Philippa Kaur

Printzen, C. & Lumbsch, H. T. Molecular evidence for the diversification of extant lichens in the late cretaceous and tertiary. Mol. Phylogenet. Evol. 17, 379–387 (2000).CAS 
PubMed 
Article 

Google Scholar 
Kraichak, E. et al. A Tale of two Hyper-diversities: Diversification dynamics of the two largest families of lichenized fungi. Sci. Rep. https://doi.org/10.1038/srep100288 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Leavitt, S. D., Lumbsch, H. T., Stenroos, S. & Clair, L. L. S. Pleistocene speciation in North American lichenized fungi and the the impact of alternative species circumscriptions and rates of molecular evolution on divergence estimates. PLoS ONE 8, e85240 (2013).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Gaya, E. et al. The adaptive radiation of lichen-forming Teloschistaceae is associated with sunscreening pigments and bark-to-rock substrate shift. PNAS 112, 11600–11605 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Schneider, K., Resl, P. & Spribille, T. Escape from the cryptic species trap: Lichen evolution on both sides of a cyanobacterial acquisition event. Mol. Ecol. 25, 3453–3468 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Widhelm, T. J. et al. Oligocene origin and drivers for diversification in the genus Sticta (Lobariaceae, Ascomycota). Mol. Phylogenetic Evol. 126, 58–73 (2018).Article 

Google Scholar 
Vamosi, J. C. & Vamosi, S. M. Factors influencing diversification in angiosperms: At the crossroads of intrinsic and extrinsic traits. Am. J. Bot. 98, 460–471 (2011).PubMed 
Article 

Google Scholar 
Wagner, C. E., Harmon, L. J. & Seehausen, O. Ecological opportunity and sexual selection together predict adaptive radiation. Nature 487, 366–369 (2012).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Karunarathne, P. et al. Intraspecific ecological niche divergence and reproductive shifts foster cytotype displacement and provide ecological opportunity to polyploids. Ann. Bot. 121, 1183–1196 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Nakov, T., Beaulieu, J. & Alverson, A. Accelerated diversification is related to life history and locomotion in a hyperdiverse lineage of microbial eukaryotes (Diatoms, Bacillariophyta). New Phytol. 219, 462–473 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Tripp, E. A. Is asexual reproduction an evolutionary dead end in lichens?. Lichenologist 48, 559–580 (2016).Article 

Google Scholar 
Tripp, E. A. & Lendemer, J. C. Twenty-seven modes of reproduction in the obligate lichen symbiosis. Brittonia 70, 1–14 (2018).Article 

Google Scholar 
Bowler, P. A. & Rundell, P. W. Reproductive strategies in lichens. Bot. J. Linn. Soc. 70, 325–340 (1975).Article 

Google Scholar 
Honegger, R. Developmental biology of lichens. New Phytol. 125, 659–677 (1993).PubMed 
Article 

Google Scholar 
Buschbom, J. & Mueller, G. M. Testing “Species Pair” hypotheses: Evolutionary processes in the lichen-forming species complex Porpidia flavocoerulescens and Porpidia melinodes. Mol. Biol. Evol. 23, 574–586. https://doi.org/10.1093/molbev/msj063 (2006).CAS 
Article 
PubMed 

Google Scholar 
Sanders, W. B. Complete life cycle of the lichen fungus Calopadia puiggarii (Pilocarpaceae, Ascomycetes) documented in situ: Propagule dispersal, establishment of symbiosis, Thallus development, and formation of sexual and asexual reproductive structures. Am. J. Bot. 101, 1836–1848 (2014).PubMed 
Article 

Google Scholar 
Poelt, J. Flechtenflora und eiszeit in Europa. Phyton (Horn) 10, 206–214 (1963).
Google Scholar 
Stofer, S. et al. Species richness of lichen functional groups in relation to land use intensity. Lichenologist 38, 331–353 (2006).Article 

Google Scholar 
Ludwig, L. R., Summerfield, T. C., Lord, J. M. & Singh, G. Characterization of the mating-type locus (MAT) reveals a heterothallic mating system in Knightiella splachnirima. Lichenologist 49, 373–385 (2017).Article 

Google Scholar 
Czarnota, P. The lichen genus Micarea (Lecanorales, Ascomycota) in Poland. Pol. Bot. Stud. 23, 1–190 (2007).
Google Scholar 
Czarnota, P. & Guzow-Krzemińska, B. A phylogenetic study of the Micarea prasina group shows that Micarea micrococca includes three distinct lineages. Lichenologist 42, 7–21 (2010).Article 

Google Scholar 
Sérusiaux, E., Brand, A. M., Motiejūnaitè, J., Orange, A. & Coppins, B. J. Lecidea doliiformis belongs to Micarea, Catillaria alba to Biatora and Biatora lignimollis occurs in Western Europe. Bryologist 113, 333–344 (2010).Article 

Google Scholar 
van den Boom, P., Brand, A., Coppins, B. & Sérusiaux, E. Two new species in the Micarea prasina group from Western Europe. Lichenologist 49, 13–25 (2017).Article 

Google Scholar 
Guzow-Krzemińska, B., Czarnota, P., Łubek, A. & Kukwa, M. Micarea soralifera sp. nov., a new sorediate species in the M. prasina group. Lichenologist 48, 161–169 (2016).Article 

Google Scholar 
Guzow-Krzemińska, B. et al. Understanding the evolution of phenotypical characters in the Micarea prasina group (Pilocarpaceae) and descriptions of six new species within the group. MycoKeys 57, 1–30 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Kantvilas, G. & Coppins, B. J. Studies on Micarea in Australasia II. A synopsis of the genus in Tasmania, with the description of ten new species. Lichenologist 51, 431–481 (2019).Article 

Google Scholar 
Launis, A. & Myllys, L. Micarea fennica, a new lignicolous lichen species from Finland. Phytotaxa 409, 179–188 (2019).Article 

Google Scholar 
Launis, A., Pykälä, J., van den Boom, P., Sérusiaux, E. & Myllys, L. Four new epiphytic species in the Micarea prasina group from Europe. Lichenologist 51, 7–25 (2019).Article 

Google Scholar 
Launis, A. et al. Sharpening species boundaries in the Micarea prasina group, with a new circumscription of the type species M. prasina. Mycologia 111, 574–592 (2019).CAS 
PubMed 
Article 

Google Scholar 
van den Boom, P., Guzow-Krzemińska, B. & Kukwa, M. Two new Micarea species (Pilocarpaceae) from Western Europe. Plant Fungal Syst. 65, 189–199. https://doi.org/10.35535/pfsyst-2020-0014 (2020).Article 

Google Scholar 
Kantelinen, A., Hyvärinen, M., Kirika, P. & Myllys, L. Four new Micarea species from the montane cloud forests of Taita Hills, Kenya. Lichenologist 53, 81–94. https://doi.org/10.1017/S0024282920000511 (2021).Article 

Google Scholar 
Coppins, B. J. A taxonomic study of the lichen genus Micarea in Europe. Bull. Br. Mus. (Nat. Hist.) Bot. 11, 17–214 (1983).
Google Scholar 
Launis, A. & Myllys, L. Micarea byssacea new to North America and Micarea hedlundii new to Maine, Michigan and Quebec. Opusc. Philolichenum 13, 84–90 (2014).
Google Scholar 
Myllys, L. & Launis, A. Additions to the diversity of lichens and lichenicolous fungi living on decaying wood in Finland. Graphis Scr. 30, 78–87 (2018).
Google Scholar 
Yahr, R., Florence, A., Škaloud, P. & Voytsekhovich, A. Molecular and morphological diversity in photobionts associated with Micarea s. str. (Lecanorales, Ascomycota). Lichenologist 47, 403–414 (2015).Article 

Google Scholar 
Spribille, T., Thor, G., Bunnell, F. L., Goward, T. & Björk, C. R. Lichens on dead wood: Species-substrate relationships in the epiphytic lichen floras of the Pacific Northwest and Fennoscandia. Ecography 31, 741–750 (2008).Article 

Google Scholar 
Resl, P., Fernańdez-Mendoza, F., Mayrhofer, H. & Spribille, T. The evolution of fungal substrate specificity in a widespread group of crustose lichens. Proc. R. Soc. B 285, 20180640. https://doi.org/10.1098/rspb.2018.0640 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Stokland, J. N., Siitonen, J. & Jonsson, B. G. Biodiversity in Dead Wood 412 (Cambridge University Press, Cambridge, 2012).Book 

Google Scholar 
Russell, M. B., Woodall, C. W., Fraver, S. & D’Amato, A. W. Estimates of downed woody debris decay class transitions for forests across the eastern United States. Ecol. Model. 251, 22–31 (2013).Article 

Google Scholar 
Russell, M. B. et al. Residence times and decay rates of downed woody debris biomass/carbon in eastern US Forests. Ecosystems 17, 765–777 (2014).CAS 
Article 

Google Scholar 
Zoller, S., Lutzoni, F. & Scheidegger, C. Genetic variation within and among populations of the threatened lichen Lobaria pulmonaria in Switzerland and implications for its conservation. Mol. Ecol. 8, 2049–2059 (1999).CAS 
PubMed 
Article 

Google Scholar 
Honegger, R., Zippler, U., Gansner, H. & Scherrer, S. Mating systems in the genus Xanthoria (lichen forming Ascomycetes). Mycol. Res. 108, 480–488 (2004).CAS 
PubMed 
Article 

Google Scholar 
Honegger, R. & Zippler, U. Mating systems in representatives of the Parmeliaceae, Ramalinaceae and Physciaceae (Lecanoromycetes, lichen-forming ascomycetes). Mycol. Res. 11, 424–432 (2007).Article 
CAS 

Google Scholar 
Ament-Velásquez, S. L. et al. The plot thickens: Haploid and triploid-like thalli, hybridization, and biased mating Type Ratios in Letharia. Front. Fungal Biol. 2, 656386. https://doi.org/10.3389/ffunb.2021.656386 (2021).Article 

Google Scholar 
van den Boom, P. & Coppins, B. J. Micarea viridileprosa sp. nov., an overlooked lichen species from Western Europe. Lichenologist 33, 87–91 (2001).Article 

Google Scholar 
Simon, J.-C., Rispe, C. & Sunnucks, P. Ecology and evolution of sex in aphids. Trends Ecol. Evol. 17, 34–39 (2002).Article 

Google Scholar 
Silvertown, J. The evolutionary maintenance of sexual reproduction: Evidence from the ecological distribution of asexual reproduction in clonal plants. Int. J. Plant Sci. 169, 157–168 (2008).Article 

Google Scholar 
Gomez-Mestre, I., Pyron, R. A. & Wiens, J. J. Phylogenetic analyses reveal unexpected patterns in the evolution of reproductive modes in frogs. Evolution 66, 3687–3700. https://doi.org/10.1111/J.1558-5646.2012.01715.X (2012).Article 
PubMed 

Google Scholar 
Dańko, A., Schaible, R. & Dańko, M. J. Salinity effects on survival and reproduction of hydrozoan Eleutheria dichotoma. Estuaries Coasts 43, 360–374. https://doi.org/10.1007/s12237-019-00675-2 (2020).CAS 
Article 

Google Scholar 
Coppins, B. J. & Tønsberg, T. A new xanthone-containing Micarea from Northwest Europe and the Pacific Northwest of North America. Lichenologist 33, 93–96 (2001).Article 

Google Scholar 
Konoreva, L., Chesnokov, S., Kuznetsova, E. & Stepanchikova, I. Remarkable records of Micarea from the Russian Far East and significant extension of Micarea laeta and M. microareolata range. Botanica 25, 186–201 (2019).Article 

Google Scholar 
Weber, L., Printzen, C., Bässler, C. & Kantelinen, A. Seven Micarea (Pilocarpaceae) species new to Germany and notes on deficiently known species in the Bavarian forest. Herzogia 34, 5–17 (2021).Article 

Google Scholar 
van den Boom, P. Some interesting records of lichens and lichenicolous fungi from The Netherlands VI. Osten. Z. Pilzk. 12 (2003).Orange, A., James, P. W. & White, F. J. Microchemical Methods for the Identification of Lichens 101 (British Lichen Society, London, 2010).
Google Scholar 
Meyer, B. & Printzen, C. Proposal for a standardized nomenclature and characterization of insoluble lichen pigments. Lichenologist 32, 571–583 (2000).Article 

Google Scholar 
Culberson, C. F. & Kristinsson, H. D. A standardized method for the identification of lichen products. J. Chromatocraphy A 46, 85–93 (1970).CAS 
Article 

Google Scholar 
Myllys, L. et al. Phylogeny of the genus Bryoria. Lichenologist 43, 617–638 (2011).Article 

Google Scholar 
Myllys, L., Lohtander, K., Källersjö, M. & Tehler, A. Sequence insertion and ITS data provide congruent information in Roccella canariensis and R. tuberculata (Arthoniales, Euascomycetes) phylogeny. Mol. Phylogenetics Evol. 12, 295–309 (1999).CAS 
Article 

Google Scholar 
White, T. J., Bruns, T., Lee, S. & Taylor, J. W. Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics. In PCR Protocols: A Guide to the Methods and Applications (eds Innis, M. A. et al.) 315–322 (Academic Press, Cambridge, 1990).
Google Scholar 
Zoller, S., Scheidegger, C. & Sperisen, C. PCR primers for the amplification of mitochondrial small subunit ribosomal DNA of lichen-forming ascomycetes. Lichenologist 31, 511–516 (1999).Article 

Google Scholar 
Leavitt, S. D., Johnson, L. A., Goward, T. & Clair, L. L. S. Species delimitation in taxonomically difficult lichen-forming fungi: an example from morphologically and chemically diverse Xanthoparmelia (Parmeliaceae) in North America. Mol. Phylogenetics Evol. 60(3), 317–332 (2011).Article 

Google Scholar 
Schmitt, I. et al. New primers for promising single-copy genes in fungal phylogenetics and systematics. Persoonia 23, 35–40 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Edgar, R. C. MUSCLE: Multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797. https://doi.org/10.1093/nar/gkh340 (2004).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kauff, F. & Lutzoni, F. Phylogeny of the Gyalectales and Ostropales (Ascomycota, Fungi): Among and within order relationships based on nuclear ribosomal RNA small and large subunits. Mol. Phylogenet. Evol. 25, 138–156 (2002).CAS 
PubMed 
Article 

Google Scholar 
Ronquist, F. & Huelsenbeck, J. P. MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinformatics 19, 1572–1574 (2003).CAS 
PubMed 
Article 

Google Scholar 
Stamatakis, A. RAxML version 8: A tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Huelsenbeck, J. P., Larget, B. & Alfaro, M. E. Bayesian phylogenetic model selection using reversible jump markov chain monte carlo. Mol. Biol. Evol. 21, 1123–1133. https://doi.org/10.1093/molbev/msh123 (2004).CAS 
Article 
PubMed 

Google Scholar 
Rambaut, A., Drummond, A. J., Xie, D., Baele, G. & Suchard, M. A. Posterior Summarization in bayesian phylogenetics using tracer 1.7. Syst. Biol. 67, 901–904 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Maddison, D. R. & Maddison, W. P. Mesquite: a modular system for evolutionary analysis. Version 3.40 http://mesquiteproject.org (2018).Pagel, M. Detecting correlated evolution on phylogenies: A general method for the comparative analysis of discrete characters. Proc. R. Soc. B. 255, 37–45 (1994).ADS 
Article 

Google Scholar  More

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.This is a summary of: Quinton, J. N. et al. Tillage exacerbates the vulnerability of cereal crops to drought. Nat. Food https://doi.org/10.1038/s43016-022-00533-8 (2022). More

Study siteThe study was conducted at the Center for Applied Research on the Environment and Sustainability (CARES) at The American University in Cairo, New Cairo, Egypt (30°01′11.7″N 31°29′59.8″E) from 12/Nov/2019 until 31st/March/2020. The experiment was carried out in a greenhouse-controlled environment with temperatures ranging from 18 to 23 °C and relative humidity between 60 and 70% during the growing period.Experimental designThe proposed design starts by treating brackish water using RO membrane separation technology, powered by an on-grid 10 kW photovoltaic solar panel as shown in Fig. 1. The permeate (freshwater) from the RO facility is directed to the aquaculture units of capacity of 1 m3, where the fish effluents are used as irrigation water and as the sole source of fertilizers for the crops.Figure 1Schematic Integrated model design. T1 Deep water culture system without sand, T2 Sandponics system with sand from October, T3 Sandponics system with sand from Beni suef, T4 Sandponics system with sand from Fayoum.Full size imageThe study followed a completely randomized design with four variants, i.e., an aquaponic deep-water culture system (T1) and three sandponics systems (T2–T4). The three sandponics systems were established with different sand collected from different sand locations in Egypt during the period between September and October 2019.Initially, an exploratory field trip was set to six different locations in Egypt to collect sand samples for lab analysis aimed at sourcing the most suitable sand for the system under study with regards to both the physical and chemical parameters. These areas include Ismailia Governorate; 30°34′55.2″N 31°50′08.1″E, 6th October governorate; 29°54′49.8″N 31°05′51.5″E, Benu Suef governorate; 28°53′18.4″N 30°45′12.9″E, Al-Minya governorate; 28.725799, 30.630305, and two sites from Fayoum governorate; 29°05′07.4″N 30°49′39.9″E.From the six locations in Egypt, preliminary sand analysis was carried out, and sand samples were also collected for both physical and chemical lab analysis at the Soil and Water Lab at the Agricultural Research Center in Dokki, Egypt. Following a thorough technical, field, mechanical, and lab chemical evaluation of the six sand samples from six locations, three sand locations/types were selected for experimentation that seemed fit and suitable for the current study. The criteria parameters for the shortlisting of sand included water retention potential of the sand by the percolation process, testing the carbonates level in the soil, the turbidity of the sand, porosity percentage and drainage potential of the sand. The three locations included 6th October (T2), Benu Suef (T3), and Fayoum site 2 (T4). In the second week of November 2019, ten cubic meter tracks of sand from the three above locations were set to collect sand from these areas to the research facility at CARES where the experiment was carried out.The study was carried out with two systems/setups, i.e., an aquaponic Deep Water Culture (DWC) and SP systems. The DWC model comprises a 1 m3 fish tank, a settlement tank, a mechanical filter, a biological filter, three grow beds, and a drainage tank. This system being the most practiced aquaponics technique was considered as the control. Fish effluent water flowed from the fish tank to the settlement tank to filter big solid wastes through the mechanical filter to remove the smaller solid wastes and the biological filter for the nitrification process. Then filtered water continues to the grow beds, where overflow drains into the drainage tank and back to the fish tank in a closed system.On the other hand, the variable in the three IAVS systems is the sand source. This system comprises three independent set-ups: a 1 m3 fish tank, three grow beds, and a drainage tank. Fish effluents flowed from the fish tank directly to the sand grow beds where water was supplied through irrigation drip lines using diaghram emitters connected with valves to ensure uniformity of water application to each grow bed.All the fish tanks were installed with the same fish stock size of 30 Nile tilapia (Oreochromis niloticus) from an existing fish stock at the research center with an average initial weight of 244 g and the same amount of water, initially 850L per tank. The fish was sourced from an already existing aquaponics system at the research center to avoid any transportation stress effects and related shocks on the small fish, leading to a lot of mortality cases. The fish were fed 3–4 times daily with commercial pellets containing 30% proteins, 5% crude lipid, 6% crude fiber, 13% Ash, and 9% moisture content supplied by Skretting Egypt. The feeding pattern and frequency were according to the fish body biomass percentage of 2–3% depending on the growth stage and upon reaching satiation.DesalinationThe experiment was entirely run with desalinated water produced from a desalination facility at the center. The desalination technology used was Reverse Osmosis (RO); in batch mode; using a Sea Water Pump with Energy Recovery Unit (model Danfoss-APP1.0/APM1.2). The RO membrane used is Hydraunatic SWC5-4040, from Lenntech company with an average salt rejection of 99.7%. Three modules were connected in a series arrangement (3 Pressure Vessels each equipped with a single module). Synthesized brackish water was prepared by dissolving industrial grade sodium chloride (sea salt) from El-Arish Governorate, Egypt. The salt chemical properties are presented in Table 1. Feedwater salinity was 10 mg/L, with an equivalent osmotic pressure equal to 8.61 bars. The osmotic pressure was calculated using Van’t Hoff relation. Permeate Total Dissolved Solids (TDS) was 192 mg/L, and brine TDS was 13.1 g/L as shown in Table 2.Table 1 Chemical properties of the used salt.Full size tableTable 2 Chemical properties of water samples used.Full size tableThe average pure water flux is 9.5 LMH and was calculated by dividing the permeate volume by the product of membrane surface area and time. Each batch run produced around 4 m3 of permeate, which was enough to irrigate the designated plant beds. The estimated average permeate recovery for the RO process is 22% and salt rejection exceeded 98.7%. The differential pressure between membrane inlet and outlet was equal to 1 bar, where membrane inlet pressure was 16 bars, and the outlet was 15 bars. The RO process operated at an average transmembrane pressure equal to 16 bars and an average permeate and brine flow rates equivalent to 3.49 and 12.41 Lpm, respectively. All experiment runs were performed at 25 °C.Plant materials and cultivation practiceSwiss chard bright lights (Beta vulgaris subsp. cicia) seeds were imported from Seed kingdom seed company in the USA. Seeds were sown in ¼ inch holes in a seed starting mix containing perlite and vermiculite and irrigated with a hand mist sprayer daily to keep the growing media always moist. Sowing was done on the 12th of November 2019, and seedlings were transplanted when they were 40 days old. Seedlings were transplanted into raised grow beds made of fiberglass material measuring 1.8 × 1.2 × 0.6 m for each of the four systems. The beds were raised off the ground by 0.5 m to allow drainage water from the bed to be collected and circulated back to the fish tank. Each bed was constructed with a drainage pipe at the bottom covered with a mesh net to prevent water blockage by the sand. Also, a 5 cm layer of small gravel was uniformly laid at the bottom of the beds to facilitate drainage, followed by sand with a height of 50 cm.In the IAVS systems, plants were irrigated using manually punched diaphragm emitters, and the irrigation flow rate was controlled using small plastic valves at the start of every irrigation tube. Emitters were installed in drip tubing at a 30 cm distance as well the tubing lines were also placed 30 cm between each other. Seedlings were transplanted 5 cm away from the emitters at 30 cm between rows and 30 cm within the row. Since the water was pumped with submersible pumps to the grow beds, regulatory pressure valves were installed in between the pump and the main irrigation line, and then water flows through the emitters into the row furrows. Water would then saturate in the sand and eventually drain at the bottom into drainage tanks and pumped back to the fish tanks.To maintain the water quality, two full cycles of water recirculation were run every day. Every irrigation cycle recirculated 25% of the fish tank, and complete drainage was allowed for a maximum of two hours. Plants were harvested upon reaching maturity for three cuts, except with the T1, which could not grow back after the second cut. Plants took 52 days from transplanting to reach the first cut, 20 days from cut 1 to cut 2, and as well 23 days from cut 2 to reach cut 3. Measurable crop parameters included plant height at harvesting/cutting, leaf area, number of leaves per plant, chlorophyll content, fresh weight per plant, and nutrient composition. Since the focus of SP is on the crops, fish were only measured to monitor their relative growth in terms of weight gained at harvesting/cutting time.Measurement of crop parametersPlants were cut 5 cm above the soil surface, and agronomical trait measurements from a representative sample of 12 plants per replicate were taken as follows.Plant heights were taken using a foot ruler and averages determined. Leaf number was obtained as the number of leaves counted per plant and averages determined. Leaf area was calculated according to the equation reported by Yeshitila and Taye16.$${text{Leaf}} , {text{ Area }}left( {{text{cm}}^{{2}} } right) = , – {422}.{973} + { 22}.{752}0{text{L }}left( {{text{cm}}} right) , + { 8}.{text{31W }}left( {{text{cm}}} right)$$where L and W represent the leaf length and Leaf width respectively, − 422.973 is a constant relating to the shape of the leaf of Swiss chard developed by the author under citation.Chlorophyll content was measured using MC-100 chlorophyll meter from Apogee Instruments, Inc, and data was expressed as SPAD averages. Fresh weight was measured using a digital weighing balance and data expressed as g/plant.Sand testSand samples were obtained and sent for analysis at the Soils, Water and Environment Research Institute, Agricultural Research Center, Giza, Egypt. The Electrical conductivity (EC) values were measured from the sand paste extract; pH values were taken from sand suspensions at ratio of 1:2.5 as described by Estefan17. The available nitrogen in the sand sample was extracted using potassium chloride (KCl) as an extractable solution with the ratio of (5gm sand to 50 ml KCl) and determined using the micro- kjeldahl method. Available potassium was determined using a flame photometer, and the other elements in the sand sample were determined by using inductively coupled plasma (ICP) Spectrometry (model Ultima 2 JY Plasma)18,19. The physical and chemical characteristics of the used sand are presented in Table 3.Table 3 (a): Chemical analysis of field sand samples, (b): Available macro, micronutrients, and heavy metals content of the sand samples.Full size tableWater analysisEvery 15 days, a measured amount of desalinated water was added to a standard mark of 850L in the fish tanks to compensate for the consumed amount of water in the system. Fish water quality parameters such as water temperature, pH, and dissolved oxygen (DO) was closely monitored using automated digital Nilebot technologies by Conative labs to fit the ideal required levels as reported by Somerville et al.20. In contrast, ammonia, nitrite, and nitrate were adjusted using an API test kit every week. These parameters’ recorded values were as follows: water temperature ranged between 25 and 28 °C, DO range between 6–7 mg/L, and pH between 6.5 and 7.0. Ammonia levels were kept below 1 mg/L. Elements in water samples were determined according to EPA methods18 using inductively coupled plasma (ICP) Spectrometry (model Ultima 2 JY Plasma) as presented in Table 4.Table 4 Water sample analysis for the different systems’ fish tanks and sump tanks.Full size tableNutritive composition analysisAccording to Official methods of analysis from the association of official analytical chemists (A.O.A.C) (1990), moisture content and Vitamin C were determined. Vitamin A was determined according to the procedures described by Aremu and Nweze21. Briefly, 100 g of the sample were homogenized, from which 1 g was obtained and soaked in 5 mL methanol for two hours at room temperature in the dark for complete extraction of a pro-vitamin A carotenoid, β-carotene. Separation of the β-carotene layer was achieved through the addition of hexane to the sample, and moisture was removed using sodium sulphonate. The absorbance of the layer was measured at 436 nm using hexane as a blank. β-carotene was calculated using the formula:$$beta {text{-carotene }}left( {{mu g}/{1}00{text{ g}}} right) , = {text{ Absorbance }}left( {text{436 nm}} right) , times {text{ V }} times {text{ D }} times { 1}00 , times { 1}00/{text{W }} times {text{ Y}}$$where: V = total volume of the extract; D = Dilution factor; W = Sample weight; Y = Percentage dry matter content of the sample.Vitamin A was then determined according to the concept of Retinol Equivalent (RE) of the β-carotene content of the vegetables using the standard conversion formula. Total hydrolyzable carbohydrates were determined as glucose using phenol–sulfuric acid reagent as described by Michel22.Vitamin C content was determined using dichlorophenol indophenol reagent. As such, 10 g of fresh leaf tissues, were crushed using a motor and pestle in the presence of 10 ml metaphosphoric acid 6% (Merck). This was followed by centrifugation at 4000×g for 5 min at 4 °C. Five mL of the supernatant were transferred into an Erlenmeyer flask, and 20 mL of 3% metaphosphoric acid were added. The extract was titrated by dichlorophenol indophenol (Sigma-Aldrich) until a rose color was observed. Vitamin C (mg/100 g FW) was then calculated and based on the standard curve of l-Ascorbic acid (Merck) concentrations.For the determination of protein and mineral content, 0.5 g of dried samples were digested using sulfuric acid (H2SO4) and hydrogen peroxide (H2O2) as described by Cottenie23. From the extracted sample, the following minerals were determined:Nitrogen was determined according to the procedures described by Plummer24. Briefly, 5 mL of the digestive solution was distilled with 10 mL of sodium hydroxide (NaOH) for 10 min to obtain ammonia. Back titration was then used to determine the amount of nitrogen present in ammonia. Protein content was calculated by multiplying total nitrogen by 6.25 according to methods of AOAC25.Phosphorus content was determined calorimetrically (660 nm) according to the procedures described by Jackson26. Potassium, Calcium, and Sodium were determined against a standard using a flame-photometer (JEN way flame photometer) as described by Piper27. Magnesium (Mg), Copper (Cu), Manganese (Mn), Zinc (Zn), and Iron (Fe) content were determined using Atomic Absorption Spectrophotometer, Pyeunican SP1900, according to methods described by Liu28.The moisture percentage of leaf samples was determined by weighing the fresh weight for each sample (Fw), then dried for 72 h at 80 °C. The dry matter weight was record as Dw. The leaf water content was then calculated as the following:$${text{Moisture}};{text{ content }}left( % right) , = , left( {{text{Fw}} – {text{Dw}}} right) , /{text{ Fw}} * {1}00$$Statistical analysisStatistical comparisons among means of more than two groups were performed with analysis of variance (ANOVA) using SPSS V22, and the difference in means was analyzed by Tukey’s test at α = 0.05. Statistical differences were considered significant at P ≤ 0.05 in triplicates and data expressed as mean ± S.D.Plant materialAll plant materials and related procedures in this study were done in accordance with the guidelines of the Institutional Review Board of the American University in Cairo and the Ministry of Agriculture and Land Reclamation in Egypt.Ethics approvalThis study followed the guidelines and approval of Committee of Animal Welfare and Research Ethics, Faculty of Agriculture, Kafrelsheikh University, Egypt. More

Zou, K. et al. eDNA metabarcoding as a promising conservation tool for monitoring fish diversity in a coastal wetland of the Pearl River Estuary compared to bottom trawling. Sci. Total Environ. 702, 134704 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Almond, R., Grooten, M. & Peterson, T. Living Planet Report 2020-Bending the Curve of Biodiversity Loss (World Wildlife Fund, 2020).
Google Scholar 
Beverton, R. Fish resources; threats and protection. Neth. J. Zool. 42, 139–175 (1991).Article 

Google Scholar 
Jackson, S. & Head, L. Australia’s mass fish kills as a crisis of modern water: Understanding hydrosocial change in the Murray-Darling Basin. Geoforum 109, 44–56 (2020).Article 

Google Scholar 
Rees, H. C. et al. REVIEW: The detection of aquatic animal species using environmental DNA—a review of eDNA as a survey tool in ecology. J. Appl. Ecol. 51, 1450–1459 (2014).CAS 
Article 

Google Scholar 
Rees, H. C. et al. The application of eDNA for monitoring of the Great Crested Newt in the UK. Ecol. Evol. 4, 4023–4032 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Wang, C. et al. Research on the biodiversity of Qinhuai River based on environmental DNA metabacroding. Acta Ecol. Sin. 42, 611–624 (2022).Article 

Google Scholar 
Deiner, K., Walser, J.-C., Mächler, E. & Altermatt, F. Choice of capture and extraction methods affect detection of freshwater biodiversity from environmental DNA. Biol. Cons. 183, 53–63 (2015).Article 

Google Scholar 
Thomsen, P. F. et al. Monitoring endangered freshwater biodiversity using environmental DNA. Mol. Ecol. 21, 2565–2573 (2012).CAS 
PubMed 
Article 

Google Scholar 
Miralles, L., Parrondo, M., Hernandez de Rojas, A., Garcia-Vazquez, E. & Borrell, Y. J. Development and validation of eDNA markers for the detection of Crepidula fornicata in environmental samples. Mar. Pollut. Bull. 146, 827–830 (2019).CAS 
PubMed 
Article 

Google Scholar 
Takahara, T., Minamoto, T., Yamanaka, H., Doi, H. & Kawabata, Z. Estimation of fish biomass using environmental DNA. PLoS ONE 7, e35868 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Aglieri, G. et al. Environmental DNA effectively captures functional diversity of coastal fish communities. Mol. Ecol. 30, 3127–3139 (2020).PubMed 
Article 

Google Scholar 
Yang, H. et al. Effectiveness assessment of using riverine water eDNA to simultaneously monitor the riverine and riparian biodiversity information. Sci. Rep. 11, 24241 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Altermatt, F. et al. Uncovering the complete biodiversity structure in spatial networks: the example of riverine systems. Oikos 129, 607–618 (2020).Article 

Google Scholar 
Stat, M. et al. Combined use of eDNA metabarcoding and video surveillance for the assessment of fish biodiversity. Conserv. Biol. 33, 196–205 (2019).PubMed 
Article 

Google Scholar 
Hallam, J., Clare, E. L., Jones, J. I. & Day, J. J. Biodiversity assessment across a dynamic riverine system: A comparison of eDNA metabarcoding versus traditional fish surveying methods. Environ. DNA 3, 1247–1266 (2021).Article 

Google Scholar 
Gao, W. Beijing Vertebrate Key (Beijing Publishing House, 1994).
Google Scholar 
Wang, H. Beijing Fish and Amphibians and Reptiles (Beijing Publishing House, 1994).
Google Scholar 
Chen, W., Hu, D. & Fu, B. Research on Biodiversity of Beijing Wetland (Science Press, 2007).
Google Scholar 
Zhang, C. et al. Fish species diversity and conservation in Beijing and adjacent areas. Biodivers. Sci. 19, 597–604 (2011).Article 

Google Scholar 
Yamamoto, S. et al. Environmental DNA metabarcoding reveals local fish communities in a species-rich coastal sea. Sci. Rep. 7, 40368 (2017).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Shaw, J. L. A. et al. Comparison of environmental DNA metabarcoding and conventional fish survey methods in a river system. Biol. Cons. 197, 131–138 (2016).Article 

Google Scholar 
Fu, M., Xiao, N., Zhao, Z., Gao, X. & Li, J. Effects of Urbanization on Ecosystem Services in Beijing. Res. Soil Water Conserv. 23, 235–239 (2016).
Google Scholar 
Hao, L. & Sun, G. Impacts of urbanization on watershed ecohydrological processes: progresses and perspectives. Acta Ecol. Sin. 41, 13–26 (2021).
Google Scholar 
Su, G. et al. Human impacts on global freshwater fish biodiversity. Science 371, 835–838 (2021).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Yan, B. et al. Effects of urban development on soil microbial functional diversity in Beijing. Res. Environ. Sci. 29, 1325–1335 (2016).CAS 

Google Scholar 
Xiao, N., Gao, X., Li, J. & Bai, J. Evaluation and Conservation Measures of Beijing Biodiversity (China Forestry Publishing House, 2018).
Google Scholar 
Xu, S., Wang, Z., Liang, J. & Zhang, S. Use of different sampling tools for comparison of fish-aggregating effects along horizontal transect at two artificial reef sites in Shengsi. J. Fish. China 40, 820–831 (2016).
Google Scholar 
Miya, M. et al. MiFish, a set of universal PCR primers for metabarcoding environmental DNA from fishes: detection of more than 230 subtropical marine species. R. Soc. Open Sci. 2, 150088 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zhang, J., Kobert, K., Flouri, T. & Stamatakis, A. PEAR: a fast and accurate Illumina Paired-End reAd mergeR. Bioinformatics (Oxford, England) 30, 614–620 (2014).CAS 
Article 

Google Scholar 
Chen, S., Zhou, Y., Chen, Y. & Gu, J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics (Oxford, England) 34, 884–890 (2018).Article 
CAS 

Google Scholar 
Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics (Oxford, England) 26, 2460–2461 (2010).CAS 
Article 

Google Scholar 
Callahan, B. J., McMurdie, P. J. & Holmes, S. P. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J. 11, 2639–2643 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Iwasaki, W. et al. MitoFish and MitoAnnotator: A mitochondrial genome database of fish with an accurate and automatic annotation pipeline. Mol. Biol. Evol. 30, 2531–2540 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wang, H. Beijing Fish Records (Beijing Publishing House, 1984).
Google Scholar 
Du, L. et al. Fish community characteristics and spatial pattern in major rivers of Beijing City. Res. Environ. Sci. 32, 447–457 (2019).
Google Scholar 
Shen, W. & Ren, H. TaxonKit: A practical and efficient NCBI taxonomy toolkit. J. Genet. Genomics 48, 844–850 (2021).PubMed 
Article 

Google Scholar 
Karr, J. R. Assessment of biotic integrity using fish communities. Fisheries 6, 21–27 (1981).Article 

Google Scholar 
Zhang, C. & Zhao, Y. Fishes in Beijing and Adjacent Areas (China. Science Press, 2013).
Google Scholar 
Wu, H. & Zhong, J. Fauna Sinica, Osteichthyes, Perciformess(Five),Gobioidei (Science Press, 2008).
Google Scholar 
Di, Y. et al. Distribution of fish communities and its influencing factors in the Nansha and Beijing sub-center reaches of the Beiyun River. Acta Sci. Circumst. 41, 156–163 (2020).
Google Scholar 
Walters, D. M., Freeman, M. C., Leigh, D. S., Freeman, B. J. & Pringle, C. M. in Effects of Urbanization on Stream Ecosystems Vol. 47 American Fisheries Society Symposium 69–85 (2005).Hu, X., Zuo, D., Liu, B., Huang, Z. & Xu, Z. Quantitative analysis of the correlation between macrobenthos community and water environmental factors and aquatic ecosystem health assessment in the North Canal River Basin of Beijing. Environ. Sci. 43, 247–255 (2022).
Google Scholar 
Kadye, W. T., Magadza, C. H. D., Moyo, N. A. G. & Kativu, S. Stream fish assemblages in relation to environmental factors on a montane plateau (Nyika Plateau, Malawi). Environ. Biol. Fishes 83, 417–428 (2008).Article 

Google Scholar 
Smith, T. A. & Kraft, C. E. Stream fish assemblages in relation to landscape position and local habitat variables. Trans. Am. Fish. Soc. 134, 430–440 (2005).Article 

Google Scholar 
Blabolil, P. et al. Environmental DNA metabarcoding uncovers environmental correlates of fish communities in spatially heterogeneous freshwater habitats. Ecol. Ind. 126, 107698 (2021).CAS 
Article 

Google Scholar 
Xie, R. et al. eDNA metabarcoding revealed differential structures of aquatic communities in a dynamic freshwater ecosystem shaped by habitat heterogeneity. Environ. Res. 201, 111602 (2021).CAS 
PubMed 
Article 

Google Scholar 
Qu, C. et al. Comparing fish prey diversity for a critically endangered aquatic mammal in a reserve and the wild using eDNA metabarcoding. Sci. Rep. 10, 16715 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pont, D. et al. Environmental DNA reveals quantitative patterns of fish biodiversity in large rivers despite its downstream transportation. Sci. Rep. 8, 10361 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Doble, C. J. et al. Testing the performance of environmental DNA metabarcoding for surveying highly diverse tropical fish communities: A case study from Lake Tanganyika. Environ. DNA 2, 24–41 (2020).Article 

Google Scholar 
Xu, N. et al. Monitoring seasonal distribution of an endangered anadromous sturgeon in a large river using environmental DNA. Sci. Nat. 105, 62 (2018).Article 
CAS 

Google Scholar 
Laramie, M. B., Pilliod, D. S. & Goldberg, C. S. Characterizing the distribution of an endangered salmonid using environmental DNA analysis. Biol. Cons. 183, 29–37 (2015).Article 

Google Scholar 
Harper, L. R. et al. Development and application of environmental DNA surveillance for the threatened crucian carp (Carassius carassius). Freshw. Biol. 64, 93–107 (2019).CAS 
Article 

Google Scholar 
Ushio, M. et al. Quantitative monitoring of multispecies fish environmental DNA using high-throughput sequencing. Metabarcoding Metagenomics 2, e2329 (2018).
Google Scholar 
Evans, N. T. et al. Quantification of mesocosm fish and amphibian species diversity via environmental DNA metabarcoding. Mol. Ecol. Resour. 16, 29–41 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V. & Egozcue, J. J. Microbiome datasets are compositional: and this is not optional. Front. Microbiol. 8, 2224 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Harrison, J. B., Sunday, J. M. & Rogers, S. M. Predicting the fate of eDNA in the environment and implications for studying biodiversity. Proc. Biol. Sci. 286, 20191409 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kelly, R. P., Shelton, A. O. & Gallego, R. Understanding PCR processes to draw meaningful conclusions from environmental DNA studies. Sci. Rep. 9, 12133 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Civade, R. et al. Spatial representativeness of environmental DNA metabarcoding signal for fish biodiversity assessment in a natural freshwater system. PLoS ONE 11, e0157366 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Barnes, M. A. et al. Environmental conditions influence eDNA persistence in aquatic systems. Environ. Sci. Technol. 48, 1819–1827 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Shogren, A. J. et al. Water flow and biofilm cover influence environmental DNA detection in recirculating streams. Environ. Sci. Technol. 52, 8530–8537 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Zhao, B., van Bodegom, P. M. & Trimbos, K. The particle size distribution of environmental DNA varies with species and degradation. Sci. Total Environ. 797, 149175 (2021).ADS 
CAS 
PubMed 
Article 

Google Scholar  More

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.This is a summary of: Van der Stocken, T. et al. Mangrove dispersal disrupted by projected changes in global seawater density. Nat. Clim. Change https://doi.org/10.1038/s41558-022-01391-9 (2022). More

Mangrove forests thrive along tropical and subtropical shorelines and their distribution extends to warm temperate regions1. They are globally recognized for the valuable ecosystem services they provide2 but are expected to be substantially influenced by climate change-related physical processes in the future3,4. Under warming winter temperatures, poleward expansion is predicted for mangroves5,6, with potential implications for ecosystem structure and functioning, as well as human livelihoods and well-being7,8. The global distribution, abundance and species richness of mangroves is governed by a broad range of biotic and environmental factors, including temperature and precipitation9 and diverse geomorphological and hydrological gradients10. Climate and aspects related to coastal geography (for example, floodplain area) determine the availability of suitable habitat for establishment11,12. However, the potential for mangroves to track changing environmental conditions and expand their distributions ultimately depends on dispersal11,13. The importance of dispersal in controlling mangrove distributions has been demonstrated by mangrove distributional responses to historical climate variability14, past mangrove (re)colonization of oceanic islands15 and from the long-term survival of mangrove seedlings planted beyond natural range limits16. As such, quantifying changes in the factors that influence dispersal is important for understanding climate-driven distributional responses of mangroves under future climate conditions.In mangroves, dispersal is accomplished by buoyant seeds and fruits (hereafter referred to as ‘propagules’). In combination with prevailing currents, the spatial scale of this process, ranging from local retention to transoceanic dispersal over thousands of kilometres13, is determined by propagule buoyancy17, that is, the density difference between that of propagules and the surrounding water. Hence, the course of dispersal trajectories for propagules from these species depends on the interaction between spatiotemporal changes in both propagule density and that of the surrounding water, rendering this process sensitive to climate-driven changes in coastal and open-ocean water properties. The biogeographic implications of such density differences were recognized more than a century ago by Henry Brougham Guppy, who discussed18 ‘the far-reaching influence on plant-distribution and on plant-development that the relation between the specific weight of seeds and fruits and the density of sea-water must possess’.Since the time of Guppy’s early observations, climate change from human activities has driven pronounced changes in ocean temperature and salinity, with further changes predicted throughout the twenty-first century19. Ocean density is a nonlinear function of temperature, salinity and pressure20; therefore, these changes may influence dispersal patterns of mangrove propagules by altering their buoyancy and floating orientation. As Guppy noted18, ‘[for] plants whose seeds or fruits are not much lighter than seawater […] the effect of increased density of the water is to extend the flotation period’ or ‘to increase the number that floated for a given period’. Guppy also reported that the seedlings of the widespread mangrove genera Rhizophora and Bruguiera present exceptional examples of propagules with densities somewhere between seawater and freshwater18. Previous studies of the impacts of climate change on mangroves have focused on factors such as sea level rise, altered precipitation regimes and increasing temperature and storm frequency4,21,22,23 but the potential impact of climate-driven changes in seawater properties on mangroves has not yet been examined. This is somewhat surprising, as the ocean is the primary dispersal medium of this ‘sea-faring’ coastal vegetation and dispersal is a key process that governs a species’ response to climate change by changing its geographical range. This knowledge gap contrasts with recent efforts to expose links between climate change and dispersal in other ecologically important marine taxa such as zooplankton and fish species24,25,26,27.In this study, we investigate predicted changes in sea surface temperature (SST), sea surface salinity (SSS) and sea surface density (SSD) for coastal waters bordering mangrove forests (hereafter referred to as ‘coastal mangrove waters’), over the next century. Using a biogeographic classification system for coastal and shelf areas28, we examine spatiotemporal changes in these surface ocean properties, with a particular focus on the world’s two major mangrove diversity hotspots: (1) the Atlantic East Pacific (AEP) region, including all of the Americas, West and Central Africa and (2) the Indo West Pacific (IWP) region, extending from East Africa eastwards to the islands of the central Pacific1. Finally, we synthesize available data on the density of mangrove propagules for different mangrove species and explore the potential impact of climate-driven changes in SSD on propagule dispersal.To assess changes in SST and SSS throughout the global range of mangrove forests, we used present (2000–2014) and future (2090–2100) surface ocean properties from the Bio-ORACLE database29,30. SSD estimates were derived from these variables using the UNESCO EOS-80 equation of state polynomial for seawater31. Changes in SST, SSS and SSD (Fig. 1) were calculated for four representative concentration pathways (RCPs) and derived for coastal waters closest to the 583,578 polygon centroids from the 2015 Global Mangrove Watch (GMW) database32. After removing duplicates, our dataset contained 10,108 unique mangrove occurrence locations, with corresponding present conditions and predicted future changes in mean SST, SSS and SSD. Under the low-warming scenario RCP 2.6, mean SST of coastal mangrove waters is predicted to change by +0.64 (±0.11) °C and mean SSS by −0.06 (±0.25) practical salinity units (PSU). Combined, this results in an average change in mean SSD of −0.25 (±0.20) kg m−3 in coastal mangrove waters by the late twenty-first century (Supplementary Table 1). These values roughly double under RCP 4.5 (Supplementary Table 2), while under RCP 6.0, a change of +1.69 (±0.14) °C in mean SST, −0.21 (±0.42) PSU in mean SSS and −0.71 (±0.32) kg m−3 in mean SSD is predicted (Supplementary Table 3). Under RCP 8.5, our study predicts a change in SST of +2.84 (±0.21) °C (range 2.11–4.01 °C), a change in SSS of −0.30 (±0.74) PSU (−2.01–1.26 PSU) and a corresponding change in SSD of −1.17 (±0.56) kg m−3 (−2.53–0.03 kg m−3) (Supplementary Table 4).Fig. 1: Global map showing the change in sea surface variables across mangrove bioregions under RCP 8.5.a–c, Change in SST (a), SSS (b) and SSD (c). Changes in SST and SSS are based on present-day (2000–2014) and future (2090–2100) marine fields from the Bio-ORACLE database29,30, from which SSD data were derived. The vertical line (19° E) separates the two major mangrove bioregions: the AEP and IWP.Full size imageSpatial variability in predicted surface ocean property changes was examined by considering the two major mangrove bioregions (AEP and IWP) (Fig. 2) and using the Marine Ecoregions of the World (MEOW) biogeographic classification28 (Fig. 3). Both the range and changes in mean SST were comparable for the AEP and IWP mangrove bioregions, for all respective RCP scenarios (Fig. 2a and Supplementary Tables 1–4). Under RCP 8.5, mean SST in both mangrove bioregions is predicted to warm ~2.8 °C by 2100, which is roughly 4.5 times the predicted increase in mean SST under RCP 2.6 (Supplementary Tables 1 and 4). Predictions for the RCP 8.5 scenario are generally consistent with reported global ocean temperature trends33 and show that the greatest warming occurs in coastal waters near the Galapagos Islands (change in mean SST of 3.92 ± 0.06 °C). Pronounced SST increases are also predicted for Hawaii (change in mean SST of 3.36 ± 0.05 °C), the Southeast Australian Shelf (3.30 ± 0.25 °C), Northern and Southern New Zealand (3.25 ± 0.07 °C and 3.34 ± 0.02 °C, respectively), Warm Temperate Northwest Pacific (3.27 ± 0.16 °C), the Red Sea and Gulf of Aden (3.24 ± 0.08 °C), Somali/Arabian Coast (3.23 ± 0.15 °C), South China Sea (3.07 ± 0.10 °C), the Tropical East Pacific (3.09 ± 0.15 °C) and the Warm Temperate Northwest Atlantic (3.14 ± 0.13 °C) (Fig. 3b and Supplementary Tables 4).Fig. 2: Change in surface ocean properties for coastal waters bordering mangrove forests and in the two major mangrove bioregions, the AEP and IWP, for different RCPs.a–c, Variation in SST (a), SSS (b) and SSD (c) under various RCP scenarios. Grey indicates global distribution (n = 10,108), orange denotes AEP (n = 3,190) and green represents IWP (n = 6,918). Data for SST and SSS consist of present-day (2000–2014) and future (2090–2100) marine fields from the Bio-ORACLE database29,30, from which SSD data were derived. The cat-eye plots50 show the distribution of the data. Median and mean values are indicated with black and white circles, respectively, and the vertical lines represent the interquartile range.Full size imageFig. 3: Global spatial variability in SST, SSS and SSD for coastal waters bordering mangrove forests under RCP 8.5.a, Global map showing the provinces (colour code and numbers) from the MEOW database28 used to investigate spatial patterns in mangrove coastal ocean water changes by 2100. b–d, Longitudinal gradient of the change in SST (b), SSS (c) and SSD (d) under RCP 8.5 in the AEP and the IWP mangrove bioregions; circles are coloured according to the MEOW province in which respective mangrove sites are located.Full size imagePredicted SSS changes exhibit an opposite trend in the AEP and IWP bioregions, with increased salinity in the AEP and reduced salinity in the IWP under global warming (RCP 2.6–RCP 8.5; Fig. 2b); this is reflected in contrasting SSD changes in both mangrove bioregions (Fig. 2c) and associated with predicted global changes in precipitation, with extensions of the rainy season over most of the monsoon domains, except for the American monsoon34. Under RCP 8.5, the spatially averaged change in mean SSS is +0.51 (±0.57) PSU in the AEP and −0.68 (±0.44) PSU in the IWP region. The maximum decrease in mean SSS (−2.01 PSU) is predicted for the Gulf of Guinea in the AEP bioregion (Fig. 3c and Supplementary Table 4). Within the IWP, the Western Indian Ocean region shows little or no changes in SSS, which contrasts with the pronounced freshening trends predicted in the eastern part of this ocean basin and the Tropical West Pacific (Figs. 1b and 3c). Increased freshening is predicted in the Bay of Bengal (SSS change: −1.17 ± 0.43 PSU), the Sunda Shelf (SSS change: −1.21 ± 0.29 PSU) and the Western Coral Triangle province (mean SSS change: −0.80 ± 0.17 PSU) (Fig. 3c and Supplementary Table 4). Within the AEP, salinity increases exceed +0.96 PSU in the Tropical Northwestern Atlantic, +0.80 in the Warm Temperate Northwest Atlantic and +0.68 in the West African Transition (Fig. 3c and Supplementary Table 4). The spatial heterogeneity in SSS across the global range of mangrove forests corresponds with observed changes in SSS35. Trends in SSD (Fig. 3d) strongly track changes in SSS (Fig. 3c) rather than SST. All RCP scenarios predict an overall decrease in SSD for both mangrove bioregions; however, the predicted decrease in SSD in the IWP region was a factor of 2 (RCP 6.0) and 2.5 (RCP 2.6, RCP 4.5 and RCP 8.5) stronger than in the AEP (Figs. 2 and 3d and Supplementary Tables 1–4).Propagule density values from our literature survey range from 1,080 kg m−3 for different mangrove species (Fig. 4 and Supplementary Table 5). The low densities reported for Heritiera littoralis propagules provide a strong contrast with the near-seawater propagule densities reported for Avicennia and members of the Rhizophoraceae (Bruguiera, Rhizophora and Ceriops). Floating characteristics of the latter may be particularly sensitive to changes in SSD. To illustrate the potential influence of changing ocean conditions on mangrove propagule dispersal, we considered threshold water density values (1,020 and 1,022 kg m−3) that are within the range where elongated propagules of important mangrove genera tend to change floating orientation (Fig. 4a). More specifically, we determined the ocean surface area with an SSD below or equal to these thresholds under different climate change scenarios (Fig. 5). Under RCP 8.5, the ocean surface covered by mangrove coastal waters (coastal waters bordering present mangrove forests) with a density ≤1,020 kg m−3 increases ~27% by 2100, notably more so in the IWP (~37%) than in the AEP (~6%) (Supplementary Table 6). A threshold of 1,022 kg m−3 results in increases of roughly +11% (global), +12% (IWP) and +8% (AEP) (Supplementary Table 7). Similar spatial patterns are observed for open-ocean waters within the global latitudinal range of mangroves (Fig. 5 and Supplementary Figs. 1 and 2).Fig. 4: Potential effect of future declines in SSD on mangrove propagule dispersal.a, Range of reported propagule density values for wide-ranging mangrove species and present and future range of SSD for coastal waters along the range of those mangrove species. Mangrove propagule data are extracted from the literature (Supplementary Table 5). H. lit, Heritiera littoralis; X. gra, Xylocarpus granatum; A. ger, Avicennia germinans; A. mar, Avicennia marina; B. gym, Bruguiera gymnorrhiza; C. tag, Ceriops tagal; R. man, Rhizophora mangle; R. muc, Rhizophora mucronata. Bottom part adapted from ref. 51. b, Conceptual figure of the potential effects of ocean warming and freshening on mangrove propagule dispersal. Ocean warming and freshening drive changes in SSD and may reduce the timeframe for opportunistic colonization. For a propagule with a specific density and floating profile under present surface ocean conditions, reduced SSD of coastal and open-ocean waters may reduce floatation time (shaded area) and hence, reduce the proportion of long-distance dispersers. For simplicity, the density of propagules is assumed to increase linearly over time, although the actual increase may be nonlinear.Full size imageFig. 5: Future changes in SSD.a–d, Spatial extent of coastal and open-ocean surface waters with a density ≤1,020 kg m−3 (a,b) and 1,022 kg m−3 (c,d), for present (2000–2014) (a,c) and future (2090–2100; RCP 8.5) (b,d) scenarios. Data are shown for surface ocean waters within the global latitudinal range of mangrove forests (between 32° N and 38° S). The two density thresholds considered are within the range of densities at which mangrove propagule buoyancy and floating orientation of several mangrove genera change, as reported in available literature. Black dots along the coast represent the global mangrove extent from the 2015 GMW dataset32. Magenta-coloured circles represent SSD values More

Dynamic in-flight flock organizationIt is commonly assumed that during flocking, flock members follow three basic interaction rules: Attraction, Repulsion and Alignment, to coordinate spatial positions between each other18. To study the spatial organization of our zebra finch flock during flight, the spatial positions of all birds in the flight section were tracked in every fifth frame (sample rate: 24 Hz (that is, frames per second)) of the synchronized footage recorded by two high-speed digital video cameras (Camera 1: centred upwind view, Fig. 1a,b; Camera 2: upturned vertical view, Fig. 1a,c) for the entire duration (51.7, 58.3, 69.2 and 127 s) of four (session 2, 5, 8 and 13) out of 13 flight sessions. Flight paths were reconstructed from the tracking data for each bird in the flock, with horizontal and vertical coordinates delivered by Camera 1 and coordinates in wind direction delivered by Camera 2. The data show that each bird mainly occupied a particular area in the flight section, and that this spatial preference was stable over different flight sessions. Bird Green, for example, was preferentially flying very low above the flight section’s floor, and bird Lilac preferred to fly at upwind positions in front of the flock (Fig. 1d, Extended Data Figs. 1 and 3 and Supplementary Information).Despite their preference in flight area, all birds constantly changed their spatial positions fast and rhythmically along the horizontal dimension of the flight section (Fig. 1e–g, Extended Data Figs. 2 and 4, Supplementary Video 1 and Supplementary Information). This behaviour is reminiscent of the flight behaviour of wild zebra finches: when being surprised in flight by a predator, zebra finches fly in a rapid zig-zag course low above the ground, heading for nearby vegetation16. Whether the sideways oscillating flight manoeuvres, which are performed by both wild birds in open space and domesticated birds in the wind tunnel’s flight section, are caused by the close proximity to the ground or are part of an escape reaction is yet unknown.From the tracking data, we further calculated the spatial distances in all three dimensions between all pairwise combinations of birds throughout the four flight sessions (sample rate: 24 Hz). When normalized to the maximum distance detected for each bird pairing, each dimension and each flight session, mean distances of bird pairings in all dimensions were narrowly distributed within a range of 27.7–38.0% of maximum distance (Fig. 1h and Supplementary Table 1). This may indicate that during flocking flight, zebra finches actively balance Attraction and Repulsion to maintain a stable 3D distance towards all other members of the flock. Owing to the spatial limitations in the wind tunnel’s flight section, we did not expect the zebra finches to perform large-scale flight manoeuvres with movements aligned between all flock members (Extended Data Fig. 5 and Supplementary Information), as can be observed, for example, in freely flying flocks of homing pigeons (Columba livia domestica)19 and white storks (Ciconia Ciconia)20.Visually guided horizontal repositioningWhen observing the dynamic spatial organization of our zebra finch flock, a question immediately arises: how do the birds prevent collisions during their frequent horizontal position changes? When considering the spatial limitation experienced by the flock of six birds during flight in the flight section and their highly dynamic flight style, collision rates seemed to be astonishingly low (median: 0.02 Hz; interquartile range (IQR): 0–0.03 Hz; n = 13 sessions) during flocking flight (in total 16 collisions in 13 min of analysed flight time). In birds, the visual system represents the main input channel for environmental information. To tackle the above question, we therefore first investigated the role of vision during flocking flight, and tested whether a bird’s viewing direction was correlated with the direction of horizontal position change. As gaze changes are governed by head movements in birds21, we used a bird’s head direction as an indicator for the orientation of its visual axis. We tracked (sample rate: 120 Hz) the position of a bird’s beak tip and neck in each frame of the footage during ten horizontal position changes (Fig. 2a and Supplementary Video 2) per bird, and found a strong interaction between a bird’s head angle relative to the wind direction and its direction of horizontal position change. During horizontal position changes, the birds always turned their heads in the direction of the position change (Fig. 2b). While the population’s median absolute angle of position change was 84.0° (IQR: 78.6–87.2°; n = 60) relative to 0° in wind direction, the population’s median absolute head turning angle was 36.0° (IQR: 26.4–42.5°; n = 60; see Supplementary Information for results on head movements during solo flight). The eyes of zebra finches are positioned laterally on their heads22 and each retina features a small region of highest ganglion cell density (fovea, that is, region of highest visual spatial resolution) at an area that receives visual input from horizontal positions at 60° relative to the midsagittal plane23. By turning their heads by about 36° during horizontal position changes, the zebra finches roughly align the foveal area in the retina of one eye with their direction of position change, and in the retina of the other eye with the wind direction (Fig. 2c,d). Thus, head turns in the direction of position change may indicate that the birds use visual cues while repositioning themselves within the flock. This hypothesis is supported by a study on zebra finch head movements performed during an obstacle avoidance task. In this study, instead of fixating on the obstacle, zebra finches turned their head in the direction of movement while navigating around the obstacle24.Fig. 2: Horizontal position changes are accompanied by head turns.a, Head and body orientation of bird Orange (ventral view) during one example of position changes to the right, tracked (sample rate: 120 Hz) in the footage of Camera 2. Circles: beak tip positions; plus signs: neck positions; upward pointing triangles: tail base positions. Cutouts of freeze frames of the footage taken with Camera 2 show the bird’s head and body posture for 11 time points during the position change. b, In all birds, the median angle of head turn during horizontal position change in flocking flight is positively correlated (linear mixed effects model (LMM), estimates ± s.e.m.: 2.05 ± 0.1, P  More

Fink, D. et al. Crowdsourcing meets ecology: he misphere wide spatiotemporal species distribution models. AI Mag. 35, 19–30. https://doi.org/10.1609/aimag.v35i2.2533 (2014).Article 

Google Scholar 
Chandler, M. et al. Contribution of citizen science towards international biodiversity monitoring. Biol. Cons. 213, 280–294. https://doi.org/10.1016/j.biocon.2016.09.004 (2017).Article 

Google Scholar 
Schmeller, D. S. et al. Advantages of volunteer-based biodiversity monitoring in Europe. Conserv. Biol. 23, 307–316. https://doi.org/10.1111/j.1523-1739.2008.01125.x (2009).Article 
PubMed 

Google Scholar 
Boakes, E. H. et al. Distorted views of biodiversity: Spatial and temporal bias in species occurrence data. PLoS Biol. https://doi.org/10.1371/journal.pbio.1000385 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
Follett, R. & Strezov, V. An analysis of citizen science based research: Usage and publication patterns. PLoS ONE https://doi.org/10.1371/journal.pone.0143687 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Zattara, E. E. & Aizen, M. A. Worldwide occurrence records suggest a global decline in bee species richness. One Earth 4, 114–123. https://doi.org/10.1016/j.oneear.2020.12.005 (2021).ADS 
Article 

Google Scholar 
Dickinson, J. L. et al. The current state of citizen science as a tool for ecological research and public engagement. Front. Ecol. Environ. 10, 291–297. https://doi.org/10.1890/110236 (2012).Article 

Google Scholar 
Kosmala, M., Wiggins, A., Swanson, A. & Simmons, B. Assessing data quality in citizen science. Front. Ecol. Environ. 14, 551–560. https://doi.org/10.1002/fee.1436 (2016).Article 

Google Scholar 
Bayraktarov, E. et al. Do big unstructured biodiversity data mean more knowledge?. Front. Ecol. Evol. https://doi.org/10.3389/fevo.2018.00239 (2019).Article 

Google Scholar 
Burgess, H. K. et al. The science of citizen science: Exploring barriers to use as a primary research tool. Biol. Cons. 208, 113–120. https://doi.org/10.1016/j.biocon.2016.05.014 (2017).Article 

Google Scholar 
Isaac, N. J. B. & Pocock, M. J. O. Bias and information in biological records. Biol. J. Lin. Soc. 115, 522–531. https://doi.org/10.1111/bij.12532 (2015).Article 

Google Scholar 
August, T., Fox, R., Roy, D. B. & Pocock, M. J. O. Data-derived metrics describing the behaviour of field-based citizen scientists provide insights for project design and modelling bias. Sci. Rep. https://doi.org/10.1038/s41598-020-67658-3 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Boakes, E. H. et al. Patterns of contribution to citizen science biodiversity projects increase understanding of volunteers’ recording behaviour. Sci. Rep. https://doi.org/10.1038/srep33051 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Di Cecco, G. J. et al. Observing the observers: How participants contribute data to iNaturalist and implications for biodiversity science. Bioscience 71, 1179–1188. https://doi.org/10.1093/biosci/biab093 (2021).Article 

Google Scholar 
Kamp, J., Oppel, S., Heldbjerg, H., Nyegaard, T. & Donald, P. F. Unstructured citizen science data fail to detect long-term population declines of common birds in Denmark. Divers. Distrib. 22, 1024–1035. https://doi.org/10.1111/ddi.12463 (2016).Article 

Google Scholar 
Altwegg, R. & Nichols, J. D. Occupancy models for citizen-science data. Methods Ecol. Evol. 10, 8–21. https://doi.org/10.1111/2041-210x.13090 (2019).Article 

Google Scholar 
Courter, J. R., Johnson, R. J., Stuyck, C. M., Lang, B. A. & Kaiser, E. W. Weekend bias in citizen science data reporting: Implications for phenology studies. Int. J. Biometeorol. 57, 715–720. https://doi.org/10.1007/s00484-012-0598-7 (2013).ADS 
Article 
PubMed 

Google Scholar 
Amano, T., Lamming, J. D. L. & Sutherland, W. J. Spatial gaps in global biodiversity information and the role of citizen science. Bioscience 66, 393–400. https://doi.org/10.1093/biosci/biw022 (2016).Article 

Google Scholar 
Geldmann, J. et al. What determines spatial bias in citizen science? Exploring four recording schemes with different proficiency requirements. Divers. Distrib. 22, 1139–1149. https://doi.org/10.1111/ddi.12477 (2016).Article 

Google Scholar 
Girardello, M. et al. Gaps in butterfly inventory data: A global analysis. Biol. Cons. 236, 289–295. https://doi.org/10.1016/j.biocon.2019.05.053 (2019).Article 

Google Scholar 
Husby, M., Hoset, K. S. & Butler, S. Non-random sampling along rural-urban gradients may reduce reliability of multi-species farmland bird indicators and their trends. Ibis https://doi.org/10.1111/ibi.12896 (2021).Article 

Google Scholar 
Petersen, T. K., Speed, J. D. M., Grøtan, V. & Austrheim, G. Species data for understanding biodiversity dynamics: The what, where and when of species occurrence data collection. Ecol. Solut. Evid. https://doi.org/10.1002/2688-8319.12048 (2021).Article 

Google Scholar 
Egerer, M., Lin, B. B. & Kendal, D. Towards better species identification processes between scientists and community participants. Sci. Total Environ. https://doi.org/10.1016/j.scitotenv.2019.133738 (2019).Article 
PubMed 

Google Scholar 
Jimenez, M. F., Pejchar, L. & Reed, S. E. Tradeoffs of using place-based community science for urban biodiversity monitoring. Conserv. Sci. Pract. https://doi.org/10.1111/csp2.338 (2021).Article 

Google Scholar 
Branchini, S. et al. Using a citizen science program to monitor coral reef biodiversity through space and time. Biodivers. Conserv. 24, 319–336. https://doi.org/10.1007/s10531-014-0810-7 (2015).Article 

Google Scholar 
Snall, T., Kindvall, O., Nilsson, J. & Part, T. Evaluating citizen-based presence data for bird monitoring. Biol. Cons. 144, 804–810. https://doi.org/10.1016/j.biocon.2010.11.010 (2011).Article 

Google Scholar 
Gardiner, M. M. et al. Lessons from lady beetles: Accuracy of monitoring data from US and UK citizen-science programs. Front. Ecol. Environ. 10, 471–476. https://doi.org/10.1890/110185 (2012).Article 

Google Scholar 
Troudet, J., Grandcolas, P., Blin, A., Vignes-Lebbe, R. & Legendre, F. Taxonomic bias in biodiversity data and societal preferences. Sci. Rep. https://doi.org/10.1038/s41598-017-09084-6 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Johansson, F. et al. Can information from citizen science data be used to predict biodiversity in stormwater ponds?. Sci. Rep. https://doi.org/10.1038/s41598-020-66306-0 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Everett, G. & Geoghegan, H. Initiating and continuing participation in citizen science for natural history. BMC Ecol. https://doi.org/10.1186/s12898-016-0062-3 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Richter, A. et al. The social fabric of citizen science drivers for long-term engagement in the German butterfly monitoring scheme. J. Insect Conserv. 22, 731–743. https://doi.org/10.1007/s10841-018-0097-1 (2018).Article 

Google Scholar 
MacPhail, V. J., Gibson, S. D. & Colla, S. R. Community science participants gain environmental awareness and contribute high quality data but improvements are needed: Insights from Bumble Bee Watch. PeerJ https://doi.org/10.7717/peerj.9141 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Maund, P. R. et al. What motivates the masses: Understanding why people contribute to conservation citizen science projects. Biol. Conserv. https://doi.org/10.1016/j.biocon.2020.108587 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Moczek, N., Nuss, M. & Kohler, J. K. Volunteering in the citizen science project “Insects of Saxony”—The larger the island of knowledge, the longer the bank of questions. Insects https://doi.org/10.3390/insects12030262 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Branchini, S. et al. Participating in a citizen science monitoring program: Implications for environmental education. PLoS ONE https://doi.org/10.1371/journal.pone.0131812 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Kelemen-Finan, J., Scheuch, M. & Winter, S. Contributions from citizen science to science education: An examination of a biodiversity citizen science project with schools in Central Europe. Int. J. Sci. Educ. 40, 2078–2098. https://doi.org/10.1080/09500693.2018.1520405 (2018).Article 

Google Scholar 
Deguines, N., Prince, K., Prevot, A. C. & Fontaine, B. Assessing the emergence of pro-biodiversity practices in citizen scientists of a backyard butterfly survey. Sci. Total Environ. https://doi.org/10.1016/j.scitotenv.2020.136842 (2020).Article 
PubMed 

Google Scholar 
Peter, M., Diekötter, T., Höffler, T. & Kremer, K. Biodiversity citizen science: Outcomes for the participating citizens. People Nat. 3, 294–311. https://doi.org/10.1002/pan3.10193 (2021).Article 

Google Scholar 
Phillips, T. B., Bailey, R. L., Martin, V., Faulkner-Grant, H. & Bonter, D. N. The role of citizen science in management of invasive avian species: What people think, know, and do. J. Environ. Manage. https://doi.org/10.1016/j.jenvman.2020.111709 (2021).Article 
PubMed 

Google Scholar 
Parrish, J. K. et al. Hoping for optimality or designing for inclusion: Persistence, learning, and the social network of citizen science. Proc. Natl. Acad. Sci. U.S.A. 116, 1894–1901. https://doi.org/10.1073/pnas.1807186115 (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Mac Domhnaill, C., Lyons, S. & Nolan, A. The citizens in citizen science: Demographic, socioeconomic, and health characteristics of biodiversity recorders in Ireland. Citiz. Sci.: Theory Pract. 5, 16. https://doi.org/10.5334/cstp.283 (2020).Article 

Google Scholar 
van der Wal, R., Sharma, N., Mellish, C., Robinson, A. & Siddharthan, A. The role of automated feedback in training and retaining biological recorders for citizen science. Conserv. Biol. 30, 550–561. https://doi.org/10.1111/cobi.12705 (2016).Article 
PubMed 

Google Scholar 
Bloom, E. H. & Crowder, D. W. Promoting data collection in pollinator citizen science projects. Citiz. Sci.: Theory Pract. 5, 3. https://doi.org/10.5334/cstp.217 (2020).Article 

Google Scholar 
Johnston, A., Fink, D., Hochachka, W. M. & Kelling, S. Estimates of observer expertise improve species distributions from citizen science data. Methods Ecol. Evol. 9, 88–97. https://doi.org/10.1111/2041-210x.12838 (2018).Article 

Google Scholar 
Kelling, S. et al. Using semistructured surveys to improve citizen science data for monitoring biodiversity. Bioscience 69, 170–179. https://doi.org/10.1093/biosci/biz010 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Koen, B., Loosveldt, G., Vandenplas, C. & Stoop, I. Response rates in the european social survey: Increasing, decreasing, or a matter of fieldwork efforts?. Surv. Methods: Insights Field https://doi.org/10.13094/SMIF-2018-00003 (2018).Article 

Google Scholar 
Gideon, L. Handbook of Survey Methodology for the Social Sciences (Springer, 2012).Book 

Google Scholar 
Wolf, C., Joye, D., Smith, T. W. & Fu, Y. C. The SAGE Handbook of Survey Methodology (SAGE Publications Ltd, 2016).Book 

Google Scholar 
Richter, A. et al. Motivation and support services in citizen science insect monitoring: A cross-country study. Biol. Conserv. 263, 109325. https://doi.org/10.1016/j.biocon.2021.109325 (2021).Article 

Google Scholar 
Johnston, A., Moran, N., Musgrove, A., Fink, D. & Baillie, S. R. Estimating species distributions from spatially biased citizen science data. Ecol. Model. https://doi.org/10.1016/j.ecolmodel.2019.108927 (2020).Article 

Google Scholar 
Isaac, N. J. B., van Strien, A. J., August, T. A., de Zeeuw, M. P. & Roy, D. B. Statistics for citizen science: Extracting signals of change from noisy ecological data. Methods Ecol. Evol. 5, 1052–1060. https://doi.org/10.1111/2041-210x.12254 (2014).Article 

Google Scholar 
Liao, H.-I., Yeh, S.-L. & Shimojo, S. Novelty vs. familiarity principles in preference decisions: Task context of past experience matters. Front. Psychol. https://doi.org/10.3389/fpsyg.2011.00043 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
Park, J., Shimojo, E. & Shimojo, S. Roles of familiarity and novelty in visual preference judgments are segregated across object categories. Proc. Natl. Acad. Sci. U.S.A. 107, 14552–14555. https://doi.org/10.1073/pnas.1004374107 (2010).ADS 
Article 
PubMed 

Google Scholar 
Tiago, P., Gouveia, M. J., Capinha, C., Santos-Reis, M. & Pereira, H. M. The influence of motivational factors on the frequency of participation in citizen science activities. Nat. Conserv.-Bulg. https://doi.org/10.3897/natureconservation.18.13429 (2017).Article 

Google Scholar 
Davis, A., Taylor, C. E. & Martin, J. M. Are pro-ecological values enough? Determining the drivers and extent of participation in citizen science programs. Hum. Dimens. Wildl. 24, 501–514. https://doi.org/10.1080/10871209.2019.1641857 (2019).Article 

Google Scholar 
Bell, S. et al. What counts? Volunteers and their organisations in the recording and monitoring of biodiversity. Biodivers. Conserv. 17, 3443–3454. https://doi.org/10.1007/s10531-008-9357-9 (2008).Article 

Google Scholar 
Toomey, A. H. & Domroese, M. C. Can citizen science lead to positive conservation attitudes and behaviors?. Hum. Ecol. Rev. 20, 50–62 (2013).Article 

Google Scholar 
Dennis, E. B., Morgan, B. J. T., Brereton, T. M., Roy, D. B. & Fox, R. Using citizen science butterfly counts to predict species population trends. Conserv. Biol. 31, 1350–1361. https://doi.org/10.1111/cobi.12956 (2017).Article 
PubMed 

Google Scholar 
Callaghan, C. T., Poore, A. G. B., Major, R. E., Rowley, J. J. L. & Cornwell, W. K. Optimizing future biodiversity sampling by citizen scientists. Proc. R. Soc. B-Biol. Sci. https://doi.org/10.1098/rspb.2019.1487 (2019).Article 

Google Scholar 
Outhwaite, C. L., Gregory, R. D., Chandler, R. E., Collen, B. & Isaac, N. J. B. Complex long-term biodiversity change among invertebrates, bryophytes and lichens. Nat. Ecol. Evol. 4, 384. https://doi.org/10.1038/s41559-020-1111-z (2020).Article 
PubMed 

Google Scholar 
Bowler, D. E. et al. Winners and losers over 35 years of dragonfly and damselfly distributional change in Germany. Divers. Distrib. https://doi.org/10.1111/ddi.13274 (2021).Article 

Google Scholar  More