Climate change is causing shifts in the ecology and biodiversity of different world regions at unprecedented rates. Global warming is also linked with changes in the risk for certain infectious diseases in humans, but also in animals and plants. There are several possible mechanisms for this. For one thing, changing weather patterns may affect how pathogens grow and reproduce. For another, the distribution ranges of animal and plant hosts of certain disease-causing pathogens are changing because of global warming. This means that the distributions of pathogens are also changing, and so is the severity of the diseases that they cause.

Increasing temperatures may also influence the physiological traits that make host species suitable for pathogens. This is because the traits that allow species to survive or adapt to changes in their environment may also make them better at hosting and transmitting the pathogens that cause disease. For example, in plant communities, rising temperatures could favor species with faster growth rates, quicker reproduction and high dispersal, and these traits are often associated with more efficient spread of disease.

Despite a lot of research into the effects of climate, it remains unclear how temperature, pathogen growth and reproduction, and host species’ traits and distributions combine and interact to alter infectious disease risk, especially in wild plant communities. To investigate this, Halliday, Jalo and Laine studied an area in southeast Switzerland where natural temperature and biodiversity change gradually through the region. The aim was to explore how relationships between plant biodiversity, pathogens and disease risk change with temperature, and to understand whether environmental or biological factors influence infectious disease risk more.

Halliday, Jalo and Laine measured the levels of fungal diseases found in the leaves of plant communities spanning 1,100 meters of elevation, showing that higher temperatures increase disease risk both directly and indirectly. Directly, higher temperatures increased pathogen growth and reproduction, and indirectly, they influenced which plants were present and therefore able to act as disease hosts. The results also indicated that temperature can affect how the traits of plants drive the transmission rates of fungal pathogens. Important predictors of disease risk were traits relating to the growth rate of host plants, which tended to increase in areas with low elevation where the surface of the soil was warm.

This study represents the first analysis, in wild plants, of how changing temperatures, the traits of shifting host species, and resident parasite populations interact to impact infectious disease risk. The insights Halliday, Jalo and Laine provided could aid in predicting how global climate change will influence infectious disease risk.

Infectious disease is strongly influenced by host community structure and abiotic conditions (Halliday et al., 2020a; Halliday et al., 2019), both of which are undergoing unprecedented change as the climate is warming (Pachauri et al., 2014) and biodiversity is being reshuffled (Díaz et al., 2019; Hillebrand et al., 2018). Understanding how biotic and abiotic conditions interact to drive the emergence and spread of infectious disease is quickly emerging as one of the greatest research challenges of the 21st century and will be the key to limiting the impacts of infectious diseases on food production systems, wildlife, and humans. Disease ecology provides a framework for achieving this goal through careful examination of interactions among hosts, parasites, and the environment (Johnson et al., 2015a; McNew, 1960; Seabloom et al., 2015; Figure 1a). Yet, we have a poor understanding of how this framework operates under natural conditions, in part because several mechanisms can operate simultaneously, making it difficult to tease apart their relative contributions to realized disease risk.

Figure 1

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Climate change involves increased environmental temperatures, which can profoundly alter disease risk (Garrett et al., 2006; Harvell et al., 2002; Rohr et al., 2011). These effects can result from direct impacts of environmental factors on parasite growth, survival and reproduction that underpin disease risk. For example, in an experiment in the Rocky mountains, host plants that grew on heated research plots showed increased disease, largely by increasing the amount of time that environmental conditions were favorable for parasite growth and reproduction (Roy et al., 2004). Importantly however, these same environmental factors can also indirectly influence disease risk by altering the composition of host or vector communities that are required for sustained parasite transmission (Elad and Pertot, 2014; Garrett et al., 2006; Harvell et al., 2002; Mordecai et al., 2019; Newton et al., 2011; Yáñez-López, 2012; Rohr et al., 2011), or by altering host defenses (Descombes et al., 2017; Pellissier et al., 2018; Wolinska and King, 2009). Thus, shifts in parasite replication that are driven by changing host or vector distributions can also determine whether and how changing environmental conditions will alter disease risk. There is growing empirical evidence in support of both direct effects that alter parasite growth and replication, as well as indirect effects that are mediated by changing host or vector community structure. However, disentangling the relative impacts of these direct and indirect effects of environmental factors on disease risk has been historically challenging, because it often requires a priori knowledge of environmental constraints acting on host and parasite populations (Garrett et al., 2006; Harvell et al., 2002; Mordecai et al., 2019; Rohr et al., 2011).

One way to disentangle direct and indirect effects of environmental conditions on disease is to consider these effects in the context of host functional traits. Host functional traits underlie ecologically important resource acquisition and allocation tradeoffs: hosts must balance allocating limited resources to maximize growth and reproduction, while also constructing tissue capable of withstanding stressful environmental conditions (Díaz et al., 2016; Reich, 2014; Reich et al., 2003; Wright et al., 2004). Thus, linking environmental conditions with relevant functional traits has become a tractable way to predict the richness and composition of communities (i.e. community structure) (Cornelissen et al., 2003; Díaz and Cabido, 1997; Funk et al., 2017; Kattge et al., 2020; Lavorel and Garnier, 2002; McMahon et al., 2011; Reich, 2014; Sundqvist et al., 2013).

The functional traits expressed by those species that are able to colonize and persist in a given location can, in turn, affect disease risk (Halliday et al., 2019; Johnson et al., 2013; Kirk et al., 2019). Specifically, an infected host’s ability to transmit disease to uninfected hosts, a trait often referred to as host competence, is often related to fast-growing, poorly defended tissues and short lifespans (Becker and Han, 2021; Cronin et al., 2014; Cronin et al., 2010; Huang et al., 2013; Johnson et al., 2012; Martin et al., 2019; Martin et al., 2016; Parker and Gilbert, 2018; Stewart Merrill and Johnson, 2020; Welsh et al., 2020). Importantly, these functional trait values also underlie ecological tradeoffs related to host growth and defense, resource acquisition and allocation, and survival and reproduction (i.e. life history) (Coley et al., 1985; Herms and Mattson, 1992; Martin et al., 2016; Reich, 2014; Reich et al., 2003; Ricklefs and Wikelski, 2002; Stearns, 1992; Stearns, 1989; Wright et al., 2004). Thus, host community competence (a community-level metric of host competence) is expected to correspond to the same functional traits (i.e. host pace-of-life) that link host community structure to shifting environmental conditions.

A trait-based framework of host community competence may explain why biodiversity loss is consistently associated with higher disease risk (Halliday et al., 2020b; Johnson et al., 2013; LoGiudice et al., 2003; Ostfeld and LoGiudice, 2003), a relationship known as the ‘dilution effect’ of biodiversity (Keesing et al., 2010; Keesing et al., 2006; Ostfeld and Keesing, 2000). This is because host species that are most resistant to biodiversity loss or best able to colonize newly disturbed habitats often rely on the same life-history strategies that are associated with higher host competence (Johnson et al., 2013; LoGiudice et al., 2003; Ostfeld and LoGiudice, 2003). For example, species that are associated with habitat fragmentation, a key anthropogenic driver of biodiversity loss, are often characterized by life history strategies favoring a ‘fast pace-of-life’ (i.e. fast growth rates, quick reproduction, and high dispersal) (Albrecht and Haider, 2013; Fay et al., 2015; Gibbs and van Dyck, 2010; Hanski et al., 2006; Keinath et al., 2017; Merckx et al., 2018; Ziv and Davidowitz, 2019). But this fast pace-of-life often comes at the cost of reduced defense against parasites (Cappelli et al., 2020; Coley et al., 1985; Cronin et al., 2014; Cronin et al., 2010; Heckman et al., 2019; Herms and Mattson, 1992; Johnson et al., 2012; Sears et al., 2015). Thus, habitat fragmentation can increase disease by increasing the density of fast pace-of-life, highly competent hosts, while slow pace-of-life, less-competent hosts are lost (Johnson et al., 2015b; Joseph et al., 2013; Mihaljevic et al., 2014). This hypothesis has widespread empirical support in a variety of systems (Johnson et al., 2019; Johnson et al., 2013; Liu et al., 2018; Ostfeld and LoGiudice, 2003). Shifting community structure during biodiversity loss may therefore predictably influence infectious disease risk (Halliday et al., 2020b).

Although relationships between host community structure and disease risk are becoming increasingly appreciated, how these relationships change across environmental gradients remain unknown (Halliday et al., 2020b; Halliday and Rohr, 2019). The relationship between host traits and host competence can be variable, and this relationship might also depend on the environmental context in which host-parasite interactions play out (Figure 1b., path d). For example, Welsh et al., 2016 showed that when hosts were reared under novel resource conditions, trait-based models of host susceptibility became increasingly inaccurate, because novel resource conditions altered how traits covaried with one another and how raw trait values predicted infection. Thus, traits associated with host community competence in one environment might not predict host community competence across environmental gradients.

We hypothesized that three non-mutually exclusive mechanisms would determine how environmental conditions influence disease risk in host communities: (1) directly, by altering parasite growth and reproduction (i.e. through abiotic constraints; Figure 1b., path a), (2) indirectly, by altering which host species occur in which locations (i.e. mediated by shifting host community structure; Figure 1b., paths b and c), and (3) indirectly, by altering how host traits influence parasite transmission (i.e. moderated by altering the relationship between host traits and host competence, which we refer to as the trait-competence relationship; Figure 1b., path d).

Here, we test the relative contributions of these three mechanisms through which environmental conditions can drive infectious disease risk (i.e. direct, mediated, and moderated) by measuring foliar fungal disease in host plant communities along a roughly 1100 m elevational gradient in Southeastern Switzerland. Foliar fungal parasites are a widely used, tractable model of disease risk that respond to small-scale variation in host community structure and environmental conditions (Cappelli et al., 2020; Halliday et al., 2019, Halliday et al., 2017; Liu et al., 2018; Liu et al., 2017; Liu et al., 2016; Mitchell et al., 2003; Mitchell et al., 2002; Rottstock et al., 2014). Host community structure and environmental conditions, in turn, vary predictably with elevation (Grinnell, 1914; Halbritter et al., 2018; Malhi et al., 2010; Sundqvist et al., 2013; Whittaker, 1956). Thus, an elevational gradient represents a natural laboratory for studying long-term, large scale changes in climate as well as interacting biotic and abiotic factors that are associated with climate change (Alexander et al., 2015; Fukami and Wardle, 2005; Sundqvist et al., 2013).

Our study reveals strong evidence that increasing temperature associated with lower elevation can directly influence disease risk, which we attribute to well-established effects of abiotic conditions (Avenot et al., 2017; Garrett et al., 2006; Harvell et al., 2002; Tapsoba and Wilson, 1997; Waugh et al., 2003) on parasite replication and growth, and can indirectly influence disease risk by shifting host community structure and by modifying the trait-competence relationship. Together, these results highlight the need to consider biotic and abiotic drivers jointly, in order to predict disease risk in the face of climate change.

To evaluate abiotic constraints on parasite replication and growth (i.e. direct effects), shifting host community structure (i.e. mediation; Baron and Kenny, 1986), and modification of the trait-competence relationship (i.e. moderation; Baron and Kenny, 1986) as mechanisms through which environmental gradients can influence disease risk, we surveyed 220, 0.5 m-diameter vegetation communities (i.e. small plots), that were established in four meadows along a 1101 m elevational gradient as part of the Calanda Biodiversity Observatory (CBO) in 2019 in order to investigate biotic and abiotic drivers of species interactions (Figure 2; Supplementary file 1a).

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Model testing effects of environmental conditions, community structure, and their interaction on disease

We tested whether the relationship between host community structure (i.e. host species richness and host community pace-of-life) and disease would change as a function of environmental conditions by fitting a linear mixed model with square-root transformed community parasite load (e.g. Halliday et al., 2019, Halliday et al., 2017; Mitchell et al., 2002) as the response. Soil-surface temperature, soil moisture, host community species richness, pace-of-life, and pairwise interactions between both measures of community structure and each abiotic variable were treated as fixed effects, with large plots, sites, and meadows as nested random intercepts. All variables that were treated as fixed effects in the model were centered so that the mean value of each variable was used as the reference value for interpreting the other variables' independent effects. This mixed model of disease revealed several independent and interactive effects of host community structure and environmental conditions on disease risk (Marginal R² = 0.227; Conditional R² = 0.497; RMSE = 0.292; LOOCV RMSE = 0.311; Table 1). Consistent with the hypothesis that host pace-of-life can determine host community competence, communities that were dominated by hosts with fast-paced life-history strategies exhibited the most disease, but this effect declined as elevation increased and temperature declined (temperature × pace-of-life: p < 0.001). This weakening effect of host community pace-of-life as soil-surface temperature declined is consistent with the hypothesis that abiotic conditions can alter which traits favor parasite transmission through the relationship between host competence and disease risk (Figure 3). These results therefore provide field evidence that an environmental gradient can alter the effect of host community structure on disease risk.

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Table 1

Predictor	Estimate	Chisq	Df	P
Soil-surface Temperature	0.044	7.4236	1	0.0064
Soil Moisture	–0.254	0.1390	1	0.7092
Host Richness	–0.009	5.3325	1	0.0209
Host Pace-of-Life	0.133	1.6970	1	0.1926
Temperature × Richness	0.004	2.6551	1	0.1032
Temperature × Pace-of-Life	0.118	11.2498	1	0.0008
Moisture × Richness	–0.288	2.7677	1	0.0962
Moisture × Pace-of-Life	–2.037	0.5647	1	0.4524

The model also revealed significant independent effects of host community structure and abiotic conditions on disease risk. Specifically, the model revealed evidence supporting the dilution effect hypothesis: increasing species richness was associated with a reduction in disease (p = 0.021), and this effect was independent of soil-surface temperature (temperature × richness: p = 0.10). Community parasite load was also positively associated with increasing soil-surface temperature (p = 0.006), consistent with the hypothesis that environmental gradients can alter parasite growth and reproduction via abiotic constraints. These effects were qualitatively similar when we included soil temperature, air temperature, or elevation in place of soil-surface temperature in the model (Supplementary file 1c).

In contrast with results involving soil-surface temperature, there was no statistically significant linear relationship between soil moisture and disease (p = 0.71), nor was there a significant interaction between soil moisture and host richness (p = 0.10) or community-weighted mean pace-of-life (p = 0.45) on disease. Because soil moisture was unrelated to elevation, pace-of-life, species richness, and disease in our models, this factor was omitted from further analyses.

Structural equation model comparing direct and indirect effects of environmental conditions on disease

Together, models of host community species richness and pace-of-life showed that declining temperature associated with increasing elevation could determine changes in host community structure, and the model of disease showed that host community structure and temperature could independently and interactively influence disease. To explore the relative influence of these direct and indirect effects on disease risk, we next constructed a structural equation model. Our data were well fit by this model (Fisher’s C = 4.111; p-value = 0.662; 6 degrees of freedom, Supplementary file 1d; Figure 4). The model leverages the strong, negative effect of elevation on soil-surface temperature (standardized path coefficient = –0.91, R² = 0.85) to compare three separate pathways through which increasing temperature can increase disease: First, increasing temperature increased community parasite load directly (standardized path coefficient = 0.24). Second, increasing temperature increased community parasite load indirectly by reducing host species richness (i.e. via mediation; product of standardized path coefficients = 0.045). Third, increasing temperature increased community parasite load indirectly by simultaneously increasing host community pace-of-life (i.e. via mediation; mean-centered standardized path coefficient = 0.39) and strengthening the relationship between host pace-of-life and disease risk (i.e. via moderation; mean-centered standardized path coefficient = 0.18; Figure 4). Together these results highlight the pressing need to consider host community context in predicting how shifting environmental gradients will alter disease risk.

Figure 4

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This study shows, to our knowledge, the first evidence under natural field conditions that, in addition to directly influencing disease risk, the abiotic environment can also indirectly influence disease both by altering host community structure (i.e. mediation) and by modifying how host community structure influences disease risk (i.e. moderation). Furthermore, this linkage between the abiotic environment and host community structure suggests that any single factor would be inadequate for explaining disease risk along our environmental gradient. Together, these results reveal the role that host communities play in determining ecosystem health across environmental gradients, suggesting that predicting how shifting abiotic conditions will influence disease risk will require explicit consideration of how host and parasite communities jointly respond to the abiotic environment.

Our results indicate that increasing temperatures associated with lower elevation, can independently influence disease. Specifically, increasing temperature increased disease, even after accounting for effects of host community structure on disease. We hypothesize that reduced temperature associated with increasing elevation may have reduced disease directly by constraining parasite growth, survival, and reproduction. Many foliar parasites grow and reproduce more successfully in warmer environmental temperatures (Avenot et al., 2017; Garrett et al., 2006; Harvell et al., 2002; Tapsoba and Wilson, 1997; Waugh et al., 2003). Warmer temperatures can also increase parasite overwintering success (Burdon and Elmqvist, 1996; Pfender and Vollmer, 1999) or allow parasites to produce more generations during a longer growing season (Garrett et al., 2006). These results corroborate past studies suggesting that environmental gradients can directly alter the strength of biotic interactions (Descombes et al., 2017; Hargreaves et al., 2019; Pellissier et al., 2014; Roslin et al., 2017; Schemske et al., 2009), including host-parasite interactions (Abbate and Antonovics, 2014; Allen et al., 2020; LaManna et al., 2017; Nunn et al., 2005). However, despite the strong and consistent effect of increasing temperature on disease, temperature was highly correlated with elevation, and we cannot rule out the possibility that these effects might be driven by some other factor associated with elevation that was not measured, such as changing humidity or soil nutrient availability. Thus, temperature effects should be interpreted with some caution.

In addition to directly influencing disease, our results indicate that increasing temperature can also indirectly influence disease by altering host community structure. Specifically, increasing temperature reduced host species richness, which, in turn, reduced disease. The reduction in host species richness with increasing temperature might be attributable to the occurrence of both low-elevation and high-elevation adapted species occupying the coolest study sites, located at the highest elevation (Colwell and Lees, 2000). Communities in the highest elevation meadow, located just below the tree line, included plant species characteristic of low elevations (e.g. Lathyrus pratensis, Lolium perenne, and Salvia pratensis) and plant species that tend to occupy high elevation grasslands (Soldanella alpina, Ranunculus montanus, and Carex sempervirens), indicating that these high-elevation sites represent an intermediate zone between subalpine and alpine vegetation communities.

Host communities with higher species richness, in turn, experienced less disease (i.e. a dilution effect; Keesing et al., 2010; Keesing et al., 2006), even after accounting for the direct effects of temperature on disease and other measures of host community structure. Past studies indicate that increasing biodiversity is often associated with a decline in disease risk because host community structure shifts during biodiversity loss to favor more competent hosts (Johnson et al., 2019; Joseph et al., 2013; Liu et al., 2018; LoGiudice et al., 2003; Ostfeld and LoGiudice, 2003; Rohr et al., 2020). However, in contrast with past studies focused on biodiversity loss, our study measured biodiversity change across a natural biodiversity gradient, which is not expected to consistently influence disease risk (Halliday et al., 2020b). We hypothesize that increasing species richness may have reduced disease risk in this system by reducing host density (Keesing et al., 2006; i.e. via encounter reduction; Mitchell et al., 2002). Encounter reduction might be particularly relevant in this system, because, in addition to altering host richness, reduced temperatures associated with increasing elevation also influence the length and timing of the growing season, which can affect peak prevalence and the duration of the epidemic season.

In addition to direct and indirect effects via mediation, our results further indicate that increasing temperature can indirectly influence disease by modifying the effect of host community structure on disease (i.e. via moderation). Specifically, disease was influenced by host community pace-of-life, but only at high temperature, low elevation sites. Because more competent hosts often exhibit fast-paced life history strategies (Cronin et al., 2010; Johnson et al., 2012; Martin et al., 2016; Parker and Gilbert, 2018; Welsh et al., 2020), we expected that host communities dominated by species with a fast pace-of-life would experience greater disease. However, a prior study suggested that the relationship between host traits and host competence might be sensitive to environmental conditions (Welsh et al., 2016), which we hypothesized would cause the relationship between host community pace-of-life and disease risk to shift across environmental gradients. Our analysis was consistent with this hypothesis: increasing temperature not only modified host community pace-of-life, but the effect of host community pace-of-life on disease was also sensitive to increasing temperature. Host community pace-of-life most strongly predicted disease risk at the highest temperatures, associated with the lowest elevation, but this effect weakened and ultimately disappeared as elevation increased and temperature declined.

These results indicate that warming temperatures can modify the effect of host community pace-of-life on disease risk, which we attribute to a change in the relationship between host traits and host competence across environmental conditions. However, we cannot rule out the possibility that the interaction between host pace-of-life and temperature could have also been driven by other mechanisms. For example, the values of functional traits expressed by a single species may have changed along the environmental gradient via a phenomenon known as intraspecific trait variation (Albert et al., 2011; Funk et al., 2017; Messier et al., 2010; Violle et al., 2012). Studies of functional traits (including this study) typically characterize each species with a single value for each trait, such as the species-level mean, under the assumption that ecologically important traits vary more among species than within species (McGill et al., 2006). However, functional traits of individuals within a species can vary due to local adaptation and phenotypic plasticity driven by local context (Albert et al., 2011; Funk et al., 2017; Messier et al., 2010; Violle et al., 2012). Thus, intraspecific shifts in the expression of key functional traits across our elevational gradient could drive the apparent interaction between host community pace-of-life and temperature. Alternatively, a reduction in infection severity with cooling temperatures could weaken the importance of investment in disease resistance (Benkman, 2013; Thompson, 1999). Thus, host species may still form strong trade-offs in fast vs slow strategies for growth vs. survival, but this pace-of-life trait would have a weak link with disease severity. Future studies should explore these mechanisms by directly measuring host and parasite functional traits across environmental gradients like elevation.

Together, the results of this study highlight the need to consider host community context in predicting how climate change will alter disease risk. Specifically, in this study, effects of the abiotic environment and changing environmental temperature on disease strongly depended on shifting host community pace-of-life. These results are consistent with a growing body of literature suggesting that the role of host communities in regulating ecosystem processes is at least partially explained by characteristics of species present in those ecosystems (Allan et al., 2015; Heilpern et al., 2020; Le Bagousse-Pinguet et al., 2019; Leitão et al., 2016; Mouillot et al., 2011; Start and Gilbert, 2019; Van de Peer et al., 2018), but that abiotic factors such as temperature can override the effects of biotic factors on ecosystem processes (Cannone et al., 2007; Laiolo et al., 2018). These results therefore suggest that predicting how climate change will influence disease may depend on complex relationships between environmental factors and the structure of host communities.

The data and code supporting the results are available on Figshare (DOI: 10.6084/m9.figshare.14058059) and Github (https://github.com/fhalliday/Calanda19/tree/Calanda19_disease_submission; copy archived at https://archive.softwareheritage.org/swh:1:rev:86ce01777c396840455fd67a3ff5cd8420e8df21).

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Author details

1. Fletcher W Halliday

   Department of Evolutionary Biology and Environmental Studies, University of Zürich, Zurich, Switzerland

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Supervision, Visualization, Writing – original draft, Writing – review and editing

   Contributed equally with

   Mikko Jalo

   For correspondence

   Fletcher.w.halliday@gmail.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3953-0861

2. Mikko Jalo

   Faculty of Biological and Environmental sciences, University of Helsinki, Helsinki, Finland

   Contribution

   Conceptualization, Data curation, Investigation, Methodology, Project administration, Visualization, Writing – original draft, Writing – review and editing

   Contributed equally with

   Fletcher W Halliday

   Competing interests

   No competing interests declared

3. Anna-Liisa Laine

   1. Department of Evolutionary Biology and Environmental Studies, University of Zürich, Zurich, Switzerland
   2. Faculty of Biological and Environmental sciences, University of Helsinki, Helsinki, Finland

   Contribution

   Conceptualization, funding-acquisition, Project administration, Supervision, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0703-5850

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