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1. Rangewide responses of Mimulus cardinalis to an extreme heat event

   https://doi.org/10.1002/ajb2.70145  

   Albano, Lucas J; Bingham, Robin A; Correa, Sulma; Laufenberg, Catherine G; Payst, Cristina; Muir, Christopher D; Sheth, Seema Nayan (February 2026, American Journal of Botany)

   Abstract PremiseExtreme events are an understudied aspect of ongoing anthropogenic climate change that could play a disproportionate role in the threat that rapid environmental shifts pose to natural populations. MethodsWe exposed plants originating from seeds that were harvested before (ancestors) and after (descendants) multiple extreme heat events from six populations across the range ofMimulus cardinalis(Phyrmaceae) to a short‐term heat‐wave treatment in controlled growth chamber environments. We assessed physiological, performance, and functional responses (stomatal conductance, leaf temperature deficit, photosystem II efficiency, relative growth rate, specific leaf area, and leaf dry matter content) to the heat‐wave treatment, along with evolutionary responses (differences between ancestors and descendants) ofM. cardinalispopulations to the recent natural extreme heat event. ResultsPlants in the heat‐wave treatment increased their overall performance, and the magnitude of increase was generally greatest among trailing‐edge populations. Despite limited overall trait differences between ancestors and descendants, there was some evidence of divergent evolutionary responses among regions to the natural extreme heat event. However, we did not find evidence of adaptive evolution that affected howM. cardinalispopulations responded to the heat‐wave treatment. ConclusionsThese results demonstrate that manyM. cardinalispopulations may reside in environments that are below their optimum average temperature, revealing potential resiliency to future warming. However, limited evolutionary responses inM. cardinalisto the recent extreme heat wave could still indicate potential for future vulnerability to extreme climate events of increased intensity, frequency, and duration. 

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2. Evolution of floral traits and mating systems under drought: a range-wide study of Mimulus cardinalis

   https://doi.org/10.1093/aobpla/plaf062  

   Wilborn-Pilotte, Olivia; Cook, Emily; Kutella, Katelin; Sheth, Seema Nayan; Diez, Jeff (October 2025, AoB PLANTS)

   Picó, F Xavier; Oakley, Christopher G (Ed.)

   Abstract Climate change is intensifying droughts across the globe, challenging species to adapt to novel conditions. While plant physiological and phenological responses to drought are well-documented, less is known about how water scarcity affects the evolution of selfing across species ranges. According to the selfing syndrome hypothesis, in environments where selfing confers a fitness advantage, selection should favour floral traits associated with increased selfing relative to outcrossing. We used a field experiment near the northern range edge of the scarlet monkeyflower (Mimulus cardinalis) to test this hypothesis both spatially (among leading-edge, central, and trailing-edge populations), and temporally (between cohorts separated by a period of historic drought). Although populations from different range positions showed genetic differentiation in some floral traits, these differences did not consistently support predictions of the selfing syndrome hypothesis. Contrary to the predictions of reduced investment in floral rewards and increased selfing ability at range edges, the sugar content of nectar was greater and autogamous seed set was smaller in leading-edge than central populations, herkogamy tended to be greater in trailing-edge populations relative to leading-edge and central ones, and nectar volume did not vary predictably among regions. There was no support for the evolution of selfing syndrome from the predrought ancestors to the postdrought descendants. Instead, in leading-edge populations, descendants evolved greater sugar content relative to ancestors, and there were no other differences between ancestors and descendants in any other trait or region. Overall, these findings suggest that mating system evolution in M. cardinalis likely reflects a complex interplay of regional factors including range position, historical adaptation, and local environmental variability, rather than simple stress-induced shifts towards selfing. 

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3. Data from: Evolutionary responses to historic drought across the range of scarlet monkeyflower

   https://doi.org/10.5061/dryad.18931zd7g  

   Sheth, Seema; Albano, Lucas; Blanchard, Charles; Cook, Emily; Diaz, Rosalinda; Gomez-Vega, Xitlaly; Kutella, Katelin; Moazed, Mariam; Patel, Macy; Prange, Julia; et al (January 2025, Dryad)

   {"Abstract":["Adaptive evolution is a key means for populations to persist under\n environmental change, yet whether populations across a species’ range can\n adapt quickly enough to keep pace with climate change remains unknown. The\n breeder’s equation predicts the evolutionary change in a trait from one\n generation to the next as the product of the selection differential and\n the narrow-sense heritability in that trait. Incorporating these aspects\n of the breeder’s equation, we performed a resurrection study with the\n scarlet monkeyflower (Mimulus cardinalis) to evaluate whether traits\n associated with drought adaptation have evolved in populations across a\n species’ range in response to extreme drought. We compared trait and\n fitness differences of pre-drought ancestors and post-drought descendants\n from six populations transplanted into three latitudinally-arrayed common\n gardens and quantified phenotypic selection and trait heritabilities. The\n strength, direction, and mode of selection varied among traits and\n gardens. Trait heritabilities were relatively low and did not differ\n dramatically among populations or gardens. Overall, instances of\n evolutionary responses between ancestors and descendants were few and\n small in magnitude, but the magnitude of these evolutionary differences\n varied among gardens. Together, these results suggest that the expression\n of genetic variation, and thus traits, depends on the environment, and\n that environmental variability in field settings may mask the genetic\n variation that is often detected in greenhouse environments. "],"TechnicalInfo":["# Data from: Evolutionary responses to historic drought across the range\n of scarlet monkeyflower\n [https://doi.org/10.5061/dryad.18931zd7g](https://doi.org/10.5061/dryad.18931zd7g) ## Description of the data and file structure These data are associated with a common garden study of scarlet monkeyflower (*Mimulus cardinalis*). In 2023, we transplanted pre-drought 2010 ancestors alongside post-drought 2017 descendants from two northern-edge, two central, and two southern-edge populations into three experimental gardens near the northern range edge, latitudinal range center, and southern range edge in the western United States. We collected data on several physiological and leaf traits associated with adaptation to drought, along with proxies for fitness, including survival and reproductive output.   ### Files and variables 1\\. **PERSIST_2023_data.csv**: 2023 trait data for all populations and cohorts in all gardens * garden: experimental garden (north, center, south) - block: experimental randomized block in garden (1 - 10 in north, 1 - 11 in center, 1 - 10 in south) * garden_block: variable that combines garden and block - row: row (y-coordinate) of experimental garden; with 4 rows per block; rows 101 - 104 are in block 1; rows 1101 - 1104 are in block 11, etc. * position: position (x-coordinate) of experimental garden; corresponds to a unique plant ID (Cross_ID_Rep), or has no plant (NA) - rowPosition: variable that combines row and position * Cross_ID_Rep: variable that combines unique ID for each full-sibling family and replicate of that family within a particular garden - Cross_ID: unique ID for each full-sibling family; each Cross_ID has a unique mom and dad * Sire_ID: unique ID for sire (father); plants with the same Sire_ID and different Dam_IDs are half-sibs - Dam_ID: unique ID for sire (mother); dams are nested within sires to yield a nested half-sib/full-sib design * Population: Population of scarlet monkeyflower (N1 and N2: northern populations; C1 and C2: central populations; S1 and S2: southern populations) - Year: Year that seeds were collected in the field (2010 ancestors and 2017 descendants) * Year1: Alternate coding for year corresponding to "ancestor" and "descendant" - Date_early: Date of early-season li-600 data collection * Time_early: Time of early-season li-600 data collection - VPDleaf_early: Leaf vapor pressure deficit at the time of early-season li-600 data collection * gsw_early: Early-season stomatal conductance to water vapor, measured at the leaf level with a li-600 porometer in units of mmol/m²/s - freshMass_g: Fresh leaf mass in grams (CDM please add something here about leaf selection) * dryMass_g: Oven-dried leaf mass in grams (CDM please add something here about leaf selection) - leafArea_cm2: Leaf area in square centimeters, derived from leaf scans (CDM please clarify) * lma_g_per_m2: Dry leaf mass in grams per area in meters squared (CDM please clarify) - ldmc: Leaf dry matter content, measured as dry leaf mass in grams divided by fresh leaf mass in grams * sla_cm2_per_g: specific leaf area, measured as leaf area in squared centimeters divided by dry leaf mass in grams - L1: Length of the primary or longest stem at first flower in centimeters * L2: Length of the second longest stem at first flower in centimeters - L3: Length of the third longest stem at first flower in centimeters * totalStemLen: Sum of the lengths of the three longest stems at first flower in centimeters - first_flower_date: Date of first flower * first_flower_doy: Day of year of first flower - last_flower_date: Date of last flower; note this is not reliable because we did not continue collecting data after a certain point in the growing season * last_flower_doy: Day of year of last flower; note this is not reliable because we did not continue collecting data after a certain point in the growing season - flowering_duration: Duration of flowering expressed as the difference between the date of last flower and the date of first flower; note this is not reliable because we did not continue collecting data after a certain point in the growing season * Date_late: Date of late-season li-600 data collection - Time_late: Time of late-season li-600 data collection * VPDleaf_late: Leaf vapor pressure deficit at the time of late-season li-600 data collection - gsw_late: Late-season stomatal conductance to water vapor, measured at the leaf level with a li-600 porometer in units of mmol/m²/s * maxHeight: Maximum stem height in centimeters at the end of the growing season - repBranchN: Number of major reproductive branches at the end of the growing season * RScount1: Number of reproductive structures (flowers, fruits, buds, and pedicels) counted on the stem with the most reproductive structures at the end of the growing season - RScount2: Number of reproductive structures (flowers, fruits, buds, and pedicels) counted on a representative major reproductive branch at the end of the growing season * RScount3: Number of reproductive structures (flowers, fruits, buds, and pedicels) counted on a representative major reproductive branch at the end of the growing season - totalRS: An estimate of the total number of reproductive structures (flowers, fruits, buds, and pedicels) on a plant, calculated as described in Supplementary Methods and Results 2\\. **PERSIST_populations_gardens_1901-2021SY.csv**: annual climate data (1951-2021) for focal populations and experimental gardens. Downloaded from climateNA v. 7.30 on 2022-09-14 (Wang T, Hamann A, Spittlehouse D, Carroll C (2016) Locally Downscaled and Spatially Customizable Climate Data for Historical and Future Periods for North America. PLoS ONE 11(6): e0156720. [https://doi.org/10.1371/journal.pone.0156720](https://doi.org/10.1371/journal.pone.0156720)) * Year: year to which climate data corresponds - ID1: identifier corresponding to population (N1 and N2: northern populations; C1 and C2: central populations; S1 and S2: southern populations) or experimental garden (N_garden: northern garden; C_garden: central garden; S_garden: southern garden) * ID2: identifier that ranks population from northernmost (1) to southernmost (6) - Latitude: y-position of each population or garden in decimal degrees * Longitude: x-position of each population or garden in decimal degrees - Elevation: meters above sea level of each population or garden All other columns are climate variables with units and definitions defined here: [https://climatena.ca/Help2](https://climatena.ca/Help2) 3\\. **subset_correlations.csv**: 2023 fitness data collected on a subset of individuals from each garden * rowPos: Variable that combines row and position (unique plant ID within each garden) - garden: Experimental garden (north, central, south) * population: Population of scarlet monkeyflower (N1 and N2: northern populations; C1 and C2: central populations; S1 and S2: southern populations) - cohort: Year that seeds were collected in the field (2010 ancestors and 2017 descendants) * repBranchN: The number of reproductive branches on an individual (used to calculate total number of reproductive structures/successful fruits) - biomass: mass of the whole plant in grams * L1: Length of the primary (usually longest) stem at first flower in centimeters - L2: Length of the second longest stem at first flower in centimeters * L3: Length of the third longest stem at first flower in centimeters - SC1: Successful fruit count for stem 1 * SC2: Successful fruit count for stem 2 - SC3: Successful fruit count for stem 3 * TC1: Total reproductive structure count for stem 1 - TC2: Total reproductive structure count for stem 2 * TC3: Total reproductive structure count for stem 3 Missing data code: NA ## Code/software #### Code and objects associated with "Evolutionary responses to historic drought across the range of scarlet monkeyflower" Manuscript is in review at The American Naturalist #### STEPS #### A. Download entire repository to desired location B. Open PERSIST-general.Rproj file in R Studio C. Install associated R packages listed at the beginning of each script. D. Create a new subdirectory with the structure "figures/2024_AmNat/manuscript" #### DIRECTORY DESCRIPTIONS data/2024_AmNat: raw data files used in analyses and figures r/2024_AmNat: script files to reproduce analyses in manuscript, numbered sequentially objects/2024_AmNat: output files created by R scripts PERSIST.Rproj: R Studio project file README.txt: text file that contains descriptions of each data file and R script #### SCRIPTS 01a_anomalies_climateNA.R: Calculate winter precipitation anomalies, make Fig. 2b and c 01b_Cardinalis_map.R: Make Fig. 2 (map of Mimulus cardinals populations and experimental gardens combined with panels from script 01a) 02_R_analyses.R: Run models with each trait as response variable to estimate trait medians, evolutionary change between ancestors and descendants and quantitative genetic parameters for each population and cohort in each garden 03_selection_analyses.R: Run models with fitness as response variable and each trait as a predictor to estimate phenotypic selection in each garden 04a_summary_R_h2_NCS.R: Summarize trait models from script 02 for traits measured in all gardens 04b_summary_R_h2_NS.R: Summarize trait models from script 02 for traits only measured in northern and southern gardens 05_model_selection_Va.R: Compare different models of additive genetic variance and make Table S9 06_plot_R_h2.R: Make figures and tables of trait medians (Fig. 3, Table S6), evolutionary change between ancestors and descendants (Fig. 6, Table S10) and quantitative genetic parameters for each population and cohort in each garden (Fig. 5, Table S8) 07a_summary_S_NCS.R: Summarize selection models from script 03 for traits measured in all gardens 07b_summary_S_NS.R: Summarize selection models from script 03 for traits only measured in northern and southern gardens 08_plot_S.R: Make figures and tables of phenotypic selection (Fig. 5, Table S7) 09_fitness-subset-correlations.R: Perform simple correlation tests among various fitness proxies measured on a subset of plants in each garden and make Fig. S1 and S2 and Table S3. 10_sample_sizes_sires_dams.R: Extract sample sizes reported in Tables S2 and S4. 11_brms_vs_mcmcglmm.R: Compare global brms model including data from all populations, cohorts, and gardens, to sub-models in brms and MCMCglmm built from each ancestral cohort of each population x garden combination (Table S5, Figure S3). 12_gxe_plot.R: Visualize genotype-by-environment interactions by plotting breeding values of each population across each pair of gardens (Figure S4). #### OBJECTS The scripts produce several intermediate objects. These are included in the repository but are not individually listed and described here. ## Access information Climate data were downloaded from climateNA v. 7.30 on 2022-09-14 (Wang T, Hamann A, Spittlehouse D, Carroll C (2016) Locally Downscaled and Spatially Customizable Climate Data for Historical and Future Periods for North America. PLoS ONE 11(6): e0156720. [https://doi.org/10.1371/journal.pone.0156720](https://doi.org/10.1371/journal.pone.0156720))"]} 

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4. Data from: Evolution of floral traits and mating systems under drought: A range-wide study of Mimulus cardinalis

   https://doi.org/10.5061/dryad.vdncjsz7s  

   Diez, Jeffrey; Wilborn-Pilotte, Olivia; Cook, Emily; Kutella, Katelin; Sheth, Seema (January 2026, Dryad)

   Climate change is intensifying droughts across the globe, challenging species to adapt to novel conditions. While plant physiological and phenological responses to drought are well-documented, less is known about how water scarcity affects the evolution of selfing across species ranges. According to the selfing syndrome hypothesis, in environments where selfing confers a fitness advantage, selection should favour floral traits associated with increased selfing relative to outcrossing. We used a field experiment near the northern range edge of the scarlet monkeyflower (Mimulus cardinalis) to test this hypothesis both spatially (among leading-edge, central, and trailing-edge populations) and temporally (between cohorts separated by a period of historic drought). Although populations from different range positions showed genetic differentiation in some floral traits, these differences did not consistently support predictions of the selfing syndrome hypothesis. Contrary to the predictions of reduced investment in floral rewards and increased selfing ability at range edges, the sugar content of nectar was greater and autogamous seed set was smaller in leading-edge than central populations, herkogamy tended to be greater in trailing-edge populations relative to leading-edge and central ones, and nectar volume did not vary predictably among regions. There was no support for the evolution of selfing syndrome from the predrought ancestors to the postdrought descendants. Instead, in leading-edge populations, descendants evolved greater sugar content relative to ancestors, and there were no other differences between ancestors and descendants in any other trait or region. Overall, these findings suggest that mating system evolution in M. cardinalis likely reflects a complex interplay of regional factors, including range position, historical adaptation, and local environmental variability, rather than simple stress-induced shifts towards selfing. This study takes advantage of a common garden that is part of a larger resurrection experiment focused on quantifying quantitative genetic parameters and evolutionary responses to a historic period of drought across the range of M. cardinalis (Figure 1; Diffenbaugh et al. 2015). For this larger study, seeds were collected from six populations of M. cardinalis (two each from its northern, central, and southern range; Figure 1) in 2010 (“pre-drought” ancestors) and 2017 (“post-drought” descendants), before and after a period of historic drought, respectively (Sheth et al. 2025; Sheth & Angert 2016; Vtipil & Sheth 2020; Wooliver et al. 2020). Locality information for each population is reported in Sheth & Angert (2016). To control for maternal and seed storage effects, plants were crossed for one generation following a nested paternal half-sibling design to allow for the estimation of quantitative genetic parameters (Sheth et al. 2025; Wooliver et al. 2020). After five weeks of greenhouse growth, seedlings were transplanted into three common gardens across the range. Due to logistical reasons that prevented the collection of floral trait data in the central and southern gardens, this study focuses on data from the northernmost garden in Eugene, Oregon (Friends of Buford Park and Mount Pisgah Native Plant Nursery). The garden had 10 blocks, with all six populations and cohorts represented in each block, for a total of 5,468 individuals. Large sample sizes and pedigreed crosses were needed for estimating additive genetic variances and covariances in a suite of traits associated with drought adaptation in the larger experiment, but were not required for the current study evaluating the evolution of floral traits across space and over time. Thus, we randomly selected 180 plants (30 from each region and cohort) for our study of floral traits.  To quantify mating system evolution across space (among populations) and time (between cohorts), we compared floral rewards, morphology, and seed set between populations using a pollination exclusion experiment. Two buds per plant were enclosed in mesh bags to prevent pollinator access before they opened. For one bagged flower per plant, we measured three traits associated with the mating system.  First, nectar volume indicates the amount of reward that the flower provides to pollinators, such that flowers producing less nectar may exhibit a higher degree of selfing than flowers producing more nectar (CITATION). We measured nectar volume in micro-liters using a 30 μL microcapillary tube inserted into the nectary. Volume was calculated from the height of the nectar column, measured with digital calipers (Carol Ann Kearns & David William Inouye 1993). Second, nectar sugar content provides information about the quality of the floral reward, with greater nectar sugar content generally associated with outcrossing. We used a refractometer (model SR0017-ATC from manufacturer Xindacheng) to measure nectar sugar content. Samples were diluted with 50 microliters of deionized water, and the undiluted sugar content (measured in degrees Brix, Brix°, where 1°Brix equals 1 gram of sucrose in 100 grams of solution) was calculated using the formula: 𝑑𝑖𝑙𝑢𝑡𝑖𝑜𝑛 𝑓𝑎𝑐𝑡𝑜𝑟 = 𝑡𝑜𝑡𝑎𝑙 𝑠𝑎𝑚𝑝𝑙𝑒 𝑣𝑜𝑙𝑢𝑚𝑒 (𝑛𝑒𝑐𝑡𝑎𝑟 + 𝑤𝑎𝑡𝑒𝑟)/𝑛𝑒𝑐𝑡𝑎r volume 𝑢𝑛𝑑𝑖𝑙𝑢𝑡𝑒𝑑 𝐵𝑟𝑖𝑥° = 𝑑𝑖𝑙𝑢𝑡𝑖𝑜𝑛 𝑓𝑎𝑐𝑡𝑜𝑟 × 𝑑𝑖𝑙𝑢𝑡𝑒𝑑 𝐵𝑟𝑖𝑥° Third, herkogamy, the spatial separation of stigma and anthers in a flower, should influence the probability of selfing, with shorter absolute distance between anthers and stigma associated with higher selfing rates (Opedal 2018). To estimate herkogamy, we used digital calipers to measure the distance between the uppermost anther pair and the stigma. To evaluate the efficacy of selfing, we collected fruits resulting from the second bagged flower on each plant to assess autogamous seed set as plants senesced. Due to the high seed count per fruit and multiple fruits per plant, we estimated the total seed set per fruit based on mass (Angert 2006). On a subset of fruits, we counted the number of seeds using photographs (Nikon D750 camera) of seeds on a white background. Images were analyzed with ImageJ to isolate the seeds, and the seeds were then weighed on an analytical balance. From this subset, seed number and seed mass were used to build a relationship to predict seed number for the remaining fruits based on their seed mass using the predict() function. # Data from: Evolution of floral traits and mating systems under drought: A range-wide study of *Mimulus cardinalis* Dataset DOI: [10.5061/dryad.vdncjsz7s](https://doi.org/10.5061/dryad.vdncjsz7s) ## Description of the data and file structure This study investigated floral trait evolution in *Mimulus cardinalis* across its range in the western United States, using a resurrection approach to compare plants originating from seeds collected before 2010 and after 2017, spanning a severe drought. In a common garden, we measured floral traits including nectar volume, sugar content, anther-stigma distance, and fecundity of self-pollinated flowers across northern-edge, central, and southern-edge populations. We hypothesized that populations from historically drier environments would shift toward selfing traits to conserve energy. Any NA represents missing data. ### Files and variables #### File: data_Dryad.csv **Description:**  ##### Variables * Region: source region of the plant (south, central, north) * Population: two populations per region * Year: cohort of the sourced plant - 2010 or 2017 * block: experimental block in the common garden * nectar_vol: nectar volume (micro liters) * nectar_sugar: nectar sugar concentration, measured in Brix * abs_asd: absolute value of the anther-stigma distance (in millimeters) * log_seeds: log of the seed production of each plant 

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5. Pushed waves, trailing edges, and extreme events: Eco‐evolutionary dynamics of a geographic range shift in the owl limpet, Lottia gigantea

   https://doi.org/10.1111/gcb.17414  

   Nielsen, Erica S; Walkes, Samuel; Sones, Jacqueline L; Fenberg, Phillip B; Paz‐García, David A; Cameron, Brenda B; Grosberg, Richard K; Sanford, Eric; Bay, Rachael A (July 2024, Global Change Biology)

   As climatic variation re‐shapes global biodiversity, understanding eco‐evolutionary feedbacks during species range shifts is of increasing importance. Theory on range expansions distinguishes between two different forms: “pulled” and “pushed” waves. Pulled waves occur when the source of the expansion comes from low‐density peripheral populations, while pushed waves occur when recruitment to the expanding edge is supplied by high‐density populations closer to the species' core. How extreme events shape pushed/pulled wave expansion events, as well as trailing‐edge declines/contractions, remains largely unexplored. We examined eco‐evolutionary responses of a marine invertebrate (the owl limpet,Lottia gigantea) that increased in abundance during the 2014–2016 marine heatwaves near the poleward edge of its geographic range in the northeastern Pacific. We used whole‐genome sequencing from 19 populations across >11 degrees of latitude to characterize genomic variation, gene flow, and demographic histories across the species' range. We estimated present‐day dispersal potential and past climatic stability to identify how contemporary and historical seascape features shape genomic characteristics. Consistent with expectations of a pushed wave, we found little genomic differentiation between core and leading‐edge populations, and higher genomic diversity at range edges. A large and well‐mixed population in the northern edge of the species' range is likely a result of ocean current anomalies increasing larval settlement and high‐dispersal potential across biogeographic boundaries. Trailing‐edge populations have higher differentiation from core populations, possibly driven by local selection and limited gene flow, as well as high genomic diversity likely as a result of climatic stability during the Last Glacial Maximum. Our findings suggest that extreme events can drive poleward range expansions that carry the adaptive potential of core populations, while also cautioning that trailing‐edge extirpations may threaten unique evolutionary variation. This work highlights the importance of understanding how both trailing and leading edges respond to global change and extreme events. 

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