More stories

  • in

    Cryopreservation of testicular tissue from Murray River Rainbowfish, Melanotaenia fluviatilis

    Animal husbandry and sample collection
    All animal handling and experimental procedures were approved by the Animal Ethics Committee B at Monash Medical Centre (MMCB/2017/39) and conducted in accordance with the Australian Code of Practice for the Care and Use of Animals for Scientific Purposes. Melanotaenia fluviatilis (Aquarium Industries, Victoria, Australia) were held at 25 °C ± 1 °C on a 12:12 light–dark cycle. At the time of experimentation, fish 5.76 cm ± 1.00 cm in length and weighing 3.25 g ± 1.38 g, were humanely killed by anesthetic overdose using aquatic anaesthetic AQUI-S (Primo Aquaculture, Queensland, Australia) and death was confirmed by destruction of the brain. The gonads were removed and placed into handling medium composed of Eagles minimum essential media (EMEM, SigmaAldrich) supplemented with 5% FBS (ThermoFisher Scientific, Victoria Australia), and 25 mM HEPES (ThermoFisher Scientific; pH 7.8) and kept on ice.
    Histology and immunohistochemistry
    Whole testes were fixed in 10% neutral buffered formalin (Merck, Victoria, Australia) for 48 h and processed by the Monash Histology Platform which included standard hematoxylin and eosin staining. Unstained sections were stained for Vasa using a zebrafish-specific anti-Vasa antibody (Sapphire Bioscience Pty. Ltd, New South Wales, Australia) and counter-stained with Hoechst (ThermoFisher Scientific). De-paraffinised sections were rehydrated through changes of xylene and a standard series of decreasing ethanol dilutions before antigen retrieval in 10 mM citrate buffer (pH 6), microwaved to boiling point for 10 min. Sections were rested in citrate buffer for 30 min prior to blocking with CAS Block (Invitrogen) for one hour followed by incubation with anti-Vasa antibody (1:200) in 5% BSA in PBS at 4 °C overnight. Sections were washed in PBS and incubated with secondary antibody, Alexa Fluor 488-conjugated goat anti-rabbit IgG (1:500; Invitrogen), and Hoechst nuclear counterstain (1:1000) in 5% BSA and PBS for one hour at room temperature.
    Images were captured using the EVOS FL Auto 2 Imaging system (ThermoFisher Scientific) and an Olympus BX43 Upright Microscope with an X-Cite Series 120 Q laser (Lumen Dynamics). Approximate cell sizes were measured using cellSens Standard imaging software (Software version: 1.16, build 15,404, Olympus) and images were analysed in FIJI23 (Software version: 2.0.0-rc-69/1.52p, Image J).
    Validation of size-based cell sorting by flow cytometry
    Using cell measurements taken from histological analysis as a guide, a size-based cell sorting method was developed to isolate our target spermatogonial cells. A set of five size-specific beads (16.5 μm, 10.2 μm, 7.56 μm, 5.11 μm, 3.3 μm, Spherotech, Lake Forest, IL, USA) were analysed on a FACS Aria Fusion flow cytometer (BD Biosciences, New South Wales, Australia). These sizes cover the range of cell sizes seen in the testis, with sperm heads being approximately 2–3 μm and spermatogonia being over 10 μm in M.fluviatilis. Due to differences in the light scattering properties of plastic beads in comparison to live cells, these bead sizes can only be interpreted as a guide of scale and not as an exact size indication for cells in suspension. Using the scatter profile produced by these beads, two gates were set: the “A” gate surrounded events in the high forward scatter region on the scatter plot, approximately 9 μm and larger to capture larger cells such as spermatogonia; the “B” gate surrounded events in a low forward scatter region, between 2—5 μm, to capture smaller germ cells such as spermatids and spermatocytes. An unstained cell suspension was then sorted through these gates and sorted cells were pelleted by centrifugation (500 g for 15mins). Images were taken of live cells in suspension using the EVOS FL Auto 2 Imaging system (ThermoFisher Scientific) and cell sizes were measured in FIJI. Samples were then fixed in 2% PFA (Thermo Fisher Scientific) for 10 min and suspended in PBS.
    Aliquots of each sample (A gate, B gate and an unsorted control) were smeared onto Superfrost Plus slides (ThermoFisher Scientific), baked overnight at 37 °C and stained with anti-Vasa antibody to determine the number of Vasa-positive cells in each sample. Briefly, the slides were washed with MilliQ water to remove any salt that was present and irrigated with wash buffer (0.1% BSA in PBS) before blocking with 10% goat serum, 0.1% Triton X in PBS for 45 min. Sections were stained with anti-Vasa antibody (1:200) in PBS containing 5% BSA for 1 h at room temperature, washed with wash buffer, incubated with Alexa Fluor 488-conjugated goat anti-rabbit IgG (1:500), and counterstained with Hoechst (1:1000). Sections were imaged on the EVOS FL Auto 2 and analysed using FIJI.
    Cryopreservation protocol
    This cryopreservation method was adapted from research described by Lee et al.14,15. Whole gonads weighing 0.0124 g ± 0.0095 g were transferred into 1.2-ml CryoTubes with 500 μl of cryomedia containing a permeating cryoprotectant, dimethyl sulfoxide (DMSO), ethylene glycol (EG), methanol or glycerol (all purchased from Merck), at concentrations ranging between 1.0 M and 2.0 M, with 0.1 M trehalose (Merck), and 1.5% BSA (Bovogen Biologicals Pty. Ltd, Victoria, Australia) in a mixed salt solution (~ 296 mOsm, pH 7.8) previously described by Lee et al.14. Control samples contained all components except the permeating cryoprotectant. Samples were equilibrated on ice for one hour and then cooled at a rate of -1 °C/minute in a CoolCell (Merck) in a -80 °C freezer for at least 3 h before being plunged into liquid nitrogen. Samples were held in liquid nitrogen for at least 24 h before thawing.
    Thawing and cell suspension preparation
    Samples were thawed in a 30 °C water bath for 1 min. The gonad was removed and gently blotted on a Kim-wipe to remove excess cryoprotectant residue and then rehydrated in three changes of handling medium (as described under “Animal husbandry and sample collection”) for 20 min per change (60 min total). After rehydration, the testis was placed in a tissue grinder with 500 μl of PBS and crushed. The tissue grinder was washed with another 500 μl of PBS resulting in a final volume of 1 ml. The cell suspension was passed through a 40 μm nylon filter to remove any large particulates prior to flow cytometry.
    Viability assessment by flow cytometry
    Cell suspensions were stained with the LIVE/DEAD Sperm Viability Kit (ThermoFisher Scientific) which included a membrane-permeating SYBR14 nucleic acid dye for detecting live cells and membrane-impermeable Propidium Iodide (PI) nucleic acid dye to detect membrane-compromised, presumably dead cells. SYBR14 was added and incubated for 5 min in the dark, followed by PI for a further 5-min incubation.
    Prior to the assessment of experimental samples, the sized beads (Spherotech) were analysed on the FACS Aria Fusion flow cytometer. Using these beads as a guide, a gate was set for the approximate size of the spermatogonial cells based on our own histological analysis of this species and previous publications on fish in general24. An unstained control and two single stain controls (PI only or SYBR14 only) were included with the experimental samples in the analysis. The sample used for the PI-only control was flash frozen in liquid nitrogen three times to ensure a high percentage of dead cell to provide an adequate count for PI staining. Flow cytometry output was analysed in FlowJoTM25. Events captured by the gate were analysed for SYBR14 and PI spectra and divided into quartiles based on the absorbance of single stain controls (Fig. 1).
    Figure 1

    Flow cytometry scatter plots and gating method. (a) Analysis of size-specific beads shows five distinct clusters. (b) A gate is set to capture events from the 9 μm measurement and above. (c) Events detected in this region are replotted to determine SYB14 and PI absorbance. Events in the Q3 region are SYB14 positive and PI negative and therefore viable. In samples treated with a negative control (d), the majority of events falls in the Q1 region, with only propidium iodide detected (e).

    Full size image

    Statistical analysis
    Statistical analysis was performed using GraphPad Prism version 8.1.2 for MacOS, GraphPad Software, La Jolla California USA, www.graphpad.com. Data is presented as mean ± standard deviation, with a p-value less than 0.05 considered statistically significant.
    For cell gating data, the proportion of cell sizes in live cell suspensions in each treatment group was analysed using a chi-square. The percentage of Vasa-positive cells in the unsorted sample and the “A” gate was analysed using an un-paired t-test; data for the “B” gate was excluded as no Vasa-positive cells were detected.
    For percentage viability data assumptions for normality and variance were met using the Shapiro–Wilk test and the Brown-Forsythe test, respectively. Following this, treatment groups were compared by one-way ANOVA and Tukey’s post hoc test. More

  • in

    Impact of local and landscape complexity on the stability of field-level pest control

    1.
    Fahrig, L. et al. Functional landscape heterogeneity and animal biodiversity in agricultural landscapes. Ecol. Lett. 14, 101–112 (2011).
    Article  Google Scholar 
    2.
    Fahrig, L. et al. Farmlands with smaller crop fields have higher within-field biodiversity. Agric. Ecosyst. Environ. 200, 219–234 (2015).
    Article  Google Scholar 

    3.
    Sirami, C. et al. Increasing crop heterogeneity enhances multitrophic diversity across agricultural regions. Proc. Natl Acad. Sci. USA 116, 16442–16447 (2019).
    CAS  Article  Google Scholar 

    4.
    Martin, E. A., Seo, B., Park, C.-R., Reineking, B. & Steffan-Dewenter, I. Scale-dependent effects of landscape composition and configuration on natural enemy diversity, crop herbivory, and yields. Ecol. Appl. 26, 448–462 (2016).
    Article  Google Scholar 

    5.
    Root, R. B. Organization of a plant–arthropod association in simple and diverse habitats: the fauna of collards (Brassica oleracea). Ecol. Monogr. 43, 95–124 (1973).
    Article  Google Scholar 

    6.
    McCann, K. The diversity–stability debate. Nature 405, 228–233 (2000).
    CAS  Article  Google Scholar 

    7.
    MacArthur, R. Fluctuations of animal populations and a measure of community stability. Ecology 36, 533–536 (1955).
    Article  Google Scholar 

    8.
    Tilman, D. Biodiversity: population versus ecosystem stability. Ecology 77, 350–363 (1996).
    Article  Google Scholar 

    9.
    Tilman, D. & Wedin, D. Productivity and sustainability influenced by biodiversity in grassland ecosystems. Nature 379, 718–720 (1996).
    CAS  Google Scholar 

    10.
    McNaughton, S. Diversity and stability of ecological communities: a comment on the role of empiricism in ecology. Am. Natur. 111, 515–525 (1977).
    Article  Google Scholar 

    11.
    Ives, A. R. & Carpenter, S. R. Stability and diversity of ecosystems. Science 317, 58–62 (2007).
    CAS  Article  Google Scholar 

    12.
    Landis, D. A., Wratten, S. D. & Gurr, G. M. Habitat management to conserve natural enemies of arthropod pests in agriculture. Annu. Rev. Entomol. 45, 175–201 (2000).
    CAS  Article  Google Scholar 

    13.
    Chaplin-Kramer, R., O’Rourke, M. E., Blitzer, E. J. & Kremen, C. A meta-analysis of crop pest and natural enemy response to landscape complexity. Ecol. Lett. 14, 922–932 (2011).
    Article  Google Scholar 

    14.
    Karp, D. S. et al. Crop pests and predators exhibit inconsistent responses to surrounding landscape composition. Proc. Natl Acad. Sci. USA 111, E7863–E7870 (2018).
    Article  CAS  Google Scholar 

    15.
    Martin, E. A. et al. The interplay of landscape composition and configuration: new pathways to manage functional biodiversity and agroecosystem services across Europe. Ecol. Lett. 22, 1083–1094 (2019).
    Article  Google Scholar 

    16.
    Larsen, A. E. & Noack, F. Identifying the landscape drivers of agricultural insecticide use leveraging evidence from 100,000 fields. Proc. Natl Acad. Sci. USA 114, 5473–5478 (2017).
    CAS  Article  Google Scholar 

    17.
    Sexton, S. E., Lei, Z. & Zilberman, D. The economics of pesticides and pest control. Int. Rev. Envir. Resour. Econ. 1, 271–326 (2007).
    Article  Google Scholar 

    18.
    Waterfield, G. & Zilberman, D. Pest management in food systems: an economic perspective. Annu. Rev. 37, 223–245 (2012).

    19.
    O’Rourke, M. E. & Jones, L. E. Analysis of landscape-scale insect pest dynamics and pesticide use: an empirical and modeling study. Ecol. Appl. 21, 3199–3210 (2011).
    Article  Google Scholar 

    20.
    Gross, K. & Rosenheim, J. A. Quantifying secondary pest outbreaks in cotton and their monetary cost with causal-inference statistics. Ecol. Appl. 21, 2770–2780 (2011).
    Article  Google Scholar 

    21.
    Rosenheim, J. A. & Meisner, M. H. Ecoinformatics can reveal yield gaps associated with crop–pest interactions: a proof-of-concept. PLoS ONE 8, e80518 (2013).
    Article  CAS  Google Scholar 

    22.
    Meisner, M. H., Zaviezo, T. & Rosenheim, J. A. Landscape crop composition effects on cotton yield, Lygus hesperus densities and pesticide use. Pest Manag. Sci. 73, 232–239 (2016).
    Article  CAS  Google Scholar 

    23.
    Farrar, J. J., Baur, M. E. & Elliott, S. F. Adoption of IPM practices in grape, tree fruit, and nut production in the western United States. J. Integr. Pest Manag. 7, 8 (2016).

    24.
    Rosenheim, J. A., Cass, B. N., Kahl, H. & Steinmann, K. P. Variation in pesticide use across crops in California agriculture: economic and ecological drivers. Sci. Total Environ. 733, 138683 (2020).
    CAS  Article  Google Scholar 

    25.
    Möhring, N., Bozzola, M., Hirsch, S. & Finger, R. Are pesticides risk decreasing? The relevance of pesticide indicator choice in empirical analysis. Agric. Econ. 51, 429–444 (2020).
    Article  Google Scholar 

    26.
    Larsen, A. E., Patton, M. & Martin, E. A. High highs and low lows: elucidating striking seasonal variability in pesticide use and its environmental implications. Sci. Total Environ. 651, 828–837 (2019).
    CAS  Article  Google Scholar 

    27.
    Dudley, N. et al. How should conservationists respond to pesticides as a driver of biodiversity loss in agroecosystems? Biol. Conserv. 209, 449–453 (2017).
    Article  Google Scholar 

    28.
    Kim, K.-H., Kabir, E. & Jahan, S. A. Exposure to pesticides and the associated human health effects. Sci. Total Environ. 575, 525–535 (2017).
    CAS  Article  Google Scholar 

    29.
    Chay, K. Y. & Greenstone, M. The impact of air pollution on infant mortality: evidence from the Clean Air Act of 1970. Q. J. Econ. 118, 1121–1167 (2003).
    Article  Google Scholar 

    30.
    Larsen, A. E., Gaines, S. D. & Deschenes, O. Agricultural pesticide use and adverse birth outcomes in the San Joaquin Valley of California. Nat. Commun. 8, 302 (2017).

    31.
    California Agricultural Statistics Review 2017–2018 1–105 (California Department of Food & Agriculture, 2018).

    32.
    Summary of Pesticide Use Report Data 2017 (California Department of Pesticide Regulation, 2018).

    33.
    Bourque, K. et al. Balancing agricultural production, groundwater management, and biodiversity goals: a multi-benefit optimization model of agriculture in Kern County, California. Sci. Total Environ. 670, 865–875 (2019).
    CAS  Article  Google Scholar 

    34.
    Larsen, A. E., Meng, K. & Kendall, B. E. Causal analysis in control–impact ecological studies with observational data. Methods Ecol. Evol. 10, 924–934 (2019).
    Article  Google Scholar 

    35.
    Just, R. E. & Pope, R. D. Stochastic specification of production functions and economic implications. J. Econ. 7, 67–86 (1978).
    Article  Google Scholar 

    36.
    Murdoch, W. W. Diversity, complexity, stability and pest control. J. Appl. Ecol. 12, 795–807 (1975).
    Article  Google Scholar 

    37.
    Van Emden, H. F. & Williams, G. Insect stability and diversity in agro-ecosystems. Annu. Rev. Entomol. 19, 455–475 (1974).
    Article  Google Scholar 

    38.
    Edwards, C. B., Rosenheim, J. A. & Segoli, M. Aggregating fields of annual crops to form larger-scale monocultures can suppress dispersal-limited herbivores. Theor. Ecol. 11, 321–331.

    39.
    O’Rourke, M. E., Rienzo-Stack, K. & Power, A. G. A multi-scale, landscape approach to predicting insect populations in agroecosystems. Ecol. Appl. 21, 1782–1791 (2011).
    Article  Google Scholar 

    40.
    Hass, A. L. et al. Landscape configurational heterogeneity by small-scale agriculture, not crop diversity, maintains pollinators and plant reproduction in Western Europe. Proc. R. Soc. B 285, 1872 (2018).
    Article  Google Scholar 

    41.
    Holzschuh, A., Dewenter, I. S. & Tscharntke, T. How do landscape composition and configuration, organic farming and fallow strips affect the diversity of bees, wasps and their parasitoids? J. Anim. Ecol. 79, 491–500 (2010).
    Article  Google Scholar 

    42.
    Rusch, A. et al. Agricultural landscape simplification reduces natural pest control: a quantitative synthesis. Agric. Ecosyst. Environ. 221, 198–204 (2016).
    Article  Google Scholar 

    43.
    Rusch, A., Bommarco, R., Jonsson, M., Smith, H. G. & Ekbom, B. Flow and stability of natural pest control services depend on complexity and crop rotation at the landscape scale. J. Appl. Ecol. 50, 345–354 (2013).
    Article  Google Scholar 

    44.
    Zhao, Z. & Reddy, G. V. P. Semi-natural habitats mediate influence of inter-annual landscape variation on cereal aphid-parasitic wasp system in an agricultural landscape. Biol. Control 128, 17–23 (2019).
    Article  Google Scholar 

    45.
    Costello, C., Quérou, N. & Tomini, A. Private eradication of mobile public bads. Eur. Econ. Rev. 94, 23–44 (2017).
    Article  Google Scholar 

    46.
    Noack, F. & Larsen, A. The contrasting effects of farm size on farm incomes and food production. Environ. Res. Lett. 14, 084024 (2019).
    Article  Google Scholar 

    47.
    Gong, Y., Baylis, K., Kozak, R. & Bull, G. Farmers’ risk preferences and pesticide use decisions: evidence from field experiments in China. Agric. Econ. 47, 411–421 (2016).
    Article  Google Scholar 

    48.
    Möhring, N., Wuepper, D., Musa, T. & Finger, R. Why farmers deviate from recommended pesticide timing: the role of uncertainty and information. Pest Manag. Sci. 76, 2787–2798 (2020).
    Article  CAS  Google Scholar 

    49.
    Larsen, A. E., Farrant, D. N. & MacDonald, A. J. Spatiotemporal overlap of pesticide use and species richness hotspots in California. Agric. Ecosyst. Environ. 289, 106741 (2020).
    CAS  Article  Google Scholar 

    50.
    Gavrilescu, M. Fate of pesticides in the environment and its bioremediation. Eng. Life Sci. 5, 497–526 (2005).
    CAS  Article  Google Scholar 

    51.
    Haan, N. L., Zhang, Y. & Landis, D. A. Predicting landscape configuration effects on agricultural pest suppression. Trends Ecol. Evol. 35, 175–186 (2020).
    Article  Google Scholar 

    52.
    Damalas, C. A. & Eleftherohorinos, I. G. Pesticide exposure, safety issues, and risk assessment indicators. Int. J. Environ. Res. Public Health 8, 1402–1419 (2011).
    CAS  Article  Google Scholar 

    53.
    Mullin, C. A., Fine, J. D., Reynolds, R. D. & Frazier, M. T. Toxicological risks of agrochemical spray adjuvants: organosilicone surfactants may not be safe. Front. Public Health 4, 320–328 (2016).
    Article  Google Scholar 

    54.
    Kniss, A. R. Long-term trends in the intensity and relative toxicity of herbicide use. Nat. Commun. 8, 14865–14867 (2017).
    CAS  Article  Google Scholar 

    55.
    Estrada, J. Mean-semivariance optimization: a heuristic approach. J. Appl. Financ. 18, 1–16 (2008).
    Article  Google Scholar 

    56.
    Finger, R., Dalhaus, T., Allendorf, J. & Hirsch, S. Determinants of downside risk exposure of dairy farms. Eur. Rev. Agric. Econ. 45, 641–674 (2018).
    Article  Google Scholar 

    57.
    Miranda, M. J. & Glauber, J. W. Providing crop disaster assistance through a modified deficiency payment program. Am. J. Agric. Econ. 73, 1233–1243 (1991).
    Article  Google Scholar 

    58.
    Wooldridge, J. M. Econometric Analysis of Cross Section and Panel Data (MIT Press, 2002).

    59.
    Cabas, J., Weersink, A. & Olale, E. Crop yield response to economic, site and climatic variables. Clim. Change 101, 599–616 (2009).
    Article  CAS  Google Scholar 

    60.
    Isik, M. & Devadoss, S. An analysis of the impact of climate change on crop yields and yield variability. Appl. Econ. 38, 835–844 (2006).
    Article  Google Scholar 

    61.
    Arellano, M. & Bond, S. Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Rev. Econ. Stud. 58, 277–297 (1991).
    Article  Google Scholar 

    62.
    Bellemare, M. F. & Wichman, C. J. Elasticities and the inverse hyperbolic sine transformation. Oxf. Bull. Econ. Stat. 82, 50–61 (2019).
    Article  Google Scholar 

    63.
    Conley, T. G. & Molinari, F. Spatial correlation robust inference with errors in location or distance. J. Econ. 140, 76–96 (2007).
    Article  Google Scholar 

    64.
    Hsiang, S. M. Temperatures and cyclones strongly associated with economic production in the Caribbean and Central America. Proc. Natl Acad. Sci. USA 107, 15367–15372 (2010).
    CAS  Article  Google Scholar 

    65.
    Fetzer, T. Can Workfare Programs Moderate Conflict? Evidence from India The Warwick Economics Research Paper Series (TWERPS) 1220 (University of Warwick, Department of Economics, 2019); https://ideas.repec.org/p/wrk/warwec/1220.html More

  • in

    Vibrational modes of water predict spectral niches for photosynthesis in lakes and oceans

    1.
    Engelmann, T. W. Über Sauerstoffausscheidung von Pflanzenzellen im Mikrospektrum. Bot. Zeit. 40, 419–426 (1882).
    Google Scholar 
    2.
    Engelmann, T. W. Farbe und assimilation. Bot. Zeit. 41, 1–29 (1883).
    Google Scholar 

    3.
    Stomp, M. et al. Adaptive divergence in pigment composition promotes phytoplankton biodiversity. Nature 432, 104–107 (2004).
    CAS  Google Scholar 

    4.
    Stomp, M., Huisman, J., Stal, L. J. & Matthijs, H. C. P. Colorful niches of phototrophic microorganisms shaped by vibrations of the water molecule. ISME J. 1, 271–282 (2007).
    CAS  Google Scholar 

    5.
    Pick, F. R. The abundance and composition of freshwater picocyanobacteria in relation to light penetration. Limnol. Oceanogr. 36, 1457–1462 (1991).
    CAS  Google Scholar 

    6.
    Vörös, L., Callieri, C., Balogh, K. V. & Bertoni, R. Freshwater picocyanobacteria along a trophic gradient and light quality range. Hydrobiologia 369–370, 117–125 (1998).
    Google Scholar 

    7.
    Stomp, M. et al. Colourful coexistence of red and green picocyanobacteria in lakes and seas. Ecol. Lett. 10, 290–298 (2007).
    Google Scholar 

    8.
    Ting, C. S., Rocap, G., King, J. & Chisholm, S. W. Cyanobacterial photosynthesis in the oceans: the origins and significance of divergent light-harvesting strategies. Trends Microbiol. 10, 134–142 (2002).
    CAS  Google Scholar 

    9.
    Grébert, T. et al. Light color acclimation is a key process in the global ocean distribution of Synechococcus cyanobacteria. Proc. Natl Acad. Sci. USA 115, E2010–E2019 (2018).
    Google Scholar 

    10.
    Luimstra, V. M., Verspagen, J. M. H., Xu, T., Schuurmans, J. M. & Huisman, J. Changes in water color shift competition between phytoplankton species with contrasting light-harvesting strategies. Ecology 101, e02951 (2020).
    PubMed  PubMed Central  Google Scholar 

    11.
    Mobley, C. D. Light and Water: Radiative Transfer in Natural Waters (Academic Press, 1994).

    12.
    Kirk, J. T. O. Light and Photosynthesis in Aquatic Ecosystems 3rd edn (Cambridge Univ. Press, 2011).

    13.
    Dall’Olmo, G., Westberry, T. K., Behrenfeld, M. J., Boss, E. & Slade, W. H. Significant contribution of large particles to optical backscattering in the open ocean. Biogeosciences 6, 947–967 (2009).
    Google Scholar 

    14.
    Morel, A. et al. Optical properties of the “clearest” natural waters. Limnol. Oceanogr. 52, 217–229 (2007).
    CAS  Google Scholar 

    15.
    Pegau, W. S., Gray, D. & Zaneveld, J. R. Absorption and attenuation of visible and near-infrared light in water: dependence on temperature and salinity. Appl. Opt. 36, 6035–6046 (1997).
    CAS  Google Scholar 

    16.
    Sogandares, F. M. & Fry, E. S. Absorption spectrum (340–640 nm) of pure water. I. Photothermal measurements. Appl. Opt. 36, 8699–8709 (1997).
    CAS  Google Scholar 

    17.
    Pope, R. M. & Fry, E. S. Absorption spectrum (380–700 nm) of pure water. II. Integrating cavity measurements. Appl. Opt. 36, 8710–8723 (1997).
    CAS  Google Scholar 

    18.
    Mason, J. D., Cone, M. T. & Fry, E. S. Ultraviolet (250–550 nm) absorption spectrum of pure water. Appl. Opt. 55, 7163–7172 (2016).
    CAS  Google Scholar 

    19.
    Mobley, C. D. & Sundman, L. K. HydroLight 5.3—EcoLight 5.3 (Sequoia Scientific Inc., 2016).

    20.
    Sathyendranath, S., Brewin, R. J., Jackson, T., Mélin, F. & Platt, T. Ocean-colour products for climate-change studies: what are their ideal characteristics? Remote Sens. Environ. 203, 125–138 (2017).
    Google Scholar 

    21.
    Neeley, A. R. & Mannino, A. (eds) IOCCG Ocean Optics and Biogeochemistry Protocols for Satellite Ocean Colour Sensor Validation, Volume 1.0. Inherent Optical Property Measurements and Protocols: Absorption Coefficient (IOCCG, 2018).

    22.
    Farrant, G. K. et al. Delineating ecologically significant taxonomic units from global patterns of marine picocyanobacteria. Proc. Natl Acad. Sci. USA 113, E3365–E3374 (2016).
    CAS  Google Scholar 

    23.
    Chisholm, S. W. et al. Prochlorococcus marinus nov. gen. nov. sp.: an oxyphototrophic marine prokaryote containing divinyl chlorophyll a and b. Arch. Microbiol. 157, 297–300 (1992).
    CAS  Google Scholar 

    24.
    Partensky, F., Hess, W. R. & Vaulot, D. Prochlorococcus, a marine photosynthetic prokaryote of global significance. Microbiol. Mol. Biol. Rev. 63, 106–127 (1999).
    CAS  PubMed  PubMed Central  Google Scholar 

    25.
    Moore, L. R., Goericke, R. & Chisholm, S. W. Comparative physiology of Synechococcus and Prochlorococcus: influence of light and temperature on growth, pigments, fluorescence and absorptive properties. Mar. Ecol. Prog. Ser. 116, 259–275 (1995).
    Google Scholar 

    26.
    Tandeau de Marsac, N. Phycobiliproteins and phycobilisomes: the early observations. Photosynth. Res. 76, 193–205 (2003).
    Google Scholar 

    27.
    Six, C. et al. Diversity and evolution of phycobilisomes in marine Synechococcus spp.: a comparative genomics study. Genome Biol. 8, R259 (2007).
    PubMed  PubMed Central  Google Scholar 

    28.
    Watanabe, M. & Ikeuchi, M. Phycobilisome: architecture of a light-harvesting supercomplex. Photosynth. Res. 116, 265–276 (2013).
    CAS  Google Scholar 

    29.
    Sanfilippo, J. E., Garczarek, L., Partensky, F. & Kehoe, D. M. Chromatic acclimation in cyanobacteria: a diverse and widespread process for optimizing photosynthesis. Annu. Rev. Microbiol. 73, 407–433 (2019).
    CAS  Google Scholar 

    30.
    Palenik, B. Chromatic adaptation in marine Synechococcus strains. Appl. Environ. Microbiol. 67, 991–994 (2001).
    CAS  PubMed  PubMed Central  Google Scholar 

    31.
    Stomp, M. et al. The timescale of phenotypic plasticity and its impact on competition in fluctuating environments. Am. Nat. 172, E169–E185 (2008).
    Google Scholar 

    32.
    Hirose, Y. et al. Diverse chromatic acclimation processes regulating phycoerythrocyanin and rod-shaped phycobilisome in cyanobacteria. Mol. Plant 12, 715–725 (2019).
    CAS  Google Scholar 

    33.
    Luimstra, V. M. et al. Blue light reduces photosynthetic efficiency of cyanobacteria through an imbalance between photosystems I and II. Photosynth. Res. 138, 177–189 (2018).
    CAS  PubMed  PubMed Central  Google Scholar 

    34.
    Humily, F. et al. A gene island with two possible configurations is involved in chromatic acclimation in marine Synechococcus. PLoS ONE 8, e84459 (2013).
    PubMed  PubMed Central  Google Scholar 

    35.
    Haverkamp, T. et al. Diversity and phylogeny of Baltic Sea picocyanobacteria inferred from their ITS and phycobiliprotein operons. Environ. Microbiol. 10, 174–188 (2008).
    CAS  Google Scholar 

    36.
    Huisman, J. et al. Cyanobacterial blooms. Nat. Rev. Microbiol. 16, 471–483 (2018).
    CAS  Google Scholar 

    37.
    Chen, F. et al. Phylogenetic diversity of Synechococcus in the Chesapeake Bay revealed by ribulose-1,5-bisphosphate carboxylase-oxygenase (RuBisCO) large subunit gene (rbcL) sequences. Aquat. Microb. Ecol. 36, 153–164 (2004).
    Google Scholar 

    38.
    Somogyi, B., Felföldi, T., Tóth, L. G., Bernát, G. & Vörös, L. Photoautotrophic picoplankton: a review on their occurrence, role and diversity in Lake Balaton. Biol. Futur. https://doi.org/10.1007/s42977-020-00030-8 (2020).

    39.
    Kardinaal, W. E. A. et al. Competition for light between toxic and nontoxic strains of the harmful cyanobacterium Microcystis. Appl. Environ. Microbiol. 73, 2939–2946 (2007).
    PubMed  PubMed Central  Google Scholar 

    40.
    Bricaud, A., Claustre, H., Ras, J. & Oubelkheir, K. Natural variability of phytoplanktonic absorption in oceanic waters: influence of the size structure of algal populations. J. Geophys. Res. 109, C11010 (2004).
    Google Scholar 

    41.
    Monteith, D. T. et al. Dissolved organic carbon trends resulting from changes in atmospheric deposition chemistry. Nature 450, 537–541 (2007).
    CAS  Google Scholar 

    42.
    Weyhenmeyer, G. A., Müller, R. A., Norman, M. & Tranvik, L. J. Sensitivity of freshwaters to browning in response to future climate change. Clim. Change 134, 225–239 (2016).
    Google Scholar 

    43.
    Kritzberg, E. S. Centennial‐long trends of lake browning show major effect of afforestation. Limnol. Oceanogr. Lett. 2, 105–112 (2017).
    Google Scholar 

    44.
    Leech, D. M., Pollard, A. I., Labou, S. G. & Hampton, S. E. Fewer blue lakes and more murky lakes across the continental U.S.: implications for planktonic food webs. Limnol. Oceanogr. 63, 2661–2680 (2018).
    CAS  PubMed  PubMed Central  Google Scholar 

    45.
    Ekvall, M. K. et al. Synergistic and species‐specific effects of climate change and water colour on cyanobacterial toxicity and bloom formation. Freshw. Biol. 58, 2414–2422 (2013).
    CAS  Google Scholar 

    46.
    Urrutia‐Cordero, P. et al. Phytoplankton diversity loss along a gradient of future warming and brownification in freshwater mesocosms. Freshw. Biol. 62, 1869–1878 (2017).
    Google Scholar 

    47.
    Wilken, S. et al. Primary producers or consumers? Increasing phytoplankton bacterivory along a gradient of lake warming and browning. Limnol. Oceanogr. 63, S142–S155 (2018).
    Google Scholar 

    48.
    Feuchtmayr, H. et al. Effects of brownification and warming on algal blooms, metabolism and higher trophic levels in productive shallow lake mesocosms. Sci. Tot. Environ. 678, 227–238 (2019).
    CAS  Google Scholar 

    49.
    Deininger, A., Faithfull, C. L. & Bergström, A. K. Phytoplankton response to whole lake inorganic N fertilization along a gradient in dissolved organic carbon. Ecology 98, 982–994 (2017).
    CAS  Google Scholar 

    50.
    Tan, X., Zhang, D., Duan, Z., Parajuli, K. & Hu, J. Effects of light color on interspecific competition between Microcystis aeruginosa and Chlorella pyrenoidosa in batch experiment. Environ. Sci. Pollut. Res. 27, 344–352 (2020).
    CAS  Google Scholar 

    51.
    Burson, A., Stomp, M., Greenwell, E., Grosse, J. & Huisman, J. Competition for nutrients and light: testing advances in resource competition with a natural phytoplankton community. Ecology 99, 1108–1118 (2018).
    Google Scholar 

    52.
    Dutkiewicz, S. et al. Dimensions of marine phytoplankton diversity. Biogeosciences 17, 609–634 (2020).
    Google Scholar 

    53.
    Johnson, Z. I. et al. Niche partitioning among Prochlorococcus ecotypes along ocean-scale environmental gradients. Science 311, 1737–1740 (2006).
    CAS  Google Scholar 

    54.
    Malmstrom, R. R. et al. Temporal dynamics of Prochlorococcus ecotypes in the Atlantic and Pacific Oceans. ISME J. 4, 1252–1264 (2010).
    Google Scholar 

    55.
    Lange, P. K. et al. Scratching beneath the surface: a model to predict the vertical distribution of Prochlorococcus using remote sensing. Remote Sens. 10, 847 (2018).
    Google Scholar 

    56.
    Wernand, M. R., van der Woerd, H. J. & Gieskes, W. W. C. Trends in ocean colour and chlorophyll concentration from 1889 to 2000, worldwide. PLoS ONE 8, e63766 (2013).
    CAS  PubMed  PubMed Central  Google Scholar 

    57.
    Dutkiewicz, S. et al. Ocean colour signature of climate change. Nat. Commun. 10, 578 (2019).
    CAS  PubMed  PubMed Central  Google Scholar 

    58.
    Bricaud, A., Morel, A. & Prieur, L. Absorption by dissolved organic matter of the sea (yellow substance) in the UV and visible domains. Limnol. Oceanogr. 26, 43–53 (1981).
    CAS  Google Scholar 

    59.
    Twardowski, M. S., Boss, E., Sullivan, J. M. & Donaghay, P. L. Modeling the spectral shape of absorption by chromophoric dissolved organic matter. Mar. Chem. 89, 69–88 (2004).
    CAS  Google Scholar 

    60.
    Babin, M. et al. Variations in the light absorption coefficients of phytoplankton, nonalgal particles, and dissolved organic matter in coastal waters around Europe. J. Geophys. Res. 108, 1–20 (2003).
    Google Scholar 

    61.
    Babin, M., Morel, A., Fournier-Sicre, V., Fell, F. & Stramski, D. Light scattering properties of marine particles in coastal and open ocean waters as related to the particle mass concentration. Limnol. Oceanogr. 48, 843–859 (2003).
    Google Scholar 

    62.
    Doxaran, D. et al. Spectral variations of light scattering by marine particles in coastal waters, from the visible to the near infrared. Limnol. Oceanogr. 54, 1257–1271 (2009).
    CAS  Google Scholar 

    63.
    Nechad, B., Ruddick, K. G. & Park, Y. Calibration and validation of a generic multisensor algorithm for mapping of total suspended matter in turbid waters. Remote Sens. Environ. 114, 854–866 (2010).
    Google Scholar 

    64.
    Petzold, T. J. Volume Scattering Functions for Selected Ocean Waters (No. SIO-REF-72-78) (Scripps Institution of Oceanography, 1972).

    65.
    Morel, A. & Gentili, B. Diffuse reflectance of oceanic waters: its dependence on sun angle as influenced by the molecular scattering contribution. Appl. Opt. 30, 4427–4438 (1991).
    CAS  Google Scholar 

    66.
    Sathyendranath, S. et al. An ocean-colour time series for use in climate studies: the experience of the Ocean-Colour Climate Change Initiative (OC-CCI). Sensors 19, 4285 (2019).
    CAS  Google Scholar 

    67.
    Holtrop, T. et al. Data: vibrational modes of water predict spectral niches for photosynthesis in lakes and oceans. https://doi.org/10.6084/m9.figshare.c.5140601.v1 (2020).

    68.
    Sanfilippo, J. E. et al. Interplay between differentially expressed enzymes contributes to light color acclimation in marine Synechococcus. Proc. Natl Acad. Sci. USA 116, 6457–6462 (2019).
    CAS  Google Scholar  More

  • in

    Tree mode of death and mortality risk factors across Amazon forests

    School of Geography, Earth and Enviornmental Sciences, University of Birmingham, Birmingham, UK
    Adriane Esquivel-Muelbert & Thomas A. M. Pugh

    School of Geography, University of Leeds, Leeds, UK
    Adriane Esquivel-Muelbert, Oliver L. Phillips, Roel J. W. Brienen, Martin J. P. Sullivan, Timothy R. Baker, Emanuel Gloor, Aurora Levesley, Simon L. Lewis, Karina Liana Lisboa Melgaço Ladvocat, Gabriela Lopez-Gonzalez, Nadir Pallqui Camacho, Julie Peacock, Georgia Pickavance & David Galbraith

    Birmingham Institute of Forest Research, University of Birmingham, Birmingham, UK
    Adriane Esquivel-Muelbert & Thomas A. M. Pugh

    School of Geography, Earth and Environmental Sciences, University of Plymouth, Plymouth, UK
    Sophie Fauset

    Department of Natural Sciences, Manchester Metropolitan University, Manchester, UK
    Martin J. P. Sullivan

    International Master Program of Agriculture, National Chung Hsing University, Taichung, Taiwan
    Kuo-Jung Chao

    Geography, College of Life and Environmental Sciences, University of Exeter, Exeter, UK
    Ted R. Feldpausch

    Instituto Nacional de Pesquisas da Amazônia, Manaus, Brazil
    Niro Higuchi, Adriano José Nogueira Lima & Carlos Quesada

    School of Mathematics, University of Leeds, Leeds, UK
    Jeanne Houwing-Duistermaat & Haiyan Liu

    Faculty of Natural Sciences, Department of Life, Imperial College London Sciences, London, UK
    Jon Lloyd

    Environmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford, UK
    Yadvinder Malhi & Simone Matias de Almeida Reis

    UNEMAT – Universidade do Estado de Mato Grosso PPG-Ecologia e Conservação, Campus de Nova Xavantina, Nova Xavantina, MT, Brazil
    Beatriz Marimon, Ben Hur Marimon Junior, Paulo Morandi, Edmar Almeida de Oliveira & Simone Matias de Almeida Reis

    Jardín Botánico de Missouri, Oxapampa, Peru
    Abel Monteagudo-Mendoza, Victor Chama Moscoso, Luis Valenzuela Gamarra & Rodolfo Vasquez Martinez

    Forest Ecology and Forest Management Group, Wageningen University and Research, Wageningen, Netherlands
    Lourens Poorter, Frans Bongers, Marielos Peña-Claros & Pieter Zuidema

    Centro de Ciências Biológicas e da Natureza, Universidade Federal do Acre, Rio Branco, AC, Brazil
    Marcos Silveira

    Instituto de Investigaciones para el Desarrollo Forestal (INDEFOR), Universidad de Los Andes, Mérida, Venezuela
    Emilio Vilanova Torre & Julio Serrano

    University of California, Berkeley, CA, USA
    Emilio Vilanova Torre

    Escuela de Ciencias Agropecuarias y Ambientales, Universidad Nacional Abierta y a Distancia, Boyacá, Colombia
    Esteban Alvarez Dávila

    Fundación ConVida, Medellín, Colombia
    Esteban Alvarez Dávila

    Instituto de Investigaciones de la Amazonia Peruana, Iquitos, Peru
    Jhon del Aguila Pasquel, Gerardo A. Aymard C., Nallaret Davila Cardozo & Eurídice Honorio Coronado

    Instituto de Biodiversidade e Florestas, Universidade Federal do Oeste do Pará, Santarém, Brazil
    Everton Almeida

    Center for Tropical Conservation, Nicholas School of the Environment, University in Durham, Durham, NC, USA
    Patricia Alvarez Loayza

    Projeto Dinâmica Biológica de Fragmentos, Instituto Nacional de Pesquisas da Amazônia Florestais, Manaus, AM, Brazil
    Ana Andrade & José Luís Camargo

    National Institute for Space Research (INPE), São José dos Campos, SP, Brazil
    Luiz E. O. C. Aragão

    Museo de Historia Natural Noel Kempff Mercado, Universidad Autónoma Gabriel Rene Moreno, Santa Cruz de la Sierra, Bolivia
    Alejandro Araujo-Murakami & Marisol Toledo

    Wageningen Environmental Research, Wageningen University and Research, Wageningen, Netherlands
    Eric Arets

    Dirección de la Carrera de Biología, Universidad Autónoma Gabriel René Moreno, Santa Cruz de la Sierra, Bolivia
    Luzmila Arroyo

    INRAE, UMR EcoFoG, CNRS, Cirad, AgroParisTech, Université des Antilles, Université de Guyane, Kourou, France
    Michel Baisie, Damien Bonal, Benoit Burban, Aurélie Dourdain, Maxime Rejou-Machain & Clement Stahl

    Department of Biological Sciences, International Center for Tropical Botany, Florida International University, Miami, FL, USA
    Christopher Baraloto

    Centro de Energia Nuclear na Agricultura, Universidade de São Paulo, Piracicaba, Brazil
    Plínio Barbosa Camargo

    Universidade Federal do Acre, Campus Floresta, Cruzeiro do Sul, Brazil
    Jorcely Barroso

    UR Forest & Societies, CIRAD, Montpellier, France
    Lilian Blanc

    Department of Biology, Utrecht, Netherlands
    René Boot

    Woods Hole Research Center, Falmouth, MA, USA
    Foster Brown

    Laboratório de Botânica e Ecologia Vegetal, Universidade Federal do Acre, Rio Branco, AC, Brazil
    Wendeson Castro

    Laboratoire Evolution et Diversite Biologique, CNRS, Toulouse, France
    Jerome Chave

    Inventory and Monitoring Program, National Park Service, Fort Collins, CO, USA
    James Comiskey

    Proyecto Castaña, Madre de Dios, Peru
    Fernando Cornejo Valverde

    Instituto de Geociências, Faculdade de Meteorologia, Universidade Federal do Para, Belém, Brazil
    Antonio Lola da Costa

    Department of Anthropology and Primate Molecular Ecology and Evolution Laboratory, University of Texas, Austin, TX, USA
    Anthony Di Fiore

    National Museum of Natural History, Smithsonian Institute, Washington, DC, USA
    Terry Erwin

    Universidad Nacional Jorge Basadre de Grohmann, Tacna, Peru
    Gerardo Flores Llampazo

    Museu Paraense Emílio Goeldi, Belém, Brazil
    Ima Célia Guimarães Vieira & Rafael Salomão

    Instituto Venezolano de Investigaciones Científicas (IVIC), Caracas, Venezuela
    Rafael Herrera

    IIAMA, Universitat Politécnica de València, València, Spain
    Rafael Herrera

    Universidad Nacional de San Antonio Abad del Cusco, Cusco, Peru
    Isau Huamantupa-Chuquimaco

    Instituto Amazónico de Investigaciones Imani, Universidad Nacional de Colombia Sede Amazonia, Leticia, Colombia
    Eliana Jimenez-Rojas

    Agteca, Santa Cruz, Bolivia
    Timothy Killeen

    College of Science and Engineering, James Cook University, Cairns, QLD, Australia
    Susan Laurance & William Laurance

    Department of Geography, University College London, London, UK
    Simon L. Lewis

    Environmental Science and Policy, George Mason University, Fairfax, VA, USA
    Thomas Lovejoy

    Research School of Biology, Australian National University, Canberra, ACT, Australia
    Patrick Meir

    School of Geosciences, University of Edinburgh, Edinburgh, UK
    Patrick Meir

    Escuela de Ciencias Forestales, Unidad Académica del Trópico, Universidad Mayor de San Simón, Cochabamba, Bolivia
    Casimiro Mendoza

    Facultad de Ingeniería Ambiental, Universidad Estatal Amazónica, Puyo, Ecuador
    David Neill

    Universidad Nacional de San Antonio Abad del Cusco, Cusco, Perú
    Percy Nuñez Vargas, Nadir Pallqui Camacho & Javier Silva Espejo

    Universidad Autónoma del Beni José Ballivián, Trinidad, Bolivia
    Guido Pardo & Vincent Vos

    Universidad Regional Amazónica Ikiam, Ikiam, Ecuador
    Maria Cristina Peñuela-Mora

    Broward County Parks Recreation, Oakland Park, FL, USA
    John Pipoly

    Keller Science Action Center, Field Museum, Chicago, IL, USA
    Nigel Pitman

    Instituto de Ciencias Naturales, Universidad Nacional de Colombia, Bogotá, Colombia
    Adriana Prieto & Agustín Rudas

    Institute of Research for Forestry Development (INDEFOR), Universidad de los Andes, Mérida, Venezuela
    Hirma Ramirez-Angulo

    Socioecosistemas y Cambio Climatico, Fundacion Con Vida, Medellín, Colombia
    Zorayda Restrepo Correa

    Centro de Conservacion, Investigacion y Manejo de Areas Naturales, CIMA Cordillera Azul, Lima, Peru
    Lily Rodriguez Bayona

    Universidade Federal Rural da Amazônia, Belém, Brazil
    Rafael Salomão & Natalino Silva

    Departamento de Biología, Universidad de La Serena, La Serena, Chile
    Javier Silva Espejo

    Guyana Forestry Commission, Georgetown, Guyana
    James Singh

    Federal University of Alagoas, Maceió, Brazil
    Juliana Stropp

    Institute for Conservation Research, Escondido, CA, USA
    Varun Swamy

    Institute for Transport Studies, University of Leeds, Leeds, UK
    Joey Talbot

    Biodiversity Dynamics, Naturalis Biodiversity Center, Leiden, The Netherlands
    Hans ter Steege

    Systems Ecology, Free University, De Boelelaan 1087, Amsterdam, Netherlands
    Hans ter Steege

    Department of Biology, University of Florida, Gainesville, FL, USA
    John Terborgh

    Iwokrama International Centre for Rainforest Conservation and Development, Georgetown, Guyana
    Raquel Thomas

    Universidad de los Andes, Mérida, Venezuela
    Armando Torres-Lezama

    School of Geography, University of Nottingham, Nottingham, UK
    Geertje van der Heijden

    Van Hall Larenstein University of Applied Sciences, Leeuwarden, Netherlands
    Peter van der Meer

    Van der Hoult Forestry Consulting, Leeuwarden, The Netherlands
    Peter van der Hout

    Núcleo de Estudos e Pesquisas Ambientais – Universidade Estadual de Campinas, Campinas, Brazil
    Simone Aparecida Vieira

    Herbario del Sur de Bolivia, Universidad de San Francisco Xavier de Chuquisaca, Sucre, Bolivia
    Jeanneth Villalobos Cayo

    Tropenbos International, Wageningen, Netherlands
    Roderick Zagt

    A.E.-M. and D.G. designed the study with contributions from O.L.P., R.J.W.B., S.F. and M.J.P.S. A.E.-M. carried out the analyses with inputs from D.G., O.L.P., R.J.W.B., S.F., M.J.P.S., J.H.-.D. and H.L. A.E.-M. wrote a first draft with contributions from D.G., M.J.P.S., T.A.M.P., S.F. and O.L.P. O.L.P., R.J.W.B., S.F., M.J.P.S., T.R.B., K.-J.C., T.R.F., N.H., Y.M., B.M., B.H.M.J., A.M.-M., L.P., M.S., E.V.T., E.A.D., J.d.A.P., E.A., P.A.L., A.A., L.E.O.CA., A.A.-M., E.Arets, L.A., G.A.A.C., M.B., C.B., P.B.C., J.B., L.B., D.B., F.B., R.J.W.B., F.Brown, B.B., J.L.C., W.C., V.C.M., J.C., J.Comiskey, F.C.V., A.L.d.C., N.D.C., A.D.F., A.D., T.E., G.F.L., I.C.G.V., R.H., E.H.C., I.H.-C., E.J.-R., T.K., S.L., W.L., S.L.L., T.L., P.M., C.M., P.Morandi, D.N., A.J.N.L., P.N.V., E.A.d.O., N.P.C., G.Prado, J.Pipoly, M.P.-C., M.C.P.-M., N.P., A.P., C.Q., H.R.-A., S.M.d.A.R., M.R.-M., Z.R.C., L.R.B., A.R., R.S., J.S., J.S.E., N.S., J.Singh, C.S., J.Stroop, V.S., J.T., H.t.S., J.T., R.T., M.T., A.T.-L., L.V.G., G.v.d.H., P.v.d.M., P.v.d.H., R.V.M., S.A.V., J.V.C., V.V., R.Z. and P.Z. led field expeditions for data collection. O.L.P., J.L. and Y.M. conceived the RAINFOR forest plot network; D.G., E.G. and T.R.B. contributed to its development. O.L.P., R.J.W.B., T.R.F., T.R.B., A.M.‐M., L.E.O.C.A., E.A.D., B.M., B.H.M.J., N.H., E.V.T., J.C., E.G. and Y.M. coordinated data collection with the help of many co‐authors. O.L.P., T.R.B., S.L.L. and G.L.-G. conceived ForestPlots.net, and M.J.P.S., A.L., J.Peacock, G.P., K.L.L.M.L., D.G. and E.G. helped to develop it. All authors read and approved the manuscript (with important insights provided by O.L.P., L.P., H.t.S., T.E., W.C., S.M.d.A.R., E.G., E.A.d.O., P.M., M.J.P.S., D.B., G.v.d.H. and P.Z.). More

  • in

    Microevolution in our megadont relative

    1.
    Broom, R. Nature 142, 377–379 (1938).
    Article  Google Scholar 
    2.
    Grine, F. E. (ed.) Evolutionary History of the “Robust” Australopithecines (Aldine de Gruyter, 1988).

    3.
    Herries, A. I. R. & Adams, J. W. J. Hum. Evol. 65, 676–681 (2013).
    Article  Google Scholar 

    4.
    Pickering, R. et al. Nature 565, 226–229 (2019).
    CAS  Google Scholar 

    5.
    Herries, A. I. R. et al. Science 368, eaaw7293 (2020).
    CAS  Article  Google Scholar 

    6.
    Martin, J. M. et al. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-020-01319-6 (2020).

    7.
    Wood, B. & Grabowski, M. in Macroevolution (eds Serrelli, E. & Gontier, N.) 345–376 (Springer, 2015).

    8.
    Vrba, E. S. in Paleoclimate and Evolution with Emphasis on Human Origins (eds Vrba, E. S. et al.) 24–45 (Yale Univ. Press, 1995).

    9.
    Potts, R. & Faith, J. T. J. Hum. Evol. 87, 5–20 (2015).
    Article  Google Scholar  More

  • in

    Epidemiological hypothesis testing using a phylogeographic and phylodynamic framework

    1.
    Lemey, P., Rambaut, A., Welch, J. J. & Suchard, M. A. Phylogeography takes a relaxed random walk in continuous space and time. Mol. Biol. Evol. 27, 1877–1885 (2010).
    CAS  PubMed Central  PubMed  Google Scholar 
    2.
    Pybus, O. G. et al. Unifying the spatial epidemiology and molecular evolution of emerging epidemics. Proc. Natl Acad. Sci. USA 109, 15066–15071 (2012).
    ADS  CAS  PubMed  PubMed Central  Google Scholar 

    3.
    Baele, G., Dellicour, S., Suchard, M. A., Lemey, P. & Vrancken, B. Recent advances in computational phylodynamics. Curr. Opin. Virol. 31, 24–32 (2018).
    PubMed  PubMed Central  Google Scholar 

    4.
    Dellicour, S., Rose, R. & Pybus, O. G. Explaining the geographic spread of emerging epidemics: a framework for comparing viral phylogenies and environmental landscape data. BMC Bioinform. 17, 1–12 (2016).
    Google Scholar 

    5.
    Jacquot, M., Nomikou, K., Palmarini, M., Mertens, P. & Biek, R. Bluetongue virus spread in Europe is a consequence of climatic, landscape and vertebrate host factors as revealed by phylogeographic inference. Proc. R. Soc. Lond. B 284, 20170919 (2017).
    Google Scholar 

    6.
    Brunker, K. et al. Landscape attributes governing local transmission of an endemic zoonosis: Rabies virus in domestic dogs. Mol. Ecol. 27, 773–788 (2018).
    CAS  PubMed Central  PubMed  Google Scholar 

    7.
    Dellicour, S., Vrancken, B., Trovão, N. S., Fargette, D. & Lemey, P. On the importance of negative controls in viral landscape phylogeography. Virus Evol. 4, vey023 (2018).
    PubMed Central  PubMed  Google Scholar 

    8.
    Minin, V. N., Bloomquist, E. W. & Suchard, M. A. Smooth skyride through a rough skyline: Bayesian coalescent-based inference of population dynamics. Mol. Biol. Evol. 25, 1459–1471 (2008).
    CAS  PubMed Central  PubMed  Google Scholar 

    9.
    Gill, M. S. et al. Improving Bayesian population dynamics inference: A coalescent-based model for multiple loci. Mol. Biol. Evol. 30, 713–724 (2013).
    CAS  PubMed  PubMed Central  Google Scholar 

    10.
    Gill, M. S., Lemey, P., Bennett, S. N., Biek, R. & Suchard, M. A. Understanding past population dynamics: Bayesian coalescent-based modeling with covariates. Syst. Biol. 65, 1041–1056 (2016).
    PubMed Central  PubMed  Google Scholar 

    11.
    Reisen, W. K. Ecology of West Nile virus in North America. Viruses 5, 2079–2105 (2013).
    PubMed Central  PubMed  Google Scholar 

    12.
    Hayes, E. B. et al. Epidemiology and transmission dynamics of West Nile virus disease. Emerg. Infect. Dis. 11, 1167–1173 (2005).
    PubMed Central  PubMed  Google Scholar 

    13.
    May, F. J., Davis, C. T., Tesh, R. B. & Barrett, A. D. T. Phylogeography of West Nile Virus: from the cradle of evolution in Africa to Eurasia, Australia, and the Americas. J. Virol. 85, 2964–2974 (2011).
    CAS  PubMed  PubMed Central  Google Scholar 

    14.
    Kramer, L. D. & Bernard, K. A. West Nile virus in the western hemisphere. Curr. Opin. Infect. Dis. 14, 519–525 (2001).
    CAS  PubMed  Google Scholar 

    15.
    Kilpatrick, A. M., Kramer, L. D., Jones, M. J., Marra, P. P. & Daszak, P. West Nile virus epidemics in North America are driven by shifts in mosquito feeding behavior. PLoS Biol. 4, 606–610 (2006).
    CAS  Google Scholar 

    16.
    Molaei, G., Andreadis, T. G., Armstrong, P. M., Anderson, J. F. & Vossbrinck, C. R. Host feeding patterns of Culex mosquitoes and West Nile virus transmission, northeastern United States. Emerg. Infect. Dis. 12, 468–474 (2006).
    PubMed Central  PubMed  Google Scholar 

    17.
    Colpitts, T. M., Conway, M. J., Montgomery, R. R. & Fikrig, E. West Nile virus: biology, transmission, and human infection. Clin. Microbiol. Rev. 25, 635–648 (2012).
    CAS  PubMed Central  PubMed  Google Scholar 

    18.
    Bowen, R. A. & Nemeth, N. M. Experimental infections with West Nile virus. Curr. Opin. Infect. Dis. 20, 293–297 (2007).
    PubMed  Google Scholar 

    19.
    Petersen, L. R. & Marfin, A. A. West Nile Virus: A primer for the clinician. Ann. Intern. Med. 137, 173–179 (2002).
    PubMed  Google Scholar 

    20.
    Petersen, L. R. & Fischer, M. Unpredictable and difficult to control—the adolescence of West Nile virus. N. Engl. J. Med. 367, 1281–1284 (2012).
    CAS  PubMed  Google Scholar 

    21.
    Lanciotti, R. S. et al. Origin of the West Nile virus responsible for an outbreak of encephalitis in the northeastern United States. Science 286, 2333–2337 (1999).
    CAS  PubMed  Google Scholar 

    22.
    Dohm, D. J., Sardelis, M. R. & Turell, M. J. Experimental vertical transmission of West Nile virus by Culex pipiens (Diptera: Culicidae). J. Med. Entomol. 39, 640–644 (2002).
    PubMed  Google Scholar 

    23.
    Goddard, L. B., Roth, A. E., Reisen, W. K. & Scott, T. W. Vertical transmission of West Nile virus by three California Culex (Diptera: Culicidae) species. J. Med. Entomol. 40, 743–746 (2003).
    PubMed  Google Scholar 

    24.
    Lequime, S. & Lambrechts, L. Vertical transmission of arboviruses in mosquitoes: A historical perspective. Infect. Genet. Evol. 28, 681–690 (2014).
    PubMed  Google Scholar 

    25.
    Ronca, S. E., Murray, K. O. & Nolan, M. S. Cumulative incidence of West Nile virus infection, continental United States, 1999–2016. Emerg. Infect. Dis. 25, 325–327 (2019).
    PubMed Central  PubMed  Google Scholar 

    26.
    George, T. L. et al. Persistent impacts of West Nile virus on North American bird populations. Proc. Natl Acad. Sci. USA 112, 14290–14294 (2015).
    ADS  CAS  PubMed  Google Scholar 

    27.
    Kilpatrick, A. M. & Wheeler, S. S. Impact of West Nile Virus on bird populations: limited lasting effects, evidence for recovery, and gaps in our understanding of impacts on ecosystems. J. Med. Entomol. 56, 1491–1497 (2019).
    PubMed Central  PubMed  Google Scholar 

    28.
    LaDeau, S. L., Kilpatrick, A. M. & Marra, P. P. West Nile virus emergence and large-scale declines of North American bird populations. Nature 447, 710–713 (2007).
    ADS  CAS  PubMed  Google Scholar 

    29.
    Davis, C. T. et al. Phylogenetic analysis of North American West Nile virus isolates, 2001–2004: evidence for the emergence of a dominant genotype. Virology 342, 252–265 (2005).
    CAS  PubMed  Google Scholar 

    30.
    Añez, G. et al. Evolutionary dynamics of West Nile virus in the United States, 1999–2011: Phylogeny, selection pressure and evolutionary time-scale analysis. PLoS Negl. Trop. Dis. 7, e2245 (2013).
    PubMed Central  PubMed  Google Scholar 

    31.
    Di Giallonardo, F. et al. Fluid spatial dynamics of West Nile Virus in the United States: Rapid spread in a permissive host environment. J. Virol. 90, 862–872 (2016).
    PubMed Central  Google Scholar 

    32.
    Hadfield, J. et al. Twenty years of West Nile virus spread and evolution in the Americas visualized by Nextstrain. PLOS Pathog. 15, e1008042 (2019).
    CAS  PubMed Central  PubMed  Google Scholar 

    33.
    Dellicour, S. et al. Using viral gene sequences to compare and explain the heterogeneous spatial dynamics of virus epidemics. Mol. Biol. Evol. 34, 2563–2571 (2017).
    CAS  PubMed  Google Scholar 

    34.
    Dijkstra, E. W. A note on two problems in connexion with graphs. Numer. Math. 1, 269–271 (1959).
    MathSciNet  MATH  Google Scholar 

    35.
    McRae, B. H. Isolation by resistance. Evolution 60, 1551–1561 (2006).
    PubMed  Google Scholar 

    36.
    La Sorte, F. A. et al. The role of atmospheric conditions in the seasonal dynamics of North American migration flyways. J. Biogeogr. 41, 1685–1696 (2014).
    Google Scholar 

    37.
    Holmes, E. C. & Grenfell, B. T. Discovering the phylodynamics of RNA viruses. PLoS Comput. Biol. 5, e1000505 (2009).
    ADS  PubMed Central  PubMed  Google Scholar 

    38.
    Faria, N. R. et al. Genomic and epidemiological monitoring of yellow fever virus transmission potential. Science 361, 894–899 (2018).
    ADS  CAS  PubMed Central  PubMed  Google Scholar 

    39.
    Carrington, C. V. F., Foster, J. E., Pybus, O. G., Bennett, S. N. & Holmes, E. C. Invasion and maintenance of dengue virus type 2 and Type 4 in the Americas. J. Virol. 79, 14680–14687 (2005).
    CAS  PubMed Central  PubMed  Google Scholar 

    40.
    Rappole, J. H. et al. Modeling movement of West Nile virus in the western hemisphere. Vector Borne Zoonotic Dis. 6, 128–139 (2006).
    PubMed  Google Scholar 

    41.
    Goldberg, T. L., Anderson, T. K. & Hamer, G. L. West Nile virus may have hitched a ride across the Western United States on Culex tarsalis mosquitoes. Mol. Ecol. 19, 1518–1519 (2010).
    PubMed  Google Scholar 

    42.
    Swetnam, D. et al. Terrestrial bird migration and West Nile virus circulation, United States. Emerg. Infect. Dis. 24, 12 (2018).

    43.
    Kwan, J. L., Kluh, S. & Reisen, W. K. Antecedent avian immunity limits tangential transmission of West Nile virus to humans. PLoS ONE 7, e34127 (2012).
    ADS  CAS  PubMed Central  PubMed  Google Scholar 

    44.
    Duggal, N. K. et al. Genotype-specific variation in West Nile virus dispersal in California. Virology 485, 79–85 (2015).
    CAS  PubMed Central  PubMed  Google Scholar 

    45.
    McMullen, A. R. et al. Evolution of new genotype of West Nile virus in North America. Emerg. Infect. Dis. 17, 785–793 (2011).
    PubMed Central  PubMed  Google Scholar 

    46.
    Hepp, C. M. et al. Phylogenetic analysis of West Nile Virus in Maricopa County, Arizona: evidence for dynamic behavior of strains in two major lineages in the American Southwest. PLOS ONE 13, e0205801 (2018).
    PubMed Central  PubMed  Google Scholar 

    47.
    Goddard, L. B., Roth, A. E., Reisen, W. K. & Scott, T. W. Vector competence of California mosquitoes for West Nile virus. Emerg. Infect. Dis. 8, 1385–1391 (2002).
    PubMed Central  PubMed  Google Scholar 

    48.
    Richards, S. L., Mores, C. N., Lord, C. C. & Tabachnick, W. J. Impact of extrinsic incubation temperature and virus exposure on vector competence of Culex pipiens quinquefasciatus say (Diptera: Culicidae) for West Nile virus. Vector Borne Zoonotic Dis. 7, 629–636 (2007).
    PubMed Central  PubMed  Google Scholar 

    49.
    Anderson, S. L., Richards, S. L., Tabachnick, W. J. & Smartt, C. T. Effects of West Nile virus dose and extrinsic incubation temperature on temporal progression of vector competence in Culex pipiens quinquefasciatus. J. Am. Mosq. Control Assoc. 26, 103–107 (2010).
    PubMed Central  PubMed  Google Scholar 

    50.
    Worwa, G. et al. Increases in the competitive fitness of West Nile virus isolates after introduction into California. Virology 514, 170–181 (2018).
    CAS  PubMed  Google Scholar 

    51.
    Duggal, N. K., Langwig, K. E., Ebel, G. D. & Brault, A. C. On the fly: interactions between birds, mosquitoes, and environment that have molded west nile virus genomic structure over two decades. J. Med. Entomol. 56, 1467–1474 (2019).
    PubMed Central  PubMed  Google Scholar 

    52.
    Reed, K. D., Meece, J. K., Henkel, J. S. & Shukla, S. K. Birds, migration and emerging zoonoses: West Nile virus, Lyme disease, influenza A and enteropathogens. Clin. Med. Res. 1, 5–12 (2003).
    PubMed Central  PubMed  Google Scholar 

    53.
    Dusek, R. J. et al. Prevalence of West Nile virus in migratory birds during spring and fall migration. Am. J. Trop. Med. Hyg. 81, 1151–1158 (2009).
    Google Scholar 

    54.
    Samuel, G. H., Adelman, Z. N. & Myles, K. M. Temperature-dependent effects on the replication and transmission of arthropod-borne viruses in their insect hosts. Curr. Opin. Insect Sci. 16, 108–113 (2016).
    PubMed Central  PubMed  Google Scholar 

    55.
    Paz, S. & Semenza, J. C. Environmental drivers of West Nile fever epidemiology in Europe and Western Asia-a review. Int. J. Environ. Res. Public Health 10, 3543–3562 (2013).
    PubMed Central  PubMed  Google Scholar 

    56.
    Dohm, D. J., O’Guinn, M. L. & Turell, M. J. Effect of environmental temperature on the ability of Culex pipiens (Diptera: Culicidae) to transmit West Nile virus. J. Med. Entomol. 39, 221–225 (2002).
    PubMed  PubMed Central  Google Scholar 

    57.
    Kilpatrick, A. M., Meola, M. A., Moudy, R. M. & Kramer, L. D. Temperature, viral genetics, and the transmission of West Nile virus by Culex pipiens mosquitoes. PLoS Path. 4, e1000092 (2008).

    58.
    DeFelice, N. B. et al. Use of temperature to improve West Nile virus forecasts. PLoS Comput. Biol. 14, e1006047 (2018).
    PubMed Central  PubMed  Google Scholar 

    59.
    Morin, C. W. & Comrie, A. C. Regional and seasonal response of a West Nile virus vector to climate change. Proc. Natl Acad. Sci. USA 110, 15620–15625 (2013).
    ADS  CAS  PubMed  Google Scholar 

    60.
    Samy, A. M. et al. Climate change influences on the global potential distribution of the mosquito Culex quinquefasciatus, vector of West Nile virus and lymphatic filariasis. PLoS ONE 11, e0163863 (2016).
    PubMed Central  PubMed  Google Scholar 

    61.
    Dellicour, S. et al. Phylodynamic assessment of intervention strategies for the West African Ebola virus outbreak. Nat. Commun. 9, 2222 (2018).
    ADS  PubMed Central  PubMed  Google Scholar 

    62.
    Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).
    CAS  PubMed Central  PubMed  Google Scholar 

    63.
    Larsson, A. AliView: a fast and lightweight alignment viewer and editor for large datasets. Bioinformatics 30, 3276–3278 (2014).
    CAS  PubMed Central  PubMed  Google Scholar 

    64.
    Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2–approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).
    ADS  PubMed Central  PubMed  Google Scholar 

    65.
    Suchard, M. A. et al. Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10. Virus Evol. 4, vey016 (2018).
    PubMed Central  PubMed  Google Scholar 

    66.
    Ayres, D. L. et al. BEAGLE 3: Improved performance, scaling, and usability for a high-performance computing library for statistical phylogenetics. Syst. Biol., https://doi.org/10.1093/sysbio/syz020 (2019).

    67.
    Tavaré, S. Some probabilistic and statistical problems in the analysis of DNA sequences. Lectures Math. Life Sci. 17, 57–86 (1986).
    MathSciNet  MATH  Google Scholar 

    68.
    Drummond, A. J., Ho, S. Y. W., Phillips, M. J. & Rambaut, A. Relaxed phylogenetics and dating with confidence. PLoS Biol. 4, 699–710 (2006).
    CAS  Google Scholar 

    69.
    Rambaut, A., Drummond, A. J., Xie, D., Baele, G. & Suchard, M. A. Posterior summarization in Bayesian phylogenetics using Tracer 1.7. Syst. Biol. 67, 901–904 (2018).
    CAS  PubMed Central  PubMed  Google Scholar 

    70.
    Fisher, A. A., Ji, X., Zhang, Z., Lemey, P. & Suchard, M. A. Relaxed random walks at scale. Syst. Biol., https://doi.org/10.1093/sysbio/syaa056 (2020).

    71.
    Lemey, P. et al. Unifying viral genetics and human transportation data to predict the global transmission dynamics of human influenza H3N2. PLoS Path. 10, e1003932 (2014).
    Google Scholar 

    72.
    Bedford, T. et al. Global circulation patterns of seasonal influenza viruses vary with antigenic drift. Nature 523, 217 (2015).
    ADS  CAS  PubMed Central  PubMed  Google Scholar 

    73.
    Hadfield, J. et al. Nextstrain: real-time tracking of pathogen evolution. Bioinformatics 34, 4121–4123 (2018).
    CAS  PubMed Central  PubMed  Google Scholar 

    74.
    Dellicour, S., Rose, R., Faria, N. R., Lemey, P. & Pybus, O. G. SERAPHIM: studying environmental rasters and phylogenetically informed movements. Bioinformatics 32, 3204–3206 (2016).
    CAS  PubMed Central  Google Scholar 

    75.
    Dellicour, S. et al. Using phylogeographic approaches to analyse the dispersal history, velocity, and direction of viral lineages–application to rabies virus spread in Iran. Mol. Ecol. 28, 4335–4350 (2019).
    PubMed Central  Google Scholar 

    76.
    Suchard, M. A., Weiss, R. E. & Sinsheimer, J. S. Models for estimating Bayes factors with applications to phylogeny and tests of monophyly. Biometrics 61, 665–673 (2005).
    MathSciNet  MATH  PubMed Central  PubMed  Google Scholar  More

  • in

    Genetic diversity and population structure in Nothofagus pumilio, a foundation species of Patagonian forests: defining priority conservation areas and management

    1.
    Silander, J. A. Temperate Forests. In: Encyclopedia of Biodiversity (Second Edition) (ed. Simon A Levin), 112–227 (Academic Press, 2001).
    2.
    Glasser, N. F., Harrison, S., Winchester, V. & Aniya, M. Late pleistocene and holocene palaeoclimate and glacier fluctuations in patagonia. Glob. Planet. Change 43, 79–101 (2004).
    ADS  Article  Google Scholar 

    3.
    Markgraf, V. Paleoenvironments and paleoclimates in Tierra del Fuego and southernmost Patagonia, South America. Palaeogeogr. Palaeoclimatol. Palaeoecol. 102, 53–67 (1993).
    Article  Google Scholar 

    4.
    Markgraf, V., McGlone, M. & Hope, G. Neogene paleoenvironmental and paleoclimatic change in southern temperate ecosystems—a southern perspective. Trends Ecol. Evol. 10(4), 143–147 (1995).
    CAS  Article  Google Scholar 

    5.
    Amoroso, M. M., Rodríguez-Catón, M., Villalba, R. & Daniels, L. D. Forest Decline in Northern Patagonia: The Role of Climatic Variability. In: Dendroecology, Ecological Studies (Analysis and Synthesis): volume 231 (ed. Amoroso, M. M.; Daniels, L. D.; Baker, P. J.; Camarero, J. J.), 325–342 (Springer, 2007).

    6.
    Rodríguez-Catón, M., Villalba, R., Morales, M. & Srur, A. Influence of droughts on Nothofagus pumilio forest decline across northern Patagonia, Argentina. Ecosphere 7(7), e01390. https://doi.org/10.1002/ecs2.1390 (2016).
    Article  Google Scholar 

    7.
    Barros, V. R. et al. Climate change in Argentina: trends, projections, impacts and adaptation. Wiley Interdiscip. Rev. Clim. Change 6, 151–169 (2005).
    Article  Google Scholar 

    8.
    Rusticucci, M. & Barrucand, M. Observed trends and changes in temperature extremes over Argentina. J. Clim. 17, 4099–4107 (2004).
    ADS  Article  Google Scholar 

    9.
    Mundo, I. A. et al. Fire history in southern Patagonia: human and climate influences on fire activity in Nothofagus pumilio forests. Ecosphere 8(9), e01932. https://doi.org/10.1002/ecs2.1932 (2017).
    Article  Google Scholar 

    10.
    Mohr-Bell, F. D. Superficies afectadas por incendios en la región bosque Andino Patagónico (BAP) durante los veranos de 2013–2014 y 2014–2015. Patagon. For. 21, 34–41 (2015).
    Google Scholar 

    11.
    Veblen, T. T., Hill, R. S. & Read, J. Ecology of Southern Chilean and Argentinean Nothofagus Forests. In: The Ecology and Biogeography of Nothofagus Forests, pp. 293–353 (Yale University, USA, 1996).

    12.
    Donoso Zegers, C. Las Especies Arbóreas de los Bosques Templados de Chile y Argentina: Autoecología. 678p (María Cuneo Ediciones, 2006)

    13.
    Soliani, C. & Aparicio, A. G. Evidence of genetic determination in the growth habit of Nothofagus pumilio (Poepp. & Endl.) Krasser at the extremes of an elevation gradient. Scand. J. For. Res. 35 (5–6), 211–220 (2020).

    14.
    Rusch, V. E. Altitudinal variation in the phenology of Nothofagus pumilio in Argentina. Rev. Chil. Hist. Nat. 66, 131–141 (1993).
    Google Scholar 

    15.
    Fajardo, A. & Piper, F. I. Intraspecific trait variation and covariation in a widespread tree species (Nothofagus pumilio) in southern Chile. N. Phytol. 189, 259–271 (2011).
    Article  Google Scholar 

    16.
    Burns, S. L., Cellini, J. M., Lencinas, M. V., Martínez Pastur, G. J. & Rivera, S. M. Description of possible natural hybrids between Nothofagus pumilio and N. antarctica at South Patagonia (Argentina). Bosque 31(1), 9–16 (2010).
    Article  Google Scholar 

    17.
    Quiroga, P., Vidal Russel, R. & Premoli, A. C. Evidencia morfológica e isoenzimática de hibridación natural entre Nothofagus antarctica y N. pumilio en el noroeste patagónico. Bosque 26(2), 25–32 (2005).
    Article  Google Scholar 

    18.
    Acosta, M. C. & Premoli, A. C. Evidence of chloroplast capture in South American Nothofagus (subgenus Nothofagus, Nothofagaceae). Mol. Phylogenet. Evol. 54, 235–242 (2010).
    Article  CAS  Google Scholar 

    19.
    Soliani, C. et al. Halfway encounters: meeting points of colonization routes among the southern beeches Nothofagus pumilio and N. antarctica. Mol. Phylogenet. Evol. 85, 197–207 (2015).
    Article  Google Scholar 

    20.
    Pastorino, M. J. & Gallo, L. A. Preliminary operational genetic management units of a highly fragmented forest tree species of southern South America. For. Ecol. Manag. 257, 2350–2358 (2009).
    Article  Google Scholar 

    21.
    Pastorino, M. J., Aparicio, A. & Azpilicueta, M. M. Regiones de Procedencia del Ciprés de la Cordillera y Bases Conceptuales para el Manejo de sus Recursos Genéticos en Argentina.108 p (Ediciones INTA, 2015).

    22.
    Azpilicueta, M. M. et al. Management of Nothofagus genetic resources: definition of genetic zones based on a combination of nuclear and chloroplast marker data. For. Ecol. Manag. 302, 414–424 (2013).
    Article  Google Scholar 

    23.
    Azpilicueta et al. Zonas Genéticas de Raulí y Roble Pellín en Argentina: Herramientas para la Conservación y el Manejo de la Diversidad Genética (ed. M.M. Azpilicueta, P. Marchelli) 50 p (Ediciones INTA, 2016).

    24.
    OTBN. Ordenamiento Territorial de Bosque Nativo/Mapa Legal CREA. https://www.crea.org.ar/mapalegal/otbn

    25.
    Bucci, G. & Vendramin, G. G. Delineation of genetic zones in the European Norway spruce natural range: preliminary evidence. Mol. Ecol. 9, 923–934 (2000).
    CAS  Article  Google Scholar 

    26.
    McKay, J. K., Christian, C. E., Harrison, S. & Rice, K. J. “How Local Is Local?”—a review of practical and conceptual issues in the genetics of restoration. Restor. Ecol. 13, 432–440 (2005).
    Article  Google Scholar 

    27.
    Williams, M. I. & Dumroese, R. K. Preparing for climate change: forestry and assisted migration. J. For. 111(4), 287–297 (2013).
    Google Scholar 

    28.
    Geburek, T. Isozymes and DNA markers in gene conservation of forest trees. Biodivers. Conserv. 6, 1639–1654 (1997).
    Article  Google Scholar 

    29.
    Ballesteros-Mejia, L., Lima, J. S. & Collevatti, R. G. Spatially-explicit analyses reveal the distribution of genetic diversity and plant conservation status in Cerrado biome. Biodivers. Conserv. 29, 1537–1554 (2018).
    Article  Google Scholar 

    30.
    Frankel, O. H., Brown, A. H. D. & Bordon, J. The Genetic Diversity of Wild Plants. In: The Conservation of Plant Biodiversity. (Cambridge University Press, Cambridge, 1995).

    31.
    Petit, R. J., El Mousadik, A. & Pons, O. Identifying populations for conservation on the basis of genetic markers. Conserv. Biol. 12, 844–855 (1998).
    Article  Google Scholar 

    32.
    van Zonneveld, M. et al. Mapping genetic diversity of cherimoya (Annona cherimola Mill.): application of spatial analysis for conservation and use of plant genetic resources. PLoS ONE 7, e29845. https://doi.org/10.1371/journal.pone.0029845 (2010).
    CAS  Article  Google Scholar 

    33.
    Soliani, C., Gallo, L. & Marchelli, P. Phylogeography of two hybridizing southern beeches (Nothofagus spp.) with different adaptive abilities. Tree Genet. Genomes 8, 659–673 (2012).
    Article  Google Scholar 

    34.
    Laikre, L. et al. Neglect of genetic diversity in implementation of the convention on biological diversity. Conserv. Biol. 24(1), 86–88 (2009).
    Article  Google Scholar 

    35.
    Fady, B. et al. Forests and global change: what can genetics contribute to the major forest management and policy challenges of the twenty-first century?. Reg. Environ. Change 16, 927–939 (2016).
    Article  Google Scholar 

    36.
    Graudal, L. et al. Global to local genetic diversity indicators of evolutionary potential in tree species within and outside forests. For. Ecol. Manag. 333, 35–51 (2014).
    Article  Google Scholar 

    37.
    Sgrò, C. M., Lowe, A. J. & Hoffmann, A. A. Building evolutionary resilience for conserving biodiversity under climate change. Evol. Appl. 4, 326–337 (2011).
    Article  Google Scholar 

    38.
    Perez, et al. Assessing population structure in the face of isolation by distance: Are we neglecting the problem?. Divers. Distrib. 24(12), 1883–1889 (2018).
    Article  Google Scholar 

    39.
    Mathiasen, P. & Premoli, A. C. Out in the cold: genetic variation of Nothofagus pumilio (Nothofagaceae) provides evidence for latitudinally distinct evolutionary histories in austral South America. Mol. Ecol. 19, 371–385 (2010).
    CAS  Article  Google Scholar 

    40.
    Jump, A. & Peñuelas, J. Extensive spatial genetic structure revealed by AFLP but not SSR molecular markers in the wind-pollinated tree, Fagus sylvatica. Mol. Ecol. 16, 925–936 (2007).
    CAS  Article  Google Scholar 

    41.
    Oddou-Muratorio, S. et al. Comparison of direct and indirect genetic methods for estimating seed and pollen dispersal in Fagus sylvatica and Fagus crenata. For. Ecol. Manag. 259, 2151–2159 (2010).
    Article  Google Scholar 

    42.
    Marchelli, P. & Gallo, L. Multiple ice-age refugia in a southern beech of South America as evidenced by chloroplast DNA markers. Conserv. Genet. 7, 591–603 (2006).
    Article  CAS  Google Scholar 

    43.
    Pastorino, M. J. & Gallo, L. A. Quaternary evolutionary history of Austrocedrus chilensis, a cypress native to the Andean-Patagonian forest. J. Biogeogr. 29, 1167–1178 (2002).
    Article  Google Scholar 

    44.
    Villagrán, C. Un modelo de la historia de la vegetación de la cordillera de la costa de Chile central-sur: la hipótesis glacial de Darwin. Rev. Chil. Hist. Nat. 74, 793–803 (2001).
    Article  Google Scholar 

    45.
    Cosacov, A., Sersic, A., Sosa, V., Johnson, L. & Cocucci, A. Multiple periglacial refugia in the Patagonian steppe and post-glacial colonization of the Andes: the phylogeography of Calceolaria polyrhiza. J. Biogeogr. 37, 1463–1477 (2010).
    Google Scholar 

    46.
    Breitman, M. F., Avila, L. J., Sites, J. W. & Morando, M. Lizards from the end of the world: phylogenetic relationships of the Liolaemus lineomaculatus section (Squamata: Iguania: Liolaemini). Mol. Phylogenet. Evol. 59, 364–376 (2011).
    Article  Google Scholar 

    47.
    Flint, R. F. & Fidalgo, F. Glacial drift in the eastern argentine Andes between latitude 41° 10’ S. and latitude 43° 10’ S. GSA Bull. 80, 1043–1052 (1969).
    Article  Google Scholar 

    48.
    Holderegger, R. & Thiel-Egenter, C. A discussion of different types of glacial refugia used in mountain biogeography and phylogeography. J. Biogeogr. 36, 476–480 (2009).
    Article  Google Scholar 

    49.
    Glasser, N. F., Jansson, K., Harrison, S. & Kleman, J. The glacial geomorphology and Pleistocene history of South America between 38°S and 56°S. Quaternary Sci. Rev. 27(3), 365–390 (2008).
    ADS  Article  Google Scholar 

    50.
    Premoli, A. C., Mathiasen, P. & Kitzberger, T. Southern-most Nothofagus trees enduring ice ages: genetic evidence and ecological niche retrodiction reveal high latitude (54°S) glacial refugia. Palaeogeogr. Palaeoclimatol. Palaeoecol. 298, 247–256 (2010).
    Article  Google Scholar 

    51.
    Derory, J. et al. What can nuclear microsatellites tell us about maritime pine genetic resources conservation and provenance certification strategies?. Ann. For. Sci. 59, 699–708 (2002).
    Article  Google Scholar 

    52.
    Honjo, M. et al. Management units of the endangered herb Primula sieboldii based on microsatellite variation among and within populations throughout Japan. Conserv. Genet. 10, 257–267 (2009).
    CAS  Article  Google Scholar 

    53.
    Väli, U., Einarsson, A., Waits, L. & Ellegren, H. To what extent do microsatellite markers reflect genome-wide genetic diversity in natural populations?. Mol Ecol. 17(17), 3808–3817 (2018).
    Article  Google Scholar 

    54.
    Reed, D. H. & Frankham, R. How closely correlated are molecular and quantitative measures of genetic variation? A meta-analysis. Evol. 55(6), 1095–1103 (2001).
    CAS  Article  Google Scholar 

    55.
    Crandall, K. A., Bininda-Emonds, O. R. P., Mace, G. M. & Wayne, R. K. Considering evolutionary processes in conservation biology. Trends Ecol. Evol. 15, 290–295 (2000).
    CAS  Article  Google Scholar 

    56.
    Widmer, A. & Lexer, C. Glacial refugia: sanctuaries for allelic richness, but not for gene diversity. Trends Ecol. Evol. 16, 267–269 (2001).
    CAS  Article  Google Scholar 

    57.
    Reed, D. H. & Frankham, R. Correlation between fitness and genetic diversity. Conserv. Biol. 17, 230–237 (2003).
    Article  Google Scholar 

    58.
    Jump, A. S., Marchant, R. & Peñuelas, J. Environmental change and the option value of genetic diversity. Trends Plant Sci. 14, 51–58 (2009).
    CAS  Article  Google Scholar 

    59.
    Prober, S. et al. Climate-adjusted provenancing: a strategy for climate-resilient ecological restoration. Front. Ecol. Evol. 3, 65 (2015).
    Article  Google Scholar 

    60.
    Thomas, E. et al. Genetic considerations in ecosystem restoration using native tree species. For. Ecol. Manag. 333, 66–75 (2014).
    Article  Google Scholar 

    61.
    Marchelli, P., Thomas, E., Azpilicueta, M. M., van Zonneveld, M. & Gallo, L. Integrating genetics and suitability modelling to bolster climate change adaptation planning in Patagonian Nothofagus forests. Tree Genet. Genomes 13, 119 (2017).
    Article  Google Scholar 

    62.
    Thomas, E. et al. Genetic diversity of Enterolobium cyclocarpum in Colombian seasonally dry tropical forest: implications for conservation and restoration. Biodivers. Conserv. 26(4), 825–842 (2016).
    Article  Google Scholar 

    63.
    Jump, A. S. & Peñuelas, J. Running to stand still: adaptation and the response of plants to rapid climate change. Ecol. Lett. 8, 1010–1020 (2005).
    Article  Google Scholar 

    64.
    Dumolin, S., Demesure, B. & Petit, R. J. Inheritance of chloroplast and mitochondrial genomes in pedunculated oak investigated with an efficient PCR method. Theor. Appl. Genet. 91, 1253–1256 (1995).
    CAS  Article  Google Scholar 

    65.
    Soliani, C., Sebastiani, F., Marchelli, P., Gallo, L. & Giovanni, G. Development of novel genomic microsatellite markers in the southern beech Nothofagus pumilio (Poepp. et Endl.) Krasser. Mol. Ecol. Resour. 10, 404–408 (2010).
    Article  Google Scholar 

    66.
    Schuelke, M. An economic method for the fluorescent labeling of PCR fragments. Nat. Biotechnol. 18, 233–234 (2000).
    CAS  Article  Google Scholar 

    67.
    Peakall, R. & Smouse, P. E. GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research—an update. J. Bioinform. 28, 2537–2539 (2012).
    CAS  Article  Google Scholar 

    68.
    Nei, M. Estimation of average heterozygosity and genetic distance from a small number of individuals. Genetics 89, 583–590 (1978).
    CAS  PubMed  PubMed Central  Google Scholar 

    69.
    Balzarini, M. & Di Rienzo, J. Info-Gen: Software para Análisis Estadístico de Datos Genéticos. Facultad de Ciencia Agropecuarias. Universidad Nacional de Córdoba. Argentina. https://www.info-gen.com.ar/. (2003).

    70.
    Oosterhout, C. V., Hutchinson, W. F., Wills, D. P. M. & Shipley, P. MICRO-CHECKER: software for identifying and correcting genotyping errors in microsatellite data. Mol. Ecol. Notes 4, 535–538 (2004).
    Article  CAS  Google Scholar 

    71.
    Chapuis, M. P. & Estoup, A. Microsatellite null alleles and estimation of population differentiation. Mol. Biol. Evol. 24, 621–631 (2006).
    Article  CAS  Google Scholar 

    72.
    Chybicki, I. J. & Burczyk, J. Simultaneous estimation of null alleles and inbreeding coefficients. J. Heredity 100, 106–113 (2009).
    CAS  Article  Google Scholar 

    73.
    Cornuet, J. M. & Luikart, G. Description and power analysis of two tests for detecting recent population bottlenecks from allele frequency data. Genetics 144, 2001–2014 (1996).
    CAS  PubMed  PubMed Central  Google Scholar 

    74.
    Wright, S. The genetical structure of populations. Ann. Eugen. 15, 323–354 (1949).
    MathSciNet  Article  Google Scholar 

    75.
    Hedrick, P. W. A standardized genetic differentiation measure. Evolution 59, 1633–1638 (2005).
    CAS  Article  Google Scholar 

    76.
    Smouse, P. E. & Peakall, R. Spatial autocorrelation analysis of individual multiallele and multilocus genetic structure. J. Heredity 82, 561–573 (1999).
    Article  Google Scholar 

    77.
    Corander, J., Waldmann, P. & Sillanpää, M. J. Bayesian analysis of genetic differentiation between populations. Genetics 163, 367–374 (2003).
    CAS  PubMed  PubMed Central  Google Scholar 

    78.
    Pastorino, M. J., Marchelli, P., Milleron, M., Soliani, C. & Gallo, L. A. The effect of different glaciation patterns over the current genetic structure of the southern beech Nothofagus antarctica. Genetica 136, 79–88 (2009).
    CAS  Article  Google Scholar 

    79.
    Pritchard, J., Stephens, M. & Donnelly, P. Inference of Population Structure Using Multilocus Genotype Data. https://www.genetics.org/content/155/2/945.long. (2000).

    80.
    Thomas, E. et al. Present spatial diversity patterns of Theobroma cacao L. in the neotropics reflect genetic differentiation in pleistocene refugia followed by human-influenced dispersal. PLoS ONE 7, e47676. https://doi.org/10.1371/journal.pone.0047676 (2012).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    81.
    Excoffier, L. & Lischer, H. E. L. Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resour. 10, 564–567 (2010).
    Article  Google Scholar 

    82.
    El Mousadik, A. & Petit, R. J. High level of genetic differentiation for allelic richness among populations of the argan tree [Argania spinosa (L.) Skeels] endemic to Morocco. Theor. Appl. Genet. 92, 832–839 (1996).
    Article  Google Scholar 

    83.
    Goudet, J. FSTAT: a Program to Estimate and Test Gene Diversities and Fixation Indices (version 2.9.3.2) https://www.unil.ch/izea/softwares/fstat.html. (2001). More

  • in

    Plant functional traits are correlated with species persistence in the herb layer of old-growth beech forests

    1.
    Watt, A. S. Pattern and process in plant community. J. Ecol. 35, 1–22 (1947).
    Article  Google Scholar 
    2.
    Ozinga, W. et al. Local above-ground persistence of vascular plants: Life-history trade-offs and environmental constraints. J. Veg. Sci. 18, 489–497 (2007).
    Article  Google Scholar 

    3.
    Økland, R. H. & Eilertsen, O. Dynamics of understory vegetation in an old-growth boreal coniferous forest, 1988–1993. J. Veg. Sci. 7, 747–762 (1996).
    Article  Google Scholar 

    4.
    Nygaard, P. H. & Ødegaard, T. Sixty years of vegetation dynamics in a south boreal coniferous forest in southern Norway. J. Veg. Sci. 10, 5–16 (1999).
    Article  Google Scholar 

    5.
    Palmer, M. W. & Rusch, G. M. How fast is the carousel? Direct indices of species mobility with examples from an Oklahoma grassland. J. Veg. Sci. 12, 305–318 (2001).
    Article  Google Scholar 

    6.
    Zobel, M., Moora, M. & Herben, T. Clonal mobility and its implications for spatio-temporal patterns of plant communities: What do we need to know next?. Oikos 119, 802–806 (2010).
    Article  Google Scholar 

    7.
    Chaideftou, E., Kallimanis, A. S., Bergmeier, E. & Dimopoulos, P. How does plant species composition change from year to year? A case study from the herbaceous layer of a submediterranean oak woodland. Comm. Ecol. 13, 88–96 (2012).
    Article  Google Scholar 

    8.
    Chapman, J. I. & McEwan, R. W. Spatiotemporal dynamics of α-and β-diversity across topographic gradients in the herbaceous layer of an old-growth deciduous forest. Oikos 122, 1679–1686 (2013).
    Article  Google Scholar 

    9.
    Graae, B. J. & Sunde, P. B. The impact of forest continuity and management on forest floor vegetation evaluated by species traits. Ecography 23, 720–730 (2000).
    Article  Google Scholar 

    10.
    Bakker, J. P., Olff, H., Willems, J. H. & Zobel, M. Why do we need permanent plots in the study of long-term vegetation dynamics?. J. Veg. Sci. 7, 147–156 (1996).
    Article  Google Scholar 

    11.
    Van der Maarel, E. Pattern and process in the plant community: fifty years after A.S. Watt. J. Veg. Sci. 7, 19–28 (1996).
    Article  Google Scholar 

    12.
    Herben, T., Krahulec, F., Hadincová, V. & Skálová, H. Small-scale variability as a mechanism for large-scale stability in mountain grasslands. J. Veg. Sci. 4, 163–170 (1993).
    Article  Google Scholar 

    13.
    Økland, R. H. Persistence of vascular plants in a Norwegian boreal coniferous forest. Ecography 18, 3–14 (1995).
    Article  Google Scholar 

    14.
    Campetella, G. et al. Patterns of plant trait-environment relationship along a forest succession chronosequence. Agric. Ecosyst. Environ. 145, 38–48 (2011).
    Article  Google Scholar 

    15.
    Canullo, R. et al. Patterns of clonal growth modes along a chronosequence of post-coppice forest regeneration in beech forest of Central Italy. Fol. Geobot. 46, 271–288 (2011).
    Article  Google Scholar 

    16.
    Rūsiņa, S., Gavrilova, I., Roze, I. & Šulcs, V. Temporal species turnover and plant community changes across different habitats in the lake Engure nature park Latvia. Proc. Latv. Acad. Sci. Sect. B. Nat. Exact Appl. Sci. 68, 68–79 (2014).
    Google Scholar 

    17.
    Norden, B. & Appelqvist, T. Conceptual problems of ecological continuity and its bioindicators. Biodivers. Conserv. 10, 779–791 (2001).
    Article  Google Scholar 

    18.
    Bartha, S., Canullo, R., Chelli, S. & Campetella, G. Unimodal relationships of understory alpha and beta diversity along chronosequence in coppiced and unmanaged beech forests. Diversity 12, 101 (2020).
    Article  Google Scholar 

    19.
    Gilliam, F. S. The ecological significance of the herbaceous layer in temperate forest ecosystems. Bioscience 57, 845–857 (2007).
    Article  Google Scholar 

    20.
    Campetella, G. et al. Scale dependent effects of coppicing on the species pool of late successional beech forest in the Central Apennines (Italy). Appl. Veg. Sci. 19, 474–485 (2016).
    Article  Google Scholar 

    21.
    Lavorel, S. & Garnier, E. Predicting changes in community composition and ecosystem functioning from plant traits: Revisiting the Holy Grail. Funct. Ecol. 16, 545–556 (2002).
    Article  Google Scholar 

    22.
    Weiher, E. et al. Challenging theophrastus: A common core list of plant traits for functional ecology. J. Veg. Sci. 10, 609–620 (1999).
    Article  Google Scholar 

    23.
    Westoby, M. A Leaf-height-seed (LHS) plant ecology strategy scheme. Plant Soil 199, 213–227 (1998).
    CAS  Article  Google Scholar 

    24.
    Wright, I. J. et al. The worldwide leaf economic spectrum. Nature 428, 821–827 (2004).
    ADS  CAS  Article  Google Scholar 

    25.
    Klimešová, J., Martínková, J. & Ottaviani, G. Belowground plant functional ecology: Towards an integrated perspective. Funct. Ecol. 32, 2115–2126 (2018).
    Article  Google Scholar 

    26.
    de Bello, F. et al. On the need for phylogenetic ‘corrections’ in functional trait-based approaches. Fol. Geobot. 50, 349–357 (2015).
    Article  Google Scholar 

    27.
    Aubin, I., Messier, C. & Bouchard, A. Can plantations develop understory biological and physical attributes of naturally regenerated forests?. Biol. Conserv. 141, 2462–2476 (2008).
    Article  Google Scholar 

    28.
    Dahlgren, J. P., Eriksson, O., Bolmgren, K., Strindell, M. & Ehrlén, J. Specific leaf area as a superior predictor of changes in field layer abundance during forest succession. J. Veg. Sci. 17, 577–582 (2006).
    Article  Google Scholar 

    29.
    Wellstein, C. et al. Effects of extreme drought on specific leaf area of grassland species: A meta-analysis of experimental studies in temperate and sub-Mediterranean systems. Glob. Change Biol. 23, 2473–2481 (2017).
    ADS  Article  Google Scholar 

    30.
    Lindacher, R., Böcker, R., Bemmerlein-Lux, F. A., Kleemann, A. & Haas, S. PHANART Datenbank der Gefäßpflanzen Mitteleuropas, Erklärung der Kennzahlen, Aufbau und Inhalt. Veröff. Geobot. Inst. ETH, Stift. Rübel 125, 1–436 (1995).
    Google Scholar 

    31.
    Turner, I. M. Sclerophylly: Primarily protective?. Funct. Ecol. 8, 669–675 (1994).
    Article  Google Scholar 

    32.
    Van Groenendael, J. M., Klimeš, L., Klimešová, J. & Hendriks, R. J. J. Comparative ecology of clonal plants. Philos. Trans. Roy. Soc. B 351, 1331–1339 (1996).
    ADS  Article  Google Scholar 

    33.
    Sammul, M., Kull, K., Niitla, T. & Mols, T. A comparison of plant communities on the basis of their clonal growth patterns. Evol. Ecol. 18, 443–467 (2004).
    Article  Google Scholar 

    34.
    Canullo, R. et al. Unravelling mechanisms of short-term vegetation dynamics in complex coppice forest systems. Fol. Geobot. 52, 71–81 (2017).
    Article  Google Scholar 

    35.
    Kidson, R. & Westoby, M. Seed mass and seedling dimensions in relation to seedling establishment. Oecologia 125, 11–17 (2000).
    ADS  CAS  Article  Google Scholar 

    36.
    Moles, A. T. & Westoby, M. Seed size and plant strategy across the whole life cycle. Oikos 113, 91–105 (2006).
    Article  Google Scholar 

    37.
    Campetella, G., Canullo, R. & Allegrini, M. C. Status and changes of ground vegetation at the CONECOFOR plots, 1999–2005. Ann. Silvicult. Res. 34, 29–48 (2008).
    Google Scholar 

    38.
    Wright, I. J., Reich, P. B. & Westoby, M. Strategy shifts in leaf physiology, structure and nutrient content between species of high- and low-rainfall and high- and low-nutrient habitats. Funct. Ecol. 15, 423–434 (2001).
    Article  Google Scholar 

    39.
    Ackerly, D. D. Functional traits of chaparral shrubs in relation to seasonal water deficit and disturbance. Ecol. Monogr. 74, 25–44 (2004).
    Article  Google Scholar 

    40.
    Kopecký, M., Hédl, R. & Szabó, P. Non-random extinctions dominate plant community changes in abandoned coppices. J. Appl. Ecol. 50, 79–87 (2013).
    Article  Google Scholar 

    41.
    Naaf, T. & Wulf, M. Traits of winner and loser species indicate drivers of herb layer changes over two decades in forests of NW Germany. J. Veg. Sci. 22, 516–527 (2011).
    Article  Google Scholar 

    42.
    Ottaviani, G., Martínková, J., Herben, T., Pausas, J. G. & Klimešová, J. On plant modularity traits: Functions and challenges. Trends Plant Sci. 22, 648–651 (2017).
    CAS  Article  Google Scholar 

    43.
    Klimešová, J. & Klimeš, L. Bud banks and their role in vegetative regeneration—A literature review and proposal for simple classification and assessment. Perspect. Plant Ecol. Evol. Syst. 8, 115–129 (2007).
    Article  Google Scholar 

    44.
    Chelli, S. et al. Climate is the main driver of clonal and bud bank traits in Italian forest understories. Persp. Plant Ecol. Evol. Syst. 40, 125478 (2019).
    Article  Google Scholar 

    45.
    Grime, J. P. Benefits of plant diversity to ecosystems: Immediate, filter and founder effects. J. Ecol. 86, 902–910 (1998).
    Article  Google Scholar 

    46.
    Alpert, P. & Simms, E. L. The relative advantages of plasticity and fixity in different environments: When is it good for a plant to adjust?. Evol. Ecol. 16, 285–297 (2002).
    Article  Google Scholar 

    47.
    Denney, D. A., Jameel, M. I., Bemmels, J. B., Rochford, M. E. & Anderson, J. T. Small spaces, big impacts: Contributions of micro-environmental variation to population persistence under climate change. AoB Plants 12, 5 (2020).
    Article  Google Scholar 

    48.
    Swenson, N. G. et al. Temporal turnover in the composition of tropical tree communities: Functional determinism and phylogenetic stochasticity. Ecology 93, 490–499 (2012).
    Article  Google Scholar 

    49.
    Kichenin, E., Wardle, D. A., Peltzer, D. A., Morse, C. W. & Freschet, G. T. Contrasting effects of plant inter-and intraspecific variation on community-level trait measures along an environmental gradient. Funct. Ecol. 27, 1254–1261 (2013).
    Article  Google Scholar 

    50.
    Petriccione, B. & Pompei, E. The CONECOFOR programme: general presentation, aims and co-ordination. J. Limnol. 61, 3–11 (2002).
    Article  Google Scholar 

    51.
    Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).
    Article  Google Scholar 

    52.
    Bagnouls, F. & Gaussen, H. Les climats biologiques et leur classification. Ann. Geogr. 335, 193–220 (1957).
    Article  Google Scholar 

    53.
    FAO/UNESCO/WMO. World map of desertification. Food and Agricultural, Organization, Rome (1997).

    54.
    EUFORGEN. Distribution map of Beech (Fagus sylvatica), www.euforgen.org (2009).

    55.
    Dupouey, J. L. Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests Part VIII. Assessment of Ground Vegetation (ICP-Forests, Hamburg, 1998).
    Google Scholar 

    56.
    Canullo, R., Campetella, G., Allegrini, M. C. & Smargiassi, V. Management of forest vegetation data series: The role of database in the frame of quality assurance procedure. J. Limnol. 61, 100–105 (2002).
    Article  Google Scholar 

    57.
    Klimeš, L., Klimešová, J., Hendriks, R. & van Groenendael, J. Clonal plant architectures: a comparative analysis of form and function. In The Ecology and Evolution of Clonal Plants (eds de Kroon, H. & van Groenendael, J.) 1–29 (Backhuys Publishers, Leiden, 1997).
    Google Scholar 

    58.
    Cerabolini, B., Ceriani, R. M., Caccianiga, M., De Andreis, R. & Raimondi, B. Seed size, shape and persistence in soil: A test on Italian flora from Alps to Mediterranean coasts. Seed Sci. Res. 13, 75–85 (2003).
    Article  Google Scholar 

    59.
    Royal Botanical Gardens Kew. Seed Information Database (SID), Version 7.1. Available from https://data.kew.org/sid/ (2008).

    60.
    Kleyer, M. et al. The LEDA Traitbase: A database of plant life-history traits of North West European Flora. J. Ecol. 96, 1266–1274 (2008).
    Article  Google Scholar 

    61.
    Wellstein, C. & Kuss, P. Diversity and frequency of clonal traits along natural and land-use gradients in grasslands of the Swiss Alps. Fol. Geobot. 46, 255–270 (2011).
    Article  Google Scholar 

    62.
    Pérez-Harguindeguy, N. et al. New handbook for standardised measurement of plant functional traits worldwide. Austr. J. Bot. 61, 167–234 (2013).
    Article  Google Scholar 

    63.
    Reinecke, J., Klemm, G. & Heinken, T. Vegetation change and homogenization of species composition in temperate nutrient deficient Scots pine forests after 45 yr. J. Veg. Sci. 25, 113–121 (2014).
    Article  Google Scholar 

    64.
    Anderson, M. J. A new method for non-parametric multivariate analysis of variance. Aust. Ecol. 26, 32–46 (2001).
    Google Scholar 

    65.
    Saitou, N. & Nei, M. The neighbor-joining method: A new method for reconstructing phylogenetic trees. Mol. Biol. Evol. 4, 406–425 (1987).
    CAS  PubMed  Google Scholar 

    66.
    Tamura, K., Nei, M. & Kumar, S. Prospects for inferring very large phylogenies by using the neighbor-joining method. Proc. Nat. Acad. Sci. USA 101, 11030–11035 (2004).
    ADS  CAS  Article  Google Scholar 

    67.
    Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: Molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549 (2018).
    CAS  Article  Google Scholar 

    68.
    Weiher, E., Clarke, G. D. P. & Keddy, P. A. Community assembly rules, morphological dispersion, and the coexistence of plant species. Oikos 81, 309 (1998).
    Article  Google Scholar 

    69.
    Breiman, L., Friedman, J. H., Olshen, R. A. & Stone, C. J. Classification and regression trees (Wadsworth International Group, Belmont, 1984).
    Google Scholar 

    70.
    Ryo, M. & Rillig, M. C. Statistically reinforced machine learning for nonlinear patterns and variable interactions. Ecosphere 8(11), e01976 (2017).
    Article  Google Scholar 

    71.
    De’ath, G. & Fabricius, K. E. Classification and regression trees: A powerful yet simple technique for ecological data analysis. Ecology 81, 3178–3192 (2000).
    Article  Google Scholar 

    72.
    Oksanen, J., Blanchet, F. G., Kindt, R., Legendre, P., Minchin, P.R., O’Hara, R.B., Simpson, G.L., Solymos, P., Stevens, M. H. H. & Wagner, H. Vegan: Community Ecology Package. R package version 2.0–7. (2013) Available at https://CRAN.R-project.org/package=vegan

    73.
    Hothorn, T., Hornik, K. & Zeileis, A. Unbiased recursive partitioning: A conditional inference framework. J. Comput. Graph. Stat. 15, 651–674 (2006).
    MathSciNet  Article  Google Scholar 

    74.
    Kembel, S. W. et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics 26, 1463–1464 (2010).
    CAS  Article  Google Scholar 

    75.
    Laliberté, E. & Legendre, P. A distance-based framework for measuring functional diversity from multiple traits. Ecology 91, 299–305 (2010).
    Article  Google Scholar 

    76.
    Fabbio, G., Manetti, M. C. & Bertini, G. Aspects of biological diversity in the CONECOFOR plots. I. Structural and species diversity in the tree community. Ann. Silvicul. Res. 30, 17–28 (2006).
    Google Scholar 

    77.
    Trabucco, A. & Zomer, R. J. Global aridity index (global-aridity) and global potential evapo-transpiration (global-PET) geospatial database. CGIAR Consortium for Spatial Information (2009). More