Investigating the Relationship Between Rainfall and Ciguatera Fish Poisoning Cases in Vanuatu

Investigating the Relationship Between Rainfall and Ciguatera Fish Poisoning Cases in Vanuatu | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Investigating the Relationship Between Rainfall and Ciguatera Fish Poisoning Cases in Vanuatu Allan Rarai, Philip Obaigwa Sagero, Eberhard Weber This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6308585/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The escalating number of ciguatera fish poisoning (CFP) cases in Vanuatu has become a public health issue. Previous studies focused on tropical cyclones and sea surface temperatures as potential causes of CFP; our study aims to investigate the relationship between rainfall and CFP cases. We use the monthly CFP dataset (1989–1996) from the South Pacific Epidemiological and Health Information Services, the daily cases (2021–2023) from the Vanuatu Ministry of Health, and the monthly and daily rainfall data from the Vanuatu Meteorology and Geo-Hazard Department. We also interviewed people about their local knowledge and perceptions of CFP causes and treatments. Cross-correlation analysis was used to determine the lag time between rainfall and CFP cases, whereas the Rainfall Anomaly Index (RAI) assessed variations in rainfall patterns. This study shows a strong positive correlation between rainfall and CFP cases, with lag times ranging from 0 to 8 months. Interview data highlight the integration of Indigenous knowledge and scientific information in CFP prevention and treatment practices. Our results emphasize the need for improved surveillance and early warning systems. This research supports targeted public health interventions and policy measures to mitigate CFP risks in Vanuatu and other Pacific Island nations. Behavioral Geography Rainfall Ciguatera Fish Poisoning Public Health Vanuatu Lag Time Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Ciguatera Fish Poisoning (CFP) is increasingly becoming a public health risk in the world, with an estimated 50,000 to 500,000 people affected every year (Kohli et al., 2015 ). The Pacific and the Caribbean regions are the most affected regions, with an estimated 25,000 to 50,000 people affected annually by the ciguatoxins after consuming reef fish (Darias-Dágfeel et al., 2024 ; Holmes et al., 2021 ; Murray et al., 2020 ). The Vanuatu Ministry of Health (VMoH) revealed an increasing trend in CFP cases, with an average of 557 CFP cases recorded annually from 1988 to 2023 (MoH, 2015). The potential impact on public health in Vanuatu is significant with the increasing cases of CFP, underscoring the pressing need to understand the issue and provide early warning to the people before CFP cases emerge. Many factors contributed to the favorable environmental conditions, resulting in the accumulation of ciguatoxins in fish, which leads to CFP. Several studies have linked ciguatera to changes in the sea surface temperature (Barrett, 2014 ; Heimann et al., 2011 ; Kohli et al., 2015 ; Tester et al., 2010 ) and tropical cyclones (Dixon et al., 2022 ; Guillemot et al., 2010 ; Puotinen et al., 2016 ; Rongo & van Woesik, 2013 ), however, there are limited studies on rainfall and ciguatera. Rainfall contributes to soil erosion, landslides, surface runoffs, and sedimentation in the coastal and marine ecosystems. Moreover, changing rainfall patterns due to climate variability and changes caused by anthropogenic activities also negatively impact the coastal and marine ecosystems (Brown et al., 2019 ). According to a WHO report, algal blooms contribute to ciguatera fish poisoning and can happen after extreme rainfall events caused by tropical cyclones (WHO, 2022). Poor agricultural practices and waste management increase the exposure of midstream and downstream nutrients and sedimentations to coastal communities (Begg et al., 2021 ; Ding, 2022 ). A similar study further elaborates that the impacts of anthropogenic land use changes on the spatiotemporal distribution of rainfall have contributed to fluvial flooding and run-offs into the marine and coastal communities, particularly on islands (Jiang et al., 2020 ). For instance, around 24% of coral reefs in Southeast Asia experienced damage from sediment accumulations due to surface run-off during extreme rainfall events as a result of land use changes (Mulyono et al., 2021 ). Soil erosion, slope factors, cropping systems, and poor management practices cause damage to the coral reefs as a result of changing rainfall patterns. There are arguments that little attention is given to threats originating from inland and terrestrial ecosystems, while priorities target marine or freshwater ecosystems when there are events of marine diseases such as fish poisoning outbreaks (Tulloch et al., 2021 ). Precipitation can trigger land and mudslides (Geertsema et al., 2009 ; Schuster & Highland, 2003 ), especially coastal landslides that combine with rising sea levels (Jakob, 2022 ) can harm habitats for marine plants such as macroalgae and seaweeds (Stancheva et al., 2021 ). Flooding, land/and mudslides frequently occur during wet and tropical cyclone seasons where rainfall is at its highest level. In 2017, a mudflow destroyed Walubuwe village on Ambae Island in Vanuatu due to a heavy rainfall event (Roy & Jong, 2018 ). These sediments and nutrients ended up in the nearshore and reef area contributed to the development of favorable conditions for the growth of dinoflagellates. Dinoflagellates are group of algae that play an important ecological role as primary producers at the base of aquatic ecosystems (Wynn et al., 2010 ) This research sought to investigate the relationship between rainfall and presence of ciguatoxins in fish. The study will provide a basis for an advisory on expected CFP during the seasonal forecast's release. That will help the Vanuatu communities and health authorities reduce and prevent CFP cases. Materials and Methods Study area Vanuatu is an archipelago of 83 islands in the Southwest Pacific extending from latitude 13 degrees south to 21 degrees south, with a land area of 12,281 square kilometers (Fig. 1 ). It exposed to many hazards, including tropical cyclones, sea level rise, earthquakes, landslides, tsunamis, and Volcanic activities (Jackson et al., 2017 ). Vanuatu has a total population of 300,019, with approximately 80% residing in rural and remote islands (Vanuatu Government, 2020). Like other Pacific Island countries, most of the Vanuatu rural population live along the coastal area where small-scale coastal fisheries play a significant domestic and livelihood needs (Campbell et al., 2024 ). Vanuatu produced 1,106 tonnes of fish for commercial purposes and 2,800 tonnes for subsistence needs for rural communities (Gillett & Group, 2011 ). These figures show that the local population consumes more coastal fish than commercially trading them. Fish play a key role as the source of protein for communities in Vanuatu. Vanuatu has two rainfall seasons: the wet season from November to April and the dry Season from May to October (Tigona et al., 2023 ; Tigona & de Freitas, 2012). The South Pacific Convergence Zone (SPCZ) influences the seasonal rainfall distribution over Vanuatu. Tropical cyclones and El-Niño Southern Oscillation (ENSO) events contribute to annual rainfall variability over Vanuatu (Pariyar et al., 2020 ). Data Rainfall Data We used daily homogenized rainfall data for eight rainfall stations in Vanuatu (Table 1 ), ranging from 1981 to 2022. The daily rainfall station data were obtained from the Vanuatu Meteorology and Geo-Hazards Department (VMGD). All stations had over 95% of available data during the observation periods except Saratamata station, with 77.8%. Table 1 Selected Stations Used for this study Sola -13.85 167.55 18m 1981–2022 41 95 Pekoa -15.52 167.22 42m 1985–2022 37 97.8 Saratamata -15.17 167.58 27m 2007–2022 16 77.8 Lamap -16.42 167.80 26m 1981–2022 41 95.2 Bauerfield -17.70 168.30 21m 1985–2022 37 98.9 Mackenzie Hill -17.81 168.38 50m 1990–2022 32 99.7 White Grass -19.45 169.22 11m 1981–2022 41 98.4 Aneityum -20.23 169.77 7m 1981–2022 41 98.4 Ciguatera Fish Poisoning Data The CFP-recorded cases were compiled from multiple sources, including the Vanuatu Ministry of Health (VMoH) and South Pacific Epidemiological and Health Information Services (SPEHIS) based at the South Pacific Community. Most of the CFP data from these sources consisted of annual records from 1981 to 2022 derived from the VMoH, with a few exceptions of monthly data covering seven years, 1989–1996, derived from SPEHIS. The daily data on CFP cases from 2021 to 2023 was obtained from the VMoH. The CFP cases are based on records of patients seeking medical attention at any health center in Vanuatu during those years. It does not include people who used local remedies to treat CFP cases. This study only uses the data from 1989 to 1996 and 2021 to 2023 for analysis. Structured Interview Data Structured interviews were conducted with a sample size of thirty-seven (37) people, consisting of twenty-eight (28) males and nine females (9) in Ambae Island, Vanuatu. This location was selected due to increasing challenges posed by the interaction between people and the environment, particularly in terms of marine resources, as a result of increased CFP cases (Goodman et al., 2003). The qualitative approach was used based on semi-structured interviews questions. We use this method to explore the understanding and knowledge of the community on CFP cases. The Interviews were carried out in seven villages. The participants were chosen from various backgrounds by our local focal point who knew of these people, their roles in the community and how long they have lived in the area. Interviews, which were approximately one hour long, were conducted by the lead author. The interviews took place in participants’ homes to ensure privacy. All interviews were recorded both in writing and through audio recordings, which were later transcribed. We used Statistical Package for Social Sciences (SPSS) software to analyze the data from the interviews. Methodology Rainfall Variability over Vanuatu Rainfall variability was assessed using Mean, standard deviation, and coefficient of variability (CV) to determine how variable is Vanuatu’ rainfall. The Coefficient of Variation (CV) value is in percentage. The lower the value of the coefficient of variation, the more precise the data are closer to the mean. A higher CV (%) indicates more significant variability in rainfall, whereas a lower CV (%) indicates more consistent rainfall from year to year. CV = \(\:\frac{\sigma\:}{\mu\:}\) x 100 …………..……………………………………………………………………………………..…………… Eq. 1 Where CV is the Coefficient variation, \(\:\varvec{\sigma\:}\) is the Standard Deviation, and \(\:\varvec{\mu\:}\) is the mean. To determine the yearly extreme wetness and dryness for different years, we used the Rainfall Anomaly Index (RAI) (Freitas, 2005 ; Araújo et al., 2009 ), Eqs. 2 and 3. RAI has been used in a few studies as an alternative to the Standard Precipitation Index (SPI) (Costa & Rodrigues, 2017 ; Hänsel et al., 2016 ; Raziei, 2021 ). The classification of RAI intensity is shown in Table 2 . RAI = \(\:3\left[\frac{N-\stackrel{-}{N}}{\stackrel{-}{M}-\stackrel{-}{N}}\right]\) , For positive anomalies……………………………………………………………..……..Eq. 2 RAI = \(\:-3\left[\frac{N-\stackrel{-}{N}}{\stackrel{-}{x}-\stackrel{-}{N}}\right]\) , For negative anomalies …………………….……………................................…..Eq. 3 Where N is the annual rainfall, \(\:\stackrel{-}{\mathbf{N}}\) is the average annual rainfall, \(\:\stackrel{-}{\mathbf{M}}\) is the average of the ten highest annual rainfall, and \(\:\stackrel{-}{\varvec{x}}\) is the average of the ten lowest annual rainfall, positive anomalies have their values above average, and negative anomalies have their values below average. Table 2 Classification of Rainfall Anomaly Index Intensity RAI range Classification Rainfall Anomaly Index (RAI) Above 4 Extremely humid 2 to 4 Very humid 0 to 2 Humid -2 to 0 Dry -4 to -2 Very dry Below − 4 Extremely dry Source: Freitas ( 2005 ) adapted by Araujo et al. (2009) Ciguatera Fish Poisoning Analysis Data Two data sets were analyzed using OriginPro software: the monthly data obtained from SPEHIS (1989–1996) and the daily CFP recorded cases (2021–2023) from VMoH. These data are completed datasets without data gaps. Using a time series chart on Origin Pro, we plot the monthly data to determine the cycle of CFP cases and use a cross-correlation function to find the correlation coefficient and the lag time between rainfall and subsequent CFP cases. The daily data CFP cases were analysed with daily rainfall data to determine the relationship between the two datasets. Cross-Correlation Function Equation We use the cross-correlation function to calculate Lag Time between rainfall and CFP cases. Eq. 4 was used to determine the lag time cross-correlation coefficient for 0 to 8 months. Given the two-time series data set, in this case rainfall ( \(\:\:{\text{x}}_{\text{t}}\:)\) and CFP cases ( \(\:{\:\text{y}}_{\text{t}\:}\) ), we delay \(\:{\text{x}}_{\text{t}}\) by T samples then calculate the cross-correlation between rainfall and CFP cases. \(\:{{\sigma\:}}_{\text{x}\text{y}}\left(\text{T}\right)=\frac{1}{\text{N}-1}\sum\:_{\text{t}=1}^{\text{N}}\left({\text{x}}_{\text{t}}-\text{T}-{{\mu\:}}_{\text{x}}\right)\left({\text{y}}_{\text{t}}-{\mu\:}\text{y}\right)\) ……………………….…………..Eq. 4 Where \(\:{{\mu\:}}_{\text{x}}\) and \(\:{{\mu\:}}_{\text{y}}\) are the mean of each time series, N is the sample in each time series. \(\:{{\sigma\:}}_{\text{x}\text{y}}\left(\text{T}\right)\) is the cross-covariance function. The normal version of the cross-correlation function is shown below; \(\:{\text{r}}_{\text{x}\text{y}}\left(\text{T}\right)=\) \(\:\frac{{\sigma\:}_{xy}\left(T\right)}{\sqrt{\sigma\:\left({x}_{t}\cdot\:{\stackrel{-}{x}}_{t}\right)\sigma\:\left({y}_{t}\cdot\:{\stackrel{-}{y}}_{t}\right)}}\) ………………………………………..……………Eq. 5 Where, \(\:{\text{r}}_{\text{x}\text{y}}\left(\text{T}\right)\) is the cross-correlation function, \(\:\left(\sigma\:\left({x}_{t}\cdot\:{\stackrel{-}{x}}_{t}\right)\sigma\:\left({y}_{t}\cdot\:{\stackrel{-}{y}}_{t}\right)\right)\) 2 is the variance of rainfall and CFP data signal. Results Rainfall Variability Table 3 shows the mean, standard deviation, and coefficient of variation (CV) for the seasonal and annual rainfall for all eight stations. On average, Vanuatu receives between 1300 mm to 4100 mm of rainfall annually. Sola station receives the highest annual rainfall of 4173 mm, whereas White Grass receives the lowest on average per year at 1316 mm. Rainfall in Vanuatu varies between the wet season (November to April) and the dry season (May to October). The analysis of seasonal rainfall contributions shows that all stations receive over 50% of their annual rainfall during the wet season and less than 40% in the dry season. The coefficient of variation (CV), expressed as a percentage, assesses the reliability of seasonal and year-to-year rainfall variability across all stations. Annually, all stations show a CV below 6%. For the seasons (Dry & wet seasons), the CV remains below 13% (Table 3 ), indicating minimal year-to-year variability of rainfall in Vanuatu. Table 3 The Mean, Standard Deviation, and Coefficient of Variability (CV) for Wet and dry Seasons and Annual Rainfall. Station Name Wet Season Dry Season Annual \(\:\text{M}\text{e}\text{a}\text{n}\) Std deviation CV % Mean Std deviation CV % Mean Std deviation CV % 1 Sola 2375.1 161.4 6.8 1798.6 158.4 8.8 4173.6 174.3 4.2 2 Pekoa 1631.2 137.5 8.4 939.4 106.5 11.3 2570.6 140.1 5.4 3 Saratamata 1348.7 91.9 6.8 691.4 59.5 8.6 2040.1 96.1 4.7 4 Lamap 1270.0 121.2 9.5 728.2 83.5 11.5 1998.3 117.9 5.9 5 Bauerfield 1572.4 148.0 9.4 747.9 87.9 11.7 2320.3 143.2 6.2 6 Mackenzie Hill 1406.6 128.0 9.1 753.2 94.0 12.5 2159.8 127.0 5.9 7 White Grass 907.3 115.6 12.7 409.0 53.4 13.0 1316.3 100.3 7.6 8 Aneityum 1489.1 162.5 10.9 811.6 84.1 10.4 2300.7 213.3 9.3 The annual rainfall cycle shows that Vanuatu has two seasons for all the eight stations analyzed (Fig. 2 ). Rainfall in Vanuatu is also spatially variable. with stations located in the north (Sola and Pekoa) (Fig. 1 ) and stations in the central (Bauerfield), receiving, on average, more rainfall than other stations (Table 3 ). Sola station receives the highest rainfall on average, while White Grass station receives the lowest rainfall. This is clearly shown in Table 3 , where the White Grass station had the highest contribution of 69%, an average of 907.3 mm to the annual rainfall during the wet season, while the Sola station had the lowest contribution of 57%, an average of 2375 mm to the annual rainfall during the wet season. Figure 3 shows the time series of annual rainfall variability from 1990 to 2022 based on the climatological period of 1991–2020 for all eight stations. The results indicate that annual average rainfall varies yearly. Based on Fig. 3 , very low (negative) RAI values indicate extremely dry arid years, contributing to very low rainfall records. In contrast, very high (positive) RAI values indicate very humid/wet years, contributing to very high rainfall records. According to the chart, the years 1988–1989, 2010/2012, and 2021–2022 are years with enhanced rainfall, while 1982–1983, 1991–1992, and 2014–2016 have depressed rainfall. Ciguatera Fish Poisoning Cases in Vanuatu The annual cycle of average monthly CFP cases is shown in Fig. 4 (a, b). Figure 4 (a) shows that the average number of ciguatera fish poisoning cases were highest in February, with 93 cases and lowest in August with 44 cases. The cases tend to be high during in the months of January to March and started to decrease to the lowest average of 44 cases in August. Figure 4 (b) shows the annual cycle of CFP cases for 2021 to 2023 period. During the three years, more cases of CFP were reported in the month of March, September, and November. Where, in the month of March an average of 13 cases were reported followed by September with 8 cases while May, August, and December recorded the lowest with one case each. The low number of average CFP cases in Fig. 4 (b) is due to only three years of dataset. Similar to the Fig. 4 (a), on average most CFP cases were reported during the first four months of the year. Figure 5 shows the distribution of the daily CFP cases for 2021, 2022, and 2023 respectively. In 2021, most CFP cases were recorded in March, with April, November, and December having less than 2 cases each and zero cases for the other months. The high daily cases for 2021 were recorded in March and the beginning of April, with over four to five CFP cases per day. In 2022, CFP cases were recorded in January, July, and September, and one case was recorded in October. For 2023, the CFP cases were recorded in July, August, and December. The highest daily cases in 2023 were recorded in February, April, May, September and October. Daily CFP cases were high in March and November during the three-year periods. Cross-Correlations Analysis of Rainfall and CFP Cases The correlation coefficients ( r ) were calculated using a cross-correlation function based on 1989–1996 and 2021–2023 rainfall and CFP cases datasets to determine the lag time between the two datasets. Figure 6 illustrates the positive and negative correlation coefficient values and the lag time from 0 to 8 months. The lag time of 0 and 1 month show a high correlation coefficient of 0.7, decreasing to 0.3 at a 2-month lag for the 1989–1996 data (Fig. 6 (a)). From the third month onward, the correlation coefficient becomes negative. For the 2021–2023 data (Fig. 6 (b)), the highest correlation coefficient of 0.4 occurs at a 1-month lag, followed by 0.2 at a 2-month lag and 0.1 at 0-month lag. Similar to Fig. 6 (a), the correlation coefficient decreases to zero at a 3-month lag and becomes negative from the 4-month to 8-month lag periods. Figure 7 shows the time series of daily rainfall records and CFP cases for 2021 to 2023. The results show that, there are recorded cases of CFP after an events of extreme rainfall. For instance, the rainfall of 101.5 mm was recorded on the 9 of January, 62.5mm on the 12 of January, 65.5 mm on the 14 of January, 95.5mm on the 24 of January, and 80.5 on the 4 of March. After the extreme rainfall events, high cases of CFP were recorded in March and the first half of April. Another high record of daily rainfall was on the 4 of May, but no cases of CFP were recorded thereafter. The same results are shown in the 21 of October, when 115mm of rainfall was recorded. This led to a few cases being recorded in November. Similarly, few cases were recorded after the 4th of December rainfall. The cases of CFP were recorded in January 2022, following 280 mm of daily rainfall on December 4, 2021 (Figure, 7(b)). The same case for 2022, where rainfall of 188 mm on January 29 and 213.5 mm on February 9 led to a few CFP cases reported in March. Additionally, rainfall in march and April resulted in CFP case in May (Figure, 7b). On May 20, an extreme rainfall event of 429 mm was recorded due to Tropical Cyclone Gina, which passed over the central islands of Vanuatu, causing heavy rainfall. A month after this event, CFP cases were reported in June, July, August, and September. Despite this being Vanuatu’s dry season, daily rainfall exceeding 40 mm was recorded during these months, contributing to the reported CFP cases. Similar results are shown in Fig. 7 (c) for the year 2023, where heavy rainfall preceded the CFP cases. Community Knowledge and Perceptions on Ciguatera Fish Poisoning We interviewed thirty-seven people including twenty-eight males and nine females. Figure 11 (a) shows the percentage of the people interviewed with 41 percent of the respondents have lived in the area for over 50 years,32 percent for more than 40 years and 13 percent for less than 30 years. All the people interviewed have experienced or have been infected with CFP sometimes in their lifetime. Their experiences and knowledge of the nature within their surrounding during these longer periods have helped them to identify the trend and the changes within their environment that related to the increase cases of CFP in the area. Sixty-three (63%) percent of the respondents are fishermen and farmers and the other 37 percent are key informants; government officials, chiefs and church leaders (Fig. 8 (a). Figure 9 shows the percentage of people with knowledge of the different causes of CFP. According to Fig. 9 , waste and sediments from land surface run-off as a result of heavy rainfall have the highest percentage of 52, followed by new coral at 20 percent, algae at 10 percent, dead coral at 6 percent, and jellyfish and knowledge of common poison fish at 4 percent. Based on local knowledge from elders, certain fish are always poisonous and can be spotted easily. Each participant knew more than one causes of CFP in the area. The participants’ knowledge of extreme weather events, such as extreme rainfall and tropical cyclones, are causes of CFP. For example, heavy sediments flow into the coastal waters after extreme weather; corals will die out, allowing algae to grow on them, allowing fish to eat the algae and get contaminated with ciguatoxins. Twenty percent of the participants know new corals as contributors to fish poisoning. We collected information from the communities on the areas where they fish, how often they have access to these areas, how they treat infected person with the ciguatoxins from eating those fish and the date they remembered of been infected with CFP. Figure 10 shows the results of access, type of fish and treatment of CP. Figure 10 (a) indicate that 61 percent of the people of Ambae have access to fish twice a week (very often), 27 percent often (once per week) while 11 percent less often (twice per month) to eat fish. Most of the fish consumed by 89 percent of the people interviewed are reef or nearshore fish while 11 percent of the participant have consumed some offshore fish. One participant stated that in 2005, he was infected with Ciguatera after eating tuna gut (stomach, intestines and colon). The participants also have knowledge or have known of people in their communities who have been infected with CFP. For instance, one responded stated on the 10 of October, 2023, more than ten people in his village were infected with CFP after eating fish taken using line fishing from nearshore. The people of Vanuatu who lived in rural and remote islands use their local knowledge to treat or cure CFP. Figure 10 (c). Show that Fifty-four percent (54%) of the responded use home remedies (local medicine) to cure CFP while 19 percent seek medical attention from health centers. About 27 percent of the people have access or used both home remedies and seek treatment from hospitals to treat CFP. Discussion The results show that rainfall over Vanuatu varies both spatially and temporally. Spatially, the stations in the north of Vanuatu, such as Sola, have higher rainfall than those in the south. Also, rainfall varies with seasons with some months recording more rainfall while others recording less. The results show inter-annual variability of rainfall in Vanuatu, with periods of enhanced and depressed rainfall. Several factors affected temporal and spatial rainfall variability in Vanuatu, including the South Pacific Convergence Zone (SPCZ), El Nino Southern Oscillation (ENSO), tropical cyclone (TC) events, and Madden Julian Oscillation (Tigona & de Freitas, 2012). The northeast-to-southwest movement of SPCZ influences the seasonality of rainfall in Vanuatu. It moves further north during the dry season (May-October) and southwards during the wet season (November-April), causing the seasonal variability of rainfall amount (Evans et al., 2016 ). Sola Stations, which is located north of Vanuatu and closer to the SPCZ, has a high monthly rainfall amount compared to White Grass stations, which is further away from the SPCZ. Tropical cyclones significantly contribute to high rainfall in Vanuatu (Deo et al., 2021 ; Magee et al., 2016 ). In Vanuatu, the tropical cyclone season coincides with the wet season (November to April). Madden Julian Oscillation (MJO), a convective activity that travels around the globe within 30 to 60 days (Diamond & Renwick, 2015 ; Tu’uholoaki et al., 2023 ), also contributes to extreme rainfall in Vanuatu during the wet season. The passage of MJO north of Vanuatu contributes to enhanced precipitation distribution and the genesis of tropical cyclones, especially during the wet season (Takemi, 2018 ). The inter-annual rainfall variability in Vanuatu is caused by the El-Niño Southern Oscillation (ENSO) events. The years with a positive rainfall anomaly index value (above-normal) are La Niña years, while those with negative RAI values (below-normal) are El Niño years. The years with zero (0) value (normal rainfall) indicate neutral years. According to the classification of the RAI intensity (Araújo et al., 2009 ), the increase in the positive values of RAI corresponds to extremely humid years which leads to higher recorded of rainfall. Both depressed and enhanced rainfall have impacts on the environment including water, agriculture, fisheries and disaster management sectors. For fisheries sector, continued enhanced rainfall caused flash flooding and surface run-off that all ended up in the near shore and coral reef system (Fabricius et al., 2014 ; Kosmas et al., 1997 ). Heavy rainfall during a La Niña event of 2017–2018 causes a landslide and mudflow in Walubuwe village on Ambae Island in Vanuatu (Roy & Jong, 2018 ). The distribution of ciguatera cases in Vanuatu is related to extreme rainfall and the frequency of natural disturbances, such as extreme climate events occurring each year (Rongo & van Woesik, 2013 ). This is in line with other studies on the increased trend of ciguatera cases around the globe, including the Pacific Islands (Chinain et al., 2020 , 2021 ; Pottier et al., 2023 ). Vanuatu communities are mainly concentrated along the coast, and their main source of protein is fish. The cases of CFP will continue to emerge if Vanuatu continues to face natural disturbances such as cyclones and rainfall, and this will continue to have a major impact on the health and socio-economic lives of the local population. The CPF has also affected other Pacific Island countries (Chinain et al., 2023 ; Gray, 2020 ; Lako et al., 2023a ; Soliño & Costa, 2020 ). Our study finds that people over 35 years have a high risk of getting poisoned by eating contaminated fish. Based on the data analyzed for this study, 57 percent of people infected with CFP in Vanuatu are males (Suppl Fig. 1). Several external factors may have contributed to this, such as Vanuatu's traditional and cultural way of life, which allows males to eat first in any feast gathering or small family homes. Males tend to eat more of the fish, including the head. Moreover, males in Vanuatu tend to have more access to fishing gear and are likely to have more time fishing than women, who usually remain home. Other studies in Fiji and Vanuatu echoed similar incidents of males being more at high risk of fish poisoning due to their cultural way of life (Goodman et al., 2003; Lako et al., 2023). Fish is an essential diet for Pacific Islanders; however, it may also cause public health risks to the local population. Knowledge of the right time to fish must be communicated to the communities to limit the cases of CFP in the future. Land factors such as surface run-off and creek discharge depend on the inter-annual variability of the seasonal rainfall and may contribute to environmental conditions that favor the growth of dinoflagellates (Seymour & McLellan, 2025 ). The changes in rainfall patterns may lead to an increase in algal blooms (cyanobacteria) inland water bodies and (dinoflagellates) in oceans and bays by nutrients washed down by events of extreme rainfall (Reichwaldt & Ghadouani, 2012 ). Surface run-off may change the acidification level of coastal waters, resulting in changing habitats, favoring environmental conditions for Gambierdiscus growth leading to ciguatoxins in fish (Rhodes et al., 2020 ). For Vanuatu, future research interest will be looking into water chemistry along coastal waters of provinces and islands with high CFP cases. The correlation coefficient decreases with increased lag time for both datasets (1989–1996 and 2021–2023). It takes roughly a lag time of 0 to 2- months for waste and sediments to be transported by land surface run-off after heavy rainfall events to the marine environment in order for favorable environmental conditions for dinoflagellate growth, which fish feed on and get contaminated with ciguatoxins, leading to CFP in people when they access and consume the fish. The results are only based on cases recorded by health centers when the person is diagnosed by a health worker and confirmed to be contaminated by CFP. Notably, from the graph, the lag time of 1 month can be used as the average lag time for the high cases of CFP to be reported after an event of rainfall. This is because of rate of run-off on volcanic islands (Ersöz et al., 2023 ) and steep slope (Chen et al., 2018 ) which contributes to sediments to the marine ecosystem. Other climatic factors such as sea surface temperature also determine the CPF cases (Zheng et al., 2020 ). The extreme rainfall during the La Niña event of 2020–2023 (Shi et al., 2023 ) may have contributed to daily CFP reported during this period. The high number of CFP cases observed in 2023 is linked to the La Niña event. The increased rainfall in 2023 resulted from the multi-year La Niña event. During multi-year ENSO events, rainfall tends to increase in the third year (Huang et al., 2024 ). This explains the high number of daily cases from June to September 2022., high rainfall was observed during the first four months and the last four months of 2022 resulting in high number of CFP cases. The people in Vanuatu used both their local knowledge and information from government agencies to make decisions concerning the prevention of fish poisoning (Rarai et al., 2024 ). The local knowledge on the prevention of the impact of ciguatera has been part of their life experiences and practices that have been passed on through generations. For example, algae growing on dead corals after tropical cyclones have damaged the corals can alert the communities of the possible cases of CFP if people consume fish from the affected area. Local knowledge plays a very vital aspect of the lives of the people dealing with CFP. Local knowledge has been used to predict the extreme weather events in Vanuatu (Rarai et al., 2022 ) and tsunamis in Indonesia (Rahman et al., 2017 ). The people in Vanuatu eat more reef fish than offshore fish (Campbell et al., 2024 ). Reef fish are easier to access, which leads to a high number of cases of CFP in the area. The fish may be contaminated with Ciguatoxins if they feed on dinoflagellates as a result of algae growing on dead coral caused by tropical cyclones or sediment cover (Rarai et al., 2024 ). Most of the CFP cases resulted from consumption of reef fish in many coastal settlements in the southeast Asia and the Pacific island (Chan, 2015 ). Treating CFP in Vanuatu is an option between local remedies and hospital treatment. Most of the people interviewed for this study used local remedies to cure CFP. Local and traditional treatment of fish poisoning is common in other pacific island (Chassagne et al., 2022 ; Lako et al., 2023). Conclusion Ciguatera Fish Poisoning (CFP) poses an escalating health risk to the people of Vanuatu, with cases on the rise. In light of the increasing incidence of CFP, there is a pressing need for earlier warning systems and heightened awareness within communities., this study finds that rainfall contributes to increased cases of CFP. While this study only looks at ciguatera cases and their correlation with rainfall, there might be biases because of data availability. Future studies should look at more extended periods of CFP data cases. Moreover, since ciguatera cases are higher in some islands and less in others in Vanuatu, future research may also target localized areas on islands with high CFP cases. Declarations The University of the South Pacific Human Research Ethics Committee approved this project. Data The data supporting this study's findings are available in the Fig share database, Acknowledgements We thank the Vanuatu Ministry of Health and the Vanuatu Ministry of Climate Change for data used in this study. This research was supported by the Association of the commonwealth universities (ACU) through the Ocean Country Partnership Programme Scholarship (OCPP) offered to the lead author. The University of the South Pacific hosted the lead author in Suva, Fiji Islands. Author Contributions Allan Rarai: Design, Conceptualization, Investigation, Formal Analysis, Write original draft, Review and editing; Philip Obaigwa Sagero: Review and editing; Eberhard Weber: Review and editing. Competing Interest The authors declare no competing interest. Additional information Supplementary information. The online version contains supplementary materials available at Correspondence and requests for materials should be addressed to Allan Rarai References Araújo, L., Moraes Neto, J., & Sousa, F. (2009). Classificação da precipitação anual e da quadra chuvosa da bacia do rio Paraíba utilizando índice de Anomalia de Chuva (IAC). Ambiente e Agua - An Interdisciplinary Journal of Applied Science , 4 (3), 93–110. https://doi.org/10.4136/ambi-agua.105 Barrett, J. R. (2014). Under the Weather with Ciguatera Fish Poisoning: Climate Variables Associated with Increases in Suspected Cases. Environmental Health Perspectives , 122 (6), A167–A167. https://doi.org/10.1289/ehp.122-A167 Begg, S. S., De Ramon N’Yeurt, A., & Iese, V. (2021). Integrated flood vulnerability assessment of villages in the Waimanu River Catchment in the South Pacific: The case of Viti Levu, Fiji. Regional Environmental Change , 21 (3), 83. https://doi.org/10.1007/s10113-021-01824-9 Brown, J. R., Lengaigne, M., Lintner, B. R., Widlansky, M. J., van der Wiel, K., Dutheil, C., Linsley, B. K., Matthews, A. J., & Renwick, J. (2020). South Pacific Convergence Zone dynamics, variability and impacts in a changing climate. Nature Reviews Earth & Environment , 1 (10), 530–543. https://doi.org/10.1038/s43017-020-0078-2 Brown, N. J., Nilsson, J., & Pemberton, P. (2019). Arctic Ocean Freshwater Dynamics: Transient Response to Increasing River Runoff and Precipitation. Journal of Geophysical Research: Oceans , 124 (7), 5205–5219. https://doi.org/10.1029/2018JC014923 Campbell, B., Steenbergen, D., Li, O., Sami, A., Nikiari, B., Delisle, A., Neihapi, P., Uriam, T., & Andrew, N. (2024). Characterising a diversity of coastal community fisheries in Kiribati and Vanuatu. Fish and Fisheries , 25 (5), 837–857. https://doi.org/10.1111/faf.12849 Campbell, B., Steenbergen, D., Li, O., Sami, A., Nikiari, B., Delisle, A., Neihapi, P., Uriam, T., & Andrew, N. (2024b). Characterising a diversity of coastal community fisheries in Kiribati and Vanuatu. Fish and Fisheries , 25 (5), 837–857. https://doi.org/10.1111/faf.12849 Chan, T. Y. K. (2015). Ciguatera Fish Poisoning in East Asia and Southeast Asia. Marine Drugs , 13 (6), Article 6. https://doi.org/10.3390/md13063466 Chassagne, F., Butaud, J.-F., Torrente, F., Conte, E., Ho, R., & Raharivelomanana, P. (2022). Polynesian medicine used to treat diarrhea and ciguatera: An ethnobotanical survey in six islands from French Polynesia. Journal of Ethnopharmacology , 292 , 115186. https://doi.org/10.1016/j.jep.2022.115186 Chen, H., Zhang, X., Abla, M., Lü, D., Yan, R., Ren, Q., Ren, Z., Yang, Y., Zhao, W., Lin, P., Liu, B., & Yang, X. (2018). Effects of vegetation and rainfall types on surface runoff and soil erosion on steep slopes on the Loess Plateau, China. CATENA , 170 , 141–149. https://doi.org/10.1016/j.catena.2018.06.006 Chinain, M., Gatti Howell, C., Roué, M., Ung, A., Henry, K., Revel, T., Cruchet, P., Viallon, J., & Darius, H. T. (2023). Ciguatera poisoning in French Polynesia: A review of the distribution and toxicity of Gambierdiscus spp., and related impacts on food web components and human health. Harmful Algae , 129 , 102525. https://doi.org/10.1016/j.hal.2023.102525 Chinain, M., Gatti, C. M. i., Darius, H. T., Quod, J.-P., & Tester, P. A. (2021). Ciguatera poisonings: A global review of occurrences and trends. Harmful Algae , 102 , 101873. https://doi.org/10.1016/j.hal.2020.101873 Chinain, M., Gatti, C. M. i, Martin-Yken, H., Roué, M., & Darius, H. T. (2020). 10 Ciguatera poisoning: An increasing burden for Pacific island communities in light of climate change? In L. M. Botana, M. C. Louzao, & N. Vilarino (Eds.), Climate Change and Marine and Freshwater Toxins (pp. 369–428). De Gruyter. https://doi.org/10.1515/9783110625738-010 Costa, J. A., & Rodrigues, G. P. (2017). Space-time distribution of rainfall anomaly index (RAI) for the Salgado Basin, Ceará State-Brazil. Ciência e Natura , 39 (3), 627–634. Darias-Dágfeel, Y., Sanchez-Henao, A., Padilla, D., Martín, M. V., Ramos-Sosa, M. J., Poquet, P., Barreto, M., Silva Sergent, F., Jerez, S., & Real, F. (2024). Effects on Biochemical Parameters and Animal Welfare of Dusky Grouper (Epinephelus marginatus, Lowe 1834) by Feeding CTX Toxic Flesh. Animals , 14 (12), Article 12. https://doi.org/10.3390/ani14121757 Deo, A., Chand, S. S., Ramsay, H., Holbrook, N. J., McGree, S., Magee, A., Bell, S., Titimaea, M., Haruhiru, A., Malsale, P., Mulitalo, S., Daphne, A., Prakash, B., Vainikolo, V., & Koshiba, S. (2021). Tropical cyclone contribution to extreme rainfall over southwest Pacific Island nations. Climate Dynamics , 56 (11), 3967–3993. https://doi.org/10.1007/s00382-021-05680-5 Diamond, H. J., & Renwick, J. A. (2015). The climatological relationship between tropical cyclones in the southwest pacific and the Madden–Julian Oscillation. International Journal of Climatology , 35 (5), 676–686. https://doi.org/10.1002/joc.4012 Ding, L. (2022). Exploring the Linkage between Land Use Type and Stream Water Quality of an Estuarine Island Applying GWR Model: A Case Study of Chongming, Shanghai. Journal of Geoscience and Environment Protection , 10 (7), Article 7. https://doi.org/10.4236/gep.2022.107017 Dixon, A. M., Puotinen, M., Ramsay, H. A., & Beger, M. (2022). Coral Reef Exposure to Damaging Tropical Cyclone Waves in a Warming Climate. Earth’s Future , 10 (8), e2021EF002600. https://doi.org/10.1029/2021EF002600 Ersöz, T., Haneda, K., Kuribayashi, A., & Gonda, Y. (2023). Temporal changes in lahar sediment run-off characteristics and run-off coefficients in the Arimura River basin of Sakurajima volcano, Japan. Earth Surface Processes and Landforms , 48 (14), 2682–2703. https://doi.org/10.1002/esp.5654 Evans, J. P., Bormann, K., Katzfey, J., Dean, S., & Arritt, R. (2016). Regional climate model projections of the South Pacific Convergence Zone. Climate Dynamics , 47 (3), 817–829. https://doi.org/10.1007/s00382-015-2873-x Fabricius, K. E., Logan, M., Weeks, S., & Brodie, J. (2014). The effects of river run-off on water clarity across the central Great Barrier Reef. Marine Pollution Bulletin , 84 (1), 191–200. https://doi.org/10.1016/j.marpolbul.2014.05.012 Freitas, M. (2005). sistema de suporte à decisão para o monitoramento de secas meteorológicas em regiões semiáridas. Rev. Tecnol.,(suppl. 19): Pp. 84-95 . Geertsema, M., Highland, L., & Vaugeouis, L. (2009). Environmental Impact of Landslides. In K. Sassa & P. Canuti (Eds.), Landslides – Disaster Risk Reduction (pp. 589–607). Springer. https://doi.org/10.1007/978-3-540-69970-5_31 Gillett, R., & Group, F. (2011). Fisheries of the Pacific Islands: Regional and national information . https://openknowledge.fao.org/handle/20.500.14283/i2092e Goodman, A., Williams, T. N., & Maitland, K. (2003a). CIGUATERA POISONING IN VANUATU. The American Journal of Tropical Medicine and Hygiene , 68 (2), 263–266. https://doi.org/10.4269/ajtmh.2003.68.263 Gray, M. J. (2020). A descriptive study of ciguatera fish poisoning in Cook Islands dogs and cats: Demographic, temporal, and spatial distribution of cases. Veterinary World , 13 (1), 10. https://doi.org/10.14202/vetworld.2020.10-20 Guillemot, N., Chabanet, P., & Le Pape, O. (2010). Cyclone effects on coral reef habitats in New Caledonia (South Pacific). Coral Reefs , 29 (2), 445–453. https://doi.org/10.1007/s00338-010-0587-4 Hänsel, S., Schucknecht, A., & Matschullat, J. (2016). The Modified Rainfall Anomaly Index (mRAI)—Is this an alternative to the Standardised Precipitation Index (SPI) in evaluating future extreme precipitation characteristics? Theoretical and Applied Climatology , 123 (3), 827–844. https://doi.org/10.1007/s00704-015-1389-y Heimann, K., Capper, A., & Sparrow, L. (2011). Ocean Surface Warming: Impact on Toxic Benthic Dinoflagellates Causing Ciguatera. In eLS . John Wiley & Sons, Ltd. https://doi.org/10.1002/9780470015902.a0023373 Holmes, M. J., Venables, B., & Lewis, R. J. (2021). Critical Review and Conceptual and Quantitative Models for the Transfer and Depuration of Ciguatoxins in Fishes. Toxins , 13 (8), Article 8. https://doi.org/10.3390/toxins13080515 Huang, A. T., Gillett, Z. E., & Taschetto, A. S. (2024). Australian Rainfall Increases During Multi-Year La Niña. Geophysical Research Letters , 51 (9), e2023GL106939. https://doi.org/10.1029/2023GL106939 Jackson, G., McNamara, K., & Witt, B. (2017). A Framework for Disaster Vulnerability in a Small Island in the Southwest Pacific: A Case Study of Emae Island, Vanuatu. International Journal of Disaster Risk Science , 8 (4), 358–373. https://doi.org/10.1007/s13753-017-0145-6 Jakob, M. (2022). Chapter 14—Landslides in a changing climate. In T. Davies, N. Rosser, & J. F. Shroder (Eds.), Landslide Hazards, Risks, and Disasters (Second Edition) (pp. 505–579). Elsevier. https://doi.org/10.1016/B978-0-12-818464-6.00003-2 Jiang, Q., He, X., Wang, J., Wen, J., Mu, H., & Xu, M. (2020). Spatiotemporal Analysis of Land Use and Land Cover (LULC) Changes and Precipitation Trends in Shanghai. Applied Sciences , 10 (21), Article 21. https://doi.org/10.3390/app10217897 Kohli, G. S., Farrell, H., & Murray, S. A. (2015). 9. Gambierdiscus, the cause of ciguatera fish poisoning: An increased human health threat influenced by climate change. In L. M. Botana, C. Louzao, & N. Vilariño (Eds.), Climate Change and Marine and Freshwater Toxins (pp. 273–312). De Gruyter. https://doi.org/10.1515/9783110333596-011 Kosmas, C., Danalatos, N., Cammeraat, L. H., Chabart, M., Diamantopoulos, J., Farand, R., Gutierrez, L., Jacob, A., Marques, H., Martinez-Fernandez, J., Mizara, A., Moustakas, N., Nicolau, J. M., Oliveros, C., Pinna, G., Puddu, R., Puigdefabregas, J., Roxo, M., Simao, A., … Vacca, A. (1997). The effect of land use on runoff and soil erosion rates under Mediterranean conditions. CATENA , 29 (1), 45–59. https://doi.org/10.1016/S0341-8162(96)00062-8 Lako, J. V., Naisilisili, S., Vuki, V. C., Kuridrani, N., & Agyei, D. (2023a). Local and Traditional Ecological Knowledge of Fish Poisoning in Fiji. Toxins , 15 (3), Article 3. https://doi.org/10.3390/toxins15030223 Magee, A. D., Verdon-Kidd, D. C., Kiem, A. S., & Royle, S. A. (2016). Tropical cyclone perceptions, impacts and adaptation in the Southwest Pacific: An urban perspective from Fiji, Vanuatu and Tonga. Natural Hazards and Earth System Sciences , 16 (5), 1091–1105. https://doi.org/10.5194/nhess-16-1091-2016 Maupin, C. R., Partin, J. W., Shen, C.-C., Quinn, T. M., Lin, K., Taylor, F. W., Banner, J. L., Thirumalai, K., & Sinclair, D. J. (2014). Persistent decadal-scale rainfall variability in the tropical South Pacific Convergence Zone through the past six centuries. Climate of the Past , 10 (4), 1319–1332. https://doi.org/10.5194/cp-10-1319-2014 Mulyono, A., Djuwansah, M. R., Narulita, I., Putra, R. D., & Surinati, D. (2021). Environmental Impact of Land-use Changes and Soil Loss on Coastal Coral Reef Cover: Study Case in the Small Tropical Island, Indonesia . https://doi.org/10.21203/rs.3.rs-148449/v1 Murray, J. S., Nishimura, T., Finch, S. C., Rhodes, L. L., Puddick, J., Harwood, D. T., Larsson, M. E., Doblin, M. A., Leung, P., Yan, M., Rise, F., Wilkins, A. L., & Prinsep, M. R. (2020). The role of 44-methylgambierone in ciguatera fish poisoning: Acute toxicity, production by marine microalgae and its potential as a biomarker for Gambierdiscus spp. Harmful Algae , 97 , 101853. https://doi.org/10.1016/j.hal.2020.101853 Pariyar, S. K., Keenlyside, N., Sorteberg, A., Spengler, T., Chandra Bhatt, B., & Ogawa, F. (2020). Factors affecting extreme rainfall events in the South Pacific. Weather and Climate Extremes , 29 , 100262. https://doi.org/10.1016/j.wace.2020.100262 Pottier, I., Lewis, R. J., & Vernoux, J.-P. (2023). Ciguatera Fish Poisoning in the Caribbean Sea and Atlantic Ocean: Reconciling the Multiplicity of Ciguatoxins and Analytical Chemistry Approach for Public Health Safety. Toxins , 15 (7), Article 7. https://doi.org/10.3390/toxins15070453 Puotinen, M., Maynard, J. A., Beeden, R., Radford, B., & Williams, G. J. (2016). A robust operational model for predicting where tropical cyclone waves damage coral reefs. Scientific Reports , 6 (1), 26009. https://doi.org/10.1038/srep26009 Rahman, A., Sakurai, A., & Munadi, K. (2017). Indigenous knowledge management to enhance community resilience to tsunami risk: Lessons learned from Smong traditions in Simeulue island, Indonesia. IOP Conference Series: Earth and Environmental Science , 56 (1), 012018. https://doi.org/10.1088/1755-1315/56/1/012018 Rarai, A., Parsons, M., Nursey-Bray, M., & Crease, R. (2022). Situating climate change adaptation within plural worlds: The role of Indigenous and local knowledge in Pentecost Island, Vanuatu. Environment and Planning E: Nature and Space , 5 (4), 2240–2282. https://doi.org/10.1177/25148486211047739 Rarai, A., Weber, E., Ruben, J., & Parsons, M. (2024). Indigenous knowledge with science forms an early warning system for ciguatera fish poisoning outbreak in Vanuatu. Communications Earth & Environment , 5 (1), 1–11. https://doi.org/10.1038/s43247-024-01931-5 Raziei, T. (2021). Revisiting the Rainfall Anomaly Index to serve as a Simplified Standardized Precipitation Index. Journal of Hydrology , 602 , 126761. https://doi.org/10.1016/j.jhydrol.2021.126761 Reichwaldt, E. S., & Ghadouani, A. (2012). Effects of rainfall patterns on toxic cyanobacterial blooms in a changing climate: Between simplistic scenarios and complex dynamics. Water Research , 46 (5), 1372–1393. https://doi.org/10.1016/j.watres.2011.11.052 Rhodes, L. L., Smith, K. F., Murray, J. S., Nishimura, T., & Finch, S. C. (2020). Ciguatera Fish Poisoning: The Risk from an Aotearoa/New Zealand Perspective. Toxins , 12 (1), Article 1. https://doi.org/10.3390/toxins12010050 Rongo, T., & van Woesik, R. (2013). The effects of natural disturbances, reef state, and herbivorous fish densities on ciguatera poisoning in Rarotonga, southern Cook Islands. Toxicon , 64 , 87–95. https://doi.org/10.1016/j.toxicon.2012.12.018 Roy, E. A., & Jong, E. de. (2018, April 25). Vanuatu: Landslide and flash flood hampers relief effort on Ambae. The Guardian . https://www.theguardian.com/world/2018/apr/25/vanuatu-landslide-and-flash-flood-hampers-relief-effort-on-ambae Schuster, R. L., & Highland, L. M. (2003). Impact of Landslides and Innovative Landslide-Mitigation Measures on the Natural Environment . Seymour, J. R., & McLellan, S. L. (2025). Climate change will amplify the impacts of harmful microorganisms in aquatic ecosystems. Nature Microbiology , 10 (3), 615–626. https://doi.org/10.1038/s41564-025-01948-2 Shi, L., Ding, R., Hu, S., Li, X., & Li, J. (2023). Extratropical impacts on the 2020–2023 Triple-Dip La Niña event. Atmospheric Research , 294 , 106937. https://doi.org/10.1016/j.atmosres.2023.106937 Soliño, L., & Costa, P. R. (2020). Global impact of ciguatoxins and ciguatera fish poisoning on fish, fisheries and consumers. Environmental Research , 182 , 109111. https://doi.org/10.1016/j.envres.2020.109111 Stancheva, M., Stanchev, H., Young, R., & Parlichev, G. (2021). Coastal erosion driven Land-Sea Interactions in Maritime Spatial Planning—A case of Bulgaria. Journal of Coastal Conservation , 25 (6), 54. https://doi.org/10.1007/s11852-021-00841-4 Takemi, T. (2018). The evolution and intensification of Cyclone Pam (2015) and resulting strong winds over the southern Pacific islands. Journal of Wind Engineering and Industrial Aerodynamics , 182 , 27–36. https://doi.org/10.1016/j.jweia.2018.09.007 Tester, P. A., Feldman, R. L., Nau, A. W., Kibler, S. R., & Wayne Litaker, R. (2010). Ciguatera fish poisoning and sea surface temperatures in the Caribbean Sea and the West Indies. Toxicon , 56 (5), 698–710. https://doi.org/10.1016/j.toxicon.2010.02.026 Tigona, R., Ongoma, V., & Weir, T. (2023). Towards improved seasonal rainfall prediction in the tropical Pacific Islands. Theoretical and Applied Climatology , 154 (1), 349–363. https://doi.org/10.1007/s00704-023-04560-8 Tigona, R. S., & de Freitas, C. R. (2012a). Relationship between the Southern Oscillation and rainfall in Vanuatu. Weather and Climate , 32 (1), 43–50. https://doi.org/10.2307/26169725 Tulloch, V. J. D., Atkinson, S., Possingham, H. P., Peterson, N., Linke, S., Allan, J. R., Kaiye, A., Keako, M., Sabi, J., Suruman, B., & Adams, V. M. (2021). Minimizing cross-realm threats from land-use change: A national-scale conservation framework connecting land, freshwater and marine systems. Biological Conservation , 254 , 108954. https://doi.org/10.1016/j.biocon.2021.108954 Tu’uholoaki, M., Espejo, A., Sharma, K. K., Singh, A., Wandres, M., Damlamian, H., & Chand, S. (2023). Influence of the Madden–Julian Oscillation (MJO) on Tropical Cyclones Affecting Tonga in the Southwest Pacific. Atmosphere , 14 (7), Article 7. https://doi.org/10.3390/atmos14071189 Vanuatu Ministry of Health, S. (2015, January 21). Ministry of Health—HOME . Ministry of Health. https://moh.gov.vu/ Vanuatu_National_Population_and_Housing_Census _-_Analytical_report_Volume_2.pdf . (2020). Retrieved March 1, 2025, from https://vnso.gov.vu/sites/default/files/2020_Vanuatu_National_Population_and_Housing_Census_-_Analytical_report_Volume_2.pdf Vale, P., & Sampayo, M. A. de M. (2003). Seasonality of diarrhetic shellfish poisoning at a coastal lagoon in Portugal: Rainfall patterns and folk wisdom. Toxicon , 41 (2), 187–197. https://doi.org/10.1016/S0041-0101(02)00276-3 Weir, T., Kumar, R., & Ngari, A. (2021). Interdecadal modulation of the effect of ENSO on rainfall in the southwestern Pacific. Journal of Southern Hemisphere Earth Systems Science , 71 (1), 53–65. https://doi.org/10.1071/ES19053 WHO-HEP-NFS-SSA-2022.1 -eng.pdf . (2022). Retrieved March 1, 2025, from https://iris.who.int/bitstream/handle/10665/352191/WHO-HEP-NFS-SSA-2022.1-eng.pdf?sequence Wynn, J., Behrens, P., Sundararajan, A., Hansen, J., & Apt, K. (2010). 6—Production of Single Cell Oils by Dinoflagellates. In Z. Cohen & C. Ratledge (Eds.), Single Cell Oils (Second Edition) (pp. 115–129). AOCS Press. https://doi.org/10.1016/B978-1-893997-73-8.50010-4 Zheng, L., Gatti, C. M. iti, Garrido Gamarro, E., Suzuki, A., & Teah, H. Y. (2020). Modeling the time-lag effect of sea surface temperatures on ciguatera poisoning in the South Pacific: Implications for surveillance and response. Toxicon , 182 , 21–29. https://doi.org/10.1016/j.toxicon.2020.05.001 Additional Declarations The authors declare no competing interests. Supplementary Files SupplFig1.png Demographics of People who have been inffected by CFP Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6308585","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":434072691,"identity":"bf883a36-6006-42a9-b9ec-5f7a7eb680a4","order_by":0,"name":"Allan Rarai","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0001-8950-0295","institution":"University of the South Pacific","correspondingAuthor":true,"prefix":"","firstName":"Allan","middleName":"","lastName":"Rarai","suffix":""},{"id":434076061,"identity":"fb2fd2b1-ba87-4851-aab1-f1add3634a6e","order_by":1,"name":"Philip Obaigwa Sagero","email":"","orcid":"https://orcid.org/0000-0001-9939-7826","institution":"University of the South Pacific","correspondingAuthor":false,"prefix":"","firstName":"Philip","middleName":"Obaigwa","lastName":"Sagero","suffix":""},{"id":434076062,"identity":"26d5b0a3-870f-44b8-a473-f295cb6bbca4","order_by":2,"name":"Eberhard Weber","email":"","orcid":"https://orcid.org/0000-0002-0870-1501","institution":"University of the South Pacific","correspondingAuthor":false,"prefix":"","firstName":"Eberhard","middleName":"","lastName":"Weber","suffix":""}],"badges":[],"createdAt":"2025-03-26 04:49:34","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6308585/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6308585/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79579642,"identity":"b64aba35-067a-4973-a861-24867b89a2d0","added_by":"auto","created_at":"2025-03-31 11:42:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":340409,"visible":true,"origin":"","legend":"\u003cp\u003eMap of Vanuatu Showing the Rainfall Stations used in this study.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-6308585/v1/21b1d083c0d8b221ddbdbe5f.png"},{"id":79579641,"identity":"907116b7-d999-4e12-9918-3aa6d471cef6","added_by":"auto","created_at":"2025-03-31 11:42:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":76220,"visible":true,"origin":"","legend":"\u003cp\u003eMean Monthly Observed Rainfall data over Vanuatu (Reference period: 1991-2020).\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-6308585/v1/90201e3c9d5dc601cbcfaa34.png"},{"id":79578429,"identity":"09c2496f-d85d-4870-b440-9b90d9cf3d85","added_by":"auto","created_at":"2025-03-31 11:34:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":35789,"visible":true,"origin":"","legend":"\u003cp\u003eInter-annual rainfall variability for each station in Vanuatu (Reference period: 1991 to 2020).\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-6308585/v1/3d372b3116a28e2cb10990b9.png"},{"id":79577614,"identity":"66bc0d78-d1be-44b1-b182-60f80495b97b","added_by":"auto","created_at":"2025-03-31 11:26:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":168215,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual Cycle of Monthly CFP Cases in Vanuatu for (a) 1989 to 1996 and (b) 2021-2023 periods.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-6308585/v1/68522714233f921d1d8f0492.png"},{"id":79577622,"identity":"d2e45a89-bed8-44ae-b49f-280f2eb5f344","added_by":"auto","created_at":"2025-03-31 11:26:12","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":82658,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual Cycle of Average Daily Ciguatera Fish Poisoning Cases for 2021, 2022 and 2023.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-6308585/v1/e3109bf5273bcb7dc46c1a3f.png"},{"id":79579643,"identity":"2163af5a-2e8b-47a0-ab2e-026cca2b2e65","added_by":"auto","created_at":"2025-03-31 11:42:12","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":35486,"visible":true,"origin":"","legend":"\u003cp\u003eLagged Cross Correlations Coefficient between Rainfall and Ciguatera Fish Poisoning for: (a) 1989-1996 and (b) 2021-2023.\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-6308585/v1/e2f2126c36c9658e42e78ea4.png"},{"id":79578430,"identity":"bca4e7b6-590d-40a8-b246-9f3864a2456e","added_by":"auto","created_at":"2025-03-31 11:34:12","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":41843,"visible":true,"origin":"","legend":"\u003cp\u003eTime series of the daily rainfall and CFP cases for 2021,2022 \u0026amp; 2023. The \u003cstrong\u003eblack line \u003c/strong\u003eindicates rainfall while the \u003cstrong\u003ered line \u003c/strong\u003eindicates the CFP cases. Days with no black or red line indicate zero (0) value for both data.\u003c/p\u003e","description":"","filename":"Fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-6308585/v1/157c9d8db7453369c99adc66.png"},{"id":79577619,"identity":"f36750f2-f3d8-41bc-a69b-0915dbd9ce25","added_by":"auto","created_at":"2025-03-31 11:26:12","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":75878,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePercentage of the frequency of people interviewed. (a)\u003c/strong\u003e Key informants, \u003cstrong\u003e(b)\u003c/strong\u003e Duration of time informants lived in the area.\u003c/p\u003e","description":"","filename":"Fig8.png","url":"https://assets-eu.researchsquare.com/files/rs-6308585/v1/64438a8aded28887b2efda39.png"},{"id":79578434,"identity":"d2a4b6e4-698d-4ab6-8ee3-94eff4ee2054","added_by":"auto","created_at":"2025-03-31 11:34:12","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":98578,"visible":true,"origin":"","legend":"\u003cp\u003eUnderstanding of the people on different causes of ciguatera fish poisoning.\u003c/p\u003e","description":"","filename":"Fig9.png","url":"https://assets-eu.researchsquare.com/files/rs-6308585/v1/2fed89501efa535938b3cb5d.png"},{"id":79578435,"identity":"605376d7-fb4e-4317-b7d9-b8fe104df01e","added_by":"auto","created_at":"2025-03-31 11:34:12","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":72081,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePercentage of interviewees on the Access to fish, Fish consumption, and Treatment options for CFP. (a) \u003c/strong\u003eaccess the fish, \u003cstrong\u003e(b) \u003c/strong\u003etype of fish consumed, and \u003cstrong\u003e(c) \u003c/strong\u003ethe treatment options people used for the CFP.\u003c/p\u003e","description":"","filename":"Fig10.png","url":"https://assets-eu.researchsquare.com/files/rs-6308585/v1/dae48b113224a642363ee8a7.png"},{"id":79580716,"identity":"6c5a2f03-1ba3-43c3-8665-c368579d1740","added_by":"auto","created_at":"2025-03-31 11:50:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1686255,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6308585/v1/90f4c61c-2882-41e4-b6d9-c5af6c117c59.pdf"},{"id":79577605,"identity":"1a11a45e-15a9-4c6d-8ae6-52de8cf312d0","added_by":"auto","created_at":"2025-03-31 11:26:12","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":44231,"visible":true,"origin":"","legend":"\u003cp\u003eDemographics of People who have been inffected by CFP\u003c/p\u003e","description":"","filename":"SupplFig1.png","url":"https://assets-eu.researchsquare.com/files/rs-6308585/v1/21bd80c56e133549d7d659c7.png"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eInvestigating the Relationship Between Rainfall and Ciguatera Fish Poisoning Cases in Vanuatu\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCiguatera Fish Poisoning (CFP) is increasingly becoming a public health risk in the world, with an estimated 50,000 to 500,000 people affected every year (Kohli et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The Pacific and the Caribbean regions are the most affected regions, with an estimated 25,000 to 50,000 people affected annually by the ciguatoxins after consuming reef fish (Darias-D\u0026aacute;gfeel et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Holmes et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Murray et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The Vanuatu Ministry of Health (VMoH) revealed an increasing trend in CFP cases, with an average of 557 CFP cases recorded annually from 1988 to 2023 (MoH, 2015). The potential impact on public health in Vanuatu is significant with the increasing cases of CFP, underscoring the pressing need to understand the issue and provide early warning to the people before CFP cases emerge.\u003c/p\u003e \u003cp\u003eMany factors contributed to the favorable environmental conditions, resulting in the accumulation of ciguatoxins in fish, which leads to CFP. Several studies have linked ciguatera to changes in the sea surface temperature (Barrett, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Heimann et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Kohli et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Tester et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and tropical cyclones (Dixon et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Guillemot et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Puotinen et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Rongo \u0026amp; van Woesik, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), however, there are limited studies on rainfall and ciguatera. Rainfall contributes to soil erosion, landslides, surface runoffs, and sedimentation in the coastal and marine ecosystems. Moreover, changing rainfall patterns due to climate variability and changes caused by anthropogenic activities also negatively impact the coastal and marine ecosystems (Brown et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). According to a WHO report, algal blooms contribute to ciguatera fish poisoning and can happen after extreme rainfall events caused by tropical cyclones (WHO, 2022).\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003ePoor agricultural practices and waste management increase the exposure of midstream and downstream nutrients and sedimentations to coastal communities (Begg et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ding, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). A similar study further elaborates that the impacts of anthropogenic land use changes on the spatiotemporal distribution of rainfall have contributed to fluvial flooding and run-offs into the marine and coastal communities, particularly on islands (Jiang et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). For instance, around 24% of coral reefs in Southeast Asia experienced damage from sediment accumulations due to surface run-off during extreme rainfall events as a result of land use changes (Mulyono et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Soil erosion, slope factors, cropping systems, and poor management practices cause damage to the coral reefs as a result of changing rainfall patterns. There are arguments that little attention is given to threats originating from inland and terrestrial ecosystems, while priorities target marine or freshwater ecosystems when there are events of marine diseases such as fish poisoning outbreaks (Tulloch et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ePrecipitation can trigger land and mudslides (Geertsema et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Schuster \u0026amp; Highland, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), especially coastal landslides that combine with rising sea levels (Jakob, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) can harm habitats for marine plants such as macroalgae and seaweeds (Stancheva et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Flooding, land/and mudslides frequently occur during wet and tropical cyclone seasons where rainfall is at its highest level. In 2017, a mudflow destroyed Walubuwe village on Ambae Island in Vanuatu due to a heavy rainfall event (Roy \u0026amp; Jong, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These sediments and nutrients ended up in the nearshore and reef area contributed to the development of favorable conditions for the growth of dinoflagellates. Dinoflagellates are group of algae that play an important ecological role as primary producers at the base of aquatic ecosystems (Wynn et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2010\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThis research sought to investigate the relationship between rainfall and presence of ciguatoxins in fish. The study will provide a basis for an advisory on expected CFP during the seasonal forecast's release. That will help the Vanuatu communities and health authorities reduce and prevent CFP cases.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy area\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eVanuatu is an archipelago of 83 islands in the Southwest Pacific extending from latitude 13 degrees south to 21 degrees south, with a land area of 12,281 square kilometers (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). It exposed to many hazards, including tropical cyclones, sea level rise, earthquakes, landslides, tsunamis, and Volcanic activities (Jackson et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Vanuatu has a total population of 300,019, with approximately 80% residing in rural and remote islands (Vanuatu Government, 2020). Like other Pacific Island countries, most of the Vanuatu rural population live along the coastal area where small-scale coastal fisheries play a significant domestic and livelihood needs (Campbell et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Vanuatu produced 1,106 tonnes of fish for commercial purposes and 2,800 tonnes for subsistence needs for rural communities (Gillett \u0026amp; Group, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). These figures show that the local population consumes more coastal fish than commercially trading them. Fish play a key role as the source of protein for communities in Vanuatu.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eVanuatu has two rainfall seasons: the wet season from November to April and the dry Season from May to October (Tigona et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Tigona \u0026amp; de Freitas, 2012). The South Pacific Convergence Zone (SPCZ) influences the seasonal rainfall distribution over Vanuatu. Tropical cyclones and El-Ni\u0026ntilde;o Southern Oscillation (ENSO) events contribute to annual rainfall variability over Vanuatu (Pariyar et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eRainfall Data\u003c/h2\u003e \u003cp\u003eWe used daily homogenized rainfall data for eight rainfall stations in Vanuatu (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), ranging from 1981 to 2022. The daily rainfall station data were obtained from the Vanuatu Meteorology and Geo-Hazards Department (VMGD). All stations had over 95% of available data during the observation periods except Saratamata station, with 77.8%.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSelected Stations Used for this study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSola\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-13.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e167.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1981\u0026ndash;2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePekoa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-15.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e167.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1985\u0026ndash;2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e97.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSaratamata\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-15.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e167.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2007\u0026ndash;2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e77.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLamap\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-16.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e167.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1981\u0026ndash;2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBauerfield\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-17.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e168.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1985\u0026ndash;2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e98.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMackenzie Hill\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-17.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e168.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1990\u0026ndash;2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e99.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite Grass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-19.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e169.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1981\u0026ndash;2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e98.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAneityum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-20.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e169.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1981\u0026ndash;2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e98.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCiguatera Fish Poisoning Data\u003c/h3\u003e\n\u003cp\u003eThe CFP-recorded cases were compiled from multiple sources, including the Vanuatu Ministry of Health (VMoH) and South Pacific Epidemiological and Health Information Services (SPEHIS) based at the South Pacific Community. Most of the CFP data from these sources consisted of annual records from 1981 to 2022 derived from the VMoH, with a few exceptions of monthly data covering seven years, 1989\u0026ndash;1996, derived from SPEHIS. The daily data on CFP cases from 2021 to 2023 was obtained from the VMoH. The CFP cases are based on records of patients seeking medical attention at any health center in Vanuatu during those years. It does not include people who used local remedies to treat CFP cases. This study only uses the data from 1989 to 1996 and 2021 to 2023 for analysis.\u003c/p\u003e\n\u003ch3\u003eStructured Interview Data\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eStructured interviews were conducted with a sample size of thirty-seven (37) people, consisting of twenty-eight (28) males and nine females (9) in Ambae Island, Vanuatu. This location was selected due to increasing challenges posed by the interaction between people and the environment, particularly in terms of marine resources, as a result of increased CFP cases (Goodman et al., 2003). The qualitative approach was used based on semi-structured interviews questions. We use this method to explore the understanding and knowledge of the community on CFP cases. The Interviews were carried out in seven villages. The participants were chosen from various backgrounds by our local focal point who knew of these people, their roles in the community and how long they have lived in the area. Interviews, which were approximately one hour long, were conducted by the lead author. The interviews took place in participants\u0026rsquo; homes to ensure privacy. All interviews were recorded both in writing and through audio recordings, which were later transcribed. We used Statistical Package for Social Sciences (SPSS) software to analyze the data from the interviews.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMethodology\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eRainfall Variability over Vanuatu\u003c/h2\u003e \u003cp\u003eRainfall variability was assessed using Mean, standard deviation, and coefficient of variability (CV) to determine how variable is Vanuatu\u0026rsquo; rainfall. The Coefficient of Variation \u003cem\u003e(CV)\u003c/em\u003e value is in percentage. The lower the value of the coefficient of variation, the more precise the data are closer to the mean. A higher CV (%) indicates more significant variability in rainfall, whereas a lower CV (%) indicates more consistent rainfall from year to year.\u003c/p\u003e \u003cp\u003e \u003cem\u003eCV =\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\sigma\\:}{\\mu\\:}\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003ex 100 \u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;..\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;..\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u003c/em\u003eEq.\u0026nbsp;1\u003c/p\u003e \u003cp\u003eWhere \u003cb\u003eCV\u003c/b\u003e is the Coefficient variation, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{\\sigma\\:}\\)\u003c/span\u003e\u003c/span\u003e is the Standard Deviation, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{\\mu\\:}\\)\u003c/span\u003e\u003c/span\u003e is the mean.\u003c/p\u003e \u003cp\u003eTo determine the yearly extreme wetness and dryness for different years, we used the Rainfall Anomaly Index (RAI) (Freitas, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Ara\u0026uacute;jo et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), Eqs.\u0026nbsp;2 and 3. RAI has been used in a few studies as an alternative to the Standard Precipitation Index (SPI) (Costa \u0026amp; Rodrigues, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; H\u0026auml;nsel et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Raziei, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The classification of RAI intensity is shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eRAI = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:3\\left[\\frac{N-\\stackrel{-}{N}}{\\stackrel{-}{M}-\\stackrel{-}{N}}\\right]\\)\u003c/span\u003e\u003c/span\u003e, For positive anomalies\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;..\u0026hellip;\u0026hellip;..Eq.\u0026nbsp;2\u003c/p\u003e \u003cp\u003eRAI = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:-3\\left[\\frac{N-\\stackrel{-}{N}}{\\stackrel{-}{x}-\\stackrel{-}{N}}\\right]\\)\u003c/span\u003e\u003c/span\u003e, For negative anomalies \u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;................................\u0026hellip;..Eq.\u0026nbsp;3\u003c/p\u003e \u003cp\u003eWhere \u003cb\u003eN\u003c/b\u003e is the annual rainfall, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{\\mathbf{N}}\\)\u003c/span\u003e\u003c/span\u003e is the average annual rainfall, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{\\mathbf{M}}\\)\u003c/span\u003e\u003c/span\u003e is the average of the ten highest annual rainfall, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{\\varvec{x}}\\)\u003c/span\u003e\u003c/span\u003e is the average of the ten lowest annual rainfall, positive anomalies have their values above average, and negative anomalies have their values below average.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClassification of Rainfall Anomaly Index Intensity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRAI range\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClassification\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u003cb\u003eRainfall Anomaly Index (RAI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbove 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExtremely humid\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 to 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery humid\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 to 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHumid\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2 to 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDry\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-4 to -2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery dry\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBelow \u0026minus;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExtremely dry\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSource: Freitas (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) adapted by Araujo et al. (2009)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eCiguatera Fish Poisoning Analysis Data\u003c/h3\u003e\n\u003cp\u003eTwo data sets were analyzed using OriginPro software: the monthly data obtained from SPEHIS (1989\u0026ndash;1996) and the daily CFP recorded cases (2021\u0026ndash;2023) from VMoH. These data are completed datasets without data gaps. Using a time series chart on Origin Pro, we plot the monthly data to determine the cycle of CFP cases and use a cross-correlation function to find the correlation coefficient and the lag time between rainfall and subsequent CFP cases. The daily data CFP cases were analysed with daily rainfall data to determine the relationship between the two datasets.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCross-Correlation Function Equation\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eWe use the cross-correlation function to calculate Lag Time between rainfall and CFP cases. Eq.\u0026nbsp;4 was used to determine the lag time cross-correlation coefficient for 0 to 8 months. Given the two-time series data set, in this case rainfall (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:{\\text{x}}_{\\text{t}}\\:)\\)\u003c/span\u003e\u003c/span\u003e and CFP cases (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\:\\text{y}}_{\\text{t}\\:}\\)\u003c/span\u003e\u003c/span\u003e), we delay \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{x}}_{\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e by \u003cem\u003eT\u003c/em\u003e samples then calculate the cross-correlation between rainfall and CFP cases.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{{\\sigma\\:}}_{\\text{x}\\text{y}}\\left(\\text{T}\\right)=\\frac{1}{\\text{N}-1}\\sum\\:_{\\text{t}=1}^{\\text{N}}\\left({\\text{x}}_{\\text{t}}-\\text{T}-{{\\mu\\:}}_{\\text{x}}\\right)\\left({\\text{y}}_{\\text{t}}-{\\mu\\:}\\text{y}\\right)\\)\u003c/span\u003e \u003c/span\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;..Eq.\u0026nbsp;4\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\mu\\:}}_{\\text{x}}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\mu\\:}}_{\\text{y}}\\)\u003c/span\u003e\u003c/span\u003e are the mean of each time series, N is the sample in each time series. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\sigma\\:}}_{\\text{x}\\text{y}}\\left(\\text{T}\\right)\\)\u003c/span\u003e\u003c/span\u003e is the cross-covariance function. The normal version of the cross-correlation function is shown below;\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{r}}_{\\text{x}\\text{y}}\\left(\\text{T}\\right)=\\)\u003c/span\u003e \u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{{\\sigma\\:}_{xy}\\left(T\\right)}{\\sqrt{\\sigma\\:\\left({x}_{t}\\cdot\\:{\\stackrel{-}{x}}_{t}\\right)\\sigma\\:\\left({y}_{t}\\cdot\\:{\\stackrel{-}{y}}_{t}\\right)}}\\)\u003c/span\u003e\u003c/span\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;..\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;Eq.\u0026nbsp;5\u003c/p\u003e \u003cp\u003eWhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{r}}_{\\text{x}\\text{y}}\\left(\\text{T}\\right)\\)\u003c/span\u003e\u003c/span\u003e is the cross-correlation function, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left(\\sigma\\:\\left({x}_{t}\\cdot\\:{\\stackrel{-}{x}}_{t}\\right)\\sigma\\:\\left({y}_{t}\\cdot\\:{\\stackrel{-}{y}}_{t}\\right)\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003csup\u003e2\u003c/sup\u003e is the variance of rainfall and CFP data signal.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eRainfall Variability\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the mean, standard deviation, and coefficient of variation (CV) for the seasonal and annual rainfall for all eight stations. On average, Vanuatu receives between 1300 mm to 4100 mm of rainfall annually. Sola station receives the highest annual rainfall of 4173 mm, whereas White Grass receives the lowest on average per year at 1316 mm. Rainfall in Vanuatu varies between the wet season (November to April) and the dry season (May to October). The analysis of seasonal rainfall contributions shows that all stations receive over 50% of their annual rainfall during the wet season and less than 40% in the dry season.\u003c/p\u003e \u003cp\u003eThe coefficient of variation (CV), expressed as a percentage, assesses the reliability of seasonal and year-to-year rainfall variability across all stations. Annually, all stations show a CV below 6%. For the seasons (Dry \u0026amp; wet seasons), the CV remains below 13% (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), indicating minimal year-to-year variability of rainfall in Vanuatu.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe Mean, Standard Deviation, and Coefficient of Variability (CV) for Wet and dry Seasons and Annual Rainfall.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStation Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eWet Season\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eDry Season\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003eAnnual\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{M}\\text{e}\\text{a}\\text{n}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd deviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eCV %\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStd deviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eCV %\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eStd deviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003eCV %\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSola\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2375.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e161.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1798.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e158.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4173.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e174.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e4.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePekoa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1631.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e137.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e939.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e106.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2570.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e140.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSaratamata\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1348.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e91.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e691.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e59.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2040.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e96.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLamap\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1270.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e121.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e728.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e83.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1998.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e117.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBauerfield\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1572.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e148.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e747.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e87.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2320.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e143.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMackenzie Hill\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1406.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e128.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e753.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e94.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2159.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e127.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhite Grass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e907.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e115.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e409.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e53.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e13.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1316.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e100.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e7.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAneityum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1489.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e162.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e811.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e84.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2300.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e213.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e9.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe annual rainfall cycle shows that Vanuatu has two seasons for all the eight stations analyzed (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Rainfall in Vanuatu is also spatially variable. with stations located in the north (Sola and Pekoa) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and stations in the central (Bauerfield), receiving, on average, more rainfall than other stations (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Sola station receives the highest rainfall on average, while White Grass station receives the lowest rainfall. This is clearly shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, where the White Grass station had the highest contribution of 69%, an average of 907.3 mm to the annual rainfall during the wet season, while the Sola station had the lowest contribution of 57%, an average of 2375 mm to the annual rainfall during the wet season.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the time series of annual rainfall variability from 1990 to 2022 based on the climatological period of 1991\u0026ndash;2020 for all eight stations. The results indicate that annual average rainfall varies yearly. Based on Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, very low (negative) RAI values indicate extremely dry arid years, contributing to very low rainfall records. In contrast, very high (positive) RAI values indicate very humid/wet years, contributing to very high rainfall records. According to the chart, the years 1988\u0026ndash;1989, 2010/2012, and 2021\u0026ndash;2022 are years with enhanced rainfall, while 1982\u0026ndash;1983, 1991\u0026ndash;1992, and 2014\u0026ndash;2016 have depressed rainfall.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCiguatera Fish Poisoning Cases in Vanuatu\u003c/h2\u003e \u003cp\u003eThe annual cycle of average monthly CFP cases is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e(a, b). Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e(a) shows that the average number of ciguatera fish poisoning cases were highest in February, with 93 cases and lowest in August with 44 cases. The cases tend to be high during in the months of January to March and started to decrease to the lowest average of 44 cases in August. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e(b) shows the annual cycle of CFP cases for 2021 to 2023 period. During the three years, more cases of CFP were reported in the month of March, September, and November. Where, in the month of March an average of 13 cases were reported followed by September with 8 cases while May, August, and December recorded the lowest with one case each. The low number of average CFP cases in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e (b) is due to only three years of dataset. Similar to the Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e(a), on average most CFP cases were reported during the first four months of the year.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the distribution of the daily CFP cases for 2021, 2022, and 2023 respectively. In 2021, most CFP cases were recorded in March, with April, November, and December having less than 2 cases each and zero cases for the other months. The high daily cases for 2021 were recorded in March and the beginning of April, with over four to five CFP cases per day. In 2022, CFP cases were recorded in January, July, and September, and one case was recorded in October. For 2023, the CFP cases were recorded in July, August, and December. The highest daily cases in 2023 were recorded in February, April, May, September and October. Daily CFP cases were high in March and November during the three-year periods.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCross-Correlations Analysis of Rainfall and CFP Cases\u003c/h2\u003e \u003cp\u003eThe correlation coefficients (\u003cem\u003er\u003c/em\u003e) were calculated using a cross-correlation function based on 1989\u0026ndash;1996 and 2021\u0026ndash;2023 rainfall and CFP cases datasets to determine the lag time between the two datasets. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e illustrates the positive and negative correlation coefficient values and the lag time from 0 to 8 months. The lag time of 0 and 1 month show a high correlation coefficient of 0.7, decreasing to 0.3 at a 2-month lag for the 1989\u0026ndash;1996 data (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e(a)). From the third month onward, the correlation coefficient becomes negative. For the 2021\u0026ndash;2023 data (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e(b)), the highest correlation coefficient of 0.4 occurs at a 1-month lag, followed by 0.2 at a 2-month lag and 0.1 at 0-month lag. Similar to Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e(a), the correlation coefficient decreases to zero at a 3-month lag and becomes negative from the 4-month to 8-month lag periods.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows the time series of daily rainfall records and CFP cases for 2021 to 2023. The results show that, there are recorded cases of CFP after an events of extreme rainfall. For instance, the rainfall of 101.5 mm was recorded on the 9 of January, 62.5mm on the 12 of January, 65.5 mm on the 14 of January, 95.5mm on the 24 of January, and 80.5 on the 4 of March. After the extreme rainfall events, high cases of CFP were recorded in March and the first half of April. Another high record of daily rainfall was on the 4 of May, but no cases of CFP were recorded thereafter. The same results are shown in the 21 of October, when 115mm of rainfall was recorded. This led to a few cases being recorded in November. Similarly, few cases were recorded after the 4th of December rainfall.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe cases of CFP were recorded in January 2022, following 280 mm of daily rainfall on December 4, 2021 (Figure, 7(b)). The same case for 2022, where rainfall of 188 mm on January 29 and 213.5 mm on February 9 led to a few CFP cases reported in March. Additionally, rainfall in march and April resulted in CFP case in May (Figure, 7b). On May 20, an extreme rainfall event of 429 mm was recorded due to Tropical Cyclone Gina, which passed over the central islands of Vanuatu, causing heavy rainfall. A month after this event, CFP cases were reported in June, July, August, and September. Despite this being Vanuatu\u0026rsquo;s dry season, daily rainfall exceeding 40 mm was recorded during these months, contributing to the reported CFP cases. Similar results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e(c) for the year 2023, where heavy rainfall preceded the CFP cases.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eCommunity Knowledge and Perceptions on Ciguatera Fish Poisoning\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eWe interviewed thirty-seven people including twenty-eight males and nine females. Figure\u0026nbsp;11 (a) shows the percentage of the people interviewed with 41 percent of the respondents have lived in the area for over 50 years,32 percent for more than 40 years and 13 percent for less than 30 years. All the people interviewed have experienced or have been infected with CFP sometimes in their lifetime. Their experiences and knowledge of the nature within their surrounding during these longer periods have helped them to identify the trend and the changes within their environment that related to the increase cases of CFP in the area. Sixty-three (63%) percent of the respondents are fishermen and farmers and the other 37 percent are key informants; government officials, chiefs and church leaders (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e(a).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e shows the percentage of people with knowledge of the different causes of CFP. According to Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, waste and sediments from land surface run-off as a result of heavy rainfall have the highest percentage of 52, followed by new coral at 20 percent, algae at 10 percent, dead coral at 6 percent, and jellyfish and knowledge of common poison fish at 4 percent. Based on local knowledge from elders, certain fish are always poisonous and can be spotted easily. Each participant knew more than one causes of CFP in the area. The participants\u0026rsquo; knowledge of extreme weather events, such as extreme rainfall and tropical cyclones, are causes of CFP. For example, heavy sediments flow into the coastal waters after extreme weather; corals will die out, allowing algae to grow on them, allowing fish to eat the algae and get contaminated with ciguatoxins. Twenty percent of the participants know new corals as contributors to fish poisoning.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe collected information from the communities on the areas where they fish, how often they have access to these areas, how they treat infected person with the ciguatoxins from eating those fish and the date they remembered of been infected with CFP. Figure\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e shows the results of access, type of fish and treatment of CP. Figure\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e (a) indicate that 61 percent of the people of Ambae have access to fish twice a week (very often), 27 percent often (once per week) while 11 percent less often (twice per month) to eat fish. Most of the fish consumed by 89 percent of the people interviewed are reef or nearshore fish while 11 percent of the participant have consumed some offshore fish. One participant stated that in 2005, he was infected with Ciguatera after eating tuna gut (stomach, intestines and colon). The participants also have knowledge or have known of people in their communities who have been infected with CFP. For instance, one responded stated on the 10 of October, 2023, more than ten people in his village were infected with CFP after eating fish taken using line fishing from nearshore. The people of Vanuatu who lived in rural and remote islands use their local knowledge to treat or cure CFP. Figure\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e(c). Show that Fifty-four percent (54%) of the responded use home remedies (local medicine) to cure CFP while 19 percent seek medical attention from health centers. About 27 percent of the people have access or used both home remedies and seek treatment from hospitals to treat CFP.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe results show that rainfall over Vanuatu varies both spatially and temporally. Spatially, the stations in the north of Vanuatu, such as Sola, have higher rainfall than those in the south. Also, rainfall varies with seasons with some months recording more rainfall while others recording less. The results show inter-annual variability of rainfall in Vanuatu, with periods of enhanced and depressed rainfall. Several factors affected temporal and spatial rainfall variability in Vanuatu, including the South Pacific Convergence Zone (SPCZ), El Nino Southern Oscillation (ENSO), tropical cyclone (TC) events, and Madden Julian Oscillation (Tigona \u0026amp; de Freitas, 2012). The northeast-to-southwest movement of SPCZ influences the seasonality of rainfall in Vanuatu. It moves further north during the dry season (May-October) and southwards during the wet season (November-April), causing the seasonal variability of rainfall amount (Evans et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Sola Stations, which is located north of Vanuatu and closer to the SPCZ, has a high monthly rainfall amount compared to White Grass stations, which is further away from the SPCZ. Tropical cyclones significantly contribute to high rainfall in Vanuatu (Deo et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Magee et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In Vanuatu, the tropical cyclone season coincides with the wet season (November to April). Madden Julian Oscillation (MJO), a convective activity that travels around the globe within 30 to 60 days (Diamond \u0026amp; Renwick, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Tu\u0026rsquo;uholoaki et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), also contributes to extreme rainfall in Vanuatu during the wet season. The passage of MJO north of Vanuatu contributes to enhanced precipitation distribution and the genesis of tropical cyclones, especially during the wet season (Takemi, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe inter-annual rainfall variability in Vanuatu is caused by the El-Ni\u0026ntilde;o Southern Oscillation (ENSO) events. The years with a positive rainfall anomaly index value (above-normal) are La Ni\u0026ntilde;a years, while those with negative RAI values (below-normal) are El Ni\u0026ntilde;o years. The years with zero (0) value (normal rainfall) indicate neutral years. According to the classification of the RAI intensity (Ara\u0026uacute;jo et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), the increase in the positive values of RAI corresponds to extremely humid years which leads to higher recorded of rainfall. Both depressed and enhanced rainfall have impacts on the environment including water, agriculture, fisheries and disaster management sectors. For fisheries sector, continued enhanced rainfall caused flash flooding and surface run-off that all ended up in the near shore and coral reef system (Fabricius et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Kosmas et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Heavy rainfall during a La Ni\u0026ntilde;a event of 2017\u0026ndash;2018 causes a landslide and mudflow in Walubuwe village on Ambae Island in Vanuatu (Roy \u0026amp; Jong, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe distribution of ciguatera cases in Vanuatu is related to extreme rainfall and the frequency of natural disturbances, such as extreme climate events occurring each year (Rongo \u0026amp; van Woesik, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This is in line with other studies on the increased trend of ciguatera cases around the globe, including the Pacific Islands (Chinain et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Pottier et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Vanuatu communities are mainly concentrated along the coast, and their main source of protein is fish. The cases of CFP will continue to emerge if Vanuatu continues to face natural disturbances such as cyclones and rainfall, and this will continue to have a major impact on the health and socio-economic lives of the local population. The CPF has also affected other Pacific Island countries (Chinain et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Gray, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lako et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e; Soli\u0026ntilde;o \u0026amp; Costa, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur study finds that people over 35 years have a high risk of getting poisoned by eating contaminated fish. Based on the data analyzed for this study, 57 percent of people infected with CFP in Vanuatu are males (Suppl Fig.\u0026nbsp;1). Several external factors may have contributed to this, such as Vanuatu's traditional and cultural way of life, which allows males to eat first in any feast gathering or small family homes. Males tend to eat more of the fish, including the head. Moreover, males in Vanuatu tend to have more access to fishing gear and are likely to have more time fishing than women, who usually remain home. Other studies in Fiji and Vanuatu echoed similar incidents of males being more at high risk of fish poisoning due to their cultural way of life (Goodman et al., 2003; Lako et al., 2023). Fish is an essential diet for Pacific Islanders; however, it may also cause public health risks to the local population. Knowledge of the right time to fish must be communicated to the communities to limit the cases of CFP in the future.\u003c/p\u003e \u003cp\u003eLand factors such as surface run-off and creek discharge depend on the inter-annual variability of the seasonal rainfall and may contribute to environmental conditions that favor the growth of dinoflagellates (Seymour \u0026amp; McLellan, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The changes in rainfall patterns may lead to an increase in algal blooms (cyanobacteria) inland water bodies and (dinoflagellates) in oceans and bays by nutrients washed down by events of extreme rainfall (Reichwaldt \u0026amp; Ghadouani, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Surface run-off may change the acidification level of coastal waters, resulting in changing habitats, favoring environmental conditions for \u003cem\u003eGambierdiscus\u003c/em\u003e growth leading to ciguatoxins in fish (Rhodes et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). For Vanuatu, future research interest will be looking into water chemistry along coastal waters of provinces and islands with high CFP cases.\u003c/p\u003e \u003cp\u003eThe correlation coefficient decreases with increased lag time for both datasets (1989\u0026ndash;1996 and 2021\u0026ndash;2023). It takes roughly a lag time of 0 to 2- months for waste and sediments to be transported by land surface run-off after heavy rainfall events to the marine environment in order for favorable environmental conditions for dinoflagellate growth, which fish feed on and get contaminated with ciguatoxins, leading to CFP in people when they access and consume the fish. The results are only based on cases recorded by health centers when the person is diagnosed by a health worker and confirmed to be contaminated by CFP. Notably, from the graph, the lag time of 1 month can be used as the average lag time for the high cases of CFP to be reported after an event of rainfall. This is because of rate of run-off on volcanic islands (Ers\u0026ouml;z et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and steep slope (Chen et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) which contributes to sediments to the marine ecosystem. Other climatic factors such as sea surface temperature also determine the CPF cases (Zheng et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe extreme rainfall during the La Ni\u0026ntilde;a event of 2020\u0026ndash;2023 (Shi et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) may have contributed to daily CFP reported during this period. The high number of CFP cases observed in 2023 is linked to the La Ni\u0026ntilde;a event. The increased rainfall in 2023 resulted from the multi-year La Ni\u0026ntilde;a event. During multi-year ENSO events, rainfall tends to increase in the third year (Huang et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This explains the high number of daily cases from June to September 2022., high rainfall was observed during the first four months and the last four months of 2022 resulting in high number of CFP cases.\u003c/p\u003e \u003cp\u003eThe people in Vanuatu used both their local knowledge and information from government agencies to make decisions concerning the prevention of fish poisoning (Rarai et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The local knowledge on the prevention of the impact of ciguatera has been part of their life experiences and practices that have been passed on through generations. For example, algae growing on dead corals after tropical cyclones have damaged the corals can alert the communities of the possible cases of CFP if people consume fish from the affected area. Local knowledge plays a very vital aspect of the lives of the people dealing with CFP. Local knowledge has been used to predict the extreme weather events in Vanuatu (Rarai et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and tsunamis in Indonesia (Rahman et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe people in Vanuatu eat more reef fish than offshore fish (Campbell et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Reef fish are easier to access, which leads to a high number of cases of CFP in the area. The fish may be contaminated with Ciguatoxins if they feed on dinoflagellates as a result of algae growing on dead coral caused by tropical cyclones or sediment cover (Rarai et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Most of the CFP cases resulted from consumption of reef fish in many coastal settlements in the southeast Asia and the Pacific island (Chan, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Treating CFP in Vanuatu is an option between local remedies and hospital treatment. Most of the people interviewed for this study used local remedies to cure CFP. Local and traditional treatment of fish poisoning is common in other pacific island (Chassagne et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lako et al., 2023).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eCiguatera Fish Poisoning (CFP) poses an escalating health risk to the people of Vanuatu, with cases on the rise. In light of the increasing incidence of CFP, there is a pressing need for earlier warning systems and heightened awareness within communities., this study finds that rainfall contributes to increased cases of CFP. While this study only looks at ciguatera cases and their correlation with rainfall, there might be biases because of data availability. Future studies should look at more extended periods of CFP data cases. Moreover, since ciguatera cases are higher in some islands and less in others in Vanuatu, future research may also target localized areas on islands with high CFP cases.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cspan\u003eThe University of the South Pacific Human Research Ethics Committee approved this project.\u003c/span\u003e\u003c/p\u003e\u003ch2\u003eData \u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe data supporting this study\u0026apos;s findings are available in the Fig share database, \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eWe thank the Vanuatu Ministry of Health and the Vanuatu Ministry of Climate Change for data used in this study. This research was supported by the Association of the commonwealth universities (ACU) through the Ocean Country Partnership Programme Scholarship (OCPP) offered to the lead author. The University of the South Pacific hosted the lead author in Suva, Fiji Islands. \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eAuthor Contributions\u0026nbsp;\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eAllan Rarai: Design, Conceptualization, Investigation, Formal Analysis, Write original draft, Review and editing; Philip Obaigwa Sagero: Review and editing; Eberhard Weber: \u0026nbsp;Review and editing. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eCompeting Interest \u0026nbsp;\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interest. \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eAdditional information \u003c/h2\u003e\n\u003cp\u003eSupplementary information.\u0026nbsp;The online version contains supplementary materials available at \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCorrespondence and requests for materials should be addressed to Allan Rarai\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAra\u0026uacute;jo, L., Moraes Neto, J., \u0026amp; Sousa, F. (2009). Classifica\u0026ccedil;\u0026atilde;o da precipita\u0026ccedil;\u0026atilde;o anual e da quadra chuvosa da bacia do rio Para\u0026iacute;ba utilizando \u0026iacute;ndice de Anomalia de Chuva (IAC). \u003cem\u003eAmbiente e Agua - An Interdisciplinary Journal of Applied Science\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e(3), 93\u0026ndash;110. https://doi.org/10.4136/ambi-agua.105\u003c/li\u003e\n \u003cli\u003eBarrett, J. R. (2014). Under the Weather with Ciguatera Fish Poisoning: Climate Variables Associated with Increases in Suspected Cases. \u003cem\u003eEnvironmental Health Perspectives\u003c/em\u003e, \u003cem\u003e122\u003c/em\u003e(6), A167\u0026ndash;A167. https://doi.org/10.1289/ehp.122-A167\u003c/li\u003e\n \u003cli\u003eBegg, S. S., De Ramon N\u0026rsquo;Yeurt, A., \u0026amp; Iese, V. (2021). Integrated flood vulnerability assessment of villages in the Waimanu River Catchment in the South Pacific: The case of Viti Levu, Fiji. \u003cem\u003eRegional Environmental Change\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e(3), 83. https://doi.org/10.1007/s10113-021-01824-9\u003c/li\u003e\n \u003cli\u003eBrown, J. R., Lengaigne, M., Lintner, B. R., Widlansky, M. J., van der Wiel, K., Dutheil, C., Linsley, B. K., Matthews, A. J., \u0026amp; Renwick, J. (2020). South Pacific Convergence Zone dynamics, variability and impacts in a changing climate. \u003cem\u003eNature Reviews Earth \u0026amp; Environment\u003c/em\u003e, \u003cem\u003e1\u003c/em\u003e(10), 530\u0026ndash;543. https://doi.org/10.1038/s43017-020-0078-2\u003c/li\u003e\n \u003cli\u003eBrown, N. J., Nilsson, J., \u0026amp; Pemberton, P. (2019). Arctic Ocean Freshwater Dynamics: Transient Response to Increasing River Runoff and Precipitation. \u003cem\u003eJournal of Geophysical Research: Oceans\u003c/em\u003e, \u003cem\u003e124\u003c/em\u003e(7), 5205\u0026ndash;5219. https://doi.org/10.1029/2018JC014923\u003c/li\u003e\n \u003cli\u003eCampbell, B., Steenbergen, D., Li, O., Sami, A., Nikiari, B., Delisle, A., Neihapi, P., Uriam, T., \u0026amp; Andrew, N. (2024). Characterising a diversity of coastal community fisheries in Kiribati and Vanuatu. \u003cem\u003eFish and Fisheries\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(5), 837\u0026ndash;857. https://doi.org/10.1111/faf.12849\u003c/li\u003e\n \u003cli\u003eCampbell, B., Steenbergen, D., Li, O., Sami, A., Nikiari, B., Delisle, A., Neihapi, P., Uriam, T., \u0026amp; Andrew, N. (2024b). Characterising a diversity of coastal community fisheries in Kiribati and Vanuatu. \u003cem\u003eFish and Fisheries\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(5), 837\u0026ndash;857. https://doi.org/10.1111/faf.12849\u003c/li\u003e\n \u003cli\u003eChan, T. Y. K. (2015). Ciguatera Fish Poisoning in East Asia and Southeast Asia. \u003cem\u003eMarine Drugs\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(6), Article 6. https://doi.org/10.3390/md13063466\u003c/li\u003e\n \u003cli\u003eChassagne, F., Butaud, J.-F., Torrente, F., Conte, E., Ho, R., \u0026amp; Raharivelomanana, P. (2022). Polynesian medicine used to treat diarrhea and ciguatera: An ethnobotanical survey in six islands from French Polynesia. \u003cem\u003eJournal of Ethnopharmacology\u003c/em\u003e, \u003cem\u003e292\u003c/em\u003e, 115186. https://doi.org/10.1016/j.jep.2022.115186\u003c/li\u003e\n \u003cli\u003eChen, H., Zhang, X., Abla, M., L\u0026uuml;, D., Yan, R., Ren, Q., Ren, Z., Yang, Y., Zhao, W., Lin, P., Liu, B., \u0026amp; Yang, X. (2018). Effects of vegetation and rainfall types on surface runoff and soil erosion on steep slopes on the Loess Plateau, China. \u003cem\u003eCATENA\u003c/em\u003e, \u003cem\u003e170\u003c/em\u003e, 141\u0026ndash;149. https://doi.org/10.1016/j.catena.2018.06.006\u003c/li\u003e\n \u003cli\u003eChinain, M., Gatti Howell, C., Rou\u0026eacute;, M., Ung, A., Henry, K., Revel, T., Cruchet, P., Viallon, J., \u0026amp; Darius, H. T. (2023). Ciguatera poisoning in French Polynesia: A review of the distribution and toxicity of \u003cem\u003eGambierdiscus\u003c/em\u003e spp., and related impacts on food web components and human health. \u003cem\u003eHarmful Algae\u003c/em\u003e, \u003cem\u003e129\u003c/em\u003e, 102525. https://doi.org/10.1016/j.hal.2023.102525\u003c/li\u003e\n \u003cli\u003eChinain, M., Gatti, C. M. i., Darius, H. T., Quod, J.-P., \u0026amp; Tester, P. A. (2021). Ciguatera poisonings: A global review of occurrences and trends. \u003cem\u003eHarmful Algae\u003c/em\u003e, \u003cem\u003e102\u003c/em\u003e, 101873. https://doi.org/10.1016/j.hal.2020.101873\u003c/li\u003e\n \u003cli\u003eChinain, M., Gatti, C. M. i, Martin-Yken, H., Rou\u0026eacute;, M., \u0026amp; Darius, H. T. (2020). 10 Ciguatera poisoning: An increasing burden for Pacific island communities in light of climate change? In L. M. Botana, M. C. Louzao, \u0026amp; N. Vilarino (Eds.), \u003cem\u003eClimate Change and Marine and Freshwater Toxins\u003c/em\u003e (pp. 369\u0026ndash;428). De Gruyter. https://doi.org/10.1515/9783110625738-010\u003c/li\u003e\n \u003cli\u003eCosta, J. A., \u0026amp; Rodrigues, G. P. (2017). Space-time distribution of rainfall anomaly index (RAI) for the Salgado Basin, Cear\u0026aacute; State-Brazil. \u003cem\u003eCi\u0026ecirc;ncia e Natura\u003c/em\u003e, \u003cem\u003e39\u003c/em\u003e(3), 627\u0026ndash;634.\u003c/li\u003e\n \u003cli\u003eDarias-D\u0026aacute;gfeel, Y., Sanchez-Henao, A., Padilla, D., Mart\u0026iacute;n, M. V., Ramos-Sosa, M. J., Poquet, P., Barreto, M., Silva Sergent, F., Jerez, S., \u0026amp; Real, F. (2024). Effects on Biochemical Parameters and Animal Welfare of Dusky Grouper (Epinephelus marginatus, Lowe 1834) by Feeding CTX Toxic Flesh. \u003cem\u003eAnimals\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(12), Article 12. https://doi.org/10.3390/ani14121757\u003c/li\u003e\n \u003cli\u003eDeo, A., Chand, S. S., Ramsay, H., Holbrook, N. J., McGree, S., Magee, A., Bell, S., Titimaea, M., Haruhiru, A., Malsale, P., Mulitalo, S., Daphne, A., Prakash, B., Vainikolo, V., \u0026amp; Koshiba, S. (2021). Tropical cyclone contribution to extreme rainfall over southwest Pacific Island nations. \u003cem\u003eClimate Dynamics\u003c/em\u003e, \u003cem\u003e56\u003c/em\u003e(11), 3967\u0026ndash;3993. https://doi.org/10.1007/s00382-021-05680-5\u003c/li\u003e\n \u003cli\u003eDiamond, H. J., \u0026amp; Renwick, J. A. (2015). The climatological relationship between tropical cyclones in the southwest pacific and the Madden\u0026ndash;Julian Oscillation. \u003cem\u003eInternational Journal of Climatology\u003c/em\u003e, \u003cem\u003e35\u003c/em\u003e(5), 676\u0026ndash;686. https://doi.org/10.1002/joc.4012\u003c/li\u003e\n \u003cli\u003eDing, L. (2022). Exploring the Linkage between Land Use Type and Stream Water Quality of an Estuarine Island Applying GWR Model: A Case Study of Chongming, Shanghai. \u003cem\u003eJournal of Geoscience and Environment Protection\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(7), Article 7. https://doi.org/10.4236/gep.2022.107017\u003c/li\u003e\n \u003cli\u003eDixon, A. M., Puotinen, M., Ramsay, H. A., \u0026amp; Beger, M. (2022). Coral Reef Exposure to Damaging Tropical Cyclone Waves in a Warming Climate. \u003cem\u003eEarth\u0026rsquo;s Future\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(8), e2021EF002600. https://doi.org/10.1029/2021EF002600\u003c/li\u003e\n \u003cli\u003eErs\u0026ouml;z, T., Haneda, K., Kuribayashi, A., \u0026amp; Gonda, Y. (2023). Temporal changes in lahar sediment run-off characteristics and run-off coefficients in the Arimura River basin of Sakurajima volcano, Japan. \u003cem\u003eEarth Surface Processes and Landforms\u003c/em\u003e, \u003cem\u003e48\u003c/em\u003e(14), 2682\u0026ndash;2703. https://doi.org/10.1002/esp.5654\u003c/li\u003e\n \u003cli\u003eEvans, J. P., Bormann, K., Katzfey, J., Dean, S., \u0026amp; Arritt, R. (2016). Regional climate model projections of the South Pacific Convergence Zone. \u003cem\u003eClimate Dynamics\u003c/em\u003e, \u003cem\u003e47\u003c/em\u003e(3), 817\u0026ndash;829. https://doi.org/10.1007/s00382-015-2873-x\u003c/li\u003e\n \u003cli\u003eFabricius, K. E., Logan, M., Weeks, S., \u0026amp; Brodie, J. (2014). The effects of river run-off on water clarity across the central Great Barrier Reef. \u003cem\u003eMarine Pollution Bulletin\u003c/em\u003e, \u003cem\u003e84\u003c/em\u003e(1), 191\u0026ndash;200. https://doi.org/10.1016/j.marpolbul.2014.05.012\u003c/li\u003e\n \u003cli\u003eFreitas, M. (2005). \u003cem\u003esistema de suporte \u0026agrave; decis\u0026atilde;o para o monitoramento de secas meteorol\u0026oacute;gicas em regi\u0026otilde;es semi\u0026aacute;ridas. Rev. Tecnol.,(suppl. 19): Pp. 84-95\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eGeertsema, M., Highland, L., \u0026amp; Vaugeouis, L. (2009). Environmental Impact of Landslides. In K. Sassa \u0026amp; P. Canuti (Eds.), \u003cem\u003eLandslides \u0026ndash; Disaster Risk Reduction\u003c/em\u003e (pp. 589\u0026ndash;607). Springer. https://doi.org/10.1007/978-3-540-69970-5_31\u003c/li\u003e\n \u003cli\u003eGillett, R., \u0026amp; Group, F. (2011). \u003cem\u003eFisheries of the Pacific Islands: Regional and national information\u003c/em\u003e. https://openknowledge.fao.org/handle/20.500.14283/i2092e\u003c/li\u003e\n \u003cli\u003eGoodman, A., Williams, T. N., \u0026amp; Maitland, K. (2003a). CIGUATERA POISONING IN VANUATU. \u003cem\u003eThe American Journal of Tropical Medicine and Hygiene\u003c/em\u003e, \u003cem\u003e68\u003c/em\u003e(2), 263\u0026ndash;266. https://doi.org/10.4269/ajtmh.2003.68.263\u003c/li\u003e\n \u003cli\u003eGray, M. J. (2020). A descriptive study of ciguatera fish poisoning in Cook Islands dogs and cats: Demographic, temporal, and spatial distribution of cases. \u003cem\u003eVeterinary World\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(1), 10. https://doi.org/10.14202/vetworld.2020.10-20\u003c/li\u003e\n \u003cli\u003eGuillemot, N., Chabanet, P., \u0026amp; Le Pape, O. (2010). Cyclone effects on coral reef habitats in New Caledonia (South Pacific). \u003cem\u003eCoral Reefs\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(2), 445\u0026ndash;453. https://doi.org/10.1007/s00338-010-0587-4\u003c/li\u003e\n \u003cli\u003eH\u0026auml;nsel, S., Schucknecht, A., \u0026amp; Matschullat, J. (2016). The Modified Rainfall Anomaly Index (mRAI)\u0026mdash;Is this an alternative to the Standardised Precipitation Index (SPI) in evaluating future extreme precipitation characteristics? \u003cem\u003eTheoretical and Applied Climatology\u003c/em\u003e, \u003cem\u003e123\u003c/em\u003e(3), 827\u0026ndash;844. https://doi.org/10.1007/s00704-015-1389-y\u003c/li\u003e\n \u003cli\u003eHeimann, K., Capper, A., \u0026amp; Sparrow, L. (2011). Ocean Surface Warming: Impact on Toxic Benthic Dinoflagellates Causing Ciguatera. In \u003cem\u003eeLS\u003c/em\u003e. John Wiley \u0026amp; Sons, Ltd. https://doi.org/10.1002/9780470015902.a0023373\u003c/li\u003e\n \u003cli\u003eHolmes, M. J., Venables, B., \u0026amp; Lewis, R. J. (2021). Critical Review and Conceptual and Quantitative Models for the Transfer and Depuration of Ciguatoxins in Fishes. \u003cem\u003eToxins\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(8), Article 8. https://doi.org/10.3390/toxins13080515\u003c/li\u003e\n \u003cli\u003eHuang, A. T., Gillett, Z. E., \u0026amp; Taschetto, A. S. (2024). Australian Rainfall Increases During Multi-Year La Ni\u0026ntilde;a. \u003cem\u003eGeophysical Research Letters\u003c/em\u003e, \u003cem\u003e51\u003c/em\u003e(9), e2023GL106939. https://doi.org/10.1029/2023GL106939\u003c/li\u003e\n \u003cli\u003eJackson, G., McNamara, K., \u0026amp; Witt, B. (2017). A Framework for Disaster Vulnerability in a Small Island in the Southwest Pacific: A Case Study of Emae Island, Vanuatu. \u003cem\u003eInternational Journal of Disaster Risk Science\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(4), 358\u0026ndash;373. https://doi.org/10.1007/s13753-017-0145-6\u003c/li\u003e\n \u003cli\u003eJakob, M. (2022). Chapter 14\u0026mdash;Landslides in a changing climate. In T. Davies, N. Rosser, \u0026amp; J. F. Shroder (Eds.), \u003cem\u003eLandslide Hazards, Risks, and Disasters (Second Edition)\u003c/em\u003e (pp. 505\u0026ndash;579). Elsevier. https://doi.org/10.1016/B978-0-12-818464-6.00003-2\u003c/li\u003e\n \u003cli\u003eJiang, Q., He, X., Wang, J., Wen, J., Mu, H., \u0026amp; Xu, M. (2020). Spatiotemporal Analysis of Land Use and Land Cover (LULC) Changes and Precipitation Trends in Shanghai. \u003cem\u003eApplied Sciences\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(21), Article 21. https://doi.org/10.3390/app10217897\u003c/li\u003e\n \u003cli\u003eKohli, G. S., Farrell, H., \u0026amp; Murray, S. A. (2015). 9. Gambierdiscus, the cause of ciguatera fish poisoning: An increased human health threat influenced by climate change. In L. M. Botana, C. Louzao, \u0026amp; N. Vilari\u0026ntilde;o (Eds.), \u003cem\u003eClimate Change and Marine and Freshwater Toxins\u003c/em\u003e (pp. 273\u0026ndash;312). De Gruyter. https://doi.org/10.1515/9783110333596-011\u003c/li\u003e\n \u003cli\u003eKosmas, C., Danalatos, N., Cammeraat, L. H., Chabart, M., Diamantopoulos, J., Farand, R., Gutierrez, L., Jacob, A., Marques, H., Martinez-Fernandez, J., Mizara, A., Moustakas, N., Nicolau, J. M., Oliveros, C., Pinna, G., Puddu, R., Puigdefabregas, J., Roxo, M., Simao, A., \u0026hellip; Vacca, A. (1997). The effect of land use on runoff and soil erosion rates under Mediterranean conditions. \u003cem\u003eCATENA\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(1), 45\u0026ndash;59. https://doi.org/10.1016/S0341-8162(96)00062-8\u003c/li\u003e\n \u003cli\u003eLako, J. V., Naisilisili, S., Vuki, V. C., Kuridrani, N., \u0026amp; Agyei, D. (2023a). Local and Traditional Ecological Knowledge of Fish Poisoning in Fiji. \u003cem\u003eToxins\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(3), Article 3. https://doi.org/10.3390/toxins15030223\u003c/li\u003e\n \u003cli\u003eMagee, A. D., Verdon-Kidd, D. C., Kiem, A. S., \u0026amp; Royle, S. A. (2016). Tropical cyclone perceptions, impacts and adaptation in the Southwest Pacific: An urban perspective from Fiji, Vanuatu and Tonga. \u003cem\u003eNatural Hazards and Earth System Sciences\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(5), 1091\u0026ndash;1105. https://doi.org/10.5194/nhess-16-1091-2016\u003c/li\u003e\n \u003cli\u003eMaupin, C. R., Partin, J. W., Shen, C.-C., Quinn, T. M., Lin, K., Taylor, F. W., Banner, J. L., Thirumalai, K., \u0026amp; Sinclair, D. J. (2014). Persistent decadal-scale rainfall variability in the tropical South Pacific Convergence Zone through the past six centuries. \u003cem\u003eClimate of the Past\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(4), 1319\u0026ndash;1332. https://doi.org/10.5194/cp-10-1319-2014\u003c/li\u003e\n \u003cli\u003eMulyono, A., Djuwansah, M. R., Narulita, I., Putra, R. D., \u0026amp; Surinati, D. (2021). \u003cem\u003eEnvironmental Impact of Land-use Changes and Soil Loss on Coastal Coral Reef Cover: Study Case in the Small Tropical Island, Indonesia\u003c/em\u003e. https://doi.org/10.21203/rs.3.rs-148449/v1\u003c/li\u003e\n \u003cli\u003eMurray, J. S., Nishimura, T., Finch, S. C., Rhodes, L. L., Puddick, J., Harwood, D. T., Larsson, M. E., Doblin, M. A., Leung, P., Yan, M., Rise, F., Wilkins, A. L., \u0026amp; Prinsep, M. R. (2020). The role of 44-methylgambierone in ciguatera fish poisoning: Acute toxicity, production by marine microalgae and its potential as a biomarker for \u003cem\u003eGambierdiscus\u003c/em\u003e spp. \u003cem\u003eHarmful Algae\u003c/em\u003e, \u003cem\u003e97\u003c/em\u003e, 101853. https://doi.org/10.1016/j.hal.2020.101853\u003c/li\u003e\n \u003cli\u003ePariyar, S. K., Keenlyside, N., Sorteberg, A., Spengler, T., Chandra Bhatt, B., \u0026amp; Ogawa, F. (2020). Factors affecting extreme rainfall events in the South Pacific. \u003cem\u003eWeather and Climate Extremes\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e, 100262. https://doi.org/10.1016/j.wace.2020.100262\u003c/li\u003e\n \u003cli\u003ePottier, I., Lewis, R. J., \u0026amp; Vernoux, J.-P. (2023). Ciguatera Fish Poisoning in the Caribbean Sea and Atlantic Ocean: Reconciling the Multiplicity of Ciguatoxins and Analytical Chemistry Approach for Public Health Safety. \u003cem\u003eToxins\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(7), Article 7. https://doi.org/10.3390/toxins15070453\u003c/li\u003e\n \u003cli\u003ePuotinen, M., Maynard, J. A., Beeden, R., Radford, B., \u0026amp; Williams, G. J. (2016). A robust operational model for predicting where tropical cyclone waves damage coral reefs. \u003cem\u003eScientific Reports\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(1), 26009. https://doi.org/10.1038/srep26009\u003c/li\u003e\n \u003cli\u003eRahman, A., Sakurai, A., \u0026amp; Munadi, K. (2017). Indigenous knowledge management to enhance community resilience to tsunami risk: Lessons learned from Smong traditions in Simeulue island, Indonesia. \u003cem\u003eIOP Conference Series: Earth and Environmental Science\u003c/em\u003e, \u003cem\u003e56\u003c/em\u003e(1), 012018. https://doi.org/10.1088/1755-1315/56/1/012018\u003c/li\u003e\n \u003cli\u003eRarai, A., Parsons, M., Nursey-Bray, M., \u0026amp; Crease, R. (2022). Situating climate change adaptation within plural worlds: The role of Indigenous and local knowledge in Pentecost Island, Vanuatu. \u003cem\u003eEnvironment and Planning E: Nature and Space\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e(4), 2240\u0026ndash;2282. https://doi.org/10.1177/25148486211047739\u003c/li\u003e\n \u003cli\u003eRarai, A., Weber, E., Ruben, J., \u0026amp; Parsons, M. (2024). Indigenous knowledge with science forms an early warning system for ciguatera fish poisoning outbreak in Vanuatu. \u003cem\u003eCommunications Earth \u0026amp; Environment\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e(1), 1\u0026ndash;11. https://doi.org/10.1038/s43247-024-01931-5\u003c/li\u003e\n \u003cli\u003eRaziei, T. (2021). Revisiting the Rainfall Anomaly Index to serve as a Simplified Standardized Precipitation Index. \u003cem\u003eJournal of Hydrology\u003c/em\u003e, \u003cem\u003e602\u003c/em\u003e, 126761. https://doi.org/10.1016/j.jhydrol.2021.126761\u003c/li\u003e\n \u003cli\u003eReichwaldt, E. S., \u0026amp; Ghadouani, A. (2012). Effects of rainfall patterns on toxic cyanobacterial blooms in a changing climate: Between simplistic scenarios and complex dynamics. \u003cem\u003eWater Research\u003c/em\u003e, \u003cem\u003e46\u003c/em\u003e(5), 1372\u0026ndash;1393. https://doi.org/10.1016/j.watres.2011.11.052\u003c/li\u003e\n \u003cli\u003eRhodes, L. L., Smith, K. F., Murray, J. S., Nishimura, T., \u0026amp; Finch, S. C. (2020). Ciguatera Fish Poisoning: The Risk from an Aotearoa/New Zealand Perspective. \u003cem\u003eToxins\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(1), Article 1. https://doi.org/10.3390/toxins12010050\u003c/li\u003e\n \u003cli\u003eRongo, T., \u0026amp; van Woesik, R. (2013). The effects of natural disturbances, reef state, and herbivorous fish densities on ciguatera poisoning in Rarotonga, southern Cook Islands. \u003cem\u003eToxicon\u003c/em\u003e, \u003cem\u003e64\u003c/em\u003e, 87\u0026ndash;95. https://doi.org/10.1016/j.toxicon.2012.12.018\u003c/li\u003e\n \u003cli\u003eRoy, E. A., \u0026amp; Jong, E. de. (2018, April 25). Vanuatu: Landslide and flash flood hampers relief effort on Ambae. \u003cem\u003eThe Guardian\u003c/em\u003e. https://www.theguardian.com/world/2018/apr/25/vanuatu-landslide-and-flash-flood-hampers-relief-effort-on-ambae\u003c/li\u003e\n \u003cli\u003eSchuster, R. L., \u0026amp; Highland, L. M. (2003). \u003cem\u003eImpact of Landslides and Innovative Landslide-Mitigation Measures on the Natural Environment\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eSeymour, J. R., \u0026amp; McLellan, S. L. (2025). Climate change will amplify the impacts of harmful microorganisms in aquatic ecosystems. \u003cem\u003eNature Microbiology\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(3), 615\u0026ndash;626. https://doi.org/10.1038/s41564-025-01948-2\u003c/li\u003e\n \u003cli\u003eShi, L., Ding, R., Hu, S., Li, X., \u0026amp; Li, J. (2023). Extratropical impacts on the 2020\u0026ndash;2023 Triple-Dip La Ni\u0026ntilde;a event. \u003cem\u003eAtmospheric Research\u003c/em\u003e, \u003cem\u003e294\u003c/em\u003e, 106937. https://doi.org/10.1016/j.atmosres.2023.106937\u003c/li\u003e\n \u003cli\u003eSoli\u0026ntilde;o, L., \u0026amp; Costa, P. R. (2020). Global impact of ciguatoxins and ciguatera fish poisoning on fish, fisheries and consumers. \u003cem\u003eEnvironmental Research\u003c/em\u003e, \u003cem\u003e182\u003c/em\u003e, 109111. https://doi.org/10.1016/j.envres.2020.109111\u003c/li\u003e\n \u003cli\u003eStancheva, M., Stanchev, H., Young, R., \u0026amp; Parlichev, G. (2021). Coastal erosion driven Land-Sea Interactions in Maritime Spatial Planning\u0026mdash;A case of Bulgaria. \u003cem\u003eJournal of Coastal Conservation\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(6), 54. https://doi.org/10.1007/s11852-021-00841-4\u003c/li\u003e\n \u003cli\u003eTakemi, T. (2018). The evolution and intensification of Cyclone Pam (2015) and resulting strong winds over the southern Pacific islands. \u003cem\u003eJournal of Wind Engineering and Industrial Aerodynamics\u003c/em\u003e, \u003cem\u003e182\u003c/em\u003e, 27\u0026ndash;36. https://doi.org/10.1016/j.jweia.2018.09.007\u003c/li\u003e\n \u003cli\u003eTester, P. A., Feldman, R. L., Nau, A. W., Kibler, S. R., \u0026amp; Wayne Litaker, R. (2010). Ciguatera fish poisoning and sea surface temperatures in the Caribbean Sea and the West Indies. \u003cem\u003eToxicon\u003c/em\u003e, \u003cem\u003e56\u003c/em\u003e(5), 698\u0026ndash;710. https://doi.org/10.1016/j.toxicon.2010.02.026\u003c/li\u003e\n \u003cli\u003eTigona, R., Ongoma, V., \u0026amp; Weir, T. (2023). Towards improved seasonal rainfall prediction in the tropical Pacific Islands. \u003cem\u003eTheoretical and Applied Climatology\u003c/em\u003e, \u003cem\u003e154\u003c/em\u003e(1), 349\u0026ndash;363. https://doi.org/10.1007/s00704-023-04560-8\u003c/li\u003e\n \u003cli\u003eTigona, R. S., \u0026amp; de Freitas, C. R. (2012a). Relationship between the Southern Oscillation and rainfall in Vanuatu. \u003cem\u003eWeather and Climate\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e(1), 43\u0026ndash;50. https://doi.org/10.2307/26169725\u003c/li\u003e\n \u003cli\u003eTulloch, V. J. D., Atkinson, S., Possingham, H. P., Peterson, N., Linke, S., Allan, J. R., Kaiye, A., Keako, M., Sabi, J., Suruman, B., \u0026amp; Adams, V. M. (2021). Minimizing cross-realm threats from land-use change: A national-scale conservation framework connecting land, freshwater and marine systems. \u003cem\u003eBiological Conservation\u003c/em\u003e, \u003cem\u003e254\u003c/em\u003e, 108954. https://doi.org/10.1016/j.biocon.2021.108954\u003c/li\u003e\n \u003cli\u003eTu\u0026rsquo;uholoaki, M., Espejo, A., Sharma, K. K., Singh, A., Wandres, M., Damlamian, H., \u0026amp; Chand, S. (2023). Influence of the Madden\u0026ndash;Julian Oscillation (MJO) on Tropical Cyclones Affecting Tonga in the Southwest Pacific. \u003cem\u003eAtmosphere\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(7), Article 7. https://doi.org/10.3390/atmos14071189\u003c/li\u003e\n \u003cli\u003eVanuatu Ministry of Health, S. (2015, January 21). \u003cem\u003eMinistry of Health\u0026mdash;HOME\u003c/em\u003e. Ministry of Health. https://moh.gov.vu/\u003c/li\u003e\n \u003cli\u003eVanuatu_National_Population_and_Housing_Census\u003cem\u003e_-_Analytical_report_Volume_2.pdf\u003c/em\u003e. (2020). Retrieved March 1, 2025, from https://vnso.gov.vu/sites/default/files/2020_Vanuatu_National_Population_and_Housing_Census_-_Analytical_report_Volume_2.pdf\u003c/li\u003e\n \u003cli\u003eVale, P., \u0026amp; Sampayo, M. A. de M. (2003). Seasonality of diarrhetic shellfish poisoning at a coastal lagoon in Portugal: Rainfall patterns and folk wisdom. \u003cem\u003eToxicon\u003c/em\u003e, \u003cem\u003e41\u003c/em\u003e(2), 187\u0026ndash;197. https://doi.org/10.1016/S0041-0101(02)00276-3\u003c/li\u003e\n \u003cli\u003eWeir, T., Kumar, R., \u0026amp; Ngari, A. (2021). Interdecadal modulation of the effect of ENSO on rainfall in the southwestern Pacific. \u003cem\u003eJournal of Southern Hemisphere Earth Systems Science\u003c/em\u003e, \u003cem\u003e71\u003c/em\u003e(1), 53\u0026ndash;65. https://doi.org/10.1071/ES19053\u003c/li\u003e\n \u003cli\u003eWHO-HEP-NFS-SSA-2022.1\u003cem\u003e-eng.pdf\u003c/em\u003e. (2022). Retrieved March 1, 2025, from https://iris.who.int/bitstream/handle/10665/352191/WHO-HEP-NFS-SSA-2022.1-eng.pdf?sequence\u003c/li\u003e\n \u003cli\u003eWynn, J., Behrens, P., Sundararajan, A., Hansen, J., \u0026amp; Apt, K. (2010). 6\u0026mdash;Production of Single Cell Oils by Dinoflagellates. In Z. Cohen \u0026amp; C. Ratledge (Eds.), \u003cem\u003eSingle Cell Oils (Second Edition)\u003c/em\u003e (pp. 115\u0026ndash;129). AOCS Press. https://doi.org/10.1016/B978-1-893997-73-8.50010-4\u003c/li\u003e\n \u003cli\u003eZheng, L., Gatti, C. M. iti, Garrido Gamarro, E., Suzuki, A., \u0026amp; Teah, H. Y. (2020). Modeling the time-lag effect of sea surface temperatures on ciguatera poisoning in the South Pacific: Implications for surveillance and response. \u003cem\u003eToxicon\u003c/em\u003e, \u003cem\u003e182\u003c/em\u003e, 21\u0026ndash;29. https://doi.org/10.1016/j.toxicon.2020.05.001\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of the South Pacific","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Rainfall, Ciguatera Fish Poisoning, Public Health, Vanuatu, Lag Time ","lastPublishedDoi":"10.21203/rs.3.rs-6308585/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6308585/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe escalating number of ciguatera fish poisoning (CFP) cases in Vanuatu has become a public health issue. Previous studies focused on tropical cyclones and sea surface temperatures as potential causes of CFP; our study aims to investigate the relationship between rainfall and CFP cases. We use the monthly CFP dataset (1989\u0026ndash;1996) from the South Pacific Epidemiological and Health Information Services, the daily cases (2021\u0026ndash;2023) from the Vanuatu Ministry of Health, and the monthly and daily rainfall data from the Vanuatu Meteorology and Geo-Hazard Department. We also interviewed people about their local knowledge and perceptions of CFP causes and treatments. Cross-correlation analysis was used to determine the lag time between rainfall and CFP cases, whereas the Rainfall Anomaly Index (RAI) assessed variations in rainfall patterns. This study shows a strong positive correlation between rainfall and CFP cases, with lag times ranging from 0 to 8 months. Interview data highlight the integration of Indigenous knowledge and scientific information in CFP prevention and treatment practices. Our results emphasize the need for improved surveillance and early warning systems. This research supports targeted public health interventions and policy measures to mitigate CFP risks in Vanuatu and other Pacific Island nations.\u003c/p\u003e","manuscriptTitle":"Investigating the Relationship Between Rainfall and Ciguatera Fish Poisoning Cases in Vanuatu","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-31 11:26:07","doi":"10.21203/rs.3.rs-6308585/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"944af964-ceec-4bb2-b4a5-5a778cd69d69","owner":[],"postedDate":"March 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":46220307,"name":"Behavioral Geography"}],"tags":[],"updatedAt":"2025-03-31T11:26:08+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-31 11:26:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6308585","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6308585","identity":"rs-6308585","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}