The Secret Life of the Middle Holocene Eastern Great Plains Bison

Photograph taken by author at Neal Smith Wildlife Refuge

Imagine yourself wandering through the Iowa tallgrass prairie, you admire the lush tranquility of the big bluestem and compass plant, perhaps you stop to pick a couple of wild blackberries while listening to the call of the red-headed woodpecker. Suddenly, as if out of nowhere, you are confronted by a thunderous sound that seems grow louder and louder and discover the source of the noise appears to be the footsteps of a herd of large, four-legged animals. As these great beasts approach, you see clearer and clearer a herd of bison. Nowadays, this scene seems almost impossible but during the middle Holocene, 7,000 to 8,500 years ago, bison ruled the prairie. This species served as a keystone species: creating necessary disturbances such as grazing and wallowing to continue the success of native prairie plants. While ecologists now know that there were indeed large populations of bison, little is known about the everyday habits of these animals. What did they eat? Where did they drink? How far did they travel? All of these questions become even more pertinent when considering that during the middle Holocene, there was an increase in aridity and drought. Current research suggests that bison respond to a drier climate by increasing their range (Fortin et al. 2003), which might change their diet and water source. So the question remains: how did the bison population in the eastern Great Plains respond to climate changes in the middle Holocene? Answering these questions will not only provide insight into bison behavior but can also reveal the history of Iowa vegetation. Since the bison’s diet consisted of the flora available, the content of their diet should reflect the prairie’s composition.

            A study conducted by Widga et al. (2010) set out to answer where the middle Holocene bison wandered and what they ate. Using fossils of bison from five different populations located in tallgrass prairie and prairie-forest border in Iowa, Minnesota and Nebraska, the researchers used isotopic analysis to determine the diet, water source and seasonal migration patterns of each herd. Isotopes are a type of a specific element containing a different number of neutrons than the typical version of the same element, making it heavier or lighter. Isotopes are useful for ecologists because certain natural processes discriminate between isotopes in their incorporation of elements. Thus, these natural processes experience different isotopic ratios than would normally be expected. Analyzing the ratios of isotopes can lend insight into the historical behaviors of bison by comparing the different isotopic ratios in the fossils of bison compared with the expected ratios in the sources of forage and water.

Map of Locations of Bison Fossils Studied (Widga et al. 2010)

The bison populations sampled were selected because there were at least 29 individuals in each herd, and their deaths appeared to occur simultaneously. This study used tooth enamel as the source of isotopes as tooth enamel can easily reveal age allowing for a comparison of behaviors within the individual and between herd mates of the same age and different ages.

            ¹³Carbon isotopes were used to analyze the bison’s’ diet. Tallgrass prairie plants incorporate carbon dioxide during photosynthesis in two distinct ways. The different pathways and the plants that utilize them are referred to as C₃ and C₄ plants. Most grasses are C₄ plants, while woody vegetation is typically C₃. The ¹³C to ¹²C ratio is higher in C₄ plants. Since bison eat these plants and incorporate the carbon throughout their bodies including their teeth, investigating the ratio of ¹³C to ¹²C in the bison teeth will lend insight into the types of plants the middle Holocene bison ate. Widga et al. (2010) found that most of the bison sampled had diets higher in C₄ plants than C₃ plants. In fact, the bison’s’ diets in all but one herd consisted of 60-85% C₄ plants. This insight on the bison’s diet enables us to better understand the composition of the tallgrass prairie during the middle Holocene, through the assumption that the bison ate a diet representational of the prairie flora. Therefore, we can conclude that the prairies in northwestern Iowa, northeastern Nebraska and southern Minnesota were roughly composed of 60-85% C₄ plants. These findings add detail to a study conducted by Baker et al. (1996), which investigated vegetation composition in northeast Iowa using a variety of methods including pollen fossils, vascular plant macrofossils and isotopic evidence. Their study found that at this time there were more mesic deciduous forest species, C3 plants, than C4 grasses in northeastern Iowa. However using the same methods, Baker et al. (1996) found that 5,500-3,500 years ago (about 2,000 years after the bison investigated by Widga et al. (2010) died) northeast Iowa experienced an ecosystem transformation from forest to prairie. Thus, the bison data reveals that prairie arrived in northwest Iowa before spreading into northeast Iowa.

            Widga et al. (2010) used ¹⁸Oxygen as a proxy to determine what types of water sources bison utilized during this period. ¹⁸O is heavier than ¹⁶O and because of the increase in mass, ¹⁸O is less likely to be evaporated than ¹⁶O. In the tallgrass prairie, there are a variety of water sources available to bison. Sources such as ephemeral or upland lakes and ponds experience intense evaporation, especially during the summer. At these water sources, there would be a high ratio of ¹⁸O to ¹⁶O. In contrast, more stable water sources such as large lakes or rivers have low ratios of ¹⁸O to ¹⁶O. Therefore, bison were more likely to consume ¹⁸O in water sources prone to evaporation. The researchers found high concentration of ¹⁸O in the teeth sampled, revealing that these bison utilized water sources susceptible to high levels of evaporation. The bison may not have found it necessary to travel outside their normal water sources in the middle Holocene despite the increase in drought conditions.

            The last question investigated by Widga et al. (2010) concerned the movement of bison. Did the bison respond to the increase in temperature with an expansion in movement and home ranges, or as with diet and water sources, did the bison stick with similar patterns before the climate change? To answer this question, researchers compared isotopic ratios of ⁸⁶Strontium and ⁸⁷Strontium found in the bison’s teeth to the ratio found in the soil of different geologic areas where the bison may have grazed. Different soil types naturally contain different concentration of ⁸⁶Sr and ⁸⁷Sr, so one can differentiate which substrate type a bison was grazing on by the concentration found in the bison’s teeth. If the ratios of ⁸⁷Sr and ⁸⁶Sr found in the bison teeth match the soil surface ratio, it would be expected that these areas were utilized by the bison. Surprisingly, few bison of the eastern Great Plains actually traveled across more than one examined area. Further, every herd’s home ranges were smaller than expected, with a radius less than 50 kilometers. These data reveal that the bison did not respond to climatic pressures by searching for literally greener patches. This finding additionally indicates that despite rising temperature, the bison had enough to eat in these prairies.

            It appears from this study that the bison during the middle Holocene adopted a policy of if ain’t (that) broken, don’t fix it. The bison did not change their diet, water source or mobility in response to the increase in temperature and the expected limitation of resources that accompanies low water availability. Surprisingly the habits of the bison from the middle Holocene closely mimic the behaviors of bison today. One explanation for this finding may be that the flora that Bison grazed in the middle Holocene may be surprisingly similar to today’s reconstructed prairies where the bison are currently confined due to the destruction of almost all of C₄ tallgrass prairie in Iowa. Importantly, these findings contradict the belief that bison movement and grazing preferences were strongly influenced by the structure of local resources. From Widga et al.’s (2010) findings, we can glean that bison roamed the eastern Great Plains tallgrass prairie year round, creating a constant influence on the behaviors and growth of tallgrass prairie species. While the ecosystem and climate have changed and shifted over thousands of years in the eastern Great Plains, bison activities concerning diet preferences, water source utilization and migration patterns have stayed fairly constant. Hence, at least for the middle Holocene eastern Great Plains bison, old habits truly die hard.

References

Baker, R. G., Bettis III, E.A., Schwert, D.P., Horton, D.G., Chumbley, C.A., Gonzalez,

            L.A. and Reagan, M.K. 1996. Holocene Paleoenvironments of Northeast Iowa.

            Ecological Monographs 66(2): 203-234.

Fortin, D., Fryxell, J.M., O'Brodovich, L. and Frandsen, D. 2003. Foraging ecology of

            bison at the landscape and plant community levels: the applicability of energy

            maximization principles. Oecologia 134: 219–237.

Widga, C., Walker, J.D. and Stockli, L.D. 2010. Middle Holocene bison diet and mobility

            in the eastern Great Plains (USA) based on 13C, 18O and 87Sr/86Sr analyses of

            tooth enamel carbonate. Quaternary Research 73 (2010) 449-460.

A Powerful New Tool for Iowa Climate Estimation from Flora Distribution

It is a self-evident fact that a plant’s potential for occupancy of a habitat is climatically limited. The thought of a palm tree in the desert is absurd - the sight of one would be bewildering. The variable distribution of plants is a phenomenon which all humans encounter in their daily lives, and leads to our basic understanding of the concept - a plant will only grow where it is able. Underlying this simple observation are the mechanisms of natural selection which allow species to diversify and occupy different niches, thus avoiding potential competitors. The connection is so strong between a plant and its climatic range that one could easily guess the climate of a place without ever physically feeling the climatic conditions - it only takes a plant or two for us to imagine what a place is like. Thus, pictures of palm trees conjure up thoughts of subtropical coasts and island habitats with warm year round conditions, while pictures of cacti might lead us to imagine an arid area with high mean annual temperature and little rainfall. Now think about this - if you were given a picture of a landscape which featured palm trees, and told that the picture was taken 10, 20, 50 thousand years ago, you would still suppose a specific climate to the area, despite the chronological distance (which I would like to coin as a neologism, “chronodistance”, because heck if that doesn’t sound cool.)

Such postulations reside under the heading of ECV, or Estimation of Climate from Vegetation.The general underlying principle behind ECV would state that plant species perform better in specific climates, resulting in a patterned correlation between species distribution and climatic variables such as temperature, precipitation, etc. Because of the complexity of floral climate ranges, the requisite mountains of data and the variability of climates across all scales, statistical models are a necessary medium for testing hypotheses regarding the connections between climate and vegetation in the real world. The use of statistical models to associate plant species distribution and climate variation has become increasingly common within scientific literature (at least common enough to have passed the acronym threshold). With fossil pollen data and plant fossils from packrat middens, such models can be (and have been) employed to estimate geographical climate variation on multiple scales across the past, and may easily be site specific (I’m lookin’ at you, Iowa).

Enter Harper and Nixon (2015) with CRACLE, Climate Reconstruction Analysis using Coexistence Likelihood Estimation. While CRACLE is but one of many ECV models, it is regarded (agreeably) by the authors as somewhat superior to its earlier counterparts for reasons which vary with comparison. One of the most appealing initial details is that CRACLE is compatible with publicly available data on plant distribution and climate variation. It is a simple recipe list which CRACLE requires: a site-specific species list and a climate profile for each species are, essentially, all that is needed. Most important, the former may easily be supplied by fossil data. This reduce the site-specificity to some degree, but not drastically enough, it seems, to limit the use of CRACLE for paleobotanical studies.

There are two basic types of ECV: taxonomic, which correlate taxa and climates, and physiognomic, which correlates leaf shape characteristics with climate. CRACLE lies in the family of taxonomic ECV, often allowing the use of free georeferenced species data in lieu of actual data sampling - a boon. While seemingly more accurate than previous taxonomic methods, physiognomic ECV is less practical as it requires extensive data acquisition regarding leaf form and abundance, and has been criticised because physiognomic correlations with climate may be geographically variable. Furthermore, for paleobotanical modeling, it is much harder to get an ample dataset of physiognomic characteristics than it is to simply see who was around.

Harper and Nixon’s study put young CRACLE through the ringer in order to test its accuracy in climate prediction compared with other ECV models. They used data on 4,000 species to infer the climate of 165 sites across the world (barring Antarctica and Europe). CRACLE’s hypothesis states that the probability of the occurrence of a single species within an area (ascribed a climate variable) can be predicted if the probability of occurrence in relation to that variable is known (See “Figure 1”). Initial climate parameters were on a rasterized scale of 0.416 degrees (~4.6km) including mean annual temperature, average annual max/min temperatures, mean annual precipitation, and the mean precipitation of the three wettest and driest months. To test the hypothesis, climate tolerance profiles were defined as the range limits of these variables that any one species was found in. To evaluate CRACLE’s performance, Harper and Nixon used the good old Pearson’s R to compare CRACLE climate estimates to the actual climate data from Worldclim. They also ran smaller scale CRACLE analyses on The Harvard Forest and Barro Colorado Island, to test for the effects of sample taxa quantity on accuracy.

Harper & Nixon found that CRACLE demonstrates more accuracy than previous ECV models, both physiognomic and taxonomic. CRACLE had an average deviation of 1.5C from the Worldclim data, whereas other models deviated up to 5C. Overall, CRACLE showed more accurate mean error and narrower estimates for mean annual temperature. In addition, the small scale trials demonstrated that CRACLE’s accuracy is not dependent on a large number of species, converging on the actual mean near 40 species (“Figure 5”).

So, CRACLE is an excellent method for estimating climate through vegetation, perhaps the most accurate to date, with the bonuses of requiring minimal field sampling and an achievable number of input taxa. There are implications here for Iowan flora and climate which should be discussed. Despite Harper and Nixon’s use of CRACLE on a global scale, nothing in their study puts a spatial limit on a geographical area of Iowa’s size. The authors stress the potential use of CRACLE in paleobotanical studies, although there may be issues regarding the climate tolerance profiles of extinct species - there do not seem to be precise ways of measuring a species’ historic climate niche change, and any assumptions or associations to the nearest living relative may be useless in timeframes beyond the Pleistocene. Moreover, while a species’ presence in the past may be confirmed or denied through fossil or pollen records, the lack of exactness of georeferenced individuals may reduce CRACLE’s accuracy.

Yet CRACLE is clearly a powerful ECV model and should not be discounted for paleobotanical studies, considering other ECV methods face many of the same hurdles. In this author’s opinion, it is, at the time of writing, across-the-board the best method for paleobotanical ECV studies. Although field sampling and sample dating are required for paleobotanical studies, CRACLE is still less labor intensive and more accurate than other ECV models, with the bonus of requiring a relatively low number of input taxa. In Iowa, it would be possible to use the pollen record to reconstruct potential climates for whenever fossil pollen represents enough to taxa to perform CRACLE analysis, as long as the timeframe is not substantial enough to possibly feature altered climate niches among relative species. It seems that, using CRACLE, we may get one step closer to cracking the case of Iowa’s climate history.

Works Cited:

Harbert, R.S., and Nixon, K.C. 2015. Climate reconstruction analysis using coexistence likelihood estimation (CRACLE): A method for the estimation of climate using vegetation. American Journal of Botany 102(8): 1277-1289.

When Who Gets There First Matters—Priority Effects in the Prairie

The tallgrass prairie ecosystem is one of the most threatened in North America. Once covering 70-80% of Iowa, it now covers less than 1/10 of a percent. The remaining fragments are scattered throughout Iowa, often highly degraded and on marginal land. European agriculture and its affiliated machinery have caused much of this transformation. However, individuals, local and state organizations are pushing to restore the prairie through new plantings. The success of these efforts depends on overcoming several obstacles, namely invasion from Eurasian agricultural and ornamental species.

How can invasive plants hurt the establishment of natives? Priority effects holds the answer to this question. A priority effect is the impact of an early arriving species on later community development by constraining which later arriving species can coexist. For example, if species A had a stronger priority effect than species B, species A would more greatly influence what other species could or could not colonize that same space. Research summarized by Wilsey et al 2014 has pointed to a stronger priority effects in exotic, Eurasian species compared to native, tallgrass prairie species. This predicts that Eurasian species will influence later community development than tallgrass prairie species.

A 2014 paper published in New Phytologist by Wilsey et al addressed why priority effects may be stronger in exotic rather than native species. They predicted that the difference in priority effects was due to differences in regenerative traits, or traits related to early development.

Wilsey et al hypothesized that priority effect differences were due to either (1) native and exotic species differing in these regenerative traits or (2) native species differing from cultivated exotic genotypes, but not from wild exotic genotypes (Figure 1). To test these hypotheses, the researchers created 14 matched sets of species, each set containing a native, a wild exotic and a cultivated exotic species (total 42 species). These species shared the same taxonomic family if not tribe and had similar ecological functions (life history, photosynthesis mechanism), spanning the 4 main functional groups of the tallgrass prairie. Additionally, all the exotic species are listed on either national or local invasive species list.  

Figure 1: Illustrated hypothesis of Wilsey et al 2014 (created by Wilsey et al 2014). The x-axis is a combination of regeneration measures with the curves showing the distributions of the native North American individuals with the exotic Eurasian individuals. They had found no evidence of (a). Therefore, that hypothesis was not investigated in their paper.

 

Their design was an additive competition greenhouse experiment. In separate pots, each of the ‘early arriving’ species (the matched 42) were seeded. These seedlings were allowed to grow for 21 days and then a mix of 39 different native species was added as the ‘late arrivals’. 11 pots were started as controls, seeded only with the late arrivals so the researchers could detect how much the early arriving species influenced the growth of the late arriving species. They harvested the plants at the end of the growing season (May 10-September 13) and analyzed diversity, species richness and late arrival biomass (Figure 2).

 

 Figure 2: Illustrated additive comparative design. Each row corresponds to a different early arrival. Each column represents a different time point. Notable findings captioned below.

 

Since the researchers were interested in regenerative traits, they measured timing of emergence, emergence rate, seedling biomass, canopy light capture, and seedling height of the early arrivals. At harvest, they measured the total aboveground biomass of late arrivals, species diversity, and species richness. These measurements described how the community assembled given which species arrived early and were used to estimate the priority effects of each early arrival group (native, wild exotic, cultivated exotic).

The regenerative traits differed significantly between exotic genotypes and native species, but not between wild and cultivated exotic species. Exotic seedlings were larger, emerged earlier and captured more sunlight. Cultivated exotic genotypes had larger biomass than wild exotic than native, but this difference was found to be a small factor in the overall priority effects. Furthermore, these different measures were highly correlated, meaning that if a species was likely to emerge early, they often captured more sunlight and vice versa.

The researchers found much lower diversity at the end of the season in the wild and cultivated exotic pots than in the native pots, often creating monocultures. Because diversity and richness were higher in native treatments compared to exotic treatments, natives were classified as having smaller priority effects. However the most interesting finding was the strong correlation between regeneration traits and community measures. Using principle component analysis, regeneration traits were strong predictors of later establishment diversity and richness. In fact, these traits fully accounted for the variability in diversity, but not total late arriving biomass. In sum, the researchers found evidence supporting hypothesis 1 but none to support hypothesis 2.

This has serious implications since previous research has shown that exotics reduce native abundance not through competition with adult plants but in reducing the establishment of native seedlings. In terms of management practices, these findings suggest focus on establishing natives early before exotics can be fully established. This will prove challenging since approximately 25% of all the species within the Iowa flora are classified as non-native. Additionally other studies mentioned by Wilsey et al (2014) have shown that the impact of priority effects can last for several years, meaning longer time scales for restoration efforts.

But all is not lost. Work by Carter and Blair 2012 has demonstrated that if a representative mix of tallgrass prairie species are used to seed restorations, given time, they can approach similar diversity as remnant prairies. Furthermore, these results are of a greenhouse experiment, meaning that resources are plentiful and weather benign, which rarely happens in the field. The next step for this research is to take the experiment out into the field to see if differences in weather cues and predator activity may alter these outcomes.

Carter, Daniel L. Blair, John M. 2012. Recovery of Native Plant Community Characteristics on a Chronosequence of Restored Prairies Seeded into Pastures in West-Central Iowa. Restoration Ecology 1-10

Wilsey, Brian J. Barber, Kaitlin. Martin, Leanne M. 2014. Exotic grassland species have stronger priority effects than natives regardless of whether they are cultivated or wild genotypes. New Phytologist 1-10

What's Killing Iowa's Trees? Diseases and Pests Putting Ecosystem Services at Risk

Over a short period of five years, my family lost seven of the ten old-growth white oaks in our surrounding yard. This does not take into account the numerous oak deaths that occurred during the same time period among our fifteen acres of northern Wisconsin old-growth forest. These massive, shade-providing trees I grew up under fell victim to oak wilt, a disease that causes rapid death and is easily spread by the transportation of firewood and lumber across states and counties. While I grieve the loss of my familial oaks because of childhood reminiscence, the widespread occurrence of tree death due to pests and disease affects the availability of numerous ecosystem services associated with forests around the Midwest and rest of the country.

             Before Euro-American settlement, the land that now makes up the state of Iowa was mostly prairie, with only roughly 18 percent of land covered by forests. This low percentage of forest cover has been reduced to approximately 7 percent today due to settlement and agriculture. Iowan land today is mostly cultivated for agricultural use, leaving riparian forests and wooded lots largely scattered around the state. The ecosystem services that these forest systems provide are vulnerable to change due to pests and diseases like Dutch Elm disease and the Emerald Ash Borer. In the 1960’s Iowa’s Elm population experienced a large decline due to the invasion of Dutch elm disease. Today, the Emerald Ash Borer presents a huge threat to the ash population in the state. Last month (10/15), the USDA mapped the effects of the pest in a map that declared the entire state of Iowa to be under quarantine, stating that wood is not to be transported across state lines or counties. The map can be seen below.

In their 2013 review article in Science, “The Consequence of Tree Pests and Diseases for Ecosystem Services,” Boyd et al discuss the introduction, consequences, and future management outlook for pests and diseases that induce mortality in global tree populations. The authors define pests and diseases in this context as herbivores or infections that “are perceived to reduce the value of trees to people,” whether that value is monetary or not. In their introduction to the concept of changes in ecosystem services, a figure is presented to display projected changes in forest structure in response to a disease that decreases abundance of native species (Figure 1). This figure shows an initial decline in a number of ecosystem services provided by the forest followed by eventual recovery of a select few as a result of compensatory growth of non-affected species. While this recovery may take place, the loss of biodiversity within the forest structure may have greater consequences, as presented by authors later in the paper.

Boyd et al. present examples of both pests and pathogens that currently account for large numbers of tree deaths globally. On the pest side, insects including the Asian longhorn beetle, Oak processionary moth, and Emerald ash borer are responsible for devastating loss of broad-leaved trees, oak species, and ash species respectively. These herbivorous creatures’ pathogenic counterparts come from a list of fungi, bacteria, viruses, and helminths that are the cause of commonly discussed diseases such as Pine wilt, Dutch elm disease, and chestnut blight. While both insect pests and disease-causing pathogens can result in widespread tree death, interaction between the two often exacerbates mortality within a forest community. Defoliation by an insect pest may leave a tree more vulnerable to infection by a pathogen that results in a deadly disease.

The geographic ranges of pest and pathogenic tree killers have increased with the evolution and development of world trade and human settlement. As discussed in the article, movement of trees beyond native ranges with the rise of international trade and agriculture brought with it the import of new pests and diseases to native tree species. Introduction of non-native insects and pathogens is detrimental to native forests that have not evolved to have mechanisms of resistance against such threats. Authors suggest that while not all introduced non-native species are harmful to native flora, genetic reassortment and hybridization can result in acquired virulence factors that become harmful to native trees.

Boyd et al. discuss evidence in support of climate warming’s detrimental influence on tree susceptibility to pest and disease. In addition to increased mortality, climate change introduces another level of importance for sustained areas of arboreal forests. Along with monetary-driven services such as pulp, timber, and food sources, trees act as a major carbon sinks. Authors suggest that large arboreal forests, when exposed to extreme pest-induced defoliation, can become carbon sources that contribute to the greater global climate change.

            In their suggestions for management and future disease control methods, the authors emphasize the need for risk-focused approaches to consider the larger set of ecosystem services being provided by arboreal species in question. Suggested approaches to management procedures are broken into three steps presented as the identification of 1) agent (whether fungal, insect, etc.) 2) pathway of transport (such as water or man made pathways.) 3) category of at-risk ecosystem services. While many tree diseases and pests affect local forest communities, the transport of timber and firewood can exacerbate the spread of such endemics.

Boyd, I. L., P. H. Freer-Smith, C. A. Gilligan, and H. C. J. Godfray. "The Consequence of Tree Pests and Diseases for Ecosystem Services." Science 342.6160 (2013): 1235773. Web.

Iowa Department of Natural Resources "Emerald Ash Borer."  Web. 15 Nov. 2015. <http://www.iowadnr.gov/Conservation/Forestry/Forest-Health/Emerald-Ash-Borer>.

How to Build a Prairie

            Less than 1 percent of the Midwest’s original tallgrass prairie remains. Iowa has been hit particularly hard, the vast majority of tallgrass prairie having been converted to agricultural land. One way conservationists are trying to deal with the loss of tallgrass prairie and other degraded ecosystems (and their ecosystem services) is ecological restoration, which involves re-creating the community structures that existed before human degradation. It’s more complicated than just putting some plants in the ground; restoration ecology draws upon fields such as wildlife biology, landscape ecology, and invasion ecology in order to re-create viable ecosystems. Restoration projects of varying sizes -including the large-scale Neal Smith National Wildlife Refuge - have become important for maintaining biodiversity and ecosystem functions in Iowa.

            In order to better restore tallgrass prairie in Iowa, researchers need to know what factors influence restoration outcomes. Ecologists disagree on the extent to which the outcomes of restoration can be determined by land management decisions. For instance, factors such as soil conditions, land-use legacy, and site history - which are largely out of manager’s control - have been proven in the past to affect species establishment and community assembly.

            A recent study (Grman, Bassett, & Brudvig, 2013) looked at which factors are most important in determining restoration outcomes. The researchers gathered data on the restoration and management of 27 different prairies in southwest Michigan. They studied four main factors:

•    Management - seed-mix species richness, grass and forb (non-grass herbaceous plant) planting density, and burn frequency

•    Historical factors - land-use history, site age, and precipitation during the planting year.

•    Site characteristics - soil type, soil heterogeneity, shade, site area, and perimeter: area ratio

•    Landscape context - what kind of habitats (forest, grassland, cropland) are surrounding the restoration?

            The researchers found that management decisions are actually quite important in determining the plant community structure of prairie restorations. Overall, management practices accounted for 65% of the variation in sown species richness. Higher forb seeding density was shown to increase species richness and decrease the richness of non-sown species (species not intentionally planted in the restoration). Using seed mixes with lower numbers of species and a higher proportion of dry prairie grasses, however, led to a higher plant beta diversity. Together, seed mix composition and forb seeding density were shown to have a significant effect on plant community composition.

            Historical factors that are out of managers’ control were also highly important in determining the restoration outcome. Older sites had lower numbers of non-sown species, and lower numbers of species in general, probably due to the increased abundance of Andropogon gerardii (a dominant C4 grass) in older sites. In formerly tilled sites, sown-species beta diversity was generally lower, and former old-fields tend to have lower diversity of non-sown species while having more exotic clonal C3 grasses.

          Surprisingly, site characteristics did not have a major effect on species richness. They shouldn’t be completely discounted, however, as sites with more heterogeneous soil had higher abundance of Poa pratensis, an exotic clonal C3 grass. It should also be pointed out that site characteristics, such as soil type, have been shown in other studies to have bigger effects on restoration outcome. The sites used in this study may have all exhibited conditions “suitable” for restoration, leading to the lack of variation based on this category. Landscape features, including the habitat types surrounding the restored prairie, were also less important, but restorations in areas dominated by row crops did have lower non-sown species beta diversity.

Figure 1. This graphic shows how much of the variation in species richness, beta diversity, and community composition among the sites is explained by each of the main factors (Management decisions, site characteristics, landscape features, site history, and unknown/residual factors). As seen in the graphic, management was a very important determinant of species richness and beta diversity.

So, what does this study in Michigan have to do with the natural history of the Iowa flora? The results of this study can be applied to tallgrass prairie restoration in Iowa, a state which has undergone similar land use changes.

If the goal of the restoration is to maximize the diversity and richness of native species restored, then the results of the paper suggest using seed mixes with a high diversity of native species, a high density of forbs, and a lower density of grasses, which is something relatively feasible for managers to accomplish. 

            However, if the ultimate goal of a restoration is to accurately restore tallgrass prairie to their conditions before human settlement, then things become more complicated. One study (Carter & Blair, 2012) compared community structure in six restored prairies (with similar landscapes and seeding regimes to the Michigan restorations) to 3 nearby remnants in Iowa. The study found that while the restorations were similar to remnants in their native species richness and abundance, species diversity, and late-appearing C3 plant abundance, they differed in many other aspects. For instance, restorations had a greater richness of late-appearing C3 species, and a lower abundance and richness of early-appearing native plants than remnants. So, while the restorations were able to create viable ecosystems, they were not able to necessarily re-create ones resembling those of Iowa’s past. The authors suggest that this discrepancy is due to management practices and not the inability of these plants to establish, as the seed mixtures most commonly used in restorations often under-represent early-appearing C3 plants.

            Restoration can have many different goals, whether it is re-creating a vanishing, historic habitat, restoring biological diversity, or reestablishing ecosystem services. The Michigan and Iowa studies suggest that the success of these goals, and which of them are most prioritized, can often depend on the management choices we make. Whether we are re-creating a historic habitat or actually creating a slightly new one can depend on something as simple as the type and amount of seeds planted in a restoration.

References

Carter, D.L. and Blair, J.M. 2012.  Recovery of Native Plant Community Characteristics on a Chronosequence of Restored Prairies Seeded into Pastures in West-Central Iowa. Ecological Restoration. 20(2): 170-179.

Grman, E. Tyler Bassett, T., and Brudvig, L.A. 2013. Confronting contingency in restoration: management and site history determine outcomes of assembling prairies, but site characteristics and landscape context have little effect. Journal of Applied Ecology. 50: 1234–1243.

            

Iowa vegetation. Here yesterday. Maybe here today. Gone tomorrow?

 -----

Lower Pine Lake, Pine Lake State Park, near Eldora Iowa. Above. View of the island from the south shore, in 2005. The Pine Creek population of eastern white pine (Pinus strobus L.), many tens of km southeast of the main body of the current species’ distribution, experienced substantial damage and mortality in an August 2009 hailstorm.  At right, several dead trees surround a more sheltered survivor, in 2015. The fate of this population, likely a relict of a time when coniferous forests were more widespread in Iowa, illustrates the vulnerability of such vestiges of Iowa’s vegetation history to chance disturbances.

In characteristically clunky prose, which I am compelled here to quote, I asked my BIO 305 (Evolution of the Iowa Flora) students to “compose a 3-4 page (double-spaced) blog post that informs a scientifically literate lay audience about a primary research article that is about the history of the Iowa flora or contains ideas or findings germane to understanding historical change in the Iowa flora. (In other words, the article does not need to be based on Iowa, but it must be Iowa-relevant).” The students’ posts, which will follow this one, should highlight interesting recent articles about vegetation past, present, and/or future and explain how those articles relate to what we know—and don’t know—about Iowa’s vegetation history and possible futures. Reading these “research blogs” should give you an entry into how scientists assemble evidence about vegetation change, as well as remind you that Iowa’s flora was, is, and, I hope, will be more than just Zea mays L. and Glycine max (L.) Merr. (You probably don’t need links to those two species.)