Philippa Kaur

Collias, N.E. & Collias, E.C. Nest Building and Bird Behavior. (Princeton University Press, 1984).Hansell, M.H. Bird nests and construction behaviour. (Cambridge University Press, 2000).Deeming, D.C. & Reynolds, S.J. Nests, eggs and incubation: New ideas about avian reproduction. (Oxford University Press, 2015).Pärssinen, V., Kalb, N., Vallon, M., Anthes, N. & Heubel, K. U. Male and female preferences for nest characteristics under paternal care. Ecol. Evol. 9, 7780–7791 (2019).PubMed 
PubMed Central 

Google Scholar 
Soler, J. J., Morales, J., Cuervo, J. J. & Moreno, J. Conspicuousness of passerine females is associated with the nest-building behaviour of males. Biol. J. Linn. Soc. 126, 824–835 (2019).
Google Scholar 
Tipton, H. C., Dreitz, V. J. & Doherty, P. F. Jr. Occupancy of Mountain Plover and Burrowing Owl in Colorado. J. Wildl. Manage. 72, 1001–1006 (2008).
Google Scholar 
Mukherjee, A., Kumara, H. N. & Bhupathy, S. Golden jackal’s underground shelters: Natal site selection, seasonal burrowing activity and pup rearing by a cathemeral canid. Mammal Res. 63, 325–339 (2018).
Google Scholar 
Berg, M. L., Beintema, N. H., Welbergen, J. A. & Komdeur, J. The functional significance of multiple nest building in the Australian Reed Warbler Acrocephalus australis. Ibis 148, 395–404 (2006).
Google Scholar 
Vergara, P., Gordo, O. & Aguirre, J. I. Nest size, nest building behaviour and breeding success in a species with nest reuse: the white stork Ciconia ciconia. Ann. Zool. Fennici 47, 184–194 (2010).
Google Scholar 
Hansell, M.H. Animal architecture. (Oxford University Press, 2005).Newton, I. Population ecology of raptors. Berkhamsted (T and AD Poyser, 1979).Ontiveros, D., Caro, J. & Pleguezuelos, J. M. Green plant material versus ectoparasites in nests of Bonelli’s Eagle. J. Zool. 274, 99–104 (2008).
Google Scholar 
Soler, J. J., Møller, A. P. & Soler, M. Nest building, sexual selection and parental investment. Evol. Ecol. 12, 427–441 (1998).
Google Scholar 
Moreno, J., Soler, M., Møller, A. P. & Linden, M. The function of stone carrying in the Black Wheatear, Oenanthe leucura. Anim. Behav. 47, 1297–1309 (1994).
Google Scholar 
Soler, J. J., Soler, M., Møller, A. P. & Martínez, J. G. Does the great spotted cuckoo choose magpie hosts according to their parenting ability?. Behav. Ecol. Sociobiol. 36, 201–206 (1995).
Google Scholar 
Soler, J. J., Cuervo, J. J., Møller, A. P. & de Lope, F. Nest building is a sexually selected behaviour in the barn swallow. Anim. Behav. 56, 1435–1442 (1998).CAS 
PubMed 

Google Scholar 
Canal, D., Mulero-Pázmány, M., Negro, J. J. & Sergio, F. Decoration increases the conspicuousness of raptor nests. PLoS ONE 11, e0157440 (2016).PubMed 
PubMed Central 

Google Scholar 
Biddle, L., Goodman, A. M. & Deeming, D. C. Construction patterns of birds’ nests provide insight into nest-building behaviours. PeerJ 5, e3010 (2017).PubMed 
PubMed Central 

Google Scholar 
Akresh, M. E., Ardia, D. R. & King, D. I. Effect of nest characteristics on thermal properties, clutch size, and reproductive performance for an open-cup nesting songbird. Avian Biol. Res. 10, 107–118 (2017).
Google Scholar 
Podofillini, S. et al. Home, dirty home: Effect of old nest material on nest-site selection and breeding performance in a cavity-nesting raptor. Curr. Zool. 64, 693–702 (2018).PubMed 
PubMed Central 

Google Scholar 
Ruiz-Castellano, C., Tomás, G., Ruiz-Rodríguez, M., Martín-Gálvez, D. & Soler, J. J. Nest material shapes eggs bacterial environment. PLoS ONE 11, e0148894 (2016).PubMed 
PubMed Central 

Google Scholar 
Tomás, G. et al. Interacting effects of aromatic plants and female age on nest-dwelling ectoparasites and blood-sucking flies in avian nests. Behav. Proc. 90, 246–253 (2012).
Google Scholar 
Suárez-Rodríguez, M. & García, C. M. An experimental demonstration that house finches add cigarette butts in response to ectoparasites. J. Avian Biol. 48, 1316–1321 (2017).
Google Scholar 
Mennerat, A. et al. Aromatic plants in nests of the blue tit Cyanistes caeruleus protect chicks from bacteria. Oecologia 161, 849–855 (2009).ADS 
PubMed 

Google Scholar 
Sanz, J. J. & García-Navas, V. Nest ornamentation in blue tits: is feather carrying ability a male status signal?. Behav. Ecol. 22, 240–247 (2011).
Google Scholar 
Östlund-Nilsson, S. & Holmlund, M. The artistic three-spined stickleback (Gasterosteus aculeatus). Behav. Ecol. Sociobiol. 53, 214–220 (2003).
Google Scholar 
Quader, S. What makes a good nest? Benefits of nest choice to female Baya Weavers (Ploceus philippinus). Auk 123, 475–486 (2006).
Google Scholar 
Møller, A. P. & Nielsen, J. T. Large increase in nest size linked to climate change: an indicator of life history, senescence and condition. Oecologia 179, 913–921 (2015).ADS 
PubMed 

Google Scholar 
De Neve, L., Soler, J. J., Soler, M. & Pérez-Contreras, T. Nest size predicts the effect of food supplementation to magpie nestlings on their immunocompetence: An experimental test of nest size indicating parental ability. Behav. Ecol. 15, 1031–1036 (2004).
Google Scholar 
Szentirmai, I., Komdeur, J. & Székely, T. What makes a nest-building male successful? Male behavior and female care in penduline tits. Behav. Ecol. 16, 994–1000 (2005).
Google Scholar 
Tomás, G. et al. Nest size and aromatic plants in the nest as sexually selected female traits in blue tits. Behav. Ecol. 24, 926–934 (2013).
Google Scholar 
Jelínek, V., Požgayová, M., Honza, M. & Procházka, P. Nest as an extended phenotype signal of female quality in the great reed warbler. J. Avian Biol. 47, 428–437 (2016).
Google Scholar 
Muth, F. & Healy, S. D. The role of adult experience in nest building in the zebrafinch, Taeniopygia guttata. Anim. Behav. 82, 185–189 (2011).
Google Scholar 
Wysocki, D. et al. Factors affecting nest size in a population of Blackbirds Turdus merula. Bird Study 62, 208–216 (2015).
Google Scholar 
Moreno, J. Avian nests and nest-building as signals. Avian Biol. Res. 5, 238–251 (2012).
Google Scholar 
Bailey, I. E., Morgan, K. V., Bertin, M., Meddle, S. L. & Healy, S. D. Physical cognition: Birds learn the structural efficacy of nest material. Proc. R. Soc. B 281, 20133225 (2014).PubMed 
PubMed Central 

Google Scholar 
Camacho-Alpízar, A., Eckersley, T., Lambert, C. T., Balasubramanian, G. & Guillette, L. M. If it ain’t broke don’t fix it: Breeding success affects nest-building decisions. Behav. Proc. 184, 104336 (2021).
Google Scholar 
Madden, J. R. Bower decorations are good predictors of mating success in the spotted bowerbird. Behav. Ecol. Sociobiol. 53, 269–277 (2003).
Google Scholar 
Mainwaring, M. C., Nagy, J. & Hauber, M. E. Sex-specific contributions to nest building in birds. Behav. Ecol. https://doi.org/10.1093/beheco/arab035 (2021).Article 

Google Scholar 
Witte, K. The differential-allocation hypothesis: Does the evidence support it?. Evolution 49, 1289–1290 (1995).PubMed 

Google Scholar 
Wright, J. & Cuthill, I. Monogamy in the European starling. Behaviour 120, 262–285 (1992).
Google Scholar 
Burley, N. Sexual selection for aesthetic traits in species with biparental care. Am. Nat. 127, 415–445 (1986).ADS 

Google Scholar 
Mainwaring, M. C., Hartley, I. R., Lambrechts, M. M. & Deeming, D. C. The design and function of birds’ nests. Ecol. Evol. 4, 3909–3928 (2014).PubMed 
PubMed Central 

Google Scholar 
Sergio, F. et al. Raptor nest decorations are a reliable threat against conspecifics. Science 331, 327–330 (2011).ADS 
CAS 
PubMed 

Google Scholar 
Heinrich, B. Why does a hawk build with green nesting material?. Northeast. Nat. 20, 209–218 (2013).
Google Scholar 
Mingju, E. et al. Old nest material functions as an informative cue in making nest-site selection decisions in the European Kestrel (Falco tinnunculus). Avian Res. 10, 43 (2019).
Google Scholar 
Martínez-Abraín, A. & Jiménez, J. Stick supply to nests by cliff-nesting raptors as an evolutionary load of past tree-nesting. IEE 12, 22–25. https://doi.org/10.24908/iee.2019.12.3.n (2019).Article 

Google Scholar 
Martínez, J. E. et al. Breeding behaviour and time-activity budgets of Bonelli’s Eagles Aquila fasciata: Marked sexual differences in parental activities. Bird Study 47, 35–44 (2020).
Google Scholar 
Cramp, S. & Simmons, K.E.L. Handbook of the Birds of the western Palearctic. Vol. 2. (Oxford University Press, 1980).Paillisson, J. M. & Chambon, R. Variation in male-built nest volume with nesting-support quality, colony, and egg production in whiskered terns. Ecol. Evol. 11, 15585–15600 (2021).PubMed 
PubMed Central 

Google Scholar 
Álvarez, E. & Barba, E. Nest quality in relation to adult bird condition and its impact on reproduction in Great Tits Parus major. Acta Ornithol. 43, 3–9 (2008).
Google Scholar 
Ferguson-Lees, J. & Christie, D. Raptors of the world. (Christopher Helm, 2001).Ontiveros, D. Águila perdicera – Aquila fasciata. In Enciclopedia Virtual de los Vertebrados Españoles. (eds. Salvador, A. & Morales, M.B.) Museo Nacional de Ciencias Naturales, Madrid; http://www.vertebradosibericos.org/ (accessed 13 September 2021) (2016).Del Hoyo, J., Elliott, A. & Sargatal, J. Handbook of the birds of the world, vol. 2. New world vultures to guineafowl. (Lynx Edicions, 1994).Ontiveros, D., Caro, J. & Pleguezuelos, J. M. Possible functions of alternative nests in raptors: the case of Bonelli’s Eagle. J. Ornithol. 149, 253–259 (2008).
Google Scholar 
Del Moral, J.C. & Molina, B. El águila perdicera en España, población reproductora en 2018 y método de censo. (SEO/BirdLife, 2018).BirdLife International. Aquila fasciata (amended version of 2016 assessment). The IUCN Red List of Threatened Species 2019. https://doi.org/10.2305/IUCN.UK.2019-3.RLTS.T22696076A155464015.en. Downloaded on 26 June 2021 (2019).Balbontín, J. & Ferrer, M. Condition of large brood in Bonelli’s Eagle Hieraaetus fasciatus. Bird Study 52, 37–41 (2005).
Google Scholar 
Martínez, J. E. et al. Copulatory behaviour in the Bonelli’s Eagle (Aquila fasciata): assessing the paternity assurance hypothesis. PLoS ONE 14, e0217175 (2019).PubMed 
PubMed Central 

Google Scholar 
Margalida, A. & Bertran, J. Nest-building behaviour of the Bearded Vulture Gypaetus barbatus. Ardea 88, 259–264 (2000).
Google Scholar 
Krüger, O. Dissecting common buzzard lifespan and lifetime reproductive success: the relative importance of food, competition, weather, habitat and individual attributes. Oecologia 133, 474–482 (2002).ADS 
PubMed 

Google Scholar 
Morrison, T. A., Yoshizaki, J., Nichols, J. D. & Bolger, D. T. Estimating survival in photographic capture–recapture studies: overcoming misidentification error. Methods Ecol. Evol. 2, 454–463 (2011).
Google Scholar 
Jiménez-Franco, M. V., Martínez, J. E., Pagán, I. & Calvo, J. F. Factors determining territory fidelity in a migratory forest raptor, the Booted Eagle Hieraaetus pennatus. J. Ornithol. 154, 311–318 (2013).
Google Scholar 
Sreekar, R. et al. Photographic capture-recapture sampling for assessing populations of the Indian Gliding Lizard Draco dussumieri. PLoS ONE 8, e55935 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Goswami, V. R. et al. Towards a reliable assessment of Asian elephant population parameters: The application of photographic spatial capture–recapture sampling in a priority floodplain ecosystem. Sci. Rep. 9, 8578 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
Méndez, D., Marsden, S. & Lloyd, H. Assessing population size and structure for Andean Condor Vultur gryphus in Bolivia using a photographic ‘capture-recapture’ method. Ibis 161, 867–877 (2019).
Google Scholar 
Zuberogoitia, J., Martínez, J. E. & Zabala, J. Individual recognition of territorial peregrine falcons Falco peregrinus: A key for long-term monitoring programmes. Munibe 61, 117–127 (2013).
Google Scholar 
Gil-Sánchez, J. M., Bautista, J., Godinho, R. & Moleón, M. Detection of individual replacements in a long-lived bird species, the Bonelli’s Eagle (Aquila fasciata), using three noninvasive methods. J. Raptor Res. https://doi.org/10.3356/JRR-20-53 (2021).Article 

Google Scholar 
García, V., Moreno-Opo, R. & Tintó, A. Sex differentiation of Bonelli’s Eagle Aquila fasciata in western Europe using morphometrics and plumage colour patterns. Ardeola 60, 261–277 (2013).
Google Scholar 
Real, J., Mañosa, S. & Codina, J. Post-nestling dependence period in the Bonelli’s Eagle Hieraaetus fasciatus. Ornis Fenn. 75, 129–137 (1998).
Google Scholar 
Mínguez, E., Angulo, E. & Siebering, V. Factors influencing length of the post-fledging period and timing of dispersal in Bonelli’s Eagle (Hieraaetus fasciatus) in southwestern Spain. J. Raptor Res. 35, 228–234 (2001).
Google Scholar 
Gil-Sánchez, J. M., Moleón, M., Otero, M. & Bautista, J. A nine-year study of successful breeding in a Bonelli’s eagle population in southeast Spain: A basis for conservation. Biol. Conserv. 118, 685–694 (2004).
Google Scholar 
Resano-Mayor, J. et al. Multi-scale effects of nestling diet on breeding performance in a terrestrial top predator inferred from stable isotope analysis. PLoS ONE 9, e95320 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Zuberogoitia, J., Martínez, J. E., Larrea, M. & Zabala, M. Parental investment of male Peregrine Falcons during incubation: Influence of experience and weather. J. Ornithol. 159, 275–282 (2018).
Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing. Available at: http://www.R-project.org/ (accessed 20 March 2021) (2021).Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Google Scholar 
Fernández, C. Nest material supplies in the Marsh Harrier Circus aeruginosus: Sexual roles, daily and seasonal activity patterns and rainfall influence. Ardea 80, 281–284 (1992).
Google Scholar 
Margalida, A., González, L. M., Sánchez, R., Oria, J. & Prada, L. Parental behaviour of Spanish Imperial Eagles Aquila adalberti: sexual differences in a moderately dimorphic raptor. Bird Study 54, 112–119 (2007).
Google Scholar 
López-López, P., Perona, A. M., Egea-Casas, O., Morant, J. & Urios, V. Tri-axial accelerometry shows differences in energy expenditure and parental effort throughout the breeding season in long-lived raptors. Curr. Zool. https://doi.org/10.1093/cz/zoab010 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Morant, J., López-López, P. & Zuberogoitia, I. Parental investment asymmetries of a globally endangered scavenger: Unravelling the role of gender, weather conditions and stage of the nesting cycle. Bird Study 66, 329–341 (2019).
Google Scholar 
Margalida, A. & Bertran, J. Breeding biology of the Bearded Vulture Gypaetus barbatus: Minimal sexual differences in parental activities. Ibis 142, 225–234 (2000).
Google Scholar 
Wimberger, P. H. The use of green plant material in bird nests to avoid ectoparasites. Auk 101, 615–618 (1984).
Google Scholar 
Dubiec, A., Gózdz, I. & Mazgagski, T. D. Green plant material in avian nests. Avian Biol. Res. 6, 133–146 (2013).
Google Scholar 
Jagiello, Z. A., Dylewski, L., Winiarska, D., Zolnierowicz, K. M. & Tobolka, M. Factors determining the occurrence of anthropogenic materials in nests of the white stork Ciconia ciconia. Environ. Sci. Pollut. Res. 25, 14726–14733 (2018).
Google Scholar 
Fargallo, J. A., de León, A. & Potti, J. Nest maintenance effort and health status in chinstrap penguins, Pygoscelis antarctica: the functional significance of stone provisioning behaviour. Behav. Ecol. Sociobiol. 50, 141–150 (2001).
Google Scholar  More

Foelix, R. F. Biology of Spiders (Oxford University Press, 2011).
Google Scholar 
World Spider Catalog. World Spider Catalog, Version 23.0. Natural History Museum Bern, online at http://wsc.nmbe.ch (2022).Nyffeler, M. & Sunderland, K. D. Composition, abundance and pest control potential of spider communities in agroecosystems: A comparison of European and US studies. Agric. Ecosyst. Environ. 95, 579–612 (2003).
Google Scholar 
Oldrati, V. et al. Peptidomic and transcriptomic profiling of four distinct spider venoms. PLoS ONE 12, e0172966 (2017).PubMed 
PubMed Central 

Google Scholar 
Herzig, V. et al. Animal toxins—Nature’s evolutionary-refined toolkit for basic research and drug discovery. Biochem. Pharmacol. 181, 114096 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Vollrath, F. & Knight, D. P. Liquid crystalline spinning of spider silk. Nature 410, 541–548 (2001).ADS 
CAS 
PubMed 

Google Scholar 
Moradmand, M. & Jäger, P. Taxonomic revision of the huntsman spider genus Eusparassus Simon, 1903 (Araneae: Sparassidae) in Eurasia. J. Nat. Hist. 46, 2439–2496 (2012).
Google Scholar 
Moradmand, M. The stone huntsman spider genus Eusparassus (Araneae: Sparassidae): Systematics and zoogeography with revision of the African and Arabian species. Zootaxa 3675, 1–108 (2013).PubMed 

Google Scholar 
Levy, G. The family of huntsman spiders in Israel with annotations on species of the Middle East (Araneae: Sparassidae). J. Zool. 217, 127–176 (1989).
Google Scholar 
Dunlop, J. A. et al. Computed tomography recovers data from historical amber: An example from huntsman spiders. Naturwissenschaften 98, 519–527 (2011).ADS 
CAS 
PubMed 

Google Scholar 
Moradmand, M., Schönhofer, A. L. & Jäger, P. Molecular phylogeny of the spider family Sparassidae with focus on the genus Eusparassus and notes on the RTA-clade and ‘Laterigradae’. Mol. Phylogenet. Evol. 74, 48–65 (2014).CAS 
PubMed 

Google Scholar 
Hutchinson, G. E. Cold spring harbor symposium on quantitative biology. Concl. Remarks 22, 415–427 (1957).
Google Scholar 
Pearman, P. B., Guisan, A., Broennimann, O. & Randin, C. F. Niche dynamics in space and time. Trends Ecol. Evol. 23, 149–158 (2008).PubMed 

Google Scholar 
Wake, D. B., Hadly, E. A. & Ackerlya, D. D. Biogeography, changing climates, and niche evolution. Proc. Natl. Acad. Sci. U. S. A. 106, 19631–19636 (2009).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Smith, A. B., Godsoe, W., Rodríguez-Sánchez, F., Wang, H. H. & Warren, D. Niche estimation above and below the species level. Trends Ecol. Evol. 34, 260–273 (2019).PubMed 

Google Scholar 
Peñalver-Alcázar, M., Jiménez-Valverde, A. & Aragón, P. Niche differentiation between deeply divergent phylogenetic lineages of an endemic newt: implications for Species Distribution Models. Zoology 144, 125852 (2021).PubMed 

Google Scholar 
Di Pasquale, G. et al. Coastal Pine-Oak Glacial Refugia in the mediterranean basin: A biogeographic approach based on charcoal analysis and spatial modelling. Forests 11, 673 (2020).
Google Scholar 
Du, Z., He, Y., Wang, H., Wang, C. & Duan, Y. Potential geographical distribution and habitat shift of the genus Ammopiptanthus in China under current and future climate change based on the MaxEnt model. J. Arid Environ. 184, 104328 (2021).ADS 

Google Scholar 
Kafash, A. et al. The Gray Toad-headed Agama, Phrynocephalus scutellatus, on the Iranian Plateau: The degree of niche overlap depends on the phylogenetic distance. Zool. Middle East 64, 47–54 (2018).
Google Scholar 
Namyatova, A. A. Climatic niche comparison between closely related trans-Palearctic species of the genus Orthocephalus (Insecta: Heteroptera: Miridae: Orthotylinae). PeerJ 8, e10517 (2020).PubMed 
PubMed Central 

Google Scholar 
Zhang, Z. et al. Lineage-level distribution models lead to more realistic climate change predictions for a threatened crayfish. Divers. Distrib. 27, 684–695 (2021).
Google Scholar 
Mammola, S. & Leroy, B. Applying species distribution models to caves and other subterranean habitats. Ecography (Cop.) 41, 1194–1208 (2018).
Google Scholar 
Mammola, S. et al. Challenges and opportunities of species distribution modelling of terrestrial arthropod predators. Divers. Distrib. 00, 1–19 (2021).
Google Scholar 
Saupe, E. E., Papes, M., Selden, P. A. & Vetter, R. S. Tracking a medically important spider: Climate change, ecological niche modeling, and the brown recluse (Loxosceles reclusa). PLoS ONE 6, 2 (2011).
Google Scholar 
Planas, E., Saupe, E. E., Lima-Ribeiro, M. S., Peterson, A. T. & Ribera, C. Ecological niche and phylogeography elucidate complex biogeographic patterns in Loxosceles rufescens (Araneae, Sicariidae) in the Mediterranean Basin. BMC Evol. Biol. https://doi.org/10.1186/s12862-014-0195-y (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Taucare-Ríos, A., Nentwig, W., Bizama, G. & Bustamante, R. O. Matching global and regional distribution models of the recluse spider Loxosceles rufescens: to what extent do these reflect niche conservatism?. Med. Vet. Entomol. 32, 490–496 (2018).PubMed 

Google Scholar 
Wang, Y., Casajus, N., Buddle, C., Berteaux, D. & Larrivée, M. Predicting the distribution of poorly-documented species, Northern black widow (Latrodectus variolus) and Black purse-web spider (Sphodros Niger), using museum specimens and citizen science data. PLoS ONE 13, e0201094 (2018).PubMed 
PubMed Central 

Google Scholar 
Jiménez-Valverde, A., Decae, A. E. & Arnedo, M. A. Environmental suitability of new reported localities of the funnelweb spider Macrothele calpeiana: An assessment using potential distribution modelling with presence-only techniques. J. Biogeogr. 38, 1213–1223 (2011).
Google Scholar 
Monsimet, J., Devineau, O., Pétillon, J. & Lafage, D. Explicit integration of dispersal-related metrics improves predictions of SDM in predatory arthropods. Sci. Rep. https://doi.org/10.1038/s41598-020-73262-2 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Salgado-Roa, F. C., Gamez, A., Sanchez-Herrera, M., Pardo-Diaz, C. & Salazar, C. Divergence promoted by the northern Andes in the giant fishing spider Ancylometes bogotensis (Araneae: Ctenidae). Biol. J. Linn. Soc. 132, 495–508 (2021).
Google Scholar 
Mammola, S., Goodacre, S. L. & Isaia, M. Climate change may drive cave spiders to extinction. Ecography (Cop.) 41, 233–243 (2018).
Google Scholar 
Ferretti, N. E., Soresi, D. S., González, A. & Arnedo, M. An integrative approach unveils speciation within the threatened spider Calathotarsus simoni (Araneae: Mygalomorphae: Migidae). Syst. Biodivers. 17, 439–457 (2019).
Google Scholar 
Pavlek, M. & Mammola, S. Niche-based processes explaining the distributions of closely related subterranean spiders. J. Biogeogr. 48, 118–133 (2021).
Google Scholar 
Bosso, L. et al. Nature protection areas of Europe are insufficient to preserve the threatened beetle Rosalia alpina (Coleoptera: Cerambycidae): evidence from species distribution models and conservation gap analysis. Ecol. Entomol. 43, 192–203 (2018).
Google Scholar 
Kafash, A. et al. Climate change produces winners and losers: Differential responses of amphibians in mountain forests of the Near East. Glob. Ecol. Conserv. 16, e00471 (2018).
Google Scholar 
Vásquez-Aguilar, A. A., Ornelas, J. F., Rodríguez-Gómez, F. & Cristina MacSwiney, G. Modeling future potential distribution of buff-bellied hummingbird (Amazilia yucatanensis) under climate change: species vs subspecies. Trop. Conserv. Sci. 25, 2 (2021).
Google Scholar 
Rosauer, D. F., Catullo, R. A., VanDerWal, J., Moussalli, A. & Moritz, C. Lineage range estimation method reveals fine-scale endemism linked to pleistocene stability in Australian rainforest herpetofauna. PLoS ONE 10, e0126274 (2015).PubMed 
PubMed Central 

Google Scholar 
Eyres, A., Eronen, J. T., Hagen, O., Böhning-Gaese, K. & Fritz, S. A. Climatic effects on niche evolution in a passerine bird clade depend on paleoclimate reconstruction method. Evolution 75, 1046–1060 (2021).PubMed 

Google Scholar 
Loyola, R. D., Lemes, P., Brum, F. T., Provete, D. B. & Duarte, L. D. S. Clade-specific consequences of climate change to amphibians in Atlantic Forest protected areas. Ecography (Cop.) 37, 65–72 (2014).
Google Scholar 
Muñoz, M. M. & Bodensteiner, B. L. Janzen’s hypothesis meets the bogert effect: Connecting climate variation, thermoregulatory behavior, and rates of physiological evolution. Integr. Org. Biol. 1, 1–12 (2019).
Google Scholar 
Entling, W., Schmidt, M. H., Bacher, S., Brandl, R. & Nentwig, W. Niche properties of Central European spiders: Shading, moisture and the evolution of the habitat niche. Glob. Ecol. Biogeogr. 16, 440–448 (2007).
Google Scholar 
Lafage, D., Maugenest, S., Bouzillé, J. B. & Pétillon, J. Disentangling the influence of local and landscape factors on alpha and beta diversities: opposite response of plants and ground-dwelling arthropods in wet meadows. Ecol. Res. 30, 1025–1035 (2015).
Google Scholar 
Peterson, A. T., Soberón, J. & Sánchez-Cordero, V. Conservatism of ecological niches in evolutionary time. Science 285, 1265–1267 (1999).CAS 
PubMed 

Google Scholar 
Wellenreuther, M., Larson, K. W. & Svensson, E. I. Climatic niche divergence or conservatism? Environmental niches and range limits in ecologically similar damselflies. Ecology 93, 1353–1366 (2012).PubMed 

Google Scholar 
Nosil, P. & Sandoval, C. P. Ecological niche dimensionality and the evolutionary diversification of stick insects. PLoS ONE 3, e1907 (2008).ADS 
PubMed 
PubMed Central 

Google Scholar 
McCormack, J. E., Zellmer, A. J. & Knowles, L. L. Does niche divergence accompany allopatric divergence in Aphelocoma jays as predicted under ecological speciation?: Insights from tests with niche models. Evolution 64, 1231–1244 (2010).PubMed 

Google Scholar 
Goudarzi, F., Hemami, M. R., Malekian, M. & Fakheran-Esfahani, S. Ecological Characterization of the breeding habitat of Luristan newt (Neurergus kaiseri) at local scale. J. Nat. Environ. 72, 113–127 (2019).
Google Scholar 
Chase, J. M. & Leibold, M. Ecological Niches: Linking Classical and Contemporary Approaches (University of Chicago Press, 2003).
Google Scholar 
Bonte, D., Vandenbroecke, N., Lens, L. & Maelfait, J. P. Low propensity for aerial dispersal in specialist spiders from fragmented landscapes. Proc. R. Soc. B Biol. Sci. 270, 1601–1607 (2003).
Google Scholar 
GBIF.org. GBIF Occurrence Download. https://doi.org/10.15468/dl.2tc2ja (2021) doi:https://doi.org/10.15468/dl.2tc2ja.Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).
Google Scholar 
Jarvis, A., Reuter, H. I., Nelson, A. & Guevara, E. Hole-Filled SRTM for the Globe Version 4. Available from the CGIAR-CSI SRTM 90m Database. (2008) doi:https ://srtm.csi.cgiar .org.Hijmans, R. J. raster: Geographic Data Analysis and Modeling. R package version 3, 3–7 (2020).
Google Scholar 
Guisan, A., Thuiller, W. & Zimmermann, N. E. Habitat suitability and distribution models: With applications in R. (2017). doi:10.1017/ 9781139028271.Quinn, G. P. & Keough, M. J. Experimental Design and Data Analysis for Biologists (Cambridge University Press, 2002).
Google Scholar 
Naimi, B. Uncertainty Analysis for Species Distribution Models. R package version (2015).Phillips, S. J., Dudík, M. & Schapire, R. E. Maxent software for modeling species niches and distributions (Version 3.4.1). Available from url: http://biodiversityinformatics.amnh.org/open_source/maxent/. Accessed on 2022–2–12.Nǎpǎruş, M. & Kuntner, M. A GIS model predicting potential distributions of a lineage: a test case on hermit spiders (Nephilidae: Nephilengys). PLoS ONE 7, e30047 (2012).ADS 
PubMed 
PubMed Central 

Google Scholar 
Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259 (2006).
Google Scholar 
Merow, C., Smith, M. J. & Silander, J. A. A practical guide to MaxEnt for modeling species’ distributions: What it does, and why inputs and settings matter. Ecography (Cop.) 36, 1058–1069 (2013).
Google Scholar 
Swets, J. A. Measuring the accuracy of diagnostic systems. Science 240, 1285–1293 (1988).ADS 
MathSciNet 
CAS 
PubMed 
MATH 

Google Scholar 
Schoener, T. W. The anolis lizards of Bimini: Resource partitioning in a complex fauna. Ecology 49, 704–726 (1968).
Google Scholar 
Warren, D. L., Glor, R. E. & Turelli, M. Environmental niche equivalency versus conservatism: Quantitative approaches to niche evolution. Evolution 62, 2868–2883 (2008).PubMed 

Google Scholar 
Warren, D. L. et al. ENMTools 1.0: an R package for comparative ecological biogeography. Ecography 44, 504–511 (2021).
Google Scholar 
Liu, C., Berry, P. M., Dawson, T. P. & Pearson, R. G. Selecting thresholds of occurrence in the prediction of species distributions. Ecography (Cop.) 28, 385–393 (2005).
Google Scholar 
Vale, C. G., Tarroso, P. & Brito, J. C. Predicting species distribution at range margins: Testing the effects of study area extent, resolution and threshold selection in the Sahara-Sahel transition zone. Divers. Distrib. 20, 20–33 (2014).
Google Scholar  More

General characteristics of study soilsTable 2 presents the descriptive statistics regarding the soil characteristics. Significant changes were observed in the distribution of sand (110–850 g kg−1), silt (50–530 g kg−1), clay (100–610 g kg−1), and soil textural class (7 texture classes) showing the diversity of natural and human processes involved in the formation and development of these soils28. Almost all soil samples were alkaline (with reaction at a range of 7.4–8.1) and calcareous (with CCE at a range of 5.5–35%). The EC of some soils was  > 4 dS/m (about 7% of the soil samples), indicating the partial salinity of the study soils. The organic carbon and total N contents of the soils were, on average, 2% (0.8–3.1%) and 0.28% (0.05–0.51%), respectively, placing them within the range of the moderate class. Likewise, the mean CEC of the soil, which is an effective indicator of soil fertility and quality, was in the moderate class of 12–25 cmol kg−129. The CEC was found to be highly correlated with clay (r = 0.76 P  Pb (58 mg kg−1)  > Ni (55.4 mg kg−1)  > Cu (38.8 mg kg−1)  > Cd (0.88 mg kg−1). In most soil samples, these ranges are comparable with data reported for other urban soils around the world—e.g. Ref.30 in Poland, Ref.31 in China, and Ref.32 in Greece. The values of Cd, Cu, and Zn were below their acceptable ranges as per the international standards4 in all soil samples. Nonetheless, the Pb and Ni contents were higher than their acceptable ranges in 13.1% and 17.4% of the samples, respectively. Furthermore, the concentrations of the five elements were higher than their background values in all urban soil samples. This difference was considerable for Cd, Pb, and Ni. The heavy metals had CV in the order of Cd (53%)  > Pb (51%)  > Ni (46%)  > Zn (21%)  > Cu (18%). This CV variation implies great variations in Cd, Pb, and Ni, which is linked to anthropogenic activities33. The background values of the metals, estimated by the median absolute deviation method10,14, were 52.3, 18.7, 0.45, 29.1, and 30.8 mg kg−1 for Zn, Cu, Cd, Pb, and Ni, respectively.We compared the concentrations of the heavy metals between urban and non-urban soils and found significant increases in the concentration of the metals in most soil types (Fig. 2). The urban soils had 17–36%, 14–21%, 41–70%, 43–69%, and 13–24% higher Zn, Cu, Cd, Pb, and Ni contents than the non-urban soils. The effluent and waste entry from multiple food processing and storage units, dying plants, metal plating facilities, and plastic production in close proximity of the study area is believed to be the reason for the high concentration of these trace elements. Research in various parts of the world, e.g., Ref.34 in India, Ref.35 in Brazil, and Ref.36 in China, has documented that the facilities have introduced significant quantities of heavy metals to soils. However, traffic and agrochemicals also play a key role in the accumulation of heavy metals in this region10.Figure 2The comparison of the mean values of Zn (a), Cu (b), Cd (c), Pb (d), and Ni (e) between urban and non-urban soils in different soil types. Different letters indicate significant differences in metal content within each soil type at P  Ni  > Cu. These findings are comparable to the results reported by37 and12. The highest EF for all five elements was observed in the Fluvisols soil type, reflecting that this soil type had been exposed to element pollution induced by urban activities to a greater extent than the other soil types. In a study on the pollution potential of four soil types in Central Greece, Ref.38 reported different ranges of element pollution across different soil types.Figure 3The comparison of the mean enrichment factor of Zn (a), Cu (b), Cd (c), Pb (d), and Ni (e) between urban and non-urban soils in different soil types. Different letters indicate significant differences in enrichment factor within each soil type at P  Pb (1.89)  > Ni (1.86)  > Cu (1.73)  > Zn (1.51). Mean PI for non-urban soils followed the order Cd (1.5)  > Zn (1.4)  > Cu (1.33)  > Pb (1.31)  > Ni (1.29). Nearly 7% and 16% of the urban soils showed moderate pollution (MP, PI = 2–3) and high pollution classes (HP, PI  > 3) of PI for Cd and 39% and 4% showed the MP and HP class of PI for Pb, respectively. However, the PI class was low pollution (PI = 1–2) for all soil samples and soil types in the non-urban soils. The results on the pollution index indicate a widespread intensification of soil pollution in urban soils across all studied heavy metals.Table 3 The level and terminology of PI and Ei of the analyzed heavy metals in urban and non-urban soils.Full size tableEcological risk, Ei was similarly found to be significantly higher in the urban soils than in the non-urban soils, even though the concentration of all elements except Cd fell within the low-risk class (Ei ≤ 40) in both urban and non-urban soils (Table 3). The mean Ei for Cd was 58.7 (moderate-risk class) and 39.2 (low-risk class) in the urban and non-urban soils, respectively. This means that urban activities have enhanced the ecological risk class of Cd by one grade. Overall, Cd had the highest EF, PI, and Ei among all heavy metals and in all soil samples, indicating a greater risk potential by Cd than Zn, Cu, Pb, and Ni across the water-soil–plant-human domain. Elevated Cd pollution by anthropogenic activities has been widely reported in the literature10,12,39. Cadmium as a Group 1 carcinogen element40 can accumulate in plant tissue without exhibiting visual symptoms. Therefore, Cd generally transfers from soil to the food chain covertly. Cadmium pollution can also influence soil quality and reduce crop yields and grain quality3.Similar to EF, PI, and Ei, the mean ER was significantly elevated in all urban soil types than the non-urban soils (Fig. 4). Among different soil types, the ER magnitude was in the order of Fluvisols (66.6%)  > Regosols (66.1%)  > Cambisols (59.8%)  > Calcisols (47%). These results indicate that Fluvisols carry a higher ecological risk potential for heavy metal accumulations than other soil types. In the study region, Fluvisols due to higher fertility and productivity are subject to more intense and extensive agronomic operations than other soil types13. Heavy application of agrochemicals (e.g., pesticides, herbicides, insecticides, and chemical fertilizers), accelerate the heavy metal input to the Fluvisols. Widespread application of nitrogen fertilizers and subsequent reduction in average soil pH markedly increases the solubility of certain heavy metals (e.g., Zn, Cu, Cd) which can be another factor increasing the ecological risk of heavy metal contamination in Fluvisols41. In addition, these Fluvisols are located on the margin of open urban wastewater channels, which are sometimes used for irrigation. A combination of mentioned processes can be implicated for higher ER of Fluvisols than that of other soil types as for BF, PI, and Ei.Figure 4The comparison of the mean ecological risk of selected heavy metals between urban and non-urban soils in different soil types. Different letters indicate significant differences in ecological risk within each soil type at P  Cu  > Ni  > Cd  > Pb in the roots, partially differing from that of the grain—Zn  > Cu  > Pb  > Ni  > Cd. Heavy metals concentrations observed in the corn roots and grains are almost comparable with those reported by42 in China and43 in Peru.Table 4 Summary statistical attributes of the concentration of heavy metals in corn root (R) and grain (G) along with their BCF and TF.Full size tableThe accumulation of heavy metals in the edible parts of corn is of higher importance. In the present study, the concentrations of these metals were lower than the acceptable level in the corn grains based on international references44. So, the consumption of corns grown in the regions should not threaten human and animal health in the short term, but caution should be exercised in their long-term consumption because some of these elements, especially Cd and Pb, which have long decomposition half-lives, gradually accumulate in body organs, especially in kidneys and livers45. Besides, the ratio of Zn, Cu, Cd, Pb, and Ni of the corn grain to their acceptable standard concentration, known as the pollution index of crop heavy metals, Ref.12 was lower than 0.7 for most corn samples, indicating the unpolluted risk class.The mean concentrations of Cd, Pb, and Ni were 5, 3.1, and 9.2 times as great in the corn roots as in their grains. This observation exhibits a notable phytoremediatory function of corn roots through restriction of radial translocation of heavy metals to the xylems and eventually into the grains. A similar trend of heavy metal accumulation in different plant organs has been reported in previous observations46,47. Based on Kabata-Pendias4 and Adriano22, plant cells can use the defensive tools of the roots to cope with heavy metals, especially Cd and Pb—highly toxic metals to plant cytosols. Accordingly, plant cells can fix these elements in the root system by such approaches as precipitating on cell walls, storing in vacuoles, and/or chelating by phytochelatins, thereby alleviating their toxic effects and inhibiting their translocation to plant shoots. For Zn, Cu, and Cd metals, a significant correlation was observed between their concentration in corn roots and grains. But, a less significant correlation (P  Cu (0.17)  > Zn (0.12)  > Ni (0.02)  > Pb (0.01). This implies that Cd, and to a smaller extent Cu is taken up by corn roots from the soil more readily, but Pb and Ni are less absorbable. These results are consistent with the reports of48 and46. The greater value of BCF-Cd may be related to a combination of the specific factors e,g., Cd concentration and chemistry, as well as soil characteristics (e.g., soil texture, pH, and calcium carbonate content)4. As was already discussed, the examined soils were characterized by high alkaline (pH = 7.4–8.1) and calcareous properties (CCE = 5.5–35%) with a high concentration of Soluble salts (EC = 0.7–6.6 dS m−1). These characteristics can result in the formation of complex Cd ions, especially CdOH+, CdCl20, CdCl+, CdSO40, and CdHCO3+4,22. These ions are plant-available, resulting in a further increase in Cd BCF. Regarding Ni and Pb, the alkaline and calcareous properties of the soils may have motivated insoluble compounds such as NiHCO3+ and NiCO30 (for Ni) and Pb(OH)2, PbCO3, PbSO4, and PbO (for Pb)4,22. These compounds cannot be uptake by plant roots, which may have resulted in a significant decrease in the BCF of these metals versus the other analyzed elements.Like BCF, the heavy metals had TF of  Pb (0.21)  > Cd (0.2)  > Ni (0.15). This implies that Zn and Cu are translocated from roots to grains readily, about four times as great as the other metals, while Ni, Cd, and Pb are translocated in smaller concentrations.The comparison of BCF and TF of Cd showed that less than 30% of Cd, on average, accumulated in the corn roots were translocated to the grains. This states that Cd is immobilized by various mechanisms before it can find its way into the grains. Some of the important mechanisms include (i) the antagonistic effects of Cd with other equivalent elements, especially Zn, Fe, and Ca, in the vascular system of corn, which reduces its mobility in the corn root-stem-grain system22, (ii) Cd sequestration in active exchange sites on the cell wall in the corn root-stem pathway10, and (iii) the binding of Cd with some specific compounds, e.g., phytochelatins of root vacuoles, which immobilizes it before its translocation to grains4,22. Lin and Aarts52 remarked that Cd mostly tends to be trapped in root vacuoles, which reduces its translocation to the upper parts of the plants. In general, it was found that corn plants have a high potential to absorb and accumulate Cd in their roots and Zn in their grains, which is consistent with previous studies41. For the majority of heavy metals, the values of BCF and TF in different soil types were in the order of Fluvisols  > Regosols  > Cambisols  > Calcisols, indicating that the great variety of soil types for the uptake and translocation of heavy metals in the soil-root-grain of the corn (Fig. 5).Figure 5Effect of soil type on the mean bioconcentration factor (a) and translocation factor (b) of selected heavy metals in urban soils. Different letters indicate significant differences in bioconcentration and translocation factors among soil types for each metal at P  Zn  > Cu  > Pb  > Ni for children, differing from that for adults (Cu  > Cd  > Pb  > Zn  > Ni). The values of HQ was  1 in over 87% of the samples, implying the low non-carcinogenic risk of this metal for corn-consuming children in the study region53. Rapidly developing children’s nervous system are highly sensitive to environmental factors, including heavy metals, so even a relatively low concentration of Cd in children’s blood may irreversibly affect their mental growth and functioning54.The highest HI was observed in children (min = 1.16, max = 2.31, mean = 1.63) followed by women and men which was similar to the found pattern of HQ (Table 7). These data show a moderate non-carcinogenic health risk (1 ≤ HI  More

IPCC. in Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (eds Masson-Delmotte, V. et al) (Cambridge University Press. In Press, 2021).Otto, F. E. L. et al. Toward an inventory of the impacts of human-induced climate change. Bull. Am. Meteorol. Soc. 101, E1972–E1979 (2020).
Google Scholar 
Stanners, D. et al. in Sustainability Indicators. A Scientific Assessment (eds Moldan, B., Hak, T. & Dahl, A. L.) 127–144 (Island Press, 2007).Cohen-Shacham, E. et al. Core principles for successfully implementing and upscaling Nature-based Solutions. Environ. Sci. Policy 98, 20–29 (2019).
Google Scholar 
Seddon, N. et al. Getting the message right on nature-based solutions to climate change. Glob. Chang. Biol. https://doi.org/10.1111/gcb.15513 (2021).Keesstra, S. et al. The superior effect of nature based solutions in land management for enhancing ecosystem services. Sci. Total Environ. 610-611, 997–1009 (2018).CAS 

Google Scholar 
Seddon, N. et al. Understanding the value and limits of nature-based solutions to climate change and other global challenges. Philos. Trans. R. Soc. Lond. B Biol. Sci. 375, 20190120 (2020).
Google Scholar 
Gómez Martín, E., Máñez Costa, M. & Schwerdtner Máñez, K. An operationalized classification of Nature Based Solutions for water-related hazards: from theory to practice. Ecol. Econ. 167 https://doi.org/10.1016/j.ecolecon.2019.106460 (2020).Doswald, N. et al. Effectiveness of ecosystem-based approaches for adaptation: review of the evidence-base. Clim. Dev. 6, 185–201 (2014).
Google Scholar 
Chausson, A. et al. Mapping the effectiveness of nature-based solutions for climate change adaptation. Glob. Chang. Biol. https://doi.org/10.1111/gcb.15310 (2020).Rebelo, A. J., Holden, P. B., Esler, K. & New, M. G. Benefits of water-related ecological infrastructure investments to support sustainable land-use: a review of evidence from critically water-stressed catchments in South Africa. R. Soc. Open Sci. 8, 201402 (2021).
Google Scholar 
Berrang-Ford, L. et al. A systematic global stocktake of evidence on human adaptation to climate change. Nat. Clim. Change 11, 989–1000 (2021).
Google Scholar 
Griscom, B. W. et al. Natural climate solutions. Proc. Natl Acad. Sci. USA 114, 11645–11650 (2017).CAS 

Google Scholar 
Bastin, J.-F. et al. The global tree restoration potential. Science 365, 76–79 (2019).CAS 

Google Scholar 
Koch, A., Brierley, C. & Lewis, S. L. Effects of Earth system feedbacks on the potential mitigation of large-scale tropical forest restoration. Biogeosciences 18, 2627–2647 (2021).CAS 

Google Scholar 
Girardin, C. A. J. et al. Nature-based solutions can help cool the planet – if we act now. Nature 593, 191–194 (2021).CAS 

Google Scholar 
Sudmeier-Rieux, K. et al. Scientific evidence for ecosystem-based disaster risk reduction. Nat. Sustain. 4, 803–810 (2021).
Google Scholar 
Otto, F. E. L. Attribution of weather and climate events. Annu. Rev. Environ. Resources 42, 627–646 (2017).
Google Scholar 
Philip, S. et al. A protocol for probabilistic extreme event attribution analyses. Adv. Stat. Climatol. Meteorol. Oceanogr. 6, 177–203 (2020).
Google Scholar 
Herring, S. C., Christidis, N., Hoell, A., Hoerling, M. P. & Stott, P. A. Explaining extreme events of 2019 from a climate perspective. Bull. Amer. Meteorol. Soc. 102, S1–S112 (2021).Otto, F. E. L. et al. Challenges to understanding extreme weather changes in lower income countries. Bull. Am. Meteorol. Soc. https://doi.org/10.1175/bams-d-19-0317.1 (2020).Pall, P. et al. Anthropogenic greenhouse gas contribution to flood risk in England and Wales in autumn 2000. Nature 470, 382–385 (2011).CAS 

Google Scholar 
Kay, A. L., Crooks, S. M., Pall, P. & Stone, D. A. Attribution of Autumn/Winter 2000 flood risk in England to anthropogenic climate change: a catchment-based study. J. Hydrol. 406, 97–112 (2011).
Google Scholar 
Schaller, N. et al. Human influence on climate in the 2014 southern England winter floods and their impacts. Nat. Clim. Change 6, 627–634 (2016).
Google Scholar 
Wolski, P., Stone, D., Tadross, M., Wehner, M. & Hewitson, B. Attribution of floods in the Okavango basin, Southern Africa. J. Hydrol. 511, 350–358 (2014).
Google Scholar 
Ross, A. C. et al. Anthropogenic influences on extreme annual streamflow into Chesapeake Bay from the Susquehanna River. Bull. Am. Meteorol. Soc. 102, S25–S32 (2021).Mitchell, D. et al. Attributing human mortality during extreme heat waves to anthropogenic climate change. Environ. Res. Lett. 11, 074006 (2016).
Google Scholar 
Botai, C., Botai, J., de Wit, J., Ncongwane, K. & Adeola, A. Drought Characteristics over the Western Cape Province, South Africa. Water https://doi.org/10.3390/w9110876 (2017).Wolski, P. How severe is Cape Town’s “Day Zero” drought? Significance 15, 24–27 (2018).
Google Scholar 
Stafford, L., Shemie, D., Kroeger, T., Baker, T. & Apse, C. The Greater Cape Town Water Fund. Assessing the return on investment for Ecological Infrastructure restoration. Business case. (The Nature Conservancy, 2018).Otto, F. E. L. et al. Anthropogenic influence on the drivers of the Western Cape drought 2015–2017. Environ. Res. Lett. https://doi.org/10.1088/1748-9326/aae9f9 (2018).Pascale, S., Kapnick, S. B., Delworth, T. L. & Cooke, W. F. Increasing risk of another Cape Town “Day Zero” drought in the 21st century. Proc. Natl. Acad. Sci. USA https://doi.org/10.1073/pnas.2009144117 (2020).Van Wilgen, B. W., Measey, J., Richardson, D. M., Wilson, J. R. & Zengeya, T. A. Biological Invasions in South Africa (Springer Nature, 2020).Le Maitre, D. et al. Impacts of plant invasions on terrestrial water flows in South Africa in Biological Invasions in South Africa (eds van Wilgen, B. W., Measey. J., Richardson, D. M., Wilson, J. R. & Zengeya, T. A.) 431–457 (Springer, 2020).Brown, A. E., Zhang, L., McMahon, T. A., Western, A. W. & Vertessy, R. A. A review of paired catchment studies for determining changes in water yield resulting from alterations in vegetation. J. Hydrol. 310, 28–61 (2005).
Google Scholar 
Dennedy-Frank, P. J. & Gorelick, S. M. Insights from watershed simulations around the world: watershed service-based restoration does not significantly enhance streamflow. Glob. Environ. Change https://doi.org/10.1016/j.gloenvcha.2019.101938 (2019).Calder, I. D. & Dye, P. Hydrological impacts of invasive alien plants. Land Use Water Resour. Res. 7, 1–12 (2001).
Google Scholar 
Trabucco, A., Zomer, R. J., Bossio, D. A., van Straaten, O. & Verchot, L. V. Climate change mitigation through afforestation/reforestation: a global analysis of hydrologic impacts with four case studies. Agric. Ecosyst. Environ. 126, 81–97 (2008).
Google Scholar 
Farley, K. A., Jobbagy, E. G. & Jackson, R. B. Effects of afforestation on water yield: a global synthesis with implications for policy. Glob. Change Biol. 11, 1565–1576 (2005).
Google Scholar 
Jackson, R. B. Trading water for carbon with biological carbon sequestration. Science 310, 1944–1947 (2005).Filoso, S., Bezerra, M. O., Weiss, K. C. B. & Palmer, M. A. Impacts of forest restoration on water yield: a systematic review. PLoS ONE 12, e0183210 (2017).
Google Scholar 
Sitzia, T., Campagnaro, T., Kowarik, I. & Trentanovi, G. Using forest management to control invasive alien species: helping implement the new European regulation on invasive alien species. Biol. Invasions 18, 1–7 (2015).
Google Scholar 
Richardson, D. M. & Rejmánek, M. Trees and shrubs as invasive alien species – a global review. Divers. Distrib. 17, 788–809 (2011).
Google Scholar 
Everard, M. et al. Can control of invasive vegetation improve water and rural livelihood security in Nepal? Ecosyst. Serv. 32, 125–133 (2018).
Google Scholar 
Everard, M. Can management of ‘thirsty’ alien trees improve water security in semi-arid India? Sci. Total Environ. 704, 135451 (2020).CAS 

Google Scholar 
Archer, S. R. et al. Woody plant encroachment: causes and consequences in Rangeland Systems Springer Series on Environmental Management (ed. Briske, D. D.) Chapter 2, 25–84 (2017).Wood, M. Bootstrapped confidence intervals as an approach to statistical inference. Organ. Res. Methods 8, 454–470 (2016).
Google Scholar 
Tan, S. H. The correct interpretation of confidence intervals. Proc. Singapore Healthc. 19 (2010).Coetsee, C., Gray, E. F., Wakeling, J., Wigley, B. J. & Bond, W. J. Low gains in ecosystem carbon with woody plant encroachment in a South African savanna. J. Trop. Ecol. 29, 49–60 (2012).
Google Scholar 
Stevens, N., Erasmus, B. F., Archibald, S. & Bond, W. J. Woody encroachment over 70 years in South African savannahs: overgrazing, global change or extinction aftershock? Philos. Trans. R. Soc. Lond. B Biol. Sci. https://doi.org/10.1098/rstb.2015.0437 (2016).Venter, Z. S., Cramer, M. D. & Hawkins, H. J. Drivers of woody plant encroachment over Africa. Nat. Commun. 9, 2272 (2018).CAS 

Google Scholar 
Forsyth, G. G., Le Maitre, D. C., Smith, J. & Lotter, D. Upper Berg River Catchment (G10A) Management Unit Control Plan. (Natural Resources Management (NRM) Department of Environmental Affairs, 2016).Dirmeyer, P. A., Balsamo, G., Blyth, E. M., Morrison, R. & Cooper, H. M. Land‐atmosphere interactions exacerbated the drought and heatwave over northern Europe during summer 2018. AGU Adv. 2, e2020AV000283 (2021).
Google Scholar 
Rejmánek, M., Richardson, D. M. & Pysek, P. Trees and shrubs as invasive alien species – 2013 update of the global database. Divers. Distrib. 19, 1093–1094 (2013).
Google Scholar 
Terrer, C. et al. A trade-off between plant and soil carbon storage under elevated CO2. Nature 591, 599–603 (2021).CAS 

Google Scholar 
Ziervogel, G. et al. Climate change impacts and adaptation in South Africa. Wiley Interdiscip. Rev. Clim. Change 5, 605–620 (2014).
Google Scholar 
Thomas, A. et al. Global evidence of constraints and limits to human adaptation. Reg. Environ. Change https://doi.org/10.1007/s10113-021-01808-9 (2021).Dow, K., Berkhout, F. & Preston, B. L. Limits to adaptation to climate change: a risk approach. Curr. Opin. Environ. Sustain. 5, 384–391 (2013).
Google Scholar 
Manning, J. & Goldblatt, P. Plants of the greater Cape Floristic Region 1: the Core Cape Flora., (South African National Biodiversity Institute, 2012).Nel, J. L. et al. Strategic water source areas for urban water security: Making the connection between protecting ecosystems and benefiting from their services. Ecosyst. Serv. 28, 251–259 (2017).
Google Scholar 
Wolski, P. What Cape Town learned from its drought. Bull. At. Sci. https://thebulletin.org/2018/04/what-cape-town-learned-from-its-drought/ (2018).D. W. S. Cape Town River Systems State of Dams on 2021-08-16. Department of Water and Sanitation. Republic of South Africa. https://www.dws.gov.za/Hydrology/Weekly/RiverSystems.aspx?river=CT (2021).Rebelo, A. J. et al. The hydrological benefits of restoration: a modelling study of alien tree clearing in four mountain catchments in South Africa. Preprint at J. Hydrol. https://doi.org/10.21203/rs.3.rs-1316834/v1.DWAF. The Assessment of Water Availability in the Berg Catchment (WMA 19) by Means of Water Resource Related Models: Report 9 (Groundwater Model): Volume 9 (Breede River Alluvium Aquifer Model). (Department of Water Affairs and Forestry, 2008).DWAF. The Assessment of Water Availability in the Berg Catchment (WMA 19) by Means of Water Resource Related Models: Report 9 (Groundwater Model): Volume 3 (Regional Conceptual Model). (Department of Water Affairs and Forestry, 2008).Blake, D., Mlisa, A. & Hartnady, C. Large scale quantification of aquifer storage and volumes from the Peninsula and Skurweberg Formations in the southwestern Cape. Water SA 36, 177–184 (2010).
Google Scholar 
Holden, P. B., Rebelo, A. J. & New, M. G. Mapping invasive alien trees in water towers: a combined approach using satellite data fusion, drone technology and expert engagement. Remote Sens. Appl.: Soc. Environ. https://doi.org/10.1016/j.rsase.2020.100448 (2021).Midgley, J. & Scott, D. The use of stable isotopes of water in hydrological studies in the Jonkershoek Valley. Water SA 20, 151–154 (1994).
Google Scholar 
Van Genuchten, M. T. A closed‐form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Sci. Soc. Am. J. 44, 892–898 (1980).
Google Scholar 
Moriasi, D. N., Gitau, M. W., Pai, N. & Daggupati, P. Hydrologic and water quality models: performance measures and evaluation criteria. Trans. ASABE 58, 1763–1785 (2015).
Google Scholar 
Stone, D. A. et al. A basis set for exploration of sensitivity to prescribed ocean conditions for estimating human contributions to extreme weather in CAM5.1-1degree. Weather Clim. Extremes 19, 10–19 (2018).
Google Scholar 
Risser, M. D., Stone, D. A., Paciorek, C. J., Wehner, M. F. & Angélil, O. Quantifying the effect of interannual ocean variability on the attribution of extreme climate events to human influence. Clim. Dyn. 49, 3051–3073 (2017).
Google Scholar 
Jones, G. S., Stott, P. A. & Christidis, N. Attribution of observed historical near-surface temperature variations to anthropogenic and natural causes using CMIP5 simulations. J. Geophys. Res. Atmos. 118, 4001–4024 (2013).
Google Scholar 
Sun, L. et al. Drivers of 2016 record Arctic warmth assessed using climate simulations subjected to factual and counterfactual forcing. Weather Clim. Extremes 19, 1–9 (2018).CAS 

Google Scholar 
Guillod, B. P. et al. weather@home 2: validation of an improved global–regional climate modelling system. Geosci. Model Dev. 10, 1849–1872 (2017).
Google Scholar 
Massey, N. et al. weather@home—development and validation of a very large ensemble modelling system for probabilistic event attribution. Q. J. R. Meteorol. Soc. 141, 1528–1545 (2014).
Google Scholar 
Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).
Google Scholar 
Flato, G. et al. in Climate change 2013: the physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change 741–866 (Cambridge University Press, 2014).Hargreaves, G. H. & Samani, Z. A. Reference crop evapotranspiration from temperature. Appl. Eng. Agriculture 1, 96–99 (1985).
Google Scholar 
Cayan, D. R., Maurer, E. P., Dettinger, M. D., Tyree, M. & Hayhoe, K. Climate change scenarios for the California region. Clim. Change 87, 21–42 (2008).
Google Scholar 
Cannon, A. J., Sobie, S. R. & Murdock, T. Q. Bias correction of GCM precipitation by quantile mapping: how well do methods preserve changes in quantiles and extremes? J. Clim. 28, 6938–6959 (2015).
Google Scholar 
R Core Team. R: A language and environment for statistical computing. (R Foundation for Statistical Computing. https://www.R-project.org/, 2020).Paciorek, C. J., Stone, D. A. & Wehner, M. F. Quantifying statistical uncertainty in the attribution of human influence on severe weather. Weather Clim. Extremes 20, 69–80 (2018).
Google Scholar 
Tadono, T. et al. Generation of the 30 M-Mesh Global Digital Surface Model by Alos Prism. ISPRS – International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B4, 157–162, https://developers.google.com/earth-engine/datasets/catalog/JAXA_ALOS_AW3D30_V3_2#description (2016).
Google Scholar 
Takaku, J., Tadono, T., Tsutsui, K. & Ichikawa, M. Validation of “Aw3d” Global Dsm Generated from Alos Prism. ISPRS Ann. Photogramm. III-4, 25–31 (2016).
Google Scholar 
Viviroli, D. Increasing dependence of lowland population on mountain water resources. Nat. Sustain. 3, 917–928 (2020).
Google Scholar 
Meybeck, M. A New typology for mountains and other relief classes: an application to global continental water resources and population distribution. Mt. Res. Dev. 21, 34–45 (2001).DWS. Surface water home. Department of Water and Sanitation. Republic of South Africa. https://www.dws.gov.za/Hydrology/Unverified/UnverifiedDataFlowInfo.aspx (2021). More

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
and JavaScript. More

Du, E. et al. Global patterns of terrestrial nitrogen and phosphorus limitation. Nat. Geosci. 13, 221–226 (2020).ADS 
CAS 

Google Scholar 
Schulze, E. Ueber einige stickstoffhaltige Bestandtheile der Keimlinge von Vicia sativa. Z. Phys. Chem. 17, 193–216 (1893).
Google Scholar 
Wishart, D. S. et al. HMDB 4.0: the human metabolome database for 2018. Nucleic Acids Res. 46, D608–D617 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kato, T., Yamagata, M. & Tsukahara, S. Guanidine compounds in fruit trees and their seasonal variations in citrus (Citrus unshiu Marc.). J. Jpn. Soc. Hortic. Sci. 55, 169–173 (1986).CAS 

Google Scholar 
Gund, P. Guanidine, trimethylenemethane, and “Y-delocalization.” Can acyclic compounds have “aromatic” stability? J. Chem. Educ. 49, 100 (1972).CAS 

Google Scholar 
Güthner, T., Mertschenk, B. & Schulz, B. In Ullmann’s Fine Chemicals vol. 2, 657–672 (Wiley-VCH, 2014).Strecker, A. Untersuchungen über die chemischen Beziehungen zwischen Guanin, Xanthin, Theobromin, Caffeïn und Kreatinin. Justus Liebigs Ann. Chem. 118, 151–177 (1861).
Google Scholar 
Iwanoff, N. N. & Awetissowa, A. N. The fermentative conversion of guanidine in urea. Biochem. Z. 231, 67–78 (1931).
Google Scholar 
Lenkeit, F., Eckert, I., Hartig, J. S. & Weinberg, Z. Discovery and characterization of a fourth class of guanidine riboswitches. Nucleic Acids Res. 48, 12889–12899 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Salvail, H., Balaji, A., Yu, D., Roth, A. & Breaker, R. R. Biochemical validation of a fourth guanidine riboswitch class in bacteria. Biochemistry 59, 4654–4662 (2020).CAS 
PubMed 

Google Scholar 
Nelson, J. W., Atilho, R. M., Sherlock, M. E., Stockbridge, R. B. & Breaker, R. R. Metabolism of free guanidine in bacteria is regulated by a widespread riboswitch class. Mol. Cell 65, 220–230 (2017).CAS 
PubMed 

Google Scholar 
Sherlock, M. E. & Breaker, R. R. Biochemical validation of a third guanidine riboswitch class in bacteria. Biochemistry 56, 359–363 (2016).
Google Scholar 
Sherlock, M. E., Malkowski, S. N. & Breaker, R. R. Biochemical validation of a second guanidine riboswitch class in bacteria. Biochemistry 56, 352–358 (2016).
Google Scholar 
Kermani, A. A., Macdonald, C. B., Gundepudi, R. & Stockbridge, R. B. Guanidinium export is the primal function of SMR family transporters. Proc. Natl Acad. Sci. USA 115, 3060–3065 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Sinn, M., Hauth, F., Lenkeit, F., Weinberg, Z. & Hartig, J. S. Widespread bacterial utilization of guanidine as nitrogen source. Mol. Microbiol. 116, 200–210 (2021).CAS 
PubMed 

Google Scholar 
Schneider, N. O. et al. Solving the conundrum: widespread proteins annotated for urea metabolism in bacteria are carboxyguanidine deiminases mediating nitrogen assimilation from guanidine. Biochemistry 59, 3258–3270 (2020).CAS 
PubMed 

Google Scholar 
Zhao, J., Zhu, L., Fan, C., Wu, Y. & Xiang, S. Structure and function of urea amidolyase. Biosci. Rep. 38, BSR20171617 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Mobley, H. L., Island, M. D. & Hausinger, R. P. Molecular biology of microbial ureases. Microbiol. Rev. 59, 451–480 (1995).CAS 
PubMed 
PubMed Central 

Google Scholar 
Mazzei, L., Musiani, F. & Ciurli, S. The structure-based reaction mechanism of urease, a nickel dependent enzyme: tale of a long debate. J. Biol. Inorg. Chem. 25, 829–845 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Uribe, E. et al. Functional analysis of the Mn2+ requirement in the catalysis of ureohydrolases arginase and agmatinase – a historical perspective. J. Inorg. Biochem. 202, 110812 (2020).CAS 
PubMed 

Google Scholar 
Perozich, J., Hempel, J. & Morris, S. M. Jr Roles of conserved residues in the arginase family. Biochim. Biophys. Acta 1382, 23–37 (1998).CAS 
PubMed 

Google Scholar 
Sekowska, A., Danchin, A. & Risler, J. L. Phylogeny of related functions: the case of polyamine biosynthetic enzymes. Microbiology 146, 1815–1828 (2000).CAS 
PubMed 

Google Scholar 
Sekula, B. The neighboring subunit is engaged to stabilize the substrate in the active site of plant arginases. Front. Plant Sci. 11, 987 (2020).PubMed 
PubMed Central 

Google Scholar 
Quintero, M. J., Muro-Pastor, A. M., Herrero, A. & Flores, E. Arginine catabolism in the cyanobacterium Synechocystis sp. strain PCC 6803 involves the urea cycle and arginase pathway. J. Bacteriol. 182, 1008–1015 (2000).CAS 
PubMed 
PubMed Central 

Google Scholar 
Lacasse, M. J., Summers, K. L., Khorasani-Motlagh, M., George, G. N. & Zamble, D. B. Bimodal nickel-binding site on Escherichia coli [NiFe]-hydrogenase metallochaperone HypA. Inorg. Chem. 58, 13604–13618 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Hoffmann, D., Gutekunst, K., Klissenbauer, M., Schulz-Friedrich, R. & Appel, J. Mutagenesis of hydrogenase accessory genes of Synechocystis sp. PCC 6803. FEBS J. 273, 4516–4527 (2006).CAS 
PubMed 

Google Scholar 
Dowling, D. P., Di Costanzo, L., Gennadios, H. A. & Christianson, D. W. Evolution of the arginase fold and functional diversity. Cell. Mol. Life Sci. 65, 2039–2055 (2008).CAS 
PubMed 
PubMed Central 

Google Scholar 
Dutta, A., Mazumder, M., Alam, M., Gourinath, S. & Sau, A. K. Metal-induced change in catalytic loop positioning in Helicobacter pylori arginase alters catalytic function. Biochem. J. 476, 3595–3614 (2019).CAS 
PubMed 

Google Scholar 
Di Costanzo, L. et al. Crystal structure of human arginase I at 1.29-Å resolution and exploration of inhibition in the immune response. Proc. Natl Acad. Sci. USA 102, 13058–13063 (2005).ADS 
PubMed 
PubMed Central 

Google Scholar 
Suzek, B. E. et al. UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches. Bioinformatics 31, 926–932 (2014).PubMed 
PubMed Central 

Google Scholar 
Alfano, M. & Cavazza, C. Structure, function, and biosynthesis of nickel-dependent enzymes. Protein Sci. 29, 1071–1089 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wang, B. et al. A guanidine-degrading enzyme controls genomic stability of ethylene-producing cyanobacteria. Nat. Commun. 12, 5150 (2021).ADS 
PubMed 
PubMed Central 

Google Scholar 
McGee, D. J. et al. Purification and characterization of Helicobacter pylori arginase, RocF: unique features among the arginase superfamily. Eur. J. Biochem. 271, 1952–1962 (2004).CAS 
PubMed 

Google Scholar 
Arakawa, N., Igarashi, M., Kazuoka, T., Oikawa, T. & Soda, K. d-Arginase of Arthrobacter sp. KUJ 8602: characterization and its identity with Zn2+-guanidinobutyrase. J. Biochem. 133, 33–42 (2003).CAS 
PubMed 

Google Scholar 
Saragadam, T., Kumar, S. & Punekar, N. S. Characterization of 4-guanidinobutyrase from Aspergillus niger. Microbiology 165, 396–410 (2019).CAS 
PubMed 

Google Scholar 
Viator, R. J., Rest, R. F., Hildebrandt, E. & McGee, D. J. Characterization of Bacillus anthracis arginase: effects of pH, temperature, and cell viability on metal preference. BMC Biochem. 9, 15 (2008).PubMed 
PubMed Central 

Google Scholar 
D’Antonio, E. L., Hai, Y. & Christianson, D. W. Structure and function of non-native metal clusters in human arginase I. Biochemistry 51, 8399–8409 (2012).PubMed 

Google Scholar 
Andresen, E., Peiter, E. & Küpper, H. Trace metal metabolism in plants. J. Exp. Bot. 69, 909–954 (2018).CAS 
PubMed 

Google Scholar 
Eisenhut, M. Manganese homeostasis in cyanobacteria. Plants 9, 18 (2019).PubMed Central 

Google Scholar 
Burnat, M. & Flores, E. Inactivation of agmatinase expressed in vegetative cells alters arginine catabolism and prevents diazotrophic growth in the heterocyst-forming cyanobacterium Anabaena. MicrobiologyOpen 3, 777–792 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Callahan, B. P., Yuan, Y. & Wolfenden, R. The burden borne by urease. J. Am. Chem. Soc. 127, 10828–10829 (2005).CAS 
PubMed 

Google Scholar 
Lewis, C. A. Jr & Wolfenden, R. The nonenzymatic decomposition of guanidines and amidines. J. Am. Chem. Soc. 136, 130–136 (2014).CAS 
PubMed 

Google Scholar 
Grobben, Y. et al. Structural insights into human Arginase-1 pH dependence and its inhibition by the small molecule inhibitor CB-1158. J. Struct. Biol. X 4, 100014 (2020).CAS 
PubMed 

Google Scholar 
Mills, L. A., McCormick, A. J. & Lea-Smith, D. J. Current knowledge and recent advances in understanding metabolism of the model cyanobacterium Synechocystis sp. PCC 6803. Biosci. Rep. 40, BSR20193325 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Giner-Lamia, J. et al. Identification of the direct regulon of NtcA during early acclimation to nitrogen starvation in the cyanobacterium Synechocystis sp PCC 6803. Nucleic Acids Res. 45, 11800–11820 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Martinez, S. & Hausinger, R. P. Biochemical and spectroscopic characterization of the non-heme Fe(II)- and 2-oxoglutarate-dependent ethylene-forming enzyme from Pseudomonas syringae pv. phaseolicola PK2. Biochemistry 55, 5989–5999 (2016).CAS 
PubMed 

Google Scholar 
Copeland, R. A. et al. An iron(IV)-oxo intermediate initiating l-arginine oxidation but not ethylene production by the 2-oxoglutarate-dependent oxygenase, ethylene-forming enzyme. J. Am. Chem. Soc. 143, 2293–2303 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Rippka, R., Deruelles, J., Waterbury, J. B., Herdman, M. & Stanier, R. Y. Generic assignments, strain histories and properties of pure cultures of cyanobacteria. Microbiology 111, 1–61 (1979).
Google Scholar 
Geyer, J. W. & Dabich, D. Rapid method for determination of arginase activity in tissue homogenates. Anal. Biochem. 39, 412–417 (1971).CAS 
PubMed 

Google Scholar 
van Anken, H. C. & Schiphorst, M. E. A kinetic determination of ammonia in plasma. Clin. Chim. Acta 56, 151–157 (1974).PubMed 

Google Scholar 
Kabsch, W. XDS. Acta Crystallogr. D 66, 125–132 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
McCoy, A. J. et al. Phaser crystallographic software. J. Appl. Crystallogr. 40, 658–674 (2007).CAS 
PubMed 
PubMed Central 

Google Scholar 
Lamzin, V. S. P. A., Wilson, K. S. In International Tables for Crystallography Vol. F (eds Arnold, E. et al.) 525–528 (Kluwer, 2012).Emsley, P., Lohkamp, B., Scott, W. G. & Cowtan, K. Features and development of Coot. Acta Crystallogr. D 66, 486–501 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Adams, P. D. et al. The Phenix software for automated determination of macromolecular structures. Methods 55, 94–106 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
Williams, C. J. et al. MolProbity: more and better reference data for improved all-atom structure validation. Protein Sci. 27, 293–315 (2018).CAS 
PubMed 

Google Scholar 
Wang, J., Wang, W., Kollman, P. A. & Case, D. A. Automatic atom type and bond type perception in molecular mechanical calculations. J. Mol. Graph. Model. 25, 247–260 (2006).ADS 
PubMed 

Google Scholar 
Maier, J. A. et al. ff14SB: improving the accuracy of protein side chain and backbone parameters from ff99SB. J. Chem. Theory Comput. 11, 3696–3713 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wang, J., Wolf, R. M., Caldwell, J. W., Kollman, P. A. & Case, D. A. Development and testing of a general amber force field. J. Comput. Chem. 25, 1157–1174 (2004).CAS 
PubMed 

Google Scholar 
Trott, O. & Olson, A. J. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem. 31, 455–461 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Ashkenazy, H. et al. ConSurf 2016: an improved methodology to estimate and visualize evolutionary conservation in macromolecules. Nucleic Acids Res. 44, W344–W350 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Lemoine, F. et al. Renewing Felsenstein’s phylogenetic bootstrap in the era of big data. Nature 556, 452–456 (2018).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Lemoine, F. et al. NGPhylogeny.fr: new generation phylogenetic services for non-specialists. Nucleic Acids Res. 47, W260–W265 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL) v4: recent updates and new developments. Nucleic Acids Res. 47, W256–W259 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Crooks, G. E., Hon, G., Chandonia, J. M. & Brenner, S. E. WebLogo: a sequence logo generator. Genome Res. 14, 1188–1190 (2004).CAS 
PubMed 
PubMed Central 

Google Scholar  More

NATURE PODCAST
09 March 2022

The AI that deciphers ancient Greek graffiti

An artificial intelligence that restores illegible inscriptions, and the project that’s reintroducing lost species in Argentina.

Nick Petrić Howe

&

Benjamin Thompson

Nick Petrić Howe

View author publications

You can also search for this author in PubMed
 Google Scholar

Benjamin Thompson

View author publications

You can also search for this author in PubMed
 Google Scholar

Twitter

Facebook

Email

Subscribe
Subscribe

iTunes
Google Podcast
acast
RSS

Listen to the latest from the world of science, with Nick Petrić Howe and Benjamin Thompson.

Your browser does not support the audio element.

Download MP3

In this episode:00:46 The AI helping historians read ancient textsResearchers have developed an artificial intelligence that can restore and date ancient Greek inscriptions. They hope that it will help historians by speeding up the process of reconstructing damaged texts. Research article: Assael et al.News and Views: AI minds the gap and fills in missing Greek inscriptionsVideo: The AI historian: A new tool to decipher ancient textsIthaca platform08:53 Research HighlightsPollinators prefer nectar with a pinch of salt, and measurements of a megacomet’s mighty size.Research Highlight: Even six-legged diners can’t resist sweet-and-salty snacksResearch Highlight: Huge comet is biggest of its kind11:10 Rewilding ArgentinaThis week Nature publishes a Comment article from a group who aim to reverse biodiversity loss by reintroducing species to areas where they are extinct. We speak to one of the Comment’s authors about the project and their hopes that it might kick start ecosystem restoration.Comment: Rewilding Argentina: lessons for the 2030 biodiversity targets21:02 Briefing ChatWe discuss some highlights from the Nature Briefing. This time, giant bacteria that can be seen with the naked eye, and how record-breaking rainfall has caused major floods in Australia.Science: Largest bacterium ever discovered has an unexpectedly complex cellNew Scientist: Record flooding in Australia driven by La Niña and climate changeThe Conversation: The east coast rain seems endless. Where on Earth is all the water coming from?Subscribe to Nature Briefing, an unmissable daily round-up of science news, opinion and analysis free in your inbox every weekday.Never miss an episode: Subscribe to the Nature Podcast on Apple Podcasts, Google Podcasts, Spotify or your favourite podcast app. Head here for the Nature Podcast RSS feed.

doi: https://doi.org/10.1038/d41586-022-00701-7

Related Articles

Read the paper: Restoring and attributing ancient texts using deep neural networks

Rewilding Argentina: lessons for the 2030 biodiversity targets

Subjects

Machine learning

History

Ecology

Latest on:

Machine learning

AI minds the gap and fills in missing Greek inscriptions
News & Views 09 MAR 22

The evolution, evolvability and engineering of gene regulatory DNA
Article 09 MAR 22

Gran Turismo champion, reimagined urine — the week in infographics
News 15 FEB 22

History

AI minds the gap and fills in missing Greek inscriptions
News & Views 09 MAR 22

Police rely on radiocarbon dating to identify forged paintings
News 09 MAR 22

Restoring and attributing ancient texts using deep neural networks
Article 09 MAR 22

Ecology

Discovery of a Ni2+-dependent guanidine hydrolase in bacteria
Article 09 MAR 22

Rewilding Argentina: lessons for the 2030 biodiversity targets
Comment 07 MAR 22

How itchy vicuñas remade a vast wilderness
Research Highlight 04 MAR 22

Jobs

Principal Investigator / Faculty

San Diego Biomedical Research Institute (SDBRI)
Multiple locations

Research Associate / Light Microscopy Imaging Specialist (m/f/x)

Technische Universität Dresden (TU Dresden)
01069 Dresden, Germany

Bioinformatician (m/f/div)

Max Planck Institute for Plant Breeding Research (MPIPZ)
Cologne, Germany

Research Coordinator / Institute Manager (div/f/m)

Max Planck Institute for Astrophysics (MPA)
Garching near Munich, Germany More

Beck, M. W. et al. The role of near shore ecosystems as fish and shellfish nurseries. Issues Ecol. 11, 1–12 (2003).
Google Scholar 
De la Torre-Castro, M., Di Carlo, G. & Jiddawi, N. S. Seagrass importance for a small-scale fishery in the tropics: The need for seascape management. Mar. Poll. Bull. 83, 398–407 (2014).
Google Scholar 
Sheaves, M., Baker, R., Nagelkerken, I. & Connolly, R. M. True value of estuarine and coastal nurseries for fish: incorporating complexity and dynamics. Estuar. Coasts 38, 401–414 (2014).
Google Scholar 
Nordlund, L. M., Unsworth, R. K. F., Gullström, M. & Cullen-Unsworth, L. C. Global significance of seagrass fishery activity. Fish. Fish. 19, 399–412 (2018).
Google Scholar 
Kimirei, I. A., Nagelkerken, I., Griffioen, B., Wagner, C. & Mgaya, Y. D. Ontogenetic habitat use by mangrove/seagrass-associated coral reef fishes shows flexibility in time and space. Estuar. Coast. Shelf Sci. 92, 47–58 (2011).ADS 

Google Scholar 
Unsworth, R. K. F. et al. Structuring of Indo-Pacific fish assemblages along the mangrove-seagrass continuum. Aquat. Biol. 5, 85–95 (2009).
Google Scholar 
Cocheret De La Morinière, E., Pollux, B. J. A., Nagelkerken, I. & van Der Velde, G. Post-settlement life cycle migration patterns and habitat preference of coral reef fish that use seagrass and mangrove habitats as nurseries. Estuar. Coast. Shelf Sci. 55, 309–321 (2002).Berkström, C., Lindborg, R., Thyresson, M. & Gullström, M. Assessing connectivity in a tropical embayment: fish migrations and seascape ecology. Biol. Conserv. 166, 43–53 (2013).
Google Scholar 
Saenger, P., Gartside, D. & Funge-Smith, S. A review of mangrove and seagrass ecosystems and their linkage to fisheries and fisheries management. FAO Regional Office for Asia and the Pacific, Bangkok, Thailand, 74 (RAP Publication, 2013).King, A. J. Density and distribution of potential prey for larval fish in the main channel of a floodplain river: pelagic versus epibenthic meiofauna. River Res. Appl. 20, 883–897 (2004).
Google Scholar 
Carassou, L., Ponton, D., Mellin, C. & Galzin, R. Predicting the structure of larval fish assemblages by a hierarchical classification of meteorological and water column forcing factors. Coral Reefs 27, 867–880 (2008).ADS 

Google Scholar 
Pinho Costa, A. C., Martins Garcia, T., Pereira Paiva, B., Ximenes Neto, A. R. & de Oliveira Soares, M. Seagrass and rhodolith beds are important seascapes for the development of fish eggs and larvae in tropical coastal areas. Mar. Environ. Res. 161, 105064 (2020).Muzaki, F. K., Giffari, A. & Saptarini, D. Community structure of fish larvae in mangroves with different root types in Labuhan coastal area, Sepulu–Madura. AIP Conf. Proc. 1854, 020025 (2017).Isari, S. et al. Exploring the larval fish community of the central Red Sea with an integrated morphological and molecular approach. PLoS ONE, 12, e0182503 (2017).Levin, P. S. Fine-scale temporal variation in recruitment of a temperate demersal fish: the importance of settlement versus post-settlement loss. Oecologia 97, 124–133 (1994).ADS 
CAS 
PubMed 

Google Scholar 
Mwaluma, J. M., Boaz Kaunda-Arara, B., Rasowo, J., Osore, M. K. & Vidar Øresland V. Seasonality in fish larval assemblage structure within marine reef National Parks in coastal Kenya. Environ. Biol. Fish. 90, 393–404 (2011).Reglero, P., Tittensor, D. P., Álvarez-Berastegui, D., Aparicio-González, A. & Worm, B. Worldwide distributions of tuna larvae: revisiting hypotheses on environmental requirements for spawning habitats. Mar. Ecol. Prog. Ser. 501, 207–224 (2014).ADS 

Google Scholar 
Leis, J. M. Ontogeny of behaviour in larvae of marine demersal fishes. Ichthyol. Res. 57, 325–342 (2010).
Google Scholar 
Tzeng, W. N. & Wang, Y. T. Hydrography and distribution dynamics of larval and juvenile fishes in the coastal waters of the Tanshui River estuary, Taiwan, with reference to estuarine larval transport. Mar. Biol. 116, 205–217 (1993).
Google Scholar 
Leis, J. M., Sweatman, H. P. A. & Reader, S. E. What the pelagic stages of coral reef fishes are doing out in blue water: Daytime field observations of larval behavioural capabilities. Mar. Freshw. Res. 47, 401–411 (1996).
Google Scholar 
Leis, J. M. & Carson-Ewart, B. M. Complex behaviour by coral-reef fish larvae in open-water and near-reef pelagic environments. Environ. Biol. Fish. 53, 259–266 (1998).
Google Scholar 
Leis, J. M. Are larvae of demersal fishes plankton or nekton?. Adv. Mar. Biol. 51, 57–141 (2006).PubMed 

Google Scholar 
Faillettaz, R., Paris, C. B. & Irisson, J. O. Larval fish swimming behavior alters dispersal patterns from marine protected areas in the North-Western Mediterranean Sea. Front. Mar. Sci. 5, 1–12 (2018).ADS 

Google Scholar 
Azeiteiro, U. M., Bacelar-Nicolau, L., Resende, P., Gonçalves, F. & Pereira, M. J. Larval fish distribution in shallow coastal waters off North Western Iberia (NE Atlantic). Estuar. Coast. Shelf Sci. 69, 554–566 (2006).ADS 

Google Scholar 
Irisson, J. O. & Lecchini, D. In situ observation of settlement behaviour in larvae of coral reef fishes at night. J. Fish Biol. 72, 2707–2713 (2008).
Google Scholar 
Teixeira Bonecker, F., de Castro, M. S. & Teixeira Bonecker, A. C. Larval fish assemblage in a tropical estuary in relation to tidal cycles, day/night and seasonal variations. Pan-Am. J. Aquat. Sci. 4, 239–246 (2009).Strydom, N. A. Patterns in larval fish diversity, abundance, and distribution in temperate South African estuaries. Estuar. Coasts 38, 268–284 (2014).
Google Scholar 
Lana, P. C. & Bernardino, A. F. (Eds). Brazilian estuaries: a benthic perspective. Brazilian Marine Biodiversity series. 212 (Springer, Cham, 2018).Donahue, M. J., Karnauskas, M., Toews, C. & Paris, C. B. Location isn’t everything: Timing of spawning aggregations optimizes larval replenishment. PLoS ONE 10, 1–15 (2015).
Google Scholar 
Reynalte-Tataje, D. A., Zaniboni-Filho, E., Bialetzki, A. & Agostinho, A. A. Temporal variability of fish larvae assemblages: influence of natural and anthropogenic disturbances. Neotrop. Ichthyol. 10, 837–846 (2012).
Google Scholar 
Somarakis, S., Tsoukali, S., Giannoulaki, M., Schismenou, E. & Nikolioudakis, N. Spawning stock, egg production and larval survival in relation to small pelagic fish recruitment. Mar. Ecol. Prog. Ser. 2018, 113–136 (2018).
Google Scholar 
Sampey, A., Meekan, M. G., Carleton, J. H., McKinnon, A. D. & McCormick, M. I. Temporal patterns in distributions of tropical fish larvae on the North West Shelf of Australia. Mar. Freshw. Res. 55, 473–487 (2004).
Google Scholar 
Rezagholinejad, S., Arshad, A., Nurul Amin, S. M. & Ehteshami, F. The influence of environmental parameters on fish larval distribution and abundance in the mangrove estuarine area of Marudu bay, Sabah, Malaysia. J. Surv. Fish. Sci. 2, 67–78 (2016).Shuai, F. et al. Temporal patterns of larval fish occurrence in a large subtropical river. PLoS ONE 11, e0156556 (2016).Nordlund, L. M. et al. Intertidal zone management in the Western Indian Ocean: assessing current status and future possibilities using expert opinions. Ambio 43, 1006–1019 (2014).PubMed 

Google Scholar 
De Oliveira, E. C. & Ferreira, E. J. G. Spawning areas, dispersion and microhabitats of fish larvae in the Anavilhanas Ecological Station, rio Negro, Amazonas State Brazil. Neotrop. Ichthyol. 6, 559–566 (2008).
Google Scholar 
Caley, M. J. et al. Recruitment and the local dynamics of open marine populations. Ann. Rev. Ecol. Syst. 27, 477–500 (1996).
Google Scholar 
Crochelet, E. et al. Validation of a fish larvae dispersal model with otolith data in the Western Indian Ocean and implications for marine spatial planning in data-poor regions. Ocean Coast Manag. 86, 13–21 (2013).
Google Scholar 
Gilroy, J. J. & Edwards, D. P. Source-sink dynamics: a neglected problem for landscape-scale biodiversity conservation in the tropics. Curr. Landsc. Ecol. Rep. 2, 51–60 (2017).
Google Scholar 
Little, M. C., Reay, P. J. & Grove, S. J. Distribution gradients of ichthyoplankton in an East African mangrove creek. Estuar. Coast. Shelf Sci. 26, 669–677 (1988).ADS 

Google Scholar 
Hedberg, P., Rybak, F. F., Gullström, M., Jiddawi, N. S. & Winder, M. Fish larvae distribution among different habitats in coastal East Africa. J. Fish Biol. 94, 29–39 (2019).CAS 
PubMed 

Google Scholar 
Heylen, B. C. & Nachtsheim, D. A. Bio-telemetry as an essential tool in movement ecology and marine conservation. In: Jungblut, S., Liebich, V. & Bode, M. (Eds), YOUMARES 8–Oceans Across Boundaries: Learning From Each Other. 83–107 (Springer, 2018).Parrish, J. Fish communities of interacting shallow-water habitats in tropical oceanic regions. Mar. Ecol. Prog. Ser. 58, 143–160 (1989).ADS 

Google Scholar 
McMahon, K. W., Berumen, M. L. & Thorrold, S. R. Linking habitat mosaics and connectivity in a coral reef seascape. Proc. Natl. Acad. Sci. USA 109, 15372–15376 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Carlson, R. R. et al. Synergistic benefits of conserving land-sea ecosystems. Glob. Ecol. Conserv. 28, e01684 (2021).Mwaluma, J. M. et al. Assemblage structure and distribution of fish larvae on the North Kenya Banks during the Southeast Monsoon season. Ocean Coast. Manag. 212, 105800 (2021).Joyeux, J. C. The abundance of fish larvae in estuaries: Within-tide variability at inlet and immigration. Estuaries 22, 889–904 (1999).
Google Scholar 
Able, K. W., Valenti, J. L. & Grothues, T. M. Fish larval supply to and within a lagoonal estuary: Multiple sources for Barnegat Bay New Jersey. Environ. Biol. Fish. 100, 663–683 (2017).
Google Scholar 
McClanahan, T. R. Seasonality in East Africa’s coastal waters. Mar. Ecol. Prog. Ser. 44, 191–199 (1988).ADS 

Google Scholar 
Aceves-Medina, G. et al. Distribution and abundance of the ichthyoplankton assemblages and its relationships with the geostrophic flow along the southern region of the California current. Lat. Am. J. Aquat. Res. 46, 104–119 (2018).
Google Scholar 
Gray, C. A. & Miskiewicz, A. G. Larval fish assemblages in south-east Australian coastal waters: Seasonal and spatial structure. Estuar. Coast. Shelf Sci. 50, 549–570 (2000).ADS 

Google Scholar 
Jiménez, M. P., Sánchez-Leal, R. F., González, C., García-Isarch, E. & García, A. Oceanographic scenario and fish larval distribution off Guinea-Bissau (north-west Africa). J. Mar. Biolog. Assoc. UK 95, 435–452.Mwaluma, J. M., Kaunda-Arara, B. & Rasowo, J. Diel and lunar variations in larval supply to Malindi Marine Park, Kenya. West Ind. Ocean J. Mar. Sci. 13, 57–67 (2014).
Google Scholar 
Stephens, J. S. Jr., Jordan, G. A., Morris, P. A., Singer, M. M. & McGowen, G. E. Can we relate larval fish abundance to recruitment or population stability? A preliminary analysis of recruitment to a temperate rocky reef. CalCOFI Rep. 27, 65–83 (1986).
Google Scholar 
Green, B. C., Smith, D. J., Grey, J. & Underwood, G. J. C. High site fidelity and low site connectivity in temperate salt marsh fish populations: A stable isotope approach. Oecologia 168, 245–255 (2012).ADS 
PubMed 

Google Scholar 
Green, J. M. & Wroblewski, J. S. Movement patterns of Atlantic cod in Gilbert Bay, Labrador: Evidence for bay residency and spawning site fidelity. J. Mar. Biolog. Assoc. UK 80, 1077–1085 (2000).
Google Scholar 
Grüss, A., Kaplan, D. M. & Hart, D. R. Relative impacts of adult movement, larval dispersal and harvester movement on the effectiveness of reserve networks. PLoS ONE 6, e19960 (2011).Luiz, O. J. et al. Adult and larval traits as determinants of geographic range size among tropical reef fishes. Proc. Natl. Acad. Sci. USA 110, 16498–16502 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Macpherson, E. & Raventos, N. Relationship between pelagic larval duration and geographic distribution of Mediterranean littoral fishes. Mar. Ecol. Prog. Ser. 327, 257–265 (2006).ADS 

Google Scholar 
Green, A. L. et al. Larval dispersal and movement patterns of coral reef fishes, and implications for marine reserve network design. Biol. Rev. 90, 1215–1247 (2015).PubMed 

Google Scholar 
Taylor, M. D., Laffan, S. D., Fielder, D. S. & Suthers, I. M. Key habitat and home range of mulloway Argyrosomus japonicus in a south-east Australian estuary: Finding the estuarine niche to optimise stocking. Mar. Ecol. Prog. Ser. 328, 237–247 (2006).ADS 

Google Scholar 
Manson, F. J., Loneragan, N. R., Skilleter, G. A. & Phinn, S. R. An evaluation of the evidence for linkages between mangroves and fisheries: A synthesis of the literature and identification of research directions. Oceanogr. Mar. Biol. 43, 483–513 (2005).
Google Scholar 
Pattrick, P. & Strydom, N. A. Composition, abundance, distribution and seasonality of larval fishes in the shallow nearshore of the proposed Greater Addo Marine Reserve, Algoa Bay South Africa. Estuar. Coast. Shelf Sci. 79, 251–262 (2008).ADS 

Google Scholar 
Sato, N., Asahida, T., Terashima, H., Hurbungs, M. D. & Ida, H. Species composition and dynamics of larval and juvenile fishes in the surf zone of Mauritius. Environ. Biol. Fish. 81, 229–238 (2008).
Google Scholar 
Jaonalison, H., Mahafina, J. & Ponton, D. Fish post-larvae assemblages at two contrasted coral reef habitats in southwest Madagascar. Reg. Stud. Mar. Sci 6, 62–74 (2016).
Google Scholar 
Azmir, I. A., Esa, Y., Amin, S. M. N., Yasin, I. S. & Yusof, F. ZMd. Identification of larval fish in mangrove areas of Peninsular Malaysia using morphology and DNA barcoding methods. J. Appl. Ichthyol. 33, 998–1006 (2017).CAS 

Google Scholar 
Macedo-Soares, L. C. P., Freire, A. S. & Muelbert, J. H. Small-scale spatial and temporal variability of larval fish assemblages at an isolated oceanic island. Mar. Ecol. Prog. Ser. 444, 207–222 (2012).ADS 

Google Scholar 
Monteleone, D. M. Seasonality and abundance of ichthyoplankton in great South Bay, New York. Estuaries 15, 230–238 (1992).
Google Scholar 
Ara, R., Arshad, A., Amin, S. M. & Mazlan, A. G. Temporal and spatial distribution of fish larvae in different ecological habitats. Asian J. Anim. Vet. Adv. 8, 53–62 (2013).
Google Scholar 
Abu El-Regal, M. Abundance and diversity of coral reef fish larvae at Hurghada, Egyptian Red Sea. Egypt. J. Aquat. Biol. Fish. 12, 17–33 (2008).
Google Scholar 
Bialetzki, A., Nakatani, K., Sanches, P. V., Baumgartner, G. & Gomes, L. C. Larval fish assemblage in the Baía River (Mato Grosso do Sul State, Brazil): temporal and spatial patterns. Environ. Biol. Fish. 73, 37–47 (2005).
Google Scholar 
Dudley, B., Tolimieri, N. & Montgomery, J. Swimming ability of the larvae of some reef fishes from New Zealand waters. Mar. Freshw. Res. 51, 783–787. https://doi.org/10.1071/MF00062 (2000).Article 

Google Scholar 
Hare, J. A. et al. Biophysical mechanisms of larval fish ingress into Chesapeake Bay. Mar. Ecol. Prog. Ser. 303, 295–310 (2005).ADS 

Google Scholar 
Watt-pringle, P. & Strydom, N. A. Habitat use by larval fishes in a temperate South African surf zone. Estuar. Coast. Shelf Sci. 58, 765–774 (2003).ADS 

Google Scholar 
Picapedra, P. H. S., Sanches, P. V. & Lansac-Tôha, F. A. Effects of light-dark cycle on the spatial distribution and feeding activity of fish larvae of two co-occurring species (Pisces: Hypophthalmidae and Sciaenidae) in a neotropical floodplain lake. Braz. J. Biol. 78, 763–772 (2018).CAS 
PubMed 

Google Scholar 
Cederlöf, U., Rydberg, L., Mgendi, M. & Mwaipopo, O. Tidal exchange in a warm tropical lagoon: Chwaka Bay, Zanzibar. Ambio 24, 458–464 (1995).
Google Scholar 
Gullström, M. et al. Assessment of changes in the seagrass-dominated submerged vegetation of tropical Chwaka Bay (Zanzibar) using satellite remote sensing. Estuar. Coast. Shelf Sci. 67, 399–408 (2006).ADS 

Google Scholar 
Gullström, M. et al. Seagrass meadows of Chwaka Bay: ecological, social and management aspects. In: de la Torre-Castro, M., Lyimo, T. J. (Eds) People, nature and research: past, present and future of Chwaka Bay, Zanzibar. ISBN: 978-9987-9559-1-6, Zanzibar Town: 89–109 (WIOMSA, 2012a)Gullström, M. et al. Connectivity and nursery function of shallow-water habitats in Chwaka Bay. In: de la Torre-Castro, M., Lyimo, T. J. (Eds) People, nature and research: past, present and future of Chwaka Bay, Zanzibar. ISBN: 978-9987-9559-1-6, Zanzibar Town: 175–192 (WIOMSA, 2012b)Rehren, J., Wolff, M. & Jiddawi, N. Holistic assessment of Chwaka Bay’s multi-gear fishery—using a trophic modeling approach. J. Mar. Syst. 180, 265–278 (2018).
Google Scholar 
Torell, E., Mmochi, A. & Palmigiano, K. Menai Bay Convernance Baseline. Coastal Resources Center, 1–18 (University of Rhode Island, 2006).Torell, E., Shalli, M., Francis, J., Kalangahe, B. & Munubi, R. Tanzania biodiversity threats assessment: Biodiversity threats and management opportunities for Fumba, Bagamoyo, and Mkuranga. 1–47 (University of Rhode Island, Narragansett, 2007).Jeyaseelan, M. J. P. Manual of fish eggs and larvae from Asian mangrove waters.193 (Paris: UNESCO Publishing, 1998).Mwaluma, J. M., Kaunda-Arara, B. & Strydom, N. A. A guide to commonly occurring larval stages of fishes in Kenyan Coastal Waters. WIOMSA Book Series No. 15. xvi + 73 (WIOMSA, 2014).Leis, J. M. & Carson-Ewart, B. M. (Eds.). The larvae of Indo-Pacific coastal fishes: an identification guide to marine fish larvae (Fauna Malesiana Handbooks 2), 804 (Brill, Leiden, 2000).Strickland, J. D. H. & Parsons, T. R. A practical handbook of seawater analysis, 2nd edn. Vol. 167. 21–26 (Bull. Fish. Res. Bd. Canada, 1972).Clarke, K. R. & Warwick, R. M. Change in Marine Communities: An Approach to Statistical Analysis and Interpretation (PRIMER-E). Plymouth Marine Laboratory, (Plymouth, UK, 2001). More