Dataset Information

Predicting the impacts of sea level rise in sea turtle nesting habitat on Bioko Island, Equatorial Guinea.

ABSTRACT: Sea level is expected to rise 44 to 74 cm by the year 2100, which may have critical, previously un-investigated implications for sea turtle nesting habitat on Bioko Island, Equatorial Guinea. This study investigates how nesting habitat will likely be lost and altered with various increases in sea level, using global sea level rise (SLR) predictions from the Intergovernmental Panel on Climate Change. Beach profiling datasets from Bioko's five southern nesting beaches were used in GIS to create models to estimate habitat loss with predicted increases in sea level by years 2046-2065 and 2081-2100. The models indicate that an average of 62% of Bioko's current nesting habitat could be lost by 2046-2065 and 87% by the years 2081-2100. Our results show that different study beaches showed different levels of vulnerability to increases in SLR. In addition, on two beaches erosion and tall vegetation berms have been documented, causing green turtles to nest uncharacteristically in front of the vegetation line. We also report that development plans are currently underway on the beach least susceptible to future increases in sea level, highlighting how anthropogenic encroachment combined with SLR can be particularly detrimental to nesting turtle populations. Identified habitat sensitivities to SLR will be used to inform the government of Equatorial Guinea to consider the vulnerability of their resident turtle populations and projected climate change implications when planning for future development. To our knowledge this is the first study to predict the impacts of SLR on a sea turtle nesting habitat in Africa.

SUBMITTER: Veelenturf CA

PROVIDER: S-EPMC7390326 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Predicting the impacts of sea level rise in sea turtle nesting habitat on Bioko Island, Equatorial Guinea.

Veelenturf Callie A CA Sinclair Elizabeth M EM Paladino Frank V FV Honarvar Shaya S

PloS one 20200729 7

Sea level is expected to rise 44 to 74 cm by the year 2100, which may have critical, previously un-investigated implications for sea turtle nesting habitat on Bioko Island, Equatorial Guinea. This study investigates how nesting habitat will likely be lost and altered with various increases in sea level, using global sea level rise (SLR) predictions from the Intergovernmental Panel on Climate Change. Beach profiling datasets from Bioko's five southern nesting beaches were used in GIS to create mo ...[more]

PMID: 32726310

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Project description:Onchocerciasis or "river blindness" is a chronic parasitic neglected tropical disease which is endemic both in mainland and insular Equatorial Guinea. We aim to estimate the current epidemiological situation of onchocerciasis in Bioko Island after vector elimination in 2005 and more than sixteen years of Community Directed Treatment with Ivermectin (CDTI) by using molecular and serological approaches for onchocerciasis diagnosis. A community-based cross-sectional study was carried out in Bioko Island from mid-January to mid-February 2014. A total of 544 study participants were recruited. A complete dermatological examination was performed and three skin snips were performed in every participant for parasitological and molecular assessments. Blood spots were also taken for determination of Ov16 IgG4 antibodies trough an "in-house" ELISA assay. Overall, we found 15 out of 522 individuals suffering any onchocerciasis specific cutaneous lesions and 16 out of 528 (3.0%) with onchocercal nodules in the skin. Nodules were significantly associated with age, being more common in subjects older than 10 years than in younger people (3.9% vs. 0%, p = 0.029). Regarding the onchocerciasis laboratory assessment, no positive parasitological test for microfilaria detection was found in the skin snips. The calculated seroprevalence through IgG4 serology was 7.9%. No children less than 10 years old were found to be positive for this test. Only one case was positive for Onchocerca volvulus (O. volvulus) after skin PCR. The present study points out that the on-going mass ivermectin treatment has been effective in reducing the prevalence of onchocerciasis and corroborates the interruption of transmission in Bioko Island. To our knowledge, this is the first time that accurate information through molecular and serological techniques is generated to estimate the onchocerciasis prevalence in this zone. Sustained support from the national program and appropriate communication and health education strategies to reinforce participation in CDTI activities are essential to ensure progress towards onchocerciasis elimination in the country.

Project description:Onchocerciasis or "river blindness" is a chronic parasitic disease caused by the filarial worm Onchocerca volvulus, transmitted through infected blackflies (Simulium spp.). Bioko Island (Equatorial Guinea) used to show a high endemicity for onchocerciasis. During the last years, the disease control programmes using different larvicides and ivermectin administration have considerably reduced the prevalence and intensity of infection. Based on this new epidemiological scenario, in the present work we aimed to assess the impact of the strategies applied against onchocerciasis in Bioko Island by an evaluation of IgG4 antibodies specific for recombinant Ov-16 in ELISA.A cross-sectional study was conducted in Bioko Island from mid-January to mid-February, 2014. Twenty communities were randomly selected from rural and urban settings. A total of 140 households were chosen. In every selected household, all individuals aged 5 years and above were recruited; 544 study participants agreed to be part of this work. No previous data on onchocerciasis seroprevalence in the selected communities were available. Blood samples were collected and used in an "ELISA in-house" prepared with recombinant Ov-16, expressed and further purified. IgG4 antibodies specific for recombinant Ov-16 were evaluated by ELISA in all of the participants.Based on the Ov-16 ELISA, the onchocerciasis seroprevalence was 7.9 %, mainly concentrated in rural settings; samples from community Catedral Ela Nguema (# 16) were missed during the field work. Among the rural setups, communities Inasa Maule (# 7), Ruiché (# 20) and Barrios Adyacentes Riaba (# 14), had the highest seropositivity percentages (29.2, 26.9 and 23.8 %, respectively). With respect to the urban settings, we did not find any positive case in communities Manzana Casa Bola (# 3), Colas Sesgas (# 6), Getesa (# 8), Moka Bioko (# 9), Impecsa (# 10), Baney Zona Baja (# 12) and Santo Tomás de Aquino (# 1). No onchocerciasis seropositive samples were found in 10-year-old individuals or younger. The IgG4 positive titles increased in older participants.A significant decline in onchocerciasis prevalence was observed in Bioko Island after years of disease-vector control and CDTI strategy. The seroprevalence increased with age, mainly in rural settings that could be due to previous exposure of population to the filarial parasite, eliminated by the control programmes introduced against onchocerciasis. A new Ov-16 serological evaluation with a larger sample size of children below 10 years of age is required to demonstrate the interruption of transmission of O. volvulus in the human population of Bioko Island (Equatorial Guinea) according to the WHO criteria.

Project description:BackgroundThe human biting rate (HBR), an important parameter for assessing malaria transmission and evaluating vector control interventions, is commonly estimated by human landing collections (HLC). Although intense efforts have been made to find alternative non-exposure mosquito collection methods, HLC remains the standard for providing reliable and consistent HBRs. The aim of this study was to assess the relationship between human landing and light trap collections (LTC), in an attempt to estimate operationally feasible conversion factors between the two. The study was conducted as part of the operational research component of the Bioko Island Malaria Control Project (BIMCP), Equatorial Guinea.MethodsMalaria mosquitoes were collected indoors and outdoors by HLCs and LTCs in three villages on Bioko Island, Equatorial Guinea during five bimonthly collections in 2009. Indoor light traps were suspended adjacent to occupied long-lasting, insecticide-treated bed nets. Outdoor light traps were placed close to the outer wall under the roof of the collection house. Collected specimens were subjected to DNA extraction and diagnostic PCR to identify species within the Anopheles gambiae complex. Data were analysed by simple regression of log-transformed values and by Bayesian regression analysis.ResultsThere was a poor correlation between the two collection methods. Results varied by location, venue, month, house, but also by the statistical method used. The more robust Bayesian analyses indicated non-linear relationships and relative sampling efficiencies being density dependent for the indoor collections, implying that straight-forward and simple conversion factors could not be calculated for any of the locations. Outdoor LTC:HLC relationships were weak, but could be estimated at 0.10 and 0.07 for each of two locations.ConclusionsLight trap collections in combination with bed nets are not recommended as a reliable method to assess human biting rates on Bioko Island. Different statistical analyses methods give variable and inconsistent results. Substantial variation in collection methods prevents the determination of reliable and operationally feasible conversion factors for both indoor and outdoor data. Until improved mosquito collection methods are developed that can provide reliable and unbiased HBR estimates, HLCs should continue to serve as the reference method for HBR estimation.

Project description:Artemisinin-based combination therapies (ACTs) resistance has emerged and could be diffusing in Africa. As an offshore island on the African continent, the island of Bioko in Equatorial Guinea is considered severely affected and resistant to drug-resistant Plasmodium falciparum malaria. However, the spatial and temporal distribution remain unclear. Molecular monitoring targeting the Pfcrt, Pfk13, Pfpm2, and Pfmdr1 genes was conducted to provide insight into the impact of current antimalarial drug resistance on the island. Furthermore, polymorphic characteristics, haplotype network, and the effect of natural selection of the Pfk13 gene were evaluated. A total of 152 Plasmodium falciparum samples (collected from 2017 to 2019) were analyzed for copy number variation of the Pfpm2 gene and Pfk13, Pfcrt, and Pfmdr1 mutations. Statistical analysis of Pfk13 sequences was performed following different evolutionary models using 96 Bioko sequences and 1322 global sequences. The results showed that the prevalence of Pfk13, Pfcrt, and Pfmdr1 mutations was 73.68%, 78.29%, and 75.66%, respectively. Large proportions of isolates with multiple copies of Pfpm2 were observed (67.86%). In Bioko parasites, the genetic diversity of Pfk13 was low, and purifying selection was suggested by Tajima's D test (-1.644, P > 0.05) and the dN/dS test (-0.0004438, P > 0.05). The extended haplotype homozygosity analysis revealed that Pfk13_K189T, although most frequent in Africa, has not yet conferred a selective advantage for parasitic survival. The results suggested that the implementation of continuous drug monitoring on Bioko Island is an essential measure. IMPORTANCE Malaria, one of the tropical parasitic diseases with a high transmission rate in Bioko Island, Equatorial Guinea, especially caused by P. falciparum is highly prevalent in this region and is commonly treated locally with ACTs. The declining antimalarial susceptibility of artemisinin-based drugs suggested that resistance to artemisinin and its derivatives is developing in P. falciparum. Copy number variants in Pfpm2 and genetic polymorphisms in Pfk13, Pfcrt, and Pfmdr1 can be used as risk assessment indicators to track the development and spread of drug resistance. This study reported for the first time the molecular surveillance of Pfpm2, Pfcrt, Pfk13, and Pfmdr1 genes in Bioko Island from 2017 to 2019 to assess the possible risk of local drug-resistant P. falciparum.

Project description:BackgroundGeospatial datasets of population are becoming more common in models used for health policy. Publicly-available maps of human population make a consistent picture from inconsistent census data, and the techniques they use to impute data makes each population map unique. Each mapping model explains its methods, but it can be difficult to know which map is appropriate for which policy work. High quality census datasets, where available, are a unique opportunity to characterize maps by comparing them with truth.MethodsWe use census data from a bed-net mass-distribution campaign on Bioko Island, Equatorial Guinea, conducted by the Bioko Island Malaria Elimination Program as a gold standard to evaluate LandScan (LS), WorldPop Constrained (WP-C) and WorldPop Unconstrained (WP-U), Gridded Population of the World (GPW), and the High-Resolution Settlement Layer (HRSL). Each layer is compared to the gold-standard using statistical measures to evaluate distribution, error, and bias. We investigated how map choice affects burden estimates from a malaria prevalence model.ResultsSpecific population layers were able to match the gold-standard distribution at different population densities. LandScan was able to most accurately capture highly urban distribution, HRSL and WP-C matched best at all other lower population densities. GPW and WP-U performed poorly everywhere. Correctly capturing empty pixels is key, and smaller pixel sizes (100 m vs 1 km) improve this. Normalizing areas based on known district populations increased performance. The use of differing population layers in a malaria model showed a disparity in results around transition points between endemicity levels.DiscussionThe metrics in this paper, some of them novel in this context, characterize how these population maps differ from the gold standard census and from each other. We show that the metrics help understand the performance of a population map within a malaria model. The closest match to the census data would combine LandScan within urban areas and the HRSL for rural areas. Researchers should prefer particular maps if health calculations have a strong dependency on knowing where people are not, or if it is important to categorize variation in density within a city.

Dataset Information

Predicting the impacts of sea level rise in sea turtle nesting habitat on Bioko Island, Equatorial Guinea.

Publications

Predicting the impacts of sea level rise in sea turtle nesting habitat on Bioko Island, Equatorial Guinea.

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