Philippa Kaur

Study area
Mount Cameroon (South-Western Province, Cameroon) is the highest mountain in West/Central Africa. This active volcano rises from the Gulf of Guinea seashore up to 4095 m asl. Its southwestern slope represents the only complete altitudinal gradient of primary forests from lowland up to the timberline (~ 2200 m asl.) in the Afrotropics. Belonging to the biodiversity hotspot, Mount Cameroon harbour numerous endemics45,46,47. With  > 12,000 mm of yearly precipitation, foothills of Mount Cameroon belong among the globally wettest places42. Most precipitation occur during the wet season (June–September;  > 2000 mm monthly), whilst the dry season (late December–February) usually lacks any strong rains42. Since 2009, most of its forests have become protected by the Mount Cameroon National Park.
Volcanism is the strongest natural disturbance on Mount Cameroon with the frequency of eruptions every ten to thirty years. Remarkably, on the studied southwestern slope, two eruptions in 1982 and 1999 created a continuous strip of bare lava rocks (in this study referred as ‘the lava flow’) interrupting the forests on the southwestern slope from above the timberline down to the seashore (Fig. 1a).
A small population of forest elephants (Loxodonta cyclotis) strongly affects forests above ca. 800 m asl. on the southwestern slope28,45. It is highly isolated from the nearest populations of the Korup NP and the Banyang-Mbo Wildlife Sanctuary, as well as from much larger metapopulations in the Congo Basin48. It has been estimated to ~ 130 individuals with a patchy local distribution28. On the southwestern slope, they concentrate around three crater lakes representing the only available water sources during the high dry season, although their local elevational range covers the gradient from lowlands to montane grasslands just above the timberline28. They rarely (if ever) cross the old lava flows, representing natural obstacles dividing forests of the southwestern slope to two blocks with different dynamics. As a result, forests on the western side of the longest lava flow have an open structure, with numerous extensive clearings and ‘elephant pastures’, whereas eastern forests are characteristic by undisturbed dense canopy (Fig. 1). To our knowledge, the two forest blocks are not influenced by any extensive human activities, nor differ in any significant environmental conditions28,45. Hereafter, we refer the forests west and east from the lava flow as disturbed and undisturbed, respectively. Effects of forest elephant disturbances on communities of trees and insects were investigated at four localities, two in an upland forest (1100 m asl.), and two in a montane forest (1850 m asl.).
Tree diversity and forest structure
At each of four sampling sites, eight circular plots (20 m radius, ~ 150 m from each other) were established in high canopy forests (although sparse in the undisturbed sites), any larger clearings were avoided. In the disturbed forest sites, the plots were previously used for a study of elevational diversity patterns40,42. In the undisturbed forest sites, plots were established specifically for this study.
To assess the tree diversity in both disturbed and undisturbed forest plots, all living and dead trees with diameter at breast height (DBH, 1.3 m) ≥ 10 cm were identified to (morpho)species (see40 for details). To study impact of elephant disturbances on forest structure, each plot was characterized by twelve descriptors. Besides tree species richness, living and dead trees with DBH ≥ 10 cm were counted. Consequently, DBH and basal area of each tree were measured and averaged per plot (mean DBH and mean basal area). Height of each tree was estimated and averaged per plot (mean height), together with the tallest tree height (maximum height) per plot. From these measurements, two additional indices were computed for each tree: stem slenderness index (SSI) was calculated as a ratio between tree height and DBH, and tree volume was estimated from the tree height and basal area49. Both measurements were then averaged per plot (mean SSI and mean tree volume). Finally, following Grote50, proxies of shrub, lower canopy, and higher canopy coverages per plot were estimated by summing the DBH of three tree height categories: 0–8 m (shrubs), 8–16 m (lower canopy), > 16 m (higher canopy).
Insect sampling
Butterflies and moths (Lepidoptera) were selected as the focal insect groups because they belong into one of the species richest insect orders, with relatively well-known ecology and taxonomy, and with well-standardized quantitative sampling methods. Moreover, they strongly differ in their habitat use29. In conclusion, butterflies51 and moths52 are often used as efficient bioindicators of changes in tropical forest ecosystems, especially useful if both groups are combined in a single study. Within each sampling plot, fruit-feeding lepidopterans were sampled by five bait traps (four in understory and one in canopy per sampling, i.e. 40 traps per sampling site, and 160 traps in total) baited by fermented bananas (see Maicher et al.42 for details). All fruit-feeding butterflies and moths (hereinafter referred as butterflies and fruit-feeding moths) were killed (this is necessary to avoid repetitive counting of the same individuals53) daily for ten consecutive days and identified to (morpho)species.
Additionally, moths were attracted by light at three ‘mothing plots’ per sampling site, established out of the sampling plots described above. These plots were selected to characterize the local heterogeneity of forest habitats and separated by a few hundred meters from each other. To keep the necessary standardisation, all mothing plots at both types of forest were established in semi-open patches, avoiding both dense forest and larger openings. Moths were attracted by a single light (see Maicher et al.42 for details) during each of six complete nights per elevation (i.e., two nights per plot). Six target moth groups (Lymantriinae, Notodontidae, Lasiocampidae, Sphingidae, Saturniidae, and Eupterotidae; hereafter referred as light-attracted moths) were collected manually, killed, and later identified into (morpho)species. The three lepidopteran datasets (butterflies, and fruit-feeding and light-attracted moths) were extracted from Maicher et al.42 for the disturbed forest plots, whilst the described sampling was performed in the undisturbed forest plots specifically for this study. Voucher specimens were deposited in the Nature Education Centre, Jagiellonian University, Kraków, Poland.
To partially cover the seasonality54, the insect sampling was repeated during transition from wet to dry season (November/December), and transition from dry to wet season (April/May) in all disturbed and undisturbed forest plots.
Diversity analyses
To check sampling completeness of all focal groups, the sampling coverages were computed to evaluate our data quality using the iNEXT package55 in R 3.5.156. For all focal groups in all seasons and at all elevations, the sampling coverages were always ≥ 0.84 (mostly even ≥ 0.90), indicating a sufficient coverage of the sampled communities (Supplementary Table S1). Therefore, observed species richness was used in all analyses57.
Effects of disturbance on species richness were analysed separately for each focal group by Generalized Estimated Equations (GEE) using the geepack package58. For trees, species richness from individual plots were used as a ‘sample’ with an independent covariance structure, with disturbance, elevation, and their interaction treated as explanatory variables. For lepidopterans, because of the temporal pseudo-replicative sampling design, species richness from a sampling day (butterflies and fruit-feeding moths) or night (light-attracted moths) at individual plot was used as a ‘sample’ with the first-order autoregressive relationship AR(1) covariance structure (i.e. repeated measurements design). Disturbance, season, elevation, disturbance × season, and disturbance × elevation were treated as explanatory variables. All models were conducted with Poisson distribution and log-link function. Pairwise post-hoc comparisons of the estimated marginal means were compared by Wald χ2 tests. Additionally, species richness of individual families of trees, butterflies, and light-attracted moths were analysed by Redundancy Analyses (RDA), a multivariate analogue of regression, based on the length of gradients in the data59. All families with  > 5 species were included in three RDA models, separately for the studied groups (the subfamily name Lymantriinae is used, because they are the only group of the hyperdiverse Erebidae family of the light-attracted moths). Fruit-feeding moth families were not analyzed because 83% of their specimens belonged to Erebidae and all other families were therefore minor in the sampled data. Species richness of individual families per plot were used as response variables, whilst interaction of disturbance and elevation were applied as factorial explanatory variable (for butterflies and light-attracted moths, the temporal variation was treated by adding season as a covariate).
Differences in composition of communities between the disturbed and undisturbed forests were analysed by multivariate ordination methods59, separately for each focal group. Firstly, the main patterns in species composition of individual plots were visualized by Non-Metric Multidimensional Scaling (NMDS) in Primer-E v660. NMDSs were generated using Bray–Curtis similarity, computed from square-root transformed species abundances per plot. Subsequently, influence of disturbance on community composition of each focal group was tested by constrained partial Canonical Correspondence Analyses (CCA) with log‐transformed species’ abundances as response variables and elevation as covariate59. Significance of all partial CCAs were tested by Monte Carlo permutation tests with 9999 permutations.
Finally, differences in the forest structure descriptors between the disturbed and undisturbed forests were analysed by partial Redundancy Analysis (RDA). Prior to the analysis, preliminary checking of the multicollinearity table among the structure descriptors was investigated. Only forest structure descriptors with pairwise collinearity More

Experimental design
This study was conducted at Gaoqiao Scientific Research Base of the Hunan Academy of Agricultural Sciences in the city of Changsha (112°58′42ʺ E, 28°11′49ʺ N), Hunan Province in China in 2018 and 2019. The soil was sandy loam. The trial crop was watermelon cultivars zaojia 8424, which was provided by Xinjiang Farmer Seed Technology Co., Ltd. China. The dazomet was provided by Beijing Sino Green Agri-Biotech Co., Ltd. Six greenhouses (30 m × 6 m) with the same background, which were cultivated watermelon under monocropping system for five years, were selected. Three of them were treated with dazomet as three replicates, others were as control group. The routine cultivation managements in all the greenhouses were the same. Every March before transplanting the watermelon seedlings, 6 kg (98% C5H18N2S2) of dazomet were applied to one greenhouse, which was then tilled the soil by a rotary immediately after spraying. Controlling the depth of tillage soil 0–20 cm to ensure that dazomet was evenly mixed into the tillage layer. As soon as the soil temperature is above 8 °C, film mulching was set up to maintain the fumes of dazomet into the soil to kill most of the soil organisms, as well as to maintain the soil moisture content at approximately 40% for the germination and growth of weeds and pathogens. After 20 days, the film was uncovered and the greenhouse was kept ventilated. Then 15 days later, the watermelon seedlings nutrition bowl was cultivated and transplanted into the greenhouse. We planted the watermelon in the greenhouse with 50–60 cm plant spacing to enable pruning the climbing vines.
We designed six different sampling times as following: 1 (March 6th, 2018, before dazomet treatment), 2 (April 24th, 2018, watermelon seedling stage), 3 (May 3rd, 2018, Fusarium wilt symptom appearance), 4 (March 6th, 2019, before dazomet treatment), 5 (April 22th, 2019, watermelon seedling stage), 6 (April 29th, 2019, Fusarium wilt symptom appearance). For each replicate, nine independent soil samples within depth of 0–20 cm in the shape of “S” from each greenhouse were pooled. Three greenhouses within same treatment regarded as three independent replicates. DAZ represents dazomet treatment group and CK represents the control group without dazomet application but using same conventional planting system. All the soil samples from greenhouses were packed into sealed sterile bags separately and brought back to the laboratory. After removing the plant roots and stones from the samples, we sieved them with a 20-μm mesh, and then divided each sample into three parts. Two of them were placed in sterile centrifuge tubes, stored at − 80 °C for sequencing analysis and Q-PCR test. While the other was used for measuring the soil properties, stored at room temperature. We have collected total of 36 samples in six different sampling times.
Field disease investigation
The incidence of Fusarium wilt was calculated during the whole watermelon onset period (Started from plants with rotted, discolored root and the vascular bundle became brown until the whole plant died). The disease incidence (%) = (number of infected plants/total number of surveys) × 100%.
Determination of soil physical and chemical properties
The soil characteristics are listed in Supplementary Table S1. Soil pH was determined in a soil: water ratio of 1:2.5 (wt./vol) using a pH meter (BPH-220, Bell Instrument Equipment Co. Ltd., Dalian, LN, China). To extract the water-soluble salts from the soil, samples of 1 mm sieved and air-dried soil weighing 20.00 g were placed in a 250 ml Erlenmeyer flask, 100 ml of distilled water was added (water: soil ratio of 5:1). Then put it into a dry triangular bottle after shaking for 5 min which was used for the determination of salt. A total of 30 ml of the soil leachate was placed in 50 ml of burnout solution. The solution temperature was measured, and then the conductivity of the solution was determined using a conductometer. The soil organic matter (SOM) was determined by oxidation with potassium dichromate by DF-101S heat collecting constant temperature magnetic stirrer (Gongyi yuhua instrument Co., Ltd, Gongyi, HN, China). Total P and K and available P and K concentrations in the soil were determined by ICP-AES (PerkinElmer 2100DV, PerkinElmer, Waltham, MA, USA) after the soils were digested using concentrated HNO3-HF-HClO4. Total nitrogen (N) and available nitrogen (AN) in the soil were determined by the Kjeldahl method and the alkali diffusion method, respectively (China Agricultural Technology Extension Service Center, 2014).
Soil microbial diversity analysis
Total genomic DNA was extracted from the soil samples using the E.Z.N.A Soil DNA kit (Omega Bio-tech, Norcross, GA, USA) according to manufacturer’s protocols. The final DNA concentration and purity were determined using a Nanodrop 2000 UV–Vis spectrophotometer (Thermo Scientific, Wilmington, DE, USA), and the DNA quality was checked by 1% agarose gel electrophoresis. Distinct regions of the 16S rRNA gene (V3-V4) and ITS1 were amplified by PCR (ABI Geneamp 9700, Applied Biosystems, Inc., Carlsbad, CA, USA) using specific primers (16S: 338F (5′-ACTCCTACGGGAGGCAGCAG-3′), 806R (5′-GGACTACHVGGGTWTCTAAT-3′); ITS1F (5′-CTTGGTCATTTAGAGGAAGTAA-3′), ITS2R (5′-GCTGCGTTCTTCATCGATGC-3′)), separately. The PCRs were conducted using the following programme: 3 min of denaturation at 95 °C, 27 cycles of 30 s at 95 °C for ITS1 rRNA gene/35 cycles of 30 s at 95 °C for 16S rRNA gene, 30 s of annealing at 55 °C, and 45 s of elongation at 72 °C with a final extension at 72 °C for 10 min, 10 °C ∞. PCR products were extracted from a 2% agarose gel and further purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA), followed by quantification using the QuantiFluor-ST kit (Promega, Madison, MI, USA) according to the manufacturer’s protocol.
Purified amplicons were pooled in equimolar amounts and sequenced (paired-end; 2 × 300 bp) on an Illumina MiSeq platform (Illumina, San Diego, CA, USA) according to the standard protocols of the Majorbio Bio-Pharm Technology Co. Ltd. (Shanghai, China). The raw reads were deposited into the NCBI Sequence Read Archive (SRA) database (Accession Number: SRP268536).
Quantitative detection of FON by real-time PCR
Distinct regions of the FON rRNA genes were amplified by PCR (Bio-Rad T100 Thermal Cycler, Bio-Rad Laboratories, Inc. Hercules, CA, USA) using specific primers (Fonq-F(5′- GTTGCTTACGGTTCTAACTGTGC -3′), Fonp1-R(5′- CTGGTACGGAATGGCCGATCAG -3′)) . Then the PCR products were used as templates to construct the standard curve of the fluorescence quantitative PCR (Bio-Rad iQ5 Optical Module, Bio-Rad Laboratories, Inc. Hercules, CA, USA) using primers (Fonq-F(5′- GTTGCTTACGGTTCTAACTGTGC -3′), Fonq-R(5′- GGTACTTGGAAGGAATTGTGGG -3′)). A 1446 bp DNA fragments containing the qPCR target sequence was amplified from soil DNA by conventional PCR (initial incubation at 94 °C for 4 min, followed by 18 cycles of 94 °C 40 s, 60 °C 40 s, 72 °C 70 s, and a final extension at 72 °C for 10 min). The PCR products were used as templates to construct the standard curve of the fluorescence quantitative PCR (reaction consisted of an initial incubation at 95 °C for 1 min, followed by 40 cycles of 95 °C 15 s, 60 °C 30 s, 72 °C 30 s). The fluorescence intensity was monitored every 0.5 °C between 65 °C-95°C to making standard melting curve13.
Data analysis
Raw FASTQ files were demultiplexed, quality-filtered by Trimmomatic and merged by FLASH with the following criteria: (i) The reads were truncated at any site receiving an average quality score  0.95. One-way ANOVA test was used to analyze significant differences of two groups. Differences between two groups were analyzed by student’s t test. Correlation heatmap analysis of the correlation coefficient between environmental factors and selected species was determined by MeV (Multi Experiment Viewer) software (http://mev.tm4.org).
Other statistical analysis was performed using SPSS version 20.0 (SPSS Inc., Chicago, IL, USA). The figures of the microbial diversity indices and relative abundance of functional profiles were prepared using Microsoft Office 2010 (Microsoft Corporation, Redmond, WA, USA)and Adobe Illustrator CS5 (Adobe Systems Incorporated, San Jose, CA, USA) (https://www.adobe.com/cn/products/illustrator.html). More

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Study species
The Egyptian goose is native to Africa and was introduced to Europe in the twentieth century27. Its native population is distributed on the sub-Saharan African continent and is one of the most common and wide spread African waterfowl species. Egg laying occurs throughout the whole year with a peak between late winter and early summer28.
The neozootic population invades Europe eastwards starting from the Netherlands, where they were introduced as ornamental species to parks27. It is now one of the most spreading neozootic bird species in Europe24. From the 1980s Egyptian geese also invade Germany where its population size increased rapidly29,30. The Egyptian goose is a resident (non-migratory / short distance migratory), monogamous, territorial bird species occurring as neozootic species in a variety of water habitats (e.g. streams, rivers, ponds, lakes,) in Europe31.
Sampling
Parasite prevalence and immunity of Egyptian geese from a native population in Namibia were investigated and compared to those of a currently spreading invasive population of the same species in Germany. In both regions, geese were sampled during ringing procedures (live trapped) or dissected after general pest control hunting (necropsy). Blood samples for immunological assays and serology exclusively originate from live trapped individuals whereas macro-parasite investigation was performed during necropsy. Micro-parasite investigation was performed in birds from both groups. Therefore, the resulting datasets are analysed separately (see method section: Statistical analysis) but a potential interplay between the different parasite prevalences and immune results is evaluated in the discussion.
Live trapping
Twenty-one Egyptian geese (9 male, 12 female) were live trapped in Namibia (22.35° S, 17.05° E) (native range) in February 2016. Additionally, data from a subset of 110 adult Egyptian geese from Germany (65 male, 45 female) investigated by Prüter et al.32 were included. German geese were sampled in the Rhine and Mosel areas (50.4° N, 7.6° E) (invasive range) in 2015 (n = 78) and 2016 (n = 32) in different months (supplementary data Table S1). Sex and reproductive status were recorded. Reproductive status was defined as breeding (e.g. guiding gosling, showing territorial behavior with a partner, having an egg-laying active cloaca) or non-breeding (e.g. not fulfilling criteria of breeding and/or being part of a non-family-flock). Numbers of breeding vs. non-breeding individuals can be found in Table S1. All Namibian birds were likely non-breeding individuals, which were sampled at a cattle feedlot were thousands of birds fly in to feed on the corn provided to the cattle. Blood was drawn from the vena metatarsalia plantaris superficialis using needles with a diameter of 0.06 mm for males and 0.04 mm for females. A fresh blood smear was prepared at capture and air dried. Blood samples were kept at 4–8 °C, centrifuged and sera were frozen in liquid nitrogen within eight hours after blood draw. Pharyngeal swabs were collected using sterile cotton swabs. Once field work finished, samples were transported to the Leibniz Institute for Zoo and Wildlife Research, Berlin, Germany and sera, blood clot and pharyngeal swabs were kept frozen at – 80 °C. Sampling in Germany was authorized by the Landesuntersuchungsamt Rheinland-Pfalz (G 15-20-005) and Landesamt für Natur, Umwelt und Verbraucherschutz Nordrhein-Westfalen (LANUV) (84-08.04.2015.A266). Permission to collect samples in Namibia was granted to GM and HK by the Ministry of Environment and Tourism (MET). Permission to export sample material from Namibia was granted by a MET export permit (No. 107513), and samples were transported to Germany in compliance with the Nagoya Protocol on Access and Benefit-sharing. All experimental procedures described in the materials and methods section were approved by the Internal Committee for Ethics and Animal Welfare of the Leibniz Institute for Zoo and Wildlife Research (permit #2014-11-03). All experiments were carried out in accordance with the approved guidelines of the Leibniz Institute for Zoo and Wildlife Research.
Necropsy
Additionally to live trapping, twenty-six free ranging Egyptian geese (17 male, 9 female) hunted during the autumn/winter season 2014/2015 and 2015/2016 in the North and West of Germany and twenty-seven Egyptian geese (11 male, 16 female), which were shot in February 2016 during regular pest control in Central Namibia were dissected. One of twenty-seven Namibian birds was live trapped and sampled before death and is thus included in both groups (live trapped and necropsy). Geese from Germany were kept frozen at – 20 °C after hunting until further analysis. Namibian geese were dissected immediately post mortem. During necropsy, ectoparasites, intestinal helminths and nasal leeches were collected. Additionally, pharyngeal swabs were taken for molecular analyses.
Parasitological and microbiological analysis
Both macro-parasites (ectoparasites, nasal leeches (Euhirundidae), intestinal helminths) and selected micro-parasites (blood parasites (Haematozoa), bacteria, viruses) of Egyptian geese from the two populations were characterized for community composition and prevalence (methods see Table S2). Hereafter we use the term “parasites” combining macro- and micro-parasites and only explicitly distinguish between the type of parasites when differences can be expected and/or occur.
During necropsy, wing and breast feathers were macroscopically checked for the presence of ectoparasites. The upper beak was cut open and macroscopically investigated for the presence of nasal leeches. Intestinal helminths were extracted from the intestine of the birds and were determined to the family level based on morphology33. Additionally, blood smears of all live-trapped animals were investigated for the presence of blood parasites during white blood cell counts34.
To compare with previously determined bacterial prevalence of adult German Egyptian geese32 (Table 2), the Namibian birds were screened for Mycoplasma spp. and Riemerella (R.) anatipestifer using conventional 16S rRNA-based PCR assays as described by Prüter et al.32. To verify the specificity of the Mycoplasma PCR assay, products with a clear band were further investigated by sequence analysis, following the procedure described by Prüter et al.32. Only samples with a clear sequencing result were designated positive.
The seroprevalence of antibodies (Ab) against Influenza A virus (IAV), Avian avulavirus 1 (AAvV-1) and West Nile virus (WNV) were determined32. For the detection of Abs against IAV, a commercial competitive enzyme linked immunosorbent assay (ELISA) was used following the manufacturer instructions (ID.vet, Grabels, France, Influenza A Antibody competition, FLUACA ver 0917DE). A commercial competitive ELISA for detection of Abs against AAvV-1 (Avian paramyxovirus 1; syn. Newcastle disease virus) was used according to the manufacturer protocol (ID.vet, Grabels, France, Newcastle Disease Competition, NDVC ver 0913 DE). Commercial competitive ELISA for Abs against Flaviviridae including WNV were applied following the manufacture protocol (ID.vet, West Nile Competition, WNC ver 1014-1P DE).
Immunological assays
Several eco-immunological tests were used to quantify both the cellular and humoral parts of the acquired and innate immune responses of Egyptian geese35. Most of the methods are species-non-specific and have been used in a wide variety of free-living avian species, including different waterfowl36,37,38. We quantified the amounts of different humoral (natural antibodies, complement, lysozyme and haptoglobin) and cellular (monocytes, heterophils, eosinophils and basophils) effectors of innate immunity. For adaptive immunity we measured the total immunoglobulin Y (IgY) concentration and the number of lymphocytes36. Sample sizes (n) for each assay were dependent on the total amount of serum available from each individual and therefore differ among the tests (Table 1).
Table 1 Total sample sizes (total n), sample sizes grouped by sex (sex ratio (♂, ♀)) and year of sampling of blood and serum samples from Namibian (native) and German (invasive) Egyptian geese (Alopochen aegyptiacus) for each immunological effector grouped by the costs of immunity (low costs vs. high costs according to Klasing39 and Lee and Klasing9.
Full size table

Immunoglobulin Y
Total IgY, the avian equivalent to mammalian IgG, was measured using a sensitive ELISA with commercial anti-chicken antibodies38,40. Ninety six-well high-binding ELISA plates (82.1581.200, Sarstedt) were coated with 100 µl of diluted serum sample (2 samples per bird 1:16,000 diluted in carbonate–bicarbonate buffer) and incubated first for 1 h at 37 °C and then overnight at 4 °C. After incubation, the plates were washed with a 200 µl solution of phosphate buffer saline and PBS–Tween, before 100 µl of a solution of 1% gelatine in PBS–Tween was added. Plates were then incubated at 37 °C for 1 h, washed with PBS–Tween and 100 µl of polyclonal rabbit anti-chicken IgY conjugated with peroxidase (A-9046, Sigma) at 1:250 (v/v) was added. Following 2 h incubation at 37 °C, the plates were washed again with PBS–Tween three times. After washing, 100 µl of revealing solution [peroxide diluted 1:1000 in ABTS (2,20-azino-bis- (3-ethylbenzthiazoline-6-sulphonic acid))] was added, and the plates were incubated for 1 h at 37 °C. The final absorbance was measured at 405 nm using a photometric microplate reader (μQuant Microplate Spectrophotometer, Biotek) and subsequently defined as total serum IgY levels41.
Lysozyme
To measure lysozyme concentration in serum, we used the lysoplate assay37: 25 μl serum were inoculated in the test holes of a 1% Noble agar gel (A5431, Sigma) containing 50 mg/100 ml lyophilized Micrococcus lysodeikticus (M3770, Sigma), a bacteria which is particularly sensitive to lysozyme concentration. Crystalline hen egg white lysozyme (L6876, Sigma) (concentration: 1, 1.25, 2.5, 5, 6.25, 10, 12.5, 20 and 25 µg/ml) was used to prepare a standard curve for each plate. Plates were incubated at room temperature (25–27 °C) for 20 h. During this period, as a result of bacterial lysis, a clear zone developed in the area of the gel surrounding the sample inoculation site. The diameters of the cleared zones are proportional to the log of the lysozyme concentration. This area was measured three times digitally using the software ImageJ (version 1.48, http://imagej.nih.gov/ij/) and the mean was converted to a semi-logarithmic plot into hen egg lysozyme equivalents (HEL equivalents, expressed in μg/mL) according to the standard curve42.
Haemolysis–haemagglutination assay
The levels of the natural antibodies and complement were assessed by using a haemolysis–haemagglutination assay as described by43 adjusted to the limited volume of serum. After pipetting 15 μl of serum into the first two columns of a U-shaped 96-well microtitre plate, 15 μl sterile PBS were added to columns 2–12. The content of the second column wells was serially diluted (1:2) until the 11th column, resulting in a dilution series for each sample from 1/1 to 1/1024. The last column of the plate was used as negative controls, containing PBS only. Fifteen μl of 1% rabbit red blood cells (supplied as 50% whole blood, 50% Alsever’s solution, Envigo) suspension was added to all wells and incubated at 37 °C for 90 min. After incubation, in order to increase the visualisation of agglutination, the plates were tilted at a 45° angle at room temperature. Agglutination and lysis, which reflect the activity of the natural antibodies and the interaction between these antibodies and complement43,44, was recorded after 20 and 90 min, respectively. Haemagglutination is characterised by the appearance of clumped red blood cells, as a result of antibodies binding multiple antigens, while during haemolysis, the red blood cells are destroyed. Titres of the natural antibodies and complement were given as the log2 of the reciprocal of the highest dilution of serum showing positive haemagglutination or lysis, respectively43,45.
Haptoglobin
We measured haptoglobin concentrations with a commercial kit (TP801, Tri-Delta Diagnostics, Inc.) following the instructions of the manufacturer. Haptoglobin concentrations (mg/ml) in undiluted serum samples were calculated according to the standard curve on each plate36.
White blood cell counts
To count leucocytes, blood smears were prepared, air-dried and stained using Giemsa- and May-Grünwald staining. Smears were examined at 1,000 fold magnification with oil immersion and the relative number and types of leucocytes were assessed by counting 100 leucocytes. The number of white blood cells of different types was expressed per 104 erythrocytes45.
Statistical analyses
Parasite prevalence
To investigate potential differences in the prevalence of parasites between native and invasive Egyptian geese, we used Fisher’s exact tests because relatively low sample sizes of dissected animals did not allow to perform multivariate analysis.
Immunity
The means and variances of the different immune effectors were compared between the invasive and native Egyptian geese populations. To this end, we used an extension of commonly applied linear models. Linear models assume that the response variable y is a function of a linear combination of n predictor variables x with coefficients c0,..,cn and an error ε:

$$y_{i} = c_{0} + c_{1} x_{1,i} + cdots + c_{n} x_{n,i} + {upvarepsilon }_{i} ,$$
(1)

where ε is the so-called residual variance which captures all the variation in the response variable that is not explained by the predictors. In linear models ε is assumed to be normally and independently distributed around zero. An additional usual assumption is that the variance of this distribution is a constant ({upsigma }_{0}), i.e.:

$${upvarepsilon }_{i} = Nleft( {0,{{ sigma }}_{0} } right),$$
(2)

which corresponds to the assumption of normally distributed residuals with homogeneous variance.
Thus, the estimated effects of the predictors c1,..,cn only describe changes in the mean of the response variable, but not in the variance around that mean.
In our analysis, models were used in which the variance was allowed to be a linear function of some predictor variables z (which might be the same or different from the predictors x of the mean in Eq. 1), i.e.:

$${upvarepsilon }_{i} = Nleft( {0,{{ sigma }}_{0} + {upsigma }_{1} z_{1,i} + cdots + {{ sigma }}_{n} z_{n,i} } right).$$
(3)

Thus, we were able to estimate simultaneously the effect and respective p-values of predictors x upon the variation in the mean of the response variable (Eq. 1) and also the effects and respective p-values of predictors z upon the residual variation around that mean (Eq. 3).
In order to appropriately capture all the potential variation in the response variables we used linear mixed-effects models (LMMs), which in addition to fixed effect predictors in Eq. (1) also included a random effect as a predictor of the mean. However, for enhanced clarity random effects were omitted in the equations above.
Different immune effectors were used as response variables (Table 1). As predictors for the mean sex (male vs. female), reproductive status (breeding vs. non-breeding) and invasion status (native vs. invasive) were included as fixed effects and month of sampling as a random effect. In this way, we control for potential confounding effects of breeding status between the two populations. As predictors for the variance, we included invasion status (native vs. invasive), sex and reproductive status, which allowed us to test our prediction that the variance in immune effects is higher among invasive individuals compared to native individuals.
Some of the immune effectors were transformed (see tables supplementary data S3, S4, S5) to ensure normality of residuals. For haptoglobin we were not able to perform a transformation that ensured normality, because of the high proportion of values below the detection threshold. To account for this, we performed a generalized linear mixed model (GLMM) with a binominal error distribution and with a binary response variable (haptoglobin being either above or below the detection threshold). In this model, it was necessary to constrain the error variance to a fixed value. Thus, for haptoglobin we were only able to test for a change in mean but not for a change in variance. In addition to analysing total leucocytes, different leucocyte subtypes were analysed separately.
The LMMs and GLMMs were implemented using the R package glmmTMB version 0.2.046. To test for differences in residual variance we used the option disformula in the function glmmTMB. Potential collinearity of predictors was tested by calculating variance inflation factors using the R package car version 2.1-647. All statistical analyses were performed using R version 3.3.248. More

Changes in radioactivity in atmospheric depositions after the accident
In March 2011, 134Cs was detected with the same activity as that of 137Cs. As 134Cs had not been detected before the accident except during the emission period resulting from the Chernobyl accident in 198611,40,41, the observed 134Cs/137Cs ratio verified that the only source of 134Cs and 137Cs was the FDNPP (Supplementary Fig. S2). Our atmospheric aerosol samples indicated that at least three plumes resulting from the FDNPP accident passed across site A (Supplementary Fig. S3). When these plumes arrived at site A, the activities of 90Sr and 137Cs in atmospheric deposition increased to 2.7 × 103 and 3.2 × 106 times, respectively, higher than those before the accident (between July 2009 and June 2010) (Fig. 1). The 137Cs/90Sr activity ratio calculated from our observational results in March 2011 was 4.5 × 103. This large difference in the rate of increase between 90Sr and 137Cs reflects the discrepancy between their emission rates, i.e., the total amounts of 90Sr and 137Cs released were estimated as 0.02 PBq39 and 14.5 PBq23, respectively. These estimations indicated that the 90Sr emission level was much lower than that of 137Cs. The monthly 137Cs deposition peak due to the FDNPP accident (2.31 × 104 Bq m−2) was much higher than those resulting from nuclear weapon tests (548 Bq m−2; June 1963) and the Chernobyl accident (131 Bq m−2; May 1986) (Fig. 1a). On the other hand, the 90Sr activity due to the FDNPP accident (5.2 Bq m−2) was lower than that due to the nuclear tests in the 1960s (170 Bq m−2; June 1963) (Fig. 1b). For comparison, the average 137Cs values in atmospheric depositions before the FDNPP accident (between July 2009 and June 2010) were 7.0 (1.2–22.5) mBq m−2 at site A and 25.0 (6.1–76.4) mBq m−2 at site B, while those for 90Sr amounted to 1.9 (ranging from not detectable (N.D.)–6.0) mBq m−2 at site A and 26.0 (6.5–116.8) mBq m−2 at site B. The possible causes of the higher depositions rates at site B than those at site A are the differences in altitude (site A: 40 m; site B: ~ 1390 m) and local environmental effects (site A: open area; site B: surrounded by forestland).
The activity of 90Sr and 137Cs in atmospheric depositions and that of 137Cs in aerosol samples rapidly decreased after the first surge in March 2011 (Fig. 2 and Supplementary Fig. S3). The decrease rate of radioactivity in atmospheric depositions at site A was due to the change in radionuclide emission, transport, and deposition processes29. We classify the period after the FDNPP accident into three phases. The first phase is dominated by direct emissions (March 2011), the second phase is dominated by tropospheric circulation and removal (from April to December 2011), and the third phase is dominated by resuspension (after January 2012). In the first phase, the direct discharge/emission of radioactive materials during the FDNPP accident and meteorological conditions governed the radionuclide concentration in the environment29,42,43,44. In the second phase, tropospheric transport of the radioactive materials remaining in the atmosphere after the FDNPP accident and their removal processes dominated atmospheric depositions17,29. The third phase (after January 2012) mainly depended on the resuspension of radioactive materials29,30,31,33,34,35,36. For comparison, the corresponding decrease rates (first, second, and third phases) resulting from the Chernobyl accident were shorter than those resulting from the FDNPP accident (for more discussion details, please refer to Supplementary Fig. S4 and the text). More discussions regarding the first and second phases were also presented in previous studies29,34, and hence the scope of the present study is restricted to the third phase.
Figure 2

Activity of 90Sr and 137Cs in atmospheric depositions after the FDNPP accident from 2011 to 2018. (a) Cesium-137 in atmospheric depositions. (b) Strontium-90 in atmospheric depositions. The black points indicate the observational results. In panel (a), the pink lines indicate the regression curves. The green and blue curves indicate the exponential curves obtained via multiple exponential fitting. The red lines indicate the preaccident levels (the average monthly deposition between June 2009 and July 2010).

Full size image

The latest average monthly 137Cs atmospheric depositions in 2018 at sites A and B reached ~ 1/8100 (2.9 Bq m−2) and ~ 1/4500 (3.0 Bq m−2), respectively, with regard to the peak levels after the accident. But these levels were still ~ 400 and ~ 130 times, respectively, higher than those before the accident (Figs. 1a and 2a, respectively). On the other hand, the 90Sr depositions in 2018 amounted to 3.0 (1.2–10.5) mBq m−2 and 33.8 (3.1–117) mBq m−2 at sites A and B, respectively (Figs. 1b and 2b, respectively). These 90Sr deposition levels were almost at the same level as the preaccident deposition levels, and we concluded that the 90Sr deposition levels at our observation sites had returned to the preaccident levels in at least 2015 (Fig. 2b).
Before the FDNPP accident, the 90Sr and 137Cs activity in atmospheric deposition showed seasonal variations (Fig. 3 and Supplementary Figs. S5 and S6). The 137Cs deposition value peaks in spring (April) at site A. On the other hand, it peaks twice in May and September at site B (Supplementary Figs. S4 and S5). Similarly, 90Sr deposition reaches peak values during the spring season (March and April) at site A and during the fall season (September and October) at site B (Fig. 3). Studies have suggested that the 90Sr and 137Cs deposition peaks during the spring season at site A are caused by local and long-range transported dust particles14,15,34,45,46.
Figure 3

Seasonal changes in 90Sr deposition from 2012 to 2018 at (a) site A and (b) site B. The black curves indicate the median values in each month after the FDNPP accident (from 2012 to 2018). The gray curves indicate those before the accident (from 2000 to 2010 at site A and from 2007 to 2010 at site B).

Full size image

After the FDNPP accident, direct emissions and their tropospheric removal processes governed the 90Sr and 137Cs activity in atmospheric depositions at sites A and B, and seasonal variations were not apparent in the first and second phases (Fig. 2). After 2012 (in the third phase), although the mean 137Cs deposition value at site A had slightly increased in spring (peaking in April), no seasonal variations in 137Cs at either site were observed (Fig. 2 and Supplementary Figs. S5 and S6). After 2014, in contrast, the seasonal variations in the 90Sr radioactivity in atmospheric deposition at both sites showed similar trends to those before the accident (Figs. 2 and 3).
Possible carriers of 90Sr and 137Cs at sites A and B
The radionuclides in the atmosphere are generally carried by aerosol particles (host particles) emitted through, for example, geochemical and biological cycles. The correlations between dust components (e.g., Al and Fe) and radionuclides (90Sr and 137Cs) within the collected samples before the accident suggest that mineral dust particles are the dominant carriers of these radionuclides at site A (Fig. 4a). Previous studies have also demonstrated that the sources of these radionuclides are mainly resuspension of contaminated dust originating from long-range transport (large-scale phenomenon) and neighboring areas (local-scale phenomenon)14,15,33,34,45,46. After the accident, chemical analysis results indicate that dust particles are the dominant carriers of 90Sr and 137Cs at site A, except from 2012 to 2014 for 90Sr when the contributions from the accident were high (Fig. 2).
Figure 4

Correlations between radionuclides and stable elements at sites (a) A and (b) B. The units for 90Sr and 137Cs are mBq m−2, and those for the stable elements are mg m−2. The red points reveal that the correlations are significant (p  More

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