Philippa Kaur

We hypothesized that functionally distinct forest types can be mapped at moderate spatial resolutions, using a combination of canopy foliar traits and canopy structure information. Our analysis of LiDAR and imaging spectroscopy data at spatial resolutions ranging from 4 to 200 m (16 m2–40,000 m2), with an emphasis on the 30 m (900 m2) spaceborne hyperspectral spatial resolution, reveals that few remotely sensed canopy properties are needed to successfully identify ecologically distinct forest types at two diverse tropical forest sites in Malaysian Borneo. In testing our second hypothesis that mapped forest types exhibit distinct ecosystem function, we found that forest types identified using remotely sensed leaf P, LMA, Max H, and canopy cover at 20 m height (Cover20) closely align with forest types defined from field-based floristic surveys29,30,31,32,33 and inventory plot-based measurements of growth and mortality rates (Fig. 4b). Our approach, however, enables mapping of their entire spatial extent (Fig. 1) and reveals important structural and functional variation within areas characterized as a single forest type in previous studies (Fig. 3). Current and forthcoming satellite hyperspectral platforms, including PRISMA (30 m), CHIME (20–30 m), and SBG (30 m), have or will have comparable spectral resolution, higher temporal revisits, and much greater geographic coverage. The ability to conduct this type of analysis using remote sensing measurements at 30 m resolution suggests that our method can be applied to these emerging spaceborne imaging spectroscopy data to reveal important differences in structure and function across the world’s tropical forests.Nested functional forest types revealedTo test our first hypothesis, rather than making an a priori decision about the number of k-means clusters (k), we explored the capacity of remotely sensed data to reveal ecologically relevant variation in forest types. Baldeck and Asner took a similar unsupervised approach to estimating beta diversity in South Africa34. Because the choice of k directly influences analysis outcomes, careful selection of k is required. Different approaches for identifying the number of clusters, using the Gapk and Wk elbow metrics35, yielded varying optimal numbers of clusters for the Sepilok and Danum landscapes (Fig. 1, Supplementary Figs. 4 and 5). However, at both sites, a comparison of results based on different values of k revealed ecologically meaningful structural and functional differences and graduated transitions between forest types (Fig. 2, Supplementary Figs. 7 and 8), indicating that the exploration of traits that aggregate or separate forest types as k changes is a valuable exercise. Overlap between the remotely sensed forest type boundaries and inventory plots within distinct forest types indicate that the series of clustered forests align closely with forest types defined based on in situ data on species composition and ecosystem structure. In part, this type of analysis requires careful selection of the number of clusters. Additionally, however, we gained valuable insights via the exploration of varying numbers of clusters as it relates to biologically meaningful categorization of forest types. Extending this method to other parts of the tropics will require similar decision-making, which will either require user input, or the development of robust automated algorithms for selecting k.Forest types capture differences in ecosystem dynamicsWe further evaluated the canopy traits and structural attributes that were most critical for mapping distinct forest types, hypothesizing that mapped forest types exhibit distinct ecosystem function. Forest types revealed by the cluster analyses were distributed along the leaf economic spectrum, where the leaf economic spectrum characterizes a tradeoff in plant growth strategies36. LMA, which can covary strongly with leaf N and P, is a key indicator of plant growth strategies along the spectrum37. At the slow-return end of the leaf economics spectrum, plants in nutrient-poor conditions with low leaf nutrient concentrations invest in leaf structure and defense, expressed as high LMA, strategizing longer-lived, tougher leaves with slower decomposition rates. This strategy comes at the cost of slower growth. At the quick-return end of the spectrum, plants in nutrient-rich environments with higher leaf nutrient concentrations invest less in structure and defense, enabling faster growth and more rapid leaf turnover, i.e., shorter leaf lifespans. This quick-return growth strategy supports higher photosynthetic rates and more rapid carbon gain36.In this study, the principal components and clustering results yielded forest types that are indicative of community level differences associated with leaf economic spectrum differences. The nutrient rich sites (Danum1 and Danum2, Supplementary Fig. 8) show high canopy N and P and low LMA compared to the nutrient poor and acidic sites (Sandstone and Kerangas), which contributes to lower leaf photosynthetic capacity (Vcmax) and growth (Fig. 4b). Foliar N:P also increased with site fertility, confirming that tropical forests are primarily limited by phosphorus, and not nitrogen38,39, with large implications for carbon sequestration in these forests. Orthogonal differences in canopy structure and architecture between Danum forest types and Sepilok Sandstone and Alluvial forests could be indicative of ecosystem scale differences in the sensitivity of these forests to endogenous disturbance processes40.The significant differences in aboveground carbon stocks and growth and mortality rates between forest types further suggests strong differences in ecosystem dynamics. In general, growth rates varied inversely to aboveground carbon, and higher aboveground carbon corresponded to lower mortality rates. As an example, the Sepilok sandstone forests, which are largely comprised of slow-growing dipterocarp species29,33, had the highest median aboveground carbon (236 Mg C ha−1), with higher canopy P and N, and lower LMA. The taller canopy and low canopy leaf nutrient concentrations are consistent with the low growth and mortality rates found in the sandstone forest, indicating a slow-growth strategy yielding larger trees and higher aboveground carbon stocks. In contrast, alluvial forests exhibit high turnover with mortality and growth rates higher relative to Sandstone forests corresponding to lower aboveground carbon on average. Kerangas forests exhibited low aboveground carbon despite an intermediate plot-level growth rate, and mortality rates that were significantly lower than the Danum or alluvial forest types. Kerangas forests, which were characterized by the highest LMA, lowest foliar P and N (Fig. 2a), and the lowest plot-level aboveground carbon density (186 Mg C ha−1; Fig. 4a), are known to have higher stem densities, lower canopy heights, and long-lived leaves5,32,41, suggesting well-developed strategies for nutrient retention42. Interestingly, despite significantly different aboveground carbon and demography, the kerangas and sandstone forests did not differ in LAI or canopy architecture (P:H); although maximum height, Cover20, and Hpeak LAI were significantly higher in the sandstone forest, highlighting the need to account for differences beyond LAI when scaling processes from leaves to ecosystems.In addition, when three forest types were distinguished at Sepilok, the alluvial inventory plot had significantly higher aboveground carbon than the remote sensing-derived alluvial forest extent (Fig. 4a, p  More

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.This is a summary of: Marin, F. R. et al. Protecting the Amazon forest and reducing global warming via agricultural intensification. Nat. Sustain. https://doi.org/10.1038/s41893-022-00968-8 (2022). More

Retrieval of E. coli plasmid sequencesAll E. coli sequences were downloaded from the NCBI FTP server in May 2020. To establish an initial collection of plasmids, only complete genomes with an associated plasmid were retained. All genomes were verified for belonging to the species E. coli using kmerfinder (https://cge.cbs.dtu.dk/services/KmerFinder/). Sequence type (ST) was determined via multi-locus sequence typing (MLST) based on the 7-gene Achtman scheme using pubMLST (https:/github.com/tseemann/mlst). Only genomes with exact matches were assigned for each ST and used for subsequent analysis. To ensure our sequences were sufficiently representative of E. coli pathogens expected in nature, a systematic literature search (see description below and Fig. S1) was conducted to establish an expected distribution of STs (Table S1). This information was used to update our initial collection to match the top 4 most prevalent STs (131, 11, 73, and 95). Specifically, to identify supplementary plasmid sequences, genome accession IDs were chosen from EnteroBase based on the following criteria: the strain was matched to the correct ST and had a high-quality genome sequence (based on N50  > 20,000 and the number of contigs  0.1, 2-tailed student t test). For the second method, all kanR plasmids were used, and instead changed the hosts such that DH5αPro cells were in competition with DH5αPro containing a spontaneous rifampicin-resistant mutant (rifR). Any rifR strain was quantified on rifampicin-containing plates, and the second strain was quantified by rifampicin CFU minus CFU obtained on blank plates. We established that rifR exhibited no fitness defects by (1) growth rates between the wild-type (WT) strain (W) and rifR (M) (Fig. S5D), and (2) directly competing the two control strains (Fig. S5E). In both cases, results were statistically indistinguishable (p  > 0.1, two-tailed student t test). KanR/cmR and WT/rifR experiments were each conducted in LB or M9CAG, respectively. In all cases, experiments were repeated with at least three independent biological replicates.Time-kill measurements in the presence of carbenicillinAll strains were grown as previously described. Time-kill experiments entailed hourly measurements of CFU in presence of carbenicillin at either 3.75 μg/mL (3x IC50) or 5 μg/mL (4x IC50) over a span of 2 or 3 h, including time 0. Specifically, overnight cultures were first diluted 1:100 into LB media containing 1 mM IPTG and 50 μg/mL kanamycin and sub-cultured for two hours in a 37 °C incubator with shaking at 250 rpm. Following this, cell density was adjusted as necessary to achieve a starting OD600 of ~0.15 in all cases. Adjusted subcultures were then aliquoted into a 96-well plate and the appropriate carbenicillin treatments were added directly to the well. Plates were sealed with a paper film and placed in a 37 °C incubator with shaking at 250 rpm. Initial collection for time=0 was acquired before carbenicillin treatment. Thereafter, 10 μL of culture was removed from the well every hour, 10-fold serial dilutions were performed and 10 μL was plated on blank LB agar with three technical replicates at each time point. Colonies were counted after plates were grown for 16 h in a 37 °C incubator to determine CFU. This procedure utilized 14 strains of DH5αPro transformed with kanR plasmids of interest – ctrl, katG, lpxM, yfbR, aroH, pld, fdtC, agp, eptC, arcA, argF, mmuM, ahr, and fabG. CFUs were averaged for all technical replicates, and experiments were conducted with at least three independent biological replicates.Oxygen consumption rateOxygen consumption rates (OCR) were obtained with the Resipher device from Lucid Scientific. The selected strains were grown overnight as previously described. Overnight cultures were resuspended in M9CAG media with 1 mM IPTG and 50 μg/mL kanamycin, and placed in 25 °C for one hour to initiate gene expression. Following this, cells were diluted 10x into M9CAG media containing kanamycin and IPTG, and 100 μL was aliquoted per well into a 96-well microtiter plate according to the manufacturer’s instructions. Plates were placed at 30 °C to minimize growth, and oxygen concentration (μM) was measured immediately thereafter. 24 wells were measured consisting of 6 technical replicates for each strain. Given the clear well-well variability (Fig. S8B, C), data shown are for one biological replicate. However, qualitative trends were consistently reproduced in multiple independent experiments.StatisticsIn all cases where t tests and ANOVA’s were used, data was first verified to be normally distributed using Kolmogorov test for normality. Otherwise, Mann-Whitney U-tests were conducted. For panels with multiple tests, Bonferroni correction was used to adjust the p values. To determine whether any metabolic category was significantly dependent on incompatibility groups, we implemented logistic regressions in MATLAB with the function fitglm. Random forest classification was used to establish the relative importance of prevalent metabolic genes and gene categories predicting the presence of antibiotic resistance genes. Chi-square tests were conducted to determine significant co-occurrence of individual antibiotic resistant and metabolism genes. Dissociative relationships were distinguished by the odds ratios from the chi-square tests. To investigate whether the strong associations and disassociations were driven by evolutionary constraints, or simply artifacts of a common ancestor, we re-ran our statistical analysis using Coinfinder [29] to take in our gene presence-absence data, along with the genome phylogeny, and compute the Bonferroni-corrected statistical likelihood of coincidence (either associations or dissociations), thereby accounting for evolutionary relatedness. More

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Basic characteristics of river sedimentsA considerable variation was found in the distribution of clay (81 to 48.4 g kg−1), silt (145 to 656 g kg−1), and sand (38 to 821 g kg−1) particles among sediment materials. The associated coefficient of variations (CV) was 57, 59.5, and 41%, respectively. Statistical data related to the physicochemical properties of sediments and their main elements are reported in Table 2. The variations in particle size distribution located sediment material in seven textural classes ranging from loamy sand to silty clay. The high variability in particle size distribution suggests that different sets of geogenic and anthropogenic processes are enacted in the development and distribution of sediments in the rivers. The pH and CCE ranged from 7.4 to 8.2 and 31 to 251 g kg−1, respectively, indicating the dominancy of alkaline-calcareous condition. None of the sediment samples exhibited salinity conditions (EC  > 4 dS m−1) with EC in the range of 0.3 to 1.4 dS m−1. A relatively low range of OM was found in all samples ranging from 7 to 61 g kg−1 with a mean value of 19 g kg−1. This range of OM coincides with the corresponding values in regional soils47. Except for pH, other sediments properties demonstrated above 35% of CV illustrating a wide range of variability in sediments’ physicochemical properties across the study rivers.Table 2 Summary statistics of sediment properties.Full size tableThe highest concentration among major elements was observed in SiO2, varying between 37.5 and 55.2%, with a mean percentage of 44.9%. This element followed in magnitude by Al2O3 (8.9–15.9%), CaO (5–14.3%), Fe2O3 (4.8–10%), MgO (2.4–17.2%), K2O (1.2–3.1%), Na2O (0.68–2.7%), SO3 (0.01–4.8% g kg−1) (Table 2). Considering the semi-arid climatic condition of the study region, higher levels of SiO2 and lower levels of Al2O3 may indicate that the silicate minerals forming the sediments of the area have not been subjected to severe weathering processes. Likewise, the Na2/K2O ratio was greater than 1 in the majority of sediment samples, implying an enrichment of potassium feldspar and the relatively intense weathering of Na-bearing minerals in the region48,49. The CIA value was in the range of 64.9 to 85.7% with a mean percentage of 72.9%, representing a moderate chemical weathering intensity of lithological materials (65%  Pb  > Cu  > Cd which varied largely among the sampling points. The level of Zn, Cu, Cd, Pb, and Ni varied in the ranges of 32.6–87.5, 14.2–33.3, 0.42–4.8, 14.5–69.5, and 20.1–183.5 mg kg-1, respectively, for winter, and 35.3–92.5, 15.6–35.1, 0.47–5.1, 15.5–73.1, 23.2–188.3 mg kg−1 for summer. The obtained ranges are comparable with data found in previous studies in Asia4,54,55,54.Figure 2The comparison of the mean concentration of Zn, Cu, Cd, Pb, and Ni elements in the study rivers’ sediments during summer and winter. Different letters show significant differences in metal content among rivers pooled over seasons at P  More

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Mistakes in a landmark paper that reported the first orangutan genomes might have implications for breeding programmes.Credit: Fiona Rogers/Nature Picture Library

Susie the Sumatran orangutan was a genetic pioneer — the first of her species to have her genome fully sequenced. Her genetic library, and that of ten other orangutans, appeared in a landmark paper in Nature in 20111 that has underpinned hundreds of subsequent studies.But in August, researchers revealed that eight of the sequences in this paper had mistakenly been assigned to the wrong orangutans2. Nature issued a correction from the authors of the original paper3.The scale of the errors sparked ire on social media, and some scientists have warned that the mistakes could have repercussions for orangutan breeding programmes. “Well that’s a bit of a f&£k up orang-utan genome researchers — only mildly embarrassing guys and girls”, tweeted Michael Sweet, a molecular ecologist at the University of Derby, UK.
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It’s not clear how these swapped identities have affected orangutan research. But researchers involved in the new analysis believe the discovery might highlight how issues in the scientific community — including the pressure to publish and a reliance on peer review to catch mistakes — could allow such errors to slip into the scientific record.“I think there are errors like this in many, many published papers,” says Graham Banes, an evolutionary biologist formerly at the University of Wisconsin–Madison who led the reanalysis of the 2011 paper. “In some ways, we’re lucky that this was just orangutans. What if this was a biomedical paper and people were developing therapies based on published data?”“It’s fairly easy for these things to occur,” adds Robert Fulton, a genomic scientist at Washington University School of Medicine in St Louis, Missouri, who was part of the team behind the original paper and is a co-author on the reanalysis. “What’s important is that that the data are now correct.” Devin Locke, who led the preparation of the 2011 paper and was formerly a colleague of Fulton’s at Washington University, did not respond to questions about the work.Hybrid headacheDetailed ‘reference’ genomes, such as those published in the 2011 Nature paper, are a key tool for biologists. In 2017, Banes and his team were using the genomes to study what happens when different species of orangutan interbreed, a process called hybridization.They noticed that the names given to some of the samples didn’t match the animals’ reported sex. For example, the 2011 paper reported that an orangutan named Dolly was male. But according to the orangutan studbook — a record of orangutans living in zoos — Dolly was female. Even stranger, Banes found that some of the genomes marked as male lacked a Y chromosome. “There was just this series of things that didn’t make sense,” he recalls.
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Banes and his colleagues eventually found that the 2011 paper had misidentified all but two of the orangutan genomes. Some mistakes seem to be the result of typos. In one case, a sample from a male orangutan was given an ID number that actually corresponded to a sample from an African pig in a tissue repository. Other samples seem to have had their identities swapped during laboratory work. The 2011 study helped to pin down when Bornean and Sumatran orangutans split into separate species, and compared their genomes with those of other primates. These conclusions are largely uncompromised by the mix-up. But Banes says that the errors could have implications for other research, including his own.Banes uses genetic data to provide zoos with recommendations about their captive breeding programmes. Zoos try to avoid crossbreeding orangutan species, partly to mimic wild populations and also because hybrids can suffer high rates of miscarriage and birth defects, says Banes. While re-examining the samples from the 2011 paper, the team realized that one of the sequences thought to be Sumatran (Pongo abelii) was actually Tapanuli (Pongo tapanuliensis), a third species of orangutan that was only described in 20174.Unfortunately, the 2011 paper had wrongly assigned the Tapanuli genome to Baldy, a male orangutan, rather than its actual owner, a female orangutan named Bubbles (both are now dead). Banes says that his team came “perilously close” to announcing in a paper that Baldy was Tapanuli.Although Baldy has no living descendants, Bubbles has several offspring at zoos around the world, all of which are Sumatran–Tapanuli hybrids. Zookeepers will now have to decide whether to stop breeding Bubbles’ descendants to avoid further hybridization, says Vincent Nijman, an anthropologist at Oxford Brookes University, UK.‘Bigger concerns’However, Nijman also argues that the errors will have little effect on orangutan conservation as a whole. Zoos often bill their animals as a back-up for endangered species, but conservationists are much more focused on the thousands of orangutans in the wild that are threatened by deforestation. “I think we have bigger concerns than some mixed-up samples,” says Erik Meijaard, a conservation scientist at Borneo Futures, a conservation consultancy company based in Bandar Seri Begawan, Brunei.
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Michael Krützen, an evolutionary geneticist at the University of Zurich in Switzerland, agrees that although the errors are “annoying”, their impact on downstream research is probably minimal. However, he says that the problems might be an example of how academia’s publish-or-perish environment could lead to “sloppy” work, as researchers race to publish their work in high-tier journals.Banes agrees that this kind of pressure — along with an over-reliance on a peer-review system that does not offer its volunteer reviewers tangible financial or professional benefits — could lead to errors slipping into published manuscripts.A spokesperson for Nature declined to comment on why the errors in the 2011 paper were not caught by peer review, citing concerns about confidentiality. (Nature’s news team is editorially independent of its academic publishing operation). “However, we would like to stress that we take our responsibility to maintain the accuracy of the scientific record very seriously,” they wrote in an e-mail. “If issues are raised about any paper we have published, we will look into them carefully and update the literature where appropriate.”Banes says that it’s important not to blame individual scientists for such errors, not least because it could discourage efforts to correct mistakes in future. “I think any scientist could have made these mistakes,” he says. “But if we all jump out and say, ‘oh my god, how could they have been so stupid?’, no one is ever going to correct anything. That shame is detrimental to science.” More