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

   https://doi.org/10.1111/1365-2656.13166  

   Becker, Daniel_J; Albery, Gregory_F; Kessler, Maureen_K; Lunn, Tamika_J; Falvo, Caylee_A; Czirják, Gábor_Á; Martin, Lynn_B; Plowright, Raina_K; Fenton, ed., Andy (January 2020, Journal of Animal Ecology)

   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. Stable isotope values of consumers, producers, and organic matter in the Shark River Slough and Taylor Slough, Everglades National Park (FCE LTER), Florida, USA, 2019 – ongoing

   https://doi.org/10.6073/pasta/da36c97eccd5461ba32eecce23a53892  

   Rezek, Ryan (January 2024, Environmental Data Initiative)

   {"Abstract":["Wetland food webs have often been characterized as detrital-based ‘brown’ energy pyramids, whereas the relative role of autotrophic (‘green’) vs. microbial (‘brown’) energy sources falls along a continuum set by physical drivers, as well as autochthonous and allochthonous inputs (Moore et al. 2004; Evans-White & Halvorson 2017) that change with ecosystem development (Schmitz et al. 2006). In the Florida Coastal Everglades (FCE), metabolic imbalances, including the collapse of calcareous periphyton mats, begin with a loss of foundation species primary production and legacy organic matter (Gaiser et al. 2006). This process likely enhances heterotrophic microbial productivity (Schulte 2016) and the supply of detrital energy to consumers by changing bioavailable and recalcitrant carbon supplies (Baggett et al. 2013). A shift from complex periphyton communities to transient planktonic communities under elevated P exposure reduces habitat structure and animal refuges but increases ‘green’ energy supplies and edibility (Trexler et al. 2015; Naja et al. 2017). Multiple sites (n=9) within the FCE were selected to document changes in coastal food webs as a result of eutrophication and increasing hydrologic variability. The project began in 2019 and is currently ongoing.\n \n References:\n Baggett, L. P., Heck, K. L., Frankovich, T. A., Armitage, A. R., & Fourqurean, J. W. (2013). Stoichiometry, growth, and fecundity responses to nutrient enrichment by invertebrate grazers in sub-tropical turtle grass (Thalassia testudinum) meadows. Marine biology, 160, 169-180.\n Evans-White, M. A., and H. M. Halvorson. 2017. Comparing the Ecological Stoichiometry in Green and Brown Food Webs – A Review and Meta-analysis of Freshwater Food Webs. Frontiers in Microbiology 8:1184. \n Gaiser, E. E., Childers, D. L., Jones, R. D., Richards, J. H., Scinto, L. J., & Trexler, J. C. (2006). Periphyton responses to eutrophication in the Florida Everglades: cross‐system patterns of structural and compositional change. Limnology and Oceanography, 51(1part2), 617-630.\n Moore, J. C., E. L. Berlow, D. C. Coleman, P. C. Ruiter, Q. Dong, A. Hastings, N. C. Johnson, K. S. McCann, K. Melville, P. J. Morin, K. Nadelhoffer, A. D. Rosemond, D. M. Post, J. L. Sabo, K. M. Scow, M. J. Vanni, and D. H. Wall. 2004. Detritus, trophic dynamics and biodiversity: Detritus, trophic dynamics and biodiversity. Ecology Letters 7:584–600. \n Naja, M., Childers, D. L., & Gaiser, E. E. (2017). Water quality implications of hydrologic restoration alternatives in the Florida Everglades, United States. Restoration Ecology, 25, S48-S58.\n Schmitz, O. J., Kalies, E. L., & Booth, M. G. (2006). Alternative dynamic regimes and trophic control of plant succession. Ecosystems, 9, 659-672.\n Schulte, Nicholas O., "Controls on Benthic Microbial Community Structure and Assembly in a Karstic Coastal Wetland" (2016). FIU Electronic Theses and Dissertations. 2447. 10.25148/etd.FIDC000233\n Trexler, J. C., Gaiser, E. E., Kominoski, J. S., & Sanchez, J. (2015). The role of periphyton mats in consumer community structure and function in calcareous wetlands: lessons from the Everglades. Microbiology of the everglades ecosystem, 155-179."]} 

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3. Modèle Atmosphérique Régional (MAR) version 3.11 regional climate model output, 1979-2019, Greenland domain, 10 kilometer (km) horizontal resolution

   https://doi.org/10.18739/A28G8FJ7F  

   Fettweis, Xavier (January 2022, NSF Arctic Data Center)

   Modèle Atmosphérique Régional (MAR) is a regional climate model that is fully coupled to a one-dimensional surface-atmosphere energy and mass transfer scheme, SISVAT (Soil Ice Snow Vegetation Atmosphere Transfer) (Fettweis et al., 2005, 2020; Lefebre et al., 2005). SISVAT employs a multilayered snowpack model, CROCUS, that simulates meltwater production, percolation, and refreeze (Brun et al., 1989), while also accounting for changes in albedo due to snow metamorphism (Brun et al., 1992). MAR has been extensively verified over the Greenland Ice Sheet and is therefore particularly well suited for analyses of Greenland ice sheet surface mass balance (Fettweis et al., 2011; Fettweis et al., 2020; Lefebre et al. 2005; Mattingly et al. 2020). Brun, E., Martin, E., Simon, V., Gendre, C., and Coléou, C. (1989). An energy and mass model of snow cover suitable for operational avalanche forecasting. Journal of Glaciology, 35, 333. https://doi.org/10.1017/S0022143000009254 Brun, E., David, P., Sudul, M., and Brunot, G. (1992). A numerical model to simulate snow-cover stratigraphy for operational avalanche forecasting. Journal of Glaciology, 38(128), 13–22. https://doi.org/10.3189/S0022143000009552 Fettweis, X., Gallée, H., Lefebre, F., and van Ypersele, J.-P. (2005). Greenland surface mass balance simulated by a regional climate model and comparison with satellite-derived data in 1990–1991. Climate Dynamics, 24(6), 623–640. https://doi.org/10.1007/s00382-005-0010-y Fettweis, X., Tedesco, M., van den Broeke, M., and Ettema, J. (2011). Melting trends over the Greenland ice sheet (1958–2009) from spaceborne microwave data and regional climate models. The Cryosphere, 5(2), 359–375. https://doi.org/10.5194/tc-5-359-2011 Fettweis, X., Hofer, S., Krebs-Kanzow, U., Amory, C., Aoki, T., Berends, C. J., et al. (2020). GrSMBMIP: intercomparison of the modelled 1980–2012 surface mass balance over the Greenland Ice Sheet. The Cryosphere, 14(11), 3935–3958. https://doi.org/10.5194/tc-14-3935-2020 Lefebre, F., Fettweis, X., Gallée, H., Van Ypersele, J.-P., Marbaix, P., Greuell, W., and Calanca, P. (2005). Evaluation of a high-resolution regional climate simulation over Greenland. Climate Dynamics, 25(1), 99–116. https://doi.org/10.1007/s00382-005-0005-8 Mattingly, K. S., Mote, T. L., Fettweis, X., van As, D., Van Tricht, K., Lhermitte, S., et al. (2020). Strong summer atmospheric rivers trigger Greenland ice sheet melt through spatially varying surface energy balance and cloud regimes. Journal of Climate, 33(16), 6809–6832. https://doi.org/10.1175/JCLI-D-19-0835.1 

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4. Proteomics Approaches to Ecoimmunology: New Insights into Wildlife Immunity and Disease

   https://doi.org/10.1093/icb/icaf044  

   Vicente-Santos, Amanda; Sandoval-Herrera, Natalia; Czirják, Gábor_Á; Neely, Benjamin_A; Becker, Daniel_J (May 2025, Integrative And Comparative Biology)

   Synopsis Understanding wildlife immune responses is crucial for assessing disease risks, environmental stress effects, and conservation challenges. Traditional ecoimmunology approaches rely on targeted assays, which, while informative, often provide a fragmented and species-limited view of immune function. Proteomics offers a powerful alternative by enabling the high-throughput, system-wide quantification of immune-related proteins, providing a functional perspective on immunity that overcomes many limitations of conventional methods. However, proteomics remains underutilized in ecoimmunology despite its potential to enhance biomarker discovery, host–pathogen interaction studies, and environmental health assessments. This perspective highlights proteomics as a transformative tool for ecoimmunology, disease ecology, and conservation biology. We discuss its unique advantages over other -omics approaches, including its ability to capture realized immune function rather than inferred gene expression, its applicability to diverse wildlife taxa, and its potential for longitudinal immune monitoring of individuals using minimally invasive sampling. We also address key challenges, including limited genomic reference resources, sample constraints, reproducibility issues, and the need for standardized protocols. To overcome these barriers, we propose practical solutions, such as leveraging proteomes of closely related species for annotation and using their annotated genomes as search spaces for peptide mapping. Additionally, we highlight the importance of alternative quality control strategies and improved data-sharing practices to enhance the utility of proteomics in wildlife research. To fully integrate proteomics into ecoimmunology, we recommend expanding public reference databases for non-model species, refining field-adapted workflows, and fostering interdisciplinary collaboration between ecologists, immunologists, and bioinformaticians. By embracing these advancements, the field can leverage proteomics to bridge the gap between molecular mechanisms and ecological processes, ultimately improving our ability to monitor wildlife health, predict disease risks, and inform conservation strategies in the face of environmental change. 

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5. Geographic Variation in Salt Marsh Structure and Function for Nekton: a Guide to Finding Commonality Across Multiple Scales

   https://doi.org/10.1007/s12237-020-00894-y  

   Ziegler, Shelby L.; Baker, Ronald; Crosby, Sarah C.; Colombano, Denise D.; Barbeau, Myriam A.; Cebrian, Just; Connolly, Rod M.; Deegan, Linda A.; Gilby, Ben L.; Mallick, Debbrota; et al (January 2021, Estuaries and Coasts)

   null (Ed.)

   Coastal salt marshes are distributed widely across the globe and are considered essential habitat for many fish and crustacean species. Yet, the literature on fishery support by salt marshes has largely been based on a few geographically distinct model systems, and as a result, inadequately captures the hierarchical nature of salt marsh pattern, process, and variation across space and time. A better understanding of geographic variation and drivers of commonalities and differences across salt marsh systems is essential to informing future management practices. Here, we address the key drivers of geographic variation in salt marshes: hydroperiod, seascape configuration, geomorphology, climatic region, sediment supply and riverine input, salinity, vegetation composition, and human activities. Future efforts to manage, conserve, and restore these habitats will require consideration of how environmental drivers within marshes affect the overall structure and subsequent function for fisheries species. We propose a future research agenda that provides both the consistent collection and reporting of sources of variation in small-scale studies and collaborative networks running parallel studies across large scales and geographically distinct locations to provide analogous information for data poor locations. These comparisons are needed to identify and prioritize restoration or conservation efforts, identify sources of variation among regions, and best manage fisheries and food resources across the globe. Introduction Understanding the drivers of geographic variation in the condition and composition of habitats is crucial to our capacity to generalize management plans across space and time and to clarify and perhaps challenge assumptions of functional equivalence among sites. Broadly defined wetland types such as salt marshes are often assumed to provide similar functions throughout their global range, such as providing nursery habitat for fishery species. However, a growing body of evidence suggests substantial geographic variation in the functioning of salt marsh and other coastal ecosystems (Bradley et al. 2020; Whalen et al. 2020). Variation in ecological patterns and processes within habitat types can alter community structure and dynamics. Local-scale patterns and processes (e.g., patch [10s of meters], local [100s of meters]) can be influenced by processes that occur at larger spatial scales (e.g., regional [kms], global), thereby causing geographic differences in the function and ecosystem service delivery of a given habitat type. Salt marshes (which include vegetated platform, interconnected tidal creeks, fringing mudflats, ponds, and pools) are widely distributed (Fig. 1) and function as valuable nursery habitats by providing key resources for many estuarine species that transition to marine or aquatic habitats as adults (Beck et al. 2001; Minello et al. 2003; Sheaves et al. 2015). However, factors that underlie variability in the delivery of ecological functions are still inadequately understood. Previous studies have explored geographic variation in the function of salt marshes for fish and mobile crustaceans (“nekton”; e.g., Minello et al. 2012, Baker et al. 2013). However, field studies that compare multiple sites across a geographical gradient are typically limited in duration and scale. In addition, the explanatory variables (e.g., elevation, flooding duration, plant structure) collected by smaller scale studies are often inconsistent and therefore limit generalizations across sites. 

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