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1. Phosphorus availability and leaching losses in annual and perennial cropping systems in an upper US Midwest landscape

   https://doi.org/10.5061/dryad.8sf7m0cpx  

   Hussain, Mir Zaman ; Hamilton, Stephen ; Robertson, G. Philip ; Basso, Bruno ( January 2021 , Dryad)

   Excessive phosphorus (P) applications to croplands can contribute to eutrophication of surface waters through surface runoff and subsurface (leaching) losses. We analyzed leaching losses of total dissolved P (TDP) from no-till corn, hybrid poplar (Populus nigra X P. maximowiczii), switchgrass (Panicum virgatum), miscanthus (Miscanthus giganteus), native grasses, and restored prairie, all planted in 2008 on former cropland in Michigan, USA. All crops except corn (13 kg P ha−1 year−1) were grown without P fertilization. Biomass was harvested at the end of each growing season except for poplar. Soil water at 1.2 m depth was sampled weekly to biweekly for TDP determination during March–November 2009–2016 using tension lysimeters. Soil test P (0–25 cm depth) was measured every autumn. Soil water TDP concentrations were usually below levels where eutrophication of surface waters is frequently observed (> 0.02 mg L−1) but often higher than in deep groundwater or nearby streams and lakes. Rates of P leaching, estimated from measured concentrations and modeled drainage, did not differ statistically among cropping systems across years; 7-year cropping system means ranged from 0.035 to 0.072 kg P ha−1 year−1 with large interannual variation. Leached P was positively related to STP, which decreased over the 7 years in all systems. These results indicate that both P-fertilized and unfertilized cropping systems may leach legacy P from past cropland management. Experimental details The Biofuel Cropping System Experiment (BCSE) is located at the W.K. Kellogg Biological Station (KBS) (42.3956° N, 85.3749° W; elevation 288 m asl) in southwestern Michigan, USA. This site is a part of the Great Lakes Bioenergy Research Center (www.glbrc.org) and is a Long-term Ecological Research site (www.lter.kbs.msu.edu). Soils are mesic Typic Hapludalfs developed on glacial outwash54 with high sand content (76% in the upper 150 cm) intermixed with silt-rich loess in the upper 50 cm55. The water table lies approximately 12–14 m below the surface. The climate is humid temperate with a mean annual air temperature of 9.1 °C and annual precipitation of 1005 mm, 511 mm of which falls between May and September (1981–2010)56,57. The BCSE was established as a randomized complete block design in 2008 on preexisting farmland. Prior to BCSE establishment, the field was used for grain crop and alfalfa (Medicago sativa L.) production for several decades. Between 2003 and 2007, the field received a total of ~ 300 kg P ha−1 as manure, and the southern half, which contains one of four replicate plots, received an additional 206 kg P ha−1 as inorganic fertilizer. The experimental design consists of five randomized blocks each containing one replicate plot (28 by 40 m) of 10 cropping systems (treatments) (Supplementary Fig. S1; also see Sanford et al.58). Block 5 is not included in the present study. Details on experimental design and site history are provided in Robertson and Hamilton57 and Gelfand et al.59. Leaching of P is analyzed in six of the cropping systems: (i) continuous no-till corn, (ii) switchgrass, (iii) miscanthus, (iv) a mixture of five species of native grasses, (v) a restored native prairie containing 18 plant species (Supplementary Table S1), and (vi) hybrid poplar. Agronomic management Phenological cameras and field observations indicated that the perennial herbaceous crops emerged each year between mid-April and mid-May. Corn was planted each year in early May. Herbaceous crops were harvested at the end of each growing season with the timing depending on weather: between October and November for corn and between November and December for herbaceous perennial crops. Corn stover was harvested shortly after corn grain, leaving approximately 10 cm height of stubble above the ground. The poplar was harvested only once, as the culmination of a 6-year rotation, in the winter of 2013–2014. Leaf emergence and senescence based on daily phenological images indicated the beginning and end of the poplar growing season, respectively, in each year. Application of inorganic fertilizers to the different crops followed a management approach typical for the region (Table 1). Corn was fertilized with 13 kg P ha−1 year−1 as starter fertilizer (N-P-K of 19-17-0) at the time of planting and an additional 33 kg P ha−1 year−1 was added as superphosphate in spring 2015. Corn also received N fertilizer around the time of planting and in mid-June at typical rates for the region (Table 1). No P fertilizer was applied to the perennial grassland or poplar systems (Table 1). All perennial grasses (except restored prairie) were provided 56 kg N ha−1 year−1 of N fertilizer in early summer between 2010 and 2016; an additional 77 kg N ha−1 was applied to miscanthus in 2009. Poplar was fertilized once with 157 kg N ha−1 in 2010 after the canopy had closed. Sampling of subsurface soil water and soil for P determination Subsurface soil water samples were collected beneath the root zone (1.2 m depth) using samplers installed at approximately 20 cm into the unconsolidated sand of 2Bt2 and 2E/Bt horizons (soils at the site are described in Crum and Collins54). Soil water was collected from two kinds of samplers: Prenart samplers constructed of Teflon and silica (http://www.prenart.dk/soil-water-samplers/) in replicate blocks 1 and 2 and Eijkelkamp ceramic samplers (http://www.eijkelkamp.com) in blocks 3 and 4 (Supplementary Fig. S1). The samplers were installed in 2008 at an angle using a hydraulic corer, with the sampling tubes buried underground within the plots and the sampler located about 9 m from the plot edge. There were no consistent differences in TDP concentrations between the two sampler types. Beginning in the 2009 growing season, subsurface soil water was sampled at weekly to biweekly intervals during non-frozen periods (April–November) by applying 50 kPa of vacuum to each sampler for 24 h, during which the extracted water was collected in glass bottles. Samples were filtered using different filter types (all 0.45 µm pore size) depending on the volume of leachate collected: 33-mm dia. cellulose acetate membrane filters when volumes were less than 50 mL; and 47-mm dia. Supor 450 polyethersulfone membrane filters for larger volumes. Total dissolved phosphorus (TDP) in water samples was analyzed by persulfate digestion of filtered samples to convert all phosphorus forms to soluble reactive phosphorus, followed by colorimetric analysis by long-pathlength spectrophotometry (UV-1800 Shimadzu, Japan) using the molybdate blue method60, for which the method detection limit was ~ 0.005 mg P L−1. Between 2009 and 2016, soil samples (0–25 cm depth) were collected each autumn from all plots for determination of soil test P (STP) by the Bray-1 method61, using as an extractant a dilute hydrochloric acid and ammonium fluoride solution, as is recommended for neutral to slightly acidic soils. The measured STP concentration in mg P kg−1 was converted to kg P ha−1 based on soil sampling depth and soil bulk density (mean, 1.5 g cm−3). Sampling of water samples from lakes, streams and wells for P determination In addition to chemistry of soil and subsurface soil water in the BCSE, waters from lakes, streams, and residential water supply wells were also sampled during 2009–2016 for TDP analysis using Supor 450 membrane filters and the same analytical method as for soil water. These water bodies are within 15 km of the study site, within a landscape mosaic of row crops, grasslands, deciduous forest, and wetlands, with some residential development (Supplementary Fig. S2, Supplementary Table S2). Details of land use and cover change in the vicinity of KBS are given in Hamilton et al.48, and patterns in nutrient concentrations in local surface waters are further discussed in Hamilton62. Leaching estimates, modeled drainage, and data analysis Leaching was estimated at daily time steps and summarized as total leaching on a crop-year basis, defined from the date of planting or leaf emergence in a given year to the day prior to planting or emergence in the following year. TDP concentrations (mg L−1) of subsurface soil water were linearly interpolated between sampling dates during non-freezing periods (April–November) and over non-sampling periods (December–March) based on the preceding November and subsequent April samples. Daily rates of TDP leaching (kg ha−1) were calculated by multiplying concentration (mg L−1) by drainage rates (m3 ha−1 day−1) modeled by the Systems Approach for Land Use Sustainability (SALUS) model, a crop growth model that is well calibrated for KBS soil and environmental conditions. SALUS simulates yield and environmental outcomes in response to weather, soil, management (planting dates, plant population, irrigation, N fertilizer application, and tillage), and genetics63. The SALUS water balance sub-model simulates surface runoff, saturated and unsaturated water flow, drainage, root water uptake, and evapotranspiration during growing and non-growing seasons63. The SALUS model has been used in studies of evapotranspiration48,51,64 and nutrient leaching20,65,66,67 from KBS soils, and its predictions of growing-season evapotranspiration are consistent with independent measurements based on growing-season soil water drawdown53 and evapotranspiration measured by eddy covariance68. Phosphorus leaching was assumed insignificant on days when SALUS predicted no drainage. Volume-weighted mean TDP concentrations in leachate for each crop-year and for the entire 7-year study period were calculated as the total dissolved P leaching flux (kg ha−1) divided by the total drainage (m3 ha−1). One-way ANOVA with time (crop-year) as the fixed factor was conducted to compare total annual drainage rates, P leaching rates, volume-weighted mean TDP concentrations, and maximum aboveground biomass among the cropping systems over all seven crop-years as well as with TDP concentrations from local lakes, streams, and groundwater wells. When a significant (α = 0.05) difference was detected among the groups, we used the Tukey honest significant difference (HSD) post-hoc test to make pairwise comparisons among the groups. In the case of maximum aboveground biomass, we used the Tukey–Kramer method to make pairwise comparisons among the groups because the absence of poplar data after the 2013 harvest resulted in unequal sample sizes. We also used the Tukey–Kramer method to compare the frequency distributions of TDP concentrations in all of the soil leachate samples with concentrations in lakes, streams, and groundwater wells, since each sample category had very different numbers of measurements. Individual spreadsheets in “data table_leaching_dissolved organic carbon and nitrogen.xls” 1.    annual precip_drainage 2.    biomass_corn, perennial grasses 3.    biomass_poplar 4.    annual N leaching _vol-wtd conc 5.    Summary_N leached 6.    annual DOC leachin_vol-wtd conc 7.    growing season length 8.    correlation_nh4 VS no3 9.    correlations_don VS no3_doc VS don Each spreadsheet is described below along with an explanation of variates. Note that ‘nan’ indicate data are missing or not available. First row indicates header; second row indicates units 1. Spreadsheet: annual precip_drainage Description: Precipitation measured from nearby Kellogg Biological Station (KBS) Long Term Ecological Research (LTER) Weather station, over 2009-2016 study period. Data shown in Figure 1; original data source for precipitation (https://lter.kbs.msu.edu/datatables/7). Drainage estimated from SALUS crop model. Note that drainage is percolation out of the root zone (0-125 cm). Annual precipitation and drainage values shown here are calculated for growing and non-growing crop periods. Variate    Description year    year of the observation crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” precip_G    precipitation during growing period (milliMeter) precip_NG    precipitation during non-growing period (milliMeter) drainage_G    drainage during growing period (milliMeter) drainage_NG    drainage during non-growing period (milliMeter)      2. Spreadsheet: biomass_corn, perennial grasses Description: Maximum aboveground biomass measurements from corn, switchgrass, miscanthus, native grass and restored prairie plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2015. Data shown in Figure 2.   Variate    Description year    year of the observation date    day of the observation (mm/dd/yyyy) crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” replicate    each crop has four replicated plots, R1, R2, R3 and R4 station    stations (S1, S2 and S3) of samplings within the plot. For more details, refer to link (https://data.sustainability.glbrc.org/protocols/156) species    plant species that are rooted within the quadrat during the time of maximum biomass harvest. See protocol for more information, refer to link (http://lter.kbs.msu.edu/datatables/36) For maize biomass, grain and whole biomass reported in the paper (weed biomass or surface litter are excluded). Surface litter biomass not included in any crops; weed biomass not included in switchgrass and miscanthus, but included in grass mixture and prairie. fraction    Fraction of biomass biomass_plot    biomass per plot on dry-weight basis (Grams_Per_SquareMeter) biomass_ha    biomass (megaGrams_Per_Hectare) by multiplying column biomass per plot with 0.01 3. Spreadsheet: biomass_poplar Description: Maximum aboveground biomass measurements from poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2015. Data shown in Figure 2. Note that poplar biomass was estimated from crop growth curves until the poplar was harvested in the winter of 2013-14. Variate    Description year    year of the observation method    methods of poplar biomass sampling date    day of the observation (mm/dd/yyyy) replicate    each crop has four replicated plots, R1, R2, R3 and R4 diameter_at_ground    poplar diameter (milliMeter) at the ground diameter_at_15cm    poplar diameter (milliMeter) at 15 cm height biomass_tree    biomass per plot (Grams_Per_Tree) biomass_ha    biomass (megaGrams_Per_Hectare) by multiplying biomass per tree with 0.01 4. Spreadsheet: annual N leaching_vol-wtd conc Description: Annual leaching rate (kiloGrams_N_Per_Hectare) and volume-weighted mean N concentrations (milliGrams_N_Per_Liter) of nitrate (no3) and dissolved organic nitrogen (don) in the leachate samples collected from corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2016. Data for nitrogen leached and volume-wtd mean N concentration shown in Figure 3a and Figure 3b, respectively. Note that ammonium (nh4) concentration were much lower and often undetectable (<0.07 milliGrams_N_Per_Liter). Also note that in 2009 and 2010 crop-years, data from some replicates are missing.    Variate    Description crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” crop-year    year of the observation replicate    each crop has four replicated plots, R1, R2, R3 and R4 no3 leached    annual leaching rates of nitrate (kiloGrams_N_Per_Hectare) don leached    annual leaching rates of don (kiloGrams_N_Per_Hectare) vol-wtd no3 conc.    Volume-weighted mean no3 concentration (milliGrams_N_Per_Liter) vol-wtd don conc.    Volume-weighted mean don concentration (milliGrams_N_Per_Liter) 5. Spreadsheet: summary_N leached Description: Summary of total amount and forms of N leached (kiloGrams_N_Per_Hectare) and the percent of applied N lost to leaching over the seven years for corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2016. Data for nitrogen amount leached shown in Figure 4a and percent of applied N lost shown in Figure 4b. Note the fraction of unleached N includes in harvest, accumulation in root biomass, soil organic matter or gaseous N emissions were not measured in the study. Variate    Description crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” no3 leached    annual leaching rates of nitrate (kiloGrams_N_Per_Hectare) don leached    annual leaching rates of don (kiloGrams_N_Per_Hectare) N unleached    N unleached (kiloGrams_N_Per_Hectare) in other sources are not studied % of N applied N lost to leaching    % of N applied N lost to leaching 6. Spreadsheet: annual DOC leachin_vol-wtd conc Description: Annual leaching rate (kiloGrams_Per_Hectare) and volume-weighted mean N concentrations (milliGrams_Per_Liter) of dissolved organic carbon (DOC) in the leachate samples collected from corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2016. Data for DOC leached and volume-wtd mean DOC concentration shown in Figure 5a and Figure 5b, respectively. Note that in 2009 and 2010 crop-years, water samples were not available for DOC measurements.     Variate    Description crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” crop-year    year of the observation replicate    each crop has four replicated plots, R1, R2, R3 and R4 doc leached    annual leaching rates of nitrate (kiloGrams_Per_Hectare) vol-wtd doc conc.    volume-weighted mean doc concentration (milliGrams_Per_Liter) 7. Spreadsheet: growing season length Description: Growing season length (days) of corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in the Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2015. Date shown in Figure S2. Note that growing season is from the date of planting or emergence to the date of harvest (or leaf senescence in case of poplar).   Variate    Description crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” year    year of the observation growing season length    growing season length (days) 8. Spreadsheet: correlation_nh4 VS no3 Description: Correlation of ammonium (nh4+) and nitrate (no3-) concentrations (milliGrams_N_Per_Liter) in the leachate samples from corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2013-2015. Data shown in Figure S3. Note that nh4+ concentration in the leachates was very low compared to no3- and don concentration and often undetectable in three crop-years (2013-2015) when measurements are available. Variate    Description crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” date    date of the observation (mm/dd/yyyy) replicate    each crop has four replicated plots, R1, R2, R3 and R4 nh4 conc    nh4 concentration (milliGrams_N_Per_Liter) no3 conc    no3 concentration (milliGrams_N_Per_Liter)   9. Spreadsheet: correlations_don VS no3_doc VS don Description: Correlations of don and nitrate concentrations (milliGrams_N_Per_Liter); and doc (milliGrams_Per_Liter) and don concentrations (milliGrams_N_Per_Liter) in the leachate samples of corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2013-2015. Data of correlation of don and nitrate concentrations shown in Figure S4 a and doc and don concentrations shown in Figure S4 b. Variate    Description crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” year    year of the observation don    don concentration (milliGrams_N_Per_Liter) no3     no3 concentration (milliGrams_N_Per_Liter) doc    doc concentration (milliGrams_Per_Liter) 

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2. Biomes as evolutionary arenas: Convergence and conservatism in the trans‐continental succulent biome

   https://doi.org/10.1111/geb.13089  

   Ringelberg, Jens J. ; Zimmermann, Niklaus E. ; Weeks, Andrea ; Lavin, Matt ; Hughes, Colin E. ; Moles, ed., Angela ( March 2020 , Global Ecology and Biogeography)

   Historically, biomes have been defined based on their structurally and functionally similar vegetation, but there is debate about whether these similarities are superficial, and about how biomes are defined and mapped. We propose that combined assessment of evolutionary convergence of plant functional traits and phylogenetic biome conservatism provides a useful approach for characterizing biomes. We focus on the little‐known succulent biome, a trans‐continentally distributed assemblage of succulent‐rich, drought‐deciduous, fire‐free forest, thicket and scrub vegetation as a useful exemplar biome to gain insights into these questions.

   Global lowland (sub)tropics.

   Present.

   Angiosperms.

   We use a model ensemble approach to model the distribution of 884 species of stem succulents, a plant functional group representing a striking example of evolutionary convergence. Using this model, phylogenies, and species occurrence data, we quantify phylogenetic succulent biome conservatism for 10 non‐succulent trans‐continental plant clades including prominent elements of the succulent biome, representing over 800 species.

   The geographical and climatic distributions of stem succulents provide an objective and quantitative proxy for mapping the distribution of the succulent biome. High fractions of succulent biome occupancy across continents suggest all 10 non‐succulent study clades are phylogenetically conserved within the succulent biome.

   The trans‐continental succulent and savanna biomes both show evolutionary convergence in key biome‐related plant functional traits. However, in contrast to the savanna biome, which was apparently assembled via repeated local recruitment of lineages via biome shifts from adjacent biomes within continents, the succulent biome forms a coherent trans‐continental evolutionary arena for drought‐adapted tropical biome conserved lineages. Recognizing the important functional differences between the succulent‐rich, grass‐poor, fire‐free succulent biome and the grass‐dominated, succulent‐poor, fire‐prone savanna biome, and defining them as distinct seasonally dry tropical biomes, occupying essentially non‐overlapping distributions, provides critical insights into tropical biodiversity and the extent of biome stasis versus biome shifting.

    

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3. Sexual selection and the ascent of women: Mate choice research since Darwin

   https://doi.org/10.1126/science.abi6308  

   Rosenthal, Gil G. ; Ryan, Michael J. ( January 2022 , Science)

   BACKGROUND Charles Darwin’s  Descent of Man, and Selection in Relation to Sex  tackled the two main controversies arising from the Origin of Species:  the evolution of humans from animal ancestors and the evolution of sexual ornaments. Most of the book focuses on the latter, Darwin’s theory of sexual selection. Research since supports his conjecture that songs, perfumes, and intricate dances evolve because they help secure mating partners. Evidence is overwhelming for a primary role of both male and female mate choice in sexual selection—not only through premating courtship but also through intimate interactions during and long after mating. But what makes one prospective mate more enticing than another? Darwin, shaped by misogyny and sexual prudery, invoked a “taste for the beautiful” without speculating on the origin of the “taste.” How to explain when the “final marriage ceremony” is between two rams? What of oral sex in bats, cloacal rubbing in bonobos, or the sexual spectrum in humans, all observable in Darwin’s time? By explaining desire through the lens of those male traits that caught his eyes and those of his gender and culture, Darwin elided these data in his theory of sexual evolution. Work since Darwin has focused on how traits and preferences coevolve. Preferences can evolve even if attractive signals only predict offspring attractiveness, but most attention has gone to the intuitive but tenuous premise that mating with gorgeous partners yields vigorous offspring. By focusing on those aspects of mating preferences that coevolve with male traits, many of Darwin’s influential followers have followed the same narrow path. The sexual selection debate in the 1980s was framed as “good genes versus runaway”: Do preferences coevolve with traits because traits predict genetic benefits, or simply because they are beautiful? To the broader world this is still the conversation. ADVANCES Even as they evolve toward ever-more-beautiful signals and healthier offspring, mate-choice mechanisms and courter traits are locked in an arms race of coercion and resistance, persuasion and skepticism. Traits favored by sexual selection often do so at the expense of chooser fitness, creating sexual conflict. Choosers then evolve preferences in response to the costs imposed by courters. Often, though, the current traits of courters tell us little about how preferences arise. Sensory systems are often tuned to nonsexual cues like food, favoring mating signals resembling those cues. And preferences can emerge simply from selection on choosing conspecifics. Sexual selection can therefore arise from chooser biases that have nothing to do with ornaments. Choice may occur before mating, as Darwin emphasized, but individuals mate multiple times and bias fertilization and offspring care toward favored partners. Mate choice can thus occur in myriad ways after mating, through behavioral, morphological, and physiological mechanisms. Like other biological traits, mating preferences vary among individuals and species along multiple dimensions. Some of this is likely adaptive, as different individuals will have different optimal mates. Indeed, mate choice may be more about choosing compatible partners than picking the “best” mate in the absolute sense. Compatibility-based choice can drive or reinforce genetic divergence and lead to speciation. The mechanisms underlying the “taste for the beautiful” determine whether mate choice accelerates or inhibits reproductive isolation. If preferences are learned from parents, or covary with ecological differences like the sensory environment, then choice can promote genetic divergence. If everyone shares preferences for attractive ornaments, then choice promotes gene flow between lineages. OUTLOOK Two major trends continue to shift the emphasis away from male “beauty” and toward how and why individuals make sexual choices. The first integrates neuroscience, genomics, and physiology. We need not limit ourselves to the feathers and dances that dazzled Darwin, which gives us a vastly richer picture of mate choice. The second is that despite persistent structural inequities in academia, a broader range of people study a broader range of questions. This new focus confirms Darwin’s insight that mate choice makes a primary contribution to sexual selection, but suggests that sexual selection is often tangential to mate choice. This conclusion challenges a persistent belief with sinister roots, whereby mate choice is all about male ornaments. Under this view, females evolve to prefer handsome males who provide healthy offspring, or alternatively, to express flighty whims for arbitrary traits. But mate-choice mechanisms also evolve for a host of other reasons Understanding mate choice mechanisms is key to understanding how sexual decisions underlie speciation and adaptation to environmental change. New theory and technology allow us to explicitly connect decision-making mechanisms with their evolutionary consequences. A century and a half after Darwin, we can shift our focus to females and males as choosers, rather than the gaudy by-products of mate choice. Mate choice mechanisms across domains of life. Sensory periphery for stimulus detection (yellow), brain for perceptual integration and evaluation (orange), and reproductive structures for postmating choice among pollen or sperm (teal). ILLUSTRATION: KELLIE HOLOSKI/ SCIENCE 

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4. Mink ( Neovison vison ) fecal microbiomes are influenced by sex, temperature, and time postdefecation

   https://doi.org/10.1093/jmammal/gyab140  

   Lafferty, Diana J. R. ; Gillman, Sierra J. ; Jeakle, Lane K. ; Roell, Brian J. ; McKenney, Erin A. ; Hayes, ed., Loren ( January 2022 , Journal of Mammalogy)

   Gut microbiomes encode myriad metabolic functions critical to mammalian ecology and evolution. While fresh fecal samples provide an efficient, noninvasive method of sampling gut microbiomes, collecting fresh feces from elusive species is logistically challenging. Nonfresh feces, however, may not accurately represent the gut microbiome of the host due to succession of gut microbial consortia postdefecation as well as colonization by microbes from the surrounding environment. Using American mink (Neovison vison) as a model species, we examined postdefecation microbial community succession to learn how ambient temperature and temporal sampling constraints influence the reliability of nonfresh feces to represent host gut microbiomes. To achieve our goal, we analyzed fresh mink feces (n = 5 females; n = 5 males) collected at the time of defecation from captive mink at a farm in the Upper Peninsula of Michigan and we subsequently subsampled each fecal specimen to investigate microbial community succession over five days, under both warm (21°C) and cold (–17°C to –1°C) temperature treatments. We found that both temperature and time influenced fecal microbiome composition; and we also detected significant sexual dimorphism in microbial community structures, with female mink microbiomes exhibiting significantly greater variation than males’ when exposed to the warm temperature treatment. Our results demonstrate that feces from unknown individuals can be a powerful tool for examining carnivore gut microbiomes, though rigorous study design is required because sex, ambient temperature, and time since defecation drive significant microbial variation and the sample size requirements necessary for detecting statistically significant differences between target populations is an important consideration for future ecologically meaningful research.

    

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5. Pollination intensity and paternity in flowering plants

   https://doi.org/10.1093/aob/mcz159  

   Christopher, Dorothy A ; Mitchell, Randall J ; Karron, Jeffrey D ( October 2019 , Annals of Botany)

   Abstract Background Siring success plays a key role in plant evolution and reproductive ecology, and variation among individuals creates an opportunity for selection to act. Differences in male reproductive success can be caused by processes that occur during two stages, the pollination and post-pollination phases of reproduction. In the pollination phase, heritable variation in floral traits and floral display affect pollinator visitation patterns, which in turn affect variation among plants in the amount of pollen exported and deposited on recipient stigmas. In the post-pollination phase, differences among individuals in pollen grain germination success and pollen tube growth may cause realized paternity to differ from patterns of pollen receipt. The maternal plant can also preferentially provision some developing seeds or fruits to further alter variation in siring success. Scope In this review, we describe studies that advance our understanding of the dynamics of the pollination and post-pollination phases, focusing on how variation in male fitness changes in response to pollen limitation. We then explore the interplay between pollination and post-pollination success, and how these processes respond to ecological factors such as pollination intensity. We also identify pressing questions at the intersection of pollination and paternity and describe novel experimental approaches to elucidate the relative importance of pollination and post-pollination factors in determining male reproductive success. Conclusions The relative contribution of pollination and post-pollination processes to variation in male reproductive success may not be constant, but rather may vary with pollination intensity. Studies that quantify the effects of pollination and post-pollination phases in concert will be especially valuable as they will enable researchers to more fully understand the ecological conditions influencing male reproductive success. 

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