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Bats are important reservoirs of zoonotic disease and are among the most gregarious of all mammals. Their population structures can be highly heterogeneous, underpinned by ecological processes across different scales, complicating assumptions regarding the nature of contacts and transmission. Although models commonly parameterise transmission using metrics of total abundance, whether this is an ecologically representative approximation of host–pathogen interactions is not routinely evaluated.

We collected a 13‐month dataset of tree‐roostingPteropusspp. from 2,522 spatially referenced trees across eight roosts to empirically evaluate the relationship between total roost abundance and tree‐level measures of abundance and density—the scale most likely to be relevant for virus transmission. We also evaluate whether roost features at different scales (roost level, subplot level, tree level) are predictive of these local density dynamics.

Roost‐level features were not representative of tree‐level abundance (bats per tree) or tree‐level density (bats per m²or m³), with roost‐level models explaining minimal variation in tree‐level measures. Total roost abundance itself was either not a significant predictor (tree‐level 3D density) or only weakly predictive (tree‐level abundance).

This indicates that basic measures, such as total abundance of bats in a roost, may not provide adequate approximations for population dynamics at scales relevant for transmission, and that alternative measures are needed to compare transmission potential between roosts. From the best candidate models, the strongest predictor of local population structure was tree density within roosts, where roosts with low tree density had a higher abundance but lower density of bats (more spacing between bats) per tree.

Together, these data highlight unpredictable and counterintuitive relationships between total abundance and local density. More nuanced modelling of transmission, spread and spillover from bats likely requires alternative approaches to integrating contact structure in host–pathogen models, rather than simply modifying the transmission function.

We develop a spatially explicit model for virus transmission within roosts of Australian tree‐dwelling bats (Pteropusspp.), parameterized to reflect Hendra virus. The spatial structure of roosts mirrors three study sites, and viral transmission between groups of bats in trees was modelled as a function of distance between roost trees. Using three levels of tree density to reflect anthropogenic changes in bat habitats, we investigate the potential effects of recent ecological shifts in Australia on the dynamics of zoonotic viruses in reservoir hosts.

We show that simulated infection dynamics in spatially structured roosts differ from that of mean‐field models for equivalently sized populations, highlighting the importance of spatial structure in disease models of gregarious taxa. Under contrasting scenarios of flying‐fox roosting structures, sparse stand structures (with fewer trees but more bats per tree) generate higher probabilities of successful outbreaks, larger and faster epidemics, and shorter virus extinction times, compared to intermediate and dense stand structures with more trees but fewer bats per tree. These observations are consistent with the greater force of infection generated by structured populations with less numerous but larger infected groups, and may flag an increased risk of pathogen spillover from these increasingly abundant roost types.

Outputs from our models contribute insights into the spread of viruses in structured animal populations, like communally roosting species, as well as specific insights into Hendra virus infection dynamics and spillover risk in a situation of changing host ecology. These insights will be relevant for modelling other zoonotic viruses in wildlife reservoir hosts in response to habitat modification and changing populations, including coronaviruses like SARS‐CoV‐2.

We provide a synthesis of how to apply macroecological approaches to the study of ecoimmunology (i.e. macroimmunology). We first review spatial factors that could generate spatial variation in defence, highlighting the need for large‐scale studies that can differentiate competing environmental predictors of immunity and detailing contexts where this approach might be favoured over small‐scale experimental studies. We next conduct a systematic review of the literature to assess the frequency of spatial studies and to classify them according to taxa, immune measures, spatial replication and extent, and statistical methods.

We review 210 ecoimmunology studies sampling multiple host populations. We show that whereas spatial approaches are relatively common, spatial replication is generally low and unlikely to provide sufficient environmental variation or power to differentiate competing spatial hypotheses. We also highlight statistical biases in macroimmunology, in that few studies characterize and account for spatial dependence statistically, potentially affecting inferences for the relationships between environmental conditions and immune defence.

We use these findings to describe tools from geostatistics and spatial modelling that can improve inference about the associations between environmental and immunological variation. In particular, we emphasize exploratory tools that can guide spatial sampling and highlight the need for greater use of mixed‐effects models that account for spatial variability while also allowing researchers to account for both individual‐ and habitat‐level covariates.

We finally discuss future research priorities for macroimmunology, including focusing on latitudinal gradients, range expansions and urbanization as being especially amenable to large‐scale spatial approaches. Methodologically, we highlight critical opportunities posed by assessing spatial variation in host tolerance, using metagenomics to quantify spatial variation in parasite pressure, coupling large‐scale field studies with small‐scale field experiments and longitudinal approaches, and applying statistical tools from macroecology and meta‐analysis to identify generalizable spatial patterns. Such work will facilitate scaling ecoimmunology from individual‐ to habitat‐level insights about the drivers of immune defence and help predict where environmental change may most alter infectious disease risk.