Ecology

Chapron, G. et al. Recovery of large carnivores in Europe’s modern human-dominated landscapes. Science 346, 1517–1519 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Lute, M. L., Carter, N. H., López-Bao, J. V. & Linnell, J. D. C. Conservation professionals’ views on governing for coexistence with large carnivores. Biol. Cons. 248, 108668 (2020).Article 

Google Scholar 
Gantchoff, M. G. & Belant, J. L. Regional connectivity for recolonizing American black bears (Ursus americanus) in southcentral USA. Biol. Cons. 214, 66–75 (2017).Article 

Google Scholar 
Ripple, W. J. et al. Status and ecological effects of the world’s largest carnivores. Science 343, 25 (2014).Article 
CAS 

Google Scholar 
Kays, R. et al. Does hunting or hiking affect wildlife communities in protected areas?. J. Appl. Ecol. 54, 242–252 (2017).Article 

Google Scholar 
Schipper, J. et al. The status of the world’s land and marine mammals: diversity, threat, and knowledge. Science 322, 225–230 (2008).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Smith, J. A., Wang, Y. & Wilmers, C. C. Top carnivores increase their kill rates on prey as a response to human-induced fear. Proc. R. Soc. B Biol. Sci. 282, 20142711 (2015).Article 

Google Scholar 
Stillfried, M., Belant, J. L., Svoboda, N. J., Beyer, D. E. & Kramer-Schadt, S. When top predators become prey: Black bears alter movement behaviour in response to hunting pressure. Behav. Proc. 120, 30–39 (2015).Article 

Google Scholar 
Støen, O.-G. et al. Physiological evidence for a human-induced landscape of fear in brown bears (Ursus arctos). Physiol. Behav. 152, 244–248 (2015).PubMed 
Article 
CAS 

Google Scholar 
Evans, M. J., Rittenhouse, T. A. G., Hawley, J. E. & Rego, P. W. Black bear recolonization patterns in a human-dominated landscape vary based on housing: New insights from spatially explicit density models. Landsc. Urban Plan. 162, 13–24 (2017).Article 

Google Scholar 
LaRue, M. A. et al. Cougars are recolonizing the midwest: Analysis of cougar confirmations during 1990–2008. J. Wildl. Manag. 76, 1364–1369 (2012).Article 

Google Scholar 
Cove, M. V., Fergus, C., Lacher, I., Akre, T. & McShea, W. J. Projecting mammal distributions in response to future alternative landscapes in a rapidly transitioning region. Remote Sens. 11, 2482 (2019).ADS 
Article 

Google Scholar 
Frid, A. & Dill, L. Human-caused disturbance stimuli as a form of predation risk. Conserv. Ecol. 6, 25 (2002).
Google Scholar 
Clinchy, M. et al. Fear of the human “super predator” far exceeds the fear of large carnivores in a model mesocarnivore. Behav. Ecol. 27, 1826–1832 (2016).
Google Scholar 
Suraci, J. P., Clinchy, M., Zanette, L. Y. & Wilmers, C. C. Fear of humans as apex predators has landscape-scale impacts from mountain lions to mice. Ecol. Lett. 22, 1578–1586 (2019).PubMed 
Article 

Google Scholar 
Gaynor, K. M., Hojnowski, C. E., Carter, N. H. & Brashares, J. S. The influence of human disturbance on wildlife nocturnality. Science 360, 1232–1235 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Smith, J. A., Thomas, A. C., Levi, T., Wang, Y. & Wilmers, C. C. Human activity reduces niche partitioning among three widespread mesocarnivores. Oikos 127, 890–901 (2018).Article 

Google Scholar 
Tucker, M. A. et al. Moving in the Anthropocene: Global reductions in terrestrial mammalian movements. Science 359, 466–469 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Carter, N. H., Brown, D. G., Etter, D. R. & Visser, L. G. American black bear habitat selection in northern Lower Peninsula, Michigan, USA, using discrete-choice modeling. Ursus 21, 57–71 (2010).Article 

Google Scholar 
Naidoo, R. & Burton, A. C. Relative effects of recreational activities on a temperate terrestrial wildlife assemblage. Conserv. Sci. Pract. 2, e271 (2020).
Google Scholar 
Geffroy, B., Samia, D. S. M., Bessa, E. & Blumstein, D. T. How nature-based tourism might increase prey vulnerability to predators. Trends Ecol. Evol. 30, 755–765 (2015).PubMed 
Article 

Google Scholar 
Geffroy, B. et al. Evolutionary dynamics in the Anthropocene: Life history and intensity of human contact shape antipredator responses. PLoS Biol. 18, e3000818 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Beeco, J. A., Hallo, J. C. & Brownlee, M. T. J. GPS visitor tracking and recreation suitability mapping: tools for understanding and managing visitor use. Landsc. Urban Plan. 127, 136–145 (2014).Article 

Google Scholar 
Thorsen, N. H. et al. Smartphone app reveals that lynx avoid human recreationists on local scale, but not home range scale. Sci. Rep. 12, 1–13 (2022).Article 
CAS 

Google Scholar 
Evans, M. J., Hawley, J. E., Rego, P. W. & Rittenhouse, T. A. G. Hourly movement decisions indicate how a large carnivore inhabits developed landscapes. Oecologia 190, 11–23 (2019).ADS 
PubMed 
Article 

Google Scholar 
Carlos, A. W. D., Bright, A. D., Teel, T. L. & Vaske, J. J. Human-black bear conflict in urban areas: an integrated approach to management response. Hum. Dimens. Wildl. 14, 174–184 (2009).Article 

Google Scholar 
Johnson, H. E. et al. Human development and climate affect hibernation in a large carnivore with implications for human–carnivore conflicts. J. Appl. Ecol. 55, 663–672 (2018).Article 

Google Scholar 
Gould, N. P., Powell, R., Olfenbuttel, C. & DePerno, C. S. Growth and reproduction by young urban and rural black bears. J. Mammal. 102, 1165–1173 (2021).Article 

Google Scholar 
Ditmer, M. A., Noyce, K. V., Fieberg, J. R. & Garshelis, D. L. Delineating the ecological and geographic edge of an opportunist: The American black bear exploiting an agricultural landscape. Ecol. Model. 387, 205–219 (2018).Article 

Google Scholar 
McFadden-Hiller, J. E. Jr. & Belant, J. L. Spatial distribution of black bear incident reports in michigan. PLoS One 11, e0154474 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Ladle, A., Steenweg, R., Shepherd, B. & Boyce, M. S. The role of human outdoor recreation in shaping patterns of grizzly bear-black bear co-occurrence. PLoS One 13, e0191730 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Wilbur, R. C., Lischka, S. A., Young, J. R. & Johnson, H. E. Experience, attitudes, and demographic factors influence the probability of reporting human–black bear interactions. Wildl. Soc. Bull. 42, 22–31 (2018).Article 

Google Scholar 
Lustig, E. J., Lyda, S. B., Leslie, D. M., Luttbeg, B. & Fairbanks, W. S. Resource selection by recolonizing American Black Bears. J. Wildl. Manage. 85, 531–542 (2021).Article 

Google Scholar 
Sun, C. C., Fuller, A. K., Hare, M. P. & Hurst, J. E. Evaluating population expansion of black bears using spatial capture-recapture. J. Wildl. Manage. 81, 814–823 (2017).Article 

Google Scholar 
Kautz, T. M. et al. Large carnivore response to human road use suggests a landscape of coexistence. Glob. Ecol. Conserv. 30, e01772 (2021).Article 

Google Scholar 
Michigan Department of Natural Resources (MIDNR) (2021).Blount, J. D., Chynoweth, M. W., Green, A. M. & Şekercioğlu, Ç. H. Review: COVID-19 highlights the importance of camera traps for wildlife conservation research and management. Biol. Cons. 256, 108984 (2021).Article 

Google Scholar 
Weather Atlas. https://www.weather-atlas.com/enEvans, J. S. Spatial Analysis and Modelling Utilities. Package ‘spatialEco’. https://cran.r-project.org/web/packages/spatialEco/spatialEco.pdf (2021).Díaz-Ruiz, F., Caro, J., Delibes-Mateos, M., Arroyo, B. & Ferreras, P. Drivers of red fox (Vulpes vulpes) daily activity: prey availability, human disturbance or habitat structure?. J. Zool. 298, 128–138 (2016).Article 

Google Scholar 
Moore, J. F. et al. Comparison of species richness and detection between line transects, ground camera traps, and arboreal camera traps. Anim. Conserv. 23, 561–572 (2020).Article 

Google Scholar 
Parsons, A. W. et al. Urbanization focuses carnivore activity in remaining natural habitats, increasing species interactions. J. Appl. Ecol. 56, 1894–1904 (2019).Article 

Google Scholar 
Allen, M. L., Sibarani, M. C., Utoyo, L. & Krofel, M. Terrestrial mammal community richness and temporal overlap between tigers and other carnivores in Bukit Barisan Selatan National Park, Sumatra. Anim. Biodiv. Conserv. 43(1), 97–107 (2020).Article 

Google Scholar 
Tian, C. et al. Temporal niche patterns of large mammals in Wanglang National Nature Reserve, China. Glob. Ecol. Conserv. 22, e01015 (2020).Article 

Google Scholar 
Meredith, M. & Ridout, M. Estimates of coefficient of overlapping for animal activity patterns. Package ‘overlap’. https://cran.r-project.org/web/packages/overlap/overlap.pdf (2020).RStudio Team. RStudio: Integrated Development for R. RStudio, PBC, Boston, MA. http://www.rstudio.com/ (2021).Ridout, M. S. & Linkie, M. Estimating overlap of daily activity patterns from camera trap data. JABES 14, 322–337 (2009).MathSciNet 
MATH 
Article 

Google Scholar 
Lashley, M. A. et al. Estimating wildlife activity curves: comparison of methods and sample size. Sci. Rep. 8, 1–11 (2018).CAS 
Article 

Google Scholar 
Rowcliffe, M. Animal Activity Statistics. Package ‘activity’. https://cran.r-project.org/web/packages/activity/activity.pdf (2021).MacKenzie, D. I., Nichols, J. D., Hines, J. E., Knutson, M. G. & Franklin, A. B. Estimating site occupancy, colonization, and local extinction when a species is detected imperfectly. Ecology 84, 2200–2207 (2003).Article 

Google Scholar 
Wei, T., & Simko, V. Visualization of a Correlation Matrix. Package ‘corrplot’. https://cran.r-project.org/web/packages/corrplot/corrplot.pdf (2017).Norton, D. C. et al. Female American black bears do not alter space use or movements to reduce infanticide risk. PLoS One 13, e0203651 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Ditmer, M. A. et al. Behavioral and physiological responses of American black bears to landscape features within an agricultural region. Ecosphere 6, 1–21 (2015).Article 

Google Scholar 
Clark, D. et al. Using machine learning methods to predict the movement trajectories of the Louisiana black bear. SMU Data Sci. Rev. 5, 25 (2021).
Google Scholar  More

Growth conditions and plant materialsTwo experiments were conducted concurrently (sites A and B) in the same screen house in 2019 between the months of May and July at the Landmark University Greenhouse and Hydroponic Technology Center, a section of the Teaching and Research Farm of the University in Omu-Aran, Kwara State Nigeria. Experiment at site B was conducted simultaneously as A so as to validate the results of experiment A. Landmark University lies within Latitude 8° 7′ 26.21388″ and 5° 5′ 0.1788″. Both experiments (A & B) involved tomato (Solanum lycopersicum L. variety cherry) and cucumber (Cucumis sativus L. variety marketer) crops. For each crop, seeds were sown into a separate seed tray filled with coco peat (Coco peat, SRIMATHI EXPORT, INDIA). Cocopeat is the mesocarp tissue or husk after the grinding of coconut fruit. It has a lightweight and high water and nutrient holding capacities, it has an acceptable pH, electrical conductivity, and other chemical attributes27. Rice husk is the by-product of rice after milling. The rice husk used was collected from the rice processing mill of Landmark University. Rice husk is a highly porous and light weighted material with a very high specific area28.Two sets of seed trays (one for organic and another for inorganic fertilizers) were used each for tomato and cucumber crops in the nursery. Both were raised in the nursery for two weeks before transplanting. Black grow bags (30 × 17 cm) filled with a coco peat/rice husk (1:4 ratio by volume) mixture with a weight of about 10 kg were arranged in a screen house. Both the nursery and establishment of crop proper take place in a screen house. The screen house has a galvanized iron as the frame, a UV covering on top, side net for screening insect pests the floor fairly covered with granite. Temperature and relative humidity within the screen house during the period of the experiment was monitored using a Thermograph and a Barograph, and they were at an average of 31 °C and 75%, respectively.The grow bags were randomly placed in the screen house for the unbiased application of amendments. For both tomato and cucumber crops, the treatment comprised of six (6) levels of liquid organic fertilizer (5, 15, 25, 35, 45, 55 mL), in-organic fertilizer, and a control (ordinary borehole water). Levels of organic fertilizers were selected based on the recommendation of 20 mL of liquid organic fertilizer by29. The eight (8) treatments both for tomato and cucumber were arranged in a Completely Randomized Design replicated three times. One healthy plant was maintained per grow bag and four grow bags represent a treatment and there were 32 plants per block each for tomato and cucumber. For both crops, the experiment lasted for 90 days.Organic and in-organic nutrient solutionsThe liquid organic fertilizer used was obtained from the biomass of Mexican sunflower (Tithonia diversifolia). Fresh biomass (mainly leaves and stems) of the plant was collected from the Teaching and Research Farms of Landmark University, Nigeria. After rinsing, they were cut with a sterile knife into pieces of ≤ 1 cm size. A sample was taken for initial physicochemical analyses by grinding in a sterile mortal, diluted with sterile water and analyzed. The biomass was then soaked in sterile water inside a clean container, and allowed to ferment spontaneously for a period of 14 days. During the fermentation, samples were taken every 4 days for microbial analyses of the major players during the fermentation. At the end of fermentation, the mixture was separated using a sieve of mesh size ≤ 2 mm. The liquid portion was then refrigerated prior to the planting regime while another sample was taken to ascertain the physicochemical and microbial qualities of the produced liquid fertilizer. The chemical analysis is presented in Table 4. For inorganic fertilizer, Water soluble fertilizers employed in hydroponics were used (Hydroponics fertilizer, Anmol chemicals, India); calcium nitrate 650 mg L−1, potassium nitrate 450 mg L−1, magnesium 400 mg L−1, chelate 20 mg L−1, mono-ammonium phosphate 400 mg L−1. The electrical conductivity (EC) of the solution was 1.9 dS m-1.Irrigation and fertigationThe tomato and cucumber plants were fertigated morning and evening daily for one hour on each occasion according to the treatments. Preparation of the nutrient solution was with borehole water and was supplied to plants by an online pressure drip irrigation system set at 2.0 L h-1 using an arrowhead on each tomato and cucumber plant. Different tanks (250 L) were installed according to the various treatments making a total of 8 tanks. The organic fertilizer was diluted according to the various treatments equivalent to 1.25, 3.75, 6.25, 8.75, 11.25, and 13.75 L per 250 L of water respectively for 5, 15, 25, 35, 45, and 55 mL treatments. The nutrient solutions were refilled when the consumption is less than 20% of the initial volume (250 L) in the tank. One day per week, crops were irrigated with ordinary water to wash out pipes and prevent deposits of salts. The same concentration of nutrient was used from transplanting to the termination of the study for both tomato and cucumber crops, however, at the flowering of the crops, the volume of fertigation was increased to 3.0 L h-1 to be able to cope with the size of the plants.Trellising, pest and diseases controlFor both tomato and cucumber crops, plant vines were supported by twisting them around a wire that is- attached to the roof of the screen house and 2 m from the ground. Lateral outgrowths were cut off every week to ensure a sturdy single stem. Pests and diseases were scouted every day. Whiteflies, aphids, and other insects were controlled with orizon (Producer, location of producer) (active ingredient, acetamiprid, and abamectin) using 0.133% v/v. Fungi were controlled using ridomil gold (Producer, Location of producer) at 2% w/v.Determination of growth and yield of tomato and cucumberThree tomato and cucumber plants were randomly selected for each treatment for the determination of growth parameters (plant height, leaf area, number of leaves per plant, and stem diameter) at mid the flowering stage of tomato and cucumber plants.The leaf area of tomato was calculated using the model (A = KL2) developed by Lyon30, where L = Length of tomato leaf, K = constant which is 0.1551, and A = leaf area of tomato. Similarly, the leaf area of cucumber was calculated using A = 0.88LW – 4.27, where L = cucumber leaf length and W = cucumber leaf width, A = leaf area of cucumber31.Tomato fruits were ready for harvest from 65 days after transplanting, harvestings were done twice every week (Mondays and Fridays) for up to 85 days after transplanting. Similarly, harvesting of cucumber fruits started 35 days after transplanting and harvestings were also done twice a week (Mondays and Fridays), harvesting was carried out till 60 days after transplanting. Tomato and cucumber fruit yields were counted and weighed at each harvest.Analysis of tomato and cucumber leaves and fruitsAt the 50% flowering stage of tomato and cucumber plants, ten leaf samples were collected from each treatment. The leaf samples were oven-dried at 75 °C for 24 h and thereafter grounded. The grounded samples were later analyzed for nitrogen (N), phosphorous (P), potassium (K), calcium (Ca), and magnesium (Mg) content using the method of described by32. At harvest, four matured tomato and cucumber fruits of uniform size were selected per treatment, and their nutrient compositions were determined using the method of33.Statistical analysisAll data collected on the growth, yield, leaf, and fruit nutrient contents of tomato and cucumber were subjected to analysis of variance (ANOVA). The SPSS V 21.0 (New York, USA) software was used to perform ANOVA and Duncan’s multiple range test (DMRT) was used to compare means at a 5% probability level.
Ethical approvalI confirm that all the research meets ethical guidelines and adheres to the legal requirements of the study country.Compliance with international, national and/or institutional guidelinesExperimental research (either cultivated or wild), comply with relevant institutional, national, and international guidelines and legislation. Experimental studies were carried out in accordance with relevant institutional, national or international guidelines or regulation. More

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

Pogoreutz C, Voolstra CR, Rädecker N, Weis V, Cardenas A, Raina J-B. The coral holobiont highlights the dependence of cnidarian animal hosts on their associated microbes. In Bosch TCG, Hadfield MG, editors. Cellular Dialogues in the Holobiont. CRC Press; 2020. pp. 91–118. https://doi.org/10.1201/9780429277375-7Rohwer F, Seguritan V, Azam F, Knowlton N. Diversity and distribution of coral-associated bacteria. Mar Ecol Prog Ser. 2002;243:1–10.
Google Scholar 
LaJeunesse TC, Parkinson JE, Gabrielson PW, Jeong HJ, Reimer JD, Voolstra CR, et al. Systematic revision of Symbiodiniaceae highlights the antiquity and diversity of coral endosymbionts. Curr Biol. 2018;28:2570–80.e6CAS 
PubMed 

Google Scholar 
Muscatine L, Porter JW. Reef corals: mutualistic symbioses adapted to nutrient-poor environments. Bioscience 1977;27:454–60.
Google Scholar 
Christian R, Voolstra DJ, Suggett RS, Peixoto JE, Parkinson KM, Quigley CB, et al. Extending the natural adaptive capacity of coral holobionts. Nature Reviews Earth & Environment. 2021;2:747–762. https://doi.org/10.1038/s43017-021-00214-3Article 

Google Scholar 
Bourne DG, Morrow KM, Webster NS. Insights into the coral microbiome: underpinning the health and resilience of reef ecosystems. Annu Rev Microbiol. 2016;70:317–40.CAS 
PubMed 

Google Scholar 
Rädecker N, Pogoreutz C, Voolstra CR, Wiedenmann J, Wild C. Nitrogen cycling in corals: the key to understanding holobiont functioning? Trends Microbiol. 2015;23:490–7.PubMed 

Google Scholar 
Matthews JL, Raina JB, Kahlke T, Seymour JR, van Oppen MJ, Suggett DJ. Symbiodiniaceae‐bacteria interactions: rethinking metabolite exchange in reef‐building corals as multi‐partner metabolic networks. Environ Microbiol 2020;22:1675–87.PubMed 

Google Scholar 
Kimes NE, Van Nostrand JD, Weil E, Zhou J, Morris PJ. Microbial functional structure of Montastraea faveolata, an important Caribbean reef‐building coral, differs between healthy and yellow‐band diseased colonies. Environ Microbiol. 2010;12:541–56.CAS 
PubMed 

Google Scholar 
Neave MJ, Apprill A, Ferrier-Pagès C, Voolstra CR. Diversity and function of prevalent symbiotic marine bacteria in the genus Endozoicomonas. Appl Environ Micro. 2016;100:8315–24.CAS 

Google Scholar 
Neave MJ, Michell CT, Apprill A, Voolstra CR. Endozoicomonas genomes reveal functional adaptation and plasticity in bacterial strains symbiotically associated with diverse marine hosts. Sci Rep. 2017;7:1–12.
Google Scholar 
Krediet CJ, Ritchie KB, Alagely A, Teplitski M. Members of native coral microbiota inhibit glycosidases and thwart colonization of coral mucus by an opportunistic pathogen. ISME J. 2013;7:980–90.CAS 
PubMed 

Google Scholar 
Raina J-B, Tapiolas D, Motti CA, Foret S, Seemann T, Tebben J, et al. Isolation of an antimicrobial compound produced by bacteria associated with reef-building corals. PeerJ 2016;4:e2275.PubMed 
PubMed Central 

Google Scholar 
Diaz JM, Hansel CM, Apprill A, Brighi C, Zhang T, Weber L, et al. Species-specific control of external superoxide levels by the coral holobiont during a natural bleaching event. Nat Commun. 2016;7:1–10.
Google Scholar 
Dunlap WC, Shick JM. Ultraviolet radiation‐absorbing mycosporine‐like amino acids in coral reef organisms: a biochemical and environmental perspective. J Phycol. 1998;34:418–30.
Google Scholar 
Webster NS, Smith LD, Heyward AJ, Watts JE, Webb RI, Blackall LL, et al. Metamorphosis of a scleractinian coral in response to microbial biofilms. Appl Environ Microbiol. 2004;70:1213–21.CAS 
PubMed 
PubMed Central 

Google Scholar 
Gómez-Lemos LA, Doropoulos C, Bayraktarov E, Diaz-Pulido G. Coralline algal metabolites induce settlement and mediate the inductive effect of epiphytic microbes on coral larvae. Sci Rep. 2018;8:1–11.
Google Scholar 
Pernice M, Raina J-B, Rädecker N, Cárdenas A, Pogoreutz C, Voolstra CR. Down to the bone: the role of overlooked endolithic microbiomes in reef coral health. ISME J. 2020;14:325–34.PubMed 

Google Scholar 
Ricci F, Marcelino VR, Blackall LL, Kühl M, Medina M, Verbruggen H. Beneath the surface: community assembly and functions of the coral skeleton microbiome. Microbiome 2019;7:1–10.
Google Scholar 
Marcelino VR, Verbruggen H. Multi-marker metabarcoding of coral skeletons reveals a rich microbiome and diverse evolutionary origins of endolithic algae. Sci Rep. 2016;6:1–9.
Google Scholar 
Verbruggen H, Marcelino VR, Guiry MD, Cremen MCM, Jackson CJ. Phylogenetic position of the coral symbiont Ostreobium (Ulvophyceae) inferred from chloroplast genome data. J Phycol. 2017;53:790–803.CAS 
PubMed 

Google Scholar 
Del Campo J, Pombert J-F, Šlapeta J, Larkum A, Keeling PJ. The ‘other’coral symbiont: Ostreobium diversity and distribution. ISME J 2017;11:296–9.PubMed 

Google Scholar 
Massé A, Domart-Coulon I, Golubic S, Duché D, Tribollet A. Early skeletal colonization of the coral holobiont by the microboring Ulvophyceae Ostreobium sp. Sci Rep. 2018;8:1–11.
Google Scholar 
Halldal P. Photosynthetic capacities and photosynthetic action spectra of endozoic algae of the massive coral Favia. Biol Bull. 1968;134:411–24.CAS 

Google Scholar 
Fork D, Larkum A. Light harvesting in the green alga Ostreobium sp., a coral symbiont adapted to extreme shade. Mar Biol. 1989;103:381–5.
Google Scholar 
Fine M, Steindler L, Loya Y. Endolithic algae photoacclimate to increased irradiance during coral bleaching. Mar Freshw Res. 2004;55:115–21.CAS 

Google Scholar 
Fine M, Roff G, Ainsworth T, Hoegh-Guldberg O. Phototrophic microendoliths bloom during coral “white syndrome”. Coral Reefs. 2006;25:577–81.
Google Scholar 
Galindo-Martínez CT, Weber M, Avila-Magaña V, Enríquez S, Kitano H, Medina M, et al. The role of the endolithic alga Ostreobium spp. during coral bleaching recovery. Sci Rep. 2022;12:1–12.
Google Scholar 
Fine M, Loya Y. Endolithic algae: an alternative source of photoassimilates during coral bleaching. Proc R Soc B Biol Sci. 2002;269:1205–10.
Google Scholar 
Schlichter D, Zscharnack B, Krisch H. Transfer of photoassimilates from endolithic algae to coral tissue. Naturwissenschaften 1995;82:561–4.CAS 

Google Scholar 
Sangsawang L, Casareto BE, Ohba H, Vu HM, Meekaew A, Suzuki T, et al. 13C and 15N assimilation and organic matter translocation by the endolithic community in the massive coral Porites lutea. R Soc Open Sci. 2017;4:171201.PubMed 
PubMed Central 

Google Scholar 
Marcelino VR, Morrow KM, van Oppen MJ, Bourne DG, Verbruggen H. Diversity and stability of coral endolithic microbial communities at a naturally high pCO2 reef. Mol Ecol. 2017;26:5344–57.CAS 
PubMed 

Google Scholar 
Marcelino VR, Van Oppen MJ, Verbruggen H. Highly structured prokaryote communities exist within the skeleton of coral colonies. ISME J. 2018;12:300–3.PubMed 

Google Scholar 
Yang S-H, Tandon K, Lu C-Y, Wada N, Shih C-J, Hsiao SS-Y, et al. Metagenomic, phylogenetic, and functional characterization of predominant endolithic green sulfur bacteria in the coral Isopora palifera. Microbiome 2019;7:1–13.
Google Scholar 
Ferrer L, Szmant A, editors. Nutrient regeneration by the endolithic community in coral skeletons. Proceedings of the 6th International Coral Reef Symposium; 1988: AIMS Townsville, Australia.Eakin CM, Devotta D, Heron S, Connolly S, Liu G, Geiger E, et al. The 2014-17 global coral bleaching event: The most severe and widespread coral reef destruction. Research Square. 2022. https://doi.org/10.21203/rs.3.rs-1555992/v1Article 

Google Scholar 
Hughes TP, Kerry JT, Álvarez-Noriega M, Álvarez-Romero JG, Anderson KD, Baird AH, et al. Global warming and recurrent mass bleaching of corals. Nature 2017;543:373–7.CAS 
PubMed 

Google Scholar 
Hughes TP, Anderson KD, Connolly SR, Heron SF, Kerry JT, Lough JM, et al. Spatial and temporal patterns of mass bleaching of corals in the Anthropocene. Science 2018;359:80–3.CAS 
PubMed 

Google Scholar 
Hughes TP, Kerry JT, Baird AH, Connolly SR, Dietzel A, Eakin CM, et al. Global warming transforms coral reef assemblages. Nature 2018;556:492–6.CAS 
PubMed 

Google Scholar 
Veron J, Stafford-Smith M, Corals of the World, Volumes 1-3. Australian Institute of Marine Science. Odyssey Publishing; 2000.Brown B, Dunne R, Phongsuwan N, Patchim L, Hawkridge J. The reef coral Goniastrea aspera: a ‘winner’becomes a ‘loser’during a severe bleaching event in Thailand. Coral Reefs. 2014;33:395–401.
Google Scholar 
Klepac C, Barshis D. Reduced thermal tolerance of massive coral species in a highly variable environment. Proc R Soc B Biol Sci. 2020;287:20201379.CAS 

Google Scholar 
Nicolas R, Evensen CR, Voolstra M, Fine G, Perna C, Buitrago-López A, et al. Empirically derived thermal thresholds of four coral species along the Red Sea using a portable and standardized experimental approach. Coral Reefs. 2022;41:239–52. https://doi.org/10.1007/s00338-022-02233-yArticle 

Google Scholar 
Madin JS, Anderson KD, Andreasen MH, Bridge TC, Cairns SD, Connolly SR, et al. The Coral Trait Database, a curated database of trait information for coral species from the global oceans. Sci Data. 2016;3:1–22.
Google Scholar 
Roth F, Karcher DB, Rädecker N, Hohn S, Carvalho S, Thomson T, et al. High rates of carbon and dinitrogen fixation suggest a critical role of benthic pioneer communities in the energy and nutrient dynamics of coral reefs. Funct Ecol. 2020;34:1991–2004.
Google Scholar 
Harrison PJ, Waters RE, Taylor F. A broad spectrum artificial sea water medium for coastal and open ocean phytoplankton. J Phycol. 1980;16:28–35.
Google Scholar 
Andersson AF, Lindberg M, Jakobsson H, Bäckhed F, Nyrén P, Engstrand L. Comparative analysis of human gut microbiota by barcoded pyrosequencing. PLoS One. 2008;3:e2836.PubMed 
PubMed Central 

Google Scholar 
Bayer T, Neave MJ, Alsheikh-Hussain A, Aranda M, Yum LK, Mincer T, et al. The microbiome of the Red Sea coral Stylophora pistillata is dominated by tissue-associated Endozoicomonas bacteria. Appl Environ Microbiol. 2013;79:4759–62.CAS 
PubMed 
PubMed Central 

Google Scholar 
Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.CAS 
PubMed 
PubMed Central 

Google Scholar 
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2012;41:D590–D6.PubMed 
PubMed Central 

Google Scholar 
Wickham H. ggplot2. Wiley Interdiscip Rev Comput Stat. 2011;3:180–5.
Google Scholar 
McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One. 2013;8:e61217.CAS 
PubMed 
PubMed Central 

Google Scholar 
Dixon P. The vegan package. J Veg Sci. 2003;14:927–30.
Google Scholar 
Lin H, Peddada SD. Analysis of compositions of microbiomes with bias correction. Nat Commun. 2020;11:1–11.CAS 

Google Scholar 
Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 2014;30:2114–20.CAS 
PubMed 
PubMed Central 

Google Scholar 
Li D, Luo R, Liu C-M, Leung C-M, Ting H-F, Sadakane K, et al. MEGAHIT v1. 0: a fast and scalable metagenome assembler driven by advanced methodologies and community practices. Methods 2016;102:3–11.CAS 
PubMed 

Google Scholar 
Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 2017;27:824–34.CAS 
PubMed 
PubMed Central 

Google Scholar 
Peng Y, Leung HC, Yiu S-M, Chin FY. Meta-IDBA: a de Novo assembler for metagenomic data. Bioinformatics 2011;27:i94–i101.CAS 
PubMed 
PubMed Central 

Google Scholar 
Gurevich A, Saveliev V, Vyahhi N, Tesler G. QUAST: quality assessment tool for genome assemblies. Bioinformatics 2013;29:1072–5.CAS 
PubMed 
PubMed Central 

Google Scholar 
Hyatt D, Chen G-L, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 2010;11:1–11.
Google Scholar 
Patro R, Duggal G, Love MI, Irizarry RA, Kingsford C. Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods. 2017;14:417–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
Menzel P, Ng KL, Krogh A. Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nat Commun. 2016;7:1–9.
Google Scholar 
Aramaki T, Blanc-Mathieu R, Endo H, Ohkubo K, Kanehisa M, Goto S, et al. KofamKOALA: KEGG ortholog assignment based on profile HMM and adaptive score threshold. Bioinformatics 2020;36:2251–2.CAS 
PubMed 

Google Scholar 
Hill MO. Diversity and evenness: a unifying notation and its consequences. Ecology 1973;54:427–32.
Google Scholar 
Bates D, Sarkar D, Bates MD, Matrix L. The lme4 package. R Package Version. 2007;2:74.
Google Scholar 
Mandal S, Van Treuren W, White RA, Eggesbø M, Knight R, Peddada SD. Analysis of composition of microbiomes: a novel method for studying microbial composition. Micro Ecol Health Dis. 2015;26:27663.
Google Scholar 
Rivera-Pinto J, Egozcue JJ, Pawlowsky-Glahn V, Paredes R, Noguera-Julian M, Calle ML. Balances: a new perspective for microbiome analysis. mSystems. 2018;3:e00053–18.CAS 
PubMed 
PubMed Central 

Google Scholar 
Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357.CAS 
PubMed 
PubMed Central 

Google Scholar 
Kang DD, Li F, Kirton E, Thomas A, Egan R, An H, et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 2019;7:e7359.PubMed 
PubMed Central 

Google Scholar 
Alneberg J, Bjarnason BS, De Bruijn I, Schirmer M, Quick J, Ijaz UZ, et al. Binning metagenomic contigs by coverage and composition. Nat Methods. 2014;11:1144–6.CAS 
PubMed 

Google Scholar 
Wu Y-W, Simmons BA, Singer SW. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics 2016;32:605–7.CAS 
PubMed 

Google Scholar 
Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.CAS 
PubMed 
PubMed Central 

Google Scholar 
Uritskiy GV, DiRuggiero J, Taylor J. MetaWRAP—a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome 2018;6:1–13.
Google Scholar 
Olm MR, Brown CT, Brooks B, Banfield JF. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. 2017;11:2864–8.CAS 
PubMed 
PubMed Central 

Google Scholar 
Chaumeil P-A, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 2020;36:1925–7.CAS 

Google Scholar 
Parks DH, Chuvochina M, Waite DW, Rinke C, Skarshewski A, Chaumeil P-A, et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat Biotechnol. 2018;36:996–1004.CAS 
PubMed 

Google Scholar 
Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 2014;30:2068–9.CAS 
PubMed 

Google Scholar 
Zhou Z, Tran P, Briester AM, Liu Y, Kieft K, Cowley ES, et al. METABOLIC: high-throughput profiling of microbial genomes for functional traits, metabolism, biogeochemistry, and community-scale functional networks. Microbiome. 2022;10:33 https://doi.org/10.1186/s40168-021-01213-8CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Na S-I, Kim YO, Yoon S-H, Ha S-M, Baek I, Chun J. UBCG: up-to-date bacterial core gene set and pipeline for phylogenomic tree reconstruction. J Microbiol. 2018;56:280–5.CAS 
PubMed 

Google Scholar 
Morel J, Jay S, Féret J-B, Bakache A, Bendoula R, Carreel F, et al. Exploring the potential of PROCOSINE and close-range hyperspectral imaging to study the effects of fungal diseases on leaf physiology. Sci Rep. 2018;8:1–13.CAS 

Google Scholar 
Calamita F, Imran HA, Vescovo L, Mekhalfi ML, La, Porta N. Early identification of root rot disease by using hyperspectral reflectance: the case of pathosystem Grapevine/Armillaria. Remote Sens. 2021;13:2436.
Google Scholar 
Brumfield KD, Huq A, Colwell RR, Olds JL, Leddy MB. Microbial resolution of whole-genome shotgun and 16S amplicon metagenomic sequencing using publicly available NEON data. PLoS One. 2020;15:e0228899.PubMed 
PubMed Central 

Google Scholar 
Khachatryan L, de Leeuw RH, Kraakman ME, Pappas N, Te Raa M, Mei H, et al. Taxonomic classification and abundance estimation using 16S and WGS—A comparison using controlled reference samples. Forensic Sci Int Genet. 2020;46:102257.CAS 
PubMed 

Google Scholar 
Cardénas A, Voolstra C. 75 Coral Endolith Bacterial Genomes (MAGs) from Red Sea corals Goniastrea edwardsi and Porites lutea (Version 1) [Data set]. Zenodo. 2021. https://doi.org/10.5281/zenodo.5606932Article 

Google Scholar 
Branson O, Bonnin EA, Perea DE, Spero HJ, Zhu Z, Winters M, et al. Nanometer-scale chemistry of a calcite biomineralization template: Implications for skeletal composition and nucleation. Proc Natl Acad Sci. 2016;113:12934–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
Sauvage T, Schmidt WE, Suda S, Fredericq S. A metabarcoding framework for facilitated survey of endolithic phototrophs with tufA. BMC Ecol. 2016;16:1–21.
Google Scholar 
Wegley L, Edwards R, Rodriguez‐Brito B, Liu H, Rohwer F. Metagenomic analysis of the microbial community associated with the coral Porites astreoides. Environ Microbiol. 2007;9:2707–19.CAS 
PubMed 

Google Scholar 
Robbins S, Song W, Engelberts J, Glasl B, Slaby BM, Boyd J, et al. A genomic view of the microbiome of coral reef demosponges. ISME J 2021;15:1641–54.CAS 
PubMed 
PubMed Central 

Google Scholar 
Yang S-Y, Lu C-Y, Tang S-L, Das RR, Sakai K, Yamashiro H, et al. Effects of ocean acidification on coral endolithic bacterial communities in Isopora palifera and Porites lobata. Front Mar Sci. 2020;7:603293.
Google Scholar 
Yang SH, Lee ST, Huang CR, Tseng CH, Chiang PW, Chen CP, et al. Prevalence of potential nitrogen‐fixing, green sulfur bacteria in the skeleton of reef‐building coral Isopora palifera. Limnol Oceanogr. 2016;61:1078–86.
Google Scholar 
Cai L, Zhou G, Tian R-M, Tong H, Zhang W, Sun J, et al. Metagenomic analysis reveals a green sulfur bacterium as a potential coral symbiont. Sci Rep. 2017;7:1–11.
Google Scholar 
Kühl M, Holst G, Larkum AW, Ralph PJ. Imaging of oxygen dynamics within the endolithic algal community of the massive coral Porites Lobata. J Phycol. 2008;44:541–50.PubMed 

Google Scholar 
Roberty S, Bailleul B, Berne N, Franck F, Cardol P. PSI Mehler reaction is the main alternative photosynthetic electron pathway in Symbiodinium sp., symbiotic dinoflagellates of cnidarians. N. Phytol. 2014;204:81–91.CAS 

Google Scholar 
Shigeoka S, Ishikawa T, Tamoi M, Miyagawa Y, Takeda T, Yabuta Y, et al. Regulation and function of ascorbate peroxidase isoenzymes. J Exp Bot. 2002;53:1305–19.CAS 
PubMed 

Google Scholar 
Roberty S, Fransolet D, Cardol P, Plumier J-C, Franck F. Imbalance between oxygen photoreduction and antioxidant capacities in Symbiodinium cells exposed to combined heat and high light stress. Coral Reefs. 2015;34:1063–73.
Google Scholar 
Petersen JM, Zielinski FU, Pape T, Seifert R, Moraru C, Amann R, et al. Hydrogen is an energy source for hydrothermal vent symbioses. Nature 2011;476:176–80.CAS 
PubMed 

Google Scholar 
McCollom T, Amend J. A thermodynamic assessment of energy requirements for biomass synthesis by chemolithoautotrophic micro‐organisms in oxic and anoxic environments. Geobiology 2005;3:135–44.CAS 

Google Scholar 
Heijnen J, Van Dijken J. In search of a thermodynamic description of biomass yields for the chemotrophic growth of microorganisms. Biotechnol Bioeng. 1992;39:833–58.CAS 
PubMed 

Google Scholar 
Bar-Even A, Noor E, Milo R. A survey of carbon fixation pathways through a quantitative lens. J Exp Bot. 2012;63:2325–42.CAS 
PubMed 

Google Scholar 
Schulze E-D, Mooney HA, Biodiversity and ecosystem function: Springer Science & Business Media; 2012.Lawton JH, Brown VK, Redundancy in ecosystems. Biodiversity and Ecosystem Function: Springer; 1994. p. 255–70.Mori AS, Furukawa T, Sasaki T. Response diversity determines the resilience of ecosystems to environmental change. Biol Rev. 2013;88:349–64.PubMed 

Google Scholar 
Nyström M. Redundancy and response diversity of functional groups: implications for the resilience of coral reefs. Ambio 2006;35:30–5.PubMed 

Google Scholar 
Rädecker N, Pogoreutz C, Gegner HM, Cárdenas A, Roth F, Bougoure J, et al. Heat stress destabilizes symbiotic nutrient cycling in corals. Proc Natl Acad Sci. 2021;118:e2022653118.PubMed 
PubMed Central 

Google Scholar 
Ziegler M, Grupstra CG, Barreto MM, Eaton M, BaOmar J, Zubier K, et al. Coral bacterial community structure responds to environmental change in a host-specific manner. Nat Commun. 2019;10:1–11.CAS 

Google Scholar 
Dikou A, Van, Woesik R. Survival under chronic stress from sediment load: spatial patterns of hard coral communities in the southern islands of Singapore. Mar Pollut Bull. 2006;52:1340–54.CAS 
PubMed 

Google Scholar 
Hennige SJ, Smith DJ, Walsh S-J, McGinley MP, Warner ME, Suggett DJ. Acclimation and adaptation of scleractinian coral communities along environmental gradients within an Indonesian reef system. J Exp Mar Biol Ecol. 2010;391:143–52.
Google Scholar 
Cárdenas A, Neave MJ, Haroon MF, Pogoreutz C, Rädecker N, Wild C, et al. Excess labile carbon promotes the expression of virulence factors in coral reef bacterioplankton. ISME J. 2018;12:59–76.PubMed 

Google Scholar 
Cárdenas A, Ye J, Ziegler M, Payet JP, McMinds R, Thurber RV, et al. Coral-associated viral assemblages from the Central Red Sea align with host species and contribute to holobiont genetic diversity. Front Microbiol. 2020;11:572534.PubMed 
PubMed Central 

Google Scholar 
McCook GD-PLJ. The fate of bleached corals: patterns and dynamics of algal recruitment. Mar Ecol Prog Ser. 2002;232:115–28.
Google Scholar 
Reshef L, Koren O, Loya Y, Zilber‐Rosenberg I, Rosenberg E. The coral probiotic hypothesis. Environ Microbiol. 2006;8:2068–73.CAS 
PubMed 

Google Scholar 
Voolstra CR, Ziegler M. Adapting with microbial help: microbiome flexibility facilitates rapid responses to environmental change. BioEssays 2020;42:2000004.
Google Scholar 
Rosenberg E, Zilber-Rosenberg I. The hologenome concept of evolution after 10 years. Microbiome 2018;6:1–14.
Google Scholar 
Wiedenmann J, D’Angelo C, Smith EG, Hunt AN, Legiret F-E, Postle AD, et al. Nutrient enrichment can increase the susceptibility of reef corals to bleaching. Nat Clim Change. 2013;3:160–4.CAS 

Google Scholar 
DeCarlo TM, Gajdzik L, Ellis J, Coker DJ, Roberts MB, Hammerman NM, et al. Nutrient-supplying ocean currents modulate coral bleaching susceptibility. Sci Adv. 2020;6:eabc5493.PubMed 
PubMed Central 

Google Scholar 
Pogoreutz C, Rädecker N, Cardenas A, Gärdes A, Voolstra CR, Wild C. Sugar enrichment provides evidence for a role of nitrogen fixation in coral bleaching. Glob Change Biol 2017;23:3838–48.
Google Scholar  More

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

Climate Class

0.65

Humid More

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

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

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