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    Fresh litter acts as a substantial phosphorus source of plant species appearing in primary succession on volcanic ash soil

    Volcanic ash soilWe used commercially available Kanuma soil as volcanic ash soil (fine-grained pumice, Akagi Engei Co., Ltd.) for growth experiment 1. Kanuma soil is a fully weathered pyroclastic fall from the eruption of Mt. Akagi 44,000 years ago19. The soil contains 30.8% aluminum (allophane and imogolite) and 1.4% iron (ferrihydrite)20. For growth experiment 2, we used three natural volcanic ash soil types—immature soil of pumice (Kanuma soil, C horizon), as well as mature soils of andosol (A–B horizon) and topsoil (the surface of andosol, P to A horizon)—collected from a riverbed in Kanuma City (36°35′ N, 139°44′ E; 200 m a.s.l.), central Japan, where the vegetation is a cypress forest. This place is managed by the Kanuma Civil Engineering Office. The topsoil was collected at a depth of approximately 0–10 cm from the soil surface after removing the fallen leaves on the soil surface. The andosol layer, typically distributed at a depth of approximately 10–75 cm, was collected from a depth of approximately 10–30 cm. Below the andosol layer, the Akadama soil layer is distributed; further below, the pumice Kanuma soil is distributed. The pumice was collected approximately 50 cm under the Akadama layer.Temporal information on soil formation was confirmed by direct radiocarbon dating of the soil samples. After removing soil carbonate with 1.0 M HCl, the total organic fraction was analyzed using an accelerator mass spectrometer (0.5MV compact AMS system, NEC) at the laboratory of radiocarbon dating, University of Tokyo. Conventional radiocarbon age after correction of isotopic fractionation with δ13C values was calibrated to a calendar date with the calibration dataset IntCal1321.The elemental analysis of total phosphorus, nitrogen, and carbon in the soil samples was performed by Createrra Inc. (http://www.createrra.co.jp/english/top.html).Plant speciesOn the volcanic ash soil of Mt. Fuji, Japan’s highest volcano, vegetation in primary succession generally changes from herbaceous plants such as Fallopia japonica (Houtt.) Ronse Decr. var. japonica to nitrogen-fixing alder plants, and finally to non-nitrogen fixing Betula ermanii Cham22,23. Hence, we used three species—F. japonica, the alder species Alnus inokumae Murai et Kusaka, and B. ermanii—owned by and grown in our research institute, Nikko Botanical Garden, for the growth experiments. Experimental research on these plants, including the collection of plant material, comply with the relevant institutional, national, and international guidelines and legislation.Litter incubation experimentSamples (1 g) of F. japonica litter leaves—collected upon leaf fall on an autumn day, dried at 80 °C for at least 48 h, and then crushed—were placed in cultivating tubes (n = 5). Then, 5 g of wet soil from the Nikko Botanical Garden (36°45′ N, 139°35′ E; 647 m a.s.l.) in Nikko, central Japan, was added to 500 mL of water and stirred (solution I). As inoculation, 0.1 mL of the supernatant of solution I was added to the tubes24. Considering that the amounts of phosphorus and nitrogen in the solution I were approximately 0.003 mg/L and 0.3 mg/L, respectively, they were determined to have not affected the initial value (t = 0). Next, 2 mL of water was added to the tubes, which were then kept at 30 °C. The tubes were left open to maintain an aerobic environment. The efflux of phosphorus and nitrogen from the leaves was measured every week for ten weeks. For these measurements, 5 mL of water was added and the tube was centrifuged for 10 min (solution II). The supernatant of solution II was then used for phosphorus and nitrogen measurements, and the residue was continuously kept at 30 °C.Growth experimentsGrowth experiments were conducted in an open-type greenhouse in Nikko Botanical Garden. The greenhouse is only vinyl on the ceiling and good ventilation to keep the temperature constant. The mean monthly highest and lowest temperatures and the monthly precipitation observed in the botanical garden during the cultivation period are provided in Table 1. In the growth experiments, irrigation with tap water was provided to the plants and litter leaves in the morning and evening. The phosphorus and nitrogen concentration of the tap water were approximately 0.03 mg/L and 0.25 mg/L respectively.Table 1 Nikko botanical garden weather data (May–October 2019).Full size tableGrowth experiment 1: Comparative experiment on the growth of plant species with and without litterThe seedlings used for the experiment were from the species F. japonica, A. inokumae, and B. ermanii. A similar seedling size was used for each plant species. Seedlings of A. inokumae coexist with N-fixing actinomycetes.Six plants per species were collected before cultivation (t = 0) and dried in an oven at 80 °C for at least 48 h to measure the dry weight. There were four experimental groups for each species: a control (Con), a nitrogen addition (N: 10 mM NH4NO3), a phosphorus addition (P: 10 mM NaH2PO4), and a nitrogen and phosphorus addition (NP: 10 mM NH4NO3 + 10 mM NaH2PO4). Once a week, 50 mL of each nutrients was added to a 0.25-L garden pot. To verify whether the addition of litter (denoted by +) improved plant growth, litter leaves of F. japonica were placed on the soils. To verify if nutrients leached from litter sustained plant growth, we also combined nutrient and litter additions (Con+, N+, P+, NP+). When nutrients were added to the soil once a week, litter bag was removed before fertilizer application and returned after that.To reproduce how litter is deposited and supplies nutrients on volcanic ash soil in primary succession, F. japonica litter was collected in Nikko in the autumn of 2018 and dried at 80 °C or 2 days or more (the same litter was used in incubation). Approximately 9 g of litter leaves was packed in a tea mesh bag25 to prevent it from flying in the wind and placed on the soil surface of the garden pots. As indicated by the equation below, the amount of litter added to the 8 × 8 cm (0.0064 m2) garden pot used in this experiment amounts to approximately three years of litter production when converted to the amount of leaf litter in a 15-year-old alder forest, i.e., about 430 g/m2 per year26.$$frac{9,g}{{430frac{g}{{ m^{2} }} yr times 0.0064 m^{2} }} cong 3.3 yr$$Six seedlings per group of A. inokumae and B. ermanii were cultivated for approximately 2 months (June 7–August 22, 2019) and 12 seedlings per group of F. japonica were cultivated for about 1 month (September 10–October 15, 2019). The experiment was stopped after 1 month for F. japonica as it grew rapidly in 2 nutrient conditions (NP, NP+) and the roots overflowed from the garden pot. At the end of the experiment, growth was evaluated by measuring dry weight after drying seedlings at 80 °C for at least 48 h. Subsequently, the total phosphorus and nitrogen content of the dried seedlings were also measured (chemical analysis).The mass of phosphorus leached from litter during the cultivation period was calculated from the difference in the phosphorus contents of the litter before and after cultivation.Growth experiment 2: Comparative experiment on plant growth with old organic matterEight F. japonica seedlings were cultivated in three different soil-types (pumice, andosol, and topsoil, as mentioned above) under three experimental conditions (Con, N, P, same nutrition as growth experiment 1) from May 29 to July 12, 2019. These plants were then harvested and oven-dried at 80 °C for at least 48 h to measure dry weight. Subsequently, the total phosphorus and nitrogen content of the seedlings were also measured (chemical analysis).Chemical analysisPhosphorusWe used the dry destruction method to pretreat total phosphorus measurements in plant tissue27. A sample of the plant (0.05 g) was burned at 550 °C for 1 h. The plant ash was dissolved in 10 mL of 2 M H2SO4 and shaken for over 16 h; then, the solution was filtered. The filtrate was diluted at a 1:10 ratio with tris(hydroxymethyl)aminomethane (pH 8.0).The soil for available phosphorus were pretreated by Truog’ s method28. The soil (0.05 g) was dissolved in 10 mL of 0.002 M H2SO4, shaken for 30 min, and the solution was filtered. The filtrate was diluted at a 1:10 ratio with water.The amount of phosphorus in the sample solution was measured by the molybdenum blue colorimetric method29.NitrogenThe total nitrogen in plant tissue was measured using an elemental analyzer (EA; Vario Macro cube, Elementar, Germany). A few milligrams of the dried plant sample were placed in a tin capsule for EA combustion. EA carried out sample combustion and N2 separation/detection from the combusted gases and provided us with nitrogen contents.The soil sample preparation for available nitrogen measurements was based on the incubation methodology30. Half of the sampled soils were analyzed fresh, and the other half incubated for four weeks at 30 °C before analysis. 2 M KCl (20 mL) was added to 2 g of the soil sample; the solution was shaken for 1 h and filtered. The filtrate was collected, and the volume of nitrogen was measured by indophenol blue absorptiometry after reducing all to ammonia using Pack Test WAK-TNi (Kyoritsu Chemical-Check Lab., Corp, Tokyo, Japan). Available nitrogen was taken as the difference in the concentration of inorganic nitrogen (NO3-N, NO2-N and NH4-N) between incubated and fresh soil.Statistical analysisAll statistical analyses were performed with EZR31 (Saitama Medical Center, Jichi Medical University, Saitama, Japan), which is a graphical user interface for R (The R Foundation for Statistical Computing, Vienna, Austria). More precisely, it is a modified version of R commander designed to add statistical functions frequently used in biostatistics. The figure’s values are mean ± SE. Intergroup differences for nutrition conditions in soil, and soil-types were evaluated using non-parametric Kruskal–Wallis with post-hoc Steel–Dwass tests. In addition, comparisons between with or without litter were evaluated using two-tailed Mann–Whitney U-test. p values are * p  More

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    Benthic estuarine communities' contribution to bioturbation under the experimental effect of marine heatwaves

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    French vote for river barriers defies biodiversity strategy

    CORRESPONDENCE
    01 June 2021

    French vote for river barriers defies biodiversity strategy

    Simon Blanchet

     ORCID: http://orcid.org/0000-0002-3843-589X

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    Pablo A. Tedesco

     ORCID: http://orcid.org/0000-0001-5972-5928

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    Simon Blanchet

    National Centre for Scientific Research (CNRS), Moulis, France.

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    Pablo A. Tedesco

    French National Research Institute for Development (IRD), Toulouse, France.

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    Europe’s rivers are disrupted by more than one million artificial barriers, including small dams, weirs and fords (see, for example, B. Belleti et al. Nature 588, 436–441; 2020). There is strong scientific evidence that such obstructions can harm both hydrological and ecological systems, yet the French parliament has voted to leave them in place (see go.nature.com/3ck9mxq).By limiting the transfer of sediments and movement of organisms, these small barriers create a succession of reaches of warming, stagnant water that threatens freshwater biodiversity (M. R. Fuller et al. Ann. NY Acad. Sci. 1335, 31–51; 2015). Dismantling such small barriers is the most effective way to restore river connectivity and is now a worldwide objective (J. E. O’Connor et al. Science 348, 496–497; 2015).The French parliament’s decision flies in the face of the EU Biodiversity Strategy. It also has no economic justification. Most small barriers cannot generate hydroelectricity and those that can contribute less than 1% to France’s electricity (see go.nature.com/2rphjch).In our view, the fate of each barrier should be decided by balancing its ecological benefits and socioeconomic costs.

    Nature 594, 26 (2021)
    doi: https://doi.org/10.1038/d41586-021-01467-0

    Competing Interests
    The authors declare no competing interests.

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    Evidence of considerable C and N transfer from peas to cereals via direct root contact but not via mycorrhiza

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    Fewer bat passes are detected during small, commercial drone flights

    Site informationThe study was conducted at the Kenauk Institute, an environmental research site, in western Quebec in July 2018, 2019 and 2020. All surveys occurred between 21h30 and 00h00 at night, with location and time of day randomized for each date of testing. Testing did not occur during inclement weather (rain or winds above 10 km/h). In 2018, during an initial field season, we surveyed bat populations using a traditional method (transect-based surveys) to determine which species were present. Six transects lasting 1.5 h each were laid out, and surveyed three times per season; three transects were located in open-canopy areas, and three were located in rugged, closed-canopy areas. Every 200 m, a flag marked a sampling point where we completed a 2-min static inventory using an Anabat SD2 (Titley Scientific, Columbia, MO). In this pilot study used to develop the main study, we observed all eight species known in Quebec, including the eastern red bat (Lasiurus borealis; 0.005 passes detected per minute in open-canopy habitat; 0.001 in closed-canopy), hoary bat (Lasiurus cinereus; 0.002 passes detected per minute in open-canopy habitat; 0.006 in closed-canopy) and tri-coloured bats (Perimyotis subflavus; 0.026 passes detected per minute in open-canopy habitat; none in closed-canopy). Species in the Eptesicus fuscus/Lasionycteris noctivagans acoustic complex were the most abundant (0.075 passes detected per minute in open-canopy habitat; 0.018 in closed-canopy) followed by Myotis species (Myotis leibii, Myotis septentrionalis, Myotis lucifugus; 0.075 passes detected per minute in open-canopy habitat; none in closed-canopy). Due to small sample sizes per species and because manual identification using spectrographic analyses can be unreliable for the differentiation of some bat species22, we pooled several bat species that had similar spectrograms into complexes. We pooled the big brown bat (Eptesicus fuscus) and the silver-haired bat (Lasionycteris noctivagans), and the Myotis species: little brown bat (Myotis lucifugus), northern long-eared Myotis (M. septentrionalis), and eastern small-footed bat (M. leibii)22. Therefore, these species are grouped together in analyses to minimize identification errors22. The big brown bat and silver-haired bat form the EPNO complex whereas the Myotis species form the MYSP complex. We identified to species the hoary bat (LACI), red bat (LABO), and tri-coloured bat (PESU)22. We identified bat passes visually using the output from the Anabat in the Anabat Insight software17,23.Detection efficiencyBecause total bat passes per minute were seven times higher in open-canopy habitats than in closed-canopy habitats, in 2019 we focused our surveying efforts in relatively open habitats. The Anabat (420 g) is too large to attach to a drone, thus in 2019 and 2020, we used Echometer Touch bat detectors (20 g; Wildlife Acoustics, Maynard, MA), commercially available and inexpensive detectors, attached to iPod 7 s (88 g; Apple Inc., Cupertino, CA). We do not directly compare between surveys done with the Anabat and the Echometer Touch, but merely used the 2018 Anabat surveys as a guide for expected bat species and distributions in 2019 and 2020. The UAV used was a commercially available Phantom 4 quadcopter from DJI (1.3 kg, DJI Technology Co. Inc., Shenzhen, China). To reduce sound interference from the drone, which could reduce the detection range of the instrument, we placed a 2-in. Sonoflat acoustic foam (Auralex, Indianapolis, IN) divider between the recorder and the drone, as recommended by past studies19,21 (Fig. 1).Figure 1Illustration of the three phases of the experiment design. A photograph of the UAV setup used in Phase 2 is presented in the top right corner. The setup consists of an Echometer Touch bat detector from Wildlife Acoustics and 2-inch Sonoflat acoustic foam from Auralex attached to a DJI Phantom 4 quadcopter using zip ties. (Images by Julian Herzog, Symbolon, FontAwesome retrieved from https://commons.wikimedia.org. Picture taken by the author).Full size imageIn both 2019 and 2020, we surveyed in three phases: (1) a 5-min recording from the ground without UAV; (2) a 5-min recording while the detector was attached to the UAV using zip ties and carabiners and while the UAV was manually flown in a 10–15 m diameter circle at canopy height (5––10 m above the pilot), depending on the survey site; and (3), identically to Phase 1, a 5-min recording taken from the ground without UAV (Fig. 1). The ground recorder, used sparsely in 2019 and consistently in 2020, was 1 m above the ground during phase 2. Based on surveys in 2018, seven sites were identified as having higher relative activity and were repeatedly monitored in 2019 and 2020 for bat activity. Of the seven study sites, five were located next to bodies of water and four were located near buildings; all were located in open areas. Open spaces and bodies of water are preferred hunting grounds for most bat species18, and make for an easier and safer drone flight. An additional bat detector (Echometer Touch 2, Wildlife Acoustics, Maynard USA) was used on the ground during Phase 2 to simultaneously monitor bat passes from the air and from the ground, to indicate whether bats were present but not detected due to UAV noise interference. In 2020, ten surveys were conducted with Echometer Touch 2 recorders on (1) the UAV, (2) on the ground, and (3) at a control site > 1 km from the current site. Control sites were only used in 2020. Because different bat detectors, as well as different classification software, detect and identify bats at different rates, we do not directly compare among different detectors or software24,25. In 2019, we used the Kaleidoscope software to identify bats automatically. We removed false identifications manually. In 2020, we used the Kaleidoscope software to identify all bats automatically. We also identified all passes visually and blind to the classification from Kaleidoscope. By classifying all bats using both software and visual identification, we aimed to determine whether our results were robust to identification technique.Data were collected beyond Phase 1 if the site had a bat density above three passes per 5 min (2019: N = 24 without ground detector; N = 5 with ground detector; 2020: N = 10 with ground detector; all sample sizes refer to experiments that included Phases 2 and 3). If insufficient bat activity was recorded at a given site after a 5-min period, data collection moved on to the next site, and data from that site was excluded from any analyses. Phase 1 was done to ensure there was an established bat presence, and to maximize sampling. The length of each phase was extended to 10 min if two passes were detected by the 5-min mark of Phase 1, allowing for the collection of more data, while maintaining the time proportions of each phase. While this process, necessary logistically to obtain a sufficient sample size, could lead to more bats detected during Phase 1, there should be no impact on Phase 3 compared to Phase 2, and thus, we used Tukey tests to examine Phase 3 relative to Phase 2, as well as Phase 1 compared with both other phases26.Each drone flight was performed by two field technicians: a pilot and an assistant. The UAV pilot held a basic operations pilot certificate for a small remotely-piloted aircraft system, visual line-of-sight (certificate number PC1917023611) in accordance with federal regulations enforced by Transport Canada. The assistant held the bat detector during Phases 1 and 3. During Phase 2, the assistant acted as the drone’s elevated launching and landing pad as the additional equipment obstructing the UAV’s landing gear. For take-off, they held the UAV upright above their head and gradually let go as the UAV gained altitude. For landing, the pilot gradually decreased the altitude of the drone until the landing gear was safely grasped by the assistant, who then held the UAV above their head until the propellers stopped moving. All methods were carried out in accordance with the guidelines of the Canadian Council for Animal Care. All experimental protocols were approved by McGill University animal care committee under protocol 2015-7599 and complied with the ARRIVE guidelines for animals.Statistical analyses were conducted using R 3.6.0 base package26. Generalized linear models (glm, Poisson distribution) were performed to determine the effect of phase (i.e., 1, 2, and 3) and detector location (detector on the UAV or on the ground) on the total number of bat passes. Tukey tests were then used to determine what phases and locations were significantly different from one another. To assess interspecific variation in detectability, the difference between the mean detection rate for Phase 1 and 3 and the detection rate in Phase 2 were calculated by species for each survey. A glm was then performed on the difference in detectability by species ([Average of Phases 1 and 3 − Average of Phase 2]–Species). Species were divided into four categories: MYSP (Myotis species complex), EPNO (big brown bat/silver-haired bat complex), LABO (eastern red bat), and LACI (hoary bat). No tri-coloured bats were detected, and are therefore absent from analyses. Detection phases were also divided into four categories in relation to the UAV flight: Phase 1 (pre-flight), Phase 2 from UAV-based detection (during flight), Phase 2 from ground-based detection (ground), and Phase 3 (post-flight).Detection capacityTo estimate the degree to which technological limitations affected the results gathered during the first experiment, a second experiment was conducted to estimate the impact of propeller-noise interference on the range of the bat detector. An Audio Generator SGA-8200 (Circuit-Test, Burnaby, Canada), connected to an Ultra Sound Advice S55/6 amplifier and loudspeaker (Ultra Sound Advice, London, UK) set to broadcast a 40 kHz sine wave at 40 dB SPLA @ 1 m, the highest dB setting, was used to replicate the high amplitude ultrasound reached by most bat species during their echolocation calls22. The Echometer Touch bat detector was moved away from the speaker along a measuring tape until the ultrasonic frequency could no longer be detected by the microphone. The procedure was then repeated with the detector attached to the flying UAV. As ambient sound perception cannot be evaluated when the microphone is attached to the UAV, the spectrogram on the Echometer Touch cellphone app (Wildlife Acoustics) connected to the detector was recorded with the screen video recording feature of the iPod 7 (Apple). These recordings were taken as the drone and bat detector were flown slowly along the ground to three distances (10 m, 15 m, 20 m) away from the ultrasound generator to better approximate the detection range. The videos were later visually assessed qualitatively by estimating the distance at which the signal from the speaker could no longer be distinguished from the noise interference of the drone.To quantify the spectral overlap of the drone with echolocation pulses, a spectral analysis of three 15 s recordings were performed using Avisoft SASLab Pro 4.40 (Avisoft Bioacoustics, Berlin Germany). These recordings included the drone flying, the drone motors running without propellers attached, and the ambient noise from the same location and time (control). Recordings were saved as 16 bit WAV files sampled at 256 kc/s and were normalized to 90% in SASLab Pro prior to parameterization. Spectrographs of those normalized recordings were generated using a Fast Fourier Transform length of 512 points, with a frame size of 100% and 75% overlap of Hann windows. This achieved a frequency resolution of 500 Hz and temporal resolution of 0.5 ms. Frequencies where noise was concentrated are evident from these spectrographs, but were confirmed by generating Logarithmic Power Spectra from each recording using Hann windowing achieving frequency resolution of 0.061 Hz. Noise is described at frequencies where the relative sound pressure level exceeded − 80 dB in those Power Spectra. More

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