More stories

  • in

    Reduced competence to arboviruses following the sustainable invasion of Wolbachia into native Aedes aegypti from Southeastern Brazil

    Mosquito lines and maintenanceTo introduce Wolbachia into Brazilian Ae. aegypti, an Australian line infected with the wMel strain21 was backcrossed for 8 generations to a natural mosquito population of Rio de Janeiro, Brazil24. Following the genetic background introgression, additional crosses and knockdown resistance (kdr) screening were undertaken to replicate natural insecticide resistance profiling and generate the line wMelRio. To assure a minimal variation in this profiling overtime, and sustain a homogeneous genetic background, wMelRio colony was refreshed with 10% wild males once in every five generations40.To maintain wMelRio, immatures (i.e. larval stages L1 to L4) were reared in dechlorinated water, at 28 °C, and fed Tetramin Flakes (Tetra GmbH, Herrenteich, Germany) until pupal formation. Following adult emersion, groups of 1000 females and 800 males were sorted and kept in BugDorm cages (MegaView Science Co Ltd., Taiwan) at 25 °C, with 10% sucrose solution ad libitum. Every three days, females were fed human blood (from blood donation centers; see details under ‘ethical considerations’), through Hemotek artificial feeders (Hemotek Ltd, UK). Note that, to avoid arboviral contamination of our colony, all blood samples were formerly tested negative for DENV, ZIKV, CHIKV, MAYV and YFV by multiplex qPCR assays36,68. Egg-laying was induced by placing dampened strips of filter paper (i.e. partially immersed in water-containing cups) inside the cages for 2–3 days, after which they were gradually dried at room temperature. Strips loaded with eggs (i.e. ovistrips) were kept at room temperature until further use, either for colony maintenance or field release. Eggs older than 40 days were discarded due to a decay in overall quality60.Egg releasesMass-reared wMel-infected Brazilian Ae. aegypti, wMelRio, were released as eggs in Jurujuba (22°56′ 00″ S, 43°07′ 00″ W), a lower-middle-class community in the city of Niterói (state of Rio de Janeiro, Brazil). Located by the shores of Guanabara bay, this community has grown from a typical fisherman settlement, with informal occupancy, to a total population of 2797 residents in 890 houses. Jurujuba encompasses a total area of 2.53 km2, divided into seven smaller sectors (i.e. sub-areas or localities within the neighborhood): Ponto Final, Várzea, Brasília, Cascarejo, Praia de Adão e Eva, Peixe-Galo and Salinas.wMelRio eggs were released in the field through special deployment devices, referred to as mosquito release containers (MRCs), which consisted of small white plastic buckets (19 cm height × 18 cm top diameter × 15.5 cm base diameter) with four small holes on the side wall, only a few centimeters away from the top lid. Each MRC was loaded with 1 L of water, 0.45 g of Tetramin Tropical Tablets (i.e. one and a half tablet) (Tetra GmbH, Herrenteich, Germany) and an ovistrip containing approximately 150–300 eggs. After six to seven days, about 80% of the immatures were pupae, and after 11 to 12 days, most of the adults had already emerged and left the device from the wall holes. Every 15 days, MRCs were checked and reloaded so that another rearing and release cycle could take place. Release sites were spatially distributed as evenly as possible (Supplementary Fig. S1), so as to maximize the spread of Wolbachia-harboring individuals and promote mating with their wild peers. The release strategy was optimized by splitting the sites into two groups, A and B, with alternate MRC loading schedules. Thus, while MRCs from group A were releasing adults, those from group B were being loaded with new ovistrips, water and food. In the following week, an opposite situation occurred, with MRCs from group B releasing adults. The release schedules, as well as the number of allocated MRCs, varied according to each Jurujuba’s sector (Supplementary Table S1).Ethical considerationsAll methods were carried out in accordance with relevant guidelines and regulations. Study protocol for Wolbachia field release was approved by the National Research Ethics Committee (CONEP, CAAE 02524513.0.1001.0008) and three government agencies: IBAMA (Ministry of Environment), Anvisa (Ministry of Health) and MAPA (Ministry of Agriculture, Livestock and Supply) to obtain the RET (Special Temporary Registry, 25351.392108/2013-96). Prior to mosquito releases, an informed consent was obtained from 70% of Jujuruba households. Also, a written informed consent was obtained from households that hosted BG-sentinel mosquito traps.For the maintenance and mass-rearing of Wolbachia-infected Ae. aegypti, adult females were fed human blood from a donation center (Hospital Antonio Pedro, Rio de Janeiro State University), with supporting regulatory approval (CONEP, CAAE 59175616.2.0000.0008) We only used blood bags which would have been discarded by the donation center, mainly due to insufficient volume to meet their quality assurance policy. Samples had no information on donor’s identity, sex, age and any clinical condition, but were tested negative for several diseases, including Hepatitis B, Hepatitis C, Chagas disease, syphilis, HIV and HTLV, as part of the Brazilian Government routine screening.For vector competence assays, human blood was obtained from Fundação Hemominas as part of a research agreement with Instituto René Rachou (Fiocruz Minas) (OF.GPO/CCO-Nr224/16).
    Wolbachia field monitoring and density level assessmentAe. aegypti field population was monitored with BG-Sentinel traps (Biogents AG, Regensburg, Germany), spread across Jurujuba in a semi-homogeneous fashion (Supplementary Fig. S2, Supplementary Table S2, Supplementary Datasheet S1). These monitoring sites were chosen among suitable households who formally agreed with hosting of a trap, and had to be reallocated according to necessity (i.e. household quits hosting the trap). Working traps were checked weekly by removing the catch bags (e.g. small meshed envelopes placed inside the BG-Sentinels to collect trapped insects) and bringing them to the laboratory for species identification and Wolbachia screening. Catch bags were barcoded according to the trap ID and site, so as to create a pipeline for field samples.Screening for Wolbachia in Ae. aegypti samples was undertaken by qPCR. Briefly, individual DNA was extracted by homogenizing head/thorax body parts in Squash Buffer (10 mM Tris–Cl, 1 mM EDTA, 25 mM NaCl, pH 8.2) supplemented with Proteinase K (200 ug/ml) and incubating at 56 °C for 5 min. Extraction ended by enzyme inactivation at 98 °C for 15 min. DNA amplifications were carried out with FastStart Essential DNA Probes Master (Roche), using specific primers and probes to Wolbachia pipientis WD0513 and Ae. aegypti rps17 markers (Supplementary Table S3). Thermocycling conditions were set on a LightCycler 96 Instrument (Roche), as follows: 95 °C for 10 min (initial denaturation), and 40 cycles of 95 °C for 15 s and 60 °C for 30 s. Samples were analyzed using absolute quantification, by comparison to serial dilutions of either gene product, cloned and amplified in the pGEMT-Easy plasmid (Promega). Negative control samples were normalized between plates, and were used as reference to determine a minimum threshold for positive samples.DENV and ZIKV isolation and replication in mosquito cellsZIKV was kindly provided by Instituto Aggeu Magalhães (IAM, Fiocruz) through viral isolation of a symptomatic patient sample from Recife (PE, Brazil) in 2015 (ZikV/H.sapiens/Brazil/BRPE243/2015). DENV was sourced following a viral isolate from a patient sample diagnosed with Dengue type 1 in Contagem (MG, Brazil), also in 2015 (Den1/H.sapiens/Brazil/BRMV09/2015). Both ZIKV and DENV samples were accompanied by patients’ written consent (CONEP, reference number 862.912), being further catalogued into the national database of genetic patrimony and associated knowledge (SISGEN, access number AA1D462).In vitro culture of viral particles were done as previously described36. Briefly, ZIKV and DENV were replicated in Aedes albopictus C6/36 cells, grown at 28 °C in Leibovitz L-15 medium (ThermoFisher) supplemented with 10% fetal bovine serum (FBS) (ThermoFisher). After seven days, supernatants were harvested and virus titers were assessed, first by Reverse Transcription (RT)-qPCR, and later by plaque assay with VERO cells grown under 37 °C, 5% carbon dioxide, in Dulbecco’s Modified Eagle Medium (DMEM) (ThermoFisher) supplemented with 3% Carboxymethylcellulose (Synth) and 2% FBS.Vector competence assaysTo perform vector competence assays with field samples of Ae. aegypti, ovitraps were mounted in both Ponto Final (Jurujuba) and Urca, a Wolbachia-free area in Rio de Janeiro. Ovitraps were collected from the field over 13 weeks, from April to June 2017, which corresponds to the time-frame between 14 and 16 months along the post-release phase in Ponto Final. Once in the insectary, eggs samples were reared to the adult stage in a controlled insectary environment (refer to ‘mosquito lines and maintenance’ for details).For virus challenging assays through oral-feeding, young females (4–6 days old) were starved for 20 to 24 h, and subsequently offered culture supernatant containing ZIKV or DENV mixed with human red blood cells (2:1 ratio), using an artificial membrane feeding system36. It is important to mention that, as for the colony maintenance protocol, blood samples used here were also submitted to quality control prior to its use in the assays, mainly due to putative arbovirus contaminations which could affect the experimental outputs. Likewise, all samples were tested negative for DENV, ZIKV, CHIKV, MAYV and YFV by multiplex qPCR assays36,68. Oral-infections were performed twice for each virus. ZIKV was offered first from fresh (initial virus titer of 4.8 × 108 PFU/mL) and second from frozen culture supernatant (initial virus titer of 7.6 × 106 PFU/mL). In contrast, DENV was offered from fresh supernatants only (virus titers of 2 × 106 PFU/mL and 6.5 × 107 PFU/mL), since frozen versions failed to infect. Specimens were allowed to feed for one hour, after which engorged females were selected and maintained with 10% sucrose solution ad libitum, during the whole extrinsic incubation period. At 14 days post-infection (dpi), viral loads were assessed in heads/thorax extracts by RT-qPCR (refer to ‘Viral diagnosis’ for more details).For saliva-mediated virus challenging assays, ZIKV and DENV pre-exposed females (14 dpi) from Jurujuba (Wolbachia +) and Urca (Wolbachia −) were starved for about 16 h (overnight) before being knocked down and kept at 4 °C for wings and legs removal. Salivation was induced by introducing a 10 µL sterile filter tip, pre-loaded with 5 µl of a solution [30% sucrose (w/v) diluted in 50% fetal bovine serum (FBS) and 50% DMEM medium], into the mosquito proboscis for 30 min. Saliva samples were individually collected, and 276 nL was intrathoracically injected into young naive females (Urca) using a Nanoject II hand held injector (Drummond), as previously described36,68. Each saliva sample was used to inoculate 8–14 naïve Wolbachia-free Ae. aegypti specimens, of which 8 were screened for infective particles. ZIKV and DENV were quantified by RT-qPCR at 5 dpi and 7 dpi, respectively (refer to ‘Viral diagnosis’ for more details). Overall Intrathoracic Saliva Infection index (OISI) was obtained by averaging the percentages (± SD) of infected individuals in each group.Viral diagnosisTo identify ZIKV and DENV particles in individual samples, whole specimens were processed into head/thorax homogenates for RNA/DNA extraction with the High Pure Viral Nucleic Acid Kit (Roche), according to manufacturer’s instructions30. Extracted samples were diluted in nuclease-free water to a concentration of 50 ng/μL. ZIKV, DENV and Wolbachia levels, in vector competence assays, were quantified by RT-qPCR using TaqMan Fast Virus 1-Step Master Mix (ThermoFisher) and specific primers and probes (Supplementary Table S3). Reactions were run on a LightCycler 96 Instrument (Roche), using the following thermocycling conditions: 50 °C for 5 min (initial RT step), 95 °C for 20 s (RT inactivation/DNA initial denaturation), and then 40 cycles of 95 °C for 3 s and 60 °C for 30 s. Each RNA/DNA sample was used in two reactions, one with ZIKV, DENV or Wolbachia primers, and another with Ae. aegypti rps17 endogenous control30. Absolute quantification was achieved by comparing amplification profiles with standard curves generated by serial dilutions of their respective gene products, amplified from a cloned sequence in pGEM-T Easy vector (Promega). Negative control samples (no virus RNA) served as reference to fix a minimum threshold for positive ones. ZIKV and DENV loads were defined as their copy number per sample (head/thorax or saliva), while Wolbachia loads were relative quantifications to the rps17 reference gene. Here, it is worth noting that, while Wolbachia titer is naturally variable and dependent on its whole-body density, the overall expression of rps17 is stable and particularly suitable for internal controls in assays with adult females69, as demonstrated previously by us and others30,62,68.Map creation and source codesThe satellite image map of Jurujuba was created with ArcGIS Desktop 10.7 (Esri Inc., https://www.esri.com/en-us/arcgis/products/arcgis-desktop/overview) using Google Earth (Google LLC) source code, under the license and in accordance with the fair use described in ‘https://about.google/brand-resource-center/products-and-services/geo-guidelines/’. Maps with geotagged MRCs and BG-Sentinel traps were created with ArcGIS Desktop 10.7 and OpenStreetMap source code (OpenStreetMap contributors), under the license CC-BY-SA 2.0.Statistical analysesGraphs and statistical analyzes were performed in GraphPad Prism 8 (GraphPad Software Inc., https://www.graphpad.com). Kruskal–Wallis test followed by Dunn’s post-hoc multiple comparisons were used to analyze Wolbachia density data from field-collected and colony samples. ZIKV and DENV loads in head/thorax extracts, from both oral and saliva-challenging samples, were compared using the Mann–Whitney U test. For all statistical inferences, ⍺ was set to 0.05. More

  • in

    Molecular detection of giant snakeheads, Channa micropeltes (Cuvier, 1831), one of the most troublesome fish species

    Ethics statementAll procedures were conducted in accordance with the current laws in Thailand on experimental animals and were approved by the safety management committee for experiments of the Laboratory Animal Center, Chiang Mai University (Project Number 2561/FA-0001). The study also followed the recommendations in the ARRIVE guidelines.Species-specific primer designAll the DNA tissue analysed originated from the mucus of the individual giant snakehead. Total DNA was extracted from the mucus sample using the Qiagen DNeasy Blood and Tissue Kit (Qiagen, Valencia, CA). Extracted DNA was used as a template for qPCR assay together with synthetic fragments. DNA samples were quantified using a Qubit fluorometer (Life Technologies) calibrated with the Quant-iT dsDNA HS Assay following the manufacturer’s instructions. For each replicate, 3 µL volumes were measured.Species-specific primers and a minor-groove binding (MGB) probe incorporating a 5′ FAM reporter dye and a 3′ non-fluorescent quencher were designed to amplify an 127 bp targeting within the 16S region for the giant snakehead (C. micropeltes), using Primer Express (V3.0, Life Technologies; Table 3). Probe and primer sequences were matched against the National Centre for Biotechnology Information (NCBI, http://www.ncbi.nlm.nih.gov/) nucleotide database with BLASTn (Basic Local Alignment Search Tool) to confirm the species’ specificity for the giant snakehead in silico assays.Table 3 Details of species-specific primers and the probe designed to amplify a 127 bp fragment of the 16S region of Channa micropeltes (Cuvier, 1831).Full size tableTo ensure that the assay only amplified the giant snakehead, it was deployed on a closely related species commonly found in Thai freshwater environments using conventional PCR amplification and visualization on a 1.5% agarose gel stained with SYBR Safe DNA Gel Stain (Life Technologies).qPCR assayThe qPCR assay was deployed using Environmental Master Mix (Applied Biosystems) on mucus samples from the giant snakehead and related species to ensure the species specificity to the qPCR assay. In addition, eDNA qPCR assay for the giant snakehead, a water sample collected from tank at Phayao Freshwater Aquarium (Phayao Inland Fisheries Research and Development Center) was known to have only the giant snakehead was included as a positive control for the presence of amplifiable eDNA in water samples. The tank contains around 4.5 m3 of water with one individual of giant snakehead resides in the tank (the fish is about 60–70 cm in length).All eDNA qPCR amplifications were performed in three replicates in a final volume of 20 µL, using 10.0 µL of 2 × TaqMan Environmental Master Mix 2.0 (Thermo Fisher Scientific), 2.0 µL of DNA template, 900 nM each of the F/R primers, and 125 nM of the probe. Samples were run under the following conditions: an initial 10 min incubation at 95 °C followed by 50 cycles of denaturation at 95 °C for 15 s and annealing/extension at 60 °C for 1 min. Negative controls with all PCR reagents but no template (three replicates) were run in parallel to assess potential contamination. The quantification cycle (Cq) was converted to quantities per unit volume using the linear regression obtained from the synthesized target gene standard curve (Integrated DNA Technologies Pte. Ltd., Singapore). The giant snakehead eDNA concentrations were then reported as copies/mL. The limit of detection (LOD) and the limit of quantification (LOQ) were also measured using the standard dilution series of synthesized target gene fragment with known copy numbers. A dilution series containing 1.5 × 101 to 1.5 × 104 copies per PCR tube were prepared and used as quantification standards. The calculation of LOD and LOQ was done using published R script by Klymus et al.26.DNA extraction from the filtersDNA trapped on the filters obtained from the aquarium experiments and field collections were extracted using Qiagen DNeasy Blood and Tissue Kit (Qiagen, Hilden, Germany) using a protocol modified from the manufacturer’s protocol with the following changes: the DNA from all samples were eluted twice with 25 µL AE buffer, in a total volume of 50 µL to obtain a more concentrated eDNA solution. The volume of ATL buffer (360 µL), Proteinase K (40 µL), AL buffer (400 µL) and Ethanol (400 µL) were doubled.Aquarium experimentAn aquarium experiment was used to test the extent to which qPCR of water samples can detect eDNA of giant snakehead at low simulated densities. The juvenile giant snakehead was obtained from the fish store and transported to a laboratory at Chiang Mai University. The giant snakeheads were then held in separate 120 L plastic holding containers in which the water was continuously filtered. The fish were fed frozen shrimp/commercially available flake fish food three times a week, and were held at 23 ± 1 °C.The sensitivity of eDNA detection in the aquaria was evaluated by conducting three aquarium experiments using plastic tanks (30 × 45 × 25 cm) filled with 120 L of aged-tap water. The water in the tanks was continuously aerated through a filter. In each experiment, the giant snakeheads were randomly assigned to the tanks (10 individuals per tank). The average size of the snakeheads was 9.7 cm (body length ranging from 9.1 to 10.6 cm). The average weight was 8.15 g (ranging from 6.7 to 10.6 g). The water in the tanks was maintained at 23 ± 1 °C. A 300 mL water sample from each tank was collected at each time point (0, 3, 6, 12, 24, 48, 72, 96, 120, 144, and 168 after removal of the fishes from the tanks) in triplicate. Collected water was filtered on a GF/F filter (0.7 μm Whatman International Ltd., Maidstone, UK). The eDNA from each sample solution was extracted using a Qiagen DNeasy Blood and Tissue Kit (Qiagen, Hilden, Germany) in a final volume of 50 µL, detailed in DNA extraction from the filters. To confirm the absence of the giant snakehead eDNA in the water prior to the experiments, three tanks without giant snakehead were prepared and water sample was collected and treated as described above.Real-time PCR was performed with the species-specific primers and probe set using a Rotor-Gene Q system (Qiagen, Hilden, Germany). The reaction conditions were the same as described in qPCR assay. Three replicates were conducted for each sample including the negative PCR control and positive control.eDNA field collectionWater samples were collected at 6 points within Kwan Payao according to the survey locations of the Inland Fisheries Research and Development Center. Additional water samples were collected from 11 and 6 locations in Ing River where water flowed into and out of Kwan Payao, respectively (Fig. 1). To avoid contamination, all field equipment was sterilized using 10% bleach, UV-Crosslinker or autoclaved and sealed prior to transport to the study site, and a separate pair of nitrile disposable gloves were used for each sample. At each site, water samples were collected three replicate in bucket that had been previously decontaminated with a 10% bleach rinse followed by two distilled water rinses.In total, water samples were collected from 6 sites (in Kwan Phayao) and from 17 sites (in the Ing River) from 15th February to 5th March 2019, the middle of the dry season. Each site was sampled in triplicate, 300 mL samples of water were collected and filtered on GF/F filter (0.7 μm Whatman International Ltd., Maidstone, UK). In total, 306 water samples were collected from the surface water of lakes and rivers. For every sampling day, deionised water (300 mL) was filtrated as a negative control. The water samples and real-time PCR was processed as described above in qPCR assay. More

  • in

    Exotic plants accumulate and share herbivores yet dominate communities via rapid growth

    1.Heger, T. & Jeschke, J. M. The enemy release hypothesis as a hierarchy of hypotheses. Oikos 123, 741–750 (2014).Article 

    Google Scholar 
    2.Elton, C. S. The Ecology of Invasions by Animals and Plants (Springer, 1958).3.Keane, R. M. & Crawley, M. J. Exotic plant invasions and the enemy release hypothesis. Trends Ecol. Evol. 17, 164–170 (2002).Article 

    Google Scholar 
    4.Mitchell, C. E. & Power, A. G. Release of invasive plants from fungal and viral pathogens. Nature 421, 625–627 (2003).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    5.Colautti, R. I., Ricciardi, A., Grigorovich, I. A. & MacIsaac, H. J. Is invasion success explained by the enemy release hypothesis? Ecol. Lett. 7, 721–733 (2004).Article 

    Google Scholar 
    6.Liu, H. & Stiling, P. Testing the enemy release hypothesis: a review and meta-analysis. Biol. Invasions 8, 1535–1545 (2006).Article 

    Google Scholar 
    7.Meijer, K., Schilthuizen, M., Beukeboom, L. & Smit, C. A review and meta-analysis of the enemy release hypothesis in plant-herbivorous insect systems. PeerJ 4, e2560v1 (2016).Article 

    Google Scholar 
    8.Jeschke, J. M. & Heger, T. (eds) Invasion Biology: Hypotheses and Evidence (CABI, 2018).9.Levine, J. M., Adler, P. B. & Yelenik, S. G. A meta-analysis of biotic resistance to exotic plant invasions. Ecol. Lett. 7, 975–989 (2004).Article 

    Google Scholar 
    10.Maron, J. L. & Vilà, M. When do herbivores affect plant invasion? Evidence for the natural enemies and biotic resistance hypotheses. Oikos 95, 361–373 (2001).Article 

    Google Scholar 
    11.Callaway, R. M. & Ridenour, W. M. Novel weapons: invasive success and the evolution of increased competitive ability. Front. Ecol. Environ. 2, 436–443 (2004).Article 

    Google Scholar 
    12.Cappuccino, N. & Arnason, J. T. Novel chemistry of invasive exotic plants. Biol. Lett. 2, 189–193 (2006).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    13.Bezemer, T. M., Harvey, J. A. & Cronin, J. T. Response of native insect communities to invasive plants. Ann. Rev. Entomol. 59, 119–141 (2014).CAS 
    Article 

    Google Scholar 
    14.Keeler, M. S. & Chew, F. S. Escaping an evolutionary trap: preference and performance of a native insect on an exotic invasive host. Oecologia 156, 559–568 (2008).ADS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    15.Eckberg, J. O., Tenhumberg, B. & Louda, S. M. Insect herbivory and propagule pressure influence Cirsium vulgare invasiveness across the landscape. Ecology 93, 1787–1794 (2012).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    16.Bürki, C. & Nentwig, W. Comparison of herbivore insect communities of Heracleum sphondylium and H. mantegazzianum in Switzerland (Spermatophyta: Apiaceae). Entomol. Gen. 22, 147–155 (1997).Article 

    Google Scholar 
    17.Cincotta, C. L., Adams, J. M. & Holzapfel, C. Testing the enemy release hypothesis: a comparison of foliar insect herbivory of the exotic Norway maple (Acer platanoides L.) and the native sugar maple (A. saccharum L.). Biol. Invasions 11, 379–388 (2008).Article 

    Google Scholar 
    18.Cronin, J. T., Bhattarai, G. P., Allen, W. J. & Meyerson, L. A. Biogeography of a plant invasion: plant-herbivore interactions. Ecology 96, 1115–1127 (2015).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    19.Hu, X.-T. & Dong, B.-C. Herbivory and nitrogen availability affect performance of an invader Alternanthera philoxeroides and its native congener A. sessilis. Flora 257, 151412 (2019).Article 

    Google Scholar 
    20.Agrawal, A. A. & Kotanen, P. M. Herbivores and the success of exotic plants: a phylogenetically controlled experiment. Ecol. Lett. 6, 712–715 (2003).Article 

    Google Scholar 
    21.Agrawal, A. A. et al. Enemy release? An experiment with congeneric plant pairs and diverse above- and belowground enemies. Ecology 86, 2979–2989 (2005).Article 

    Google Scholar 
    22.Parker, J. D. & Hay, M. E. Biotic resistance to plant invasions? Native herbivores prefer non-native plants. Ecol. Lett. 8, 959–967 (2005).Article 

    Google Scholar 
    23.Parker, J. D., Burkepile, D. E. & Hay, M. E. Opposing effects of native and exotic herbivores on plant invasions. Science 311, 1459–1461 (2006).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    24.Parker, I. M. & Gilbert, G. S. When there is no escape: the effects of natural enemies on native, invasive, and non-native plants. Ecology 88, 1210–1224 (2007).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    25.Dostál, P. et al. Enemy damage of exotic plant species is similar to that of natives and increases with productivity. J. Ecol. 101, 388–399 (2013).Article 

    Google Scholar 
    26.Meijer, K. et al. Phytophagous insects on native and non-native host plants: combining the community approach and the biogeographical approach. PLoS ONE 10, e0125607 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    27.Schultheis, E. H., Berardi, A. E. & Lau, J. A. No release for the wicked: enemy release is dynamic and not associated with invasiveness. Ecology 96, 2446–2457 (2015).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    28.Beckstead, J. & Parker, I. M. Invasiveness of Ammophila arenaria: release from soil-borne pathogens? Ecology 84, 2824–2831 (2003).Article 

    Google Scholar 
    29.van Kleunen, M., Weber, E. & Fischer, M. A meta-analysis of trait differences between invasive and non-invasive plant species. Ecol. Lett. 13, 235–245 (2010).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    30.Ashton, I. W. & Lerdau, M. T. Tolerance to herbivory, and not resistance, may explain differential success of invasive, naturalized, and native North American temperate vines. Divers. Distrib. 14, 169–178 (2008).Article 

    Google Scholar 
    31.Seebens, H. et al. No saturation in the accumulation of alien species worldwide. Nat. Commun. 8, 14435 (2017).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    32.Eppinga, M. B., Rietkerk, M., Dekker, S. C., De Ruiter, P. C. & van der Putten, W. H. Accumulation of local pathogens: a new hypothesis to explain exotic plant invasions. Oikos 114, 168–176 (2006).Article 

    Google Scholar 
    33.Bufford, J. L. et al. Taxonomic similarity, more than contact opportunity, explains novel plant–pathogen associations between native and alien taxa. N. Phytol. 212, 657–667 (2016).CAS 
    Article 

    Google Scholar 
    34.White, E. M., Wilson, J. C. & Clarke, A. R. Biotic indirect effects: a neglected concept in invasion biology. Divers. Distrib. 12, 443–455 (2006).Article 

    Google Scholar 
    35.Allen, W. J. in Plant Invasions: The Role of Species Interactions (CABI Publishing, 2020).36.Holt, R. D. Predation, apparent competition, and the structure of prey communities. Theor. Popul. Biol. 12, 197–229 (1977).MathSciNet 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    37.Holt, R. D. & Bonsall, M. B. Apparent competition. Annu. Rev. Ecol. Evol. Syst. 48, 447–471 (2017).Article 

    Google Scholar 
    38.Sessions, L. & Kelly, D. Predator-mediated apparent competition between an introduced grass, Agrostis capillaris, and a native fern, Botrychium australe (Ophioglossaceae), in New Zealand. Oikos 96, 102–109 (2002).Article 

    Google Scholar 
    39.Dangremond, E. M., Pardini, E. A. & Knight, T. M. Apparent competition with an invasive plant hastens the extinction of an endangered lupine. Ecology 91, 2261–2271 (2010).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    40.Bhattarai, G. P., Meyerson, L. A. & Cronin, J. T. Geographic variation in apparent competition between native and invasive Phragmites australis. Ecology 98, 349–358 (2017).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    41.Simberloff, D. & Von Holle, B. Positive interactions of nonindigenous species: invasional meltdown? Biol. Invasions 1, 21–32 (1999).Article 

    Google Scholar 
    42.Allen, W. J. et al. Community-level direct and indirect impacts of an invasive plant favour exotic over native species. J. Ecol. 108, 2499–2510 (2020).Article 

    Google Scholar 
    43.Morris, R. J., Lewis, O. T. & Godfray, C. J. Experimental evidence for apparent competition in a tropical forest food web. Nature 428, 310–313 (2004).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    44.Tack, A. J. M., Gripenberg, S. & Roslin, T. Can we predict indirect interactions from quantitative food webs? – an experimental approach. J. Anim. Ecol. 80, 108–118 (2011).PubMed 
    Article 

    Google Scholar 
    45.Frost, C. M. et al. Apparent competition drives community-wide parasitism rates and changes in host abundance across ecosystem boundaries. Nat. Commun. 7, 12644 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    46.Bardgett, R. D. & Wardle, D. A. Aboveground–Belowground Linkages: Biotic Interactions, Ecosystem Processes and Global Change (Oxford University Press, 2010).47.Heinen, R., Biere, A., Harvey, J. A. & Bezemer, T. M. Effects of soil organisms on aboveground plant-insect interactions in the field: patterns, mechanisms and the role of methodology. Front. Ecol. Evol. 6, 106 (2018).Article 

    Google Scholar 
    48.Bever, J. D., Westover, K. M. & Antonovics, J. Incorporating the soil community into plant population dynamics: the utility of the feedback approach. J. Ecol. 85, 561–573 (1997).Article 

    Google Scholar 
    49.Kulmatiski, A., Beard, K. H., Stevens, J. R. & Cobbold, S. M. Plant–soil feedbacks: a meta‐analytical review. Ecol. Lett. 11, 980–992 (2008).PubMed 
    Article 

    Google Scholar 
    50.Levine, J. M., Pachepsky, E., Kendall, B. E., Yelenik, S. G. & Lambers, J. H. Plant-soil feedbacks and invasive spread. Ecol. Lett. 9, 1005–1014 (2006).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    51.Suding, K. N., Harpole, W. S., Fukami, T., Kulmatiski, A., MacDougall, A. S., Stein, C. & van der Putten, W. H. Consequences of plant–soil feedbacks in invasion. J. Ecol. 101, 298–308 (2013).Article 

    Google Scholar 
    52.Crawford, K. M. et al. When and where plant-soil feedback may promote plant coexistence: a meta-analysis. Ecol. Lett. 22, 1274–1284 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    53.Cornelissen, T., Fernandes, G. W. & Vasconcellos-Neto, J. Size does matter: variation in herbivory between and within plants and the plant vigor hypothesis. Oikos 117, 1121–1130 (2008).Article 

    Google Scholar 
    54.Price, P. W. The plant vigor hypothesis and herbivore attack. Oikos 62, 244–251 (1991).Article 

    Google Scholar 
    55.Waller, L. P. et al. Biotic interactions drive ecosystem responses to plant invaders. Science 368, 967–972 (2020).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    56.Kozlov, M. V., Lanta, V., Zverev, V. & Zvereva, E. L. Global patterns in background losses of woody plant foliage to insects. Glob. Ecol. Biogeogr. 24, 1126–1135 (2015).Article 

    Google Scholar 
    57.Zas, R., Moreira, X. & Sampedro, L. Tolerance and induced resistance in a native and an exotic pine species: relevant traits for invasion ecology. J. Ecol. 99, 1316–1326 (2011).Article 

    Google Scholar 
    58.Croy, J. R., Meyerson, L. A., Allen, W. J., Bhattarai, G. P. & Cronin, J. T. Lineage and latitudinal variation in Phragmites australis tolerance to herbivory: implications for invasion success. Oikos 129, 1341–1357 (2020).Article 

    Google Scholar 
    59.Liu, G., Huang, Q.-Q., Lin, Z.-G., Huang, F.-F., Liao, H.-X. & Peng, S.-L. High tolerance to salinity and herbivory stresses may explain the expansion of Ipomoea cairica to salt marshes. PLoS ONE 7, e48829 (2012).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    60.Paynter, Q. et al. Why did specificity testing fail to predict the field host-range of the gorse pod moth in New Zealand. Biol. Control 46, 453–462 (2008).Article 

    Google Scholar 
    61.Groenteman, R., Fowler, S. V. & Sullivan, J. J. St. John’s wort beetles would not have been introduced to New Zealand now: a retrospective host range test of New Zealand’s most successful weed biocontrol agents. Biol. Control 57, 50–58 (2011).Article 

    Google Scholar 
    62.Blossey, B. & Nötzold, R. Evolution of increased competitive ability in invasive nonindigenous plants: a hypothesis. J. Ecol. 83, 887–889 (1995).Article 

    Google Scholar 
    63.Felker-Quinn, E., Schweitzer, J. A. & Bailey, J. K. Meta-analysis reveals evolution in invasive plant species but little support for evolution of increased competitive ability (EICA). Ecol. Evol. 3, 739–751 (2013).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    64.Rotter, M. C. & Holeski, L. M. A meta-analysis of the evolution of increased competitive ability hypothesis: genetic-based trait variation and herbivory resistance trade-offs. Biol. Invasions 20, 2647–2660 (2018).Article 

    Google Scholar 
    65.Shelby, N. et al. No difference in the competitive ability of introduced and native Trifolium provenances when grown with soil biota from their introduced and native ranges. AoB Plants 8, plw016 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    66.Harvey, J. A., Bukovinszky, T. & van der Putten, W. H. Interactions between invasive plants and insect herbivores: a plea for a multitrophic perspective. Biol. Conserv. 143, 2251–2259 (2010).Article 

    Google Scholar 
    67.Allen, W. J. et al. Multitrophic enemy release of invasive Phragmites australis and its introduced herbivores in North America. Biol. Invasions 17, 3419–3432 (2015).Article 

    Google Scholar 
    68.Kim, T. N. & Underwood, N. Plant neighborhood effects on herbivory: damage is both density and frequency dependent. Ecology 96, 1431–1437 (2015).PubMed 
    Article 

    Google Scholar 
    69.Bartomeus, I., Vilà, M. & Santamaría, L. Contrasting effects of invasive plants in plant-pollinator networks. Oecologia 155, 761–770 (2008).ADS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    70.Lekberg, Y., Gibbons, S. M., Rosendahl, S. & Ramsey, P. W. Severe plant invasions can increase mycorrhizal fungal abundance and diversity. ISME J. 7, 1424–1433 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    71.Fernandez-Conradi, P., Jactel, H., Robin, C., Tack, A. J. M. & Castagneyrol, B. Fungi reduce preference and performance of insect herbivores on challenged plants. Ecology 99, 300–311 (2018).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    72.Reinhart, K. O. & Callaway, R. M. Soil biota and invasive plants. N. Phytol. 170, 445–457 (2006).Article 

    Google Scholar 
    73.Gioria, M. & Osborne, B. A. Resource competition in plant invasions: emerging patterns and research needs. Front. Plant Sci. 5, 501 (2014).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    74.Frost, C. M. et al. Using ecological network theory to predict biological invasions. Trends Ecol. Evol. 34, 831–843 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    75.Sauve, A. M. C., Thébault, E., Pocock, M. J. O. & Fontaine, C. How plants connect pollination and herbivory networks and their contribution to community stability. Ecology 97, 908–917 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    76.Pilosof, S., Porter, M. A., Pascual, M. & Kéfi, S. The multilayer nature of ecological networks. Nat. Ecol. Evol. 1, 0101 (2017).Article 

    Google Scholar 
    77.Weir, B. S., Turner, S. J., Silvester, W. B., Park, D. C. & Young, J. M. Unexpectedly diverse Mesorhizobium strains and Rhizobium leguminosarum nodulate native legume genera of New Zealand, while introduced legume weeds are nodulated by Bradyrhizobium species. Appl. Environ. Microbiol. 70, 5980–5987 (2004).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    78.Cappuccino, N. & Carpenter, D. Invasive exotic plants suffer less herbivory than non-invasive exotic plants. Biol. Lett. 1, 435–438 (2005).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    79.Liu, H., Stiling, P. & Pemberton, R. W. Does enemy release matter for invasive plants? evidence from a comparison of insect herbivore damage among invasive, non-invasive and native congeners. Biol. Invasions 9, 773–781 (2007).Article 

    Google Scholar 
    80.Howell, C. Consolidated List of Environmental Weeds in New Zealand. DOC Research & Development Series 292 (Department of Conservation, 2008).81.Ghanizadeh, H. & Harrington, K. C. Weed management in New Zealand pastures. Agronomy 9, 448 (2019).CAS 
    Article 

    Google Scholar 
    82.Kos, M., Tuijl, M. A. B., de Roo, J., Mulder, P. P. J. & Bezemer, T. M. Species-specific plant–soil feedback effects on aboveground plant-insect interactions. J. Ecol. 103, 904–914 (2015).CAS 
    Article 

    Google Scholar 
    83.Heinen, R., Biere, A. & Bezemer, T. M. Plant traits shape soil legacy effects on individual plant–insect interactions. Oikos 129, 261–273 (2020).CAS 
    Article 

    Google Scholar 
    84.Bezemer, T. M et al. Above‐and below‐ground herbivory effects on below‐ground plant–fungus interactions and plant–soil feedback responses. J. Ecol. 101, 325–333 (2013).Article 
    CAS 

    Google Scholar 
    85.Heinze, J., Wacker, A. & Kulmatiski, A. Plant–soil feedback effects altered by aboveground herbivory explain plant species abundance in the landscape. Ecology 101, e03023 (2020).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    86.Müller, C. B., Adriaanse, I. C. T., Belshaw, R. & Godfray, H. C. J. The structure of an aphid-parasitoid community. J. Anim. Ecol. 68, 346–370 (1999).Article 

    Google Scholar 
    87.R Core Team. R: a language and environment for statistical computing. Version 3.6.1. R Foundation for Statistical Computing http://www.R-project.org (2019).88.Bates, D. et al. lme4: linear mixed-effects models using ‘Eigen’ and S4. R package version 1.1-21 http://CRAN.R-project.org/package=lme4 (2019).89.Lenth, R., Singmann, H., Love, J., Buerkner, P. & Herve, M. emmeans: estimated marginal means, aka least-squares means. R package version 1.3.5.1 http://CRAN.R-project.org/package=emmeans (2019).90.Dormann, C. F., Fruend, J. & Gruber, B. bipartite: visualising bipartite networks and calculating some (ecological) indices. R package version 2.13 http://CRAN.R-project.org/package=bipartite (2019). More

  • in

    First comprehensive quantification of annual land use/cover from 1990 to 2020 across mainland Vietnam

    1.Gómez, C., White, J. C. & Wulder, M. A. Optical remotely sensed time series data for land cover classification: A review. ISPRS J. Photogramm. Remote. Sens. 116, 55–72 (2016).ADS 
    Article 

    Google Scholar 
    2.Dale, V. H. The relationship between land-use change and climate change. Ecol. Appl. 7, 753–769 (1997).Article 

    Google Scholar 
    3.Dai, A. Increasing drought under global warming in observations and models. Nat. Clim. Chang. 3, 52–58 (2013).ADS 
    Article 

    Google Scholar 
    4.Houghton, R. A. et al. Carbon emissions from land use and land-cover change. Biogeosciences 9, 5125–5142 (2012).ADS 
    CAS 
    Article 

    Google Scholar 
    5.Lambin, E. F. et al. The causes of land-use and land-cover change: Moving beyond the myths. Glob. Environ. Chang. 11, 261–269 (2001).Article 

    Google Scholar 
    6.Song, X. P. et al. Global land change from 1982 to 2016. Nature 560, 639–643 (2018).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    7.Grimm, N. B. et al. Global change and the ecology of cities. Science 319, 756–760 (2008).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    8.Utkin, L. V. et al. A weighted random survival forest. Knowl. Based Syst. 177, 136–144 (2019).Article 

    Google Scholar 
    9.Gašparović, M., Zrinjski, M. & Gudelj, M. Automatic cost-effective method for land cover classification (ALCC). Comput. Environ. Urban Syst. 76, 1–10 (2019).Article 

    Google Scholar 
    10.Hu, Y., Dong, Y. & Batunacun. ,. An automatic approach for land-change detection and land updates based on integrated NDVI timing analysis and the CVAPS method with GEE support. ISPRS J. Photogramm. Remote Sens. 146, 347–359 (2018).ADS 
    Article 

    Google Scholar 
    11.ESA. Sentinel-2 Mission Requirements Document. Earth (2007).12.Main-Knorn, M. et al. Sen2Cor for Sentinel-2. In 3 (2017). https://doi.org/10.1117/12.2278218.13.Gong, P. et al. Stable classification with limited sample: Transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017. Sci. Bull. 64, 370–373 (2019).Article 

    Google Scholar 
    14.Homer, C., Huang, C., Yang, L., Wylie, B. & Coan, M. Development of a 2001 National Land-Cover Database for the United States. Photogramm. Eng. Remote. Sens. 70, 829–840 (2004).Article 

    Google Scholar 
    15.Fry, J. A. et al. Completion of the 2006 national land cover database for the conterminous united states. Photogramm. Eng. Remote Sens. 77, 858–864 (2011).
    Google Scholar 
    16.Homer, C. et al. Completion of the 2011 national land cover database for the conterminous United States—Representing a decade of land cover change information. Photogramm. Eng. Remote Sens. 81, 345–354 (2015).
    Google Scholar 
    17.Yang, L. et al. A new generation of the United States National Land Cover Database: Requirements, research priorities, design, and implementation strategies. ISPRS J. Photogramm. Remote Sens. 146, 108–123 (2018).ADS 
    Article 

    Google Scholar 
    18.Chen, J. et al. Global land cover mapping at 30 m resolution: A POK-based operational approach. ISPRS J. Photogramm. Remote Sens. 103, 7–27 (2015).ADS 
    Article 

    Google Scholar 
    19.Hoang, T. T., Truong, V. T., Hayashi, M., Tadono, T. & Nasahara, K. N. New JAXA high-resolution land use/land cover map for Vietnam aiming for natural forest and plantation forest monitoring. Remote Sens. 12, 2707 (2020).20.Phan, D. C., Trung, T. H., Nasahara, K. N. & Tadono, T. JAXA high-resolution land use/land cover map for Central Vietnam in 2007 and 2017. Remote Sens. 10, 1406 (2018).ADS 
    Article 

    Google Scholar 
    21.Nemani, R. Nasa earth exchange: Next generation earth science collaborative. ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. XXXVIII-8/, 17–17 (2012).22.Mugiraneza, T., Nascetti, A. & Ban, Y. Continuous monitoring of urban land cover change trajectories with landsat time series and landtrendr-google earth engine cloud computing. Remote Sens. 12, 2883 (2020).23.Jin, Y., Liu, X., Yao, J., Zhang, X. & Zhang, H. Mapping the annual dynamics of cultivated land in typical area of the Middle-lower Yangtze plain using long time-series of Landsat images based on Google Earth Engine. Int. J. Remote Sens. 41, 1625–1644 (2020).ADS 
    Article 

    Google Scholar 
    24.Hu, Y. & Hu, Y. Detecting forest disturbance and recovery in Primorsky Krai, Russia, using annual landsat time series and multi-source land cover products. Remote Sens. 12, 129 (2020).Article 

    Google Scholar 
    25.Miettinen, J., Shi, C. & Liew, S. C. 2015 Land cover map of Southeast Asia at 250 m spatial resolution. Remote Sens. Lett. 7, 701–710 (2016).Article 

    Google Scholar 
    26.Ghorbanian, A. et al. Improved land cover map of Iran using Sentinel imagery within Google Earth Engine and a novel automatic workflow for land cover classification using migrated training samples. ISPRS J. Photogramm. Remote Sens. 167, 276–288 (2020).ADS 
    Article 

    Google Scholar 
    27.Huang, H. et al. The migration of training samples towards dynamic global land cover mapping. ISPRS J. Photogramm. Remote Sens. 161, 27–36 (2020).ADS 
    Article 

    Google Scholar 
    28.Bagan, H. & Yamagata, Y. Land-cover change analysis in 50 global cities by using a combination of Landsat data and analysis of grid cells. Environ. Res. Lett. 9, 064015 (2014).29.Radoux, J. et al. Automated training sample extraction for global land cover mapping. Remote Sens. 6, 3965–3987 (2014).ADS 
    Article 

    Google Scholar 
    30.Tran, H., Tran, T. & Kervyn, M. Dynamics of land cover/land use changes in the Mekong Delta, 1973–2011: A Remote sensing analysis of the Tran Van Thoi District, Ca Mau Province, Vietnam. Remote Sens. 7, 2899–2925 (2015).ADS 
    Article 

    Google Scholar 
    31.Chi, V. K. et al. Land transitions in Northwest Vietnam: An integrated analysis of biophysical and socio-cultural factors. Hum. Ecol. 41, 37–50 (2013).Article 

    Google Scholar 
    32.Villamor, G. B., Catacutan, D. C., Truong, V. A. T. & Thi, L. D. Tree-cover transition in Northern Vietnam from a gender-specific land-use preferences perspective. Land Use Policy 61, 53–62 (2017).Article 

    Google Scholar 
    33.Truong, V. T. et al. JAXA annual forest cover maps for Vietnam during 2015–2018 Using ALOS-2/PALSAR-2 and auxiliary data. Remote Sens. 11, 2412 (2019).ADS 
    Article 

    Google Scholar 
    34.And, R. D. of A. Vietnam’S Modified Submission on Refreence Levels for Redd+ Results Based Payments Under Unfccc. https://redd.unfccc.int/files/vietnam_frl_modified__submission_final_for_posting.pdf (2016).35.Xu, X., Jain, A. K. & Calvin, K. V. Quantifying the biophysical and socioeconomic drivers of changes in forest and agricultural land in South and Southeast Asia. Glob. Chang. Biol. 25, 2137–2151 (2019).ADS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    36.Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science (80- ) 342, 850–853 (2013).ADS 
    CAS 
    Article 

    Google Scholar 
    37.Di Gregorio, A., and Jansen, L. J. M. Land Cover Classification System (LCCS): Classification Concepts and User Manual. Fao http://www.fao.org/3/x0596e/x0596e00.htm (2000).38.Van Thinh, T., Phan, D. C., Nasahara, K. N. & Tadono, T. How does land use/land cover map’s accuracy depend on number of classification classes? Sci. Online Lett. Atmos. 15, 28–31 (2019).
    Google Scholar 
    39.Talukdar, S. et al. Land-use land-cover classification by machine learning classifiers for satellite observations—a review. Remote Sens. 12, 1135 (2020).ADS 
    Article 

    Google Scholar 
    40.Song, S., Gong, W., Zhu, B. & Huang, X. Wavelength selection and spectral discrimination for paddy rice, with laboratory measurements of hyperspectral leaf reflectance. ISPRS J. Photogramm. Remote Sens. 66, 672–682 (2011).ADS 
    Article 

    Google Scholar 
    41.Szantoi, Z., Smith, S. E., Strona, G., Koh, L. P. & Wich, S. A. Mapping orangutan habitat and agricultural areas using Landsat OLI imagery augmented with unmanned aircraft system aerial photography. Int. J. Remote Sens. 38, 2231–2245 (2017).ADS 
    Article 

    Google Scholar 
    42.Hill, R. A. & Thomson, A. G. Mapping woodland species composition and structure using airborne spectral and LiDAR data. Int. J. Remote Sens. 26, 3763–3779 (2005).ADS 
    Article 

    Google Scholar 
    43.Kontgis, C., Schneider, A. & Ozdogan, M. Mapping rice paddy extent and intensification in the Vietnamese Mekong River Delta with dense time stacks of Landsat data. Remote Sens. Environ. 169, 255–269 (2015).ADS 
    Article 

    Google Scholar 
    44.Kontgis, C. et al. Monitoring peri-urbanization in the greater Ho Chi Minh City metropolitan area. Appl. Geogr. 53, 377–388 (2014).Article 

    Google Scholar 
    45.D’Amour, C. B. et al. Future urban land expansion and implications for global croplands. Proc. Natl. Acad. Sci. USA 114, 8939–8944 (2017).Article 
    CAS 

    Google Scholar 
    46.Ha, T. V., Tuohy, M., Irwin, M. & Tuan, P. V. Monitoring and mapping rural urbanization and land use changes using Landsat data in the northeast subtropical region of Vietnam. Egypt. J. Remote Sens. Sp. Sci. 23, 11–19 (2020).
    Google Scholar 
    47.Nhan, T. Q., Van Ly, L. T. & Tan, L. V. How much do rice farmers earn from their crops? Evidence from a rice-exporting country. J. Agric. Stud. 8, 302 (2020).
    Google Scholar 
    48.Truong, T. D. & Do, L. H. Mangrove forests and aquaculture in the Mekong river delta. Land Use Policy 73, 20–28 (2018).Article 

    Google Scholar 
    49.Lam-Dao, N., Pham-Bach, V., Nguyen-Thanh, M., Pham-Thi, M.-T. & Hoang-Phi, P. Change detection of land use and riverbank in Mekong Delta, Vietnam using time series remotely sensed data. J. Resour. Ecol. 2, 370–374 (2011).
    Google Scholar 
    50.Ha, T. T. P., van Dijk, H. & Visser, L. Impacts of changes in mangrove forest management practices on forest accessibility and livelihood: A case study in mangrove-shrimp farming system in Ca Mau Province, Mekong Delta, Vietnam. Land Use Policy 36, 89–101 (2014).Article 

    Google Scholar 
    51.Le, T. N., Bregt, A. K., van Halsema, G. E., Hellegers, P. J. G. J. & Nguyen, L. D. Interplay between land-use dynamics and changes in hydrological regime in the Vietnamese Mekong Delta. Land Use Policy 73, 269–280 (2018).Article 

    Google Scholar 
    52.Khuc, Q. V., Tran, B. Q., Meyfroidt, P. & Paschke, M. W. Drivers of deforestation and forest degradation in Vietnam: An exploratory analysis at the national level. For. Policy Econ. 90, 128–141 (2018).Article 

    Google Scholar 
    53.Meyfroidt, P., Vu, T. P. & Hoang, V. A. Trajectories of deforestation, coffee expansion and displacement of shifting cultivation in the Central Highlands of Vietnam. Glob. Environ. Chang. 23, 1187–1198 (2013).Article 

    Google Scholar 
    54.Ngo-Duc, T., Kieu, C., Thatcher, M., Nguyen-Le, D. & Phan-Van, T. Climate projections for Vietnam based on regional climate models. Clim. Res. 60, 199–213 (2014).Article 

    Google Scholar 
    55.Lindesay, J. A. et al. International geosphere-biosphere programme/international global atmospheric chemistry SAFARI-92 field experiment: Background and overview. J. Geophys. Res. Atmos. 101, 23521–23530 (1996).ADS 
    CAS 
    Article 

    Google Scholar 
    56.Klemas, V. V., Dobson, J. E., Ferguson, R. L. & Haddad, K. D. A coastal land cover classification system for the NOAA Coastwatch Change Analysis Project. J. Coast. Res. 9, 862–872 (1993).
    Google Scholar 
    57.Saah, D. et al. Primitives as building blocks for constructing land cover maps. Int. J. Appl. Earth Obs. Geoinf. 85, 101979 (2020).Article 

    Google Scholar 
    58.Keys, R. G. Cubic convolution interpolation for digital image processing. IEEE Trans. Acoust. 29, 1153–1160 (1981).MathSciNet 
    MATH 
    Article 

    Google Scholar 
    59.Farr, T. G. et al. The shuttle radar topography mission. Rev. Geophys. 45, RG2004 (2007).60.Filipponi, F. Sentinel-1 GRD preprocessing workflow. Proceedings 18, 11 (2019).Article 

    Google Scholar 
    61.Soenen, S. A., Peddle, D. R. & Coburn, C. A. SCS+C: A modified sun-canopy-sensor topographic correction in forested terrain. IEEE Trans. Geosci. Remote Sens. 43, 2148–2159 (2005).ADS 
    Article 

    Google Scholar 
    62.Saleous, N. & Kutler, J. LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2 Summary: Data Citation: Model Product Description: Oak Ridge National Laboratory Distributed Active Archive Center http://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1146 (2012) 10.3334/ORNLDAAC/1146.63.Vermote, E., Justice, C., Claverie, M. & Franch, B. Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sens. Environ. 185, 46–56 (2016).ADS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    64.Foga, S. et al. Cloud detection algorithm comparison and validation for operational Landsat data products. Remote Sens. Environ. 194, 379–390 (2017).ADS 
    Article 

    Google Scholar 
    65.Louis, J. et al. Sentinel-2 Global Surface Reflectance Level-2a Product Generated with Sen2Cor. In International Geoscience and Remote Sensing Symposium (IGARSS) 8522–8525 (2019). https://doi.org/10.1109/IGARSS.2019.8898540.66.Roy, D. P. et al. A general method to normalize Landsat reflectance data to nadir BRDF adjusted reflectance. Remote Sens. Environ. 176, 255–271 (2016).ADS 
    Article 

    Google Scholar 
    67.Roy, D. P. et al. Examination of Sentinel-2A multi-spectral instrument (MSI) reflectance anisotropy and the suitability of a general method to normalize MSI reflectance to nadir BRDF adjusted reflectance. Remote Sens. Environ. 199, 25–38 (2017).ADS 
    Article 

    Google Scholar 
    68.Lee, J. S., Ainsworth, T. L., Wang, Y. & Chen, K. S. Polarimetric SAR speckle filtering and the extended sigma filter. IEEE Trans. Geosci. Remote Sens. 53, 1150–1160 (2015).ADS 
    Article 

    Google Scholar 
    69.Roy, D. P. et al. Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity. Remote Sens. Environ. 185, 57–70 (2016).ADS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    70.Claverie, M. et al. The Harmonized Landsat and Sentinel-2 surface reflectance data set. Remote Sens. Environ. 219, 145–161 (2018).ADS 
    Article 

    Google Scholar 
    71.Flood, N. Seasonal composite landsat TM/ETM+ Images using the medoid (a multi-dimensional median). Remote Sens. 5, 6481–6500 (2013).ADS 
    Article 

    Google Scholar 
    72.Kaufman, Y. J. & Tanré, D. Atmospherically Resistant Vegetation Index (ARVI) for EOS-MODIS. IEEE Trans. Geosci. Remote Sens. 30, 261–270 (1992).ADS 
    Article 

    Google Scholar 
    73.Saah, D. et al. Land cover mapping in data scarce environments: Challenges and opportunities. Front. Environ. Sci. 7, 150 (2019).Article 

    Google Scholar 
    74.Li, C., Wang, J., Wang, L., Hu, L. & Gong, P. Comparison of classification algorithms and training sample sizes in urban land classification with landsat thematic mapper imagery. Remote Sens. 6, 964–983 (2014).ADS 
    Article 

    Google Scholar 
    75.Richards, J. A. Remote sensing digital image analysis: An introduction. Remote Sensing Digital Image Analysis: An Introduction vol. 9783642300 (2013).76.Kruse, F. A. et al. The spectral image processing system (SIPS)-interactive visualization and analysis of imaging spectrometer data. Remote Sens. Environ. 44, 145–163 (1993).ADS 
    Article 

    Google Scholar 
    77.Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).MATH 
    Article 

    Google Scholar 
    78.He, Y., Lee, E. & Warner, T. A. A time series of annual land use and land cover maps of China from 1982 to 2013 generated using AVHRR GIMMS NDVI3g data. Remote Sens. Environ. 199, 201–217 (2017).ADS 
    Article 

    Google Scholar 
    79.Zhao, H. & Chen, X. Use of normalized difference bareness index in quickly mapping bare areas from TM/ETM+. In International Geoscience and Remote Sensing Symposium (IGARSS) vol. 3 1666–1668 (IEEE, 2005).80.Baloloy, A. B., Blanco, A. C., Raymund Rhommel, R. R. C. & Nadaoka, K. Development and application of a new mangrove vegetation index (MVI) for rapid and accurate mangrove mapping. ISPRS J. Photogramm. Remote Sens. 166, 95–117 (2020).ADS 
    Article 

    Google Scholar 
    81.García, M. J. L. & Caselles, V. Mapping burns and natural reforestation using thematic mapper data. Geocarto Int. 6, 31–37 (1991).Article 

    Google Scholar 
    82.Wright, C. & Gallant, A. Improved wetland remote sensing in Yellowstone National Park using classification trees to combine TM imagery and ancillary environmental data. Remote Sens. Environ. 107, 582–605 (2007).ADS 
    Article 

    Google Scholar 
    83.Hutchinson, C. F. Techniques for combining Landsat and ancillary data for digital classification improvement. Photogramm. Eng. Remote Sens. 48, 123–130 (1982).
    Google Scholar 
    84.Tadono, T. et al. Precise global DEM generation by ALOS PRISM. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. II–4, 71–76 (2014).Article 

    Google Scholar 
    85.Jokar Arsanjani, J., Zipf, A., Mooney, P. & Helbich, M. OpenStreetMap in GIScience. OpenStreetMap in GIScience: Experiences, Research, Applications (2015). https://doi.org/10.1007/978-3-319-14280-7.86.Open Development Mekong. OpenDevelopmentMekong. East-West Management Institute https://data.opendevelopmentmekong.net/organization/6f37a27d-2790-4b9a-8570-a36cb1d8108f?res_format=KML (2015).87.Truong, V. T. et al. JAXA annual forest cover maps for Vietnam during 2015–2018 Using ALOS-2/PALSAR-2 and auxiliary data. Remote Sens. 11 (2019).88.Olofsson, P. et al. Good practices for estimating area and assessing accuracy of land change. Remote Sens. Environ. 148, 42–57 (2014).ADS 
    Article 

    Google Scholar 
    89.Congalton, R. G. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens. Environ. 37, 35–46 (1991).ADS 
    Article 

    Google Scholar 
    90.Schmidt, M. The Sankey diagram in energy and material flow management—part II: Methodology and current applications. J. Ind. Ecol. 12, 173–185 (2008).Article 

    Google Scholar 
    91.Zha, Y., Gao, J. & Ni, S. Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. Int. J. Remote Sens. 24, 583–594 (2003).ADS 
    Article 

    Google Scholar 
    92.Lacaux, J. P., Tourre, Y. M., Vignolles, C., Ndione, J. A. & Lafaye, M. Classification of ponds from high-spatial resolution remote sensing: Application to Rift Valley Fever epidemics in Senegal. Remote Sens. Environ. 106, 66–74 (2007).ADS 
    Article 

    Google Scholar 
    93.Tucker, C. J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8, 127–150 (1979).ADS 
    Article 

    Google Scholar 
    94.McFeeters, S. K. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. Int. J. Remote Sens. 17, 1425–1432 (1996).ADS 
    Article 

    Google Scholar 
    95.Huete, A. R. A soil-adjusted vegetation index (SAVI). Remote Sens. Environ. 25, 295–309 (1988).ADS 
    Article 

    Google Scholar 
    96.Penuelas, J., Baret, F. & Filella, I. Semi-empirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance. Photosynthetica 31, 221–230 (1995).CAS 

    Google Scholar 
    97.Jayamanna, S., Kawamura, M. & Tsujiko, Y. Relation between social and environmental conditions in colombo, sri lanka and the urban index estimated by satellite remote sensing data. Int. Arch. Photogram. Remote Sens. 31, 321–326 (1996).
    Google Scholar 
    98.Shen, L. & Li, C. Water body extraction from Landsat ETM+ imagery using adaboost algorithm. In 2010 18th International Conference on Geoinformatics, Geoinformatics 2010 (2010). https://doi.org/10.1109/GEOINFORMATICS.2010.5567762.99.Perry, C. R. & Lautenschlager, L. F. Functional equivalence of spectral vegetation indices. Remote Sens. Environ. 14, 169–182 (1984).ADS 
    Article 

    Google Scholar 
    100.As-syakur, A. R., Adnyana, I. W. S., Arthana, I. W. & Nuarsa, I. W. Enhanced built-UP and bareness index (EBBI) for mapping built-UP and bare land in an urban area. Remote Sens. 4, 2957–2970 (2012).ADS 
    Article 

    Google Scholar 
    101.Liu, H. Q. & Huete, A. Feedback based modification of the NDVI to minimize canopy background and atmospheric noise. IEEE Trans. Geosci. Remote Sens. 33, 457–465 (1995).ADS 
    Article 

    Google Scholar 
    102.Gitelson, A. A., Viña, A., Ciganda, V., Rundquist, D. C. & Arkebauer, T. J. Remote estimation of canopy chlorophyll content in crops. Geophys. Res. Lett. 32, 1–4 (2005).Article 
    CAS 

    Google Scholar  More

  • in

    Value of Mexican nature reserve is more than monetary

    Our monetary compensation programme for poor rural communities in Mexico’s Sierra de Manantlán Biosphere Reserve encouraged them to forgo exploitation of their natural resources to provide ecosystem services for the city of Colima (see Nature 591, 178; 2021). But focusing solely on the monetary value of ecosystems isn’t enough.The National Forestry Commission of Mexico and the Fund for Natural Protected Areas have made compensatory payments of almost US$1 million to communities in the Cerro Grande region of the reserve since 2003, backed since 2013 by a local fiduciary fund of voluntary contributions from Colima’s citizens. However, the voluntary payments are minimal because most people don’t acknowledge the importance of the mountain forest that provides 90% of their water.As well as monetary schemes, the cutural traditions and the land-tenure rights of communal organizations must be recognized. They should be empowered to draw up contracts between owners of natural resources and urban beneficiaries that will promote their common social, economic and livelihood interests. To increase productivity and family income for impoverished small landowners, payment for ecosystem services could be implemented by using diverse marketing approaches that include sustainable agroforestry and livestock production. More

  • in

    Effects of cadmium stress on growth and physiological characteristics of sassafras seedlings

    1.Ali, B. et al. Physiological and ultra-structural changes in Brassica napus seedlings induced by cadmium stress. Biol Plant 58(1), 131–138 (2014).CAS 
    Article 

    Google Scholar 
    2.Tang, Y. et al. Cadmium-accumulator straw application alleviates cadmium stress of lettuce (Lactuca sativa) by promoting pgotosynthetic activity and antioxidative enzyme activities. Environ. Sci. pollut. Res. 25, 30671–30679 (2018).CAS 
    Article 

    Google Scholar 
    3.Jia, L. et al. Hormesis effects induced by cadmium on growth and photosynthetic performance in a hyperaccumulator, Lonicera japonica. Thunb. J Plant Growth Regul 34(1), 13–21 (2015).CAS 
    Article 

    Google Scholar 
    4.Gallego, S. M., Benavides, M. P. (2019) Cadmium-induced oxidative and nitrosative stress in plants. Cadmium Toxicity and Tolerance in Plants. Elsevier, pp. 233–274.5.Rizwan, M. et al. Cadmium minimization in wheat: a critical review. Ecotoxicol. Environ. Saf. 130, 43–53 (2016).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    6.Zou, J. et al. Transcriptional, physiological and cytological analysis validated the roles of some key genes linked Cd stress in Salix matsudanaKoidz. Environ. Exp. Bot. 134, 116–129 (2017).CAS 
    Article 

    Google Scholar 
    7.Chen, H. C. et al. The effects of exogenous organic acids on the growth, photosynthesis and cellular ultrastructure of Salix variegata Franch Under Cd stress. Ecotoxicol. Environ. Saf. 187, 1–10 (2020).
    Google Scholar 
    8.Sarvajeet, S. G., Nafees, A. K. & Narendra, T. Cadmium at high dose perturbs growth, photosynthesis and nitrogen metabolism while at low dose it up regulates sulfur assimilation and antioxidant machinery in garden cress (Lepidium sativum L.). Plant Sci. 182, 112–120 (2011).
    Google Scholar 
    9.Daniel, H., Tereza, C., Tom´a, V. & Radka, P. The effect of nanoparticles on the photosynthetic pigments in cadmium-zinc interactions. Environ. Sci. Pollut. Res. 26(4), 4147–4151 (2019).Article 
    CAS 

    Google Scholar 
    10.Tian, X. et al. Measurement of metal bioaccessibility in vegetables to improve human exposure assessments: field study of soil–plant–atmosphere transfers in urban areas South China. Environ. Geochem. Health 38(6), 1283–1301 (2016).Article 
    CAS 

    Google Scholar 
    11.He, J. et al. A transcriptomic network underlies microstructural and physiological responses to cadmium in Populus× _canescens. Plant Physiol. 162, 424–439 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    12.He, J. et al. Cadmium tolerance in six poplar species. Environ. Sci. Pollut. Res. 20, 163–174 (2013).CAS 
    Article 

    Google Scholar 
    13.He, N. et al. Draft genome sequence of the mulberry tree Morus notabilis. Nat. Commun. 4, 1–9 (2013).Article 
    CAS 

    Google Scholar 
    14.Wu, P., Luo, Z. (1981) Precious sassafras of Guizhou[J]. Guizhou Forest. Sci. Technol.15.Flora of China, 1982, vol. 31, p. 238.16.Xiyou, C. Study on Growth of Sassafras in different Mixed ways[J]. Anhui Forest. Sci. Technol. 4, 9–11 (2015).
    Google Scholar 
    17.Cheng Yong, Wu. et al. Storage test of sassafras seeds[J]. Hunan Forest. Sci. Technol. 2, 28–30 (2014).
    Google Scholar 
    18.Shen, Y. et al. Study on biomass and productivity of natural secondary Sassafras Mixed Forest[J]. J. Central South Univ. Forest. Technol. 5, 26–30 (2011).
    Google Scholar 
    19.Jin, Y. Q. et al. Efficient adsorption of methylene blue and lead ions in aqueous solutions by 5-sulfosalicylic acid modified lignin[J]. Int. J. Biol. Macromol. 123, 50–58 (2019).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    20.Cheng, Y. F. et al. Rapid method for protein quantitation by Bradford assay after elimination of the interference of polysorbate 80[J]. Anal Biochem 494, 37–39 (2016).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    21.Abdelgawad, H., Zinta, G., Badreldin, A. H., et al. (2019) Maize roots and shoots show distinct profiles of oxidative stress and antioxidant defense under heavy metal toxicity[J]. Environ. Pollut., p. 11370522.Donahue, J. L. et al. Responses of antioxidants to paraquat in pea leaves (relationships to resistance) [J]. Plant Physiol 113(1), 249–257 (1997).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    23.Merey, H. A. et al. Validated UPLC method for the determination of guaiphenesin, oxeladin citrate, diphenhydramine, and sodium benzoate in their quaternary mixture used in treatment of cough, in the presence of guaiphenesin-related substance (guaiacol)[J]. Chem. Pap. 72(9), 2247–2254 (2018).CAS 
    Article 

    Google Scholar 
    24.Beers, R. F. & Sizer, I. W. A spectrophotometric method for measuring the breakdown of hydrogen peroxide by catalase[J]. J. Biol. Chem. 195(1), 133–140 (1952).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    25.Zhao, F. J., Jiang, R. F., Dunham, S. J. & McGrath, S. P. Cadmium uptake, translocation and tolerance in the hyperaccumulator Arabidopsis halleri. New Phytol. J. 172, 646–654 (2006).CAS 
    Article 

    Google Scholar 
    26.Lichtenthaler, H. K. & Wellburn, A. R. Determinations of total carotenoids and chlorophylls a and b of leaf extracts in different solvents. Analysis 11(5), 591–592 (1983).CAS 

    Google Scholar 
    27.Zipiao, Ye. Andvances in models of photosynthetic response to light and CO2[J]. Chin. J. Plant Ecol. 06, 727–740 (2010).
    Google Scholar 
    28.Saidi, I. et al. Oxidative damages induced by short-term exposure to cadmium in bean plants: protective role of salicylic acid. S Afr. J. Bot. 85, 32–38 (2013).CAS 
    Article 

    Google Scholar 
    29.Anwaar, S. A. et al. Silicon (Si) alleviates cotton (Gossypium hirsutum L.) fromzinc (Zn) toxicity stress by limiting Zn uptake and oxidative damage. Environ. Sci. Pollut. Res. 22, 3441–3450 (2014).Article 
    CAS 

    Google Scholar 
    30.Fuzhong, Wu. et al. Effects of cadmium stress on the growth, nutrient accumulation, distribution and utilization of Osmanthus fragrans. J. Plant Ecol. 34(10), 1220–1226 (2010).
    Google Scholar 
    31.Cengiz, K., Nudrat, A., Akram, M., Ashraf, M., Nasser, A., Parvaiz, A. (2020) Exogenously supplied silicon (Si) improves cadmium tolerance in pepper (Capsicum annuum L.) by upregulating the synthesis of nitric oxide and hydrogen sulfide[J]. J. Biotechnol., p. 31632.Wang, H. et al. Effects of cadmium stress at different concentrations on photosynthesis, lipid peroxidation and antioxidant enzyme activities in maize seedlings [J]. J. Plant Nutrition Fertilizer 14(01), 36–42 (2008).CAS 

    Google Scholar 
    33.Awasthi, P., Mahajan, V., Jamwal, V. L. et al. (2016) Cloning and expression analysis of chalcone synthase gene from Coleus forskohlii. J. Genet.34.Ahmad, P., Jaleel, C. A., Salem, M. A., Nabi, G. & Sharma, S. Roles of enzymatic and nonenzymatic antioxidants in plants during abiotic stress. Crit. Rev. Biotechnol. 30(3), 161–175 (2010).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    35.Chen, H. et al. H2O2 mediates nitrate-induced iron chlorosis by regulating iron homeostasis in rice. Plant Cell Environ. 41, 767–781 (2018).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    36.Kohli, S. K., Khanna, K., Bhardwaj, R., Abd_Alla, E. F., Corpas, F. J. (2019) Assessment of subcellular ros and no metabolism in higher plants: multifunctional signaling molecules. Antioxidants, vol 8, no 1237.Meng Jie, A. & Hai Jiang, W. Effects of modifiers on the growth, photosynthesis, and antioxidant enzymes of cotton under cadmium toxicity. J. Plant Growth Regulat. 38, 1196–1205 (2019).Article 
    CAS 

    Google Scholar 
    38.Wei, X. et al. Effects of different breaking dormancy ways on the photosynthetic characteristics and activities of protective enzymes of ‘misty’ blueberry leaves. Sci. Agric. Sin. 48(22), 4517–4528 (2015).CAS 

    Google Scholar 
    39.Chaabene, Z. et al. Copper toxicity and date palm (Phoenix dactylifera) seedling tolerance: monitoring of related biomarkers. Environ. Toxicol. Chem. 37(3), 797–806 (2018).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    40.Ozfidan-Konakci, C. et al. The humic acid-induced changes in the water status, chlorophyll fluorescence and antioxidant defense systems of wheat leaves with cadmium stress. Ecotoxicol. Environ. Saf. 155, 66–75 (2018).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    41.Liu, Q. S. et al. Transcriptomic responses of dove tree (Davida involucrata Baill) to heat stress at the seedling stage[J]. Forest 10(8), 656 (2019).Article 

    Google Scholar 
    42.Yang, L. P. et al. Effect of Cd on growth, physiological response, Cd subcellular distribution and chemical forms of Koelreuteria paniculate[J]. Ecotox Environ. Safe 160, 10–18 (2018).CAS 
    Article 

    Google Scholar 
    43.Zhang, Y. L. et al. The physiological characteristics of ornamental kale for cold resistance[J]. Act. Agric. 31(4), 168–176 (2016).CAS 

    Google Scholar 
    44.Rady, M. M. & Hemida, K. A. Modulation of cadmium toxicity and enhancing cadmium-tolerance in wheat seedlings by exogenous application of polyamines. Ecotoxicol Environ. Saf 119, 178–185 (2015).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    45.Chen, Y. H. et al. Study on the characteristics of proline and active oxygen metabolism in red sea under salt stress [J]. J. Xiamen Univ. Nat. Sci. 43(03), 402–405 (2004).CAS 

    Google Scholar 
    46.Niu, M. G. et al. Effects of drought, waterlogging and low temperature stress on physiological and biochemical characteristics of wheat [J]. Seed 04, 17–19 (2003).
    Google Scholar 
    47.Deng, F.-F., Yang, S.-L. & Gong, M. Regulation of proline metabolism in abiotic plants by cell signaling molecules [J]. J. Plant Physiol. 51(10), 1573–1582 (2015).CAS 

    Google Scholar 
    48.Samuel, D. et al. Proline inhibits aggre-gation during protein refolding[J]. Protein Sci. 9(2), 344–352 (2010).Article 

    Google Scholar 
    49.Abd Allah, E. F. et al. Calcium application enhances growth and alleviates the damaging effects induced by Cd stress in sesame (Sesamum indicum L.). J. Plant Interact. 12(1), 237–243 (2017).Article 
    CAS 

    Google Scholar 
    50.Zhang, X. D. et al. Annotation and characterization of Cd-responsive metal transporter genes in rapeseed (Brassica napus). Bio Metals 31(1), 107–121 (2018).CAS 

    Google Scholar 
    51.Chen, K. et al. Physiological response and cold resistance evaluation of the leaves of Parashorea chinensis seedlings to low temperature stress[J]. J NW For Univ 34(3), 67–73 (2019).CAS 

    Google Scholar 
    52.Ge, W. & Jiao, Y. Changes of soluble protein content of two poplar trees under cadmium stress [J]. Modern Agric. Sci. Technol. 1, 199–200 (2012).
    Google Scholar 
    53.Aina, R. et al. Thiol-petide level and proteomic changes in response to cadmium toxicity in Oryza sativa L. rotts[J]. Environ. Exp. Botany 59(3), 381–392 (2007).CAS 
    Article 

    Google Scholar 
    54.Xu, J. J. et al. Effects of Cd stress on antioxidant enzymes activity of Sonchus asper L. Hill and Zea mays L. in intercropping system[J]. J. Yunnan Agric. Univ. Nat Sci. Ed. 30(2), 348–355 (2016).
    Google Scholar 
    55.Hendrik, K., Frithjof, K. & Martin, S. Environmental relevance of heavy metal-substituted chlorophylls using the example of water plants[J]. J. Exp. Bot. 47(2), 259–266 (1996).Article 

    Google Scholar 
    56.Chen, X. X. et al. Effects of thallium and cadmiun stress on the growth and photosynthetic characteristics of Arundinacea[J]. Guangxi Plants 39(6), 743–751 (2019).
    Google Scholar 
    57.Ahanger, M. A., U Aziz, Alsahli, A. A., Alyemeni, M. N., Ahmad, P. (2020). Combined kinetin and spermidine treatments ameliorate growth and photosynthetic inhibition in vigna angularis by up-regulating antioxidant and nitrogen metabolism under cadmium stress. Biomolecules, vol. 10, no 158.Sun Xiaolin, Xu. et al. Response of photosynthetic pigments in plant leaves to shading[J]. Chin. J. Plant Ecol. 34(8), 989–999 (2010).
    Google Scholar 
    59.Chen, X.-X. et al. Effects of cadmium stress on growth and photosynthetic characteristics of asparagus spears[J]. Plants Guangxi 39(6), 743–751 (2019).
    Google Scholar 
    60.Lu, Y. et al. Effects of heavy metals on photosynthetic and physiological growth characteristics of halophytes[J]. Acta Botanica Northwestern Sinica 31(2), 370–376 (2011).CAS 

    Google Scholar 
    61.Farquhar, G. D. & Sharkey, T. D. Stomatal Conductance and Photosynthesis[J]. Annu. Rev. Plant Physiol. 33(1), 317–345 (1982).CAS 
    Article 

    Google Scholar 
    62.Haizhen, W. et al. Response of chlorophyll fluorescence characteristics to high temperature in heteromorphous leaves of Populus eureka [J]. Acta Ecol. Sin. 9, 100–109 (2011).
    Google Scholar 
    63.Liyuan, Li. et al. Photosynthetic light response simulation of leaves of Quercus variabilis and Robinia pseudoacacia under different light environments[J]. Chin. J. Appl. Ecol. 29(7), 2295–2306 (2016).
    Google Scholar 
    64.Wang, F.-K. et al. Photosynthetic light response curve of Populus microphylla under different slope orientation[J]. Water Soil Conservat. Res. 22(113), 182–187 (2015).
    Google Scholar 
    65.Xin, Qi., Qunfang, C. & Yulong, F. Adaptation of photosynthesis to growth light intensity in seedlings of three tree species of Putaoia in tropical rain forest [J]. Chin. J. Plant Ecol. 01, 34–41 (2004).
    Google Scholar  More

  • in

    Relationship of contact angle of spray solution on leaf surfaces with weed control

    The results were divided into two factors, both were an independent process that was occurring on the surfaces, together, the two factors explained 60.2% of the total variation of the original data. Factor 1, which consists of the contact angle and the weed control, represent 60.1% of the data, and Factor 2, which consists of the contact angle of the abaxial surface of I. grandifolia and the adaxial surface of A. curassavica, represent 0.9% of the total variation (Table 1).Table 1 Result of the analysis of factors containing the first two factors (processes) with their respective factorial loads that represent the correlation coefficients between the foliar surfaces and control and each Factor.Full size tableThe process contained in the contact angle and weed control is the most important for this study since it is derived from the higher eigenvalue and has a higher percentage of explanation (60.1%), and the variables that contribute the most are represented by artificial surface (0.98), lantana adaxial and abaxial face (0.75 and 0.68, respectively), shakeshake adaxial and abaxial face (0.95 and 0.96, respectively), morning glory adaxial face (0.81), sicklepod adaxial and abaxial face (0.88 and 0.94), castor bean adaxial and abaxial face (0.52 and 0.85), bloodflower milkweed abaxial face (0.73), control in shakeshake 5, 11 and 16 DAA (−0.50, −0.77 and −0.92, respectively), control in lantana 5, 11 and 16 DAA (−0.51, −0.84 and −0.95, respectively). Furthermore, according to the signs of the factorial loads, the contact angle and weed control factor are directly and strongly correlated with the surfaces, because the contact angle showed the same positive sign for both surfaces, as well as to the control shakeshake and lantana showed the same negative sign.Considering that the factors are orthogonal (uncorrelated), the processes retained in the contact angle, weed control (Factor 1), and the factor morning glory abaxial surface, bloodflower milkweed adaxial surface (Factor 2) are considered independent. Thus, the analysis was performed with the scores of the contact angle and control factor (Fig. 1) and the morning glory abaxial surface factor, and bloodflower milk weed adaxial surface (Fig. 2). A significant difference (F = 109.58; p = 0.0001) was found between the treatments when the surfaces and weed control were evaluated (Factor 1). There were differences between the spray solutions and water treatment.Figure 1Graphical representation with the scores of Factor 1 (surface and control) as a function of the evaluated samples. Vertical bars represent confidence intervals of 0.95 (F = 109.58; p = 0.0001). Spray solution: 1-No adjuvant; 2-herbicide associated with vegetable oil; 3- herbicide associated with mineral oil; 4-herbicide associated with lecithin, and Control.Full size imageFigure 2Graphical representation with Factor 2 (Ipomoea grandifolia (abaxial) and Asclepias curassavica (adaxial) scores) as a function of the evaluated samples. Vertical bars represent confidence intervals of 0.95 (F = 4.036, p = 0.009). Spray solution: 1-No adjuvant; 2-herbicide associated with vegetable oil; 3-herbicide associated with mineral oil; 4-herbicide associated with lecithin, and Control.Full size imageFor factor 2 there was also a difference (F = 4.036; p = 0.009) when the plants were evaluated together. The spray solution did not differ from the control. When the spray solutions were compared, there was a difference only between the herbicide spray solution with no lecithin and the herbicide spray solution with lecithin (Fig. 2).Correlating with the results of the univariate analysis, it is possible to observe that the separation of factors is related to the result obtained from the contact angle of the bloodweed milkweed and morningglory surfaces, because, for bloodflower milkweed, the treatments and control were not significant (p  > 0.05), with no significance for the dosage and adjuvant interaction (p  > 0.05). The two experiments to control the weeds were complementary, did not differ from each other (p  > 0.05).For lantana on the adaxial surface, the control treatment differed from the treatments (p = 0.001), however, the spray solution, doses, and interaction were not significant (p  > 0.05, 0.0844, and 0.0616, respectively) (Table 2). For the abaxial surface, the spray solutions and the interaction of the factors were not significant (p = 0.0535 and 0.1353, respectively). However, the herbicide doses were statistically different (p  > 0.0001) (Figs. 3, 4).Table 2 Average and standard deviation of the contact angle (°) after drop deposition in surfaces of the leaves of the weeds.Full size tableFigure 3Percentage of control of Crotalaria incana L. plants after herbicide solution spraying. (A) Aminopyralid + fluroxypyr at 155.3 L of active ingredient ha−1. (B) Aminopyralid + fluroxypyr at 360.6 L of active ingredient ha−1. Equal letters within the evaluation days, the adjuvants do not differ from each other by the Tukey test (p  > 0.05).Full size imageFigure 4Percentage of control of Lantana camara L. plants after herbicide solution spraying. (A) Aminopyralid + fluroxypyr at 155.3 L of active ingredient ha−1. (B) Aminopyralid + fluroxypyr at 360.6 L of active ingredient ha−1. Equal letters within the evaluation days, the adjuvants do not differ from each other by the Tukey test (p  > 0.05).Full size imageThe contact angle of the shakeshake adaxial surface did not differ for spray solutions (p = 0.666), dose and control factors versus the treatments were significant (p  > 0.0001 and 0.0001), but for interaction, there was no significance (p = 0.5327) (Table 2). The abaxial surface, the spray solution, doses, and the control treatment versus the treatments were significant (p = 0.0056, 0.0428, and 0.0001), but the interaction was not significant (0.4453) (Table 2). For sicklepods adaxial and abaxial surfaces, doses, spray solution, control versus treatments and interaction were significant (p  > 0.0001). The surfaces of the species shakeshake and sicklepod showed higher values to the control, the abaxial surface sicklepod presented the contact angle value of approximately 177° (Table 2; Figs. 5 and 6), for shakeshake the addition of vegetable oil decreased the contact angle on the adaxial surface at any dose, and for sicklepod the addition of lecithin resulted in the lowest contact angle (Table 2).Figure 5Surface adaxial in plants of the Crotalaria incana L. (A), Lantana camara L. (B), Asclepias curassavica L. (C), Senna obtusifolia (L.) H.S.Irwin & Barneby (D), Ricinus communis L. (E) and Ipomoea grandifolia (Dammer) O’Donell (F) taken on the Stereoscopic Microscope with × 1.0.Full size imageFigure 6Surface abaxial in plants of the Crotalaria incana L. (A), Lantana camara L. (B), Asclepias curassavica L. (C), Senna obtusifolia (L.) H.S.Irwin & Barneby (D), Ricinus communis L. (E) and Ipomoea grandifolia (Dammer) O’Donell (F) taken on the Stereoscopic Microscope with × 1.0.Full size imageFor the castor bean, on the adaxial surface, the spray solution and doses were not significant (p = 0.06126 and 0.1761, respectively), the control treatment and the interaction were significant (p  > 0.0001), but for the abaxial surface the spray solution, dose, interaction, and control treatment were significant (p  > 0.0001). The morningglory on the adaxial surface showed a significant difference between the spray solution, doses, control treatment (p = 0.0001, 0.0072, and 0.0001) but it was not significant for the interaction (p = 0.2283), on the abaxial surface, the spray solution and the doses were not significant (p = 0.0755 and 0.3025), but the interaction and the control treatment were significant (p = 0.0007 and 0.0018). For morningglory the addition of lecithin resulted in the lowest contact angle on the adaxial surface, for the abaxial addition of mineral oil and lecithin presented the lowest values, for the castor beans the lecithin resulted in the lowest contact angle on the adaxial surface for the dose of 155.3 L ha−1, however, at a dose of 310.6 L ha−1, the spray solution without adjuvant or mineral or vegetable oil showed lower values of contact angle.For the milkweed bloodflower, on the adaxial surface, the sprays solution, doses, interaction, and control treatment were not significant (p = 0.1320, 0.6804, 0.0848, and 0.800, respectively), the abaxial surface, the sprays solution, doses, and interaction were not significant (p = 0.2016, 0.7371 and 0.8916, respectively), so the contact angle values were statistically similar to each other. However, its species presented a lower average value of the contact angle (43.93°) compared to other plants (Fig. 5 and 6).The standard surface (Parafilm) sprays solutions, control treatment and doses were significant (p  > 0.0001) but there was no significance for interaction (p = 0.2077), thus, regardless of the dose, the addition of lecithin resulted in the lowest contact angle of the drops.In the weed control experiment, shakeshake and lantana plants showed rapid damage caused by the herbicide aminopyralid + fluroxypyr (Fig. 3 and 4). For shakeshake, at 5 DAP the spray solution and dose were not significant (p  > 0.05), the control treatment versus the treatments and the interaction was significant (p = 0.0019 and 0.0149, respectively), the interaction was significant only in mineral oil in that the level of control was lower at the dose of 155.3 L ha−1. At 11 DAP the spray solution was not significant (p  > 0.05), but the doses, the interaction, and the control treatment versus the treatments were significant (p = 0.0009, 0.0413, and 0.0001, respectively), the interaction was significant because there were differences in lecithin at the control level, in which the dose of 155.3 L ha−1 resulted in the lowest level of control. In the last evaluation at 16 DAP, the spray solution was not significant (p  > 0.05), dose, interaction, and control versus treatments were significant (p = 0.0001, 0.0353 and 0.001), the interaction was significant for the spray solutions that had the addition of an adjuvant, in which the highest level of control was observed at the dose of 310.6 L ha−1 (Fig. 3 and 4).The level of lantana control at 5 DAP showed significance for spray solution (p = 0.0257) and control versus treatments (0.007), the dose and interaction were not significant (p  > 0.05), at 11DAP the spray solution, dose, and interaction were not significant (p = 0.1377, 0.0706 and 0.6540, respectively) there was only significance for control versus treatments (p = 0.0001). At 16 DAP the spray solution, dose, and interaction were not significant (p = 0.4703, 0.1734, and 0.4210, respectively), there was significance for the control versus treatments (p = 0.0004) (Fig. 3 and 4).In general, at 5 DAP, both species showed symptoms of twisting of the leaves next of the apical region was observed differences between treatments with no adjuvant and the doses used. The dose of 310.6 L ha−1 showed a better percentage of control in both species, and independent of the treatments used (Fig. 3 and 4).At 16 DAP, lantana plants showed 100% control independent of the dose used, but the shakeshake plants had control of close to 80% independent of the dose (Fig. 4). Shakeshake plants have a lower percentage of control due to the surface being more hydrophobic to the surface of the lantana, hydrophobicity can cause the drops to ricochet, with no drops being deposited, therefore, the product does not absorb (Fig. 3).The addition of adjuvants to the spray solution did not result in differences between the treatment without addition, despite the decrease in the contact angle with the addition of the herbicide. In the shakeshake, a higher percentage of control was observed in the treatment without the addition of an adjuvant, in the dose of 155.3 L ha−1, in the dose of 310.6 L ha−1 with the addition of lecithin resulted in a higher percentage of control (Fig. 3). For lantana, the addition or not of the adjuvant did not result in different control percentage values, independent of the dose, that is, only the herbicide was necessary to obtain 100% control of the plants, this is due to the lower angle value of contact compared to shakeshake (Fig. 4). More

  • in

    Wide and increasing suitability for Aedes albopictus in Europe is congruent across distribution models

    1.Medlock, J. M. et al. An entomological review of invasive mosquitoes in Europe. Bull. Entomol. Res. 105, 637–663 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    2.Benedict, M. Q., Levine, R. S., Hawley, W. A. & Lounibos, L. P. Spread of the Tiger. Vector Borne Zoonotic Dis. 7, 76–85 (2007).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    3.Bonizzoni, M., Gasperi, G., Chen, X. & James, A. A. The invasive mosquito species Aedes albopictus: current knowledge and future perspectives. Trends Parasitol. 29, 460–468 (2013).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    4.Jourdain, F. et al. Towards harmonisation of entomological surveillance in the mediterranean area. PLoSNegl. Trop. Dis. 13, 1–28 (2019).
    Google Scholar 
    5.Kraemer, M. U. et al. Past and future spread of the arbovirus vectors Aedes aegypti and Aedes albopictus. Nat. Microbiol. 4, 854–863 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    6.Kamal, M., Kenawy, M. A., Rady, M. H., Khaled, A. S. & Samy, A. M. Mapping the global potential distributions of two arboviral vectors Aedes aegypti and Ae. albopictus under changing climate. PLoS ONE 13, 1–21 (2018).
    Google Scholar 
    7.Proestos, Y. et al. Present and future projections of habitat suitability of the Asian tiger mosquito, a vector of viral pathogens, from global climate simulation. Philos. Trans. R. Soc. B Biol. Sci. 370, 20130554 (2015).Article 

    Google Scholar 
    8.Campbell, L. P. et al. Climate change influences on global distributions of dengue and chikungunya virus vectors. Philos. Trans. R. Soc. B Biol. Sci. 370, 20140135 (2015).Article 

    Google Scholar 
    9.Gossner, C. M., Ducheyne, E. & Schaffner, F. Increased risk for autochthonous vector-borne infections transmitted by Aedes albopictus in continental europe. Eurosurveillance 23, 2–7 (2018).
    Google Scholar 
    10.ECDC, E. C. for D. P. and C. & EFSA, E. F. S. A. Aedes albopictus—current known distribution: September 2020. Mosquito maps [internet]. https://ecdc.europa.eu/en/disease-vectors/surveillance-and-disease-data/mosquito-maps (2020).11.Ding, F., Fu, J., Jiang, D., Hao, M. & Lin, G. Mapping the spatial distribution of Aedes aegypti and Aedes albopictus. Acta Trop. 178, 155–162 (2018).PubMed 
    Article 

    Google Scholar 
    12.Kraemer, M. U. et al. The global distribution of the arbovirus vectors Aedes aegypti and Ae. albopictus. Elife 4, 1–18 (2015).Article 

    Google Scholar 
    13.Santos, J. & Meneses, B. M. An integrated approach for the assessment of the Aedes aegypti and Aedes albopictus global spatial distribution, and determination of the zones susceptible to the development of Zika virus. Acta Trop. 168, 80–90 (2017).PubMed 
    Article 

    Google Scholar 
    14.Kuhlisch, C., Kampen, H. & Walther, D. The Asian tiger mosquito Aedes albopictus (Diptera: Culicidae) in Central Germany: surveillance in its northernmost distribution area. Acta Trop. 188, 78–85 (2018).PubMed 
    Article 

    Google Scholar 
    15.Vaux, A. G. C. et al. The challenge of invasive mosquito vectors in the U.K. during 2016–2018: a summary of the surveillance and control of Aedes albopictus. Med. Vet. Entomol. https://doi.org/10.1111/mve.12396 (2019).PubMed 
    Article 

    Google Scholar 
    16.Metelmann, S. et al. The UK’s suitability for Aedes albopictus in current and future climates. J. R. Soc. Interface 16, 20180761 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    17.Petrić, M., Lalić, B., Ducheyne, E., Djurdjević, V. & Petrić, D. Modelling the regional impact of climate change on the suitability of the establishment of the Asian tiger mosquito (Aedes albopictus) in Serbia. Clim. Chang. 142, 361–374 (2017).ADS 
    Article 

    Google Scholar 
    18.Fischer, D., Thomas, S. M., Niemitz, F., Reineking, B. & Beierkuhnlein, C. Projection of climatic suitability for Aedes albopictusSkuse (Culicidae) in Europe under climate change conditions. Glob. Planet. Chang. 78, 54–64 (2011).ADS 
    Article 

    Google Scholar 
    19.Caminade, C. et al. Suitability of European climate for the Asian tiger mosquito Aedes albopictus: recent trends and future scenarios. J. R. Soc. Interface 9, 2708–2717 (2012).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    20.Fischer, D., Thomas, S. M., Neteler, M., Tjaden, N. B. & Beierkuhnlein, C. Climatic suitability of Aedes albopictus in Europe referring to climate change projections: comparison of mechanistic and correlative niche modelling approaches. Eurosurveillance 19, 1–13 (2014).
    Google Scholar 
    21.Kraemer, M. U. G. et al. The global compendium of Aedes aegypti and Ae. albopictus occurrence. Sci. Data 2, 1–8 (2015).Article 

    Google Scholar 
    22.Cunze, S., Kochmann, J., Koch, L. K. & Klimpel, S. Aedes albopictus and its environmental limits in Europe. PLoS ONE 11, 1–14 (2016).Article 
    CAS 

    Google Scholar 
    23.Shragai, T. & Harrington, L. C. Aedes albopictus (Diptera: Culicidae) on an invasive edge: abundance, spatial distribution, and habitat usage of larvae and pupae across urban and socioeconomic environmental gradients. J. Med. Entomol. 56, 472–482 (2019).PubMed 
    Article 

    Google Scholar 
    24.Buisson, L., Thuiller, W., Casajus, N., Lek, S. & Grenouillet, G. Uncertainty in ensemble forecasting of species distribution. Glob. Chang. Biol. 16, 1145–1157 (2010).ADS 
    Article 

    Google Scholar 
    25.LaDeau, S. L., Allan, B. F., Leisnham, P. T. & Levy, M. Z. The ecological foundations of transmission potential and vector-borne disease in urban landscapes. Funct. Ecol. 29, 889–901 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    26.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).
    Google Scholar 
    27.Acevedo, P. & Real, R. Favourability: concept, distinctive characteristics and potential usefulness. Naturwissenschaften 99, 515–522 (2012).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    28.Radosavljevic, A. & Anderson, R. P. Making better Maxent models of species distributions: complexity, overfitting and evaluation. J. Biogeogr. 41, 629–643 (2014).Article 

    Google Scholar 
    29.Pearson, R. G. Species’ distribution modeling for conservation educators and practitioners. Synth. Am. Museum Nat. Hist. 50, 54–89 (2007).
    Google Scholar 
    30.Capinha, C., Larson, E. R., Tricarico, E., Olden, J. D. & Gherardi, F. Effects of climate change, invasive species, and disease on the distribution of native european crayfishes. Conserv. Biol. 27, 731–740 (2013).PubMed 
    Article 

    Google Scholar 
    31.Castellanos, A. A., Huntley, J. W., Voelker, G. & Lawing, A. M. Environmental filtering improves ecological niche models across multiple scales. Methods Ecol. Evol. 10, 481–492 (2019).Article 

    Google Scholar 
    32.Machín, L., Aschemann-Witzel, J., Curutchet, M. R., Giménez, A. & Ares, G. Traffic light system can increase healthfulness perception: implications for policy making. J. Nutr. Educ. Behav. 50, 668–674 (2018).PubMed 
    Article 

    Google Scholar 
    33.OECD. Redefining Urban: A New Way to Measure Metropolitan Areas (OECD, 2012).Book 

    Google Scholar 
    34.Tippelt, L., Werner, D. & Kampen, H. Tolerance of three Aedes albopictus strains (Diptera: Culicidae) from different geographical origins towards winter temperatures under field conditions in northern Germany. PLoS ONE 14, e0219553 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    35.Li, R. et al. Climate-driven variation in mosquito density predicts the spatiotemporal dynamics of dengue. Proc. Natl. Acad. Sci. U. S. A. 116, 3624–3629 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    36.Murray, K. A. et al. Global biogeography of human infectious diseases. Proc. Natl. Acad. Sci. U. S. A. 112, 12746–12751 (2015).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    37.Shragai, T., Tesla, B., Murdock, C. & Harrington, L. C. Zika and chikungunya: mosquito-borne viruses in a changing world. Ann. N. Y. Acad. Sci. 1399, 61–77 (2017).ADS 
    PubMed 
    Article 

    Google Scholar 
    38.Tjaden, N. B., Caminade, C., Beierkuhnlein, C. & Thomas, S. M. Mosquito-borne diseases: advances in modelling climate-change impacts. Trends Parasitol. 34, 227–245 (2018).PubMed 
    Article 

    Google Scholar 
    39.Paupy, C., Delatte, H., Bagny, L., Corbel, V. & Fontenille, D. Aedes albopictus, an arbovirus vector: from the darkness to the light. Microb. Infect. 11, 1177–1185 (2009).CAS 
    Article 

    Google Scholar 
    40.Mariconti, M. et al. Estimating the risk of arbovirus transmission in Southern Europe using vector competence data. Sci. Rep. 9, 1–10 (2019).CAS 
    Article 

    Google Scholar 
    41.Egizi, A., Fefferman, N. H. & Fonseca, D. M. Evidence that implicit assumptions of ‘no evolution’ of disease vectors in changing environments can be violated on a rapid timescale. Philos. Trans. R. Soc. B Biol. Sci. 370, 1–10 (2015).Article 

    Google Scholar 
    42.Zeller, H., Marrama, L., Sudre, B., Van Bortel, W. & Warns-Petit, E. Mosquito-borne disease surveillance by the European Centre for Disease Prevention and Control. Clin. Microbiol. Infect. 19, 693–698 (2013).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    43.Fernandes, J. N., Moise, I. K., Maranto, G. L. & Beier, J. C. Revamping mosquito-borne disease control to tackle future threats. Trends Parasitol. 34, 359–368 (2018).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    44.Lwande, O. W., Obanda, V., Lindstro, A., Ahlm, C. & Evander, M. Risk factors for arbovirus pandemics. Vector-Borne Zoonotic Dis. 20, 71–81 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    45.Colón-González, F. J. et al. Limiting global-mean temperature increase to 1.5–2 °C could reduce the incidence and spatial spread of dengue fever in Latin America. Proc. Natl. Acad. Sci. U. S. A. 115, 6243–6248 (2019).ADS 
    Article 
    CAS 

    Google Scholar 
    46.Zheng, X., Zhong, D., He, Y. & Zhou, G. Seasonality modeling of the distribution of Aedes albopictus in China based on climatic and environmental suitability. Infect. Dis. Poverty 8, 1–9 (2019).CAS 
    Article 

    Google Scholar 
    47.Schaffner, F., Medlock, J. M. M. & Van Bortel, W. Public health significance of invasive mosquitoes in Europe. Clin. Microbiol. Infect. 19, 685–692 (2013).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    48.Rückert, C. & Ebel, G. D. How do virus-mosquito interactions lead to viral emergence?. Trends Parasitol. 34, 310–321 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    49.Sanna, M. & Hsieh, Y. H. Ascertaining the impact of public rapid transit system on spread of dengue in urban settings. Sci. Total Environ. 598, 1151–1159 (2017).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    50.Valerio, L. et al. Host-feeding patterns of Aedes albopictus (Diptera: Culicidae) in urban and rural contexts within Rome province, Italy. Vector-Borne Zoonotic Dis. 10, 291–294 (2010).PubMed 
    Article 

    Google Scholar 
    51.Wen, T. H., Lin, M. H. & Fang, C. T. Population movement and vector-borne disease transmission: differentiating spatial-temporal diffusion patterns of commuting and noncommuting dengue cases. Ann. Assoc. Am. Geogr. 102, 1026–1037 (2012).Article 

    Google Scholar 
    52.Rogers, D. J. Dengue: recent past and future threats. Philos. Trans. R. Soc. B Biol. Sci. 370, 1–18 (2015).Article 

    Google Scholar  More