Ecology

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

Insect rearingThe cotton mealybugs used in this study were originally collected from Rose of Sharon, Hibiscus syriacus L. (Malvales: Malvaceae) in Jinhua, Zhejiang Province, China, in June 2016. They were maintained on fresh tomato plants (cv. Hezuo-903, Shanghai Changzhong Seeds Industry Co., Ltd, China) in a climatically controlled chamber maintained at 27 ± 1 °C, 75% relative humidity (RH), and a photoperiod of 14:10 (L:D). For detailed insect rearing and tomato cultivation methods see ref. 56.Scanning electron microscopy (SEM) of P. solenopsis waxSEM was used to observe changes in wax on the body surface of adult P. solenopsis females according to the methods of Huang et al.57. Briefly, collected insects were taped onto a stub and dried in an ion sputter (Hatachi, Tokyo, Japan) under a vacuum. After gold sputtering, the samples were observed using a TM-1000 SEM (Hatachi, Tokyo, Japan). Photos were scanned from the dorsal part of the third thoracic segment. Thirty insects were used for both RNAi-treated and control groups.Chemical composition analysis of mealybug waxA small soft brush was used to collect wax filaments from the body surface of P. solenopsis females. Prior to use, the brush was washed successively by 70% ethanol, sterile water, and 1× sterile phosphate-buffered saline (PBS, pH 7.4). The wax was collected into a clean chromatography vial for the following experiments. Two vials of wax, each collected from 1000 adult females, were dissolved in 1 ml of methanol and 1 ml of n-hexane, respectively. The vials were stirred gently for 3 min, kept at room temperature for 30 min, and then put into an S06H ultrasonic vibrator (Zealway, Xiamen, China) for 30 min to dissolve the wax sufficiently. The samples were analyzed on a TRACE 1310 (Thermo Scientific, Waltham, USA) gas chromatograph (GC) equipped with an ISQ single quadrupole MS and interfaced with the Chromeleon 7.2 data analysis system (Thermo Scientific, Waltham, USA), with a constant flow of helium at 1 ml/min. For each sample, a splitless injection of 1.0 μl was respectively made into a polar TG-WaxMS (Thermo Scientific, Waltham, USA) and a nonpolar TG-5MS (Thermo Scientific, Waltham, USA) 30 m × 0.25 mm × 0.25 μm capillary column. The temperature program for polar column samples was as follows: 40 °C for 2 min, then 5 °C/min to 240 °C, hold 10 min; the program for nonpolar column samples was: 40 °C for 2 min, then 5 °C/min to 300 °C, hold 5 min. Injector and detector temperatures were, respectively, set at 250 and 230 °C for polar column samples, and at 300 and 300 °C for nonpolar column samples. Mass detection for all samples was run under an EI mode with a 70 eV ionization potential and an effective m/z range of 35–450 at a scan rate of 5 scan/s. Chemical compounds were identified by mapping against the NIST database. The relative content of each compound was calculated by peak area which was determined using the Agilent MassHunter system.RNA extraction and RT-qPCRTotal RNA was isolated using TRIzol reagent (Invitrogen, Carlsbad, CA) following the manufacturer’s instructions, and RNA quality was accessed using agarose gel electrophoresis and a Biodrop μLite. 800 ng of total RNA was used for cDNA synthesis using the HiScript III RT SuperMixfor qPCR (+gDNA wiper) (Vazyme Biotech Co., Ltd., Nanjing, China), according to the manufacturer’s instructions. Quantitative RT-PCR (RT-qPCR) was conducted using an AriaMx real-time PCR system (Agilent Technologies, USA), using a 20 μl reaction containing 2 μl of 10-fold diluted cDNA, 0.8 μl of each primer, and 10 μl ChamQ SYBR Color qPCR Master Mix (Vazyme Biotech Co., Ltd., Nanjing, China). The RT-qPCR thermocycling protocol was 95 °C for 30 s, followed by 40 cycles of 95 °C for 10 s and 60 °C for 30 s. The PsActin gene was used as an internal control. At least three biological replicates were used for each experiment. Quantitative variations were evaluated using the relative quantitative method (2−ΔΔCt)58.Transcriptome analysis of integumentary and non-integumentary tissuesTo obtain the integument and other tissues, adult P. solenopsis females were dissected in 1× sterile PBS (pH 7.4) on a sterile Petri dish. Dissected fresh tissues were directly used or frozen in liquid nitrogen and stored at −80 °C for follow-up experiments. We sequenced the transcriptomes of integumentary and non-integumentary tissues (all other tissues without integument) dissected from 150 adult females, with each sample being repeated in triplicate. mRNAs were purified from total RNA via oligo (dT) magnetic beads, and the fragmented mRNAs were then reverse transcribed into cDNA using random primers. Constructed pair-end libraries were sequenced using an Illumina HiSeq X Ten platform in Novogene (Beijing, China). After quality control, the clean RNA-Seq data of the six libraries were aligned with the P. solenopsis genome (http://v2.insect-genome.com/Organism/624) using HISTAT259. Then featureCounts60 and DESeq261 were used for the differential expression analysis of genes. The threshold for differentially expressed genes (DEGs) was defined by log2fold ≥ 1 or ≤−1 and a padj-value  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

This study quantitatively assessed waterbody loss due to urban expansion of large Chinese cities. We first extracted multi-temporal urban boundaries to determine the expansion of cities of over one million in population from 1990 to 2018. The monthly surface-water dataset was then used to identify surface waterbodies in the study period. Depending on the ratio of surface waterbody area to urban area, cities were further divided into three categories (i.e. water-abundant, water-medium, water-deficient). Finally, we quantified the rate of waterbody loss and evaluated the spatial and temporal variation of waterbody loss as a function of urban expansion and according to city type.GUB datasetThe Global Urban Boundary (GUB) dataset (http://data.ess.tsinghua.edu.cn) was used to determine urban expansion. GUB provides data on built-up areas over 30 years, with a spatial resolution of 30 m. In the GUB dataset, nonurban areas (such as green space and water space) surrounded by artificial impervious areas are filled within the urban boundary and removed by the algorithm, which is consistent with global mapping methods. The continuous urban boundary was demarcated by morphological image processing methods, which have an overall accuracy of over 90%. In this dataset, extensive water and forests are excluded, and the impervious surface within the urban boundaries accounts for about 60% of the total surface area47. Compared with urban boundaries obtained from night-time light, GUB better separates urban areas from surrounding nonurban areas.Monthly waterbody datasetWe selected the JRC Monthly Water History V1.3 dataset(https://global-surface-water.appspot.com/), which is available from the Google Earth Engine, as the basis for representing surface waterbodies48. This data collection, which was produced by using images from the Landsat series, contains 442 images of global monthly waterbody area from March 1984 to December 2020. In this dataset, the validation confirmed that fewer than 1% of waterbodies were incorrectly detected, and fewer than 5% of waterbodies were missed altogether. We chose this dataset due to the long-term spatial distribution of waterbodies and due to mountain shadows and urban-constructions masking, which reflects the real changes in waterbodies.Theoretical backgroundIt is well known that cities have high concentrations of population and resources and expand spatially during development. There are many different perspectives on the size of cities, and studies have mostly used urban density and population to characterize them. However, because it is challenging to standardize data sources and quality, there is no unified quantitative standard49. Urban construction has concentrated human activity and brought about changes in land types. Cities are also identified as physical spaces, which can be defined as the built environment50,51. The built environment, which includes structures like buildings, roads, and other artificial constructions, is sometimes referred to as a non-natural environment52.Rural is the antithesis of urban. As large cities have spread outward in developing nations like Asia, a transitional fringe has been created by the gradual blurring of the line separating urban and rural areas53. According to McGee, good locations, easy access, and sizable agricultural land all contribute to the development potential of large cities. Thus, between urban and rural areas, there are transitional areas of active spatial morphological change known as desakota33,54. The peri-urban areas, like desakota, are gradually developed and incorporated into original built-up urban areas in urbanization. The original landscape, which included agricultural land, vegetation, and waterbodies, gradually changed into an urban land use type, i.e. impervious surface, and thus the city continues to expand outwards. Waterbody, an essential ecological element, has been heavily developed or filled in during urbanization, which may present dangerous ecological risks. In this paper, we identified the urban boundaries based on physical space to explore the encroachment activities on waterbodies during the urbanization of large cities. We determined whether existing waterbodies were transformed into urban waterbodies or encroached upon and whether waterbodies were increased in the expansion of urban boundaries, thus proposing strategies for protecting waterbodies in the future.Extracting the extent of large Chinese cities from GUB datasetTo characterize urban expansion, GUB data are selected as the original data for urban boundary selection. The Chinese administrative scale of municipalities is not exclusively urban, but also includes rural areas. In our study, cities were defined as municipal districts excluding the vast countryside within the administrative boundaries of prefecture-level cities. We identified urban areas based on the physical boundaries from the perspective of remote sensing, which can precisely track urban expansion51.In this work, we selected 159 cities with a population of over one million in 2018 based on the average annual population of urban districts from the 2019 China City Statistical Yearbook (Fig. S1). Taiwan, Hong Kong, and Macau are omitted. According to statistics, China had 160 cities with populations exceeding one million in 2018. However, due to the lack of data for the built-up area in 1990, Guang’an was not included in the study. We thus obtained 159 cities from the GUB dataset. Due to numerous fragmented patches within the administrative boundary, the population identified the main urban areas, and max patch areas were comprehensively based on the urban boundaries. Through manual detection and adjustment of the map, we determined that the location of the extracted urban area was consistent with that of the municipal government, and the boundary was extracted for each period. We took the growth area as the expansion area, with the original area being the city at the onset of each period (Fig. S3).We used the average annual urban growth (AUG) rate to characterize the rate of urban expansion, as is widely done to evaluate urban expansion55,56. It is calculated as$${text{AUG}} = left[ {frac{{Land_{t1} }}{{Land_{t0} }}^{{frac{1}{t1 – t0}}} – 1} right] times 100% ,$$
where (Land_{t0}) and (Land_{t1}) represent the urban land area at time t0 and t1, where t0 and t1 are the start and end of the given study period.Identification of urban waterbodiesUrban waterbodies contain all the components of urban flow networks above the ground and include natural waterbodies such as lakes, rivers, streams, and wetlands and artificial waterbodies such as parks and ponds48. We identified all waterbodies existing within the urban boundary as urban waterbody. Considering urban expansion, urban waterbodies vary as urban boundary shift at different stages. Our study explored how the original waterbodies changed under urban expansion, including whether they were kept as urban waterbodies or encroached upon. Considering the dryness or wetness of each year, we used the data for 3 years (36 months) around each period (1990, 1995, 2000, 2005, 2010, 2015, and 2018) to describe the waterbody. Not all waterbodies could be detected for each month of the year; for example, freezing may prevent waterbodies from being detected. To cover seasonal and permanent waterbodies, we used the waterbody frequency index (WFI), which is calculated as the fraction of waterbody months within the 3 years to identify stable waterbodies pixel by pixel57. The spatial distribution of each waterbody was then mapped comprehensively for each period. By comparing the extracted waterbody with the long-time-series high-resolution remote-sensing images from Google Earth, we found that the extracted waterbodies fit the actual waterbody distribution quite well (Fig. S2):$$WFIleft( i right) = frac{WMleft( i right)}{{DMleft( i right)}}$$
where WFI(i) is the water occurrence for pixel i in the images before and after the given year, and i is the pixel number for the study area. WM(i) is the number of months during which the waterbody is detected in i pixel over the 3 years. DM(i) is the number of months during which the data are available in pixel i. If the waterbody frequency index of a pixel is greater than 25%, this pixel is considered as a waterbody; otherwise, it is not.City classification based on surface waterbodyCities with over one million in population may not be short of waterbodies, but significant differences remain in surface waterbody abundance. Due to large differences in city size, it is inappropriate to use waterbody area as a criterion. Considering the influence of urban expansion, we ranked 159 cities according to the indicator of waterbody fraction (WF), namely the fraction of the original surface water within the urban boundary in 2018. Waterbodies not impacted by urbanization were taken as the original surface waterbody, which used the average surface waterbody from 1985 to 1991 as baseline. We used the natural break method to divide cities into abundant, moderate, and deficient levels (referred to as Type I, Type II, and Type III, respectively) and evaluate the abundance of waterbodies in cities. Based on the waterbody fraction (WF) value, which is calculated as follows:$${text{WF}} = frac{{Water_{origin} }}{{Land_{2018} }}$$
where WF is used to judge the urban waterbody abundance in cities. (Water_{1990}) is the origin surface waterbody area (used the year in 1985–1991) in the urban boundary of 2018, (Land_{2018}) the urban land area in the urban boundary of 2018.Temporal characteristic of waterbody loss and gainTo understand the spatial–temporal features of surface waterbodies, we used five normalized indicators to compare waterbody variations between cities during urban expansion from the overall perspective and from the city perspective.The variation in original natural waterbodies reflects the intensity of the natural resource development in urban expansion. We summarized the reduction and preservation of original waterbodies in urban expansion areas with a population of over one million to represent the encroachment of urban expansion on waterbodies:$$WL = frac{{sum NWL_{t0_t1} left( i right)}}{{sum W_{t0} left( i right)}} times 100%$$$$WP = frac{{sum (W_{t0} left( i right) – NWL_{t0_t1} left( i right))}}{{sum W_{t0} left( i right)}} times 100%$$
where i labels the city within the 159 cities, WL and WP are the fractions of waterbody loss and preservation in urban expansion areas of all cities, (NWL_{t0_t1}) is the net waterbody loss during period t0–t1 (, and W_{t0}) is the natural waterbody in the urban expansion area at time t0.To estimate the net waterbody loss caused by urban expansion at various stages, we used the standardized indicator, annual average net waterbody loss rate (ANWL), to compare waterbody loss speeds over time. This indicator is independent of the difference in waterbody abundance and can be compared over time. Waterbody loss is one part of the impact of urbanization; the other is waterbody gain. We used the same method to evaluate the annual average net waterbody gain rate (ANWG). The formulas are$$A{text{NWL}} = frac{{NWL_{t0_t1} }}{{W_{t0} left( {t1 – t0} right)}} times 100%$$$$ANWG = frac{{NWG_{t0 – t1} }}{{W_{t0} left( {t1 – t0} right)}} times 100%$$
where NWL and NWG are the net waterbody loss and gain, respectively, and the other abbreviations are the same as above.Considering the direct impact of urban expansion, we used a normalized indicator, the average net waterbody loss velocity of urban expansion ((AWLV)), which refers to the amount of waterbody encroachment per unit urban expansion area. It quantifies the time-heterogeneity of waterbody loss due to urban expansion and is calculated as follows:$$AWLV = frac{{NWL_{t0_t1} }}{{Land_{t1} – Land_{t0} }}$$We calculated these indicators for the six expansion periods (1990–1995, 1995–2000, 2000–2005, 2005–2010, 2010–2015, and 2015–2018) (Fig. 3). In the study, if the waterbody pixel count is zero at the onset of the period, the indicator for the period is abnormal and thus excluded. More

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Asian elephants spend most of their time outside protected areas because they prefer the food they find there, an international team of scientists reports. But this behaviour is putting the animals and people in harm’s way, say researchers.The finding has important implications for the long-term survival of the animals because protected areas are a cornerstone of global conservation strategies to protect threatened species, say researchers.If protected areas do not contain animals’ preferred habitats, they will wander out, says Ahimsa Campos-Arceiz, who studies Asian elephants (Elephas maximus) at the Chinese Academy of Sciences’ Xishuangbanna Tropical Botanical Garden in Menglun, China. “It’s a good intention, but doesn’t always work out that way.”Human–elephant conflict is the biggest threat for Asian elephants. Over the past few decades, animals in protected areas have increasingly wandered into villages. They often cause destruction, damaging crops and infrastructure and injuring and even killing people.Wandering elephantsTo understand how effective protected areas are for conserving Asian elephants, Campos-Arceiz and his colleagues set out to get a precise picture of Asian-elephant movements. They collared 102 individuals in Peninsular Malaysia and Borneo, recording 600,000 GPS locations over a decade. They found that most elephants spent most of their time in habitats outside the protected areas, at the forest edge and in areas of regrowth. The findings were published in the Journal of Applied Ecology1 on 18 October.The researchers suspect that the elephants venture out because they like to eat grasses, bamboo, palms and fast-growing trees, which are common in disturbed forests and relatively scarce under the canopy of old-growth forests.Philip Nyhus, a conservation biologist who specializes in human–wildlife conflict at Colby College in Waterville, Maine, says Asian elephants live deep in dense forest and so are much more difficult to study than African elephants, which roam open savannahs. “The sample size is impressive,” he says.The finding is not unexpected given past anecdotal observations of elephant behaviour, says Nyhus. But now the data show that this is a common strategy for the survival of these animals, and not just something seen in a subset of the population. The research provides strong evidence for how to set up suitable protected areas that reduce the risk of elephants wandering out, he says.‘There will be conflict’The results do not diminish the importance of protected areas, which provide long-term safety for the animals, says Campos-Arceiz, who did the field work while at the University of Nottingham Malaysia in Selangor. “But they are clearly not enough.”The study suggests that “there will be conflict between humans and elephants”, says Guo Xianming, director of the Research Institute of Xishuangbanna National Nature Reserve in Jinghong.Asian elephants wander into villages owing to a combination of reasons: an increase in elephant populations, forests in many reserves have grown denser and have become unsuitable for the animals, and increasing habitat loss and degradation outside.Last year, two herds of elephants made global headlines as they wandered out of the Xishuangbanna National Nature Reserve and travelled for hundreds of kilometers, wreaking havoc along the way. One herd spent five weeks at the botanical garden where Campos-Arceiz works. “It was intense,” he says.There is an urgent need to understand how people and elephants can better share the landscape, says Guo. And the first step is by better protecting people’s lives and livelihoods. “It’s the only way of peaceful co-existence.”
The reporting of the story was supported by International Women’s Media Foundation’s Howard G. Buffett Fund for Women Journalists. More

Adolescents losing sleepEpidemiological studies in US school students aged 14–18 have shown that declines in mental health mirror reductions in the amount of sleep they are getting. Although it is hard to show a causal link between these changes, the authors of this Comment article argue that ensuring that young people get enough sleep is crucial for them to thrive. Various factors could be contributing to this drop-off in sleep, they say, including the use of digital media before bed, schoolwork pressures and extracurricular activities late in the evening or early in the morning.

Sources: J. M. Twenge et al. Sleep Med. 39, 47–53 (2017)/US CDC YRBSS

Monkeypox trajectoryAlmost six months after the monkeypox virus started to spread globally, vaccination efforts and behavioural changes seem to be containing the current strain — at least in the United States and Europe. The number of cases in these regions peaked in August and is now falling. But the situation could still play out in several ways, as this News story reports. At best, the outbreak might fizzle out over the next few months or years. At worst, the virus could become endemic outside Africa.

Source: WHO

The most valuable soilsThis map shows the regions of the world where the conservation of soil should be prioritized. Soils contain a wealth of biodiversity, such as bacteria, fungi, nematode worms and earthworms. These organisms have important roles in ecosystem processes, such as carbon and nutrient cycling, water storage and supporting plant growth. The authors of a paper in Nature set out to identify global hotspots for conservation by surveying soil biodiversity and ecosystem functions at 615 sites around the world. They found hotspots of biodiversity in temperate and Mediterranean regions and in alpine tundra, whereas hotspots of species uniqueness occurred in the tropics and drylands. More than 70% of the hotspots are not adequately covered by protected areas. More

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