Eric Menges has been studying rare plants and fire ecology at Archbold Biological Station for more than 30 years. I just learned that he’s the subject (and narrator) of a short, 16-minute video on Vimeo that I’ve embedded above. Please take the time to watch it. You’ll learn a lot.
If you’re reading this post, you know that my colleagues and I have been studying Protea for more than a decade. A lot of our work has focused on documenting and understanding trait-environment associations. We’ve studied those associations both among populations within species (Protea repens: https://doi.org/10.1093/aob/mcv146), among populations within a small, closely related clade (Protea sect. Exsertae: https://doi.org/ and https://doi.org/), and across the entire genus (https://doi.org/10.1086/680051). But all of those studies look at the relationship between the climate as it is now (as reflected in the South African Atlas of Agrohydrology and Climatology). They haven’t examined how traits have evolved in response to changes in climate.
Our latest paper, begins to address that shortcoming. We use the highly resolved phylogeny of Protea that Nora Mitchell constructed as part of her dissertation (http://darwin.eeb.uconn.edu/uncommon-ground/blog/2017/01/23/a-new-phylogeny-for-protea/ and https://doi.org/10.3732/ajb.1600227), and we reconstruct estimates of how traits changed over evolutionary time in concert (or not) with climates. Our reconstructions depend on particular models of evolutionary change, and we explore several alternatives. Here’s the abstract:
Evolutionary radiations are responsible for much of Earth’s diversity, yet the causes of these radiations are often elusive. Determining the relative roles of adaptation and geographic isolation in diversification is vital to understanding the causes of any radiation, and whether a radiation may be labeled as “adaptive” or not. Across many groups of plants, trait–climate relationships suggest that traits are an important indicator of how plants adapt to different climates. In particular, analyses of plant functional traits in global databases suggest that there is an “economics spectrum” along which combinations of functional traits covary along a fast–slow continuum. We examine evolutionary associations among traits and between trait and climate variables on a strongly supported phylogeny in the iconic plant genus Protea to identify correlated evolution of functional traits and the climatic-niches that species occupy. Results indicate that trait diversification in Protea has climate associations along two axes of variation: correlated evolution of plant size with temperature and leaf investment with rainfall. Evidence suggests that traits and climatic-niches evolve in similar ways, although some of these associations are inconsistent with global patterns on a broader phylogenetic scale. When combined with previous experimental work suggesting that trait–climate associations are adaptive in Protea, the results presented here suggest that trait diversification in this radiation is adaptive.
Mitchell, N., J.E. Carlson, and K.E. Holsinger. 2018. Correlated evolution between climate and suites of traits along a fast–slow continuum in the radiation of Protea. Ecology and Evolution 8:1853–1866. doi: 10.1002/ece3.3773.
Some of you know that Carl Schlichting and I co-advise Tanisha Williams. If you know that, you almost certainly know that Tanisha spent the 2015-2016 academic year as a Fulbright Fellow in South Africa. She was based at the Cape Peninsula University of Technology, and she used her time not only to collect seeds of Pelargonium and establish experimental gardens at Kirstenbosch Botanical Garden and Rhodes University but also to work with two non-profit environmental organizations. She posted an article about her experience on the blog of the Fulbright Student Program. Here’s an excerpt to whet your appetite:
Among the many experiences I had, I must say the residents from the Khayelitsha township have taken a special place in my heart. This is where I taught girls and young women math, science, computer tutoring, life skills, and female empowerment through a community center program. It was such an impactful experience, as these girls are growing up in a community with high rates of unemployment, violence, and other socioeconomic issues. It was empowering for me to see the curiosity and determination these girls had for learning and changing their community. They thought I was there to teach them from my own experiences being raised in a comparable situation and now working on my doctorate as a scientist, but I know I was the one that gained the most from our time together. I learned what it truly means to have hope and persevere. These lessons, along with the ecological and evolutionary insights from my academic research, will be ones that I always remember.
Last January I mentioned that I co-authored a paper that appeared on bioRxiv in which we combined tree ring and growth increment data to predict growth from weather and biophysical data. The paper has now appeared in Ecosphere, an open acces journal from the Ecological Society of America. Here’s the abstract. You’ll find the full citation below.
Fusing tree-ring and forest inventory data to infer influences on tree growth
Better understanding and prediction of tree growth is important because of the many ecosystem services provided by forests and the uncertainty surrounding how forests will respond to anthropogenic climate change. With the ultimate goal of improving models of forest dynamics, here we construct a statistical model that combines complementary data sources, tree-ring and forest inventory data. A Bayesian hierarchical model was used to gain inference on the effects of many factors on tree growth—individual tree size, climate, biophysical conditions, stand-level competitive environment, tree-level canopy status, and forest management treatments—using both diameter at breast height (dbh) and tree-ring data. The model consists of two multiple regression models, one each for the two data sources, linked via a constant of proportionality between coefficients that are found in parallel in the two regressions. This model was applied to a data set of ~130 increment cores and ~500 repeat measurements of dbh at a single site in the Jemez Mountains of north-central New Mexico, USA. The tree-ring data serve as the only source of information on how annual growth responds to climate variation, whereas both data types inform non-climatic effects on growth. Inferences from the model included positive effects on growth of seasonal precipitation, wetness index, and height ratio, and negative effects of dbh, seasonal temperature, southerly aspect and radiation, and plot basal area. Climatic effects inferred by the model were confirmed by a dendroclimatic analysis. Combining the two data sources substantially reduced uncertainty about non-climate fixed effects on radial increments. This demonstrates that forest inventory data measured on many trees, combined with tree-ring data developed for a small number of trees, can be used to quantify and parse multiple influences on absolute tree growth. We highlight the kinds of research questions that can be addressed by combining the high-resolution information on climate effects contained in tree rings with the rich tree- and stand-level information found in forest inventories, including projection of tree growth under future climate scenarios, carbon accounting, and investigation of management actions aimed at increasing forest resilience.
Evans, M. E. K., D. A. Falk, A. Arizpe, T. L. Swetnam, F. Babst, and K. E. Holsinger. 2017. Fusing tree-ring and forest inventory data to infer influences on tree growth. Ecosphere 8(7):e01889. doi: 10.1002/ecs2.1889
Ecologists and paleoecologists have used the width of tree rings for years as a way of inferring past climates. In fact, tree ring data were an important component of the proxy data Mann et al. (1998) used when they constructed their famous1 hockey stick representing global surface temperatures over the last millennium. I don’t have anything as earth shattering as a hockey stick to share with you, but I am pleased to report that a paper on which I am a co-author demonstrates how to combine tree ring and growth increment data (with other data) to predict growth of forest trees. Here’s tha abstract and a link to the paper on bioRxiv.
Fusing tree-ring and forest inventory data to infer influences on tree growth
Better understanding and prediction of tree growth is important because of the many ecosystem services provided by forests and the uncertainty surrounding how forests will respond to anthropogenic climate change. With the ultimate goal of improving models of forest dynamics, here we construct a statistical model that combines complementary data sources: tree-ring and forest inventory data. A Bayesian hierarchical model is used to gain inference on the effects of many factors on tree growth (individual tree size, climate, biophysical conditions, stand-level competitive environment, tree-level canopy status, and forest management treatments) using both diameter at breast height (DBH) and tree-ring data. The model consists of two multiple regression models, one each for the two data sources, linked via a constant of proportionality between coefficients that are found in parallel in the two regressions. The model was applied to a dataset developed at a single, well-studied site in the Jemez Mountains of north-central New Mexico, U. S. A. Inferences from the model included positive effects of seasonal precipitation, wetness index, and height ratio, and negative effects of seasonal temperature, southerly aspect and radiation, and plot basal area. Climatic effects inferred by the model compared well to results from a dendroclimatic analysis. Combining the two data sources did not lead to higher predictive accuracy (using the leave-one-out information criterion, LOOIC), either when there was a large number of increment cores (129) or under a reduced data scenario of 15 increment cores. However, there was a clear advantage, in terms of parameter estimates, to the use of both data sources under the reduced data scenario: DBH remeasurement data for ~500 trees substantially reduced uncertainty about non-climate fixed effects on radial increments. We discuss the kinds of research questions that might be addressed when the high-resolution information on climate effects contained in tree rings are combined with the rich metadata on tree- and stand-level conditions found in forest inventories, including carbon accounting and projection of tree growth and forest dynamics under future climate scenarios.