Philippa Kaur

A hippo swims in Colombia’s Magdalena River, near where Pablo Escobar’s compound was located.Credit: Fernando Vergara/AP/Shutterstock

Colombian environment minister Susana Muhamad has triggered fear among researchers that she will protect, rather than reduce, a growing population of invasive hippos that threaten the country’s natural ecosystems and biodiversity. Although she did not directly mention the hippos — a contentious issue in Colombia — Muhamad said during a speech in late January that her ministry would create policies that prioritize animal well-being, including the creation of a new division of animal protection.
Landmark Colombian bird study repeated to right colonial-era wrongs
The hippos escaped from drug-cartel leader Pablo Escobar’s estate after he died in 1993. Left alone, the male and three females that Escobar had illegally imported from a US zoo established themselves in Colombia’s Magdalena River and some small lakes nearby — part of the country’s main watershed. After years of breeding, the ‘cocaine hippos’ have multiplied to about 150 individuals, scientists estimate.Given that the hippos (Hippopotamus amphibius) — considered the largest invasive animal in the world — have no natural predators in Colombia and have been mating at a steady rate, their population could reach 1,500 in 16 years, according to a modelling study published in 20211. “I do not understand what the government is waiting for to act,” says Nataly Castelblanco Martínez, a Colombian conservation biologist at the Autonomous University of Quintana Roo in Chetumal, Mexico, and co-author of the study. “If we don’t do anything, 20 years from now the problem will have no solution.”Researchers have called for a strict management plan that would eventually reduce the wild population to zero, through a combination of culling some animals and capturing others, then relocating them to facilities such as zoos. But the subject of what to do with the hippos has polarized the country, with some enamoured by the animals’ charisma and value as a tourist attraction and others concerned about the threat they pose to the environment and local fishing communities.‘A bit surreal’Several studies and observations suggest how destructive it could be to allow the Colombian hippo population to explode. A 2019 paper2, for example, showed that, compared with lakes without hippos, those where the animals have taken up residence contain more nutrients and organic matter that favour the growth of cyanobacteria — aquatic microbes associated with toxic algal blooms. These blooms can reduce water quality and cause mass fish deaths, affecting local fishing communities.

A sign near Doradal, Colombia, warns passersby of the danger of invasive hippos.Credit: Juancho Torres/Anadolu Agency via Getty

Other scientists have predicted that the hippos could displace endangered species that are native to the Magdalena River, such as the Antillean manatee (Trichechus manatus manatus), by outcompeting them for food and space. They caution that traffic accidents and attacks on people caused by the hippos will become more common. And they warn that wildlife traffickers are already taking advantage of the situation by illegally selling baby hippos — a trend that could intensify.“It’s a bit surreal,” says Jorge Moreno Bernal, a vertebrate palaeontologist at the University of the North in Barranquilla, Colombia. “This is just a taste of what may come.”When Colombian authorities first recognized the speed at which the hippo population was growing, during the 2000s, they acted to reduce their numbers. But in 2009, when photos appeared online after soldiers gunned down Pepe, Escobar’s fugitive male hippo, the outcry from animal-rights activists and others plunged the environment ministry into an “institutional paralysis”, says Sebastián Restrepo Calle, an ecologist at Javeriana University in Bogotá.Researchers say that the hippos don’t belong in Colombia — they are native to sub-Saharan Africa. Simulations run by Castelblanco Martínez and her colleagues suggest that to reduce the population to zero by 2033, about 30 hippos would need to be removed from the wild population per year1. No other course of action, including sterilization or castration, would eradicate them, according to the modelling of various management scenarios, says Castelblanco Martínez.The cost of inactionThe worry now is that, instead of basing decisions on evidence and expertise in conservation, the government is listening to popular opinion, says Restrepo Calle. Neither Muhamad nor representatives of the environment ministry replied to Nature’s requests for comment.
Ancient stone tools suggest early humans dined on hippo
“Why prioritize one species over our own ecosystems?” — especially a species that isn’t native, asks Alejandra Echeverri, a Colombian conservation scientist at Stanford University in California. Along with her colleagues, Echeverri published a study last month showing that Colombia has few policies governing invasive species compared with its overall number of biodiversity policies3.Animals-rights advocates, meanwhile, argue that they aren’t ignoring environmental concerns. Luis Domingo Gómez Maldonado, an animal-rights activist and specialist in animal law at Saint Thomas University in Bogotá, says “It’s not about saving the hippos on a whim,” but rather about solving the issue while also giving the hippos justice. “My indisputable position is: let’s save as many individuals as possible, let’s do it ethically.”Researchers, too, say they have the animals’ best interests at heart. “Even if [advocates] don’t see it, we care about the hippos,” Castelblanco Martínez says. “The more time that passes, the more hippos will either have to be culled, castrated or captured.”The question is whether environmental authorities will act swiftly to draft and enforce a management plan that is both ethical and effective. Should they sit on the issue for too long, Castelblanco Martínez warns, rural communities that are most affected by the hippos might take matters into their own hands.If the government doesn’t cull them, she says, people will use shotguns to do it. More

EthicsNo ethics approval was required for this study. Our study was conducted in a state-owned industrial oil palm plantation where we established a cooperation with the estate owner to access the site and collect data. No endangered or protected species were sampled. Research permits were obtained from the Ministry of Research, Technology and Higher Education, and sample collection and sample export permits were obtained from the Ministry of Environment and Forestry of the Republic of Indonesia.Study area and experimental designOur study was conducted in a state-owned industrial oil palm plantation (PTPN VI) located in Jambi, Indonesia (1.719° S, 103.398° E, 73 m above sea level). Initial planting of oil palms within the 2,025 ha plantation area started in 1998 and ended in 2002; planting density was 142 palms ha−1, spaced 8 m apart in each row and between rows, and palms were ≥16 years old during our study period of 2016–2020. The study sites have a mean annual temperature of 27.0 ± 0.2 °C and a mean annual precipitation of 2,103 ± 445 mm (2008–2017, Sultan Thaha Airport, Jambi). The management practices in large-scale oil palm plantations typically result in three contrasting management zones: (1) a 2 m radius around the base of the palm that was weeded (four times a year) and raked before fertilizer application, hereafter called the ‘palm circle’; (2) an area occurring every second inter-row, where pruned senesced palm fronds were piled up, hereafter called ‘frond piles’; and (3) the remaining area of the plantation where less weeding (two times a year) and no fertilizer were applied, hereafter called ‘inter-rows’.Within this oil palm plantation, we established a management experiment in November 2016 with full factorial treatments of two fertilization rates × two weeding practices: conventional fertilization rates at PTPN VI and other large-scale plantations (260 kg N–50 kg P–220 kg K ha−1 yr−1), reduced fertilization rates based on quantified nutrient export by harvest (136 kg N–17 kg P ha−1 yr−1–187 kg K ha−1 yr−1), herbicide and mechanical weeding15. The reduced fertilization treatment was based on quantified nutrient export from fruit harvest, calculated by multiplying the nutrient content of fruit bunches with the long-term yield data of the plantation. Fertilizers were applied yearly in April and October following weeding and raking of the palm circle. The common practice at PTPN VI and other large-scale plantations on acidic Acrisol soils is to apply lime and micronutrients, and these were unchanged in our management experiment. Before each N–P–K fertilizer application, dolomite and micronutrients were applied to the palm circle in all treatment plots using the common rates)52: 426 kg ha−1 yr−1 dolomite and 142 kg Micro-Mag ha−1 yr−1 (containing 0.5% B2O3, 0.5% CuO, 0.25% Fe2O3, 0.15% ZnO, 0.1% MnO and 18% MgO). Herbicide treatment was carried out using glyphosate in the palm circle (1.50 l ha−1 yr−1, split into four applications per year) and in the inter-rows (0.75 l ha−1 yr−1, split into two applications per year). Mechanical weeding was done using a brush cutter in the same management zones and at the same frequency as the herbicide treatment.The 22 factorial design resulted in four treatment combinations: conventional fertilization with herbicide treatment, reduced fertilization with herbicide treatment, conventional fertilization with mechanical weeding and reduced fertilization with mechanical weeding. The four treatments were randomly assigned on 50 m × 50 m plots replicated in four blocks, totalling 16 plots. The effective measurement area was the inner 30 m × 30 m area within each replicate plot to avoid any possible edge effects. For indicators (below) that were measured within subplots, these subplots were distributed randomly within the inner 30 m × 30 m of a plot. All replicate plots were located on flat terrain and on an Acrisol soil with a sandy clay loam texture.Ecosystem functions and multifunctionalityOur study included multiple indicators for each of the eight ecosystem functions23, described in details below (Supplementary Tables 1 and 2). All the parameters were expressed at the plot level by taking the means of the subplots (that is, biological parameters) or the area-weighted average of the three management zones per plot (that is, soil parameters). (1) Greenhouse gas (GHG) regulation was indicated by NEP, soil organic C (SOC) and soil GHG fluxes. (2) Erosion prevention was signified by the understory vegetation cover during the four-year measurements. (3) Organic matter decomposition was indicated by leaf litter decomposition and soil animal decomposer activity. (4) Soil fertility was signified by gross N mineralization rate, effective cation exchange capacity (ECEC), base saturation and microbial biomass N. (5) Pollination potential was designated by pan-trapped arthropod abundance and nectar-feeding bird activity. As such, it does not quantify the pollination potential for oil palm, which is mainly pollinated by a single weevil species, but rather as a proxy for a general pollination potential for other co-occurring plants. (6) Water filtration (the capacity to provide clean water) was indicated by leaching losses of the major elements. (7) Plant refugium (the capacity to provide a suitable habitat for plants) as signified by the percentage ground cover of invasive plants to the total ground cover of understory vegetation during the four-year measurements. (8) Biological control (the regulation of herbivores via predation) was indicated by insectivorous bird and bat activities and the soil arthropod predator activity.All the ecosystem functions were merged into a multifunctionality index using the established average and threshold approaches12. For average multifunctionality, we first averaged the z-standardized values (Statistics) of indicators for each ecosystem function and calculated the mean of the eight ecosystem functions for each plot. For threshold multifunctionality, this was calculated from the number of functions that exceeds a set threshold, which is a percentage of the maximum performance level of each function12; we investigated the range of thresholds from 10% to 90% to have a complete overview. The maximum performance was taken as the average of the three highest values for each indicator per ecosystem function across all plots to reduce effect of potential outliers. For each plot, we counted the number of indicators that exceeded a given threshold for each function and divided by the number of indicators for each function12.Indicators of GHG regulationWe calculated annual NEP for each plot as: net ecosystem C exchange – harvested fruit biomass C (ref. 16), whereby net ecosystem C exchange = Cout (or heterotrophic respiration) – Cin (or net primary productivity)53. The net primary productivity of oil palms in each plot was the sum of aboveground biomass production (aboveground biomass C + frond litter biomass C input + fruit biomass C) and belowground biomass production. Aboveground biomass production was estimated using allometric equations developed for oil palm plantations in Indonesia54, using the height of palms measured yearly from 2019 to 2020. Annual frond litter biomass input was calculated from the number and dry mass of fronds pruned during harvesting events of an entire year in each plot and was averaged for 2019 and 2020. Aboveground biomass production was converted to C based on C concentrations in wood and leaf litter55. Annual fruit biomass C production (which is also the harvest export) was calculated from the average annual yield in 2019 and 2020 and the measured C concentrations of fruit bunches. Belowground root biomass and litter C production were taken from previous work in our study area55, and it was assumed constant for each plot. Heterotrophic respiration was estimated for each plot as: annual soil CO2 C emission (below) × 0.7 (based on 30% root respiration contribution to soil respiration from a tropical forest in Sulawesi, Indonesia56) + annual frond litter biomass C input × 0.8 (~80% of frond litter is decomposed within a year in this oil palm plantation8). SOC was measured in March 2018 from composite samples collected from two subplots in each of the three management zones per plot down to 50 cm depth. Soil samples were air dried, finely ground and analysed for SOC using a CN analyser (Vario EL Cube, Elementar Analysis Systems). SOC stocks were calculated using the measured bulk density in each management zone, and values for each plot were the area-weighted average of the three management zones (18% for palm circle, 15% for frond piles and 67% for inter-rows)15,22.From July 2019 to June 2020, we conducted monthly measurements of soil CO2, CH4 and N2O fluxes using vented, static chambers permanently installed in the three management zones within two subplots per plot11,57. Annual soil CO2, CH4 and N2O fluxes were trapezoidal interpolations between measurement periods for the whole year, and values for each plot were the area-weighted average of the three management zones (above).Indicators of erosion preventionDiversity and abundance of vascular plants were assessed once a year from 2016 to 2020 before weeding in September–November. In five subplots per plot, we recorded the occurrence of all vascular plant species and estimated the percent cover of the understory vegetation. The percentage cover and plant species richness of each measurement year were expressed in ratio to that of 2016 to account for initial differences among the plots before the start of the experiment. For example, percentage cover in 2017 was:$$mathrm{Cover}_{2017} = frac{{left( {mathrm{Cover}_{2017} – mathrm{Cover}_{2016}} right)}}{{mathrm{Cover}_{2016}}}$$The values from five subplots were averaged to represent each plot.Indicators of organic matter decompositionLeaf litter decomposition was determined using litter bags (20 cm × 20 cm with 4 mm mesh size) containing 10 g of dry oil palm leaf litter8. Three litter bags per plot were placed on the edge of the frond piles in December 2016. After eight months of incubation in the field, we calculated leaf litter decomposition as the difference between initial litter dry mass and litter dry mass following incubation. Soil animal decomposer activity is described below (Soil arthropods).Indicators of soil fertilityAll these indicators were measured in February–March 2018 in the three management zones within two subplots per plot22. Gross N mineralization rate in the soil was measured in the top 5 cm depth on intact soil cores incubated in situ using the 15N pool dilution technique58. ECEC and base saturation were measured in the top 5 cm depth as this is the depth that reacts fast to changes in management22. The exchangeable cation concentrations (Ca, Mg, K, Na, Al, Fe, Mn) were determined by percolating the soil with 1 mol l−1 of unbuffered NH4Cl, followed by analysis of the percolates using an inductively coupled plasma-atomic emission spectrometer (ICP-AES; iCAP 6300 Duo view ICP Spectrometer, Thermo Fisher Scientific). Base saturation was calculated as the percentage exchangeable bases (Mg, Ca, K and Na) on ECEC. Microbial biomass N was measured from fresh soil samples using the fumigation-extraction method59. The values for each plot were the mean of the two subplots that were the area-weighted average of the three management zones (above)15,22.Indicators of general pollination potentialFluorescent yellow pan traps were used to sample aboveground arthropods (to determine pollinator communities60) in November 2016, September 2017 and June 2018. The traps were attached to a platform at the height of the surrounding vegetation within a 2 × 3 grid centred in the inter-rows of each plot in six clusters of three traps, totalling 18 traps per plot. Traps were exposed in the field for 48 h. We stored all trapped arthropods in 70% ethanol and later counted and identified to order and species level. The abundance of trapped arthropods in 2017 and 2018 were calculated as the ratio to the abundance in 2016 to account for initial differences among the plots before the start of the experiment. The activity of nectar-feeding birds is described below (Birds and bats).Indicators of water filtrationElement leaching losses were determined from analyses of soil-pore water sampled monthly at 1.5 m depth using suction cup lysimeters (P80 ceramic, maximum pore size 1 μm; CeramTec) over the course of one year (2017–2018)15. Lysimeters were installed in the three management zones within two subplots per plot. Dissolved N was analysed using continuous flow injection colorimetry (SEAL Analytical AA3, SEAL Analytical), whereas these other elements were determined using ICP-AES. The values for each plot were the mean of the two subplots that were the area-weighted average of the three management zones15,22.Indicators of plant refugiumIn five subplots per plot, the percentage cover and species richness of invasive understory plant species were assessed once a year from 2016 to 2020 before weeding in September–November. We defined invasive species as those plants non-native to Sumatra61 and among the ten dominant species (excluding oil palm) in the plantation for each year. The percentage cover of invasive understory plant species of each measurement year was expressed in a ratio to that of 2016 to account for initial differences among the plots before the start of the experiment. The values for each plot were represented by the average of five subplots.Indicators of biological controlThe activities of insectivorous birds and bats are described below (Birds and bats). In five subplots per plot, soil invertebrates were collected (Soil arthropods), counted, identified to taxonomic order level and subsequently classified according to their trophic groups that include predators60. The values from five subplots were average to represent each plot.BiodiversityBiodiversity was measured by the taxonomic richness of seven multitrophic groups, described in details below (Supplementary Tables 1 and 2).Understory plant species richnessThe method is described above (Indicators of erosion prevention), using the number of species as an indicator (Supplementary Table 2).Soil microorganism richnessThis was determined in May 2017 by co-extracting RNA and DNA from three soil cores (5 cm diameter, 7 cm depth) in five subplots per plot62. While DNA extraction describes the entire microbial community, RNA represents the active community. The v3–v4 region of the 16S rRNA gene was amplified and sequenced with a MiSeq sequencer (Illumina). Taxonomic classification was done by mapping curated sequences against the SILVA small subunit (SSU) 138 non-redundant (NR) database63 with the Basic Local Alignment Search Tool (BLASTN)64.Soil arthropod order richnessFor determination of soil arthropods, we collected soil samples (16 cm × 16 cm, 5 cm depth) in five subplots per plot in October–November 2017. We extracted the animals from the soil using a heat-gradient extractor65, collected them in dimethyleneglycol-water solution (1:1) and stored in 80% ethanol. The extracted animals were counted and identified to taxonomic order level61. They were also assigned to the trophic groups decomposers, herbivores and predators based on the predominant food resources recorded in previous reviews and a local study66,67. Orders with diverse feeding habits were divided into several feeding groups, for example, Coleoptera were divided into mostly predatory families (Staphylinidae, Carabidae), herbivorous families (for example, Curculionidae) and decomposer families (for example, Tenebrionidae). The total number of individuals per taxonomic group in each subplot was multiplied by the group-specific metabolic rate, which were summed to calculate soil animal decomposer activity. The values from five subplots were average to represent each plot.Aboveground arthropod order and insect family richnessIn addition to the fluorescent yellow pan traps described above (Indicators of general pollination potential), sweep net and Malaise trap samplings were conducted in June 2018, which targeted the general flying and understory dwelling arthropod communities. Sweep net sampling was conducted within the understory vegetation along two 10 m long transects per plot, with ten sweeping strokes performed per transect. In each plot, we installed a single Malaise trap between two randomly chosen palms and exposed it for 24 h. Arthropods were counted, identified to taxonomic order level and the insects to taxonomic family level and values from the three methods were summed to represent each plot.Birds and batsBirds and bats passing at each replicate plot were sampled in September 2017 using SM2Bat + sound recorders (Wildlife Acoustics) with two microphones (SMX-II and SMX-US) placed at a height of 1.5 m in the middle of each plot68. We assigned the bird vocalization to species with Xeno-Canto69 and the Macaulay library70. Insectivorous bat species richness was computed by dividing them into morphospecies based on the characteristics of their call (call frequency, duration, shape). In addition, we gathered information on proportional diet preferences of the bird species using the EltonTrait database71. We defined birds feeding on invertebrates (potential biocontrol agents) as the species with a diet of at least 80% invertebrates and feeding on nectar (potential pollinators), if the diet included at least 20% of nectar.Economic indicatorsWe used six indicators linked to the level and stability of yield and profit: yield, lower fifth quantile of the yield per palm per plot, shortfall probability, management costs, profit and relative gross margin. We assessed fruit yield by weighing the harvested fruit bunches from each palm within the inner 30 m × 30 m area of each plot. The harvest followed the schedule and standard practices of the plantation company: each palm was harvested approximately every ten days and the lower fronds were pruned. For each plot, we calculated the average fruit yield per palm and scaled up to a hectare, considering the planting density of 142 palms per ha. Because the palms in each plot have different fruiting cycles and were harvested continuously, the calculation of an annual yield may lead to misleading differences between treatments. Therefore, we calculated the cumulative yield from the beginning of the experiment to four years (2017–2020), which should account for the inter- and intra-annual variations in fruit production of the palms in the plots and thus allowing for comparison among treatments. As effects of management practices on yield may be delayed46, we also calculated the cumulative yield during two consecutive years (2017–2018 and 2019–2020) and checked for treatment effects on yield and profit indicators separately for these two periods.We computed risk indicators on the cumulative yield and on the yield between the two periods. We used the lowest fifth quantile of the yield per palm per plot (left side of the distribution) to indicate the production of the palms with lowest performance. Also, we determined the yield shortfall probability (lower partial moment 0th order), defined as the share of palms that fell below a predefined threshold of yield; the thresholds chosen were 630 kg−1 per palm for cumulative yield and 300 kg−1 per palm per year for the two-year yield, which corresponded to 75% of the average yield.Revenues and costs were calculated as cumulative values during four years of the experiment (2017–2020) using the same prices and costs for all the years. This was because we were interested in assessing the economic consequences of different management treatments, and they might be difficult to interpret when changes in prices and costs between calendar years are included, which are driven by external market powers rather than the field-management practices. For the same reason, we abstained from discounting profits. Given the usually high discount rates applied to the study area, slight differences in harvesting activities between calendar years or months might lead to high systematic differences between the management treatments, which are associated with the variation in work schedule within the plantation rather than the actual difference among management treatments. Revenues were calculated from the yield and the average price of the fruit bunches in 2016 and 201761. Material costs were the sum of the costs of fertilizers, herbicide and gasoline for the brush cutter. Labour costs were calculated from the minimum wage in Jambi and the time (in labour hours) needed for the harvesting, fertilizing and weeding operations, which were recorded in 2017 for each plot. The weeding labour included the labour for raking the palm circle before fertilization, which was equal in all treatments, and the weeding in the palm circle and inter-rows either with herbicide or brush cutter. In addition, we included the time to remove C. hirta, which must be removed mechanically from all plots once a year, calculated from the average weed-removal time in the palm circle and the percentage cover of C. hirta in each plot for each year. We then calculated the profit as the difference between revenues and the total management costs and the relative gross margin as the gross profit proportion of the revenues.StatisticsTo test for differences among management treatments for each ecosystem function and across indicators of biodiversity, the plot-level value of each indicator was first z standardized (z = (actual value − mean value across plots) / standard deviation)4. This prevents the dominance of one or few indicators over the others, and z standardization allows several distinct indicators to best characterize an ecosystem function or biodiversity4. Standardized values were inverted (multiplied by −1) for indicators of which high values signify undesirable effect (that is, NEP, soil N2O and CH4 fluxes, element leaching losses, invasive plant cover, yield shortfall, management costs) for intuitive interpretations. For a specific ecosystem function (Supplementary Figs. 1 and 2) and across indicators of biodiversity (Fig. 2), linear mixed-effects (LME) models were used to assess differences among management treatments (fertilization, weeding and their interaction) as fixed effects with replicate plots and indicators (Supplementary Tables 2 and 3) as random effects. The significance of the fixed effects was evaluated using ANOVA72. The LME model performance was assessed using diagnostic residual plots73. As indicator variables may systematically differ in their responses to management treatments, we also tested the interaction between indicator and treatment (Table 1). For testing the differences among management treatments across ecosystem functions (that is, multifunctionality; Fig. 1), we used for each replicate plot the average of z-standardized indicators of each ecosystem function and ranges of thresholds (that is, number of functions that exceeds a set percentage of the maximum performance of each function12; Supplementary Fig. 3). The LME models had management treatments (fertilization, weeding and their interaction) as fixed effects and replicate plots and ecosystem functions as random effects; the interaction between ecosystem function and treatment were also tested to assess if there were systematic differences in their responses to management treatments (Table 1). As we expected that the type of weeding will influence ground vegetation, we tested for differences in ground cover of understory vegetation, measured from 2016 to 2020, using LME with management treatments as fixed effect and replicate plots and year as random effects. Differences among management treatments (fertilization, weeding and their interaction) in yield and profit indicators, which were cumulative values over four years (Fig. 3) or for two separate periods (2017–2018 and 2019–2020; Supplementary Fig. 4), were assessed using linear model ANOVA (Table 1). For clear visual comparison among management treatments across ecosystem functions, multitrophic groups for biodiversity, and yield and profit indicators, the fifth and 95th percentiles of their z-standardized values were presented in a petal diagram (Fig. 4 and Supplementary Fig. 5). Data were analysed using R (version 4.0.4), using the R packages ‘nlme’ and ‘influence.ME’73.Reporting summaryFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article. More

RESEARCH BRIEFINGS
01 March 2023

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

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

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

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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

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