Philippa Kaur

Bell, A. M. Behavioural differences between individuals and two populations of stickleback (Gasterosteus aculeatus). J. Evol. Biol. 18, 464–473 (2005).Article 
CAS 
PubMed 

Google Scholar 
Dochtermann, N. A. & Jenkins, S. H. Behavioural syndromes in Merriam’s kangaroo rats (Dipodomys merriami): A test of competing hypotheses. Proc. R. Soc. Lond. B 274, 2343–2349 (2007).
Google Scholar 
Tremmel, M. & Müller, C. Insect personality depends on environmental conditions. Behav. Ecol. 24, 386–392 (2013).Article 

Google Scholar 
Zidar, J. et al. A comparison of animal personality and coping styles in the red junglefowl. Anim. Behav. 130, 209–220 (2017).Article 

Google Scholar 
Sih, A., Bell, A. & Johnson, J. C. Behavioral syndromes: An ecological and evolutionary overview. Trends Ecol. Evol. 19, 372–378 (2004).Article 
PubMed 

Google Scholar 
Réale, D. & Dingemanse, N. J. Personality and individual social specialization. In Social behaviour: Genes, ecology and evolution (eds Székely, T. et al.) 417–441 (Cambridge University Press, 2010).Chapter 

Google Scholar 
Dingemanse, N. J. & Dochtermann, N. A. Quantifying individual variation in behaviour. Mixed-effect modelling approaches. J. Anim. Ecol. 82, 39–54 (2013).Article 
PubMed 

Google Scholar 
Dingemanse, N. J., Kazem, A. J. N., Reale, D. & Wright, J. Behavioural reaction norms: Animal personality meets individual plasticity. Trends Ecol. Evol. 25, 81–89 (2010).Article 
PubMed 

Google Scholar 
Wolf, M., van Doorn, G. S. & Weissing, F. J. Evolutionary emergence of responsive and unresponsive personalities. Proc. Natl. Acad. Sci. USA 105, 15825–15830 (2008).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ólafsdóttir, G. Á. & Magellan, K. Interactions between boldness, foraging performance and behavioural plasticity across social contexts. Behav. Ecol. Sociobiol. 70, 1879–1889 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Mathot, K. J., Wright, J., Kempenaers, B. & Dingemanse, N. J. Adaptive strategies for managing uncertainty may explain personality-related differences in behavioural plasticity. Oikos 121(7), 1009–1020 (2012).Article 

Google Scholar 
Réale, D., Reader, S. M., Sol, D., McDougall, P. T. & Dingemanse, N. J. Integrating animal temperament within ecology and evolution. Biol. Rev. 82, 291–318 (2007).Article 
PubMed 

Google Scholar 
Coppens, C. M., de Boer, S. F. & Koolhaas, J. M. Coping styles and behavioural flexibility: Towards underlying mechanisms. Philos. Trans. R. Soc. B Biol. Sci. 365, 4021–4028 (2010).Article 

Google Scholar 
Benus, R. F., Daas, S. D., Koolhaas, J. M. & van Oortmerssen, G. A. Routine formation and flexibility in social and non-social behaviour of aggressive and non-aggressive male mice. Behaviour 112, 176–193 (1990).Article 

Google Scholar 
Dall, S. R., Houston, A. I. & McNamara, J. M. The behavioural ecology of personality: Consistent individual differences from an adaptive perspective. Ecol. Lett. 7, 734–739 (2004).Article 

Google Scholar 
Mitchell, D. J. & Biro, P. A. Is behavioural plasticity consistent across different environmental gradients and through time?. Proc. R. Soc. B. 284(1860), 20170893 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Stamps, J. A. Individual differences in behavioural plasticities. Biol. Rev. 91, 534–567 (2016).Article 
PubMed 

Google Scholar 
Stamps, J. A. & Biro, P. A. Personality and individual differences in plasticity. Curr. Opin. Behav. Sci. 12, 18–23 (2016).Article 

Google Scholar 
Dingemanse, N. J., Both, C., Drent, P. J. & Tinbergen, J. M. Fitness consequences of avian personalities in a fluctuating environment. Proc. R. Soc. Lond. B 271, 847 (2004).Article 

Google Scholar 
Smith, B. R. & Blumstein, D. T. Fitness consequences of personality: a meta-analysis. Behav. Ecol. 19, 448–455 (2008).Article 

Google Scholar 
Dingemanse, N. J. & Réale, D. Natural selection and animal personality. Behaviour 142, 1159–1184 (2005).Article 

Google Scholar 
Duque-Wilckens, N., Trainor, B. C. & Marler, C. A. Aggression and territoriality. In Encyclopedia of animal behavior (ed. Choe, J. C.) 539–546 (Elsevier, 2019).Chapter 

Google Scholar 
AmphibiaWeb. AmphibiaWeb: Information on amphibian biology and conservation. Available at https://amphibiaweb.org (2022).Ringler, M. et al. Acoustic ranging in poison frogs—It is not about signal amplitude alone. Behav. Ecol. Sociobiol. 71, 114 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Ringler, M., Ursprung, E. & Hödl, W. Site fidelity and patterns of short- and long-term movement in the brilliant-thighed poison frog Allobates femoralis (Aromobatidae). Behav. Ecol. Sociobiol. 63, 1281–1293 (2009).Article 

Google Scholar 
Ringler, M., Ringler, E., Magaña Mendoza, D. & Hödl, W. Intrusion experiments to measure territory size: Development of the method, tests through simulations, and application in the frog Allobates femoralis. PLoS ONE 6, e25844 (2011).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ringler, E., Ringler, M., Jehle, R. & Hödl, W. The female perspective of mating in A. femoralis, a territorial frog with paternal care—A spatial and genetic analysis. PLoS ONE 7, e40237 (2012).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ursprung, E., Ringler, M., Jehle, R. & Hödl, W. Strong male/male competition allows for nonchoosy females: High levels of polygynandry in a territorial frog with paternal care. Mol. Ecol. 20, 1759–1771 (2011).Article 
PubMed 

Google Scholar 
Pröhl, H. Territorial behavior in dendrobatid frogs. J Herpetol 39, 354–365 (2005).Article 

Google Scholar 
Peignier, M. et al. Exploring links between personality traits and their social and non-social environments in wild poison frogs. Behav. Ecol. Sociobiol. 76, 93 (2022).Article 

Google Scholar 
Chaloupka, S. et al. Repeatable territorial aggression in a Neotropical poison frog. Front. Ecol. Evol. 10, 398 (2022).Article 

Google Scholar 
Amézquita Torres, A. et al. Masking interference and the evolution of the acoustic communication system in the Amazonian dendrobatid frog Allobates femoralis. Evolution 60, 1874–1887 (2006).
Google Scholar 
Rodríguez López, C., Amézquita Torres, A., Ringler, M., Pašukonis, A. & Hödl, W. Calling amplitude flexibility and acoustic spacing in the territorial frog Allobates femoralis. Behav. Ecol. Sociobiol. 74, 1–10 (2020).
Google Scholar 
Asab. Guidelines for the treatment of animals in behavioural research and teaching. Anim. Behav. 159, 1–11 (2020).
Google Scholar 
Du Percie Sert, N. et al. The ARRIVE guidelines 2.0: Updated guidelines for reporting animal research. PLoS Biol 18, e3000410 (2020).Article 

Google Scholar 
Ringler, E., Mangione, R. & Ringler, M. Where have all the tadpoles gone? Individual genetic tracking of amphibian larvae until adulthood. Mol. Ecol. Resour. 15, 737–746 (2015).Article 
PubMed 

Google Scholar 
Ringler, M. et al. High-resolution forest mapping for behavioural studies in the Nature Reserve ‘Les Nouragues’, French Guiana. J. Maps 12, 26–32 (2016).Article 

Google Scholar 
Keith, D. A. et al. A function-based typology for Earth’s ecosystems. Nature 610, 513–518 (2022).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kaefer, I. L., Montanarin, A., da Costa, R. S. & Lima, P. A. Temporal patterns of reproductive activity and site attachment of the brilliant-thighed frog Allobates femoralis from central Amazonia. J. Herpetol. 46, 549–554 (2012).Article 

Google Scholar 
Rasband, W. S. ImageJ (U. S. National Institutes of Health, 1997–2021).Bolger, D. T., Morrison, T. A., Vance, B., Lee, D. & Farid, H. A computer-assisted system for photographic mark–recapture analysis. Methods Ecol. Evol. 3, 813–822 (2012).Article 

Google Scholar 
Narins, P. M., Hödl, W. & Grabul, D. S. Bimodal signal requisite for agonistic behavior in a dart-poison frog, Epipedobates femoralis. Proc. Natl. Acad. Sci. USA 100, 577–580 (2003).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Gasser, H., Amézquita Torres, A. & Hödl, W. Who is calling? Intraspecific call variation in the aromobatid frog Allobates femoralis. Ethology 115, 596–607 (2009).Article 

Google Scholar 
Hödl, W. Dendrobates femoralis (Dendrobatidae): a handy fellow for frog bioacoustics in Proceedings of the 4th Ordinary General meeting of the Societas Europaea Herpetologica, (ed.van Gelder, J. J., Strijbosch, H. & Bergers, P.) (1987).Ursprung, E., Ringler, M. & Hödl, W. Phonotactic approach pattern in the neotropical frog Allobates femoralis: A spatial and temporal analysis. Behaviour 146, 153–170 (2009).Article 

Google Scholar 
Sonnleitner, R., Ringler, M., Loretto, M.-C. & Ringler, E. Experience shapes accuracy in territorial decision-making in a poison frog. Biol. Lett. 16, 20200094 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Hödl, W. Phyllobates femoralis (Dendrobatidae): Rufverhalten und akustische Orientierung der Männchen (Freilandaufnahmen) in Bundesstaatliche Hauptstelle für Wissenschaftliche Kinematographie (1983).Tumulty, J. P. et al. Brilliant-thighed poison frogs do not use acoustic identity information to treat territorial neighbours as dear enemies. Anim. Behav. 141, 203–220 (2018).Article 

Google Scholar 
Fernandes, I. Y. et al. Unlinking the speciation steps: Geographical factors drive changes in sexual signals of an Amazonian Nurse-Frog through body size variation. Evol. Biol. 48, 81–93 (2021).Article 

Google Scholar 
Garcia, M. J. et al. Dueling frogs: do male green tree frogs (Hyla cinerea) eavesdrop on and assess nearby calling competitors?. Behav. Ecol. Sociobiol. 73(2), 1041 (2019).Article 

Google Scholar 
Gingras, B., Böckle, M., Herbst, C. T. & Fitch, W. T. Call acoustics reflect body size across four clades of anurans. J Zool 289(2), 143–150 (2013).Article 

Google Scholar 
Stoffel, M. A., Nakagawa, S. & Schielzeth, H. rptR: Repeatability estimation and variance decomposition by generalized linear mixed-effects models. Methods Ecol. Evol. 8, 1639–1644 (2017).Article 

Google Scholar 
Fox, J. et al. Package ‘sem’: Structural Equation Models. https://CRAN.R-project.org/package=sem (2022).Burnham, K. P. & Anderson, D. R. Model selection and multimodel inference: A practical information-theoretic approach 2nd edn. (Springer, 2002).MATH 

Google Scholar 
Hertel, A. G., Niemelä, P. T., Dingemanse, N. J. & Mueller, T. A guide for studying among-individual behavioral variation from movement data in the wild. Mov. Ecol. 8, 30 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Hadfield, J. D. MCMC methods for multi-response generalized linear mixed models: The MCMCglmmR package. J. Stat. Softw. 33, 1–22 (2010).Article 

Google Scholar 
Bürkner, P.-C. brms: An R package for Bayesian multilevel models using stan. J. Stat. Softw. 80, 1–28 (2017).Article 

Google Scholar 
Gelman, A., Hwang, J. & Vehtari, A. Understanding predictive information criteria for Bayesian models. Stat. Comput. 24, 997–1016 (2014).Article 
MathSciNet 
MATH 

Google Scholar 
Whalen, A. & Hoppitt, W. J. E. Bayesian model selection with network based diffusion analysis. Front. Psychol. 7, 409 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
R Core Team. R: A language and environment for statistical computing (R Foundation for Statistical Computing, 2019).
Google Scholar 
Ryan, M. J., Bartholomew, G. A. & Rand, A. S. Energetics of reproduction in a neotropical frog, Physalaemus pustulosus. Ecology 64, 1456–1462 (1983).Article 

Google Scholar 
Taigen, T. L. & Wells, K. D. Energetics of vocalization by an anuran amphibian (Hyla versicolor). J. Comp. Physiol. 155, 163–170 (1985).Article 

Google Scholar 
Pough, F. H. & Taigen, T. L. Metabolic correlates of the foraging and social behaviour of dart-poison frogs. Anim. Behav. 39, 145–155 (1990).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 
PubMed 
PubMed Central 

Google Scholar 
Kelleher, S. R., Silla, A. J. & Byrne, P. G. Animal personality and behavioral syndromes in amphibians: A review of the evidence, experimental approaches, and implications for conservation. Behav. Ecol. Sociobiol. 72, 10539 (2018).Article 

Google Scholar 
Moser-Purdy, C., MacDougall-Shackleton, E. A. & Mennill, D. J. Enemies are not always dear: Male song sparrows adjust dear enemy effect expression in response to female fertility. Anim. Behav. 126, 17–22 (2017).Article 

Google Scholar  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

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

Balashov, S. P. et al. Xanthorhodopsin: a proton pump with a light-harvesting carotenoid antenna. Science 309, 2061–2064 (2005).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Imasheva, E. S., Balashov, S. P., Choi, A. R., Jung, K.-H. & Lanyi, J. K. Reconstitution of Gloeobacter violaceus rhodopsin with a light-harvesting carotenoid antenna. Biochemistry 48, 10948–10955 (2009).Article 
CAS 
PubMed 

Google Scholar 
Fuhrman, J. A., Schwalbach, M. S. & Stingl, U. Proteorhodopsins: an array of physiological roles? Nat. Rev. Microbiol. 6, 488–494 (2008).Article 
CAS 
PubMed 

Google Scholar 
Vollmers, J. et al. Poles apart: Arctic and Antarctic Octadecabacter strains share high genome plasticity and a new type of xanthorhodopsin. PLoS ONE 8, e63422 (2013).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bertsova, Y. V., Arutyunyan, A. M. & Bogachev, A. V. Na+-translocating rhodopsin from Dokdonia sp. PRO95 does not contain carotenoid antenna. Biochem. Mosc. 81, 414–419 (2016).Article 
CAS 

Google Scholar 
Misra, R., Eliash, T., Sudo, Y. & Sheves, M. Retinal–salinixanthin interactions in a thermophilic rhodopsin. J. Phys. Chem. B 123, 10–20 (2019).Article 
CAS 
PubMed 

Google Scholar 
Béjà, O. et al. Bacterial rhodopsin: evidence for a new type of phototrophy in the sea. Science 289, 1902–1906 (2000).Article 
ADS 
PubMed 

Google Scholar 
Béjà, O., Spudich, E. N., Spudich, J. L., Leclerc, M. & DeLong, E. F. Proteorhodopsin phototrophy in the ocean. Nature 411, 786–789 (2001).Article 
ADS 
PubMed 

Google Scholar 
Atamna-Ismaeel, N. et al. Widespread distribution of proteorhodopsins in freshwater and brackish ecosystems. ISME J. 2, 656–662 (2008).Article 
CAS 
PubMed 

Google Scholar 
Frigaard, N.-U., Martinez, A., Mincer, T. J. & DeLong, E. F. Proteorhodopsin lateral gene transfer between marine planktonic Bacteria and Archaea. Nature 439, 847–850 (2006).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Finkel, O. M., Béjà, O. & Belkin, S. Global abundance of microbial rhodopsins. ISME J. 7, 448–451 (2013).Article 
CAS 
PubMed 

Google Scholar 
Gómez-Consarnau, L. et al. Microbial rhodopsins are major contributors to the solar energy captured in the sea. Sci. Adv. 5, eaaw8855 (2019).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
DeLong, E. F. & Béjà, O. The light-driven proton pump proteorhodopsin enhances bacterial survival during tough times. PLoS Biol. 8, e1000359 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
Munson-McGee, J. H. et al. Decoupling of respiration rates and abundance in marine prokaryoplankton. Nature 612, 764–770 (2022).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wang, W.-W., Sineshchekov, O. A., Spudich, E. N. & Spudich, J. L. Spectroscopic and photochemical characterization of a deep ocean proteorhodopsin. J. Biol. Chem. 278, 33985–33991 (2003).Article 
CAS 
PubMed 

Google Scholar 
Man, D. Diversification and spectral tuning in marine proteorhodopsins. EMBO J. 22, 1725–1731 (2003).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Lanyi, J. K. & Balashov, S. P. in Halophiles and Hypersaline Environments (eds. Ventosa, A., Oren, A. & Ma, Y.) 319–340 (Springer, 2011).Balashov, S. P. et al. Reconstitution of Gloeobacter rhodopsin with echinenone: role of the 4-keto group. Biochemistry 49, 9792–9799 (2010).Article 
CAS 
PubMed 

Google Scholar 
Kopejtka, K. et al. A bacterium from a mountain lake harvests light using both proton-pumping xanthorhodopsins and bacteriochlorophyll-based photosystems. Proc. Natl Acad. Sci. USA 119, e2211018119 (2022).Article 
CAS 
PubMed 

Google Scholar 
Pushkarev, A. & Béjà, O. Functional metagenomic screen reveals new and diverse microbial rhodopsins. ISME J. 10, 2331–2335 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Pushkarev, A. et al. A distinct abundant group of microbial rhodopsins discovered using functional metagenomics. Nature 558, 595–599 (2018).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Chazan, A. et al. Diverse heliorhodopsins detected via functional metagenomics in freshwater Actinobacteria, Chloroflexi and Archaea. Environ. Microbiol. 24, 110–121 (2022).Article 
CAS 
PubMed 

Google Scholar 
Inoue, K. et al. A light-driven sodium ion pump in marine bacteria. Nat. Commun. 4, 1678 (2013).Article 
ADS 
PubMed 

Google Scholar 
Bhosale, P. & Bernstein, P. S. Microbial xanthophylls. Appl. Microbiol. Biotechnol. 68, 445–455 (2005).Article 
CAS 
PubMed 

Google Scholar 
Demmig-Adams, B., Polutchko, S. K. & Adams, W. W. Structure–function–environment relationship of the isomers zeaxanthin and lutein. Photochem 2, 308–325 (2022).Article 

Google Scholar 
Barreiro C. & Barredo J. L. Microbial Carotenoids: Methods and Protocols (Humana Press, 2018).Ram, S., Mitra, M., Shah, F., Tirkey, S. R. & Mishra, S. Bacteria as an alternate biofactory for carotenoid production: a review of its applications, opportunities and challenges. J. Funct. Foods 67, 103867 (2020).Article 
CAS 

Google Scholar 
Shibata, M. et al. Oligomeric states of microbial rhodopsins determined by high-speed atomic force microscopy and circular dichroic spectroscopy. Sci. Rep. 8, 8262 (2018).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Luecke, H. et al. Crystallographic structure of xanthorhodopsin, the light-driven proton pump with a dual chromophore. Proc. Natl Acad. Sci. USA 105, 16561–16565 (2008).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Chuon, K. et al. Assembly of natively synthesized dual chromophores into functional actinorhodopsin. Front. Microbiol. 12, 652328 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Yoshizawa, S., Kawanabe, A., Ito, H., Kandori, H. & Kogure, K. Diversity and functional analysis of proteorhodopsin in marine Flavobacteria. Environ. Microbiol. 14, 1240–1248 (2012).Article 
CAS 
PubMed 

Google Scholar 
Ahmed, F. et al. Profiling of carotenoids and antioxidant capacity of microalgae from subtropical coastal and brackish waters. Food Chem. 165, 300–306 (2014).Article 
CAS 
PubMed 

Google Scholar 
Shihoya, W. et al. Crystal structure of heliorhodopsin. Nature 574, 132–136 (2019).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Kishi, K. E. et al. Structural basis for channel conduction in the pump-like channelrhodopsin ChRmine. Cell 185, 672–689.e23 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Balashov, S. P., Imasheva, E. S., Wang, J. M. & Lanyi, J. K. Excitation energy-transfer and the relative orientation of retinal and carotenoid in xanthorhodopsin. Biophys. J. 95, 2402–2414 (2008).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Lakowicz, J. R. (ed.) in Principles of Fluorescence Spectroscopy 27–61 (Springer, 2006).Dana, J. et al. Testing the fate of nascent holes in CdSe nanocrystals with sub-10 fs pump–probe spectroscopy. Nanoscale 13, 1982–1987 (2021).Article 
CAS 
PubMed 

Google Scholar 
Polívka, T. et al. Femtosecond carotenoid to retinal energy transfer in xanthorhodopsin. Biophys. J. 96, 2268–2277 (2009).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Iyer, E. S. S., Gdor, I., Eliash, T., Sheves, M. & Ruhman, S. Efficient femtosecond energy transfer from carotenoid to retinal in Gloeobacter rhodopsin–salinixanthin complex. J. Phys. Chem. B 119, 2345–2349 (2015).Article 
CAS 
PubMed 

Google Scholar 
Doi, S., Tsukamoto, T., Yoshizawa, S. & Sudo, Y. An inhibitory role of Arg-84 in anion channelrhodopsin-2 expressed in Escherichia coli. Sci. Rep. 7, 41879 (2017).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Nagiri, C. et al. Crystal structure of human endothelin ETB receptor in complex with peptide inverse agonist IRL2500. Commun. Biol. 2, 236 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Yamashita, K., Hirata, K. & Yamamoto, M. KAMO: towards automated data processing for microcrystals. Acta Crystallogr. D Struct. Biol. 74, 441–449 (2018).Article 
CAS 
PubMed 
PubMed Central 

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

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

Google Scholar 
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Emsley, P., Lohkamp, B., Scott, W. G. & Cowtan, K. Features and development of Coot. Acta Crystallogr. D Biol. Crystallogr. 66, 486–501 (2010).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Afonine, P. V. et al. Towards automated crystallographic structure refinement with phenix.refine.Acta Crystallogr. D Biol. Crystallogr. 68, 352–367 (2012).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Zivanov, J. et al. New tools for automated high-resolution cryo-EM structure determination in RELION-3.eLife 7, e42166 (2018).Punjani, A., Rubinstein, J. L., Fleet, D. J. & Brubaker, M. A. cryoSPARC: algorithms for rapid unsupervised cryo-EM structure determination. Nat. Methods 14, 290–296 (2017).Punjani, A., Zhang, H. & Fleet, D. J. Non-uniform refinement: adaptive regularization improves single-particle cryo-EM reconstruction. Nat. Methods 17, 1214–1221 (2020).Article 
CAS 
PubMed 

Google Scholar 
Rosenthal, P. B. & Henderson, R. Optimal determination of particle orientation, absolute hand, and contrast loss in single-particle electron cryomicroscopy. J. Mol. Biol. 333, 721–745 (2003).Article 
CAS 
PubMed 

Google Scholar 
Emsley, P. & Cowtan, K. Coot: model-building tools for molecular graphics. Acta Crystallogr. D Biol. Crystallogr. 60, 2126–2132 (2004).Article 
PubMed 

Google Scholar 
Adams, P. D. et al. PHENIX: a comprehensive Python-based system for macromolecular structure solution. Acta Crystallogr. D Biol. Crystallogr. 66, 213–221 (2010).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Yamashita, K., Palmer, C. M., Burnley, T. & Murshudov, G. N. Cryo-EM single-particle structure refinement and map calculation using Servalcat. Acta Crystallogr. D Struct. Biol. 77, 1282–1291 (2021).Article 
CAS 
PubMed 
PubMed Central 

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

Google Scholar 
Inoue, K. et al. Exploration of natural red-shifted rhodopsins using a machine learning-based Bayesian experimental design. Commun. Biol. 4, 362 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Salazar, G. et al. Gene expression changes and community turnover differentially shape the global ocean metatranscriptome. Cell 179, 1068–1083.e21 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Chen, I.-M. A. et al. The IMG/M data management and analysis system v.6.0: new tools and advanced capabilities. Nucleic Acids Res. 49, D751–D763 (2021).Article 
CAS 
PubMed 

Google Scholar 
Nayfach, S. et al. A genomic catalog of Earth’s microbiomes. Nat. Biotechnol. 39, 499–509 (2021).Article 
CAS 
PubMed 

Google Scholar 
Sunagawa, S. et al. Metagenomic species profiling using universal phylogenetic marker genes. Nat. Methods 10, 1196–1199 (2013).Article 
CAS 
PubMed 

Google Scholar 
Wickham, H. in ggplot2 (eds Gentleman, R., Hornik, K. & Parmigiani, G.) 189–201 (Springer, 2016).Katoh, K., Misawa, K., Kuma, K. & Miyata, T. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 30, 3059–3066 (2002).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Capella-Gutiérrez, S., Silla-Martínez, J. M. & Gabaldón, T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25, 1972–1973 (2009).Article 
PubMed 
PubMed Central 

Google Scholar 
Nguyen, L.-T., Schmidt, H. A., von Haeseler, A. & Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).Article 
CAS 
PubMed 

Google Scholar 
Hoang, D. T., Chernomor, O., von Haeseler, A., Minh, B. Q. & Vinh, L. S. UFBoot2: improving the ultrafast bootstrap approximation. Mol. Biol. Evol. 35, 518–522 (2018).Article 
CAS 
PubMed 

Google Scholar  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

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

In co-culture with the bacterivorous flagellate Poteriospumella lacustris, the prey bacterium Pseudomonas putida exhibited a characteristic succession of predation defenses. The initial and the final defense differed substantially from one another with regard to their mechanism and their population-level benefits to the bacteria.Our results strongly indicate that the initial bacterial defense falls into the category of chemical defense, and is regulated by phenotypic plasticity. This would require P. putida to be able to sense predator density and to regulate the excretion of inhibitory substances accordingly. Because a considerable proportion of the P. putida genome is known to be involved in regulation and signal transduction allowing for very flexible responses to environmental triggers [41] both conditions are likely to be met. The filtrate exposure tests (Fig. 3) provide specific evidence for the ability of P. putida KT2440 to up- and downregulate the excretion of compounds inhibiting flagellate growth in response to grazing pressure. Previous research [25] corroborated the ability of P. putida to escape grazing from bacterivorous flagellates through induced responses like aggregation or biofilm formation.To provide a possible characterization for the apparent bacterial toxin, the whole-genome sequences of P. putida KT2440 obtained here were aligned against the antiSMASH [42] database. The output suggests the existence of non-ribosomal peptide synthetase clusters mediating the production of pyoverdines, a particular class of siderophores. The latter are molecules released by bacteria into the environment, which enhance the uptake of essential metals like, e.g., iron under deficient conditions. Specific pyoverdines associated with P. putida KT2440 have previously been identified [43]. Recent findings have shown that the benefits from siderophore production are not limited to competitive advantages gained from enhanced resource exploitation [44]. Pyoverdines were also demonstrated to determine the virulence of Pseudomonads via the damage of mitochondria in colonized hosts [45]. Moreover, pyoverdines were shown to be involved in the inducible defense of P. putida against predatory myxobacteria [46]. Such multiple functions have been reported for a number of bacterial metabolites, especially in Pseudomonads [47], and the particular combination of pyoverdin effects would explain the observed simultaneous flagellate inhibition and promoted bacterial growth.In contrast to the initial chemical defense of P. putida, the subsequent filamentation clearly provides an example of rapid evolution. Although the responsible mutation(s) could only be pinpointed in a few isolates so far (Table S1), there is no doubt about the genetic manifestation and heritability of the filamentous phenotype due to its demonstrated non-reversible nature.Only recently, similar observations were made by long-term co-cultivation of Pseudomonas fluorescence with the amoeboid predator Neaglena grubei [48]. In that system, protective adaptations like enhanced biofilm formation and altered motility were traced down to mutations in two particular genes (wspF, amrZ).From the perspective of the bacterial population, filamentation appears to be a much less efficient defense mechanism than toxin production. This is clearly reflected by the ratio of prey to predator biomass, which differed by two orders of magnitude between the initial and final defense (Table 6). It raises the question of why bacteria would abandon a highly effective form of defense in favor of a much less effective one. As demonstrated experimentally, adaptation of predators to the toxin can be excluded as a cause (Fig. 4). Moreover, it was not instantly evident how the small-sized flagellate was ultimately able to persist in large numbers given a very high proportion of completely inedible prey individuals (Fig. 1D and Fig. S2).Table 6 Average abundance of predator and prey during the temporary steady state following the initial bacterial defense (day 13–16) and during the final steady state (beyond day 30).Full size tableTo develop a comprehensive understanding of the system addressing the questions raised above, we set up a semi-continuous differential equation model to simulate the dynamics of predator and prey phenotypes. The model considers seven state variables (carbon, densities of four bacterial phenotypes, flagellate density, and toxin concentration) whose dynamics are controlled by nine processes (Table 3, Fig. 2). In addition to microbial growth and grazing, the model implements a phenotypically plastic predation defense (toxin production) as well as a genetic defense (filamentation) which arises via mutation. The particular assumptions implemented in the model are as follows:Dual effect of bacterial metabolitesIn line with the above discussion on siderophore-like compounds, secondary metabolites excreted by P. putida were assumed to exhibit a dual function, both inhibiting the growth of flagellates and allowing for a more efficient exploitation of the resources by bacteria. The inhibition of predators was demonstrated directly (Figs. 3 and 4) while enhanced resource exploitation was inferred from bacterial abundances in co-cultures exceeding the carrying capacity observed in predator-free controls (Fig. 1A, day 11–18).Metabolite production is costlyThe production of bacterial metabolites was assumed to be associated with a slight fitness cost [49] since resources are diverted from reproduction, thus resulting in a lowered growth rate of toxin-producing bacteria. The assumed fitness cost of 11% (parameter cBx in Table 5) allowed for the best agreement between simulated and observed data and is in agreement with data on the cost of pyoverdine production by P. aeruginosa [50]. The cost only manifests when toxin production is upregulated.Predator recognition and quorum sensing interactIn the model, the production of bacterial metabolites is upregulated when the two conditions of high flagellate abundance and high bacterial abundance coincide. That is, the expression of the toxin-based bacterial defense is assumed to be jointly controlled by predator recognition and quorum sensing (QS). Examples for such joint control of bacterial defenses have been reported previously [8, 26, 51]. The involvement of QS in chemical defense strategies is particularly likely as effective toxin concentrations can only be reached when producers are highly abundant. While multiple QS systems have been described for other Pseudomonads, only a single system has been identified in P. putida KT2440 so far [52, 53].Mutation rates are conditional on stressThe emergence of mutations resulting in the filamentation of P. putida was assumed to be conditional on a high ambient concentration of bacterial metabolites. The latter was considered as a proxy for bacterial stress which can affect mutagenesis either directly or indirectly by a variety of mechanisms [54,55,56]. Without this assumption, the almost synchronous appearance of filaments in all replicates at a late point in time would be very difficult to explain. Specifically, if mutation frequencies were high, filaments would become the predominant phenotype early (Fig. S3) which contradicts observations. On the other hand, if frequencies were low but unconditional, the timing of filament appearance should vary between replicates, which is in contrast to observations either (Fig. 1B).Filamentation is associated with a fitness costMeasurements of growth rate constants revealed a significant fitness disadvantage of filamentous isolates in comparison to single-celled, undefended isolates (p  More