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Title: Ecoimmunology at spatial scales
Abstract In Focus

Becker, D. J., Albery, G. F., Kessler, M. K., Lunn, T. J., Falvo, C. A., Czirják, G. Á., Martin, L. B., & Plowright, R. K. (2020). Macroimmunology: The drivers and consequences of spatial patterns in wildlife immune defence.Journal of Animal Ecology,89, 972–995. Ecoimmunology seeks to identify and explain natural variation in immune function. Most research so far has focused on differences among individuals within populations, which are often driven by trade‐offs in resource allocation between energetically costly immunity and competing processes such as reproduction. In their review article, Becker et al. (2020) have proposed a framework to explicitly address habitat‐ and population‐level differences in wildlife immune phenotypes. Termed macroimmunology, this concept integrates principles from ecoimmunology and macroecology. Becker et al. (2020) have highlighted three non‐mutually exclusive habitat features that are likely to vary at spatial scales and influence immune function: (a) parasite pressure, (b) abiotic and biotic factors and (c) anthropogenic changes. However, a large and robust body of literature suitable for synthesis to detect macroimmunology patterns and effect sizes is not yet available. Through their systematic review and critical assessment, Becker et al. (2020) identified common problems in existing research that hinders spatial inferences, such as a need for spatial replication in study design and statistical analyses that account for spatial dependence. Overall, macroimmunology has the potential to identify and even predict spatial patterns in immune phenotypes that form the mechanistic underpinnings of important wildlife disease processes, and this review represents an important step to realizing these goals.

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Journal of Animal Ecology
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p. 2210-2213
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National Science Foundation
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  1. 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.

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  2. Obeid, Iyad Selesnick (Ed.)
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We used a variety of techniques such as the file locking mechanism, multithreading, circular buffers, real-time event decoding, and signal-decision plotting to realize the system. A video demonstrating the system is available at: The final conference submission will include a more detailed analysis of the online performance of each module. ACKNOWLEDGMENTS Research reported in this publication was most recently supported by the National Science Foundation Partnership for Innovation award number IIP-1827565 and the Pennsylvania Commonwealth Universal Research Enhancement Program (PA CURE). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the official views of any of these organizations. REFERENCES [1] A. Craik, Y. He, and J. L. Contreras-Vidal, “Deep learning for electroencephalogram (EEG) classification tasks: a review,” J. Neural Eng., vol. 16, no. 3, p. 031001, 2019. [2] A. C. Bridi, T. Q. Louro, and R. C. L. Da Silva, “Clinical Alarms in intensive care: implications of alarm fatigue for the safety of patients,” Rev. Lat. Am. Enfermagem, vol. 22, no. 6, p. 1034, 2014. [3] M. Golmohammadi, V. Shah, I. Obeid, and J. Picone, “Deep Learning Approaches for Automatic Seizure Detection from Scalp Electroencephalograms,” in Signal Processing in Medicine and Biology: Emerging Trends in Research and Applications, 1st ed., I. Obeid, I. Selesnick, and J. Picone, Eds. New York, New York, USA: Springer, 2020, pp. 233–274. [4] “CFM Olympic Brainz Monitor.” [Online]. Available: [Accessed: 17-Jul-2020]. [5] M. L. Scheuer, S. B. Wilson, A. Antony, G. Ghearing, A. Urban, and A. I. Bagic, “Seizure Detection: Interreader Agreement and Detection Algorithm Assessments Using a Large Dataset,” J. Clin. Neurophysiol., 2020. [6] A. Harati, M. Golmohammadi, S. Lopez, I. Obeid, and J. Picone, “Improved EEG Event Classification Using Differential Energy,” in Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium, 2015, pp. 1–4. [7] V. Shah, C. Campbell, I. Obeid, and J. Picone, “Improved Spatio-Temporal Modeling in Automated Seizure Detection using Channel-Dependent Posteriors,” Neurocomputing, 2021. [8] W. Tatum, A. Husain, S. Benbadis, and P. Kaplan, Handbook of EEG Interpretation. New York City, New York, USA: Demos Medical Publishing, 2007. [9] D. P. Bovet and C. Marco, Understanding the Linux Kernel, 3rd ed. O’Reilly Media, Inc., 2005. [10] V. Shah et al., “The Temple University Hospital Seizure Detection Corpus,” Front. Neuroinform., vol. 12, pp. 1–6, 2018. [11] F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” J. Mach. Learn. Res., vol. 12, pp. 2825–2830, 2011. [12] J. Gotman, D. Flanagan, J. Zhang, and B. Rosenblatt, “Automatic seizure detection in the newborn: Methods and initial evaluation,” Electroencephalogr. Clin. Neurophysiol., vol. 103, no. 3, pp. 356–362, 1997. 
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  5. Abstract

    An understudied aspect of vertebrate ecoimmunology has been the relative contributions of environmental factors (E), genetic background (G) and their interaction (G × E) in shaping immune development and function. Environmental temperature is known to affect many aspects of immune function and alterations in temperature regimes have been implicated in emergent disease outbreaks, making it a critical environmental factor to study in the context of immune phenotype determinants of wild animals.

    We assessed the relative influences of environmental temperature, genetic background and their interaction on first‐year development of innate and adaptive immune defences of captive‐born garter snakesThamnophis elegansusing a reciprocal transplant laboratory experiment. We used a full‐factorial design with snakes from two divergent life‐history ecotypes, which are known to differ in immune function in their native habitats, raised under conditions mimicking the natural thermal regime—that is, warmer and cooler—of each habitat.

    Genetic background (ecotype) and thermal regime influenced innate and adaptive immune parameters of snakes, but in an immune‐component specific manner. We found some evidence of G × E interactions but no indication of adaptive plasticity with respect to thermal environment. At the individual level, the effects of thermal environment on resource allocation decisions varied between the fast‐ and the slow‐paced life‐history ecotypes. Under warmer conditions, which increased food consumption of individuals in both ecotypes, the former invested mostly in growth, whereas the latter invested more evenly between growth and immune development.

    Overall, immune parameters were highly flexible, but results suggest that other environmental factors are likely more important than temperature per se in driving the ecotype differences in immunity previously documented in the snakes under field conditions. Our results also add to the understanding of investment in immune development and growth during early postnatal life under different thermal environments. Our finding of immune‐component specific patterns strongly cautions against oversimplification of the highly complex immune system in ecoimmunological studies. In conjunction, these results deepen our understanding of the degree of immunological flexibility wild animals present, information that is ever more vital in the context of rapid global environmental change.

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