Ecology

Satellite imagery provides insight into where and how Earth’s surface changes, particularly in remote areas where in situ measurements are generally lacking. With the large volumes of data produced by satellites, we need streamlined computational pipelines for optimized processing capabilities. Although a multitude of platforms exists to process satellite data, these often have expensive license requirements that price out much of the geospatial community. Moreover, many of these platforms are propriety, but transparency is key when developing geospatial processing workflows. Open-source programming is critical to the creation of efficient imagery processing pipelines. More

Lawrence, D. & Vandecar, K. Effects of tropical deforestation on climate and agriculture. Nat. Clim. Change 5, 27–36 (2015).Article 
ADS 

Google Scholar 
Spracklen, D. V., Baker, J. C. A., Garcia-Carreras, L. & Marsham, J. H. The effects of tropical vegetation on rainfall. Annu. Rev. Environ. Resour. 43, 193–218 (2018).Article 

Google Scholar 
Bonan, G. B. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320, 1444–1449 (2008).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Spracklen, D. V., Arnold, S. R. & Taylor, C. M. Observations of increased tropical rainfall preceded by air passage over forests. Nature 489, 282–285 (2012).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Staal, A. et al. Forest-rainfall cascades buffer against drought across the Amazon. Nat. Clim. Change 8, 539–543 (2018).Article 
ADS 

Google Scholar 
Baker, J. C. A. & Spracklen, D. V. Divergent representation of precipitation recycling in the Amazon and the Congo in CMIP6 models. Geophys. Res. Lett. 49, e2021GL095136 (2022).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Guan, K. et al. Photosynthetic seasonality of global tropical forests constrained by hydroclimate. Nat. Geosci. 8, 284–289 (2015).Article 
ADS 
CAS 

Google Scholar 
Staal, A. et al. Hysteresis of tropical forests in the 21st century. Nat. Commun. 11, 4978 (2020).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Zemp, D. C. et al. Self-amplified Amazon forest loss due to vegetation-atmosphere feedbacks. Nat. Commun. 8, 14681 (2017).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–854 (2013).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Chagnon, F. J. F. & Bras, R. L. Contemporary climate change in the Amazon. Geophys. Res. Lett. 32, L13703 (2005).Article 
ADS 

Google Scholar 
Khanna, J., Medvigy, D., Fueglistaler, S. & Walko, R. Regional dry-season climate changes due to three decades of Amazonian deforestation. Nat. Clim. Change 7, 200–204 (2017).Article 
ADS 

Google Scholar 
Garcia-Carreras, L. & Parker, D. J. How does local tropical deforestation affect rainfall? Geophys. Res. Lett. 38, L19802 (2011).Article 
ADS 

Google Scholar 
Leite-Filho, A. T., Soares-Filho, B. S., Davis, J. L., Abrahão, G. M. & Börner, J. Deforestation reduces rainfall and agricultural revenues in the Brazilian Amazon. Nat. Commun. 12, 2591 (2021).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
McAlpine, C. A. et al. Forest loss and Borneo’s climate. Environ. Res. Lett. 13, 044009 (2018).Chapman, S. et al. Compounding impact of deforestation on Borneo’s climate during El Niño events. Environ. Res. Lett. 15, 084006 (2020).Spracklen, D. V. & Garcia-Carreras, L. The impact of Amazonian deforestation on Amazon basin rainfall. Geophys. Res. Lett. 42, 9546–9552 (2015).Article 
ADS 

Google Scholar 
Jiang, Y. et al. Modeled response of South American climate to three decades of deforestation. J. Clim. 34, 2189–2203 (2021).Article 
ADS 

Google Scholar 
Harris, I., Osborn, T. J., Jones, P. & Lister, D. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci. Data 7, 109 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Fassoni-Andrade, A. C. et al. Amazon hydrology from space: scientific advances and future challenges. Rev. Geophys. 59, e2020RG000728 (2021).Article 
ADS 

Google Scholar 
Haiden, T., Janousek, M., Vitart, F., Ferranti, L. & Prates, F. Evaluation of ECMWF Forecasts, Including the 2019 Upgrade. ECMWF Technical Memorandum No. 853 (ECMWF, 2019).Esquivel-Muelbert, A. et al. Compositional response of Amazon forests to climate change. Glob. Change Biol. 25, 39–56 (2019).Article 
ADS 

Google Scholar 
Brum, M. et al. ENSO effects on the transpiration of eastern Amazon trees. Philos. Trans. R. Soc. B 373, 20180085 (2018).Article 

Google Scholar 
Bagley, J. E., Desai, A. R., Harding, K. J., Snyder, P. K. & Foley, J. A. Drought and deforestation: has land cover change influenced recent precipitation extremes in the Amazon? J. Clim. 27, 345–361 (2014).Article 
ADS 

Google Scholar 
Wunderling, N. et al. Recurrent droughts increase risk of cascading tipping events by outpacing adaptive capacities in the Amazon rainforest. Proc. Natl Acad. Sci. USA 119, e2120777119 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Fu, R. & Li, W. The influence of the land surface on the transition from dry to wet season in Amazonia. Theor. Appl. Climatol. 78, 97–110 (2004).Article 
ADS 

Google Scholar 
Leite-Filho, A. T., de Sousa Pontes, V. Y. & Costa, M. H. Effects of deforestation on the onset of the rainy season and the duration of dry spells in southern Amazonia. J. Geophys. Res. Atmos. 124, 5268–5281 (2019).Article 
ADS 

Google Scholar 
Negri, A. J., Adler, R. F., Xu, L. & Surratt, J. The Impact of Amazonian deforestation on dry season rainfall. J. Clim. 17, 1306–1319 (2004).Article 
ADS 

Google Scholar 
Chagnon, F. J. F., Bras, R. L. & Wang, J. Climatic shift in patterns of shallow clouds over the Amazon. Geophys. Res. Lett. 31, L24212 (2004).Article 
ADS 

Google Scholar 
Chambers, J. Q. & Artaxo, P. Biosphere–atmosphere interactions: deforestation size influences rainfall. Nat. Clim. Change 7, 175–176 (2017).Article 
ADS 

Google Scholar 
Baudena, M., Tuinenburg, O. A., Ferdinand, P. A. & Staal, A. Effects of land-use change in the Amazon on precipitation are likely underestimated. Glob. Change Biol. 27, 5580–5587 (2021).Article 
CAS 

Google Scholar 
Duku, C. & Hein, L. The impact of deforestation on rainfall in Africa: a data-driven assessment. Environ. Res. Lett. 16, 064044 (2021).Akkermans, T., Thiery, W. & Van Lipzig, N. P. M. The regional climate impact of a realistic future deforestation scenario in the Congo basin. J. Clim. 27, 2714–2734 (2014).Article 
ADS 

Google Scholar 
Staal, A. et al. Feedback between drought and deforestation in the Amazon. Environ. Res. Lett. 15, 044024 (2020).Xu, X. et al. Deforestation triggering irreversible transition in Amazon hydrological cycle. Environ. Res. Lett. 17, 034037 (2022).Kooperman, G. J. et al. Forest response to rising CO2 drives zonally asymmetric rainfall change over tropical land. Nat. Clim. Change 8, 434–440 (2018).Article 
ADS 

Google Scholar 
Chen, Z. et al. Global land monsoon precipitation changes in CMIP6 projections. Geophys. Res. Lett. 47, e2019GL086902 (2020).Stickler, C. M. et al. Dependence of hydropower energy generation on forests in the Amazon Basin at local and regional scales. Proc. Natl Acad. Sci. USA 110, 9601–9606 (2013).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Challinor, A. J. et al. A meta-analysis of crop yield under climate change and adaptation. Nat. Clim. Change 4, 287–291 (2014).Article 
ADS 

Google Scholar 
Strand, J. et al. Spatially explicit valuation of the Brazilian Amazon forest’s ecosystem services. Nat. Sustain. 1, 657–664 (2018).Article 

Google Scholar 
Potapov, P. et al. Global maps of cropland extent and change show accelerated cropland expansion in the twenty-first century. Nat. Food 3, 19–28 (2022).Article 

Google Scholar 
Li, Y. et al. Deforestation-induced climate change reduces carbon storage in remaining tropical forests. Nat. Commun. 13, 1964 (2022).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Aragão, L. E. O. C. et al. Interactions between rainfall, deforestation and fires during recent years in the Brazilian Amazonia. Philos. Trans. R. Soc. B 363, 1779–1785 (2008).Article 

Google Scholar 
Marengo, J. A. et al. Changes in climate and land use over the Amazon region: current and future variability and trends. Front. Earth Sci. https://doi.org/10.3389/feart.2018.00228 (2018).Jiang, Y. et al. Widespread increase of boreal summer dry season length over the Congo rainforest. Nat. Clim. Change https://doi.org/10.1038/s41558-019-0512-y (2019).Van Der Ent, R. J. & Savenije, H. H. G. Length and time scales of atmospheric moisture recycling. Atmos. Chem. Phys. 11, 1853–1863 (2011).Article 
ADS 

Google Scholar 
Sorí, R., Nieto, R., Vicente-Serrano, S. M., Drumond, A. & Gimeno, L. A Lagrangian perspective of the hydrological cycle in the Congo River basin. Earth Syst. Dyn. 8, 653–675 (2017).Article 
ADS 

Google Scholar 
van der Ent, R. J., Savenije, H. H. G., Schaefli, B. & Steele-Dunne, S. C. Origin and fate of atmospheric moisture over continents. Water Resour. Res. 46, W09525 (2010).ADS 

Google Scholar 
Feng, Y. et al. Doubling of annual forest carbon loss over the tropics during the early twenty-first century. Nat. Sustain. 4, 441–451 (2022).
Google Scholar 
Tuinenburg, O. A., Bosmans, J. H. C. & Staal, A. The global potential of forest restoration for drought mitigation. Environ. Res. Lett. 17, 034045 (2022).Met Office. Cartopy: a cartographic python library with a Matplotlib interface 2010–2015. Met Office https://scitools.org.uk/cartopy (2022).Hoyer, S. & Hamman, J. xarray: N-D labeled arrays and datasets in Python. J. Open Res. Softw. https://doi.org/10.5334/jors.148 (2017).Zhuang, J. xESMF. Zenodo https://doi.org/10.5281/zenodo.1134365 (2022).Baker, J. C. A. & Spracklen, D. V. Climate benefits of intact Amazon forests and the biophysical consequences of disturbance. Front. For. Glob. Change https://doi.org/10.3389/ffgc.2019.00047 (2019).Schaaf, C. & Wang, Z. MCD43A3 MODIS/Terra+Aqua BRDF/Albedo Daily L3 Global – 500m V006. NASA EOSDIS Land Processes DAAC https://doi.org/10.5067/modis/mcd43a3.006 (2015).Waskom, M. Seaborn: statistical data visualization. J. Open Source Softw. 6, 3021 (2021).Article 
ADS 

Google Scholar 
Chen, M. et al. Global land use for 2015–2100 at 0.05° resolution under diverse socioeconomic and climate scenarios. Sci. Data 7, 320 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Funk, C. et al. The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Sci. Data 2, 150066 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Xie, P. et al. NOAA Climate Data Record (CDR) of CPC Morphing technique (CMORPH) high resolution global precipitation estimates, version 1. NOAA National Centers for Environmental Information https://doi.org/10.25921/w9va-q159 (2019).Xie, P. et al. A gauge-based analysis of daily precipitation over East Asia. J. Hydrometeorol. 8, 607–626 (2007).Article 
ADS 

Google Scholar 
Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146, 1999–2049 (2020).Article 
ADS 

Google Scholar 
Elke, R., Hänsel, S., Finger, P., Schneider, U. & Ziese, M. GPCC Climatology Version 2022 at 0.25°: monthly land-surface precipitation climatology for every month and the total year from rain-gauges built on GTS-based and historical data. GPCC https://doi.org/10.5676/DWD_GPCC/CLIM_M_V2022_025 (2022).Huffman, G. J. A., Behrangi, R. F., Adler, D. T., Bolvin, E. J. & Nelkin, G. G. Introduction to the new version 3 GPCP monthly global precipitation analysis. GPCP https://docserver.gesdisc.eosdis.nasa.gov/public/project/MEaSUREs/GPCP/Release_Notes.GPCPV3.2.pdf (2022).Hou, A. Y. et al. The global precipitation measurement mission. Bull. Am. Meteorol. Soc. 95, 701–722 (2014).Article 
ADS 

Google Scholar 
Kobayashi, S. et al. The JRA-55 reanalysis: general specifications and basic characteristics. J. Meteorol. Soc. Japan 93, 5–48 (2015).Article 
ADS 

Google Scholar 
Gelaro, R. et al. The modern-era retrospective analysis for research and applications, version 2 (MERRA-2). J. Clim. 30, 5419–5454 (2017).Article 
ADS 

Google Scholar 
Chen, M., Xie, P. & Janowiak, J. E. Global land precipitation: a 50-yr monthly analysis based on gauge observations. J. Hydrometeorol. 3, 249–266 (2002).Article 
ADS 

Google Scholar 
Nguyen, P. et al. The CHRS data portal, an easily accessible public repository for PERSIANN global satellite precipitation data. Sci. Data 6, 1180296 (2019).Article 

Google Scholar 
Ashouri, H. et al. PERSIANN-CDR: daily precipitation climate data record from multisatellite observations for hydrological and climate studies. Bull. Am. Meteorol. Soc. 96, 69–83 (2015).Article 
ADS 

Google Scholar 
Nguyen, P. et al. Persiann dynamic infrared–rain rate (PDIR-now): a near-real-time, quasi-global satellite precipitation dataset. J. Hydrometeorol. 21, 2893–2906 (2020).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Sadeghi, M. et al. PERSIANN-CCS-CDR, a 3-hourly 0.04° global precipitation climate data record for heavy precipitation studies. Sci. Data 8, 157 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Huffman, G. J. et al. The TRMM Multisatellite Precipitation Analysis (TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J. Hydrometeorol. 8, 38–55 (2007).Article 
ADS 

Google Scholar 
Matsuura, K. & Willmott, C. J. Terrestrial precipitation: 1900-2017 gridded monthly time series. Global Precipitation Archive http://climate.geog.udel.edu/~climate/html_pages/Global2017/README.GlobalTsP2017.html (2018). More

RESEARCH BRIEFINGS
01 March 2023

Tropical deforestation affects local and regional precipitation, but the effects are uncertain and have not been determined using observations. Satellite data sets were used to show reductions in precipitation over areas of tropical forest loss, with stronger reductions seen as the deforested area expands. More

RESEARCH BRIEFINGS
01 March 2023

Global spatial and temporal patterns of coastal phytoplankton blooms were characterized using daily satellite imaging between 2003 and 2020. These blooms were identified on the coast of 126 of the 153 ocean-bordering countries examined. The extent and frequency of blooms have increased globally over the past two decades. More

There is a recent change in the modus operandi of Brazilian Amazon deforestation. The proportion of illegal deforestation in public land increased from ~43–44% (2015–2018) to ~49–52% (2019–2021)10. Land grabbers occupy public lands (deforesting or raising cattle) in a high-risk expectation of receiving title to the land and/or trading the land with significant returns (land speculation)6,7. Therefore, we argue that it is crucial to rapidly assign most of the Amazon’s UPFs to land tenure regimes associated with conservation. Land-tenure security will bring greater governance and protection to these areas. Achieving this goal requires a combination of three measures: (1) careful attention to the choice of land tenure categories for UPFs, (2) technological improvements, and (3) law enforcement.Choice of land tenure category for UPFsPublic lands in Brazil include several categories, such as conservation areas (with several subcategories under law number 9985/2000), Indigenous lands, and rural settlements, among others. Therefore, the category choice for each undesignated public land area requires studies to determine those lands’ social, environmental, or productive suitability, taking note of their histories of occupation, cultural importance, and potential uses. The unpopulated forest is a myth. Most of the areas in the Amazon have been occupied by human populations—traditional communities, indigenous villages, uncontacted tribes, “riverside” (ribeirinho) peoples, or small farmers—for generations. Ancestral occupation of land without proof or associated studies, however, does not guarantee land rights. Therefore, to avoid unfair competition for land and unilateral political decisions, the best choice of land category for a given UPF to meet social, ecological and economic demands would benefit from active social participation, multidisciplinary scientific studies, in situ observations, and innovative technologies (e.g., remote sensing, data processing capabilities, machine learning, cloud computing) to provide fast, scalable, and quality information.Final allocation decisions, however, must be preceded by participatory and transparent consultation processes to avoid conflicts and safeguard land rights. The measure of assigning tenure categories to the UPFs has a high level of complexity in itself and may benefit from the support of multi-actors (e.g., governments, academia, civil society, private sector) at multi-levels (e.g., studies, participation processes, decision-making processes) and multi-scales (local, regional and national). Despite the complexity, there are examples in the early 2000s of joint efforts to allocate land (“Terra Legal” Program) and create protected areas on a large scale and in a short period of time in the Brazilian Amazon. We emphasize, however, that the tenure categories selected for the UPFs need to maintain forest cover, remain in the public domain in compliance with national laws, and enhance long-term Amazon conservation, respecting the rights of resident populations.Technological improvements to control land grabbing in UPFLasting conservation of the Amazon rainforest depends on ending land-grabbing and illegal deforestation in public forests (designated or undesignated). However, land grabbers are using a self-declaratory tool to declare illegally invaded public lands as private properties, which demands immediate technological improvements to the system.The Rural Environmental Registry (CAR is the Portuguese acronym) is a mechanism of environmental oversight of private lands under the Brazilian Forest Code (Law 12,651/2012). CARs are registered on a web-based platform (Rural Environmental Registry System – SICAR). By law, landowners must self-declare their property boundaries and land use types (e.g., residential, agricultural, protection) in SICAR, respecting legally required protection of certain forest areas and watercourses. Then, a state environmental agency must validate the information. Unfortunately, the validation process has been extremely slow (e.g., More

Réale, D. et al. Personality and the emergence of the pace-of-life syndrome concept at the population level. Philos. Trans. R. Soc. Lond. B 365, 4051–4063 (2010).Article 

Google Scholar 
Gosling, S. D. From mice to men: What can we learn about personality from animal research?. Psychol. Bull. 127, 45 (2001).Article 
CAS 

Google Scholar 
Dingemanse, N. J., Class, B. & Holtmann, B. Nonrandom mating for behavior in the wild?. Trends Ecol. Evol. 36, 177–179 (2021).Article 

Google Scholar 
Croft, D. P. et al. Behavioural trait assortment in a social network: Patterns and implications. Behav. Ecol. Sociobiol. 63, 1495–1503 (2009).Article 

Google Scholar 
Morton, F. B., Weiss, A., Buchanan-Smith, H. M. & Lee, P. C. Capuchin monkeys with similar personalities have higher-quality relationships independent of age, sex, kinship and rank. Anim. Behav. 105, 163–171 (2015).Article 

Google Scholar 
Su, X. et al. Agonistic behaviour and energy metabolism of bold and shy swimming crabs Portunus trituberculatus. J. Exp. Biol. https://doi.org/10.1242/jeb.188706 (2019).Article 

Google Scholar 
Jolles, J. W., King, A. J. & Killen, S. S. The role of individual heterogeneity in collective animal behaviour. Trends Ecol. Evol. 35, 278–291 (2020).Article 

Google Scholar 
Bell, A. M., Hankison, S. J. & Laskowski, K. L. The repeatability of behaviour: A meta-analysis. Anim. Behav. 77, 771–783 (2009).Article 

Google Scholar 
Frost, A. J., Winrow-Giffen, A., Ashley, P. J. & Sneddon, L. U. Plasticity in animal personality traits: Does prior experience alter the degree of boldness?. P. Roy. Soc. B-Biol. Sci. 274, 333–339 (2007).
Google Scholar 
Krause, J., James, R. & Croft, D. P. Personality in the context of social networks. Philos. Trans. R. Soc. Lond. B 365, 4099 (2010).Article 
CAS 

Google Scholar 
David, M., Auclair, Y. & Cézilly, F. Personality predicts social dominance in female zebra finches, Taeniopygia guttata, in a feeding context. Anim. Behav. 81, 219–224 (2011).Article 

Google Scholar 
Favati, A., Leimar, O. & Løvlie, H. Personality predicts social dominance in male domestic fowl. PLoS ONE 9, e103535 (2014).Article 
ADS 

Google Scholar 
McGhee, K. E. & Travis, J. Repeatable behavioural type and stable dominance rank in the Bluefin killifish. Anim. Behav. 79, 497–507 (2010).Article 

Google Scholar 
Krause, J., Croft, D. P. & James, R. Social network theory in the behavioural sciences: Potential applications. Behav. Ecol. Sociobiol. 62, 15–27 (2007).Article 
CAS 

Google Scholar 
Flack, J. C., Girvan, M., de Waal, F. & Krakauer, D. C. Policing stabilizes construction of social niches in primates. Nature 439, 426–429 (2006).Article 
ADS 
CAS 

Google Scholar 
Croft, D. P., James, R. & Krause, J. Exploring Animal Social Networks (Princeton University Press, 2008).Book 

Google Scholar 
Patriquin, K. J., Leonard, M. L., Broders, H. G. & Garroway, C. J. Do social networks of female northern long-eared bats vary with reproductive period and age?. Behav. Ecol. Sociobiol. 64, 899–913 (2010).Article 

Google Scholar 
Gomes, A. C. R., Beltrão, P., Boogert, N. J. & Cardoso, G. C. Familiarity, dominance, sex and season shape common waxbill social networks. Behav. Ecol. 33, 526–540 (2022).Article 

Google Scholar 
Croft, D. P., Krause, J. & James, R. Social networks in the guppy (Poecilia reticulata). P. Roy. Soc. B-Biol. Sci. 271, S516–S519 (2004).Article 

Google Scholar 
Pike, T. W., Samanta, M., Lindström, J. & Royle, N. J. Behavioural phenotype affects social interactions in an animal network. P. Roy. Soc. B-Biol. Sci. 275, 2515–2520 (2008).
Google Scholar 
Aplin, L. M. et al. Individual personalities predict social behaviour in wild networks of great tits (Parus major). Ecol. Lett. 16, 1365–1372 (2013).Article 
CAS 

Google Scholar 
Massen, J. J. & Koski, S. E. Chimps of a feather sit together: Chimpanzee friendships are based on homophily in personality. Evol. Hum. Behav. 35, 1–8 (2014).Article 

Google Scholar 
Rault, J.-L. Friends with benefits: Social support and its relevance for farm animal welfare. Appl. Anim. Behav. Sci. 136, 1–14 (2012).Article 

Google Scholar 
Schneider, G. & Krueger, K. Third-party interventions keep social partners from exchanging affiliative interactions with others. Anim. Behav. 83, 377–387 (2012).Article 

Google Scholar 
Fraser, O. N. & Bugnyar, T. Do ravens show consolation? Responses to distressed others. PLoS ONE 5, e10605 (2010).Article 
ADS 

Google Scholar 
Rose, P. & Croft, D. The potential of social network analysis as a tool for the management of zoo animals. Anim. Welf. 24, 123–138 (2015).Article 

Google Scholar 
Clark, F. E. Space to choose: network analysis of social preferences in a captive chimpanzee community, and implications for management. Am. J. Primatol. 73, 748–757 (2011).Article 

Google Scholar 
Corner, L., Pfeiffer, D. & Morris, R. Social-network analysis of Mycobacterium bovis transmission among captive brushtail possums (Trichosurus vulpecula). Prev. Vet. Med. 59, 147–167 (2003).Article 
CAS 

Google Scholar 
Hansen, H., McDonald, D. B., Groves, P., Maier, J. A. & Ben-David, M. Social networks and the formation and maintenance of river otter groups. Ethology 115, 384–396 (2009).Article 

Google Scholar 
Radosevich, L. M., Jaffe, K. E. & Minier, D. E. The utility of social network analysis for informing zoo management: Changing network dynamics of a group of captive hamadryas baboons (Papio hamadryas) following an introduction of two young males. Zoo Biol. 40, 503–516 (2021).Article 

Google Scholar 
Pacheco, X. P. & Madden, J. R. Does the social network structure of wild animal populations differ from that of animals in captivity?. Behav. Processes 190, 104446 (2021).Article 

Google Scholar 
Watters, J. V. & Powell, D. M. Measuring animal personality for use in population management in zoos: Suggested methods and rationale. Zoo Biol. 31, 1–12 (2012).Article 

Google Scholar 
Koski, S. E. Social personality traits in chimpanzees: temporal stability and structure of behaviourally assessed personality traits in three captive populations. Behav. Ecol. Sociobiol. 65, 2161–2174 (2011).Article 

Google Scholar 
Račevska, E. & Hill, C. M. Personality and social dynamics of zoo-housed western lowland gorillas (Gorilla gorilla gorilla). J. Zoo Aqua. Res. 5, 116–122 (2017).
Google Scholar 
Stoinski, T. S., Jaicks, H. F. & Drayton, L. A. Visitor effects on the behavior of captive western lowland gorillas: The importance of individual differences in examining welfare. Zoo Biol. 31, 586–599 (2012).Article 

Google Scholar 
Wielebnowski, N. C. Behavioral differences as predictors of breeding status in captive cheetahs. Zoo Biol. 18, 335–349 (1999).Article 

Google Scholar 
Barrett, L. P. et al. Personality assessment of headstart Texas horned lizards (Phrynosoma cornutum) in human care prior to release. Appl. Anim. Behav. Sci. 254, 105690 (2022).Article 

Google Scholar 
Rose, P. E., Brereton, J. E. & Croft, D. P. Measuring welfare in captive flamingos: Activity patterns and exhibit usage in zoo-housed birds. Appl. Anim. Behav. Sci. 205, 115–125 (2018).Article 

Google Scholar 
Rose, P. E. & Croft, D. P. Social bonds in a flock bird: Species differences and seasonality in social structure in captive flamingo flocks over a 12-month period. Appl. Anim. Behav. Sci. 193, 87–97 (2017).Article 

Google Scholar 
Rose, P. E. & Croft, D. P. Quantifying the social structure of a large captive flock of greater flamingos (Phoenicopterus roseus): Potential implications for management in captivity. Behav. Processes 150, 66–74 (2018).Article 

Google Scholar 
Rose, P. E., Croft, D. P. & Lee, R. A review of captive flamingo (Phoenicopteridae) welfare: A synthesis of current knowledge and future directions. Intern. Zoo Yearb. 48, 139–155 (2014).Article 

Google Scholar 
Rose, P. E. & Croft, D. P. Evaluating the social networks of four flocks of captive flamingos over a five-year period: Temporal, environmental, group and health influences on assortment. Behav. Processes 175, 104118 (2020).Article 

Google Scholar 
Munson, A. A., Jones, C., Schraft, H. & Sih, A. You’re just my type: Mate choice and behavioral types. Trends Ecol. Evol. 35, 823–833 (2020).Article 

Google Scholar 
Schuett, W., Tregenza, T. & Dall, S. R. Sexual selection and animal personality. Biol. Rev. 85, 217–246 (2010).Article 

Google Scholar 
Jackson, W. M. Why do winners keep winning?. Behav. Ecol. Sociobiol. 28, 271–276 (1991).Article 

Google Scholar 
Dammhahn, M. & Almeling, L. Is risk taking during foraging a personality trait? A field test for cross-context consistency in boldness. Anim. Behav. 84, 1131–1139 (2012).Article 

Google Scholar 
Van Oers, K., Drent, P. J., De Goede, P. & Van Noordwijk, A. J. Realized heritability and repeatability of risk-taking behaviour in relation to avian personalities. P. Roy. Soc. B-Biol. Sci. 271, 65–73 (2004).Article 

Google Scholar 
Hinton, M. G. et al. Patterns of aggression among captive American flamingos (Phoenicopterus ruber). Zoo Biol. 32, 445–453 (2013).Article 

Google Scholar 
Royer, E. A. & Anderson, M. J. Evidence of a dominance hierarchy in captive Caribbean flamingos and its relation to pair bonding and physiological measures of health. Behav. Processes 105, 60–70 (2014).Article 

Google Scholar 
Carere, C., Drent, P. J., Privitera, L., Koolhaas, J. M. & Groothuis, T. G. Personalities in great tits, Parus major: Stability and consistency. Anim. Behav. 70, 795–805 (2005).Article 

Google Scholar 
Jouventin, P., Lequette, B. & Dobson, F. S. Age-related mate choice in the wandering albatross. Anim. Behav. 57, 1099–1106 (1999).Article 
CAS 

Google Scholar 
Black, J. M. Partnerships in Birds: The Study of Monogamy (Oxford University Press, USA, 1996).
Google Scholar 
Estevez, I., Andersen, I.-L. & Nævdal, E. Group size, density and social dynamics in farm animals. Appl. Anim. Behav. Sci. 103, 185–204 (2007).Article 

Google Scholar 
Pickering, S. The comparative breeding biology of flamingos Phoenicopteridae at the Wildfowl and Wetlands Trust Centre, Slimbridge. Intern. Zoo Yearbook 31, 139–146 (1992).Article 

Google Scholar 
Whitehead, H. Analyzing Animal Societies: Quantitative Methods for Vertebrate Social Analysis (University of Chicago Press, 2008).Book 

Google Scholar 
Wilson, A. D., Krause, S., Dingemanse, N. J. & Krause, J. Network position: A key component in the characterization of social personality types. Behav. Ecol. Sociobiol. 67, 163–173 (2013).Article 

Google Scholar 
Renner, M. J. & Kelly, A. L. Behavioral decisions for managing social distance and aggression in captive polar bears (Ursus maritimus). J. Appl. Anim. Welf. Sci. 9, 233–239 (2006).Article 
CAS 

Google Scholar 
Stevens, E. F. & Pickett, C. Managing the social environments of flamingos for reproductive success. Zoo Biol. 13, 501–507 (1994).Article 

Google Scholar 
Franks, D. W., Ruxton, G. D. & James, R. Sampling animal association networks with the gambit of the group. Behav. Ecol. Sociobiol. 64, 493–503 (2010).Article 

Google Scholar 
Haddadi, H. et al. Determining association networks in social animals: Choosing spatial–temporal criteria and sampling rates. Behav. Ecol. Sociobiol. 65, 1659–1668 (2011).Article 

Google Scholar 
Whitehead, H. & Dufault, S. Techniques for analyzing vertebrate social structure using identified individuals. Adv. Stud. Behav. 28, 33–74 (1999).Article 

Google Scholar 
Borgatti, S.P., M., E., G., & C., F.L. UCINET for windows: software for social network analysis. Analytic Technologies: Harvard, MA (2002).Borgatti, S. P. NetDraw: graph visualization software (Analytic Technologies, 2002).
Google Scholar 
Bejder, L., Fletcher, D. & Bräger, S. A method for testing association patterns of social animals. Anim. Behav. 56, 719–725 (1998).Article 
CAS 

Google Scholar 
Farine, D. R. & Whitehead, H. Constructing, conducting and interpreting animal social network analysis. J. Anim. Ecol. 84, 1144–1163 (2015).Article 

Google Scholar 
Perdue, B. M., Gaalema, D. E., Martin, A. L., Dampier, S. M. & Maple, T. L. Factors affecting aggression in a captive flock of Chilean flamingos (Phoenicopterus chilensis). Zoo Biol. 30, 59–64 (2011).
Google Scholar 
IBMCorp. IBM SPSS Statistics for Windows. IBM Corp: Armonk, NY (2012).Clarke, K.R. & Gorley, R.N. PRIMER v6: User Manual/Tutorial. PRIMER-E, Plymouth. (2006).Kassambara, A. & Mundt, F. factoextra: Extract and Visualize the Results of Multivariate Data Analyses. (2020).RCoreTeam. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. (2021).Budaev, S. V. Using principal components and factor analysis in animal behaviour research: Caveats and guidelines. Ethology 116, 472–480 (2010).Article 

Google Scholar 
Whitehead, H. SOCPROG programs: Analysing animal social structures. Behav. Ecol. Sociobiol. 63, 765–778 (2009).Article 

Google Scholar 
Whitehead, H. SOCPROG: Programs for analyzing social structure: Whitehead Lab (2019).Hanneman, R.A. & Riddle, M., Chapter 18: Some Statistical Tools. In: Introduction to Social Network Methods. (University of California, Riverside 2005). http://faculty.ucr.edu/~hanneman/.(2005) More

Global biodiversity has been dramatically declining over the last decades1,2,3,4. The current biodiversity crisis is primarily driven by human-induced factors, the most serious of which are land-use change, habitat fragmentation, and climate change5. While global public awareness of climate change matters is high6,7, public recognition of biodiversity loss has, historically, been low8. The understanding of biodiversity concepts highly varies among countries and social groups9,10,11: in Nigeria, the biodiversity concept was known of 20.5% of non-professional Nigerians (with basic education or no formal training) while among 88.8% of professionals with tertiary education, it reached 88.8%; 60% of participants in a study in Switzerland had never heard the term biodiversity and Chinese farmers in another pilot study have never heard about biodiversity. In the European Union, the global leader of the environmental movement on both the political and discursive levels12,13, in 2018, 71% of EU citizens had heard of biodiversity, but only around 41% of these knew what biodiversity meant14. This illiteracy is a significant constraint for conservation strategies because the development and success of actions to halt and reverse biodiversity loss strongly rely on public support15.If general awareness of biodiversity loss is low, knowledge about plant diversity is even lower16. Plants have traditionally been overlooked, and expressions such as “plant blindness”, defined as a human tendency to ignore plant species17, perfectly illustrate the situation in terms of plant conservation. And yet, current estimates suggest that two out of five plant species are threatened with extinction18. Moreover, plants play a crucial role in the world ecosystems by providing habitat, shelter, oxygen, and food, including for humans19. Local community support boosts the effectiveness of biodiversity conservation actions20,21,22. However, how biodiversity is perceived and the benefits it provides to local populations have a significant influence on this support23. Therefore, stopping the loss of plant biodiversity and the impact it has on ecosystem health and human well-being must also strive to raise public awareness on the importance of plant conservation24.A big challenge, however, is to engage people with conservation. Nowadays, in a world where a large part of the human population lives in urban areas, the contact of people with nature is declining. This is a trend that will be even more accentuated in the future25. Perhaps society’s interest in plants is decreasing because of limited exposure to plants in daily lives, schools, and work. However, by critically examining our roles as plant scientists and educators, we realize that there are probably things we could, and should, do differently. New strategies to connect people to nature are required to spark people’s interest in and knowledge of plants. Citizen science programs and mobile applications (apps) are noteworthy initiatives that are helping to achieve this goal.Citizen science is defined as the general public involvement in scientific research activities and currently is a mainstream approach to collect information and data on a wide range of scientific subjects26,27. The development of mobile technologies and the widespread use of smartphones have boosted citizen science and enabled the development of mobile apps, which are digital tools that integrate, in real-time, data from multiple sources28.The goal of this article is to show how citizen science and mobile apps can be used as educational tools to raise awareness about plant biodiversity and conservation among the general public. We focused on formal education activities, at the Bachelor of Science (BSc) level, that were designed to collect data on various aspects of plant community and functional ecology. We also present the outcomes of two informal education initiatives that used citizen science to gather data on the distribution of plant diversity. We discuss these activities and results in light of their potential to engage the public into biodiversity conservation, and as educational and outreach tools.Formal education: UniversityDuring the COVID-19 pandemic (2021), Ecology practical classes of the Bologna Bachelor Degree in Biology (Faculty of Sciences of the University of Lisbon) had to be adapted to remote learning. Fortunately, during the States of Emergency imposed by the Portuguese Government, citizens were allowed to take brief walks. Taking advantage of citizen’s ability to briefly travel outdoors, we created three activities for students, as alternatives to those typically carried out in the classroom/campus, which we describe below.Activity 1—Analysis of the impact of disturbance on plant diversity in grasslandsThe objective of this activity was for students to explore the impact of disturbance and site attributes (such as soil type) on the diversity of the herbaceous plant community and its associated pollinators. This was undertaken in grasslands located near their homes, within walking distance (due to COVID lockdown movement restrictions). To achieve this goal, we developed a comprehensive sampling protocol that included methods for (i) selecting and characterizing sampling sites based on the level of human perturbation, (ii) soil characterization, (iii) sampling, identifying, and registering plants using the iNaturalist/Biodiversity4All platform and Flora-on web (Box 1), and (iv) pollinator sampling (Supplementary Data 1). To ensure accurate plant and pollinators identification, all observations were verified by professors responsible for each topic.First, each student chose one sampling site and teachers, using photographs, classified all sites regarding their perturbation level (low, medium, and high). Then, using the sampling protocol, students were invited to study different aspects of their sampling site, in loco or at their homes. Soil samples were analysed using simple methods and available household instruments (such as plastic cups, kitchen scale, and oven). Students were introduced to soil biodiversity as well as soil parameters (humidity, texture, structure, infiltration and draining) during the remote classes. Plants were sampled using a home-made 1 m2 quadrat. All species within were counted and identified to the lowest taxonomic level possible, using the mentioned apps and website. Before plant sampling, students were also asked to count and identify pollinators within their quadrats (broad taxonomic groups, bees, butterflies, flies, beetles) for 5 min, again using the apps to aid identification.Following field sampling, students were asked to calculate two taxonomic indices of plant communities. These included species richness, which measures the number of different species that occur in a sample, and the Simpson Diversity Index, which evaluates the probability that two individuals randomly selected from a sample will belong to the same species. Students also calculated functional diversity indices such as Functional Richness and Functional Dissimilarity, since functional diversity explores functional differences between species and how these differences reflect and affect the interactions with the environment and with other species29. Then, students assessed the relation between these indices and perturbation level. They analysed several functional traits of plants that are likely to respond to local perturbation (e.g., height, leaf size). Finally, they attempted to relate plant indices with the occurrence of pollinators.Overall, students sampled 147 grasslands that were affected by low (n = 17); medium (n = 86) and high (n = 40) levels of perturbation, scattered across mainland Portugal (Fig. 1a). In total, 3015 observations corresponding to 543 species of plant and 88 of insects (Fig. 1b) were registered in the iNaturalist/Biodiversity4All project Ecologia2_FCUL, created specifically to record all of the diversity data associated with this activity. Other registered taxa included six species of molluscs and 13 of arachnids, and other occasional soil macrofauna.Fig. 1: Analysis of the impact of disturbance on plant diversity in grasslands.a Location of grasslands sampled; b Banner and overview of main results of the project created in the platform iNaturalist/Biodiversity4All to register the sampled species; c Boxplots include data of the taxonomic diversity indices (plant species richness and Simpson Diversity Index) of sampled grasslands at three different perturbation levels: low, medium and high. Central lines represent median values, box limits indicate the upper and lower quartiles, whiskers correspond to 1.5 × the interquartile range above and below the upper and lower quartiles and points are the outliers. Boxplots with different letters indicate statistically significant differences among perturbation levels based on multiple pairwise comparisons.Full size imageThe results showed that the number of species (richness) decreased consistently with the level of perturbation. Simpson Diversity Index values increased, indicating low diversity values in highly perturbed herbaceous plant communities (Fig. 1c). Results revealed a trend towards an increase in the proportion of species with lower stature as perturbation increased. However, with no clear relationship with either biodiversity or perturbation. Finally, results indicated no clear relation of pollinator abundance or richness with plant richness and diversity, although field records relate a lower number of pollinators as wind intensity increased. In fact, pollinator sampling is extremely weather sensitive, which may have contributed to the lack of consistent relationships between pollinator diversity and perturbation.Box 1 Citizen science platforms and apps used for formal and informal educational activitiesiNaturalist (https://www.inaturalist.org/home): is a social network of naturalists, citizen scientists, and biologists that is based on mapping and sharing biodiversity observations. They describe themselves as “an online social network of people sharing biodiversity information in order to help each other learn about nature”. iNaturalist may be accessed via website or mobile app. Records are validated by the iNaturalist community. Observations reached approximately 110 million as of July 2022. This app allows the development of both open-access and registration-restricted projects. BioDiversity4All (https://www.biodiversity4all.org/) is a Portuguese biodiversity citizen science platform created by the Biodiversity for All Association. This platform was founded in 2010 and is currently linked to the “iNaturalist” network43. All the projects presented in this article were developed on the Biodiversity4All platform.Flora-on (https://flora-on.pt/): this portal contains occurrence data of vascular plants from the Portuguese flora collected by project collaborators (over 575,000 records as of July 2022). Flora-on was created by the Botanical Society of Portugal (SPBotânica), a Portuguese association devoted to the promotion and study of botany in Portugal. Botanists and naturalists provide most of the data, but occasional contributors are welcomed. Records are supervised by the portal editors, ensuring the dataset’s quality level. The portal includes stunning images of leaves, flowers, fruits, and other plant parts for 2299 of the 3300 taxa occurring in Portugal44. Additionally, the portal includes a powerful search engine that allows geographical, morphological, and taxonomical searches.LeafBite (https://zoegp.science/leafbyte): is a free, open-source iPhone app that measures total leaf area as well as consumed leaf area when herbivory is present45.Leaf-IT is a free and simple Android app created for scientific purposes. It was designed to measure leaf area under challenging field conditions. It has simple features for area calculation and data output, and can be used for ecological research and education46.Activity 2—Leaf trait assessment of shrub and tree speciesStudents were asked to assess three leaf traits Specific leaf area (SLA), Specific leaf mass (LMA), and Leaf Water Content (LWC) of two or three shrub or tree species. Each species should ideally fall into one of three functional groups known for their water adaptations, namely Hydrophytes, Mesophytes and Xerophytes. Students were challenged to choose charismatic Mediterranean species that grew nearby, such as Olea europaea, Nerium oleander or Phillyrea angustifolia. Alternatively, they could take the “Quercus challenge”, which involved ranking the Portuguese oak species based on their drought tolerance. A detailed protocol was developed to assist students for this purpose (Supplementary Data 2). In this protocol was demonstrated how to calculate the leaf area using the LeafBite and Leaf-IT apps (Box 1).The students calculated the SLA, LMA, and LWC of a total of 104 species (Supplementary Data 3) belonging to the main functional groups under study. Regarding the “Quercus challenge”, they were able to classify the six most representative oak species in Portugal and confirm the relationship among these indices and their tolerance to drought (Fig. 2).Fig. 2: Leaf trait assessment of shrub and tree species: Quercus challenge.Classification of Portuguese oak species regarding their drought tolerance (higher tolerance, left-up, lower tolerance right-down).Full size imageOne of the students, accomplished to present his own learning experience related to these activities at the XXIII Conference of the Environmental Research Network of Portuguese-speaking Nations – REALP, under the title “Plant Ecology during Confinement – A Digital Approach”.Activity 3—Evaluating the impact on the biodiversity of lawn management at the University of Lisbon campusAlthough, after the lockdown, practical classes returned to the laboratories and the field in 2021/22, we continued to use the iNaturalist/Biodiversity4All platform and the Flora-on website for biodiversity registering and identification, because of the success of the activities, as evidenced by the positive comments we received from students.The goal of this activity was to study the impact of lawn management on plant diversity and pollination on the University of Lisbon campus. To accomplish this, the students described the herbaceous communities and pollinators on four lawns (named C8, RL, RR, and TT) that had different management practices (mowing and irrigation). A comprehensive document with sampling guidelines was developed (Supplementary Data 4).The project Ecologia 2 Relvados 2022 registered 100 plant and 17 pollinator species (Fig. 3a). Given that the sampling took place during a cold and rainy week, which limited pollinator activity, the low number of pollinators registered was expectable (Lawson and Rands 2019). Following these analyses, the TT lawn (Fig. 3b), which had low levels of mowing and no watering, showed a significantly higher value of diversity, indicating it had the best management strategy for these systems (Fig. 3c), if the goal is to increase biodiversity.Fig. 3: Evaluating the impact on the biodiversity of lawn management at the University of Lisbon campus.a Banner and overview of main results of the project Ecologia 2 Relvados created in the platform iNaturalist/BioDiversity4All to register the sampled species; b Location of the lawns sampled in the Campus of the University of Lisbon; c Boxplots include data of the taxonomic diversity indices (plant species richness and Simpson Diversity Index) of sampled grasslands. Central lines represent median values, box limits indicate the upper and lower quartiles, whiskers correspond to 1.5 × the interquartile range above and below the upper and lower quartiles and points are the outliers. Boxplots with different letters indicate statistically significant differences among lawns based on multiple pairwise comparisons.Full size imageInformal education: BioBlitzesIntense biological surveys known as “BioBlitz” are carried out to record all organisms found in certain locations, such as cities, protected areas, or even entire countries. They are being used all over the world to collect and share georeferenced biodiversity data30. We developed two Plant Bioblitzes based on the BioDiversity4All/iNaturalist and Flora-on platforms. Social media, such as Facebook, Instagram, and Twitter, were used to promote these events and engage citizens (Fig. 4). The BioBlitzes were developed by SPBotânica in collaboration with BioDiversity4All.Fig. 4: Bioblitz I & II – Flora of Portugal.Posters created for the promotion of the two Flora of Portugal Bioblitzes.Full size imageBioblitz I & II – Flora of PortugalThe celebration of Fascination of Plants Day (18th of May) served as the backdrop for the organization of two-weekend Bioblitzes: Bioblitz Flora of Portugal I and Bioblitz Flora of Portugal II.In 2021, the Bioblitz was solely focused on project members, which meant that only those who had voluntarily joined the initiative could participate. In total, the 119 project members registered 4234 observations of 890 plant species. In contrast, the 2022 Bioblitz was an open project (no registration required). In total, the 323 observers made 6547 records of 1198 species. To evaluate the impact of the Bioblitz events, we compared the data registered in BioDiverstiy4All during the weekends of both events (2021 and 2022) with (i) the data registered in the platform during the equivalent weekends of 2019 and 2020 and (ii) also during the weekends before both Bioblitzes. The number of species, observations, and observers increased significantly from 2019 to 2020, 2021, and 2022, but, when comparing values from 2020 with 2021 and 2022, this rise was only verified during the Bioblitz weekends, proving the importance of Bioblitzes in this increase (Fig. 5).Fig. 5: Number of observations, species and observers registered on the BioDiversity4All/iNaturalist platform over equivalent weekends in 2019, 2020, 2021, and 2022.Numbers for 2021 and 2022 correspond to the weekends in which Bioblitzes I & II – Flora of Portugal were conducted, as well as previous ones.Full size image More

Wilkinson, D. At cross purposes. Nature 412, 485 (2001).Article 
ADS 
CAS 
PubMed 

Google Scholar 
de Bary, H. A. Über Symbiose [On Symbiosis]. Tageblatt für die Versammlung Dtsch. Naturforscher und Aerzte (in Cassel) [Daily J. Conf. Ger. Sci. Phys.] (in Ger. 51, 121–126 (1878).Lücking, R., Leavitt, S. D. & Hawksworth, D. L. Species in lichen-forming fungi: balancing between conceptual and practical considerations, and between phenotype and phylogenomics. Fungal Div.109, 99–154 (Springer, Netherlands, 2021).de Vries, J. & Archibald, J. M. Plant evolution: Landmarks on the path to terrestrial life. New Phytol. 217, 1428–1434 (2018).Article 
PubMed 

Google Scholar 
Ahmadjian, V. The Lichen Symbiosis (John Wiley & Sons, 1993).
Google Scholar 
Lücking, R., Hodkinson, B. P. & Leavitt, S. D. The 2016 classification of lichenized fungi in the Ascomycota and Basidiomycota-approaching one thousand genera. Bryologist 119, 361–416 (2016).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).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wedin, M., Döring, H. & Gilenstam, G. Saprotrophy and lichenization as options for the same fungal species on different substrata: Environmental plasticity and fungal lifestyles in the Stictis-Conotrema complex. New Phytol. 164, 459–465 (2004).Article 

Google Scholar 
Muggia, L., Baloch, E., Stabentheiner, E., Grube, M. & Wedin, M. Photobiont association and genetic diversity of the optionally lichenized fungus Schizoxylon albescens. FEMS Microbiol. Ecol. 75, 255–272 (2011).Article 
CAS 
PubMed 

Google Scholar 
Sanders, W. B., Moe, R. L. & Ascaso, C. Ultrastructural study of the brown alga Petroderma maculiforme (Phaeophyceae) in the free-living state and in lichen symbiosis with the intertidal marine fungus Verrucaria tavaresiae (Ascomycotina). Eur. J. Phycol. 40, 353–361 (2005).Article 
CAS 

Google Scholar 
Vondrák, J. et al. From Cinderella to Princess. Preslia 94, 143–181 (2022).Article 

Google Scholar 
Hawksworth, D. L. The variety of fungal-algal symbioses, their evolutionary significance, and the nature of lichens. Bot. J. Linn. Soc. 96, 3–20 (1988).Article 

Google Scholar 
Larsson, K. H. & Ryvarden, L. Corticioid fungi of Europe 1. Acanthobasidium–Gyrodontium. Synop. Fungorum 43, 1–266 (2021).
Google Scholar 
Albertini, J. B., von Schweinitz, L. D. Conspectus fungorum in Lusatiae Superioris agro Niskiensi crescentium, e methodo Persooniana. (DE: Sumtibus Kummerianis, Lipsiae 1805) https://doi.org/10.5962/bhl.title.3601.Poelt, J. & Jülich, W. Über die Beziehungen zweier corticioider Basidiomyceten zu Algen. Österr. Bot. Zeitschrift 116, 400–410 (1969).Article 

Google Scholar 
Voytsekhovich, A., Ordynets, O. & Akimov, Y. Optionally lichenized fungi of Hyphodontia (Agaricomycetes, Schizoporaceae) and their photobiont composition. Aктyaльнi Пpoблeми Бoтaнiки Ta Eкoлoгiї. Maтepiaли Miжнapoднoї Кoнфepeнцiї Moлoдиx Учeниx 65 (2013).Voytsekhovich, A., Mikhailyuk, T., Akimov, Y., Ordynets, A., Gustavs, L. Optionally lichenized fungi of Hyphodontia (Agaricomycetes, Schizoporaceae). 8th Congress of the International Symbiosis Society, Lisbon, 12–18 July 2015. Lisbon, PT:, 217 (Conf. abstract) (2015).Gustavs L, Schiefelbein U, Darienko T, P. T. Symbioses of the green algal genera Coccomyxa and Elliptochloris (Trebouxiophyceae, Chlorophyta). in Algal and Cyanobacteria Symbioses (ed. Grube M, Seckbach J) 169–208 (2017).Darienko, T., Gustavs, L., Eggert, A., Wolf, W. & Pröschold, T. Evaluating the species boundaries of green microalgae (Coccomyxa, Trebouxiophyceae, Chlorophyta) using integrative taxonomy and DNA barcoding with further implications for the species identification in environmental samples. PLoS ONE 10, 1–31 (2015).Article 

Google Scholar 
Malavasi, V. et al. DNA-based taxonomy in ecologically versatile microalgae: A re-evaluation of the species concept within the coccoid green algal genus Coccomyxa (Trebouxiophyceae, Chlorophyta). PLoS ONE 11, e0151137 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Green, T. G. A., Nash, T. H. Lichen Biology. In Lichen Biology, Second Edition 152–181 (Cambridge University Press, Cambridge, 2008) https://doi.org/10.1017/CBO9780511790478.Lindgren, H. et al. Cophylogenetic patterns in algal symbionts correlate with repeated symbiont switches during diversification and geographic expansion of lichen-forming fungi in the genus Sticta (Ascomycota, Peltigeraceae). Mol. Phylogenet. Evol. 150, 106860 (2020).Article 
PubMed 

Google Scholar 
Kulichová, J., Škaloud, P. & Neustupa, J. Molecular diversity of green corticolous microalgae from two sub-mediterranean European localities. Eur. J. Phycol. 49, 345–355 (2014).Article 

Google Scholar 
Pröschold, T. & Darienko, T. The green puzzle Stichococcus (Trebouxiophyceae, Chlorophyta): New generic and species concept among this widely distributed genus. Phytotaxa 441, 113–142 (2020).Article 

Google Scholar 
Meier, F. A., Scherrer, S. & Honegger, R. Faecal pellets of lichenivorous mites contain viable cells of the lichen-forming ascomycete Xanthoria parietina and its green algal photobiont. Trebouxia arboricola. Biol. J. Linn. Soc. 76, 259–268 (2002).Article 

Google Scholar 
Bernicchia, A. & Gorjón, S. P. Corticiaceae s.l. 1008 (2010), ISBN: 9788890105791.Parmasto, E. Descriptiones taxorum novorum. Combinationes novae. Proc. Acad. Sci. Est. SSR. Biol. 16, 377–394 (1967).
Google Scholar 
Hjortstam, K., Larsson, K., Ryvarden, L. & Eriksson, J. The Corticiaceae of North Europe. (Oslo: Fungiflora, 1988).Jaag, O. Coccomyxa schmidle Monographie einer algengattung. Beitr. Kryptogamenflora Schweiz 8, 1–132 (1933).
Google Scholar 
Oberwinkler, F. Die gattungen der Basidiolichenen. Vorträge aus dem Gesamtgebiet der Botanik. Herausgegeb. v. d. Deutsch. bot. Ges. Neue Folge 4, 139–169 (1970).
Google Scholar 
Poelt, J. Basidienflechten, eine in den Alpen lange übersehene Pflanzengruppe. Jahrb. Vereins Schutze Alpenpfl. Tiere 40, 81–92 (1975).
Google Scholar 
Eriksson, J., Hjortstam, K. The Corticiaceae of North Europe. Vol. 6. (Grønlands Eskefabrikk, 1981).Oberwinkler, F. Basidiolichens. In Fungal Association 211–225 (Springer, Berlin Heidelberg, Berlin, 2001). https://doi.org/10.1007/978-3-662-07334-6_12.Chapter 

Google Scholar 
Jülich, W. A new lichenized Athelia from Florida. Persoonia 10, 149–151 (1978).
Google Scholar 
Zavada, M. S. & Simoes, P. The possible demi-lichenization of the basidiocarps of Trametes Versicolor (L.:Fries) pilat (polyporaceae). Northeast. Nat. 8, 101–112 (2001).
Google Scholar 
Neustroeva, N., Mukhin, V., Novakovskaya, I. & Patova, E. Biodiversity of symbiotic algae of wood decay Basidimycetes in the Central Urals. III Russ. Natl. Conf. “Information Technol. Biodivers. Res. 1, 83–92 (2020).
Google Scholar 
Zavada, M. S., DiMichele, L. & Toth, C. R. The possible demi-lichenization of Trametes versicolor (L.: Fries) Pilát (Polyporaceae): The transfer of fixed 14CO2 from epiphytic algae to T. versicolor. Northeast. Nat. 11, 33–40 (2004).Article 

Google Scholar 
Mukhin, V. A., Patova, E. N., Kiseleva, I. S., Neustroeva, N. V. & Novakovskaya, I. V. Mycetobiont symbiotic algae of wood-decomposing fungi. Russ. J. Ecol. 47, 133–137 (2016).Article 
CAS 

Google Scholar 
Sanders, W. B. & Masumoto, H. Lichen algae: The photosynthetic partners in lichen symbioses. Lichenologist 53, 347–393 (2021).Article 

Google Scholar 
Krause, G. & Weis, E. Chlorophyll fluorescence and photosynthesis: the basics. Annu. Rev. Plant Biol. 42(1), 313–349 (1991).Article 
CAS 

Google Scholar 
Lüttge, U. & Büdel, B. Resurrection kinetics of photosynthesis in desiccation-tolerant terrestrial green algae (Chlorophyta) on tree bark. Plant Biol. 12, 437–444 (2010).Article 
PubMed 

Google Scholar 
Lange, O. L. Moisture content and CO2 exchange of lichens: I. Influence of temperature on moisture-dependent net photosynthesis and dark respiration in Ramalina maciformis. Oecologia 45, 82–87 (1980).Article 
ADS 
PubMed 

Google Scholar 
Palmqvist, K. & Sundberg, B. Light use efficiency of dry matter gain in five macrolichens: Relative impact of microclimate conditions and species-specific traits. Plant Cell Environ. 23, 1–14 (2000).Article 

Google Scholar 
Vondrak, J. & Kubásek, J. Algal stacks and fungal stacks as adaptations to high light in lichens. Lichenol. 45(1), 115 (2013).Article 

Google Scholar 
Smith, N. G. & Dukes, J. S. Plant respiration and photosynthesis in global-scale models: Incorporating acclimation to temperature and CO2. Glob. Chang. Biol. 19, 45–63 (2013).Article 
ADS 
PubMed 

Google Scholar 
Medeiros, P. M. & Simoneit, B. R. T. Analysis of sugars in environmental samples by gas chromatography-mass spectrometry. J. Chromatogr. A 1141, 271–278 (2007).Article 
CAS 
PubMed 

Google Scholar 
Honegger, R. Functional aspects of the lichen symbiosis. Annu. Rev. Plant Physiol. Plant Mol. Biol. 42, 553–578 (1991).Article 
CAS 

Google Scholar 
Honegger, R. The lichen symbiosis—What is so spectacular about it?. Lichenologist 30, 193–212 (1998).Article 

Google Scholar 
Kirk, P. M. et al. (eds) Dictionary of the Fungi 10th edn. (CABI, Netherlands, 2008).
Google Scholar 
Ahmadjian, V. The lichen alga Trebouxia: Does it occur free-living?. Plant Syst. Evol. 158, 243–247 (1988).Article 

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).Article 
PubMed 

Google Scholar 
Rindi, F. & Guiry, M. Composition and spatial variability of terrestrial algal assemblages occurring at the bases of urban walls in Europe. Phycologia 43, 225–235 (2004).Article 

Google Scholar 
Stonyeva, M. P., Uzunov, B. A. & Gärtner, G. Aerophytic green algae, epimycotic on Fomes fomentarius (L. ex Fr.) Kickx. Annu. Sofia Univ “St. Kliment Ohridski”. Fac. Biol. 99, 19–25 (2015).
Google Scholar 
Aras, S. & Cansaran, D. Isolation of DNA for sequence analysis from herbarium material of some lichen specimens. Turk. J. Bot. 30, 449–453 (2006).
Google Scholar 
Hall, T. BioEdit: A userfriendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucleic Acids Symp. Ser. 41, 95–98 (1999).CAS 

Google Scholar 
Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Posada, D. jModelTest: Phylogenetic model averaging. Mol. Biol. Evol. 25, 1253–1256 (2008).Article 
CAS 
PubMed 

Google Scholar 
Huelsenbeck, J. P. & Ronquist, F. MRBAYES: Bayesian inference of phylogenetic trees. Bioinformatics 17, 754–755 (2001).Article 
CAS 
PubMed 

Google Scholar 
Vondrák, J. & Kubásek, J. Algal stacks and fungal stacks as adaptations to high light in lichens. Lichenol. 45, 115–124 (2013).Article 

Google Scholar 
Kubásek, J., Hájek, T. & Glime, J. M. Bryophyte photosynthesis in sunflecks: Greater relative induction rate than in tracheophytes. J. Bryol. 36, 110–117 (2014).Article 

Google Scholar 
Kubásek, J. et al. Moss stomata do not respond to light and CO2 concentration but facilitate carbon uptake by sporophytes: A gas exchange, stomatal aperture, and C-13-labelling study. New Phytol. 230, 1815–1828 (2021).Article 
PubMed 

Google Scholar 
Feige, G. & Kremer, B. Unusual carbohydrate pattern in Trentepohlia species. Phytochemistry 19, 1844–1845 (1980).Article 
CAS 

Google Scholar 
Tonon, T., Li, Y. & McQueen-Mason, S. Mannitol biosynthesis in algae: More widespread and diverse than previously thought. New Phytol. 213, 1573–1579 (2017).Article 
PubMed 

Google Scholar 
Gustavs, L., Görs, M. & Karsten, U. Polyol patterns in biofilm-forming aeroterrestrial green algae (Trebouxiophyceae, Chlorophyta). J. Phycol. 47, 533–537 (2011).Article 
PubMed 

Google Scholar  More