Research Article |
Corresponding author: Vasiliy T. Lakoba ( vtlakoba@gmail.com ) Academic editor: Elizabeth Wandrag
© 2021 Vasiliy T. Lakoba, Gregory E. Welbaum, John R. Seiler, Jacob N. Barney.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation:
Lakoba VT, Welbaum GE, Seiler JR, Barney JN (2021) A perennial invader’s seed and rhizome differ in cold tolerance and apparent local adaptation. NeoBiota 70: 1-21. https://doi.org/10.3897/neobiota.70.64614
|
Extreme cold plays a key role in the range boundaries of plants. Winter survival is central to their persistence, but not all structures are equally susceptible to frost kill and, therefore, limiting to distributions. Furthermore, we expect intraspecific variation in cold tolerance both within and among tissue types. In a laboratory setting, we determined freezing tolerances of two overwintering propagule types – seeds and rhizomes – of the globally invasive Johnsongrass (Sorghum halepense), testing apparent emergence and electrolyte leakage as a proxy for cell death. We used 18 genotypes from agricultural and non-agricultural habitats spanning the climatic extremes occupied by Johnsongrass in the US. Single node rhizome fragments had an average LT90 of -5.1 °C with no significant variation based on home climate or ecotype. Seeds frozen at -85 °C suffered a decline in germinability to 10% from 25% at 22 °C. Population origin did not affect seed response to any temperature. However, non-agricultural seeds germinated more and faster than agricultural seeds from the coldest climates, with a reversed relationship among warmest origin seeds. Regardless of ecotype, seeds from the cold/dry and wet/warm sectors of Johnsongrass’s range germinated more and faster. Drastic differences in cold tolerance between seeds and rhizome and evidence for seeds’ local adaptation to land use and climate suggest that its spread is likely limited by winter rhizome survival, as well as adaptability of germination behavior to longer winters. These findings shed light on Johnsongrass’ dispersal dynamics and help identify future avenues for mechanistically understanding its range limitation.
Cold tolerance, invasive plants, land use change, local adaptation, range boundaries
Species range limits are often dictated by climatic tolerances at large spatial scales. For most plants, temperature and moisture availability play a leading, though not unilateral, role in defining distributions (
Both sexual and vegetative structures need cold tolerance to survive between growing seasons in non-tropical climates. While individual perennation is dependent on winter cold tolerance (
Perenniality is a boon to plant fitness because it reduces each subsequent year’s demand for vegetative-to-reproductive allocation (
Perenniality can also buffer against challenging growing conditions (e.g., cold winter temperatures,
Advantages of having rhizomes (perennial underground stems) are evident across many plant systems (e.g.,
The cosmopolitan invader Johnsongrass (Sorghum halepense) sexually reproduces annually through seed, while its perenniality is achieved by rhizome survival through the winter (
Rhizome cold tolerance may also be related to overall rhizome development, which responds to resource inputs. Fertilization and irrigation can be responsible for rhizome development changes compared to growth in non-agricultural settings (
Specific to Johnsongrass,
In the broader context of plant invasion biology, we set out to test whether adaptation to different land uses can yield divergent stress adaptation in a relatively short period of time (i.e., decades to centuries). While other studies have investigated differences between geographic ecotypes and home climates as predictors of perennial plant cold tolerance (
We sourced propagules from our collection of >200 Johnsongrass populations representing the full geographic and climatic variation of its US range. In particular, we drew from a subset of this collection that consisted of seed produced in a common garden setting to account for maternal effects. For this study, we systematically chose populations representing both agricultural and non-agricultural origins, as well as the extremes of mean annual precipitation (MAP) and minimum January temperature (MinT), each averaged across a 30-year span (1981–2010). We used MAP to account for general moisture availability, which interacts with temperature, but is not the focus of our stress tolerance study. However, we chose January MinT as a proxy for the extreme cold experienced at a given location, which may correspond more directly with adapted cold tolerance rather than the annual mean (
The Johnsongrass populations selected for the rhizome and seed experiments (see details in Table
A complete list of the Johnsongrass populations used in the seed freezing and rhizome freezing experiments. The ecotype source and the population is indicated, as well as the 30-year normal of mean annual precipitation (MAP) and minimum January temperature (MinT).
Population | Ecotype | MAP(mm) | MinT(°C) | Rhizome data | Seed data |
---|---|---|---|---|---|
CA-2 | non-agricultural | 262 | 3.26 | yes | yes |
TX-1 | agricultural | 923 | 2.7 | yes | yes |
NM-4 | non-agricultural | 234 | -1.48 | yes | no |
AL-10 | non-agricultural | 1456 | -0.64 | yes | yes |
GA-6 | agricultural | 1197 | 0.69 | yes | yes |
KS-4 | agricultural | 801 | -6.81 | yes | yes |
TX-2 | agricultural | 1481 | 5.1 | yes | yes |
AZ-2 | non-agricultural | 306 | 4.0 | yes | yes |
FL-3 | non-agricultural | 1287 | 4.16 | yes | no |
AZ-3 | non-agricultural | 199 | 4.29 | yes | yes |
OH-7 | agricultural | 1022 | -7.75 | yes | no |
KS-2 | non-agricultural | 771 | -6.74 | yes | yes |
OH-1 | agricultural | 980 | -7.79 | yes | yes |
NM-12 | agricultural | 458 | -5.04 | yes | yes |
OH-8 | agricultural | 924 | -7.49 | yes | yes |
NE-1 | non-agricultural | 790 | -9.45 | yes | yes |
CA-1 | agricultural | 259 | 3.06 | yes | yes |
TX-4 | non-agricultural | 462 | -5.21 | yes | yes |
To release seeds from dormancy, we treated them with commercial strength sodium hypochlorite (Clorox Regular-Bleach, The Clorox Company, Oakland CA) for 4 hours followed by a 1 hour water rinse (
Ten rhizome segments (10–20 mm long; containing only one node) from each plant (representing a single population) were sealed individually in capped 5 mL plastic culture tubes (Samco DCT, Thermo Fischer Scientific, Waltham MA) and submerged in cooling baths of 50:50 ethylene glycol:water solution for the cold treatments. We limited the rhizome segments to one node due to the known inverse relationship between segment length and probability of emergence in Johnsongrass (
To determine the effect of cold treatments on rhizome viability, after treatment application all samples were removed from the plastic culture tubes and half of the rhizome segments (5) of each population’s replicates were planted at ~2 cm depth in potting mix in plastic transplant trays (Vacuum Plug Tray, The H.C. Companies, Twinsburg OH). Trays consisted of ninety-eight 32 cm3 cells for the individual rhizome segment. Propagation trays were maintained in light and uncovered at room temperature (~24 °C) in the laboratory and were watered to maintain even moisture (every ~3 days). We recorded binary success/failure to emerge, as well as days from treatment to emergence for each rhizome fragment sample.
The other half (5) of the replicates were submerged in 10 mL of deionized water in individual glass test tubes at ~24 °C for electrolyte leakage assessment. Electrolyte leakage, in which K ions play a critical role (
RC = ECt / ECd
where EC = electrical conductivity (i.e., specific conductance), t = post-treatment, d = dead (microwaved). This, in turn, served as a proxy for the proportion of rhizome tissue damaged by the treatment.
We established a relationship between rhizome emergence and RC across all populations and temperatures with a logistic model and extracted the lethal dose (LD90) value – the RC value at which at least 90% of rhizomes do not emerge. We then fitted a Gompertz curve to the RC responses of each population to the treatment temperatures. Each of these curves was then used to inversely predict the temperature at which the LD90 RC value was achieved, yielding each population’s 90% lethal temperature (LT90) value. We then conducted stepwise linear regression of population LT90s on mean sample mass, as well as ecotype identity, MinT, MAP, and second order interactions, optimizing for the corrected Akaike Information Criterion (AICc) via backward selection. Mean sample mass was not subject to model reduction. The logistic model and LD90 extraction were performed in R (3.5.0; R Core Team 2018) using packages ‘aod’ (Lesnoff and Lancelot 2012), ‘ggplot2’ (Wickham 2016), ‘MASS’ (Venables and Ripley 2002), and ‘popbio’ (Stubben and Milligan 2007). Inverse prediction of LT90 and linear regression were performed with JMP Pro, Version 15 (SAS Institute, Inc., Cary, NC).
To conduct the seed freezing experiment, we selected the same 18 populations (9 agricultural, 9 non-agricultural) from the rhizome freezing experiment (Table
We recorded the proportion of seeds in each replicate that successfully germinated (GP), as well as the number of days elapsed between treatment and germination. We also derived a mean time to germination (MGT) in days per seed for each 20-seed Petri dish. Data were collected until no new germination occurred, which was within 12 days of treatment application.
We conducted mixed effects linear regression models of GP and MGT on experimental and populations origin variables. The model included fixed effects of ecotype, treatment temperature, MinT, and MAP, block as a random effect, as well as all possible second order interactions among the fixed effects. We then performed backward model selection, removing non-significant predictors in order to optimize AICc. Block was the only factor intentionally conserved in both models. All statistical analyses on seed freezing data were performed using JMP Pro, Version 15 (SAS Institute, Inc., Cary, NC).
Experimental data are provided in an associated file (Suppl. material
No rhizomes emerged after the -10 °C treatment, but all other treatments (-6 °C, -4 °C, -2 °C) yielded partial emergence. Logistic regression of rhizome emergence on treatment temperature found a significant effect (p < 0.0001) with a predicted LT90 of -7.1 °C, which we calculated instead of the LT50 due to the baseline emergence rate (at the warmest temperatures) of ~50%. Furthermore, rhizome emergence was erratic within populations due to node viability or other uncontrollable qualities, making results too variable for direct estimates of population-level LT90. Therefore, we used the logistic relationship between RC and emergence (p = 0.0081, Fig.
Plots of A the logit model of rhizome emergence response to 5-replicate relative conductivity (RC) means (p = 0.0081), yielding a lethal dose for 90% of samples (LD90) of 0.309247 and B population and whole species lethal temperature for 90% of samples (LT90) values in degrees C based on rhizome RC values, in ascending order.
Rhizome LT90 was generally lower among agricultural (-5.36 ± 0.17) than non-agricultural (-4.98 ± 0.13) populations, but this relationship was not significant (p = 0.079). Rhizome LT90 also did not correlate with MinT (p = 0.640), MAP (p = 0.848), or sample mass (p = 0.478). Population LT90 values ranged from -5.67 °C (OH-8) to -4.43 °C (GA-6), with overall Johnsongrass rhizome LT90 calculated at -5.08 °C (Fig.
Colder treatment temperatures decreased both GP and MGT (p < 0.0001; Table
Effect tests of each linear model of seed germination percentage (GP) and mean germination time (MGT) as reduced via backward stepwise selection for optimized Akaike Information Criterion (AICc). Both response variables were log-transformed to meet model assumptions. Square brackets around variable names indicate variable locking prior to stepwise selection. Alpha level of significance indicated by *** = 0.0005, ** = 0.005, * = 0.05.
log10(GP) | log10(MGT) | |||||||
---|---|---|---|---|---|---|---|---|
DF | SS | F | p | DF | SS | F | p | |
[Block] | 4 | 2.041 | 3.809 | 0.0526 | 4 | 0.191 | 3.101 | 0.0800 |
Ecotype | 1 | 0.940 | 1.753 | 0.1872 | 1 | 0.073 | 1.190 | 0.2768 |
MinT | 1 | 2.541 | 4.741 | 0.0308* | 1 | 0.170 | 2.760 | 0.0985 |
MAP | 1 | 5.667 | 10.576 | 0.0014** | 1 | 0.086 | 13.907 | 0.0003** |
Treatment Temp | 1 | 17.980 | 33.552 | <0.0001*** | 1 | 2.187 | 35.590 | <0.0001*** |
Ecotype*MinT | 1 | 2.845 | 5.309 | 0.0224* | 1 | 0.687 | 11.176 | 0.001** |
MinT*MAP | 1 | 14.554 | 27.159 | <0.0001*** | 1 | 1.423 | 23.156 | <0.0001*** |
There were marked population differences in germination percentage (GP) within and across treatments (Fig.
We found an interactive effect of MinT and MAP on both GP and MGT (p < 0.0001 for both; see Table
Interactive effects of A minimum January temperature (MinT) and mean annual precipitation (MAP) on germination percentage (GP) (p < 0.0001) and B MinT and MAP on mean germination time (MGT) (p < 0.0001) show greater and faster germination associated with populations from cold-and-dry and hot-and-humid climates. Both response variables are log10 transformed to meet model assumptions.
We found that both Johnsongrass seed and rhizome are affected by exposure to acute treatment temperature minima, but on very different scales. While rhizome emergence showed a sharp decline from ~50% emergence to non-viability in the vicinity of -5 °C, seed germinability declined very gradually from ~25% to 10% across the gradient of 22 to -85 °C. The rhizome survival threshold of approximately -5 °C confirmed
We found no differences in seed germination or rhizome emergence response – and therefore no differences in cold tolerance – to freezing treatments based on home MAP and MinT. There were, however, inherent differences in seed germination response based on home MAP and MinT. This yielded a response surface where cool/dry and warm/humid origin Johnsongrass populations germinated more and faster than cool/humid and warm/dry origin populations. It is possible that reduced and delayed germination on dry sites may be a conservative strategy selected for by drought stress, which can be especially damaging for seedlings, compared to seeds or mature plants (
No differences were found between agricultural and non-agricultural populations’ seed or rhizome responses to freezing treatment temperatures, indicating no differences in cold tolerance based on ecotype identity. However, we again found inherent differences in GP and MGT based on home MinT as mediated by ecotype identity. Non-agricultural populations germinated more and faster than agricultural ones when originating from colder climates; however this did not translate to any differential response to our cold treatments. Given the smooth decrease in germinability from +22 °C to -85 °C treatments across all populations, it makes sense that population differences based on a MinT range of -10 °C to +5 °C are unrelated to survival of -85 °C or even -20 °C treatments. Tolerance of the extreme cold could not have been selected for in the landscape, as Johnsongrass seed does not encounter these temperatures. Unfortunately, we were limited by available equipment to test temperatures between -10 and -85 °C. We had posited that any differences in cold tolerance between ecotypes could be driven by energy assimilation and storage from a more favorable preceding season; however, this could not have been the case for seed, as we accounted for maternal effects by using only germplasm that had been grown out in a common environment for a generation.
Cold tolerance differences between seed and rhizome were so vast that they cannot be compared by LT90 values. Seed GP approached 0.1 (analogous to LT90) around -85 °C and no colder treatment was available, meaning that a true dose response curve could not be built for seed as it was for rhizome. This extreme cold tolerance across Johnsongrass populations informed us that seed freezing is likely not range limiting. Given no origin MAP or MinT differences in rhizome LT90, we also cannot test whether the annual climate niche is significantly different between populations. Instead, our evidence points to propagule pressure and phenology as likely factors of northern range limitation. Given the relatively high winter temperatures that rhizomes cannot survive, rhizome segments likely cease to be feasible propagules for range expansion in regions with climates similar to southern Ontario, where populations persist only via seed (
Dormant and non-dormant seeds are clearly not range limiting to Johnsongrass as a species, nor limited based on ecotype or home climate. In other words, seed from anywhere in the North American range can survive winter temperature minima anywhere on the continent. Given that Johnsongrass persists in places with colder winter temperatures than the rhizome LT90 of -5 °C, thermal dynamics of soil are clearly a factor that prevents us from simply predicting cold temperature range limitation. Lack of apparent climate or ecotype adaptation of rhizome cold tolerance tells us that this may be a stable trait within the species, while an expanding “annual range” beyond the perennial range is feasible. However, even though seeds may always be cold tolerant, seedlings are likely to be much more vulnerable to stressors such as late frosts and droughts (
One of the primary challenges in interpreting rhizome cold tolerance and forming hypotheses about continental distributions is the interaction of climate change with snow cover and, thereby, insulation of soils in winter. Rhizome carbohydrate storage, bud formation, survival, and phenology of spring emergence are known to be sensitive to winter snow depths (
By uncovering drastic differences in cold tolerance and between organs and populations, we are able to better understand their potential contributions to species distributions. We can begin to deduce which organs may or may not be limiting to overall plant stress tolerance and whether there are other physiological or phenological drivers of known range limits. Likewise, we can narrow possible drivers of range limitation and connect them to spatially explicit habitat parameters. However, we must be careful not to conflate experimentally isolated stress limits with distribution boundaries (
We see that not all propagules of a plant respond similarly to all stresses – cold temperatures being a key example. In studying and managing invasive plants, this can inform our understanding of likely dispersal vectors. Our findings on Johnsongrass, in corroboration with
We thank Valerie Thomas and Brian Strahm for providing manuscript draft feedback. We thank Dave Mitchem for consultation on laboratory methods. We thank David Haak for laboratory equipment use. We thank Edward Gaines for common garden care.
This work was partially supported by the Virginia Tech College of Agriculture and Life Sciences and the National Institute of Food and Agriculture Global Food Security CAP [2015-68004-23492 to JNB].
We would like to acknowledge support in the publication of this article from Virginia Tech's Open Access Subvention Fund.
Table S1
Data type: propagule emergence
Explanation note: A table of emergence response by population, temperature, and propagule type.