Ecology

Good, S. P., Noone, D. & Bowen, G. Hydrologic connectivity constrains partitioning of global terrestrial water fluxes. Science 349, 175–177 (2015).ADS 
CAS 
PubMed 

Google Scholar 
Masson-Delmotte, V. et al. Climate change 2021: The physical science basis. contribution of working group I to the sixth assessment report of the intergovernmental panel of climate change. Global warming of 1.5 C. An IPCC Special Report (2021).Milly, P. C. D., Dunne, K. A. & Vecchia, A. V. Global pattern of trends in streamflow and water availability in a changing climate. Nature 438, 347–350 (2005).ADS 
CAS 
PubMed 

Google Scholar 
Konapala, G., Mishra, A. K., Wada, Y. & Mann, M. E. Climate change will affect global water availability through compounding changes in seasonal precipitation and evaporation. Nat. Commun. 11, 3044 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Miralles, D. G., Gentine, P., Seneviratne, S. I. & Teuling, A. J. Land-atmospheric feedbacks during droughts and heatwaves: state of the science and current challenges. Ann. N. Y. Acad. Sci. 1436, 19–35 (2019).ADS 
PubMed 

Google Scholar 
Schwalm, C. R. et al. Global patterns of drought recovery. Nature 548, 202–205 (2017).ADS 
CAS 
PubMed 

Google Scholar 
Sippel, S. et al. Drought, heat, and the carbon cycle: a review. Curr. Clim. Change Rep. 4, 266–286 (2018).
Google Scholar 
Peterson, T. J., Saft, M., Peel, M. C. & John, A. Watersheds may not recover from drought. Science 372, 745–749 (2021).ADS 
CAS 
PubMed 

Google Scholar 
Vicente-Serrano, S. M., Beguería, S. & L`ópez-Moreno, J. I. A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. J. Clim. 23, 1696–1718 (2010).
Google Scholar 
Anderson, M. C. et al. The evaporative stress index as an indicator of agricultural drought in brazil: an assessment based on crop yield impacts. Remote Sens. Environ. 174, 82–99 (2016).ADS 

Google Scholar 
Fisher, J. B. et al. The future of evapotranspiration: global requirements for ecosystem functioning, carbon and climate feedbacks, agricultural management, and water resources. Water Resour. Res. 53, 2618–2626 (2017).ADS 

Google Scholar 
Kalma, J. D., McVicar, T. R. & McCabe, M. F. Estimating land surface evaporation: a review of methods using remotely sensed surface temperature data. Surv. Geophys. 29, 421–469 (2008).ADS 

Google Scholar 
Melton, F. S. et al. Openet: Filling a critical data gap in water management for the western united states. JAWRA Journal of the American Water Resources Association (2021). https://onlinelibrary.wiley.com/doi/abs/10.1111/1752-1688.12956. https://onlinelibrary.wiley.com/doi/pdf/10.1111/1752-1688.12956.Lawrence, D. M. et al. The community land model version 5: Description of new features, benchmarking, and impact of forcing uncertainty. J. Adv. Modeling Earth Syst. 11, 4245–4287 (2019).ADS 

Google Scholar 
Niu, G.-Y. et al. The community noah land surface model with multiparameterization options (noah-mp): 1. model description and evaluation with local-scale measurements. J. Geophys. Res.: Atmosph. 116 (2011). https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2010JD015139.Miralles, D. G. et al. Global land-surface evaporation estimated from satellite-based observations. Hydrol. Earth Syst. Sci. 15, 453–469 (2011).ADS 

Google Scholar 
Fisher, J. B., Tu, K. P. & Baldocchi, D. D. Global estimates of the land-atmosphere water flux based on monthly avhrr and islscp-ii data, validated at 16 fluxnet sites. Remote Sens. Environ. 112, 901–919 (2008).ADS 

Google Scholar 
Mu, Q., Zhao, M. & Running, S. W. Improvements to a modis global terrestrial evapotranspiration algorithm. Remote Sens. Environ. 115, 1781–1800 (2011).ADS 

Google Scholar 
Mueller, B. & Seneviratne, S. I. Systematic land climate and evapotranspiration biases in cmip5 simulations. Geophys. Res. Lett. 41, 128–134 (2014).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Koppa, A., Alam, S., Miralles, D. G. & Gebremichael, M. Budyko-based long-term water and energy balance closure in global watersheds from earth observations. Water Resour. Res. 57, e2020WR028658 (2021).ADS 
PubMed 
PubMed Central 

Google Scholar 
Fisher, J. B. et al. The future of evapotranspiration: global requirements for ecosystem functioning, carbon and climate feedbacks, agricultural management, and water resources. Water Resour. Res. 53, 2618–2626 (2017).ADS 

Google Scholar 
Penman, H. L. & Keen, B. A. Natural evaporation from open water, bare soil and grass. Proc. R. Soc. Lond. Ser. A. Math. Phys. Sci. 193, 120–145 (1948).ADS 
CAS 

Google Scholar 
Priestley, C. H. B. & Taylor, R. J. On the assessment of surface heat flux and evaporation using large-scale parameters. Monthly Weather Rev. 100, 81–92 (1972).
Google Scholar 
Maes, W. H., Gentine, P., Verhoest, N. E. C. & Miralles, D. G. Potential evaporation at eddy-covariance sites across the globe. Hydrol. Earth Syst. Sci. 23, 925–948 (2019).ADS 

Google Scholar 
Zhao, W. L. et al. Physics-constrained machine learning of evapotranspiration. Geophys. Res. Lett. 46, 14496–14507 (2019).ADS 

Google Scholar 
Miralles, D. G. et al. The wacmos-et project – part 2: Evaluation of global terrestrial evaporation data sets. Hydrol. Earth Syst. Sci. 20, 823–842 (2016).ADS 

Google Scholar 
Green, J. K., Berry, J., Ciais, P., Zhang, Y. & Gentine, P. Amazon rainforest photosynthesis increases in response to atmospheric dryness. Sci. Adv. 6 (2020). https://advances.sciencemag.org/content/6/47/eabb7232. https://advances.sciencemag.org/content/6/47/eabb7232.full.pdf.Verhoef, A. & Egea, G. Modeling plant transpiration under limited soil water: Comparison of different plant and soil hydraulic parameterizations and preliminary implications for their use in land surface models. Agric. For. Meteorol. 191, 22–32 (2014).ADS 

Google Scholar 
Powell, T. L. et al. Confronting model predictions of carbon fluxes with measurements of amazon forests subjected to experimental drought. N. Phytologist 200, 350–365 (2013).
Google Scholar 
Wu, X. et al. Parameterization of the water stress reduction function based on soil–plant water relations. Irrig. Sci. 39, 101–122 (2021).
Google Scholar 
Zhang, J., Liu, P., Zhang, F. & Song, Q. Cloudnet: Ground-based cloud classification with deep convolutional neural network. Geophys. Res. Lett. 45, 8665–8672 (2018).ADS 

Google Scholar 
Hengl, T. et al. Soilgrids250m: global gridded soil information based on machine learning. PLoS ONE 12, 1–40 (2017).
Google Scholar 
Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).ADS 
CAS 
PubMed 

Google Scholar 
Jung, M. et al. The fluxcom ensemble of global land-atmosphere energy fluxes. Sci. Data 6, 74 (2019).PubMed 
PubMed Central 

Google Scholar 
McGovern, A. et al. Using artificial intelligence to improve real-time decision-making for high-impact weather. Bull. Am. Meteorological Soc. 98, 2073–2090 (2017).
Google Scholar 
Kratzert, F. et al. Toward improved predictions in ungauged basins: exploiting the power of machine learning. Water Resour. Res. 55, 11344–11354 (2019).ADS 

Google Scholar 
Reichstein, M. et al. Deep learning and process understanding for data-driven earth system science. Nature 566, 195–204 (2019).ADS 
CAS 
PubMed 

Google Scholar 
Rasp, S., Pritchard, M. S. & Gentine, P. Deep learning to represent subgrid processes in climate models. Proc. Natl Acad. Sci. USA 115, 9684–9689 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
de Bézenac, E., Pajot, A. & Gallinari, P. Deep learning for physical processes: incorporating prior scientific knowledge. J. Stat. Mech.: Theory Exp. 2019, 124009 (2019).MathSciNet 
MATH 

Google Scholar 
Kraft, B., Jung, M., Körner, M. & Reichstein, M. Hybrid modeling: Fusion of a deep learning approach and a physics-based model for global hydrological modeling. Int. Arch. Photogramm., Remote Sens. Spat. Inf. Sci. XLIII-B2-2020, 1537–1544 (2020).
Google Scholar 
Chen, H., Huang, J. J., Dash, S. S., Wei, Y. & Li, H. A hybrid deep learning framework with physical process description for simulation of evapotranspiration. J. Hydrol. 606, 127422 (2022).
Google Scholar 
Martens, B. et al. Gleam v3: satellite-based land evaporation and root-zone soil moisture. Geoscientific Model Dev. 10, 1903–1925 (2017).ADS 

Google Scholar 
Gash, J. H. C. An analytical model of rainfall interception by forests. Q. J. R. Meteorological Soc. 105, 43–55 (1979).ADS 

Google Scholar 
Grossiord, C. et al. Plant responses to rising vapor pressure deficit. N. Phytologist 226, 1550–1566 (2020).
Google Scholar 
Urban, J., Ingwers, M., McGuire, M. A. & Teskey, R. O. Stomatal conductance increases with rising temperature. Plant Signal. Behav. 12, e1356534 (2017). PMID: 28786730.PubMed 
PubMed Central 

Google Scholar 
Matthews, J. S. A., Vialet-Chabrand, S. & Lawson, T. Role of blue and red light in stomatal dynamic behaviour. J. Exp. Bot. 71, 2253–2269 (2019).PubMed Central 

Google Scholar 
Xu, Z., Jiang, Y., Jia, B. & Zhou, G. Elevated-co2 response of stomata and its dependence on environmental factors. Front. Plant Sci. 7, 657 (2016).PubMed 
PubMed Central 

Google Scholar 
Jung, M. et al. Recent decline in the global land evapotranspiration trend due to limited moisture supply. Nature 467, 951–954 (2010).ADS 
CAS 
PubMed 

Google Scholar 
Peng, Y., Bloomfield, K. J., Cernusak, L. A., Domingues, T. F. & Colin Prentice, I. Global climate and nutrient controls of photosynthetic capacity. Commun. Biol. 4, 462 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Knoben, W. J. M., Freer, J. E. & Woods, R. A. Technical note: Inherent benchmark or not? comparing nash–sutcliffe and kling–gupta efficiency scores. Hydrol. Earth Syst. Sci. 23, 4323–4331 (2019).ADS 

Google Scholar 
Pagán, B. R., Maes, W. H., Gentine, P., Martens, B. & Miralles, D. G. Exploring the potential of satellite solar-induced fluorescence to constrain global transpiration estimates. Remote Sens. 11 (2019). https://www.mdpi.com/2072-4292/11/4/413.Jonard, F. et al. Value of sun-induced chlorophyll fluorescence for quantifying hydrological states and fluxes: current status and challenges. Agric. For. Meteorol. 291, 108088 (2020).ADS 

Google Scholar 
Bauer, P. et al. The digital revolution of earth-system science. Nat. Comput. Sci. 1, 104–113 (2021).
Google Scholar 
Bonan, G. B. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320, 1444–1449 (2008).ADS 
CAS 
PubMed 

Google Scholar 
Pastorello, G. et al. The fluxnet2015 dataset and the oneflux processing pipeline for eddy covariance data. Sci. Data 7, 225 (2020).PubMed 
PubMed Central 

Google Scholar 
Wei, Z. et al. Revisiting the contribution of transpiration to global terrestrial evapotranspiration. Geophys. Res. Lett. 44, 2792–2801 (2017).ADS 

Google Scholar 
Stoy, P. C. et al. Reviews and syntheses: turning the challenges of partitioning ecosystem evaporation and transpiration into opportunities. Biogeosciences 16, 3747–3775 (2019).ADS 
CAS 

Google Scholar 
Poyatos, R. et al. Global transpiration data from sap flow measurements: the sapfluxnet database. Earth Syst. Sci. Data 13, 2607–2649 (2021).ADS 

Google Scholar 
Falster, D. S. et al. Baad: a biomass and allometry database for woody plants. Ecology 96, 1445–1445 (2015).
Google Scholar 
Granier, A. & Loustau, D. Measuring and modelling the transpiration of a maritime pine canopy from sap-flow data. Agric. For. Meteorol. 71, 61–81 (1994).ADS 

Google Scholar 
Aumann, H. et al. Airs/amsu/hsb on the aqua mission: design, science objectives, data products, and processing systems. IEEE Trans. Geosci. Remote Sens. 41, 253–264 (2003).ADS 

Google Scholar 
Wielicki, B. A. et al. Clouds and the earth’s radiant energy system (ceres): an earth observing system experiment. Bull. Am. Meteorological Soc. 77, 853–868 (1996).ADS 

Google Scholar 
Moesinger, L. et al. The global long-term microwave vegetation optical depth climate archive (vodca). Earth Syst. Sci. Data 12, 177–196 (2020).ADS 

Google Scholar 
Abadi, M. et al. TensorFlow: Large-scale machine learning on heterogeneous systems (2015). https://www.tensorflow.org/. Software available from tensorflow.org.Gupta, H. V., Kling, H., Yilmaz, K. K. & Martinez, G. F. Decomposition of the mean squared error and NSE performance criteria: implications for improving hydrological modelling. J. Hydrol. 377, 80–91 (2009).ADS 

Google Scholar 
Yu, L., Wen, J., Chang, C. Y., Frankenberg, C. & Sun, Y. High-resolution global contiguous sif of oco-2. Geophys. Res. Lett. 46, 1449–1458 (2019).ADS 

Google Scholar 
Koppa, A., Rains, D., Hulsman, P., Poyatos, R. & Miralles, D. G. A Deep learning-based hybrid model of global terrestrial evaporation (2022). https://doi.org/10.5281/zenodo.5886608.Koppa, A., Rains, D., Hulsman, P., Poyatos, R. & Miralles, D. G. A Deep learning-based hybrid model of global terrestrial evaporation (2022). https://doi.org/10.5281/zenodo.6343005. More

In this study we analysed how tree and arborescent palm species richness was related to aboveground carbon stock, commercially relevant timber stock, and commercially relevant NTFP abundance in tropical forests, and how these relationships were influenced by environmental stratification at different spatial scales. We found that species richness showed significant relationships with all three ecosystem services stock components, but its relationships were strongly influenced by variation across forest types and biogeographical strata. This is further explained below.Across the Guiana Shield, species richness showed a positive relationship with carbon stock and timber, but not with NTFP abundance. Although relationships only differed in significance among the biogeographical subregions, they differed in direction between terra firme forests and white sand forests. Species richness was positively related to carbon stock and timber stock in terra firme forests, whereas it was negatively related to NTFP abundance in white sand forests. The positive species-carbon relationship across forests of the Guiana Shield is in line with the effects described by hypotheses such as the ‘niche complementarity’ and ‘selection effect’10 and is in line with previous findings at regional spatial scales6,21. To our knowledge, the relationship between species richness and timber stock has not been previously analysed for tropical forests. Interestingly, the observed positive species-timber relationship in terra firme forests of the Guiana Shield contrasts with the negative species-timber relationship found for subtropical forests in both the U.S.A. and Spain20, although this may be explained by the difference in ecosystems. The non-significant species-NTFP abundance relationship across the Guiana Shield and the negative relationship within white sand forests seems to contradict previous findings. Steur et al.24 found a negative species-NTFP abundance relationship for tropical forests in Suriname. However, this negative relationship was found across multiple forest types, including flooded forests that had low species richness and high NTFP abundance. These flooded forests most likely influenced the species-NTFP abundance relationship across all forest types.In contrast to the relationship between species richness and carbon stock, no mechanism has been proposed for how species richness would influence commercial timber stock and NTFP abundance. Although our results suggest that species richness had a positive relationship with timber, the relationship was not found within multiple biogeographical subregions. For NTFP abundance, species richness did not contribute to explaining variation when variation across biogeographical subregions was accounted for (i.e. was included as an explanatory variable). We here tentatively propose that both commercial relevant timber stock and NTFP abundance are driven by variation in species floristic composition, rather than by species richness. For services such as commercial timber and NTFP provisioning, only a subset of all species is relevant (in this study, 9.4% of all morphospecies for timber and 3.8% for NTFPs), and such subsets are likely not random selections. For example, for Suriname, it was found that variation in commercially relevant NTFP abundance was driven by a particularly small selection of NTFP producing species with high abundances (referred to as ‘NTFP oligarchs’)24, and for commercial relevant timber stock, it is commonly known that selections tend to include more abundant than rare species. Additionally, as the relative abundance of species tends to vary across floristic regions in Amazonia, where, for example, certain species are dominant in particular forest types and biogeographical regions31,32, it can be expected that commercial timber stock and NTFP abundance are determined by floristic composition. In support, for NTFP abundance in Suriname tropical forests, Steur et al.24 found that floristic composition was a stronger predictor of NTFP abundance than species richness.Across all of Amazonia, species richness had a positive relationship with carbon stock, but only when variation among biogeographical regions was accounted for. The positive species-carbon relationship across Amazonia partly contrasts with previous findings at continental spatial scales11,13. When variation across climatic and/or edaphic variables was accounted for, Sullivan et al.13 found no significant species-carbon relationship across South-America, while Poorter et al.33 did find a positive relationship across Meso- and South-America. Here, we propose that accounting for differences among biogeographical regions can explain the previously found contrasts at continental spatial scales. In our dataset, for individual regions, we found either a positive relationship or a non-significant, but weakly positive, relationship between carbon stock and species richness (Fig. 2). However, when the data were aggregated across all regions, this resulted in a non-significant, and weakly negative, relationship. This reflects a known statistical phenomenon referred to as a ‘Simpson’s paradox’34, in which a relationship appears in multiple distinct groups but disappears or reverses when the groups are combined. Additional post-hoc tests of leaving one region out at a time showed that this pattern was not dependent of any particular biogeographical region. This is the first time that an analysis based on empirical data provides evidence for a Simpson’s paradox in species-ecosystem service relationships.It is likely that the observed differences in carbon stock across the biogeographical regions of Amazonia are influenced by multiple factors. For example, the biogeographical regions used in our analyses were recognised according to differences in substrate history, geological age and floristic composition, which could all contribute to variation in carbon stock. The substrate history and geological age of the biogeographical regions have been related to differences in soil fertility35, while multiple spatial gradients in floristic composition identified across the Amazon coincide with a spatial gradient in wood density28. However, further analysis is needed to obtain better insight into the relative contributions of these and other variables to explain the observed variation in carbon stock across the biogeographical regions. This requires data on multiple environmental variables, including floristic composition, climatic variables such as the length of the dry period, soil conditions, and intensity of disturbance.In our analyses, terra firme forests determined the relationship of species richness with the carbon stock, timber stock, and NTFP abundance across the datasets. Although this is most likely the effect of unequal sample sizes, with terra firme forests being the dominant forest type in terms of sample size (n = 130 vs. n = 21 for the Guiana Shield dataset; n = 257 vs. n = 26 for the Amazonia dataset), we expect that the observed relationships reflect the general pattern. Terra firme forests are the most dominant forest type in terms of geographical area32 and were representatively sampled. Regardless, the analyses per forest type had added value. The significant relationship between species richness and NTFP abundance in white sand forests across the Guiana Shield would otherwise have been overlooked.Due to the known scarcity of reliable and adequate information on which timber and NTFP species are being commercially traded36,37,38,39, we used a fixed set of timber and NTFP species to apply across the Guiana Shield plots. However, in reality, timber and NTFP species can be expected to vary according to socio-economic factors, such as culture, access, and harvest costs, which may change over space and time. Therefore, estimates of timber stock and NTFP abundance can be expected to differ across spatial gradients, and thus, their possible relationships with species richness cannot be easily generalised. To circumvent this, timber stock and NTFP abundance would have to be estimated on the basis of ‘flexible’ species selections that can change according to local socio-economic contexts. To this end, detailed information on both commercially relevant timber and NTFP species is urgently needed. Yet, for our study area, we did not observe major differences in selected species, and we included broad selections of species, which should make timber stock and NTFP abundance robust against small deviations in species selection. It must be noted that our approach of quantifying commercial relevant timber stock and NTFP abundance does not consider the value of timber and NTFPs for subsistence use. In addition, NTFPs can also be derived from other growth forms, such as lianas, shrubs and herbs. Last, because NTFP production data was not available we used NTFP abundance as a proxy for NTFP stock, following similar assessments of NTFP stock 24,40. A limitation of this approach is that each NTFP species individual has an equal contribution to NTFP stock, whereas it can be expected that large individuals may have a larger contribution than smaller individuals and that production volumes can differ for different types of NTFPs, for example barks vs. seeds.Our findings illustrate the importance of considering environmental stratification and spatial scale when analysing relationships between biodiversity and ecosystem services. First, environmental stratification can help detect relationships that are otherwise obscured by environmental heterogeneity. For example, although the association between species richness and carbon stock across Amazonia was relatively weak (explaining ~ 3% of total variation vs. ~ 15% in the Guiana Shield) and was obscured by variation in carbon stock across biogeographical strata, by using environmental stratification the positive relationship remained detectable. Second, environmental heterogeneity tends to vary with spatial scale; therefore, its importance needs to be checked according to spatial scale. For example, at the regional scale of the Guiana Shield, biogeographical subregions explained a moderate amount of variation in carbon stock (~ 20%), while at the spatial scale of Amazonia, biogeographical regions explained more than half of total variation in carbon stock (~ 55%). Such an increase and ultimate importance of variation across biogeographical strata might also explain the absence of a significant relationship between species richness and carbon stock across African and/or Asian tropical forests as reported by Sullivan et al.13.In our analyses, we found evidence of a positive relationship between species richness and carbon stock across and within Amazonia. This supports the notion that win–win scenarios are possible in conservation approaches, where, for example, REDD+ can be expected to help conserve tropical forests that contain large amounts of carbon stock and high concentrations of species9. However, we conclude that species richness is not always a strong predictor of biomass-based ecosystem services. In our analyses, NTFP abundance was not driven by species richness, and we ultimately expect the same for timber stock. We expect that differences in floristic composition, linked to differences across forest types and biogeographical strata, will be more relevant than species richness in explaining variation in timber stock and NTFP abundance. This would mean that conserving timber and NTFP related ecosystem services requires the development of additional region-specific strategies that account for differences in floristic composition. For example, areas with high concentrations of timber or NTFPs could be considered in the designation of multiple use protected areas41, such as the extractive reserves in Brazil, or be included as ‘high conservation value areas’ (HCVAs) in sustainable forest management certification42. More

Pollard W, Omelon C, Andersen D, McKay C. Perennial spring occurrence in the Expedition Fiord area of western Axel Heiberg Island, Canadian High Arctic. Can J Earth Sci. 1999;36:105–20.CAS 
Article 

Google Scholar 
Andersen DT. Cold springs in permafrost on Earth and Mars. J Geophys Res. 2002;107:4–1-4-7.
Google Scholar 
Niederberger TD, Perreault NN, Tille S, Lollar BS, Lacrampe-Couloume G, Andersen D, et al. Microbial characterization of a subzero, hypersaline methane seep in the Canadian High Arctic. ISME J. 2010;4:1326–39.CAS 
PubMed 
Article 

Google Scholar 
Goordial J, Lamarche-Gagnon G, Lay CY, Whyte L. Left out in the cold: life in cryoenvironments. In: Seckbach J, Oren A, Stan-Lotter H, editors. Polyextremophiles. New York: Springer; 2013. p. 335–64.Gilichinsky D, Rivkina E, Bakermans C, Shcherbakova V, Petrovskaya L, Ozerskaya S, et al. Biodiversity of cryopegs in permafrost. FEMS Microbiol Ecol. 2005;53:117–28.CAS 
PubMed 
Article 

Google Scholar 
Rivkina EM, Friedmann EI, McKay CP, Gilichinsky DA. Metabolic activity of permafrost bacteria below the freezing point. Appl Environ Microbiol. 2000;66:3230–3.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Brown MV, Bowman JP. A molecular phylogenetic survey of sea-ice microbial communities (SIMCO). FEMS Microbiol Ecol. 2001;35:267–75.CAS 
PubMed 
Article 

Google Scholar 
Murray AE, Kenig F, Fritsen CH, McKay CP, Cawley KM, Edwards R, et al. Microbial life at -13 degrees C in the brine of an ice-sealed Antarctic lake. Proc Natl Acad Sci USA. 2012;109:20626–31.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Orosei R, Lauro SE, Pettinelli E, Cicchetti A, Coradini M, Cosciotti B, et al. Radar evidence of subglacial liquid water on Mars. Science. 2018;361:490–3.CAS 
PubMed 
Article 

Google Scholar 
Lauro SE, Pettinelli E, Caprarelli G, Guallini L, Pio Rossi A, Mattei E, et al. Multiple subglacial water bodies below the south pole of Mars unveiled by new MARSIS data. Nat Astron. 2021;5:63–70.Article 

Google Scholar 
Bishop JL, Yesilbas M, Hinman NW, Burton ZFM, Englert PAJ, Toner JD, et al. Martian subsurface cryosalt expansion and collapse as trigger for landslides. Sci Adv. 2021;7:1–13.
Google Scholar 
Allen CC, Oehler DZ. A case for ancient springs in Arabia Terra, Mars. Astrobiology. 2008;8:1093–112.CAS 
PubMed 
Article 

Google Scholar 
Battler MM, Osinski GR, Banerjee NR. Mineralogy of saline perennial cold springs on Axel Heiberg Island, Nunavut, Canada and implications for spring deposits on Mars. Icarus. 2013;224:364–81.CAS 
Article 

Google Scholar 
Leask EK, Ehlmann BL. Evidence for deposition of chloride on Mars from small‐volume surface water events into the Late Hesperian‐Early Amazonian. AGU Adv. 2022;3:1–19.Article 

Google Scholar 
Howell SM, Pappalardo RT. NASA’s Europa Clipper-a mission to a potentially habitable ocean world. Nat Commun. 2020;11:1–4.Article 

Google Scholar 
Farley KA, Williford KH, Stack KM, Bhartia R, Chen A, de la Torre M, et al. Mars 2020 mission overview. Space Sci Rev. 2020;216:1–41.Article 

Google Scholar 
Kargel JS, Kaye JZ, Head JW, Marion GM, Sassen R, Crowley JK, et al. Europa’s crust and ocean: origin, composition, and the prospects for life. Icarus. 2000;148:226–65.CAS 
Article 

Google Scholar 
Taubner RS, Pappenreiter P, Zwicker J, Smrzka D, Pruckner C, Kolar P, et al. Biological methane production under putative Enceladus-like conditions. Nat Commun. 2018;9:1–11.CAS 
Article 

Google Scholar 
Lamarche-Gagnon G, Comery R, Greer CW, Whyte LG. Evidence of in situ microbial activity and sulphidogenesis in perennially sub-0 degrees C and hypersaline sediments of a high Arctic permafrost spring. Extremophiles. 2015;19:1–15.CAS 
PubMed 
Article 

Google Scholar 
Lay CY, Mykytczuk NC, Yergeau E, Lamarche-Gagnon G, Greer CW, Whyte LG. Defining the functional potential and active community members of a sediment microbial community in a high-arctic hypersaline subzero spring. Appl Environ Microbiol. 2013;79:3637–48.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Menzel P, Ng KL, Krogh A. Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nat Commun. 2016;7:1–9.Article 

Google Scholar 
Gruber-Vodicka HR, Seah BKB, Pruesse E. phyloFlash: rapid small-subunit rRNA profiling and targeted assembly from metagenomes. mSystems. 2020;5:1–16.Article 

Google Scholar 
Li D, Liu CM, Luo R, Sadakane K, Lam TW. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics. 2015;31:1674–6.CAS 
PubMed 
Article 

Google Scholar 
Kang DD, Froula J, Egan R, Wang Z. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ. 2015;3:1–15.Article 

Google Scholar 
Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chen IA, Chu K, Palaniappan K, Ratner A, Huang J, Huntemann M, et al. The IMG/M data management and analysis system v.6.0: new tools and advanced capabilities. Nucleic Acids Res. 2020;49:D751–D63.PubMed Central 
Article 

Google Scholar 
Mukherjee S, Stamatis D, Bertsch J, Ovchinnikova G, Sundaramurthi JC, Lee J, et al. Genomes OnLine Database (GOLD) v.8: overview and updates. Nucleic Acids Res. 2020;49:D723–D733.PubMed Central 
Article 

Google Scholar 
Chaumeil PA, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics. 2019;36:1925–7.PubMed Central 

Google Scholar 
Schmieder R, Edwards R. Fast identification and removal of sequence contamination from genomic and metagenomic datasets. PLoS ONE. 2011;6:1–11.Article 

Google Scholar 
Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012;19:455–77.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kopylova E, Noe L, Touzet H. SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics. 2012;28:3211–7.CAS 
PubMed 
Article 

Google Scholar 
Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Anders S, Pyl PT, Huber W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics. 2015;31:166–9.CAS 
PubMed 
Article 

Google Scholar 
Royo-Llonch M, Sanchez P, Ruiz-Gonzalez C, Salazar G, Pedros-Alio C, Sebastian M, et al. Compendium of 530 metagenome-assembled bacterial and archaeal genomes from the polar Arctic Ocean. Nat Microbiol. 2021;6:1561–74.CAS 
PubMed 
Article 

Google Scholar 
Ghosh W, Dam B. Biochemistry and molecular biology of lithotrophic sulfur oxidation by taxonomically and ecologically diverse bacteria and archaea. FEMS Microbiol Rev. 2009;33:999–1043.CAS 
PubMed 
Article 

Google Scholar 
Boden R. Reclassification of Halothiobacillus hydrothermalis and Halothiobacillus halophilus to Guyparkeria gen. nov. in the Thioalkalibacteraceae fam. nov., with emended descriptions of the genus Halothiobacillus and family Halothiobacillaceae. Int J Syst Evol Microbiol. 2017;67:3919–28.CAS 
PubMed 
Article 

Google Scholar 
Sorokin DY, Abbas B, van Zessen E, Muyzer G. Isolation and characterization of an obligately chemolithoautotrophic Halothiobacillus strain capable of growth on thiocyanate as an energy source. FEMS Microbiol Lett. 2014;354:69–74.CAS 
PubMed 
Article 

Google Scholar 
Meier DV, Pjevac P, Bach W, Hourdez S, Girguis PR, Vidoudez C, et al. Niche partitioning of diverse sulfur-oxidizing bacteria at hydrothermal vents. ISME J. 2017;11:1545–58.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Headd B, Engel AS. Evidence for niche partitioning revealed by the distribution of sulfur oxidation genes collected from areas of a terrestrial sulfidic spring with differing geochemical conditions. Appl Environ Microbiol. 2013;79:1171–82.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Preisig O, Zufferey R, Thoney-Meyer L, Appleby CA, Hennecke H. A high-affinity cbb3-type cytochrome oxidase terminates the symbiosis-specific respiratory chain of Bradyrhizobium japonicum. J Bacteriol. 1996;178:1532–8.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mikucki JA, Pearson A, Johnston DT, Turchyn AV, Farquhar J, Schrag DP, et al. A contemporary microbially maintained subglacial ferrous “ocean”. Science. 2009;324:397–400.CAS 
PubMed 
Article 

Google Scholar 
Ruff SE, Biddle JF, Teske AP, Knittel K, Boetius A, Ramette A. Global dispersion and local diversification of the methane seep microbiome. Proc Natl Acad Sci USA. 2015;112:4015–20.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lloyd KG, Lapham L, Teske A. An anaerobic methane-oxidizing community of ANME-1b archaea in hypersaline Gulf of Mexico sediments. Appl Environ Microbiol. 2006;72:7218–30.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Maignien L, Parkes RJ, Cragg B, Niemann H, Knittel K, Coulon S, et al. Anaerobic oxidation of methane in hypersaline cold seep sediments. FEMS Microbiol Ecol. 2013;83:214–31.CAS 
PubMed 
Article 

Google Scholar 
Campen R, Kowalski J, Lyons WB, Tulaczyk S, Dachwald B, Pettit E, et al. Microbial diversity of an Antarctic subglacial community and high-resolution replicate sampling inform hydrological connectivity in a polar desert. Environ Microbiol. 2019;21:2290–306.PubMed 
Article 

Google Scholar 
Cooper ZS, Rapp JZ, Carpenter SD, Iwahana G, Eicken H, Deming JW. Distinctive microbial communities in subzero hypersaline brines from Arctic coastal sea ice and rarely sampled cryopegs. FEMS Microbiol Ecol. 2019;95:1–15.Article 

Google Scholar 
Winkel M, Mitzscherling J, Overduin PP, Horn F, Winterfeld M, Rijkers R, et al. Anaerobic methanotrophic communities thrive in deep submarine permafrost. Sci Rep. 2018;8:1–13.CAS 

Google Scholar 
Lay CY, Mykytczuk NC, Niederberger TD, Martineau C, Greer CW, Whyte LG. Microbial diversity and activity in hypersaline high Arctic spring channels. Extremophiles. 2012;16:177–91.CAS 
PubMed 
Article 

Google Scholar 
Bhattarai S, Cassarini C, Lens PNL. Physiology and distribution of archaeal methanotrophs that couple anaerobic oxidation of methane with sulfate reduction. Microbiol Mol Biol Rev. 2019;83:1–31.Article 

Google Scholar 
Kleindienst S, Ramette A, Amann R, Knittel K. Distribution and in situ abundance of sulfate-reducing bacteria in diverse marine hydrocarbon seep sediments. Environ Microbiol. 2012;14:2689–710.CAS 
PubMed 
Article 

Google Scholar 
Timmers PH, Welte CU, Koehorst JJ, Plugge CM, Jetten MS, Stams AJ. Reverse methanogenesis and respiration in methanotrophic archaea. Archaea. 2017;2017:1–22.Article 

Google Scholar 
Leu AO, Cai C, McIlroy SJ, Southam G, Orphan VJ, Yuan Z, et al. Anaerobic methane oxidation coupled to manganese reduction by members of the Methanoperedenaceae. ISME J. 2020;14:1030–41.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Haroon MF, Hu S, Shi Y, Imelfort M, Keller J, Hugenholtz P, et al. Anaerobic oxidation of methane coupled to nitrate reduction in a novel archaeal lineage. Nature. 2013;500:567–70.CAS 
PubMed 
Article 

Google Scholar 
Cai C, Leu AO, Xie GJ, Guo J, Feng Y, Zhao JX, et al. A methanotrophic archaeon couples anaerobic oxidation of methane to Fe(III) reduction. ISME J. 2018;12:1929–39.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Oshkin IY, Wegner CE, Luke C, Glagolev MV, Filippov IV, Pimenov NV, et al. Gammaproteobacterial methanotrophs dominate cold methane seeps in floodplains of West Siberian rivers. Appl Environ Microbiol. 2014;80:5944–54.PubMed 
PubMed Central 
Article 

Google Scholar 
Cabrol L, Thalasso F, Gandois L, Sepulveda-Jauregui A, Martinez-Cruz K, Teisserenc R, et al. Anaerobic oxidation of methane and associated microbiome in anoxic water of Northwestern Siberian lakes. Sci Total Environ. 2020;736:1–16.Article 

Google Scholar 
Orcutt B, Boetius A, Elvert M, Samarkin V, Joye SB. Molecular biogeochemistry of sulfate reduction, methanogenesis and the anaerobic oxidation of methane at Gulf of Mexico cold seeps. Geochim Cosmochim Acta. 2005;69:4267–81.CAS 
Article 

Google Scholar 
Knittel K, Losekann T, Boetius A, Kort R, Amann R. Diversity and distribution of methanotrophic archaea at cold seeps. Appl Environ Microbiol. 2005;71:467–79.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Schubert CJ, Coolen MJ, Neretin LN, Schippers A, Abbas B, Durisch-Kaiser E, et al. Aerobic and anaerobic methanotrophs in the Black Sea water column. Environ Microbiol. 2006;8:1844–56.CAS 
PubMed 
Article 

Google Scholar 
Wang J, Hua M, Cai C, Hu J, Wang J, Yang H, et al. Spatial-temporal pattern of sulfate-dependent anaerobic methane oxidation in an intertidal zone of the East China Sea. Appl Environ Microbiol. 2019;85:1–15.
Google Scholar 
Dyksma S, Bischof K, Fuchs BM, Hoffmann K, Meier D, Meyerdierks A, et al. Ubiquitous Gammaproteobacteria dominate dark carbon fixation in coastal sediments. ISME J. 2016;10:1939–53.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Perreault NN, Greer CW, Andersen DT, Tille S, Lacrampe-Couloume G, Lollar BS, et al. Heterotrophic and autotrophic microbial populations in cold perennial springs of the high Arctic. Appl Environ Microbiol. 2008;74:6898–907.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Cordero PRF, Bayly K, Man Leung P, Huang C, Islam ZF, Schittenhelm RB, et al. Atmospheric carbon monoxide oxidation is a widespread mechanism supporting microbial survival. ISME J. 2019;13:2868–81.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Nigro LM, Elling FJ, Hinrichs KU, Joye SB, Teske A. Microbial ecology and biogeochemistry of hypersaline sediments in Orca Basin. PLoS ONE. 2020;15:1–25.Article 

Google Scholar 
Rath KM, Fierer N, Murphy DV, Rousk J. Linking bacterial community composition to soil salinity along environmental gradients. ISME J. 2019;13:836–46.CAS 
PubMed 
Article 

Google Scholar 
Yoon JH, Lee MH, Kang SJ, Oh TK. Salegentibacter salinarum sp. nov., isolated from a marine solar saltern. Int J Syst Evol Microbiol. 2008;58:365–9.CAS 
PubMed 
Article 

Google Scholar 
Sangwan N, Xia F, Gilbert JA. Recovering complete and draft population genomes from metagenome datasets. Microbiome. 2016;4:1–11.Article 

Google Scholar 
Goordial J, Raymond-Bouchard I, Zolotarov Y, de Bethencourt L, Ronholm J, Shapiro N, et al. Cold adaptive traits revealed by comparative genomic analysis of the eurypsychrophile Rhodococcus sp. JG3 isolated from high elevation McMurdo Dry Valley permafrost, Antarctica. FEMS Microbiol Ecol. 2016;92:1–11.
Google Scholar 
Laso-Perez R, Wegener G, Knittel K, Widdel F, Harding KJ, Krukenberg V, et al. Thermophilic archaea activate butane via alkyl-coenzyme M formation. Nature. 2016;539:396–401.CAS 
PubMed 
Article 

Google Scholar 
Dombrowski N, Teske AP, Baker BJ. Expansive microbial metabolic versatility and biodiversity in dynamic Guaymas Basin hydrothermal sediments. Nat Commun. 2018;9:1–13.CAS 
Article 

Google Scholar 
Oren A. Thermodynamic limits to microbial life at high salt concentrations. Environ Microbiol. 2011;13:1908–23.CAS 
PubMed 
Article 

Google Scholar 
Gunde-Cimerman N, Plemenitas A, Oren A. Strategies of adaptation of microorganisms of the three domains of life to high salt concentrations. FEMS Microbiol Rev. 2018;42:353–75.CAS 
PubMed 
Article 

Google Scholar 
Hechler T, Pfeifer F. Anaerobiosis inhibits gas vesicle formation in halophilic. Archaea Mol Microbiol. 2009;71:132–45.CAS 
PubMed 
Article 

Google Scholar 
Stokke R, Roalkvam I, Lanzen A, Haflidason H, Steen IH. Integrated metagenomic and metaproteomic analyses of an ANME-1-dominated community in marine cold seep sediments. Environ Microbiol. 2012;14:1333–46.CAS 
PubMed 
Article 

Google Scholar 
Wegener G, Krukenberg V, Riedel D, Tegetmeyer HE, Boetius A. Intercellular wiring enables electron transfer between methanotrophic archaea and bacteria. Nature. 2015;526:587–90.CAS 
PubMed 
Article 

Google Scholar 
Skennerton CT, Chourey K, Iyer R, Hettich RL, Tyson GW, Orphan VJ. Methane-fueled syntrophy through extracellular electron transfer: uncovering the genomic traits conserved within diverse bacterial partners of anaerobic methanotrophic archaea. mBio. 2017;8:1–14.Article 

Google Scholar 
Krukenberg V, Riedel D, Gruber-Vodicka HR, Buttigieg PL, Tegetmeyer HE, Boetius A, et al. Gene expression and ultrastructure of meso- and thermophilic methanotrophic consortia. Environ Microbiol. 2018;20:1651–66.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Youssef NH, Rinke C, Stepanauskas R, Farag I, Woyke T, Elshahed MS. Insights into the metabolism, lifestyle and putative evolutionary history of the novel archaeal phylum ‘Diapherotrites’. ISME J. 2015;9:447–60.CAS 
PubMed 
Article 

Google Scholar 
Castelle CJ, Brown CT, Anantharaman K, Probst AJ, Huang RH, Banfield JF. Biosynthetic capacity, metabolic variety and unusual biology in the CPR and DPANN radiations. Nat Rev Microbiol. 2018;16:629–45.CAS 
PubMed 
Article 

Google Scholar 
Rinke C, Schwientek P, Sczyrba A, Ivanova NN, Anderson IJ, Cheng JF, et al. Insights into the phylogeny and coding potential of microbial dark matter. Nature. 2013;499:431–7.CAS 
PubMed 
Article 

Google Scholar 
Dombrowski N, Lee JH, Williams TA, Offre P, Spang A. Genomic diversity, lifestyles and evolutionary origins of DPANN archaea. FEMS Microbiol Lett. 2019;366:1–12.Article 

Google Scholar 
Wong HL, MacLeod FI, White RA 3rd, Visscher PT, Burns BP. Microbial dark matter filling the niche in hypersaline microbial mats. Microbiome. 2020;8:1–14.Article 

Google Scholar 
Schut GJ, Nixon WJ, Lipscomb GL, Scott RA, Adams MW. Mutational analyses of the enzymes involved in the metabolism of hydrogen by the hyperthermophilic archaeon Pyrococcus furiosus. Front Microbiol. 2012;3:1–6.Article 

Google Scholar 
Ruuskanen MO, Colby G, St. Pierre KA, St. Louis VL, Aris‐Brosou S, Poulain AJ. Microbial genomes retrieved from High Arctic lake sediments encode for adaptation to cold and oligotrophic environments. Limnol Oceanogr. 2020;65:S233–S247.CAS 
Article 

Google Scholar 
Vigneron A, Cruaud P, Lovejoy C, Vincent WF. Genomic evidence of functional diversity in DPANN archaea, from oxic species to anoxic vampiristic consortia. ISME Commun. 2022;2:1–10.Article 

Google Scholar 
Parks DH, Chuvochina M, Waite DW, Rinke C, Skarshewski A, Chaumeil PA, et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat Biotechnol. 2018;36:996–1004.CAS 
PubMed 
Article 

Google Scholar 
Meheust R, Castelle CJ, Matheus Carnevali PB, Farag IF, He C, Chen LX, et al. Groundwater Elusimicrobia are metabolically diverse compared to gut microbiome Elusimicrobia and some have a novel nitrogenase paralog. ISME J. 2020;14:2907–22.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hahn CR, Farag IF, Murphy CL, Podar M, Elshahed MS, Youssef NH. Microbial diversity and sulfur cycling in an early earth analogue: from ancient novelty to modern commonality. mBio. https://doi.org/10.1128/mbio.00016-22. (in press).Yang J, Yan R, Roy A, Xu D, Poisson J, Zhang Y. The I-TASSER Suite: protein structure and function prediction. Nat Methods. 2015;12:7–8.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rummel JD, Beaty DW, Jones MA, Bakermans C, Barlow NG, Boston PJ, et al. A new analysis of Mars “Special Regions”: findings of the second MEPAG Special Regions Science Analysis Group (SR-SAG2). Astrobiology. 2014;14:887–968.PubMed 
Article 

Google Scholar 
Harris RL, Schuerger AC, Wang W, Tamama Y, Garvin ZK, Onstott TC. Transcriptional response to prolonged perchlorate exposure in the methanogen Methanosarcina barkeri and implications for Martian habitability. Sci Rep. 2021;11:1–16.Article 

Google Scholar 
Webster CR, Mahaffy PR, Atreya SK, Moores JE, Flesch GJ, Malespin C, et al. Background levels of methane in Mars’ atmosphere show strong seasonal variations. Science. 2018;360:1093–6.CAS 
PubMed 
Article 

Google Scholar 
Oehler DZ, Etiope G. Methane seepage on Mars: where to look and why. Astrobiology. 2017;17:1233–64.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Marlow JJ, Larowe DE, Ehlmann BL, Amend JP, Orphan VJ. The potential for biologically catalyzed anaerobic methane oxidation on ancient Mars. Astrobiology. 2014;14:292–307.CAS 
PubMed 
Article 

Google Scholar 
Ji M, Greening C, Vanwonterghem I, Carere CR, Bay SK, Steen JA, et al. Atmospheric trace gases support primary production in Antarctic desert surface soil. Nature. 2017;552:400–3.CAS 
PubMed 
Article 

Google Scholar 
Berg JS, Ahmerkamp S, Pjevac P, Hausmann B, Milucka J, Kuypers MMM. How low can they go? Aerobic respiration by microorganisms under apparent anoxia. FEMS Microbiol Rev. 2022;fuac006. https://doi.org/10.1093/femsre/fuac006.Berg JS, Pjevac P, Sommer T, Buckner CRT, Philippi M, Hach PF, et al. Dark aerobic sulfide oxidation by anoxygenic phototrophs in anoxic waters. Environ Microbiol. 2019;21:1611–26.CAS 
PubMed 
Article 

Google Scholar 
Stamenković V, Ward LM, Mischna M, Fischer WW. O2 solubility in Martian near-surface environments and implications for aerobic life. Nat Geosci. 2018;11:905–9.Article 

Google Scholar  More

FennelThe main effect of fertilization (F) significantly impacted all measured parameters of fennel. Intercropping (I) pattern affected all parameters except plant height and 1000-seed weight. Significant I × F interactions occurred for umbel number, seed yield, essential oil content (EO), EO yield, oil content, and oil yield (Table 2).Table 2 Analysis of variance for the effect of cropping pattern and fertilization on evaluated traits in fennel.Full size tablePlant heightThe tallest fennel plants (125.8 cm) occurred in the sole cropping (Fs), while the shortest plants (98.6 cm) occurred in 1F:2FG. Across intercropping patterns, the Fs treatment had 28%, 14%, 11% taller fennel plants than 1F:2FG, 2F:2FG, and 2F:4FG, respectively (Fig. 1A). Compared to the unfertilized control, HA and BFS increased fennel plant height by 10% and 13%, respectively (Fig. 1B).Figure 1Means comparison for the main effects of cropping patterns [Fs (fennel sole cropping), 1F:2FG, 2F:2FG, 2F:4FG (ratios of fennel and fenugreek in the intercropping patterns)] on plant height (A), and fertilization [C (control), HA (humic acid), BFS (biofertilizers)] on plant height (B) and 1000-seed weight (C) of fennel. Different lower-case letters above the bars indicate significant (p ≤ 0.05) differences.Full size image1000-seed weightCompared to the unfertilized control (3.9 g per 1000 seeds), BFS and HA increased the 1000-seed weight of fennel by 24.1% and 14.5% (4.9 and 4.5 g per 1000 seeds), respectively (Fig. 1C).Umbel numberThe fennel sole cropping fertilized with HA produced the most umbels of fennel (51.5), while 2F:4FG without fertilization produced the least (32). Averaged across fertilizer types within each intercropping system, 1F:2FG, 2F:2FG, and 2F:4FG had 21.1%, 16.3%, and 26.7% fewer umbels than fennel sole cropping, respectively. Across intercropping systems, HA and BFS increased the umbel number by 17.8% and 16.5% compared with the unfertilized control, respectively (Fig. 2A).Figure 2Means comparison for the interaction effect of fertilization [C (control), HA (humic acid), BFS (biofertilizers)] and different cropping patterns [Fs (fennel sole cropping), 1F:2FG, 2F:2FG, 2F:4FG (ratios of fennel and fenugreek in the intercropping patterns)] on umbel number (A) and seed yield (B) of fennel. Different lower-case letters above the bars indicate significant (p ≤ 0.05) differences.Full size imageSeed yieldThe different intercropping patterns had lower fennel seed yields than fennel sole cropping. Sole cropping fertilized with BFS and HA produced the highest fennel seed yields (2233 and 2209 kg ha–1, respectively), followed by unfertilized sole cropping (1960 kg ha–1). The lowest seed yields occurred in the unfertilized controls in 1F:2FG (933 kg ha–1) and 2F:4FG (1033 kg ha–1). Averaged across fertilization treatments, fennel seed yield in 1F:2FG, 2F:2FG, and 2F:4FG decreased by 41.7, 26.8, and 36.3%, respectively, compared to fennel sole cropping (Fs). Averaged across intercropping patterns, HA and BFS increased fennel seed yield by 33.3% and 39.5% compared with the unfertilized control, respectively (Fig. 2B).Essential oil content and yieldThe different intercropping patterns produced higher EO contents of fennel than fennel sole cropping. The highest absolute EO content of fennel (4.22%) occurred in 2F:2FG fertilized with BFS, although this did not statistically differ from the 2F:2FG fertilized with HA (4.04%) or 2F:4FG fertilized with HA or BFS (3.8% and 4.00%, respectively) (Fig. 3A). The lowest EO contents occurred in the unfertilized control (2.38%), HA (2.55%), and BFS (2.57%) in the Fs system. Averaged across fertilization treatments, the EO content of fennel in 1F:2FG, 2F:2FG, and 2F:4FG increased by 36%, 52%, and 44% compared to fennel sole cropping, respectively. Within each intercropping pattern, and with the exception of Fs, the HA and BFS treatments had higher EO contents of fennel, none of which significantly differed, increasing by 25% and 29%, respectively (Fig. 3A).Figure 3Means comparison for the interaction effect of fertilization [C (control), HA (humic acid), BFS (biofertilizers)] and different cropping patterns [Fs (fennel sole cropping), 1F:2FG, 2F:2FG, 2F:4FG (ratios of fennel and fenugreek in the intercropping patterns)] on essential oil content (A), essential oil yield (B), oil content (C), and oil yield (D) of fennel. Different lower-case letters above the bars indicate significant (p ≤ 0.05) differences.Full size imageMaximum EO yields of fennel occurred with HA or BFS applied in 2F:2FG (65.2 and 66.6 kg ha–1) and 2F:4FG (60.7 and 65.5 kg ha–1), respectively, while the lowest EO yields occurred in the unfertilized control in 1F:2FG (27.2 kg ha–1), 2F:2FG (33.2 kg ha–1), and 2F:4FG (32.1 kg ha–1). Averaged across intercropping patterns, the EO yield of fennel increased by 66.1% and 74.7% with HA and BFS, respectively (Fig. 3B).Fennel essential oil compositionGC–FID and GC–MS analyses identified 14 components in the fennel EO (representing 97.4–99.9% of the total composition) (Table 3), with the main constituents being trans-anethole (78.3–84.85%), estragole (3.02–7.17%), fenchone (4.14–7.52%), and limonene (3.15–4.88%). The highest percentage of (E)-anethole, estragole, and fenchone occurred in 2F:2FG with BFS. The highest limonene content occurred in 2F:4 FG with HA. The relative contents of trans-anethole, fenchone, and limonene increased by 3.9%, 16.6%, and 8.4% compared with fennel sole cropping. Notably, the contents of most compounds increased with HA and BFS. Compared to the unfertilized control, trans-anethole, fenchone, and limonene contents increased by 2.9%, 21.5%, and 7.9% with BFS and 2.3%, 22.4%, and 11.9% with HA, respectively (Table 3).Table 3 Proportion of fennel essential oil constituents under different cropping patterns and fertilization.Full size tableFennel oil content and yieldAmong the studied treatments, the highest fennel oil content occurred with HA or BFS application in 1F:2FG (16.3% and 16.6%) and 2F:2FG (16.3% and 17.4%), respectively. The lowest fennel oil contents occurred in the unfertilized control, HA, and BFS treatments (12.5%, 12.8%, and 12.9%, respectively) under fennel sole cropping, and the unfertilized control in 2F:4FG (12.6%). Averaged across fertilizer treatments, fennel oil content in 1F:2FG, 2F:2FG, and 2F:4FG increased by 22.8%, 26.0%, and 12.6% compared with fennel sole cropping, respectively. Across intercropping patterns, HA and BFS increased fennel oil content by 13.5% and 16.5%, respectively (Fig. 3C).The maximum oil yield of fennel (318.6 kg ha–1) occurred in 2F:2FG fertilized with BFS, while the lowest oil yield (129.3 kg ha–1) occurred in 1F:2FG without fertilization. Across intercropping patterns, HA and BFS increased fennel oil yield by 50.8% and 62.6%, respectively (Fig. 3D).Oil compoundsGC–FID and GC–MS analyses identified nine constituents that represented 94.3–97.9% of the total fennel oil composition. The main oil constituents were oleic acid (39.2–48.3%), linoleic acid (17.1–24.8%), stearic acid (10.9–15.4%), lauric acid (10.1–14.00%), and arachidic acid (2.2–3.4%). The highest oleic and linoleic acid contents occurred in 2F:4FG and 2F:2FG fertilized with BFS, respectively. Across fertilizer treatments, oleic and linoleic acid contents increased by 6% and 21%, respectively, under different intercropping patterns compared with fennel sole cropping. Across systems, HA and BFS enhanced oleic acid content by 1.8% and 8% and linoleic acid by 7.9% and 8.2%, respectively, compared with the unfertilized control. The highest percentage of stearic and lauric acids occurred in the unfertilized control of fennel sole cropping. Conversely, the lowest stearic and lauric acid contents occurred in 2F:2FG and 2F:4FG fertilized with BFS, 16.1% and 14.2% higher than fennel sole cropping, respectively. Finally, HA and BFS decreased stearic acid content by an average of 5.4% and 7.2%, respectively (Table 4).Table 4 Proportion of fennel oil constituents under different cropping patterns and fertilization.Full size tablePhenolic compoundsThe main phenolic compounds of fennel were chlorogenic acid (10.4–15.3 ppm), quercetin (7.0–17.2 ppm), and cinnamic acid (4.1–8.9 ppm). The highest chlorogenic acid and quercetin contents occurred in 2F:2FG fertilized with BFS and HA, respectively, while the lowest contents occurred in the fennel sole cropping system without fertilizer. Averaged across the three intercropping patterns, the chlorogenic acid and quercetin contents were 18.5% and 80.1% higher than the fennel sole cropping system. The chlorogenic acid and quercetin contents increased by 13% and 17% with BFS and 22% and 15% with HA, respectively (Table 5).Table 5 Concentration of phenolic compounds in fennel under different cropping patterns and fertilization.Full size tableFenugreekThe main effects of intercropping (I) pattern (C) and fertilizer (F) were significant for all parameters analyzed in fenugreek. Significant I × F interactions occurred for plant height, pod number per plant, seed yield, oil content, and oil yield of fenugreek (Table 6).Table 6 Analysis of variance for the effects of cropping patterns and fertilization on evaluated traits in fenugreek.Full size tablePlant heightThe 2F:2FG intercropping system fertilized with BFS produced the tallest fenugreek plants (63 cm), followed by 1F:2FG with BFS (53.3 cm) and 2F:4FG with BFS (56 cm), and 2F:2FG with HA (55 cm). The unfertilized control produced the shortest fenugreek plants (42 cm) in the sole cropping. Most fertilizer treatments across different intercropping patterns produced taller fenugreek plants than their sole cropping counterparts. Across fertilizer treatments, 1F:2FG, 2F:2FG, and 2F:4FG produced 16.2%, 26.8%, and 14.6% taller fenugreek plants than sole cropping, respectively. Across cropping patterns, BFS and HA increased fenugreek plant height by 5.7% and 15.2% compared with the unfertilized control, respectively (Fig. 4A).Figure 4Means comparison for the interaction effect of fertilization [C (control), HA (humic acid), BFS (biofertilizers)] and different cropping patterns [FGs (fenugreek sole cropping), 1F:2FG, 2F:2FG, 2F:4FG (ratios of fennel and fenugreek in the intercropping patterns)] on plant height (A) and pod number per plant (B) of fenugreek. Different lower-case letters above the bars indicate significant (p ≤ 0.05) differences.Full size imagePod number per plantThe fenugreek sole cropping with BFS and HA and 2F:4FG with BFS produced the most pods per plant (21.3, 20.3, and 20, respectively), while the unfertilized controls in 1F:2FG, 2F:2FG, and 2F:4FG produced the least (11.6, 12, and 13.3, respectively). Across fertilization treatments, 1F:2FG, 2F:2FG, and 2F:4FG had 30.1%, 25.6%, and 14.3% fewer pods per plant, respectively, than the fenugreek sole cropping system. Across cropping systems, HA and BFS increased pod number per plant in fenugreek by 25% and 33%, respectively, relative to the corresponding sole cropping (Fig. 4B).Seed number per podAcross fertilization treatments, fenugreek sole cropping produced the most seeds per pod (7.09), followed by 2F:4FG (6.02), 2F:2FG (4.93), and 1F:2FG (4.41) (Fig. 5A). In relative terms, sole cropping produced 60.5%, 43.9%, and 17.6% more seeds per pod than 1F:2FG, 2F:2FG, and 2F:4FG (Fig. 5A). Across cropping patterns, BFS and HA increased seed number per pod by 8.1% and 17.4% compared with the unfertilized control, respectively (Fig. 5B).Figure 5Means comparison for the main effects of cropping patterns [FGs (fenugreek sole cropping), 1F:2FG, 2F:2FG, 2F:4FG (ratios of fennel and fenugreek in the intercropping patterns)] on seed number per pod (A) and 1000-seed weight (C), and fertilization [C (control), HA (humic acid), BFS (biofertilizers)] on seed number per pod (B) and 1000-seed weight (D) of fennel. Different lower-case letters above the bars indicate significant (p ≤ 0.05) differences.Full size image1000-seed weightAmong different cropping patterns, sole cropping and 1F:2FG produced the highest (10.45 g) and lowest (8.34 g) fenugreek seed weights, respectively. In relative terms, fenugreek sole cropping produced 25.3%, 21.8%, and 12.4% higher seed weights than 1F:2FG, 2F:2FG, and 2F:4FG, respectively (Fig. 5C). Across cropping patterns, BFS and HA increased fenugreek seed weight by 3.7% and 5.7% compared with the control, respectively (Fig. 5D).Seed yieldMeans comparisons showed that sole cropping produced higher fenugreek seed yields than intercropping patterns. Sole cropping with BFS (1240 kg ha–1) and HA (1217 kg ha–1) produced the highest seed yields followed by the unfertilized control (Fig. 6A). The unfertilized control in 1F:2FG (437 kg ha–1) and 2F:2FG (467 kg ha–1) produced the lowest fenugreek seed yields. In all cases, and within each cropping pattern, BFS and HS produced higher fenugreek seed yields than the unfertilized control. As a result, BFS and HA increased fenugreek seed yield by 25.2% and 31.5% compared with the unfertilized control, respectively (Fig. 6A).Figure 6Means comparison for the interaction effects of fertilization [C (control), HA (humic acid), BFS (biofertilizers)] and different cropping patterns [FGs (fenugreek sole cropping), 1F:2FG, 2F:2FG, 2F:4FG (ratios of fennel and fenugreek in the intercropping patterns)] on seed yield (A), oil content (B), and oil yield (C) of fenugreek. Different lower-case letters above the bars indicate significant (p ≤ 0.05) differences.Full size imageOil content and yieldThe 2F:2FG cropping pattern with BFS produced the highest fenugreek oil content (8.3%), while the unfertilized control in sole cropping produced the lowest (5.9%). Across fertilizer treatments, 1F:2FG, 2F:2 FG, and 2F:4 FG produced 11.7%, 18.5%, and 15.7% higher fenugreek oil contents than sole cropping, respectively. In the 2F:2FG and 2F:4FG cropping patterns, BFS produced higher oil content (%) than HA. As a result, across cropping patterns, HA and BFS increased fenugreek oil content by 12.3% and 19.4%, respectively (Fig. 6B).Sole cropping with HA and BFS and 2F:2FG with BFS produced the highest fenugreek oil yields (77.1, 80.0, and 74.4 kg ha–1, respectively), while the unfertilized controls in 1F:2FG and 2F:4FG produced the lowest (27.51 and 29.8 kg ha–1, respectively). The 1F:2FG, 2F:2FG, and 2F:4FG cropping patterns produced 45.9%, 20.7%, and 41.5% lower fenugreek oil yields than fenugreek sole cropping, respectively. Moreover, except for sole cropping, BFS produced the highest fenugreek oil yield, followed by HA and the unfertilized control (Fig. 6C).Oil compoundsGC–FID and GC–MS analyses identified seven constituents (representing 91.09–99.27% of the total composition) in fenugreek oil. The main oil constituents were linoleic acid (26.1–37.1%), linolenic acid (16.9–22.4%), oleic acid (15.1–21.2%), palmitic acid (11.2–17.1%), lauric acid (5.0–12.3%), and myristic acid (3.1–6.4%). The highest linoleic and oleic acid percentages occurred in 1F:2FG and 2F:4FG with BFS. The 1F:2FG cropping pattern with BFS also had the highest linolenic acid percentage. The fenugreek sole cropping system without fertilization (control) had the lowest content of these three compounds. The intercropping patterns had 17%, 18.2%, and 17.1% higher oleic, linoleic, and linolenic acid contents than fenugreek sole cropping. In addition, HA and BFS increased oleic acid content by 15.6% and 8.8%, linoleic acid content by 12.8% and 7%, and linolenic acid content by 7.5% and 12.9%, respectively. Fenugreek sole cropping without fertilization produced the highest lauric acid and palmitic contents, 29.33% and 22.81% higher than the intercropping patterns (Table 7).Table 7 Proportion of fenugreek oil constituents under different cropping patterns and fertilization.Full size tablePhenolic compoundsThe main phenolic compounds in fenugreek were chlorogenic acid (2.01–5.49 ppm), caffeic acid (2.42–4.93 ppm), quercetin (1.98–4.45 ppm), comaric (1.09–2.43 ppm), apigenin (1.97–2.99 ppm), and gallic acid (1.76–2.92 ppm). The 2F:2FG cropping pattern with HA produced the highest quercetin and gallic acid contents, and 2F:4FG with HA produced the highest chlorogenic and caffeic acid contents. The 2F:2FG and 2F:4FG cropping patterns with BFS produced the highest comaric and apigenin contents, respectively. In contrast, fenugreek sole cropping without fertilization produced the lowest contents of the abovementioned compounds (Table 8).Table 8 Proportion of fenugreek concentration of phenolic compounds under different cropping patterns and fertilization.Full size tableLand equivalent ratio (LER)The 2F:4FG and 2F:2FG intercropping patterns treated with BFS had the highest partial LERs for fennel (0.82) and fenugreek (0.72), respectively. In addition, 2F:2FG with BFS and 1F:2FG without fertilization produced the highest (1.42) and lowest (0.86) total LERs, respectively (Fig. 7).Figure 7Partial and total land equivalent ratio (LER) for seed yields of different fennel and fenugreek intercropping patterns [1F:2FG, 2F:2FG, 2F:4FG (ratios of fennel and fenugreek in the intercropping patterns)] and fertilization [C (Control), HA (humic acid), BFS (biofertilizers)]. Different lower-case letters above the bars indicate significant (p ≤ 0.05) differences.Full size image More

Abbott RJ, Brennan AC (2014) Altitudinal gradients, plant hybrid zones and evolutionary novelty. Philos Trans R Soc B Biol Sci 369:6–9Article 

Google Scholar 
Avise JC (2000) Phylogeography: the history and formation of species. Harvard University Press, Cambridge, MABook 

Google Scholar 
Baldassarre DT, White TA, Karubian J, Webster MS (2014) Genomic and morphological analysis of a semipermeable avian hybrid zone suggests asymmetrical introgression of a sexual signal. Evolution 68:2644–2657PubMed 
Article 
PubMed Central 

Google Scholar 
Barr KR, Dharmarajan G, Rhodes OE, Lance R, Leberg PL (2007) Novel microsatellite loci for the study of the black-capped vireo (Vireo atricapillus). Mol Ecol Notes 7:1067–1069CAS 
Article 

Google Scholar 
Barton NH, Gale KS (1993) Hybrid zones and the evolutionary process. In: Harrison RG (ed.) Hybrid Zones and the Evolutionary Process. Oxford University Press, New York, NY
Google Scholar 
Barton NH, Hewitt GM (1989) Adaption, speciation and hybrid zones. Nature 341:497–503CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Billerman SM, Murphy MA, Carling MD (2016) Changing climate mediates sapsucker (Aves: Sphyrapicus) hybrid zone movement. Ecol Evol 6:7976–7990PubMed 
PubMed Central 
Article 

Google Scholar 
Bell RC, Irian CG (2019) Phenotypic and genetic divergence in reed frogs across a mosaic hybrid zone on São Tomé Island. Biol J Linn Soc 128:672–680Article 

Google Scholar 
Bensch S, Price T, Kohn J (1997) Isolation and characterization of microsatellite loci in a Phylloscopus warbler. Mol Ecol 6:91–92CAS 
PubMed 
Article 

Google Scholar 
Bradbury IR, Bowman S, Borza T, Snelgrove PVR, Hutchings JA, Berg PR et al. (2014) Long distance linkage disequilibrium and limited hybridization suggest cryptic speciation in Atlantic cod. PLoS ONE 9:e106330Article 
CAS 

Google Scholar 
Brelsford A, Irwin DE (2009) Incipient speciation despite little assortative mating: the yellow-rumped warbler hybrid zone. Evolution 63:3050–3060PubMed 
Article 

Google Scholar 
Burg TM, Croxall JP (2004) Global population structure and taxonomy of the wandering albatross species complex. Mol Ecol 13:2345–2355CAS 
PubMed 
Article 

Google Scholar 
Carling MD, Zuckerberg B (2011) Spatio-temporal changes in the genetic structure of the Passerina bunting hybrid zone. Mol Ecol 20:1166–1175PubMed 
Article 

Google Scholar 
Carling MD, Thomassen HA (2012) The role of environmental heterogeneity in maintaining reproductive isolation between hybridizing Passerina (Aves: Cardinalidae) buntings. Int J Ecol 2012:295463Article 

Google Scholar 
Carpenter AM, Graham BA, Spellman GM, Klicka J, Burg TM (2021) Genetic, bioacoustic and morphological analyses reveal cryptic speciation in the warbling vireo complex (Vireo gilvus: Vireonidae: Passeriformes). Zool J Linn Soc zlab036 https://doi.org/10.1093/zoolinnean/zlab036Cicero C, Johnson NK (1998) Molecular phylogeny and ecological diversification in a clade of New World songbirds (genus Vireo). Mol Ecol 7:1359–1370CAS 
PubMed 
Article 

Google Scholar 
Chenuil A, Cahill AE, Délémontey N, Du Salliant du Luc E, Fanton H (2019) Problems and questions posed by cryptic species. A framework to guide future studies. Assessing to conserving biodiversity. History, philosophy and theory of the life sciences, Vol. 24. Springer. Daubenmire, Cham
Google Scholar 
Cheviron ZA, Brumfield RT (2012) Genomic insights into adaptation to high-altitude environments. Heredity 108:354–361CAS 
PubMed 
Article 

Google Scholar 
Coyne JA, Orr HA (2004) Speciation. Sinauer and Associates, Sunderland, Massachusetts
Google Scholar 
Culumber ZW, Shepard DB, Colemans SW, Rosenthal GG, Tobler M (2012) Physiological adaptation along environmental gradients and replicated hybrid zone structure in swordtails (Teleostei: Xiphophorus). J Evol Biol 25:1800–1814CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Dubay SG, Witt CC (2014) Differential high-altitude adaptation and restricted gene flow across a mid-elevation hybrid zone in Andean tit-tyrant flycatchers. Mol Ecol 23:3551–3565PubMed 
Article 
PubMed Central 

Google Scholar 
Garroway CJ, Bowman J, Cascaden TJ, Holloway GL, Mahan CG, Malcolm JR et al. (2010) Climate change induced hybridization in flying squirrels. Glob Chang Biol 16:113–121Article 

Google Scholar 
Grabenstein KC, Taylor SA (2018) Breaking barriers: Causes, consequences, and experimental utility of human-mediated hybridization. Trends Ecol Evol 33:198–212PubMed 
Article 

Google Scholar 
Graham BA, Cicero C, Strickland D, Woods JG, Coneybeare H, Dohms KM et al. (2021) Cryptic genetic diversity and cytonuclear discordance characterize contact among Canada jay (Perisoreus canadensis) morphotypes in western North America. Biol J Linn Soc 132:725–740Article 

Google Scholar 
Hammer Ø, Harper DA, Ryan PD (2001) Paleontological statistics software package for education and data analysis. Palaeontol Electron 4:9Haselhorst MSH, Parchman TL, Buerkle CA (2019) Genetic evidence for species cohesion, substructure and hybrids in spruce. Mol Ecol 28:2029–2045PubMed 
Article 

Google Scholar 
Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978Article 

Google Scholar 
Hawley DM (2005) Isolation and characterization of eight microsatellite loci from the house finch (Carpodactus mexicanus). Mol Ecol Notes 5:443–445CAS 
Article 

Google Scholar 
Hebert PDN, Stoeckle MY, Zemlak TS, Francis CM (2004) Identification of birds through DNA barcodes. PLoS Biol 2:e312PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Hewitt GM (1988) Hybrid zones-natural laboratories for evolutionary studies. Trends Ecol Evol 3:158–167CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Hewitt GM (2001) Speciation, hybrid zones and phylogeography—or seeing genes in space and time. Mol Ecol 10:537–549CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978Article 

Google Scholar 
Hindley JA, Graham BA, Pulgarin-R PC, Burg TM (2018) The influence of latitude, geographic distance, and habitat discontinuities on genetic variation in a high latitude montane species. Sci Rep. 8:11846CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hinojosa JC, Koubínová D, Szenteczki MA, Pitteloud C, Dincă V, Alvarez N et al. (2019) A mirage of cryptic species: Genomics uncover striking mitonuclear discordance in the butterfly Thymelicus sylvestris. Mol Ecol 28:3857–3868PubMed 
Article 
PubMed Central 

Google Scholar 
Hubisz MJ, Falush D, Stephens M, Pritchard JK (2009) Inferring weak population structure with the assistance of sample group information. Mol Ecol Res 9:1322–1332Article 

Google Scholar 
Irwin DE (2020) Assortative mating in hybrid zones is remarkably ineffective in promoting speciation. Evolution 195:E150–E167
Google Scholar 
Johnson NK (1995) Speciation in vireos. I. Macrogeographic patterns of allozymic variation in the Vireo solitarius complex in the contiguous United States. Condor 97:903–919Article 

Google Scholar 
Johnson NK, Cicero C (2004) New mitochondrial DNA data affirm the importance of Pleistocene speciation in North American birds. Evolution 58:1122–1130PubMed 
Article 
PubMed Central 

Google Scholar 
Larson EL, Tinghitella RM, Taylor SA (2019) Insect hybridization and climate change. Front Ecol Evol 7:348Article 

Google Scholar 
Legendre P, Fortin M-J (2010) Comparison of the Mantel test and alternative approaches for detecting complex multivariate relationships in the spatial analysis of genetic data. Mol Ecol Resour 10:831–844PubMed 
Article 

Google Scholar 
Lovell SF, Lein MR, Rogers SM (2021) Cryptic speciation in the warbling vireo (Vireo gilvus). Ornithology 138:ukaa071Article 

Google Scholar 
MacDonald ZG, Dupuis JR, Davis CS, Acorn JH, Nielsen SE, Sperling FAH (2020) Gene flow and climate-associated genetic variation in a vagile habitat specialist. Mol Ecol 29:3889–3906PubMed 
Article 

Google Scholar 
Manthey JD, Klicka J, Spellman GM (2011) Cryptic diversity in a widespread North American songbird: phylogeography of the brown creeper (Certhia americana). Mol Phylogenet Evol 58:502–512PubMed 
Article 

Google Scholar 
Marchetti K, Price T, Richman A (1995) Correlates of wing morphology with foraging behaviour and migration distance in the genus Phylloscopus. J Av Biol 26:177–181Article 

Google Scholar 
Martin H, Touzet P, Dufay M, Gode C, Schmitt E, Lahiani E et al. (2017) Lineages of Silene nutans developed rapid, strong, asymmetric postzygotic reproductive isolation in allopatry. Evolution 71:1519–1531CAS 
PubMed 
Article 

Google Scholar 
Martinez JG, Soler JJ, Soler M, Moller AP, Burke T (1999) Comparative population structure and gene flow of a brood parasite, the great spotted cuckoo (Clamator glandarius) and its primary host, the magpie (Pica pica). Evolution 53:269–278CAS 
PubMed 
PubMed Central 

Google Scholar 
Mettler RD, Spellman GM (2009) A hybrid zone revisited: Molecular and morphological analysis of the maintenance, movement, and evolution of a Great Plains avian (Cardinalidae: Pheucticus) hybrid zone. Mol Ecol 18:3256–3267CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Meirmans PG, Van Tienderen PH (2004) GENOTYPE and GENODIVE: two programs for the analysis of genetic diversity of asexual organisms. Mol Ecol Notes 4:792–794Article 

Google Scholar 
Nowakowski JK, Szulc J, Remisiewicz M (2014) The further the flight, the longer the wing: relationship between wing length and migratory distance in Old World reed and bush warblers (Acrocephalidae and Locustellidae). Ornis Fennica 91:178–186
Google Scholar 
Pavolova A, Amos JN, Joseph L, Loynes K, Austin JJ, Keogh JS et al. (2013) Perched at the mito-nuclear crossroads: divergent mitochondrial lineages correlate with environment in the face of ongoing nuclear gene flow in an Australian bird. Evol 67:3412–3428Article 
CAS 

Google Scholar 
Piertney SB, Marquiss M, Summers R (1998) Characterization of tetranucleotide microsatellite markers in the Scottish crossbill (Loxia scotica). Mol Ecol 7:1261–1263CAS 
PubMed 
Article 

Google Scholar 
Porras-Hurtado L, Ruiz Y, Santos C, Phillips C, Carracedo A, Lareu MV (2013) An overview of STRUCTURE: Applications, parameter settings, and supporting software. Front Genet 4:98PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Pritchard J, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Reding DM, Castañeda-Rico S, Shirazi S, Hofman CA, Cancellare IA, Lance SL et al. (2021) Mitochondrial genomes of the United States distribution of gray fox (Urocyon cinereoargenteus) reveal a major phylogeographic break at the Great Plains suture zone. Front Ecol Evol. https://doi.org/10.3389/fevo.2021.666800.Richardson DS, Jury FL, Dawson DA, Salgueiro P, Komdeur J, Burke T (2003) Fifty Seychelles warbler (Acrocephalus sechellensis) microsatellite loci polymorphic in Sylviidae species and their cross-species amplification in other passerine birds. Mol Ecol 9:2225–2230Article 

Google Scholar 
Riordan EC, Gugger PF, Ortego J, Smith C, Gaddis K, Thompson P et al. (2016) Association of genetic and phenotypic variability with geography and climate in three southern California oaks. Am J Bot 103:73–85PubMed 
Article 

Google Scholar 
Rush AC, Cannings RJ, Irwin DE (2009) Analysis of multilocus DNA reveals hybridization in a contact zone between Empidonax flycatchers. J Avian Biol 40:614–624Article 

Google Scholar 
Sartor CC, Cushman SA, Wan HY, Kretschmer R, Pereira JA, Bou N et al. (2021) The role of the environment in the spatial dynamics of an extensive hybrid zone between two neotropical cats. J Evol Biol 34:614–627PubMed 
Article 

Google Scholar 
Schukman JM, Lira-Noriega A, Townsend Peterson A (2011) Multiscalar ecological characterization of Say’s and eastern phoebes and their zone of contact in the Great Plains. Condor 113:372–384Article 

Google Scholar 
Seehausen O, Takimoto G, Roy D, Jokela J (2008) Speciation reversal and biodiversity dynamics with hybridization in changing environments. Mol Ecol 17:30–44PubMed 
Article 
PubMed Central 

Google Scholar 
Semenchuk GP (1992) The Atlas of Breeding Birds of Alberta. Fed. of Alberta Naturalists, Edmonton, p 243
Google Scholar 
Peakall R, Smouse PE (2012) GenAlEx 6.5: Genetic analysis in Excel. Population genetic software for teaching and research–an update. Bioinformatics 28:2537–2539CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Sorenson MD, Ast JC, Dimcheff DE, Yuri T, Mindell DP (1999) Primers for a PCR-based approach to mitochondrial genome sequencing in birds and other vertebrates. Mol Phylogent Evol 12:105–114CAS 
Article 

Google Scholar 
Spellman GM, Klicka J (2007) Phylogeography of the white-breasted nuthatch (Sitta carolinensis): diversification in North American pine and oak woodlands. Mol Ecol 16:1729–1740CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Stenzler LM, Fitzpatrick JW (2002) Isolation of microsatellite loci in the Florida scrub jay Aphelocoma coerulescens. Mol Ecol Notes 2:547–550CAS 
Article 

Google Scholar 
Swenson NG (2006) GIS-based niche models reveal unifying climatic mechanisms that maintain location of avian hybrid zones in a North America suture zone. J Evol Biol. 19:717–725CAS 
PubMed 
Article 

Google Scholar 
Swenson NG, Howard DJ (2005) Clustering of contact zones, hybrid zones, and phylogeographic breaks in North America. Am Nat 166:581–591PubMed 
Article 

Google Scholar 
Tarr CL, Fleischer RC (1998) Primers for polymorphic GT microsatellites isolated from the Mariana crow, Corvus kubaryi. Mol Ecol 7:253–255CAS 
PubMed 
Article 

Google Scholar 
Tarroso P, Pereira RJ, Martínez-Freiría F, Godinho R, Brito JC (2014) Hybridization at an ecotone: Ecological and genetic barriers between three Iberian vipers. Mol Ecol 23:1108–1123CAS 
PubMed 
Article 

Google Scholar 
Taylor SA, Larson EL, Harrison RG (2015) Hybrid zones: windows on climate change. Trends Ecol Evol 30:398–406PubMed 
PubMed Central 
Article 

Google Scholar 
Toews DPL, Mandic M, Richards JG, Irwin DE (2014) Migration, mitochondria and the yellow-rumped warbler. Evolution 68:241–255CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Toews DPL, Campagna L, Taylor SA, Balakrishnan CN, Baldassarre DT, Deane-Coe PE et al. (2016) Genomic approaches to understanding population divergence and speciation in birds. Auk 133:13–30Article 

Google Scholar 
Toews DPL, Irwin DE (2008) Cryptic speciation in a Holarctic passerine revealed by genetic and bioacoustic analyses. Mol Ecol 17:2691–2705CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
van Els P, Cicero C, Klicka J (2012) High latitudes and high genetic diversity: Phylogeography of a widespread boreal bird, the gray jay (Perisoreus canadensis). Mol Phylogenet Evol 63:456–465PubMed 
Article 
PubMed Central 

Google Scholar 
Voelker G, Rohwer S (1998) Contrasts in scheduling of molt and migration in eastern and western warbling vireos. Auk 155:142–155Article 

Google Scholar 
Walsh J, Billerman SM, Rohwer VG, Butcher BG, Lovette IJ (2020) Genomic and plumage variation across the controversial Baltimore and Bullock’s oriole hybrid zone. Auk 137:1–15Article 

Google Scholar 
Walsh J, Rowe RJ, Olsen BJ, Shriver WG, Kovach AI (2016) Genotype-environment associations support a mosaic hybrid zone between two tidal marsh birds. Ecol Evol 6:279–294PubMed 
Article 
PubMed Central 

Google Scholar 
Walsh P, Metzger D, Higuchi R (1991) Chelex 100 as a medium for simple extraction of DNA for PCR-based typing from forensic material. Biotechniques 10:506–513CAS 
PubMed 
PubMed Central 

Google Scholar 
Weir JT, Schluter D (2004) Ice sheets promote speciation in boreal birds. Proc R Soc B 271:1881–1887PubMed 
PubMed Central 
Article 

Google Scholar 
Williams JW (2003) Variations in tree cover in North America since the last glacial maximum. Glob Planet Change 35:1–23Article 

Google Scholar 
Williams DA, Berg EC, Hale AM, Hughes CR (2004) Characterization of microsatellites for parentage studies of white-throated magpie-jays (Calocitta formosa) and brown jays (Cyanocorax morio). Mol Ecol Notes 4:509–511CAS 
Article 

Google Scholar 
Zwartjes PW (2001) Genetic structuring among migratory populations of the black-whiskered vireo, with a comparison to the red-eyed vireo. Condor 103:439–448Article 

Google Scholar  More

Paez-Espino D, Eloe-Fadrosh EA, Pavlopoulos GA, Thomas AD, Huntemann M, Mikhailova N, et al. Uncovering Earth’s virome. Nature. 2016;536:425–30.CAS 
PubMed 

Google Scholar 
Anderson PK, Cunningham AA, Patel NG, Morales FJ, Epstein PR, Daszak P. Emerging infectious diseases of plants: pathogen pollution, climate change and agrotechnology drivers. Trends Ecol Evol. 2004;19:535–44.PubMed 

Google Scholar 
Taylor LH, Latham SM, Woolhouse MEJ. Risk factors for human disease emergence. Philos Trans R Soc B Biol Sci. 2001;356:983–9.CAS 

Google Scholar 
White R, Murray S, Rohweder M. Pilot analysis of global ecosystems: grassland ecosystems. 2000 World Resources Institute. Washington, DC.Zhao Y, Liu Z, Wu J. Grassland ecosystem services: a systematic review of research advances and future directions. Landsc Ecol. 2020;35:793–814.
Google Scholar 
Trubl G, Jang HBin, Roux S, Emerson JB, Solonenko N, Vik DR, et al. Soil viruses are underexplored players in ecosystem carbon processing. mSystems. 2018;3:e00076–18.CAS 
PubMed 
PubMed Central 

Google Scholar 
Emerson JB, Roux S, Brum JR, Bolduc B, Woodcroft BJ, Jang HBin, et al. Host-linked soil viral ecology along a permafrost thaw gradient. Nat Microbiol. 2018;3:870–80.CAS 
PubMed 
PubMed Central 

Google Scholar 
Zablocki O, Adriaenssens EM, Frossard A, Seely M, Ramond J-B, Cowan D. Metaviromes of extracellular soil viruses along a Namib desert aridity gradient. Genome Announc. 2017;5:e01470–16.PubMed 
PubMed Central 

Google Scholar 
Jin M, Guo X, Zhang R, Qu W, Gao B, Zeng R. Diversities and potential biogeochemical impacts of mangrove soil viruses. Microbiome. 2019;7:58.PubMed 
PubMed Central 

Google Scholar 
Adriaenssens EM, Kramer R, Van Goethem MW, Makhalanyane TP, Hogg I, Cowan DA. Environmental drivers of viral community composition in Antarctic soils identified by viromics. Microbiome. 2017;5:83.PubMed 
PubMed Central 

Google Scholar 
Williamson KE, Fuhrmann JJ, Wommack KE, Radosevich M. Viruses in soil ecosystems: an unknown quantity within an unexplored territory. Annu Rev Virol. 2017;4:201–19.CAS 
PubMed 

Google Scholar 
Starr EP, Nuccio EE, Pett-Ridge J, Banfield JF, Firestone MK. Metatranscriptomic reconstruction reveals RNA viruses with the potential to shape carbon cycling in soil. Proc Natl Acad Sci. 2019;116:25900–8.CAS 
PubMed 
PubMed Central 

Google Scholar 
Wu R, Davison MR, Gao Y, Nicora CD, Mcdermott JE, Burnum-Johnson KE, et al. Moisture modulates soil reservoirs of active DNA and RNA viruses. Commun Biol. 2021;4:1–11.
Google Scholar 
Hurwitz BL, Sullivan MB. The Pacific Ocean Virome (POV): a marine viral metagenomic dataset and associated protein clusters for quantitative viral ecology. PLoS One. 2013;8:e57355.CAS 
PubMed 
PubMed Central 

Google Scholar 
Breitbart M, Bonnain C, Malki K, Sawaya NA. Phage puppet masters of the marine microbial realm. Nat Microbiol. 2018;3:754–66.CAS 
PubMed 

Google Scholar 
Wolf YI, Kazlauskas D, Iranzo J, Lucía-Sanz A, Kuhn JH, Krupovic M, et al. Origins and evolution of the Global RNA virome. MBio. 2018;9:e02329–18.PubMed 
PubMed Central 

Google Scholar 
Shi M, Lin XD, Tian JH, Chen LJ, Chen X, Li CX, et al. Redefining the invertebrate RNA virosphere. Nature. 2016;540:539–43.CAS 

Google Scholar 
Callanan J, Stockdale SR, Shkoporov A, Draper LA, Ross RP, Hill C. Expansion of known ssRNA phage genomes: from tens to over a thousand. Sci Adv. 2020;6:eaay5981.CAS 
PubMed 
PubMed Central 

Google Scholar 
Koonin EV, Dolja VV, Krupovic M, Varsani A, Wolf YI, Yutin N, et al. Global organization and proposed megataxonomy of the virus world. Microbiol Mol Biol Rev. 2020;84:e00061-19.PubMed 
PubMed Central 

Google Scholar 
Cobbin JC, Charon J, Harvey E, Holmes EC, Mahar JE. Current challenges to virus discovery by meta-transcriptomics. Curr Opin Virol. 2021;51:48–55.CAS 
PubMed 

Google Scholar 
Trubl G, Hyman P, Roux S, Abedon ST. Coming-of-age characterization of soil viruses: a user’s guide to virus isolation, detection within metagenomes, and viromics. Soil Syst. 2020;4:1–34. MDPI AG.
Google Scholar 
Santos-Medellin C, Zinke LA, ter Horst AM, Gelardi DL, Parikh SJ, Emerson JB. Viromes outperform total metagenomes in revealing the spatiotemporal patterns of agricultural soil viral communities. ISME J. 2021;15:1–15.
Google Scholar 
Adriaenssens EM, Farkas K, Harrison C, Jones DL, Allison HE, McCarthy AJ. Viromic analysis of wastewater input to a river catchment reveals a diverse assemblage of RNA viruses. mSystems. 2018;3:e00025–18.CAS 
PubMed 
PubMed Central 

Google Scholar 
Bibby K, Peccia J. Identification of viral pathogen diversity in sewage sludge by metagenome analysis. Environ Sci Technol. 2013;47:1945–51.CAS 
PubMed 
PubMed Central 

Google Scholar 
Culley A. New insight into the RNA aquatic virosphere via viromics. Virus Res. 2018;244:84–89.CAS 
PubMed 

Google Scholar 
Withers E, Hill PW, Chadwick DR, Jones DL. Use of untargeted metabolomics for assessing soil quality and microbial function. Soil Biol Biochem. 2020;143:107758.CAS 

Google Scholar 
Trubl G, Solonenko N, Chittick L, Solonenko SA, Rich VI, Sullivan MB. Optimization of viral resuspension methods for carbon-rich soils along a permafrost thaw gradient. PeerJ. 2016;4:e1999.PubMed 
PubMed Central 

Google Scholar 
Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. J. 2011;17:10.
Google Scholar 
Joshi N, Fass J. Sickle: a sliding-window, adaptive, quality-based trimming tool for FastQ files. 2011.Schmieder R, Edwards R. Quality control and preprocessing of metagenomic datasets. Bioinformatics. 2011;27:863–4.CAS 
PubMed 
PubMed Central 

Google Scholar 
Kopylova E, Noé L, Touzet H. SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics. 2012;28:3211–7.CAS 
PubMed 

Google Scholar 
Li D, Liu C-M, Luo R, Sadakane K, Lam T-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics. 2015;31:1674–6.CAS 
PubMed 

Google Scholar 
Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2014;12:59–60. Nature Publishing Group.PubMed 

Google Scholar 
Huson DH, Beier S, Flade I, Górska A, El-Hadidi M, Mitra S. et al.MEGAN Community Edition – interactive exploration and analysis of large-scale microbiome sequencing data.PLOS Comput Biol. 2016;12:e1004957PubMed 
PubMed Central 

Google Scholar 
Hyatt D, Chen GL, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 2010;11:119.
Google Scholar 
Mistry J, Finn RD, Eddy SR, Bateman A, Punta M. Challenges in homology search: HMMER3 and convergent evolution of coiled-coil regions. Nucleic Acids Res. 2013;41:e121–e121.CAS 
PubMed 
PubMed Central 

Google Scholar 
Li W, Godzik A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics. 2006;22:1658–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
Roux S, Adriaenssens EM, Dutilh BE, Koonin EV, Kropinski AM, Krupovic M, et al. Minimum information about an uncultivated virus genome (MIUViG). Nat Biotechnol. 2018;37:29–37.PubMed 
PubMed Central 

Google Scholar 
Germain P-L, Vitriolo A, Adamo A, Laise P, Das V, Testa G. RNAontheBENCH: computational and empirical resources for benchmarking RNAseq quantification and differential expression methods. Nucleic Acids Res. 2016;44:5054–67.CAS 
PubMed 
PubMed Central 

Google Scholar 
Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, et al. vegan: Community Ecology Package. 2019.Wickham H. ggplot2: elegant graphics for data analysis. 2016. Springer-Verlag New York.Conway JR, Lex A, Gehlenborg N. UpSetR: an R package for the visualization of intersecting sets and their properties. Bioinformatics. 2017;33:2938–40.CAS 
PubMed 
PubMed Central 

Google Scholar 
Katoh K. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 2002;30:3059–66.CAS 
PubMed 
PubMed Central 

Google Scholar 
Price MN, Dehal PS, Arkin AP. FastTree 2 – approximately maximum-likelihood trees for large alignments. PLoS One. 2010;5:e9490.PubMed 
PubMed Central 

Google Scholar 
Letunic I, Bork P. Interactive Tree Of Life (iTOL) v4: recent updates and new developments. Nucleic Acids Res. 2019;47:W256–W259.CAS 
PubMed 
PubMed Central 

Google Scholar 
Roux S, Emerson JB, Eloe-Fadrosh EA, Sullivan MB. Benchmarking viromics: an in silico evaluation of metagenome-enabled estimates of viral community composition and diversity. PeerJ. 2017;5:e3817.PubMed 
PubMed Central 

Google Scholar 
Ayllón MA, Turina M, Xie J, Nerva L, Marzano SYL, Donaire L, et al. ICTV virus taxonomy profile: botourmiaviridae. J Gen Virol. 2020;101:454–5.PubMed 
PubMed Central 

Google Scholar 
Krishnamurthy SR, Janowski AB, Zhao G, Barouch D, Wang D. Hyperexpansion of RNA bacteriophage diversity. PLOS Biol. 2016;14:e1002409.PubMed 
PubMed Central 

Google Scholar 
Hillman BI, Cai G. The family Narnaviridae. Simplest of RNA viruses. Adv Virus Res. 2013;86:149–76.
Google Scholar 
Obbard DJ, Shi M, Roberts KE, Longdon B, Dennis AB. A new lineage of segmented RNA viruses infecting animals. Virus Evol. 2020;6:61.
Google Scholar 
Xu X, Bei J, Xuan Y, Chen J, Chen D, Barker SC, et al. Full-length genome sequence of segmented RNA virus from ticks was obtained using small RNA sequencing data. BMC Genom. 2020;21:1–8.
Google Scholar 
Roossinck MJ. The good viruses: viral mutualistic symbioses. Nat Rev Microbiol. 2011;9:99–108. Nature Publishing Group.CAS 
PubMed 

Google Scholar 
Milgroom MG, Cortesi P. Biological control of chestnut blight with hypovirulence: a critical analysis. Annu Rev Phytopathol. 2004;42:311–38. Annual ReviewsCAS 
PubMed 

Google Scholar 
Zell R, Delwart E, Gorbalenya AE, Hovi T, King AMQ, Knowles NJ, et al. ICTV virus taxonomy profile: Picornaviridae. J Gen Virol. 2017;98:2421–2.CAS 
PubMed 
PubMed Central 

Google Scholar 
Valles SM, Chen Y, Firth AE, Guérin DMA, Hashimoto Y, Herrero S, et al. ICTV virus taxonomy profile: Dicistroviridae. J Gen Virol. 2017;98:355–6.CAS 
PubMed 
PubMed Central 

Google Scholar 
Barrios E. Soil biota, ecosystem services and land productivity. Ecol Econ. 2007;64:269–85.
Google Scholar 
Vainio EJ, Chiba S, Ghabrial SA, Maiss E, Roossinck M, Sabanadzovic S, et al. ICTV virus taxonomy profile: Partitiviridae. J Gen Virol. 2018;99:17–18.CAS 
PubMed 

Google Scholar 
Yong CY, Yeap SK, Omar AR, Tan WS. Advances in the study of nodavirus. PeerJ. 2017;2017:e3841.
Google Scholar 
Schmitt AP, Lamb RA. Escaping from the cell: assembly and budding of negative-strand RNA viruses. In: Kawaoka Y (ed). Biology of negative-strand RNA viruses: the power of reverse genetics. 2004. (Springer Berlin Heidelberg, Berlin, Heidelberg, pp 145–96.Käfer S, Paraskevopoulou S, Zirkel F, Wieseke N, Donath A, Petersen M, et al. Re-assessing the diversity of negative-strand RNA viruses in insects. PLoS Pathog. 2019;15:e1008224.PubMed 
PubMed Central 

Google Scholar 
Bejerman N, Debat H, Dietzgen, RG. The plant negative-sense RNA virosphere: virus discovery through new eyes. Front. Microbiol. 2020;11:588427.PubMed 
PubMed Central 

Google Scholar 
Wolf YI, Silas S, Wang Y, Wu S, Bocek M, Kazlauskas D, et al. Doubling of the known set of RNA viruses by metagenomic analysis of an aquatic virome. Nat Microbiol. 2020;5:1262–70.CAS 
PubMed 
PubMed Central 

Google Scholar 
Adriaenssens EM, Kramer R, van Goethem MW, Makhalanyane TP, Hogg I, Cowan DA. Environmental drivers of viral community composition in Antarctic soils identified by viromics. Microbiome. 2017;5:1–14.
Google Scholar 
Mahmoud H, Jose L. Phage and nucleocytoplasmic large viral sequences dominate coral viromes from the Arabian Gulf. Front Microbiol. 2017;8:2063.PubMed 
PubMed Central 

Google Scholar 
Koyama A, Steinweg JM, Haddix ML, Dukes JS, Wallenstein MD. Soil bacterial community responses to altered precipitation and temperature regimes in an old field grassland are mediated by plants. FEMS Microbiol Ecol. 2018;94:fix156.
Google Scholar 
Hurwitz BL, Hallam SJ, Sullivan MB. Metabolic reprogramming by viruses in the sunlit and dark ocean. Genome Biol. 2013;14:R123.PubMed 
PubMed Central 

Google Scholar  More

Seymour, C. L. et al. Caught on camera: The impacts of urban domestic cats on wild prey in an African city and neighbouring protected areas. Glob. Ecol. Conserv. 23, e01198 (2020).Article 

Google Scholar 
Mori, E. et al. License to Kill? Domestic Cats Affect a Wide Range of Native Fauna in a Highly Biodiverse Mediterranean Country. Front. Ecol. Evol. 7, 477 (2019).Kays, R. et al. The small home ranges and large local ecological impacts of pet cats. Anim. Conserv. 23, 516–523 (2020).Loss, S. R., Will, T. & Marra, P. P. The impact of free-ranging domestic cats on wildlife of the United States. Nat. Commun. 4, 1396 (2013).ADS 
Article 

Google Scholar 
Van Heezik, Y., Smyth, A., Adams, A. & Gordon, J. Do domestic cats impose an unsustain386 able harvest on urban bird populations?. Biol. Conserv. 143, 121–130 (2010).Article 

Google Scholar 
Woods, M., McDonald, R. A. & Harris, S. Predation of wildlife by domestic cats Felis catus in Great Britain. Mammal Rev. 33, 174–188 (2003).Article 

Google Scholar 
Li, Y. et al. Estimates of wildlife killed by free-ranging cats in China. Biol. Conserv. 253, 108929 (2021).Article 

Google Scholar 
Barratt, D. G. Home range size, habitat utilisation and movement patterns of suburban and farm cats Felis catus. Ecography 20, 271–280 (1997).Article 

Google Scholar 
Moseby, K. E., Stott, J. & Crisp, H. Movement patterns of feral predators in an arid environment–implications for control through poison baiting. English. Wildl. Res. 36, 422–435 (2009).Article 

Google Scholar 
Hall, C. M. et al. Factors determining the home ranges of pet cats: A meta-analysis. Biol. Conserv. 203, 313–320 (2016).Article 

Google Scholar 
Castañeda, I. et al. Trophic patterns and home-range size of two generalist urban carnivores: A review. J. Zool. 307, 79–92 (2019).Article 

Google Scholar 
Hebblewhite, M. & Haydon, D. T. Distinguishing technology from biology: A critical review of the use of GPS telemetry data in ecology. Philos. Trans. R. Soc. B Biol. Sci. 365, 2303–2312 (2010).Article 

Google Scholar 
Allen, A. M. et al. Scaling up movements: From individual space use to population patterns. Ecosphere 7, e01524 (2016).
Google Scholar 
Trouwborst, A., McCormack, P. C. & Martínez Camacho, E. Domestic cats and their impacts on biodiversity: A blind spot in the application of nature conservation law. People Nat. 2, 235–250 (2020).Article 

Google Scholar 
Sims, V., Evans, K. L., Newson, S. E., Tratalos, J. A. & Gaston, K. J. Avian assemblage structure and domestic cat densities in urban environments. Divers. Distrib. 14, 387–399 (2008).Article 

Google Scholar 
Lepczyk, C. A., Mertig, A. G. & Liu, J. Landowners and cat predation across rural-to-urban landscapes. Biol. Conserv. 115, 191–201 (2004).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing R Foundation for Statistical Computing (Vienna, Austria, 2021).Heggøy, O. & Shimmings, P. Huskattens predasjon på fugler i Norge. En vurdering basert på en litteraturgjennomgang tech. rep. 36 (2018).Morgan, S. et al. Urban cat (Felis catus) movement and predation activity associated with a wetland reserve in New Zealand. Wildl. Res. 36, 574–580 (2009).Calver, M., Grayson, J., Lilith, M. & Dickman, C. Applying the precautionary principle to the issue of impacts by pet cats on urban wildlife. Biol. Conserv. 144, 1895–1901 (2011).Article 

Google Scholar 
Crowley, S., Cecchetti, M. & Mcdonald, R. Diverse perspectives of cat owners indicate bar riers to and opportunities for managing cat predation of wildlife. Front. Ecol. Environ. 18, 544–549 (2020).Treves, A., Krofel, M., Ohrens, O. & van Eeden, L. M. Predator control needs a standard of unbiased randomized experiments with cross-over design. Front. Ecol. Evol. 7, 462 (2019).Ferreira, G. A., Machado, J. C., Nakano-Oliveira, E., Andriolo, A. & Genaro, G. The effect of castration on home range size and activity patterns of domestic cats living in a natural area in a protected area on a Brazilian island. Appl. Anim. Behav. Sci. 230, 105049 (2020).Bengsen, A. J. et al. Feral cat home-range size varies predictably with landscape productivity and population density. J. Zool. 298, 112–120 (2016).Article 

Google Scholar 
López-Jara, M. J. et al. Free-roaming domestic cats near conservation areas in Chile: Spatial movements, human care and risks for wildlife. Perspect. Ecol. Conserv. 19, 387–398 (2021).Gillies, C. & Clout, M. The prey of domestic cats (Felis catus) in two suburbs of Auckland City, New Zealand. J. Zool. 259, 309–315 (2003).Article 

Google Scholar 
Pirie, T. J., Thomas, R. L. & Fellowes, M. D. E. Pet cats (Felis catus) from urban boundaries use different habitats, have larger home ranges and kill more prey than cats from the suburbs. Landsc. Urban Plan. 220, 104338 (2022).Article 

Google Scholar 
Vucetich, J. A., Hebblewhite, M., Smith, D. W. & Peterson, R. O. Predicting prey population dynamics from kill rate, predation rate and predator-prey ratios in three wolf-ungulate systems. J. Anim. Ecol. 80, 1236–1245 (2011).Article 

Google Scholar 
Kennedy, M., Phillips, B. E. N. L., Legge, S., Murphy, S. A. & Faulkner, R. A. Do dingoes suppress the activity of feral cats in northern Australia?. Austral Ecol. 37, 134–139 (2012).Article 

Google Scholar 
Crooks, K. R. & Soule, M. E. Mesopredator release and avifaunal extinctions in a fragmented system. English. Nature 400, 563–566 (1999).ADS 
CAS 
Article 

Google Scholar 
Ferreira, J. P., Leita, O. I., Santos-Reis, M. & Revilla, E. Human-related factors regulate the spatial ecology of domestic cats in sensitive areas for conservation. PLOS ONE 6, e25970 (2011).ADS 
CAS 
Article 

Google Scholar 
Brook, L. A., Johnson, C. N. & Ritchie, E. G. Effects of predator control on behaviour of an apex predator and indirect consequences for mesopredator suppression. J. Appl. Ecol. 49, 1278–1286 (2012).Article 

Google Scholar 
Laundre, J. W., Hernandez, L. & Altendorf, K. B. Wolves, elk, and bison: Reestablishing the “landscape of fear’’ in Yellowstone National Park, USA. English. Can. J. Zool. 79, 1401–1409 (2001).Article 

Google Scholar 
Ritchie, E. G. & Johnson, C. N. Predator interactions, mesopredator release and biodiversity conservation. English. Ecol. Lett. 12, 9820–998 (2009).Article 

Google Scholar 
Milleret, C. et al. GPS collars have an apparent positive effect on the survival of a large carnivore. Biol. Lett. 17, 20210128 (2021).Cecchetti, M., Crowley, S. L., Goodwin, C. E. D. & McDonald, R. A. Provision of high meat content food and object play reduce predation of wild animals by domestic cats Felis catus. Curr. Biol. 31, 1107-1111.e5 (2021).CAS 
Article 

Google Scholar 
Linklater, W., Farnworth, M., van Heezik, Y., Stafford, K. & Macdonald, E. Prioritizing cat owner behaviors for a campaign to reduce wildlife depredation. Conserv. Sci. Pract. 1, 1:e29 (2019).Selinske, M. J. et al. Identifying and prioritizing human behaviors that benefit biodiversity. Conserv. Sci. Pract. 2, e249 (2020).
Google Scholar 
McDonald, J. L., Maclean, M., Evans, M. R. & Hodgson, D. J. Reconciling actual and perceived rates of predation by domestic cats. Ecol. Evol. 5, 2745–2753 (2015).Article 

Google Scholar 
Bischof, R. et al. Estimating and forecasting spatial population dynamics of apex predators using transnational genetic monitoring. Proc. Natl. Acad. Sci. 117, 30531–30538 (2020).CAS 
Article 

Google Scholar 
Bischof, R., Gjevestad, J. G. O., Ordiz, A., Eldegard, K. & Milleret, C. High frequency GPS bursts and path-level analysis reveal linear feature tracking by red foxes. Sci. Rep. 9, 8849 (2019).ADS 
Article 

Google Scholar 
Gupte, P. R. et al. A guide to pre-processing high-throughput animal tracking data. J. Anim. Ecol. 91, 287–307 (2022).Article 

Google Scholar 
Morris, G. & Conner, L. Assessment of accuracy, fix success rate, and use of estimated horizontal position error (EHPE) to filter inaccurate data collected by a common commercially available GPS logger. PLoS ONE 12, e0189020 (2017).Article 

Google Scholar 
Clapp, J. G., Holbrook, J. D. & Thompson, D. J. GPSeqClus: An R package for sequential clustering of animal location data for model building, model application and field site investigations. Methods Ecol. Evol. 12, 787–793 (2021).Article 

Google Scholar 
Nielson, M., R., Sawyer, H. & McDonald, T. L. BBMM: Brownian Bridge Movement Model R Package Version 3.0 (2013).Horne, J. S., Garton, E. O., Krone, S. M. & Lewis, J. S. Analyzing animal movements using Brownian bridges. Ecology 88, 2354–2363 (2007).Article 

Google Scholar 
Sawyer, H., Kauffman, M. J., Nielson, R. M. & Horne, J. S. Identifying and prioritizing ungulate migration routes for landscape-level conservation. Ecol. Appl. 19, 2016–2025 (2009).Article 

Google Scholar 
Fischer, J. W., Walter, W. D. & Avery, M. L. Brownian bridge movement models to characterize birds’ home ranges. Condor 115, 298–305 (2013).Article 

Google Scholar 
Seidler, R., Long, R., Berger, J., Bergen, S. & Beckmann, J. Identifying impediments to long-distance mammal migrations. Conserv. Biol. 29 (2014).Collins, G. Seasonal distribution and routes of pronghorn in the Northern Great Basin. West. N. Am. Nat. 76, 101–112 (2016).Article 

Google Scholar  More

Phylogenetic analysis with multiple markersThe final alignment of 5670 bp length contained 843 variable sites (14.7%). Missing data accounted for 53.5% of the alignment cells and the relative GC content was 39.5%. Our phylogeny suggests that the investigated Holarctic taxa of the niger clade sensu Ref.34 are divided into two major clades with strong statistical support (Fig. 1). The first major clade (L. niger group) consists exclusively of Palearctic species (L. niger, L. platythorax, L. japonicus, L. emarginatus, L. balearicus, L. grandis, L. cinereus, the L. alienus-complex, L. sakagamii, L. productus and L. hayashi), with the exception of an unnamed Nearctic subclade recovered as sister to the rest of the group. This unnamed subclade we describe as a new species below (L. ponderosae sp. nov.). Lasius ponderosae sp. nov. corresponds to what was previously known as the Nearctic form of “L. niger” sensu ref.17, but includes some western Nearctic populations formerly assigned to “L. alienus”17,52 as well. Monophyly of L. ponderosae sp. nov. was fully supported by Bayesian inference (pp = 1) and moderately supported by maximum likelihood (66% bootstrap support, Fig. 1). Lasius ponderosae sp. nov. is distantly related to L. niger; and L.niger is a close relative of L. japonicus and L. platythorax, as well as other Palearctic taxa. The second major clade (L. brunneus group) within the investigated Holarctic members of the L. niger clade contains both Nearctic and Palearctic species not closely related to the taxa of interest (Fig. 1).Figure 1Molecular phylogeny of 26 Holarctic ant taxa belonging to the subgenus Lasius sensu Wilson (1955) and two outgroup taxa (L. pallitarsis and L. mixtus). The phylogeny was calculated under the coalescent model and incorporates data from 9 genes (mtDNA: COI, COII, 16S, nuDNA: Defensin, H3, LR, Wg, Top1 & 28S). Names of species native to the Nearctic are shown in red and those of species native to the Palearctic in blue. Node labels show posterior probability (Bayesian inference) followed by bootstrap support (Maximum likelihood). The scale bar indicates the length of 0.01 substitutions/site.Full size imageDNA-barcodingThe native North American species L. ponderosae sp. nov. contains at least 15 COI-mitotypes (n = 28 sequenced specimens) belonging to four distinct deep lineages, with divergences of up to 5.9%. Haplotype diversity was 0.899 and nucleotide diversity was 0.012. None of the mitotypes of this species was found to be widespread or particularly abundant. In striking contrast, low genetic diversity was found in L. niger across its entire distribution (Fig. 2). No more than 7 different COI-mitotypes were detected in samples from distant localities representing most of the known range (n = 70 specimens from 12 countries), from Spain in the West to the Siberian Baikal-region in the East (Fig. 2). Their maximum pairwise divergence was only 0.6%, with a haplotype diversity of 0.682 and a nucleotide diversity below 0.001. One mitotype of L. niger is highly dominant within the native range, occurring from Western Europe to Central Siberia (mitotype h2 in Fig. 2).Figure 2Mitotype tree and distribution maps for 98 DNA-barcodes belonging to 7 mitotypes of the ant Lasius niger (blue, n = 70) and 15 mitotypes of L. ponderosae sp. nov. (red, n = 28). The red dashed line delimits the expected natural range of L. ponderosae sp. nov.53 Maps have been created using the free R-package “ggmap” v3.0.0 (https://github.com/dkahle/ggmap) in R v4.1.1. Map tiles by Stamen Design, under CC BY 3.0.Full size imageRecent Palearctic L. niger introduction to CanadaPalearctic Lasius niger was introduced to several localities in coastal Canada in recent times, where at least 11 populations were found in two metropolitan areas (Vancouver and Halifax areas, see Table S2 for details). Those populations consist of the most dominant Palearctic mitotype of L. niger (h2). However, in 3 localities in the Vancouver area, 3 specimens with a second mitotype were found (mitotype h4, Fig. 2, Table S2) in syntopy with those carrying the most common mitotype h2. This second Canadian COI-mitotype (h4) was not found among our samples from the Old World, although it only differs by a single nucleotide substitution from mitotypes found there. A review of BOLD data revealed that the Canadian barcoded specimens of L. niger were mostly collected in anthropogenic habitats such as schoolyards (Supplementary Table S2).Description of Lasius ponderosae sp. novLasius ponderosae Schär, Talavera, Rana, Espadaler, Cover, Shattuck and Vila. ZooBank LSID: urn:lsid:zoobank.org:act:22E2743A-2F1C-4870-B318-A1F2DF2B464C Etymology: ponderosae alludes to the ponderosa pine tree (Pinus ponderosa) that is at the centre of occurrence in the ponderosa pine—gambel oak communities in the western Rocky Mountains and northern Arizona.Type material: located at the Museum of Comparative Zoology, Cambridge, USA. Two paratype workers each will be deposited at the collections of University of California Davis (UCDC), the University of Utah (JTLC) and the Natural History Museum of Los Angeles County (LACM).Holotype: worker, Fig. 3a–c. Type locality: USA, Utah: Uintah Co., Uintah Mtns., 2408 m. 18.6 mi N. Jct. Rt. 40 on Rt. 191, 40.66378°N, − 109.47918°E, leg. 15.VII.2013, S. P. Cover; J. D. Rana, collection code SPC 8571. Measurements [mm]: HL: 0.899, HW: 0.823, SL: 0.821, EL: 0.239, EW: 0.189, ProW: 0.56, ML: 1.069, HTL: 0.863, CI: 92, SI: 100.Figure 3Frontal, lateral and dorsal view of the holotype worker (a–c), a paratype gyne (d–f) and a paratype male of Lasius ponderosae sp. nov. (g–i).Full size imageParatypes: 15 workers, two gynes (Fig. 3d–f), two males (Fig. 3g–i) from the same series as the holotype, morphometric data is given in the Appendix, Table S5 and Table S6. CO1 mitotype h17: Genbank Accession no. LT977508.Description of the worker caste: A member of a complex of cryptic species resembling L. niger. Intermediate in overall body size, antennal scape length and eye size and comparable to related species (Table 1). Terminal segment of maxillary palps and torulo-clypeal distance relative to head size shorter than in related Palearctic species (Table 1). Mandibles with 8 or rarely 7 or 9 regular denticles and lacking offset teeth at their basal angle. Penultimate and terminal basal mandibular teeth of subequal size, and the gap in between with subequal area than the basal tooth. Anterior margin of clypeus evenly rounded. Dorsofrontal profile of pronotum slightly angular (Fig. 4a). Propodeal dome short and flat, usually lower than mesonotum (Fig. 4a). Body with abundant and long pilosity, especially lateral propodeum, genae, hind margin and underside of head. Pilosity of tibiae and antennal scapes variable, ranging from almost no setae (“L. alienus”-like phenotype) to very hairy (“L. niger”-like phenotype). Microscopic pubescent hairs on forehead between frontal carinae long and fine. Clypeus typically with only few scattered pubescent hairs (Figs. 3, 4c). Coloration of body dark brown, occasionally yellowish- or reddish-brown or slightly bicolored with head and thorax lighter than abdomen. Femora and antennal scapes brown. Mandibles and distal parts of legs yellowish to dark brown. Specimens of all 3 castes are shown in Fig. 3a–i and morphometric data are summarized in Table 1 and raw measurements are available in Table S5 and S6.Table 1 Morphometric data of Lasius ponderosae sp. nov. and comparison to morphologically similar Palearctic species.Full size tableFigure 4Average thorax profile of Lasius ponderosae sp. nov. (a) and members of the Palearctic L. niger-complex (b). Figures were created by image averaging (L. ponderosae sp. nov n = 35; Palearctic L. niger-complex n = 30 specimens). Frontal view of head and detail of clypeus of the Holotype worker of L. ponderosae sp. nov. (c) and a non-type worker of L. niger (d).Full size imageDiagnosis: Lasius ponderosae sp. nov. workers key out to “L. niger” using Wilson’s 1955 key to the Nearctic Lasius species. However, some populations with reduced pilosity may also be identified as “L. alienus” using this key. Lasius alienus is a Eurasian species not known from North America33. The Nearctic “L. alienus” sensu Wilson (1955) includes both, L. americanus as well as populations of L. ponderosae sp. nov. with sparse setae counts on tibia and/or scapes. Lasius ponderosae sp. nov. can be distinguished from L. americanus by the presence of abundant, long setae surpassing the sides of the head in full face view (nGen  > 5 and nOcc  > 10 vs. nGen  0.8 across models and runs). The strongest predictors were: Annual Mean Temperature (mean variable importance = 0.32), Mean Temperature of Coldest Quarter (0.23), Temperature Annual Range (0.23) and Temperature Seasonality (0.24). The contribution of land cover was low (0.02). The model predicted high probabilities of occurrence of L. niger in the eastern United States and southeastern Canada, including the island of Newfoundland, and small areas of suitable habitat in southwestern Canada and the Aleutians (Fig. 6). The area with high predicted occurrence probability of L. niger in the New World includes the two sites where populations have actually established (which were not used in the modeling): Nova Scotia and Vancouver. Further areas with high occurrence probabilities are New England, Southern Ontario, the Great Lakes-region and the Northern Appalachians. Low occurrence probabilities were found for the central North American prairies as well as arctic, boreal, arid, subtropical and tropical regions (Fig. 6). Considering the highest occurrence probability range (0.8–1 on a 0–1 probability scale), the area of suitable habitats for L. niger is 4,547,537 km2 in Europe and 1,308,920 km2 in North America. For an intermediate to high occurrence probability range (0.5–1) we estimated 5,371,055 km2 in Europe and 3,054,283 km2 in North America, and for the widest probability range (0.2–1) we estimated 6,155,643 km2 of suitable areas in Europe and 6,889,745 km2 in North America (Fig. 6).Figure 6Projected occurrence probability from ecological niche modeling for the Palearctic ant Lasius niger which has been introduced to Canada, based on 19 climatic and one land use variable. The intensity of blue colour indicates the probability of occurrence on a 0–1 scale based on 180 presences (black circles) and 182 absences (white circles) in the native range in the Old World (a). The model was then projected to North America to estimate areas of suitable habitat for this introduced species (b). These maps have been created using the free R-package “ggplot2” v3.3.5 (https://ggplot2.tidyverse.org) in R v4.1.1.Full size image More