Philippa Kaur

Root, T. L. et al. Fingerprints of global warming on wild animals and plants. Nature 421, 57–60 (2003).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Meehl, G. A. et al. How much more global warming and sea level rise?. Science 307, 1769–1772 (2005).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Stocker, T. F. et al. (Cambridge University Press, 2013).Kopp, R. E. et al. Probabilistic 21st and 22nd century sea-level projections at a global network of tide-gauge sites. Earth’s Future 2, 383–406 (2014).ADS 
Article 

Google Scholar 
Church, J. A. & White, N. J. A 20th century acceleration in global sea‐level rise. Geophys. Res. Lett. 33 (2006).Church, J. A. & White, N. J. Sea-level rise from the late 19th to the early 21st century. Surv. Geophys. 32, 585–602 (2011).ADS 
Article 

Google Scholar 
Vermeer, M. & Rahmstorf, S. Global sea level linked to global temperature. Proc. Natl. Acad. Sci. 106, 21527–21532 (2009).ADS 
CAS 
PubMed Central 
Article 
PubMed 

Google Scholar 
Horton, B. P., Rahmstorf, S., Engelhart, S. E. & Kemp, A. C. Expert assessment of sea-level rise by AD 2100 and AD 2300. Quatern. Sci. Rev. 84, 1–6 (2014).ADS 
Article 

Google Scholar 
Day, J. W., Pont, D., Hensel, P. F. & Ibañez, C. Impacts of sea-level rise on deltas in the Gulf of Mexico and the Mediterranean: The importance of pulsing events to sustainability. Estuaries 18, 636–647 (1995).CAS 
Article 

Google Scholar 
Feagin, R. A., Sherman, D. J. & Grant, W. E. Coastal erosion, global sea-level rise, and the loss of sand dune plant habitats. Front. Ecol. Environ. 3, 359–364 (2005).Article 

Google Scholar 
Nicholls, R. J. Planning for the impacts of sea level rise. Oceanography 24, 144–157 (2011).Article 

Google Scholar 
Hinkel, J. et al. Coastal flood damage and adaptation costs under 21st century sea-level rise. Proc. Natl. Acad. Sci. 111, 3292–3297 (2014).ADS 
CAS 
PubMed Central 
Article 
PubMed 

Google Scholar 
Deutsch, C. A. et al. Impacts of climate warming on terrestrial ectotherms across latitude. Proc. Natl. Acad. Sci. 105, 6668–6672 (2008).ADS 
CAS 
PubMed Central 
Article 
PubMed 

Google Scholar 
Duarte, H. et al. Can amphibians take the heat? Vulnerability to climate warming in subtropical and temperate larval amphibian communities. Glob. Change Biol. 18, 412–421 (2012).ADS 
Article 

Google Scholar 
Licht, P. & Brown, A. G. Behavioral thermoregulation and its role in the ecolgy of the red-bellied newt, Taricha rivularis. Ecology 48, 598–611 (1967).Article 

Google Scholar 
Feder, M. E. & Pough, F. H. Temperature selection by the red-backed salamander, Plethodon c. cinereus (Green) (Caudata: Plethodontidae). Comp. Biochem. Physiol. Part A Physiol. 50, 91–98 (1975).CAS 
Article 

Google Scholar 
Keen, W. H. & Schroeder, E. E. Temperature selection and tolerance in three species of Ambystoma larvae. Copeia 1975, 523–530 (1975).Article 

Google Scholar 
Hoppe, D. M. Thermal tolerance in tadpoles of the chorus frog Pseudacris triseriata. Herpetologica. 318–321 (1978).Cupp Jr, P. V. Thermal tolerance of five salientian amphibians during development and metamorphosis. Herpetologica. 234–244 (1980).Howard, J. H., Wallace, R. L. & Stauffer, J. R. Critical thermal maxima in populations of Ambystoma macrodactylum from different elevations. J. Herpetol. 17, 400–402 (1983).Article 

Google Scholar 
Floyd, R. B. Ontogenetic change in the temperature tolerance of larval Bufo marinus (Anura: Bufonidae). Comp. Biochem. Physiol. A Physiol. 75, 267–271 (1983).Article 

Google Scholar 
Floyd, R. B. Effects of photoperiod and starvation on the temperature tolerance of larvae of the giant toad, Bufo marinus. Copeia 1985, 625–631 (1985).MathSciNet 
Article 

Google Scholar 
Manis, M. L. & Claussen, D. L. Environmental and genetic influences on the thermal physiology of Rana sylvatica. J. Therm. Biol 11, 31–36 (1986).Article 

Google Scholar 
Layne, J., Claussen, D. & Manis, M. Effects of acclimation temperature, season, and time of day on the critical thermal maxima and minima of the crayfish Orconectes rusticus. J. Therm. Biol 12, 183–187 (1987).Article 

Google Scholar 
Lutterschmidt, W. I. & Hutchison, V. H. The critical thermal maximum: History and critique. Can. J. Zool. 75, 1561–1574 (1997).Article 

Google Scholar 
Simon, M. N., Ribeiro, P. L. & Navas, C. A. Upper thermal tolerance plasticity in tropical amphibian species from contrasting habitats: Implications for warming impact prediction. J. Therm. Biol 48, 36–44 (2015).Article 
PubMed 

Google Scholar 
Boutilier, R., Donohoe, P., Tattersall, G. & West, T. Hypometabolic homeostasis in overwintering aquatic amphibians. J. Exp. Biol. 200, 387–400 (1997).CAS 
Article 
PubMed 

Google Scholar 
Shoemaker, V. & Nagy, K. A. Osmoregulation in amphibians and reptiles. Annu. Rev. Physiol. 39, 449–471 (1977).CAS 
Article 
PubMed 

Google Scholar 
Viertel, B. Salt tolerance of Rana temporaria: Spawning site selection and survival during embryonic development (Amphibia, Anura). Amphibia-Reptilia 20, 161–171 (1999).Article 

Google Scholar 
Wu, C.-S. & Kam, Y.-C. Thermal tolerance and thermoregulation by Taiwanese rhacophorid tadpoles (Buergeria japonica) living in geothermal hot springs and streams. Herpetologica 61, 35–46 (2005).Article 

Google Scholar 
Gomez-Mestre, I. & Tejedo, M. Local adaptation of an anuran amphibian to osmotically stressful environments. Evolution 57, 1889–1899 (2003).Article 
PubMed 

Google Scholar 
Christy, M. T. & Dickman, C. R. Effects of salinity on tadpoles of the green and golden bell frog (Litoria aurea). Amphibia-Reptilia 23, 1–11 (2002).Article 

Google Scholar 
Wu, C.-S. & Kam, Y.-C. Effects of salinity on the survival, growth, development, and metamorphosis of Fejervarya limnocharis tadpoles living in brackish water. Zool. Sci. 26, 476–482 (2009).Article 

Google Scholar 
Wu, C. S., Yang, W. K., Lee, T. H., Gomez-Mestre, I. & Kam, Y. C. Salinity acclimation enhances salinity tolerance in tadpoles living in brackish water through increased Na+, K+-ATPase expression. J. Exp. Zool. A Ecol. Genet. Physiol. 321, 57–64 (2014).CAS 
Article 
PubMed 

Google Scholar 
Alexander, L. G., Lailvaux, S. P., Pechmann, J. H. & DeVries, P. J. Effects of salinity on early life stages of the Gulf Coast toad, Incilius nebulifer (Anura: Bufonidae). Copeia 2012, 106–114 (2012).Article 

Google Scholar 
Bernabò, I., Bonacci, A., Coscarelli, F., Tripepi, M. & Brunelli, E. Effects of salinity stress on Bufo balearicus and Bufo bufo tadpoles: Tolerance, morphological gill alterations and Na+/K+-ATPase localization. Aquat. Toxicol. 132, 119–133 (2013).Article 
CAS 
PubMed 

Google Scholar 
Kearney, B. D., Pell, R. J., Byrne, P. G. & Reina, R. D. Anuran larval developmental plasticity and survival in response to variable salinity of ecologically relevant timing and magnitude. J. Exp. Zool. A Ecol. Genet. Physiol. 321, 541–549 (2014).Article 
PubMed 

Google Scholar 
Hsu, W. T., Wu, C. S., Hatch, K., Chang, Y. M. & Kam, Y. C. Full compensation of growth in salt-tolerant tadpoles after release from salinity stress. J. Zool. 304, 141–149 (2018).Article 

Google Scholar 
Hsu, W.-T. et al. Salinity acclimation affects survival and metamorphosis of crab-eating frog tadpoles. Herpetologica 68, 14–21 (2012).Article 

Google Scholar 
Lai, J.-C., Kam, Y.-C., Lin, H.-C. & Wu, C.-S. Enhanced salt tolerance of euryhaline tadpoles depends on increased Na+, K+-ATPase expression after salinity acclimation. Comp. Biochem. Physiol. A: Mol. Integr. Physiol. 227, 84–91 (2019).CAS 
Article 

Google Scholar 
Brown, M. E. & Walls, S. C. Variation in salinity tolerance among larval anurans: Implications for community composition and the spread of an invasive, non-native species. Copeia 2013, 543–551 (2013).Article 

Google Scholar 
Balinsky, J. B. Adaptation of nitrogen metabolism to hyperosmotic environment in Amphibia. J. Exp. Zool. A Ecol. Genet. Physiol. 215, 335–350 (1981).CAS 

Google Scholar 
Duellman, W. & Trueb, L. Biology of Amphibians (John Hopkins University Press, 1994).
Google Scholar 
Alcala, A. C. Breeding behavior and early development of frogs of Negros, Philippine Islands. Copeia 1962, 679–726 (1962).Article 

Google Scholar 
Gordon, M. S. & Tucker, V. A. Osmotic regulation in the tadpoles of the crab-eating frog (Rana cancrivora). J. Exp. Biol. 42, 437–445 (1965).CAS 
Article 

Google Scholar 
Dunson, W. A. Tolerance to high temperature and salinity by tadpoles of the Philippine frog, Rana cancrivora. Copeia 1977, 375–378 (1977).Article 

Google Scholar 
Uchiyama, M., Murakami, T., Wakasugi, C. & Yoshizawa, H. Structure of the kidney in the crab-eating frog, Rana cancrivora. J. Morphol. 204, 147–156 (1990).CAS 
Article 
PubMed 

Google Scholar 
Heo, K., Kim, Y. I., Bae, Y., Jang, Y. & Borzée, A. First report of Dryophytes japonicus tadpoles in saline environment. Russ. J. Herpetol. 26, 87–90 (2019).Article 

Google Scholar 
Jian, C. Y., Cheng, S. Y. & Chen, J. C. Temperature and salinity tolerances of yellowfin sea bream, Acanthopagrus latus, at different salinity and temperature levels. Aquac. Res. 34, 175–185 (2003).Article 

Google Scholar 
Sardella, B. A., Sanmarti, E. & Kültz, D. The acute temperature tolerance of green sturgeon (Acipenser medirostris) and the effect of environmental salinity. J. Exp. Zool. A Ecol. Genet. Physiol. 309, 477–483 (2008).Article 
PubMed 

Google Scholar 
Everatt, M. J., Worland, M. R., Convey, P., Bale, J. S. & Hayward, S. A. The impact of salinity exposure on survival and temperature tolerance of the Antarctic collembolan Cryptopygus antarcticus. Physiol. Entomol. 38, 202–210 (2013).Article 

Google Scholar 
Kerby, J. L., Richards-Hrdlicka, K. L., Storfer, A. & Skelly, D. K. An examination of amphibian sensitivity to environmental contaminants: are amphibians poor canaries?. Ecol. Lett. 13, 60–67 (2010).Article 
PubMed 

Google Scholar 
Chang, Y. M., Wu, C. S., Huang, Y. S., Sung, S. M. & Hwang, W. Occurrence and reproduction of anurans in brackish water in a coastal forest in Taiwan. Herpetol. Notes 9, 291–295 (2016).
Google Scholar 
Peng, T. R., Hsieh, Y. H. & Liu, T. S. Hydro chemical characteristics and salinization of groundwater in Yunlin area. J. Chin. Soil Water Conserv. 32, 173–189 (2005).
Google Scholar 
Gosner, K. L. A simplified table for staging anuran embryos and larvae with notes on identification. Herpetologica 16, 183–190 (1960).
Google Scholar 
Phillips, S. J., Anderson, R. P., Dudík, M., Schapire, R. E. & Blair, M. E. Opening the black box: An open-source release of Maxent. Ecography 40, 887–893 (2017).Article 

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

Google Scholar 
Groff, L. A., Marks, S. B. & Hayes, M. P. Using ecological niche models to direct rare amphibian surveys: A case study using the Oregon Spotted Frog (Rana pretiosa). Herpetol. Conserv. Biol. 9, 354–368 (2014).
Google Scholar 
Kumar, P. Assessment of impact of climate change on Rhododendrons in Sikkim Himalayas using Maxent modelling: limitations and challenges. Biodivers. Conserv. 21, 1251–1266 (2012).Article 

Google Scholar 
Pineda, E. & Lobo, J. M. Assessing the accuracy of species distribution models to predict amphibian species richness patterns. J. Anim. Ecol. 78, 182–190 (2009).Article 
PubMed 

Google Scholar 
Yuan, H.-S., Wei, Y.-L. & Wang, X.-G. Maxent modeling for predicting the potential distribution of Sanghuang, an important group of medicinal fungi in China. Fungal Ecol. 17, 140–145 (2015).Article 

Google Scholar 
Chinathamby, K., Reina, R. D., Bailey, P. C. & Lees, B. K. Effects of salinity on the survival, growth and development of tadpoles of the brown tree frog, Litoria ewingii. Aust. J. Zool. 54, 97–105 (2006).Article 

Google Scholar 
Metcalfe, N. B. & Monaghan, P. Compensation for a bad start: Grow now, pay later?. Trends Ecol. Evol. 16, 254–260 (2001).Article 
PubMed 

Google Scholar 
Metzger, D. C., Healy, T. M. & Schulte, P. M. Conserved effects of salinity acclimation on thermal tolerance and hsp70 expression in divergent populations of threespine stickleback (Gasterosteus aculeatus). J. Comp. Physiol. B. 186, 879–889 (2016).CAS 
Article 
PubMed 

Google Scholar 
Sanabria, E. et al. Effect of salinity on locomotor performance and thermal extremes of metamorphic Andean Toads (Rhinella spinulosa) from Monte Desert, Argentina. J. Therm. Biol. 74, 195–200 (2018).CAS 
Article 
PubMed 

Google Scholar 
Sokolova, I. M. Energy-limited tolerance to stress as a conceptual framework to integrate the effects of multiple stressors. Integr. Comp. Biol. 53, 597–608 (2013).Article 
PubMed 

Google Scholar 
Kikawada, T. et al. Dehydration-induced expression of LEA proteins in an anhydrobiotic chironomid. Biochem. Biophys. Res. Commun. 348, 56–61 (2006).CAS 
Article 
PubMed 

Google Scholar 
Sanzo, D. & Hecnar, S. J. Effects of road de-icing salt (NaCl) on larval wood frogs (Rana sylvatica). Environ. Pollut. 140, 247–256 (2006).CAS 
Article 
PubMed 

Google Scholar 
Wood, L. & Welch, A. M. Assessment of interactive effects of elevated salinity and three pesticides on life history and behavior of southern toad (Anaxyrus terrestris) tadpoles. Environ. Toxicol. Chem. 34, 667–676 (2015).CAS 
Article 
PubMed 

Google Scholar 
Gomez-Mestre, I., Tejedo, M., Ramayo, E. & Estepa, J. Developmental alterations and osmoregulatory physiology of a larval anuran under osmotic stress. Physiol. Biochem. Zool. 77, 267–274 (2004).CAS 
Article 
PubMed 

Google Scholar 
Dent, J. N. Hormonal interaction in amphibian metamorphosis 1 2. Am. Zool. 28, 297–308 (1988).CAS 
Article 

Google Scholar 
Bodensteiner, B. L. et al. Thermal adaptation revisited: How conserved are thermal traits of reptiles and amphibians?. J. Exp. Zool. Part A Ecol. Integr. Physiol. 335, 173–194 (2021).Article 

Google Scholar 
Rezende, E. L., Tejedo, M. & Santos, M. Estimating the adaptive potential of critical thermal limits: Methodological problems and evolutionary implications. Funct. Ecol. 25, 111–121 (2011).Article 

Google Scholar 
Mitchell, J. D., Hewitt, P. & Van Der Linde, T. D. K. Critical thermal limits and temperature tolerance in the harvester termite Hodotermes mossambicus (Hagen). J. Insect Physiol. 39, 523–528 (1993).Article 

Google Scholar 
Plummer, M. V., Williams, B. K., Skiver, M. M. & Carlyle, J. C. Effects of dehydration on the critical thermal maximum of the desert box turtle (Terrapene ornata luteola). J. Herpetol. 37, 747–751 (2003).Article 

Google Scholar 
Lee, S. et al. Effects of feed restriction on the upper temperature tolerance and heat shock response in juvenile green and white sturgeon. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 198, 87–95 (2016).CAS 
Article 
PubMed 

Google Scholar 
Blaustein, A. R. & Wake, D. B. Declining amphibian populations: A global phenomenon?. Trends Ecol. Evol. 5, 203–204 (1990).Article 

Google Scholar 
Kiesecker, J. M., Blaustein, A. R. & Belden, L. K. Complex causes of amphibian population declines. Nature 410, 681 (2001).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Rohr, J. R. & Raffel, T. R. Linking global climate and temperature variability to widespread amphibian declines putatively caused by disease. Proc. Natl. Acad. Sci. 107, 8269–8274 (2010).ADS 
CAS 
PubMed Central 
Article 
PubMed 

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

Google Scholar 
Skelly, D. & Freidenburg, L. Effects of beaver on the thermal biology of an amphibian. Ecol. Lett. 3, 483–486 (2000).Article 

Google Scholar 
Radchuk, V. et al. Adaptive responses of animals to climate change are most likely insufficient. Nat. Commun. 10, 1–14 (2019).CAS 
Article 

Google Scholar  More

Larson, D. W., Matthes, U. & Kelly, P. E. Cliff Ecology (Cambridge University Press, 2000).Book 

Google Scholar 
Cooper, A. Plant species coexistence in cliff habitats. J. Biogeogr. 24, 483–494 (1997).Article 

Google Scholar 
Davis, P. H. Cliff vegetation in the eastern Mediterranean. J. Ecol. 39, 63–93 (1951).Article 

Google Scholar 
Snogerup, S. Evolutionary and plant geographical aspects of chasmophytic communities. In Plant life of South-West Asia (eds Davis, P. H. et al.) 157–170 (Bot. Soc. Edinb, 1971).
Google Scholar 
Baskin, J. M. & Baskin, C. C. Endemism in rock outcrop plant communities of unglaciated eastern United States: An evaluation of the roles of the edaphic, genetic and light factors. J. Biogeogr. 15, 829–840 (1988).Article 

Google Scholar 
Medina, B. M. O. & Fernandes, G. W. The potential of natural regeneration of rocky outcrop vegetation on rupestrian field soils in Serra do Cipo, Brazil. Braz. J. Bot. 30, 665–678 (2007).Article 

Google Scholar 
Alves, R. J. V., Cardin, L. & Kropf, M. S. Angiosperm disjunction “Campos Rupestres-Restingas”: Are-evaluation. Acta Bot. Bras. 2, 675–685 (2007).Article 

Google Scholar 
Harley, R. M. Introduction. In Flora of the Pico das Almas, Chapada Diamantina, Bahia, Brazil (eds Stannard, B. L., Harvey, Y. B. & Harley, R. M) 1–42 (Royal Botanic Gardens, 1995).Hubbell, S. P. Neutral theory in ecology and the evolution of ecological equivalence. Ecology 87, 1387–1398 (2006).PubMed 
Article 

Google Scholar 
Conceição, A. A., Pirani, J. R. & Meirelles, S. T. Floristics, structure and soil of insular vegetation in four quartzite-sandstone outcrops of “Chapada Diamantina”, Northeast Brazil. Rev. Bras. Bot. 30, 641–656 (2007).Article 

Google Scholar 
Le Stradic, S., Buisson, E. & Wilson, F. G. Vegetation composition and structure of some Neotropical mountain grasslands in Brazil. J Mt Sci 12:864–77. An. Acad. Bras. Ciênc. 87(4), 2097–2110 (2015).Article 
CAS 

Google Scholar 
Nunes, J. A. et al. Soil–vegetation relationships on a banded ironstone ‘island’, Carajás Plateau, Brazilian Eastern Amazonia. An. Acad. Bras. Cienc. 87(4), 2097–2110 (2015).CAS 
PubMed 
Article 

Google Scholar 
Silva, W. A. Gradiente vegetacional e pedológico em complexo rupestre de quartzito no Quadrilátero Ferrífero, Minas Gerais, Brasil. MSc Thesis. (Universidade Federal de Viçosa, 2013).Vincent, R. C. & Meguro, M. Influence of soil properties on the abundance of plant species in ferruginous rocky soils vegetation, southeastern Brazil. Braz. J. Bot. 31, 377–388 (2008).Article 

Google Scholar 
Porembski, S. Tropical inselbergs: Habitat types, adaptive strategies and diversity patterns. Rev. Bras. de Bot. 30, 579–586 (2007).Article 

Google Scholar 
De Paula, L. F. A., Forzza, R. C., Neri, A. V., Bueno, M. L. & Porembski, S. Sugar Loaf Land in south-eastern Brazil: A center of diversity for mat-forming bromeliads on inselbergs. Bot. J. Linn. Soc. 181, 459–476 (2016).Article 

Google Scholar 
Rezende, M. G., Elias, R. C. L., Salimena, F. R. G. & Neto, L. M. Flora vascular da Serra da Pedra Branca, Caldas, Minas Gerais e relações florísticas com áreas de altitude da Região Sudeste do Brasil. Biota Neotrop. 13, 201–224 (2013).Article 

Google Scholar 
Sarthou, C., Villiers, J. F. & Ponge, J. F. Shrub vegetation on tropical granitic inselbergs in French Guiana. J. Veg. Sci. 14, 645–652 (2003).Article 

Google Scholar 
Tinti, B. V. et al. Plant diversity on granite/gneiss rock outcrop at Pedra do Pato, Serra do Brigadeiro State Park, Brazil. Check List 11, 1780 (2015).Article 

Google Scholar 
Barbara, T., Martinelli, G., Fay, M. F., Mayo, S. J. & Lexer, C. Population differentiation and species cohesion in two closely related plants adapted to neotropical high-altitude “inselbergs”, Alcantarea imperialis and Alcantarea geniculata (Bromeliaceae). Mol. Ecol. 16, 1981–1992 (2007).CAS 
PubMed 
Article 

Google Scholar 
Boisselier-Dubayle, M. C., Leblois, R., Samadi, S., Lambourdière, J. & Sarthou, C. Genetic structure of the xerophilous bromeliad Pitcairnia geyskesii on inselbergs in French Guiana—A test of the forest refuge hypothesis. Ecography 33, 175–184 (2010).Article 

Google Scholar 
Domingues, R. et al. Genetic variability of an endangered Bromeliaceae species (Pitcairnia albiflos) from the Brazilian Atlantic rainforest. Genet. Mol. Res. 10, 2482–2491 (2011).CAS 
PubMed 
Article 

Google Scholar 
Hmeljevski, K. V. et al. Conservation assessment of an extremely restricted bromeliad highlights the need for population-based conservation on granitic inselbergs of the Brazilian Atlantic Forest. Flora Morpho. Distribut. Funct. Ecolo. Plants. 209, 250–259 (2014).Article 

Google Scholar 
Palma-Silva, C. et al. Sympatric bromeliad species (Pitcairnia spp.) facilitate tests of mechanisms involved in species cohesion and reproductive isolation in Neotropical inselbergs. Mol. Ecol. 20, 3185–3201 (2011).CAS 
PubMed 
Article 

Google Scholar 
Gomes, P. & Alves, M. Floristic diversity of two crystalline rocky outcrops in the Brazilian northeast semi-arid region. Rev. Bras. Bot. 33(4), 661–676 (2010).Article 

Google Scholar 
Nunes, J. A., Villa, P. M., Neri, A. V., Silva, W. A. & Schaefer, C. E. G. R. Seasonality drives herbaceous community beta diversity in lithologically different rocky outcrops in Brazil. Plant. Ecol. Evol. 153(2), 208–218 (2020).Article 

Google Scholar 
Speziale, K. L. & Ezcurra, C. The role of outcrops in the diversity of Patagonian vegetation: Relicts of glacial palaeofloras?. Flora Morphol. Distrib. Funct. Ecol. Plant. 207, 141–149 (2012).
Google Scholar 
Speziale, K. L., Ruggiero, A. & Ezcurra, C. Plant species richness–environment relationships across the Subantarctic-Patagonian transition zone. J. Biogeogr. 37, 449–464 (2010).Article 

Google Scholar 
Yates, C. J. et al. High species diversity and turnover in granite inselberg floras highlight the need for a conservation strategy protecting many outcrops. Ecol. Evol. 9, 7660–7675 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Gaston, K. J. Geographic range limits: Achieving synthesis. Proc. R. Soc. B Biol. Sci. 276, 1395–1406 (2009).Article 

Google Scholar 
McGann, T. D. How insular are ecological ‘islands’? An example from the granitic outcrops of the New England Batholith of Australia. Proc. R. Soc. Queensland. 110, 1–13 (2002).
Google Scholar 
Parmentier, I., Stévart, T. & Hardy, O. J. The inselberg flora of Atlantic Central Africa. I. Determinants of species assemblages. J. Biogeogr. 32, 685–696 (2005).Article 

Google Scholar 
Changwe, K. & Balkwill, K. Floristics of the Dunbar Valley serpentinite site, Songimvelo Game Reserve, South Africa. Bot. J. Linn. Soc. 143, 271–285 (2003).Article 

Google Scholar 
Clarke, P. J. Habitat islands in fire-prone vegetation: Do landscape features influence community composition?. J. Biogeogr. 29, 677–684 (2002).Article 

Google Scholar 
De Bello, F., Leps, J. & Sebastia, M. T. Variations in species and functional plant diversity along climatic and grazing gradients. Ecography 29(6), 801–810 (2006).Article 

Google Scholar 
Porembski, S., Martinelli, G., Ohlemüller, R. & Barthlott, W. Diversity and ecology of saxicolous vegetation mats on inselbergs in the Brazilian Atlantic rainforest. Divers. Distrib. 4, 107–119 (1998).Article 

Google Scholar 
Porembski, S., Szarzynski, J., Mund, J. P. & Barthlott, W. Biodiversity and vegetation of small-sized inselbergs in a West African rain forest (Taï, Ivory Coast). J. Biogeogr. 23, 47–55 (1996).Article 

Google Scholar 
Rahmanian, S. et al. Effects of livestock grazing on soil, plant functional diversity, and ecological traits vary between regions with different climates in northeastern Iran. Ecol. Evol. 9, 8225–8237 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Speziale, K. L. & Ezcurra, C. Patterns of alien plant invasions in northwestern Patagonia, Argentina. J. Arid Environ. 75, 890–897 (2011).ADS 
Article 

Google Scholar 
Qian, H., Chen, S. H. & Zhang, J. L. Disentangling environmental and spatial effects on phylogenetic structure of angiosperm tree communities in China. Sci. Rep. 7, 5864 (2017).ADS 
Article 
CAS 

Google Scholar 
Farzam, M. & Ejtehadi, H. Effects of drought and canopy facilitation on plant diversity and abundance in a semiarid mountainous rangeland. J. Plant. Ecol. 10(4), 626–633 (2016).
Google Scholar 
Heino, J. & Tolonen, K. T. Ecological drivers of multiple facets of beta diversity in a lentic macroinvertebrate metacommunity. Limnol. Oceanogr. 62, 2431–2444. https://doi.org/10.1002/lno.10577 (2017).ADS 
Article 

Google Scholar 
Miranda, J. D., Armas, C., Padilla, F. M. & Pugnaire, F. I. Climatic change and rainfall patterns: Effects on semi-arid plant communities of the Iberian Southeast. J. Arid. Environ. 75, 1302–1309 (2011).ADS 
Article 

Google Scholar 
Pashirzad, M., Ejtehadi, H., Vaezi, J. & Shefferson, R. P. Multiple processes at different spatial scales determine beta diversity patterns in a mountainous semi-arid rangeland of Khorassan-Kopet Dagh floristic province, NE Iran. Plant. Ecol. 220(9), 829–844 (2019).Article 

Google Scholar 
Victorero, L., Robert, K., Robinson, L. F., Taylor, M. L. & Huvenne, V. A. I. Species replacement dominates megabenthos beta diversity in a remote seamount setting. Sci. Rep. 8, 4152 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Deil, U. Rock communities in tropical Arabia. Flora et Vegetation Mundi 9, 175–187 (1991).
Google Scholar 
Dimopoulos, P., Sýkora, K. V., Mucina, L. & Georgiadis, T. The high-rank syntaxa of the rock-cliff and scree vegetation of the mainland Greece and Crete. Folia Geobot. 32, 313–334 (1997).Article 

Google Scholar 
Hein, P., Kürschner, H. & Parolly, G. Phytosociological studies on high mountain plant communities of the Taurus Mountains (Turkey) 2. Rock communities. Phytocoenologia 28, 465–563 (1998).Article 

Google Scholar 
Nowak, A., Nowak, S., Nobis, M. & Nobis, A. Vegetation of rock clefts and ledges in the Pamir Alai Mts, Tajikistan (Middle Asia). Cent. Eur. J. Biol. 9, 444–460 (2014).
Google Scholar 
Urbis, A. & Blazyca, B. Rock vascular plant species of the Kraków-Częstochowa, Uplands. Thaiszia J. Bot. 21, 207–214 (2011).
Google Scholar 
Wiser, S. K., Peet, R. K. & White, P. S. High-elevation rock outcrop vegetation of the Southern Appalachian Mountains. J. Veg. Sci. 7, 703–722 (1996).Article 

Google Scholar 
Cadotte, M. W. Experimental evidence that evolutionarily diverse assemblages result in higher productivity. PNAS 110(22), 8996–9000 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Swenson, G.N. Functional and Phylogenetic Ecology in R (Use R!) Kindle Edition (2014).Cadotte, M. W. & Davies, P. R. Why phylogenies do not always predict ecological differences. Ecol. Monogr. 87(4), 535–551 (2016).Article 

Google Scholar 
De Bello, F., LepŠ, J. A. N. & Sebastià, M. T. Predictive value of plant traits to grazing along a climatic gradient in the Mediterranean. J. Appl. Ecol. 42(5), 824–833 (2005).Article 

Google Scholar 
Funk, J. et al. Revisiting the Holy Grail: Using plant functional traits to understand ecologica processes. Biol. Rev. 92(2), 1156–1173 (2017).PubMed 
Article 

Google Scholar 
Lavorel, S. & Garnier, É. Predicting changes in community composition and ecosystem functioning from plant traits: Revisiting the Holy Grail. Funct. Ecol. 16(5), 545–556 (2002).Article 

Google Scholar 
Violle, C. et al. Let the concept of trait be functional!. Oikos 116, 882–892 (2007).Article 

Google Scholar 
Zheng, S., Li, W., Lan, Z., Ren, H. & Wang, K. Functional trait responses to grazing are mediated by soil moisture and plant functional group identity. Sci. Rep. 5, 18163 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gillison, A. N. Plant functional types and traits at the community, ecosystem and world level. In Vegetation Ecology (eds van der Maarel, E. & Franklin, J.) 347–386 (Wiley, 2013).Chapter 

Google Scholar 
Loreau, M. Biodiversity and ecosystem functioning: Recent theoretical advances. Oikos 91, 3–17 (2000).Article 

Google Scholar 
Akhani, H., Djamali, M., Ghorbanalizadeh, A. & Ramezani, E. Plant biodiversity of Hyrcanian relict forests, N Iran: An overview of the flora, vegetation, paleoecology and conservation. Pak. J. Bot. 42, 231–258 (2010).
Google Scholar 
Hamzehee, B. et al. Phytosociological survey of remnant Alnus glutinosa ssp. barbata communities in the lowland Caspian forests of northern Iran. Pytocoenologia. 38, 117–132 (2008).Article 

Google Scholar 
Moradi, H. et al. Elevational gradient and vegetation-environmental relationships in the central Hyrcanian forests of northern Iran. Nord. J. Bot. 34, 1–14 (2016).Article 

Google Scholar 
Naqinezhad, A., Esmailpoor, A. & Jafari, N. A new record of Pyrola minor (Pyrolaceae) for the flora of Iran as well as a description of its surrounding habitats. Taxon. Biosyst. 22, 71–80 (2015).
Google Scholar 
Naqinezhad, A., Zare-Maivan, H. & Gholizadeh, H. A floristic survey of the Hyrcanian forests in Northern Iran, using two lowland-mountain transects. J. For. Res. 26, 187–199 (2015).CAS 
Article 

Google Scholar 
Sagheb-Talebi, K., Sajedi, T. & Pourhashemi, M. Forests of Iran (Springer Sci, 2014).Book 

Google Scholar 
Siadati, S. et al. Botanical diversity of Hyrcanian forests; a case study of a transect in the Kheyrud protected lowland mountain forests in northern Iran. Phytotaxa 7, 1–18 (2010).Article 

Google Scholar 
Akhani, H. & Ziegler, H. Photosynthetic pathways and habitats of grasses in Golestan National Park (NE Iran), with an emphasis on the C 4-grass dominated rock communities. Phytocoenologia 32, 455–501 (2002).Article 

Google Scholar 
Akhani, H., Mahdavi, P., Noroozi, J. & Zarrinpour, V. Vegetation patterns of the Irano-Turanian steppe along a 3,000 m altitudinal gradient in the Alborz Mountains of Northern Iran. Folia Geobot. 48, 229–255 (2013).Article 

Google Scholar 
Klein, J. C. The altitudinal vegetation Alborez The Central (Iran) between the Iranian-Turanian and Euro-Siberian regions (French) (Institut Français de Recherche en Iran, 2001).
Google Scholar 
Noroozi, J. Case study: High Mountain Regions in Iran 255–260. of Chapter 7 (Endemism in mainland regions-case studies). In Endemism in Vascular plants. Plant. Veg. (ed Hobohm, C.) 9. (Springer, 2014).Noroozi, J., Akhani, H. & Willner, W. Phytosociological and ecological study of the high alpine vegetation of Tuchal Mountains (Central Alborz, Iran). Phytocoenologia 40, 293–321 (2010).Article 

Google Scholar 
Do Carmo, F. F. & Jacobi, C. M. Diversity and plant trait-soil relationships among rock outcrops in the Brazilian Atlantic rainforest. Plant Soil. 403, 7–20 (2015).Article 
CAS 

Google Scholar 
Cavender-Bares, J., Kozak, K. H., Fine, P. V. A. & Kembel, S. The merging of community ecology and phylogenetic biology. Ecol Lett. 12, 693–715 (2009).PubMed 
Article 

Google Scholar 
Heydari, M., Poorbabaei, H., Esmailzadeh, O., Salehi, A. & EshaghiRad, J. Indicator plant species in monitoring forest soil conditions using logistic regression model in Zagros Oak (Quercus brantii var. persica) forest ecosystems. Ilam city. J. Plant Res. 27(5), 811–828 (2014).
Google Scholar 
Speziale, K. L. & Ezcurra, C. Rock outcrops as potential biodiversity refugia under climate change in North Patagonia. Plant Ecol. Diver. 8, 353–361 (2014).Article 

Google Scholar 
Rahmanian, S. et al. Effects of livestock grazing on plant species diversity vary along a climatic gradient in northeastern Iran. Appl. Veg. Sci. 23, 551–561 (2020).Article 

Google Scholar 
Huston, M. A. Biological Diversity: The Coexistence of Species in Changing Landscape (Cambridge University, 1994).
Google Scholar 
Mason, N. W., Mouillot, D. & Lee, W. G. Functional richness, functional evenness and functional divergence: The primary components of functional diversity. Oikos 111, 112–118 (2005).Article 

Google Scholar 
Stubbs, W. J. & Wilson, J. B. Evidence for limiting similarity in a sand dune community. J. Ecol. 92, 557567 (2004).Article 

Google Scholar 
Stanisci, A. et al. Functional composition and diversity of leaf traits in subalpine versus alpine vegetation in the Apennines. Ann. Bot. Comp. plants. 12, plaa004 (2020).CAS 

Google Scholar 
Chesson, P. et al. Resource pulses, species interactions, and diversity maintenance in arid and semi-arid environments. Oecologia 141, 236–253 (2004).ADS 
PubMed 
Article 

Google Scholar 
Rosbakh, S. et al. Contrasting effects of extreme drought and snowmelt patterns on mountain plants along an elevation gradient. Front. Plant Sci. 8, 1478 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Korner, C. Alpine Treelines: Functional Ecology of the Global High Elevation tree Limits (Springer Sci. & Business Media, 2012).Book 

Google Scholar 
Reich, P. B. et al. Generality of leaf trait relationships: A test across six biomes. Ecology 80, 1955–1969 (1999).Article 

Google Scholar 
Westoby, M., Falster, D. S., Moles, A. T., Vesk, P. A. & Wright, I. J. Plant ecological strategies: Some leading dimensions of variation between species. Ann. Rev. Ecol. Syst. 33, 125–159 (2002).Article 

Google Scholar 
Hautier, Y., Niklaus, P. A. & Hector, A. Competition for light causes plant biodiversity loss after eutrophication. Science 324, 636–638 (2009).ADS 
CAS 
PubMed 
Article 

Google Scholar 
De Bello, F. D. et al. Hierarchical effects of environmental filters on the functional structure of plant communities: A case study in the French Alps. Ecography 36, 393–402 (2013).Article 

Google Scholar 
Korner, C., Neumayer, M., Menendez-Riedl, S. P. & Smeets-Scheel, A. Functional morphology of mountain plants. Flora 182, 353–383 (1989).Article 

Google Scholar 
Rosbakh, S., Römermann, C. & Poschlod, P. Specific leaf area correlates with temperature new evidence of trait variation at the population, species and community levels. Alp. Bot. 125, 79–86 (2015).Article 

Google Scholar 
Ordonez, J. C. et al. Global study of relationships between leaf traits, climate and soil measures of nutrient fertility. Glob. Ecol. Biogeogr. 18, 137–149 (2009).Article 

Google Scholar 
Li, W. et al. Community-weighted mean traits but not functional diversity determine the changes in soil properties during wetland drying on the Tibetan Plateau. Solid Earth. 8, 137–147 (2017).ADS 
Article 

Google Scholar 
Bardgett, R. D., Mommer, L. & De Vries, F. T. Going underground: Root traits as drivers of ecosystem processes. Trends Ecol. Evol. 29, 692–699 (2014).PubMed 
Article 

Google Scholar 
Lane, D. R., Coffin, D. P. & Lauenroth, W. K. Effects of soil texture and precipitation on above-ground net primary productivity and vegetation structure across the Central Grassland region of the United States. J. Veg. Sci. 9, 239–250 (1998).Article 

Google Scholar 
Noy-Meir, I. Multivariate analysis of the semi-arid vegetation of southern Australia. II. Vegetation catenae an environmental gradients. Aust. J. Bot. 22, 40–115 (1973).
Google Scholar 
Moura, M. R., Villalobos, F., Costa, G. C. & Garcia, P. C. A. Disentangling the role of climate, topography and vegetation in species richness gradients. PLoS ONE 11(3), 0152468 (2016).Article 
CAS 

Google Scholar 
Neri, A. V. et al. Soil and altitude drives diversity and functioning of Brazilian Páramos (Campo de Altitude). J. plant. Ecol. 10(5), 771–779 (2016).
Google Scholar 
Benites, V. M., Schaefer, C. E. G. R., Simas, F. N. B., Santos, H. G. & Mendonca, B. A. F. Soils associated to rock outcrops in the Brazilian mountain ranges Mantiqueira and Espinhaço. Rev. Bras. Bot. 30, 569–577 (2007).Article 

Google Scholar 
Flynn, D. F. B. et al. Loss of functional diversity under land use intensification across multiple taxa. Ecol. Lett. 12, 22–33 (2009).PubMed 
Article 

Google Scholar 
Zuo, X. A. et al. Testing associations of plant functional diversity with along a restoration gradient of sandy grassland. Front. Plant. Sci. 7, 1–11 (2016).ADS 
Article 

Google Scholar 
Myers-Smith, I. H. et al. Shrub expansion in tundra ecosystems: Dynamics, impacts and research priorities. Environ. Res. Lett. 6, 045509 (2011).ADS 
Article 

Google Scholar 
Vankoughnett, M. R. & Grogan, P. Nitrogen isotope tracer acquisition in low and tall birch tundra plant communities: A 2-year test of the snow–shrub hypothesis. Biogeochemistry 118, 291–306 (2014).CAS 
Article 

Google Scholar 
Pescador, D. S., de Bello, F., Valladares, F. & Escudero, A. Plant trait variation along an altitudinal gradient in Mediterranean high mountain grasslands: Controlling the species turnover effect. PLoS ONE 10, e0118876 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Pescador, D. S., Sierra-Almeida, A., Torres, P. J. & Escudero, A. Summer freezing resistance: A critical filter for plant community assemblies in Mediterranean high mountains. Front. Plant. Sci. 7, 194 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Heydarnejad, S. & Ranjbar, A. Investigation of the effect of salinity stress on growth characteristic and ion accumulation in plants. J. Desert Ecos. Eng. 3(4), 1–10 (2013).
Google Scholar 
Perez-Harguindeguy, N. et al. New handbook for standardized measurement of plant functional traits worldwide. Aust. J. Bot. 61, 167–234 (2013).Article 

Google Scholar 
Cornelissen, J. H. C. et al. A handbook of protocols for standardised and easy measurement of plant functional traits worldwide. Aust. J. Bot. 51, 335–380 (2003).Article 

Google Scholar 
Raunkiaer, C. The Life Forms of Plants and Statistical Plant Geography (Oxford University Press, 1934).
Google Scholar 
Gee, G. W. & Bauder, J. W. Particle size analysis. In Methods of Soil Analysis. Part 1, 2nd ed. (ed Klute, A.) Agronomy Monographs, Vol. 9, 383–409 (Am. Soc. Agr., 1986).Bremner, J. M. In Nitrogen-Total Methods of Soil Analysis. (eds Sparks, D. L.) Soil Sci Soc Am J. 1085–1122 (Am Soc Agr. Inc, 1996).Walkley, A. & Black, I. A. An examination of the Degtjareff method for determining soil organic matter, and a proposed modification of the chromic acid titration method. Soil Sci. 37, 29–38 (1934).ADS 
CAS 
Article 

Google Scholar 
Nelson, D. W. & Sommers, L. Total carbon, organic carbon, and organic matter 1. Methods of soil analysis. Part 2. Chemical and microbi‐ological properties, (methodsofsoilan2), 539–579 (1982).Miller, R. H. & Keeney, D. R. Methods of soil analysis, 2nd ed. In Part 2. Chemical and Microbiological Properties (eds Page, A. L. et al.) 1–129 (ASA, SSSA, 1982).
Google Scholar 
Food and Agriculture Organization-FAO. Management of gypsiferous soils. Soil Bulletin, 62, (FAO, 1990).Chao, A. et al. Rarefaction and extrapolation with Hill numbers: A framework for sampling and estimation in species diversity studies. Ecol. Monogr. 84, 45–67 (2014).Article 

Google Scholar 
Shipley, B., Vile, D. & Garnier, É. from plant traits to plant communities: A statistica mechanistic approach to biodiversity. Science 314(5800), 812–814 (2006).ADS 
MathSciNet 
CAS 
PubMed 
MATH 
Article 

Google Scholar 
Zhu, J., Jiang, L. & Zhang, Y. Relationships between functional diversity and aboveground biomass production in the Northern Tibetan alpine grasslands. Sci. Rep. 6, 34105 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Laliberte, E. & Legendre, P. A distance-based framework for measuring functional diversity from multiple traits. Ecology 91(1), 299–305 (2010).PubMed 
Article 

Google Scholar 
Wheeler, D. & Tiefelsdorf, M. Multicollinearity and correlation among local regression coefficients in geographically weighted regression. J. Geogr. Syst. 7, 161–187 (2005).Article 

Google Scholar 
Fox, J. & Weisberg, S. A review of: an R companion to applied regression, second edition. J. Biopharm. Stat. 22, 418–419 (2011).
Google Scholar 
Brien, R. M. A caution regarding rules of thumb for variance inflation factors. Qual. Quant. 41, 673–690 (2007).Article 

Google Scholar 
Dray, S., Legendre, P. & Blanchet, F. G. packfor: forward selection with permutation (Canoco p. 46). (2011) http://R-Forge.R-project.org/projects/sedar (Accessed 7 Nov 2016).Blanchet, F. G., Legendre, P. & Borcard, D. Forward selection of explanatory variables. Ecology 89, 2623–2632 (2008).PubMed 
Article 

Google Scholar 
Oksanen, J. et al. vegan: Community Ecology Package (2017).Wickham, H. et al. Ggplot2: Elegant Graphics for Data Analysis 2nd edn. (Springer International Publishing, 2016).MATH 
Book 

Google Scholar  More

Jones, J. W. et al. The DSSAT cropping system model. Eur. J. Agron. 18, 235–265 (2003).Article 

Google Scholar 
van Diepen, C. A., Wolf, J., van Keulen, H. & Rappoldt, C. WOFOST: a simulation model of crop production. Soil Use Manag. 5, 16–24 (1989).Article 

Google Scholar 
Cao, J. et al. Integrating multi-source data for rice yield prediction across China using machine learning and deep learning approaches. Agric. For. Meteorol. 297, 108275 (2021).ADS 
Article 

Google Scholar 
Khanal, S., Kushal, K. C., Fulton, J. P., Shearer, S. & Ozkan, E. Remote sensing in agriculture—accomplishments, limitations, and opportunities. Remote Sens. 12, 3783 (2020).ADS 
Article 

Google Scholar 
Maas, S. J. Parameterised model of gramineous crop growth: II. within-season simulation calibration. Agron. J. 85, 354–358 (1993).Article 

Google Scholar 
Nguyen, V., Jeong, S., Ko, J., Ng, C. & Yeom, J. Mathematical integration of remotely-sensed information into a crop modelling process for mapping crop productivity. Remote Sens. 11, 2131 (2019).Article 

Google Scholar 
Huang, J. et al. Assimilation of remote sensing into crop growth models: current status and perspectives. Agric. For. Meteorol. 276–277, 107609 (2019).ADS 
Article 

Google Scholar 
Jin, X. et al. A review of data assimilation of remote sensing and crop models. Eur. J. Agron. 92, 141–152 (2018).Article 

Google Scholar 
Shawon, A. R. et al. Assessment of a proximal sensing-integrated crop model for simulation of soybean growth and yield. Remote Sens. 12, 410 (2020).ADS 
Article 

Google Scholar 
Shawon, A. R. et al. Two-dimensional simulation of barley growth and yield using a model integrated with remote-controlled aerial imagery. Remote Sens. 12, 3766 (2020).ADS 
Article 

Google Scholar 
Shin, T. et al. Simulation of wheat productivity using a model integrated with proximal and remotely controlled aerial sensing information. Front. Plant Sci. https://doi.org/10.3389/fpls.2021.649660 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Huang, J. et al. Assimilating a synthetic Kalman filter leaf area index series into the WOFOST model to improve regional winter wheat yield estimation. Agric. For. Meteorol. 216, 188–202 (2016).ADS 
Article 

Google Scholar 
Khaki, S., Wang, L. & Archontoulis, S. V. A CNN-RNN framework for crop yield prediction. Front. Plant Sci. https://doi.org/10.3389/fpls.2019.01750 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Kim, N. et al. An artificial intelligence approach to prediction of corn yields under extreme weather conditions using satellite and meteorological data. Appl. Sci. 10, 3785 (2020).CAS 
Article 

Google Scholar 
Kumar, P. et al. Comprehensive evaluation of soil moisture retrieval models under different crop cover types using C-band synthetic aperture radar data. Geocarto Int. 34, 1022–1041 (2019).Article 

Google Scholar 
Everingham, Y., Sexton, J., Skocaj, D. & Inman-Bamber, G. Accurate prediction of sugarcane yield using a random forest algorithm. Agron. Sustain. Dev. 36, 27 (2016).Article 

Google Scholar 
Feng, P., Wang, B., Li Liu, D., Waters, C. & Yu, Q. Incorporating machine learning with biophysical model can improve the evaluation of climate extremes impacts on wheat yield in south-eastern Australia. Agric. For. Meteorol. 275, 100–113 (2019).ADS 
Article 

Google Scholar 
Shahhosseini, M., Hu, G., Huber, I. & Archontoulis, S. V. Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt. Sci. Rep. 11, 1606 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Cai, Y. et al. Detecting in-season crop nitrogen stress of corn for field trials using UAV- and CubeSat-based multispectral sensing. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 12, 5153–5166 (2019).ADS 
Article 

Google Scholar 
van Klompenburg, T., Kassahun, A. & Catal, C. Crop yield prediction using machine learning: a systematic literature review. Comput. Electron. Agric. 177, 105709 (2020).Article 

Google Scholar 
Kamilaris, A. & Prenafeta-Boldú, F. X. Deep learning in agriculture: a survey. Comput. Electron. Agric. 147, 70–90 (2018).Article 

Google Scholar 
Bui, D. T., Tsangaratos, P., Nguyen, V.-T., Liem, N. V. & Trinh, P. T. Comparing the prediction performance of a deep learning neural network model with conventional machine learning models in landslide susceptibility assessment. CATENA 188, 104426 (2020).Article 

Google Scholar 
Sahoo, A. K., Pradhan, C. & Das, H. Performance evaluation of different machine learning methods and deep-learning based convolutional neural network for health decision making. In Nature Inspired Computing for Data Science (eds Rout, M. et al.) (Springer International Publishing, 2020).
Google Scholar 
Jeong, S. et al. Development of Variable Threshold Models for detection of irrigated paddy rice fields and irrigation timing in heterogeneous land cover. Agric. Water Manag. 115, 83–91 (2012).Article 

Google Scholar 
Peng, D., Huete, A. R., Huang, J., Wang, F. & Sun, H. Detection and estimation of mixed paddy rice cropping patterns with MODIS data. Int. J. Appl. Earth Obs. Geoinf. 13, 13–23 (2011).ADS 

Google Scholar 
Jeong, S., Ko, J. & Yeom, J.-M. Nationwide projection of rice yield using a crop model integrated with geostationary satellite imagery: a case study in South Korea. Remote Sens. 10, 1665 (2018).ADS 
Article 

Google Scholar 
Xiao, X. et al. Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images. Remote Sens. Environ. 100, 95–113 (2006).ADS 
Article 

Google Scholar 
Ozdogan, M. & Gutman, G. A new methodology to map irrigated areas using multi-temporal MODIS and ancillary data: an application example in the continental US. Remote Sens. Environ. 112, 3520–3537 (2008).ADS 
Article 

Google Scholar 
Yeom, J.-M., Jeong, S., Deo, R. C. & Ko, J. Mapping rice area and yield in northeastern Asia by incorporating a crop model with dense vegetation index profiles from a geostationary satellite. GISci. Remote Sens. 58, 1–27 (2021).Article 

Google Scholar 
Yeom, J.-M. et al. Monitoring paddy productivity in North Korea employing geostationary satellite images integrated with GRAMI-rice model. Sci. Rep. 8, 16121 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Jeong, S., Ko, J., Choi, J., Xue, W. & Yeom, J.-M. Application of an unmanned aerial system for monitoring paddy productivity using the GRAMI-rice model. Int. J. Remote Sens. 39, 2441–2462 (2018).Article 

Google Scholar 
Jeong, S. et al. Geographical variations in gross primary production and evapotranspiration of paddy rice in the Korean Peninsula. Sci. Total Environ. 714, 136632 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Roger, P., Vermote, E. & Ray, J. MODIS Surface Reflectance User’s Guide. Collection 6 (2015).Scharlemann, J. P. W. et al. Global data for ecology and epidemiology: a novel algorithm for temporal Fourier processing MODIS data. PLoS ONE 3, e1408 (2008).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pede, T. & Mountrakis, G. An empirical comparison of interpolation methods for MODIS 8-day land surface temperature composites across the conterminous Unites States. ISPRS J. Photogramm. Remote Sens. 142, 137–150 (2018).ADS 
Article 

Google Scholar 
Kilibarda, M. et al. Spatio-temporal interpolation of daily temperatures for global land areas at 1 km resolution. J. Geophys. Res. Atmos. 119, 2294–2313 (2014).ADS 
Article 

Google Scholar 
Nunez, M. The development of a satellite-based insolation model for the tropical western Pacific Ocean. Int. J. Climatol. 13, 607–627 (1993).Article 

Google Scholar 
Otkin, J. A., Anderson, M. C., Mecikalski, J. R. & Diak, G. R. Validation of GOES-based insolation estimates using data from the U.S. Climate reference network. J. Hydrometeorol. 6, 460–475 (2005).ADS 
Article 

Google Scholar 
Pinker, R. & Laszlo, I. Modeling surface solar irradiance for satellite applications on a global scale. J. Appl. Meteorol. 31, 194–211 (1992).ADS 
Article 

Google Scholar 
Kawamura, H., Tanahashi, S. & Takahashi, T. Estimation of insolation over the Pacific Ocean off the Sanriku coast. J. Oceanogr. 54, 457–464 (1998).Article 

Google Scholar 
Yeom, J.-M., Seo, Y.-K., Kim, D.-S. & Han, K.-S. Solar radiation received by slopes using COMS imagery, a physically based radiation model, and GLOBE. J. Sens. 2016, 1–15 (2016).Article 

Google Scholar 
Yeom, J.-M., Han, K.-S. & Kim, J.-J. Evaluation on penetration rate of cloud for incoming solar radiation using geostationary satellite data. Asia-Pac. J. Atmos. Sci. 48, 115–123 (2012).ADS 
Article 

Google Scholar 
Kawai, Y. & Kawamura, H. Validation and improvement of satellite-derived surface solar radiation over the Northwestern Pacific Ocean. J. Oceanogr. 61, 79–89 (2005).Article 

Google Scholar 
Tanahashi, S., Kawamura, H., Matsuura, T., Takahashi, T. & Yusa, H. A system to distribute satellite incident solar radiation in real-time. Remote Sens. Environ. 75, 412–422 (2001).ADS 
Article 

Google Scholar 
Elbern, H., Schmidt, H., Talagrand, O. & Ebel, A. 4D-variational data assimilation with an adjoint air quality model for emission analysis. Environ. Model. Softw. 15, 539–548 (2000).Article 

Google Scholar 
Press, W. H., Teukolsky, S. A., Vetterling, W. T. & Flannery, B. P. Numerical Recipes: The Art of Scientific Computing (Cambridge University Press, 1992).MATH 

Google Scholar 
Ko, J. et al. Simulation and mapping of rice growth and yield based on remote sensing. J. Appl. Remote Sens. 9, 096067 (2015).Article 

Google Scholar 
Emami Javanmard, M., Ghaderi, S. F. & Hoseinzadeh, M. Data mining with 12 machine learning algorithms for predict costs and carbon dioxide emission in integrated energy-water optimization model in buildings. Energy Convers. Manag. 238, 114153 (2021).CAS 
Article 

Google Scholar 
Diebold, F. X. & Shin, M. Machine learning for regularized survey forecast combination: partially-egalitarian LASSO and its derivatives. Int. J. Forecast. 35, 1679–1691 (2019).Article 

Google Scholar 
Khosla, E., Dharavath, R. & Priya, R. Crop yield prediction using aggregated rainfall-based modular artificial neural networks and support vector regression. Environ. Dev. Sustain. 22, 5687–5708 (2020).Article 

Google Scholar 
Wang, S., Azzari, G. & Lobell, D. B. Crop type mapping without field-level labels: random forest transfer and unsupervised clustering techniques. Remote Sens. Environ. 222, 303–317 (2019).ADS 
Article 

Google Scholar 
Ustuner, M. & Balik, S. F. Polarimetric target decompositions and light gradient boosting machine for crop classification: a comparative evaluation. ISPRS Int. J. Geo Inf. 8, 97 (2019).Article 

Google Scholar 
Jeong, S., Ko, J. & Yeom, J.-M. Predicting rice yield at pixel scale through synthetic use of crop and deep learning models with satellite data in South and North Korea. Sci. Total Environ. 802, 149726 (2022).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Nash, J. E. & Sutcliffe, J. V. River flow forecasting through conceptual models part I: a discussion of principles. J. Hydrol. 10, 282–290 (1970).ADS 
Article 

Google Scholar  More

Choice of biowaste treatment plants and sample identifiersCompost samples were collected from four central municipal biowaste treatment plants (denominated as #1 to #4) in Baden-Wurttemberg, Germany (Table 1). All plants used a state-of-the-art two-stage biowaste treatment process comprising of (a) anaerobic digestion/biogas production and (b) subsequent composting of the solid digestate to produce a high-quality mature compost sold for direct use as fertilizer in agriculture. The composts were regularly analyzed by an independent laboratory for quality and residual contamination and consistently fulfilled the quality requirements of the label RAL-GZ 251 Gütezeichen Kompost of the German Bundesgütegemeinschaft Kompost e.V. (www.gz-kompost.de). Plants #1 and #3 produce in addition a liquid fertilizer, which is separated from the solid digestate at the end of stage a) by press filtration and which is also intended for direct use on agricultural soil (replacement of liquid manure). In case of plants #1, #3, and #4 up to 25 wt% of shrub/tree cuttings were added to the solid digestate for composting. All plants used sieving (typically with a 12 or a 20 mm mesh) at the end of the process to assure the necessary purity of their finished composts. Whenever technically possible, we as well took samples of the pre-compost immediately before this final sieving step to evaluate its contribution to the removal of residual BPD fragments. For analysis, composts were passed consecutively through two sieves with mesh sizes of 5 mm and 1 mm, yielding two fragment preparations for IR-analysis namely a > 5 mm fraction corresponding to the contamination by residual “macroplastic” (5 mm is a commonly used upper size limit for “microplastic”, anything larger is macroplastic) and a 1–5 mm fraction corresponding to the regulatory relevant residual contamination by microplastic. The lower limit of 1 mm rather than 2 mm was chosen in anticipation of the expected changes in regulation, where the replacement of the 2 mm limit by a 1 mm limit is imminent.Table 1 Technical data of the investigated plants and incidence of BDP fragments in the sampled composts.Full size tableOccurrence of plastic fragments  > 1 mm in the sampled compostsComposting times of 5–9 weeks were used in the investigated plants (Table 1), which is shorter than the 12 weeks indicated in EN 13432 for the 90% disintegration of a compostable plastic material, but a realistic time span for state-of-the-art technical waste treatment. Since we were not in a position to estimate the quantity of BDP entering the plants, since for technical reasons we were unable to obtain a representative sample, we cannot say, whether any residual BDP detected by us in the finished composts was due to a yet incomplete disintegration process or whether it corresponds to the 10% material still permissible by EN 13432 even after the full composting step. However, in 7 out of the 12 sampled composts and pre-composts fragments with chemical signatures corresponding to the BDPs poly (lactic acid) (PLA) and poly (butylene-adipate-co-terephthalate) (PBAT) were identified in the > 5 mm and/or the 1–5 mm sieving fractions using FTIR analysis3 (Fig. 1; Table 1). All recovered fragments appeared to stem from foils, bags or packaging, since they were thin compared to their length and width (see Suppl Figure S1 for typical examples). Fragments with overlapping signatures, most likely PBAT/PLA mixtures or blends, were also found (see Suppl Figure S2 for the interpretation of the spectra). In addition, the recorded BDP fragment spectra (Fig. 1A) showed high similarity to the FTIR spectra of commercial compostable bags sold in the vicinity of the biowaste treatment plants (Fig. 1B), which together with the geometry of the recovered fragments led us to assuming that the majority of the BDP entered the biowaste in the form of such bags.Figure 1FTIR spectra of BDP fragments from composts and commercial bags. (A) BDP fragments recovered from the composts and (B) the commercial compostable bags. Fragments were coded as follows: p or f for pre-compost or finished compost, followed by the plant number (#1 to #4), an indication of the size fraction ( > 5 mm or 1–5 mm) in which the fragment was found, and finally, the fragment number. Fragment F#1_5mm_4 therefore represents the 4th fragment collected in the  > 5 mm size fraction from the finished compost of plant number 1. Bags were arbitrarily numbered 1–10, see Suppl Table S1 for supplier information. The spectra (in grey) of the reference materials for PLA and PBAT are given as basis for the interpretation. Spectra in red refer to test samples consisting only of PBAT, while those in blue indicate samples composed of PBAT/PLA mixtures.Full size imageThe BDP fragments were found alongside fragments of commodity plastics (mostly PE) in all cases. Finished composts tended to contain fewer and smaller fragments than the corresponding pre-composts. The final sieving of the pre-composts to prepare the finished composts hence appears to be quite effective in removing such fragments, in particular those from the > 5 mm size fraction (Table 1) and for that reason has become state-of-the-art in preparing quality composts (contamination by plastic fragments > 2 mm of less than 0.1 wt%). Given that the size of the fragments is a crucial factor regarding ecological risk, we analyzed the sizes (length Î width) of the BDP fragments in comparison to that of the plastic fragments with signatures of commodity plastics such as PE (Fig. 2). BDP fragments found in a given compost sample tended to be smaller than the fragments stemming from non-BDP materials, which may indicate that BDPs degrade faster or tend to disintegrate into tinier particles than commodity plastics. This may also explain why in the compost from plant #2, no BDP fragments were found in the particle fraction retained by the 5 mm sieve ( > 5 mm fraction), while 19 such particles were found in the fraction then retained by the 1 mm sieve (1–5 mm fraction). Interestingly, plant #2 is the only one included in our study that uses no mechanical breakdown of the incoming biowaste. This reduces the mechanical stress on the incoming material. Mechanical stress can alter the properties of plastic foils such as the crystallinity whereby crystallinity has been shown to influence the biological degradation of BDP such as PLA7.Figure 2Size distribution of plastic fragments  > 1 mm. (A) Fragments found in the finished compost from plant #1, (B) in the finished compost from plant #2, and (C) in the pre-compost from plant #3. For reasons of statistical relevance, only samples containing more than 20 BDP fragments per kg of compost were included in the analysis.Full size imageMaterial characteristics of BDP fragments in comparison to those of commercial biodegradable bagsIn order to verify whether the BDP fragments recovered from the composts differed from the compostable bags in any parameter with possible relevance for biodegradation and environmental impact16, the physico-chemical properties of bags and fragments were studied in detail. Since we wanted to have a maximum of information of the BDP fragments, size/weight was a limiting factor in selecting fragments for analysis. Fragments of at least 1 mg were required for the FT-IR analysis. 5 mg-fragments could be analyzed in addition by 1H-NMR, while the full set of analytics (FT-IR, 1H-NMR, and DSC) required at least 10 mg of sample.For insight into the chemical composition, 1H-NMR spectra of the commercial bags and all suitable BDP fragments were compared (Fig. 3). In case of material mixtures and blends, the 1H-NMR analysis allows quantification of the PBAT/PLA weight ratio in the materials and also of the ratio of the butylene terephthalate (BT) and butylene adipate (BA) units in the involved PBAT polyesters.Figure 31H NMR spectra of BDP fragments from composts and commercial bags. (A) BDP fragments recovered from the composts and (B) the commercial compostable bags. Fragments were coded as follows: p or f for pre-compost or finished compost, followed by the plant number (#1 to #4), an indication of the size fraction ( > 5 mm or 1–5 mm) in which the fragment was found, and finally, the fragment number. Bags were arbitrarily numbered 1–10, see Suppl Table S1 for supplier information. The spectra (in grey) of the reference materials for PLA and PBAT are given as basis for the interpretation. Spectra in red refer to test samples consisting only of PBAT, while those in blue indicate samples composed of PBAT/PLA mixtures. (C) Chemical structures of PLA and PBAT, chemical shifts of the protons are assigned as indicated in the reference spectra in (B).Full size imageThe 1H-NMR spectra corroborate the FTIR measurements in that all investigated commercial bags were made from PBAT/PLA mixtures of varied composition (Table 2). By comparison, some of the fragments, for instance, f#1_5mm_4, appeared to consist of only PBAT. Other fragments, e.g., f#1_1mm_9, were mixtures of PLA and PBAT (Table 2). However, even in the case of PBAT/PLA mixtures, the average PBAT content tended to be higher in the fragments than in the bags, while the BT/BA monomer ratio in the respective PBATs, was also significantly higher in the fragments than in the bags. If we assume the fragments to stem from similar compostable bags as the ones included in our comparison, this would mean that during composting of such a bag, the PLA degrades more quickly than the PBAT, whereas within a given PBAT polyester, the BA unit is more easily degraded than the BT unit. Evidence can indeed be found in the pertinent literature that PLA has faster biodegradation kinetics than PBAT, while BT is more resistant to mineralization than BA17,18.Table 2 Composition of commercial compostable bags and BDP fragments recovered from the composts as analyzed by 1H-NMR.Full size tableNext, differential scanning calorimetry (DSC) was used to analyze fragments compared to commercial bags in regard to the presence of amorphous vs. crystalline domains, a parameter expected to affect biodegradation kinetics and therefore the putative environmental impact of the produced microplastic16 upon release into the environment with the composts. Whereas amorphous domains show glass transition, crystalline domains show melting, both of which can be discerned by the respective phase transition enthalpy in the DSC curves (Fig. 4).Figure 4DSC curves of BDP fragments and compostable bags #1 and #7. Curves for the reference materials (in grey) for PLA and PBAT are given for comparison. Curves were recorded during the first heating run (temperature range: − 50 °C to 200 °C, heating rate: 10 °C min−1). (A) and (B) curves in red refer to test samples consisting only of PBAT, while those in blue indicate samples composed of PBAT/PLA mixtures. Fragments were coded as follows: p or f for pre-compost or finished compost, followed by the plant number (#1 to #4), an indication of the size fraction ( > 5 mm or 1–5 mm) in which the fragment was found, and finally, the fragment number.Full size imageThe curve for the reference PBAT shows a glass transition temperature (Tg) of − 29 °C and a broad melting range between 100 and 140 °C for the crystalline domains, while that of the PLA reference shows a glass transition temperature of 58 °C and a narrower melting peak between 144 °C and 162 °C. The curve for commercial bag #1, which had a comparatively high PLA content, shows a pronounced melting peak in the expected range; the same is the case for fragment p#3_5mm_1 and to a lesser extent for fragment p#3_5mm_9, two fragments, which also have high PLA contents. The DSC curves of the other fragments and bag #1 are undefined in comparison, which is due to their high PBAT content. According to the DSC curves, most of the investigated materials are semicrystalline, i.e., contain both amorphous (glass transition) and crystalline (melting) domains. However, the DCS data alone allow only a qualitative discussion of the differences between fragments and bags.To obtain quantitative data on the crystallinity differences, wide angle X-ray scattering (WAXS) spectra were recorded. WAXS requires fragments at least 3 cm long, which restricted the number of fragment samples to three, all of which were found in pre-compost samples. The corresponding curves are shown in Fig. 5A–C. The spectra of the commercial biodegradable bags are shown in Suppl Figure S3. Foils were in addition prepared by heat pressing from the reference materials for PLA and PBAT in order to include them into the WAXS measurements (Fig. 5D). While the foils produced from the PBAT reference material produced crystallinity peaks at 16.2°, 17.3°, 20.4°, 23.2°, and 24.8°, the foil prepared from the PLA reference material showed only an amorphous halo at 15.5° and 31.5°, which is in accordance with values published in the literature19. A more pronounced crystallinity peak was obtained in the case of an additionally annealed PLA foil.Figure 5WAXS curves with Lorenz fitting for (A) fragment p#3_5mm_1, (B) fragment p#3_5mm_9, and (C) fragment p#4_5mm_2. (D) WAXS curves for foils produced from the PBAT and PLA reference materials; the percent values indicate the crystallinity. The dash lines are the fitting peak curves for the XRD spectrum. Crystallinity can be obtained by dividing the integration area of the fitted peaks by the integration area of the entire spectrum. Fragments were coded as follows: p or f for pre-compost or finished compost, followed by the plant number (#1 to #4), an indication of the size fraction ( > 5 mm or 1–5 mm) in which the fragment was found, and finally, the fragment number.Full size imageIn case of the fragments and bags, the peaks of PLA and PBAT overlapped to some extent in the WAXS spectra, but by conducting Lorenz fitting using Origin software, the overall crystallinity could be calculated as follows:$$chi = { 1}00% , *{text{ Aa}}/left( {{text{Aa }} + {text{ Ac}}} right)$$where χ is the crystallinity and Aa and Ac represent the areas of the amorphous and crystalline peaks.Using this equation, crystallinities of 55% (fragments p#3_5mm_1), 34% (p#3_5mm_9), and 34% (p#4_5mm_2) were calculated for the fragments. The foils prepared in house for the reference materials had similar crystallinities (43% in case of the annealed PLA foil and 26% of the PBAT foil), while the simple PLA foil was amorphous. By comparison, for eight of the commercial bags, crystallinities in the range from 1% to 7% were calculated, whereas these values were 14% and 15% for the remaining two bag types (Suppl Figure S3).The high crystallinity of the larger fragments recovered from the pre-compost samples suggests that crystalline domains of BDP materials may indeed disintegrate more slowly than the amorphous ones, as prior studies on microbial biodegradation have suggested7,8. Admittedly, such large fragments per se would not enter the environment, since the final sieving step used to prepare the finished composts is quite efficient at removing them. However, it is tempting to extrapolate that residual BDP in general are remnants of the more crystal domains of the original material, even though experimental proof of this assumption is at present not possible. 10 wt% of a BDP bag is allowed to remain after standard composting. It is usually assumed that any such residues continue to degrade with comparable speed. However, should these residues correspond to the more crystalline domains, rather than degrading with similar speed as the bulk material, the more crystalline fragments can be expected to persist for a much longer and at present unpredictable length of time in the environment, e.g. when applied to the soil with the composts; in particular, when they are also enriched in PBAT and BT units as suggested by our analysis of the chemical composition. Data from the use of biodegradable foils in agriculture show that the degradation in the environment may take years20. Altogether this may have unforeseen economic and environmental consequences, especially when considering the high fraction of BDP fragments < 5 mm. Putative consequences include changes in soil properties, the soil microbiome and therefore in plant performance21, a factor indispensable for worldwide nutrition.Residues of BDP fragments  1 mm were found in the collected LF samples. This is hardly surprising, given that the LF is produced by press filtration of the digestate after the anaerobic stage. Such a filtration step can be expected to retain fragments > 1 mm in the produced filter cake, which goes into the composting step, leaving the filtrate, i.e. the LF, essentially free of such particles. Anaerobic digestion is currently not assumed to contribute significantly to the degradation of BDP17,22, but the process conditions (mixing, pumping) may promote breakdown of larger fragments, particularly when additives such as plasticizers23 leach out of the material.Since the residual solids content of the LF is low (plant #1: 8.6 wt%, plant #3: 5.8 wt%), a combination of enzymatic-oxidative treatment and µFTIR imaging originally developed for environmental samples from aqueous systems24,25 could be adapted for the analysis (size and chemical signature) of particles in the LF down to a size of 10 µm. The corresponding data are compiled in Table 3. In all cases, residual fragments from PBAT-based polymers represented the dominant plastic fraction in the investigated samples; i.e. approximately 53% of all plastic particles in the LF from plant #1 (11,520 BDP particles per liter) and 65% in the case of plant #3 (12,480 BDP particles per liter). Liquid manure is applied several times a year to fields at a concentration of 2–3 L m−2. According to our analysis > 20,000 BDP microparticles of a size ranging from 10 µm to 500 µm enter each m2 of agricultural soil whenever LF is applied on agricultural surfaces.Table 3 Microplastic fragments (BDP/all) found per liter of liquid fertilizer.Full size tableDue to the complexity of the matrix, a similar analysis of individual plastic fragments  1 mm. Six compost samples representing the more contaminated ones based on the content of fragments > 1 mm, namely, f#1, f#2, p#3, f#3, p#4 and f#4 (nomenclature: f or p for finished or pre-compost, followed by plant number), were extracted with a 90/10 vol% chloroform/methanol mixture. The amounts of PBAT and PLA in the obtained extracts were then quantified via 1H-NMR (Table 4). Briefly, the intensity of characteristic signals in the extract spectra of the compost samples (see Suppl Figure S4) were compared to peak intensities produced by calibration standards of the pure polymer dissolved at a known concentration in the chloroform/methanol. All samples and standards were normalized using the 1,2-dichloroethan signal at 3.73 ppm as internal standard. See also Suppl Figure S5 for an exemplification of the quantification of the PBAT/PLA ratios. Based on the amounts of PBAT and PLA extracted from a known amount of compost, the total mass concentration (wt% dry weight) of these polymers in the composts was calculated.Table 4 Evidence of PBAT and PLA residues caused by fragments  2 mm. Moreover, residues of PBAT and PLA were found in all investigated compost samples, including the finished compost from plant #4, which had shown no contamination by larger BPD fragments (Table 1). The pre-compost from that plant had shown a few contaminating BDP fragments in the > 5 mm fraction. However, in regard to the fragments More

Very high-resolution (VHR) satellite imagery allows us to survey regularly remote and large areas of the ocean, difficult to access by boats or planes. The interest in using VHR satellite imagery for the study of great whales (including sperm whales and baleen whales) has grown in the past years1,2,3,4,5 since Abileah6 and Fretwell et al.7 showed its potential. This growing interest may be linked to the improvement in the spatial resolution of satellite imagery, which increased in 2014 from 46 cm to 31 cm. This upgrade enhanced the confidence in the detection of whales in satellite imagery, as more details could be seen, such as whale-defining features (e.g. flukes).Detecting whales in the imagery is either conducted manually1,4,5,7, or automatically2,3. A downside of the manual approach is that it is time-demanding, with manual counter often having to view hundred and sometimes thousands of square kilometres of open ocean. The development of automated approaches to detect whales by satellite would not only speed up this application, but also reduce the possibility of missing whales due to observer fatigue and standardize the procedure. Various automated approaches exist from pixel-based to artificial intelligence. Machine learning, an application of artificial intelligence, seems to be the most appropriate automated method to detect whales efficiently in satellite imagery2,3,8,9.In machine learning an algorithm learns how to identify features by repeatedly testing different search parameters against a training dataset10,11. Concerning whales, the algorithm needs to be trained to detect the wide variety of shapes and colour characterising whales. Shapes and colour will be influenced by the type of species, the environment (e.g. various degree of turbidity), the light conditions, and the behaviours (e.g. foraging, travelling, breaching), as different behaviours will result in different postures. The larger a training dataset is, the more accurate and transferable to other satellite images the algorithm will be. At the time of writing, such a dataset does not exist or is not publicly available.Creating a large enough dataset necessary to train algorithms to detect whales in VHR satellite imagery will require the various research groups analysing VHR satellite imagery to openly share examples of whales and non-whale objects in VHR satellite imagery, which could be facilitated by uploading such data on a central open source repository, similar to the GenBank12 for DNA code or OBIS-Seamap13 for marine wildlife observations. Ideally clipped out image chips of the whale objects would be shared as tiff files, which retains most of the characteristics of the original image. However, all VHR satellites are commercially owned, except for the Cartosat-3 owned by the government of India14, which means it is not possible to publicly share image chips as tiff file. Instead, image chips could be shared in a png or jepg format, which involve loosing some spectral information. If tiff files are required, georeferenced and labelled boxes encompassing the whale objects could also be shared, including information on the satellite imagery to allow anyone to ask the commercial providers for the exact imagery.Here we present a database of whale objects found in VHR satellite imagery. It represents four different species of whales (i.e. southern right whale, Eubalaena australis; grey whale, Eschrichtius robustus; humpback whale, Megaptera novaeangliae; fin whale, Balaenoptera physalus; Fig. 1), which were manually detected in images captured by different satellites (i.e., GeoEye-1, Quickbird-2, WorldView-2, WorldView-3). We created the database by (i) first detecting whale objects manually in satellite imagery, (ii) then we classified whale objects as either “definite”, “probable” or “possible” as in Cubaynes et al.1; and (iii) finally we created georeferenced and labelled points and boxes centered around each whale object, as well as providing image chips in a png format. With this database made publicly available, we aim to initiate the creation of a central database that can be built upon.Fig. 1Database of annotated whales detected in satellite imagery covering different species and areas. Humpback whales were detected in Maui Nui, US (a); grey whales in Laguna San Ignacio, Mexico (b); fin whales in the Pelagos Sanctuary, France, Monaco and Italy (c); southern right whales were observed in three areas, off the Peninsula Valdes, Argentina (d); off Witsand, South Africa (e); and off the Auckland Islands, New Zealand (f). The dot size represents the number of annotated whales per location. Whale silhouettes were sourced from philopic.com (the grey and humpback whales silhouettes are from Chris Luh).Full size image More

Lim, N., Kelt, D. A., Lim, K. K. & Bernard, H. Vertebrate scavengers control abundance of diarrheal-causing bacteria in tropical plantations. Zool. Stud. 59, 1–10 (2020).
Google Scholar 
Beasley, J. C., Olson, Z. H. & DeVault, T. L. Ecological role of vertebrate scavengers. In: Carrion Ecology, Evolution and their Applications. (eds Benbow, E.M., Tomberlin, J. & Tarone, A.) 107–127 (CRC Press, 2015).
Ogada, D. L., Keesing, F. & Virani, M. Z. Dropping dead: Causes and consequences of vulture population declines worldwide. Ann. N. Y. Acad. Sci. 1249, 57–71 (2012).ADS 
PubMed 
Article 

Google Scholar 
Reid, W. V. et al. Ecosystems and Human Well-Being-Synthesis: A Report of the Millennium Ecosystem Assessment (Island Press, 2005).
Google Scholar 
Wilson, E. E. & Wolkovich, E. M. Scavenging: How carnivores and carrion structure communities. Trends Ecol. Evol. 26, 129–135 (2011).PubMed 
Article 

Google Scholar 
Moleón, M., Sánchez-Zapata, J. A., Selva, N., Donázar, J. A. & Owen-Smith, N. Inter-specific interactions linking predation and scavenging in terrestrial vertebrate assemblages. Biol. Rev. 89, 1042–1054. https://doi.org/10.1111/brv.12097 (2014).Article 
PubMed 

Google Scholar 
Fonseca, C. R. & Ganade, G. Species functional redundancy, random extinctions and the stability of ecosystems. J. Ecol. 89, 118–125 (2001).Article 

Google Scholar 
Mori, A. S., Furukawa, T. & Sasaki, T. Response diversity determines the resilience of ecosystems to environmental change. Biol. Rev. 88, 349–364. https://doi.org/10.1111/brv.12004 (2013).Article 
PubMed 

Google Scholar 
Huijbers, C. M. et al. Limited functional redundancy in vertebrate scavenger guilds fails to compensate for the loss of raptors from urbanized sandy beaches. Divers. Distrib. 21, 55–63 (2015).Article 

Google Scholar 
Ceballos, G. et al. Accelerated modern human–induced species losses: Entering the sixth mass extinction. Sci. Adv. 1, e1400253 (2015).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Buechley, E. R. & Şekercioğlu, Ç. H. The Avian scavenger crisis: Looming extinctions, trophic cascades, and loss of critical ecosystem functions. Biol. Cons. 198, 220–228 (2016).Article 

Google Scholar 
Hill, J. E., DeVault, T. L., Wang, G. & Belant, J. L. Anthropogenic mortality in mammals increases with the human footprint. Front. Ecol. Environ. 18, 13–18. https://doi.org/10.1002/fee.2127 (2019).Article 

Google Scholar 
Sebastián-González, E. et al. Scavenging in the Anthropocene: Human impact drives vertebrate scavenger species richness at a global scale. Glob. Change Biol. 25, 3005–3017 (2019).ADS 
Article 

Google Scholar 
Sebastián-González, E. et al. Network structure of vertebrate scavenger assemblages at the global scale: Drivers and ecosystem functioning implications. Ecography 43, 1–13. https://doi.org/10.1111/ecog.05083 (2020).Article 

Google Scholar 
Marneweck, C. J., Katzner, T. E. & Jachowski, D. S. Predicted climate-induced reductions in scavenging in eastern North America. Glob. Change Biol. 27, 3383–3394. https://doi.org/10.1111/gcb.15653 (2021).Article 

Google Scholar 
Mokany, K., Ash, J. & Roxburgh, S. Functional identity is more important than diversity in influencing ecosystem processes in a temperate native grassland. J. Ecol. 96, 884–893. https://doi.org/10.1111/j.1365-2745.2008.01395.x (2008).Article 

Google Scholar 
Gagic, V. et al. Functional identity and diversity of animals predict ecosystem functioning better than species-based indices. Proc. R. Soc. B Biol. Sci. 282, 20142620 (2015).Article 

Google Scholar 
Mateo-Tomás, P., Olea, P. P., Selva, N. & Sánchez-Zapata, J. A. Species and individual replacements contribute more than nestedness to shape vertebrate scavenger metacommunities. Ecography 42, 365–375 (2019).Article 

Google Scholar 
Sebastián-González, E. et al. Functional traits driving species role in the structure of terrestrial vertebrate scavenger networks. Ecology https://doi.org/10.1002/ecy.3519 (2021).Article 
PubMed 

Google Scholar 
DeVault, T. L., Rhodes, O. E. Jr. & Shivik, J. A. Scavenging by vertebrates: Behavioral, ecological, and evolutionary perspectives on an important energy transfer pathway in terrestrial ecosystems. Oikos 102, 225–234 (2003).Article 

Google Scholar 
Allen, M. L., Elbroch, L. M., Wilmers, C. C. & Wittmer, H. U. The comparative effects of large carnivores on the acquisition of carrion by scavengers. Am. Nat. 185, 822–833 (2015).PubMed 
Article 

Google Scholar 
Moleón, M., Sánchez-Zapata, J. A., Sebastián-González, E. & Owen-Smith, N. Carcass size shapes the structure and functioning of an African scavenging assemblage. Oikos 124, 1391–1403 (2015).Article 

Google Scholar 
Gutiérrez-Cánovas, C. et al. Large home range scavengers support higher rates of carcass removal. Funct. Ecol. 34, 1921–1932 (2020).Article 

Google Scholar 
Walker, M. A. et al. Factors influencing scavenger guilds and scavenging efficiency in Southwestern Montana. Sci. Rep. https://doi.org/10.1038/s41598-021-83426-3 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Winfree, R., Fox, J., Williams, N. M., Reilly, J. R. & Cariveau, D. P. Abundance of common species, not species richness, drives delivery of a real-world ecosystem service. Ecol. Lett. 18, 626–635. https://doi.org/10.1111/ele.12424 (2015).Article 
PubMed 

Google Scholar 
Mateo-Tomás, P., Olea, P. P., Moleón, M., Selva, N. & Sánchez-Zapata, J. A. Both rare and common species support ecosystem services in scavenger communities. Glob. Ecol. Biogeogr. 26, 1459–1470. https://doi.org/10.1111/geb.12673 (2017).Article 

Google Scholar 
Butler, J. R. A. & du Toit, J. T. Diet of free-ranging domestic dogs (Canis familiaris) in rural Zimbabwe: Implications for wild scavengers on the periphery of wildlife reserves. Anim. Conserv. 5, 29–37. https://doi.org/10.1017/s136794300200104x (2002).Article 

Google Scholar 
DeVault, T. L., Olson, Z. H., Beasley, J. C. & Rhodes, O. E. Jr. Mesopredators dominate competition for carrion in an agricultural landscape. Basic Appl. Ecol. 12, 268–274 (2011).Article 

Google Scholar 
Ogada, D. L., Torchin, M. E., Kinnaird, M. F. & Ezenwa, V. O. Effects of vulture declines on facultative scavengers and potential implications for mammalian disease transmission. Conserv. Biol. 26, 453–460. https://doi.org/10.1111/j.1523-1739.2012.01827.x (2012).CAS 
Article 
PubMed 

Google Scholar 
Morales-Reyes, Z. et al. Scavenging efficiency and red fox abundance in Mediterranean mountains with and without vultures. Acta Oecol. 79, 81–88. https://doi.org/10.1016/j.actao.2016.12.012 (2017).ADS 
Article 

Google Scholar 
Inagaki, A. et al. Vertebrate scavenger guild composition and utilization of carrion in an East Asian temperate forest. Ecol. Evol. 10, 1223–1232 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Blazquez, M., Sanchez-Zapata, J. A., Botella, F., Carrete, M. & Eguía, S. Spatio-temporal segregation of facultative avian scavengers at ungulate carcasses. Acta Oecol. 35, 645–650 (2009).ADS 
Article 

Google Scholar 
Inger, R., Cox, D. T. C., Per, E., Norton, B. A. & Gaston, K. J. Ecological role of vertebrate scavengers in urban ecosystems in the UK. Ecol. Evol. 6, 7015–7023. https://doi.org/10.1002/ece3.2414 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Hill, J. E., DeVault, T. L., Beasley, J. C., Rhodes, O. E. Jr. & Belant, J. L. Effects of vulture exclusion on carrion consumption by facultative scavengers. Ecol. Evol. 8, 2518–2526. https://doi.org/10.1002/ece3.3840 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Olson, Z., Beasley, J., DeVault, T. L. & Rhodes, O. E. Jr. Scavenger community response to the removal of a dominant scavenger. Oikos 121, 77–84 (2012).Article 

Google Scholar 
Pardo-Barquín, E., Mateo-Tomás, P. & Olea, P. P. Habitat characteristics from local to landscape scales combine to shape vertebrate scavenging communities. Basic Appl. Ecol. 34, 126–139. https://doi.org/10.1016/j.baae.2018.08.005 (2019).Article 

Google Scholar 
Turner, K. L., Conner, L. M. & Beasley, J. C. Effect of mammalian mesopredator exclusion on vertebrate scavenging communities. Sci. Rep. 10, 1–9 (2020).Article 
CAS 

Google Scholar 
Ohashi, H. et al. Differences in the activity pattern of the wild boar Sus scrofa related to human disturbance. Eur. J. Wildl. Res. 59, 167–177. https://doi.org/10.1007/s10344-012-0661-z (2013).Article 

Google Scholar 
Saito, M. & Koike, F. Distribution of wild mammal assemblages along an urban–rural–forest landscape gradient in warm-temperate East Asia. PLoS ONE 8, e65464. https://doi.org/10.1371/journal.pone.0065464 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

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. https://doi.org/10.1126/science.aar7121 (2018).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Tsunoda, M. et al. Human disturbance affects latrine-use patterns of raccoon dogs. J. Wildl. Manag. 83, 728–736. https://doi.org/10.1002/jwmg.21610 (2019).Article 

Google Scholar 
Watabe, R. & Saito, M. U. Effects of vehicle-passing frequency on forest roads on the activity patterns of carnivores. Landsc. Ecol. Eng. 17, 225–231. https://doi.org/10.1007/s11355-020-00434-7 (2021).Article 

Google Scholar 
Luna, Á., Romero-Vidal, P. & Arrondo, E. Predation and scavenging in the city: A review of spatio-temporal trends in research. Diversity 13, 46. https://doi.org/10.3390/d13020046 (2021).Article 

Google Scholar 
Huijbers, C. M., Schlacher, T. A., Schoeman, D. S., Weston, M. A. & Connolly, R. M. Urbanisation alters processing of marine carrion on sandy beaches. Landsc. Urban Plan. 119, 1–8 (2013).Article 

Google Scholar 
Fukushima Prefectural Government. Transition of evacuation designated zones. https://www.pref.fukushima.lg.jp/site/portal-english/en03-08.html. (2019). Accessed 20 Apr 2022.Steinhauser, G., Brandl, A. & Johnson, T. E. Comparison of the Chernobyl and Fukushima nuclear accidents: A review of the environmental impacts. Sci. Total Environ. 470, 800–817 (2014).ADS 
PubMed 
Article 
CAS 

Google Scholar 
Center for International Earth Science Information Network (CIESIN)—Columbia University. (NASA Socioeconomic Data and Applications Center (SEDAC), Palisades, NY, 2018).Lyons, P. C., Okuda, K., Hamilton, M. J., Hinton, T. G. & Beasley, J. C. Rewilding of Fukushima’s human evacuation zone in the presence of radioactive stressors. Front. Ecol. Environ. 18, 127–134 (2020).Article 

Google Scholar 
Deryabina, T. G. et al. Long-term census data reveal abundant wildlife populations at Chernobyl. Curr. Biol. 25, R824–R826. https://doi.org/10.1016/j.cub.2015.08.017 (2015).CAS 
Article 
PubMed 

Google Scholar 
Webster, S. C. et al. Where the wild things are: Influence of radiation on the distribution of four mammalian species within the Chernobyl Exclusion Zone. Front. Ecol. Environ. 14, 185–190. https://doi.org/10.1002/fee.1227 (2016).Article 

Google Scholar 
Schlichting, P. E., Love, C. N., Webster, S. C. & Beasley, J. C. Efficiency and composition of vertebrate scavengers at the land–water interface in the Chernobyl Exclusion Zone. Food Webs 18, e00107. https://doi.org/10.1016/j.fooweb.2018.e00107 (2019).Article 

Google Scholar 
Newsome, T. M. et al. Monitoring the dead as an ecosystem indicator. Ecol. Evol. 11, 5844–5856. https://doi.org/10.1002/ece3.7542 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Turner, K. L., Abernethy, E. F., Mike Conner, L., Rhodes, O. E. Jr. & Beasley, J. C. Abiotic and biotic factors modulate carrion fate and vertebrate scavenging communities. Ecology 98, 2413–2424 (2017).PubMed 
Article 

Google Scholar 
Ruzicka, R. E. & Conover, M. R. Does weather or site characteristics influence the ability of scavengers to locate food?. Ethology 118, 187–196 (2012).Article 

Google Scholar 
Paula, J. J. S. et al. Camera-trapping as a methodology to assess the persistence of wildlife carcasses resulting from collisions with human-made structures. Wildl. Res. 41, 717–725. https://doi.org/10.1071/WR14063 (2015).Article 

Google Scholar 
Selva, N., Jędrzejewska, B., Jędrzejewski, W. & Wajrak, A. Factors affecting carcass use by a guild of scavengers in European temperate woodland. Can. J. Zool. 83, 1590–1601 (2005).Article 

Google Scholar 
Nakama, S., Yoshimura, K., Fujiwara, K., Ishikawa, H. & Iijima, K. Temporal decrease in air dose rate in the sub-urban area affected by the Fukushima Dai-ichi Nuclear Power Plant accident during four years after decontamination works. J. Environ. Radioact. 208–209, 106013. https://doi.org/10.1016/j.jenvrad.2019.106013 (2019).CAS 
Article 
PubMed 

Google Scholar 
Ministry of the Environment of Japan. Off-Site Environmental Remediation in Affected Areas in Japan. http://josen.env.go.jp/en/decontamination/ (2020). Accessed 20 Apr 2022.Japan Meteorological Agency. Climate in Namie in 2018: Monthly Overview Data. http://www.data.jma.go.jp/obd/stats/etrn/view/monthly_a1.php?prec_no=36&block_no=0295&year=2018&month=7&day=&view=p1 (2018). Accessed 1 Apr 2019.De Vault, T. L., Brisbin, J., Lehr, I., Rhodes, J. & Olin, E. Factors influencing the acquisition of rodent carrion by vertebrate scavengers and decomposers. Can. J. Zool. 82, 502–509 (2004).Article 

Google Scholar 
Kane, A., Healy, K., Guillerme, T., Ruxton, G. D. & Jackson, A. L. A recipe for scavenging in vertebrates—The natural history of a behaviour. Ecography 40, 11. https://doi.org/10.1111/ecog.02817 (2017).Article 

Google Scholar 
Natusch, D. J. D., Lyons, J. A. & Shine, R. How do predators and scavengers locate resource hotspots within a tropical forest?. Aust. Ecol. 42, 742–749. https://doi.org/10.1111/aec.12492 (2017).Article 

Google Scholar 
Japan Aerospace Exploration Agency. High-resolution land use land cover map of Japan (ver.16.09). https://www.eorc.jaxa.jp/ALOS/en/lulc/lulc_index.htm (2011). Accessed 1 Apr 2019.Newkirk, E. S. CPW Photo Warehouse. http://cpw.state.co.us/learn/Pages/ResearchMammalsSoftware.aspx (2016). Accessed 1 Apr 2019.Therneau, T. M. A Package for Survival Analysis in R. R package version 3.3-1 (2022).Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48. https://doi.org/10.18637/jss.v067.i01 (2015).Article 

Google Scholar 
Anderson, D. et al. Introgression dynamics from invasive pigs into wild boar following the March 2011 natural and anthropogenic disasters at Fukushima. Proc. R. Soc. B Biol. Sci. 288, 20210874. https://doi.org/10.1098/rspb.2021.0874 (2021).CAS 
Article 

Google Scholar 
Ishiniwa, H., Onuma, M. & Tamaoki, M. Behavior of Radionuclides in the Environment III 463–472 (Springer, 2022).Book 

Google Scholar 
Nemoto, Y. et al. Effects of 137Cs contamination after the TEPCO Fukushima Dai-ichi Nuclear Power Station accident on food and habitat of wild boar in Fukushima Prefecture. J. Environ. Radioact. 225, 106342. https://doi.org/10.1016/j.jenvrad.2020.106342 (2020).CAS 
Article 
PubMed 

Google Scholar 
Olson, Z. H., Beasley, J. C. & Rhodes, O. E. Jr. Carcass type affects local scavenger guilds more than habitat connectivity. PLoS ONE 11, e0147798 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
DeVault, T. L., Seamans, T. W., Linnell, K. E., Sparks, D. W. & Beasley, J. C. Scavenger removal of bird carcasses at simulated wind turbines: Does carcass type matter?. Ecosphere. https://doi.org/10.1002/ecs2.1994 (2017).Article 

Google Scholar 
Sugiura, S., Tanaka, R., Taki, H. & Kanzaki, N. Differential responses of scavenging arthropods and vertebrates to forest loss maintain ecosystem function in a heterogeneous landscape. Biol. Cons. 159, 206–213 (2013).Article 

Google Scholar 
Enari, H. & Enari, H. S. Not avian but mammalian scavengers efficiently consume carcasses under heavy snowfall conditions: A case from northern Japan. Mamm. Biol. 101, 419–428. https://doi.org/10.1007/s42991-020-00097-9 (2021).Article 

Google Scholar 
Selva, N., Jedrzejewska, B., Jedrzejewski, W. & Wajrak, A. Scavenging on European bison carcasses in Bialowieza primeval forest (eastern Poland). Ecoscience 10, 303–311 (2003).Article 

Google Scholar 
Jojola-Elverum, S. M., Shivik, J. A. & Clark, L. Importance of bacterial decomposition and carrion substrate to foraging brown treesnakes. J. Chem. Ecol. 27, 1315–1331. https://doi.org/10.1023/a:1010357024140 (2001).CAS 
Article 
PubMed 

Google Scholar 
Abernethy, E. F., Turner, K. L., Beasley, J. C. & Rhodes, O. E. Jr. Scavenging along an ecological interface: Utilization of amphibian and reptile carcasses around isolated wetlands. Ecosphere 8, e01989. https://doi.org/10.1002/ecs2.1989 (2017).Article 

Google Scholar 
Sugiura, S. & Hayashi, M. Functional compensation by insular scavengers: The relative contributions of vertebrates and invertebrates vary among islands. Ecography 41, 1173–1183 (2018).Article 

Google Scholar 
Matsuo, R. & Ochiai, K. Dietary overlap among two introduced and one native sympatric carnivore species, the raccoon, the masked palm civet, and the raccoon dog, in Chiba Prefecture, Japan. Mammal Study 34, 187–194 (2009).Article 

Google Scholar 
Drygala, F. & Zoller, H. Diet composition of the invasive raccoon dog (Nyctereutes procyonoides) and the native red fox (Vulpes vulpes) in north-east Germany. Hystrix Italian J. Mammal. 24, 190–194 (2014).
Google Scholar 
Elmeros, M. et al. The diet of feral raccoon dog (Nyctereutes procyonoides) and native badger (Meles meles) and red fox (Vulpes vulpes) in Denmark. Mammal Res. 63, 405–413. https://doi.org/10.1007/s13364-018-0372-2 (2018).Article 

Google Scholar 
Sekizawa, R., Ichii, K. & Kondo, M. Satellite-based detection of evacuation-induced land cover changes following the Fukushima Daiichi nuclear disaster. Remote Sensing Lett. 6, 824–833 (2015).Article 

Google Scholar 
Ishihara, M. & Tadono, T. Land cover changes induced by the great east Japan earthquake in 2011. Sci. Rep. 7, 45769–45769. https://doi.org/10.1038/srep45769 (2017).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Focardi, S., Materassi, M., Innocenti, G. & Berzi, D. Kleptoparasitism and scavenging can stabilize ecosystem dynamics. Am. Nat. 190, 398–409 (2017).PubMed 
Article 

Google Scholar 
Osugi, S., Trentin, B. E. & Koike, S. Impact of wild boars on the feeding behavior of smaller frugivorous mammals. Mamm. Biol. 97, 22–27 (2019).Article 

Google Scholar 
Duľa, M. & Krofel, M. A cat in paradise: Hunting and feeding behaviour of Eurasian lynx among abundant naive prey. Mamm. Biol. 100, 685–690. https://doi.org/10.1007/s42991-020-00070-6 (2020).Article 

Google Scholar 
Smith, J. B., Laatsch, L. J. & Beasley, J. C. Spatial complexity of carcass location influences vertebrate scavenger efficiency and species composition. Sci. Rep. 7, 10250. https://doi.org/10.1038/s41598-017-10046-1 (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Moleón, M. et al. Carrion availability in space and time. In Carrion Ecology and Management (eds Olea, P.P., Mateo-Tomás, P. & Sánchez-Zapata, J.A.) 23–44 (Springer International Publishing, 2019).
DeVault, T. L. & Rhodes, O. E. Jr. Identification of vertebrate scavengers of small mammal carcasses in a forested landscape. Acta Theriol. 47, 185–192 (2002).Article 

Google Scholar 
Bumann, G. B. & Stauffer, D. F. Scavenging of ruffed grouse in the Appalachians: Influences and implications. Wildl. Soc. Bull. 1973–2006(30), 853–860 (2002).
Google Scholar 
Young, A., Stillman, R., Smith, M. J. & Korstjens, A. H. An experimental study of vertebrate scavenging behavior in a Northwest European woodland context. J. Forensic Sci. 59, 1333–1342. https://doi.org/10.1111/1556-4029.12468 (2014).Article 
PubMed 

Google Scholar 
Abernethy, E. F. et al. Carcasses of invasive species are predominantly utilized by invasive scavengers in an island ecosystem. Ecosphere 7 (2016).DeVault, T. L. & Krochmal, A. R. Scavenging by snakes: An examination of the literature. Herpetologica 58, 429–436 (2002).Article 

Google Scholar 
Shivik, J. A. & Clark, L. Ontogenetic shifts in carrion attractiveness to brown tree snakes (Boiga irregularis). J. Herpetol. 33, 334–336. https://doi.org/10.2307/1565737 (1999).Article 

Google Scholar 
Campobasso, C. P., Di Vella, G. & Introna, F. Factors affecting decomposition and Diptera colonization. Forensic Sci. Int. 120, 18–27 (2001).CAS 
PubMed 
Article 

Google Scholar  More

Wilkins, M. R., Seddon, N. R. & Safran, R. J. Evolutionary divergence in acoustic signals: causes and consequences. Trends Ecol. Evol. 28, 156–166 (2013).PubMed 
Article 

Google Scholar 
Wei, C. Sound production and propagation in cetacean. In Neuroendocrine Regulation of Animal Vocalization (eds Rosenfeld, C. S. & Hoffmann, F.) 267–291 (Academic Press, 2021).Chapter 

Google Scholar 
Nakakara, F. Social functions of cetacean acoustic communication. Fish. Sci. 68, 298–301 (2002).Article 

Google Scholar 
Caldwell, M. C. & Caldwell, D. K. Vocalization of naive captive dolphins in small groups. Science 159, 1121–1123 (1968).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Caldwell, M. C., Caldwell, D. K. & Tyack, P. L. Review of the signature-whistle-hypothesis for the Atlantic bottlenose dolphin. In The bottlenose dolphin (eds Leatherwood, S. & Reeves, R. R.) 199–234 (Academic Press, 1990).Chapter 

Google Scholar 
Ford, J. B. Vocal traditions among resident killer whales (Orcinus orca) in coastal waters of British Columbia. Can. J. Zool. 69, 1454–1483 (1991).Article 

Google Scholar 
Weilgart, L. & Whitehead, H. Group-specific dialects and geographical variation in coda repertoire in South Pacific sperm whales. Behav. Ecol. Sociobiol. 40, 277–285 (1997).Article 

Google Scholar 
Deeck, V. B., Ford, J. K. B. & Spong, P. Dialect change in resident killer whales: implications for vocal learning and cultural transmission. Anim. Behav. 60, 629–638 (2000).Article 

Google Scholar 
Chen, Z. & Wiens, J. J. The origins of acoustic communication in vertebrates. Nat. Commun. 11, 369 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Morton, E. S. Sources of selection on avian sounds. Am. Nat. 109, 17–34 (1975).ADS 
Article 

Google Scholar 
Irwin, D. E., Thimgan, M. P. & Irwin, J. H. Call divergence is correlated with geographic and genetic distance in greenish warblers (Phylloscopus trochiloides): A strong role for stochasticity in signal evolution?. J. Evol. Biol. 21, 435–448 (2008).CAS 
PubMed 
Article 

Google Scholar 
Campbell, P. et al. Geographic variation in the songs of Neotropical singing mice: Testing the relative importance of drift and local adaptation. Evol. 64, 1955–1972 (2010).
Google Scholar 
Connor, R. C., Wells, R. S., Mann, J. & Read, A. J. The bottlenose dolphin: Social relationships in a fission-fusion society. In Cetacean societies: Field studies of dolphins and whales (eds Mann, J. et al.) 91–126 (University of Chicago Press, Chicago, 2000).
Google Scholar 
Janik, V. M. & Sayigh, L. S. Communication in bottlenose dolphins: 50 years of signature whistle research. J. Comp. Physiol. A https://doi.org/10.1007/s00359-013-0817-7 (2013).Article 

Google Scholar 
MacFarlane, N. et al. Signature whistles facilitate reunions and/or advertise identity in Bottlenose Dolphins. JASA 141, 3543 (2017).Article 

Google Scholar 
Buckstaff, K. C. Effects of watercraft noise on the acoustic behaviour of bottlenose dolphins, Tursiops truncatus, in Sarasota Bay, Florida. Mar. Mam. Sci. 20, 709–725 (2004).Article 

Google Scholar 
Cook, M. L. H., Sayigh, L. S., Blum, J. E. & Wells, R. S. Signature-whistle production in undisturbed free-ranging bottlenose dolphins (Tursiops truncatus). Proc. R. Soc. Lond. B. 271, 1043–1049 (2004).Article 

Google Scholar 
Watwood, S. L., Owen, E. C. G., Tyack, P. L. & Wells, R. S. Signature whistle use by temporarily restrained and free-swimming bottlenose dolphins, Tursiops truncatus. Anim. Behav. 69, 1373–1386 (2005).Article 

Google Scholar 
Sayigh, L. S., Tyack, P. L., Wells, R. S., Scott, M. D. & Irvine, A. B. Sex difference in signature whistle production of free-ranging bottle-nosed dolphins, Tursiops-truncatus. Beh. Ecol. Soc. 36, 171–177 (1995).Article 

Google Scholar 
Tyack, P. L. & Sayigh, L. S. Vocal learning in cetaceans. In Social influences on vocal development (eds Snowdon, C. T. & Hausberger, M.) 208–233 (Cambridge University Press, 1997).Chapter 

Google Scholar 
Miksis, J. L., Tyack, P. & Buck, J. R. Captive dolphins, Tursiops truncatus, develop signature whistles that match acoustic features of human-made model sounds. JASA 112, 728–739 (2002).Article 

Google Scholar 
Fripp, D. et al. Bottlenose dolphin (Tursiops truncatus) calves appear to model their signature whistles on the signature whistles of community members. Anim. Cogn. 8, 17–26 (2005).PubMed 
Article 

Google Scholar 
Janik, V. M. & Slater, P. J. B. Context-specific use suggests that bottlenose dolphin signature whistles are cohesion calls. Anim. Behav. 56, 829–838 (1998).CAS 
PubMed 
Article 

Google Scholar 
Sayigh, L. S., Tyack, P. L., Wells, R. S. & Scott, M. D. Signature whistles of free-ranging bottlenose dolphins, Tursiops truncatus: mother offspring comparisons. Behav. Ecol. Sociobiol. 26, 247–260 (1990).Article 

Google Scholar 
Watwood, S. L., Tyack, P. L. & Wells, R. S. Whistle sharing in paired male bottlenose dolphins, Tursiops truncatus. Behav. Ecol. Sociobiol. 55, 531–543 (2004).Article 

Google Scholar 
Janik, V. M., Dehnhardt, G. & Todt, D. Signature whistle variations in a bottlenosed dolphin, Tursiops truncatus. Behav. Ecol. Sociobiol. 35, 243–248 (1994).Article 

Google Scholar 
Esch, H. C., Sayigh, L. S. & Wells, R. S. Quantifying parameters of bottlenose dolphin signature whistles. Mar. Mam. Sci. 24, 976–986 (2009).Article 

Google Scholar 
Gridley, T. Geographic and species variation in bottlenose dolphin (Tursiops spp.) signature whistle types. PhD Thesis Biology. University of St Andrews (2011).King, S. L. & Janik, V. M. Bottlenose dolphins can use learned vocal labels to address each other. Proc Natl Acad Sci USA 110, 13216–13221 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kriesell, H., Elwen, S. H., Nastasi, A. & Gridley, T. Identification and characteristics of signature whistles in wild bottlenose dolphins (Tursiops truncatus) from Namibia. PLoS ONE 9, e106317 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Luis, A. R., Couchinho, M. N. & dos Santos, M. E. Signature whistles in wild bottlenose dolphins: Long term stability and emission rates. Acta Ethol. https://doi.org/10.1007/s10211-015-0230-z (2015).Article 

Google Scholar 
Wang, D. W., Würsig, B. & Evans, W. E. Whistles of bottlenose dolphins: Comparisons among populations. Aquatic Mam. 21, 65–77 (1995).
Google Scholar 
May-Collado, L. J. & Wartzok, D. A comparison of bottlenose dolphin whistles in the Atlantic Ocean: Factors promoting whistle variation. J. Mammal. 89, 1229–1240 (2008).Article 

Google Scholar 
Papale, E. et al. Acoustic divergence between bottlenose dolphin whistles from the Central-Eastern North Atlantic and Mediterranean Sea. Acta Ethol. 17, 155–165 (2014).Article 

Google Scholar 
La Manna, G., Rako-Gospić, N., Manghi, M., Picciulin, M. & Sarà, G. Assessing geographical variation on whistle acoustic structure of three Mediterranean populations of common bottlenose dolphin (Tursiops truncatus). Beh. 154, 583–607 (2017).Article 

Google Scholar 
La Manna, G. et al. Whistle variation in Mediterranean common bottlenose dolphin: The role of geographical, anthropogenic, social, and behavioral factors. Ecol. Evol. 00, 1–7 (2020).
Google Scholar 
Natoli, A., Birkun, A., Aguilar, A., Lopez, A. & Rus Hoelzel, A. Habitat structure and the dispersal of male and female bottlenose dolphins (Tursiops truncatus) based on microsatellite and mitochon-drial DNA analyses. Proc. R. Soc. Lond. B. 272, 1217–2122 (2005).CAS 

Google Scholar 
Richardson, W. J., Greene, C. R., Malme, C. I. & Thomson, D. H. Marine mammals and noise (Academic Press, London, 1995).
Google Scholar 
Gnone, G., et al. TursioMed: An international project to assess the conservation status of the bottlenose dolphin in the Mediterranean Sea. Final Report (2019).La Manna, G. & Ronchetti, F. Relazione sul monitoraggio della presenza e distribuzione del tursiope Tursiops truncatus nell’area del nord Sardegna comprendente l’Area Marina Protetta Capo Caccia – Isola Piana. Report AMP, 42 (2018).La Manna, G., Ronchetti, F., Sarà, G., Ruiu, A. & Ceccherelli, G. Common bottlenose dolphin protection and sustainable boating: species distribution modeling for effective coastal planning. Front. Mar. Sci. 7, 542648 (2020).Article 

Google Scholar 
Pace, D. S. et al. An integrated approach for cetacean knowledge and conservation in the central Mediterranean Sea using research and social media data sources. Aquat. Conserv. 29, 1302–1323 (2019).Article 

Google Scholar 
Pace, D. S. et al. Capitoline Dolphins: Residency patterns and abundance estimate of Tursiops truncatus at the Tiber River Estuary (Mediterranean Sea). Biology 10, 275 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Pulcini, M., Pace, D. S., La Manna, G., Triossi, F. & Fortuna, C. M. Distribution and abundance estimates of bottlenose dolphins (Tursiops truncatus) around Lampedusa Island (Sicily Channel, Italy). Implications for their management. J. Mar. Biol. Assoc. UK 6, 1175–1184 (2013).
Google Scholar 
La Manna, G., Ronchetti, F. & Sarà, G. Predicting common bottlenose dolphin habitat preference to dynamically adapt management measures from a Marine Spatial Planning perspective. Ocean Coast. Manag. 130, 317–327 (2016).Article 

Google Scholar 
Santostasi, N. L., Bonizzoni, S., Bearzi, G., Eddy, L. & Gimenez, O. A robust design capture-recapture analysis of abundance, survival and temporary emigration of three odontocete species in the Gulf of Corinth, Greece. PLoS ONE 11, e0166650 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Bearzi, G., Bonizzoni, S. & Gonzalvo, J. Mid-distance movements of common bottlenose dolphins in the coastal waters of Greece. J. Ethol 29, 369–374 (2011).Article 

Google Scholar 
Bearzi, G. et al. Dolphins in a scaled-down Mediterranean: The Gulf of Corinth’s odontocetes. In Adv. Mar. Biol. Vol. 75 (eds NotarbartolodiSciara, G. et al.) 297–331 (Academic Press, 2016).
Google Scholar 
Pleslić, G. et al. The abundance of common bottlenose dolphins (Tursiops truncatus) in the former special marine reserve of the Cres-Lošinj Archipelago, Croatia. Aquat. Conserv. 25, 125–137 (2015).Article 

Google Scholar 
Rako-Gospić, N. et al. Factor associated variations in the home range of a resident Adriatic common bottlenose dolphin population. Mar. Pol. Bul. 124, 234–244 (2017).Article 
CAS 

Google Scholar 
Janik, V. M., King, S. L., Sayigh, L. S. & Wells, R. S. Identifying signature whistles from recordings of groups of unrestrained bottlenose dolphins (Tursiops truncatus). Mar Mam. Sci 29, 1–14 (2013).Article 

Google Scholar 
La Manna, G., Manghi, M., Pavan, G., Lo Mascolo, F. & Sarà, G. Behavioural strategy of common bottlenose dolphins (Tursiops truncatus) in response to different kinds of boats in the waters of Lampedusa Island (Italy). Aquat. Conserv. 23, 745–757 (2013).
Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2015).
Google Scholar 
Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. A. & Smith, G. H. Mixed effects models and extensions in ecology with R, 579 (Springer, 2009).MATH 
Book 

Google Scholar 
Garamszegi, L. Z. A simple statistical guide for the analysis of behaviour when data are constrained due to practical or ethical reasons. Anim. Beh. 120, 223–234 (2015).Article 

Google Scholar 
Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D., & R Core Team. nlme: Linear and Nonlinear Mixed Effects Models. R package version 3.1–137 (2018).Janik, V. M. Source levels and the estimated active space of bottlenose dolphin (Tursiops truncatus) whistles in the Moray Firth, Scotland. J. Comp. Physiol. A Sens. Neural Behav. Physiol 186, 673–680 (2000).CAS 
Article 

Google Scholar 
Quintana-Rizzo, E., Mann, D. A. & Wells, R. S. Estimated communication range of social sounds used by bottlenose dolphins (Tursiops truncatus). JASA 120, 1671–1683 (2006).Article 

Google Scholar 
Sayigh, L. S. Development and function of signature whistles of free ranging bottlenose dolphins, Tursiops truncatus. MIT/WHOI joint program (1992).Janik, V. M., Sayigh, L. S. & Wells, R. S. Signature whistle shape conveys identity information to bottlenose dolphins. PNAS 103, 8293–8297 (2006).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Papale, E., Gamba, M., Perez-Gil, M., Martin, V. M. & Giacoma, C. Dolphins adjust species-specific frequency parameters to compensate for increasing background noise. PLoS ONE 10, e0121711 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
La Manna, G., Rako-Gospić, N., Manghi, M. & Ceccherelli, G. Influence of environmental, social and behavioural variables on the whistling of the common bottlenose dolphin (Tursiops truncatus). Behav. Ecol. Sociobiol. 73, 12 (2019).Article 

Google Scholar 
Ballard, S. M. & Lee, K. M. The acoustics of marine sediments. JASA 13, 18–18 (2017).
Google Scholar 
Smolker, R. & Pepper, J. W. Whistle convergence among allied male bottlenose dolphins (Delphinidae, Tursiops sp). Ethology 105, 595–617 (1999).Article 

Google Scholar 
Sayigh, L. S., Esch, H. C., Wells, R. S. & Janik, V. M. Facts about signature whistles of bottlenose dolphins (Tursiops truncatus). Anim. Behav. 74, 1631–1642 (2007).Article 

Google Scholar 
Jourdan J., et al. Distribution and abundance of bottlenose dolphin (Tursiops truncatus) along French Provençal coast. In Proceeding of the 30th European Cetacean Society Conference, Madeira (2016).Labach, H. et al. Distribution and abundance of common bottlenose dolphin (Tursiops truncatus) over the French Mediterranean continental shelf. Mar. Mam. Sci. 2021, 1–11 (2021).
Google Scholar 
Terranova, F. et al. Signature whistles of the demographic unit of bottlenose dolphins (Tursiops truncatus) inhabiting the Eastern Ligurian Sea: characterisation and comparison with the literature. Eur. Zool. J. 88, 771–781 (2021).Article 

Google Scholar  More

Gilbert, S. F., Sapp, J. & Tauber, A. I. A symbiotic view of life: we have never been individuals. Q. Rev. Biol. 87, 325–341 (2012).PubMed 
Article 

Google Scholar 
Bass, D., Stentiford, G. D., Wang, H.-C., Koskella, B. & Tyler, C. R. The pathobiome in animal and plant diseases. Trends Ecol. Evol. 34, 996–1008 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Husnik, F. & Keeling, P. J. The fate of obligate endosymbionts: reduction, integration, or extinction. Curr. Opin. Genet. Dev. 58-59, 1–8 (2019).CAS 
PubMed 
Article 

Google Scholar 
Berg, G. et al. Microbiome definition re-visited: old concepts and new challenges. Microbiome 8, 103 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Kwong, W. K. & Moran, N. A. Gut microbial communities of social bees. Nat. Rev. Microbiol. 14, 374–384 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hammer, T. J., Janzen, D. H., Hallwachs, W., Jaffe, S. P. & Fierer, N. Caterpillars lack a resident gut microbiome. Proc. Nat Acad. Sci. USA 114, 9641–9646 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Holt, C. C., van der Giezen, M., Daniels, C. L., Stentiford, G. D. & Bass, D. Spatial and temporal axes impact ecology of the gut microbiome in juvenile European lobster (Homarus gammarus). ISME J. 14, 531–543 (2020).PubMed 
Article 

Google Scholar 
Pollock, F. J. et al. Coral-associated bacteria demonstrate phylosymbiosis and cophylogeny. Nat. Commun. 9, 4921 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Thomas, T. et al. Diversity, structure and convergent evolution of the global sponge microbiome. Nat. Commun. 7, 11870 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Engelberts, J. P. et al. Characterization of a sponge microbiome using an integrative genome-centric approach. ISME J. 14, 1100–1110 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ley, R. E. et al. Evolution of mammals and their gut microbes. Science 320, 1647–1651 (2008).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mallot, E. K. & Amato, K. R. Host specificity of the gut microbiome. Nat. Rev. Microbiol. 19, 639–653 (2021).Article 
CAS 

Google Scholar 
Colston, T. J. & Jackson, C. R. Microbiome evolution along divergent branches of the vertebrate tree of life: what is known and unknown. Mol. Ecol. 25, 3776–3800 (2016).PubMed 
Article 

Google Scholar 
Levin, D. et al. Diversity and functional landscapes in the microbiota of animals in the wild. Science 372, eabb5352 (2021).CAS 
PubMed 
Article 

Google Scholar 
Nishida, A. H. & Ochman, H. Rates of gut microbiome divergence in mammals. Mol. Ecol. 27, 1884–1897 (2013).Article 

Google Scholar 
Brooks, A. W., Kohl, K. D., Brucker, R. M., van Opstal, E. J. & Bordenstein, S. R. Phylosymbiosis: relationships and functional effects of microbial communities across host evolutionary history. PLoS Biol. 14, e2000225 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Mazel, F. et al. Is host filtering the main driver of phylosymbiosis across the tree of life? mSystems 3, https://doi.org/10.1128/mSystems.00097-18 (2018).Lutz, H. L. et al. Ecology and host identity outweigh evolutionary history in shaping the bat microbiome. mBio 4, 6 (2019).
Google Scholar 
Grond, K. et al. No evidence for phylosymbiosis in Western chipmunk species. FEMS Microbiol. Ecol. 96, fiz182 (2020).CAS 
PubMed 
Article 

Google Scholar 
Song, S. J. et al. Comparative analyses of vertebrate gut microbiomes reveal convergence between birds and bats. mBio 11, 1 (2020).Article 

Google Scholar 
Trevelline, B. K., Sosa, J., Hartup, B. K. & Kohl, K. D. A bird’s-eye view of phylosymbiosis: weak signatures of phylosymbiosis among all 15 species of cranes. Proc. R. Soc. B 287, 20192988 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Muegge, B. D. et al. Diet drives convergence in gut microbiome functions across mammalian phylogeny and within humans. Science 332, 970–974 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Youngblut, N. D. et al. Host diet and evolutionary history explain different aspects of gut microbiome diversity among vertebrate clades. Nat. Commun. 10, 2200 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Amato, K. R. et al. Evolutionary trends in host physiology outweigh dietary niche in structuring primate gut microbiomes. ISME J. 13, 576–587 (2019).CAS 
PubMed 
Article 

Google Scholar 
Moeller, A. H. et al. Social behavior shapes the chimpanzee pan-microbiome. Sci. Adv. 2, e1500997 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Eckert, E. M., Anicic, N. & Fontaneto, D. Freshwater zooplankton microbiome composition is highly flexible and strongly influenced by the environment. Mol. Ecol. 30, 1545–1558 (2021).PubMed 
Article 

Google Scholar 
Yatsunenko, T. et al. Human gut microbiome viewed across age and geography. Nature 486, 222–228 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bik, H. M. Microbial metazoa are microbes too. mSystems 4, e00109–e00119 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Schuelke, T., Pereira, T. J., Hardy, S. M. & Bik, H. M. Nematode-associated microbial taxa do not correlate with host phylogeny, geographic region or feeding morphology in marine sediment habitats. Mol. Ecol. 27, 1930–1951 (2018).PubMed 
Article 

Google Scholar 
Guidetti, R. et al. Further insights in the Tardigrada microbiome: phylogenetic position and prevalence of infection of four new Alphaproteobacteria putative endosymbionts. Zool. J. Linn. Soc. 188, 925–937 (2020).Article 

Google Scholar 
Giere, O. Meiobenthology (Springer-Verlag, 2009).Laumer, C. E. et al. Revisiting metazoan phylogeny with genomic sampling of all phyla. Proc. R. Soc. B 286, 20190831 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hammer, T. J., Sanders, J. G. & Fierer, N. Not all animals need a microbiome. FEMS Microbiol. Lett. 366, fnz117 (2019).CAS 
PubMed 
Article 

Google Scholar 
Alejandre-Colomo, C. et al. Cultivable Winogradskyella species are genomically distinct from the sympatric abundant candidate species. ISME Commun. 1, 51 (2021).Article 

Google Scholar 
Husnik, F. et al. Bacterial and archaeal symbioses with protists. Curr. Biol. 31, R862–R877 (2021).CAS 
PubMed 
Article 

Google Scholar 
Salje, J. Cells within cells: Rickettsiales and the obligate intracellular bacterial lifestyle. Nat. Rev. Microbiol. 19, 375–390 (2021).CAS 
PubMed 
Article 

Google Scholar 
Neave, M. J., Apprill, A., Ferrier-Pagès, C. & Voolstra, C. R. Diversity and function of prevalent symbiotic marine bacteria in the genus Endozoicomonas. Appl. Microbiol. Biotechnol. 100, 8315–8324 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Weiland-Bräuer, N. et al. Composition of bacterial communities associated with Aurelia aurita changes with compartment, life stage, and population. Appl. Environ. Microbiol. 81, 6038–6052 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Bik, E. M. et al. Marine mammals harbor unique microbiotas shaped by and yet distinct from the sea. Nat. Commun. 7, 10516 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Burns, A. R. et al. Contribution of neutral processes to the assembly of gut microbial communities in the zebrafish over host development. ISME J. 10, 655–664 (2016).CAS 
PubMed 
Article 

Google Scholar 
McFall-Ngai, M. Adaptive immunity: care for the community. Nature 445, 153 (2007).CAS 
PubMed 
Article 

Google Scholar 
Ruehland, C. & Dubilier, N. Gamma- and epsilonproteobacterial ectosymbionts of a shallow-water marine worm are related to deep-sea hydrothermal vent ectosymbionts. Environ. Microbiol. 12, 2312–2326 (2010).CAS 
PubMed 

Google Scholar 
Gruber-Vodicka, H. R. et al. Two intracellular and cell-type specific bacterial symbionts in the placozoan Trichoplax H2. Nat. Microbiol. 4, 1465–1474 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Schockaert, E. R. in Methods for the Examination of Organismal Diversity in Soils and Sediments (ed. Hall, G. S.) 211–225 (CABI, 1996).Higgins, R. P. in Introduction to the Study of Meiofauna (eds. Higgins, R. P. and Thiel, H.) 328–331 (SIP, 1988).Schram, M. D. & Davison, P. G. Irwin Loops—a history and method of constructing homemade loops. Trans. Kans. Acad. Sci. 115, 35–40 (1903).Article 

Google Scholar 
Medlin, L., Elwood, H. J., Stickel, S. & Sogin, M. L. The characterization of enzymatically amplified eukaryotic 16S-like rRNA-coding regions. Gene 71, 491–499 (1988).CAS 
PubMed 
Article 

Google Scholar 
Bower, S. M. et al. Preferential PCR amplification of parasitic protistan small subunit rDNA from metazoan tissues. J. Eukaryot. Microbiol. 51, 325–332 (2004).CAS 
PubMed 
Article 

Google Scholar 
Comeau, A. M., Li, W. K. W., Tremblay, J.-E., Carmack, E. C. & Lovejoy, C. Arctic ocean microbial community structure before and after the 2007 record sea ice minimum. PLoS ONE 6, e27492 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zhang, R.-Y. et al. Design of targeted primers based on 16S rRNA sequences in meta-transcriptomic datasets and identification of a novel taxonomic group in the Asgard archaea. BMC Microbiol. 20, 25 (2020).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Lane, D. J. in Nucleic Acid Techniques in Bacterial Systematics (eds Stackebrandt, E. & Goodfellow, M) 115–175 (Wiley, 1991).Parada, A. E., Needham, D. M. & Fuhrman, J. A. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ. Microbiol. 18, 1403–1414 (2016).CAS 
PubMed 
Article 

Google Scholar 
Marcel, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10 (2011).
Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).Callahan, B. J. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naïve Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Davis, N. M., Proctor, D. M., Holmes, S. P., Relman, D. A. & Callahan, B. J. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome 6, 226 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).Love, M. I., Huber, W. & Anders, S. Moderate estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).Kurtz, Z. D. Sparse and compositionally robust inference of microbial ecological networks. PLoS Comput. Biol. 11, e1004226 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Csardi, G. & Nepusz, T. The igraph Software Package for Complex Network Research (InterJournal, 2006).Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).Kolde, R. pheatmap: pretty heatmaps. R package version 1.0.12 https://CRAN.R-project.org/package=pheatmap (2015).Lin, H. & Das Peddada, S. Analysis of composition of microbiomes with bias correction. Nat. Commun. 11, 3514 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Oksanen, J. vegan: Community Ecology Package. R package version 2.5.7 https://CRAN.R-project.org/package=vegan (2020).Rouse, G., Pleijel, F. & Tilic, E. Annelida (Oxford Univ. Press, 2022).Ahmed, M. & Holovachov, O. Twenty years after De Ley and Blaxter—How far did we progress in understanding the phylogeny of the phylum Nematoda? Animals 11, 3479 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Van Steenkiste, N. W. L., Herbert, E. R. & Leander, B. S. Species diversity in the marine microturbellarian Astrotorhynchus bifidus sensu lato (Platyhelminthes: Rhabdocoela) from the Northeast Pacific Ocean. Mol. Phylogenet. Evol. 120, 259–273 (2018). More