Philippa Kaur

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

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

Data sourcesMODIS on the Aqua satellite provides a global coverage within 1–2 days. All images acquired by this satellite mission from January 2003 to December 2020 were used in our study to detect global coastal phytoplankton blooms, with a total of 0.76 million images. MODIS Level-1A images were downloaded from the Ocean Biology Distributed Active Archive Center (OB.DAAC) at NASA Goddard Space Flight Center (GSFC), and were subsequently processed with SeaDAS software (version 7.5) to obtain Rayleigh-corrected reflectance (Rrc (dimensionless), which was converted using the rhos (in sr−1) product (rhos × π) from SeaDAS)41, remote sensing reflectance (Rrs (sr−1)) and quality control flags (l2_flags). If a pixel was flagged by any of the following, it was then removed from phytoplankton bloom detection: straylight, cloud, land, high sunglint, high solar zenith angle and high sensor zenith angle (https://oceancolor.gsfc.nasa.gov/atbd/ocl2flags/). MODIS level-3 product for aerosol optical thicknesses (AOT) at 869 nm was also obtained from OB.DAAC NASA GSFC (version R2018.0), which was used to examine the impacts of aerosols on bloom trends.We examined the algal blooms in the EEZs of 153 ocean-bordering countries (excluding the EEZs in the Caspian Sea or around the Antarctic), 126 of which were found with at least one bloom in the past two decades. The EEZ dataset is available at https://www.marineregions.org/download_file.php?name=World_EEZ_v11_20191118.zip. The EEZs are up to 200 nautical miles (or 370 km) away from coastlines, which include all continental shelf areas and offer the majority of marine resources available for human use. Regional statistics of algal blooms were also performed for LMEs. LMEs encompass global coastal oceans and outer edges of coastal currents areas, which are defined by various distinct features of the oceans, including hydrology, productivity, bathymetry and trophically dependent populations42. Of the 66 LMEs identified globally, we excluded the Arctic and Antarctic regions and examined 54 LMEs. The boundaries of LMEs were obtained from https://www.sciencebase.gov/catalog/item/55c77722e4b08400b1fd8244.We used HAEDAT to validate our satellite-detected phytoplankton blooms in terms of presence or absence. The HAEDAT dataset (http://haedat.iode.org) is a collection of records of HAB events, maintained under the UNESCO Intergovernmental Oceanographic Commission and with data archives since 1985. For each HAB event, the HAEDAT records its bloom period (ranging from days to months) and geolocation. We merged duplicate entries when both the recorded locations and times of the HAEDAT events were very similar to one another, and a total number of 2,609 HAEDAT events were ultimately selected between 2003 and 2020.We used the ¼° resolution National Oceanic and Atmospheric Administration Optimum Interpolated SST (v. 2.1) data to examine the potential simulating effects of warming on the global phytoplankton trends. We also estimated the SST gradients following the method of Martínez-Moreno33. As detailed in ref. 33, the SST gradient can be used as a proxy for the magnitude of oceanic mesoscale currents (EKE). We used the SST gradient to explore the effects of ocean circulation dynamics on algal blooms.Fertilizer uses and aquaculture production for different countries was used to examine the potential effects of nutrient enrichment from humans on global phytoplankton bloom trends. Annual data between 2003 and 2019 on synthetic fertilizer use, including nitrogen and phosphorus, are available from https://ourworldindata.org/fertilizers. Annual aquaculture production includes cultivated fish and crustaceans in marine and inland waters, and sea tanks, and the data between 2003 and 2018 are available from https://ourworldindata.org/grapher/aquaculture-farmed-fish-production.The MEI, which combines various oceanic and atmospheric variables36, was used to examine the connections between El Niño–Southern Oscillation activities and marine phytoplankton blooms. The dataset is available from https://psl.noaa.gov/enso/mei/.Development of an automated bloom detection methodA recent study by the UNESCO Intergovernmental Oceanographic Commission revealed that globally reported HAB events have increased6. However, such an overall increasing trend was found to be highly correlated with recently intensified sampling efforts6. Once this potential bias was accounted for by examining the ratio between HAB events to the number of samplings5, there was no significant global trend in HAB incidence, though there were increases in certain regions. With synoptic, frequent, and large-scale observations, satellite remote sensing has been extensively used to monitor algal blooms in oceanic environments17,18,19. For example, chlorophyll a (Chla) concentrations, a proxy for phytoplankton biomass, has been provided as a standard product by NASA since the proof-of-concept Coastal Zone Color Scanner (1978–1986) era43,44. The current default algorithm used to retrieve Chla products is based on the high absorption of Chla at the blue band45,46, which often shows high accuracy in the clear open oceans but high uncertainties in coastal waters. This is because, in productive and dynamic coastal oceans, the absorption of Chla in the blue band can be obscured by the presence of suspended sediments and/or coloured dissolved organic matter (CDOM)47. To address this problem, various regionalized Chla algorithms have been developed48. Unfortunately, the concentrations of the water constituents (CDOM, sediment and Chla) can vary substantially across different coastal oceans. As a result, a universal Chla algorithm that can accurately estimate Chla concentrations in global coastal oceans is not currently available.Alternatively, many spectral indices have been developed to identify phytoplankton blooms instead of quantifying their bloom biomass, including the normalized fluorescence line height21 (nFLH), red tide index49 (RI), algal bloom index47 (ABI), red–blue difference (RBD)50, Karenia brevis bloom index50 (KBBI) and red tide detection index51 (RDI). In practice, the most important task for these index-based algorithms is to determine their optimal thresholds for bloom classification. However, such optimal thresholds can be regional-or image-specific20, due to the complexity of optical features in coastal waters and/or the contamination of unfavourable observational conditions (such as thick aerosols, thin clouds, and so on), making it difficult to apply spectral-index-based algorithms at a global scale.To circumvent the difficulty in determining unified thresholds for various spectral indices across global coastal oceans, an approach from a recent study to classify algal blooms in freshwater lakes52 was adopted and modified here. In that study, the remotely sensed reflectance data in three visible bands (red, green and blue) were converted into two-dimensional colour space created by the Commission Internationale del’éclairage (CIE), in which the position on the CIE chromaticity diagram represented the colour perceived by human eyes (Extended Data Fig. 1a). As the algal blooms in freshwater lakes were manifested as greenish colours, the reflectance of bloom-containing pixels was expected to be distributed in the green gamut of the CIE chromaticity diagram; the stronger the bloom, the closer the distance to the upper border of the diagram (the greener the water).Here, the colour of phytoplankton blooms in the coastal oceans can be greenish, yellowish, brownish, or even reddish53, owing to the compositions of bloom species (diatoms or dinoflagellates) and the concentrations of different water constituents. Furthermore, the Chla concentrations of the coastal blooms are typically lower than those in inland waters, thus demanding more accurate classification algorithms. Thus, the algorithm proposed by Hou et al.52 was modified when using the CIE chromaticity space for bloom detection in marine environments. Specifically, we used the following coordinate conversion formulas to obtain the xy coordinate values in the CIE colour space:$$begin{array}{c}x=X/(X+Y+Z)\ y=Y/(X+Y+Z)\ X=2.7689R+1.7517G+1.1302B\ Y=1.0000R+4.5907G+0.0601B\ Z=0.0000R+0.0565G+5.5943Bend{array}$$
(1)
where R, G and B represent the Rrc at 748 nm, 678 nm (fluorescence band) and 667 nm in the MODIS Aqua data, respectively. By contrast, the R, G and B channels used in Hou et al.52 were the red, green and blue bands. We used the fluorescence band for the G channel because, for a given region, the 678 nm signal increases monotonically with the Chla concentration for blooms of moderate intensity21, which is similar to the response of greenness to freshwater algal blooms. Thus, the converted y value in the CIE coordinate system represents the strength of the fluorescence. In practice, for pixels with phytoplankton blooms, the converted colours in the chromaticity diagram will be located within the green, yellow or orange–red gamut (see Extended Data Fig. 1a); the stronger the fluorescence signal is, the closer the distance to the upper border of the CIE diagram (larger y value). By contrast, for bloom-free pixels without a fluorescence signal, their converted xy coordinates will be located in the blue or purple gamut. Therefore, we can determine a lower boundary in the CIE two-dimensional coordinate system to separate bloom and non-bloom pixels, similar to the method proposed by Hou et al.52.We selected 53,820 bloom-containing pixels from the MODIS Rrc data as training samples to determine the boundary of the CIE colour space. These sample points were selected from nearshore waters worldwide where frequent phytoplankton blooms have been reported (Extended Data Fig. 2); the algal species included various species of dinoflagellates and diatoms20. A total of 80 images was used, which were acquired from different seasons and across various bloom magnitudes, to ensure that the samples used could almost exhaustively represent the different bloom conditions in the coastal oceans.We combined the MODIS FLHRrc (fluorescence line height based on Rrc) and enhanced red–green–blue composite (ERGB) to delineate bloom pixels manually. The FLHRrc image was calculated as:$$begin{array}{c}{{rm{FLH}}}_{{rm{Rrc}}}={R}_{{rm{rc}}678}times {F}_{678}-[{R}_{{rm{rc}}667}times {F}_{667}+({R}_{{rm{rc}}748}times {F}_{748}\ ,,-,{R}_{{rm{rc}}667}times {F}_{667})times (678-667)/(748-667)]end{array}$$
(2)
where Rrc667, Rrc678 and Rrc748 are the Rrc at 667, 678 and 748 nm, respectively, and F667, F678 and F748 are the corresponding extraterrestrial solar irradiance. ERGB composite images were generated using Rrc of three bands at 555 (R), 488 (G) and 443 nm (B). Although phytoplankton-rich and sediment-rich waters have high FLHRrc values, they appear as darkish and bright features in the ERGB images (Extended Data Fig. 3), respectively21. In fact, visual examination with fluorescence signals and ERGB has been widely accepted as a practical way to delineate coastal algal blooms on a limited number of images21,54,55. Note that the FLHRrc here was slightly different from the NASA standard nFLH product56, as the latter is generated using Rrs (corrected for both Rayleigh and aerosol scattering) instead of Rrc (with residual effects of aerosols). However, when using the NASA standard algorithm to further perform aerosol scattering correction over Rrc, 20.7% of our selected bloom-containing pixels failed to obtain valid Rrs (without retrievals or flagged as low quality), especially for those with strong blooms (see examples in Extended Data Fig. 4). Likewise, we also found various nearshore regions with invalid Rrs retrievals. By contrast, Rrc had valid data for all selected samples and showed more coverage in nearshore coastal waters. The differences between Rrs and Rrc were because the assumptions for the standard atmospheric correction algorithm do not hold for bloom pixels or nearshore waters with complex optical properties57. In fact, Rrc has been used as an alternative to Rrs in various applications in complex waters58,59.We converted the Rrc data of 53,820 selected sample pixels into the xy coordinates in the CIE colour space (Extended Data Fig. 1a). As expected, these samples of bloom-containing pixels were located in the upper half of the chromaticity diagram (the green, yellow and orange–red gamut) (Extended Data Fig. 1a). We determined the lower boundary of these sample points in the chromaticity diagram, which represents the lightest colour and thus the weakest phytoplankton blooms; any point that falls above this boundary represents stronger blooms. The method to determine the boundary was similar to Hou et al.52: we first binned the sample points according to the x value in the chromaticity diagram and estimated the 1st percentile (Q1%) of the corresponding Y for each bin; then, we fit the Q1% using two-order polynomial regression. Sensitivity analysis with Q0.3% (the three-sigma value) resulted in minor changes ( 1/3 AND y  > y2), it is classified as a ‘bloom’ pixel.Depending on the local region and application purpose, the meaning of ‘phytoplankton bloom’ may differ. Here, for a global application, the pixelwise bloom classification is based on the relationship (represented using the CIE colour space) between Rrc in the 667-, 678- and 754-nm bands derived from visual interpretation of the 80 pairs of FLHRrc and ERGB imagery. Instead of a simple threshold, we used a lower boundary of the sample points in the chromaticity diagram to define a bloom. In simple words, a pixel is classified as a bloom if its fluorescence signal is detectable (the associated xy coordinate in the CIE colour space located above the lower boundary). Histogram of the nFLH values from the 53,820 training pixels demonstrated the minimum value of ~0.02 mW cm−2 μm−1 (Extended Data Fig. 1a), which is in line with the lower-bound signal of K. brevis blooms on the West Florida shelf21,47. Note that, such a minimum nFLH is determined from the global training pixels, and it does not necessarily represent a unified lower bound for phytoplankton blooms across the entire globe, especially considering that fluorescence efficiency may be a large variable across different regions. Different regions may have different lower bounds of nFLH to define a bloom, and such variability is represented by the predefined boundary in the CIE chromaticity diagram in our study. Correspondingly, although the accuracy of Chla retrievals may have large uncertainties in coastal waters, the histogram of the 53,820 training pixels shows a lower bound of ~1 mg m−3 (Extended Data Fig. 1a). Similarly to nFLH, such a lower bound may not be applicable to all coastal regions, as different regions may have different lower bounds of Chla for bloom definition.Although the MODIS cloud (generated by SeaDAS with Rrc869 0.12) and Index2 ( More

Chazdon, R. L. et al. Carbon sequestration potential of second-growth forest regeneration in the Latin American tropics. Sci. Adv. 2, e1501639 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
IKI. The Bonn Challenge. https://www.bonnchallenge.org/ (2022).UNCCD. Land Degradation Neutrality. https://www.unccd.int/land-and-life/land-degradation-neutrality/overview (2022).Bastin, J.-F. et al. The global tree restoration potential. Science 365, 76–79 (2019).Article 
CAS 
PubMed 

Google Scholar 
Brancalion, P. H. S. et al. Global restoration opportunities in tropical rainforest landscapes. Sci. Adv. 5, eaav3223 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Strassburg, B. B. N. et al. Global priority areas for ecosystem restoration. Nature 586, 724–729 (2020).Article 
CAS 
PubMed 

Google Scholar 
Erbaugh, J. T. et al. Global forest restoration and the importance of prioritizing local communities. Nat. Ecol. Evol. 4, 1472–1476 (2020).Article 
CAS 
PubMed 

Google Scholar 
Fleischman, F. et al. Restoration prioritization must be informed by marginalized people. Nature 607, E5–E6 (2022).Article 
CAS 
PubMed 

Google Scholar 
Chaturvedi, R. et al. Restoration Opportunities Atlas of India. www.india.restorationatlas.org/methodology (2022).McLain, R., Lawry, S., Guariguata, M. R. & Reed, J. Toward a tenure-responsive approach to forest landscape restoration: a proposed tenure diagnostic for assessing restoration opportunities. Land Use Policy 104, 103748 (2021).Article 

Google Scholar 
Binod, B., Bhattarcharjee, A. & Ishwar, N. M. Bonn Challenge and India: Progress on Restoration Efforts Across States and Landscapes (IUCN, 2018).Government of India. Aspirational Districts Phase 1 (vikaspedia, 2018).Government of India. Census of India. https://censusindia.gov.in/2011census/dchb/DCHB.html (2011).DeFries, R. et al. Land management can contribute to net zero. Science 376, 1163–1165 (2022).Article 
CAS 
PubMed 

Google Scholar 
Borah, B., Bhattacharya, A. & Ishwar, N. M. Bonn Challenge and India. Progress On Restoration Efforts Across States and Landscapes. https://www.bonnchallenge.org/pledges/india (2018).Gopalakrishna, T. et al. Existing land uses constrain climate change mitigation potential of forest restoration in India. Conserv. Lett. https://doi.org/10.1111/conl.12867 (2022).Dhyani, S. et al. Agroforestry to achieve global climate adaptation and mitigation targets: are South Asian countries sufficiently prepared? Forests 12, 303 (2021).Article 

Google Scholar 
Nerlekar, A. N. et al. Removal or utilization? Testing alternative approaches to the management of an invasive woody legume in an arid Indian grassland. Restor. Ecol. https://doi.org/10.1111/rec.13477 (2022).Coleman, E. A. et al. Limited effects of tree planting on forest canopy cover and rural livelihoods in Northern India. Nat Sustain 4, 997–1004 (2021).Article 

Google Scholar 
Ramprasad, V., Joglekar, A. & Fleischman, F. Plantations and pastoralists: afforestation activities make pastoralists in the Indian Himalaya vulnerable. Ecol. Soci. https://doi.org/10.5751/ES-11810-250401 (2020).DeFries, R. et al. Improved household living standards can restore dry tropical forests. Biotropica https://doi.org/10.1111/btp.12978 (2021).Lele, S., Khare, A. & Mokashi, S. Estimating and Mapping CFR Potential (ATREE, 2020).Agarwala, M. et al. Impact of biogas interventions on forest biomass and regeneration in southern India. Global Ecol. Conservation 11, 213–223 (2017).Article 

Google Scholar 
Menon, A. & Schmidt-Vogt, D. Effects of the COVID-19 pandemic on farmers and their responses: a study of three farming systems in Kerala. South India. Land 11, 144 (2022).
Google Scholar 
Fremout, T. et al. Diversity for Restoration (D4R): Guiding the selection of tree species and seed sources for climate‐resilient restoration of tropical forest landscapes. J. Appl. Ecol. 59, 664–679 (2022).Article 

Google Scholar 
Hughes, K. A. et al. Can restoration of the commons reduce rural vulnerability? A Quasi-experimental comparison of COVID-19 livelihood-based coping strategies among rural households in three Indian States. Int. J. Common. 16, 189 (2022).Article 

Google Scholar 
Madhusudan, M. D. & Vanak, A. Mapping the Distribution and Extent of India’s Semi-arid Open Natural Ecosystems. https://doi.org/10.1002/essoar.10507612.1 (2021).Vanak, A. T., Hiremath, A. J., Ganesh, T. & Rai, N. D. Filling in the (Forest) Blanks: the Past, Present and Future of India’s Savanna Grasslands (ATREE, 2017).Oxford Poverty & Human Development Initiative. Global Multidimensional Poverty Index 2018. The Most Detailed Picture to Date of the World’s Poorest People. https://ophi.org.uk/wp-content/uploads/G-MPI_2018_2ed_web.pdf (2018).Hijmans, R. J. et al. raster: Geographic Data Analysis and Modeling. https://rspatial.org/raster (2023).Bivand, R. et al. rgdal: Bindings for the ‘Geospatial’ Data Abstraction Library. https://cran.r-project.org/web/packages/rgdal/index.html (2023).QGIS Development Team. QGIS Geographic Information System (Open Source Geospatial Foundation Project, 2022). More

OverviewThis study establishes a framework for mapping carbon stocks at the level of individual trees at a sub-continental scale in semi-arid sub-Saharan Africa north of the Equator. We used satellite imagery from the early dry season (Extended Data Fig. 1). The deep learning method developed by a previous study1 allowed us to map billions of discrete tree crowns at the 50-cm scale from West Africa to the Red Sea. Then we used allometry to convert tree crown area into tree wood, foliage and root carbon for the 0–1,000 mm year−1 precipitation zone in which our allometry was collected (Extended Data Fig. 2). We introduce a viewer that enables the billions of trees to be viewed at different scales, with information on location, metadata of the Maxar satellite image used, tree crown area and the estimated wood, foliage and root carbon content based on our allometry (Fig. 4). We also make available our output data for the 1,000 mm year−1 precipitation zone southward to 9.5° N latitude with information on location, precipitation, metadata of the Maxar satellite image used, tree crown area, tree wood carbon, tree root carbon and tree leaf carbon.Satellite imageryWe used 326,523 Maxar multispectral images from the QuickBird-2, GeoEye-1, WorldView-2 and WorldView-3 satellites collected from 2002 to 2020 from November to March from 9.5° N to 24° N latitude within Universal Transverse Mercator (UTM) zones 28–37 for Africa (Extended Data Table 1a). These images were obtained by NASA through the NextView License from the National Geospatial-Intelligence Agency. Data were assembled over several years with a focus on later years to achieve a relatively recent and complete wall-to-wall coverage.When using satellite data from different satellites over several years, with varying sun–target–satellite angles, with varying radiometric calibration of satellite spectral bands and different atmospheric compositions through which the surface is imaged, there are two possibilities for using hundreds of thousands of satellite images together quantitatively. One approach, used extensively in NASA’s, NOAA’s and the European Space Agency’s Earth-viewing satellite programmes, is to quantitatively inter-calibrate radiometrically the satellite channels through time; correct these data for time-dependent atmospheric effects such as aerosols, clouds, haze, smoke, dust and other atmospheric constituent effects and then normalize the viewing perspective to the same sun–target–satellite angle38. Another approach is to use the satellite data as collected; assemble training data of trees viewed from different satellites under different sun–target–satellite angles, different times, different atmospheric conditions and use machine learning with high-performance computing to perform the tree mapping at the 50-cm scale. The key to successful machine learning is to account for all the sources of variation within the domain of study in the training data to ensure accurate identification of trees under all circumstances. We included trees viewed substantially off-nadir, trees collected under different aerosol optical thicknesses, trees collected under cirrus cloud conditions, trees viewed in the forward and backward scan directions, trees on sandy soils, trees on clay soils, trees on burn scars, trees in laterite areas and trees in riverine settings. Our training data were collected by one team member and are a carefully selected manual delineation of 89,899 individual trees under a range of atmospheric conditions, viewing perspectives and ecological settings.All multispectral and panchromatic bands associated with our Maxar images were orthorectified to a common mapping basis. We next pan-sharpened all multispectral bands to the 0.5-m scale with the associated panchromatic band. The absolute locational uncertainty of pixels at the 0.5-m scale from orbit is approximately ±11 m, considering the root-mean-square location errors among the QuickBird-2, GeoEye-1, WorldView-2 and WorldView-3 satellites (Extended Data Table 1). We formed the normalized difference vegetation index (NDVI)39 from every image in the traditional way from the pan-sharpened red and near-infrared bands. We also associated the panchromatic band with the NDVI band and ensured that the panchromatic and NDVI bands were highly co-registered. The NDVI was used to distinguish tree crowns from non-vegetated background because the images were taken from a period when only woody plants were photosynthetically active in this area36. Our training data were labelled on images from the early dry season when only trees have green leaves. Because most semi-arid savannah trees continue to photosynthesize in the early dry season after herbaceous vegetation senesces, green leaf tree crowns are easily mapped because of their higher NDVI values than their senescent herbaceous vegetation surroundings. We substantiate this by analysis of 308 individual trees using NDVI time series with 4-m PlanetScope imagery that emphasized the importance of satellite data from the November, December and January early dry-season months (Extended Data Fig. 1).We next formed our data into mosaics by applying a set of decision rules, resulting in a collection of 16 × 16-km tiles within each UTM zone from 9.5° N to 24° N latitude for Africa. The initial round of scoring considered percentage cloud cover, sun elevation angle and sensor off-nadir angle: preference was given to imagery that had lower cloud cover, then higher sun elevation angle and finally view angles closest to nadir. In the second round of scoring, selections were assigned priority to favour early dry-season months and off-nadir view angles: preference was given to imagery from November, December and January with off-nadir angle less than ±15°; second to imagery from November to January with off-nadir angle between ±15° and ±30°; third to imagery from February or March with off-nadir angle less than ±15°; and finally to imagery from February or March with off-nadir angle between ±15° and ±30°. Image mosaics were necessary to eliminate multiple counting of trees. We formed mosaics using 94,502 images for tree segmentation, with 94% of these being from November, December and January. Ninety percent of our selected mosaic imagery was within ±15° of nadir, 87% were acquired between 2010 and 2020 and 94% were from the early dry season (Extended Data Fig. 7). A summary of month, year, solar elevation and off-nadir angle by UTM zone can be found in Supplemental Information Fig. 1.Possible obscuration of the surface by clouds totalled 4.1% of our input mosaic data area and aerosol optical depth >0.6 at 470-nm (ref. 40) areas totalled 3.4% of our input data. However, we mapped 691,477,772 trees in our possible cloud-cover-affected and aerosol-affected areas, indicating that cloud and aerosol effects were lower than these numbers. In addition, 0.9% of our input data did not process. We include a data layer in our viewer for these three conditions.Mapping tree crowns with deep learningWe used convolutional neural network models developed by a previous study1. The models were trained with manually delineated and annotated 89,899 individual trees along a north–south gradient from 0 to 1,000 mm year−1 rainfall1. Only features that showed a distinct crown area and associated shadow were included, which excluded small bushes, grass tussocks, rocks and other features that might have green leaves or cast a shadow from our classification. All training data and model training was done in UTM zones 28 and 29. Because tree floristic diversity in the 0–1,000 mm year−1 zone of our study is highly similar from the Atlantic Ocean to the Red Sea across Africa41,42,43, we added no further training data as our study moved further eastward. We used state-of-the-art deep learning to segment trees crowns at the 50-cm scale1. We used two different models based on a U-Net architecture, one for lower-rainfall desert regions with 150 mm year−1. Details about the network architecture, training process and hyperparameter choices can be found in ref. 1. Previous evaluation showed that early dry-season images performed better than late dry-season images, which was a limitation of our previous study. We reduced this error by using early dry-season images with only 6% of our area being covered by images from February and March. The models were also designed to separate clumped trees by highlighting spaces between different crowns during the learning process, similar to a strategy for separating touching cells in microscopic imagery22.AllometryVery-high-resolution satellite images and deep learning have achieved mapping of individual trees over large areas1. Each tree is georeferenced in the satellite data and defined by crown area. The challenge was to develop allometric equations for foliage, wood and root dry masses or carbon based on crown area regardless of species. This was met by reanalysing existing Sahelian and Sudanian woody plant data from destructive sampling. Overall, the seasonal maximum foliage, wood and root dry masses were measured on 900, 698 and 26 trees or shrubs from 27, 26 and 5 species, respectively, for which crown area was also measured. Several allometric regression models tested for foliage, wood or root masses are power functions and independent of species. All the regression outputs were inter-compared for fit indicators, by systematic estimates of prediction uncertainty and by root-to-wood ratios and foliage-to-wood ratios over the range of crown areas. This resulted in a set of ordinary least squares log–log equations with crown area as the independent variable. The Sahelian and Sudanian allometry equations were also compared with published allometry equations for tropical trees, primarily from more humid tropics, which are generally based on stem diameter, tree height and wood density. Our allometric predictions are within the range of other allometry predictions, reinforcing the confidence in their use beyond the Sahelian and Sudanian domains into sub-humid savannahs for discrete trees19.On the basis of ref. 19, we predicted the wood (w), foliage (f) and root (r) dry mass as functions of the crown area (A) of a single tree as:$$begin{array}{c}{text{mass}}_{{rm{w}}}(A)=3.9448times {A}^{1.1068},({N}_{{rm{w}}}=698)\ {text{mass}}_{{rm{f}}}(A)=0.2693times {A}^{0.9441},({N}_{{rm{f}}}=900)\ {text{mass}}_{{rm{r}}}(A)=0.8339times {A}^{1.1730},({N}_{{rm{r}}}=26)end{array}$$The tree mass components of wood, leaves and roots were combined to predict the total mass(A) in kg of a tree from its crown area A in m2:$$text{mass}left(Aright)={text{mass}}_{{rm{w}}}left(Aright)+{text{mass}}_{{rm{f}}}left(Aright)+{text{mass}}_{{rm{r}}}left(Aright)$$As in ref. 1, a crown area of size A  > 200 m2 was split into ({rm{lfloor }}A/100{rm{rfloor }}) areas of size 100 m2 and one area with the remaining m2 if necessary. We converted dry mass to carbon by multiplying with a factor of 0.47 (ref. 44).Uncertainty analysisWe evaluated the uncertainty of our tree crown area mapping and carbon estimation in two ways. First, we quantified our tree crown mapping omission and commission errors by inspecting randomly selected areas from UTM zones 28–37, validating that our neural network generalized over UTM zones consistently (Extended Data Fig. 8).Second, we quantified the relative error of our tree crown area estimation. We consider the uncertainty Δx of a quantity x and the corresponding relative uncertainty δx defined by the absolute and relative error, respectively45. To assess the relative error in crown area estimation resulting from errors by the neural network, we considered external validation data from ref. 1, which were not used in the model-building process. We considered expert-labelled tree crowns as well as the predicted tree crowns from 78 plots of 256 × 256 pixels. The hand-labelled set contained 5,925 trees and the system delineated 5,915 trees. The total hand-labelled tree crown area was 118,327 m2 and the neural network predicted 121,898 m2. This gave a relative error in crown area mapping of δarea = 3.3%. We matched expert-labelled and predicted tree crowns and computed the root-mean-square error (RMSE) per tree, taking overlapping areas and missed trees into account (see Extended Data Fig. 8). We estimated the allometric uncertainty (δallometric) using the data from ref. 19 (see below). The two relative errors δarea and δallometric were combined to an overall uncertainty estimate for the carbon prediction of ±19.8% (see below).Omission and commission errorsWe evaluated our tree crown mapping accuracy by analysis of 1,028 randomly selected 512 × 256-pixel areas over the 9.5° N to 24° N latitude within UTM zones 28–37. Because the drier 60% of our study area only contains 1% of the 9,947,310,221 trees we mapped in the 0–1,000 mm year−1 rainfall zone, we applied an 80% bias for selecting evaluation areas above the 200 mm year−1 precipitation line46, as >98% of tree identifications were above the 200 mm year−1 precipitation isoline. Identified tree polygons were further categorized into tree crown area classes from 0–15 m2, 15–50 m2, 50–200 m2 and >200 m2, with a total of 50,570 trees evaluated. Although a previous study reported greatest uncertainty in both the smallest and largest area classes1, our more expansive work found the greatest uncertainty in our smallest tree class. We excluded from evaluation any tiles that had annual precipitation46 >1,000 mm year−1 and all areas that were devoid of vegetation, leaving us with 850 areas.Seven members of our team evaluated the accuracy in terms of commission and omission by tree crown area classes for the 850 areas. Input data provided for every area were the NDVI layer, the panchromatic shadow layer and the neural net mapping results in each of the four crown area classes. Ancillary data available to evaluators included the centre coordinates for comparison with Google Earth data, the Funk et al.46 rainfall, the acquisition date of the area evaluated and the viewing perspective.We identified areas wrongly classified as tree crowns (commission errors), missed trees (omission errors) and crown areas corresponding to clumped trees (Extended Data Fig. 8). Clumped trees were most common for >200 m2 tree crown area. They were rare in the 3–15 m2 and 15–50 m2 tree classes, which comprise 88% of our tree crowns. In the 850 patches, the number of trees ranged from one tree to 326 trees, with a total of 50,570 trees evaluated and 3,765 errors identified. Overall, the commission and omission error rates were 4.9% and 2.7%, respectively, a net uncertainty of 2.2%.Allometric uncertainty estimationThe prediction of tree carbon from the crown area for a single tree based on crown area alone is inherently uncertain47,48. As the allometric equations are based on three different datasets, we compute their uncertainties independently, combine them and put them in relation to the total carbon measured in the three datasets.The allometric equations were established using an optimal least-squares fit of an affine linear model predicting the logarithmic carbon from the logarithmic tree crown area19. To estimate the uncertainty of the allometric equations, we repeated the fitting using random subsampling. The datasets were randomly split into training data (80%) for fitting the allometric equations and validation data (20%) for assessing the uncertainty. For example, from the root measurements, (({A}_{1},{y}_{1}),ldots ,({A}_{{N}_{{rm{r}}}},,{y}_{{N}_{{rm{r}}}})), we compute ({mu }_{{rm{r}}}=frac{1}{{N}_{{rm{r}}}}mathop{sum }limits_{i=1}^{{N}_{{rm{r}}}}{y}_{i}) and ({hat{mu }}_{{rm{r}}}=frac{1}{{N}_{{rm{r}}}}mathop{sum }limits_{i=1}^{{N}_{{rm{r}}}}{text{mass}}_{{rm{r}}}({A}_{i})). The corresponding error is ({varDelta }_{{rm{r}}}=|{mu }_{{rm{r}}}-{hat{mu }}_{{rm{r}}}|).Because the total carbon for a tree with a certain crown area is the sum of the three carbon components, we add the absolute uncertainties assuming independence45.$${varDelta }_{{rm{a}}{rm{l}}{rm{l}}{rm{o}}{rm{m}}{rm{e}}{rm{t}}{rm{r}}{rm{i}}{rm{c}}}simeq sqrt{{varDelta }_{{rm{f}}}^{2}+{varDelta }_{{rm{w}}}^{2}+{varDelta }_{{rm{r}}}^{2}}$$and compute the relative uncertainty as ({delta }_{text{allometric}}=frac{{varDelta }_{text{allometric}}}{{mu }_{text{mass}}}), in which the average mass μmass is given by the sum of the averages for wood (μw), leaves (μf) and root (μr). This process was repeated ten times, resulting in a mean relative uncertainty of$${bar{delta }}_{{rm{allometric}}}=19.5 % .$$Total carbon uncertaintyWe combine the uncertainties from the neural net mapping and our allometric equations, which can be viewed as considering (1 + A)·(1 + B) with A and B being random variables with standard deviations δarea and δallometric. Neglecting higher-order and interaction terms, we combine the two sources of uncertainty to (delta simeq sqrt{{delta }_{{rm{area}}}^{2}+{bar{delta }}_{{rm{allometric}}}^{2}}), resulting in an uncertainty in total tree carbon for our study of ±19.8%. See also Extended Data Fig. 9 for the RMSEs of our predicted crown areas calculated on external validation data from ref. 1, binned on the basis of the 50th quantiles of the hand-labelled crown areas and converted also into carbon. Extended Data Fig. 10 is a flow diagram summarizing our methods.Our viewerVisualizing our large tree-mapping dataset in an interactive format was essential for quality-control purposes, exploration of the data and hypothesis creation. Creating a web-based viewer serves the purpose of being the initial point of interaction with our dataset for fellow researchers, local stakeholders or the general public. The visualization of more than 10 billion trees in a web browser required maintaining performance, interactivity and individual metadata for each polygon. Users should be able to zoom in to any area within the dataset to view individual tree polygons and query their statistics while at the same time accurately depicting the overall trends of the dataset at lower zoom levels. The visualization also needed to clearly denote where data were missing or possibly affected by clouds or aerosols. Finally, the extent and origin of the source imagery, its acquisition date and a preview of the imagery needed to be available. To accomplish these goals, a vector-tile-based approach was taken, with the data visualized in a Mapbox GL JS map within a React web application. To create vector tiles covering the entire study area, we developed a data-processing pipeline using high-performance computing resources to transform the data into compatible formats, as well as to package, optimize and combine the vector tiles themselves.We used two tracks to store and visualize the results of this study on the web: vector polygon data and generalized rasters representing tree crown density. At the native spatial resolution of 50 cm, the map shows the full-resolution tree polygon dataset. At lower-spatial-resolution zoom levels, rasterized representations of tree density are shown. Visualizing generalized rasters in place of vector polygons improves performance substantially. As users zoom in to higher spatial resolutions, the raster layer fades away and is replaced by the full-resolution polygon layer. Once zoomed far enough to resolve individual polygons, users can click to select a polygon to show a map overlay containing various properties of the tree, as well as the date on which the source imagery was acquired and a link to preview the source imagery.Rainfall dataWe used the rainfall data of Funk et al. to estimate annual rainfall at 5.6-m grids46. We averaged the available data from 1982 to 2017 and extracted the mean annual rainfall for each mapped tree and bilinearly interpolated it to 100 × 100-m resolution. The rainfall data were also used to classify the study area into mean annual precipitation zones: hyper-arid from 0–150 mm year−1, arid from 150–300 mm year−1, semi-arid from 300–600 mm year−1 and sub-humid from 600–1,000 mm year−1 zones. The rainfall data are found at https://data.chc.ucsb.edu/products/CHIRPS-2.0/africa_monthly/ (ref. 46). More

Tucker, C. et al. Nature 615, 80–86 (2023).Article 

Google Scholar 
Bayala, J. et al. Agric. Ecosyst. Environ. 205, 25–35 (2015).Article 

Google Scholar 
Keesstra, S. D. et al. Soil 2, 111–128 (2016).Article 

Google Scholar 
Dewi, S. et al. Int. J. Biodivers. Sci. Ecosyst. Serv. Mgmt 13, 312–329 (2017).Article 

Google Scholar 
Ahlström, A. et al. Science 348, 895–899 (2015).Article 
PubMed 

Google Scholar 
Poulter, B. et al. Nature 509, 600–603 (2014).Article 
PubMed 

Google Scholar 
Prăvălie, R. et al. Environ. Res. 201, 111580 (2021).Article 
PubMed 

Google Scholar 
Reij, C. P. & Smaling, E. M. A. Land Use Policy 25, 410–420 (2008).Article 

Google Scholar 
Zomer, R. J., Bossio, D. A., Trabucco, A., van Noordwijk, M. & Xu, J. Circ. Agric. Syst. 2, 3 (2022).Article 

Google Scholar 
Chomba, S., Sinclair, F., Savadogo, P., Bourne, M. & Lohbeck, M. Front. For. Glob. Change 3, 571679 (2020).Article 

Google Scholar 
Dakpogan, A., Bayala, J., Ouattara, I. & Ellington, E. in United for Lands: From National Coalitions to a Pipeline of Bankable Projects for the Great Green Wall 54–56 (United Nations, 2022).
Google Scholar 
Garrity, D. P. & Bayala, J. in Sustainable Development Through Trees on Farms: Agroforestry in its Fifth Decade (ed. van Noordwijk, M.) 153–175 (World Agroforestry, 2019).
Google Scholar 
Schnell, S., Kleinn, C. & Ståhl, G. Environ. Monit. Assess. 187, 600 (2015).Article 
PubMed 

Google Scholar  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

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