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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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    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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    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

  • 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

    Gentrified gardens

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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

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    Evaluating the temporal and spatio-temporal niche partitioning between carnivores by different analytical method in northeastern Japan

    Gause, G. F. Experimental analysis of Vito Volterra’s mathematical theory of the struggle for existence. Science 79, 16–17 (1934).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Amarasekare, P. Competitive coexistence in spatially structured environments: A synthesis. Ecol. Lett. 6, 1109–1122 (2003).Article 

    Google Scholar 
    HilleRisLambers, J., Adler, P. B., Harpole, W. S., Levine, J. M. & Mayfield, M. M. Rethinking community assembly through the lens of coexistence theory. Annu. Rev. Ecol. Evol. Syst. 43, 227–248 (2012).Article 

    Google Scholar 
    Wisz, M. S. et al. The role of biotic interactions in shaping distributions and realised assemblages of species: Implications for species distribution modelling. Biol. Rev. 88, 15–30 (2013).PubMed 
    Article 

    Google Scholar 
    Frey, S., Fisher, J. T., Burton, A. C. & Volpe, J. P. Investigating animal activity patterns and temporal niche partitioning using camera-trap data: Challenges and opportunities. Remote Sens. Ecol. Conserv. 3, 123–132 (2017).Article 

    Google Scholar 
    Davis, C. L. et al. Ecological correlates of the spatial co-occurrence of sympatric mammalian carnivores worldwide. Ecol. Lett. 21, 1401–1412 (2018).PubMed 
    Article 

    Google Scholar 
    Durant, S. M. Competition refuges and coexistence: An example from Serengeti carnivores. J. Anim. Ecol. 67, 370–386 (1998).Article 

    Google Scholar 
    Fedriani, J. M., Fuller, T. K., Sauvajot, R. M. & York, E. C. Competition and intraguild predation among three sympatric carnivores. Oecologia 125, 258–270 (2000).ADS 
    PubMed 
    Article 

    Google Scholar 
    Kamler, J. F., Ballard, W. B., Gilliland, R. L. & Mote, K. Spatial relationships between swift foxes and coyotes in northwestern Texas. Can. J. Zool. 81, 168–172 (2003).Article 

    Google Scholar 
    Vanak, A. T. et al. Moving to stay in place: Behavioral mechanisms for coexistence of African large carnivores. Ecology 94, 2619–2631 (2013).PubMed 
    Article 

    Google Scholar 
    Donadio, E. & Buskirk, S. W. Diet, morphology, and interspecific killing in carnivora. Am. Nat. 167, 524–536 (2006).PubMed 
    Article 

    Google Scholar 
    Tsunoda, H. et al. Food niche segregation between sympatric golden jackals and red foxes in central Bulgaria. J. Zool. 303, 64–71 (2017).Article 

    Google Scholar 
    Palomares, F. & Caro, T. M. Interspecific killing among mammalian carnivores. Am. Nat. 153, 492–508 (1999).CAS 
    PubMed 
    Article 

    Google Scholar 
    Linnell, J. D. C. & Strand, O. Interference interactions, co-existence and conservation of mammalian carnivores. Divers. Distrib. 6, 169–176 (2000).Article 

    Google Scholar 
    Kamler, J. F., Stenkewitz, U., Klare, U., Jacobsen, N. F. & MacDonald, D. W. Resource partitioning among cape foxes, bat-eared foxes, and black-backed jackals in South Africa. J. Wildl. Manag. 76, 1241–1253 (2012).Article 

    Google Scholar 
    Di Bitetti, M. S., Di Blanco, Y. E., Pereira, J. A., Paviolo, A. & Pírez, I. J. Time Partitioning favors the coexistence of sympatric crab-eating foxes (Cerdocyon thous) and Pampas Foxes (Lycalopex gymnocercus). J. Mammal. 90, 479–490 (2009).Article 

    Google Scholar 
    Lesmeister, D. B., Nielsen, C. K., Schauber, E. M. & Hellgren, E. C. Spatial and temporal structure of a mesocarnivore guild in Midwestern North America. Wildl. Monogr. 191, 1–61 (2015).Article 

    Google Scholar 
    Di Bitetti, M. S., De Angelo, C. D., Di Blanco, Y. E. & Paviolo, A. Niche partitioning and species coexistence in a Neotropical felid assemblage. Acta Oecologica 36, 403–412 (2010).ADS 
    Article 

    Google Scholar 
    Monterroso, P., Alves, P. C. & Ferreras, P. Plasticity in circadian activity patterns of mesocarnivores in southwestern Europe: Implications for species coexistence. Behav. Ecol. Sociobiol. 68, 1403–1417 (2014).Article 

    Google Scholar 
    Tsunoda, H., Ito, K., Peeva, S., Raichev, E. & Kaneko, Y. Spatial and temporal separation between the golden jackal and three sympatric carnivores in a human-modified landscape in central Bulgaria. Zool. Ecol. 28, 172–179 (2018).Article 

    Google Scholar 
    Tsunoda, H. et al. Spatio-temporal partitioning facilitates mesocarnivore sympatry in the Stara Planina Mountains, Bulgaria. Zoology 141, 125801 (2020).PubMed 
    Article 

    Google Scholar 
    Ramesh, T., Kalle, R., Sankar, K. & Qureshi, Q. Spatio-temporal partitioning among large carnivores in relation to major prey species in Western Ghats. J. Zool. 287, 269–275 (2012).Article 

    Google Scholar 
    Gómez-Ortiz, Y., Monroy-Vilchis, O. & Castro-Arellano, I. Temporal coexistence in a carnivore assemblage from central Mexico: Temporal-domain dependence. Mammal Res. 64, 333–342 (2019).Article 

    Google Scholar 
    Ridout, M. S. & Linkie, M. Estimating overlap of daily activity patterns from camera trap data. J. Agric. Biol. Environ. Stat. 14, 322–337 (2009).MathSciNet 
    MATH 
    Article 

    Google Scholar 
    Meredith, M. & Ridout, M. Overlap: Estimates of coefficient of overlapping for animal activity patterns. https://cran.r-project.org/web/packages/overlaphttps://cran.r-project.org/web/packages/overlap/index.html (2018).Marinho, P. H., Fonseca, C. R., Sarmento, P., Fonseca, C. & Venticinque, E. M. Temporal niche overlap among mesocarnivores in a Caatinga dry forest. Eur. J. Wildl. Res. 66, 1–13 (2020).Article 

    Google Scholar 
    Vilella, M., Ferrandiz-Rovira, M. & Sayol, F. Coexistence of predators in time: Effects of season and prey availability on species activity within a Mediterranean carnivore guild. Ecol. Evol. 10, 11408–11422 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Zhao, G. et al. Spatio-temporal coexistence of sympatric mesocarnivores with a single apex carnivore in a fine-scale landscape. Glob. Ecol. Conserv. 21, e00897 (2020).Article 

    Google Scholar 
    Farmer, M. J., Allen, M. L., Olson, E. R., Van Stappen, J. & Van Deelen, T. R. Agonistic interactions and island biogeography as drivers of carnivore spatial and temporal activity at multiple scales. Can. J. Zool. 99, 309–317 (2021).Article 

    Google Scholar 
    Watabe, R. & Saito, M. U. Diel activity patterns of three sympatric medium-sized carnivores during winter and spring in a heavy snowfall area in northeastern Japan. Mammal Study 46, 69–75 (2021).Article 

    Google Scholar 
    Lashley, M. A. et al. Estimating wildlife activity curves: comparison of methods and sample size. Sci. Rep. 8, 4173 (2018).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Niedballa, J., Wilting, A., Sollmann, R., Hofer, H. & Courtiol, A. Assessing analytical methods for detecting spatiotemporal interactions between species from camera trapping data. Remote Sens. Ecol. Conserv. 5, 272–285 (2019).Article 

    Google Scholar 
    Karanth, K. U. et al. Spatio-temporal interactions facilitate large carnivore sympatry across a resource gradient. Proc. R. Soc. B Biol. Sci. 284, 20161860 (2017).Article 

    Google Scholar 
    Cusack, J. J. et al. Revealing kleptoparasitic and predatory tendencies in an African mammal community using camera traps: A comparison of spatiotemporal approaches. Oikos 126, 812–822 (2017).Article 

    Google Scholar 
    Balme, G. et al. Big cats at large: density, structure, and spatio-temporal patterns of a leopard population free of anthropogenic mortality. Popul. Ecol. 61, 256–267 (2019).Article 

    Google Scholar 
    Li, Z. et al. Coexistence of two sympatric flagship carnivores in the human-dominated forest landscapes of Northeast Asia. Landsc. Ecol. 34, 291–305 (2019).Article 

    Google Scholar 
    Lahkar, D., Ahmed, M. F., Begum, R. H., Das, S. K. & Harihar, A. Inferring patterns of sympatry among large carnivores in Manas National Park: A prey-rich habitat influenced by anthropogenic disturbances. Anim. Conserv. 24, 589–601 (2021).Article 

    Google Scholar 
    Paúl, M. J., Layna, J. F., Monterroso, P. & Álvares, F. Resource partitioning of sympatric African Wolves (Canis lupaster) and side-striped jackals (Canis adustus) in an arid environment from West Africa. Diversity 12, 477 (2020).Article 

    Google Scholar 
    Prat-Guitart, M., Onorato, D. P., Hines, J. E. & Oli, M. K. Spatiotemporal pattern of interactions between an apex predator and sympatric species. J. Mammal. 101, 1279–1288 (2020).Article 

    Google Scholar 
    Stone, L. & Roberts, A. The checkerboard score and species distributions. Oecologia 85, 74–79 (1990).ADS 
    PubMed 
    Article 

    Google Scholar 
    Griffith, D. M., Veech, J. A. & Marsh, C. J. Cooccur: Probabilistic species co-occurrence analysis in r. J. Stat. Softw. 69, 1–17 (2016).Article 

    Google Scholar 
    Noor, A., Mir, Z. R., Veeraswami, G. G. & Habib, B. Activity patterns and spatial co-occurrence of sympatric mammals in the moist temperate forest of the Kashmir Himalaya, India. Folia Zool. 66, 231–241 (2017).Article 

    Google Scholar 
    de Satgé, J., Teichman, K. & Cristescu, B. Competition and coexistence in a small carnivore guild. Oecologia 184, 873–884 (2017).ADS 
    PubMed 
    Article 

    Google Scholar 
    Kass, J. M., Tingley, M. W., Tetsuya, T. & Koike, F. Co-occurrence of invasive and native carnivorans affects occupancy patterns across environmental gradients. Biol. Invasions 22, 2251–2266 (2020).Article 

    Google Scholar 
    Louppe, V., Herrel, A., Pisanu, B., Grouard, S. & Veron, G. Assessing occupancy and activity of two invasive carnivores in two Caribbean islands: implications for insular ecosystems. J. Zool. 313, 182–194 (2020).Article 

    Google Scholar 
    Proulx, G. et al. World distribution and status of the genus Martes in 20. In Martens and Fishers (Martes) in Human-Altered Environments (eds Harrison, D. J. et al.) 21–76 (Springer, Berlin, 2005). https://doi.org/10.1007/b99487.Chapter 

    Google Scholar 
    Ohdachi, S. D., Ishibashi, Y., Iwasa, M., Fukuki, D. & Saitoh, T. The Wild Mammals of Japan 2nd edn. (Shokadoh Book Seller, Kyoto, 2015).
    Google Scholar 
    Kauhala, K. & Saeki, M. Nyctereutes procyonoides. The IUCN Red List of Threatened Species. https://www.iucnredlist.org/species/14925/85658776 (2016).Yamamoto, Y. Comparative analyses on food habits of Japanese marten, red fox, badger and raccoon dog in the Mt. Nyugasa, Nagano Prefecture, Japan. Nat. Environ. Sci. Res. 7, 45–52 (1994) (in Japanese with English summary).
    Google Scholar 
    Hisano, M. et al. A comparison of visual and genetic techniques for identifying Japanese marten scats enabling diet examination in relation to seasonal food availability in a sub-alpine area of Japan. Zool. Sci. 34, 137–146 (2017).Article 

    Google Scholar 
    Lindstrom, E. R., Brainerd, S. M., Helldin, J. O. & Overskaug, K. Pine marten-red fox interactions: A case of intraguild predation?. Ann. Zool. Fenn. 32, 123–130 (1995).
    Google Scholar 
    Waggershauser, C. N., Ruffino, L., Kortland, K. & Lambin, X. Lethal interactions among forest-grouse predators are numerous, motivated by hunger and carcasses, and their impacts determined by the demographic value of the victims. Ecol. Evol. 11, 7164–7186 (2021).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Watabe, R., Saito, M. U., Enari, H. S. & Enari, H. Mammalian fauna of the Kaminagawa Experimental Forest of Yamagata University detected by camera traps. Tohoku J. For. Sci. 25, 37–40 (2020) (in Japanese).
    Google Scholar 
    Hofmeester, T. R., Rowcliffe, J. M. & Jansen, P. A. A simple method for estimating the effective detection distance of camera traps. Remote Sens. Ecol. Conserv. 3, 81–89 (2017).Article 

    Google Scholar 
    Di Bitetti, M. S., Paviolo, A. & De Angelo, C. Camera trap photographic rates on roads vs. off roads: Location does matter. Mastozoología Neotrop. 21, 37–46 (2014).
    Google Scholar 
    Borcard, D. & Legendre, P. Is the Mantel correlogram powerful enough to be useful in ecological analysis? A simulation study. Ecology 93, 1473–1481 (2012).PubMed 
    Article 

    Google Scholar 
    Oksanen, J. et al. Vegan: community ecology package. https://cran.r-project.org/web/packages/veganhttps://cran.r-project.org/web/packages/vegan/index.html (2019).R Core Team. R: a language environment for statistical computing. r foundation for statistical computing, Vienna, Austria. https://www.r-project.org/https://www.r-project.org/ (2021).Linkie, M. & Ridout, M. S. Assessing tiger-prey interactions in Sumatran rainforests. J. Zool. 284, 224–229 (2011).Article 

    Google Scholar 
    Watabe, R. & Saito, M. U. Effects of vehicle-passing frequency on forest roads on the activity patterns of carnivores. Landsc. Ecol. Eng. 17, 225–231 (2021).Article 

    Google Scholar 
    Furukawa, G. genkiFurukawa/rSetDayNightAttr documentation. https://rdrr.io/github/genkiFurukawa/rSetDayNightAhttps://rdrr.io/github/genkiFurukawa/rSetDayNightAttr/ (2019).Mielke, P. W., Berry, K. J. & Johnson, E. S. Multi-response permutation procedures for a priori classifications. Commun. Stat. Theory Methods 5, 1409–1424 (1976).MATH 
    Article 

    Google Scholar 
    Kronfeld-Schor, N. & Dayan, T. Partitioning of time as an ecological resource. Annu. Rev. Ecol. Evol. Syst. 34, 153–181 (2003).Article 

    Google Scholar 
    Monterroso, P., Alves, P. C. & Ferreras, P. Catch me if you can: Diel activity patterns of mammalian prey and predators. Ethology 119, 1044–1056 (2013).Article 

    Google Scholar 
    Hendrichsen, D. K. & Tyler, N. J. C. How the timing of weather events influences early development in a large mammal. Ecology 95, 1737–1745 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    Herfindal, I. et al. Weather affects temporal niche partitioning between moose and livestock. Wildlife Biol. https://doi.org/10.2981/wlb.00275 (2017).Article 

    Google Scholar 
    Haswell, P. M., Jones, K. A., Kusak, J. & Hayward, M. W. Fear, foraging and olfaction: How mesopredators avoid costly interactions with apex predators. Oecologia 187, 573–583 (2018).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Barrull, J. et al. Factors and mechanisms that explain coexistence in a Mediterranean carnivore assemblage: An integrated study based on camera trapping and diet. Mamm. Biol. 79, 123–131 (2014).Article 

    Google Scholar 
    Tattersall, E. R., Burgar, J. M., Fisher, J. T. & Burton, A. C. Boreal predator co-occurrences reveal shared use of seismic lines in a working landscape. Ecol. Evol. 10, 1678–1691 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Moll, R. J. et al. Humans and urban development mediate the sympatry of competing carnivores. Urban Ecosyst. 21, 765–778 (2018).Article 

    Google Scholar 
    McCreadie, J. W. & Bedwell, C. R. Patterns of co-occurrence of stream insects and an examination of a causal mechanism: Ecological checkerboard or habitat checkerboard?. Insect Conserv. Divers. 6, 105–113 (2013).Article 

    Google Scholar  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