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I have installed SandboxLink extension that provides each user their own sandbox accessible through their personal menu bar (top right)
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Climate change affects both terrestrial<ref>Diffenbaugh, N. S. and Field, C. B., 2013. Changes in Ecologically Critical Terrestrial Climate Conditions. Science, 341(6145), pp. 486-492. [https://doi.org/10.1126/science.1237123 doi: 10.1126/science.1237123]</ref> and aquatic biomes<ref>Hoegh-Guldberg, O., and Bruno, J. F., 2010. The Impact of Climate Change on the World’s Marine Ecosystem. Science, 328(5985), pp. 1523-1528. [https://doi.org/10.1126/science.1189930 doi: 10.1126/science.1189930]</ref> causing significant effects on ecosystem functions and biodiversity<ref name=":0">Bellard, C., Berteslsmeier, C., Leadley, P., Thuiller, W., and Courchamp, F., 2012. Impacts of climate change on the future of biodiversity. Ecological Letters, 15(4), pp. 365-377. [https://doi.org/10.1111/j.1461-0248.2011.01736.x doi: 10.1111/j.1461-0248.2011.01736.x] [//www.enviro.wiki/images/a/a4/Bellard2012.pdf Article pdf]</ref>. Climate change is affecting several key ecological processes and patterns that will have cascading impacts on wildlife and habitat<ref name=":1">Inkley, D. B., Anderson, M. G., Blaustein, A. R., Burkett, V. R., Felzer, B., Griffith, B., Price, J., and Root, T. L., 2004. Global Climate Change and Wildlife in North America. Wildlife Society Technical Review 04-2. The Wildlife Society, Bethesda, MD, 26 pp. [//www.enviro.wiki/images/f/f1/Inkley2004.pdf Report pdf]</ref>. For example, sea-level rise, changes in the timing and duration of growing seasons, and changes in primary production are mainly driven by changes to global environmental variables (e.g., temperature and atmospheric CO<sub>2</sub>). Climate-induced changes in the environment ultimately impact wildlife population abundance and distributions.  
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Proteomics is the analysis of proteins present in a sample. Proteogenomics is the combined use of proteomics with genomics and transcriptomics to support protein identifications and analyses. As tools, proteomics and proteogenomics allow researchers and practitioners to understand the functional gene products and relevant microbial metabolisms in a system, which in turn can lead to informed decision-making in remediation situations.  
 
 
<div style="float:right;margin:0 0 2em 2em;">__TOC__</div>
 
<div style="float:right;margin:0 0 2em 2em;">__TOC__</div>
  
 
'''Related Article(s):'''
 
'''Related Article(s):'''
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*[[Molecular Biological Tools - MBTs]]
 
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*[[Metagenomics]]
 
  
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*[[Climate Change Primer|Climate Change]]
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*[[Predicting Species Responses to Climate Change with Population Models]]
  
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'''Contributor(s):''' Dr. Kate H. Kucharzyk, Dr. Morgan V. Evans, Dr. Robert W. Murdoch, and Dr. Fadime Kara Murdoch
 
  
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'''Contributor(s):''' Dr. Breanna F. Powers and Dr. Julie A. Heath
  
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'''Key Resources(s):'''
 
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*[[Media: Arsene-Ploetze2015.pdf | Proteomic tools to decipher microbial community structure and functioning]]<ref name="Arsène-Ploetze2015">Arsène-Ploetze, F., Bertin, P.N., and Carapito, C., 2015.  Proteomic tools to decipher microbial community structure and functioning. Environmental Science and Pollution Research, 22, pp. 13599-13612. [https://doi.org/10.1007/s11356-014-3898-0 DOI: 10.1007/s11356-014-3898-0] [[Media: Arsene-Ploetze2015.pdf | Article]]</ref>.
 
  
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==Introduction==
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'''Key Resource(s):'''
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'''Proteomics''' is the comprehensive analysis of the proteins produced by single organisms or by microbial community (e.g., “meta”-proteomics). In this regard, proteomics represents the identification of functional gene products, providing information and insight into structural proteins and the molecular machinery produced and utilized by organisms to sustain the metabolic processes. While proteomics data can be analyzed in a de novo manner by comparing to global protein sequence databases, from a systems biology perspective, the ideal starting point for all considerations and the key enabling information is the (meta)genome of the sample under study.
 
  
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'''Proteogenomics''' combines proteomics with (meta)genomics and/or transcriptomics to better analyze and identify proteins<ref name="Helbling2012">Helbling, D.E., Ackermann, M., Fenner, K., Kohler, H.P., and Johnson, D.R., 2012.  The activity level of a microbial community function can be predicted from its metatranscriptome. The ISME Journal, 6(4), pp. 902-904. [https://doi.org/10.1038/ismej.2011.158 doi: 10.1038/ismej.2011.158] [[Media: Helbling2012.pdf | Article]]</ref>. For environmental applications, proteomics can be applied to soil, groundwater, sediment, or other environmental samples. Proteomics allows for functional characterization of a sample, enabling investigators to infer what relevant metabolisms may be active in a system (e.g., hydrocarbon degradation, reductive dechlorination). The large-scale characterization of any given proteome is accomplished by comparing measured peptide spectra with predicted protein or peptide data derived from (meta)genomic information. Thus, it is vital to have complete (meta)genome sequence information for the system being studied <ref name="Ansong2008">Ansong C., Purvine, S.O., Adkins, J.N., Lipton, M.S., and Smith, R.D., 2008.  Proteogenomics: needs and roles to be filled by proteomics in genome annotation. Briefings in Functional Genomics, 7(1), pp. 50-62. [https://doi.org/10.1093/bfgp/eln010 doi: 10.1093/bfgp/eln010] [[Media: Ansong2008.pdf | Article]]</ref>.This has led to the term proteogenomics to describe the strong linkage between genomics and proteomics. As implied, the quality of the proteomic measurements is inextricably linked to the quality of the genomic or metagenomic sequence data. Proteogenomics is an inherently more uncertain technique when compared to nucleic acid sequencing or qPCR technologies, yet it provides unparalleled global insights into biological structure and function.
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*Global climate change and wildlife in North America. Wildlife Society Technical Review 04-2<ref name=":1" />
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*Impacts of climate change on the future of biodiversity<ref name=":0" />
  
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DNA-based methods such as shotgun metagenomics and 16S rRNA amplicon sequencing provide important information and guidance for potential function of microbial communities. Like shotgun proteomics, both methods also provide <u>relative abundances of many features</u>. On the other hand, quantitative real time PCR (qPCR) provides <u>absolute quantities of few features</u> with greater sensitivity (at least 1 order of magnitude) than metagenomics approaches<ref name="Clark2018">Clark K., Taggart, D.M., Baldwin, B.R., Ritalahti, K.M., Murdoch, R.W., Hatt, J.K., and Löffler, F.E., 2018. Normalized Quantitative PCR Measurements as Predictors for Ethene Formation at Sites Impacted with Chlorinated Ethenes. Environmental Science & Technology, 52(22), pp. 13410-13420. [https://doi.org/10.1021/acs.est.8b04373 doi: 10.1021/acs.est.8b04373]</ref>.As with all nucleic-acid-based (eg. DNA or RNA) methods, qPCR only informs potential activity, not actual activity. By detecting gene expression rather than simply genes, RNA-based methods such as metatranscriptomics provide insight into which genes are active<ref name="Czaplicki2016">Czaplicki, L.M. and Gunsch, C.K., 2016. Reflection on Molecular Approaches Influencing State-of-the-Art Bioremediation Design: Culturing to Microbial Community Fingerprinting to Omics. Journal of Environmental Engineering, 142(10), pp. 1-13. [https://doi.org/10.1061/(ASCE)EE.1943-7870.0001141 doi: 10.1061/(ASCE)EE.1943-7870.0001141]</ref>, but at the cost of additional challenges posed by increased difficulty with RNA isolation and instability. Proteomics provides the actual catalytic activity by detection and quantification of proteins of interest.
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==Introduction==
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[[File:PowersFig1.png|thumb|900px|left | Figure 1. The predicted extinction risk (by percentage with 95% CIs) from climate change by different regions, colors represent a gradient from least to most extinction risks (green to red) based upon the number of relevant studies (n). Figure is from Urban (2015)<ref name=":2" />. Reprinted with permission from AAAS. Any use of this figure requires the prior written permission of AAAS]]
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Global climate change will affect ecosystem functions and cycles such as nutrient, hydraulic, and carbon cycles, changing aspects of environmental conditions such as temperature, soil moisture, and precipitation<ref>Davidson, E. A. and Janssens, I. A., 2006. Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature, 440, pp. 165-173.[https://doi.org/10.1038/nature04514 doi: 10.1038/nature04514]</ref><ref>Melillo, J. M., McGuire, A. D., Kicklighter, D. W., Moore, B., Vorosmarty, C. J., and Schloss, A. L., 1993. Global climate change and terrestrial net primary production. Nature, 363, pp. 234-240. [https://doi.org/10.1038/363234a0 doi: 10.1038/363234a0]</ref>. Wildlife species are adapted to their environments and changes to the environment and habitat conditions will mediate effects, either directly or indirectly, on species survival, fecundity and ultimately population persistence<ref>Alig, R. J., Technical Coordinator, 2011. Effects of Climate Change on Natural Resources and Communities: A Compendium of Briefing Papers. U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, General Technical Report, [https://www.fs.usda.gov/treesearch/pubs/37513 PNW-GTR-837], Portland, OR, 169p. [https://doi.org/10.2737/PNW-GTR-837 doi:10.2737/PNW-GTR-837] [//www.enviro.wiki/images/f/f2/Pnwgtr837.pdf Report pdf]</ref><ref>Acevedo-Whitehouse, K., and Duffus, A. L. J., 2009. Effects of environmental change on wildlife health. Philosophical Transactions of the Royal Society B: Biological Sciences, 364(1534), pp. 3429-3438. [https://doi.org/10.1098/rstb.2009.0128 doi: 10.1098/rstb.2009.0128]</ref><ref>Milligan, S. R., Holt, W. V., and Lloyd, R., 2009. Impacts of climate change and environmental factors on reproduction and development in wildlife. Philosophical Transactions of the Royal Society B: Biological Sciences, 364(1534), pp. 3313-3319. [https://doi.org/10.1098/rstb.2009.0175 doi: 10.1098/rstb.2009.0175] [//www.enviro.wiki/images/a/ad/Milligan2009.pdf Article pdf]</ref>. The ability to adapt to changing habitat conditions as a result of climate change will differ across individual species and between populations. Some wildlife species may be more vulnerable to climate change than other species (Figure 1). Vulnerability is often linked to particular life-history traits (e.g., specialized habitat needs or limited dispersal abilities, see Pacifici et al. 2015P<ref>Pacifici, M., Foden, W., and Visconti, P., 2015. Assessing species vulnerability to climate change. Nature Climate Change 5, pp. 215-225. [https://doi.org/10.1038/nclimate2448 doi: 10.1038/nclimate2448]</ref> for a review on species vulnerability to climate change) or genetic composition. For example, grassland birds may be more vulnerable to changing climate than forest birds as forests can buffer change more so than grasslands<ref>Jarzyna, M. A., Zuckerberg, B., Finley, A. O., and Porter, W. F., 2016. Synergistic effects of climate change and land cover: grassland birds are more vulnerable to climate change. Landscape Ecology, 31(10), pp. 2275-2290. [https://doi.org/10.1007/s10980-016-0399-1 doi: 10.1007/s10980-016-0399-1]</ref>. Projected changes in the climate will generally have adverse effects of wildlife populations<ref>IPCC, 2001. Climate Change 2001: Synthesis Report. A Contribution of Working Groups I, II, and III to the Third Assessment Report of the Intergovernmental Panel on Climate Change. R.T. Watson and the Core Writing Team (eds.). Cambridge University Press, Cambridge, United Kingdom, and New York, NY, USA, 398p. [//www.enviro.wiki/images/5/56/IPCC2001.pdf Report pdf]</ref>, though there are some species coping with climate change or benefitting from environmental change.  For example, American kestrels (Falco sparverius) have shifted their breeding phenology to earlier in the year and may now raise two broods of young within a breeding season<ref>Smith, S. H., Steenhof, K., McClure, C. J. W., and Heath, J. A. 2017. Earlier nesting by generalist predatory bird is associated with human responses to climate change. Journal of Animal Ecology, 86(1), pp. 98-107. [https://doi.org/10.1111/1365-2656.12604 doi: 10.1111/1365-2656.12604] [//www.enviro.wiki/images/5/5d/Smith2017.pdf Article pdf]</ref>.  
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[[File:PowersFig2.png|thumb|900px|right| Figure 2. Conceptual diagram showing how increased water temperatures and pCO₂ (partial pressure of carbon dioxide) affect the early life stages of fish. Where arrow direction indicated increasing rate ( ↑ ), decreasing rate ( ↓ ), or both directions depending on other environmental variables ( ↕️ ). Figure is from Pankhurst and Munday (2011)<ref name=":4" />.]]
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==The influence of climate change on wildlife and habitats==
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Climate change effects on wildlife include increases and changes in disease and pathogens distribution, patterns, and outbreaks in wildlife<ref>Bradley, B. A., Wilcove, D. S., and Oppenheimer, M., 2010. Climate change increases risk of plant invasion in the Eastern United States. Biological Invasions, 12, pp.1855-1872. [https://doi.org/10.1007/s10530-009-9597-y doi: 10.1007/s10530-009-9597-y]</ref><ref>Cudmore, T. J., Björklund, N., Carroll, A. L., and Lindgren, B. S., 2010. Climate change and the range expansion of an aggressive bark beetle: evidence of higher beetle reproduction in naïve host tree populations. Journal of Applied Ecology, 47(5), pp. 1036-1043. [https://doi.org/10.1111/j.1365-2664.2010.01848.x doi: 10.1111/j.1365-2664.2010.01848.x] [//www.enviro.wiki/images/b/b3/Cudmore2010.pdf Article pdf]</ref><ref>Price, S. J., Leung, W. T., Owen, C. J., Puschendorf, R., Sergeant, C., Cunningham, A. A., Balloux, F., Garner, T. W., and Nichols, R. A., 2019. Effects of historic and projected climate change on the range and impacts of an emerging wildlife disease. Global Change Biology, 25(8), pp. 2648-2660. [https://doi.org/10.1111/gcb.14651 doi: 10.1111/gcb.14651]</ref>; changes in range distributions and shifts in latitudinal and elevational gradients; changes in phenology or the timing of life cycle events that may create phenological mismatches<ref>Renner, S. S., and Zohner, C. M., 2018. Climate Change and Phenological Mismatch in Trophic Interactions Among Plants, Insects, and Vertebrates. Annual Review of Ecology, Evolution, and Systematics, 49, pp. 165-182. [https://doi.org/10.1146/annurev-ecolsys-110617-062535 doi: 10.1146/annurev-ecolsys-110617-062535]</ref>; and extinction or population reduction<ref name=":2">Urban, M. C., 2015. Accelerating extinction risk from climate change. Science, 348(6234), pp. 571-573. [https://doi.org/ 10.1126/science.aaa4984 doi: 10.1126/science.aaa4984] [//www.enviro.wiki/images/1/15/Urban2015.pdf Article pdf]</ref>. The effects of climate change across a species’ range will most likely not be homogenous, meaning it can vary substantially, especially if a species’ range spans across different continents as exhibited by many migratory birds. Other changes in habitat include shifting vegetation (i.e., tree-lines are shifting to higher elevations), changes in nutrients in plants, earlier snowmelt and run-off, increase in invasive species, warming of streams and rivers, reduction or degradation of habitat (i.e., glacial melt), and an increase in large wildfires<ref>Barbero, R., Abatzoglou, J. T., Larkin, N. K., Kolden, C. A., and Stocks, B., 2015. Climate change presents increased potential for very large fires in the contiguous United States. International Journal of Wildland Fire, 24(7), pp. 892-899. [https://doi.org/10.1071/WF150830128 doi: 10.1071/WF150830128] [//www.enviro.wiki/images/0/08/Barbero2015.pdf Article pdf]</ref>and droughts<ref>Schlaepfer, D. R., Bradford, J. B., Lauenroth, W. K., Munson, S. M., Tietjen, B., Hall, S. A., Wilson, S. D., Duniway, M. C., Jia, G., Pyke, D. A., Lkhagva, A., and Jamiyansharav, K., 2017. Climate change reduces extent of temperate drylands and intensifies drought in deep soils. Nature Communications, 8, pp. 14196. [https://doi.org/10.1038/ncomms14196 doi: 10.1038/ncomms14196]</ref>.
  
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==Advantages==
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Although climate change effects on wildlife often are linked to species-specific traits, there are general impacts associated with taxonomic groups<ref name=":1" />. For fish it can affect reproduction<ref name=":4">Pankhurst, N. W., and Munday, P. L., 2011. Effects of climate change on fish reproduction and early life history stages. Marine and Freshwater Research, 62(9), pp. 1015-1026. [https://doi.org/10.1071/MF10269 doi: 10.1071/MF10269] [[Special:FilePath/Pankhurst2011.pdf| Article pdf]]</ref>, growth, and recruitment<ref name=":3">Lynch, A. J., Myers, B. J. E., Chu, C., Eby, L. A., Falke, J. A., Kovach, R. P., Krabbenhoft, T. J., Kwak, T. J., Lyons, J., Paukert, C. P., and Whitney, J. E., 2016. Climate Change Effects on North American Inland Fish Populations and Assemblages. Fisheries, 41(7), pp. 346-361. [https://doi.org/10.1080/03632415.2016.1186016 doi: 10.1080/03632415.2016.1186016]</ref>(Figure 2). Cold-water fish such as inland North American species are highly affected with the warming of streams and rivers<ref name=":3" />. Amphibians are highly sensitive to their environment and changes in temperature and moisture can affect development, range, abundance, and phenology<ref>Blaustein, A.R., Walls, S.C., Bancroft, B.A., Lawler, J.J., Searle, C.L., and Gervasi, S.S., 2010. Direct and Indirect Effects of Climate Change on Amphibian Populations. Diversity, 2(2), pp. 281-313.[https://doi.org/10.3390/d2020281 doi: 10.3390/d2020281] [[Media:Blaustein 2010.pdf | Article pdf]]</ref><ref name=":1" /><ref>Ficetola, G. F., and Maiorano, L., 2016. Contrasting effects of temperature and precipitation change on amphibian phenology, abundance and performance. Oecologia, 181(3), pp. 683-693. [https://doi.org/10.1007/s00442-016-3610-9 doi: 10.1007/s00442-016-3610-9]</ref>. In reptiles, climate change effects can alter thermoregulation patterns, affect female reproduction and in some species, change sex ratios with increasing temperature<ref name=":1" />. Furthermore, for many bird species the timing of migration and other phenological events are affected by climate change<ref>Crick, H. Q. P., 2004. The impact of climate change on birds. Ibis, 146(s1), pp. 48-56. [https://doi.org/10.1111/j.1474-919X.2004.00327.x doi: 10.1111/j.1474-919X.2004.00327.x] [//www.enviro.wiki/images/c/c9/Crick2004.pdf Article pdf]</ref>. Range shifts, growth size, and survival are linked to climate change for mammals<ref name=":1" />. Arctic marine mammals are closely linked to sea ice dynamics and a changing climate will affect these dynamics<ref>Kovacs, K. M., Lydersen, C., Overland, J. E., and Moore, S. E., 2011. Impacts of changing sea-ice conditions on Arctic marine mammals. Marine Biodiversity, 41, pp. 81-194. [https://doi.org/10.1007/s12526-010-0061-0 doi: 10.1007/s12526-010-0061-0]</ref>. Therefore, it is increasingly important for conservation and management plans to consider the effects of climate change on wildlife and habitat for the geographic location<ref>Mawdsley, J. R., O’Malley, R., and Ojima, D. S. 2009. A Review of Climate-Change Adaptations Strategies For Wildlife Management and Biodiversity Conservation. Conservation Biology, 23(5), pp. 1080-1089. [https://doi.org/10.1111/j.1523-1739.2009.01264.x doi: 10.1111/j.1523-1739.2009.01264.x]</ref>. 
  
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The two main advantages of the application of metaproteomics are:
 
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#the ability to directly measure microbial enzymes (not just potential for enzyme synthesis), and
 
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#the ability to generate detailed information on hundreds of microorganisms and proteins in one assay.
 
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The process of obtaining the information does not require culturing of microorganisms or performance of any molecular assays. To measure features that are directly correlated to microbial activity, metaproteomics can be used to identify and relatively quantify proteins<ref name="Hettich2013">Hettich, R.L., Pan, C., Chourey, K., and Giannone, R.J., 2013.  Metaproteomics: Harnessing the Power of High Performance Mass Spectrometry to Identify the Suite of Proteins That Control Metabolic Activities in Microbial Communities. Analytical Chemistry, 85(9), pp. 4203-4214. [https://doi.org/10.1021/ac303053e doi: 10.1021/ac303053e] [[Media: Hettich2013.pdf | Article]]</ref><ref name="Keller2009">Keller, M. and Hettich, R.L., 2009.  Environmental Proteomics: a Paradigm Shift in Characterizing Microbial Activities at the Molecular Level. Microbiology and Molecular Biology Reviews, 73(1), pp. 62-70. [https://doi.org/10.1128/MMBR.00028-08 doi: 10.1128/MMBR.00028-08] [[Media: Keller2009.pdf | Article]]</ref><ref name="Schneider2010">Schneider, T. and Riedel, K., 2010.  Environmental proteomics: Analysis of structure and function of microbial communities. Proteomics, 10(4), pp. 785-98. [https://doi.org/10.1002/pmic.200900450 doi: 10.1002/pmic.200900450]</ref>. These proteins provide important information about community activities, such as which microbial organisms are most active and what proteins are present (including proteins catalyzing reactions involved in bioremediation)<ref name="Arsène-Ploetze2015"/><ref name="Johnson2015">Johnson, D.R., Helbling, D.E., Men, Y., and Fenner, K., 2015.  Can meta-omics help to establish causality between contaminant biotransformations and genes or gene products? Environmental Science: Water Research & Technology, 1, pp. 272-278. [https://doi.org/10.1039/C5EW00016E doi: 10.1039/C5EW00016E] [[Media: Johnson2015.pdf | Article]]</ref>.
 
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==Limitations==
 
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Limitations associated with this technology are related to composition of the proteome to be analyzed, mainly concerning protein expression levels and limitations of the analytical equipment. Limitations are summarized as follows:
 
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*'''''Sample preparation''''' - the vast diversity of protein molecular sizes, charge states, conformational states, post-translational modifications etc., make it unfeasible to use a single sample preparation protocol that captures the entire proteome for a given microbial community. Thus, use of a protocol that allows isolation of protein content from a biological sample and eliminates non-specific contaminants (e.g., keratins, fatty acids, plastic polymers, nucleic acids and salt clusters) should be developed and tested prior to sample analysis<ref name="Chourey2010">Chourey, K., Jansson, J., VerBerkmoes, N., Shah, M., Chavarria, K.L., Tom, L.M., Brodie, E.L., and Hettich, R.L., 2010. Direct Cellular Lysis/Protein Extraction Protocol for Soil Metaproteomics. Journal of Proteome Research, 9(12), pp. 6615-6622. [https://doi.org/10.1021/pr100787q doi: 10.1021/pr100787q]</ref><ref name="Qian2017">Qian, C. and Hettich, R.L., 2017. Optimized Extraction Method To Remove Humic Acid Interferences from Soil Samples Prior to Microbial Proteome Measurements. Journal of Proteome Research, 16(7), pp. 2537-2546. [https://doi.org/10.1021/acs.jproteome.7b00103 doi: 10.1021/acs.jproteome.7b00103]</ref>
 
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*'''''Proteins are not expressed in equal amounts''''' and there may be large differences in protein levels in proteomes in samples collected from the same site. A proteomic analysis must employ proper technologies for the detection of all proteins or proteins of interest. In a small sample volume that is usually used in a proteomic analysis, a large percentage of the expressed proteins occur at low abundance levels and cannot be readily detected in the analysis due to high-abundance proteins effectively monopolizing the “sampling effort”. These rare proteins may be of particular interest in environmental samples because proteins associated with contaminant degradation are often a very small fraction of the total expressed proteins. The practical protein detection limit for Liquid Chromatography-Tandem Mass Spectrometry-Time-of-Flight (LC-MS/MS-TOF) analysis lies in the femtomol (10-15 [fmol]) range. However, due to losses during protein extraction and sample clean up and dilution, the sufficient protein concentration for detection is more realistically in the low picomol (10-12 [pmol]) to high fmol range. This limitation can be addressed by collecting a greater volume of groundwater for analysis; however, this may not be possible at all sampling locations.
 
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*'''''Detection of proteins at low concentrations''''' (low abundance) may be limited by other proteins present in high concentrations (high abundance). The successful search for low-abundance proteins may be mitigated by use of chromatography for separation of high-abundance proteins and precipitation for elution of proteins of interest prior to analytical detection. However, complete removal of high-abundance proteins may not be recommended because they may trap the low-abundance proteins along with their associated fragments and peptides, which will be lost and not detected. An alternative approach relies on 2-dimensional chromatography coupled with tandem mass spectrometry.
 
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*'''''Success in the identification of proteins may vary with the sensitivity of the mass spectrometer.''''' Proper analytic equipment can be costly. Of the most sensitive mass spectrometers, electrospray ionization and laser desorption ionization-based instruments can detect peptides with low detection limits.
 
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*'''''Not all biological variation can be accounted for.''''' Despite great improvements in the costs of genomic sequencing and gene prediction, there remain some aspects of biology that cannot be accurately or consistently predicted.  For example, the presence and variety of post-translational modifications remains problematic and, if present and not accounted for during analysis, such modified proteins will not be efficiently detected.
 
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*'''''Selection of the appropriate analytical method determines the success of the study.''''' Depending on the study goals and context, some methodological adjustments should be considered, including pre-enrichment of key low-abundance proteins, adjustment of protein extraction methods or tuning of the analytical equipment.  A single methodological approach is not suitable for all purposes.
 
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==Assessing Changes in Microbial Community Composition and Dynamics – Common Applications==
 
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Environmental metaproteomics is used in applied in research areas such as:
 
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#'''Bioenergy''' – characterization of feedstock conversion into energy e.g., cellulose or lignin degradation to biofuels<ref name="Ndimba2013"> Ndimba, B.K., Ndimba, R.J., Johnson, T.S., Waditee-Sirisattha, R., Baba, M., Sirisattha, S., Shiraiwa, Y., Agrawal, G.K., and Rakwal, R, 2013. Biofuels as a sustainable energy source: an update of the applications of proteomics in bioenergy crops and algae Journal of Proteomics, 20(93), pp. 234-244. [https://doi.org/10.1016/j.jprot.2013.05.041 doi: 10.1016/j.jprot.2013.05.041]</ref>
 
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#'''Human health''' – characterization of microbial involvement in impact/control of disease vs health in human bodies<ref name="Brooks2015">Brooks, B., Mueller, R.S., Young, J.C., Morowitz, M.J., Hettich, R.L., and Banfield, J.F., 2015. Strain-resolved microbial community proteomics reveals simultaneous aerobic and anaerobic function during gastrointestinal tract colonization of a preterm infant. Frontiers in Microbiology, 1(6) pp. 654. [https://doi.org/10.3389/fmicb.2015.00654 doi: 10.3389/fmicb.2015.00654] [[Media: Brooks2015.pdf | Article]]</ref><ref name="Carr2014">Carr, S.A., Abbatiello, S.E., Ackermann, B.L., Borchers, C., Domon, B., Deutsch, E.W.,, Grant, R.P., Hoofnagle, A.N., Hüttenhain, R., Koomen, J.M., Liebler, D.C., Liu, T., MacLean, B., Mani, D.R., Mansfield, E., Neubert, H., Paulovich, A.G., Reiter, L., Vitek, O., Aebersold, R., Anderson, L., Bethem, R., Blonder, J., Boja, E., Botelho, J., Boyne, M., Bradshaw, R.A., Burlingame, A.L., Chan, D., Keshishian, H., Kuhn, E., Kinsinger, C., Lee, J.S., Lee, S.W., Moritz, R., Oses-Prieto, J., Rifai, N., Ritchie, J., Rodriguez, H., Srinivas, P.R., Townsend, R.R., Van Eyk, J., Whiteley, G., Wiita, A., and Weintraub, S., 2014.  Targeted peptide measurements in biology and medicine: best practices for mass spectrometry-based assay development using a fit-for-purpose approach. Molecular and Cellular Proteomics, 13(3), pp. 907-917. [https://doi.org/10.1074/mcp.M113.036095 doi: 10.1074/mcp.M113.036095] [[Media: Carr2014.pdf | Article]]</ref>
 
−
#'''Bioremediation''' – characterization of degradation of contaminants in sediments, soils, and groundwater by microorganisms<ref name="Bansal2009">Bansal, R., Deobald, L.A., Crawford, R.L., Paszczynski, A.J., 2009. Proteomic detection of proteins involved in perchlorate and chlorate metabolism. Biodegradation, 20(5), pp.603-620. [https://doi.org/10.1007/s10532-009-9248-0 doi: 10.1007/s10532-009-9248-0]</ref><ref name="Fuller2020">Fuller, M. E., van Groos, P. G. K., Jarrett, M., Kucharzyk, K. H., Minard-Smith, A., Heraty, L. J., and Sturchio, N. C., 2020. Application of a multiple lines of evidence approach to document natural attenuation of hexahydro-1, 3, 5-trinitro-1, 3, 5-triazine (RDX) in groundwater. Chemosphere. 250, pp. 126210. [https://doi.org/10.1016/j.chemosphere.2020.126210 doi: 10.1016/j.chemosphere.2020.126210]</ref><ref name="Kucharzyk2020">Kucharzyk, K. H., Meisel, J. E., Kara-Murdoch, F., Murdoch, R. W., Higgins, S. A., Vainberg, S., and Löffler, F. E., 2020. Metagenome-guided proteomic quantification of reductive dehalogenases in the Dehalococcoides mccartyi-containing consortium SDC-9. Journal of Proteome Research, 9(4), pp. 1812-1823. [https://doi.org/10.1021/acs.jproteome.0c00072 doi: 10.1021/acs.jproteome.0c00072]</ref><ref name="Kucharzyk2018">Kucharzyk, K.H., Rectanus, H.V., Bartling, C., Chang, P., Rosansky. S., Neil, K., and Chaudhry, T., 2018. Assessment of post remediation performance of a biobarrier oxygen injection system at a methyl tert-butyl ether (MTBE)-contaminated site, Marine Corps Base Camp Pendleton San Diego, California. Environmental Security Technology Certification Program, Alexandria, VA. ER-201588 [[Media: Kucharzyk2018.pdf | Report]]</ref><ref name="Michalsen2020a"> Michalsen, M. M., Kucharzyk, K. H., Bartling, C., Meisel, J. E., Hatzinger, P., Wilson, J., Istok, J., and Loffler, F, 2020. Validation of advanced molecular biological tools to monitor chlorinated solvent bioremediation and estimate cVOC degradation rates. Environmental Security Technology Certification Program, Alexandria, VA. ER201726. [[Media: Michalsen2020a.pdf | Report]]</ref><ref name="Michalsen2020b">Michalsen, M. M., King, A. S., Istok, J. D., Crocker, F. H., Fuller, M. E., Kucharzyk, K. H., and Gander, M. J., 2020. Spatially distinct redox conditions and degradation rates following field-scale bioaugmentation for RDX-contaminated groundwater remediation. Journal of Hazardous Materials, 387, 121529. [https://doi.org/10.1016/j.jhazmat.2019.121529 doi: 10.1016/j.jhazmat.2019.121529]</ref>
 
−
#'''Carbon cycling''' – characterization of a role of microorganisms in carbon flow in an ecosystem.
 
−
#'''Agricultural metabolism''' – characterization of microbial interactions with plants<ref name="Tan2017">Tan, B.C, Lim, Y.S., and Lau, S.E., 2017. Proteomics in commercial crops: An overview. Journal of Proteomics, 169, pp. 176-188. [https://doi.org/10.1016/j.jprot.2017.05.018 doi:10.1016/j.jprot.2017.05.018]</ref>
 
−
 
−
==Targeted vs Shotgun Proteomics==
 
−
Proteomics techniques can be broadly classified into two categories:
 
−
#'''Untargeted or shotgun proteomics,''' aimed at comprehensively identifying and characterizing relative abundances of the totality of proteins in a sample.
 
−
#'''Targeted proteomics,''' focused on identifying and absolutely quantifying one protein.
 
−
''Shotgun proteomics'' refers to digestion of the total proteome and subjecting all resulting peptides to separation, mass-spectroscopy, and identification based on a reference protein database (ideally derived from in silico translation of the (meta)genome). 
 
−
Notably, shotgun proteomics generates only an approximated ''relative abundance'' for identified proteins. On the other hand, ''targeted proteomics'' allows for ''absolute quantification'' of a single protein within a complex sample, which in turn allows for analysis of any potential correlation to a degradation rate. Prediction of degradation rates based on enzyme concentration is a crucial step towards better understanding of the molecular events underlying metabolic processes. By measuring key biomarkers, proteomic studies present an opportunity to gain profound into ecosystem health, degradation of recalcitrant compounds, and bioremediation. Quantitative proteomics can also guide regulatory agencies to make better site management decisions, thereby minimizing radiation costs and chemically induced adverse effects.
 
−
 
−
''Targeted Proteomics'' targets peptides of a specific protein in a complex mixture of other peptides and determines their presence (if they are above the detection limit) and quantity in one sample or across multiple samples (Figure 1)<ref name="Zhang2013"/>. This analysis usually utilizes a triple quadrupole mass spectrometer (QqQ-MS), an instrument which has traditionally been used to quantify small molecules. Only recently has it been utilized for peptides. Parallel Reaction Monitoring (PRM) and data independent analysis are alternative options that can be far cheaper than developing a method for a QqQ-MS.
 
−
Each targeted proteomics assay must be carefully developed.  The assay specifically “targets” peptides enzymatically digested from the target protein, necessitating careful selection of peptides and method. Development of a new assay also requires preliminary analyses to ensure the method is robust. Once a method is developed and verified, it can be applied to an unlimited number of samples.
 
−
[[File: ProteomicsFig1.png|thumb|650px|left | Figure 1. Targeted proteomic workflow<ref name="Zhang2013">Zhang, Y., Fonslow, B.R, Shan, B., Baek, M.C., and Yates, J.R. III., 2013. Protein analysis by shotgun/bottom-up proteomics. Chemical Reviews, 113(4), pp 2343-94. [https://doi.org/10.1021/cr3003533 doi:10.1021/cr3003533]</ref>]]
 
−
 
−
'''Bottom-Up Proteomics'''
 
−
 
−
''Bottom-up proteomics'' (Figure 2) begins by analyzing of mass spectrum fragmentation patterns of peptides, which are generated after proteolytic digestion of proteins<ref name="Zhang2013"/>. Spectra are identified by comparison to a database of reference proteins which are digested ''in silico''.  The creation or selection of this database is one of the most crucial steps in the analysis; ideally, the database consists of proteins encoded by the genome or metagenome of the organisms present in the sample under study.  While the rapidly decreasing cost of DNA sequencing and emergence of standard assembly and annotation pipelines make obtaining (meta)genomes increasingly cost-effective, it remains an option to use global reference protein databases, such as UniProt (see [https://unipept.ugent.be/ unipept.ugent.be]). However, making peptide-spectrum matches (a.k.a. PSMs) is a statistically uncertain process, as the alignment of mass fragmentation pattern of the peptide to the database is seldom perfect. Several algorithms have been developed to tackle this problem (Comet, X! Tandem, MyriMatch, OMSSA, Tide, etc.). PSM matching must carefully consider factors such as what enzyme was used to digest the proteome and how specific and complete digestion was.  Additionally, peptide modifications, chemical modification of amino acids which lead to differences in mass and fragmentation pattern, are a wide-spread complication that must be accounted for. Some are intentional, for example carbiodomethylation of cysteine residues, which is applied to protect sulfur residues from becoming oxidized. Other modifications occur unintentionally during sample preparation, such as oxidation of methionine residues.
 
−
PSMs are inherently statistically uncertain. A given shotgun proteome may involve hundreds of thousands of PSMs, which leads to the danger of false discovery. Traditionally, a formal false discovery rate (FDR) is applied; this is an adjustment of the threshold for statistical significance based on the number of tests performed, i.e. the more PSMs, the more stringent the testing must be.  The risk is greatly exacerbated by using reference protein databases that do not reflect the sample. Use of the (meta)genome correlating to the sample under study makes this danger of false identification much less risky when compared to traditional approaches.  It is generally acknowledged that rigid adherence to FDR thresholds is a serious impediment to thorough PSM assignment<ref name="Heyer2017"> Heyer, R., Schallert, K., Zoun, R., Becher, B., Saake, G., and Benndorf, D., 2017. Challenges and perspectives of metaproteomic data analysis. Journal of Biotechnology, 261, pp. 24–36. [https://doi.org/10.1016/j.jbiotec.2017.06.1201 doi: 10.1016/j.jbiotec.2017.06.1201] [[Media: Heyer2017.pdf | Article]]</ref> but is generally advisable when analyzing a proteome without any pre-existing knowledge on sample composition. 
 
−
Following PSM assignment, whether by use of a global reference protein database with application of FDR or by comparison to a sample-specific reference (meta)genome, peptides are matched to proteins. This step acts as a second statistical filtering step and can employ several criteria such as whether the peptide identified is unique in the database, how many peptides match a given protein, and what score was assigned to the PSMs by the PSM algorithm. A metaproteome protein identification might, for example, require three or six matching PSMs.
 
−
Software for making PSMs and protein identifications can be obtained as individual packages, but several convenient open-source packages are available, some of which include several PSM algorithms (SearchGUI) and even pipelines for making further sample comparisons (PatternLab for Proteomics) or functional interpretations (MetaProteomeAnalyzer). Many of these features are also available in commercial software packages, such as Progenesis QI (Waters) and ProteinPilot (SCIEX).  Global bottom-up shotgun proteomics data analysis remains an actively evolving discipline.
 
−
[[File: ProteomicsFig2.png|thumb|650px|Figure 2. Bottom- up shotgun proteomics]]
 
−
 
−
==Selecting Sample Locations==
 
−
Below are a few guidelines for selecting sampling locations to aid in drawing conclusions from metaproteomics data.
 
−
 
−
*'''Background:'''Samples from non-impacted background area can be compared with results from impacted areas to examine the impact of contamination on composition of microbial proteomes that reflect the ongoing metabolical processes.
 
−
*'''Baseline:'''These samples are collected and analyzed prior to treatment as a baseline for evaluating changes in the microbial metabolism in response to the remediation.
 
−
*'''Plume:'''These samples are collected from distinct zones within the source area or contaminant plume to reflect variations in contaminant concentrations, geochemical conditions, and other site-specific criteria.
 
−
 
−
==Sample Collection, Preservation, and Shipping==
 
−
Sampling procedures for proteomics analyses are straightforward and readily integrated into existing monitoring programs. Almost any type of sample matrix (soil, sediment, groundwater), filters (on-site filtration) can be analyzed. All samples should be shipped to the laboratory on ice or dry ice (-20 °C) using an overnight carrier to minimize the potential for changes in the microbial community between collection and analysis and keep integrity of proteins.
 
−
Groundwater samples (typically 1 L) can be shipped directly to the laboratory or filtered in the field. For on-site filtration, groundwater is pumped through a Sterivex<sup>&reg;</sup> or Bio-Flo<sup>&reg;</sup> filter using standard low flow sampling techniques. The groundwater may then be discarded appropriately. As with other sample types, filters should be shipped on ice (4 °C) using an overnight carrier.
 
 
==References==
 
==References==
 
<references />
 
<references />
 +
 
==See Also==
 
==See Also==
−
*[[Media: Cantarel2011.pdf | Strategies for Metagenomic-Guided Whole-Community Proteomics of Complex Microbial Environments]]
 
−
*[https://www.nature.com/articles/nbt.1661  Options and considerations when selecting a quantitative proteomics strategy]
 
−
*[[Media: Ge2013.pdf | Environmental OMICS: Current Status and Future Directions]]
 
−
*[https://pubs.acs.org/doi/abs/10.1021/acs.jproteome.6b00239  An Alignment-Free “Metapeptide” Strategy for Metaproteomic Characterization of Microbiome Samples Using Shotgun Metagenomic Sequencing]
 
−
*[https://www.sciencedirect.com/science/article/abs/pii/S0038071713003799  Environmental proteomics: A long march in the pedosphere]
 
−
*[https://www.sciencedirect.com/science/article/abs/pii/S0038071713003799  Proteome profile and proteogenomics of the organohalide-respiring bacterium Dehalococcoides mccartyi strain CBDB1 grown on hexachlorobenzene as electron acceptor]
 
−
*[https://pubs.acs.org/doi/10.1021/acs.jproteome.8b00716  Unipept 4.0: Functional Analysis of Metaproteome Data]
 
−
*[https://www.nature.com/articles/nbt0808-860  Guidelines for reporting the use of mass spectrometry in proteomics]
 
−
*[https://www.nature.com/articles/nbt1329  The minimum information about a proteomics experiment (MIAPE)]
 
−
*[https://pubs.acs.org/doi/10.1021/es501673s  Next-Generation Proteomics: Toward Customized Biomarkers for Environmental Biomonitoring]
 
−
*[[Media: Wilmes2015.pdf | A decade of metaproteomics: Where we stand and what the future holds]]
 

Latest revision as of 14:07, 25 April 2022

Climate change affects both terrestrial[1] and aquatic biomes[2] causing significant effects on ecosystem functions and biodiversity[3]. Climate change is affecting several key ecological processes and patterns that will have cascading impacts on wildlife and habitat[4]. For example, sea-level rise, changes in the timing and duration of growing seasons, and changes in primary production are mainly driven by changes to global environmental variables (e.g., temperature and atmospheric CO2). Climate-induced changes in the environment ultimately impact wildlife population abundance and distributions.

Related Article(s):


Contributor(s): Dr. Breanna F. Powers and Dr. Julie A. Heath


Key Resource(s):

  • Global climate change and wildlife in North America. Wildlife Society Technical Review 04-2[4]
  • Impacts of climate change on the future of biodiversity[3]

Introduction

Figure 1. The predicted extinction risk (by percentage with 95% CIs) from climate change by different regions, colors represent a gradient from least to most extinction risks (green to red) based upon the number of relevant studies (n). Figure is from Urban (2015)[5]. Reprinted with permission from AAAS. Any use of this figure requires the prior written permission of AAAS

Global climate change will affect ecosystem functions and cycles such as nutrient, hydraulic, and carbon cycles, changing aspects of environmental conditions such as temperature, soil moisture, and precipitation[6][7]. Wildlife species are adapted to their environments and changes to the environment and habitat conditions will mediate effects, either directly or indirectly, on species survival, fecundity and ultimately population persistence[8][9][10]. The ability to adapt to changing habitat conditions as a result of climate change will differ across individual species and between populations. Some wildlife species may be more vulnerable to climate change than other species (Figure 1). Vulnerability is often linked to particular life-history traits (e.g., specialized habitat needs or limited dispersal abilities, see Pacifici et al. 2015P[11] for a review on species vulnerability to climate change) or genetic composition. For example, grassland birds may be more vulnerable to changing climate than forest birds as forests can buffer change more so than grasslands[12]. Projected changes in the climate will generally have adverse effects of wildlife populations[13], though there are some species coping with climate change or benefitting from environmental change. For example, American kestrels (Falco sparverius) have shifted their breeding phenology to earlier in the year and may now raise two broods of young within a breeding season[14].

Figure 2. Conceptual diagram showing how increased water temperatures and pCO₂ (partial pressure of carbon dioxide) affect the early life stages of fish. Where arrow direction indicated increasing rate ( ↑ ), decreasing rate ( ↓ ), or both directions depending on other environmental variables ( ↕️ ). Figure is from Pankhurst and Munday (2011)[15].

The influence of climate change on wildlife and habitats

Climate change effects on wildlife include increases and changes in disease and pathogens distribution, patterns, and outbreaks in wildlife[16][17][18]; changes in range distributions and shifts in latitudinal and elevational gradients; changes in phenology or the timing of life cycle events that may create phenological mismatches[19]; and extinction or population reduction[5]. The effects of climate change across a species’ range will most likely not be homogenous, meaning it can vary substantially, especially if a species’ range spans across different continents as exhibited by many migratory birds. Other changes in habitat include shifting vegetation (i.e., tree-lines are shifting to higher elevations), changes in nutrients in plants, earlier snowmelt and run-off, increase in invasive species, warming of streams and rivers, reduction or degradation of habitat (i.e., glacial melt), and an increase in large wildfires[20]and droughts[21].

Although climate change effects on wildlife often are linked to species-specific traits, there are general impacts associated with taxonomic groups[4]. For fish it can affect reproduction[15], growth, and recruitment[22](Figure 2). Cold-water fish such as inland North American species are highly affected with the warming of streams and rivers[22]. Amphibians are highly sensitive to their environment and changes in temperature and moisture can affect development, range, abundance, and phenology[23][4][24]. In reptiles, climate change effects can alter thermoregulation patterns, affect female reproduction and in some species, change sex ratios with increasing temperature[4]. Furthermore, for many bird species the timing of migration and other phenological events are affected by climate change[25]. Range shifts, growth size, and survival are linked to climate change for mammals[4]. Arctic marine mammals are closely linked to sea ice dynamics and a changing climate will affect these dynamics[26]. Therefore, it is increasingly important for conservation and management plans to consider the effects of climate change on wildlife and habitat for the geographic location[27].

References

  1. ^ Diffenbaugh, N. S. and Field, C. B., 2013. Changes in Ecologically Critical Terrestrial Climate Conditions. Science, 341(6145), pp. 486-492. doi: 10.1126/science.1237123
  2. ^ Hoegh-Guldberg, O., and Bruno, J. F., 2010. The Impact of Climate Change on the World’s Marine Ecosystem. Science, 328(5985), pp. 1523-1528. doi: 10.1126/science.1189930
  3. ^ 3.0 3.1 Bellard, C., Berteslsmeier, C., Leadley, P., Thuiller, W., and Courchamp, F., 2012. Impacts of climate change on the future of biodiversity. Ecological Letters, 15(4), pp. 365-377. doi: 10.1111/j.1461-0248.2011.01736.x Article pdf
  4. ^ 4.0 4.1 4.2 4.3 4.4 4.5 Inkley, D. B., Anderson, M. G., Blaustein, A. R., Burkett, V. R., Felzer, B., Griffith, B., Price, J., and Root, T. L., 2004. Global Climate Change and Wildlife in North America. Wildlife Society Technical Review 04-2. The Wildlife Society, Bethesda, MD, 26 pp. Report pdf
  5. ^ 5.0 5.1 Urban, M. C., 2015. Accelerating extinction risk from climate change. Science, 348(6234), pp. 571-573. 10.1126/science.aaa4984 doi: 10.1126/science.aaa4984 Article pdf
  6. ^ Davidson, E. A. and Janssens, I. A., 2006. Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature, 440, pp. 165-173.doi: 10.1038/nature04514
  7. ^ Melillo, J. M., McGuire, A. D., Kicklighter, D. W., Moore, B., Vorosmarty, C. J., and Schloss, A. L., 1993. Global climate change and terrestrial net primary production. Nature, 363, pp. 234-240. doi: 10.1038/363234a0
  8. ^ Alig, R. J., Technical Coordinator, 2011. Effects of Climate Change on Natural Resources and Communities: A Compendium of Briefing Papers. U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, General Technical Report, PNW-GTR-837, Portland, OR, 169p. doi:10.2737/PNW-GTR-837 Report pdf
  9. ^ Acevedo-Whitehouse, K., and Duffus, A. L. J., 2009. Effects of environmental change on wildlife health. Philosophical Transactions of the Royal Society B: Biological Sciences, 364(1534), pp. 3429-3438. doi: 10.1098/rstb.2009.0128
  10. ^ Milligan, S. R., Holt, W. V., and Lloyd, R., 2009. Impacts of climate change and environmental factors on reproduction and development in wildlife. Philosophical Transactions of the Royal Society B: Biological Sciences, 364(1534), pp. 3313-3319. doi: 10.1098/rstb.2009.0175 Article pdf
  11. ^ Pacifici, M., Foden, W., and Visconti, P., 2015. Assessing species vulnerability to climate change. Nature Climate Change 5, pp. 215-225. doi: 10.1038/nclimate2448
  12. ^ Jarzyna, M. A., Zuckerberg, B., Finley, A. O., and Porter, W. F., 2016. Synergistic effects of climate change and land cover: grassland birds are more vulnerable to climate change. Landscape Ecology, 31(10), pp. 2275-2290. doi: 10.1007/s10980-016-0399-1
  13. ^ IPCC, 2001. Climate Change 2001: Synthesis Report. A Contribution of Working Groups I, II, and III to the Third Assessment Report of the Intergovernmental Panel on Climate Change. R.T. Watson and the Core Writing Team (eds.). Cambridge University Press, Cambridge, United Kingdom, and New York, NY, USA, 398p. Report pdf
  14. ^ Smith, S. H., Steenhof, K., McClure, C. J. W., and Heath, J. A. 2017. Earlier nesting by generalist predatory bird is associated with human responses to climate change. Journal of Animal Ecology, 86(1), pp. 98-107. doi: 10.1111/1365-2656.12604 Article pdf
  15. ^ 15.0 15.1 Pankhurst, N. W., and Munday, P. L., 2011. Effects of climate change on fish reproduction and early life history stages. Marine and Freshwater Research, 62(9), pp. 1015-1026. doi: 10.1071/MF10269 Article pdf
  16. ^ Bradley, B. A., Wilcove, D. S., and Oppenheimer, M., 2010. Climate change increases risk of plant invasion in the Eastern United States. Biological Invasions, 12, pp.1855-1872. doi: 10.1007/s10530-009-9597-y
  17. ^ Cudmore, T. J., Björklund, N., Carroll, A. L., and Lindgren, B. S., 2010. Climate change and the range expansion of an aggressive bark beetle: evidence of higher beetle reproduction in naïve host tree populations. Journal of Applied Ecology, 47(5), pp. 1036-1043. doi: 10.1111/j.1365-2664.2010.01848.x Article pdf
  18. ^ Price, S. J., Leung, W. T., Owen, C. J., Puschendorf, R., Sergeant, C., Cunningham, A. A., Balloux, F., Garner, T. W., and Nichols, R. A., 2019. Effects of historic and projected climate change on the range and impacts of an emerging wildlife disease. Global Change Biology, 25(8), pp. 2648-2660. doi: 10.1111/gcb.14651
  19. ^ Renner, S. S., and Zohner, C. M., 2018. Climate Change and Phenological Mismatch in Trophic Interactions Among Plants, Insects, and Vertebrates. Annual Review of Ecology, Evolution, and Systematics, 49, pp. 165-182. doi: 10.1146/annurev-ecolsys-110617-062535
  20. ^ Barbero, R., Abatzoglou, J. T., Larkin, N. K., Kolden, C. A., and Stocks, B., 2015. Climate change presents increased potential for very large fires in the contiguous United States. International Journal of Wildland Fire, 24(7), pp. 892-899. doi: 10.1071/WF150830128 Article pdf
  21. ^ Schlaepfer, D. R., Bradford, J. B., Lauenroth, W. K., Munson, S. M., Tietjen, B., Hall, S. A., Wilson, S. D., Duniway, M. C., Jia, G., Pyke, D. A., Lkhagva, A., and Jamiyansharav, K., 2017. Climate change reduces extent of temperate drylands and intensifies drought in deep soils. Nature Communications, 8, pp. 14196. doi: 10.1038/ncomms14196
  22. ^ 22.0 22.1 Lynch, A. J., Myers, B. J. E., Chu, C., Eby, L. A., Falke, J. A., Kovach, R. P., Krabbenhoft, T. J., Kwak, T. J., Lyons, J., Paukert, C. P., and Whitney, J. E., 2016. Climate Change Effects on North American Inland Fish Populations and Assemblages. Fisheries, 41(7), pp. 346-361. doi: 10.1080/03632415.2016.1186016
  23. ^ Blaustein, A.R., Walls, S.C., Bancroft, B.A., Lawler, J.J., Searle, C.L., and Gervasi, S.S., 2010. Direct and Indirect Effects of Climate Change on Amphibian Populations. Diversity, 2(2), pp. 281-313.doi: 10.3390/d2020281 Article pdf
  24. ^ Ficetola, G. F., and Maiorano, L., 2016. Contrasting effects of temperature and precipitation change on amphibian phenology, abundance and performance. Oecologia, 181(3), pp. 683-693. doi: 10.1007/s00442-016-3610-9
  25. ^ Crick, H. Q. P., 2004. The impact of climate change on birds. Ibis, 146(s1), pp. 48-56. doi: 10.1111/j.1474-919X.2004.00327.x Article pdf
  26. ^ Kovacs, K. M., Lydersen, C., Overland, J. E., and Moore, S. E., 2011. Impacts of changing sea-ice conditions on Arctic marine mammals. Marine Biodiversity, 41, pp. 81-194. doi: 10.1007/s12526-010-0061-0
  27. ^ Mawdsley, J. R., O’Malley, R., and Ojima, D. S. 2009. A Review of Climate-Change Adaptations Strategies For Wildlife Management and Biodiversity Conservation. Conservation Biology, 23(5), pp. 1080-1089. doi: 10.1111/j.1523-1739.2009.01264.x

See Also

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