Ecology

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These authors contributed equally: Laura H. Antão, Benjamin Weigel.These authors jointly supervised this work: Tomas Roslin, Anna-Liisa Laine.Research Centre for Ecological Change, Organismal and Evolutionary Biology Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, FinlandLaura H. Antão, Benjamin Weigel, Giovanni Strona, Maria Hällfors, Elina Kaarlejärvi, Otso Ovaskainen, Marjo Saastamoinen, Jarno Vanhatalo, Tomas Roslin & Anna-Liisa LaineDepartment of Biological Sciences, University of South Carolina, Columbia, SC, USATad DallasDepartment of Biology, Lund University, Lund, SwedenØystein H. OpedalFinnish Environment Institute (SYKE), Helsinki, FinlandJanne Heliölä, Mikko Kuussaari, Juha Pöyry & Kristiina VuorioNatural Resources Institute Finland (Luke), Helsinki, FinlandHeikki Henttonen, Otso Huitu, Andreas Lindén, Päivi Merilä, Maija Salemaa & Tiina TonteriSection of Ecology, Department of Biology, University of Turku, Turku, FinlandErkki KorpimäkiFinnish Museum of Natural History, University of Helsinki, Helsinki, FinlandAleksi LehikoinenKainuu Centre for Economic Development, Transport and the Environment, Kajaani, FinlandReima LeinonenUniversity of Helsinki, Helsinki, FinlandHannu PietiäinenDepartment of Biological and Environmental Science, University of Jyväskylä, Jyväskylä, FinlandOtso OvaskainenCentre for Biodiversity Dynamics, Department of Biology, Norwegian University of Science and Technology, Trondheim, NorwayOtso OvaskainenHelsinki Institute of Life Science, University of Helsinki, Helsinki, FinlandMarjo SaastamoinenDepartment of Mathematics and Statistics, Faculty of Science, University of Helsinki, Helsinki, FinlandJarno VanhataloSpatial Foodweb Ecology Group, Department of Agricultural Sciences, University of Helsinki, Helsinki, FinlandTomas RoslinSpatial Foodweb Ecology Group, Department of Ecology, Swedish University of Agricultural Sciences, Uppsala, SwedenTomas RoslinDepartment of Evolutionary Biology and Environmental Studies, University of Zürich, Zürich, SwitzerlandAnna-Liisa Laine More

The Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) last month released its annual report on elephant poaching. It reveals a downward trend across African range states, based on data from its Monitoring the Illegal Killing of Elephants programme. The decline correlates with reduced ivory trading over the period, particularly in the Chinese market.
Competing Interests
The author declares no competing interests. More

Bacterial density and growth potential in the rearing water were related to the microbial carrying capacityQuantifying the bacterial density in each tank verified that we obtained a higher bacterial load in the systems with added organic material. The bacterial density was, on average, 7.8× higher in the systems with high compared to low bacterial carrying capacity. This difference was particularly evident at 2 (34.8×, Kruskal–Wallis p = 0.0008) and 9 DPH (9.1×, Kruskal–Wallis p = 0.0007) (Fig. 1). The bacterial density increased throughout the experiment for the tanks with low microbial carrying capacity (treatment group MMS−, FTS−), reflecting increased larval feeding and defecation. Contrastingly, the bacterial density was relatively stable over time in the MMS+ treatment and even decreased over time in the FTS+ treatment. When averaging the densities at 11 and 15 DPH within each rearing treatment, we observed that the ‘MMS+ to FTS+’ had a considerable difference in the bacterial density between incoming and rearing water (24.2×). In contrast, this difference was below 8.2× in all other treatment tanks. Such differences in density indicated that some communities were below the microbial carrying capacity of the systems. We thus investigated the growth potential to determine if carrying capacity was reached in the rearing water.Figure 1Bacterial density (million bacterial cells mL−1) at various days post-hatching (DPH) in incoming and rearing tank water. Note that the y-axis is log scaled. Colours indicate the rearing treatment, and shape signifies rearing (filled circle) and incoming water (filled triangle).Full size imageThe bacterial net growth potential in the intake and rearing water was quantified as the number of cell doublings after incubation for 3 days11. Generally, the FTS− and MMS− rearing water had net growth potential with an average of 0.2 and 0.1, respectively (Supplementary Fig. 2). In contrast, the rearing water of the FTS+ and MMS+ had a negative net growth potential with averages of −0.2 and −0.06, respectively. In the case of negative net growth potential, the bacterial density decreased during the incubation. A negative net growth potential suggested that the rearing water bacterial communities were at the tank’s microbial carrying capacity at the time of sampling. Thus, the bacterial communities were at the carrying capacity of the high (+) carrying capacity systems and below in the low (−) systems. To gain a deeper understanding of the bacterial community characteristics the 16S rRNA gene of the bacterial community was sequenced at 1 and 9 DPH.Initial rearing condition did not leave a legacy effect on bacterial α-diversityThe bacterial α-diversity of the rearing water was investigated at 1 and 12 DPH (Fig. 2). At 1 DPH, the richness was comparable between the FTS−, FTS+ and MMS+ treatments, but on average, 1.5× higher for the MMS− treatment (307 vs 205 ASVs, Tukey’s test p  More

G.M., L.O.A., L.V.G. and L.E.O.C.A. thank the São Paulo Research Foundation (FAPESP) for funding (grants 2019/25701-8, 2020/08916-8, 2016/02018-2 and 2020/15230-5, respectively). L.O.A. and L.E.O.C.A. thank the National Council for Scientific and Technological Development (CNPq) for funding (grants 314473/2020-3 and 314416/2020-0, respectively). G.d.O. thanks the University of South Alabama Faculty Development Council Grant for funding (grant 279600-2022). More

Description of the study areaThe study covers urban, peri-urban and rural wetlands in the Bamenda Municipality of the North West Region of Cameroon that have evolved concomitantly with different stages of urbanization (Fig. 1). In this study, urbanisation is considered a loose term that is aimed at giving a geographical expression to the variation in the economic, social and cultural practices in the milieu. The central town with many economic activities is termed the urban, the fringe area with sprawls is termed peri-urban while the rural has typical peasant activities and make-shift structures. From the variation of human activities in the three sub-zones, a variety of chemical substances are discharged into drains, playing a substantial role in soil quality, and therefore plant macrophyte diversity. The Plants studied were ubiquitous in the area and verification of their IUCN conservation status in the red data book of plants of Cameroon confirmed their abundance14. Information on protected sites in Cameroon does not place the study area under conservation status. In line with that, permits are not required to undertake academic and research studies as well as do a responsible collection of plants in the study area. The urbanization rate of Bamenda is 42%, and the population grew from 48,111 inhabitants in 1976 to 488,883 inhabitants in 201015, with 150–200 inhabitants/km2.Figure 1Relief Map of Bamenda showing the Bamenda escarpment, topography and the location for quadrat sites.Full size imageThe study area is part of the Bamenda escarpment that is located between latitudes 5° 55″ N and 6° 30″ N and longitudes 10° 25″ E and 10° 67″ E. The town shows an altitudinal range of 1200–1700 m and is divided into two parts by escarpments—a low-lying and gently undulating part with altitudes ranging from 1200 to 1400 m, with many flat areas that are usually inundated for most parts of the year, and an elevated part that range from 1400 to 1700 m altitude. Most of the streams take their rise from this elevated part (Fig. 1).This area experiences two seasons—a rainy season (mid-March to mid-October) and a short dry season (mid-October to mid-March). The thermic and hyperthermic temperature regimes dominate in the area. The mean annual temperature stands at 19.9 °C. January and February are the hottest months with mean monthly temperatures of 29.1 and 29.7 °C, respectively. This area is dominated by the Ustic and Udic moisture regimes with the Udic extending to the south9. Annual rainfall ranges from 1300 to 3000 mm16. The area has a rich hydrographical network with intense human activities and a dense population along different water courses in the watershed. The area is bounded on the West, North and East by the Cameroon Volcanic Line (made up of basalts, trachytes, rhyolites and numerous salt springs). The geologic history of this area originates from the Precambrian era when there was a vast formation of geosynclinal complexes, which became filled with clay-calcareous, and sandstone sediments9. These materials, crossed by intrusions of crystalline rocks, were folded in a generally NE-SW direction and underwent variable metamorphism9. The Rocks in the area are thus of igneous (granitic and volcanic) and metamorphic (migmatites) origin17, which gives rise to ferralitic soils18.Agriculture is the principal human activity in and around this region18. The area equally harbours the commercial center that has factories ranging from soap production, and mechanic workshops to metallurgy, which may be potential sources of pollutants that can influence wetland Geochemistry. Raffia farinifera bush, which is largely limited to the wetlands, is an important vegetation type in this area. R. farinifera provides raffia wine, a vital economic resource to the inhabitants who are fighting against the cultivation of these wetlands by vegetable farmers.Methods of the studyMacrophyte diversity studyThe plant diversity of the wetlands was evaluated using quadrats in the dry season for accessibility reasons. For each of the three wetlands (the urban, peri-urban and rural areas), three transects were established on which representative quadrats, each measuring 10 m × 10 m, were mapped out in uncultivated areas for the determination of plant species cover-abundance and diversity. It is perceived that the different zones receive different mixtures of chemical substances and thus influence macrophyte diversity differently.According to a publication by14 on the vascular plants of Cameroon and a taxonomic checklist with IUCN assessment, the plants of the area are placed under the Least Concern Category(LC), and therefore not in the risky category. Diversity studies involved the identification of a specific area called “relevé” by progressively increasing test quadrat size and sampling for specific diversity until the smallest area with adequate species representation was reached. The relevé size determined here was 1 m2, making a total of 300 sub-quadrats (relevé) in the entire study ie. 100 in each main quadrat). For each site (main quadrat), 10 representative relevés were sampled and all plant species were enumerated. Most plant species in each of them were identified in the field by the Botanist, Dr Ndam Lawrence Monah using visual observation of the morphology of the leaves and flowers, a self-made field guide, the Flora of West Africa and the Flora of Cameroon. 10 unidentified plants were appropriately collected where there were in abundance, placed onto a conventional plant press and dried in the field. Voucher specimens were tagged and transported to the Limbe Botanic Gardens (SCA: Southern Cameroon, the code of the Limbe Botanic Gardens Herbarium) for identification. Mr Elias Ndive, the Taxonomist of the Limbe Botanic Gardens compared unidentified specimens with authentic herbarium specimens and other taxonomic guides and finally identified them. Voucher specimens of the 10 plants were given identification numbers and deposited in the Herbarium of the Limbe Botanic Gardens.The Braun–Banquet method was used19 for the assessment of species cover abundance. Relative abundance and abundance ratings were determined using the Braun–Banquet rating scheme (Table 1).Table 1 Braun-Blanquet rating scheme for vegetation cover-abundance, Source19.Full size tableSimpson’s diversity indexSpecies richness was evaluated using Simpson’s diversity index (D), which takes into account both species richness and the Braun-Blanquet rating scheme for vegetation cover abundance and evenness of abundance among the species present. In essence, D measures the probability that two individuals that are randomly selected from an area will belong to the same species. The formula for calculating D is presented as:$${text{D}} = frac{{sum {{text{n}}_{i} left( {{text{n}}_{i} – 1} right)} }}{{{text{N}}({text{N}} – 1)}}$$where ni = the total number of each species; N = the total number of individuals of all species.The value of D ranges from 0 to 1. With this index, 0 represents infinite diversity and 1 represents no diversity. That is, the larger the value the lower the diversity.Alternatively, Simpson’s Diversity Index, = 1–D,1-D was used as a measure of diversity because it is more logical and less likely to cause confusion. The scale then gives a score from 0 to 1 with higher scores showing higher diversity (instead of being associated with low scores).The Simpson index is a dominance index because it gives more weight to common or dominant species. In this case, a few rare species with only a few representatives will not affect the diversity.
Soil sampling and analysisSoil sampling was done in and around the three quadrats laid in the urban, peri-urban and rural wetlands for macrophytes sampling. Twenty-one (21) composite samples (0–25 cm) were randomly collected (Fig. 2) and taken to the laboratory in black plastic bags. Each composite sample was a collection of 5 dried core soil samples. Due to the observed greater heterogeneity in the urban sector, the sampling density was intensified. The soil samples were air-dried and screened through a 2-mm sieve. They were analyzed in duplicate for their physicochemical properties in the Environmental and Analytical Chemistry Laboratory of the University of Dschang, Cameroon. Particle size distribution, cation exchange capacity (CEC), exchangeable bases, electrical conductivity (EC) and pH were determined by standard procedures20. Soil pH was measured both in water and KCl (1:2.5 soil/water mixture) using a glass electrode pH meter. Part of the soil was ball-milled for organic carbon (Walkley–Black method) and total nitrogen (Macro-Kjeldahl method) as largely described by20. Available phosphorus (P) was determined by Bray I method. Exchangeable cations were extracted using 1 N ammonium acetate at pH 7. Potassium (K) and sodium (Na) in the extract were determined using a flame photometer and magnesium (Mg) and calcium (Ca) were determined by complexiometric titration. Exchange acidity was extracted with 1 M KCl followed by quantification of Al and H by titration20. Effective cation exchange capacity (ECEC) was determined as the sum of bases and exchanged acidity.Figure 2Adapted from the 1980 land use map of the Bamenda City Area showing soil sampling points: Source Bamenda City Council.Map of the study area in freshwater wetlands of Bamenda Municipality.Full size imageApparent CEC (CEC at pH 7) was determined directly as outlined by20. Based on critical values of nutrients established for vegetables, nutrients were declared sufficient or deficient.
Statistical analysisThe data were subjected to statistical analysis using Microsoft Excel 2007 and SPSS statistical package 20.0. Soil properties were assessed for their variability using the coefficient of variation (CV) and compared with variability classes (Table 2).$$CV=frac{Sd}{X}X 100$$where: Sd = standard deviation; = X arithmetic mean of soil properties.Table 2 Grouping coefficient of variation into variability classes.Full size tableThe hierarchical cluster analysis (HCA) was used to group the area under managing units. The main goal of the hierarchical agglomerative cluster analysis is to spontaneously classify the data into groups of similarity (clusters). This is done by searching objects in the n-dimensional space that is located in the closest neighborhood and separating a stable cluster from other clusters. The sampling sites were considered objects for classification. Each object was determined by a set of variables (chemical concentrations of the soils in this case). More

ResultsSoil physiochemical propertiesThe preliminary status of the soil analyzed before the commencement of the field preparatory activities revealed that the soil was subtlety fertile with regard to the physical and chemical properties (Table 1).Table 1 Physicochemical properties of the soil.Full size tableAssessment of disease incidence at sowing dates during each year in the trial studyIn the trial study, very low and statistically significant (p  More

Study population and study designThis is a prospective interventional cohort study of healthy S. aureus carriers and noncarriers in the Netherlands. All experiments were performed in accordance with the Dutch Medical Research Involving Human Subjects Act (WMO). The study protocol was approved by the local Medical Ethical Committee of the Erasmus University Medical Centre Rotterdam, The Netherlands (MEC-2018-091). Written informed consent was obtained for all participants. Participants were recruited through advertisements at Dutch universities and the research teams social networks. Exclusion criteria were age  8 CFU/mL for each culture. Noncarriers were defined as 2 S. aureus-negative cultures. Intermittent S. aureus carriers were excluded from further participation in the study. Eligible volunteers were enrolled on a first-come, first-served basis.Eligible participants were asked to fill out a questionnaire regarding risk factors for S. aureus acquisition. All participants received decolonization treatment. Decolonization consisted of mupirocin nasal ointment (2%, GlaxoSmithKline BV, Zeist, the Netherlands) twice daily and chlorhexidine gluconate cutaneous solution (4%w/v, Regent Medical Overseas Limited, Oldham, UK) once daily, both for 5 days.Nasal samples were taken 1 day before decolonization (D0) and 2 days (D7), 1 month (M1), 3 months (M3) and 6 months (M6) after decolonization. All participants received a personal demonstration for nasal sampling by the executive researcher. Thereafter, all specimens were taken by the participants by inserting a swab (ESwab, 490CE.A, Copan Italia, Brescia, Italy) into one nostril and rotating 5 times, repeating this in the second nostril using the same swab. Swabs were collected in a container filled with 1 ml modified Liquid Amies, a collection and transport solution, and sent through regular mail service (non-temperature controlled) or deposited at the laboratory personally.
Staphylococcus aureus quantitative cultureQuantitative S. aureus cultures were conducted to examine the dynamics of S. aureus carriage over the 6-month follow-up period after decolonization. Swab containers were vortexed for 20 s before plating. Serial dilutions of Amies medium were plated onto phenol mannitol salt agar (PHMA) and incubated for 2 days at 37 °C. Swabs were placed in phenol mannitol salt broth (PHMB) and incubated for 7 days at 37 °C for enrichment. S. aureus growth was confirmed by a latex agglutination test (Staph Plus Latex Kit, Diamondial, Vienna, Austria). Morphologically different S. aureus colonies were selected for spa typing and methicillin resistance screening using BBL CHROMagar MRSA II agar (BD, Breda, The Netherlands).
Spa typingMolecular typing of S. aureus isolates was performed to infer whether recolonization with S. aureus in decolonized carriers involved the same spa-type. Typing was limited to the last S. aureus positive culture moment and the last S. aureus positive culture moment after decolonization in recolonised carriers. S. aureus DNA lysates were prepared by boiling in 10 mM Tris–HCl, 1 mM disodium EDTA, pH 8.0 or extraction with the QIAamp DNA Mini Kit (QIAGEN, Venlo, The Netherlands) according to the manufacturer’s instructions. Amplification of the S. aureus protein A (spa) repeat region was performed by PCR with 2 sets of primers. One set consisted of forward primer spa-1113, 5′-TAAAGACGATCCTTCGGTGAGC-3′ and reverse primer spa-1514, 5′-CAGCAGTAGTGCCGTTTGCTT-3′24. The other set consisted of forward primers spa-F1, 5′-AACAACGTAACGGCTTCATCC-3′ and spa-F2 5′-AGACGATCCTTCAGTGAGC-3′ and reverse primer spa-R1 5′-GCTTTTGCAATGTCATTTACTG-3′. Amplicons were purified with ExoSAP-IT (Applied Biosystems) according to the manufacturer’s instructions and sent for sequence analysis (Baseclear, Leiden, The Netherlands). Resulting sequences were analysed using BioNumerics v7.6 (Applied Maths NV, Sint-Martens-Latem, Belgium) and the spa types were assigned by use of the RidomStaphType database (Ridom GmbH, Würzburg, Germany).16S ribosomal RNA sequencing of nasal microbiotaThe impact of decolonization on the nasal microbiome and the recovery of the microbiome structure after decolonization were examined by means of 16S rRNA metabarcoding. Amies medium from each nasal swab container was stored at − 80 °C on the day of receipt at the study laboratory in Rotterdam, NL, then sent at − 80 °C to the microbiome analysis laboratory in Lyon, FR. To properly capture the impact of decolonization on the living microbiota, metabarcoding used RNA-based 16S ribosomal RNA (rRNA, which is preserved in living cells but quickly cleared after cell death or lysis) rather than the DNA coding sequence, as DNA can persist for prolonged time periods after cell death25,26,27,28. RNA was extracted using the Mag Bind® Total RNA 96 Kit (Omega Bio-tek) tissue protocol from 150 µL of samples’ material. Cell lysis was performed using beads (Disruptor plate C plus—Omega Bio-tek) and proteinase K for 15 min at 2600 rpm, followed by 10 min at room temperature without agitation, and finished with a DNase I digestion of 20 min at room temperature. RNA was quantified using QuantiFluor RNA kit on Tecan Safire (TECAN). 10 ng total RNA was used for reverse transcription using FIREScript RT cDNA synthesis kit (Solis Biodyne) with random primers, then cDNA was purified with SPRIselect reagent (Beckman coulter) and quantified.The rRNA V1–V3 region was PCR amplified using the 5× HOT BIOAmp® BlendMaster Mix 12,5 mM MgCl 2 (Biofidal), 10× GC rich Enhancer (Biofidal) and BSA 20 mg/mL. The PCR reaction consisted of 30 cycles at 56 °C using the forward primer 27F, 5′-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG AGAGTTTGATCCTGGCTCAG-3′ and reverse primer 534R, 5′-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGATTACCGCGGCTGCTGG-3′ in 25 µL of solution. PCR products were purified using SPRIselect beads (Beckman Coulter) in 20 µL nuclease-free water and quantified using QuantiFluor dsDNA (Promega). Samples were indexed with lllumina’s barcodes with the same PCR reagents during a 12 cycles PCR, then purified and quantified as previously mentioned. Samples were normalized and pooled, then sequenced using Illumina MiSeq V3 Flow Cell following the constructor’s recommendations for a 2 × 300 bp paired-end application. A mean of 130 k proofread reads per sample was obtained.Experiment buffers were used as negative controls to detect contamination by out-of-sample bacterial RNA. RNA extraction was controlled using an in-house mix of live Staphylococcus aureus ATCC29213 and Escherichia coli ATCC25922 in equal proportions, allowing for assessing extraction bias in Gram-positive and -negative bacteria. PCR amplification bias was controlled using a commercial DNA mix of 8 bacterial species (ZymoBIOMICS™ Microbial Community DNA Standard).Bioinformatics and statistical analysesSequencing reads were quality checked and trimmed. Paired-ended read pairs were merged using BBMap version 38.49 (available at https://sourceforge.net/projects/bbmap/), with default options besides a minimum single size of 150 bp with an average Phred quality score higher than 10, and a total pair size of minimum 400 bp. PCR adapters were removed with cutadapt v.2.1 (Martin 2011) then dereplicated using vsearch v.2.12.029 with the sizeout option. For species assignment, reads were aligned to sequences of NCBI blast 16S_ribosomal_RNA database (version date 03.12.2020) using Blastn v.2.11.0+30,31, keeping a maximum of 20 reference targets. Read counts per bacterial species were normalized to account for taxon-specific variations of the copy number of 16S rRNA genes using NCBI rrnDB-5.5 database based on the mean gene copy number in the taxon.To optimize the resolution of sequencing read taxonomic assignment, we used in-house bioinformatic software publicly available at https://github.com/rasigadelab/taxonresolve. Briefly, when a read matches sequences from several species with identical alignment scores, taxonomic assignment pipelines typically output the higher taxonomic level such as the genus (e.g., Staphylococcus spp. when a read matches S. aureus and S. epidermidis). This loss of information can be problematic when species-level discrimination is important. To prevent losing species-level information, the taxonresolve software assigns reads with uncertain species to groups of species rather than to genera.Bacterial species deemed present from contaminating sources such as kits reagents and found in negative controls, mostly from the Bacillus genera, were removed. A total of 1376 species or group of species were retained. The rarefaction curves corresponding to the sequencing effort to assess the species richness within samples are shown in Supplementary Fig. 3. Most samples reached a plateau after 40,000 sequences.Given the small sample size compared to the number of variables and species considered in this study, no hypothesis testing was performed, and we provide a descriptive assessment of the results. In figures, 95% confidence intervals of the means were computed based on normal approximation, after log transformation for CFU/mL and log odds transformation for quantities restricted to the [0, 1] interval, such as proportions.In microbial diversity analyses, we retained the 9 most prevalent bacterial species and pooled the other species into an ‘Others’ category. To assess the disruption and possible recovery of the microbiota, the divergence of sampled microbiota relative to the initial, pre-treatment microbiota (D0) was assessed using the Bray–Curtis dissimilarity at each sampling time point relative to the first sample of the same patient.Software code of the analyses are available at https://github.com/rasigadelab/macotra-metabarcoding. Data are available at https://zenodo.org/record/6382657. Analyses and figures used R software v3.6.032 with packages dplyr33, ggplot234, vegan35, and MicrobiomAnalyst available at https://www.microbiomeanalyst.ca36,37. More