraduate ecological forecasting education
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Ecological forecasting has become important for predicting the future state of ecosystems and their services and offers a promising approach for introducing a diverse group of researchers to quantitative methods in ecology. A competent forecasting workforce requires equitable quantitative training in ecology, which is still in development at the undergraduate level. Understanding where the current curriculum landscape allows for targeted interventions to improve educational opportunities. We compiled existing resources for teaching and learning ecological forecasting at three curriculum levels ranging from open-access, online resources to university courses on ecological forecasting, to characterize the existing curriculum. We combined this analysis with direct conversations with ecological forecasting educators, practitioners, and students to gain a more holistic perspective into the current curriculum gaps. Using this curriculum analysis approach, we sought to answer the following questions: “What ecological forecasting topics are being taught to undergraduate students, and what is not being taught that is important for preparing students for research careers?” and “Who has access, and who does not have access, to the online resources and courses related to ecological forecasting?” Providing insight into these questions offers the opportunity to concentrate curriculum development efforts in areas that presently lack resources.
Biotic interactions moderate the climate-vegetation relationship over the last 2,000 years of the pre-Industrial Holocene in the Upper Midwest, U.S.
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Forest aboveground biomass comprises a substantial proportion of terrestrial carbon storage. Understanding the drivers of forest aboveground biomass change, especially in the context of global climate change, is critical for making informed policy and management decisions. Change in forest aboveground biomass is driven by a combination of environmental variables and biotic interactions (e.g., competition, facilitation). Because of the long lifespans of forest trees, the contributions of climate drivers and biotic interactions to changes in forest aboveground biomass only become fully apparent over long time periods (decades to centuries). As a consequence of the lower availability of time series data spanning decades to centuries, processes occurring over long time spans are often overlooked. Understanding the degree to which biotic interactions moderate the relationship between climate and forest aboveground biomass is crucial to informing process models making long-term forecasts of the carbon cycle.
Comparing model-data fusion methods for parameter estimation and state variable prediction using a forest gap model
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Models represent our hypotheses of real ecological systems, and consequently, they may incompletely or incorrectly quantify ecosystem dynamics, leading to imprecise predictions. One way to improve model performance is via model-data fusion, a class of methods that combine model predictions and observations. One model-data fusion method is parameter optimization, in which model simulations are conducted with different parameter combinations and predicted state variables are compared post hoc to data. The parameter combinations that lead to the most accurate predictions are sselected as the “true” parameter values. Another method is data assimilation, which involves iteratively adjusting an ensemble of model predictions with data. When parameters are varied in each simulation and updated along with the state variables via ensemble weighting, both parameters and state data assimilation are performed simultaneously.
Both environment and feedbacks are important drivers of macroscale forest, savanna, and prairie biome distributions in the Upper Midwest, USA
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Macroscale environmental conditions explain spatiotemporal patterns in the distribution of vegetation communities and the biomes they comprise at broad scales. However, similar environmental conditions can support distinct communities in some regions. This is the case near the boundaries of savanna biomes with forest and prairie biomes, such as in the Upper Midwest, USA (UMW) during pre-industrial times, and in tropical Africa and South America today. In these regions, vegetation-environment feedbacks, such as the promotion of fire by open vegetation, drive the distribution of biomes and have important implications for the resilience of these biomes to changes in the environment. The spatiotemporal scales at which feedbacks are important for determining the distribution of biomes are disputed, however. Here, we used reconstructions of relative abundance of eleven tree taxa over 2,000 years and the UMW from fossil pollen records, along with reconstructions of climatic and soil conditions, to quantify the relationship between environment and vegetation. We found that the distribution of vegetation communities in the UMW is broadly explained by average annual temperature, total annual precipitation, precipitation seasonality, and soil texture. After accounting for their joint dependence on such environmental variables, however, strong residual relationships remained between taxa characteristic of the savanna-forest and savanna-prairie biome boundaries. Specifically, the strong negative residual correlations between oak (characteristic of savanna) and maple (characteristic of forest) taxa (-0.60 [-0.83, -0.43] mean [95% CI]) and between oak and elm (characteristic of prairie) taxa (-0.59 [-0.82, -0.42]) indicate that, under the same environmental conditions, savanna taxa remain systematically segregated from forest and prairie taxa. This suggests a potential role for vegetation-environment feedbacks in explaining the distribution of vegetation in this region, even at regional and millennial scales. This work highlights the importance of incorporating feedbacks into predictive models of the long-term and large-scale consequences of environmental change on savanna, forest, and prairie communities.