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Present study aimed to develop a risk model to identify the risk localities in the dengue high risk areas. Kernel density and Euclidean distance based approaches are widely used in raster development of GIS modelling. Kernel density was used to fit a smoothly tapered surface to point layers while Euclidean distance was used to identify close exposures of polygon layers17. The risk values were ranked for each layer depending on their contribution to the transmission of dengue incidences. Based on the ILWIS Applications Guide18, the maximum risk value for the developed model was assigned as 10. Previous study conducted on mathematical modelling of dengue incidences in the Gampaha District have stated exponential influence of previous month cases on current month disease transmission in the district19. Further, investigation on adult and immature stages of dengue vector mosquitoes indicated that DENV are present in adult dengue vector mosquitoes and significant correlations of entomological indices with patient cases in the same district20,21. Therefore, patient locations and positive breeding container layers were selected as maximum risk variables and assigned the risk value of 10 as these variables are directly involved in disease transmission. In the modelling, dispersed risk distance patient cases and breeding places were selected less than average flight distance of dengue vector mosquitoes which is 400m22. Further, these study areas are considered to be highly congested areas and therefore, total building and home garden layers were given second highest ranking. Previous study conducted in Indonesia reported that consistent high number of dengue cases in larger areas of buildings even though the correlation is weak23. Further, higher dengue vector population densities were reported around home gardens from many countries24,25,26. Therefore, moderate risk level, risk value of 6, was assigned to total buildings and home garden layers. Recent study conducted in the Gampaha District demonstrated the contribution of daily commutes of people for transmission of dengue in the district27. When people visited to urban areas, there is higher probability of acquiring of dengue as these urban and suburban areas may act as dengue hot spots and artificial reservoirs which has been documented previously in Sri Lanka as well as other countries28,29,30,31. Therefore, land use layer for urban areas was given third highest ranking in the risk model. Another study conducted in Sri Lanka reported roads are important aspects for transmission of dengue25 and households in the present study areas located along main roads or have access roads. Further, previous study in Sri Lanka reported the potentials of public places play as artificial reservoirs dengue32 because of higher prevalence of breeding places around public places. It is a well-known fact that distribution of dengue vector mosquitoes varies with the elevation depending on geographical areas. Therefore, roads, public places and elevation layers were ranked in the third position with risk level of 4. However, the lower risk distances were assigned to road and contour layers as the layers are not related directly for transmission of the disease even though they play important role. Previous study conducted in Kenya and Uganda has reported higher dengue vector mosquito populations close to vegetation and marshy lands33 which may provide resting places of dengue vectors, especially for male mosquitos. When considering the study areas, with the exception of the 3rd Kurana study area, all other study areas have close proximity to marshy areas and therefore these areas were included as a variable in the present study. Since it is not directly involving in mosquito population increase or disease transmission, the lowest rank was assigned to marshy areas of land use layers.
When comparing the generated risk maps with satellite imageries, vegetation covers were observed in high risk localities in all study areas. The reason could be the vegetation covers make better resting places for dengue vector mosquitoes. Even though the Ae. aegypti mosquitoes, the main vector of DENV, rest indoors34, previous studies conducted in Malaysia and Kenya reported the preference of Ae. aegypti to rest and breed outdoors due to increased breeding opportunities without affecting lifespan or gonotrophic activity35,36. Meanwhile, it is well-known fact that Ae. albopictus, the subsidiary vector of DENV, prefers vegetation to rest and breeding in both natural and man-made containers37,38.
When comparing the intensity maps generated from the Poisson point process model with generated risk maps, differences in localization of intensities were observed specially in Eriyawetiya and Welikadamulla study areas. In the risk map of Eriyawetiya study area, risk localities were located mainly along the roads in the area and this observation was even statistically significant in Pearson correlation analysis. However, when considering the intensity map from the Poisson point process model, lower predicted intensity was observed in most of the locations in the study area and high intensities were observed around the southern border along the Devasumithrarama road and in central area. When considering the Welikadamulla study area, even though risk map indicates that dengue is high virtually all over the area, the predicted intensity map illustrates that dengue may high in central and northern border of the area along the Welikadamulla road. Interestingly, while the dengue high intensity localities in both Eriyawetiya and Welikadamulla study areas are mainly used as home gardens, these localities have close proximity to crowded public places, such as schools, temples, community halls, etc. Perhaps, these public places may have acted as artificial reservoirs of dengue. This is further observation in the high density localities in Akbar Town and 3rd Kurana study areas. In the Akbar Town area, high intensities were observed around mosques. In the 3rd Kurana study area, many public places, such as schools and churches, are located in the central and southern area where intensities were high. However, the lowest dengue intensities were observed from the 3rd Kurana study area.
In the Poisson point process model, highest intensity range was observed in the Eriyawetiya study area while the lowest was observed from 3rd Kurana. Eriyawetiya study area is located close to the northern border of Colombo, the commercial capital in Sri Lanka, where highest number of dengue cases are reported in the country39. Recent study reported that human commutes to risk areas in Colombo and transportations may play significant role transmission of dengue in the nearby areas, such as Eriyawetiya study area, leading to higher intensities21. However, the overall lowest intensities reported from 3rd Kurana study area may be due to continuous encouragement of dwellers in the area to remove dengue vector mosquito breeding places and use of protective measures by the churches and clergies.
The results of Pearson correlation analysis and Poisson multivariate point process model were also different especially with respect to positive breeding locations and roads layers. Positive correlation was observed between breeding places and patient locations in Pearson correlation analysis, which can be expected as dengue vector mosquitoes are anthropophilic mosquitoes with low flying ranges, were different from the results of Poisson point process model. In the model, no or negative correlation was observed between patient locations and breeding places. In a multivariate model, all explanatory variables are modelled to capture the true variation of the response variable while in Pearson correlation only one explanatory variable is considered at a time. The negative correlation in Poisson model with breeding places may be due to the hidden breeding places. These breeding places may be unidentified due to level of personal expertise, restrictions of accessibility to household, limitations due to inadequate resources, etc. which lead to differences between actual adult population and larval indices21. Further, even though road layers were shown similar behaviours for 3rd Kurana and Welikadamulla study areas both in Pearson correlations and Poisson modelling, differences were observed in Eriyawetiya and Akbar Town. The positive correlations observed between patient locations and road layers could probably be because of high congestion of households alongside the roads and therefore, even single DENV infected mosquitos can spread the disease to all households as these mosquitoes probe many humans during blood feeding. Similar observation has been reported in previous study conducted in West Indies40. The study further states that more dengue cases being found within 1–3 km away from various types of roads. This may be the reason for the observed negative estimates from multivariate Poisson model in Eriyawetiya and Akbar Town study areas as the patient locations are very close to access roads.
When analysing the observed (K) -functions of the developed Poisson multivariate models for the study areas, both clustering and dispersions were observed for Eriyawetiya and 3rd Kurana study areas while only clustering was observed in the Akbar Town and Welikadamulla areas. Interestingly, in Eriyawetiya and 3rd Kurana study areas, clustering was observed a radius of approximately 150 m. This is comparable to the general flying range of dengue vector mosquitoes, especially with regards to the Ae. aegypti41, the main dengue vector mosquito. Further, this may be an indicative of that patients in a small areal cluster are prompted due to a single infected dengue vector mosquito. During the analysis, both isotropic42 and translation43 edge correction methods were considered, therefore, edge effects arising from the unobserved patient locations outside study area can be hampered when estimating the (K)-functions. The estimations of (K)-functions were within the upper and lower envelopes of simulated functions in Akbar Town, 3rd Kurana and Welikadamulla study areas, that is, given particular distance, the data and simulated patterns were statistically equivalent. This indicates that dengue patient locations in the study areas were undergone a complete random pattern or CSR except for Eriyawetiya study area. This observation is further confirmed by the results of Maximum Absolute Deviation (MAD) and the Diggle-Cressie-Loosmore-Ford (DCLF) non-graphical tests44.
Among four monsoon seasons, the first inter-monsoon season occurs during March and April months. The Southwest monsoon period starts in May and it lasts till September. During the October and November, the second inter-monsoon period occurs and the Northeast monsoon lasts for three months from December to February. When analysing the distribution of dengue incidences in the monsoon periods, the highest number of dengue incidences were reported from the Southwest monsoon period in all study areas. The Gampaha District is located in the western part of Sri Lanka and during the monsoon period, the district experiences a rainfall of 750–2000 mm. In other monsoon periods, rainfall of the Gampaha District is less than 1000 mm45. The reason for higher precipitation in the Southwest monsoon period includes the presence of abundant water bodies, such as Arabian Sea and Indian Ocean, leading to higher accumulation of moisture in Southwest monsoon winds46. The higher rainfalls increase not only the availability of the breeding containers for dengue vector mosquitoes, but also favourable environmental conditions, viz. humidity and temperature, for its development. This will lead to increased disease transmission during the Southwest monsoon season compared to other monsoon seasons.
The developed models can be used to identify risk localities easily for healthcare workers and decision makers. The Poisson point process models can be developed using freely available software and packages. Further, road maps can be easily obtained for freely available sources and modified easily using freely available GIS software. With the advantages of technology, correct GPS locations of positive dengue vector mosquito breeding places and patients can be easily obtained using mobile devices with minimum wage during vector control programmes and export directly into GIS software. Since roads, land use, buildings and contour being not changing frequently in a particular area, with the aid of available data on patient locations as well as positive breeding places, it is possible to develop risk maps monthly or biannually to assess the risk levels of high risk areas. Further, when health authorities have risk map of particular area over few years, then it is possible to identify risk localities and transmission of dengue in an area in advance. This is particularly important in outbreaks and epidemic progression, so that they can have a better scenario of undergone situation to use scarce health resources effectively to control disease transmission. Meantime, the model can be further enhanced by incorporating serotype data which may lead identify index cases and initial clusters. A combined approach of predictive mathematical models19 and genetic approaches to identify the virulence of circulating dengue viruses21 will provide sufficient information for health authorities to take timely actions, such as intensive source reduction programmes, targeted intervention programmes or deploy vector reduction tools such as ovitraps9, to manage the situation to prevent propagation of outbreaks and epidemics. More

Study area
The study took place in the south-western Kalahari region of Botswana, known as the Bakgalagadi Schwelle (S 24.35°, E 20.62°), including the Botswana side of the Kgalagadi Transfrontier Park. The vegetation forms an open savanna, overlying deep sandy substrate with limited free-standing water. There is an intermittent river, Nossob river, in the south, ~ 80 km from the centre of the study area. A characteristic of this area is the highly mineralized, clay-rich depressions called pans, which retain water for variable periods after rain6. Air temperatures exceed 40 °C in summer and fall below 1 °C in winter6. Rainfall is seasonal but erratic, falling primarily during short-duration, high-intensity thunderstorms between November and April6. Mean annual rainfall in the Schwelle region ranges between 250 and 350 mm13.
Climatic variables
A free-standing miniature black globe thermometer (“miniglobe”), identical to the collar miniglobe thermometer, was placed within the area used by the animals in direct sun, 1 m aboveground, and recorded temperature (°C) every hour (S 24.307°, E 20.745°; reference miniglobe). Dry-bulb air temperature (°C), wind speed (ms−1), and solar radiation (Wm−2) data were obtained from the Agricultural Research Council (ARC) weather station located at the Nossob campsite (S 25.4°, E 20.6°). Normalised Difference Vegetation Index (NDVI) (MODIS Terra 16-day) and local rainfall (mm; CHIRPS) data covering the study area (S 24.434°, E 20.293°) were obtained from Google Earth Engine14.
Study species and data collection
In August 2013, eight individual female gemsbok and eight individual female wildebeest, each from separate herds, were darted by a veterinarian from a helicopter. Each dart consisted of Thiafentanil (gemsbok: 7–8 mg, wildebeest: 4–6 mg, Thianil, Kyron Laboratories, Johannesburg, South Africa), medetomidine hydrochloride (gemsbok: 3–6 mg, wildebeest: 2–4 mg, Kyron Laboratories, Johannesburg, South Africa) and ketamine (gemsbok: 75–150 mg, wildebeest: 50–150 mg Pfizer Animal Health, Sandton, South Africa). Each individual was fitted with a GPS collar (African Wildlife Tracking, Pretoria, South Africa) that supported a miniglobe attached to the top to record the thermal environment that the individual bearing it occupied15. Miniglobe temperatures and GPS locations were recorded hourly. In addition, each individual underwent surgery to implant miniature temperature-sensitive data loggers in the retroperitoneal space and had a motion-sensitive data logger tethered to the abdominal muscle wall (see7 for details). The data loggers were covered with biologically and chemically inert wax (Sasol, South Africa) and sterilised in instant sterilant (F10 Sterilant with rust inhibitor, Health and Hygiene (Pty) Ltd., Roodepoort, South Africa) before implantation. Once the individual animal was immobile, it was placed in sternal recumbency with its head elevated and supported by sandbags. Following intubation, anaesthesia was maintained with 2–5% isoflurane (Aerrane, Astra Zeneca, Johannesburg, South Africa), administered in 100% oxygen. Incision sites were shaved and sterilised with chlorhexidine gluconate (Hibitane, Zeneca, Johannesburg, South Africa). A local anaesthetic (3 ml subcutaneously (S.C.); lignocaine hydrochloride, Bayer Animal Health (Pty) Ltd., Isando, South Africa) was administered to the incision site. After placement of the loggers, the incision was sutured closed. Respiratory rate, heart rate, arterial oxygen saturation, and rectal temperature were monitored throughout the surgery, which lasted approximately 30–45 min. Each individual animal also received an antibiotic (~ 0.04 ml kg−1, intra muscularly (I.M.), Duplocillin, Schering-Plough Animal Health Ltd., New Zealand), and anti-inflammatory (~ 0.5 mg kg−1 I.M., Metacam, Meloxicam injectable solution, Boehringer Ingelheim Vetmedica, Inc, St. Joseph, U.S.A.) medication. Following surgery and termination of inhalation anaesthesia, the immobilization drugs were completely reversed by a combination of naltrexone (gemsbok: 75–120 mg, wildebeest: 60–100 mg, I.M. Naltrexone, Kyron Laboratories, Johannesburg, South Africa) and atipamezole (gemsbok: 10–20 mg; wildebeest: 10–15 mg, I.M. Antisedan, Orion Corporation, Orion Pharma, Finland).
The temperature-sensitive data loggers (DST centi-T, Star-Oddi, Iceland) recorded body temperature at 10-min intervals (Fig. 1a,b) and the motion-sensitive data logger recorded whole body movements (i.e., motion changes) as activity counts within the first 10 s of each 5-min interval. The motion-sensitive logger had a triaxial accelerometer (ADXL345, Sigma Delta Technologies, Australia) with equal sensitivity across three planes (resolution one-fourth 4 mg/least significant bit). We adjusted the activity units to be relative to the maximum activity count for the entire study period per logger, to account for differences in the sensitivity of the individual motion-sensitive loggers. The data loggers and the collar weighed less than 1% of the individual animal’s body mass and is unlikely to have adversely affected their behaviour.
Figure 1

Ten-min recordings of body temperature from a representative female wildebeest (a) and female gemsbok (b) over the study period (September 2013 to November 2014); and the monthly dry-bulb air temperature (solid black line), rainfall (grey bars) and monthly composited vegetation greenness (NDVI; dashed grey line) over two years (c) highlighting drought conditions in the first year. The light grey boxes represent the two hot-dry seasons compared in the current study.

Full size image

Two wildebeest were never relocated, possibly as a result of collar failure or predation. Three gemsbok died in October 2013. The remaining 11 animals were recaptured in May 2015, and data loggers and collars were removed. Thereafter the animals were released. Because of the inability to relocate all animals, animal deaths, and technological failures, we recovered a sample of 11 internal body temperature loggers (five gemsbok and six wildebeest); eight internal motion-sensitive loggers (four gemsboks and four wildebeest); nine GPS units (five gemsboks and four wildebeest) and nine miniglobe temperature sensors from the collars (five gemsboks and four wildebeest).
All procedures were approved by the Animal Ethics Screening Committee of the University of the Witwatersrand (protocol no. 2012/24/04) and all experiment procedures were performed in accordance with relevant guidelines and regulations as well as the ARRIVE guidelines (https://arriveguidelines.org/). The Government of Botswana via the Ministry of Environment, Wildlife and Tourism and Department of Wildlife and National Parks granted approvals and permits [numbers EWT 8/36/4 XX (32), EWT 8/36/4 XXVII (15), EWT 8/36/4 XXIV (102)] to conduct the study.
Data analysis
During the study period, the first hot-dry season (September to November 2013, ‘drought’) occurred at the end of a prolonged dry period, whereas the second hot-dry season (September to November 2014, ‘non-drought’) followed more typical rainfall conditions (Fig. 1c). Miniglobe temperature (24 h mean, minimum and maximum) and dry-bulb air temperature (24 h minimum and maximum), as well as mean 24 h wind speed and solar radiation were similar between the two hot-dry periods (Table 1). We averaged 16-day NDVI composites per season as an index of vegetation greenness in response to prior rainfall. Rainfall during the wet season prior to the commencement of the study (December to May 2013) was less than 40% ( More

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Study area
We established field survey sites in four different habitat types (BW: breakwater wall; EB: eelgrass bed; IF: intertidal flat; SB: subtidal bottom) within Matsunaga Bay, located in the eastern Seto Inland Sea, Japan (Fig. 1). Matsunaga Bay is a small semi-closed bay with an area of approximately 12 km2. It is connected with other water bodies through the Tozaki-Seto Strait (width: approx. 400 m) and the Onomichi Strait (width: approx. 200 m)29,30. Water depths are mostly less than 20 m throughout the bay. The water depths at our four survey sites were approximately 4.5 m, but part of site SB located near a shipping channel reached depths of 10–13 m. Intertidal flats cover 35% (4.3 km2) of the bay area, whereas eelgrass beds cover 1.7% (0.2 km2)31. The bottom sediment type is mainly muddy throughout the bay, although some parts of EB and SB have sandy and muddy bottoms (see Supplementary Table S1)30.
Figure 1

Locations of sampling sites in Matsunaga Bay, Hiroshima, Japan. This map was created based on coordinate data from Google (http://www.gis-tool.com/mapview/maptocoordinates.html). The four habitats examined in this study are indicated by BW (breakwater wall; grey circle), EB (eelgrass bed; grey circle), IF (intertidal flat; grey rectangle), and SB (subtidal bottom; grey polygon).

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Although human activities along the coast of Matsunaga Bay appear to be associated with artificial structures (e.g., industrial plants, marinas, and lumber yards), natural habitats are still relatively well-preserved compared to areas in the eastern Seto Inland Sea29. The total population of towns within 5 km of the coastline of the bay can be expected to exceed 100,000 people, which ranks within the top 20% of administrative districts in Japan, including prefectures, towns, villages, and the 23 wards of Tokyo32.
Data collection
We conducted one field survey in September 2016 (summer) and another in January 2017 (winter) to collect data on benthic community structures and environmental conditions in each habitat. We established five sampling sites within each habitat to obtain replicated samples. To reduce biases due to tidal cycle, we performed all field sampling and measurements at high tide, when all habitats were underwater.
We used a standard sample area (approximately 0.15 m2 per sample) at each sampling site irrespective of sampling method to obtain comparable data on benthic communities. Some of our sampling methods involved Smith–McIntyre grab samplers and quadrats that could not cover the standard sample area in a single sample; for these methods, we combined the data from three samples to make up a single sampling site. At BW, benthic macrofauna (hereafter referred to as “benthos”) samples were collected by SCUBA divers. We established 15 sampling positions in a 5 × 3 grid (i.e., five depths [sampling sites] and three replicates) based on distance from the seafloor at the breakwater wall (Supplementary Fig. S1). The SCUBA divers used scraper blades, 0.1-mm mesh bags, and 22.5 cm × 22.5 cm quadrats because the benthic communities were mainly composed of sessile organisms. At EB, we employed different sampling methods for the above- and belowground components. SCUBA divers collected aboveground samples of eelgrass-associated benthos and eelgrass shoots using a mesh bag (mesh size: 0.1 mm; bag diameter: 45 cm). They then cut away the eelgrass shoots near each aboveground site and collected belowground samples of the benthos on top of and within the sediment by using the bucket part of a Smith–McIntyre grab sampler (sampling area: 22.5 cm × 22.5 cm). At IF and SB, benthos and bottom sediment samples were collected from a ship using a Smith–McIntyre grab sampler (sampling area: 22.5 cm × 22.5 cm).
All benthos was extracted using a 1-mm sieve and preserved in buffered 10% formalin in the field as soon as possible after sampling. The samples were identified to the lowest possible taxonomic unit and counted in the laboratory. After identification, we organized the dominant benthic species (or taxa) according to their primary feeding types and common life forms with reference to the World Register of Marine Species (http://www.marinespecies.org/) and the literature. No vertebrate species were targeted in this study. We defined the primary life forms of adult benthic species on/in their substrates as “common life forms.”
Although differences in environmental conditions were not the focus of our study, we did assess whether there were considerable water quality differences among sites. The purpose of this assessment was to try to identify sites with similar conditions so that exogenous impacts on biological communities could be discounted as much as possible in the analysis. We measured water and sediment conditions at each sampling site (except at BW, where sediment conditions were not measured due to the absence of sediment). Prior to benthos and sediment sampling, we measured water temperature, salinity, pH, and dissolved oxygen concentration at each site at a depth directly above the seafloor by using a multi-parameter water quality meter (AAQ‐RINKO, JFE Advantech Co. Ltd., Japan). At BW, where the substrate (i.e., the breakwater) is oriented vertically (see Supplementary Fig. S1), we measured water conditions in the middle of the water column. We also measured temperature, pH, oxidation–reduction potential (ORP), water content, and median particle size (D50) in the sediment. Sediment temperature, pH, and ORP were measured by using a portable ion meter (IM-32P, DKK-TOA Co., Japan) immediately after each sample was collected. Sediment water content and D50 were measured in the laboratory once benthic species had been removed from the sample.
Data analysis
First, we identified how many species were shared between all habitat pairs to understand inter-habitat species-sharing relationships. Second, we compared species compositions and abundances among habitats using similarity indices and multivariate analysis (described below).
To detect species sharing in terms of species commonality and endemism among the four habitats, we classified benthic species into the following three categories: common, endemic, and shifting. Common species were defined as species that occurred across all habitats. Endemic species are those that were found in only one habitat. Shifting species were defined as those that occurred in multiple habitats (but not across all habitats) and therefore showed a broad allowable range of habitat types or conditions. To analyse the importance of habitat sets in maintaining local species diversity, we further categorized the shifting species into two- or three-habitat users (i.e., those that occurred in two or three different habitats). Moreover, we calculated the numbers and proportions (i.e., using the Jaccard similarity index) of shared species in each habitat pair to evaluate the potential strength of any inter-habitat relationships. The Jaccard similarity index ((J)) is calculated as follows:

$$ J = frac{{S_{alpha beta } }}{{S_{alpha } + S_{beta } – S_{alpha beta } }}, $$

where (S_{alpha }) is the number of species in habitat (alpha), (S_{beta }) is the number of species in habitat (beta), and (S_{alpha beta }) is the number of species that are shared among habitats (alpha) and (beta).
In terms of the functional groups, we analysed abundance matrices of abundant species grouped by primary feeding types and common life forms. Focusing on abundant species is a useful way to reflect the functional characteristics of biological communities14. Therefore, we identified the most abundant species from each sample before constructing the abundance data matrices. To determine how many species to select for analysis, we calculated the number e of equally-abundant species that would be required to obtain the Simpson diversity index of each community (i.e., the effective number of species33). We then selected e abundant species from each sample in rank order from most to least abundant. If multiple species of the same rank occupied this cut-off threshold, we selected all of them. This selection method, which is unique to our study, successfully identified dominant species that accounted for over 70% of the total abundance in each community in all habitats.
To compare benthic macrofaunal communities among habitats, we performed non‐metric multidimensional scaling (NMDS)34 based on dissimilarity matrices obtained by using the metaMDS function in the vegan package35. To compare species compositions, we constructed Jaccard dissimilarity matrices based on presence/absence data, and to compare species abundance and functional compositions, we constructed Bray–Curtis dissimilarity matrices based on abundance datasets. All abundance datasets were square-root transformed before calculating the Bray–Curtis dissimilarity matrices to reduce the influence of abundance bias. We accepted the NMDS ordinations if stress values were less than 0.2 to maintain the accuracy of the two‐dimensional ordinations34. Then, we tested the effects of habitat type and sampling time by conducting a two-way permutational multivariate analysis of variance (two-way PERMANOVA)36 using the adonis function in the vegan package. Here, we considered four habitats (Habitat), two sampling times (Time), and their interaction term as explanatory factors. Although our main focus was differences in community compositions among habitats at each sampling time, we also examined the magnitude of variation in each habitat by comparing two stable seasonal extremes (i.e., summer and winter). If the results of the PERMANOVA were significant, we performed post-hoc tests (pairwise PERMANOVA) to identify which pairs of community structures were significantly different by using the pairwise.perm.manova function in the RVAideMemoire package37. We used 9999 permutations for the NMDS ordination, PERMANOVA, and pairwise PERMANOVA. P-values calculated during the pairwise PERMANOVA were corrected using the false discovery rate method38. For the benthic community data collected at each sampling point at EB, above- and belowground datasets were integrated to reflect spatial representativeness (see Figs. S3 and S4). All analyses were performed using R version 3.5.139. More

The Litho-technical characterization of the deposit
The litho-technical characterisation of the sediments has resulted from: the geological inspection of the cores in the liners and of the undisturbed geotechnical samples; the paleogeographic reconstruction of the soil deposition29,39; the soil geotechnical index properties; the geochemical and the mineralogical analyses. Here-forth, Fig. 7a reports the litho-technical section N–N′ whose trace is shown in Fig. 7b.
Figure 7

(a) Litho-technical section N–N′; (b) I Bay and location of all the investigated sections. Key: (1) 2017 campaign projected borehole; (2) top of the calcareous bedrock according to30 (3) bathymetry (Port authority 1947–1978); (4) significant content of organic matter; (5) fishing net (anthropogenic material); (6) coastline; (7) stratigraphic contact; (8) 1stLTU; (9) 2ndLTU, of consistency from very soft to soft and occasional presence of sand or silty sand, from very loose to loose (a); (10) 3rdLTU, of consistency increasing with depth, from very soft to soft (a), from soft to firm (b), firm (c), stiff (d)66,67, and occasional layers rich in sand (e), gravel (f) and peaty levels (g); (11) Possible disturbed top layers of the ASP formation; (12) ASP formation, with clayey silt or silty clay of very stiff consistency, and sandy levels (Su = 200–500 kPa) (a), or Grey-bluish marly-silty clay (Su  > 500 kPa) (b).

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A First litho-technical unit, hereafter 1stLTU (light yellow colour in Fig. 7a), of about 1.5 m thickness, has been found to cover the whole deposit. It is formed of either clay with silt, or sandy to slightly sandy silt with clay, deposited in recent times up to present, according to the sedimentology and paleogeographic studies. The corresponding grading curves (Fig. 8) show that its clay fraction, CF, varies in the range 27–53%, its silt fraction, MF, in the range 39–57%, and its sand fraction, SF, is minor, except for site S1, close to the Porta Napoli channel (Fig. 2). It is rich in organic matter and the pocket penetrometer Su data (Su  More