Macroimmunology: The drivers and consequences of spatial patterns in wildlife immune defence

Abstract

The prevalence and intensity of parasites in wild hosts varies across space and is a key determinant of infection risk in humans, domestic animals and threatened wildlife. Because the immune system serves as the primary barrier to infection, replication and transmission following exposure, we here consider the environmental drivers of immunity. Spatial variation in parasite pressure, abiotic and biotic conditions, and anthropogenic factors can all shape immunity across spatial scales. Identifying the most important spatial drivers of immunity could help pre-empt infectious disease risks, especially in the context of how large-scale factors such as urbanization affect defence by changing environmental conditions. 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.

Keywords: ecoimmunology; host competence; macroecology; resistance; spatial autocorrelation; zoonoses.

Figure 1.

Conceptual schematic for how sampling designs over broad spatial extents (a) capture more variance (σ²) in environmental conditions than over narrow spatial extents (b) and how inference for spatial relationships with immunity is affected by scale. The map displays environmental conditions (e.g., annual mean temperature for illustrative purposes) at the resolution of 2.5 minutes latitude from WorldClim (Hijmans et al. 2005). Data were extracted from 20 randomly distributed sampling points using the raster package in R within a large and a narrow spatial extent. Estimates of σ² in the underlying environmental gradient are shown for both sampling designs. Mean immune phenotype data per site were generated by adding normally distributed noise to the environmental gradient; lines show fits from generalized least square models accounting for the spatial dependence of sampled sites. The sampling design using a broad spatial extent reveals a strong association between environmental variation and immunity (χ²=20.5), whereas that using a narrow spatial extent detects no relationship (χ²=0.5).

Figure 2.

The proportion of our sample of ecoimmunology studies (n=456) that studied multiple host populations. (A) The estimated prevalence across all data is shown in black with the 95% confidence interval in grey (46%); stratification of these data by wildlife taxa suggested that fish had a higher proportion of multi-site studies than birds and mammals, although the latter two taxa had been studied more frequently. (B) While the overall number of multi-site studies has increased for ecoimmunology over time (points are scaled by the number of studies per year), the relative abundance of multi-site studies has remained unchanged since the 1980s.

Figure 3.

Description of immunology data and spatial mechanisms assessed in our sample of multi-site studies in the ecoimmunology literature. For all 210 studies included in our systematic review, we classified immunology data into seven categories (A). Where possible, we also assessed if studies measured innate or adaptive immunity (B). For the 70% of studies that assessed spatial variation in wildlife immunology, we classified if studies assessed each of the three mechanisms linking environmental and immunological variation as well as space only (C). Barplots are stacked by wildlife taxa.

Figure 4.

Spatial replication and spatial extent in multi-site ecoimmunology studies. (A) The histogram for the number of sites per study (e.g., spatial replication) is shown in grey with the fitted distributions (size and shading are proportional to the Akaike weights; Table S1). (B) The means and 95% confidence intervals from a GLM with Gamma-distributed errors are shown for wildlife taxa alongside the raw data; spatial replication did not vary across wildlife. (C) Estimated spatial extent is shown for all 157 studies in which coordinate data were available or for which extent was reported, with shading of the bounding boxes corresponding to wildlife taxa. (D) Fitted values and 95% confidence intervals (grey) for the top linear model describing the relationship between spatial replication and spatial extent (both with a log₁₀ transformation).

Figure 5.

Statistical approaches used in our sample of multi-site studies within the ecoimmunology literature. For all 210 studies included in our systematic review, we quantified the scale of data analysis (A), if studies assessed spatial autocorrelation (B), and if studies controlled for spatial dependence (C). For the 43 studies that controlled for spatial dependence, we also quantified how space was treated in the analyses. Barplots are stacked by taxa.

Figure 6.

Passerines and rodents are two possible model taxa for macroimmunology. The phylogeny of the 270 species included in our literature sample is displayed with the number of studies per species shown as bars (color is proportional to size); highlighted are the clades containing these two orders. We visualize how these orders overlay in trait space (body size and geographic range size) to emphasize the relative ease of live capture and sampling these species alongside their broad distributions (encompassing high environmental variation). Also displayed are the distributions of spatial replication per study for both orders. See the Online Supplement for more information about sources of phylogenetic and trait data.