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Title: Declines in an abundant aquatic insect, the burrowing mayfly, across major North American waterways

Seasonal animal movement among disparate habitats is a fundamental mechanism by which energy, nutrients, and biomass are transported across ecotones. A dramatic example of such exchange is the annual emergence of mayfly swarms from freshwater benthic habitats, but their characterization at macroscales has remained impossible. We analyzed radar observations of mayfly emergence flights to quantify long-term changes in annual biomass transport along the Upper Mississippi River and Western Lake Erie Basin. A single emergence event can produce 87.9 billion mayflies, releasing 3,078.6 tons of biomass into the airspace over several hours, but in recent years, production across both waterways has declined by over 50%. As a primary prey source in aquatic and terrestrial ecosystems, these declines will impact higher trophic levels and environmental nutrient cycling.

 
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Award ID(s):
1840230
NSF-PAR ID:
10131479
Author(s) / Creator(s):
; ; ; ; ;
Publisher / Repository:
Proceedings of the National Academy of Sciences
Date Published:
Journal Name:
Proceedings of the National Academy of Sciences
Volume:
117
Issue:
6
ISSN:
0027-8424
Page Range / eLocation ID:
p. 2987-2992
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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  1. null (Ed.)
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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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  3. Abstract

    Drying intermittent stream networks often have permanent water refuges that are important for recolonisation. These habitats may be hotspots for interactions between fishes and invertebrates as they become isolated, but densities and diversity of fishes in these refuges can be highly variable across time and space.

    Insect emergence from streams provides energy and nutrient subsidies to riparian habitats. The magnitude of such subsidies may be influenced by in‐stream predators such as fishes.

    We examined whether benthic macroinvertebrate communities, emerging adult insects, and algal biomass in permanent grassland stream pools differed among sites with naturally varying densities of fishes. We also manipulated fish densities in a mesocosm experiment to address how fishes might affect colonisation during recovery from hydrologic disturbance.

    Fish biomass had a negative impact on invertebrate abundance, but not biomass or taxa richness, in natural pools. Total fish biomass was not correlated with total insect emergence in natural pools, but orangethroat darter (Etheostoma spectabile) biomass was inversely correlated with emerging Chironomidae biomass and individual midge body size. The interaction in our models between predatory fish biomass and date suggested that fishes may also delay insect emergence from natural pools, altering the timing of aquatic–terrestrial subsidies.

    There was an increase over time in algal biomass (chlorophyll‐a) in mesocosms, but this did not differ among fish density treatments. Regardless, fish presence in mesocosms reduced the abundance of colonising insects and total invertebrate biomass. Mesocosm invertebrate communities in treatments without fishes were characterised by more Chironomidae, Culicidae, and Corduliidae.

    Results suggest that fishes influence invertebrates in habitats that represent important refuges during hydrologic disturbance, hot spots for subsidy exports to riparian food webs, and source areas for colonists during recovery from hydrologic disturbance. Fish effects in these systems include decreasing invertebrate abundance, shifting community structure, and altering patterns of invertebrate emergence and colonisation.

     
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  4. Abstract

    Understanding insect and fish interactions from a spatial and temporal perspective can have implications on large‐scale phenology in freshwater systems, yet current information is limited. We employed a novel approach of combining information from acoustic telemetry for six freshwater fish species and weather radar to assess the phenology of mayfly emergence and foraging patterns of freshwater fish. We hypothesized that freshwater fish conduct synchronous movements with annual mayfly hatches as a pulse resource opportunity. Generalized additive models were developed to assess movement distance as a function of species and time; before, during, and after annual mayfly hatch events. A cross‐section abundance index was also employed to quantify dynamics of aerial mayflies. Hatch dynamics revealed nocturnal emergence behaviour with annual variations in intensity, spatial extent, and origin. We found that the hatch was likely a pulse resource feeding opportunity for channel catfish, common carp, freshwater drum, and walleye instead of a synchronized feeding event. Bigmouth buffalo and lake sturgeon utilized riverine habitat away from the hatch and did not likely forage on the emerging mayflies. Remote sensing of fishes and emergent insects using our approach is the first attempt at bridging the capabilities of fisheries ecology and aeroecology to advance movement ecology.

     
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  5. Abstract

    Although there is a well‐known association between tree cover and soil texture in savannahs, the hydrological drivers of tree cover variation have not been systematically explored, particularly in parallel with factors such as fire, herbivory, and tree–grass interactions. The relationship between hydrological factors and tree cover is important for resolving the relative contribution of bottom‐up versus top‐down factors in structuring savannah vegetation.

    We quantified soil moisture dynamics across eight 1‐km transects spanning tree cover gradients from open to woody savannah in Serengeti National Park in Tanzania using soil moisture sensors coupled with dataloggers. We mapped tree cover at two spatial scales through supervised classification of high‐resolution satellite imagery. We simultaneously produced water retention curves in open and woody habitats within each transect to compare soil hydrological properties and to convert volumetric water content (θ) from dataloggers to plant‐available water over the course of an annual cycle. We also quantified grass biomass at 100 locations per transect, estimated fire frequency from MODIS satellite data, and quantified herbivore occupancy with paired camera traps situated in open and woody habitats within each transect.

    We found a positive relationship between tree cover and soil moisture drainage rate, and found that open habitats had more negative water potentials than woody habitats for a given value ofθ. In contrast, we found no evidence for a consistent relationship between grass biomass or fire frequency and tree cover. We found evidence for higher browser occupancy in woody than open habitats, but no habitat effects on herbivores as a group (browsers plus grazers), suggesting that herbivory is unlikely to be the dominant factor explaining variation in tree cover.

    Synthesis. Our results suggest that variation in tree cover is partly driven by hydrological (edaphic) factors unrelated to fire, herbivory, tree–grass interactions or mean annual precipitation at these spatial scales in Serengeti. We contrast our findings with previous work attributing tree cover shifts in Serengeti to precipitation gradients.

     
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