Ecology

Resistance of H. zea populations to Cry1AcTo measure variation in resistance of H. zea populations across field locations during 2017 and 2018, larval offspring of insects collected from non-Bt maize were subjected to a diet-overlay bioassay containing a diagnostic concentration of Cry1Ac (29 µg/cm2) corresponding to the mean LC95 of four Cry1Ac susceptible H. zea populations. Overall, larval survivorship varied significantly among years (Fig. 1).Figure 1Survival of H. zea larvae collected in 2017 and 2018 following exposure to 29 µg/cm2 Cry1Ac in diet-overlay assay. Dashed line is mean survival of a known susceptible field population. ***Years significantly different F = 25.38; df = 1, 58; P  More

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Isolation of marine sediment-derived actinomycetes from west-central PhilippinesThe geographical sites identified in this study were evaluated to explore the actinomycete distribution in west-central Philippines (Fig. 1a). A total of 16 sediment cores were recovered from the 11 sampling sites and were processed in the laboratory using cultured-dependent actinomycete isolation (Supplementary Table S2). The seawater’s physicochemical conditions in all sampling sites were uniform with pH 7.0 and salinity ranging from 3.1 to 3.2. The characteristics of marine sediments and the distance of the actual collection sites identified using the given criteria varied per sampling location. The sediment characteristics vary from coarse to fine sand with mixture of broken corals and pebbles (Supplementary Table S3). Culture-dependent isolation revealed that actinomycete-like colonies and spores were observed in the minimal marine media after 30 to 60 days of incubation. Actinomycete isolates were repeatedly sub-cultured in enriched marine medium 1 (MM1) to obtain pure cultures as shown in Fig. 1b. Actinomycete growth observed in enriched media was white to gray aerial spores with brown to yellow mycelia or without diffusible pigmentations. Notably, there were strains that produced colonies with no diffusible pigmentations.Figure 1Distribution, abundance, and antibacterial activity of marine sediment-derived actinomycetes in the Philippines. (a) The overall map showing the 11 sampling sites situated within the west-central region in the Philippines. The enlarged map showed the details of the sampling sites and their corresponding actinomycetes abundance. Sampling sites are number-coded as shown inside the circle. The color gradient indicates strain abundance. (b) Actinomycete colonies were sub-cultured repeatedly to obtain pure culture of isolates. (c) A total of 92 out of 2212 actinomycetes strains have confirmed antibacterial activities as verified using microbroth susceptibility assay. The map with Streptomyces abundance plot was generated using ggplot2 package in Rstudio ver. 1.2.5042 (https://www.rstudio.com/).Full size imageIn this work, a total of 2212 pure actinomycete strains were isolated from marine sediments collected in 11 geographically distant sampling sites across the west-central Philippines (Fig. 1a). Actinomycete strains were highly abundant in Negros Occidental with 580 isolates (26%), followed by Southern Antique with 348 isolates (16%) and Southeastern Iloilo with 228 (10%). We recovered least actinomycete strains in Occidental Mindoro and South Central Visayas with only 94 and 81 strains, respectively.Antibacterial activity profile of actinomycete strainsWe assessed the antibacterial activities of actinomycete strains against a multidrug-resistant Gram-positive bacterium (S. aureus ATCC BAA-44) and three Gram-negative bacteria (E. coli ATCC 25922, P. aeruginosa ATCC 27853, and E. aerogenes ATCC 13048) using resazurin agar overlay assay and microbroth susceptibility assay as initial and confirmatory screenings, respectively. A total of 218 (9.9%) out of the 2212 actinomycete isolates have antibacterial activities in the initial screening as indicated by positive results or retained blue resazurin color in wells containing actinomycete overlaid with the test pathogens (Supplementary Fig. S2). The 218 active isolates were fermented to produce biomass for secondary metabolite extraction and to confirm their antibacterial activities by microbroth susceptibility testing. Figure 1c showed the confirmed 92 (4.1%) antibiotic-producing actinomycete strains. The majority of the strains (71 isolates) exhibited activity against Gram-positive S. aureus ATCC BAA-44. Thirty-nine (39) strains (42%) were active against E. coli ATCC 25922. Six strains were active against P. aeruginosa ATCC 27853, while all strains tested were inactive against E. aerogenes ATCC 13048 as indicated with no or less than 50% growth inhibition. Twenty-three (23) active strains targeted 2–3 test pathogens, while 69 active strains were only active against one test pathogen (Supplementary Table S4).Phylogenetic diversity of multiple antibiotic-producing strainsThe 92 active actinomycete strains were further identified and confirmed as Streptomyces species based on genomic analysis of their 16S rRNA and rpoB gene sequences. Comparison of 16S rRNA gene sequences (ranging from 1150 to 1500 nucleotides) and rpoB (700–995 nucleotides) gene sequences with their similar matches in the GenBank verified that the 92 active strains were closely related (97 to 100%) with 19 species under the genus Streptomyces. The nearly complete 16S rRNA and rpoB gene sequences were analyzed in a phylogenetic tree using maximum likelihood algorithms. The 16S rRNA and rpoB gene sequences of active strains reported in the present study were deposited in the GenBank nucleotide database (Supplementary Table S5).Phylogenetic analysis revealed multiple strains with identical 16S rRNA gene sequences which clustered together into 13 major clusters (shown by the colored nodes in the tree) with high bootstrap values ( > 90%) in the phylogenetic tree (Supplementary Fig. S3). Thirty-three strains (36%) were highly similar to S. parvulus presented in red circle. Followed by 12 strains with high similarity to S. enissocaesilis (light blue circle), 11 S. rochei strains (dark pink), six S. mutabilis strains (dark blue), five S. diastaticus strains (light green), four S. kunmingensis strains (green) and three S. geysiriensis strains (light orange). A phylogenetic analysis of rpoB gene sequence was conducted to provide a better resolution of the evolutionary relationship among strains within and between species supporting the taxonomic identity of the phylogenetically identical strains.Similarly, the rpoB gene sequences phylogenetic tree (Fig. 2) showed 13 major clusters that were highly supported with bootstrap replicates  > 90%, except for monophyletic clusters III (Streptomyces sp. strain DSD176) and X (Streptomyces sp. strain DSD1006) with low bootstrap replicates ( 1% abundance) can be recovered in both methods. We also noted that some species were exclusively recovered using a specific method. Eight species were exclusively recovered in DSM compared to 6 species in HSM. All active strains of S. kunmingensis, S. mutabilis, S. sedi and S. olivaceus were only recovered by HSM. Contrary, the active strains of S. carpaticus and S. harbinensis were only isolated using DSM.Carbon source composition of marine minimal media, along with effective isolation techniques, was crucial for the isolation of antibiotic-producing Streptomyces. Among the five minimal marine media used, three carbon sources yielded high isolation rate: glucose, mannitol, and trehalose yielded nine species with 32 strains (35%), nine species with 18 strains (20%), and ten species with 18 strains (20%) respectively. However, only eight species (12 strains, 20%) and two species (12 strains, 20%) were isolated in raffinose, and starch-based media, respectively. In the contrary, high diversity indices were observed in trehalose (2.197), mannitol (2.0), and raffinose (1.979) (Fig. 5a). As expected, the starch-containing media had the lowest isolation rate and diversity as only two species (S. enissocaesilis and S. parvulus) were able to utilize a more complex carbon source.Figure 5Diversity of antibiotic-producing Streptomyces using five different carbon sources. (a) From the five carbon sources in the minimal marine media utilized by Streptomyces strains in this study, mannitol yielded the highest number of active strains, while high diversity was recorded in active strains that utilized glucose (n = 92). (b) Venn diagram of five carbon sources showed that two Streptomyces species can be isolated using all five carbon sources.Full size imageInterestingly, co-isolation of species in different carbon sources was shown in the Venn diagram (Fig. 5b). Eight species can be recovered from at least two different carbon sources, whereas 11 species were exclusively isolated from a specific carbon source. Bioactive S. enissocaesilis and S. parvulus strains were recovered from all of the carbon sources utilized in this study. Active S. rochei were isolated in four media but not in starch-based media. In contrast, more exclusive species were isolated in trehalose with four species (S. harbinensis strains, Streptomyces sp. strain DSD3025, S. pseudogriseolus, and S. xiamenensis). Followed by glucose with three species (Streptomyces sp. strain DSD742, S. carpaticus and S. sedi), and two species each on mannitol- and raffinose-based media (Fig. 5b). The results indicated that diverse Streptomyces species preferred simple sugars-containing one or two sugar molecule as nutrient source compared to complex sugars.
Streptomyces abundance and diversity in geographical sampling locationsBioactive Streptomyces species were widely distributed across the different sampling locations in west-central Philippines (Fig. 6a). Although Southern Antique, Negros Occidental and Negros Oriental have highest number of active strains isolated, we found that Southern Antique, Southern Iloilo, and Western Antique were the most diverse sampling sites (Fig. 6b). We have isolated the greatest number of antibiotic-producing Streptomyces species which were evenly distributed in Southern Antique. This indicates that Southern Antique is stable with many potential niches that can support highly diverse Streptomyces species.Figure 6(a) The abundance profile of 19 antibiotic-producing Streptomyces species in different geographical location across the Philippines revealed that S. parvulus was the most abundant species. (b) Diversity, richness and evenness of antibiotic-producing Streptomyces species in different sites were calculated using vegan package in RStudio ver1.2.5042 (https://www.rstudio.com/). (c) Recovery profile and abundance of antibiotic-producing strain per depth layer, indicated by colored circles and its size, revealed that strains in the bottom sediments were the most diverse as compared to other depth layer and S. parvulus was the most abundant species recovered.Full size imageStreptomyces parvulus emerged as the dominant antibiotic-producing species in this study. Out of the 33 active S. parvulus strains, the majority were isolated in Negros Occidental (11 strains) and Negros Oriental (ten strains). Although Negros Occidental and Negros Oriental have high abundance, its microbial community is highly dominated by one species, S. parvulus, supported by the low species richness, evenness, and diversity (Fig. 6b). This finding implies that these sites may have few potential niches that only a few species dominate. Bioactive S. enissocaesilis strains were recovered in four sampling locations only; specifically, Southern Antique (five strains), Western Antique (three strains), Southeastern Iloilo (three strains), and Northwestern Antique (one strain). Active S. rochei were isolated and evenly distributed in seven sampling locations, but were not present in Western Antique, Northwestern Antique, South Central Visayas, and Tubbataha Reefs. Notably, we observed that no bioactive S. parvulus, S. enissocaesilis, and S. rochei were isolated in Tubbataha Reefs, but antibiotic-producing S. cacaoi, S. psuedogriseolus and S. mutabilis strains were isolated only in Tubbataha Reefs marine sediments. Meanwhile, site-specific species such as S. sedi were recovered only in Occidental Mindoro. The isolation of site-specific species within genus Streptomyces can offer insight on the adaptive capacity of strains to inhibit locally coexisting resource competitors within and among these distinct locations.Distribution of bioactive Streptomyces species at different sediment depthsWe further investigated the distribution of antibiotic-producing Streptomyces strains along with the 110-cm sediment depth in different sampling sites. From the sediments that were partitioned according to depth with five categories at 25-cm increments, heterogeneous distributions of bioactive species were observed at deeper sediment with different dominant species in each depth (Fig. 6c). Although, S. parvulus, S. rochei, and S. enissocaesilis strains were ubiquitous in all depths, several species thrive abundantly in specific depths compared to other species. Streptomyces parvulus was the most dominant species in surface sediment. Meanwhile, Streptomyces rochei and S. enissocaesilis strains were more adapted in surface and sub-bottom sediments, respectively. Depth-specific Streptomyces strains were also identified as follows: Streptomyces sp. strain DSD3025 was isolated in subsurface sediments; S. mutabilis strains were abundant in the middle sediment layer; Streptomyces sp. strain DSD1006 and S. pseudogriseolus strain were recovered from sub-bottom sediments; and Streptomyces strain sp. DSD742, S. albus, S. sedi and S. xiamenensis strains were obtained from bottom sediments. High species diversity was positively correlated with increasing sediment depth, where surface sediments are known to be more prone to dispersal and wash-offs by environmental factors such as deep ocean currents42. Furthermore, the depth-specific species identified largely influenced the species richness in varying sediment depth. More

Study regions and farmsTen European regions from boreal to Mediterranean were selected (Supplementary Table 1). They represented major agricultural land uses such as arable crops including horticulture, mixed farming, grassland and perennial crops (vineyards and olives). Within each region, a pool of ~20–40 farms was selected from which 12–20 farms were randomly selected (169 in total) that belonged to the same farm type, produced under homogeneous climatic and environmental circumstances and fulfilled specific criteria regarding their main production branch. In case the selected farms were not willing to participate, we asked other farms from the pool till the sufficient number has been reached. The selected organic farms had all been certified for at least five years. Farmers were asked if they were willing to participate in the study. If they refused, additional random sampling was conducted. In the region NL, 11 organic farms agreed to participate but only three non-organic farms, whereas seven organic farms and 11 non-organic farms were available in the region HU. During the study, one non-organic farmer in the region CH ceased participation.Habitat maps and farm interviewsThe complete area of all selected farms was mapped, using the BioHab method36. Excluded from the farm area were woody and aquatic habitats larger than 800 m2 and summer pastures. Within the farm area, areal and linear habitats were recorded. For an areal habitat, the minimal mapping unit was 400 m2 with a width of at least 5 m. More narrow habitats, between 0.5 and 5 m wide and at least 30 m long, were mapped as linear habitats. Habitats were distinguished in habitat types according to Raunkiær life forms, environmental conditions and management evidence28. Further, a farmland class was assigned to each habitat that described whether the habitat was managed for agricultural production or other objectives such as e.g. nature conservation. In face-to-face interviews following a standardized questionnaire, farmers provided detailed information on field management and yield.Categorization as production fields and semi-natural habitatsBased on the habitat maps and available information about management intensity, we categorized all habitats as either semi-natural habitats or production fields. In agricultural landscapes, these two categories are often not clearly distinguishable. There is a gradient from more intensively managed production fields to less intensively used semi-natural habitats. In addition, a categorization at the local scale can be different from an approach at a European scale (29 and see p. 45 of37). Here, we applied the same criteria for all ten study regions.In all cases, we categorized as production fields: arable crops, intensively managed grasslands (following main plant species observed, management evidence and objectives, with fertilization and/or two or more cuts a year), horticultural crops, and vineyards.We categorized as semi-natural habitats: linear habitats, habitats that were managed for nature conservation objectives, habitats where mainly geophytes, helophytes or hydrophytes were growing, grasslands with woody vegetation (shrubs and/or trees), and extensively managed grasslands (no fertilization, no or one cut a year).Species samplingVascular plant, earthworm, spider and bee species were sampled in all different habitat types of a farm. One plot per habitat type was randomly selected per farm for species sampling. This resulted in 1402 selected habitat plots on 169 farms (Supplementary Table 2). In the selected habitats, species were sampled during one growing season, using standardized protocols19,38. Plant species were identified in squares of 10 × 10 m in areal habitats and in rectangular strips of 1 × 10 m in linear habitats. Earthworms were collected at three random locations of 30 × 30 cm per habitat. First, a solution of allyl isothiocyanate (AITC) was poured out to extract earthworms from the soil. Afterwards, a 20-cm-deep soil core from the same location was hand sorted to find additional specimens. Identification took place in the lab. Spiders were sampled on three dates at five random locations per habitat within a circle of 0.1 m2. Using a modified vacuum shredder, spiders were taken from the soil surface, transferred to a cool box, frozen, or put in ethanol, sorted and identified in the lab. Bees (wild bees and bumble bees) were sampled on three dates, during dry, sunny and warm weather conditions. They were captured with an entomological aerial net along a 100 m long and 2 m wide transect, transferred to a killing jar and identified in the lab.Grouping of species dataSpecies data were pooled per taxa, habitat and region, and three sub-communities were formed with species (1) exclusively found in semi-natural habitats, i.e. unique to semi-natural habitats, (2) exclusively found in production fields, i.e. unique to production fields, and (3) found in both habitat categories i.e. shared by production fields and semi-natural habitats. For calculations of effects over all four taxa, species richness was the sum of the individual taxa species richnesses.Estimating species richnessSpecies richness was estimated using coverage- and sample-size-based rarefaction and extrapolation curves31,39,40. Rarefaction and extrapolation, including confidence intervals (bootstrap method) and sampling coverage, were calculated in R 3.4.041 using package iNEXT42. Detailed information is provided below for each topic.Estimating richness of unique species to compare semi-natural habitats and production fieldsTo legitimately compare the richness of species unique to semi-natural habitats and to production fields, we used the coverage-based method, i.e. we standardized the samples by their completeness30. The point of comparison was determined by the so-called ‘base coverage’ identified by the following procedure31: (1) select the maximum sample coverage at reference sample size (number of sampling units) of the sub-communities under comparison, (2) select the minimum sample coverage at twice the reference sample size of the sub-communities under comparison, (3) identify the maximum of the results from step (1) and step (2) as ‘base coverage’. The species richness estimates were then read off from the species sample-size-based rarefaction and extrapolation curves at the ‘base coverage’ for each sub-community being compared. If zero or exactly one species was unique to a sub-community at the reference sample size, no sample coverage could be calculated. In this case, we set the species richness at 0 or 1, respectively. The species richness estimate of the other sub-community under comparison was then read off at twice the reference sample size on the curve.The ‘base coverage’ was individually defined for each region and each taxonomic group since the mixed effects models used to analyze the data took into account the variation among regions and taxonomic groups.Differences in species richness unique to semi-natural habitats and production fieldsThe difference between the species richness unique to semi-natural habitats and unique to production fields was tested with mixed effects models using package lme4 (Version 1.1-12) in R43. The data were (Sij | β, b, x) ~ Poisson(µij) from i = 1, …, 10 regions. The model is:$${{{rm{ln}}}}left({mu }_{{ij}}right)={beta }_{0}+{beta }_{1}{x}_{1i}+{b}_{1i}$$
(1)
$${b}_{1} sim N(0,sigma 2)$$where ({beta }_{0}) is a fixed intercept, ({beta }_{1}) a fixed effect sub-community ({x}_{1{ij}}) (species unique to semi-natural habitats versus species unique to production fields), b1i are random intercepts for region i. Random effects are normally distributed with mean 0 and variance σ2. The significance of term ({beta }_{1}) was calculated by log-likelihood ratio tests with one degree of freedom. For the models over all four taxa, an additional random intercept was included, i.e. b2j with mean 0 and variance σ2 for j = 1, …, 4 taxa (Fig. 1b).Differences in species richness between organic and non-organic systemsThe comparison between organic and non-organic systems of species unique to semi-natural habitats and to production fields, and of species shared by the two habitat categories, relied on coverage-based extrapolation as described above. Differences between management systems were tested for significance using mixed-effects models with management system ({beta }_{1}) ({x}_{1{ij}}) as fixed effect in (1).Estimating species loss due to conversion of semi-natural habitats to production fieldsTo predict the species loss due to conversion of semi-natural habitats to production fields, we relied on sample-size-based extrapolations31 with species incidence frequencies. We estimated the richness of the species pool for the total number of mapped habitats including the extrapolated species richness unique to semi-natural habitats and unique to production fields, and the observed richness of shared species for each of the four taxa. This species pool provided the basis for the calculation of the species loss or gain (Table 1 and Supplementary Table 7). To model the species richness decrease for any amount of semi-natural habitats converted to production fields, we calculated and drew backward the curve composed of the accumulation curve for species unique to semi-natural habitats, to which the estimated total species richness unique to production fields (constant) and the corresponding gain of species unique to production fields (increases with increasing area of production fields as semi-natural habitats are converted), and the richness of observed shared species (constant) were added. This is the species decrease curve (Supplementary Fig. 2). If started at the observed species richness, this curve corresponds exactly to a species richness curve calculated by a cumulative random removal of semi-natural habitats one by one from the pool of all habitats. The four taxa decrease curves were added for the curve in Fig. 2. Confidence intervals (CI, 95%) shown in Figs. 2 and 3 are calculated by bootstrapping within the calculation of the species accumulation curves (iNEXT42), upper and lower bounds of the 95% CI of the four taxa being added. From the species decrease curve, we read off the predicted species richness for a conversion of 50% and 90% of the semi-natural habitats, and a conversion required to increase production by 10%.As species were sampled in 20% of all mapped habitats on average per region (min. 8%, max. 35%), extrapolated species accumulation curves used to build the species decrease curve were calculated for more than two to three times the reference sample size, which is the suggested range for reliable extrapolation of the species richness estimator31,44. Obviously, the confidence intervals (CI) of the species richness extrapolations here became wide (Supplementary Fig. 4). As we still wanted to show the impact of a conversion of the whole semi-natural area into production fields on the production gain in the ten regions, we used the uncertainty (upper and lower bounds of the 95% CI of the four taxa added) to define two situations in addition to the average case to predict species richness for a 50% and a 90% semi-natural habitat conversion, and a conversion required to increase production by 10%: (1) a worst case situation with the upper bound of the CI of the expected species richness unique to semi-natural habitats, the lower bound of the CI of the expected species richness unique to production fields, and shared species assumed not to be able to survive without semi-natural habitats and considered like species unique to semi-natural habitats (i.e. upper bound); and (2) a best case situation with the lower bound of the CI of the expected species richness unique to semi-natural habitats, the upper bound of the CI of the expected species richness unique to production fields, and the lower bound of the CI of the expected shared species richness.Estimating production gainFarmer interviews delivered an average yield per crop type per farm for the years 2008–2010 (Supplementary Data45 shows details for organic and non-organic systems separately). Farmers indicated yield in kilograms or tons per hectare. This was transformed into energy units, i.e. mega joules per hectare (MJ ha−1) using standard values46. From this, for each region, the average yield (MJ ha−1) was calculated by first multiplying individual crop type yields by the corresponding crop type areas to obtain the production per crop type, then summing up the production of all crop types, and finally dividing this sum by the total area of the crop types. For livestock farms, the fodder production of grasslands was estimated based on the average requirements per livestock unit, accounting for the amount of feed grain, legumes, silage maize and of imported feedstuff. All yields relate to plant biomass production and do not comprise livestock products. The average yield takes into account the relative cover of the different crop types in the regions. Therefore, the conversion of the semi-natural area to production fields was region-specific. The production of certain semi-natural habitats as e.g. olive groves in Spain was not part of the production calculation. The reason is that data on production for semi-natural habitats were mainly not available and/or negligible, e.g. extensively used grassland in CH or in HU, and we decided to apply the same treatment to all the regions. Consequently, in case of olive groves in Spain the effective increase in production is overestimated. To calculate the production gain per region, the production field area added by the conversion of semi-natural habitat area was multiplied by the average yield. In practice, in many regions it may be impossible to convert semi-natural habitat to productive land due to geomorphological constraints and poor soils, and even if land were converted, yields would be much lower than these averages. The results presented here, especially the 90% scenario, are therefore over-optimistic. On the other hand, our calculations are based on the area of semi-natural habitat available for conversion on existing farms, but in some regions other sources of semi-natural land may be available for conversion, e.g. former agricultural land that has been abandoned.Species loss and production gain for three scenariosWe calculated the change of species richness and the production gain under current day production efficiency for two scenarios: (1) a conversion of 90% of the semi-natural area into production fields. The 10% of semi-natural area remaining is considered unsuitable for agricultural use or even impossible to cultivate; (2) a conversion of 50% of the semi-natural area into production fields, and (3) a necessary conversion of the semi-natural area into production fields to achieve a 10% production increase per region.Standardization for organic and non-organic systemsAlthough the overall mapped area, the number of semi-natural habitats, the number of production fields and the average habitat size did not significantly differ between the two management systems (Supplementary Table 5), we standardized the number and size of habitats to the average across both systems per region to compare the species loss and production gain at current day production efficiency in the organic and non-organic systems. The total production in organic and non-organic systems per region was calculated based on the respective yield and the average mapped area of the production fields across both systems as described in section “Estimation of production gain”. The impact on biodiversity was analyzed for the scenario that organic systems should achieve the same level of production as non-organic systems by converting semi-natural habitats to production fields. We calculated the amount of the required area to be converted into production fields and the corresponding species change.Differences between management systems were again tested for significance using mixed-effects models with management system ({{{{rm{beta }}}}}_{1}) ({{{{rm{x}}}}}_{1{{{rm{ij}}}}}) as fixed effect in (1).Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

Africa’s population is rapidly growing, with its share of the global population projected to increase from 17% in 2020 to 39% by 2100 (ref. 8). The continent is already grappling with low agricultural productivity and food security challenges. Tremendous efforts are needed to increase food production; however, arable land continues to undergo widespread degradation due to issues such as nutrient mining, erosion, overgrazing and pollution. Climate change and more frequent weather extremes, such as floods and droughts, further degrade land and reduce agricultural productivity.Some efforts to counteract low productivity, however, can increase greenhouse gas emissions and derail efforts to meet global climate targets. Poor water management, fertilizer application and residue burning in rice production are, for example, major sources of potent greenhouse gases such as methane and nitrous oxide9,10. To ensure that the United Nations sustainable development goals and the African Union’s Agenda 2063 for food and water security are realized at minimal environmental cost, science-based land management practices are needed to decouple agricultural productivity from greenhouse gas emissions.
Credit: majimazuri21/PixabayThe Agriculture, Forestry and Other Land Uses (AFOLU) sector contributes the largest share of greenhouse gas emissions in Africa11. Thus, developing large-scale agronomic, livestock and forest management practices that increase productivity and reduce emissions is key to achieving enhanced production and environmental sustainability. However, it is impossible to effectively manage greenhouse gas emissions if there is limited capacity to quantify them in Africa.Improved data infrastructure and research are needed to quantify emissions associated with specific land management practices under different land uses. Similarly, land use mitigation strategies should be informed by existing and potential future land use changes and their impact on greenhouse gas emissions under different climate scenarios. However, past studies that examined land use changes at various temporal scales mainly used coarse resolution satellite imagery and suffered from limited availability or poor-quality of data, partly due to cost. Such challenges have resulted in limited knowledge of land management practices that reduce greenhouse gas emissions while increasing agricultural productivity.Improved greenhouse gas observation networks and in situ measurements12 will enable the development of country-specific emission factors (IPCC tier 2/3)13 and quantification and management of land use specific greenhouse emissions. It will reduce uncertainties in emissions inventory data on Agriculture, Forestry and Other Land Uses14, which are currently estimated using emission factors extracted from default value databases (tier 1 methodologies).Free earth observation data, such as those from the European Space Agency and United States Geological Surveys, are becoming increasingly available. Together with improvements in cloud-based computing infrastructure, this presents an opportunity to advance research into current and future land use and vegetation dynamics. Coupled with accurately quantified greenhouse gas emissions, this can support current and future land management practices that contribute to mitigation and adaptation objectives of countries. More

Convergence is a common feature of evolution and has great effect on the succession of microbial communities. For natural microbial communities such as the microbiome of gut [1], soil [2], sediment [3], rhizosphere [4], and phyllosphere [5], convergence generally means that different communities converge towards a similar species composition, which is accompanied by species loss and acquisition. Such a convergence can be reproduced in simplified synthetic communities [6,7,8], or even in single-species populations, in which convergence can still be achieved at sub-species level [9, 10]. Unlike the convergence of natural microbial community, those experiments carried out in a sterile laboratory environment only involves the loss of species. Specifically, the main manifestation of convergence in the synthetic community containing stably coexisting species lies in that the relative proportion of species tend to become consistent [7, 8]. Nonetheless, synthetic community opens a window for us to investigate the ecological mechanism. Previous studies of synthetic communities have revealed that the convergence of bacterial community can be regulated by pH [11], mortality [12], and particularly nutrient availability [13, 14]. Most existing studies focus on the changes in species proportions, but there is a lack of in-depth understanding of the gene expression changes driven by the community species interaction.In this study, we constructed a synthetic community with two model microorganisms, Escherichia coli K-12 (EC) and Pseudomonas putida KT2440 (PP), and reproduced a convergent community assembly in closed broth-culture system. In monocultures, the growth curves of both E. coli and P. putida fitted well with the bacterial growth model, and fell into a logarithmic phase at the first 4 h of bacterium culture and a stationary phase at subsequent 20 h (after the first 4 h) (Fig. 1a). When same quantities of bacteria were grown in cocultures, their quantities were basically similar to those in monocultures, particularly in the logarithmic phase (Fig. 1b–d). By contrast, the quantities of minority species in cocultures continued to increase, and they were close to the quantities in monocultures at 24 h post co-cultivation (Fig. 1b–d). Besides, statistical analysis showed that the quantities of P. putida in all three cocultures were overall greater than that in monoculture, while E. coli quantities were no more than its monoculture (Fig. 1b–d), suggesting that P. putida has a negative effect on the growth of E. coli, but E. coli promotes that of P. putida.Fig. 1: Convergence of community structure and gene expression.a–d Growth curves of E. coli and P. putida in monoculture (a) and the “1:1000”, “1:1”, “1000:1” cocultures (b–d). In b–d subplots, the growth curves of monocultures were placed on the background layer (dashed lines), and the significant differences in cell quantity between coculture and corresponding monoculture were shown (ns, non-significant; *p  More

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NEWS AND VIEWS
01 September 2021

Policy, drought and fires combine to affect biodiversity in the Amazon basin

Analysis of the ranges of nearly 15,000 plant and vertebrate species in the Amazon basin reveals that, from 2001 to 2019, a majority were affected by fire. Drought and forest policy were the best predictors of fire outcomes.

Thomas W. Gillespie

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Thomas W. Gillespie

Thomas W. Gillespie is in the Department of Geography and at the Institute of the Environment and Sustainability at the University of California, Los Angeles, Los Angeles, California 90095, USA.

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The Amazon basin contains the largest continuous area of tropical rainforests in the world, and has a crucial role in regulating Earth’s climate1. Rates of tropical-rainforest deforestation and the impacts of fire and drought there are well established2,3. Less is known, however, about how these factors might interact to affect biodiversity, and about the role that forest policy and its enforcement have had over time. Writing in Nature, Feng et al.4 address these issues.

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doi: https://doi.org/10.1038/d41586-021-02320-0

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