DOI: 10.5061/dryad.7h44j104w
title: Agricultural land use and ensuing eutrophication both shape parasitic trematode communities in rural African lakes

description: Land use is a major driver of biodiversity loss, but how it impacts
 parasite communities is scarcely documented. Crater lakes and their
 catchments in rural western Uganda greatly vary in the intensity of
 anthropogenic disturbance, thus providing opportunity to assess the
 effects of land use on snail-borne parasitic trematodes. We applied
 state-of-the-art molecular biomonitoring to 2385 Bulinus tropicus snails
 from 34 lakes to detect and genotype trematode infections. The 45
 trematode taxa recovered infect a wide range of final vertebrate hosts,
 and some can cause health burdens of significant public importance. Using
 constrained ordinations and generalised additive models, we found that B.
 tropicus reaches peak abundance in lakes with catchments partly under
 agriculture, whereas trematode infections increase with B. tropicus
 abundance and peak at intermediate aquatic productivity. Trematode
 diversity also increases with aquatic productivity, levelling off only in
 the most productive lakes. These relationships likely reflect the higher
 abundance and variety of final hosts sustained by more productive lakes.
 Finally, we found that land use affects trematode community composition,
 with more livestock parasites and less bird parasites occurring in
 agricultural catchments. Our results indicate that both land use and lake
 eutrophication affect the distribution of hotspots for parasitic disease
 transmission.
description: A.
 Environmental data <em>Tabulated in
 the file <strong>data_envi.csv</strong></em>
 At 34 crater lakes located in western Uganda (map in Fig. 1 of
 the article, coordinates in Table S1), surface-water conductivity and pH
 were recorded using a Hydrolab Quanta multimeter, to confirm freshwater
 conditions and to avoid low-pH habitats that are naturally less suitable
 for aquatic snails. Additionally, we retrieved high-quality environmental
 data on each of the sampled lakes from the literature. Estimates of
 aquatic productivity (and associated lake trophic status) are based on
 multi-seasonal measurements of the ubiquitous photosynthetic pigment
 chlorophyll-a (Chl-a) as proxy of phytoplankton biomass in 18 lakes [15],
 and the documented relationship between water transparency and Chl-a
 concentration [1] in the other lakes (details in Table S1). Chl-a values
 were log10-transformed to reduce skewness. Surface water transparency data
 are provided by [2] or [3] for all lakes except Nyamirima, for which
 missing data were replaced with the median values of the entire dataset
 (Table 1). The average slope of terrain within each crater basin was
 retrived from [4]. Estimates of total sediment influx to the lakes
 resulting from soil erosion, as proxy for the compound impact of land use
 on the aquatic ecosystem (through excess nutrient input and siltation),
 were obtained from [4]. These sediment yields were calculated by applying
 the Revised Universal Soil Loss Equation [5] and a sediment delivery
 distributed model [6] to Sentinel-2A satellite images [7]. For lakes with
 inflowing streams, sediment yield estimates were averaged over the range
 reported by [4]. Specific data on the distribution of different types of
 natural and anthropogenic land cover in each catchment, as inferred from
 Sentinel-2A satellite images [4], were used to calculate the fraction (%
 area) of each catchment occupied by (semi-) natural vegetation (forest,
 woodland or grassland), timber plantations (eucalyptus and pine), and crop
 fields (banana, coffee, manioc, maize and vegetable gardens) as proxy for
 variation in land use intensity. Finally, we retrieved reanalysis data on
 mean annual precipitation (MAP) and mean air temperature (MAT) at each
 study site from WorldClim [8]. B. Snail
 data <em>Tabulated in the file
 <strong>data_envi.csv</strong></em>
 In February 2019, we collected molluscs (snails and bivalves)
 from the 34 selected lakes by scooping nearshore aquatic vegetation and
 unconsolidated sediments for 45 minutes and down to ~1m water depth. All
 collected specimens were sorted, counted and identified to species or
 genus level using shell-diagnostic features [9]. They were then sacrificed
 by heat shock at ~70 °C for ~45 seconds and preserved in 80%
 analytical-grade ethanol. We obtained in total 8865 molluscs from 32 of
 the surveyed lakes; none were found in Katinda and Kisibendi. For the
 purpose of this study we focused on <em>B. tropicus</em>, the
 most abundant species (3262 specimens; 36.8%) and found at 24 lakes. To
 ensure that our findings are consistent with the known regional
 distribution of <em>B. tropicus</em>, we compared our field
 data to those of two earlier snail surveys [3,10]. Results were highly
 similar, except that we did not find <em>B. tropicus</em> in
 four lakes (Kanyamukali, Kayihara, Murabyo and Nyahirya) where it had been
 recorded before at very low abundance. For analyses of <em>B.
 tropicus</em> distribution in relation to environmental factors we
 made our data set representative of all three surveys by assuming that a
 nominal number of five <em>B. tropicus</em> specimens were
 found at these four lakes. C. Trematode
 data <em>Processed ASV data in the
 file <strong>trematode_ASV_table.csv</strong> / Maximum
 likelihood tree in the file
 <strong>ML_trematode_tree.nexus</strong> / information on
 species taxonomy and host preference in file
 <strong>trematode_spe_info.csv</strong></em>
 Genetic screening was limited to the 16 lakes where &gt;50
 <em>B. tropicus</em> specimens were collected to ensure
 accurate characterisation of local trematode diversity and community
 composition. In lakes with abundant <em>B. tropicus</em> we
 limited infection screening to <em>ca</em> 200 randomly
 selected specimens per lake. In total, we screened 2385 <em>B.
 tropicus</em> (73% of those collected) for trematode infection with
 a multiplex polymerase chain reaction [11]. Specifically, all soft tissues
 were separated from the shell, homogenised with a sterilised scalpel and
 subjected to genomic DNA extraction using the E.Z.N.A. Mollusc DNA Kit
 (OMEGA Biotek, Norcross, GA, USA). DNA extracts were diluted in ultrapure
 water at 1:10 concentration and screened for the presence of trematode DNA
 using multiplex PCR. Subsequently, we genotyped the trematodes of all
 infected <em>B. tropicus</em> (n=861, or 36.1% of those
 screened) with high-throughput amplicon sequencing (HTAS; [12]). The
 sequencing data were subjected to quality control and processed into
 Amplicon Sequence Variants (ASVs) using <em>dada2</em> [13].
 Our quality-controlled ASVs are deposited in GenBank under accession
 numbers OQ548105-OQ549888 (ITS2); OQ543469-OQ543563 (cox1 I);
 OQ606413-OQ606758 (cox1 II); OQ573734-OQ574607 (NAD1) and
 OQ574626-OQ575328 (cytb). DNA vouchers are deposited at the Royal Museum
 for Central Africa (Tervuren, Belgium) under BE_RMCA_MOL_DNA codes
 numbered 000001 to 002472. Finally, we used these sequences to identify
 the trematode species present via BLAST and phylogenetic analyses using
 the methodology detailed in supplementary materials (Text S1).
 We characterised trematode infracommunities from all ASVs
 recovered per infected snail. Using these infracommunities we
 reconstructed the total trematode communities (<em>i.e.</em>,
 component communities) infecting the <em>B. tropicus</em>
 population of each studied lake. The composition of these communities was
 tabulated in the form of lake-specific abundance matrices which we used to
 estimate local trematode species richness and Hill-Shannon diversity (a
 version of the Shannon diversity index expressed in units of species)
 using iNEXT 2.0.20 [14]. To obtain comparable estimates of community
 diversity across lakes, we standardised sampling by estimating
 Hill-Shannon diversity at 0.95 coverage of the trematode community.We also
 characterised the phylogenetic diversity of each lake’s trematode
 community by computing the mean pairwise phylogenetic distance (MPD; [41])
 among all trematodes recovered from a lake, using
 <em>picante</em> 1.8.2 [15]. Finally, based on the observed
 number of parasite infections, we also calculated the adjusted trematode
 abundance per lake, which accounts for the bias of characterising parasite
 infracommunities from <em>ca</em> 200 snails in lakes where
 &gt;200 specimens were collected. Per lake, we multiplied the observed
 number of infections by the ratio between the total number of <em>B.
 tropicus</em> collected and those screened for trematode
 infections. Finally, to understand how anthropogenic
 impacts propagate through the studied system we classified the trematode
 taxa within each community according to their inferred final host(s),
 namely livestock; birds; multiple final hosts (these are versatile
 trematode taxa able to infect either mammals, birds or other animals); and
 unknown host animals (See Table S3 and references herein).
   Verschuren D, Plisnier P-D,
 Cocquyt C, Hughes H, Lebrun J, Gelorini V, Rumes B, Mahy G, André L. 2011
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 report. </em>Research programme science for a sustainable
 development. Brussels: Belgian Science. De
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 of western Uganda. <em>Limnologica</em>
 <strong>90</strong>, 125891.
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 C, Albrecht C. 2022 Factors controlling the distribution of intermediate
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 A machine learning approach. <em>Front. Environ. Sci.</em>
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description: # Agricultural land use and ensuing eutrophication both shape parasitic
 trematode communities in rural African lakes
 [https://doi.org/10.5061/dryad.7h44j104w](https://doi.org/10.5061/dryad.7h44j104w) ## Description of the data and file structure The data was collected through field sampling and compilation from published data sources with the aim to document 1) the environmental setting of crater lakes in Uganda; 2) the abundance of *Bulinus tropicus* snails in these lakes; 3) the composition of the communities of trematode parasites infecting these snails, as well as their abundance. In doing so, our ultimate goal was to link ongoing environmental changes with variations in parasite communities. ### Files and variables #### File: data\_envi.csv **Description:** File containing the environmental data linked to each crater lake studied, as well as data on the snails collected during field sampling. Each row represents a lake. ##### Variables * cluster: Name of the cluster to which the lake belongs. * lake: Short identifier for each lake. * lakeID: Name of each lake. * protected: Variable indicating whether the lake is located in a protected natural area or not. * MAP: Mean annual precipitation in mm. * MAT: Mean annual temperatures in °C. * lat: Latitude in decimal degrees. * long: Longitude in decimal degrees. * alt: Altitude in m. * cond: Aquatic conductivity in µs/cm. * ph: Water pH. * pristine_vegetation: Fraction of the area of the lake catchment covered by natural vegetation. * crop_fields: Fraction of the area of the lake catchment covered by crop fields. * timber_plantations: Fraction of the area of the lake catchment covered by timber plantations. * mean_slope_grad: Mean slope in the catchment in °. * sed_yield: Annual sediment yield in the lake in t/ha*yr. * prod: Aquatic productivity in μg Chl-a/L. * prod_status: Status of the aquatic productivity.  * n_Btrop: Number of *Bulinus tropicus* specimens collected. * nBtrop_tested: Number of *Bulinus tropicus* specimens tested for parasite infection. * n_RD_pos: Number of *Bulinus tropicus* specimens positive for parasite infection in multiplex PCR. #### File: trematode\_spe\_info.csv **Description:** File providing details on the taxonomic status and host preference for the trematode taxa encountered in our study. Each row represents one trematode taxon. ##### Variables * species: Short identifier specific to each species. * Family: Family to which the taxon belongs. * suborder: Suborder to which the taxon belongs. * Taxon: Taxon at lowest resolution of identification. * Spe: Trematode species nomenclature at lowest resolution of identification. * Secondary: Expected intermediate host category. * num_hosts: Expected number of hosts in the life cycle. * zoonotic human: Expected possibility of being zoonotic to humans. * Host: Expected final host category. #### File: ML\_trematode\_tree.nexus **Description:**  Multi-locus maximum likelihood trematode phylogenetic tree including DNA sequences newly generated in this study from trematode sampling in the Ugandan crater lakes. For a graphic representation of the tree including DNA sequences of reference species extracted from GenBank, see Fig. S2. #### File: trematode\_ASV\_table.csv **Description:** Quality-checked Amplicon Sequence Variants (ASV) table with read support and closest BLAST identity. Each row accounts for the occurrence of a specific ASV in a specific sample. ##### Variables * sampleID: Unique identifier to each snail specimen, including (separated by "_"); the year of collection; the country code (UG = Uganda); the lake where the snail was collected (same identifier as the "lake" variable in the "data_envi.csv" file); the temporary taxonomic status of the snail at the time of collection (BUL = *Bulinus*, BTR = *Bulinus truncatus*); a unique number, sometimes accompanied with the letter "C" if cercariae were collected from the snail. * sequence: DNA sequence of the ASV. * count: Number of reads from the Illumina MiSeq run attributed to the ASV, in the specific snail sample considered. * tot_reads: Total number of reads from the Illumina MiSeq run attributed to the specific snail sample considered (all ASVs included). * prop: Ratio between "count" and "tot_reads". * marker: Molecular marker sequenced. * BLAST: Closest match in BLAST+ search. * identity: % of bases identical between the ASV considered and the closest match in BLAST+ search * lake: Lake where the snail was collected (same identifier as the "lake" variable in the "data_envi.csv" file) * spe: Trematode species nomenclature at lowest resolution of identification (same as the "Spe" variable in the "trematode_spe_info.csv" file). ## Code/software Annotated R script used to run the following tasks. *Please note that you will need to place the input data files in you current working environment. An exhaustive list of all packages used and their version is provided at the end of the task list.* 1. Load and collate the data in the R environment (*dplyr*, *stringr, reshape2, tidyr*). 2. Tabulate the trematode communities per lake, and estimate taxonomic (*iNext*) and phylogenetic (*picante, ape, iCAMP, seqinr*) diversity per lake . 3. Perform principal component analysis (*Hmisc, Rmisc*) and correlation plots (*corrplot*) among environmental variables. 4. Perform generalized additive modelling of *Bulinus tropicus* abundance, trematode taxonomic and phylogenetic diversity, and trematode abundance (*mgcv, DescTools*). 5. Perform multidimensional scaling and distance-based redundancy analysis of trematode community composition (*vegan*)*.* 6. Illustrate the outputs of our analyses (*ggplot2, ggrepel, ggsci*). List of packages: *textshape* version 1.7.5 *ggplot2* version 3.5.0 *tidyr* version 1.3.1 *Rmisc* version 1.5.1 *Hmisc* version 5.1-3 *stringr* version 1.5.1 *dplyr* version 1.1.4 *reshape2* version 1.4.4 *seqinr* version 4.2-36 *ggsci* version 3.0.3 *vegan* version 2.6-4 *iNEXT* version 3.0.1 *iCAMP* version 1.5.12 *picante* version 1.8.2 *ape* version 5.8 *ggrepel* version 0.9.5 *DescTools* version 0.99.54 *corrplot* version 0.92 *mgcv* version 1.9-1 *gratia* version 0.9.0 ## Access information Other publicly accessible locations of the data: * None. Data was derived from the following sources: * Part of the data was retrieved from the following published material (details in the methodology):  1. Verschuren D, Plisnier P-D, Cocquyt C, Hughes H, Lebrun J, Gelorini V, Rumes B, Mahy G, André L. 2011 *Climatic and anthropogenic impacts on African ecosystems. Final report. *Research programme science for a sustainable development. Brussels: Belgian Science. 2. De Crop W, Verschuren D. 2021 Mixing regimes in the equatorial crater lakes of western Uganda. *Limnologica* **90**, 125891. (doi:10.1016/j.limno.2021.125891) 3. Tabo Z, Neubauer TA, Tumwebaze I, Stelbrink B, Breuer L, Hammoud C, Albrecht C. 2022 Factors controlling the distribution of intermediate host snails of *Schistosoma* in crater lakes in Uganda: A machine learning approach. *Front. Environ. Sci.* **10**, 871735. (doi:10.3389/fenvs.2022.871735) 4. De Crop W, Verschuren D, Ryken N, Basooma R, Okuonzia JT, Verdoodt A. 2023 Accelerated soil erosion and sedimentation associated with agricultural activity in crater-lake catchments of western Uganda. *Land* **12**, 976. (doi:10.3390/land12050976) 5.  Renard KG, Foster GR, Weesies GA, McCool DK, Yoder DC. 1997 *Predicting soil erosion by water: A guide to conservation planning with the revised universal soil loss equation (RUSLE).* U.S. Department of Agriculture, Agricultural Research Service. 6. Ferro V, Porto P. 2000 Sediment delivery distributed (SEDD) Model. *J. Hydrol. Eng.* **5**, 411–422. (doi:10.1061/(ASCE)1084-0699(2000)5:4(411)) 7. E.S.A. 2016 *Copernicus Sentinel-2A*. Data; ESA: Paris, France. 8.  Fick SE, Hijmans RJ. 2017 WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas. *Intl Journal of Climatology* **37**, 4302–4315. (doi:10.1002/joc.5086)