Margaret E Evans
- Assistant Professor, Dendrochronology
- Adjunct Lecturer, Ecology and Evolutionary Biology
- Assistant Professor, Natural Resources
- Assistant Professor, Arid Lands Resources Sciences - GIDP
- Member of the Graduate Faculty
No activities entered.
No activities entered.
Natural Resources SeminarRNR 596B (Fall 2021)
Natural Resources SeminrRNR 496B (Fall 2021)
Conservation BiologyECOL 406R (Spring 2021)
Conservation BiologyECOL 506R (Spring 2021)
PreceptorECOL 391 (Spring 2021)
ThesisECOL 910 (Spring 2021)
ResearchECOL 900 (Fall 2020)
Biology Lecture TutorECOL 497B (Spring 2020)
Conservation BiologyECOL 406R (Spring 2020)
Conservation BiologyECOL 506R (Spring 2020)
ThesisECOL 910 (Spring 2020)
ResearchECOL 900 (Fall 2019)
Tpcs in DendrochronologyGEOS 595E (Fall 2019)
Conservation BiologyECOL 406R (Spring 2019)
Conservation BiologyRNR 506R (Spring 2019)
ResearchECOL 900 (Spring 2019)
Tpcs in DendrochronologyGEOS 595E (Spring 2019)
Undgrad Tching Trng EcolECOL 497A (Spring 2019)
ResearchECOL 900 (Fall 2018)
Conservation BiologyECOL 406R (Spring 2018)
Conservation BiologyECOL 506R (Spring 2018)
Independent StudyECOL 499 (Spring 2018)
Intrnship Present+PlanECOL 610C (Spring 2018)
Rsrch Ecology+EvolutionECOL 610A (Spring 2018)
Undgrad Tching Trng EcolECOL 497A (Spring 2018)
Intrnship Present+PlanECOL 610C (Fall 2017)
Conservation BiologyECOL 406R (Spring 2017)
Conservation BiologyRNR 506R (Spring 2017)
Intrnship Present+PlanECOL 610C (Spring 2017)
Undgrad Tching Trng EcolECOL 497A (Spring 2017)
Intrnship Present+PlanECOL 610C (Fall 2016)
Rsrch Ecology+EvolutionECOL 610A (Fall 2016)
Conservation BiologyECOL 406R (Spring 2016)
Conservation BiologyECOL 506R (Spring 2016)
Conservation BiologyRNR 506R (Spring 2016)
Rsrch Ecology+EvolutionECOL 610A (Spring 2016)
Undgrad Tching Trng EcolECOL 497A (Spring 2016)
- Dennhart, A. J., Evans, M. E., Dechner, A., Hunt, L. E., & Maurer, B. A. (2016). Macroecology and the Theory of Island Biogeography: Abundant Utility for Applications in Restoration Ecology. In Foundations of Restoration Ecology, Second Edition.
- Soulebeau, A., Pellens, R., Lowry, P. P., Aubriot, X., Evans, M. E., & Haevermans, T. (2016). Conservation of Phylogenetic Diversity in Madagascar's largest endemic plant family, Sarcolaenaceae. In Biodiversity Conservation and Phylogenetic Systematics(pp 355-374). Springer. doi:10.1007/978-3-319-22461-9_18
- Klesse, S., DeRose, R. J., Shaw, J. D., Babst, F., Anderegg, L., Axelson, J., Black, B., Ettinger, A., Greisbauer, H., Guiterman, C., Harley, G., Harvey, J., Lo, Y., Lynch, A., O'Connor, C., Restaino, C., Sauchyn, D., Smith, D., Wood, L., , Villanueva, J., et al. (2020). Continental-scale tree ring-based projection of Douglas-fir growth - testing the limits of space-for-time substitution. Global Change Biology, 26, 5146-5163.
- Ricker, M., Gutierrez-Garcia, G., Juarez-Guerrero, D., & Evans, M. E. (2020). Statistical age determination of tree rings. PLoS One, 15(9), e0239052. doi:https://doi.org/10.1371/journal.pone.0239052
- Babst, F., Evans, M. E., Bodesheim, P., Zhang, Z., Charney, N., Turton, R., Friend, A., Trouet, V. M., Girardin, M., Record, S., Klesse, S., Poulter, B., Mahecha, M., Moore, D. J., Seftigen, K., Frank, D. C., Bjorklund, J., Enquist, B. J., Bouriaud, O., , Eckes, A., et al. (2018). When tree rings go global: challenges and opportunities for retro- and prospective insight. Quarternary Science Reviews, 197, 1-20. doi:https://doi.org/10.1016/j.quascirev.2018.07.009
- Evans, M. E., Gugger, P. F., Lynch, A. M., Guiterman, C. H., Fowler, J. C., Klesse, S., & Riordan, E. C. (2018). Dendroecology meets genomics in the common garden: new insights on climate adaptation (invited Commentary). New Phytologist, 218, 401-403.
- Hearn, D. J., Evans, M. E., Wolf, B., McGinty, M., & Wen, J. (2017). Dispersal is associated with morphological innovation, but not increased diversification, in Cyphostemma (Vitaceae). Journal of Systematics and Evolution, 56(4), 340-359. doi:doi: 10.1111/jse.12417
- Klesse, S., DeRose, R. J., Guiterman, C. H., Lynch, A. M., O'Connor, C. D., Shaw, J. D., & Evans, M. E. (2018). Sampling bias overestimates climate change impacts on forest growth in the Southwestern United States. Nature Communications, 9, 5336. doi:doi.org/10.1038/s41467-018-07800-y
- Evans, M. E., Falk, D. A., Arizpe, A., Swetnam, T., Babst, F., & Holsinger, K. E. (2017). Fusing tree-ring and forest inventory data to infer influences on tree growth. EcoSphere, e01889. doi:10.1002/ecs2.1889
- Pillet, M., Joetzjer, E., Belmin, C., Chave, J., Ciais, P., Dourdain, A., Evans, M. E., Herault, B., Luyssaert, S., & Poulter, B. (2017). Disentangling competitive vs. climatic effects on tropical forest mortality. Journal of Ecology. doi:10.1111/1365-2745.12876
- Vanderwel, M., Rozendaal, D., & Evans, M. E. (2017). Predicting the abundance of forest types across the eastern U.S. through inverse modelling of tree-level demography. Ecological Applications, 27, 2128-2141. doi:10.1002/eap.1596
- Charney, N., Babst, F., Poulter, B., Record, S., Trouet, V. M., Frank, D. C., Enquist, B. J., & Evans, M. E. (2016). Observed forest sensitivity to climate implies larger reductions in 21st century forest growth. Ecology Letters.
- Evans, M. E., Merow, C., Record, S., McMahon, S., & Enquist, B. J. (2016). Making process-based range forecasts for many species. Trends in Ecology and Evolution, 31(11), 860-871. doi:http://dx.doi.org/10.1016/j.tree.2016.08.005
- Evans, M. E., Aubriot, X., Hearn, D., Lanciaux, M., Lavergne, S., Cruaud, C., Lowry, P. P., & Haevermans, T. (2014). Insights on the Evolution of Plant Succulence from a Remarkable Radiation in Madagascar (Euphorbia). Systematic Biology, 63(5), 698-711.
- Merow, C., Dahlgren, J. P., Metcalf, C. J., Childs, D. Z., Evans, M. E., Jongejans, E., Record, S., Rees, M., Salguero-Gomez, R., & McMahon, S. M. (2014). Advancing population ecology with integral projection models: a practical guide. Methods in Ecology and Evolution, 99-110.
- Babst, F., & Evans, M. E. (2020, August). Hotspots of change in major tree species under climate warming. Annual Meeting of the Ecological Society of America. held virtually: Ecological Society of America.More infoBackground/Question/MethodsWarming alters the variability and trajectories of tree growth around the world by intensifying or alleviating energy and water limitation. This insight from regional to global-scale research emphasizes the susceptibility of forest ecosystems and resources to climate change. However, globally-derived estimates are not necessarily meaningful for local nature conservation or management considerations, if they lack specific information on present or prospective tree species habitats. This is particularly the case towards the edge of their distribution, where shifts in growth trajectories may be imminent or already occurring. Importantly, the geographic space occupied by a tree species is not only constrained by climate, but often reflects biotic pressure such as competition for resources with other species. Hence, distinguishing climatic from competitive niche boundaries becomes a central challenge to identifying areas where tree species are most susceptible to climate change.Here, we employ a novel concept to characterize each position within a species’ bioclimatic niche based on two criteria: a climate sensitivity index (CSI) and a habitat occupancy index (HOI). The CSI is derived from step-wise multiple linear regression models that explain variability in annual radial tree growth as a function of monthly climate anomalies. The HOI is based on an ensemble of five species distribution models calculated from a combination of observed species occurrences and twenty-five bioclimatic variables.Results/ConclusionsWe calculated these two indices for 11 widespread tree species across the Northern Hemisphere. The combination of climate sensitivity and habitat suitability indicated hotspots of change, where tree growth is mainly limited by competition (low HOI and low CSI), as well as areas that are particularly sensitive to climate variability (low HOI and high CSI). In the former, we expect that forest management geared towards adjusting the competitive balance between several candidate species will be most effective under changing environmental conditions. In the latter areas, selecting particularly drought-tolerant accessions of a given species may reduce forest susceptibility to the predicted warming and drying.
- DeRose, R. J., Evans, M. E., & Klesse, S. (2020, August). Building the North American National Forest Inventory tree-ring database. Annual Meeting of the Ecological Society of America. held virtually: Ecological Society of America.
- Evans, M. E. (2020, August). Continental-scale tree ring-based projection of Douglas-fir growth - Testing the limits of space-for-time substitution. Annual Meeting of the Ecological Society of America. held virtually: Ecological Society of America.More infoBackground/Question/MethodsA central challenge in global change research is the projection of the future behavior of a system based upon past observations. Tree-ring data have been used increasingly over the last decade to project tree growth and forest ecosystem vulnerability under future climate conditions. But how can the response of tree growth to past climate variation predict the future, when the future does not look like the past? Space-for-time substitution (SFTS) is one way to overcome the problem of extrapolation: the response at a given location in a warmer future is assumed to follow the response at a warmer location today.Here we developed and evaluated a SFTS approach to projecting future growth of Douglas-fir (Pseudotsuga menziesii [Mirb.] Franco), a species that occupies an exceptionally large environmental and geographic space in North America – from 17°N to 55°N in latitude, and across mean annual temperatures ranging from -0.5 to 19.5°C and cumulative annual precipitation from 300 to 4800 mm. We compiled a dataset of 30,388 tree-ring time series from 2,699 sampling sites, totaling 2,706,098 ring widths over the 1902-2016 period of analysis. We then fit a hierarchical mixed effects model to these data to evaluate variation in individual tree growth in response to spatial and temporal variation in climate.Results/ConclusionsWe found opposing gradients of productivity and climate sensitivity, with largest growth rings and weakest response to interannual climate variation in the mesic coastal part of Douglas-fir’s range vs. narrower rings and stronger climate sensitivity across the semi-arid interior. Ring width variation in response to spatial vs. temporal temperature variation was opposite in sign: across space, average ring width was greater at warmer locations, but at a given location, ring widths were almost always smaller in response to warmer-than-average temperatures. This suggests that spatial variation in productivity, which is caused at least partly by local adaptation and other slow processes, cannot appropriately be used to anticipate changes in productivity caused by rapid climate change. We thus adopted an approach substituting only climate sensitivities when projecting future tree growth. Under this approach to SFTS, growth declines were projected across much of Douglas-fir’s distribution, with largest relative decreases in the semiarid U.S. Interior West and smallest in the mesic Pacific Northwest. Better understanding of the limits of SFTS is critical for ecological forecasting in a nonstationary world.
- Heilman, K. A., Dietze, M. C., Shaw, J. D., DeRose, R. J., Klesse, S., Finley, A. O., Gray, A. T., Arizpe, A. H., & Evans, M. E. (2020, August). Assimilation of tree ring and forest inventory data to forecast future growth responses of Pinus ponderosa. Ecological Society of America. Virtual: Ecological Society of America.More infoBackground/Question/MethodsForest responses to future climate are highly uncertain, but critical for forecasting and managing for forest carbon dynamics. To improve ecological forecasts of forest response, we harness the strengths of two large ecological datasets: tree-ring time series data that provide annually resolved growth responses, and spatially extensive forest inventory (FIA) data. We use a Bayesian state space model to assimilate these two ecological data sets, and quantify the effects of precipitation, maximum temperature, tree size, stand density, site index and two-way interactions between these factors on tree growth. We implemented a two-stage approach to model Pinus ponderosa responses in Arizona. Stage 1 leverages tree-ring increment data and repeat diameter measurements of 515 trees to estimate effects on tree growth. Posterior parameter estimates from stage 1 were then used as priors in stage 2, where data were included from an additional 5,794 trees in the forest inventory that only have repeat bole diameter measurements.Results/ConclusionsPrecipitation has a strong positive effect on Pinus ponderosa growth in Arizona, leading to growth declines under drier future conditions. Maximum temperature does not have a strong direct effect on growth, but a positive interaction between temperature and precipitation drives decreased growth under hot and dry future conditions. Tree size, stand density, and site factors all have considerable direct effects on annual tree growth, and can modify climate responses, such that larger trees and trees with high site quality have greater growth increments, but high stand density reduces growth increments, particularly at high temperatures. Interactions between stand-level properties and climate sensitivity provide opportunities to manage for forests that optimize carbon storage and climate resilience. Fusing information from 5794 repeat diameter measurements reduces uncertainty about stand-level processes, and allows us to forecast annual growth increment in forest plots without tree ring data. Assimilating tree ring and forest inventory data can help inform current management, constrain uncertainties about the effects of climate change, and provides a framework for iterative ecological forecasts.
- Riordan, E. C., & Evans, M. E. (2020, August). Spatial data aggregation underestimates variability in tree-growth response to climate. Annual Meeting of the Ecological Society of America. held virtually: Ecological Society of America.More infoBackground/Question/MethodsIncreasingly, scientists are using tree growth–climate relationships quantified from tree-ring data to spatially project climate change impacts on forests, from local to global scales. Local ring-width measurements are often combined through statistical aggregation to represent larger regions. Aggregation maximizes the common climate signal in tree-ring time-series while dampening local factors such that climate emerges as a strong predictor of variability in ring-widths, particularly at broad spatial scales. We ask whether spatial aggregation of ring-width data influences estimates of tree growth–climate sensitivity and biases projections of future forest response to climate change. We aggregated plot-level annual ring-width time-series of Douglas-fir in the southwestern United States, collected by the USFS Forest Inventory Analysis (FIA) program, and corresponding climate data from small (e.g., 40 km grain) to large (e.g., 600 km grain) spatial scales. We then modeled aggregated annual ring-widths as a function of aggregated annual warm season temperature and cool season precipitation, two important drivers of tree growth in the region identified by earlier research.Results/ConclusionsAs aggregation scale increased, so did the proportion of variation in ring-widths explained by climate (coefficient of determination). This was not accompanied by an increase in the magnitude of climate sensitivity (regression slopes), which remained stable, on average, across aggregation scales. Rather, increasing aggregation scale resulted in a strong reduction in the variability of regression slope estimates. The fine scale heterogeneity in climate sensitivity was partially explained by climatic conditions (normals) that vary geographically, with greater tree growth–climate sensitivity at warmer and drier locations. While we did not find an effect of aggregation on the average change in ring-width predicted under future climate scenarios, the decreased variability of climate sensitivity estimates at large spatial scales resulted in predictions that underestimate the variability of future tree growth response to climate. Detecting such landscape-level heterogeneity in climate sensitivity may be particularly important for understanding and managing forest resilience to climate change -- i.e., identifying and actively managing climate refugia. Hierarchical models, which can draw inference from local tree growth and nest effects from multiple spatial scales, may offer a more robust alternative to widely used spatial data aggregation practices.
- Schultz, E. L., & Evans, M. E. (2020, August). Incorporating large-scale disturbances into demographic range models: The importance of fire as a factor limiting the distribution of Pinus edulis. Annual Meeting of the Ecological Society of America. held virtually: Ecological Society of America.More infoBackground/Question/MethodsMaking accurate predictions about species’ future distributions requires a holistic understanding of the factors that determine species range limits. Previous studies of range limits have focused primarily on the direct effects of climate and competition as drivers of range limits. However, climate may also play an indirect role in shaping species distributions via its effect on disturbance regimes. Indeed, previous research on P. edulis demonstrated that climate and competition together were not enough to explain the geographic distribution: projected population growth rates were high at high-elevation locations where climate is was favorable but P. edulis is absent. Here, we used a stochastic demographic range model to evaluate the role of disturbance, particularly fire, as a factor influencing the geographic distribution of Pinus edulis, a tree species at the arid edge of the forest biome. We parameterized the model with census data on 23,426 trees in 1,941 forest inventory plots and incorporated fire as a stochastic disturbance based on fire occurrence data. We then projected the expected P. edulis distribution based on its long-term demographic performance.Results/ConclusionsFire damage was observed in more frequently in plots where P. edulis was absent than in plots where P. edulis was present (4.7% vs 1.7%). In plots that experienced fire, mortality rates of P. edulis were 54.9% in plots experiencing only ground fire damage, 78.6% in plots experiencing ground fire and crown fire damage, and 96.5% in plots experiencing crown fire damage, suggesting that fire can be a major driver of mortality in P. edulis. Applying stochastic fire return intervals (FRI) characteristic of three forest types of the Colorado Plateau region – Ponderosa pine, mixed conifer, and spruce-fir forests – we find that the long-term stochastic growth rate of P. edulis in the presence of fire is below the replacement level of 1.0 in the two forest types with lower FRI. While the direct effects of climate are an important driver of geographic distributions, the fundamental Earth system process of fire is likely necessary to explain P. edulis’ distribution. Cross-scale interactions between climate and disturbance can lead to complex dynamics that are not predictable from models that include only climate and competition.
- Evans, M. E. (2019, September). Demographic range modeling reveals that climate and competition are insufficient to explain a species’ distribution.. 1) annual meeting of the Ecological Society of America, Aug, 2018, New Orleans, LA; 2) MountainClim meeting, September, 2018, Crested Butte, CO; 3) US Forest Service FIA Stakeholder Science Meeting, November, 2019, Knoxville, TN.
- Giebink, C., DeRose, R. J., Castle, M., Shaw, J. D., & Evans, M. E. (2019, November). Updating the Forest Vegetation Simulator with climate response recorded in tree rings. U. S. Forest Service Forest Inventory and Analysis (FIA) Stakeholder Science Meeting. Knoxville, Tennessee: U. S. D. A. Forest Service.
- Klesse, S., & Evans, M. E. (2019, May). Back to the future - Continental-scale tree-ring based projection of Douglas-fir growth. 1) WorldDendro (February 2018, Bhutan), 2) Ecological Society of America's Annual meeting (August 2018, New Orleans, Louisiana), 3) TRACE conference (May 2019, San Leucio, Italy). San Leucio, Caserta, italy: PAGES.
- Evans, M. E., Dietze, M., DeRose, R. J., & Arizpe, A. (2018, February). Lightning Talks on Ecological Networks, Knowledge, and Forecasts. Ecological Knowledge and Predictions: Integrating across Networks and National Observatories. University of Arizona: National Science Foundation.
- Babst, F., Bouriaud, O., Poulter, B., Zhang, Z., Trouet, V. M., Evans, M. E., Charney, N., Record, S., Enquist, B. J., Seftigen, K., Bjorklund, J., Klesse, S., Bodesheim, P., Mahecha, M., Girardin, M., Friend, A., & Frank, D. C. (2017, April). When tree rings go global: challenges and opportunities for retro- and prospective insights. European Geosciences Union General Assembly. Vienna, Austria: European Geosciences Union.
- Evans, M. E., DeRose, R. J., Klesse, S., Aragon, J., Grey, A., Pillet, M., Arizpe, A., Shaw, J., & Dietze, M. (2017, Aug-Dec). Assimilation of tree-ring and forest inventory data to understand the influences of climate, tree size, and stand density on tree growth: a regional analysis of Pinus ponderosa. 1) Ecological Society of America's Annual Meeting, 2) Forest Inventory and Analysis Stakeholder Science Meeting, and 3) Uncertainty Quantification Group, Mathematics Department, University of Arizona. 1) Portland, Oregon, 2) Park City, Utah, and 3) Tucson, Arizona: 1) Ecological Society of America, 2) U. S. Forest Service, 3) Department of Mathematics, University of Arizona.
- Itter, M. S., Bradford, J. B., D'Amato, A. W., Evans, M. E., Finley, A. O., Foster, J. R., & Palik, B. J. (2017, August). Assimilation of tree-ring and repeat census data to model interactions between climate and past forest dynamics. Ecological Society of America's Annual Meeting. Portland, Oregon: Ecological Society of America.
- Klesse, S., DeRose, R. J., & Evans, M. E. (2017, August). Differences in sampling design influence tree-ring derived climate sensitivity: implications for forest vulnerability assessment. Ecological Society of America's Annual Meeting. Portland, Oregon: Ecological Society of America.
- Evans, M. E. (2016, June). Resilience ecology from a forest conservation perspective: from historical roots to practical application. Summer School Modeling Environmental Resilience - Agroecology, Climate, Ecology, Ocean, Society. Paris, France: Ecole Normale Superieure.
- Evans, M. E., Falk, D. A., Arizpe, A., Swetnam, T. L., Babst, F., & Holsinger, K. E. (2016, March). Combining tree-ring and forest inventory data to infer climatic niche: a hierarchical Bayesian approach. In Symposium: “Tree rings and dynamic vegetation models.”. Third American Dendrochronology Conference (AmeriDendro 2016). Mendoza, Argentina: Tree Ring Society.
- Evans, M. E., Falk, D. A., Arizpe, A., Swetnam, T. L., Babst, F., & Holsinger, K. E. (2016, October). Forecasting future tree growth from tree-ring data, and combining tree-ring and forest inventory data. Climate Ecology & Tree Growth Workshop. Petersham, Massachusetts: Harvard Forest, Harvard University.