More stories

  • in

    Saving hawksbill sea turtles from rats, cats and Hurricane Ida

    Download PDF

    It was turtle-nesting season when this photograph was taken one night in June. I am on Needham’s Point beach measuring a critically endangered female hawksbill turtle (Eretmochelys imbricata). As field director of the Barbados sea turtle project, I run the day-to-day conservation activities and train and manage volunteers.We also run research projects that inform our conservation activities. We collect data such as shell length, which can tell us the age at which females become sexually mature and can indicate growth rates. These data help us to keep track of turtle health and survival. For example, if we start seeing smaller turtles, this could indicate that they are maturing faster, or that food is scarce and the turtles are growing more slowly.In August, the baby turtles hatch. I was on call 7 days a week for around 8 hours a day, responding to emergencies. These included hatchlings wandering off in the wrong direction, putting them at risk of being hit by a car or eaten by predators such as rats and cats. We took the hatchlings to a safe spot on the beach and released them. I also had to prepare for the expected swells as Hurricane Ida passed us by: when beaches flood, nests can wash away. We took rescued eggs and premature hatchlings to a makeshift intensive-care unit until they were ready for release. We aim to leave no turtle behind.I have worked at the project for 15 years. I recently finished a master’s degree on the coloration of the Barbados bullfinch (Loxigilla barbadensis) at the University of the West Indies, which hosts the turtle project. Next year I hope to start a PhD, part of which will look at the conflict between tourism and sea-turtle survival in Barbados. Here, interactions between sea turtles and humans occur at every stage of the turtles’ lives and can affect their survival. After my doctorate, I will continue to focus on helping sea turtles in the Caribbean. There is something addictive about making a real-time, tangible difference to their lives.

    Nature 598, 532 (2021)
    doi: https://doi.org/10.1038/d41586-021-02851-6

    Related Articles

    230-million-year-old turtle fossil deepens mystery of reptile’s origins

    Breeding the sweetest biofuels in the business

    Dexterous sea turtles use flippers as grippers

    Subjects

    Careers

    Conservation biology

    Latest on:

    Careers

    How I wrote a pop-science book
    Career Column 15 OCT 21

    How AI is helping the natural sciences
    Career Guide 13 OCT 21

    Eight career tips from Nobel Laureates
    Career Column 08 OCT 21

    Jobs

    W 3-Professorship for Biochemistry

    University of Kiel (CAU)
    Kiel, Germany

    Laboratory Associate

    Center for Molecular Fingerprinting (CMF) Research Nonprofit LLC
    Multiple locations

    Scientific Computing Officer – Structural Studies – Scientific Computing – LMB 1697

    MRC Laboratory of Molecular Biology
    Cambridge, United Kingdom

    Head of Laser Lab Operations

    Center for Molecular Fingerprinting (CMF) Research Nonprofit LLC
    Multiple locations More

  • in

    Carbon dioxide levels in initial nests of the leaf-cutting ant Atta sexdens (Hymenoptera: Formicidae)

    1.Hughes, W. O. H. & Goulson, D. The use of alarm pheromones to enhance bait harvest by grass-cutting ants. Bull. Entomol. Res. 92, 213–218 (2002).CAS 
    Article 

    Google Scholar 
    2.Staab, M. & Kleineidam, C. J. Initiation of swarming behavior and synchronization of mating flights in the leaf-cutting ant Atta vollenweideri Forel, 1893 (Hymenoptera: Formicidae). Myrmecol. News 19, 93–102 (2014).
    Google Scholar 
    3.Sales, T. A., Toledo, A. M. O. & Lopes, J. F. S. The best of heavy queens: Influence of post-flight weight on queens’ survival and productivity in Acromyrmex subterraneus (Forel, 1893) (Hymenoptera: Formicidae). Insectes Soc. 67, 383–390 (2020).Article 

    Google Scholar 
    4.Camargo, R. S., Forti, L. C., Fujihara, R. T. & Roces, F. Digging effort in leaf-cutting ant queens (Atta sexdens rubropilosa) and its effects on survival and colony growth during the claustral phase. Insectes Soc. 58, 17–22 (2011).Article 

    Google Scholar 
    5.Autuori, M. Contribuição para o conhecimento da saúva (Atta spp.) (Hymenoptera: Formicidae). I. Evolução do sauveiro (Atta sexdens rubropilosa Forel, 1908). Arq. Inst. Biol. 12, 197–228 (1941).
    Google Scholar 
    6.Aylward, F. O. et al. Leucoagaricus gongylophorus produces diverse enzymes for the degradation of recalcitrant plant polymers in leaf-cutter ant fungus gardens. Appl. Environ. Microbiol. 79, 3770–3778 (2013).ADS 
    CAS 
    Article 

    Google Scholar 
    7.Costa, A. N., Vasconcelos, H. L., Vieira-Neto, E. H. M. & Bruna, E. M. Do herbivores exert top-down effects in Neotropical savannas? Estimates of biomass consumption by leaf-cutter ants. J. Veg. Sci. 19, 849–854 (2008).Article 

    Google Scholar 
    8.Bollazzi, M., Forti, L. C. & Roces, F. Ventilation of the giant nests of Atta leaf-cutting ants: Does underground circulating air enter the fungus chambers?. Insectes Soc. 59, 487–498 (2012).Article 

    Google Scholar 
    9.Sousa-Souto, L. et al. Increased CO2 emission and organic matter decomposition by leaf-cutting ant nests in a coastal environment. Soil Biol. Biochem. 44, 21–25 (2012).CAS 
    Article 

    Google Scholar 
    10.Hasin, S. et al. CO2 efflux from subterranean nests of ant communities in a seasonal tropical forest, Thailand. Ecol. Evol. 4, 3929–3939 (2014).Article 

    Google Scholar 
    11.Tschinkel, W. R. The nest architecture of the Florida harvester ant, Pogonomyrmex badius. J. Insect Sci. 4, 21 (2004).Article 

    Google Scholar 
    12.Kleineidam, C. & Roces, F. Carbon dioxide concentrations and nest ventilation in nests of the leaf-cutting ant Atta vollenweideri. Insectes Soc. 47, 241–248 (2000).Article 

    Google Scholar 
    13.Currie, J. A. Gas diffusion through soil crumbs: The effects of compaction and wetting. J. Soil Sci. 35, 1–10 (1984).CAS 
    Article 

    Google Scholar 
    14.Kleineidam, C., Ernst, R. & Roces, F. Wind-induced ventilation of the giant nests of the leaf-cutting ant Atta vollenweideri. Naturwissenschaften 88, 301–305 (2001).ADS 
    CAS 
    Article 

    Google Scholar 
    15.Vogel, S., Ellington, C. P. & Kilgore, D. L. Wind-induced ventilation of the burrow of the prairie-dog, Cynomys ludovicianus. J. Comp. Physiol. 85, 1–14 (1973).Article 

    Google Scholar 
    16.Jonkman, J. C. M. The external and internal structure and growth of nests of the leaf-cutting ant Atta vollenweideri Forel, 1893 (Hym: Formicidae) Part II. Zeitschrift für Angew. Entomol. 89, 158–173 (1980).Article 

    Google Scholar 
    17.Gutiérrez, J. L. & Jones, C. G. Physical ecosystem engineers as agents of biogeochemical heterogeneity. Bioscience 56, 227–236 (2006).Article 

    Google Scholar 
    18.Fernandez-Bou, A. S. et al. The role of the ecosystem engineer, the leaf-cutter ant Atta cephalotes, on soil CO2 dynamics in a wet tropical rainforest. J. Geophys. Res. Biogeosciences 124, 260–273 (2019).ADS 
    CAS 
    Article 

    Google Scholar 
    19.Moitinho, M. R. et al. Does fresh farmyard manure introduce surviving microbes into soil or activate soil-borne microbiota?. J. Environ. Manag. 11, 1–15 (2021).
    Google Scholar 
    20.Roces, F. Variable thermal sensitivity as output of a circadian clock controlling the bimodal rhythm of temperature choice in the ant Camponotus mus. J. Comp. Physiol. A 177, 637–643 (1995).Article 

    Google Scholar 
    21.Römer, D., Bollazzi, M. & Roces, F. Carbon dioxide sensing in an obligate insect-fungus symbiosis: CO2 preferences of leaf-cutting ants to rear their mutualistic fungus. PLoS ONE 12, e0174597 (2017).Article 

    Google Scholar 
    22.Halboth, F. & Roces, F. The construction of ventilation turrets in Atta vollenweideri leaf-cutting ants: Carbon dioxide levels in the nest tunnels, but not airflow or air humidity, influence turret structure. PLoS ONE 12, e0188162 (2017).Article 

    Google Scholar 
    23.Kleineidam, C. & Tautz, J. Perception of carbon dioxide and other “air-condition” parameters in the leaf cutting ant Atta cephalotes. Naturwissenschaften 83, 566–568 (1996).ADS 
    CAS 

    Google Scholar 
    24.Kleineidam, C., Romani, R., Tautz, J. & Isidoro, N. Ultrastructure and physiology of the CO2 sensitive sensillum ampullaceum in the leaf-cutting ant Atta sexdens. Arthropod Struct. Dev. 29, 43–55 (2000).CAS 
    Article 

    Google Scholar 
    25.Camargo, R. S. & Forti, L. C. Queen lipid content and nest growth in the leaf cutting ant (Atta sexdens rubropilosa) (Hymenoptera: Formicidae). J. Nat. Hist. 47, 65–73 (2013).Article 

    Google Scholar 
    26.Seal, J. N. Scaling of body weight and fat content in fungus-gardening ant queens: Does this explain why leaf-cutting ants found claustrally?. Insectes Soc. 56, 135–141 (2009).Article 

    Google Scholar 
    27.Camargo, R. D. S., Fonseca, J. A., Lopes, J. F. S. & Forti, L. C. Influência do ambiente no desenvolvimento de colônias iniciais de formigas cortadeiras (Atta sexdens rubropilosa). Ciência Rural 43, 1375–1380 (2013).Article 

    Google Scholar 
    28.Silva, E. J., da Silva Camargo, R. & Forti, L. C. Flight and digging effort in leaf-cutting ant males and gynes. Sociobiology 62, 334–339 (2015).Article 

    Google Scholar 
    29.Kuzyakov, Y. Sources of CO2 efflux from soil and review of partitioning methods. Soil Biol. Biochem. 38, 425–448 (2006).CAS 
    Article 

    Google Scholar 
    30.Camargo, R. S., Silva, E. J., Forti, L. C. & Matos, C. A. O. Initial development and production of CO2 in colonies of the leaf-cutting ant Atta sexdens during the claustral foundation. Sociobiology 63, 720–723 (2016).Article 

    Google Scholar 
    31.Cribari-Neto, F. & Zeileis, A. Beta regression in R. J. Stat. Softw. 34, 1–24 (2010).Article 

    Google Scholar 
    32.Ferrari, S. & Cribari-Neto, F. Beta regression for modelling rates and proportions. J. Appl. Stat. 31, 799–815 (2004).MathSciNet 
    Article 

    Google Scholar 
    33.Smithson, M. & Verkuilen, J. A better lemon squeezer? Maximum-likelihood regression with beta-distributed dependent variables. Psychol. Methods 11, 54 (2006).Article 

    Google Scholar  More

  • in

    Identification and analyses of the chemical composition of a naturally occurring albino mutant chanterelle

    All fungal specimens were collected on Crown Lands for which no permission is required. Specimens of fresh, field-collected mushroom fruiting bodies were either air-dried at a temperature of 30–35 °C or frozen at − 80 °C until processing, and air-dried voucher specimens have been deposited at the Dr. Laurie L. Consaul Herbarium, London, Canada (UWO) and the National Mycological Herbarium of Canada, Ottawa (DAOM) (Table 1).DNA extraction, PCR amplification and sequencingGenomic DNA was extracted from air-dried specimens following Thorn et al.9. Primers ITS1 and ITS6R were used to amplify the ITS region, LS1 and LR3 to amplify ~ 650 bases of the 5’-LSU region and Canth-ef1a983-F and Canth-ef1a-1567-R to amplify Tef-19,41,42,43,44. The PCR products were checked using gel electrophoresis and successful products were cleaned using Bio Basic EZ-10 Spin Column PCR Products Purification Kit. Cleaned PCR products were submitted to the sequencing facility of Robarts Institute (University of Western Ontario) to obtain sequences through Sanger sequencing with amplification primers, and internal sequencing primers CanthITS1_Internal-R, 5.8S-R-Canth, and ITS86R-Canth for the ITS region9. New sequences produced for this study were deposited in GenBank as accessions MN181445–MN181461 and MN206911–MN206945.Phylogenetic analysesSequences of the ITS, LSU and Tef-1 regions were cleaned and assembled with SeqEd v.1.03, then, together with sequences of related species downloaded from GenBank, each region was aligned separately with MAFFT v.745 under the G-INS-i strategy, with “leave gappy regions” selected. The draft Cantharellus cibarius genome (QOWL00000000.1)18 was searched by BLASTn for ribosomal and Tef-1 sequences. The full-length match to our Tef-1 sequences was found in a single scaffold (QOWL01010594_RC), but no ITS sequences and only partial LSU sequences were found (in QOWL01010930_RC, QOWL01012415, QOWL01007530_RC, and QOWL01005980_RC). Alignments were imported into MEGA X46,47, trimmed and concatenated into a single ITS-LSU-Tef-1 dataset, then optimized manually. Phylogenetic trees were constructed with maximum likelihood (ML), with 1000 bootstrapping replicates in MEGA X. Analyses were repeated with Bayesian inference using MrBayes 3.2.6 with 4 chains and 5 million generations, discarding the first 25% of trees, when the average standard deviation of split frequencies had stabilized below 0.0148. Tree topologies were compared, and Bayesian prior probabilities transferred to the ML bootstrap tree in Adobe Acrobat.Genetic analysis of carotenoid synthesis genesIn order to design primers to amplify portions of the Al-1 and Al-2 genes in white and golden chanterelles, these genes were first located in three published Cantharellus genome sequences18 using tBLASTn49 to query the genomes with protein sequences from N. crassa (PRJNA132; Al-1 XM_959620.2 and Al-2 XM_960632.3)19. Candidate gene sequences from Cantharellus appalachiensis (QLPK00000000.1; Al-1 Scaffold 4647: QLPK01003932.1 and Al-2 Scaffold 1419: QLPK01001208.1), C. cibarius (QOWL00000000.1; Al-1 Scaffold 15338: QOWL01009792.1 and Al-2 Scaffold 1560: QOWL01001169.1), and C. cinnabarinus (QLPJ00000000.1; Al-1 Scaffold 1057: QLPJ01000880.1 and Al-2 Scaffold 2474: QLPJ01001998.1) were annotated in Geneious using a discontiguous megablast against GenBank to search for homologous motifs50. Based on these alignments, putative ORFs were annotated and aligned, and an overlapping set of PCR primers for AL-1 and AL-2 were designed in Geneious (Table 5). Designed primers were tested for specificity using the BLAST algorithm against the three Cantharellus genomes18. PCR amplified products were assessed for quality, cleaned, sent for sequencing, and assembled as above. Assembled sequences of white and gold samples were compared, along with their putative amino acid products determined using ExPASy51, guided by the translations of the Neurospora crassa Al-1 (XM_959620.2) and Al-2 (XM_960632.3) genes. Partial sequences of the Al-1 and Al-2 genes of gold and white variants have been deposited in GenBank as MW442833–MW442836.Table 5 PCR primers designed to amplify portions of the phytoene desaturase gene (Al-1) and phytoene synthase gene (Al-2) from Cantharellus species, based on genomic sequences from Cantharellus appalachiensis, C. cibarius, and C. cinnabarinus18, listed in “Materials and methods” section.Full size tablePigment analysisField-collected mushroom fruiting bodies were weighed while fresh, wrapped in aluminum foil, and frozen at − 80 °C for pigment analyses, and other samples were weighed fresh and then dried to obtain a conversion for fresh to dry weight. Pigments were extracted with ice-cold 100% acetone at 4 °C and dim light. The supernatant was filtered through a 0.22 µm syringe filter and samples were stored at − 80 °C until analysed. Pigments were separated and quantified by high-performance liquid chromatography (HPLC) as described previously20, with some modifications. The system consisted of a Beckman System Gold programmable solvent module 126, diode array detector module 168 (Beckman Instruments, San Ramon, California, USA), CSC-Spherisorb ODS-1 reverse-phase column (5 mm particle size, 25 × 0.46 cm I.D.) with an Upchurch Perisorb A guard column (both columns from Chromatographic Specialties Inc., Concord, Ontario, Canada). Samples were injected using a Beckman 210A sample injection valve with a 20 μL sample loop. Pigments were eluted isocratically for 6 min with a solvent system of acetonitrile:methanol:0.1 M Tris–HCl (pH 8.0), (72:8:3.5, v/v/v), followed by a 2 min linear gradient to 100% methanol:hexane (75:25, v/v) which continued isocratically for 4 min. Total run time was 12 min. Flow rate was 2 mL min−1. Absorbance was detected at 440 nm and peak areas were integrated by Beckman System Gold software. Retention times and response factors of Chl a, Chl b, lutein and ß-carotene were determined by injection of known amounts of pure standards purchased from Sigma (St. Louis, MO, USA). The retention times of zeaxanthin, antheraxanthin, violaxanthin and neoxanthin were determined by using pigments purified by thin-layer chromatography as described by Diaz et al.52.Extraction and analysis of chanterelle lipidsSamples of each chanterelle species were homogenized to fine powder in a cryomill (Reitch, Germany) and 100 mg of the homogenized powder mixed with 1 mL methanol (MeOH), 1 mL chloroform (CHCl3) and 0.8 mL water following Pham et al.53. The sample mixture was thoroughly vortexed, then centrifuged (Sorvall Legend XT/XF centrifuge; ThermoFisher Scientific, Mississauga, Ontario) at 2500 rpm for 15 min. The organic layer was transferred to new vials, dried under nitrogen and then reconstituted in 1 mL chloroform:methanol (1:1 v/v). Aliquots were then used for either gas chromatography with mass spectrometric and flame ionization detection (GC–MS/FID) or ultra-high-performance liquid chromatography with heated electrospray ionization high resolution accurate mass tandem mass spectrometric analysis (UHPLC-HESI-HRAM/MS–MS) for fatty acids and intact lipids analysis, respectively.For GC–MS/FID analysis, chanterelle fatty acids were converted to fatty acid methyl esters (FAMEs) as follows: To 300 µL aliquot of the lipid extract, 50 μL of C18:0 alkane (1 mg mL−1 in chloroform: methanol 1:1 v/v) was added as internal standards and the samples dried under nitrogen and the fatty acids esterified by adding 400 µL methanolic HCl (1.5 N). The samples were then incubated in a pre-heated oven at 60 °C for 30 min. After incubation, 0.8 mL of distilled water was added to the cooled samples and the FAMEs extracted with 3 aliquots each of 500 μL of hexane. The fractions were combined, dried under N2, re-suspended in 50 μL hexane, and the FAMEs analyzed using a Trace 1300 gas chromatograph coupled to a Flame Ionization Detector and TSQ 8000 mass spectrometer (Thermo Fisher Scientific). The FAMEs were separated on a BPX70 high-resolution column (10 m × 0.1 mm ID × 0.2 μm, Canadian Life Science, Peterborough, Ontario) using helium as the carrier gas at a flow rate of 1 mL min−1. One μL of each sample was injected in split mode (1:15) using a Tri-plus auto-sampler (Thermo Fisher Scientific). The operation conditions were as follows: initial oven temperature set at 50 °C for 0.75 min, increased to 155 °C at 4 °C min−1, ramped to 210 °C at 6 °C min−1, then 240 °C at 15 °C min−1 and final temperature held for 2 min. Methylated fatty acids were determined by comparison with retention times and mass spectra obtained from commercial standards (Supelco 37 component mix, Supelco PUFA No. 3, Sigma Aldrich, Oakville, Ontario) and the NIST database (Thermo Fisher Scientific). Standard curves were employed to determine the amount of individual fatty acids, and values are presented as nmole%.For the UHPLC-HESI-HRAM/MS–MS analysis, a Q-Exactive Orbitrap mass spectrometer (Thermo Fisher Scientific) coupled to an automated Dionex UltiMate 3000 UHPLC system was used to analyze the intact chanterelle lipids according to our previously published method53. Briefly, the intact lipids were resolved using an Accucore C30 column (150 mm × 2 mm I.D., particle size: 2.6 µm, pore diameter: 150 Å) and the following solvent systems: (i) Solvent A consisted of acetonitrile:water (60:40 v/v) containing 10 mM ammonium formate and 0.1% formic acid and (ii) Solvent B consisted of isopropanol:acetonitrile:water (90:10:1 v/v/v) with 10 mM ammonium formate and 0.1% formic acid. The conditions used for separation were 30 °C (column oven temperature), flow rate of 0.2 mL min−1, and 10 µL of sample injected. The gradient system used was as follow: solvent B increased to 30% in 3 min; 43% in 5 min, 50% in 1 min, 90% in 9 min, 99% in 8 min, and finally maintained at 99% for 4 min. The column was re-equilibrated for 5 min before each new injection. Full scans and tandem MS acquisitions were performed in both negative and positive modes using the following parameters: sheath gas: 40, auxiliary gas: 2, ion spray voltage: 3.2 kV, capillary temperature: 300 °C; S-lens RF: 30 V; mass range: 200–2000 m/z; full scan at 70,000 m/z resolution; top-20 data-dependent MS/MS resolution at 35,000 m/z, collision energy of 35 (arbitrary unit); injection time of 35 min for C30RP chromatography; isolation window: 1 m/z; automatic gain control target: 1e5 with dynamic exclusion setting of 5.0 s. The instrument was externally calibrated to 1 ppm using electrospray ionization (ESI); negative and positive calibration solutions (Thermo Fisher Scientific) were used to calibrate the instrument at 1 ppm. Tune parameters were optimized using PC 18:1(9Z)/18:1(9Z), Cer d18:1/18:1(9Z), PG 18:1(9Z)/18:1(9Z), sulfoquinovosyl diacylglycerols [SQDG] 18:3(9Z,12Z,15Z)/16:0, monogalactosyl diglyceride [MGDG] 18:3(9Z,12Z,15Z)/16:3(7Z,10Z,13Z), and digalactosyldiacylglycerol [DGDG] 18:3(9Z,12Z,15Z)/18:3(9Z,12Z,15Z) lipid standards (Avanti Polar Lipids, Alabaster, AL, USA) in both negative and positive ion modes. The data were processed using either X-Calibur 4.0 (Thermo Fisher Scientific) or LipidSearch version 4.1 (Mitsui Knowledge Industry, Tokyo, Japan) software packages.Phenolics analysis by GC–MSReagent grade phenolic acid standards including benzoic acids, p-hydroxybenzoic acid, vanillic acid, gallic acid, 3,4-dihydroxybenzoic acid, syringic acid, gentisic acid, veratric acid, salicylic acid, cinnamic acid, o-coumaric acid, m-coumaric acid, p-coumaric acid, ferulic acid, sinapic acid, caffeic acid, sodium hydroxide, N,O-Bis(trimethylsilyl)trifluoroacetamide (BSTFA-TCMS) were purchased from Sigma Aldrich. Methanol, ethyl acetate, and hydrochloric acid (36% w/v) were purchased from VWR (Mississauga, Ontario, Canada). For alkaline hydrolysis of powdered chanterelles, 100 µL of aqueous 3,4-dihydroxybenzoic acid solution (0.2 mg mL−1) was added to a mixture containing 4 g of sample in 8 mL 1 M sodium hydroxide. The resultant mixture was incubated in the dark for 24 h at 25 °C on an orbital shaker (50 rpm). The pH of the reaction mixture was adjusted to 2.0–2.5 using concentrated HCl then vortexed. The organic components were extracted four times with 4 mL methanol: ethyl acetate (1:3 ratio) into pre-weighed vials. The solvent was evaporated under nitrogen at 35 °C to determine the crude extraction yield. The extracts were resuspended in 1 mL ethyl acetate, vortexed, then 300 µL of extract transferred into a pre-weighed vial, dried under nitrogen, and 50 µL of BSTFA-TCMS and 50 µL of pyridine added. The resultant mixture was incubated at 70 °C in darkness for 30 min then transferred to GC vials for GC–MS analysis. Standard solutions were derivatized in a similar manner.A Thermo Scientific Trace 1300 gas chromatograph coupled to a Triple Quad mass spectrometer (Thermo Fisher Scientific) was used for the analysis and the compounds resolved on a ZB-5MS non-polar stationary phase column (30 m × 0.25 mm I.D., 0.25 μm film thickness, Phenomenex, Torrance, CA, USA) with helium as the carrier gas (flow rate of 0.6 mL min−1). One microliter of the standard or sample was injected in basic mode (15:0) using a Tri-plus auto-sampler. The oven temperature program was as follows: the initial oven temperature was 70 °C (held for 1 min), was increased at 12 °C min−1 to 220 °C (held for 3 min), 15 °C min−1 to reach 250 °C and held for 1 min. Identification of the phenolic acids (as trimethylsilyl ether, TMS) was based on the comparison of their retention times and mass spectra with that of the NIST library and commercial standards, with quantities calculated and expressed as nmole%.
    Analysis of the volatile profile of chanterelles by SPME-GC/MSVolatile metabolites were extracted and analysed by Solid-Phase Microextraction and Gas Chromatography/Mass Spectrometry (SPME-GC/MS) following Vidal et al.54. Briefly, 100 mg of sample powder obtained after cryo homogenization was placed in 10 mL headspace glass vials and kept at 50 °C for 5 min (sample equilibration) before volatile metabolites extraction and analysis began. A divinylbenzene/carboxen/polydimethylsyloxane (DVB/CAR/ PDMS) coated fibre (1 cm long, 50/30 μm film thickness; Supelco, Sigma-Aldrich), was inserted into the headspace of the sample vial and held there for 60 min55,56. Chanterelle volatile composition was analyzed using a Trace 1300 gas chromatograph coupled to a TSQ 8000 Triple Quadrupole mass spectrometer (Thermo Fisher Scientific). The extracted volatile compounds were separated using a ZB-5MS non-polar stationary phase column (30 m × 0.25 mm I.D., 0.25 μm film thickness; Phenomenex) with He used as the carrier at a flow rate of 1 mL min−1. After extraction the fibre was desorbed for 10 min in the injection port and the instrument operated as follows: splitless mode with a purge time of 5 min, initial oven temperature set at 50 °C (5 min hold) and increased to 290 °C at 4 °C min−1 (2 min hold). Ion source and quadrupole mass analyzer temperatures were set at 230 and 150 °C respectively, injector and detector temperatures held at 250 and 290 °C respectively, mass spectra ionization energy set at 70 eV, and data acquisition done in scan mode. After each sample desorption, the fiber was cleaned for 10 min at 250 °C in the conditioning station. Volatile compounds were identified by matching the obtained mass spectra with those of available standards, and mass spectra from commercial libraries NIST/EPA/NIH (version 2.2, Thermo Fisher Scientific) or the scientific literature55,56. Volatile compounds in the chanterelle samples were semi-quantified based on the area counts × 10−6 of the base peak. Compounds with lower abundances than 10−6 area counts were considered as traces. Although the chromatographic response factor of each compound is different, the area counts determined are useful for comparison of the relative abundance of each compound in the different samples analysed55,56.Statistics and reproducibilityResults of analyses of lipids and phenolics are presented as means and standard errors of 4 replicates and those of head-space analyses of volatiles are based on 2 replicates (Tables 2, 3, 4). The values of all dependent variables were tested for normal distribution and homoscedasticity by Shapiro–Wilk and Levene tests, respectively. For variables with homogeneous variance, parametric one-way analysis of variance (ANOVA) was used to determine if there were significant differences between chanterelle samples. Where significance was detected, the means were compared with Fisher’s Least Significant Difference (LSD), α = 0.05. Where the assumption of normality was not met (pentanal, 3-octen-2-one, 2-nonanone, 2-undecanone, (E)-α-Ionone, 3-hydroxy-α-ionene, all phenolic acids, and some minor fatty acids 10:0, 15:0, 14:1, 16:1n5, 20:1n9 and 24:1n9), no significance was detected after the data were treated with a non-parametric Kruskal–Wallis test and significance (p  More

  • in

    Fitness consequences of targeted gene flow to counter impacts of drying climates on terrestrial-breeding frogs

    1.Lande, R. & Shannon, S. The role of genetic variation in adaptation and population persistence in a changing environment. Evolution 50, 434–437 (1996).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    2.Barrett, R. D. & Schluter, D. Adaptation from standing genetic variation. Trends Ecol. Evol. 23, 38–44 (2008).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    3.Young, A., Boyle, T. & Brown, T. The population genetic consequences of habitat fragmentation for plants. Trends Ecol. Evol. 11, 413–418 (1996).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    4.Cushman, S. A. Effects of habitat loss and fragmentation on amphibians: a review and prospectus. Biol. Conserv. 128, 231–240 (2006).Article 

    Google Scholar 
    5.Opdam, P. & Wascher, D. Climate change meets habitat fragmentation: linking landscape and biogeographical scale levels in research and conservation. Biol. Conserv. 117, 285–297 (2004).Article 

    Google Scholar 
    6.Broadhurst, L. M. et al. Seed supply for broadscale restoration: maximizing evolutionary potential. Evol. Appl. 1, 587–597 (2008).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    7.Vitt, P., Havens, K., Kramer, A. T., Sollenberger, D. & Yates, E. Assisted migration of plants: changes in latitudes, changes in attitudes. Biol. Conserv. 143, 18–27 (2010).Article 

    Google Scholar 
    8.Aitken, S. N. & Bemmels, J. B. Time to get moving: assisted gene flow of forest trees. Evol. Appl. 9, 271–290 (2016).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    9.Evans, B. J. et al. Speciation over the edge: gene flow among non-human primate species across a formidable biogeographic barrier. R. Soc. Open Sci. 4, 170351 (2017).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    10.Weeks, A. R. et al. Assessing the benefits and risks of translocations in changing environments: a genetic perspective. Evol. Appl. 4, 709–725 (2011).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    11.Pavlova, A. et al. Severe consequences of habitat fragmentation on genetic diversity of an endangered Australian freshwater fish: a call for assisted gene flow. Evol. Appl. 10, 531–550 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    12.Aitken, S. N. & Whitlock, M. C. Assisted gene flow to facilitate local adaptation to climate change. Annu. Rev. Ecol. Evol. Syst. 44, 367–388 (2013).Article 

    Google Scholar 
    13.Rajpurohit, S. & Nedved, O. Clinal variation in fitness related traits in tropical drosophilids of the Indian subcontinent. J. Therm. Biol. 38, 345–354 (2013).Article 

    Google Scholar 
    14.Kawecki, T. J. & Ebert, D. Conceptual issues in local adaptation. Ecol. Lett. 7, 1225–1241 (2004).Article 

    Google Scholar 
    15.Kottler, E. J., Dickman, E. E., Sexton, J. P., Emery, N. C. & Franks, S. J. Draining the swamp hypothesis: little evidence that gene flow reduces fitness at range edges. Trends Ecol. Evol. https://doi.org/10.1016/j.tree.2021.02.004 (2021).16.Kelly, E. & Phillips, B. L. Targeted gene flow for conservation. Conserv. Biol. 30, 259–267 (2016).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    17.Macdonald, S. L., Llewelyn, J., Moritz, C. & Phillips, B. L. Peripheral isolates as sources of adaptive diversity under climate change. Front. Ecol. Evol. 5, 88 (2017).Article 

    Google Scholar 
    18.Edmands, S. Between a rock and a hard place: evaluating the relative risks of inbreeding and outbreeding for conservation and management. Mol. Ecol. 16, 463–475 (2007).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    19.Edmands, S. Heterosis and outbreeding depression in interpopulation crosses spanning a wide range of divergence. Evolution 53, 1757–1768 (1999).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    20.Frankham, R. et al. Predicting the probability of outbreeding depression. Conserv. Biol. 25, 465–475 (2011).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    21.Whiteley, A. R., Fitzpatrick, S. W., Funk, W. C. & Tallmon, D. A. Genetic rescue to the rescue. Trends Ecol. Evol. 30, 42–49 (2015).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    22.Schierup, M. H. & Christiansen, F. B. Inbreeding depression and outbreeding depression in plants. Heredity 77, 461–468 (1996).Article 

    Google Scholar 
    23.Bjorkman, A. D., Vellend, M., Frei, E. R. & Henry, G. H. Climate adaptation is not enough: warming does not facilitate success of southern tundra plant populations in the high Arctic. Glob. Change Biol. 23, 1540–1551 (2017).Article 

    Google Scholar 
    24.Frankham, R. Where are we in conservation genetics and where do we need to go? Conserv. Genet. 11, 661–663 (2010).Article 

    Google Scholar 
    25.Tallmon, D. A., Luikart, G. & Waples, R. S. The alluring simplicity and complex reality of genetic rescue. Trends Ecol. Evol. 19, 489–496 (2004).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    26.Weeks, A. R. et al. Genetic rescue increases fitness and aids rapid recovery of an endangered marsupial population. Nat. Commun. 8, 1071 (2017).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    27.Le Cam, S., Perrier, C., Besnard, A.-L., Bernatchez, L. & Evanno, G. Genetic and phenotypic changes in an Atlantic salmon population supplemented with non-local individuals: a longitudinal study over 21 years. Proc. Roy. Soc. B-Biol. Sci. 282, 20142765 (2015).Article 
    CAS 

    Google Scholar 
    28.Fitzpatrick, S. W. et al. Gene flow from an adaptively divergent source causes rescue through genetic and demographic factors in two wild populations of Trinidadian guppies. Evol. Appl. 9, 879–891 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    29.Robinson, Z. L. et al. Experimental test of genetic rescue in isolated populations of brook trout. Mol. Ecol. 26, 4418–4433 (2017).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    30.Byrne, P. G. & Silla, A. J. An experimental test of the genetic consequences of population augmentation in an amphibian. Conserv. Sci. Pract. 2, e194 (2020).31.Stuart, S. N. et al. Status and trends of amphibian declines and extinctions worldwide. Science 306, 1783–1786 (2004).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    32.Urban, M. C., Richardson, J. L. & Freidenfelds, N. A. Plasticity and genetic adaptation mediate amphibian and reptile responses to climate change. Evol. Appl. 7, 88–103 (2014).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    33.Carey, C. & Alexander, M. A. Climate change and amphibian declines: is there a link? Divers. Distrib. 9, 111–121 (2003).Article 

    Google Scholar 
    34.Parmesan, C. & Yohe, G. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421, 37–42 (2003).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    35.Pounds, J. A. et al. Widespread amphibian extinctions from epidemic disease driven by global warming. Nature 439, 161–167 (2006).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    36.Thomas, C. D. et al. Extinction risk from climate change. Nature 427, 145 (2004).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    37.Rudin-Bitterli, T. S., Evans, J. P. & Mitchell, N. J. Geographic variation in adult and embryonic desiccation tolerance in a terrestrial-breeding frog. Evolution 74, 1186–1199 (2020).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    38.Eads, A., Mitchell, N. J. & Evans, J. Patterns of genetic variation in desiccation tolerance in embryos of the terrestrial-breeding frog, Pseudophryne guentheri. Evolution 66, 2865–2877 (2012).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    39.Cummins, D., Kennington, W. J., Rudin‐Bitterli, T. & Mitchell, N. J. A genome‐wide search for local adaptation in a terrestrial‐breeding frog reveals vulnerability to climate change. Glob. Change Biol. 25, 3151–3162 (2019).Article 

    Google Scholar 
    40.Bureau of Meteorology. Climate Data Online, http://www.bom.gov.au/climate/data/ (2020).41.Turelli, M. & Moyle, L. C. Asymmetric postmating isolation: Darwin’s corollary to Haldane’s rule. Genetics 176, 1059–1088 (2007).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    42.Dobzhansky, T. Studies on hybrid sterility. II. Localization of sterility factors in Drosophila pseudoobscura hybrids. Genetics 21, 113 (1936).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    43.Muller, H. J. Isolating mechanisms, evolution and temperature. Biol. Symp. 6, 71–125 (1942).
    Google Scholar 
    44.Orr, H. A. The population genetics of speciation: the evolution of hybrid incompatibilities. Genetics 139, 1805–1813 (1995).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    45.Arntzen, J. W., Jehle, R., Bardakci, F., Burke, T. & Wallis, G. P. Asymmetric viability of reciprocal-cross hybrids between crested and marbled newts (Trituris cristatus and Trituris marmoratus). Evolution 63, 1191–1202 (2009).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    46.Lee-Yaw, J. A., Jacobs, C. G. C. & Irwin, D. E. Individual performance in relation to cytonuclear discordance in a northern contact zone between long-toed salamander (Ambystoma macrodactylum) lineages. Mol. Ecol. 23, 4590–4602 (2014).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    47.Sanchez, S. et al. Within-colony spatial segregation leads to foraging behaviour variation in a seabird. Mar. Ecol. Prog. Ser. 606, 215–230 (2018).Article 

    Google Scholar 
    48.Sasa, M. M., Chippindale, P. T. & Johnson, N. A. Patterns of postzygotic isolation in frogs. Evolution 52, 1811–1820 (1998).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    49.Sánchez‐Guillén, R., Córdoba‐Aguilar, A., Cordero‐Rivera, A. & Wellenreuther, M. Genetic divergence predicts reproductive isolation in damselflies. J. Evol. Biol. 27, 76–87 (2014).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    50.Coyne, J. A. & Orr, H. A. Patterns of speciation in Drosophila. Evolution 43, 362–381 (1989).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    51.Kelemen, L. & Moritz, C. Comparative phylogeography of a sibling pair of rainforest Drosophila species (Drosophila serrata and D. birchii). Evolution 53, 1306–1311 (1999).PubMed 
    PubMed Central 

    Google Scholar 
    52.Hercus, M. J. & Hoffmann, A. A. Desiccation resistance in interspecific Drosophila crosses: genetic interactions and trait correlations. Genetics 151, 1493–1502 (1999).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    53.Rudin-Bitterli, T. S., Mitchell, N. J. & Evans, J. P. Extensive geographical variation in testes size and ejaculate traits in a terrestrial-breeding frog. Biol. Lett. 16, 20200411 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    54.Shaver, J., Barch, S. & Shivers, C. Tissue-specificity of frog egg-jelly antigens. J. Exp. Zool. 151, 95–103 (1962).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    55.Bradford, D. F. & Seymour, R. S. Influence of environmental PO2 on embryonic oxygen consumption, rate of development, and hatching in the frog, Pseudophryne bibroni. Physiol. Zool. 61, 475–482 (1988).Article 

    Google Scholar 
    56.Seymour, R. S., Geiser, F. & Bradford, D. F. Metabolic cost of development in terrestrial frog eggs (Pseudophryne bibronii). Physiol. Zool. 64, 688–696 (1991).Article 

    Google Scholar 
    57.Warkentin, K. M. Adaptive plasticity in hatching age: a response to predation risk trade-offs. Proc. Natl Acad. Sci. USA 92, 3507–3510 (1995).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    58.Webb, P. Effect of body form and response threshold on the vulnerability of four species of teleost prey attacked by largemouth bass (Micropterus salmoides). Can. J. Fish. Aquat. Sci. 43, 763–771 (1986).Article 

    Google Scholar 
    59.Watkins, T. B. Predator-mediated selection on burst swimming performance in tadpoles of the Pacific tree frog, Pseudacris regilla. Physiol. Zool. 69, 154–167 (1996).Article 

    Google Scholar 
    60.Wilson, R. & Franklin, C. Thermal acclimation of locomotor performance in tadpoles of the frog Limnodynastes peronii. J. Comp. Physiol. B 169, 445–451 (1999).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    61.Teplitsky, C. et al. Escape behaviour and ultimate causes of specific induced defences in an anuran tadpole. J. Evol. Biol. 18, 180–190 (2005).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    62.Walker, J., Ghalambor, C., Griset, O., McKenney, D. & Reznick, D. Do faster starts increase the probability of evading predators? Funct. Ecol. 19, 808–815 (2005).Article 

    Google Scholar 
    63.Langerhans, R. B. Morphology, performance, fitness: functional insight into a post-Pleistocene radiation of mosquitofish. Biol. Lett. 5, 488–491 (2009).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    64.Plowman, M. C., Grbac-lvankovic, S., Martin, J., Hopfer, S. M. & Sunderman, F. W. Jr Malformations persist after metamorphosis of Xenopus laevis tadpoles exposed to Ni2+, Co2+, or Cd2+ in FETAX assays. Teratog. Carcinog. Mutagen. 14, 135–144 (1994).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    65.Lynch, M. & Walsh, B. Genetics and Analysis of Quantitative Traits. Vol. 1 (Sinauer Sunderland, MA, 1998).66.Remington, D. L. & O’Malley, D. M. Whole-genome characterization of embryonic stage inbreeding depression in a selfed loblolly pine family. Genetics 155, 337–348 (2000).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    67.Lynch, M. The genetic interpretation of inbreeding depression and outbreeding depression. Evolution 45, 622–629 (1991).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    68.Armbruster, P., Bradshaw, W. E., Steiner, A. L. & Holzapfel, C. M. Evolutionary responses to environmental stress by the pitcher-plant mosquito, Wyeomyia smithii. Heredity 83, 509–519 (1999).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    69.Marr, A. B., Keller, L. F. & Arcese, P. Heterosis and outbreeding depression in descendants of natural immigrants to an inbred population of song sparrows (Melospiza melodia). Evolution 56, 131–142 (2002).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    70.Marshall, T. & Spalton, J. Simultaneous inbreeding and outbreeding depression in reintroduced Arabian oryx. Anim. Conserv. 3, 241–248 (2000).Article 

    Google Scholar 
    71.Rudin-Bitterli, T. S., Mitchell, N. J. & Evans, J. P. Environmental stress increases the magnitude of nonadditive genetic variation in offspring fitness in the frog Crinia georgiana. Am. Nat. 192, 461–478 (2018).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    72.Drummond, E., Short, E. & Clancy, D. Mitonuclear gene X environment effects on lifespan and health: How common, how big? Mitochondrion 49, 12–18 (2019).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    73.Morales, H. E. et al. Concordant divergence of mitogenomes and a mitonuclear gene cluster in bird lineages inhabiting different climates. Nat. Ecol. Evol. 2, 1258–1267 (2018).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    74.Schmid, M., Evans, B. J. & Bogart, J. P. Polyploidy in amphibia. Cytogenet. Genome Res. 145, 315–330 (2015).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    75.Silla, A. J. Artificial fertilisation in a terrestrial toadlet (Pseudophryne guentheri): effect of medium osmolality, sperm concentration and gamete storage. Reprod. Fertil. Dev. 25, 1134–1141 (2013).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    76.Phillip, G. B. & Keogh, J. S. Extreme sequential polyandry insures against nest failure in a frog. Proc. Roy. Soc. B-Biol. Sci. 276, 115–120 (2009).Article 

    Google Scholar 
    77.Brandies, P., Peel, E., Hogg, C. J. & Belov, K. The value of reference genomes in the conservation of threatened species. Genes 10, 846 (2019).CAS 
    PubMed Central 
    Article 

    Google Scholar 
    78.Scheele, B. C. et al. Interventions for reducing extinction risk in chytridiomycosis‐threatened amphibians. Conserv. Biol. 28, 1195–1205 (2014).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    79.Osborne, W. S. & Norman, J. A. Conservation genetics of Corroboree frogs, Psuedophryne corroboree (Anura: Myobatrachidae): population subdivision and genetic divergence. Aust. J. Zool. 39, 285–297 (1991).Article 

    Google Scholar 
    80.Browne, R. K. et al. Sperm collection and storage for the sustainable management of amphibian biodiversity. Theriogenology 133, 187–200 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    81.Silla, A. J. & Byrne, P. G. Hormone-induced ovulation and artificial fertilisation in four terrestrial-breeding anurans. Reprod. Fertil. Dev. https://doi.org/10.1071/RD20243 (2021).82.O’Brien, D. M., Keogh, J. S., Silla, A. J. & Byrne, P. G. Female choice for related males in wild red-backed toadlets (Pseudophryne coriacea). Behav. Ecol. 30, 928–937 (2019).Article 

    Google Scholar 
    83.Gosner, K. L. A simplified table for staging anuran embryos and larvae with notes on identification. Herpetologica 16, 183–190 (1960).
    Google Scholar 
    84.Anstis, M. Tadpoles and Frogs of Australia. (New Holland Publishers, 2013).85.CSIRO, and Bureau of Meteorology. State of the Climate 2018 (CSIRO Publishing, 2018).86.Andrich, M. A. & Imberger, J. The effect of land clearing on rainfall and fresh water resources in Western Australia: a multi-functional sustainability analysis. Int. J. Sustain. Dev. World Ecol. 20, 549–563 (2013).Article 

    Google Scholar 
    87.Raut, B. A., Jakob, C. & Reeder, M. J. Rainfall changes over southwestern Australia and their relationship to the Southern Annular Mode and ENSO. J. Clim. 27, 5801–5814 (2014).Article 

    Google Scholar 
    88.Arnold, G. in Greenhouse: Planning for Climate Change (ed. Pearman, G. I.) 375–386 (CSIRO Publishing, 1988).89.Hobbs, R. J. Effects of landscape fragmentation on ecosystem processes in the Western Australian wheatbelt. Biol. Conserv. 64, 193–201 (1993).Article 

    Google Scholar 
    90.Silla, A. J. Effect of priming injections of luteinizing hormone-releasing hormone on spermiation and ovulation in Gϋnther’s toadlet, Pseudophryne guentheri. Reprod. Biol. Endocrinol. 9, 68 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    91.Lymbery, R. A., Kennington, W. J. & Evans, J. P. Multivariate sexual selection on ejaculate traits under sperm competition. Am. Nat. 192, 94–104 (2018).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    92.Browne, R. K., Clulow, J. & Mahony, M. Short-term storage of cane toad (Bufo marinus) gametes. Reproduction 121, 167–173 (2001).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    93.Kouba, A. J., Vance, C. K., Frommeyer, M. A. & Roth, T. L. Structural and functional aspects of Bufo americanus spermatozoa: effects of inactivation and reactivation. J. Exp. Zool. A. Comp. Exp. Biol. 295, 172–182 (2003).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    94.Abràmoff, M. D., Magalhães, P. J. & Ram, S. J. Image processing with Image. J. Biophotonics Int. 11, 36–42 (2004).
    Google Scholar 
    95.Noldus, L. P., Spink, A. J. & Tegelenbosch, R. A. EthoVision: a versatile video tracking system for automation of behavioral experiments. Behav. Res. Methods Instrum. Comput. 33, 398–414 (2001).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    96.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. Journal of Statistical Software 67, 1–48. https://doi.org/10.18637/jss.v067.i01 (2014).97.Bolker, B. M. et al. Generalized linear mixed models: a practical guide for ecology and evolution. Trends Ecol. Evol. 24, 127–135 (2009).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    98.Harrison, X. A. Using observation-level random effects to model overdispersion in count data in ecology and evolution. PeerJ 2, e616 (2014).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    99.Rudin-Bitterli, T. S., Evans, J. P. & Mitchell, N. J. Fitness consequences of targeted gene flow to counter impacts of drying climates on terrestrial-breeding frogs. Data sets. https://doi.org/10.5061/dryad.6m905qg09 (2021). More

  • in

    Iterative human and automated identification of wildlife images

    1.Steenweg, R. et al. Scaling-up camera traps: monitoring the planet’s biodiversity with networks of remote sensors. Front. Ecol. Environ. 15, 26–34 (2017).Article 

    Google Scholar 
    2.Rich, L. N. et al. Assessing global patterns in mammalian carnivore occupancy and richness by integrating local camera trap surveys. Global Ecol. Biogeogr. 26, 918–929 (2017).Article 

    Google Scholar 
    3.Barnosky, A. D. et al. Has the Earth’s sixth mass extinction already arrived? Nature 471, 51–57 (2011).Article 

    Google Scholar 
    4.Ahumada, J. A. et al. Wildlife insights: a platform to maximize the potential of camera trap and other passive sensor wildlife data for the planet. Environ. Conserv. 47, 1–6 (2020).MathSciNet 
    Article 

    Google Scholar 
    5.Norouzzadeh, M. S. et al. Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning. Proc. Natl Acad. Sci. 115, E5716–E5725 (2018).Article 

    Google Scholar 
    6.Miao, Z. et al. Insights and approaches using deep learning to classify wildlife. Sci. Rep. 9, 8137 (2019).Article 

    Google Scholar 
    7.Liu, Z. et al. Large-scale long-tailed recognition in an open world. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 2537–2546 (IEEE, 2019).8.Liu, Z. et al. Open compound domain adaptation. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition 12406–12415 (IEEE, 2020).9.Hautier, Y. et al. Anthropogenic environmental changes affect ecosystem stability via biodiversity. Science 348, 336–340 (2015).Article 

    Google Scholar 
    10.Barlow, J. et al. Anthropogenic disturbance in tropical forests can double biodiversity loss from deforestation. Nature 535, 144–147 (2016).Article 

    Google Scholar 
    11.Ripple, W. J. et al. Conserving the world’s megafauna and biodiversity: the fierce urgency of now. Bioscience 67, 197–200 (2017).Article 

    Google Scholar 
    12.Dirzo, R. et al. Defaunation in the Anthropocene. Science 345, 401–406 (2014).Article 

    Google Scholar 
    13.O’Connell, A. F., Nichols, J. D. & Karanth, K. U. Camera Traps in Animal Ecology: Methods and Analyses (Springer Science & Business Media, 2010).14.Burton, A. C. et al. Wildlife camera trapping: a review and recommendations for linking surveys to ecological processes. J. Appl. Ecol. 52, 675–685 (2015).Article 

    Google Scholar 
    15.Kays, R., McShea, W. J. & Wikelski, M. Born-digital biodiversity data: millions and billions. Divers. Distrib. 26, 644–648 (2020).Article 

    Google Scholar 
    16.Swanson, A. et al. Snapshot Serengeti, high-frequency annotated camera trap images of 40 mammalian species in an African savanna. Sci. Data 2, 1–14 (2015).Article 

    Google Scholar 
    17.Ahumada, J. A. et al. Community structure and diversity of tropical forest mammals: data from a global camera trap network. Philos. Trans. R. Soc. B Biol. Sci. 366, 2703–2711 (2011).Article 

    Google Scholar 
    18.Pardo, L. E. et al. Snapshot Safari: a large-scale collaborative to monitor Africa’s remarkable biodiversity. South Africa J. Sci. https://doi.org/10.17159/sajs.2021/8134 (2021).19.Anderson, T. M. et al. The spatial distribution of African savannah herbivores: species associations and habitat occupancy in a landscape context. Philos. Trans. R. Soc. B Biol. Sci. 371, 20150314 (2016).Article 

    Google Scholar 
    20.Palmer, M., Fieberg, J., Swanson, A., Kosmala, M. & Packer, C. A ‘dynamic’ landscape of fear: prey responses to spatiotemporal variations in predation risk across the lunar cycle. Ecol. Lett. 20, 1364–1373 (2017).Article 

    Google Scholar 
    21.Tabak, M. A. et al. Machine learning to classify animal species in camera trap images: applications in ecology. Methods Ecol. Evol. 10, 585–590 (2019).Article 

    Google Scholar 
    22.Whytock, R. C. et al. Robust ecological analysis of camera trap data labelled by a machine learning model. Methods Ecol. Evol 12, 1080–1092 (2021).Article 

    Google Scholar 
    23.Beery, S., Van Horn, G. & Perona, P. Recognition in terra incognita. In Proc. European Conference on Computer Vision (ECCV) 456–473 (IEEE, 2018).24.Tabak, M. A. et al. Improving the accessibility and transferability of machine learning algorithms for identification of animals in camera trap images: MLWIC2. Ecol. Evol. 10, 10374–10383 (2020).Article 

    Google Scholar 
    25.Shahinfar, S., Meek, P. & Falzon, G. How many images do I need? Understanding how sample size per class affects deep learning model performance metrics for balanced designs in autonomous wildlife monitoring. Ecol. Inform. 57, 101085 (2020).Article 

    Google Scholar 
    26.Norouzzadeh, M. S. et al. A deep active learning system for species identification and counting in camera trap images. Methods Ecol. Evol. 12, 150–161 (2020).Article 

    Google Scholar 
    27.Willi, M. et al. Identifying animal species in camera trap images using deep learning and citizen science. Methods Ecol. Evol. 10, 80–91 (2019).Article 

    Google Scholar 
    28.Schneider, S., Greenberg, S., Taylor, G. W. & Kremer, S. C. Three critical factors affecting automated image species recognition performance for camera traps. Ecol. Evol. 10, 3503–3517 (2020).Article 

    Google Scholar 
    29.Kays, R. et al. An empirical evaluation of camera trap study design: how many, how long and when? Methods Ecol. Evol. 11, 700–713 (2020).Article 

    Google Scholar 
    30.Prach, K. & Walker, L. R. Four opportunities for studies of ecological succession. Trends Ecol. Evol. 26, 119–123 (2011).Article 

    Google Scholar 
    31.Mech, L. D., Isbell, F., Krueger, J. & Hart, J. Gray wolf (Canis lupus) recolonization failure: a Minnesota case study. Can. Field-Nat. 133, 60–65 (2019).Article 

    Google Scholar 
    32.Taylor, G. et al. Is reintroduction biology an effective applied science? Trends Ecol. Evol. 32, 873–880 (2017).Article 

    Google Scholar 
    33.Clavero, M. & Garcia-Berthou, E. Invasive species are a leading cause of animal extinctions. Trends Ecol. Evol. 20, 110 (2005).Article 

    Google Scholar 
    34.Caravaggi, A. et al. An invasive-native mammalian species replacement process captured by camera trap survey random encounter models. Remote Sens. Ecol. Conserv. 2, 45–58 (2016).Article 

    Google Scholar 
    35.Arjovsky, M., Bottou, L., Gulrajani, I. & Lopez-Paz, D. Invariant risk minimization. Preprint at https://arxiv.org/abs/1907.02893 (2019).36.Yosinski, J., Clune, J., Bengio, Y. & Lipson, H. How transferable are features in deep neural networks? In Advances in Neural Information Processing Systems 3320–3328 (IEEE, 2014).37.Deng, J. et al. ImageNet: a large-scale hierarchical image database. In Proc. 2009 IEEE Conference on Computer Vision and Pattern Recognition 248–255 (IEEE, 2009).38.Pimm, S. L. et al. The biodiversity of species and their rates of extinction, distribution and protection. Science https://doi.org/10.1126/science.1246752 (2014).39.Liu, W., Wang, X., Owens, J. & Li, Y. Energy-based out-of-distribution detection. In Advances in Neural Information Processing Systems (eds Larochelle, H. et al.) 21464–21475 (Curran Associates, 2020).40.Lee, D.-H. Pseudo-label: the simple and efficient semi-supervised learning method for deep neural networks. In Workshop on Challenges in Representation Learning, ICML, Vol. 3 (2013).41.He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 770–778 (IEEE, 2016).42.Hinton, G., Vinyals, O. & Dean, J. Distilling the knowledge in a neural network. Preprint at https://arxiv.org/abs/1503.02531 (2015).43.Gaynor, K. M., Daskin, J. H., Rich, L. N. & Brashares, J. S. Postwar wildlife recovery in an African savanna: evaluating patterns and drivers of species occupancy and richness. Anim. Conserv. 24, 510–522 (2020).Article 

    Google Scholar 
    44.Paszke, A. et al. in Advances in Neural Information Processing Systems Vol. 32 (eds Wallach, H. et al.) 8024–8035 http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (Curran Associates, 2019)45.Chen, T., Kornblith, S., Norouzi, M. & Hinton, G. A simple framework for contrastive learning of visual representations. Preprint at https://arxiv.org/abs/2002.05709 (2020).46.He, K., Fan, H., Wu, Y., Xie, S. & Girshick, R. Momentum contrast for unsupervised visual representation learning. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition 9729–9738 (IEEE, 2020).47.Xiao, T., Wang, X., Efros, A. A. & Darrell, T. What should not be contrastive in contrastive learning. Preprint at https://arxiv.org/abs/2008.05659 (2020). More

  • in

    Constraining photosynthesis with ∆17O in CO2

    The net uptake of CO2 by the biosphere offsets roughly a quarter of current fossil fuel emissions. However, climate change is expected to impact photosynthesis and ecosystem respiration differently. Quantification of these individual processes is required to better understand and predict the consequences for carbon cycling. Variations in oxygen isotope signatures (δ18O and Δ17O) in atmospheric CO2 can be used as tracers for photosynthesis. Δ17O is much less dependent on variations in the hydrological cycle, which often obscure photosynthesis signals in the more widely measured δ18O. Although, measurement techniques for Δ17O in tropospheric CO2 only became sufficiently accurate to interpret variations since the ~2010s, providing new insights into the carbon cycle. More

  • in

    Identifying thresholds in the impacts of an invasive groundcover on native vegetation

    1.Vilà, M. et al. Ecological impacts of invasive alien plants: A meta-analysis of their effects on species, communities and ecosystems. Ecol. Lett. 14, 702–708 (2011).PubMed 
    Article 

    Google Scholar 
    2.Pyšek, P. et al. A global assessment of invasive plant impacts on resident species, communities and ecosystems: The interaction of impact measures, invading species’ traits and environment. Glob. Chang. Biol. 18, 1725–1737 (2012).ADS 
    PubMed Central 
    Article 
    PubMed 

    Google Scholar 
    3.Barney, J. N., Tekiela, D. R., Dollete, E. S. & Tomasek, B. J. What is the “real” impact of invasive plant species?. Front. Ecol. Environ. 11, 322–329 (2013).Article 

    Google Scholar 
    4.O’Loughlin, L. S., Gooden, B., Barney, J. N. & Lindenmayer, D. B. Surrogacy in invasion research and management: Inferring “impact” from “invasiveness”. Front. Ecol. Environ. 17, 464–473 (2019).Article 

    Google Scholar 
    5.Crystal-Ornelas, R. & Lockwood, J. L. The ‘known unknowns’ of invasive species impact measurement. Biol. Invasions https://doi.org/10.1007/s10530-020-02200-0 (2020).Article 

    Google Scholar 
    6.Foster, C. N. et al. How practitioners integrate decision triggers with existing metrics in conservation monitoring. J. Environ. Manage. 230, 94–101 (2019).PubMed 
    Article 

    Google Scholar 
    7.Hulme, P. E. Weed risk assessment: A way forward or a waste of time?. J. Appl. Ecol. 49, 10–19 (2012).Article 

    Google Scholar 
    8.Meyerson, L. A., Simberloff, D., Boardman, L. & Lockwood, J. L. Toward, “rules” for studying biological invasions. Bull. Ecol. Soc. Am. 100, 1689–1699 (2019).Article 

    Google Scholar 
    9.Hulme, P. E. et al. Bias and error in understanding plant invasion impacts. Trends Ecol. Evol. 28, 212–218 (2013).PubMed 
    Article 

    Google Scholar 
    10.Bradley, B. A. et al. Disentangling the abundance–impact relationship for invasive species. Proc. Natl. Acad. Sci. USA 116, 9919–9924 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    11.Panetta, F. D. & Gooden, B. Managing for biodiversity: Impact and action thresholds for invasive plants in natural ecosystems. NeoBiota 34, 53–66 (2017).Article 

    Google Scholar 
    12.Gooden, B., French, K., Turner, P. J. & Downey, P. O. Impact threshold for an alien plant invader, Lantana camara L., on native plant communities. Biol. Conserv. 142, 2631–2641 (2009).Article 

    Google Scholar 
    13.Bernard-Verdier, M. & Hulme, P. E. Alien plants can be associated with a decrease in local and regional native richness even when at low abundance. J. Ecol. 107, 1343–1354 (2019).Article 

    Google Scholar 
    14.Panetta, F. D., O’Loughlin, L. S. & Gooden, B. Identifying thresholds and ceilings in plant community recovery for optimal management of widespread weeds. NeoBiota 42, 1–18 (2019).Article 

    Google Scholar 
    15.Sofaer, H. R., Jarnevich, C. S. & Pearse, I. S. The relationship between invader abundance and impact. Ecosphere 9, e02415 (2018).Article 

    Google Scholar 
    16.Gooden, B. & French, K. Impacts of alien plant invasion on native plant communities are mediated by functional identity of resident species, not resource availability. Oikos 124, 298–306 (2015).Article 

    Google Scholar 
    17.Fried, G. & Panetta, F. D. Comparing an exotic shrub’s impact with that of a native life form analogue: Baccharis halimifolia vs Tamarix gallica in Mediterranean salt marsh communities. J. Veg. Sci. 27, 812–823 (2016).Article 

    Google Scholar 
    18.Chabrerie, O., Loinard, J., Perrin, S., Saguez, R. & Decocq, G. Impact of Prunus serotina invasion on understory functional diversity in a European temperate forest. Biol. Invasions 12, 1891–1907 (2010).Article 

    Google Scholar 
    19.O’Loughlin, L. S., Green, P. T. & Morgan, J. W. The rise and fall of Leptospermum laevigatum: Plant community change associated with the invasion and senescence of a range-expanding native species. Appl. Veg. Sci. 18, 323–331 (2015).Article 

    Google Scholar 
    20.Case, E. J., Harrison, S. & Cornell, H. V. Do high-impact invaders have the strongest negative effects on abundant and functionally similar resident species?. Funct. Ecol. 30, 1447–1453 (2016).Article 

    Google Scholar 
    21.González-Moreno, P., Diez, J. M., Ibáñez, I., Font, X. & Vilà, M. Plant invasions are context-dependent: Multiscale effects of climate, human activity and habitat. Divers. Distrib. 20, 720–731 (2014).Article 

    Google Scholar 
    22.Jauni, M., Gripenberg, S. & Ramula, S. Non-native plant species benefit from disturbance: A meta-analysis. Oikos https://doi.org/10.1111/oik.01416 (2014).Article 

    Google Scholar 
    23.Gill, R. A. et al. Niche opportunities for invasive annual plants in dryland ecosystems are controlled by disturbance, trophic interactions, and rainfall. Oecologia 187, 1–11 (2018).ADS 
    Article 

    Google Scholar 
    24.Didham, R. K., Tylianakis, J. M., Gemmell, N. J., Rand, T. A. & Ewers, R. M. Interactive effects of habitat modification and species invasion on native species decline. Trends Ecol. Evol. 22, 489–496 (2007).PubMed 
    Article 

    Google Scholar 
    25.Sokol, N. W., Kuebbing, S. E. & Bradford, M. A. Impacts of an invasive plant are fundamentally altered by a co-occurring forest disturbance. Ecology 98, 2133–2144 (2017).PubMed 
    Article 

    Google Scholar 
    26.Iacarella, J. C., Mankiewicz, P. S. & Ricciardi, A. Negative competitive effects of invasive plants change with time since invasion. Ecosphere 6, 1–14 (2015).Article 

    Google Scholar 
    27.McAlpine, K. G., Lamoureaux, S. L. & Westbrooke, I. Ecological impacts of ground cover weeds in New Zealand lowland forests. N. Z. J. Ecol. 39, 50–60 (2015).
    Google Scholar 
    28.MacDougall, A. S. & Turkington, R. Are invasive species drivers or passengers of change in degraded ecosystems?. Ecology 86, 42–55 (2005).Article 

    Google Scholar 
    29.Didham, R. K., Tylianakis, J. M., Hutchison, M. A., Ewers, R. M. & Gemmell, N. J. Are invasive species the drivers of ecological change?. Trends Ecol. Evol. 20, 470–474 (2005).PubMed 
    Article 

    Google Scholar 
    30.Kettenring, K. M. & Adams, C. R. Lessons learned from invasive plant control experiments: A systematic review and meta-analysis. J. Appl. Ecol. 48, 970–979 (2011).Article 

    Google Scholar 
    31.D’Antonio, C. & Flory, S. L. Long-term dynamics and impacts of plant invasions. J. Ecol. 105, 1459–1461 (2017).Article 

    Google Scholar 
    32.Prober, S. M., Thiele, K. R. & Speijers, J. Competing drivers lead to non-linear native: Exotic relationships in endangered temperate grassy woodlands. Biol. Invasions 18, 3001–3014 (2016).Article 

    Google Scholar 
    33.Alvarez, M. E. & Cushman, J. H. Community-level consequences of a plant invasion: Effects on three habitats in Coastal California. Ecol. Appl. 12, 1434 (2002).Article 

    Google Scholar 
    34.Standish, R. J., Robertson, A. W. & Williams, P. A. The impact of an invasive weed Tradescantia fluminensis on native forest regeneration. J. Appl. Ecol. 38, 1253–1263 (2001).Article 

    Google Scholar 
    35.Zeeman, B. J., McDonnell, M. J., Kendal, D. & Morgan, J. W. Biotic homogenization in an increasingly urbanized temperate grassland ecosystem. J. Veg. Sci. 28, 550–561 (2017).Article 

    Google Scholar 
    36.Hejda, M. Do species of invaded communities differ in their vulnerability to being eliminated by the dominant alien plants?. Biol. Invasions 15, 1989–1999 (2013).Article 

    Google Scholar 
    37.Hejda, M., Štajerová, K., Pergl, J. & Pyšek, P. Impacts of dominant plant species on trait composition of communities: Comparison between the native and invaded ranges. Ecosphere 10, 20 (2019).Article 

    Google Scholar 
    38.Kuebbing, S. E. & Nuñez, M. A. Negative, neutral, and positive interactions among nonnative plants: Patterns, processes, and management implications. Glob. Change Biol. 21, 926–934 (2015).ADS 
    Article 

    Google Scholar 
    39.O’Loughlin, L. S. et al. Invasive shrub re-establishment following management has contrasting effects on biodiversity. Sci. Rep. 9, 4083 (2019).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    40.Grime, J. P. Competitive Exclusion in Herbaceous Vegetation. Nature 242, 344–347 (1973).ADS 
    Article 

    Google Scholar 
    41.Connell, J. H. Diversity in tropical rain forests and coral reefs. Science 199, 1302–1310 (1978).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    42.Barney, J. N., Smith, L. L. & Tekiela, D. R. Weed risk assessments can be useful, but have limitations. Invasions Plant Sci. Manage. 9, 84–85 (2016).Article 

    Google Scholar 
    43.Dugdale, T., McLaren, D. & Conran, J. The biology of Australian weeds 65. ‘Tradescantia fluminensis’ Vell. Plant Prot. Q. 30, 116 (2015).
    Google Scholar 
    44.Fowler, S. V. et al. Tradescantia fluminensis, an exotic weed affecting native forest regeneration in New Zealand: Ecological surveys, safety tests and releases of four biocontrol agents from Brazil. Biol. Control 64, 323–329 (2013).Article 

    Google Scholar 
    45.Morin L. Information package to support application to release the white smut-like fungus Kordyana brasiliensis for the biological control of wandering trad (Tradescantia fluminensis) in Australia. (CSIRO, Australia, 2017).46.CABI. Tradescantia fluminensis (wandering Jew). In Invasive Species Compendium (2019).47.Butcher, E. R. & Kelly, D. Physical and anthropogenic factors predict distribution of the invasive weed Tradescantia fluminensis. Austral. Ecol. 36, 621–627 (2011).
    Google Scholar 
    48.Standish, R. J. et al. Invasion by a perennial herb increases decomposition rate and alters nutrient availability in warm temperate lowland forest remnants. Biol. Invasions. 6, 71–81 (2004).Article 

    Google Scholar 
    49.Toft, R. J., Harris, R. J. & Williams, P. A. Impacts of the weed Tradescantia fluminensis on insect communities in fragmented forests in New Zealand. Biol. Conserv. 102, 31–46 (2001).Article 

    Google Scholar 
    50.Bureau of Meteorology. Climate Data Online. http://www.bom.gov.au/climate/data/index.shtm (2020).51.Maisey, A. C., Nimmo, D. G. & Bennett, A. F. Habitat selection by the Superb Lyrebird (Menura novaehollandiae), an iconic ecosystem engineer in forests of south-eastern Australia. Austral. Ecol. 44, 503–513 (2019).Article 

    Google Scholar 
    52.Incoll, B., Maisey, A. & Adam, J. T. years of forest restoration in the Upwey Corridor, Dandenong Ranges, Victoria. Ecol. Manage. Restor. 19, 189–197 (2018).Article 

    Google Scholar 
    53.Gooden, B. & French, K. Non-interactive effects of plant invasion and landscape modification on native communities. Divers. Distrib. 20, 626–639 (2014).Article 

    Google Scholar 
    54.Mason, T. J. & French, K. Management regimes for a plant invader differentially impact resident communities. Biol. Conserv. 136, 246–259 (2007).Article 

    Google Scholar 
    55.Sullivan, J. J., Timmins, S. M. & Williams, P. A. Movement of exotic plants into coastal native forests from gardens in northern New Zealand. N. Z. J. Ecol. 29, 1–10 (2005).
    Google Scholar 
    56.R Core Team. R: A Language and Environment for Statistical Computing. (2019).57.Zuur, A., Ieno, E., Walker, N., Saveliev, A. & Smith, G. Mixed Effects Models and Extensions in Ecology with R (Springer, 2009).MATH 
    Book 

    Google Scholar  More

  • in

    Resilience of spider communities affected by a range of silvicultural treatments in a temperate deciduous forest stand

    1.Lindner, M. et al. Climate change impacts, adaptive capacity, and vulnerability of European forest ecosystems. For. Ecol. Manag. 259, 698–709. https://doi.org/10.1016/j.foreco.2009.09.023 (2010).Article 

    Google Scholar 
    2.Gamfeldt, L. et al. Higher levels of multiple ecosystem services are found in forests with more tree species. Nat. Commun. 4, 8. https://doi.org/10.1038/ncomms2328 (2013).CAS 
    Article 

    Google Scholar 
    3.Van Meerbeek, K., Jucker, T. & Svenning, J.-C. Unifying the concepts of stability and resilience in ecology. J. Ecol. 109, 3114–3132. https://doi.org/10.1111/1365-2745.13651 (2021).Article 

    Google Scholar 
    4.FAO and UNEP. The State of the World’s Forests (SOFO). (FAO and UNEP, 2020).5.Forest Europe. State of Europe’s Forests 2015. Ministerial Conference on the Protection of Forests in Europe. www.foresteurope.org. (Forest Europe, 2015).6.Matthews, J. D. Silvicultural Systems (Oxford University Press, 1991).
    Google Scholar 
    7.Chaudhary, A., Burivalova, Z., Koh, L. P. & Hellweg, S. Impact of forest management on species richness: Global meta-analysis and economic trade-offs. Sci. Rep. 6, 10. https://doi.org/10.1038/srep23954 (2016).CAS 
    Article 

    Google Scholar 
    8.Gustafsson, L., Kouki, J. & Sverdrup-Thygeson, A. Tree retention as a conservation measure in clear-cut forests of northern Europe: A review of ecological consequences. Scand. J. For. Res. 25, 295–308. https://doi.org/10.1080/02827581.2010.497495 (2010).Article 

    Google Scholar 
    9.Raymond, P., Bédard, S., Roy, V., Larouche, C. & Tremblay, S. The irregular shelterwood system: Review, classification, and potential application to forests affected by partial disturbances. J. For. 107, 405–413 (2009).
    Google Scholar 
    10.Csépányi, P. & Csór, A. Economic assessment of European beech and Turkey oak stands with close-to-nature forest management. Acta Silvat. Lignar. Hung. 13, 9–24 (2017).Article 

    Google Scholar 
    11.Ebeling, A. et al. Plant Diversity Impacts Decomposition and Herbivory via Changes in Aboveground Arthropods. PLoS ONE 9, 8. https://doi.org/10.1371/journal.pone.0106529 (2014).CAS 
    Article 

    Google Scholar 
    12.Chen, B. R. & Wise, D. H. Bottom-up limitation of predaceous arthropods in a detritus-based terrestrial food web. Ecology 80, 761–772. https://doi.org/10.1890/0012-9658(1999)080[0761:Bulopa]2.0.Co;2 (1999).Article 

    Google Scholar 
    13.Zuev, A. et al. Different groups of ground-dwelling spiders share similar trophic niches in temperate forests. Ecol. Entomol. 45, 1346–1356. https://doi.org/10.1111/een.12918 (2020).Article 

    Google Scholar 
    14.Moulder, B. C. & Reichle, D. E. Significance of Spider Predation in the Energy Dynamics of Forest-Floor Arthropod Communities. Ecol. Monogr. 42, 473–498. https://doi.org/10.2307/1942168 (1972).Article 

    Google Scholar 
    15.Lawrence, K. L. & Wise, D. H. Unexpected indirect effect of spiders on the rate of litter disappearance in a deciduous forest. Pedobiologia 48, 149–157. https://doi.org/10.1016/j.pedobi.2003.11.001 (2004).Article 

    Google Scholar 
    16.Oxbrough, A. & Ziesche, T. Spiders in Forest Ecoystems. In Integrative approaches as an opportunity for the conservation of forest biodiversity (eds Kraus, D. & Krumm, F.) 186–193 (European Forest Institute, 2013).
    Google Scholar 
    17.Clarke, R. D. & Grant, P. R. An experimental study of the role of spiders as predators in a forest litter community. Part 1. Ecology 49, 1152–1154. https://doi.org/10.2307/1934499 (1968).Article 

    Google Scholar 
    18.Wermelinger, B. et al. Impact of windthrow and salvage-logging on taxonomic and functional diversity of forest arthropods. For. Ecol. Manag. 391, 9–18. https://doi.org/10.1016/j.foreco.2017.01.033 (2017).Article 

    Google Scholar 
    19.Gallé, R., Szabó, A., Császár, P. & Torma, A. Spider assemblage structure and functional diversity patterns of natural forest steppes and exotic forest plantations. For. Ecol. Manag. 411, 234–239. https://doi.org/10.1016/j.foreco.2018.01.040 (2018).Article 

    Google Scholar 
    20.Buddle, C. M., Langor, D. W., Pohl, G. R. & Spence, J. R. Arthropod responses to harvesting and wildfire: Implications for emulation of natural disturbance in forest management. Biol. Cons. 128, 346–357. https://doi.org/10.1016/j.biocon.2005.10.002 (2006).Article 

    Google Scholar 
    21.Oxbrough, A. G., Gittings, T., O’Halloran, J., Giller, P. S. & Smith, G. F. Structural indicators of spider communities across the forest plantation cycle. For. Ecol. Manag. 212, 171–183. https://doi.org/10.1016/j.foreco.2005.03.040 (2005).Article 

    Google Scholar 
    22.Ingle, K. et al. Winter-active spider fauna is affected by plantation forest type. Env. Entomol. 49, 601–606. https://doi.org/10.1093/ee/nvaa025 (2020).Article 

    Google Scholar 
    23.Munevar, A., Rubio, G. D. & Zurita, G. A. Changes in spider diversity through the growth cycle of pine plantations in the semi-deciduous Atlantic forest: The role of prey availability and abiotic conditions. For. Ecol. Manag. 424, 536–544. https://doi.org/10.1016/j.foreco.2018.03.025 (2018).Article 

    Google Scholar 
    24.Matveinen-Huju, K. & Koivula, M. Effects of alternative harvesting methods on boreal forest spider assemblages. Can. J. For. Res. 38, 782–794. https://doi.org/10.1139/x07-169 (2008).Article 

    Google Scholar 
    25.Buddle, C. M. & Shorthouse, D. P. Effects of experimental harvesting on spider (Araneae) assemblages in boreal deciduous forests. Can. Entomol. 140, 437–452 (2008).Article 

    Google Scholar 
    26.Kovács, B., Tinya, F., Németh, C. & Ódor, P. Unfolding the effects of different forestry treatments on microclimate in oak forests: results of a 4-yr experiment. Ecol. Appl. 30, e02043. https://doi.org/10.1002/eap.2043 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    27.Kovács, B. et al. The Short-Term Effects of Experimental Forestry Treatments on Site Conditions in an Oak-Hornbeam Forest. Forests 9, 406 (2018).Article 

    Google Scholar 
    28.Pommerening, A. & Murphy, S. T. A review of the history, definitions and methods of continuous cover forestry with special attention to afforestation and restocking. Forestry 77, 27–44. https://doi.org/10.1093/forestry/77.1.27 (2004).Article 

    Google Scholar 
    29.Tinya, F. et al. Initial understory response to experimental silvicultural treatments in a temperate oak-dominated forest. Eur. J. For. Res. 138, 65–77. https://doi.org/10.1007/s10342-018-1154-8 (2018).Article 

    Google Scholar 
    30.Tinya, F. et al. Initial regeneration success of tree species after different forestry treatments in a sessile oak-hornbeam forest. For. Ecol. Manag. 459, 117810. https://doi.org/10.1016/j.foreco.2019.117810 (2020).Article 

    Google Scholar 
    31.Boros, G., Kovács, B. & Ódor, P. Green tree retention enhances negative short-term effects of clear-cutting on enchytraeid assemblages in a temperate forest. Appl. Soil Ecol. 136, 106–115. https://doi.org/10.1016/j.apsoil.2018.12.018 (2019).Article 

    Google Scholar 
    32.Elek, Z. et al. Taxon-specific responses to different forestry treatments in a temperate forest. Sci. Rep. 8, 16990. https://doi.org/10.1038/s41598-018-35159-z (2018).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    33.Connell, J. H. Intermediate-disturbance hypothesis. Science 204, 1345–1345 (1979).CAS 
    Article 

    Google Scholar 
    34.Chen, K. C. & Tso, I. M. Spider diversity on Orchid Island, Taiwan: A comparison between habitats receiving different degrees of human disturbance. Zool. Stud. 43, 598–611 (2004).
    Google Scholar 
    35.Szinetar, C. & Samu, F. Intensive grazing opens spider assemblage to invasion by disturbance-tolerant species. J. Arachnol. 40, 59–70 (2012).Article 

    Google Scholar 
    36.Pinzon, J., Spence, J. R. & Langor, D. W. Responses of ground-dwelling spiders (Araneae) to variable retention harvesting practices in the boreal forest. For. Ecol. Manag. 266, 42–53. https://doi.org/10.1016/j.foreco.2011.10.045 (2012).Article 

    Google Scholar 
    37.Pinzon, J., Spence, J. R. & Langor, D. W. Effects of prescribed burning and harvesting on ground-dwelling spiders in the Canadian boreal mixedwood forest. Biodivers. Conserv. 22, 1513–1536. https://doi.org/10.1007/s10531-013-0489-1 (2013).Article 

    Google Scholar 
    38.Samu, F. et al. Differential ecological responses of two generalist arthropod groups, spiders and carabid beetles (Araneae, Carabidae), to the effects of wildfire. Commun. Ecol. 11, 129–139. https://doi.org/10.1556/ComEc.11.2010.2.1 (2010).Article 

    Google Scholar 
    39.Morel, L. et al. Spontaneous recovery of functional diversity and rarity of ground-living spiders shed light on the conservation importance of recent woodlands. Biodivers. Conserv. 28, 687–709. https://doi.org/10.1007/s10531-018-01687-3 (2019).Article 

    Google Scholar 
    40.Seedre, M., Felton, A. & Lindbladh, M. What is the impact of continuous cover forestry compared to clearcut forestry on stand-level biodiversity in boreal and temperate forests? A systematic review protocol. Env. Evid. 7, 28. https://doi.org/10.1186/s13750-018-0138-y (2018).Article 

    Google Scholar 
    41.Garcia-Tejero, S., Spence, J. R., O’Halloran, J., Bourassa, S. & Oxbrough, A. Natural succession and clearcutting as drivers of environmental heterogeneity and beta diversity in North American boreal forests. PLoS ONE 13, 16. https://doi.org/10.1371/journal.pone.0206931 (2018).CAS 
    Article 

    Google Scholar 
    42.Andrési, D., Bali, L., Tuba, K. & Szinetár, C. Comparative study of ground beetle and ground-dwelling spider assemblages of artificial gap openings. Commun. Ecol. 19, 133–140. https://doi.org/10.1556/168.2018.19.2.5 (2018).Article 

    Google Scholar 
    43.Arganaraz, C. I. et al. Ground-dwelling spiders and understory vascular plants on Fuegian austral forests: Community responses to variable retention management and their association to natural ecosystems. For. Ecol. Manag. 474, 12. https://doi.org/10.1016/j.foreco.2020.118375 (2020).Article 

    Google Scholar 
    44.Dorow, W. H. O., Blick, T., Pauls, S. U. & Schneider, A. Waldbindung ausgewählter Tiergruppen Deutschlands (BfN-Skripten 544, 2019).
    Google Scholar 
    45.Szmatona-Túri, T., Magos, G., Vona-Túri, D., Gál, B. & Weiperth, A. Review of habitats occupied by Urocoras longispinus: A little-known spider species, and responses to grassland management. Biologia 73, 523–529. https://doi.org/10.2478/s11756-018-0061-2 (2018).Article 

    Google Scholar 
    46.Haraguchi, T. F., Uchida, M., Shibata, Y. & Tayasu, I. Contributions of detrital subsidies to aboveground spiders during secondary succession, revealed by radiocarbon and stable isotope signatures. Oecologia 171, 935–944. https://doi.org/10.1007/s00442-012-2446-1 (2013).ADS 
    Article 
    PubMed 

    Google Scholar 
    47.Carvalho, J. C. et al. Taxonomic divergence and functional convergence in Iberian spider forest communities: Insights from beta diversity partitioning. J. Biogeogr. 47, 288–300. https://doi.org/10.1111/jbi.13722 (2020).Article 

    Google Scholar 
    48.Samu, F., Horváth, A., Neidert, D., Botos, E. & Szita, É. Metacommunities of spiders in grassland habitat fragments of an agricultural landscape. Basic Appl. Ecol. 31, 92–103. https://doi.org/10.1016/j.baae.2018.07.009 (2018).Article 

    Google Scholar 
    49.Frost, C. M., Didham, R. K., Rand, T. A., Peralta, G. & Tylianakis, J. M. Community-level net spillover of natural enemies from managed to natural forest. Ecology 96, 193–202. https://doi.org/10.1890/14-0696.1 (2015).Article 
    PubMed 

    Google Scholar 
    50.Stewart-Oaten, A., Murdoch, W. W. & Parker, K. R. Environmental impact assessment: “pseudoreplication” in time?. Ecology 67, 929–940. https://doi.org/10.2307/1939815 (1986).Article 

    Google Scholar 
    51.Lemmon, P. E. A new instrument for measuring forest overstory density. J. For. 55, 667–668 (1957).
    Google Scholar 
    52.Jimenez-Valverde, A. & Lobo, J. M. Establishing reliable spider (Araneae, Araneidae and Thomisidae) assemblage sampling protocols: estimation of species richness, seasonal coverage and contribution of juvenile data to species richness and composition. Acta Oecol. 30, 21–32 (2006).ADS 
    Article 

    Google Scholar 
    53.SAS Institute. JMP Statistics and Graphics Guide, Release 6. (SAS Institute Inc., 2005).54.Smilauer, P. & Leps, J. Multivariate Analysis of Ecological Data Using CANOCO 5 2nd edn. (Cambridge University Press, 2014).Book 

    Google Scholar 
    55.ter Braak, C. J. F. & Smilauer, P. Canoco Reference Manual and User’s Guide: Software for Ordination (version 5.0) (Microcomputer Power, 2012).
    Google Scholar 
    56.McCune, B. & Mefford, M. PC-ORD. Multivariate Analysis ofEcological Data. Version 6. (MjM software design, 2011).57.Van den Brink, P. J. & Braak, C. J. F. T. Principal response curves: Analysis of time-dependent multivariate responses of biological community to stress. Environ. Toxicol. Chem. 18, 138–148. https://doi.org/10.1002/etc.5620180207 (1999).Article 

    Google Scholar 
    58.Weiher, E. & Boylen, C. W. Patterns and prediction of α and β diversity of aquatic plants in Adirondack (New York) lakes. Can. J. Bot. 72, 1797–1804. https://doi.org/10.1139/b94-221 (1994).Article 

    Google Scholar 
    59.Koleff, P., Gaston, K. J. & Lennon, J. J. Measuring beta diversity for presence-absence data. J. Anim. Ecol. 72, 367–382. https://doi.org/10.1046/j.1365-2656.2003.00710.x (2003).Article 

    Google Scholar 
    60.Podani, J. & Schmera, D. A new conceptual and methodological framework for exploring and explaining pattern in presence—absence data. Oikos 120, 1625–1638. https://doi.org/10.1111/j.1600-0706.2011.19451.x (2011).Article 

    Google Scholar  More