DOI: 10.5061/dryad.mpg4f4r4t
title: Year 1 Tanzanian ponds snail-parasite dynamics

description: Different populations of hosts and parasites experience distinct
 seasonality in environmental factors, depending on local-scale biotic and
 abiotic factors. This can lead to highly heterogeneous disease outcomes
 across host ranges. Variable seasonality characterizes urogenital
 schistosomiasis, a neglected tropical disease caused by parasitic
 trematodes (Schistosoma haematobium). Their intermediate hosts are aquatic
 Bulinus snails that are highly adapted to extreme rainfall seasonality,
 undergoing prolonged dormancy yearly. While Bulinus snails have a
 remarkable capacity for rebounding following dormancy, we investigated the
 extent to which parasite survival within snails is diminished. We
 conducted an investigation of seasonal snail-schistosome dynamics in 109
 ponds of variable ephemerality in Tanzania from August 2021 to July 2022.
 First, we found that ponds have two synchronized peaks of schistosome
 infection prevalence and observed cercariae, though of lower magnitude in
 the fully-desiccating than non-desiccating ponds. Second, we evaluated
 total yearly schistosome prevalence across an ephemerality gradient,
 finding ponds with intermediate ephemerality to have the highest infection
 rates. We also investigated dynamics of non-schistosome trematodes, which
 lacked synonymity with schistosome patterns. We found peak schistosome
 transmission risk at intermediate pond ephemerality, thus the impacts of
 anticipated increases in landscape desiccation could result in increases
 or decreases in transmission risk with global change.
description: Sampling sites We surveyed 109 ponds monthly
 in six Tanzanian districts of the Lake Victoria watershed from 23 August
 2021–27 July 2022 (Figure 1). These ponds are created or modified by
 village communities to increase year-round water availability for the
 purpose of human household use (“Kisima”), for cattle use (“Lambo”), or
 for longer term water storage dams with unspecified use (“Bwawa”). A small
 number of ephemeral rivers and streams (“Mto” or “Kijito”) were also
 included in the study. Many of these ponds dry completely for several
 months of the year (Figure 2A) or dramatically decrease in size (Figure
 2B) in the dry season. All ponds were chosen with approval of, and surveys
 were conducted in collaboration with, local village leaders. A larger
 pilot study was conducted in 2020–2021 including 467 pond sites that were
 identified by local leaders as potential transmission sites across the six
 districts, based on frequent human and cattle use (31). In some cases,
 this included all ponds in the village and in other villages leaders
 identified 6–8 ponds. For the current study, only villages where
 schistosome infected snails were found were retained and up to five ponds
 were retained per village. All sampling was conducted with permission from
 the Medical Research Coordination Committee of the National Institute for
 Medical Research (NIMR) in Mwanza (ethics approval certificate number
 NIMR/HQ/R.8a/Vol.IX/3462). The Lake Victoria watershed
 is typically characterized by short and long rainy seasons,
 <em>Vuli</em> (October-December) and
 <em>Masika</em> (March-May), respectively. However, a delayed
 <em>Vuli</em> commencing in December 2021 resulted in a
 combining of the two rainy periods in our sampling period (Figure 3A).
 Rainfall data (in mm per day) was obtained from the Mwanza weather station
 between 23 August 2021 and 27 July 2022 (32). During
 monthly site visits, each pond was surveyed for maximum length and width
 perpendicular to maximum length in meters using a tape measure, and depth
 at center in meters using a pole and tape measure. Dry ponds for which
 these dimensions were 0 meters were noted as such. All ponds that were too
 deep in the center to measure were assigned a depth of 2 meters.
 Snail surveying and collection We conducted
 snail surveys in small rainfall catchment areas in Northern Tanzania which
 are occupied by <em>Bulinus nasutus</em> snails (22). This
 species is morphologically differentiated from other
 <em>Bulinus</em> species in Tanzania, <em>B.
 africanus</em> and <em>B. globosus</em>. Two researchers
 conducted time-constrained net sampling using metal mesh scoop nets to
 collect <em>B. nasutus </em>snails for 15 minutes (leading to
 a total of 30 minutes of surveying per pond per month). This provides a
 representation of the contact experience of a person standing in the water
 for 30 minutes. In addition, this method is as effective as quadrat
 methods in assessing snail population dynamics across long time periods
 (33). Researchers searched for snails across the area of the pond, with
 special focus on microhabitats (submerged and floating vegetation, and
 other floating objects).  Snails were placed in Nalgene containers in a
 cooler and brought back to the lab at the NIMR Mwanza Centre for cleaning,
 counting, and quantifying parasites (“shedding”).
 Identifying and quantifying parasite shedding
 <em>Bulinus nasutus</em> snails were shed for patent
 infections in individual 30 ml beakers with 25 ml bottled water for 24
 hours in natural light conditions. Following this full day shed, beakers
 were examined under a dissecting microscope at 10–25x for the presence of
 cercariae (larval forms) of schistosome and non-schistosome trematodes.
 The cercariae of these two groups are distinguished by size, shape, and
 movement (34). <em>Schistosoma haematobium</em> cannot be
 morphologically distinguished from <em>S. bovis</em> or their
 hybrids (35), therefore we represented all these individuals as
 “schistosomes”. Non-schistosomes were overwhelmingly represented by
 xiphiodiocercariae. If the presence of trematodes was confirmed, cercarial
 intensity was quantified after staining with Lugol’s Iodine and
 homogenization by gentle pipetting. For schistosomes, if the estimated
 number of cercariae was below 200, all cercariae were counted. If the
 number was larger, a subsample of 18.5% of the beaker’s bottom area was
 counted and multiplied by 5.412 to extrapolate for the total area of the
 beaker. For non-schistosome trematodes, only the subsample approach was
 taken due to higher intensities being typical.
 Identifying potential infected aestivators
 We identified if snails carried infections through aestivation
 using the timing of infections relative to emergence from aestivation in
 desiccating ponds. The prepatent period that is necessary for infections
 to develop until cercariae release typically take 6–18 weeks in a
 laboratory setting for <em>Schistosoma haematobium</em> (36).
 Ponds need to refill for snails to revive from aestivation and be
 receptive to miracidia once the water has returned. This could take just a
 few days or several weeks from the onset of rain, depending on the size of
 the pond and where in the pond snails are aestivating. As a result, we
 conservatively infer that infections that were detected less than 60 days
 following the last dry survey were acquired before aestivation, with
 increased confidence in those detected &lt;30 days after the last dry
 survey. While <em>Bulinus </em>snails aestivate in dry
 microhabitats within non-desiccating ponds when water conditions are not
 ideal, it was not possible to identify infected asetivators using our
 methodology as these ponds did not have any dry surveys as a time
 reference point. Statistical analyses
 In this study, we assessed transmission risk in four ways: (1)
 snail abundance, (2) the proportion of snails infected, (3) per capita
 release of parasite cercariae from sampled snails, and (4) the total
 parasitic cercariae observed from all sampled snails. Snail abundance is
 defined as the number of <em>B. nasutus </em>snails recovered
 per 30-minute pond survey. Thereafter, we assess the proportion of these
 snails that sheds schistosome or non-schistosome parasites within a
 24-hour shedding window. In terms of cercarial production, we first looked
 at the average number of cercariae released by individual snails of each
 parasite group (per capita) and we also summed the total number of
 cercariae observed of each parasite group by the infected snails that we
 collected in each pond survey (total observed release).
 We utilized Generalized Additive Mixed Models (GAMMs) in the R
 package mgcv to evaluate how several metrics changed as a function of time
 and binary pond status (desiccating or non-desiccating). Specifically, we
 ran individual analyses to test how the following dependent variables
 changed over the course of a year: water depth (Gamma distribution using
 depth+0.01cm and link=“log”), snail abundance (Quasipoisson distribution),
 and infection prevalence (binomial distributions) and per capita and total
 observed cercariae in pond surveys (Quasipoisson distributions) of the two
 parasite groups. GAMMs are effective at evaluating smoothed, non-linear
 relationships over time. Thus, in each analysis we represented the annual
 trend as a smooth term. For Gamma and binomial error distributions, we fit
 models with Restricted Maximum Likelihood (REML), whereas as for
 Quasipoisson models, we fit with Quasi-Penalized Likelihood (37). Our
 independent variable of time is defined as days from the first day of
 sampling (23 July 2021) ranging from 0 to 338. We fit models with
 continuous autoregressive-1 error structures to account for repeated
 measures, except for the prevalence and per capita cercariae models due to
 failed convergence. For all models, we fit the dynamics of non-desiccating
 waterbodies with a reference temporal smooth and tested for significantly
 different dynamics in desiccating waterbodies with a temporal difference
 smooth (37). Lastly, we included pond ID as a random effect in the GAMMs
 to account for nonindependence in the monthly repeated observations from
 these replicated sites. All GAMMs were set up with a similar structure to
 this schistosome prevalence model below. See script for variation per
 model type. <pre style="padding-left:40px;">gamm(cbind(schisto_pos,
 schisto_neg)~ s(ContDate)+<br>s(ContDate, by=Aest) + s(Waterbody,
 bs="re"), family =
 binomial("logit"),<br>data=infected_ponds,
 method="REML")</pre> We ran Generalized Linear
 Models (GLMs) with the R package glmmTMB with binomial error distributions
 on all snails collected from each pond to assess if cumulative yearly
 infection prevalence varied among the two ephemerality categories. We also
 ran similar binomial GLMs to assess if yearly transmission risk differs
 with intensity of ephemerality (% pond reduction in pond area in a year).
 We summed the total number of infected and uninfected snails per pond per
 year for each parasite group to evaluate cumulative yearly parasite
 transmission risk. We then calculated the surface area of each pond on
 each visit by assuming an elliptical shape (Surface area =
 1/2length*1/2width*p ) and the percentage reduction in area of each pond
 ((max-min)/max*100) as a measure of ephemerality intensity.
description: This README file was generated on 2024-01-08 by Naima Starkloff. GENERAL
 INFORMATION 1. Title of Dataset: Year 1 Tanzanian ponds snail-parasite
 dynamics 2. Author Information A. Principal Investigator Contact
 Information Name: David J. Civitello Institution: Emory University
 Address: Atlanta, GA USA Email: B.
 Associate or Co-investigator Contact Information Name: Naima C. Starkloff
 Institution: Emory University Address: Atlanta, GA USA Email:
 3. Date of data collection (single date,
 range, approximate date): August 2021-July 2022 4. Geographic location of
 data collection: Northwestern Tanzania (Busega, Kishapu, Kwimba, Magu,
 Misungwi And Sengerema districts) 5. Information about funding sources
 that supported the collection of the data: N.C.S. and D.J.C. were
 supported by the US National Institute of Allergy and Infectious Diseases
 R01 AI50774-01. All fieldwork expenses were also funded by this source.
 SHARING/ACCESS INFORMATION 1. Licenses/restrictions placed on the data:
 CC0 1.0 Universal (CC0 1.0) Public Domain 2. Links to publications that
 cite or use the data: Starkloff, N.C., Angelo, T., Mahalila, M.P.,
 Charles, J., Kinung'hi, S. & Civitello, D.J. (2024).
 Spatio-temporal variability in transmission risk of human schistosomes and
 animal trematodes in a seasonally desiccating East African landscape.
 Proceedings B. 1. Links to
 other publicly accessible locations of the data: None 2.
 Links/relationships to ancillary data sets: None 3. Was data derived from
 another source? No A. If yes, list source(s): NA 4. Recommended citation
 for this dataset: Starkloff, N.C., Angelo, T., Mahalila, M.P., Charles,
 J., Kinung'hi, S. & Civitello, D.J. (2024). Data from:
 Spatio-temporal variability in transmission risk of human schistosomes and
 animal trematodes in a seasonally desiccating East African landscape.
 Dryad Digital Repository. 
 DATA & FILE OVERVIEW 1. File List: A) waterbody_data.csv B)
 snail_data.csv C) weather_data.csv 1. Relationship between files, if
 important: None 2. Additional related data collected that was not included
 in the current data package: None 3. Are there multiple versions of the
 dataset? No A. If yes, name of file(s) that was updated: NA i. Why was the
 file updated? NA ii. When was the file updated? NA
 \#########################################################################
 DATA-SPECIFIC INFORMATION FOR: waterbody_data.csv 1. Number of variables:
 20 2. Number of cases/rows: 1308 3. Variable List: * Waterbody: name of
 waterbody/pond (n=109) surveyed monthly * Collection_Date: date of monthly
 survey at waterbody * Cont_Date: variable of time defined as days from the
 first day of sampling (23 July 2021) ranging from 0 to 338 * Phase:
 monthly survey category (Categories: August_2021 to July_2022) * District:
 name of district in which waterbody is located * Village: name of village
 in which waterbody is located * Waterbody_Type: type of waterbody
 (Categories: Dam, Pond, River, Stream) * Long_dimension: maximum length of
 the waterbody in meters * Perp_dimension: length of the waterbody
 perpendicular to Long_dimension in meters * Depth_centre: depth at the
 center of the waterbody in meters. In cases that ponds were too deep to
 measure, a depth of 2m was assigned. All dry waterbodies were assigned a
 depth of 0m. * Water_level: categorical assignment of water presence by
 field research team (Categories: Dry, Low, Normal, Flooded) *
 Bulinus_Number_Collected: Number of Bulinus nasutus snails collected in a
 waterbody survey * Infected_Bulinus_Number: Number of Bulinus nasutus
 snails collected in a waterbody survey that were infected with Schistosome
 parasites (based on lab shedding) * Schist_Fails: Number of Bulinus
 nasutus snails collected in a waterbody survey that were not infected with
 Schistosome parasites (based on lab shedding) * NSInfectedSnails: Number
 of Bulinus nasutus snails collected in a monthly waterbody survey that
 were infected with non-schistosome parasites (based on lab shedding) *
 NS_Fails: Number of Bulinus nasutus snails collected in a monthly
 waterbody survey that were not infected with non-schistosome parasites
 (based on lab shedding) * WB_SchistNumber: total number of schistosome
 shed in a monthly waterbody survey (compiled from additional datasheet on
 individual snail data below) * WB_NonSchistNumber: total number of
 non-schistosome shed in a monthly waterbody survey (compiled from
 additional datasheet on individual snail data below) * Aest: binary
 variable indicating if waterbody dried for at least one survey period or
 never dried between August 2021 to July 2022 (Categories: 1, 0) * WB_Area:
 surface area of waterbody in monthly survey. Calculated assuming an
 elliptical shape (Surface area = 1/2Long_dimension*1/Perp_dimension*π) 4.
 Missing data codes: None 5. Specialized formats or other abbreviations
 used: None
 \#########################################################################
 DATA-SPECIFIC INFORMATION FOR: snail_data.csv 1. Number of variables:
 30137 2. Number of cases/rows: 13 3. Variable List: * BatchNumber: all
 snails collected were sorted into batches of 32 for measuring and
 shedding. Batch numbers are unique to each waterbody and monthly survey. *
 Snail: numerical ID provided to each Bulinus nasutus snail between 1 and
 32 per monthly waterbody survey batch. * Waterbody: name of waterbody/pond
 (n=109) surveyed monthly * Collection_Date: date of monthly survey at
 waterbody * Cont_Date: variable of time defined as days from the first day
 of sampling (23 July 2021) ranging from 0 to 338 * Phase: monthly survey
 category (Categories: August_2021 to July_2022) * District: name of
 district in which waterbody is located * Village: name of village in which
 waterbody is located * Schisto_Pos: individual snail's infection
 status for schistosome infection (Categories: 1, 0) * Nonschisto_Pos:
 individual snail's infection status for non-schistosome infection
 (Categories: 1, 0) * Total_Schist_No: quantification of release of
 schistosome parasite cercariae by individual snail following 24 hours in
 beaker * Total_Nonschist_No: quantification of release of non-schistosome
 parasite cercariae by individual snail following 24 hours in beaker *
 Aest: binary variable indicating if waterbody dried for at least one
 survey period or never dried (Categories: 1, 0) 4. Missing data codes:
 None 5. Specialized formats or other abbreviations used: None
 \#########################################################################
 DATA-SPECIFIC INFORMATION FOR: weather_data.csv 1. Number of variables: 3
 2. Number of cases/rows: 339 3. Variable List: * datetime: day, month and
 year of data collection from Mwanza weather center between 23 August 2021
 and 27 July 2022 (the survey period) * precip: the mm of rainfall recorded
 in a given day * Cont_Date: variable of time defined as days from the
 first day of sampling (23 July 2021) ranging from 0 to 338 4. Missing data
 codes: None 5. Specialized formats or other abbreviations used: None