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\begin{document}

\title{Reducing disease and producing food: Effects of 13 agrochemicals on
snail biomass and human schistosomes}



\author[1]{Christopher Haggerty}%
\author[2]{Neal  Halstead}%
\author[3]{David Civitello}%
\author[1]{Jason Rohr}%
\affil[1]{University of Notre Dame}%
\affil[2]{Wildlands Conservation}%
\affil[3]{Emory University}%


\vspace{-1em}



  
  \date{May 24, 2024}


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\selectlanguage{english}
\begin{abstract}
Agrochemical use is predicted to increase 2-5 fold by 2050 to meet food
demand. Evidence suggests that agrochemical pollution could increase
snails that transmit the disease schistosomiasis to 250 million people,
but most agrochemicals remain unexamined. Here we quantify the relative
effects of fertilizer, six insecticides, and six herbicides on snail
genera responsible for 90\% of global schistosomiasis cases. We
identified fertilizers and 4 of 6 insecticides as high risk for
increasing snail biomass by increasing snail habitat (aquatic
vegetation) or food (periphyton) and reducing snail predators,
respectively. Herbicides generally had negative effects on snails by
reducing habitat, with two herbicides increasing snails in the absence
of aquatic vegetation. Parasite production, which reflects human
infection risk, scaled positively to snail biomass. Our findings suggest
that fertilizers and insecticides present higher chances of increasing
human schistosomiasis than herbicides, and revealed several low risk
agrochemicals might help increase crop production without increasing
schistosomiasis.%
\end{abstract}%



\sloppy


\textbf{Introduction}

Recent evidence suggests that increasing agriculture to meet the growing
global food demand could simultaneously increase human infectious
diseases (Rohr et al., 2019), necessitating studies that identify means
of sustainably improving food production without increasing disease. One
such human disease that is positively associated with agricultural
expansion is schistosomiasis, a disease caused by parasites released
from freshwater snails, with an estimated 779 million people at risk
globally (Vos et al., 2016). Over 90\% of global cases
(\textasciitilde{}200 million cases) occur in sub-Saharan Africa and
involve \emph{Biomphalaria} and \emph{Bulinus} snail species as
intermediate hosts (Gryseels, Polman, Clerinx, \& Kestens, 2006;
Steinmann, Keiser, Bos, Tanner, \& Utzinger, 2006). Schistosomiasis has
been positively linked to expanding snail habitat and loss of native
snail predators (Huang \& Manderson, 1992; Savaya Alkalay et al., 2014;
Steinmann et al., 2006) because each infected snail host can release
thousands of cercariae, the human-infectious life stage, daily. Parasite
production by snails can be influenced by local aquatic factors
(Haggerty, Bakhoum, Civitello, et al., 2020), and thus, human infection
rates respond rapidly to changes in local ecological factors (Huang \&
Manderson, 1992), emphasizing the importance of freshwater ecology in
infection dynamics.

Agrochemical pollution has the potential to influence ecological factors
that directly or indirectly influence schistosomiasis transmission. Both
fertilizers and herbicides can increase periphyton resources required
for snail growth and cercarial production (Halstead et al., 2014), and
have been linked to increased trematode infection intensities in frogs
in agricultural wetlands (Rohr et al., 2008). Herbicides kill
phytoplankton that absorb light from the water column, which can
indirectly increase growth of periphyton, attached algae that is a major
resource for snails (Halstead et al., 2018). Additionally, fertilizers
and herbicides can increase and decrease, respectively, submerged
vegetation that acts as primary habitat for snails and may influence
snail predation. Invertebrate predators that consume snails and help
regulate snail populations can be killed by some insecticides even at
environmentally relevant concentrations, leading to top-down insecticide
effects that increase snails (Halstead et al., 2014). Snail intermediate
hosts are difficult to control (Gryseels et al., 2006; Steinmann et al.,
2006), and understanding the strength of various agrochemicals on snails
might be key to understanding recent increases in human schistosomiasis
coincident with agricultural expansion and preventing any future surges
(Hoover et al., 2020).

Agrochemical use is predicted to increase 2-5 fold over the next few
decades (Foley et al., 2011; Tilman, Balzer, Hill, \& Befort, 2011;
Tilman et al., 2001), yet laboratory studies of pesticides using species
in isolation could miss the net effects of pesticides in natural
communities (Clements \& Rohr, 2009; Rohr \& Crumrine, 2005; Rohr,
Kerby, \& Sih, 2006). Submerged aquatic vegetation that acts as snail
habitat might decrease the toxicity of certain insecticides by changing
pH (Brogan \& Relyea, 2014), or influence predation by providing refugia
(Davis, Purrenhage, \& Boone, 2012). Thus, because agrochemicals in
natural environments are differently subject to the above influences,
simulating natural communities using mesocosm experiments is needed to
improve predictions of the direct and indirect effects of agrochemicals
on freshwater communities, such as those containing snail hosts (Rohr,
Salice, \& Nisbet, 2016).

We created experimental natural freshwater pond communities to determine
the predictability of top-down effects of insecticides and bottom-up
effects of fertilizer or herbicides on the biomass of two genera of
snail hosts that can transmit schistosomiasis (\emph{Biomphalaria
glabrata} and \emph{Bulinus truncatus} ). More specifically, we examined
the top-down effects of six insecticides belonging to two insecticide
classes (the organophosphates chlorpyrifos, malathion, and terbufos, and
the pyrethroids esfenvalerate, \selectlanguage{greek}λ-\selectlanguage{english}cyhalothrin, and permethrin), and the
bottom-up effects of both fertilizer and six herbicides belonging to two
herbicide classes (the chloroacetanilides acetochlor, alachlor, and
metachlor, and the triazines atrazine, propazine, and simazine) in the
presence of snail predators. We then performed a follow-up experiment
using all six herbicides but without snail predators to determine if
herbicide bottom-up effects on snails are only evident in the absence of
strong top-down effects. In the follow-up experiment, we also examined
if total parasite production by snails was predicted by their total
biomass, and, thus, whether snail biomass changes associated with
different agrochemical exposures could be related to human exposure to
parasites.

\textbf{Material and methods}

\emph{Experimental Design}

\emph{Experiment One}

To examine top-down and bottom-up effects in the presence of snail
predators, we established experimental freshwater ponds in 75 800L
mesocosms filled with 500L of water (mean pH = 9.09) in the summer of
2011 at a facility approximately 20 miles southeast of Tampa, FL, USA.
Tap water in mesocosms was allowed to age for 48 hours before being
seeded with algae (periphyton and phytoplankton) and zooplankton
collected from local ponds. Algal and zooplankton communities were
allowed to establish over a four-week period and water was mixed among
tanks weekly to attempt to homogenize initial communities before
application of agrochemical treatments. Plastic containers with sediment
(1 L play sand and 1 L organic topsoil (The Scotts Company, Marysville,
OH, USA)) and five rooted shoots of \emph{H. verticillata} were added to
each tank on 5 July 2011. Immediately before application of agrochemical
treatments on 11 July 2011 (Week 0), we added snails (21 \emph{Bi}
.\emph{glabrata} {[}NMRI strain{]} and 12 \emph{Bu.
truncatus}{[}Egyptian strain{]}, provided by NIAID Schistosomiasis
Resource Center) and snail predators (2 juvenile \emph{Procambarus
alleni}crayfish, 7 \emph{Belostoma flumineum} water bugs, and
3\emph{Lethocerus} spp. water bugs collected from local ponds) to each
tank.

Tanks were randomly assigned to one of fifteen treatments (5 tanks per
treatment) including twelve pesticides at their respective EEC
(estimated environmental concentration), fertilizer (440 \selectlanguage{greek}µ\selectlanguage{english}g/L
Phosphorus; 4400 \selectlanguage{greek}µ\selectlanguage{english}g/L Nitrogen, observed levels from the field; (Chase,
2003)), solvent control (0.0625 mL/L acetone), and water control in five
replicated spatial blocks. Water and solvent controls were used to
ensure that any observed effects of pesticides could not be attributed
to the presence of solvent. All pesticides were dissolved in acetone and
applied at their respective estimated peak environmental concentrations
(Tables S1-S2). EEC values were determined using the USEPA's GENEEC
(v2.0, USEPA, Washington, D.C.) software, manufacturers' label
application recommendations, and the physicochemical properties of each
pesticide (Table S1-S2).

\emph{Experiment Two}

To examine bottom-up effects of the six herbicides in the absence of
snail predators, we performed a follow-up experiment in 2015 using the
same general experimental setup above in the same location but with a
few alterations from 2011: 1) 64 mesocosm tanks were employed with 8
tanks per treatment, including the water and solvent controls, 2) five
crayfish predators were randomly assigned to half of the tanks in each
treatment, 3) 24 \emph{Bi} . \emph{glabrata} (NMRI strain) snails were
placed into each tank, and 4) tanks lacked \emph{H. verticillata} .

\emph{Data Collection}

\emph{Experiment One}

Data collection for algae and snail eggs was made at weeks 1, 2, 4, 6, 8
and 12 of the experiment. Periphyton measurements were recorded from 100
cm\textsuperscript{2} clay tiles suspended vertically 15 cm from the
bottom of each tank (approximately 20 cm below the water's surface),
facing south along the northern wall of each tank. Five clay tiles were
added to each tank when they were initially filled with water in June
2011 and July 2015. Each visit, a tile was collected and the
phytoplankton removed using a scrub brush and wash bottle of de-ionized
water to fill a 50mL falcon tube. A separate 50 mL water sample from the
center of the tank was collected for phytoplankton abundance.
Phytoplankton and periphyton chlorophyll (measured as
F\textsubscript{0}), were measured from samples stored in darkness for
1h, using a handheld fluorometer (Z985 Cuvette AquaPen, Qubit Systems
Inc., Kingston, Ontario, Canada).

Snail egg masses and hatchling density were estimated using two 15 x 30
cm pieces of Plexiglass placed in each tank; one suspended vertically 10
cm from the bottom of each tank and one resting horizontally along the
tank bottom. Visual searches for dead crayfish in tanks with crayfish
predators occurred at 24 and 48 hrs after insecticide addition, and upon
each snail egg sampling session. At the end of the experiment (week 12),
we quantified the number and mass (g) of live snails of each species and
live crayfish in predator tanks. All snails and macroinvertebrates were
collected and subsequently preserved in 70\% ethanol. We determined
snail infection under a dissecting microscope by cracking each snail's
shell and inspecting the hepatopancreas and gonad.

\emph{Snail infections}

\emph{Schistosoma mansoni} (NMRI strain) and \emph{S.
haematobium}(Egyptian strain) eggs were collected from infected
Swiss-Webster mice and Siberian hamsters, respectively, from the NIAID
Schistosomiasis Resource Center. Five infected mice and hamsters each
were euthanatized on dates (30 August 2011, 1 September 2011, and 6
September 2011), and\emph{S. mansoni} and \emph{S. haematobium} eggs
were collected from the livers and intestines, respectively. Three mL
egg aliquots were prepared for each schistosome species and added to the
tanks within two hours. An additional three egg aliquots were placed in
artificial spring water (Cohen, Neimark, \& Eveland, 1980) and average
egg viability is provided in Supplementary Table S3.

\emph{Experiment Two}

Algae, snail eggs, and snail hatchlings were quantified using the same
methodology as the first experiment. At the end of the experiment (week
12), we recorded the number and mass of live predators and snails. All
snails and macroinvertebrates were collected and subsequently preserved
in 70\% ethanol. We determined snail infection under a dissecting
microscope by cracking each snail's shell and inspecting the
hepatopancreas and gonad. We estimated snail tissue biomass (mg dry
weight) using a mass-shell length (mm) regression where mass =
0.0096*L\textsuperscript{3~}as fully described by (Civitello, Fatima,
Johnson, Nisbet, \& Rohr, 2018). To assess parasite production in the
second experiment, we randomly collected 24 snails from each tank
between 9 -- 12 AM on September 10 and September 24 (days 45 and 59 post
initial egg introduction, respectively) using a small net, measured
their mass (g), and then shed them individually in artificial spring
water in 30 mL beakers in the field for 1 hr. After shedding, snails
were returned to their tank of capture. Collected cercariae were stained
with Lugol's solution and counted in the laboratory under a microscope.
At the end of both experiments, we added pool shock (71.8\%
trichloro-s-triazinetrione, Recreational Water Products , Buford, GA,
USA) to each tank at 0.15g/L to kill any snails or infective schistosome
cercariae (Halstead et al., 2018).

\emph{Snail infections}

\emph{Schistosoma mansoni} (NMRI strain) eggs were collected from
infected Swiss-Webster mice provided by the NIAID Schistosomiasis
Resource Center. Sixteen infected mice were euthanatized on each date of
egg addition to tanks (27 July 2015, 10 August 2015, 24 August 2015 and
8 September 2015), and \emph{S. mansoni} eggs were collected from the
livers. Egg isolation, viability testing, and deployment to tanks within
2 h was performed as described above for experiment one. Average egg
viability for the four egg harvests in experiment two was 48 percent
(Table S3).

Experimental biosafety protocols were approved by USF Biosafety (IBC \#
1334) to minimize the risk of human infection via personal protective
gear and to minimize snail escape using methods fully described by
Halstead et al. (2018).

\emph{Data Analysis}

\emph{Experiment One}

We used the \emph{lme4} () package to examine whether pesticide type,
class, or chemical identity explained the greatest amount of variation
in snail biomass. To do so, we separately predicted infected and
uninfected \emph{B. glabrata} and \emph{B. truncatus} biomass using a
categorical random term for pesticide, nested within class, nested
within type to account for the nested structure of our experimental
design (1 \textbar{} type / class / chemical). These models did not
consider the control tanks because they were not hierarchically nested.
To determine whether our nested experimental treatments had significant
effects on snails, we then performed a likelihood ratio test to compare
a full nested model for each snail species to a null (intercept only)
linear model that did not include the random effect. If our nested model
was significant, we then used the experimental level that explained the
most variation in snails as a fixed categorical predictor of either
infected or uninfected \emph{B. glabrata} or uninfected \emph{B.
truncatus} total snail biomass using separate regressions with a
Gaussian error distribution in the \emph{glmmTMB} package (Brooks et
al., 2017) in R v4.0 (RCoreTeam, 2013). We did not analyze
infected\emph{B. truncatus} biomass due to very low egg viability, and,
thus, infection rates for this species (Table S3). For each response, we
also fit models including a random term for spatial block and/or a term
for zero-inflation. We considered models having water and solvent tanks
treated separately or combined as one control group, resulting in 9
possible models for each species. We ranked models using AICc values,
and assessed significance of predictors for each response only for the
model with the lowest AICc value (Tables S4-S6). We plotted the results
of our regression models using the \emph{visreg} package (Rosseel,
2012).

A combined factor and path analysis was used for the first experiment to
explore potential causal pathways of agrochemicals on snail egg
production and biomass using the \emph{lavaan} package (Rosseel, 2012)
in R statistical software (RCoreTeam, 2013). Because our sample size of
75 tanks restricted the number of causal pathways we could infer, we
first constructed a latent variable for phytoplankton chlorophyll a
(F\textsubscript{0}) in all weeks, periphyton chlorophyll a
(F\textsubscript{0}) in all weeks, snail egg counts in all weeks,
and\emph{H. verticillata} abundance in all weeks (1-12). For each latent
variable, we used modification indices to add any missing covariances
until each model fit the observed data (\emph{CFI} \textgreater{} 0.95).
The scores for each latent variable model were then extracted using the
predict function in lavaan and used for construction of final structural
equation models to separately predict \emph{Bi. glabrata} and \emph{Bu.
truncatus} biomass (Tables S7-S8). We examined pathways that were
previously known for agrochemicals i.e. top down-effects of insecticides
on snail predators and bottom-up effects of fertilizer or herbicide on
periphyton (Halstead et al., 2018). We also examined d-separate tests
and included any potential missing pathways until our model had a good
fit to observed data

\emph{Experiment Two}

We performed the same regressions as for experiment one to predict
infected, uninfected, and total snail biomass in experiment two using a
categorical random term for pesticide, nested within class, and compared
it to a null (intercept) model using likelihood ratio tests. To identify
which herbicides have strong effects in the absence of predation, we
used the fixed categorical predictor for pesticide identity to
separately predict natural-log transformed infected, uninfected, and
total \emph{B. glabrata} snail biomass, in tanks without crayfish
predators, using a Gaussian error distribution in the
\emph{glmmTMB}package (Brooks et al., 2017) in R v4.0 (RCoreTeam, 2013).
Total biomass included living and dead snails to examine whether
herbicides caused increases in snails early in the experiment despite
those snails perishing before the end of the experiment.

To test for an interaction between crayfish predators and herbicides in
experiment two, we separately predicted log-transformed uninfected,
infected, and total \emph{Bi. glabrata} snail biomass, using Gaussian
error distributions that considered all possible interactions between a
fixed categorical term for crayfish presence and fixed categorical terms
for pesticide, class, or type. We also considered a random term for
block, a term for zero-inflation, and whether water and solvent tanks
should be treated separately or as a single control group, resulting in
20 possible crayfish interaction models. We compared all interaction
models to one another, a model using only crayfish presence and a null
(intercept only) model using AICc. We also used a Pearsons' correlation
coefficient to assess the relationship between infected and total snail
biomass, and performed a negative binomial regression to predict total
cercariae counts in tanks using shedding snail biomass.

\textbf{Results}

\emph{Experiment One}

Pesticide type explained 43 percent of the variation in \emph{Bi.
glabrata} biomass (Table S4), whereas most of the variation in \emph{Bu.
truncatus} biomass (35 percent) was explained by pesticide identity
nested within class nested within type (Table S4). Likelihood ratio
tests determined that the above pesticide effects were significant for
both snail species (Table S5). Model selection favored using pesticide
type and pesticide nested within class within type for \emph{Bi.
glabrata} and \emph{Bu. truncatus} biomass, respectively, and the lowest
AICc values included a term for zero-inflation and treating water and
solvent as a single control group for both snail species (Tables S6-S7).
Model selection (Table S6) indicated significant positive fertilizer
effects (bottom-up) on \emph{Bi. glabrata} biomass (Fig. 1a; Table 1).
In contrast, bottom-up effects of herbicides for \emph{Bi.
glabrata}biomass were generally not significant in the presence of
predators (Table 1; \emph{p} \textgreater{} 0.05). Insecticides were
associated with positive top-down effects on \emph{Bi. glabrata} biomass
(Fig. 1a; Table 1). Similar to \emph{Bi. glabrata} , fertilizers had
significant positive bottom-up effects for \emph{Bu. truncatus} biomass,
after model selection (Table S7), while none of the six herbicides were
significantly associated with \emph{Bu. truncatus} biomass in the
presence of predators (Fig. 1b; Table 1). Similar to \emph{Bi. glabrata}
, insecticides were associated with positive top-down effects on
\emph{Bu. truncatus} biomass (Fig. 1b). Four insecticides led to
significant increases in average \emph{Bu. truncatus} biomass relative
to the controls (chlorpyrifos: 55\% increase, terbufos: 36\%,
esfenvalerate: 28\%, and permethrin: 61\%), while one insecticide from
each insecticide class had no significant effects (malathion and
\selectlanguage{greek}λ-\selectlanguage{english}cyhalothrin both changed average biomass by \textless{} 10\%).

Structural equation modeling (SEM) was consistent with the above
relationships (Fig. 2). The final SEM was a good fit for \emph{Bu.
truncatus} (\emph{CFI} = 0.99, \selectlanguage{greek}χ\selectlanguage{english}\textsuperscript{2} = 15.25, \emph{df} =
26, \emph{p} = 0.95; Table S8) and \emph{Bi. glabrata} (\emph{CFI} =
0.99, \selectlanguage{greek}χ\selectlanguage{english}\textsuperscript{2} = 28.04, \emph{df} = 26, \emph{p} = 0.36;
Table S9). The SEM model for \emph{Bu. truncatus} suggested positive and
indirect top-down effects of insecticides on snail biomass by lowering
24 hr crayfish survival, which lowered final crayfish abundance (Fig.
2). Fertilizer increased both phytoplankton and periphyton chlorophyll
a, the latter of which had positive but non-significant effects upon
snail eggs and biomass (Table S8). Instead, significant positive
bottom-up effects of fertilizer occurred via increasing submerged
vegetation, which increased both snail eggs and biomass (Fig. 2). The
sum of standardized coefficients of indirect pathways from fertilizer to
snail eggs or biomass was over an order of magnitude greater via
submerged vegetation than via periphyton chlorophyll a alone. Submerged
vegetation was reduced by crayfish via consumption, which also formed a
significant and negative indirect pathway from crayfish predators to
snail eggs or biomass that was independent of crayfish predation on
snails (Fig. 2). Thus, insecticide-induced reductions in crayfish were
positively associated with snails both by reducing predation and
increasing snail habitat. The significance and direction of pathways in
the SEM model for \emph{Bi. glabrata} were identical to \emph{Bu.
truncatus} except that both snail eggs and submerged vegetation pathways
were negative for \emph{Bi. glabrata} biomass (Table S9). \emph{Bi.
glabrata} biomass at the end of the experiment was also approximately an
order of magnitude greater than for \emph{Bu. truncatus} (Table S10).

\emph{Experiment Two}

Given that the top-down effects of crayfish were so strong, essentially
eliminating juvenile snails, we were concerned that the top-down effects
of crayfish were masking any positive effects of herbicides on snail
production and schistosomiasis risk. Consequently, we conducted a
follow-up experiment where we crossed the same herbicide exposures with
the presence and absence of crayfish. However, using five crayfish led
to complete depredation of snails in the predator tanks, reducing our
number of tanks with live snails in half (\emph{n} = 32). Subsequently,
we found that little variance in \emph{Bi. glabrata} biomass was
explained by treatment (Table S4), and likelihood ratio tests did not
detect significant herbicide effects in our nested models (Table S5). An
interaction term between crayfish presence and herbicide type for
infected, uninfected, and total \emph{Bi. glabrata} biomass, after model
selection (Tables S11-S13), showed no significant interaction between
crayfish presence/absence and herbicides (Table 2). Focusing on tanks
without crayfish predators, the herbicides atrazine and acetochlor were
associated with increases in the average biomass of infected snails (55
\%) and total snails (131 \%), respectively (Table S13; Fig. 3a,3c). In
contrast, the herbicide alachlor was associated with a decrease in
uninfected snail biomass (126\%) in tanks without predators (Table S14;
Fig. 3b). A Pearson's correlation between snail biomass and infected
snail biomass suggested a significant positive association (\emph{rho} =
0.67, \emph{t\textsubscript{30}} =4.96, \emph{p} \textless{} 0.001; Fig.
1c). Moreover, infected snail biomass was a significant positive
predictor of total cercariae in tanks (Fig. 1d).

\textbf{Discussion}

Given that recent evidence indicates that increasing agriculture to meet
the growing global food demand could simultaneously increase human
infectious diseases (Rohr et al., 2019), such as human schistosomiasis,
we conducted a study to identify agrochemicals that could sustainably
improve food production without increasing schistosomiasis. Expanding
upon previous work on predators in isolation (Halstead, Civitello, \&
Rohr, 2015), our study confirmed that most insecticides pose a high
toxicity risk to snail predators at environmental concentrations in
simulated aquatic communities. We identified that, within their
pesticide class, the organophosphate malathion and the pyrethroid
\selectlanguage{greek}λ-\selectlanguage{english}cyhalothrin had the lowest risks of increasing \emph{Bu.
truncatus}snails that transmit urinary schistosomiasis in Africa.
Bottom-up effects of fertilizer on snail biomass in natural communities
occurred as fertilizers increased both snail food (periphyton) and
habitat (aquatic vegetation), whereas herbicides decreased snail habitat
(experiment one). Thus, fertilizer bottom-up effects are expected to be
generally stronger than those for herbicides in natural communities.
Positive bottom-up effects on snails for the herbicides atrazine and
acetochlor were found in the absence of aquatic macrophytes and snail
predators (experiment two), whereas some herbicides, such as alachlor,
were negatively associated with snails under the same conditions. We
found that snail biomass positively predicted their cercarial
production, suggesting that changes in snail biomass associated with
agrochemical pollution could have strong potential to influence human
exposure to schistosome cercariae. Our findings highlight that
maximizing the benefits of agricultural intensification, while
minimizing indirect costs on human health, will require identifying
bottom-up and top-down effects of agrochemicals within natural aquatic
communities where the snails that transmit schistosome parasites are
found.

Initial (24hr) mortality of the crayfish \emph{Procambarus alleni} in
our mesocosm experiment generally matched expectations from previous lab
experiments, with the exception of the toxicity of some pyrethroids
being lower in outdoor mesocosms. We confirmed previous findings that
malathion is a low risk organophosphate, potentially because of its very
high LC\textsubscript{50} values for \emph{P. alleni} (Halstead et al.,
2015). Halstead et al. (2015) reported that all three of the pyrethroids
that we tested were highly toxic to \emph{P. alleni} . However, we found
that \selectlanguage{greek}λ-\selectlanguage{english}cyhalothrin, unlike other pyrethroids, did not lead to top-down
effects on snails in our natural communities when applied at
concentrations approximating the 96h LC\textsubscript{50} for \emph{P.
alleni} (Halstead et al., 2015). One potential reason for this result
may be that submerged vegetation, such as the \emph{H. verticillata}used
in our study, has been linked to reduced toxicity of pH-sensitive
insecticides, including malathion and \selectlanguage{greek}λ-\selectlanguage{english}cyhalothrin (Brogan \& Relyea,
2014; Leistra et al., 2004). While the organophosphate chlorpyrifos and
the pyrethroid esfenvalerate also exhibit pH-sensitive hydrolysis
(Hertfordshire, 2013), they were likely applied at sufficiently high
concentrations in our study (2x and 10x their respective 96h
LC\textsubscript{50} estimates) to preclude any increased degradation
from mitigating acute toxicity to \emph{P. alleni} . Thus, our findings
suggest that, of the insecticides we tested, malathion and \selectlanguage{greek}λ-\selectlanguage{english}cyhalothrin
should represent the weakest top-down effects on snails in natural
communities with aquatic vegetation, and the lowest risk of increasing
snail biomass.

Fertilizers can increase submerged vegetation that acts as snail
habitat, potentially making fertilizers more likely to have positive
bottom-up effects on snails than herbicides and even modifying top-down
effects. Unlike other agrochemicals, fertilizers are both commercially
available and locally sourced via manure products, leading to their wide
application even by rural subsistence farmers. Consistent with previous
mesocosm experiments, we found that fertilizers can increase snails by
increasing attached algae that snails eat (Halstead et al., 2018).
However, we also documented that stronger bottom-up effects of
fertilizer exist by increasing the submerged vegetation that serves as
snail habitat. By increasing \emph{H. verticillata} , fertilizers
indirectly benefited \emph{Bu. truncatus} biomass by increasing both
snail reproduction and growth. In contrast, the same associations were
negative for \emph{Bi. glabrata} , likely because of resource depletion
in some tanks given that \emph{Bi. glabrata} infection rates and biomass
were both an order of magnitude greater than for \emph{Bu. truncatus} .
In early weeks, algal food resources for snails fueled by fertilizer
would be high and available for snail reproduction, growth, and parasite
production by infected snails (Civitello et al., 2018). As \emph{Bi.
glabrata} populations quickly grew, per-capita resources in tanks
decline such that snail populations crash as periphyton resources become
limiting in microcosms (Civitello et al., 2018; Rohr, Halstead, \&
Raffel, 2012). Thus, aquatic vegetation likely fueled snail reproduction
for both snail species, but \emph{Bi. glabrata} biomass
outpaced\emph{Bu. truncatus} biomass, likely increasing the likelihood
of population crashes (negative associations) toward the end of our
experiment. We know of no available studies documenting that such
resource limitations occur in aquatic systems where schistosomiasis is
transmitted to humans. In fact, available field studies suggest that
aquatic vegetation is highly beneficial to both \emph{Bu. truncatus}
and\emph{Bi. glabrata} populations in natural systems and to their
parasite production (Haggerty, Bakhoum, Civitello, et al., 2020).

In our first experiment, presence of \emph{H. verticillata} in the tank
provided an alternative resource for crayfish (Lodge, Kershner, Aloi, \&
Covich, 1994), in addition to providing potential refugia and increased
resource availability for snails (Bronmark, 1989). Crayfish of the
genera \emph{Procambarus} and \emph{Oronectes} have been associated with
declines in both submerged macrophytes and snails (Lodge et al., 1994;
Rodr\selectlanguage{ngerman}íguez, Bécares, \& Fernández-Aláez, 2003). Reductions in macrophytes
by crayfish occur through both consumptive and non-consumptive removal
of plant biomass (Lodge et al., 1994; Rodríguez et al., 2003), while
reductions in snails are primarily consumptive (Hobbs, Jass, \& Huner,
1989). We found that crayfish were negatively associated with \emph{H.
verticillata} cover, consistent with crayfish consuming this macrophyte.
However, submerged plants actually were associated with reduced final
crayfish counts, possibly by providing refugia for snails thus reducing
crayfish predation rates on this primary protein source. In addition to
providing potential refugia from snail predators, macrophytes provide
increased surface area for epiphytic periphyton growth, and grazing of
epiphytes by snails can, in turn, increase macrophyte growth (Bronmark,
1989; Lodge et al., 1994; Schmidt, Koehler, \& Alfermann, 2011;
Underwood, 1991). Nutrients, particularly ammonia, released by snails
can also facilitate macrophyte growth (Underwood, 1991). Thus, aquatic
vegetation can increase snails by both providing resources to snails and
reducing predation rates on snails. In contrast to the potential for
fertilizers to foster the mutualism between snails and vegetation, we
found that effects of herbicides on snails in natural communities were
generally weaker, potentially due to a negative association between
herbicides and submerged plants in experiment one.

Herbicides generally had negative effects on snails by reducing aquatic
vegetation, with positive bottom-up effects of atrazine and acetochlor
only in the absence of aquatic vegetation and snail predators. The
positive association that we found between some herbicides, such as
atrazine, one of the most common herbicides globally, and snails in our
second experiment is consistent with previous studies (Halstead et al.,
2018; Rohr et al., 2008). Halstead et al. (2018) actually detected
positive atrazine effects while controlling for crayfish density.
However, Halstead et al. (2018) did not include submerged aquatic
vegetation, which likely increased the chances of detecting bottom-up
herbicide effects because it omits the negative herbicide effects on
snail habitat that we found in experiment one. Atrazine and other
herbicides can increase snails by killing phytoplankton that competes
with periphyton algae eaten by snails (Halstead et al., 2018). Instead
of such mechanisms leading to consistent positive effects on snails
among herbicides, we found positive and negative effects in our study
for acetochlor and alachlor, respectively. As algae absorb available
nutrients, both periphyton and snails that consume these nutrients,
eventually hit a carrying capacity after which their populations can
decline or even crash (Rohr et al., 2012). Herbicide effects can be
negative overall when considering only data points at the end of the
experiment after snail populations exceed carrying capacity and
populations subsequently crash (Rohr, 2017). Thus, we think that
variation in snail population growth among tanks likely influenced
alachlor and acetochlor associations with dead and total snails,
respectively, when measured at the end of our second experiment.
Nonetheless, our results suggest that fertilizers are far more likely
than herbicides to have positive bottom-up effects on snails, and
especially in communities where snail predators are present. Our finding
that fertilizers can increase snail habitat and resources is consistent
with field observations in sub-Saharan Africa that revealed positive
associations between fertilizer use and snail habitat, snail abundance,
and human schistosomiasis infections (Haggerty, Bakhoum, Chamberlin, et
al., 2020).

The importance of other functional groups, such as aquatic vegetation,
in mediating the indirect effects of contaminants on snails that
transmit the parasites causing schistosomiasis in humans underscores the
need to include these relationships when assessing environmental risk of
contaminants. This becomes even more critical when assessing the risk of
contaminant mixtures. The combined indirect effects of multiple
contaminants can either mitigate or exacerbate the predicted effects of
a single-contaminant scenario (Halstead et al., 2014). Our study
inferences are restricted to observing chemicals individually and we
could not simulate all biotic components of the aquatic communities that
occur in transmission sites that might influence snail responses to
agrochemicals. For example, eutrophication from long-term fertilizer
inputs could cause unpredictable changes to snail populations or species
richness, potentially eliminating important functional redundancies that
can alleviate losses in functionally-important sensitive species (Hooper
et al., 2005). Thus, the extent to which changes in biodiversity and
abiotic conditions can mediate indirect effects of contaminants (either
individually or as mixtures) on \emph{Biomphalaria} and
\emph{Bulinus}snails warrants further study. This is especially true
given that changes in snail biomass associated with agrochemicals that
we found appear very likely to change parasite production. Further,
human co-infection by parasites released by \emph{Biomphalaria}
and\emph{Bulinus} snails is not uncommon in Africa.

Overall, our findings suggest the need to identify low risk
insecticides, such as malathion, that could reduce crop pests without
increasing snails. For example, our findings recommend against
pesticides that harm crayfish, because these predators reduce
transmission directly by consuming snails and indirectly by consuming
snail habitat. The more consistent and stronger positive impact of
fertilizers on snails than herbicides, by increasing both their food and
habitat, highlights that the removal of aquatic vegetation could offer
important public health benefits by limiting its bottom-up effects on
schistosomiasis risk.

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\textbf{Table 1.} Linear mixed effects regression after model selection
for \emph{Biomphalaria} \emph{glabrata} and \emph{Bulinus
truncatus}snail biomass in experiment one (\emph{n} = 75). Positive
insecticide and fertilizer effects represent top-down and bottom-up
effects on snails, respectively. Herbicide effects were neither
significant nor consistent with positive bottom-up effects. Infected
\emph{Biomphalaria glabrata} biomass did not vary with experimental
treatments (see Supplementary Information).\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
\emph{Biomphalaria} Biomass & Parameter & Estimate & Std.Error & z-value
& p-value\tabularnewline
\midrule
\endhead
& (Intercept) & 1.503 & 0.321 & 4.687 &
\textbf{\textless{}0.001}\tabularnewline
& fertilizer & 1.567 & 0.500 & 3.133 & \textbf{0.002}\tabularnewline
& herbicide & -0.710 & 0.371 & -1.913 & 0.056\tabularnewline
& insecticide & 0.950 & 0.365 & 2.605 & \textbf{0.009}\tabularnewline
& ZI (Intercept) & -1.609 & 0.438 & -3.678 &
\textbf{\textless{}0.001}\tabularnewline
\emph{Bulinus} & Parameter & Estimate & Std.Error & z-value &
p-value\tabularnewline
Biomass & & & & &\tabularnewline
Insecticides & (Intercept) & 0.229 & 0.119 & 1.926 &
0.054\tabularnewline
& \selectlanguage{greek}λ-\selectlanguage{english}cyhalothrin & -0.227 & 0.203 & -1.119 & 0.263\tabularnewline
& chlorpyrifos & 1.260 & 0.199 & 6.345 &
\textbf{\textless{}0.001}\tabularnewline
& malathion & -0.229 & 0.204 & -1.126 & 0.260\tabularnewline
& terbufos & 0.965 & 0.222 & 4.342 &
\textbf{\textless{}0.001}\tabularnewline
& esfenvalerate & 0.773 & 0.241 & 3.210 & \textbf{0.001}\tabularnewline
& permethrin & 1.387 & 0.214 & 6.480 &
\textbf{\textless{}0.001}\tabularnewline
Herbicides & acetochlor & -0.209 & 0.203 & -1.032 & 0.302\tabularnewline
& alachlor & -0.208 & 0.203 & -1.026 & 0.305\tabularnewline
& atrazine & -0.229 & 0.204 & -1.126 & 0.260\tabularnewline
& metolachlor & -0.229 & 0.204 & -1.126 & 0.260\tabularnewline
& propazine & -0.229 & 0.204 & -1.126 & 0.260\tabularnewline
& simazine & -0.229 & 0.204 & -1.126 & 0.260\tabularnewline
& fertilizer & 0.932 & 0.199 & 4.691 &
\textbf{\textless{}0.001}\tabularnewline
& ZI (Intercept) & -2.390 & 0.669 & -3.571 &
\textbf{\textless{}0.001}\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 2.} Linear mixed effects regression after model selection
for infected and uninfected \emph{Biomphalaria} \emph{glabrata} snail
biomass considering an interaction between crayfish predators and
herbicides in experiment two (\emph{n} = 64). Herbicide effects did not
significantly interact with top-down effects.\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
Response & Parameter & Estimate & Std.error & z-value &
p-value\tabularnewline
\midrule
\endhead
Infected \emph{Biom.} biomass & (Intercept) & 1.53 & 0.11 & 13.4 &
\textless{}0.01\tabularnewline
& CrayfishYes & -1.45 & 0.16 & -9.01 & \textless{}0.01\tabularnewline
& typeherbicide & 0.13 & 0.13 & 1 & 0.32\tabularnewline
& CrayfishYes:typeherbicide & -0.13 & 0.19 & -0.72 & 0.48\tabularnewline
& ZI (Intercept) & -1.68 & 0.42 & -4.01 & \textless{}0.01\tabularnewline
Uninfected \emph{Biom.} biomass & (Intercept) & 2.67 & 0.17 & 15.69 &
\textless{}0.01\tabularnewline
& CrayfishYes & -2.59 & 0.24 & -10.7 & \textless{}0.01\tabularnewline
& typeherbicide & -0.13 & 0.2 & -0.65 & 0.52\tabularnewline
& CrayfishYes:typeherbicide & 0.13 & 0.28 & 0.46 & 0.65\tabularnewline
& ZI(Intercept) & -3.52 & 1.02 & -3.47 & \textless{}0.01\tabularnewline
Total \emph{Biom.} biomass & (Intercept) & 3.29 & 0.10 & 31.47 &
\textless{}0.01\tabularnewline
& CrayfishYes & -1.30 & 0.15 & -8.49 & \textless{}0.01\tabularnewline
& typeherbicide & 0.06 & 0.12 & 0.53 & 0.60\tabularnewline
& CrayfishYes:typeherbicide & -0.02 & 0.18 & -0.14 & 0.89\tabularnewline
& ZI(Intercept) & -3.01 & 0.59 & -5.09 & \textless{}0.01\tabularnewline
\bottomrule
\end{longtable}

\textbf{Figure 1.} Linear mixed effects model results for natural-log
transformed \emph{B. glabrata} (a) and \emph{B. truncatus} (b) biomass
in experiment one (\emph{n} = 75) using a Gaussian error distribution,
scatterplot of natural-log transformed snail biomass versus infected
snail biomass (\emph{n} = 32) in experiment two (c), and a negative
binomial model predicting total cercariae using a fixed predictor for
infected snail biomass (\emph{n} = 21) in experiment two (d).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 2. Combined factor and path analysis for \emph{B.
truncatus} biomass demonstrating top-down and bottom-up effects of
insecticides and fertilizer, respectively.} Top-down effects:
insecticide exposure reduced final crayfish counts, which directly
increased snail biomass by reducing predation, and also indirectly
increased snail biomass by increasing snail habitat \emph{H.
verticillata} (crayfish ate \emph{H. verticillata} ). Bottom-up effects:
Fertilizer increased periphyton and \emph{H. verticillata} , which
increased snail biomass because they are food and habitat, respectively,
for snails. Herbicides reduced \emph{H. verticillata} , snail habitat,
and, thus, on average, had net negative effects on snails. P-values for
paths in the model are reported below each standardized coefficient. Red
arrows denote significant negative paths; black arrows denote
significant positive paths; dashed arrows denote non-significant
pathways. Boxes represent directly measured variables and circles
represent latent variables. Indicator variables for latent variables
have been omitted from the figure to reduce visual complexity, but are
reported in Table S5. The path model had a good fit to observed data
(\emph{CFI} = 0.99, \selectlanguage{greek}χ\selectlanguage{english}\textsuperscript{2} = 26.3, \emph{df} =
26,\emph{p-value} = 0.45).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Figure 3.} Linear mixed effects model results using a fixed
categorical predictor for each herbicide to predict natural-log
transformed infected (a), uninfected (b), and total \emph{B.
glabrata}biomass (c) in tanks without predators in experiment two
(\emph{n} = 32) using a Gaussian error distribution. Only atrazine and
acetochlor were positively associated with infected and total \emph{B.
glabrata} biomass (\emph{p} \textless{}0.05), respectively, whereas
alachlor was negatively associated with uninfected \emph{B. glabrata}
biomass (\emph{p} \textless{}0.05).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\selectlanguage{english}
\FloatBarrier
\end{document}

