# Co-circulation and co-infection: parasite interactions across scales

Giacomo Zilio, Alison B Duncan, Vanessa O Ezenwa, Andy Fenton, Erida Gjini, Oliver Kaltz, Sara Magalhães, Yannis Michalakis, Nicole Mideo, Amy Pedersen, et al.

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Giacomo Zilio, Alison B Duncan, Vanessa O Ezenwa, Andy Fenton, Erida Gjini, et al.. Co-circulation and co-infection: parasite interactions across scales. Trends in Ecology & Evolution, In press. hal-05436071

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1 Co-circulation and co-infection: parasite interactions across scales

2 Authorship

3 Giacomo Zilio<sup>1,2</sup>, Alison B. Duncan<sup>2</sup>, Vanessa O. Ezenwa<sup>3</sup>, Andy Fenton<sup>4</sup>, Erida Gjini<sup>5</sup>, Oliver Kaltz<sup>2</sup>,

4 Sara Magalhães<sup>6</sup>, Yannis Michalakis<sup>7</sup>, Nicole Mideo<sup>8</sup>, Amy Pedersen<sup>9</sup>, Luna Taguchi<sup>8</sup>, Charlotte

5 Tollenaere<sup>10</sup>, Flore Zélé<sup>2</sup>, Andrew D. Dean<sup>4,a</sup>

6 Giacomo Zilio<sup>1,2</sup>, ORCID ID: https://orcid.org/0000-0002-4448-3118

7 Alison B. Duncan<sup>2</sup>, ORCID ID: https://orcid.org/0000-0002-6499-2913

8 Vanessa O. Ezenwa<sup>3</sup>, ORCID ID: https://orcid.org/0000-0002-8078-1913

9 Andy Fenton<sup>4</sup>, ORCID ID: https://orcid.org/0000-0002-7676-917X

10 Erida Gjini<sup>5</sup>, ORCID ID: https://orcid.org/0000-0002-0493-3102

11 Oliver Kaltz<sup>2</sup>, ORCID ID: https://orcid.org/0000-0002-7154-0456

12 Sara Magalhães<sup>6</sup>, ORCID ID: https://orcid.org/0000-0002-8609-7768

13 Yannis Michalakis<sup>7</sup>, ORCID ID: https://orcid.org/0000-0003-1929-0848

14 Nicole Mideo<sup>8</sup>, ORCID ID: https://orcid.org/0000-0002-8719-3620

15 Amy Pedersen<sup>9</sup>, ORCID ID: https://orcid.org/0000-0002-1385-1360

16 Luna Taguchi<sup>8</sup>, ORCID ID: https://orcid.org/0009-0006-2269-1887

17 Charlotte Tollenaere<sup>10</sup>, ORCID ID: https://orcid.org/0000-0003-2045-2824
18 Flore Zélé², ORCID ID: https://orcid.org/0000-0003-2954-5488

19 Andrew D. Dean⁴,ª, andrew.dean@liverpool.ac.uk, ORCID ID: https://orcid.org/0000-0001-

20 9033-7560

21 ¹ CEFE, University of Montpellier, CNRS, EPHE, IRD, Montpellier, France

22 ² ISEM, University de Montpellier, CNRS, EPHE, IRD, Montpellier, France

23 ³ Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT, USA

24 ⁴ Institute of Infection, Veterinary & Ecological Sciences, University of Liverpool, UK

25 ⁵ Center for Computational and Stochastic Mathematics, Instituto Superior Técnico, University of
26 Lisbon, Lisbon, Portugal

27 ⁶ Centre for Ecology, Evolution and Environmental Changes (cE3c) & CHANGE - Global Change
28 and Sustainability Institute, Universidade de Lisboa, Lisboa, Portugal Departamento de Biologia
29 Animal, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal

30 ⁷ MIVEGEC, Université Montpellier, CNRS, IRD, Montpellier, France

31 ⁸ Department of Ecology & Evolutionary Biology, University of Toronto, Toronto ON, Canada

32 ⁹ Institute of Ecology and Evolution, School of Biological Sciences, University of Edinburgh,
33 Edinburgh EH9 3FL, UK

34 ¹⁰ PHIM Plant Health Institute, Univ Montpellier, IRD, CIRAD, INRAE, Institut Agro, Montpellier,
35 France

36 ª Corresponding author: andrew.dean@liverpool.ac.uk
37 **Keywords**

38 Competition, Facilitation, Epidemics, Host-parasite dynamics, Multiple infections

39 **Highlights**

40 Climate change and human activities are shifting parasite distributions, and thus causing novel
41 parasite co-occurrences.

42 Multiple parasites (strains or species) co-circulating within a host population often cause co-
43 infections, i.e., the simultaneous infection of an individual host by multiple parasites.

44 Co-circulating parasites can interact at both the host individual and host population scales, with
45 crucial implications for transmission, host health, host-parasite evolution and disease
46 management. However, interactions at one scale are often investigated without considering the
47 other.

48 We present a conceptual framework that explicitly recognizes the two scales of parasite-parasite
49 interactions. This framework identifies multiple pathways by which parasites interact and
50 highlights the ecological, epidemiological, and evolutionary consequences of these interactions.

51
52 **Abstract**

53 Parasite-parasite interactions occur both within and between hosts, but the two scales are rarely
54 considered together. Consequently, there is a gap in our ability to predict the integrated effects
55 of interactions occurring across scales, disentangle their relative contributions to key ecological
56 outcomes, and accurately identify the drivers of host-parasite evolutionary responses. Here, we
57 extend the standard Susceptible-Infected framework of theoretical epidemiology to explicitly
58 incorporate parasite-parasite interactions across scales. We identify where in each step of the
59 transmission process such interactions may occur, at the within- or between-host levels,
60 providing empirical examples where possible. Thus, we demonstrate how integrating the two
61 scales provides a more complete understanding of the evolutionary ecology of multi-parasite
62 systems, and suggest future avenues of investigation.

63
64 **Main text**

65 **Parasite interactions across scales**

66 A single population can host multiple parasites (strains or species). This **co-circulation** of parasites
67 (see **Glossary**) can lead to the **co-infection** of individual hosts. Co-infecting parasites interact
68 through a range of mechanisms, with implications for host health, population dynamics and host-
69 parasite evolution [1–3]. Co-circulating parasites can also interact at the host population level,
70 without necessarily co-infecting the same host individual. For example, measles and whooping
71 cough can interfere with each other by removing susceptible individuals from the host population
72 via effects on host behaviour and mortality [4,5]. Although interactions among parasites occur
73 both **within hosts (individual level)** and **between-hosts (population level)**, these distinct scales
74 of interaction are rarely considered concomitantly [6,7].

75 We argue that focusing on individual- and population-level parasite-parasite interactions as
76 independent phenomena has resulted in a fundamental gap in our understanding of their
77 ecological, epidemiological and evolutionary consequences. Indeed, interactions at one scale
78 may compound, confound or obscure interactions at another (Figure 1). To address this gap, we
79 use a Susceptible-Infected (SI) transmission framework to delineate the range of ways that
80 parasite-parasite interactions at the individual and population levels can affect parasite
81 transmission dynamics. We illustrate these concepts with specific examples at both levels across
82 diverse host-parasite systems. We then discuss the broader implications: why studying
83 individual- and population-level interactions in combination generates novel insights into
84 infectious disease ecology and evolution; how consideration of both scales may be necessary for
successful disease control; and how appropriate modelling and experiments can help disentangle
when interactions at one scale affect those at another. Finally, we identify outstanding questions
and future directions for building a more holistic perspective on parasite-parasite interactions.

A transmission framework for individual- and population-level interactions

We use the Susceptible-Infected (SI) transmission framework (Box 1) to illustrate how both
individual- and population-level interactions among parasites determine epidemiological
dynamics [8]. In this framework, transmission is conceptualised as a three-step process. First, a
susceptible host must make contact with an infected one; the infective stage of the parasite must
then be successfully transferred from the infected to the susceptible; finally, the parasite must
become established within the new host [9]. Although the precise details and mechanisms at
play in each of these steps will differ between systems, this sequence highlights the elements
determining transmission: the susceptible and infected host subpopulations, their **rate of
contact**, host **susceptibility** and host **infectiousness**.

Here, we extend this framework from a single parasite (the **focal parasite**, i.e. the one whose
transmission we are interested in) to incorporate a **background parasite**. Although we do not
explicitly consider transmission of this background parasite, its presence may affect the focal
parasite's transmission dynamics via any of the elements of transmission. Particularly, the
presence of a background parasite divides the focal-susceptible subpopulation into uninfected
and background singly-infected, and the focal-infected subpopulation into focal singly-infected
and co-infected. This framework therefore yields seven pathways (four subpopulations and three
traits) by which the background parasite may affect transmission of the focal parasite (see Box
106 1). Table 1 illustrates how these pathways may include both within-host (WH) or between-host (BH) interactions. Note that we consider only two-parasite systems for illustrative purposes, but the ideas are readily extended to more via the consideration of multiple background parasites and their possible interactions with the focal parasite (see below).
107
108
109

110 By definition, within-host interactions occur between parasites infecting the same host, but not
111 necessarily at the same time. Thus, this category includes within-host interference competition
112 between parasites, and indirect interactions mediated by a shared host, such as via the within-
113 host resources or the host immune response [10]. In contrast, between-host interactions occur
114 at the population level, via hosts but not within them. By altering host behaviour, condition or
115 abundance, a background parasite may indirectly affect the transmission of the focal parasite [5].
116 Theoretical and empirical examples of interactions at both levels are outlined in Table 1.

117 Although rarely investigated (but see [11]), contact rates may decrease as a consequence of co-infection promoting convalescence or conspecific avoidance, such as through increased
118 pathology [12] (Table 1, WH1). At the population level, host behavioural defences against one
119 parasite could affect transmission-relevant contact rates for others (Table 1, BH1). For example,
120 the use of face masks, self-isolation, lockdown, and several other non-pharmaceutical
121 interventions against SARS-CoV-2 reduced contact rates and thereby the circulation of other
122 respiratory viruses [13]. Similar behavioural modifications may be seen in non-human animals:
123 garden ant colonies (Lasius niger) exposed to the fungus Metarhizium brunneum altered their
124 behaviour and social networks, which may inhibit the transmission of the fungus [14], and
125 possibly other parasites. Parasites can also directly affect host behaviour via host manipulation
126
[15,16]. Whether co-infecting parasites benefit from this will depend on whether their interests align. For example, the protozoan *Toxoplasma gondii* modifies behaviour of infected rats to enhance predation by cats, its definitive host [17]; the consequences for transmission of a co-infecting parasite then depends on whether it also infects the same definitive host.

Susceptibility and infectiousness are also sensitive to co-infection. Infection by the background parasite can make a host more or less susceptible to the focal parasite by weakening its immune response, depleting its condition, or priming the immune system to resist the focal parasite. The same processes might increase or decrease infectiousness in co-infected hosts. Research supports host infectiousness and susceptibility being impacted by within-host interactions (Table 1, WH2-3). For instance, narrow-leafed plantain (*Plantago lanceolata*) plants co-infected with two different strains of powdery mildew (*Podosphaera plantaginis*) released more spores than singly infected plants [18], and acquired immunity to paramyxoviruses in humans reduced susceptibility to infections from other strains [19]. Susceptibility can also be affected by population level interactions (Table 1, BH3), such as the release of volatiles by infected plants, upregulating immune responses in neighbouring conspecifics, thus reducing susceptibility to co-circulating parasites [20].

The size of susceptible or infected subpopulations are critical determinants of transmission. Within-host interactions may: reduce fecundity, thereby diminishing the uninfected pool, decreasing transmission (Table 1, WH4); impede recovery, lengthening the time an individual is infected, increasing transmission (Table 1, WH7); or increase mortality, reducing transmission (Table 1, WH7). *Daphnia galeata* co-infected with a protozoan (*Caullerya mesnili*) and a fungus
(Metschnikowia sp.) showed increased mortality (shorter transmission window) and reduced fecundity (fewer susceptibles) compared to single infections [21], reducing transmission. Similarly, co-infection with two viruses resulted in high disease severity and low fecundity in barley (Hordeum vulgare) [22]. However, co-infection might also decrease mortality, as observed in co-infecting Trypanosoma brucei strains in mice (Mus musculus), promoting host survival through apparent competition via the host immune response [23]. At the population level (Table 1, BH4-6), increased host mortality due to the background parasite may decrease both focal-susceptible and focal-infected subpopulations via reductions in background singly-infected and co-infected hosts. If increased mortality releases the wider host population from density-dependent constraints, promoting an influx of new individuals via increased births or immigration, this may increase the susceptible pool [24]. Host competition also provides opportunity for between-host interactions; if infection by a background parasite alters host competitiveness relative to hosts singly infected by the focal parasite, the corresponding subpopulations will change in abundance [25].

## Consequences and implications

Co-infection interactions can have dramatic consequences for the transmission and population dynamics of parasites. These effects can manifest by altering the parasite basic reproduction number, changing the regions of parameter space that lead to coexistence, and hence probabilities of parasite co-circulation, and altering host and parasite population dynamics, quantitatively and qualitatively [26–28]. Importantly, the magnitude and direction of these effects can depend on the specific nature of the co-infection [29]. Work in wood mice (Apodemus sylvaticus) found a strong negative within-host interaction between co-infecting nematodes
(Heligmosomoides polygyrus) and coccidians (Eimeria hungaryensis) [30]. However, analysing population-level patterns of association between these two parasites revealed no signal of the interaction; instead, infection levels of the two parasites were positively correlated across hosts [31]. This likely arises from correlated exposure due to their shared transmission route (faecal-oral), swamping the signal of the within-host interaction. Hence, population-level patterns of parasite co-circulation may poorly reflect the occurrence or direction of within-host interactions [32]. Only when data were examined at local spatial scales around co-infected hosts did the negative within-host effect become apparent [33]. Further empirical evidence of conflicting relationships between scales was found in African buffalo (Syncerus caffer) infected with bovine tuberculosis (bTB). bTB increased individual susceptibility to brucellosis (Brucella sp.), but given a concomitant increase in mortality among co-infected individuals, models predicted that brucellosis presence should reduce bTB prevalence at the population level [34]. These examples demonstrate how designing experiments and combining approaches which are explicitly multiscale in scope leads to a fuller understanding of host-parasite dynamics, and is necessary to properly investigate hypothesized parasite interactions. Research that focuses strictly on one level of interaction may erroneously attribute observed phenomena to interactions at that level, thus leading to flawed interpretations of the dynamics and consequences of co-circulating parasites (see also Box 2).

When two or more parasites are present, interdependence between co-circulation and coinfection interactions may generate complex and nonlinear fitness landscapes [28,29]. Interactions that change population-level prevalence or mean parasite abundance will quantitatively affect the overall **force of infection**, and therefore the risk and implications of (co-
192 )infection for individual hosts [28,29]. Bidirectional cross-scale effects may occur across various
193 parasite systems whenever co-infection interactions shape population-level transmission
194 dynamics, and co-circulation interactions, in turn, shape co-infection risk. This type of feedback
195 has potential consequences for disease control and evolutionary dynamics. Transmission
196 reduction or eradication of a focal parasite may unintentionally remove constraints on a
197 background parasite, a notorious example being pneumococcal serotype replacement post-
198 vaccination, in which prevalence of target serotypes decreases but that of non-target serotypes
199 increases [35]. To understand the outcomes of such multi-level interactions will therefore require
200 dynamic frameworks, either in the form of individual-based models with explicit interactions
201 [36], mathematical models along the lines of the 'essentially nested' models [37] or coinfection-
202 replicator systems with mechanistic fitness [28,29]. This should be informed and tested,
203 whenever possible, by field data and experimental systems.

204 Concluding remarks and future perspectives

205 Given the range of possible interactions between parasites and the complex nature of their
206 emergent multi-scale effects, mathematical models play a vital role in studying their occurrence
207 and consequences. Existing models on co-circulating parasites with and without co-infection
208 provide a well-developed theoretical foundation to build upon [29,38]. Such models are generally
209 restricted to microparasites (e.g., viruses, bacteria) or different parasite strains, with direct
210 (contact) transmission and SI-type dynamics, including their extension to include recovery and/or
211 reinfection. There is clear potential to broaden the scope of these models to include different
212 host traits (resistance, tolerance, dispersal) and parasite types (micro- and macroparasites,
213 parasites with different transmission modes) [39], revealing generalities and/or specificities of
any conclusions, and advancing our understanding of host-parasite dynamics and their
predictability [28,40]. For example, macroparasite-specific models generally extend SI theory, in
which infection status is binary, to explicitly incorporate parasite load [41], thus allowing
transmission, mortality, etc. to be load-dependent. Interactions among parasites may themselves
be load-dependent, perhaps via increased parasite-parasite encounter rates or host behavioural
modification at high loads, leading to complexities only captured by such models. Simple
predictions could also be obtained by considering whether co-infecting parasites have aligned
interests, especially when sharing a transmission route [42]. Apart from the recognised conflict
between horizontal and vertical transmission strategies (the latter better matches host interests
as it exploits host fecundity [43]), parasites with complex life cycles also illustrate such conflicts
[44]. For parasites co-infecting the same intermediate host but having different definitive hosts
(e.g., fish vs. bird), there will be a strong selective pressure to reach the “correct” host and avoid
a dead-end route, no matter the interests of the other parasites.

Our framework has been developed with a single host species in mind. However, the concepts
have important implications for multi-host systems too, particularly in the context of biological
invasions and disease management with spillover and spillback among host species [25]. In such
a context, parasite-parasite interactions like those already discussed may occur in both host
species, but with the added complication of how the background parasite affects interspecies
transmission of the focal parasite. For instance, if a background parasite reduces the density of
optimal hosts for the focal parasite, the focal may switch onto alternative, sub-optimal hosts [45].
A related process is apparent competition, in which one parasite suppresses a dominant host
species, thereby allowing a less competitive host to persist. In such cases, specialized parasites
that depend exclusively on the subdominant host may, in effect, rely on the presence of the first
parasite in order to persist. The consequences of such interactions will be complex, depending
on the precise relationships between the multiple host and parasite species.

The framework may also be applied to multi-parasite communities (i.e., those with more than
two parasites, see Box 2). Using time-series data in natural populations can allow building a
complete web of parasite interactions within the community for different transmission
components, such as susceptibility [46]. As suggested by our framework, this requires as a first
step considering all possible combinations of focal and background parasites, and precise
knowledge on all pairwise within- and between-host interactions with their direction and
magnitude. However, multiple background parasites result in correspondingly more possible
interactions with the focal; these interactions may be enhanced or confounded by interactions
between background parasites. Furthermore, introducing a third parasite could feasibly result in
interactions between parasites that were previously unaffected by one another (i.e., high-order
interactions). Thus, simply including additional background parasites in an additive manner may
not fully reflect the true nature of the composite web of interactions [46].

Multi-scale parasite-parasite interactions also have evolutionary implications [47]. For instance,
if a background parasite increases the fecundity (BH4) or mortality (BH5) of a suitable host, these
changes in host availability could in turn affect patterns of local adaptation of a focal parasite. At
the same time, interactions increasing the probability of coinfection, such as a background
parasite making a host more attractive to a focal parasite (WH3), may make (co)evolution in
coinfections more likely, with consequences for virulence and transmission [48].
In conclusion, multi-parasite systems cannot be fully understood without taking into account
interactions among parasites at both the individual and population levels. To achieve this will
require multiscale experimental design, with data collected at both levels and across temporal
scales, in conjunction with carefully designed statistical and mathematical models. Our
framework should therefore be employed to both inform methodological approaches that
incorporate the different levels and types of parasite-parasite interaction, and to interpret the
resulting data. Cross-disciplinary approaches among ecologists, epidemiologists, and
evolutionary biologists, including sampling natural populations together with controlled eco-
evolutionary (e.g., mesocosm) experiments, will help to generate new research questions and
refine predictions (Outstanding Questions). This will advance our knowledge on host-parasite
interactions across scales and their consequences for the parasites involved, and their hosts.

## Acknowledgments

This is publication ISEM-2025-292 of the Institut des Sciences de l'Evolution - Montpellier. We
thank the anonymous reviewers for their constructive suggestions. Y.M. acknowledges support
from CNRS and IRD. A.D.D., A.F and A.P. acknowledge support from NERC under grant
NE/X01424X/1. This work was funded by a grant from the Agence Nationale de Recherche to O.K.
(grant no. ANR-20-CE02-0023-01), by an ANR grant to A.B.D. (EVOLVIR: ANR-20-CE35-0013), The
Mariano Gago Prize for Bilateral Cooperation in Research with Portugal from the French Ministry
of Higher Education and Research and the French Academy of Sciences and the Academy of
Sciences of Lisbon to A.B.D. and S.M, and by the Mourou/Strickland Mobility Program
(Ambassade de France au Canada) to N.M. V.O.E acknowledges support from the U. S. National
Institutes of Health (R01GM131319).
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<figure><img src="image_3.png" /><figcaption>432 **Figure 1: Parasite-parasite interactions.** Here, a host population (elephants) can be infected with two parasites (X; red circle and Y; blue triangle). Parasites can interact (double-headed arrow) at different levels in the host population. (A) Most research has focused on interactions that occur within a co-infected host (within-host parasite-parasite interactions), as illustrated by the co-infected elephant (red circle and blue triangle). (B) In contrast, two parasites that co-circulate in the host population can interact at the population level (between-host parasite-parasite interactions), a process which does not necessarily involve parasites ever infecting the same host. (C) Importantly, interactions among parasites infecting the same host population can occur simultaneously at both levels, with interactions at one level potentially affecting interactions at the other. The importance of considering such cases is at the heart of this paper.</figcaption></figure>

443
448 **Text Boxes**

449 **Box 1: Individual- and population-level interactions in a Susceptible-Infected transmission**
450 **framework**

451 In the Susceptible-Infected (SI) transmission framework, widely used in theoretical epidemiology
452 [8], the density of infected individuals *I* in a host population is governed by the equation

$$
453 \qquad \frac{dI}{dt} = \beta SI + \dots
$$

454
455 with *S* being the density of susceptible individuals and *β* the transmission coefficient. We limit
456 ourselves to density-dependent transmission for brevity, but the ideas herein are readily
457 translatable to frequency-dependent transmission. The term *βSI* encodes transmission as being
458 proportional to the product of the densities of susceptible and infected individuals; the ellipsis
459 represents processes such as population turnover and recovery, the details of which are system-
460 specific.

461 This framework is formulated for a single parasite (the **focal parasite**), whose transmission we
462 are interested in. We consider this in the context of a **background parasite**, whose transmission
463 we do not explicitly incorporate, but which may affect the focal parasite’s transmission dynamics.
464 Thus, *S* and *I* are, respectively, the susceptible and infected host subpopulations for the focal
465 parasite, some of which may also be infected by the background parasite. We therefore write

$$
466 \qquad S = U + I_B
$$

$$
467 \qquad I = I_F + I_C
$$
where $U$ are hosts uninfected hosts by either parasite, $I_B$ are infected by only the background parasite, $I_F$ by only the focal parasite, and $I_C$ are co-infected. The transmission coefficient of the focal parasite can be decomposed into

$\beta = \kappa \times \iota \times \sigma$

where $\kappa$ is the **contact rate** between individuals, $\iota$ is the **infectiousness** of focal-infected individuals and $\sigma$ is the **susceptibility** of focal-uninfected individuals [9]. The values of $\kappa$, $\sigma$ and $\iota$ may differ depending on infection status by the background parasite, but we do not codify this mathematically for the present purpose. Thus, the transmission function $\beta SI$ comprises seven pathways by which the background parasite may affect transmission of the focal parasite (Table 1): three traits (contact rate, susceptibility and infectiousness) and four subpopulations (uninfected, the two singly infected and co-infected hosts). How parasites may interact along these pathways, at both the within-host and between-host levels, is explored in more detail in the main text.
485 **Box 2: Application of our framework to spider mites (Tetranychus spp.)**

486 Spider mites are ectoparasites that feed on several plant species, causing damage to agricultural
487 and natural ecosystems. Within this group, *Tetranychus urticae* (a generalist) co-circulates in the
488 Mediterranean region with several other spider mite species [61], including *T. evansi* (a specialist
489 of *Solanaceae* plants) [62]. Spider mites are easy to manipulate in controlled conditions (Figure
490 I), making them an ideal system for the application of our framework.

491 In laboratory experiments, *T. evansi* suppresses the tomato plant (*Solanum lycopersicum*)
492 defences [63], facilitating infection by *T. urticae* [64], (Table 1, WH2). However, on whole plants,
493 *T. urticae* is generally excluded, unless it arrives first [65], possibly because *T. evansi's* web
494 prevent *T. urticae* from accessing leaves [66]. This exclusion could have population-level
495 consequences, if *T. evansi* infection prevents *T. urticae* from accessing immunocompromised
496 plants (Table 1, BH5). Thus, we might conclude that *T. evansi* is a better competitor, and should
497 always exclude *T. urticae*. However, results from a field work study only partially corroborate
498 this: during 10 years of co-circulation in eastern Spain, *T. evansi* tended to displace *T. urticae* from
499 non-crop plants but did not completely exclude them; both mites still occurred in co-infection
500 (on 4.2% of sampled plants) [67].

501 Applying our conceptual framework can provide further insights and reconcile these
502 experimental and large-scale patterns. For instance, in laboratory settings, the presence of *T.
503 evansi* increases the rate at which *T. urticae* infects new leaf discs [68] (e.g., contact rate). This
504 could facilitate its spread to new hosts (Table 1, BH1) while avoiding or reducing within-host
505 competition. Interestingly, lima bean plants (*Phaseolus lunatus*) infected with *T. urticae* emit
volatile compounds that trigger immune-related responses in unexposed neighbouring plants, thus increasing defences against further conspecific infections [69] (Table 1, BH3). Although lima bean is not a suitable host for *T. evansi*, the shared tomato host emits similar volatiles upon infection [70], which could be further investigated following infection with both spider mite species.

These within- and between-host interactions highlight that this is a fertile system to explore how interaction mechanisms at different scales contribute to parasite epidemiology (Figure I). It nicely illustrates how neglecting one level could produce an incomplete depiction of actual infection dynamics, or even erroneous interpretations (exclusion vs. coexistence). The full picture requires both levels to be considered, alongside factors such as parasite genetic background, multi-host communities, environmental conditions, and evolution.

<figure><img src="image_4.png" /><figcaption>**Figure I: Spider mite parasite system across scales.** Illustrative pictures of how spider mite co-infections and co-circulation can be studied at the within- and between-host levels in laboratory</figcaption></figure>
520 settings on leaf discs (A) and entire plants (B), in semi-field (C) and field (D) conditions. Photo

521 credits: (A, B, D) Flore Zélé (author); (C) Alison Duncan (author); all photos used with permission.

522
523 **Glossary**

524 **Background parasite**: The parasite that co-circulates within the same host population as the
525 focal parasite, possibly but not necessarily co-infecting the same individuals.

526 **Between-host or population-level interactions**: Parasites affect one another indirectly, through
527 their hosts, without necessarily infecting the same host individual.

528 **Co-circulation**: The presence of two or more parasite species (or strains) within the same host
529 population at the same time, but not necessarily within the same individual hosts.

530 **Co-infection**: The presence of two or more parasite species (or strains) within the same host, at
531 the same time.

532 **Contact rate**: The rate at which encounters of sufficient length to transmit parasite infective
533 stages occur between uninfected and infected individuals or infective stages (depending on
534 transmission mode).

535 **Focal parasite**: The parasite currently being considered, the focus of study.

536 **Force of infection**: The rate at which susceptible hosts acquire infections.

537 **Infectiousness**: The propensity of an infected individual to transmit infection to another
538 individual upon contact.

539 **Susceptibility**: The propensity of a host to become infected upon exposure.
540 **Within-host or individual-level interactions:** Parasites affect one another, directly or indirectly,

541 due to their presence within the same host individual. Infection can be concurrent (co-infection)

542 or not (sequential, non-overlapping infections by different parasites of the same host).

543
544 Outstanding Questions

545 • How do parasite interactions at the host individual and population scales affect one another?

547 • What are the consequences of cross-scale interactions for parasite coexistence, host population dynamics, and individual host infection risk and health?

549 • How can we differentiate between co-infection and co-circulation interactions in real-world systems?

551 • What is the relative importance of within- and between-host interactions in explaining epidemiological dynamics?

553 • How can we better evaluate outcomes of potential disease management strategies and improve predictability in a multi parasite?

555 • How can we best integrate theory, field and experimental data on parasite interactions?
556 Which empirical results require new or adapted theory to help explain them, and which
557 theories have not yet been sufficiently tested empirically?

558 • Do interactions occurring at both scales affect basic theoretical predictions?

559 • How do parasite interactions shift along environmental or ecological gradients?

560 • Can other biotic and abiotic factors modify transmission opportunities and epidemiological dynamics for co-circulating and co-infecting parasites?

562 • Some pathways of interaction between parasites predicted by our framework are yet to receive much consideration; are they rare or simply overlooked?

564