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    Free hand hitting of stone-like objects in wild gorillas

    Gifford-Gonzalez, D. Bones are not enough: Analogues, knowledge, and interpretive strategies in zooarchaeology. J. Anthropol. Archaeol. 10, 215–254 (1991).Article 

    Google Scholar 
    Pobiner, B. L. The zooarchaeology and paleoecology of early hominin scavenging. Evol. Anthropol. 2, 68–82 (2020).Article 

    Google Scholar 
    Rodriguez, A. et al. Right or left? Determining the hand holding the tool from use traces. J. Archaeol. Sci. Rep. 31, 102316 (2020).
    Google Scholar 
    Feix, T., Kivell, T. L., Pouydebat, E. & Dollar, A. M. Estimating thumb-index finger precision grip and manipulation potential in extant and fossil primates. J. R. Soc. Interface. https://doi.org/10.1098/rsif.2015.0176 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bardo, A. et al. The implications of thumb movements for Neanderthal and modern human manipulation. Sci. Rep. 10, 19323 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Stout, D., Semaw, S., Rogers, M. J. & Cauche, D. Technological variation in the earliest Oldowan from Gona, Afar, Ethiopia. J. Hum. Evol. 58, 474–491 (2010).PubMed 
    Article 

    Google Scholar 
    Tennie, C., Premo, L. S., Braun, D. R. & McPherron, S. P. Resetting the null hypothesis: Early stone tools and cultural transmission. Curr. Anthrop. 58, 652–672 (2017).Article 

    Google Scholar 
    Tennie, C. The zone of latent solution (ZLS) account remains the most parsimonious explanation for early stone tools. Curr. Anthrop. 60, 331–332 (2019).
    Google Scholar 
    Tennie, C., Braun, D. R., Premo, L. S. & McPherron, S. P. The Island Test for Cumulative Culture in Paleolithic Cultures. In The Nature of Culture. Series: Vertebrate Paleobiology and Paleoanthropology (eds Haidle, M. N. et al.) (Springer, 2016).
    Google Scholar 
    Perreault, C. The Quality of the Archaeological Record (University of Chicago Press, 2019).Book 

    Google Scholar 
    Proffitt, T. et al. Wild monkeys flake stone tools. Nature 539, 85–88 (2016).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Carvalho, S., Cunha, E., Sousa, C. & Matsuzawa, T. Chaînes opératoires and resource-exploitation strategies in chimpanzee (Pan troglodytes) nut cracking. J. Hum. Evol. 55, 148–163 (2008).PubMed 
    Article 

    Google Scholar 
    Westergaard, G. C. & Suomi, S. J. The stone tools of capuchins (Cebus apella). Int. J. Primatol. 16, 1017–1024 (1995).Article 

    Google Scholar 
    De la Torre, I. & Mora, R. Technological Strategies in the Lower Pleistocene at Olduvai Beds I and II (Service de Prehistoire, Universite de Liege, 2005).
    Google Scholar 
    M. D. O. M. Í. Dominguez-Rodrigo, 3.3-Million-Year-Old Stone Tools and Butchery Traces? More Evidence Needed. PaleoAnthropology. 9 (2016).Harmand, S. et al. 3.3-million-year-old stone tools from Lomekwi 3, West Turkana, Kenya. Nature 521, 310–315 (2015).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Andrefsky, W. Lithics: Macroscopic Approaches to Analysis (Cambridge University Press, 2005).Book 

    Google Scholar 
    Malaivijitnond, S. et al. Stone-tool usage by Thai long-tailed macaques (Macaca fascicularis). Am. J. Primatol. 69, 227–233 (2007).PubMed 
    Article 

    Google Scholar 
    Luncz, L. V. et al. Resource depletion through primate stone technology. eLife 6, e23647 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Leca, J. B., Gunst, N. & Huffman, M. Complexity in object manipulation by Japanese macaques (Macaca fuscata): A cross-sectional analysis of manual coordination in stone handling patterns. J. Comp. Psychol. 125, 61 (2011).PubMed 
    Article 

    Google Scholar 
    Toth, N., Schick, K. D., Savage-Rumbaugh, E. S., Sevcik, R. A. & Rumbaugh, D. M. Pan the tool-maker: Investigations into the stone tool-making and tool-using capabilities of a bonobo (Pan paniscus). J. Archaeol. Sci. 20, 81–91 (1993).Article 

    Google Scholar 
    Wright, R. V. S. Imitative learning of a flaked stone technology-The case of an orangutan. Mankind 8, 296–306 (2009).
    Google Scholar 
    Bandini, E. et al. Naïve, unenculturated chimpanzees fail to make and use flaked stone tools. Open Res. Eur. https://doi.org/10.12688/openreseurope.13186.2 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    J. Henrich, C. Tennie, in Chimpanzees and Human Evolution, M. Muller, R. Wrangham, D. Pilbeam, Eds. (Harvard University Press, Cambridge, MA, (2017), 645–702.Breuer, T., Ndoundou-Hockemba, M. & Fishlock, V. First observation of tool use in wild gorillas. PLoS Biol. 3, 2041–2043 (2005).CAS 
    Article 

    Google Scholar 
    Wittiger, L., Society, W. C., River, C. & Project, G. Tool use during display behavior in wild cross river gorillas. Am. J. Primat. 5, 1–5 (2007).
    Google Scholar 
    Kinani, J. F. & Zimmerman, D. Tool use for food acquisition in a wild mountain gorilla (Gorilla beringei beringei). Am. J. Primat. 77, 353–357 (2015).Article 

    Google Scholar 
    Grueter, C. C., Robbins, M. M., Ndagijimana, F. & Stoinski, T. S. Possible tool use in a mountain gorilla. Behav. Processes. 100, 160–162 (2013).PubMed 
    Article 

    Google Scholar 
    Parker, S. T., Kerr, M., Markowitz, H. & Gould, J. A survey of tool use in zoo gorillas. In The Mentalities of Gorillas and Orangutans: Comparative Perspectives (eds Parker, S. T. et al.) (Cambridge University Press, 1999).Chapter 

    Google Scholar 
    Shumaker, R. W., Walkup, K. R. & Beck, B. B. Animal Tool Behavior: The Use and Manufacture of Tools by Animals (Johns Hopkins University Press, 2011).
    Google Scholar 
    Pouydebat, E., Berge, C., Gorce, P. & Coppens, Y. Use and manufacture of tools to extract food by captive Gorilla gorilla gorilla: Experimental approach. Folia Primat. 76, 180–183. https://doi.org/10.1159/000084381 (2005).Article 

    Google Scholar 
    Haslam, M. ‘Captivity bias’ in animal tool use and its implications for the evolution of hominin technology. PTRBAE 368, 20120421 (2013).PubMed 
    PubMed Central 

    Google Scholar 
    Van Schaik, C. P., Deaner, R. O. & Merrill, M. Y. The conditions for tool use in primates: Implications for the evolution of material culture. J. Hum. Evol. 36, 719–741 (1999).PubMed 
    Article 

    Google Scholar 
    Pouydebat, E., Gorce, P., Coppens, Y. & Bels, V. Biomechanical study of grasping according to the volume of the object: Human versus non-human primates. J. Biomech. 42, 266–272 (2009).PubMed 
    Article 

    Google Scholar 
    Pouydebat, E., Laurin, M., Gorce, P. & Bels, V. Evolution of grasping among anthropoids. J. Evol. Bio. 21, 1732–1743 (2008).CAS 
    Article 

    Google Scholar 
    Bardo, A., Cornette, R., Borel, A. & Pouydebat, E. Manual function and performance in humans, gorillas and orangutans during the same tool use task. Am. J. Phys. Anthropol. https://doi.org/10.1002/ajpa.2332 (2017).Article 
    PubMed 

    Google Scholar 
    A. Bardo, A. Borel, H. Meunier, J. P. Guéry, E. Pouydebat, Manual abilities in great apes during a tool use task. Am. J. Phys. Anthropol. doi: 10.1002 (2016).W. C. McGrew, Why is ape tool use so confusing. Comparative socioecology: the behavioural ecology of humans and other mammals. 457–472 (1989).Cipolletta, C. et al. Termite feeding by Gorilla gorilla gorilla at Bai Hokou, Central African Republic. Int. J. Primatol. 28, 457–476 (2007).Article 

    Google Scholar 
    Salmi, R., Rahman, U. & Doran-Sheehy, D. M. Hand preference for a novel bimanual coordinated task during termite feeding in wild western gorillas (Gorilla gorilla gorilla). Int. J. Primatol. 37, 200–212 (2016).Article 

    Google Scholar 
    Masi, S. et al. The influence of seasonal frugivory on nutrient and energy intake in wild western gorillas. PLoS ONE 10, e0129254 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Redford, K. H. & Dorea, J. G. The nutritional value of invertebrates with emphasis on ants and termites as food for mammals. J. Zool. 203, 385–395 (1984).CAS 
    Article 

    Google Scholar 
    McGrew, W. C. The ‘other faunivory’revisited: Insectivory in human and non-human primates and the evolution of human diet. J. Hum. Evol. 71, 4–11 (2014).PubMed 
    Article 

    Google Scholar 
    Tennie, C., O’Malley, R. C. & Gilby, I. C. Why do chimpanzees hunt? Considering the benefits and costs of acquiring and consuming vertebrate versus invertebrate prey. J. Hum. Evol. 71, 38–45 (2014).PubMed 
    Article 

    Google Scholar 
    McBrearty, S. Consider the humble termite: Termites as agents of post-depositional disturbance at African archaeological sites. J. Archaeol. Sci. 17, 111–143 (1990).Article 

    Google Scholar 
    Okwakol, M. J. N. Effects of Cubitermes testaceus (Williams) on some physical and chemical properties of soil in a grassland area of Uganda. Afr. J. Ecol. 25, 147–153 (1987).Article 

    Google Scholar 
    Altmann, J. Observational study of behavior: Sampling methods. Behavior 49, 227–267 (1974).CAS 
    Article 

    Google Scholar 
    Robira, B. et al. Handedness in gestural and manipulative actions in male hunter-gatherer Aka pygmies from Central African Republic. Am. J. Phys. Anthropol. 166(481–491), 19 (2018).
    Google Scholar 
    Meguerditchian, A., Calcutt, S. E., Lonsdorf, E. V., Ross, S. R. & Hopkins, W. D. Brief communication: Captive gorillas are right-handed for bimanual feeding. Am. J. Phys. Anthropol. 141, 638–645 (2010).PubMed 
    PubMed Central 

    Google Scholar 
    Dapena, J. E. S. Ú. S., William, J., Anderst, N. P. & Toth, The biomechanics of the arm swing in Oldowan stone flaking. In The Oldowan: Case Studies into the Earliest Stone Age (No. 1). Gosport (eds Toth, N. P. & Schick, K. D.) (Stone Age Institute Press, 2006).
    Google Scholar 
    Nowell, A. A. & Fletcher, A. W. The development of feeding behaviour in wild western lowland gorillas (Gorilla gorilla gorilla). Behaviour 145, 171–193 (2008).Article 

    Google Scholar 
    Pouydebat, E., Gorce, P., Coppens, Y. & Bels, V. Substrate optimization in nuts cracking by capuchin monkeys. Am. J. Primatol. 68, 1017–1024 (2006).CAS 
    PubMed 
    Article 

    Google Scholar 
    Boinski, S., Quatrone, R. P. & Swarttz, H. Substrate and tool use by brown capuchins in Suriname: Ecological contexts and cognitive bases. Am. Anthropol. 102, 741–761 (2000).Article 

    Google Scholar 
    Panger, M. A. Object-use in free-ranging white-faced capuchins (Cebus capucinus) in Costa Rica. Am. J. Phys. Anthropol. 106, 311–321 (1998).CAS 
    PubMed 
    Article 

    Google Scholar 
    Parker, S. T. & Gibson, K. R. Object manipulation, tool use and sensorimotor intelligence as feeding adaptations in cebus monkeys and great apes. J. Hum. Evol. 6, 623–641 (1977).Article 

    Google Scholar 
    Heldstab, S. A., Isler, K., Schuppli, C. & van Schaik, C. P. When ontogeny recapitulates phylogeny: Fixed neurodevelopmental sequence of manipulative skills among primates. Sci. Adv. 6, eabb4685 (2020).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Clutton-Brock, T. H. Some aspects of intraspecific variation in feeding and ranging behaviour in primates. In Primate Ecology Studies of Feeding And Ranging Behavior in Lemurs, Monkeys and Apes (ed. Clutton-Brock, T. H.) (Academic Press, 1977).
    Google Scholar 
    Key, C. & Ross, C. Sex differences in energy expenditure in non-human primates. Proc. R. Soc. Lond. B. 266, 2479–2485 (1999).CAS 
    Article 

    Google Scholar 
    Lockman, J. J. A perception–action perspective on tool use development. Child Dev. 71, 137–144 (2000).CAS 
    PubMed 
    Article 

    Google Scholar 
    Masi, S. et al. Unusual feeding behavior in wild great apes, a window to understand origins of self-medication in humans: Role of sociality and physiology on learning process. Physiol. Behav. 105, 337–349 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    Falótico, T. & Ottoni, E. B. The manifold use of pounding stone tools by wild capuchin monkeys of Serra da Capivara National Park, Brazil. Behaviour 153, 421–442 (2016).Article 

    Google Scholar 
    Falótico, T. & Ottoni, E. B. Stone throwing as a sexual display in wild female bearded capuchin monkeys, Sapajus libidinosus. PLoS ONE 8, e79535 (2013).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Mannu, M. & Ottoni, E. B. The enhanced tool-kit of two groups of wild bearded capuchin monkeys in the Caatinga: Tool making, associative use, and secondary tools. Am. J. Primatol. 71, 242–251 (2009).PubMed 
    Article 

    Google Scholar 
    Gumert, M. D., Kluck, M. & Malaivijitnond, S. Thephysical characteristics and usage patterns of stoneaxe and pounding hammers used by long-tailedmacaques in the Andaman Sea region of Thailand. Am. J. Primatol. 71, 594–608. https://doi.org/10.1002/ajp.20694 (2009).Article 
    PubMed 

    Google Scholar 
    Marzke, M. W. Precision grips, hand morphology, and tools. Am. J. Phys. Anthropol. 102, 91–110 (1997).CAS 
    PubMed 
    Article 

    Google Scholar 
    Matsuzawa, T. Chimpanzee Intelligence in Nature and in Captivity Isomorphism of Symbol Use and Tool Use (Cambridge University Press, 1996).Book 

    Google Scholar 
    Westergaard, G. C. & Suomi, S. J. A simple stone-tool technology in monkeys. J. Hum. Evol. 27, 399–404 (1994).Article 

    Google Scholar 
    Liu, Q. et al. Kinematics and energetics of nut-cracking in wild capuchin monkeys (Cebus libidinosus) in Piauí, Brazil. Am. J. Phys. Anthropol. 138, 210–220 (2009).CAS 
    PubMed 
    Article 

    Google Scholar 
    Günther, M. M. & Boesch, C. Energetic Cost of Nut-cracking Behaviour in Wild Chimpanzees. In Hands of Primates 109–129 (Springer, 1993).
    Google Scholar 
    Roach, N. T., Venkadesan, M., Rainbow, M. J. & Lieberman, D. E. Elastic energy storage in the shoulder and the evolution of high-speed throwing in Homo. Nature 498, 483–486 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Young, N. M., Capellini, T. D., Roach, N. T. & Alemseged, Z. Fossil hominin shoulders support an African ape-like last common ancestor of humans and chimpanzees. PNAS 112, 11829–11834 (2015).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Doran-Sheehy, D., Mongo, P., Lodwick, J. & Conklin-Brittain, N. L. Male and female western gorilla diet: Preferred foods, use of fallback resources, and implications for ape versus old world monkey foraging strategies. Am. J. Phys. Anthropol. 140, 727–738 (2009).CAS 
    PubMed 
    Article 

    Google Scholar 
    Breuer, T., Hockemba, M. B. N., Olejniczak, C., Parnell, R. J. & Stokes, E. J. Physical maturation, life-history classes and age estimates of free-ranging western gorillas – Insights from Mbeli Bai, Republic of Congo. Am. J. Primatol. 71, 106–119 (2009).PubMed 
    Article 

    Google Scholar 
    Hopkins, W. D. et al. The use of bouts and frequencies in the evaluation of hand preferences for a coordinated bimanual task in chimpanzees (Pan troglodytes): An empirical study comparing two different indices of laterality. J. Comp. Psychol. 115, 294–299 (2001).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Byrne, R. W. & Byrne, J. M. Manual dexterity in the gorilla: bimanual and digit role differentiation in a natural task. Anim. Cogn. 4, 347–361 (2001).CAS 
    PubMed 
    Article 

    Google Scholar  More

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    Evaluating changes in growth and pigmentation of Cladosporium cladosporioides and Paecilomyces variotii in response to gamma and ultraviolet irradiation

    Gamma source and dose modelingThe general literature contains conflicting results on whether the energies of photons interacting with fungi affects the radiotrophic response. As such, we sought to control critical variables while irradiating the fungi with ionizing radiation from a sealed Cs-137 source and a UV source. The Cs-137 source emitted a photon at 662 keV along with other lower energy photons near 30 keV (Table S1).A review of previous studies was conducted to identify the gamma dose rate and total dose that should be targeted for exposure (Table S2). Those dose rates ranged from 600,000 rad delivered in 1.5 h to 0.08 rad delivered in 16 h. Even among studies examining the same fungi attributes, the total dose varied dramatically. For the present study, we used a Health Physics code to target a 50-rad dose over a one-week exposure. This dose was selected as it changes blood count observed in most humans24. We hypothesized that this dose would induce physiological changes in the fungi without causing a high rate of lethality. A MicroShield (Grove Software, Inc.) model was created to identify the quantity of radioactive material and distance between source and sample necessary to achieve the dose of 50 rad in seven days. From a sensitivity analysis of the MicroShield model, it was determined that ~ 350 µCi of Cs-137 would create a dose rate of ~ 50 rad in seven days (Fig. 1; Table S3), if placed 1.8 cm from the surface of the fungi. It should be noted that Microshield values are often conservative and likely underestimate the actual dose on target. In addition, 50 rad falls in the middle of the large range for energies previously reported in the general literature (Table S2).Figure 1Time required on target to achieve an exposure of 50 rad determined in MicroShield and based on an activity of ~ 350 µCi for Cs-137 source and the vertical distance between the source and fungus.Full size imageThe dose from the Cs-137 source on the fungal mycelium is also dependent on the radial growth of the fungus from the center plug used to initiate growth. As the fungus grows away from the source, the leading edge will experience a lower total dose of radiation. Although a uniform dose would have been ideal, a source with activity sufficient to create a uniform radiation field would have initiated a variety of safety controls deemed impractical for this experiment. The background radiation dose at the testing site in Albuquerque is approximately 10 µrem h−1; the dose at the outermost area of the Petri dish was measured at 65,553 µrad h−1. As this dose was primarily from gamma emissions, rad and rem can be considered equivalent. To validate the simulation, a dose rate study was performed using thermoluminescent dosimeters (TLD) placed at varying distances from the center of the source. The TLD placed directly under the source measured ~ 100 rad over the seven-day exposure, which is double the prediction from the simulation (50 rad; Fig. 2A). However, at a radial distance of 3.5 cm, the measured and estimated total dose over seven days were much closer, 12.3 and 11.4 rad, respectively. A comparison of the measured and estimated dose on target demonstrated a non-linear correlation (Fig. 2B), in which the simulation better approximated the dose at larger radial distances from the source.Figure 2(A) The total gamma dose on the fungal mycelial at 7 days as a function of the radial distance from the central mycelium plug based on empirical measurements (-●-) and estimated from simulations (-○-). (B) Observed correlation between the measured and estimated doses at varying radial distances.Full size imageIn order to normalize the energy deposited in the fungi from Cs-137 and the UV lamp sources, the units of MeV g−1 s−1 were selected for additional simulations. Monte Carlo N-Particle transport code (MCNP) simulations were used to determine this quantity for the Cs-137. The materials and geometry of the Petri dish and fungus used for these simulations are shown in Fig. 3. The Cs-137 was simulated as a point source located 1.5 cm from the top of the fungi. The Petri dish was set on a bakelite table. The setup was located in the center of a notional 5 m × 5 m × 5 m room with 30 cm thick concrete walls and filled with air. Leads bricks set on the table surrounded the petri dish and source. The International Commission on Radiological Protection (ICRP) material definitions did not contain data for fungal mycelia. Thus, we selected for skin as the closest approximation of the properties of the fungal mycelium25. This simulation gave a result for the energy deposited per particle as 6.53 × 10–4 MeV g−1, which for a 350 μCi activity, the rate of energy deposition was determined to be 7907 MeV g−1 s−1.Figure 3Top (upper left) and side (upper right) view of the Petri dish and fungi materials and distances used to determine energy deposition rates in MCNP. The overall geometry used for the radiation transport simulations, including the lead bricks, is shown from the top down (lower left) and from the side (lower right).Full size imageUV source and irradiationOur intent was to match the energy absorbed by the fungi to control for all variables except the photon energy difference between the Cs-137 source and UV lamp. The spectrum of energies emitted from the Cs-137 source varied significantly from those of the UV lamp, which in this case was a 30 W deuterium lamp that emitted from 185 to 400 nm (Fig. S1). This wide bandwidth represented photon energies ranging from 3.1 to 6.7 eV. The bandwidth of the UV exposure was limited to 300–350 nm using a 50-nm bandpass filter centered at 325 nm to ensure that incident photons would be in the UV energy range and not form ozone. Because we chose to match the overall energy deposited from the UV source to the gamma source it was necessary to attenuate the beam to the right power level. We assumed that all the UV energy would be absorbed near the surface rather than in the bulk since the fungi were melanized. This simplified the calculations and reduced risk, given the challenge of accurately estimating the absorbance of the fungi. The power deposited by the gamma source was calculated as the rate of energy deposition was determined to be 7907 MeV g−1 s−1 (1.3 nW g−1 s−1). Given the initial size of the plug was 1 cm in diameter, the desired lamp fluence needed to be ~ 2.8 nW cm−2. Across the spectrum of interest, the lamp power was determined to be 3.202 × 10–4 mW, thus requiring an attenuation of 8.7 × 10–9 (OD 8.06), reducing the lamp power to ~ 3 pW cm−2 and achieving a reasonably close power density to the target. Due to the sensitivity of UV detectors, the required power densities could not be measured directly. Alternatively, we measured the neutral density filters to verify the prescription was indeed correct.Response of P. variotii to irradiationUniform plugs (~ 5-mm in diameter) of actively growing mycelia of P. variotii were cut using the end of a Pasteur pipette and transferred a Petri plate containing potato dextrose agar (PDA) one day prior to initiating exposure experiments. The diameter of the mycelium was measured from four images, separated by precisely six hours, over the course of seven days and used to measure the growth rate. Differences in the pigmentation of the fungi under the different conditions was quantified in Fiji26 through analysis of grayscale images collected at day seven, following the method described by Brilhante et al.27 A ratiometric value was derived from the grayscale values and the white background, which corrected for variations in lighting across or between images.Significant differences in the pigmentation but not growth rates of P. variotii were associated with exposure to UV and gamma to irradiation, based on One-Way ANOVA analyses (Fig. 4A; Table S4). P. variotii is a ubiquitous filamentous fungus commonly inhabiting soil, decaying plants, and food products and was reported to be present on the surface of the walls of Unit-4 at ChNPP22,28. P. variotii is also a common food contaminant and is resistant to high temperature and metals29,30, despite being more sensitive to gamma irradiation than other fungi such as Aspergillus fumigatus31. In the present study, we hypothesized that positive radiation-induced effects in P. variotii would result in enhanced growth rates due to gamma irradiation. Across all conditions, the average growth rate of P. variotii was ~ 5.6 ± 0.9 mm d−1 (mean ± standard deviation). While the growth rate of P. variotii exposed to gamma irradiation was greater compared with the control and UV-irradiated samples (Fig. 4A), the difference in the mean growth rates was not significant (P = 0.255) by ANOVA.Figure 4(A) Growth rate and pigmentation of control (orange square), gamma- (blue square), and UV- (red square) irradiated cultures of P. variotti (mean ± standard deviation). (B) Estimated total irradiation dose experienced by the mycelial as a function of the distance from the central source. Exponential decay fit: − 3.6 + 105.7*exp(− 0.75*x); Adjusted R2 = 0.998. (C) Graphical representation of the irradiation dose based on the growth rate and duration of exposure for zones of mycelia as a function of radial distance from the central plug.Full size imageWe also hypothesized that the pigmentation of P. variotii would increase with exposure to gamma and UV irradiation. While P. variotti does not produce melanin, it does produce a pigment, Ywa1, from a polyketide synthesis (PKS) gene cluster and has been shown to protect the fungus against UV-C irradiation28. In some melanized fungi, Ywa1 serves as precursor and can be hydrolyzed to 1,3,6,8-tetrahydroxynaphthalen (T4HN). T4HN may then be converted to 1,8-dihydroxynaphthalene (1,8-DHN) melanin through the DHN pathway32. However, Lim et al.28 concluded that P. variotii does not produce true melanin as the pigmentation was maintained when the DHN-melanin pathway was inhibited. Significant differences in the pigmentation of P. variotii were observed among the three different sample types (P  More

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    The regional impact of the COVID-19 lockdown on the air quality in Ji'nan, China

    Overall characteristics of air pollutantsThe results of previous studies indicated that local pollution is highly important in determining the emissions of air pollutant. Therefore, in this study, we estimated the changes in pollution and the AQI between the pre-COVID and COVID lockdown periods and among the different regions in Ji’nan. A comparison of the different pollutant concentrations analysed in this study shows that the concentrations of almost all pollutants decreased during the COVID lockdown period; only the concentration of O3 increased continuously as the COVID lockdown period progressed (Fig. 1).Figure 1Spatial distributions of the different observation sites and industrial enterprises above a designated size threshold in Ji’nan city. JCE, machine tool factory No. 2; LSX, technical college; JNS, Ji’nan fourth building group; KFQ, economic development zone; KGS, Kegansuo; LWZ, Laiwu memorial hall; NKS, Agricultural Scientific Institute; SZZ, Seed warehouse of Shandong Province; SJC, Ji’nan monitoring station; TXG, Taixing company; CQD, Changqing school. Red circles, red triangles and red squares represent stations in urban, urban-industrial and suburban regions, respectively. The map of Observation site was completed by the geostatistical analysis module of ArcGIS (version 10.3, https://developers.arcgis.com/).Full size imageDuring the observation period, the daily average mass concentrations of PM10, PM2.5, SO2, NO2, CO, and O3 in Ji’nan were 137.09 µg/m3, 101.35 µg/m3, 22.70 µg/m3, 39.77 µg/m3, 1.28 mg/m3, and 71.84 µg/m3, respectively (Fig. 2). The mass concentrations of PM10 and PM2.5 exceeded the daily average Grade I values (50 µg/m3 and 35 µg/m3) of the Ambient Air Quality Standard of China (CAAQS, GB 3095-2012) during the whole observation period. In contrast, the mass concentrations of NO2, SO2, CO and O3 were substantially lower than the daily average Grade I values (80 µg/m3, 50 µg/m3, 4 mg/m3 and 100 µg/m3, respectively) of the CAAQS each day. During the pre-COVID period, the daily average mass concentrations of PM10, PM2.5, SO2, NO2, CO, and O3 in Ji’nan were 177.03 µg/m3, 125.94 µg/m3, 26.39 µg/m3, 54.52 µg/m3, 1.59 mg/m3, and 60.72 µg/m3, respectively. The mass concentrations of all these pollutants, except NO2, CO and O3, exceeded the daily average Grade I values of the CAAQS. The mass concentration trends during the COVID lockdown period were consistent with those during the pre-COVID period, but there were significant differences in the concentrations between the periods. In summary, the air quality in Ji’nan was generally good from January 24 to February 7, 2020, mainly due to the strict prevention and control measures for COVID-19.Figure 2Temporal variations in the mass concentrations of air pollutants (PM10, PM2.5, NO2, SO2, CO and O3) at the urban site in Ji’nan during the observation period.Full size imageEffects of regional differences and lockdown on air pollutantsOur results reveal that the PM10, PM2.5, NO2, SO2, CO and O3 concentrations in the urban, suburban and urban-industrial regions differed significantly between the COVID lockdown and pre-COVID periods (Figs. 3, 4).Figure 3Mean concentrations (± SD, mg/m3) of PM10, PM2.5, NO2, SO2, CO and O3 during the pre-COVID and COVID lockdown periods in 2020; the values were determined by combining the urban, suburban and urban-industrial areas at the regional scale. *, ** and *** represent significant differences between the pre-COVID and COVID lockdown periods in the same region (Duncan test, *p = 0.05; **p = 0.01; ***p = 0.001), with nonsignificant results being excluded.Full size imageFigure 4General reductions in the concentrations of major air pollutants.Full size imageNOx, one of the most important pollutants and a major health hazard, was studied in different countries across the world during COVID-19-related lockdowns. In all three regions studied herein, the highest rate of reduction in NO2 concentrations was observed during the COVID lockdown period (Fig. 4), with the NO2 levels in the COVID lockdown period being 54.02% on average lower than those during the pre-COVID period (53.07% in urban area, 48.31% in the suburban areas and 55.74% in the urban-industrial area) (Fig. 4); this reduction is greater than that reported at other sites by 26–42%11 and 14–38%18 but lower than that (50–62%) in Barcelona and Madrid in Spain33. As shown in Fig. 3E, the NO2 concentrations in the urban, suburban and urban-industrial areas were significantly higher in the pre-COVID period than in the COVID lockdown period, with the pre-COVID the NO2 levels in the urban area being 13.46% and 27.63% higher than those in the suburban and urban-industrial areas, respectively. During the COVID-19 lockdown period, the NO2 levels in urban areas were 4.69% and 31.75% higher than those in the suburban and urban-industrial areas, respectively. Blocking and controlling the air pollution associated with COVID-19 has helped reduce ground NO2 levels34 and this effect might be correlated with the tropospheric NO2 column density27. Among all sources of NO2, automobile emissions and power generation are the most important5. A systematic review confirmed that a short-term increase in the NO2 concentration in urban areas correlates to an increase in the number of pneumonia hospitalizations5,35.The trends in the CO concentration were similar to those in the NO2 level. During the COVID-lockdown period, the average CO mass concentrations in the urban, suburban and urban industrial areas were 1.08 mg/m3, 1.16 mg/m3 and 1.14 mg/m3, respectively, which decreased by 27.78%, 29.46% and 36.61%, respectively, compared with those during the pre-COVID period. The highest levels of PM10 were also observed during the pre-COVID period in the urban, suburban, and urban-industrial areas in Ji’nan (Fig. 4). The reductions in PM2.5 and CO emissions in urban and urban-industrial areas are generally higher than those in suburban areas25, supporting our findings. Notably, PM2.5 and CO are generated mainly by construction activities and from road dust, natural soil dust and dust from urban-industrial activities36. In contrast, the differences in the PM10 concentrations among the three regions were not significant during either the pre-COVID period or the COVID-lockdown period (Fig. 3A), which suggests that particles in Ji’nan are strongly diffused. However, the COVID lockdown period had a significant effect on the PM10 concentrations, with 42.86%, 44.26% and 50.60% differences in the PM10 concentration between the pre-COVID and COVID lockdown periods in the urban, suburban and urban-industrial areas, respectively (average of 44.92%, Fig. 4). The main reasons for the decreases in the concentration of PM were the severe restrictions on vehicle traffic, the cessation of industrial activities, and the stopping of construction projects, which are important sources of floating dust in the urban air37. Despite the overall consistency among the observed changes in all regions for the different air pollutants (except O3), at the regional level, some differences were statistically significant, while others were not due to the variability among stations, with the differences being more pronounced at the urban, suburban and urban-industrial stations.O3 is a secondary pollutant involved in different atmospheric reaction mechanisms and acts as both a source and sink. Generally, the impact of lockdowns on O3 was mixed, with its levels generally falling within ± 20%38, but total O3 levels remained relatively stable18. In this study, by comparing the regional mean concentrations throughout the COVID-19 period, we found that O3 concentrations were higher during the COVID lockdown period than during the pre-COVID period, especially in the urban regions (Fig. 3). Furthermore, the mean O3 concentration at all stations during the COVID lockdown period was 37.42% higher than that during the pre-COVID period (46.84% in the urban areas, 18.27% in the suburban area, and 19.84% in the urban-industrial areas) (Fig. 4); this finding is consistent with the outcomes of other studies, which reported that O3 concentrations increased by (on average) 20% during lockdowns39, potentially due, in part, to atmospheric reactivity37. The higher lockdown O3 concentrations can be attributed to the following three reasons: (1) low PM concentrations can result in more sunlight passing through the atmosphere, encouraging increased photochemical activities and thus higher O3 production40; (2) a reduction in NOx emissions increases O3 formation41; and (3) lower PM2.5 concentrations means their role as a sink for hydroperoxy radicals (HO2) is less effective, which would increase peroxy radical-mediated O3 production42. During the pre-COVID period, the O3 levels were not significantly different among the region, and the same results were observed during the COVID lockdown period. However, in the urban and urban-industrial areas, the O3 levels during the COVID lockdown period were significantly higher than those in the pre-COVID period (p  More

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    Version 3 of the Global Aridity Index and Potential Evapotranspiration Database

    Calculating Potential Evapotranspiration using Penman-MonteithAmong several equations used to estimate PET, an implementation of the Penman-Monteith equation originally presented by the Food and Agriculture Organization FAO-561, is considered a standard method3,12,13,49. FAO-561 defined PET as the ET of a reference crop (ET0) under optimal conditions, in this case with the specific characteristics of well-watered grass with an assumed height of 12 centimeters, a fixed surface resistance of 70 seconds per meter and an albedo of 0.231. Less specifically, “reference evapotranspiration”, generally referred to as “ET0”, measures the rate at which readily available soil water is evaporated from specified vegetated surfaces2,13, i.e., from a uniform surface of dense, actively growing vegetation having specified height and surface resistance, not short of soil water, and representing an expanse of at least 100 m of the same or similar vegetations1,13. ET0 is one of the essential hydrological variables used in many research efforts, such as study of the hydrologic water balance, crop yield simulation, irrigation system management and in water resources management, allowing researchers and practitioners to study the evaporative demand of the atmosphere independent of crop type, crop development and management practices2,4,13,49. ET0 values measured or calculated at different locations or in different seasons are comparable as they refer to the ET from the same reference surface. The factors affecting ET0 are climatic parameters, and crop specific resistances coefficients solved for reference vegetation. Other crop specific coefficients (Kc) may then be used to determine the ET of specific crops (ETc), and which can in turn be determined from ET01.As the Penman-Monteith methodology is predominately a climatic approach, it can be applied globally as it does not require estimations of additional site-specific parameters. However, a major drawback of the Penman-Monteith method is its relatively high need for specific data for a variety of parameters (i.e., windspeed, relative humidity, solar radiation). Zomer et al.18 compared five methods of calculating PET with parameters from data available at the time and settled upon using a Modified Hargreaves-Thornton equation50 which required less parametrization to produce the Global-AI_PET_v116,17,18. Several other attempts to produce global PET datasets with concurrently available global datasets came to similar conclusions51,52,53. The Modified Hargreaves-Thornton method required less parameterization with relatively good results, relying on datasets which were available at the time for a globally applicable modeling effort. The Global-AI_PET_v1 used the WorldClim_v1.420 downscaled climate dataset (30 arcseconds; averaged over the period 1960–1990) for input into the global geospatial implementation of the Modified Hargreaves-Thornton equation, applied on a per grid cell basis at approximately 1 km resolution (30 arcseconds). More recently, the UK Climate Research Unit released the “CRU_TS Version 4.04”, which now includes a Penman-Monteith calculated PET (ET0) global coverage, however at a relatively coarse resolution of 0.5 × 0.5 degrees. A number of satellite-based remote sensing datasets22,54,55,56,57 are now available and in use to provide the parameters for ET0 estimates, in some cases providing high spatial and/or temporal resolution and are likely to become increasingly utilized as the historical data record lengthens and sensors improve.The latest 2.0 versions of WorldClim58 (currently version 2.1; released January 2020), in addition to being updated with improved data and analysis, and a revised baseline (1970–2000), includes several additional primary climatic variables, beyond temperature and precipitation, namely: solar radiation, wind speed and water vapor pressure. The addition of these variables allowed that the global data now available was sufficient to effectively parameterize the FAO-56 equation to estimate ET0 globally at the 30 arc seconds scale (~1 km at equator).The FAO-56 Penman-Monteith equation, described in detail below, has been implemented on a per grid cell basis at 30 arc seconds resolution, using the Python programming language (version 3.2). The data to parametrize the various components equations required to arrive at the ET0 estimate were obtained from the Worlclim 2.158 climatological dataset, which provides values averaged over the time period 1970–2000 for minimum, maximum and average temperature; solar radiation; wind speed, and water vapor pressure. Subroutines in the program include calculation of the psychrometric constant (aerodynamic resistance), saturation vapor pressure, vapor pressure deficit, slope of vapour pressure curve, air density at constant pressure, net shortwave radiation at crop surface, clear-sky solar radiation, net longwave radiation at crop surface, net radiation at the crop surface, and the calculation of daily and monthly ET0. This process is described below. Geospatial processing and analysis were done using ArcGIS Pro v 2.9 (ESRI, 2020), Python (ArcPy) programming language (version 3.2), and Microsoft Excel for further data analysis, graphics and presentation.Global Reference Evapotranspiration (Global-ET0)Penman59, in 1948, first combined the radiative energy balance with the aerodynamic mass transfer method and derived an equation to compute evaporation from an open water surface from standard climatological records of sunshine, temperature, humidity and wind speed. This combined approach eliminated the need for the parameter “most difficult” to measure, surface temperature, and allowed for the first time an opportunity to make theoretical estimates of ET from standard meteorological data. Consequently, these estimates could also now be made retrospectively. This so-called combination method was further developed by many researchers and extended to cropped surfaces by introducing resistance factors. Among the various derivations of the Penman equation is the inclusion of a bulk surface resistance term60, with the resulting equation now called the Penman-Monteith equation3, as standardized in FAO-561 and subsequently by the American Society of Civil Engineers – Technical Committee on Standardization of Reference Evapotranspiration12,13,49,61. The FAO-56 Penman-Monteith form of the combination equation to estimate ET0 is calculated as:$$ETo=frac{Delta left({R}_{n}-Gright)+{rho }_{a}{c}_{p}frac{({e}_{s}-{e}_{a})}{{r}_{a}}}{Delta +gamma left(1+frac{{r}_{s}}{{r}_{a}}right)}$$
    (1)
    WhereET0 is the evapotranspiration for reference crop, as mm day−1Rn is the net radiation at the crop surface, as MJ m−2 day−1G is the soil heat flux density, as MJ m−2 day−1cp is the specific heat of dry airpa is the air density at constant pressurees is the saturation vapour pressure, as kPaea is the actual vapour pressure, as kPaes – ea is the saturation vapour pressure deficit, as kPa(Delta ) is the slope vapour pressure curve, as kPa °C−1(gamma ) is the psychrometric constant, as kPa °C−1rs is the bulk surface resistance, as m s−1ra is the aerodynamic resistance, as m s−1Psychrometric Constant (γ)The Atmospheric Pressure (Pr, [KPa]) is the pressure exerted by the weight of the atmosphere and is thus dependent on elevation (elev, [m]). To a certain (and limited) extent evaporation is promoted at higher elevations:$$Pr=101.3ast {left(frac{293-0.0065ast elev}{293}right)}^{5.26}$$
    (2)
    Instead, the psychrometric constant, [γ, kPa C−1] is expressed as:$$gamma =frac{{c}_{p}ast Pr}{varepsilon ast lambda }=frac{0.001013ast Pr}{0.622ast 2.45}$$
    (3)
    Where cp is the specific heat at constant pressure [MJ kg−1 °C−1] and is equal to 1.013 10−3, λ is the latent heat of vaporization [MJ kg−1] and is equal to 2.45, while ε is the molecular weight ratio between water vapour and dry air and is equal to 0.622.Elevation data has been obtained from the Shuttle Radar Topography Mission (SRTM) aggregated to 30 arc-second spatial resolution62 and combined with the USGS GTOPO3063 database for the areas north of 60°N and south of 60°S where no SRTM data was available (available at https://worldclim.org).Air Density at Constant Pressure [ρa]The mean Air Density at Constant Pressure [ρa, Kg m−3] can be represented as:$${rho }_{a}=frac{Pr}{{T}_{Kv}ast R}$$
    (4)
    While R is the specific heat constant (0.287, KJ Kg−1 K−1), the virtual temperature TKv can be represented as well as:$${T}_{Kv}=1.01ast ({T}_{avg}+273)$$
    (5)
    With Tavg as the mean daily air temperature at 2 m height [C°].Saturation Vapor Pressure [KPa]Saturation Vapor Pressure [KPa] is strictly related to temperature values (T)$${e}_{s_T}=0.6108ast ex{p}^{left[frac{17.27ast T}{T+237.3}right]}$$
    (6)
    Values of saturation vapor pressures, as function of temperature, are calculated for both Minimum Temperature [Tmin, C°] and Maximum temperature [Tmax, C°]. Due to nonlinearity of the equation, the mean saturation vapour pressure [es, KPa] is calculated as the average of saturation vapour pressure at minimum [es_min] and maximum temperature [es_max]$${e}_{s}=frac{{e}_{s_Tmax}+{e}_{s_Tmin}}{2}$$
    (7)
    The actual vapour pressure [ea, KPa] is the vapour pressure exerted by the water in the air and is usually calculated as function of Relative Humidity [RH]. Water vapour pressure is already available as one of the Worldclim 2.1 variables.$${e}_{a}=RH/100,ast ,{e}_{s}$$
    (8)
    The vapour pressure deficit (es-ea), [KPa] is the difference between the saturation (es) and actual vapour pressure (({e}_{a})).Slope of Saturation Vapor Pressure (Δ)The Slope of Saturation Vapor Pressure [Δ, kPa C−1] at a given temperature is given as function of average temperature:$$Delta =frac{4098ast 0.6108,ex{p}^{left(frac{17.27ast {T}_{avg}}{{T}_{avg}+237.3}right)}}{{left({T}_{avg}+237.3right)}^{2}}$$
    (9)
    Where Tavg [C°] is the average temperature.Net Radiation At The Crop Surface (R
    n)Net radiation [Rn, MJ m−2 day−1] is the difference between the net shortwave radiation [Rns, MJ m−2 day−1] and the net longwave radiation [Rnl, MJ m−2 day−1], and is calculated using solar radiation (Rs). In Worldclim 2.1 solar radiation (Rs) is given as KJ m−2 day−1. Thus, for computation of ET0, its unit should be converted to MJ m−2 day−1 and thus its value should be divided by 1000. The net accounting of either longwave and shortwave radiation sums up the incoming and outgoing components.$${R}_{n}={R}_{ns}-{R}_{nl}$$
    (10)
    The net shortwave radiation [Rns, MJ m−2 day−1] is the fraction of the solar radiation Rs that is not reflected from the surface. The fraction of the solar radiation reflected by the surface is known as the albedo [α]. For the green grass reference crop, α is assumed to have a value of 0.23. The value of Rns is:$${R}_{ns}={R}_{s},ast ,(1-alpha )$$
    (11)
    The difference between outgoing and incoming longwave radiation is called the net longwave radiation [Rnl]. As the outgoing longwave radiation is almost always greater than the incoming longwave radiation, Rnl represents an energy loss. Longwave energy emission is related to surface temperature following Stefan-Boltzmann law. Thus, longwave radiation emission is calculated as positive in the outward direction, while shortwave radiation is positive in the downward direction. The net energy flux leaving the earth’s surface is influenced as well by humidity and cloudiness$${R}_{nl}=sigma ast left(frac{{T}_{max,,K}^{4}+{T}_{min,,K}^{4}}{2}right)ast left(0.34-0.14ast sqrt{{e}_{a}}right)ast left(1.35ast frac{{R}_{s}}{{R}_{so}}-0.35right)$$
    (12)
    Where σ represent the Stefan-Boltzmann constant (4.903 10-9 MJ K−4 m−2 day−1), Tmax,K and Tmin,K the maximum and minimum absolute temperature (in Kelvin; K = C° + 273.16), ea is the actual vapour pressure; Rs the measured solar radiation [MJ m−2 day−1] and Rso is the calculated clear-sky radiation [MJ m−2 day−1]. Rso is calculated as function of extraterrestrial solar radiation [Ra, MJ m−2 day−1] and elevation (elev, m):$${R}_{so}={R}_{a}ast (0.75+0.00002ast elev)$$
    (13)
    The extraterrestrial radiation, [Ra, MJ m−2 day−1], is estimated from the solar constant, solar declination and day of the year. It requires specific information about latitude and Julian day to accomplish a trigonometric computation of the amount of solar radiation reaching the top of the atmosphere following trigonometric computations as shown in Allen et al.1.Although the soil heat flux is small compared to Rn, particularly when the surface is covered by vegetation, changes of soil heat flux may still be relevant at monthly scale. However, accurate assessments of soil heat flux may require computation of soil heat capacity, related to its mineral composition and water content, which in turn may be rather inaccurate at global scale at resolution of 30 arc sec. Thus, for simplicity, changes in soil heat fluxes are ignored (G = 0).Bulk Surface Resistance (r
    s)The resistance nomenclature distinguishes between aerodynamic resistance and surface resistance factors. The surface resistance parameters are often combined into one parameter, the ‘bulk’ surface resistance parameter which operates in series with the aerodynamic resistance. The surface resistance, rs, describes the resistance of vapour flow through stomata openings, total leaf area and soil surface. The aerodynamic resistance, ra, describes the resistance from the vegetation upward and involves friction from air flowing over vegetative surfaces. Although the exchange process in a vegetation layer is too complex to be fully described by the two resistance factors, good correlations can be obtained between measured and calculated evapotranspiration rates, especially for a uniform grass reference surface.A general equation for the bulk surface resistance (rs, [s m−1]) describes a ratio between the bulk stomatal resistance of a well illuminated leaf (rl) and the active sunlit leaf area of the vegetation:$${r}_{s}=frac{{r}_{l}}{LA{I}_{active}}$$
    (14)
    The stomatal resistance of a single leaf under well-watered conditions has a value of about 100 s m−1. It can be assumed that about half (0.5) of the total LAI is actively contributing to vapour transfer, while it can also be roughly generalized that for short crops there is a linear relation between LAI and crop height (h):$$LAI=24ast h$$
    (15)
    When the evapotranspiration simulated with the Penman-Monteith method is referred to a specific reference crop, denoted as ET0, a simplified computation of the method can occur that defines a priori specific variables into constant values. In this case, the reference surface is a hypothetical grass reference crop, well-watered grass of uniform height, actively growing and completely shading the ground, with an assumed crop height of 0.12 m, and an albedo of 0.23. The surface resistance for this hypothetical grass can be simplified to the following:$${r}_{s}=frac{100}{0.5ast 24ast h}$$
    (16)
    For such reference crop the surface resistance is fixed to 70 s m−1 and implies a moderately dry soil surface resulting from about a weekly irrigation frequency.Aerodynamic Resistance (r
    a)The aerodynamic resistance [s m−1] verifies the transfer of water vapour and heat from the vegetation surface into the air, and is controlled by both vegetation status but also atmospheric turbulence under theoretical aspect as:$${r}_{a}=frac{lnleft[frac{{z}_{m}-d}{{z}_{om}}right]ast lnleft[frac{{z}_{h}-d}{{z}_{oh}}right]}{{k}^{2}{u}_{z}}$$
    (17)
    Zm [m] is the height [h] of wind measurements and Zh [m] is the height of humidity measurements. These are normally set at 2 meters height, although several climate models may provide them for higher heights (e.g. 10 m). The zero plane displacement (d [m]) term can be estimated as two thirds of crop height, while Zom is the roughness length governing momentum transfer, and can be calculated as Zom = 0.123 * h.The roughness length governing transfer of heat and vapour, Zoh [m], can be approximated as one tenth of Zom. k is the von Karman’s constant, equal to 0.41, and uz [m s-1] is the wind speed at height z.The reference surface, as stated, is a hypothetical grass reference crop, well-watered grass of uniform height, actively growing and completely shading the ground, with an assumed crop height of 0.12 m, and an albedo of 0.23. For such reference crop the surface resistance is fixed to 70 s m-1 and implies a moderately dry soil surface resulting from about a weekly irrigation frequency.When crop height is equal to 0.12 and wind/humidity measurements are taken at 2 meters height, then the aerodynamic resistance can be simplified as:$${r}_{a}=frac{208}{{u}_{2}}$$
    (18)
    Reference Evapotranspiration (ET
    0)Given the above, and the specific properties of the standard reference crop, the FAO-56 Penman-Monteith method to estimate ET0 then can be calculated as:$$ETo=frac{0.408ast Delta ast left({R}_{n}-Gright)+gamma frac{900}{{T}_{avg}+273}ast {u}_{2}ast left({e}_{s}-{e}_{a}right)}{Delta +gamma left(1+frac{{r}_{s}}{{r}_{a}}right)}$$
    (19)
    Aridity Index (AI)Aridity is often expressed as a generalized function of precipitation and PET. The ratio of precipitation over PET (or ET0). That is, the precipitation available in relation to atmospheric water demand64 quantifies water availability for plant growth after ET demand has been met, comparing incoming moisture totals with potential outgoing moisture65.Geospatial analysis and global mapping of the AI for the averaged 1970–2000 time period has been calculated on a per grid cell basis, as:$$Al=MA_Prec/MA_E{T}_{0}$$
    (20)
    where:AI = Aridity IndexMA_Prec = Mean Annual PrecipitationMA_ET0 = Mean Annual Reference EvapotranspirationMean annual precipitation (MA_Prec) values were obtained from the WorldClim v 2.158, as averaged over the period 1970–2000, while ET0 datasets estimated on a monthly average basis by the Global-ET0 (i.e., modeled using the method described above) were aggregated to mean annual values (MA_ET0). Using this formulation, AI values are unitless, increasing with more humid condition and decreasing with more arid conditions.As a general reference, a climate classification scheme for Aridity Index values provided by UNEP64 provides an insight into the climatic significance of the range of moisture availability conditions described by the AI.
    Aridity Index Value

    Climate Class

    0.65

    Humid More

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    Social support correlates with glucocorticoid concentrations in wild African elephant orphans

    Wu, A. Social buffering of stress – Physiological and ethological perspectives. Appl. Anim. Behav. Sci. 239, 105325 (2021).
    Google Scholar 
    Hennessy, M. B., Kaiser, S. & Sachser, N. Social buffering of the stress response: diversity, mechanisms, and functions. Front. Neuroendocrinol. 30, 470–482 (2009).CAS 
    PubMed 

    Google Scholar 
    Young, C., Majolo, B., Heistermann, M., Schülke, O. & Ostner, J. Responses to social and environmental stress are attenuated by strong male bonds in wild macaques. Proc. Natl Acad. Sci. USA 111, 18195–18200 (2014).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Stanton, M. E., Patterson, J. M. & Levine, S. Social influences on conditioned cortisol secretion in the squirrel monkey. Psychoneuroendocrinology 10, 125–134 (1985).CAS 
    PubMed 

    Google Scholar 
    Caldji, C., Diorio, J. & Meaney, M. J. Variations in maternal care in infancy regulate the development of stress reactivity. Biol. Psychiatry 48, 1164–1174 (2000).CAS 
    PubMed 

    Google Scholar 
    Novak, M. A., Hamel, A. F., Kelly, B. J., Dettmer, A. M. & Meyer, J. S. Stress, the HPA axis, and nonhuman primate well-being: a review. Appl. Anim. Behav. Sci. 143, 135–149 (2013).PubMed 
    PubMed Central 

    Google Scholar 
    Sapolsky, R. M., Romero, L. M. & Munck, A. U. How do glucocorticoids influence stress responses? Integrating permissive, suppressive, stimulatory, and preparative actions. Endocr. Rev. 21, 55–89 (2000).CAS 
    PubMed 

    Google Scholar 
    Liu, D. et al. Maternal Care, hippocampal glucocorticoid receptors, and hypothalamic-pituitary-adrenal responses to stress. Sci. Ment. Heal. Stress Brain 9, 75–78 (1997).
    Google Scholar 
    Gjerstad, J. K., Lightman, S. L. & Spiga, F. Role of glucocorticoid negative feedback in the regulation of HPA axis pulsatility. Stress 21, 403–416 (2018).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Spiga, F., Walker, J. J., Terry, J. R. & Lightman, S. L. HPA axis-rhythms. Compr. Physiol. 4, 1273–1298 (2014).PubMed 

    Google Scholar 
    Sapolsky, R. M. Why Zebras Don’t Get Ulcers (Henry Holt and Company, LLC, 2004).Campos, F. A. et al. Glucocorticoid exposure predicts survival in female baboons. Sci. Adv. 7, 1–10 (2021).
    Google Scholar 
    Banerjee, S. B., Arterbery, A. S., Fergus, D. J. & Adkins-Regan, E. Deprivation of maternal care has long-lasting consequences for the hypothalamic-pituitary-adrenal axis of zebra finches. Proc. R. Soc. B Biol. Sci. 279, 759–766 (2012).
    Google Scholar 
    Hennessy, M. B., Nigh, C. K., Sims, M. L. & Long, S. J. Plasma cortisol and vocalization responses of postweaning age guinea pigs to maternal and sibling separation: evidence for filial attachment after weaning. Dev. Psychobiol. 28, 103–115 (1995).CAS 
    PubMed 

    Google Scholar 
    Hennessy, M. B., O’Leary, S. K., Hawke, J. L. & Wilson, S. E. Social influences on cortisol and behavioral responses of preweaning, periadolescent, and adult guinea pigs. Physiol. Behav. 76, 305–314 (2002).CAS 
    PubMed 

    Google Scholar 
    Wiener, S. G., Johnson, D. F. & Levine, S. Influence of postnatal rearing conditions on the response of squirrel monkey infants to brief perturbations in mother-infant relationships. Physiol. Behav. 39, 21–26 (1987).CAS 
    PubMed 

    Google Scholar 
    Girard-Buttoz, C. et al. Early maternal loss leads to short-but not long-term effects on diurnal cortisol slopes in wild chimpanzees. Elife 10, e64134 (2021).Rosenbaum, S. et al. Social bonds do not mediate the relationship between early adversity and adult glucocorticoids in wild baboons. Proc. Natl Acad. Sci. USA 117, 20052–20062 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Moss, C. Elephant Memories: Thirteen Years in the Life of an Elephant Family (Univ. Chicago Press, 1988).Douglas-Hamilton, I., Bhalla, S., Wittemyer, G. & Vollrath, F. Behavioural reactions of elephants towards a dying and deceased matriarch. Appl. Anim. Behav. Sci. 100, 87–102 (2006).
    Google Scholar 
    Shoshani, J., Kupsky, W. J. & Marchant, G. H. Elephant brain. Part I: gross morphology, functions, comparative anatomy, and evolution. Brain Res. Bull. 70, 124–157 (2006).PubMed 

    Google Scholar 
    Goldenberg, S. Z. & Wittemyer, G. Orphaned female elephant social bonds reflect lack of access to mature adults. Sci. Rep. 7, 14408 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    Goldenberg, S. Z. & Wittemyer, G. Orphaning and natal group dispersal are associated with social costs in female elephants. Anim. Behav. 143, 1–8 (2018).
    Google Scholar 
    Lee, P. C. Allomothering among African elephants. Anim. Behav. 35, 278–291 (1987).
    Google Scholar 
    Parker, J. M. et al. Poaching of African elephants indirectly decreases population growth through lowered orphan survival. Curr. Biol. 31, 4156–4162.e5 (2021).Wittemyer, G. et al. Where sociality and relatedness diverge: the genetic basis for hierarchical social organization in African elephants. Proc. R. Soc. B Biol. Sci. 276, 3513–3521 (2009).
    Google Scholar 
    Goldenberg, S. Z., Douglas-Hamilton, I. & Wittemyer, G. Vertical transmission of social roles drives resilience to poaching in elephant metworks. Curr. Biol. 26, 75–79 (2016).CAS 
    PubMed 

    Google Scholar 
    Gobush, K. S., Mutayoba, B. M. & Wasser, S. K. Long-term impacts of poaching on relatedness, stress physiology, and reproductive output of adult female African elephants. Conserv. Biol. 22, 1590–1599 (2008).CAS 
    PubMed 

    Google Scholar 
    Gobush, K. S. et al. Loxodonta africana (African Savanna Elephant). Loxodonta africana: the IUCN red list of threatened species 2021 e.T181008073A181022663 https://doi.org/10.2305/IUCN.UK.2021-1.RLTS.T181008073A181022663.en (2021).Wittemyer, G. et al. Illegal killing for ivory drives global decline in African elephants. Proc. Natl Acad. Sci. USA 111, 13117–13121 (2014).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Wittemyer, G., Daballen, D. & Douglas-Hamilton, I. Comparative Demography of an At-Risk African Elephant Population. PLoS ONE 8, e53726 (2013).McCormick, S. D. & Romero, L. M. Conservation endocrinology. Bioscience 67, 429–442 (2017).
    Google Scholar 
    Wittemyer, G. The elephant population of Samburu and Buffalo Springs National Reserves, Kenya. Afr. J. Ecol. 39, 357–369 (2001).
    Google Scholar 
    Cockrem, J. F. Individual variation in glucocorticoid stress responses in animals. Gen. Comp. Endocrinol. 181, 45–58 (2013).CAS 
    PubMed 

    Google Scholar 
    Taff, C. C., Schoenle, L. A. & Vitousek, M. N. The repeatability of glucocorticoids: a review and meta-analysis. Gen. Comp. Endocrinol. 260, 136–145 (2018).CAS 
    PubMed 

    Google Scholar 
    Hooten, M. B. & Hobbs, N. T. A guide to Bayesian model selection for ecologists. Ecol. Monogr. 85, 3–28 (2015).
    Google Scholar 
    Wittemyer, G. & Getz, W. M. Hierarchical dominance structure and social organization in African elephants, Loxodonta africana. Anim. Behav. 73, 671–681 (2007).
    Google Scholar 
    Heim, C., Ehlert, U. & Hellhammer, D. H. The potential role of hypocortisolism in the pathophysiology of stress-related bodily disorders. Psychoneuroendocrinology 25, 1–35 (2000).CAS 
    PubMed 

    Google Scholar 
    Dickens, M. J. & Romero, L. M. A consensus endocrine profile for chronically stressed wild animals does not exist. Gen. Comp. Endocrinol. 191, 177–189 (2013).CAS 
    PubMed 

    Google Scholar 
    Ma, D., Serbin, L. A. & Stack, D. M. How children’s anxiety symptoms impact the functioning of the hypothalamus–pituitary–adrenal axis over time: a cross-lagged panel approach using hierarchical linear modeling. Dev. Psychopathol. 31, 1–15 (2018).
    Google Scholar 
    Blas, J., Bortolotti, G. R., Tella, J. L., Baos, R. & Marchant, T. A. Stress response during development predicts fitness in a wild, long lived vertebrate. Proc. Natl Acad. Sci. USA 104, 8880–8884 (2007).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Boonstra, R. Reality as the leading cause of stress: rethinking the impact of chronic stress in nature. Funct. Ecol. 27, 11–23 (2013).
    Google Scholar 
    Gunnar, M. R. & Vazquez, D. M. Low cortisol and a flattening of expected daytime rhythm: Potential indices of risk in human development. Dev. Psychopathol. 13, 515–538 (2001).CAS 
    PubMed 

    Google Scholar 
    Perry, R. E. et al. Corticosterone administration targeting a hypo-reactive HPA axis rescues a socially-avoidant phenotype in scarcity-adversity reared rats. Dev. Cogn. Neurosci. 40, 100716 (2019).Fries, E., Hesse, J., Hellhammer, J. & Hellhammer, D. H. A new view on hypocortisolism. Psychoneuroendocrinology 30, 1010–1016 (2005).CAS 
    PubMed 

    Google Scholar 
    Dorsey, C., Dennis, P., Guagnano, G., Wood, T. & Brown, J. L. Decreased baseline fecal glucocorticoid concentrations associated with skin and oral lesions in black rhinoceros (Diceros bicornis). J. Zoo. Wildl. Med. 41, 616–625 (2010).PubMed 

    Google Scholar 
    Pawluski, J. et al. Low plasma cortisol and fecal cortisol metabolite measures as indicators of compromised welfare in domestic horses (Equus caballus). PLoS ONE 12, 1–18 (2017).
    Google Scholar 
    Feng, X. et al. Maternal separation produces lasting changes in cortisol and behavior in rhesus monkeys. Proc. Natl Acad. Sci. USA 108, 14312–14317 (2011).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    González Ramírez, C. et al. The NR3C1 gene expression is a potential surrogate biomarker for risk and diagnosis of posttraumatic stress disorder. Psychiatry Res. 284, 112797 (2020).PubMed 

    Google Scholar 
    Cluver, L., Fincham, D. S. & Seedat, S. Posttraumatic stress in AIDS-orphaned children exposed to high levels of trauma: the protective role of perceived social support. J. Trauma. Stress 22, 106–112 (2009).PubMed 

    Google Scholar 
    Bastille-Rousseau, G. et al. Landscape-scale habitat response of African elephants shows strong selection for foraging opportunities in a human dominated ecosystem. Ecography 43, 149–160 (2020).
    Google Scholar 
    Foley, C. A. H., Papageorge, S. & Wasser, S. K. Noninvasive stress and reproductive measures of social and ecological pressures in free-ranging African elephants. Conserv. Biol. 15, 1134–1142 (2001).
    Google Scholar 
    Wittemyer, G., Getz, W. M., Vollrath, F. & Douglas-Hamilton, I. Social dominance, seasonal movements, and spatial segregation in African elephants: a contribution to conservation behavior. Behav. Ecol. Sociobiol. 61, 1919–1931 (2007).
    Google Scholar 
    Wittemyer, G., Daballen, D. & Douglas‐Hamilton, I. Differential influence of human impacts on age‐specific demography underpins trends in an African elephant population. Ecosphere 12, e03720 (2021).Brown, J. L. et al. Individual and environmental risk factors associated with fecal glucocorticoid metabolite concentrations in zoo-housed Asian and African elephants. PLoS ONE 14, 1–18 (2019).
    Google Scholar 
    Goldenberg, S. Z. et al. Increasing conservation translocation success by building social functionality in released populations. Glob. Ecol. Conserv. 18, e00604 (2019).Dantzer, B., Fletcher, Q. E., Boonstra, R. & Sheriff, M. J. Measures of physiological stress: a transparent or opaque window into the status, management and conservation of species? Conserv. Physiol. 2, 1–18 (2014).
    Google Scholar 
    Kaisin, O., Fuzessy, L., Poncin, P., Brotcorne, F. & Culot, L. A meta-analysis of anthropogenic impacts on physiological stress in wild primates. Conserv. Biol. 0, 1–14 (2020).CAS 

    Google Scholar 
    Ganswindt, A., Rasmussen, H. B., Heistermann, M. & Hodges, J. K. The sexually active states of free-ranging male African elephants (Loxodonta africana): defining musth and non-musth using endocrinology, physical signals, and behavior. Horm. Behav. 47, 83–91 (2005).CAS 
    PubMed 

    Google Scholar 
    Santymire, R. M. et al. Using ACTH challenges to validate techniques for adrenocortical activity analysis in various African wildlife species. Int. J. Anim. Vet. Adv. 4, 99–108 (2012).CAS 

    Google Scholar 
    Watson, R. et al. Development of a versatile enzyme immunoassay for non-invasive assessment of glucocorticoid metabolites in a diversity of taxonomic species. Gen. Comp. Endocrinol. 186, 16–24 (2013).CAS 
    PubMed 

    Google Scholar 
    Oduor, S. et al. Differing physiological and behavioral responses to anthropogenic factors between resident and non-resident African elephants at Mpala Ranch, Laikipia County, Kenya. PeerJ 8, e10010 (2020).Brown, J. L., Kersey, D. C., Freeman, E. W. & Wagener, T. Assessment of diurnal urinary cortisol excretion in Asian and African elephants using different endocrine methods. Zoo. Biol. 29, 274–283 (2010).PubMed 

    Google Scholar 
    Justice, C. O. et al. The moderate resolution imaging spectroradiometer (MODIS): land remote sensing for global change research. IEEE Trans. Geosci. Remote Sens. 36, 1228–1249 (1998).
    Google Scholar 
    Lafferty, D. J. R., Zimova, M., Clontz, L., Hackländer, K. & Mills, L. S. Noninvasive measures of physiological stress are confounded by exposure. Sci. Rep. 9, 1–6 (2019).
    Google Scholar 
    O’Dwyer, K., Dargent, F., Forbes, M. R. & Koprivnikar, J. Parasite infection leads to widespread glucocorticoid hormone increases in vertebrate hosts: a meta-analysis. J. Anim. Ecol. 89, 519–529 (2020).PubMed 

    Google Scholar 
    Parker, J. M., Goldenberg, S. Z., Letitiya, D. & Wittemyer, G. Strongylid infection varies with age, sex, movement and social factors in wild African elephants. Parasitology 147, 348–359 (2020).PubMed 

    Google Scholar 
    Gibbons, L., Jacobs, D. E., Fox, M. T. & Hansen, J. The RVC/FAO guide to veterinary diagnostic parasitology. McMaster egg-counting technique. http://www.rvc.ac.uk/review/Parasitology/EggCount/Purpose.htm (2004)R Core Team. A language and environment for statistical computing. https://www.r-project.org/. (2020).Rstudio Team. RStudio: integrated development for R. http://www.rstudio.com/ (2020).Plummer, M. rjags: Bayesian graphical models using MCMC. https://cran.r-project.org/package=rjags (2019).Brooks, S. P. & Gelman, A. General methods for monitoring convergence of iterative simulations. J. Comput. Graph. Stat. 7, 434–455 (1998).
    Google Scholar 
    Gelman, A. & Rubin, D. B. Inference from iterative simulation using multiple sequences. Stat. Sci. 7, 457–511 (1992).
    Google Scholar 
    Wickham, H. ggplot2: elegant graphics for data analysis. https://ggplot2.tidyverse.org (2016).Youngflesh, C. MCMCvis: tools to visualize, manipulate, and summarize MCMC output. J. Open Source Softw. 3, 640 (2018).
    Google Scholar 
    Parker, J. M. The Physiological Condition of Orphaned African Elephants (Loxodonta africana). Doctoral dissertation, Colorado State University. (2021). More

  • in

    Increasing climatic decoupling of bird abundances and distributions

    Brondizio, E. S., Settele, J., Díaz, S. & Ngo, H. T. IPBES (2019): Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES Secretariat, 2019).Urban, M. C. Accelerating extinction risk from climate change. Science 348, 571–573 (2015).CAS 
    PubMed 

    Google Scholar 
    Warren, R., Price, J., Graham, E., Forstenhaeusler, N. & VanDerWal, J. The projected effect on insects, vertebrates, and plants of limiting global warming to 1.5 °C rather than 2 °C. Science 360, 791–795 (2018).CAS 
    PubMed 

    Google Scholar 
    Schloss, C. A., Nuñez, T. A. & Lawler, J. J. Dispersal will limit ability of mammals to track climate change in the Western Hemisphere. Proc. Natl Acad. Sci. USA 109, 8606–8611 (2012).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Travis, J. M. J. Climate change and habitat destruction: a deadly anthropogenic cocktail. Proc. Biol. Sci. 270, 467–473 (2003).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hill, J. K. et al. Impacts of landscape structure on butterfly range expansion. Ecol. Lett. 4, 313–321 (2001).
    Google Scholar 
    Guo, F., Lenoir, J. & Bonebrake, T. C. Land-use change interacts with climate to determine elevational species redistribution. Nat. Commun. 9, 1315 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    McLaughlin, J. F., Hellmann, J. J., Boggs, C. L. & Ehrlich, P. R. Climate change hastens population extinctions. Proc. Natl Acad. Sci. USA 99, 6070–6074 (2002).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Jetz, W., Wilcove, D. S. & Dobson, A. P. Projected impacts of climate and land-use change on the global diversity of birds. PLoS Biol. 5, e157 (2007).PubMed 
    PubMed Central 

    Google Scholar 
    Mantyka-Pringle, C. S. et al. Climate change modifies risk of global biodiversity loss due to land-cover change. Biol. Conserv. 187, 103–111 (2015).
    Google Scholar 
    Conradie, S. R., Woodborne, S. M., Cunningham, S. J. & McKechnie, A. E. Chronic, sublethal effects of high temperatures will cause severe declines in southern African arid-zone birds during the 21st century. Proc. Natl Acad. Sci. USA 116, 14065–14070 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Forister, M. L. et al. Compounded effects of climate change and habitat alteration shift patterns of butterfly diversity. Proc. Natl Acad. Sci. USA 107, 2088–2092 (2010).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Oliver, T. H. & Morecroft, M. D. Interactions between climate change and land use change on biodiversity: attribution problems, risks, and opportunities. Wiley Interdiscip. Rev. Clim. Change 5, 317–335 (2014).
    Google Scholar 
    MacLean, S. A. & Beissinger, S. R. Species’ traits as predictors of range shifts under contemporary climate change: a review and meta-analysis. Glob. Change Biol. 23, 4094–4105 (2017).
    Google Scholar 
    Pacifici, M. et al. Species’ traits influenced their response to recent climate change. Nat. Clim. Change 7, 205–208 (2017).
    Google Scholar 
    Root, T. Energy constraints on avian distributions and abundances. Ecology 69, 330–339 (1988).
    Google Scholar 
    Whitfield, M. C., Smit, B., McKechnie, A. E. & Wolf, B. O. Avian thermoregulation in the heat: scaling of heat tolerance and evaporative cooling capacity in three southern African arid-zone passerines. J. Exp. Biol. 218, 1705–1714 (2015).PubMed 

    Google Scholar 
    McKechnie, A. E. et al. Avian thermoregulation in the heat: evaporative cooling in five Australian passerines reveals within-order biogeographic variation in heat tolerance. J. Exp. Biol. 220, 2436–2444 (2017).PubMed 

    Google Scholar 
    Platts, P. J. et al. Habitat availability explains variation in climate-driven range shifts across multiple taxonomic groups. Sci. Rep. 9, 15039 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Pearson, R. G. Climate change and the migration capacity of species. Trends Ecol. Evol. 21, 111–113 (2006).PubMed 

    Google Scholar 
    Partners in Flight. Avian Conservation Assessment Database Version 2021 (accessed 5 February 2021); http://pif.birdconservancy.org/ACADHill, M. J. & Guerschman, J. P. The MODIS global vegetation fractional cover product 2001–2018: characteristics of vegetation fractional cover in grasslands and savanna woodlands. Remote Sens. (Basel) 12, 406 (2020).
    Google Scholar 
    Wiebe, K. L. & Gerstmar, H. Influence of spring temperatures and individual traits on reproductive timing and success in a migratory woodpecker. Auk 127, 917–925 (2010).
    Google Scholar 
    Viana, D. S. & Chase, J. M. Ecological traits underlying interspecific variation in climate matching of birds. Glob. Ecol. Biogeogr. 31, 1021–1034 (2022).
    Google Scholar 
    Kellermann, V., Van Heerwaarden, B., Sgrò, C. M. & Hoffmann, A. A. Fundamental evolutionary limits in ecological traits drive Drosophila species distributions. Science 325, 1244–1246 (2009).CAS 
    PubMed 

    Google Scholar 
    Devictor, V. et al. Differences in the climatic debts of birds and butterflies at a continental scale. Nat. Clim. Change 2, 121–124 (2012).
    Google Scholar 
    Mason, L. R. et al. Population responses of bird populations to climate change on two continents vary with species’ ecological traits but not with direction of change in climate suitability. Clim. Change 157, 337–354 (2019).
    Google Scholar 
    Coyle, J. R., Hurlbert, A. H. & White, E. P. Opposing mechanisms drive richness patterns of core and transient bird species. Am. Nat. 181, E83–E90 (2013).PubMed 

    Google Scholar 
    Valiela, I. & Martinetto, P. Changes in bird abundance in eastern North America: urban sprawl and global footprint? BioScience 57, 360–370 (2007).
    Google Scholar 
    Smith, S. J., Edmonds, J., Hartin, C. A., Mundra, A. & Calvin, K. Near-term acceleration in the rate of temperature change. Nat. Clim. Change 5, 333–336 (2015).
    Google Scholar 
    Winkler, K., Fuchs, R., Rounsevell, M. & Herold, M. Global land use changes are four times greater than previously estimated. Nat. Commun. 12, 2501 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Rosenberg, K. V. et al. Decline of the North American avifauna. Science 366, 120–124 (2019).CAS 
    PubMed 

    Google Scholar 
    Currie, D. J. & Venne, S. Climate change is not a major driver of shifts in the geographical distributions of North American birds. Glob. Ecol. Biogeogr. 26, 333–346 (2017).
    Google Scholar 
    Socolar, J. B., Epanchin, P. N., Beissinger, S. R. & Tingley, M. W. Phenological shifts conserve thermal niches in North American birds and reshape expectations for climate-driven range shifts. Proc. Natl Acad. Sci. USA 114, 12976–12981 (2017).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Barnagaud, J.-Y. et al. Relating habitat and climatic niches in birds. PLoS ONE 7, e32819 (2012).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ponti, R., Arcones, A., Ferrer, X. & Vieites, D. R. Seasonal climatic niches diverge in migratory birds. Ibis 162, 318–330 (2020).
    Google Scholar 
    Zurell, D., Gallien, L., Graham, C. H. & Zimmermann, N. E. Do long-distance migratory birds track their niche through seasons? J. Biogeogr. 45, 1459–1468 (2018).
    Google Scholar 
    Stephens, P. A. et al. Consistent response of bird populations to climate change on two continents. Science 352, 84–87 (2016).CAS 
    PubMed 

    Google Scholar 
    Ralston, J., DeLuca, W. V., Feldman, R. E. & King, D. I. Population trends influence species ability to track climate change. Glob. Change Biol. 23, 1390–1399 (2017).
    Google Scholar 
    Magurran, A. E. et al. Long-term datasets in biodiversity research and monitoring: assessing change in ecological communities through time. Trends Ecol. Evol. 25, 574–582 (2010).PubMed 

    Google Scholar 
    Jarzyna, M. A. & Jetz, W. A near half-century of temporal change in different facets of avian diversity. Glob. Change Biol. 23, 2999–3011 (2017).
    Google Scholar 
    van der Bolt, B., van Nes, E. H., Bathiany, S., Vollebregt, M. E. & Scheffer, M. Climate reddening increases the chance of critical transitions. Nat. Clim. Change 8, 478–484 (2018).
    Google Scholar 
    Bowler, D. E., Heldbjerg, H., Fox, A. D., O’Hara, R. B. & Böhning-Gaese, K. Disentangling the effects of multiple environmental drivers on population changes within communities. J. Anim. Ecol. 87, 1034–1045 (2018).PubMed 

    Google Scholar 
    Zurell, D., Graham, C. H., Gallien, L., Thuiller, W. & Zimmermann, N. E. Long-distance migratory birds threatened by multiple independent risks from global change. Nat. Clim. Change 8, 992–996 (2018).
    Google Scholar 
    Northrup, J. M., Rivers, J. W., Yang, Z. & Betts, M. G. Synergistic effects of climate and land-use change influence broad-scale avian population declines. Glob. Change Biol. 25, 1561–1575 (2019).
    Google Scholar 
    Guisan, A. et al. Predicting species distributions for conservation decisions. Ecol. Lett. 16, 1424–1435 (2013).PubMed 
    PubMed Central 

    Google Scholar 
    Pardieck, K. L., Ziolkowski, D. J. Jr, Lutmerding, M., Aponte, V. & Hudson, M.-A. R. North American Breeding Bird Survey Dataset 1966–2018 Version 2018.0. (US Geological Survey, 2019); https://www.sciencebase.gov/catalog/item/5d65256ae4b09b198a26c1d7Harris, D. J., Taylor, S. D. & White, E. P. Forecasting biodiversity in breeding birds using best practices. PeerJ 6, e4278 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Wickham, H., Francois, R., Henry, L. & Müller, K. dplyr: a Grammar of Data Manipulation. R package version 1.0.0 https://cran.r-project.org/web/packages/dplyr/index.html (2020).Wickham, H. & Henry, L. tidyr: Tidy Messy Data. R package version 1.1.0 https://cran.r-project.org/web/packages/tidyr/index.html (2020).Hijmans, R. J. raster: Geographic Data Analysis and Modeling. R package version 3.0-12 https://cran.r-project.org/web/packages/raster/index.html (2015).Bivand, R., Pebesma, E. J. & Gómez-Rubio, V. Applied Spatial Data Analysis with R (Springer, 2013).Hijmans, R. J. geosphere: Spherical Trigonometry. R package version 1.5–10 https://cran.r-project.org/web/packages/geosphere/index.html (2019).Hart, E. M. & Bell, K. prism. R package version 0.0.6 https://github.com/ropensci/prism (2015).Senyondo, H. et al. rdataretriever: R interface to the data retriever. J. Open Source Softw. 6, 2800 (2021).
    Google Scholar 
    Morris, B. D. & White, E. P. The EcoData retriever: improving access to existing ecological data. PLoS ONE 8, e65848 (2013).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Senyondo, H. et al. Retriever: data retrieval tool. J. Open Source Softw. 2, 451 (2017).
    Google Scholar 
    Hurlbert, A. H. & White, E. P. Disparity between range map- and survey-based analyses of species richness: patterns, processes and implications. Ecol. Lett. 8, 319–327 (2005).
    Google Scholar 
    Harris, D. J. Generating realistic assemblages with a joint species distribution model. Methods Ecol. Evol. 6, 465–473 (2015).
    Google Scholar 
    Sheard, C. et al. Ecological drivers of global gradients in avian dispersal inferred from wing morphology. Nat. Commun. 11, 2463 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Eyres, A., Böhning-Gaese, K. & Fritz, S. A. Quantification of climatic niches in birds: adding the temporal dimension. J. Avian Biol. 48, 1517–1531 (2017).
    Google Scholar 
    Martin, A. E. & Fahrig, L. Habitat specialist birds disperse farther and are more migratory than habitat generalist birds. Ecology 99, 2058–2066 (2018).PubMed 

    Google Scholar 
    Sauer, J. R. & Link, W. A. Analysis of the North American Breeding Bird Survey using hierarchical models. Auk 128, 87–98 (2011).
    Google Scholar 
    García Molinos, J., Schoeman, D. S., Brown, C. J. & Burrows, M. T. VoCC: an R package for calculating the velocity of climate change and related climatic metrics. Methods Ecol. Evol. 10, 2195–2202 (2019).
    Google Scholar 
    Krenek, S., Berendonk, T. U. & Petzoldt, T. Thermal performance curves of Paramecium caudatum: a model selection approach. Eur. J. Protistol. 47, 124–137 (2011).PubMed 

    Google Scholar 
    Bahn, V. & McGill, B. J. Can niche-based distribution models outperform spatial interpolation? Glob. Ecol. Biogeogr. 16, 733–742 (2007).
    Google Scholar 
    Dobson, L. L., La Sorte, F. A., Manne, L. L. & Hawkins, B. A. The diversity and abundance of North American bird assemblages fail to track changing productivity. Ecology 96, 1105–1114 (2015).PubMed 

    Google Scholar 
    Roberts, D. R. et al. Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography 40, 913–929 (2017).
    Google Scholar 
    Tikhonov, G. et al. Joint species distribution modelling with the R-package HMSC. Methods Ecol. Evol. 11, 442–447 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    Greenwell, B., Boehmke, B., Cunningham, J. & GBM Developers. gbm: Generalized boosted regression models. R package version 2.1.5 https://cran.r-project.org/web/packages/gbm/index.html (2019).Wood, S. N. Generalized Additive Models: an Introduction with R (CRC Press/Taylor & Francis Group, 2017).Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444–448 (2012).CAS 
    PubMed 

    Google Scholar 
    Revell, L. J. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).
    Google Scholar 
    Paradis, E. & Schliep, K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35, 526–528 (2019).CAS 
    PubMed 

    Google Scholar 
    Bürkner, P.-C. brms: an R package for Bayesian multilevel models using Stan. J. Stat. Softw. 80, 1–28 (2017).
    Google Scholar 
    Stan Development Team. Stan Modeling Language Users Guide and Reference Manual (2020); https://mc-stan.org/users/documentation/ More

  • in

    Plant rarity in fire-prone dry sclerophyll communities

    Mouillot, D. et al. Rare species support vulnerable functions in high-diversity ecosystems. PLoS Biol. 11, e1001569 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Leitão, R. P. et al. Rare species contribute disproportionately to the functional structure of species assemblages. Proc. R Soc. B 283, 20160084 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Enquist, B. J. et al. The commonness of rarity: Global and future distribution of rarity across land plants. Sci. Adv. 5, eaaz0414 (2019).Bevill, R. L. & Louda, S. M. Comparisons of related rare and common species in the study of plant rarity. Conserv. Biol. 13, 493–498 (1999).Article 

    Google Scholar 
    Murray, B. R., Thrall, P. H., Gill, A. M. & Nicotra, A. B. How plant life-history and ecological traits relate to species rarity and commonness at varying spatial scales. Austral Ecol. 27, 291–310 (2002).Article 

    Google Scholar 
    Gaston, K. J. Common ecology. Bioscience 61, 354–362 (2011).Article 

    Google Scholar 
    Kraft, N. J. et al. Community assembly, coexistence and the environmental filtering metaphor. Funct. Ecol. 29, 592–599 (2015).Article 

    Google Scholar 
    Gaston, K. J. What is rarity? in Rarity 1–21 (Springer, 1994).Rabinowitz, D. Seven forms of rarity. in The biological aspects of rare plant conservation (ed. Synge, H.) 205–217 (John Wiley and Sons: Chichester, UK, 1981).Sykes, L., Santini, L., Etard, A. & Newbold, T. Effects of rarity form on species’ responses to land use. Conserv. Biol. 34, 688–696 (2019).PubMed 
    Article 

    Google Scholar 
    Patykowski, J. et al. The effect of prescribed burning on plant rarity in a temperate forest. Ecol. Evol. 8, 1714–1725 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Ames, G. M., Wall, W. A., Hohmann, M. G. & Wright, J. P. Trait space of rare plants in a fire-dependent ecosystem. Conserv. Biol. 31, 903–911 (2017).PubMed 
    Article 

    Google Scholar 
    Foster, C. N. et al. Effects of fire regime on plant species richness and composition differ among forest, woodland and heath vegetation. Appl. Veg. Sci. 21, 132–143 (2018).Article 

    Google Scholar 
    Fernández-García, V. et al. Fire regimes shape diversity and traits of vegetation under different climatic conditions. Sci. Total Environ. 716, 137137 (2020).ADS 
    PubMed 
    Article 
    CAS 

    Google Scholar 
    Bassett, M., Leonard, S. W. J., Chia, E. K., Clarke, M. F. & Bennett, A. F. Interacting effects of fire severity, time since fire and topography on vegetation structure after wildfire. For. Ecol. Manag. 396, 26–34 (2017).Article 

    Google Scholar 
    Miller, B. P., Symons, D. R. & Barrett, M. D. Persistence of rare species depends on rare events: Demography, fire response and phenology of two plant species endemic to a semiarid Banded Iron Formation range. Aust. J. Bot. 67, 268–280 (2019).Article 

    Google Scholar 
    Etchells, H., O’Donnell, A. J., Lachlan McCaw, W. & Grierson, P. F. Fire severity impacts on tree mortality and post-fire recruitment in tall eucalypt forests of southwest Australia. For. Ecol. Manag. 459, 117850 (2020).Article 

    Google Scholar 
    Bradstock, R. A., Tozer, M. G. & Keith, D. A. Effects of high frequency fire on floristic composition and abundance in a fire-prone heathland near Sydney. Aust. J. Bot. 45, 641–655 (1997).Article 

    Google Scholar 
    Penman, T. D., Binns, D. L., Brassil, T. E., Shiels, R. J. & Allen, R. M. Long-term changes in understorey vegetation in the absence of wildfire in south-east dry sclerophyll forests. Aust. J. Bot. 57, 533–540 (2010).Article 

    Google Scholar 
    Ooi, M. K. The importance of fire season when managing threatened plant species: A long-term case-study of a rare Leucopogon species (Ericaceae). J. Environ. Manage. 236, 17–24 (2019).PubMed 
    Article 

    Google Scholar 
    Pausas, J. G., Bradstock, R. A., Keith, D. A. & Keeley, J. E. Plant functional traits in relation to fire in crown-fire ecosystems. Ecology 85, 1085–1100 (2004).Article 

    Google Scholar 
    Australian Bureau of Meteorology. Climate Data Online. www.bom.gov.au (2019).Abell, R. S. Geoscience map of Jervis Bay Territory and Beecroft peninsula (1:25000 scale). Australian Geological Survey Organisation (1992).Taws, N. Vegetation survey and mapping of Jervis Bay Territory. (Taws Botanical Research, 1997).Taylor, G., Abell, R. & Paterson, I. Geology, geomorphology, soils and earth resources. in Jervis Bay (eds. Cho Arthur, G., Georges, Stoutjesdikj Richard, R., & Longmore) .-. (Australian Nature Conservation Agency, 1995).Keith, D. A. Ocean shores to desert dunes: the native vegetation of NSW and the ACT (Selected Extracts). (Department of Environment and Conservation (NSW), 2004).Keith, D. A. & Tozer, M. G. Vegetation dynamics in coastal heathlands of the Sydney basin. in Proceedings of the Linnean Society of New South Wales vol. 134 (2012).Lindenmayer, D. B. et al. Contrasting mammal responses to vegetation type and fire. Wildl. Res. 35, 395–408 (2008).Article 

    Google Scholar 
    Bradstock, R. A. & Kenny, B. J. An application of plant functional types to fire management in a conservation reserve in southeastern Australia. J. Veg. Sci. 14, 345–354 (2003).Article 

    Google Scholar 
    Bowd, E. J., Banks, S. C., Bissett, A., May, T. W. & Lindenmayer, D. B. Direct and indirect disturbance impacts in forests. Ecol. Lett. https://doi.org/10.1111/ele.13741 (2021).Article 
    PubMed 

    Google Scholar 
    Thompson, C. G., Kim, R. S., Aloe, A. M. & Becker, B. J. Extracting the variance inflation factor and other multicollinearity diagnostics from typical regression results. Basic Appl. Soc. Psychol. 39, 81–90 (2017).Article 

    Google Scholar 
    Fox, J. & Weisberg (Sage, 2019).
    Google Scholar 
    Venables, W. N. R., B. D. Modern Applied Statistics with S. Fourth Edition. (Springer, 2002).Morrison, D. A. et al. Effects of fire frequency on plant species composition of sandstone communities in the Sydney region: Inter-fire interval and time-since-fire. Aust. J. Ecol. 20, 239–247 (1995).ADS 
    Article 

    Google Scholar 
    Burnham, K. P., Anderson, D. R. & Huyvaert, K. P. AIC model selection and multimodel inference in behavioral ecology: Some background, observations, and comparisons. Behav. Ecol. Sociobiol. 65, 23–35 (2011).Article 

    Google Scholar 
    Hartig, F. DHARMa: Residual diagnostics for hierarchical (multi-Level / mixed) regression models. (2020).Falster, D. et al. AusTraits, a curated plant trait database for the Australian flora. Sci. Data 8, 254 (2021).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Tozer, M. G. & Bradstock, R. A. Fire-mediated effects of overstorey on plant species diversity and abundance in an eastern Australian heath. Plant Ecol. 164, 213–223 (2003).Article 

    Google Scholar 
    Gosper, C. R., Yates, C. J., Prober, S. M. & Parsons, B. C. Contrasting changes in vegetation structure and diversity with time since fire in two Australian Mediterranean-climate plant communities. Austral Ecol. 37, 164–174 (2012).Article 

    Google Scholar 
    Foster, C., Barton, P., Robinson, N., MacGregor, C. & Lindenmayer, D. B. Effects of a large wildfire on vegetation structure in a variable fire mosaic. Ecol. Appl. 27, 2369–2381 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    Preston, F. W. The commonness, and rarity, of species. Ecology 29, 254–283 (1948).Article 

    Google Scholar 
    McGill, B. J. A renaissance in the study of abundance. Science 314, 770–772 (2006).CAS 
    PubMed 
    Article 

    Google Scholar 
    Silvertown, J. Plant coexistence and the niche. Trends Ecol. Evol. 19, 605–611 (2004).Article 

    Google Scholar 
    Lyons, K. G. & Schwartz, M. W. Rare species loss alters ecosystem function—invasion resistance. Ecol. Lett. 4, 358–365 (2001).Article 

    Google Scholar 
    Dee, L. E. et al. When do ecosystem services depend on rare species?. Trends Ecol. Evol. 34, 746–758 (2019).PubMed 
    Article 

    Google Scholar 
    Smith, M. D. & Knapp, A. K. Dominant species maintain ecosystem function with non-random species loss. Ecol. Lett. 6, 509–517 (2003).Article 

    Google Scholar 
    Lennon, J. J., Koleff, P., Greenwood, J. J. & Gaston, K. J. Contribution of rarity and commonness to patterns of species richness. Ecol. Lett. 7, 81–87 (2004).Article 

    Google Scholar 
    Foster, C. N. et al. Herbivory and fire interact to affect forest understory habitat, but not its use by small vertebrates. Anim. Conserv. 19, 15–25 (2016).Article 

    Google Scholar 
    Lamont, B. B., Enright, N. J. & He, T. Fitness and evolution of resprouters in relation to fire. Plant Ecol. 212, 1945–1957 (2011).Article 

    Google Scholar 
    Tolhurst, K. G. & Turvey, N. D. Effects of bracken (Pteridium esculentum (forst. f.) cockayne) on eucalypt regeneration in west-central Victoria. For. Ecol. Manag. 54, 45–67 (1992).Candeias, M. & Warren, R. J. Rareness starts early for disturbance-dependent grassland plant species. Biodivers. Conserv. 25, 2771–2785 (2016).Article 

    Google Scholar 
    Beadle, N. Soil phosphate and the delimitation of plant communities in eastern Australia. Ecology 35, 370–375 (1954).CAS 
    Article 

    Google Scholar 
    Orians, G. H. & Milewski, A. V. Ecology of Australia: The effects of nutrient-poor soils and intense fires. Biol. Rev. 82, 393–423 (2007).PubMed 
    Article 

    Google Scholar 
    Vesk, P. A. & Westoby, M. Funding the bud bank: A review of the costs of buds. Oikos 106, 200–208 (2004).Article 

    Google Scholar 
    Wilfahrt, P. et al. Temporal rarity is a better predictor of local extinction risk than spatial rarity. Ecology https://doi.org/10.1002/ecy.3504 (2021).Article 
    PubMed 

    Google Scholar 
    Miller, B. P. et al. Persistence of rare species depends on rare events: Demography, fire response and phenology of two plant species endemic to a semiarid Banded Iron Formation range. Aust. J. Bot. 67, 268–280 (2019).Article 

    Google Scholar 
    Gillespie, I. G. & Allen, E. B. Fire and competition in a southern California grassland: Impacts on the rare forb Erodium macrophyllum. J. Appl. Ecol. 41, 643–652 (2004).Article 

    Google Scholar 
    Maire, V. et al. Habitat filtering and niche differentiation jointly explain species relative abundance within grassland communities along fertility and disturbance gradients. New Phytol. 196, 497–509 (2012).PubMed 
    Article 

    Google Scholar 
    Yenni, G., Adler, P. B. & Ernest, S. M. Do persistent rare species experience stronger negative frequency dependence than common species?. Glob. Ecol. Biogeogr. 26, 513–523 (2017).Article 

    Google Scholar 
    Mayberry, R. J. & Elle, E. Conservation of a rare plant requires different methods in different habitats: Demographic lessons from Actaea elata. Oecologia 164, 1121–1130 (2010).ADS 
    PubMed 
    Article 

    Google Scholar 
    Rabinowitz, D. & Rapp, J. K. Dispersal abilities of seven sparse and common grasses froma Missouri prairie. Am. J. Bot. 68, 616–624 (1981).Article 

    Google Scholar 
    McIntyre, S. Comparison of a common, rare and declining plant species in the Asteraceae: Possible causes of rarity. Pac. Conserv. Biol. 2, 177–190 (1995).Article 

    Google Scholar 
    Hopfensperger, K. N. A review of similarity between seed bank and standing vegetation across ecosystems. Oikos 116, 1438–1448 (2007).Article 

    Google Scholar 
    Cross, A. T. et al. Defining the role of fire in alleviating seed dormancy in a rare Mediterranean endemic subshrub. AoB Plants 9, (2017). More

  • in

    Towards 3D basic theories of plant forms

    Cremers, G. Presence of 10 models of plant architecture in Euphorbes-Malgaches. Comptes Rendus Hebd. des. Seances de. L Academie des. Sci. Ser. D. 281, 1575–1578 (1975).
    Google Scholar 
    Balduzzi, M. et al. Reshaping plant biology: qualitative and quantitative descriptors for plant morphology. Front. Plant Sci. 8, 117 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Albert, C. H. et al. A multi-trait approach reveals the structure and the relative importance of intra- vs. interspecific variability in plant traits. Funct. Ecol. 24, 1192–1201 (2010).Article 

    Google Scholar 
    Farnsworth, K. D. & Niklas, K. J. Theories of optimization, form and function in branching architecture in plants. Funct. Ecol. 9, 355–363 (1995).Article 

    Google Scholar 
    Enquist, B. J. et al. in Advances in Ecological Research (eds Pawar, S.et al.), 249–318 (Academic Press, 2015).Niklas, K. J. & Spatz, H. C. Allometric theory and the mechanical stability of large trees: proof and conjecture. Am. J. Bot. 93, 824–828 (2006).PubMed 
    Article 

    Google Scholar 
    Price, C. A. et al. The metabolic theory of ecology: prospects and challenges for plant biology. N. Phytol. 188, 696–710 (2010).Article 

    Google Scholar 
    Martone, P. T. et al. Mechanics without muscle: biomechanical inspiration from the plant world. Integr. Comp. Biol. 50, 888–907 (2010).PubMed 
    Article 

    Google Scholar 
    West, G. B. & Brown, J. H. The origin of allometric scaling laws in biology from genomes to ecosystems: towards a quantitative unifying theory of biological structure and organization. J. Exp. Biol. 208, 1575–1592 (2005).PubMed 
    Article 

    Google Scholar 
    Enquist, B. J. Universal scaling in tree and vascular plant allometry: toward a general quantitative theory linking plant form and function from cells to ecosystems. Tree Physiol. 22, 1045–1064 (2002).PubMed 
    Article 

    Google Scholar 
    Anfodillo, T. et al. An allometry-based approach for understanding forest structure, predicting tree-size distribution and assessing the degree of disturbance. Proc. R. Soc. Lond. B Biol. Sci. 280, 20122375 (2013).
    Google Scholar 
    Duncanson, L. I., Dubayah, R. O. & Enquist, B. J. Assessing the general patterns of forest structure: quantifying tree and forest allometric scaling relationships in the United States. Glob. Ecol. Biogeogr. 24, 1465–1475 (2015).Article 

    Google Scholar 
    West, G. B., Brown, J. H. & Enquist, B. J. The fourth dimension of life: Fractal geometry and allometric scaling of organisms. Science 284, 1677–1679 (1999).CAS 
    PubMed 
    Article 

    Google Scholar 
    Winter, C. L. & Tartakovsky, D. M. Theoretical foundation for conductivity scaling. Geophys. Res. Lett. 28, 4367–4369 (2001).Article 

    Google Scholar 
    Reich, P. B. et al. Universal scaling of respiratory metabolism, size and nitrogen in plants. Nature 439, 457–461 (2006).CAS 
    PubMed 
    Article 

    Google Scholar 
    Choi, S. et al. Application of the metabolic scaling theory and water–energy balance equation to model large‐scale patterns of maximum forest canopy height. Glob. Ecol. Biogeogr. 25, 1428–1442 (2016).Article 

    Google Scholar 
    Osler, G. H. R., West, P. W. & Downes, G. M. Effects of bending stress on taper and growth of stems of young Eucalyptus regnans trees. Trees 10, 239–246 (1996).
    Google Scholar 
    Berthier, S. et al. Irregular heartwood formation in maritime pine (Pinus pinaster Ait): consequences for biomechanical and hydraulic tree functioning. Ann. Bot. 87, 19–25 (2001).Article 

    Google Scholar 
    Fournier, M. et al. Integrative biomechanics for tree ecology: beyond wood density and strength. J. Exp. Bot. 64, 4793–4815 (2013).CAS 
    PubMed 
    Article 

    Google Scholar 
    Sone, K., Noguchi, K. & Terashima, I. Dependency of branch diameter growth in young Acer trees on light availability and shoot elongation. Tree Physiol. 25, 39–48 (2005).PubMed 
    Article 

    Google Scholar 
    Anten, N. P. & Schieving, F. The role of wood mass density and mechanical constraints in the economy of tree architecture. Am. Nat. 175, 250–260 (2010).PubMed 
    Article 

    Google Scholar 
    Jelonek, T. et al. The biomechanical formation of trees (Prace Naukowe, Doniesienia, Komunikaty, 2019).Muller‐Landau, H. C. et al. Testing metabolic ecology theory for allometric scaling of tree size, growth and mortality in tropical forests. Ecol. Lett. 9, 575–588 (2006).PubMed 
    Article 

    Google Scholar 
    McMahon, T. A. & Kronauer, R. E. Tree structures: deducing the principle of mechanical design. J. Theor. Biol. 59, 443–466 (1976).CAS 
    PubMed 
    Article 

    Google Scholar 
    Alméras, T. & Fournier, M. Biomechanical design and long-term stability of trees: morphological and wood traits involved in the balance between weight increase and the gravitropic reaction. J. Theor. Biol. 256, 370–381 (2009).PubMed 
    Article 

    Google Scholar 
    West, G. B., Brown, J. H. & Enquist, B. J. A general model for the origin of allometric scaling laws in biology. Science 276, 122–126 (1997).CAS 
    PubMed 
    Article 

    Google Scholar 
    Mäkelä, A. & Valentine, H. T. Crown ratio influences allometric scaling in trees. Ecol 87, 2967–2972 (2006).Article 

    Google Scholar 
    Duursma, R. A. et al. Self‐shading affects allometric scaling in trees. Funct. Ecol. 24, 723–730 (2010).Article 

    Google Scholar 
    Pretzsch, H. & Dieler, J. Evidence of variant intra-and interspecific scaling of tree crown structure and relevance for allometric theory. Oecologia 169, 637–649 (2012).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Lin, Y. et al. Plant interactions alter the predictions of metabolic scaling theory. PloS One 8, e57612 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Cheng, D. et al. Scaling relationship between tree respiration rates and biomass. Biol. Lett. 6, 715–717 (2010).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Ogawa, K. Scaling relations based on the geometric and metabolic theories in woody plant species: A review. Perspect. Plant Ecol. Evol. Syst. 40, 125480 (2019).Article 

    Google Scholar 
    Risto, S. et al. Functional–structural plant models: a growing paradigm for plant studies. Ann. Bot. 114, 599–603 (2014).Article 

    Google Scholar 
    Jackson, T. et al. Finite element analysis of trees in the wind based on terrestrial laser scanning data. Agric. Meteorol. 265, 137–144 (2019).Article 

    Google Scholar 
    Disney, M. Terrestrial LiDAR: a three‐dimensional revolution in how we look at trees. N. Phytol. 222, 1736–1741 (2019).Article 

    Google Scholar 
    Malhi, Y. et al. New perspectives on the ecology of tree structure and tree communities through terrestrial laser scanning. Interface Focus 8, 20170052 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Bayer, D., Seifert, S. & Pretzsch, H. Structural crown properties of Norway spruce (Picea abies [L.] Karst.) and European beech (Fagus sylvatica [L.]) in mixed versus pure stands revealed by terrestrial laser scanning. Trees 27, 1035–1047 (2013).Article 

    Google Scholar 
    Lin, Y. & Herold, M. Tree species classification based on explicit tree structure feature parameters derived from static terrestrial laser scanning data. Agric. Meteorol. 216, 105–114 (2016).Article 

    Google Scholar 
    Tanago, J. G. et al. Estimation of above‐ground biomass of large tropical trees with terrestrial LiDAR. Methods Ecol. Evol. 9, 223–234 (2018).Article 

    Google Scholar 
    Takoudjou, S. M. et al. Using terrestrial laser scanning data to estimate large tropical trees biomass and calibrate allometric models: A comparison with traditional destructive approach. Methods Ecol. Evol. 9, 905–916 (2018).Article 

    Google Scholar 
    Dassot, M., Fournier, M. & Deleuze, C. Assessing the scaling of the tree branch diameters frequency distribution with terrestrial laser scanning: methodological framework and issues. Ann. Sci. 76, 66 (2019).Article 

    Google Scholar 
    Klockow, P. A. et al. Allometry and structural volume change of standing dead southern pine trees using non-destructive terrestrial LiDAR. Remote Sens. Environ. 241, 111729 (2020).Article 

    Google Scholar 
    Stovall, A. E., Anderson-Teixeira, K. J. & Shugart, H. H. Assessing terrestrial laser scanning for developing non-destructive biomass allometry. Ecol. Manag. 427, 217–229 (2018).Article 

    Google Scholar 
    Dai, J. et al. Drought-modulated allometric patterns of trees in semi-arid forests. Commun. Biol. 3, 1–8 (2020).Article 

    Google Scholar 
    Ogawa, K., Hagihara, A. & Hozumi, K. Growth analysis of a seedling community of Chamaecyparis obtusa. VI. Estimation of individual gross primary production by the summation method. In Transactions of the 30th Meeting of Chubu Branch of Japanese Forestry Society, 179–181 (Honda Kiyoshi, 1985).Yokota, T. & Hagihara, A. Dependence of the aboveground CO2 exchange rate on tree size in field-grown hinoki cypress (Chamaecyparis obtusa). J. Plant Res. 109, 177–184 (1996).Article 

    Google Scholar 
    Enquist, B. J. et al. Biological scaling: does the exception prove the rule? Nature 445, E9–E10 (2007).CAS 
    PubMed 
    Article 

    Google Scholar 
    Lau, A. et al. Estimating architecture-based metabolic scaling exponents of tropical trees using terrestrial LiDAR and 3D modelling. Ecol. Manag. 439, 132–145 (2019).Article 

    Google Scholar 
    Li, Y. et al. Retrieval of tree branch architecture attributes from terrestrial laser scan data using a Laplacian algorithm. Agric. Meteorol. 284, 107874 (2020).Article 

    Google Scholar 
    Noyer, E. et al. Biomechanical control of beech pole verticality (Fagus sylvatica) before and after thinning: theoretical modelling and ground‐truth data using terrestrial LiDAR. Am. J. Bot. 106, 187–198 (2019).PubMed 
    Article 

    Google Scholar 
    Jackson, T. et al. A new architectural perspective on wind damage in a natural forest. Front. Glob. Chang. 1, 13 (2019).Article 

    Google Scholar 
    Jackson, T. Strain Measurements on 21 Trees in Wytham Woods, UK. NERC Environmental Information Data Centre. https://doi.org/10.5285/533d87d3-48c1-4c6e-9f2f-fda273ab45bc (2018).Kozłowski, J. & Konarzewski, M. Is West, Brown and Enquist’s model of allometric scaling mathematically correct and biologically relevant? Funct. Ecol. 18, 283–289 (2004).Article 

    Google Scholar 
    Kleiber, M. Body size and metabolic rate. Physiol. Rev. 27, 511–541 (1947).CAS 
    PubMed 
    Article 

    Google Scholar 
    Hay, M. J. M. et al. Branching responses of a plagiotropic clonal herb to localised incidence of light simulating that reflected from vegetation. Oecologia 127, 185–190 (2001).CAS 
    PubMed 
    Article 

    Google Scholar 
    Cordero, R. A., Fetcher, N. & Voltzow, J. Effects of wind on the allometry of two species of plants in an elfin cloud forest. Biotropica 39, 177–185 (2010).Article 

    Google Scholar 
    Anfodillo, T. et al. Allometric trajectories and “stress”: a quantitative approach. Front. Plant Sci. 7, 1681 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Louarn, G. & Song, Y. Two decades of functional-structural plant modelling: now addressing fundamental questions in systems biology and predictive ecology. Ann. Bot. 126, 501–509 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Poorter, H. & Sack, L. Pitfalls and possibilities in the analysis of biomass allocation patterns in plants. Front. Plant Sci. 3, 259 (2012).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Thomas, S. C. Reproductive allometry in Malaysian rain forest trees: biomechanics versus optimal allocation. Evol. Ecol. 10, 517–530 (1996).Article 

    Google Scholar 
    Kempes, C. P. et al. Predicting maximum tree heights and other traits from allometric scaling and resource limitations. PLoS One 6, e20551 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Blanchard, E. et al. Contrasted allometries between stem diameter, crown area, and tree height in five tropical biogeographic areas. Trees 30, 1953–1968 (2016).Article 

    Google Scholar 
    Swetnam, T. L., O’Connor, C. D. & Lynch, A. M. Tree morphologic plasticity explains deviation from metabolic scaling theory in semi-arid conifer forests, southwestern USA. PLoS One 11, e0157582 (2016).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Loehle, C. Biomechanical constraints on tree architecture. Trees 30, 2061–2070 (2016).Article 

    Google Scholar 
    Guillon, T., Dumont, Y. & Fourcaud, T. Numerical methods for the biomechanics of growing trees. Comput. Math. Appl. 64, 289–309 (2012).Article 

    Google Scholar 
    Olson, M. E., Rosell, J. A., Muñoz, S. Z. & Castorena, M. Carbon limitation, stem growth rate and the biomechanical cause of Corner’s rules. Ann. Bot. 122, 583–592 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    West, G. B., Enquist, B. J. & Brown, J. H. A general quantitative theory of forest structure and dynamics. Proc. Natl Acad. Sci. USA 106, 7040–7045 (2009).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar  More