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\begin{document}

\title{Host-pathogen interactions under pressure: a review and meta-analysis of
stress-mediated effects on disease dynamics}



\author[1]{Amanda Vicente-Santos}%
\author[2]{Beatriz Willink}%
\author[3]{Kacy Nowak}%
\author[1]{David Civitello}%
\author[1]{Thomas Gillespie}%
\affil[1]{Emory University}%
\affil[2]{Stockholm University}%
\affil[3]{Emory University School of Public Health}%


\vspace{-1em}



  
  \date{May 20, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Human activities have increased the intensity and frequency of natural
stressors and created novel stressors, altering host-pathogen
interactions, and changing the risk of emerging infectious diseases.
Despite the ubiquity of such anthropogenic impacts, predicting the
directionality of outcomes has proven challenging. Here, we conduct a
review and meta-analysis to determine the primary mechanisms through
which stressors affect host-pathogen interactions and to evaluate the
impacts stress has on host fitness (survival and fecundity) and pathogen
infectivity (prevalence and intensity). We assessed 891 effect sizes
from 71 host species (representing seven taxonomic groups) and 78
parasite taxa from 98 studies. We found that infected and uninfected
hosts had similar sensitivity to stressors and that responses varied
according to stressor type. Specifically, limited resources compromised
host fecundity and decreased pathogen intensity, while abiotic
environmental stressors (e.g., temperature and salinity) decreased host
survivorship and increased pathogen intensity, and pollution increased
mortality but decreased pathogen prevalence. We then used our
meta-analysis results to develop Susceptible-Infected theoretical models
to illustrate scenarios where infection rates are expected to increase
or decrease in response to resource limitation or environmental stress
gradients. Our results carry implications for conservation and disease
emergence and reveal areas for future work.%
\end{abstract}%



\sloppy


\textbf{Article type:} Synthesis

\textbf{Host-pathogen interactions under pressure: a review and
meta-analysis of stress-mediated effects on disease dynamics}

Amanda Vicente-Santos\textsuperscript{1*}, Beatriz
Willink\textsuperscript{2,3,4}, Kacy Nowak\textsuperscript{5}, David J.
Civitello\textsuperscript{1,6}, and Thomas R.
Gillespie\textsuperscript{1,5,7*}

\textsuperscript{1}Population Biology, Ecology, and Evolution Program,
Emory University, Atlanta, GA 30322, USA

\textsuperscript{2}Department of Zoology, Stockholm University,
Stockholm 106-91, Sweden

\textsuperscript{3}Department of Biological Sciences, National
University of Singapore, Singapore 117558, Singapore

\textsuperscript{4}School of Biology, University of Costa Rica, San Jos\selectlanguage{ngerman}é
11501-2060, Costa Rica

\textsuperscript{5}Department of Environmental Health, Rollins School of
Public Health, Emory University, Atlanta, GA 30322, USA

\textsuperscript{6} Department of Biology, Emory University, Atlanta, GA
30322, USA

\textsuperscript{7}Department of Environmental Sciences, Emory
University, Atlanta, GA 30322, USA

*\textbf{Correspondence:} Amanda Vicente-Santos. Sutton Hall 107,
Biology Department, University of Oklahoma, Norman, OK 73019, USA
amanda.vicente@ou.edu Tel. +1(404) 276-9990; and

Thomas R. Gillespie. 400 Dowman Drive, Suite E510, Department of
Environmental Sciences, Emory University, Atlanta, GA 30322, USA
thomas.gillespie@emory.edu Tel: +1(404) 727-7926

\textbf{Running title:} Stress-mediated effects on disease dynamics

\textbf{Keywords} : resource limitation, environmental stress,
pollution, survival, fecundity, infectivity, epidemiological models

\textbf{Email addresses:}amanda.vicente@ou.edu,
beatriz.willink@zoologi.su.se, kkacy.nnowak@gmail.com,
david.james.civitello@emory.edu, thomas.gillespie@emory.edu

\textbf{Statement of authorship:} AVS conceived the study. AVS, BW, DJC,
and TRG designed the project and plans for data collection. AVS, BW, and
KN collected data. AVS, BW, KN, and DJC analyzed the data. AVS, BW, and
DJC designed figures and tables with inputs from all authors. AVS and
DJC conceived and created the models. AVS, BW, and TRG drafted the
manuscript. All authors revised and edited the manuscript for
intellectual content.

\textbf{Data accessibility statement:} Data is available from the Dryad
Digital Repository (doi:10.5061/dryad.zw3r228cd).

Word number of abstract \textbf{(198)} ; word number of main
text\textbf{(7463)} ; word number in text box 1 \textbf{(197)} ; word
number in text box 2 \textbf{(134)} ; number of references
\textbf{(143)} ; number of figures \textbf{(5)} ; number of tables
\textbf{(0)} ; number of text boxes \textbf{(2)}

\textbf{Abstract:} Human activities have increased the intensity and
frequency of natural stressors and created novel stressors, altering
host-pathogen interactions, and changing the risk of emerging infectious
diseases. Despite the ubiquity of such anthropogenic impacts, predicting
the directionality of outcomes has proven challenging. Here, we conduct
a review and meta-analysis to determine the primary mechanisms through
which stressors affect host-pathogen interactions and to evaluate the
impacts stress has on host fitness (survival and fecundity) and pathogen
infectivity (prevalence and intensity). We assessed 891 effect sizes
from 71 host species (representing seven taxonomic groups) and 78
parasite taxa from 98 studies. We found that infected and uninfected
hosts had similar sensitivity to stressors and that responses varied
according to stressor type. Specifically, limited resources compromised
host fecundity and decreased pathogen intensity, while abiotic
environmental stressors (e.g., temperature and salinity) decreased host
survivorship and increased pathogen intensity, and pollution increased
mortality but decreased pathogen prevalence. We then used our
meta-analysis results to develop Susceptible-Infected theoretical models
to illustrate scenarios where infection rates are expected to increase
or decrease in response to resource limitation or environmental stress
gradients. Our results carry implications for conservation and disease
emergence and reveal areas for future work.

\textbf{INTRODUCTION}

Accelerating anthropogenic impacts are modifying habitats and disrupting
interactions between coevolved species (Barnosky \emph{et al.} 2012),
including host-pathogen dynamics, raising concern for human and animal
health, biodiversity conservation, and ecosystem structure and function
(Jones \emph{et al.} 2008; Wiethoelter \emph{et al.} 2015; Allen\emph{et
al.} 2017; Rohr \emph{et al.} 2019; Gibb \emph{et al.} 2020). However,
given the complexity and ubiquity of anthropogenic impacts, teasing
apart the effects of perturbations on disease dynamics has proven
difficult. A key to solving this challenge is identifying how
human-induced stressors affect processes that mechanistically impact
epidemiological dynamics, such as host survival and fecundity and
pathogen infectivity (i.e., the ability of a pathogen to establish an
infection and replicate in a host).

Stressors affect transmission dynamics in three fundamental mechanistic
ways. First, when stressors reduce host survival and fecundity, they
reduce host density, and by extension, transmission of density-dependent
pathogens (McCallum \emph{et al.} 2001). Second, host behavioral and
immunological traits that influence the acquisition, proliferation, and
dissemination of pathogens, a series of processes often summarized as
host competence (Barron \emph{et al.} 2015). Host competence may
increase under stressful conditions that erode immune response to
pathogens (resource limitation or agrochemical exposure) (Knutie\emph{et
al.} 2017; Rohr \emph{et al.} 2008). Third, stressors can have direct
and indirect effects on pathogens. Host condition can shape pathogen
fitness by mediating intra-host resource availability and host immune
response, as reviewed and modeled by Cressler \emph{et al.}(2014).
Pollution and environmental conditions may also negatively affect
pathogens, especially in free-living stages (Pietrock \& Marcogliese
2003). Given that these three distinct mechanisms predict different
outcomes, it is imperative to consider them collectively when examining
stress-mediated effects on disease dynamics.

We aim to synthesize the current understanding of how human-induced
stressors affect disease dynamics and consider the implications of these
stressors for mitigating disease emergence and threatened species
population declines. Here we define stress as any change that causes
actual or perceived threats to the homeostasis of an organism (pathogen
or host), precluding it \selectlanguage{english}from controlling fitness-critical variables
(Del Giudice \emph{et al.} 2018). We began by reviewing the literature
to assess how stressors may affect host-pathogen interactions by
altering (1) host density, (2) host defenses, and (3) pathogen
infectivity. Further, we conducted a systematic search and meta-analysis
of studies where host fitness (host survival and fecundity) and pathogen
prevalence and intensity have been evaluated under benign and stressful
conditions (low resources, adverse environmental conditions, and
pollution) for infected and uninfected hosts. Given that host defenses
and pathogen infectivity are rarely evaluated independently, we used
infection prevalence and intensity to capture these two processes
(hereafter infectivity). Specifically, we evaluated how different types
of stressors affected host fitness and pathogen infectivity, if fitness
effects of stressors were more severe for infected vs. uninfected hosts,
and whether infectivity traits were more susceptible to stress than host
fitness traits.

To further synthesize our results, we incorporated our empirical
findings into two theoretical Susceptible-Infected (SI) models to
elucidate scenarios where infection rates were expected to increase or
decrease in response to the simultaneous trait changes (i.e., host
fitness and pathogen infectivity) occurring over resource and
environmental stress gradients. Our meta-analysis revealed similarly
negative responses of infected and uninfected hosts to stressors and
identified stressor type as determinant of infection outcomes. Our
results provide insights for predicting and mitigating the impacts of
stressor-pathogen interactions on human and animal health, more relevant
than ever, as human-induced perturbations are a growing threat
worldwide.

\textbf{MECHANISTIC LINKS BETWEEN STRESSORS AND PATHOGEN TRANSMISSION}

Stressors modulate host density

A key assumption of many infectious disease models is that contact rates
between infected and uninfected individuals increase as population
density increases (Anderson \emph{et al.} 1986; McCallum \emph{et
al.}2001). Therefore, if stressors negatively impact host fitness by
restricting host population growth via reduced fecundity or increased
mortality or emigration, pathogens will be less frequently transmitted,
and prevalence is expected to decline. This reasoning justifies culling
campaigns, where infection rates are reduced or pathogens are extirpated
by reducing host density below a critical transmission threshold
(Lafferty \& Holt 2003; Prentice \emph{et al.} 2019). Although, to our
knowledge, no studies have explicitly evaluated the
stressor-density-disease relationship, studies have shown that human
pressures indirectly increase host-density thresholds resulting in
epidemics. For instance, overfishing of predatory lobsters
(\selectlanguage{english}\emph{Panulirus interruptus} ) has led to dense purple urchin
(\selectlanguage{english}\emph{Strongylocentrotus purpuratus} ) populations, more likely to
experience urchin-specific bacterial (\emph{Vibrio} bacteria) epidemics
(Lafferty 2004). Similarly, although thermal stress increases the
susceptibility of corals to disease, it only leads to white syndrome
outbreaks where corals are at high density (Bruno \emph{et al.} 2007).

Alternatively, stressors may contribute to increased local host density
without increasing fecundity. For instance, behavioral responses to
stressors, such as changes in migration patterns (Satterfield \emph{et
al.} 2018; S\selectlanguage{ngerman}ánchez \emph{et al.} 2020), foraging behaviors
(Epstein\emph{et al.} 2006), and aggregations in low-quality
food-provisioned sites (intentional or unintentional) (Becker \emph{et
al.} 2015), have been associated with higher host density. Consequently,
higher local density may intensify disease transmission via increased
contact rates, as illustrated by theoretical models (Becker \& Hall
2014).

Disease transmission can also be sustained at low population density.
For instance, in social species, the frequency of social contact can
govern disease epidemics independently of host density (Johnson \emph{et
al.} 2011; Rimbach \emph{et al.} 2015; Rushmore \emph{et al.} 2017).
Given that density-independent transmission (e.g., sexual or
vector-borne transmission) does not require a minimum host density for
parasites to invade a population (Hopkins \emph{et al.} 2020), it is
expected that a combination of stressors and pathogen infection would
drive populations to extinction more frequently than density-dependent
transmissions (Castro \emph{et al.} 2005; Ryder \emph{et al.} 2007).

Stressors may affect the fitness of infected and uninfected hosts
differently. Infection increases sensitivity to other stressors, as
infected hosts are more energetically constrained (Marcogliese \&
Pietrock 2011). Such a combined effect of stress (warming temperatures)
and infection (e.g., \emph{Vibrio coralliilyticus} ) may be responsible
for the rapid global coral reef decline (Maynard \emph{et al.} 2015).
Despite many examples of synergistic tolls that stressors and pathogens
have on host fitness (Crain \emph{et al.} 2008), few have tested whether
stressors have a differential impact on the fitness of infected compared
to uninfected hosts (Marcogliese \& Pietrock 2011; Beldomenico \& Begon
2016).

Stressors constrain host defenses

Hosts invest resources to defend themselves from pathogens via
behavioral or physiological mechanisms. While avoidance behavior is less
understood (Buck \emph{et al.} 2018), physiological mechanisms, such as
infection resistance or disease tolerance, are well documented
(Råberg\emph{et al.} 2007, 2009; Svensson \& Råberg 2010). Resistance
mechanisms control parasite growth and reproduction, reducing infection
intensity, while tolerance reduces or compensates for infection-induced
pathology without reducing pathogen burden (Boots 2008;
Medzhitov\emph{et al.} 2012). Although resistance limits pathogen
replication while tolerance does not, leading to different disease
implications (Schneider \& Ayres 2008), both strategies have high
energetic requirements, and hosts should only elicit them if parasite
infections reduce their fitness (Ayres \& Schneider 2009; Cumnock
\emph{et al.}2018). Consequently, trade-offs exist between immune
response and other energetically costly physiological processes, such as
reproduction and growth (Lochmiller \& Deerenberg 2000), in both
vertebrates (Gustafsson\emph{et al.} 1994) and invertebrates (Schwenke
\emph{et al.} 2016). Furthermore, there is recent evidence that
trade-offs between reproduction and immune function exist at the
transcriptomic level and may be conserved across animals (Rodrigues
\emph{et al.} 2021). Given these trade-offs, host defense may be
compromised under stressful conditions (Sheldon \& Verhulst 1996;
Gervasi \emph{et al.} 2015).

Stressors may modulate host defensive mechanisms against infections.
Malnutrition can impair immune function by reducing T-cell-mediated
immune response (Alonso-Alvarez \& Tella 2001), toxicants can
immunocompromise a host (Caren 1981) or upregulate host immunity
(Pölkki\emph{et al.} 2012), and extreme temperature variation can impair
immunity leading to species declines (Rohr \& Raffel 2010). Owen\emph{et
al} . (2021) showed that food-deprived robins (\emph{Turdus migratorius}
) developed higher West Nile Virus titers and were infectious longer
than robins fed normally. Similarly, amphibians exposed to pesticides
have experienced eosinophil recusation (a resistance mechanism) and
associated increases in trematode infections and subsequent limb
malformations (Kiesecker 2002). Conversely, infection tolerance in
Galapagos mockingbirds (\emph{Mimus parvulus} ) has been impaired by
climatically-induced food stress, exhibiting lower fledging success in
dry years (when resources were scarce) compared to wet years, due to
inability to compensate for costs of parasitic fly nest infestations
(McNew \emph{et al.} 2019). These examples show that host susceptibility
to infections and/or pathogen transmission may increase under stressful
conditions.

Pathogens are affected by stressors as well

Pathogens can be affected by stressors directly or indirectly through
their hosts. It is critical to distinguish these mechanisms, as each may
affect host populations differently. By definition, pathogens rely on
host resources to grow and reproduce (Casadevall \& Pirofski 2002);
therefore, pathogens compete for resources with host physiological
processes that mediate disease outcome (i.e., reproduction, growth,
immune defense; Cressler \emph{et al.} 2014). Direct manipulation of
immune response by pathogens has been documented (Maizels \&
Yazdanbakhsh 2003; Schmid-Hempel 2008), but pathogens may also
outcompete host immune response by direct resource consumption
(Cressler\emph{et al.} 2014). For example, in a \emph{Daphnia} -fungal
parasite system, more resources equate to greater epidemics due to both
higher\emph{Daphnia} reproductive rates (i.e., host density driven) and
higher infection intensity (Civitello \emph{et al.} 2015), suggesting
that food stress lowers parasitism in the \emph{Daphnia} -fungal
parasite system.

On the other hand, a common sickness behavior, reduced food consumption,
may be an adaptive host response (Murray \& Murray 1979; Exton 1997;
Ayres \& Schneider 2009). Parasite-mediated anorexia can improve host
health and recovery (Wang \emph{et al.} 2016), much like fever
(Kluger\emph{et al.} 1996). Anorexia appears to intensify with higher
levels of parasite exposure or intensity (as reviewed by Hite et al.
(2020)); however, the advantages or disadvantages of anorexia depend on
nutrient stores and quality, and ambient conditions (McKenzie \&
Townsend 2007; Johnson \emph{et al.} 2010; Becker \emph{et al.} 2015;
Hite \emph{et al.} 2020). Sometimes a low-quality resource may be
inadequate for the host while sufficient for the pathogen (Dallas \&
Drake 2014) or lead to fewer resources for the parasite (Kyriazakia
\emph{et al.} 1998; Hall\emph{et al.} 2009a, b). Conversely, hosts may
increase food intake to compensate for energy lost fighting infections
(i.e., resource compensation hypothesis (Christe \emph{et al.} 1996)).
As a result, high-resource diets may increase host tolerance to
infections by reducing resource competition between hosts and parasites
without negatively affecting parasite fitness (Knutie \emph{et al.}
2017), with possible implications for the evolution of pathogen
virulence (Hite\emph{et al.} 2020).

Environmental stressors may also directly impact pathogens with
environmental stages (Riggs \emph{et al.} 1987). Fluctuating
environmental conditions and pollutants can negatively affect pathogens
(Pietrock \& Marcogliese 2003). For instance, deviations from
temperature and salinity optima can reduce survival and lifespan in
free-living helminths (Pechenik \& Fried 1995; Measures 1996), and
reduced longevity decreases infective periods. Similarly, elevated
nitrate concentrations can reduce free-living spore survival, which may
counteract the effects of increased intensity within
\emph{Daphnia}(Dallas and Drake 2014). Even when pathogens survive
stressors, their capacity to infect hosts could be affected. For
instance, metals can impact sensory receptors of environmental stages of
parasites, such as cercariae, impairing their ability to locate,
recognize and infect hosts (Ghandour \& Webbe 1975; King \& Higashi
1992; Morley \emph{et al.}2002).

Finally, differential effects of stressors on directly vs. indirectly
transmitted pathogens (i.e., vector-borne or intermediate hosts) may
lead to divergent outcomes (Hopkins \emph{et al.} 2020). For instance,
Studer \emph{et al.} (2010) showed that although increased temperatures
favored the development of the trematode \selectlanguage{english}\emph{Maritrema
novaezealandensis} within their \selectlanguage{english}intermediate amphipod
host,\emph{Paracalliope novizealandiae} , warmer temperatures increased
amphipod mortality, creating a bottleneck for pathogen transmission.
Similarly, qualitative differences between aquatic and terrestrial
systems, due to life history differences and the greater taxonomic
diversity of aquatic parasites and hosts (Harvell \emph{et al.} 2002;
McCallum \emph{et al.} 2004; Byers 2021), may result in divergent
disease outcomes. For example, environmental transmission dominates
aquatic systems (Lafferty 2017), making pathogens more susceptible to
direct effects of stressors.

\textbf{META-ANALYSIS}

We conducted a systematic literature search and meta-analysis to
evaluate the impacts of three broad types of environmental stressors on
disease dynamics. First, we confirmed that pathogen exposure in
laboratory studies typically negatively affected host fitness. We then
proceeded with our main meta-analyses focused on two specific questions:
Q1) were stressor fitness effects more severe for infected vs.
uninfected hosts?, and Q2) was infectivity more susceptible to
environmental stress than host fitness traits? To address these
questions with data from primary studies, we used infection intensity
and prevalence as proxies for infectivity and survivorship and fecundity
as proxies of host fitness.

Literature survey and study selection

To identify studies that evaluated the effects of environmental
stressors on infectivity and host fitness traits in host-parasite
systems, on February 9\textsuperscript{th} of 2021, we conducted a
systematic literature search in Web of Science using the search terms:
(parasit* OR pathogen* OR disease) AND (environment* OR temperature OR
pollution OR resource OR provision* OR toxi* OR contamination) AND
(infection OR load OR yield OR resistance) AND (''birth rate'' OR
''death rate'' OR surviv* OR mortality OR reproduct* OR fecundity). We
limited our search to journal articles published in English between 2010
and 2020 and scanned titles and, if relevant, abstracts of all 20,684
hits. This initial screening effort was split and carried out by two
experienced independent reviewers (AVS and BW). We identified ten
additional studies from references of selected studies. One experienced
reviewer or two student reviewers further examined articles documenting
effects of environmental stressors on infectivity and host fitness.

We classified stressors into three groups: 1) environmental factors,
which can vary naturally but are also subject to human-induced
perturbation (hereafter ``endogenous environment''); 2) presence or
quantity of chemical pollutants (hereafter ``chemical pollution''), that
lead to negative expected outcomes for hosts; and 3) resource
availability for hosts (hereafter ``resource limitation''). Although, in
natural systems, these stressors often overlap (e.g., increased
temperature can alter resource availability), we included studies where
only one stressor was evaluated to facilitate the interpretations of our
results. We excluded studies if stressful and control environments
differed due to additional antagonistic biotic interactions (e.g.,
presence of predators or competitors) or by the presence of substances
purposely used as therapeutic interventions on infected hosts (e.g.,
chlorine as water treatment). Furthermore, we limited our search to
studies with animal hosts and excluded studies on parasitoid infections
(Fig. 1).

We included only experimental studies with hosts exposed to or infected
by parasites under laboratory conditions. We only included studies if
infected hosts were exposed to stressful and control treatments and both
host fitness (fecundity and/or survivorship) and pathogen infectivity
(prevalence and/or intensity) were reported from the same experiment
(i.e., same pool of individuals divided between stressful and control
treatments) at matched timepoint(s) (Fig. 1). For example, if a study
reported infection intensity at 24 h and 72 h post-infection (hpi), but
survivorship was only recorded at 72 hpi, we used 72h data. If a study
recorded both fitness and infectivity at multiple time intervals, we
included all matched intervals in data collection. We accounted for
non-independence of these effects and their sampling errors in the
random structure of our statistical models (see
sections\emph{Meta-analyses} and \emph{Publication bias} ). Studies were
further excluded for pseudoreplication, missing sample size information,
or when estimates were reported without associated errors (Fig. 1).

Data collection and transformations

We obtained primary literature data directly from main text, tables,
supporting material, or raw data files whenever available. Otherwise, we
digitized data from figures using PlotDigitizer
(https://plotdigitizer.com). Stressor effects were standardized to
unbiased mean differences (Hedge's \emph{g} ) from both continuous and
discrete variables (Hedges 1981). For continuous variables, we obtained
mean and standard deviation (SD) of fitness traits and infectivity
metrics in environments with different exposure to stressors. If SD was
not reported, an error estimate (standard error (SE), 95\% confidence
interval (CI) or Wald's CI) was converted to SD, assuming normality. If
a study reported median instead of mean (n = 13 effects in four
studies), we estimated the mean following Hozo et al. (2005). If
dispersion was only reported as data range or interquartile range (n = 8
effects in one study and n = 5 effects in three studies, respectively),
we approximated SD (Lajeunesse 2013; Wan \emph{et al.} 2014). Mean and
SD of response variables were then used to calculate standardized mean
differences (d) and their variances.

Many studies (n = 67) used discrete variables to quantify infection
prevalence and/or survivorship. In these cases, we calculated odds
ratios between environmental treatments and estimated variances
(Rosenberg \emph{et al.} 2013). In cases where at least one category had
no observations (e.g., no survival in polluted treatment), we applied
Yate's continuity correction to avoid dividing by zero (Yates 1934). Log
odds ratios were then converted to \emph{d} , and variances of log odds
ratios were converted to variances of \emph{d} , assuming a continuous
logistic distribution underlies each discrete trait (Hasselblad \&
Hedges 1995). Finally, we estimated Hedge's \emph{g} and its variance by
applying sample size correction \emph{J} to all values of \emph{d} and
their variances (Hedges 1981).

Most experiments (n = 108) contrasted host fitness traits and
infectivity across three or more environmental treatments or in more
than one-time interval. For example, a control group could be compared
to two levels of chemical pollution or at both 24 and 48 hpi. In these
cases, stressor effects and sampling errors were not independent, as
they shared control group or time baseline. To account for correlated
sampling errors between these effects, we computed covariances in
sampling errors between effects in multiple-comparison designs following
Viechtbauer (2010). We included these variance-covariance matrices in
our statistical analyses (see below). For a few experiments (n = 8)
where large covariances between effects and small sample sizes resulted
in variance-covariance matrices with negative eigenvalues (i.e., not
positive definite), we adjusted covariance estimates to produce the
nearest positive definite matrix using the R package
\emph{Matrix}(Douglas \& Maechler 2021). As an alternative approach to
estimating sampling error covariances, we adjusted fixed effect
coefficients using the robust variance estimator (RVE) (Hedges \emph{et
al.} 2010), as implemented in the R package \emph{clubSandwich}
(Pustejovsky 2020). Here, we focus on results with estimated covariances
and show results under the RVE in Supporting Material.

Moderators

Our first analysis aimed to determine whether pathogen exposure in
selected studies led to reduced host fitness without environmental
stressors. For this analysis, we used the response trait category
(fecundity or survivorship) as the only moderator and included only data
from hosts in control (i.e., no environmental stress) conditions. We
then focused on effects of environmental stressors on host-pathogen
dynamics and examined three factors that could moderate the magnitude of
these effects. For Q1, we considered infection status (infected and
uninfected), stressor type, and response trait (fecundity and
survivorship) as moderators. We were specifically interested in whether
infection status amplified any negative fitness consequences of
stressors. As mentioned above, stressors were of three types: 1)
endogenous environmental factors (e.g., temperature, humidity, salinity,
dissolved oxygen, and habitat structural complexity), 2) chemical
pollution by toxins or synthetic compounds typically derived from
pesticides or herbicides, and 3) resource limitation (restricted access
to food or specific nutrients, like nitrogen and phosphorus). For
response traits, fecundity was typically recorded as total number of
offspring, whereas survivorship was reported as proportion alive, number
alive, and sometimes, time to death.

In Q2, we focused exclusively on infected individuals under the
abovementioned criteria. We investigated stressor type and response
trait as moderators. We aimed to contrast effects of stress on fitness
vs. infectivity responses. We, therefore, included two additional
response traits as infectivity proxies: infection intensity and
prevalence. Prevalence was always reported as number or percentage of
infected individuals. Infection intensity was often quantified in
different ways for different types of pathogens, for example, (log) copy
number for viruses, colony-forming units for bacteria, mean number of
cercaria for helminths, and spore counts for fungi. To compare relative
sensitivity of fitness and infectivity, and because prevalence and
infection intensity represent the opposite of host defense, signs of
unbiased standardized mean differences were flipped. By doing so, a
positive effect size reflects greater defense and a beneficial outcome
for hosts, whereas, for fitness traits, a positive sign indicates higher
survivorship or fecundity.

We complemented these main models for Q2 with two additional moderators
in separate analyses. We investigated whether transmission environment
(terrestrial or aquatic) or transmission mode (direct or indirect)
modulates effects of environmental stressors on infectivity and host
fitness responses. For hosts that occupy different environments across
life stages, we categorized transmission environment based on life stage
of hosts exposed or infected in each study, which was typically the most
susceptible life stage to the target pathogen. We classified pathogen
transmission as `indirect' if it met one of three conditions: 1)
pathogen required an ecologically distinct intermediate host to complete
its life cycle, 2) pathogen was transmitted between ecologically similar
hosts via vectors, or 3) pathogen could survive independently of host
during free-living stage. Otherwise, pathogens were considered as having
`direct' transmission between ecologically similar hosts.

Meta-analyses

We analyzed effect sizes (Hedge's \emph{g} ) for both Q1 and Q2 with
multi-level meta-analytic (MLMA) models, fitted in R v 4.1.2 (R Core
Team 2021) and using the package \emph{metafor} version 3.0-2
(Viechtbauer 2010). We employed a model selection approach based on the
Akaike Information Criterion (AIC) to identify the most important
moderators explaining heterogeneity in effect sizes and the most
parsimonious model (Arnold 2010). This required first fitting the full
model and all reduced models via maximum likelihood (ML) estimation. For
Q1, the full model included the moderator variables infection status,
fitness trait, stressor type, and all their interactions. The full model
for Q2 included response trait, stressor type, and their interaction.

All models accounted for non-independence of effects and sampling errors
measured in the experiment. Models also included observation-level
random intercepts, so residual variation within studies could be
estimated. Full and reduced models (including intercept-only model) were
compared using the `dredge' function of the R package \emph{MuMIn} v
1.43.17 (Bart\selectlanguage{ngerman}ón 2020). The highest-ranking model based on small sample
size corrected AIC (AICc) was then refitted via restricted
maximum-likelihood (REML) estimation to interpret moderators and
evaluate publication bias and heterogeneity.

We report meta-analytic mean estimates and 95\% confidence intervals for
effects of moderators in final models. Meta-analysis results were
plotted using the R package \emph{orchaRd} (Nakagawa \emph{et al.}2021).
We tested significance of statistical contrasts between fitness and
infectivity response variables in Q2 using Wald-type chi-square tests,
computed with the function `anova'.

Heterogeneity

We estimated proportion of heterogeneity relative to sampling error
(I\textsuperscript{2}; Higgins and Thompson 2002) and partitioned it
into between-study heterogeneity and within-study heterogeneity
(Nakagawa \& Santos 2012). Current formulations of
I\textsuperscript{2}do not accommodate sampling-error covariances for
multivariate meta-analytic models. We, therefore, fitted simpler models
with only observation-level variances to estimate I\textsuperscript{2}.
While this is not ideal, we note that meta-analytic effects of
moderators accounting for sampling-error covariances are robust to these
simpler models after adjustment with RVE (see Supporting Material).

Publication bias

Following Nakagawa et al. (2022), we relied on two complementary
approaches to assess small study effects, which may result from
publication bias. First, we visualized the relationship between effect
sizes and precision (SE) using funnel plots. To do this, we re-fitted
selected models as random effect models and computed residual effect
sizes conditional on experiment, observation, and factor level, for
factors included as moderators in the main analyses. These conditional
residuals have the advantage of taking some within-experiment
non-independence into account, but they still make unlikely assumptions
about sampling variances (Nakagawa \emph{et al.} 2022).

We, therefore, complemented funnel plots with a two-step, modified
Egger's test for multilevel meta-analysis (Nakagawa \emph{et al.} 2022).
In the first step of this test, SE of effect sizes is included as the
only moderator in a meta-regression with the same random effect
structure as our main MLMA analyses. A significant slope of this
moderator means that studies with low precision tended to report either
more negative or more positive effects than studies with higher
precision. Therefore, if the SE slope is different from zero, the second
step of the test is to fit a meta-regression with the variance of effect
sizes as the only moderator. The intercept of this second
meta-regression is then a more appropriate estimate of the overall
meta-analytic effect (Stanley \& Doucouliagos 2014). Because we
uncovered evidence consistent with publication bias in Q1 and Q2, we
tested the robustness of the meta-analytic effects of moderators by
fitting a multi-level meta-regression (MLMR) with variance in addition
to the moderators of interest for each question in our study (see
Supporting Material).

Summary of literature survey

Our final data set included 98 studies and 891 effects (Fig. 1). While
most studies reported results from a single experiment, 21 studies
included two to four experiments, resulting in a total of 122
experiments. Host taxa included arthropods (n = 20 species, classes
Brachiopoda, Copepoda, Insecta, and Malacostraca), molluscs (n = 13
species, classes Bivalvia and Gastropoda),~fish (n = 13 species),
amphibians (n = 21 species), and several vertebrates species (two bird,
one reptile, and one mammal) (Fig. S1a). Parasite taxa comprised viruses
(n = 37), bacteria (n = 14), fungi (n = 6), parasitic animals (n = 13,
helminths and myxozoan), and protozoans (n = 8) (Fig. S1b).

Q1: Fitness effects of stressors on infected and uninfected hosts

After confirming that pathogen infections in surveyed literature reduce
host fitness (Fig. S2; Fig. S3; Table S1), we asked if effects of
stressors on fitness are modulated by infection status (Q1). The lowest
AICc model for Q1 included stressor type, response trait, and their
interaction as moderators (Table S2). Our data, therefore, does not
support differential effects of environmental stressors between infected
and uninfected hosts (Fig. S4). The interaction between stressor type
and response trait resulted primarily from a relatively strong negative
effect of resource limitation on fecundity (Table S3; Fig. 2) and a
relatively strong negative effect of endogenous environmental stressors
on survivorship (Table S3; Fig. 2). Pollution also negatively affected
survivorship (Table S3; Fig. 2), but this effect was contingent on
results of low precision studies (see Evidence of publication bias).
These contrasting effects of the three stressor types were qualitatively
similar if the RVE was used instead of modeling sampling-error
covariances (Fig. S5). Differences in effect sizes both within
(I\textsuperscript{2} = 40.42\%) and between (I\textsuperscript{2} =
53.41\%) experiments contributed to relatively high total heterogeneity
(I\textsuperscript{2} = 93.83\%).

Q2: Sensitivity of host fitness and infectivity responses to stress

We contrasted fitness and infectivity effects of stressors on infected
hosts. The full model, including stressor type, response trait, and
their interaction, had the lowest AICc score (Table S4). In this model,
the interaction arose not only due to differential sensitivity of
fecundity and survivorship responses to stressor type but also because
the direction of infectivity responses only aligned with fitness
responses for endogenous environmental stressors (Table S5; Fig. 3).
Effects of resource limitation differed between response variables
(fecundity vs. intensity: p \textless{} 0.001; fecundity vs. prevalence:
p = 0.006; survivorship vs. infection intensity: p = 0.010; and
survivorship vs. prevalence: p \textgreater{} 0.05). When resources were
limited, not only was host fecundity reduced (as noted in Q1), but
infection intensity was also reduced (Table S5; Fig. 3). In contrast,
chemical pollution impacted survivorship more than either proxy of
infectivity (survivorship vs. infection intensity: p = 0.024,
survivorship vs. prevalence: p = 0.018). We found that pollution
decreased both host survival and pathogen prevalence (Table S5).
Finally, perturbation of the endogenous environment tended to decrease
host survival and increase pathogen intensity, both of which had
negative consequences for host fitness and health (Table S5; Fig. 3, all
infectivity vs. fitness contrasts p \textgreater{} 0.05; however,
survivorship vs. prevalence: p = 0.057).

We obtained a similar pattern of interaction among stressors and fitness
and infectivity responses when the RVE was used to account for
non-independence of sampling errors (Fig. S6). Despite these contrasting
effects of moderators, heterogeneity remained high (total
I\textsuperscript{2} = 90.26\%), both between (I\textsuperscript{2} =
64.11\%) and within (I\textsuperscript{2} = 25.15\%) experiments.

Effects of stressors on host fitness and infectivity traits also
depended on environment and mode of pathogen transmission. While the
negative effect of resource limitation on host fecundity was consistent
in both environments, resource limitation only lowered pathogen
intensity for aquatic hosts (Table S6; Fig. S7). Similarly, chemical
pollution reduced pathogen prevalence, and endogenous environmental
stressors reduced host survivorship and increased infection intensity in
aquatic but not terrestrial hosts (Table S6; Fig. S7). For host-pathogen
systems with indirect transmission modes, resource limitation decreased
host fecundity and pathogen intensity, and chemical pollution reduced
host survival and pathogen prevalence (Table S7; Fig. S8). While effects
of endogenous environmental stressors were generally consistent between
transmission modes, mortality was more pronounced in hosts exposed to
pathogens with direct transmission (Table S7; Fig. S8). Although our
results show potential distinctions and similarities between
environments and transmission modes, we note that most effects were from
aquatic (495 of 686) and indirect transmission (509 of 686) systems,
possibly biasing our findings towards these systems.

Evidence of publication bias

More negative effects of stressors in studies with lower precision
suggest that publication bias may partially explain our results for both
Q1 and Q2 (Fig. 4). We confirmed these negative relationships between
effect size and precision using a two-step modified Egger's test (Table
S8). We thus adjusted meta-analytic estimates for analyses in Q1 and Q2
by including variance as an additional moderator in both models.

Some results in Q1 differed qualitatively after adjusting for small
study effects. Specifically, effects of endogenous environmental
stressors and pollution became non-significant when variance was
included as a moderator (Table S9; Fig. S9). Moreover, the effect of
resource limitation on survivorship changed direction after the
small-study adjustment. However, we note that this effect was
indistinguishable from zero in both unadjusted and adjusted models and
was based on few studies (n = 8).

In Q2, our qualitative results remained essentially unchanged after
adjusting for publication bias. Overall, effects of endogenous
environmental stressors reduced host survival and increased both
infectivity traits (Table S10; Fig. S10). As in our primary analysis,
resource limitation in the adjusted model negatively affected fecundity,
but the meta-analytic effect on intensity was marginally non-significant
(Table S10). Finally, the negative impact of chemical pollution on host
survival and prevalence in our primary analysis (Table S5; Fig. 3)
became indistinguishable from zero in the adjusted model (Table S10;
Fig. S10). However, we caution that this result was based on a
relatively small number of experiments (n = 9).

\textbf{INTEGRATING EMPIRICAL RESULTS INTO EPIDEMIOLOGICAL MODELS}

When considering effects of stress on infected host fitness and
infectivity, responses varied depending on stressor type. Environmental
stress decreased host survivorship and increased infection intensity,
pollution decreased host survival and pathogen prevalence, and limiting
resources decreased host reproduction and pathogen intensity.

We integrated the best-supported relationships from our meta-analysis
into mathematical models to evaluate the net impact of these
simultaneous effects of stressors on host-pathogen interactions. We
built two dynamic Susceptible-Infected (SI) models. An SI-Resource model
following the framework of Civitello \emph{et al.} (2018) where key
processes (i.e., host reproduction and pathogen transmission) could
depend on resource availability (Box 1). And an SI-Environmental
gradient model following the framework of Lafferty \& Holt (2003) where
key processes (i.e., host survivorship and pathogen transmission) could
depend on an abiotic environmental factor (Box 2). Because our
meta-analysis suggested no proportional difference between uninfected
and infected hosts for survival or reproduction, we incorporated this
result by including a common parameter for the strength of these effects
on both groups (Box 1 and Box 2).

We used the models to determine equilibria of disease prevalence as a
function of resource availability and environmental stress gradients,
using the numerical integration function ``lsoda'' in the R
package\emph{deSolve} (Soetaert \emph{et al.} 2010). We examine
different scenarios in which fecundity and infectivity, or background
death and infectivity, had different sensitivities to either resource
(Box 1) or environmental stress gradients (Box 2), respectively. We
simulated epidemiological dynamics of each model across a gradient of
either resource availability or environmental stress, then plotted
equilibrium infection prevalence and host density against such gradients
for each model (Fig. 5).

Model predictions

Using our dynamical models (Box 1 and Box 2), we evaluated whether the
patterns of trait sensitivity to stressors we documented in the
meta-analysis reduce or increase infection prevalence across stress
gradients and how stressors ultimately impact host population densities.
The SI-Resource model predicts that a decrease in resource productivity
decreases infection prevalence (Fig. 5A), in part because host densities
also decrease with limited resources (Fig. 5C). Once a pathogen
establishes in a population, there is stabilizing feedback, where
pathogens suppress host density, increasing resources, and further
increasing transmission (Fig. S11). Therefore, in all scenarios of
sensitivity of pathogen transmissibility to resources (smaller values of
the half-saturation transmission constant (h\textsubscript{t}) increase
sensitivity of transmission rate (\selectlanguage{greek}β\selectlanguage{english}) to resources), the model reaches
the same prevalence equilibrium. However, although population density
also stabilizes, impacts on host density are different for each
scenario: populations more sensitive to resources available will reach
smaller population sizes compared to less sensitive populations (Fig.
5C)

The SI-Environmental stress gradient models revealed that population
density decreases regardless of effects of stress on hosts
susceptibility due to increased mortality. But it exponentially
decreases host populations when transmission rate is sensitive to the
environmental factor (Fig. 5B and D). Specifically, when stress
increases host susceptibility (i.e., greater values of
\selectlanguage{greek}β\selectlanguage{english}\textsubscript{E}), infection prevalence will increase rapidly (Fig.
5B) but at the cost of increasing host mortality (Fig. 5D). Therefore,
infection prevalence will have a maximum at intermediate stress level
but will drop as population densities are too low to sustain
transmission. In contrast, as transmission is more negatively affected
by stressors (i.e., pathogens are negatively affected by stressors),
infection prevalence will quickly reach zero with increasing
environmental stress (Fig. 5B). But as stress increases and persists,
populations will decline after pathogen extirpation (Fig. 5D).
Importantly, our models suggest that high pathogen prevalence and/or
stressors can result in host population extinction.

Our models illustrate that consequences of stress gradients on disease
can depend on the sensitivity that host traits, such as births and
deaths, and shared host-pathogen traits, such as transmission (i.e., \selectlanguage{greek}β\selectlanguage{english})
have to stressors. Interestingly, and consistent with Lafferty \& Holt
(2003) simulations, our models showed that increased environmental
stress generally decreased disease, mainly driven by host density
reductions. Although stress can make hosts more likely to become
infected at the individual level, at the population level, negative
impacts on host survival and reproduction may be driving pathogen and
host local extinctions (Lafferty \& Holt 2003).

\textbf{DISCUSSION}

Stressor type modulates host fitness and infectivity in different ways

Our meta-analysis documented the dominant effects of stressors on host
fitness and pathogen infectivity. Interestingly, we found that infected
and uninfected hosts had proportionally similar sensitivity to stressors
in relation to survival and fecundity. Furthermore, stressor type
determined host fitness and pathogen infectivity outcomes. Although we
found that resource limitation decreased host fecundity and pathogen
intensity, other authors have described positive, negative, and unimodal
relationships across animal taxa. For example, Cressler \emph{et
al.}(2014) found that as invertebrates increased their resource uptake,
they increased their pathogen intensity, whereas increased resource
consumption decreased pathogen intensity in vertebrates. They argued
this differential response could be due to distinct immune systems and
body sizes (Cressler \emph{et al.} 2014). Contrary to their results, we
found that both vertebrate and invertebrate hosts (which represented
most of our data) reproduced less and carried a lower pathogen burden
when facing limiting resources. One possible explanation is that hosts
invest resources in immune defense at the cost of reproduction. In
support of this hypothesis, it has been proposed that illness-mediated
anorexia may enhance immune function by acting as a ``master switch''
that reduces investment in other physiological processes (Hite \emph{et
al.} 2020). For example, Cumnock \emph{et al.} (2018) showed that
malaria-infected mice reduced their food intake and switched from
burning sugar (glycolysis) to fats (ketosis), which influenced host
tolerance to infections. Alternatively, resource limitation could
negatively affect pathogens, decreasing their capacity to reproduce
within hosts. Lastly, resource-limited hosts are often smaller and may
carry fewer pathogens, reducing pathogen intensity. This has been
reported in the snail-Schistosome system, where smaller snails carry
fewer parasites (Civitello \emph{et al.} 2022). Moreover,
in\emph{Daphnia} populations, food shortage reduced body size with
subsequent reductions in spore loads of a microsporidian parasite
(Pulkkinen \& Ebert 2004).

Regarding endogenous environmental stressors, we found that stressed
hosts survive less but have higher pathogen intensity. Coping with
fluctuating abiotic environments can be energetically demanding for
hosts, and human activities may exacerbate the frequency and severity of
naturally occurring fluctuations. For example, temperature variation
occurs naturally, but climate change makes it unpredictable or more
drastic (Harvell \emph{et al.} 2002; Marcogliese 2008). When stressed,
hosts may not resist infections (increasing pathogen proliferation)
and/or compensate for damage done by the pathogen (tolerating
infection), as seen when higher temperatures increase coral
(\emph{Gorgonia ventalina} ) susceptibility to fungus (\emph{Aspergillus
sydowii} ), while also increasing fungal growth and virulence
(Ward\emph{et al.} 2007).

Finally, we found hosts exposed to pollutants had higher mortality but
lower pathogen prevalence. However, we note that these results must be
interpreted cautiously, given that the experimental studies included in
our meta-analysis intentionally used sub-lethal toxin doses. Low
prevalence may be due to hosts dying before replicating and transmitting
the pathogen. This result is consistent with mechanistic models of how
toxicants influence pathogen transmission showing that infection
prevalence was lower in more contaminated landscapes due to high host
mortality (S\selectlanguage{ngerman}ánchez \emph{et al.} 2020). Although pollution can decrease
parasitism if infected hosts suffer more than uninfected hosts from
pollutant exposure, our analysis showed that hosts are equally sensitive
to toxins regardless of infection status. Alternatively, parasites could
also be negatively affected by pollution. For example, mortality
increased in infected hosts as zinc concentration increased, but
parasite burden peaked at intermediate zinc concentrations in a
fish-parasite system (Gheorgiu \emph{et al.} 2006). A follow-up study
revealed that both parasite lifespan and fecundity were also negatively
affected by zinc (Gheorgiu \emph{et al.} 2007).

Implications for biodiversity conservation and disease transmission

While there are many examples of human activities conspicuously causing
wildlife population declines (Dirzo \emph{et al.} 2014), more subtle
disruptions of host-pathogen interactions can also impact population
dynamics. The worldwide amphibian decline constitutes an important
example. Although mass amphibian mortalities have been linked to chytrid
fungus infections (Lötters \emph{et al.} 2009), the pathogen alone is
not sufficient to cause of ongoing declines (Alford \emph{et al.} 2007;
Rollins-Smith \emph{et al.} 2011; Scheele \emph{et al.} 2019). Global
warming, another culprit of population declines, degrades amphibian
condition (Reading 2007), increasing susceptibility to the fungus
(Garner \emph{et al.} 2009; Rollins-Smith \emph{et al.} 2011;
Cohen\emph{et al.} 2019a, b, 2020). In the wild, when pathogens are
highly virulent, sick individuals are seldom found, probably due to
reduced survivorship and diminished activity when ill. However, sick or
dead individuals are conspicuous at infrequent times, such as the
beforementioned amphibian mass mortality events (Lötters \emph{et
al.}2009). As sick animals become abundant, they could be more commonly
detected, indicating an ongoing population decline (green lines in Fig.
5B and C) (Beldomenico \& Begon 2016). It is important to note that
other strategies to monitor and manage wildlife diseases exist, like
targeted surveillance on single species that dominate transmission
(Streicker \emph{et al.} 2013; Charlier \emph{et al.} 2022).

Effects of multiple stressors (e.g., environmental stressors plus
infection) can perpetuate cycles, where hosts in poor condition may not
respond adequately to infection (e.g., reduced infection resistance or
tolerance), further reducing their condition and increasing
susceptibility to stressors and additional infections (Beldomenico \&
Begon 2016). As most known pathogens are multi-host (Woolhouse \emph{et
al.} 2001), such cycles could affect population and community-level
dynamics (Beldomenico \& Begon 2016). For example, Lafferty \& Holt
(2003) showed a positive association between stress and disease because
transmission did not decrease as a specific host population became rare
(as in our models with a single species), posing a threat to other
species. White-nose syndrome, an emerging fungal disease in bats,
constitutes another notable example. While the disease has severely
decimated some bat species populations, other sympatric and closely
related species have been largely unaffected while sustaining
transmission (Langwig \emph{et al.} 2012, 2016; Cheng \emph{et
al.}2021).

Although most of the taxa examined (arthropods, molluscs, amphibians,
and fish) are not commonly associated with zoonotic events, insights are
gained by identifying generalities across taxa and comparing them with
other systems. For instance, we found that pathogen intensity increased
in hosts exposed to environmental stressors, suggesting negative
implications for public health. Under stressful conditions, individuals
could become superspreaders, amplifying pathogen transmission potential
and disease risk (Lloyd-Smith \emph{et al.} 2005; Gervasi \emph{et
al.}2015; Faust \emph{et al.} 2017). Consequently, they could increase
intra- and inter-species transmission and pose a risk for spillover to
humans and domesticated animals (Plowright \emph{et al.} 2017;
Faust\emph{et al.} 2018). For example, nutritional stress has been
identified a primary risk factors for Hendra virus infection in flying
foxes (\emph{Pteropus} sp.), leading to spillover events that affected
both livestock and humans (Plowright \emph{et al.} 2015; Becker \emph{et
al.}2022; Eby \emph{et al.} 2023).

Future directions and concluding remarks

Our analyses included only experimental studies, with hosts exposed to a
single parasite species and a single stressor. This approach, although
easier to interpret and valuable to tease apart stressor effects in
host-pathogen interactions, is difficult to translate to the natural
world, where populations are likely exposed to multiple pathogens and a
combination of stressors. When considering co-infections, for instance,
stressors may compromise one arm of immune defense, making hosts more
vulnerable to pathogens that require such response. For example, food
restriction increased levels of eosinophils in capybaras (a Th2 immune
response) and consequently reduced nematode burden (where resistance
relies on the Th2 response), but coccidian infection intensity increased
due to inadequate Th1 immune response (Eberhardt \emph{et al.} 2013).
Future studies should use a combination of field and laboratory
experiments to perturb processes that covary with stressors to determine
how and why results vary comparing laboratory and real-world conditions.

As a next level of complexity, host-pathogen systems do not occur in
isolation, and some other biotic stressors and interactions can
indirectly affect disease dynamics. For example, hosts compete for
resources with other species and are consumed by predators.
Consequently, stressors can affect other community members in ways that
could enhance or negate epidemiological effects on hosts and pathogens
(Strauss \emph{et al.} 2015, 2016). Furthermore, most known pathogens
infect multiple host species (Woolhouse \emph{et al.} 2001), but some
host species are disproportionately responsible for parasite
transmission (Haydon \emph{et al.} 2002). Generally, ecologically
resilient species exhibit fast life histories and invest less in immune
defense compared to more disturbance-sensitive species (Johnson \emph{et
al.} 2012; Previtali \emph{et al.} 2012; Pap \emph{et al.} 2015),
predicting that resilient species will have insufficient immune response
to prevent pathogen replication and transmission, resulting in higher
transmission rates. Therefore, future research is sorely needed to
evaluate the effects stressors have on different host species and their
relative contribution to community disease transmission.

Moreover, combining experimental and modeling approaches is needed to
move beyond associational patterns toward a mechanistic understanding of
how stressors affect hosts and pathogens due to the common occurrence of
multiple simultaneous stressors. Approaches are available for
incorporating stressors into epidemiological models, such as examining
variation in R\textsubscript{0}, the basic reproductive number of a
parasite (Anderson \& May 1991). Pinpointing when and how stressors
increase or decrease R\textsubscript{0} is crucial to understanding
their roles in infectious disease dynamics. Though multiple mechanisms
(including changes in host contact rates and per-contact probability of
transmission) are often subsumed in the transmission parameter \selectlanguage{greek}β, \selectlanguage{english}these
need not be fixed, as we have illustrated with our models. The same
applies to birth and death rates, and even to pathogen virulence, given
that variation in host immune defenses alters per-contact transmission
probabilities and the duration of the infectious period. As a next step,
integrating a series of models with empirical results will inform the
generality of predicted patterns.

Finally, our study highlights the need to expand empirical research at
the interface of stress and infectious disease in highly relevant
systems for zoonotic disease emergence. The studies included in our
meta-analysis had low coverage of both vertebrates and terrestrial
systems, yet terrestrial vertebrates such as rodents and bats have been
linked repeatedly to zoonotic diseases affecting humans and livestock
(Luis \emph{et al.} 2013; Han \emph{et al.} 2016). However, only one
rodent study rodents provided sufficient data to be included in our
meta-analysis (Eze \emph{et al.} 2013).

As anthropogenic activities continue to alter ecosystems in ways that
facilitate disease emergence worldwide, we must consider stressor
effects on disease dynamics. Our findings improve our understanding of
this interplay and provide insights for predicting and mitigating the
impacts of stressor-pathogen synergies on human, animal, and planetary
health.

\textbf{Acknowledgments}

We thank Emory undergraduate students for their help harvesting data
(Olivia Milloway, Christie Jones, Maddy Koenig, Melanie Yu, and
Sivasomasundari Arunarasu).

\textbf{Funding}

AVS was supported by a U.S. Department of State -- Fulbright Fellowship
and the Laney Graduate School of Emory University. BW was funded by a
Postdoctoral Fellowship from the Swedish Research Council
(Vetenskapr\selectlanguage{ngerman}ådet no. 2019-06444). DJC was supported by NSF IOS 1755002
and NIAID 1R01 AI150774. TRG was supported by Emory University and the
Halle Foundation.

\textbf{Conflict of interest}

The authors declare no conflict of interest.

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\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 1.} PRISMA diagram documenting our study screening for
inclusion and exclusion for the meta-analysis. Each stage of the data
collection process is highlighted with different colored pipes (blue:
literature search; orange: title/abstract screening; green: full-text
screening).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Figure 2.} Orchard plot for the best multi-level meta-analytic
(MLMA) model of the effects of environmental stressors on host fitness
traits. The model includes two factorial moderators: stressor type,
coded as ``endogenous environment'' (EE), ``chemical pollution'' (CP),
and ``resource limitation'' (RL), and fitness response trait
(``fecundity'' or ``survivorship''). Nodes in the same color show
effects of the same stressor. The overall mean effect sizes (Hedge's g)
for each combination of stressor and response trait are shown as circles
with black border lines. 95\% confidence intervals are represented by
the thick black bars, and prediction intervals are represented by the
thin bars. The number of effects for each category (k) is given in
parentheses. Circle size is proportional to effect size precision.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Figure 3.} Orchard plot for the best multi-level meta-analytic
(MLMA) model of the effects of environmental stressors on host fitness
traits and infectivity. The model includes two factorial moderators:
stressor type, coded as ``endogenous environment'' (EE), ``chemical
pollution'' (CP), and ``resource limitation'' (RL), and response trait
(``prevalence'', ``intensity'', ``fecundity'' or ``survivorship'').
Negative effect sizes imply reduced fecundity, survivorship, infection
prevalence, or intensity. Nodes in the same color show effects of the
same stressor on the same category of the response variable (fitness or
infectivity). The overall mean effect sizes (Hedge's g)~for each
combination of stressor and response variable are shown as circles with
black border lines. 95\% confidence intervals are represented by the
thick black bars, and prediction intervals are represented by the thin
bars. The number of effects for each category (k) is given in
parentheses. Circle size is proportional to effect size precision.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{Figure} \textbf{4} . Funnel plots showing the relation between
precision (SE) and conditional residuals of the effects of environmental
stressors on A) fitness and B) fitness and infectivity responses in
animal hosts. Dark and light grey areas show bounds of 90\% and 95\% CIs
for conditional residuals given the SE. Circles represent individual
effects and are colored by precision, with dark red representing greater
precision.\selectlanguage{english}
\begin{longtable}[]{@{}l@{}}
\toprule
\textbf{Box 1. SI-Resource model} Susceptible (S) and infected hosts (I)
are foraging on available resources (R), while resources grow
logistically. Hosts require resources to reproduce, determined by the
conversion efficiency (e, births per unit of resource) and foraging rate
(f\textsubscript{M}). From our meta-analysis results, limited resources
affected host reproduction (decreased fecundity) of both susceptible and
infected hosts. However, infected hosts reproduce at a lower rate than
susceptible hosts (\selectlanguage{greek}ρ, \selectlanguage{english}relative fecundity of I compared to S). Hosts die
at a background death rate (d), but infected hosts have increased
mortality due to the pathogen (\selectlanguage{greek}ν, \selectlanguage{english}virulence). Hosts become infected at a
transmission rate (\selectlanguage{greek}β\selectlanguage{english}\textsubscript{M}). To determine how fast traits
rise with resource availability, we use half saturation reproduction
constant (hr) and half saturation transmission constant (ht) as part of
type II functional response. The latter increases as hosts are more
sensitive to resource availability. Resources grow at a growth rate (r)
and have a carrying capacity (K). Parameters used in our simulations: e
= 0.5, f\textsubscript{M} = 0.1, \selectlanguage{greek}ρ \selectlanguage{english}= 0.25, d = 0.01, \selectlanguage{greek}ν \selectlanguage{english}= 0.04,
\selectlanguage{greek}β\selectlanguage{english}\textsubscript{M} = 0.01, hr = 4, ht = varies (0, 1, 2, 4, 8), r = 1, K
= 10.\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{longtable}[]{@{}l@{}}
\toprule
\textbf{Box 2. SI- Environmental stress gradient model} Susceptible
hosts (S) grow logistically and have a density at which birth rate (b)
hits zero from competition (K, carrying capacity). As an assumption of
relative change, transmission rate (\selectlanguage{greek}β\selectlanguage{english}) and background death (d) are
sensitive to environmental stressors (E), \selectlanguage{greek}β\selectlanguage{english}\textsubscript{E} and
d\textsubscript{E}, respectively. From our meta-analysis results,
environmental stress (and pollution) affected survival, regardless of
infection status. However, infected hosts (I) reproduce at a smaller
rate than susceptible hosts (\selectlanguage{greek}ρ, \selectlanguage{english}relative fecundity of I compared to S)
and have increased mortality due to the pathogen (\selectlanguage{greek}ν\selectlanguage{english}). Parameters used in
our simulations: b = 0.5, K = 10, \selectlanguage{greek}β \selectlanguage{english}= 0.01, d = 0.01, E = varies (0-1),
\selectlanguage{greek}β\selectlanguage{english}\textsubscript{E} = varies (-8, -4, -2, 0, 2, 4, 8), d\textsubscript{E}
= 2, \selectlanguage{greek}ρ \selectlanguage{english}= 0.25, \selectlanguage{greek}ν \selectlanguage{english}= 0.04.\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}

\textbf{Figure 5.} Contrasting outcomes for equilibrium prevalence (A
and B) and host density (C and D) from hypothetical epidemiological
models that illustrate dynamics that rise when fitness traits (survival
and fecundity) and infectivity (transmission rate) vary with stressors,
as demonstrated by our meta-analysis results. A and C are simulation
outcomes of SI-Resource model. The half-saturation transmission constant
(h\textsubscript{t}) determines the transmission rate (\selectlanguage{greek}β\selectlanguage{english}) response to
resource availability, where a greater value of h\textsubscript{t} makes
the \selectlanguage{greek}β \selectlanguage{english}less sensible to resources, and vice versa. B and D are simulation
outcomes of SI-Environmental stress model. In the model, \selectlanguage{greek}β \selectlanguage{english}could have
different sensitivities to environmental factors (\selectlanguage{greek}β\selectlanguage{english}\textsubscript{E}),
ranging from positive to negative. For parameters used in each model
simulation, see Box 1 and 2, respectively.

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

