Ecology

Study area and dateIn the north-west of France, the macrotidal Bourgneuf Bay (1°-2° W, 46°-47° N; total area ~340 km2; Fig. 1) has an intertidal zone largely dominated by mudflats (exposed surface area ~100 km2). Bourgneuf Bay is situated south of the Loire estuary and is open to the sea along 12 km from the west to the north-west. C. gigas aquaculture here is of national importance and wild C. gigas reefs can account for over double the biomass of their farmed conspecifics65. Analysis of satellite observations covering 30 years of MPB biomass in the bay confirmed the co-occurrence of high MPB biomass with wild oyster reefs and cultivated stocks16 (Supplementary Methods: Wider Situation of the Reefs). Two small (each  > 750 m2) wild C. gigas reefs and their immediate surroundings (10–100 m) in the north of Bourgneuf Bay were deemed suitable for experimental manipulation (yellow and orange regions in Fig. 1). Méléder et al.18 described MPB biomass as mostly concentrating around the 2 m isobath, the Falleron river channel (closest point ~400 m NNE from the eastern reef), and oyster farms. Covering this isobath, we superimposed a 350 * 350 m grid (12.25 hectares) to cover the two wild oyster reefs, orientated so that the ‘Y’ axis runs parallel to the slope of bathymetry (Fig. 1). The grid was split regularly into 49 ‘grand-cells’ of 50 * 50 m (i.e., n = 49) and each of those split into 25 cells of 10 * 10 m (i.e., n = 1225; Fig. 1). Four cells per grand-cell were chosen randomly for the sampling of meiofauna, granulometry, OM (see Table 3 for specific methodology), and macrofauna. Of these cells, only every second cell was processed for meiofauna because of time constraints in assessing their abundance.Table 3 Summary of study variables and their sampling methodologies.Full size tableAlthough there were only two oyster reef complexes (‘reefs’, hereon) in this study, multiple sampling cells fell on, or in close proximity to, each reef, so that each reef had many potential (though not independent) distance decay transects running from it capturing natural variation in spatial structure66. Comparing the ecological change following the experimental burning of oyster reefs (described below) against ecological change occurring at these two reefs over the previous 25 years16 also allowed us greater confidence to disentangle the treatment effects from typical variation. Through the centres of five grand-cells to the south of the extent, a transect forming an ‘L’ shape (Fig. 1) was sampled every 10 m for in situ MPB pigment composition and biomass. We used these data to complete the remote sensing approach for MPB biomass estimation (see below, Microphytobenthos). The western reef was slightly larger than the eastern reef and contained a large rock, ‘Roche Bonnet’, rising 0.5–1 m from the sediment. Outside the grid, another larger (200 * 80 m) wild oyster reef lies WSW at ~260 m distance from the western reef. The grid was sampled for the variables listed in Table 3 during the winter MPB low and early autumn peak seasons (see also ground-truthing in16), on the dates 18-19th September 2013 and 17-18th March 2014, before treatment, and on 7-8th October 2014 after treatment.MicrophytobenthosWe mapped MPB biomass by satellite remote sensing, following the method described in detail in Echappé et al. (2018). We used the same long-term record of high-resolution satellite images to analyse the spatial distribution of the normalised difference vegetation index (NDVI), a proxy of MPB chlorophyll a concentration at the sediment’s surface18,67, before and after treatment (individual image details in captions of Fig. 2 and Supplementary Figs. S4–S7). After atmospheric correction (FLASH and US40 aerosol model), the satellite-derived NDVI was validated against associated field measurements (r2 = 0.85, root-mean-square deviation, RMSE = 0.04, n = 57, P 20°) was limited (Supplementary Results: Additional MPB Images). An optimal image was chosen as representative of MPB biomass patterns per season16. The study area would ideally be tidally uncovered for ~2 h before the image was taken, whereupon MPB biomass is concentrated at the sediment surface. The optimal images met this condition (i.e., Fig. 2).To complete NDVI maps, in situ MPB pigment composition and biomass were retrieved by HPLC analysis from the 25 triplicates of sediment. These had been sampled using contact-cores to freeze the top 2 mm of sediment in situ with liquid nitrogen, with a metal surface 56 mm in diameter. Biomass was expressed by Chl a concentration (mg m−2), and dominance of MPB taxa was broadly assessed by ratio of pigment sources to Chl a: Fucoxanthin (Fuco), Diadinoxanthin (DD), Diatoxanthin (DT) and Chl c for diatoms. The ratio of unknown carotenoids (interpreted as by-products due to the low resolution of their absorption spectra) to Chl a was also analysed for ecological purposes (dominant taxa), whereas grazing pressure was investigated using the ratio of pheophorbid a to Chl a (methodological discussion in28).Sediment variablesFor laser granulometry, we sampled two depths, 0–5 cm and 5–10 cm, in triplicate at each cell. Each of the triplicate samples was put in a vial with water and sonicated. The particle size distribution was determined on a Mastersizer 3000 with a reporting range 50 nm to 3 mm. We also determined sediment percentage OM at two depths by mass loss on ignition in comparison to the oven-dried original (procedure as described for Macrofauna, also Table 3).MacrofaunaWe sampled macrofauna by a single 200 * 200 mm (depth * diameter) core per cell. Contents were placed into labelled buckets and sieved onshore (1 mm mesh). Soft-bodied polychaetes were picked out with forceps and preserved in buffered formalin during sieving. All material left on the sieve was bagged and preserved in formalin at the laboratory. Individuals were counted and measured by the longest axis (accuracy 0.1 mm, calipers); the deep-burrowing polychaete Diopatra biscayensis was counted by the presence of visible tubes above the sediment. Calibration curves from length to mass per species per season were built by identifying size classes by Sturges rule. Multiple individuals per size class (ideally n = 100) were measured to estimate mean organic mass per individual of each size class. Shell matter was physically separated from tissue, before both being dried in aluminium foil cups for 48 h at 60 °C and weighed (g) for tissue dry mass using a mass balance. Dry mass was then incinerated for four hours at 450 °C and reweighed (g; decrease in mass of the aluminium cup was also accounted for), the difference giving the organic matter mass (including residue in the shell matter), or ash free dry weight (AFDW). This number was divided by number of individuals. Calibration curves per species used first order polynomial curves for bivalves, unless numbers of size classes and individuals were small (1% of the total abundance. All mapping and analyses were performed in the statistical computing environment R (v4.0.2)73.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

Xu, L., Saatchi, S.S., Yang, Y., Yu, Y., Pongratz, J., Bloom, A.A., Bowman, K., Worden, J., Liu, J., Yin, Y. & Domke, G. Changes in global terrestrial live biomass over the 21st century. Sci. Adv. 7(27), p.eabe9829 (2001).Hoecker, T. J., Higuera, P. E., Kelly, R. & Hu, F. S. Arctic & boreal paleofire records reveal drivers of fire activity & departures from Holocene variability. Ecology 101(9), e03096 (2020).Article 

Google Scholar 
Bradshaw, C. J. & Warkentin, I. G. Global estimates of boreal forest carbon stocks & flux. Global Planet. Change 128, 24–30 (2015).Article 

Google Scholar 
Kuhry, P. & Turunen, J. The postglacial development of boreal and subarctic peatlands in Boreal Peatland Ecosystems, 25–46 (Springer, 2006).Walker, X. J. et al. Increasing wildfires threaten historic carbon sink of boreal forest soils. Nature 572(7770), 520–523 (2019).CAS 
Article 

Google Scholar 
Gauthier, S., Bernier, P., Kuuluvainen, T., Shvidenko, A. Z. & Schepaschenko, D. G. Boreal forest health & global change. Science 349(6250), 819–822 (2015).CAS 
Article 

Google Scholar 
Walker, X. J. et al. Cross-scale controls on carbon emissions from boreal forest megafires. Global Change Biol. 24(9), 4251–4265 (2018).Article 

Google Scholar 
Flannigan, M. D., Logan, K. A., Amiro, B. D., Skinner, W. R. & Stocks, B. J. Future area burned in Canada. Clim. Change 72(1), 1–16 (2005).CAS 
Article 

Google Scholar 
Balshi, M. S. et al. Assessing the response of area burned to changing climate in western boreal North America using a Multivariate Adaptive Regression Splines (MARS) approach. Global Change Biol. 15(3), 578–600 (2009).Article 

Google Scholar 
Johnstone, J. F. & Chapin, F. S. Fire interval effects on successional trajectory in boreal forests of northwest Canada. Ecosystems 9(2), 268–277 (2006).Article 

Google Scholar 
Viereck, L.A. & Little, E.L. Alaska trees & shrubs. US Forest Service 410, (1972).Paine, R. T., Tegner, M. J. & Johnson, E. A. Compounded perturbations yield ecological surprises. Ecosystems 1(6), 535–545 (1998).Article 

Google Scholar 
Buma, B. Disturbance interactions: characterization, prediction, & the potential for cascading effects. Ecosphere 6(4), 1–15 (2015).Article 

Google Scholar 
Burton, P. J., Jentsch, A. & Walker, L. R. The ecology of disturbance interactions. Bioscience 70(10), 854–870 (2020).Article 

Google Scholar 
Brown, C. D. & Johnstone, J. F. Once burned, twice shy: Repeat fires reduce seed availability & alter substrate constraints on Picea mariana regeneration. Forest Ecol. Manage. 266, 34–41 (2012).Article 

Google Scholar 
Buma, B., Brown, C. D., Donato, D. C., Fontaine, J. B. & Johnstone, J. F. The impacts of changing disturbance regimes on serotinous plant populations & communities. Bioscience 63(11), 866–876 (2013).Article 

Google Scholar 
Coop, J. D. et al. Wildfire-driven forest conversion in western North American landscapes. Bioscience 70(8), 659–673 (2020).Article 

Google Scholar 
Enright, N. J., Fontaine, J. B., Bowman, D. M., Bradstock, R. A. & Williams, R. J. Interval squeeze: altered fire regimes & demographic responses interact to threaten woody species persistence as climate changes. Front. Ecol. Enviro 13(5), 265–272 (2015).Article 

Google Scholar 
Burns, R.M., & Honkala B.H. Silvics of North America US Department of Agriculture, Forest Service, Ag. Handbook 654, (1990).Hayes, K. & Buma, B. Effects of short-interval disturbances continue to accumulate, overwhelming variability in local resilience. Ecosphere 12(3), 03379 (2021).Article 

Google Scholar 
Mack, M. C. et al. Carbon loss from boreal forest wildfires offset by increased dominance of deciduous trees. Science 372(6539), 280–283 (2021).CAS 
Article 

Google Scholar 
Viereck, L. A., Dyrness, C. T. & Foote, M. J. An overview of the vegetation & soils of the floodplain ecosystems of the Tanana River, interior Alaska. Can. J. For. Res. 23(5), 889–898 (1993).Article 

Google Scholar 
Hoy, E. E., Turetsky, M. R. & Kasischke, E. S. More frequent burning increases vulnerability of Alaskan boreal black spruce forests. Enviro. Res. Lett. 11(9), 095001 (2016).Article 

Google Scholar 
Whitman, E., Parisien, M. A., Thompson, D. K. & Flannigan, M. D. Short-interval wildfire & drought overwhelm boreal forest resilience. Sci. Rep. 9(1), 1–12 (2019).Article 

Google Scholar 
Greene, D. F. et al. The reduction of organic-layer depth by wildfire in the North American boreal forest & its effect on tree recruitment by seed. Can. J. For. Res. 37(6), 1012–1023 (2007).Article 

Google Scholar 
Héon, J., Arseneault, D. & Parisien, M. A. Resistance of the boreal forest to high burn rates. PNAS 111(38), 13888–13893 (2014).Article 

Google Scholar 
Buma, B., Weiss, S., Hayes, K. & Lucash, M. Wildland fire reburning trends across the US West suggest only short-term negative feedback & differing climatic effects. Enviro. Res. Lett. 15(3), 034026 (2020).Article 

Google Scholar 
Thompson, D. K. et al. Fuel accumulation in a high-frequency boreal wildfire regime: from wetland to upland. Can. J. For. Res. 47(7), 957–964 (2017).Article 

Google Scholar 
Kasischke, E. S. et al. Alaska’s changing fire regime—implications for the vulnerability of its boreal forests. Can. J. For. Res. 40(7), 1313–1324 (2010).Article 

Google Scholar 
Kelly, R. et al. Recent burning of boreal forests exceeds fire regime limits of the past 10,000 years. PNAS 110(32), 13055–13060 (2013).CAS 
Article 

Google Scholar 
Gaboriau, D. M. et al. Temperature & fuel availability control fire size/severity in the boreal forest of central Northwest Territories, Canada. Quat. Sci. Rev. 250, 106697 (2020).Article 

Google Scholar 
Johnstone, J. F., Rupp, T. S., Olson, M. & Verbyla, D. Modeling impacts of fire severity on successional trajectories & future fire behavior in Alaskan boreal forests. Landscape Ecol. 26(4), 487–500 (2011).Article 

Google Scholar 
Hess, K. A. et al. Satellite-based assessment of grassland conversion & related fire disturbance in the Kenai Peninsula, Alaska. Rem. Sens. 11(3), 283 (2019).Article 

Google Scholar 
Hollingsworth, T. N., Breen, A. L., Hewitt, R. E. & Mack, M. C. Does fire always accelerate shrub expansion in Arctic tundra? Examining a novel grass-dominated successional trajectory on the Seward Peninsula. A. A. A. Res. 53(1), 93–109 (2021).
Google Scholar 
Turner, M. G., Romme, W. H. & Tinker, D. B. Surprises & lessons from the 1988 Yellowstone fires. Frontiers Ecol. Environ. 1(7), 351–358 (2003).Article 

Google Scholar 
Shvidenko, A. Z. et al. Impact of wildfire in Russia between 1998–2010 on ecosystems & the global carbon budget. Dokl. Earth Sci. 441(2), 1678–1682 (2011).CAS 
Article 

Google Scholar 
Alaska Fire Service 2021. Alaska Interagency Coordination Center, Bureau of L& Management, Alaska Fire Service. https://fire.ak.blm.gov/arcgis/rest/services/Map&FeatureServices/FireHistory/MapServer/1French, N. H. et al. Using Landsat data to assess fire & burn severity in the North American boreal forest region: an overview and summary of results. Int. J. Wildland Fire 17(4), 443–462 (2008).Article 

Google Scholar 
Morimoto, M. & Juday, G. Perspectives on Sustainable Forest Management in Interior Alaska Boreal Forest: Recent History and Challenges. Forests 10(6), 484 (2019).Article 

Google Scholar 
NASA/METI/AIST/Japan Spacesystems, & U.S./Japan ASTER Science Team. ASTER Global Digital Elevation Model V003. distributed by NASA EOSDIS L& Processes DAAC, https://doi.org/10.5067/ASTER/ASTGTM.003 (2018)Fick, S. E. & Hijmans, R. J. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Clim 37(12), 4302–4315 (2017).Article 

Google Scholar 
US Environmental Protection Agency, Level III Ecoregions of the Continental United States, Corvallis, Oregon: U.S. EPA— National Health & Environmental Effects Research Laboratory https://epa.gov/eco-research/level-iii-&-ivecoregions-continental-united-states (2013)Wang, J.A., et al. ABoVE: Landsat-derived Annual Dominant Land Cover Across ABoVE Core Domain, 1984-2014. ORNL DAAC, Oak Ridge, Tennessee, USA (2019).. https://doi.org/10.3334/ORNLDAAC/1691Debeer, D. & Strobl, C. Conditional permutation importance revisited. BMC Bioinform. 21(1), 1–30 (2020).Article 

Google Scholar 
Strobl, C., Boulesteix, A. L., Kneib, T., Augustin, T. & Zeileis, A. Conditional variable importance for r&om forests. BMC Bioinform. 9(1), 1–11 (2008).Article 

Google Scholar 
Moisen, G. & Frescino, T. Comparing five modelling techniques for predicting forest characteristics. Ecol. Mod. 157, 209–225 (2002).Article 

Google Scholar 
R Core Team R: A language & environment for statistical computing (2021).Pebesma, E.J. & Bivand, R.S. Classes and methods for spatial data in R. R News 5 (2), https://cran.r-project.org/doc/Rnews/. (2005)Hijmans, R.J. raster: Geographic Data Analysis and Modeling. R package version 3.4–5. (2020) https://CRAN.R-project.org/package=rasterStrobl, C., Boulesteix, A.-L., Kneib, T., Augustin, T. & Zeileis, A. Conditional variable importance for random forests. BMC Bioinform. 9, 307 (2008).Article 

Google Scholar 
Debeer, D., Hothorn, T. & Strobl, C permimp: Conditional Permutation Importance. R package version 1.0–1. https://CRAN.R-project.org/package=permimp (2021)Schneider, G., Chicken, E., & Becvarik, R. NSM3:Functions and Datasets to Accompany Hollander, Wolfe, and Chicken – Nonparametric Statistical Methods, Third Edition. R package version 1.16. https://CRAN.R-project.org/package=NSM3 (2021) More

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doi: https://doi.org/10.1038/d41586-022-00832-x

ReferencesLiu, J. et al. Conserv. Biol. https://doi.org/10.1111/cobi.13907 (2022).Article 

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Siver et al.19 identified three categories of fossil Mallomonas species uncovered in the extensive Giraffe Pipe locality. One group of species had scales with morphological characteristics similar to, and difficult to separate from, modern congeners. Based on a morphological species concept, these could be viewed as representing the same species. A second group had morphologically different scales, but ones that could be linked to one or more modern species. The third group possessed scales that could not be directly linked to any modern species. The majority of the species contained in the latter group lacked a V-rib and well developed dome, and were considered as stem organisms within the broad section Planae. Siver et al.19 further reported that the mean size of scales in the group containing the extinct stem taxa was larger than those fossil taxa grouped with modern congeners.The current study adds additional modern and fossil species to the database used by Siver et al.19, including the oldest known taxon from the Cretaceous Wombat locality, and provides the first attempt to reconstruct cell size for fossil Mallomonas species. Based on the expanded database, several trends with respect to the evolution of scale and cell size of Mallomonas taxa can be made. First, there is a strong relationship between scale width and scale length that was similar for both fossil and modern forms. Second, as a group, fossil taxa had scales that are significantly larger than those produced by modern species, especially with respect to surface area. The five species with the largest scales belong to extinct fossil species, four of which belong to the group of stem taxa within section Planae. These scales are massive compared with modern forms, and support the concept of scale gigantism for early members of the Mallomonas clade containing species with scales that lack a V-rib and dome (Fig. 1; subclade A2). Third, assuming the model relating scale and cell size can be applied to the geologic record, fossil species produced significantly larger cells than modern forms.Because the models relating scale length to scale width were similar for modern and fossil species, the assumption is that the models developed relating scale size to cell size are appropriate for fossil taxa. In addition, the precise overlapping pattern of scales comprising the cell covering on modern species has recently been documented for Eocene fossil species22, indicating that this architectural design was well evolved by at least the early Eocene. Thus, making the assumption that other fossil taxa had similarly constructed cell coverings is reasonable, and further supports the application of the models relating scale and cell size to these fossil forms.Based on the model estimates, the mean cell size of the fossil species is approximately twice as large as the average cell produced by modern organisms. This doubling of cell size was also observed for the smallest species. The mean size of the five smallest modern species (M. canina, M. mangofera, M. dickii, M. madagascariensis, and M. gutata) was 9.3 × 5 µm, compared to the mean cell size estimated for the five smallest fossil taxa (M. pseudohamata, M. preisigii, M. dispar, M. bakeri and M. GP4) of 18 × 8.7 µm. The cell size discrepancy is even greater for fossil species that lack modern congeners, and especially for the extinct stem species within section Planae that possessed an average cell size of 69.2 × 20.8 µm, with a maximum cell size of 81.7 × 22.7 µm for M. GP13. The scales produced by these large fossil cells were not only massive in size, but also robust and heavily silicified. It is likely that these large cells covered with large, heavy and cumbersome scales would have been slow swimmers that expended significantly more energy to maintain their position in the water column than modern species. Perhaps these cells were also more prone to predation by larger zooplankton, and a combination of decreased motility and greater predation provided the evolutionary pressure for smaller and faster cells with less dense siliceous components, and ultimately caused the demise of the large-celled fossil species. In contrast, it is also possible that the stimulus initially resulting in the evolution of the larger species was the fact that they were too big to be preyed upon by smaller invertebrates.Several points regarding the models used to estimate cell size are warranted. First, it is important to note that because the scale sizes used to estimate cell sizes for the larger fossil taxa are at the end of the range used to produce the model, caution needs to be exercised. The assumption is being made that the linear relationship of the model holds for the larger scales, and that the linear relationship does not begin to flatten and reach a maximum cell size. However, there is no indication that the relationship is reaching an asymptote, nor reason to assume that the model would not hold for organisms that produce larger siliceous components. Second, the scale and cell size data used to produce the models consisted of the midpoint values of the ranges given in the literature. Thus, the cell sizes inferred from the models represent a midpoint estimate of the range for each species, and not an upper size limit. Third, there is more data available in the literature documenting scale size than there is for cell size for most modern Mallomonas species. Additional data on cell size, especially inclusion of mean values, may help to further fine-tune the models. Lastly, the formula of an ellipse was used to estimate scale surface area for the few species with “square-shaped” scales. Although this may slightly underestimate the surface area, using a formula for a square or rectangle would have resulted in an overestimation. Because the few species with square-shaped scales were primarily the extinct fossil taxa lacking modern congeners, their cell size may actually have been slightly larger than estimated in this study.Interestingly, fossil scales that have morphologically similar (identical) modern counterparts were not significantly different in size from each other, implying that their corresponding cells were also of similar size. These taxa have significantly smaller scales compared to those species with gigantic scales, and closer to the mean of modern species. Perhaps, this is why the lineages of these morphologically-identical species have survived for tens of millions of years. Despite maintaining virtually identical scale types, the degree of genetic difference from a physiological or reproductive perspective between taxa with virtually identical siliceous components remains unknown19,23.The extinct scale types are not only significantly larger than those of species with modern congeners, but some have a tendency of being more rectangular to square-shaped. In contrast, fossil scale types that can be linked to modern species, along with their contemporary counterparts, tend to have elliptical-shaped scales. This is especially true of body scales15,16,19. Although a few smaller species of Mallomonas form spherical cells, the vast majority of species produce ellipsoidal-shaped cells, and this is especially true of species forming larger cells15,16. Smaller elliptical-shaped scales would be more efficient in covering a curving ellipsoidal cell surface than larger and square-shaped scales, and allow for a closer fitting cell covering. Jadrná et al.26 recently reported that scales of the closely related synurophyte genus, Synura, have also become smaller and more elongate over geologic time, complementing the observations for Mallomonas. Taken together, these findings support the idea that the evolutionary trend for synurophyte organisms has been towards smaller, elliptical scales.Cyanobacteria, a prokaryotic group of organisms estimated to have evolved by 3.5–3.4 Ga, represent one of the earliest known and smallest life forms on Earth27. Since the evolution of these early prokaryotes, Smith et al.28 estimated that the maximum body size of subsequent life forms has increased approximately 18-fold, with large jumps occurring with the evolution of eukaryote cells, and another concurrent with the advent of multicellularity. In contrast, shifts in the sizes of siliceous scales and corresponding cells of Mallomonas species are small in comparison, within an order of magnitude, and similar to changes observed for prokaryote organisms and other unicellular protists over the Geozoic28,29.Despite the overall lack of historical information on cell size for the majority of unicellular eukaryote lineages, there are data for some organisms that build resistant cell walls or coverings that are taxonomically diagnostic and become incorporated into the fossil record. Diatoms produce a siliceous cell wall known as the frustule, a structure composed of top and bottom pieces called valves that are held together with additional structures called girdle bands. Frustules, or their valve components, can be uncovered from the fossil record and used to provide a direct measure of cell size. Using this technique, Finkel et al.29 reported that the size of planktic marine diatoms declined over the Cenozoic, and correlated the shift with abiotic forcing factors, including a rise in sea surface temperature and water column stratification. Foraminifera are heterotrophic marine protists that build shells out of calcium carbonate, the latter of which can also become part of the fossil record. Changes in the size of foraminifera shells over the Cenozoic have also been correlated with shifts in the intensity of water column stratification30. At this time, it is not known if the decline in cell size for Mallomonas species in the section Planae lineage recorded in the current study was the result of abiotic variables (e.g. energy expenditure or temperature), biotic factors (e.g. predation), or a combination of forcing variables.The current study has provided a means to link scale size to cell size for Mallomonas that, in turn, can be used to trace shifts in cell size over geologic time. As additional scales of Mallomonas species are uncovered from the fossil record, the scale-to-cell size model will be a valuable tool for continuing to unravel the evolutionary history of cell size for this important photosynthetic organism. Other groups of unicellular protists, including euglyphids, heliozoids and rotosphaerids, are similar to synurophytes in that they build cell coverings using numerous overlapping siliceous scales or plates that can become fossilize. Perhaps the same technique of relating scale size to cell size could be used to develop models for these protist organisms, and similarly applied to the fossil record.It is interesting to note that most modern Mallomonas species with large body scales are found in warm tropical regions, including M. bronchartiana Compère, M. pseudobronchartiana Gusev, Siver & Shin, M. velari Gusev, Siver & Shin31, M. vietnamica Gusev, Kezlya & Trans32, M. gusakovii33 and several varieties of M. matvienkoae16. In addition, the modern tropical taxa M. neoampla Gusev & Siver and M. vietnamica share several rare features of their scales and bristles with fossil species recorded from the Giraffe locality, suggesting a possible link between the modern tropical and fossil floras. During the early to middle Eocene, the Earth experienced warm greenhouse conditions and lacked a cryosphere34. The Giraffe locality, positioned near the Arctic Circle, had an estimated mean annual temperature 17 °C warmer, and a mean annual precipitation over four times higher, than present conditions35. In fact, the assemblage of plants and animals in the Eocene Arctic has been described as analogous to those found today in eastern Asia36. Perhaps tropical regions, especially in southeastern Asia, offered refugia for some of the ancient Mallomonas lineages.In summary, multiple extinct fossil species of the diverse and common synurophyte genus, Mallomonas, are reported here to have possessed gigantic scales that are significantly larger than those found on modern species. Based on a model relating scale to cell size, cells of fossil Mallomonas species were estimated to be, on average, twice as large as modern species. A combination of larger cells with heavy siliceous scales that fit less effectively around the cell may have resulted in slower cells more prone to predation, heavier cells requiring more energy resources to maintain their position in the water column, and ultimately their demise. Additional fossil species, especially representing other localities and time periods, will ultimately strengthen our understanding of the evolution of scale and cell size in synurophyte algae. More

Pimm, S. L. et al. Emerging technologies to conserve biodiversity. Trends Ecol. Evol. 30, 685–696 (2015).Article 

Google Scholar 
Marvin, D. C. et al. Integrating technologies for scalable ecology and conservation. Glob. Ecol. Conserv. 7, 262–275 (2016).Article 

Google Scholar 
Wall, J., Wittemyer, G., Klinkenberg, B. & Douglas-Hamilton, I. Novel opportunities for wildlife conservation and research with real-time monitoring. Ecol. Appl. 24, 593–601 (2014).Article 

Google Scholar 
Snaddon, J., Petrokofsky, G., Jepson, P. & Willis, K. J. Biodiversity technologies: tools as change agents. Biol. Lett. 9, 20121029 (2013).Article 

Google Scholar 
Pettorelli, N., Safi, K., Turner, W. Satellite remote sensing, biodiversity research and conservation of the future. Philos. Trans. R. Soc. B Biol. Sci. 369, 20130190 (2014).Ripperger, S. P. et al. Thinking small: Next-generation sensor networks close the size gap in vertebrate biologging. PLOS Biol. 18, e3000655 (2020).CAS 
Article 

Google Scholar 
Xu, H., Wang, K., Vayanos, P. & Tambe, M. Strategic coordination of human patrollers and mobile sensors with signaling for security games. 8 (2018).Liu, Y. et al. AI for Earth: Rainforest conservation by acoustic surveillance. 2 (2019).Joppa, L. N. Technology for nature conservation: an industry perspective. Ambio 44, 522–526 (2015).Article 

Google Scholar 
Koh, L. P. & Wich, S. A. Dawn of drone ecology: low-cost autonomous aerial vehicles for conservation. Trop. Conserv. Sci. 5, 121–132 (2012).Article 

Google Scholar 
Hahn, N. et al. Unmanned aerial vehicles mitigate human–elephant conflict on the borders of Tanzanian Parks: a case study. Oryx 51, 513–516 (2017).Article 

Google Scholar 
Pomerantz, A. et al. Real-time DNA barcoding in a rainforest using nanopore sequencing: opportunities for rapid biodiversity assessments and local capacity building. GigaScience 7, (2018).Van Doren, B. M. & Horton, K. G. A continental system for forecasting bird migration. Science 361, 1115–1118 (2018).ADS 
Article 

Google Scholar 
Howson, P. Building trust and equity in marine conservation and fisheries supply chain management with blockchain. Mar. Policy 115, 103873 (2020).Article 

Google Scholar 
Speaker, T. et al. A global community-sourced assessment of the state of conservation technology. Conserv. Biol. cobi. https://doi.org/10.1111/cobi.13871 (2022).Article 

Google Scholar 
Pearce, J. M. Building research equipment with free Open-Source Hardware. Science 337, 1303–1304 (2012).ADS 
CAS 
Article 

Google Scholar 
Gibb, R., Browning, E., Glover-Kapfer, P. & Jones, K. E. Emerging opportunities and challenges for passive acoustics in ecological assessment and monitoring. Methods Ecol. Evol. 10, 169–185 (2019).Article 

Google Scholar 
current constraints and future priorities for development. Glover-Kapfer, P., Soto-Navarro, C. A. & Wearn, O. R. Camera-trapping version 3.0. Remote Sens. Ecol. Conserv. 5, 209–223 (2019).Article 

Google Scholar 
Norouzzadeh, M. S. et al. Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning. Proc. Natl. Acad. Sci. 115, E5716–E5725 (2018).CAS 
Article 

Google Scholar 
Berger-Tal, O. & Lahoz-Monfort, J. J. Conservation technology: the next generation. Conserv. Lett. 11, 1–6 (2018).Article 

Google Scholar 
Hill, A. P. et al. AudioMoth: Evaluation of a smart open acoustic device for monitoring biodiversity and the environment. Methods Ecol. Evol. 9, 1199–1211 (2018).Article 

Google Scholar 
Zárybnická, M., Kubizňák, P., Šindelář, J. & Hlaváč, V. Smart nest box: a tool and methodology for monitoring of cavity-dwelling animals. Methods Ecol. Evol. 7, 483–492 (2016).Article 

Google Scholar 
Kalmár, G. et al. Animal-Borne Anti-Poaching System. in Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services 91–102 (ACM, 2019). https://doi.org/10.1145/3307334.3326080.Weise, F. J. et al. Lions at the gates: Trans-disciplinary design of an early warning system to improve human-lion coexistence. Front. Ecol. Evol. 6, 242 (2019).Article 

Google Scholar 
Beery, S., Van Horn, G. & Perona, P. Recognition in Terra Incognita. in Proceedings of the European Conference on Computer Vision (ECCV) (eds. Ferrari, V., Hebert, M., Sminchisescu, C. & Weiss, Y.) 472–489 (Springer International Publishing, 2018). https://doi.org/10.1007/978-3-030-01270-0_28.Crego, R. D., Masolele, M. M., Connette, G. & Stabach, J. A. Enhancing animal movement analyses: spatiotemporal matching of animal positions with remotely sensed data using google earth engine and R. Remote Sens. 13, 4154 (2021).ADS 
Article 

Google Scholar 
Gorelick, N. et al. Google earth engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).ADS 
Article 

Google Scholar 
Vulcan. EarthRanger. https://earthranger.com.Ahumada, J. A. et al. Wildlife insights: A platform to maximize the potential of camera trap and other passive sensor wildlife data for the planet. Environ. Conserv. 47, 1–6 (2020).MathSciNet 
Article 

Google Scholar 
Lahoz-Monfort, J. J. et al. A call for international leadership and coordination to realize the potential of conservation technology. Bioscience 69, 823–832 (2019).Article 

Google Scholar 
Group Gets – AudioMoth. https://groupgets.com/manufacturers/open-acoustic-devices/products/audiomoth.Kulits, P., Wall, J., Bedetti, A., Henley, M. & Beery, S. ElephantBook: A semi-automated human-in-the-loop system for elephant re-identification. in ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS) 88–98 (ACM, 2021). https://doi.org/10.1145/3460112.3471947.Pardo, L. E. et al. Snapshot Safari: A large-scale collaborative to monitor Africa’s remarkable biodiversity. South Afr. J. Sci. 117, (2021).Iacona, G. et al. Identifying technology solutions to bring conservation into the innovation era. Front. Ecol. Environ. 17, 591–598 (2019).Article 

Google Scholar 
Cooper, R. G. What’s next?: After stage-gate. Res.-Technol. Manag. 57, 20–31 (2014).ADS 

Google Scholar 
Cooper, R. G. The drivers of success in new-product development. Ind. Mark. Manag. 76, 36–47 (2019).Article 

Google Scholar 
Pearce, J. M. The case for open source appropriate technology. Environ. Dev. Sustain. 14, 425–431 (2012).Article 

Google Scholar 
Mair, J., Battilana, J. & Cardenas, J. Organizing for society: A typology of social entrepreneuring models. J. Bus. Ethics 111, 353–373 (2012).Article 

Google Scholar 
Meissner, D. Public-private partnership models for science, technology, and innovation cooperation. J. Knowl. Econ. 10, 1341–1361 (2019).Article 

Google Scholar 
Likert, R. A technique for the measurement of attitudes. Arch. Psychol. 22, 1–55.Mayer, A. L. & Wellstead, A. M. Questionable survey methods generate a questionable list of recommended articles. Nat. Ecol. Evol. 2, 1336–1337 (2018).Article 

Google Scholar 
Archie, K. M., Dilling, L., Milford, J. B. & Pampel, F. C. Climate Change and Western Public Lands: a Survey of U.S. Federal Land Managers on the Status of Adaptation Efforts. Ecol. Soc. 17 (2012).Jimenez, M. F. et al. Underrepresented faculty play a disproportionate role in advancing diversity and inclusion. Nat. Ecol. Evol. 3, 1030–1033 (2019).Article 

Google Scholar 
Christensen, R. ordinal – Regression Models for Ordinal Data. R package version 2019.12-10. (2019).R Core Team. R: A language and environment for statistical computing. (2020).Arnold, T. W. Uninformative parameters and model selection using Akaike’s information criterion. J. Wildl. Manag. 74, 1175–1178 (2010).Article 

Google Scholar 
QSR International Pty Ltd. Nvivo 12 Pro. (2020).Glesne, C. Making words fly: Developing understanding through interviewing. Becom. Qual. Res. Introd. 3, (2006).Creswell, J. W. & Creswell, J. D. Research design: Qualitative, quantitative, and mixed methods approaches. (Sage publications 2017). More

Balami, S., Vašutová, M., Godbold, D., Kotas, P. & Cudlín, P. Soil fungal communities across land use types. Forest Biogeosci. For. 13, 548–558 (2020).
Google Scholar 
Deacon, J. Fungal Biology (Wiley, 2009).
Google Scholar 
Ruiz-Almenara, C., Gándara, E. & Gómez-Hernández, M. Comparison of diversity and composition of macrofungal species between intensive mushroom harvesting and non-harvesting areas in Oaxaca, Mexico. PeerJ 7, e8325 (2019).PubMed 
PubMed Central 

Google Scholar 
Moore, J. C. et al. Detritus, trophic dynamics and biodiversity. Ecol. Lett. 7, 584–600 (2004).ADS 

Google Scholar 
Egli, S. Mycorrhizal mushroom diversity and productivity—an indicator of forest health?. Ann. For. Sci. 68, 81–88 (2011).
Google Scholar 
Westover, K. M. & Bever, J. D. Mechanisms of plant species coexistence: Roles of rhizosphere bacteria and root fungal pathogens. Ecology 82, 3285–3294 (2001).
Google Scholar 
Deacon, J. Fungal Biology (Wiley, 2006).
Google Scholar 
Fernandez, C. W., Nguyen, N. H. U. H., Stefanski, A. & Han, Y. Ectomycorrhizal fungal response to warming is linked to poor host performance at the boreal-temperate ecotone. Glob. Chang. Biol. 23, 1598–1609 (2017).ADS 
PubMed 

Google Scholar 
Heilmann-Clausen, J. et al. A fungal perspective on conservation biology. Conserv. Biol. 29, 61–68 (2015).PubMed 

Google Scholar 
Shay, P.-E., Winder, R. S. & Trofymow, J. A. Nutrient-cycling microbes in coastal Douglas-fir forests: Regional-scale correlation between communities, in situ climate, and other factors. Front. Microbiol. 6, 5897 (2015).
Google Scholar 
van der Heijden, M. G. A., Bardgett, R. D. & van Straalen, N. M. The unseen majority: Soil microbes as drivers of plant diversity and productivity in terrestrial ecosystems. Ecol. Lett. 11, 296–310 (2008).PubMed 

Google Scholar 
Richter, A., Schöning, I., Kahl, T., Bauhus, J. & Ruess, L. Regional environmental conditions shape microbial community structure stronger than local forest management intensity. For. Ecol. Manag. 409, 250–259 (2018).
Google Scholar 
Monkai, J., Hyde, K. D., Xu, J. & Mortimer, P. E. Diversity and ecology of soil fungal communities in rubber plantations. Fungal Biol. Rev. 31, 1–11 (2017).
Google Scholar 
White, F. Vegetation of Africa—a descriptive memoir to accompany the UNESCO/AETFAT/UNSO vegetation map of Africa, Natural Resources Research Report XX. U.N. Educational, Scientific and Cultural Organization, Paris (1983).Aynekulu, E. et al. Plant diversity and regeneration in a disturbed isolated dry Afromontane forest in northern Ethiopia. Folia Geobot. 51, 115–127 (2016).
Google Scholar 
Wassie, A., Sterck, F. J. & Bongers, F. Species and structural diversity of church forests in a fragmented Ethiopian Highland landscape. J. Veg. Sci. 21, 938–948 (2010).
Google Scholar 
Alem, D., Dejene, T., Oria-de-Rueda, J. A. & Martín-Pinto, P. Survey of macrofungal diversity and analysis of edaphic factors influencing the fungal community of church forests in Dry Afromontane areas of Northern Ethiopia. For. Ecol. Manag. 496, 119391 (2021).
Google Scholar 
Aerts, R. et al. Conservation of the Ethiopian church forests: Threats, opportunities and implications for their management. Sci. Total Environ. 551–552, 404–414 (2016).ADS 
PubMed 

Google Scholar 
Wassie, A., Teketay, D. & Powell, N. Church forests in North Gonder administrative zone, Northern Ethiopia. For. Trees Livelihoods 15, 349–373 (2005).
Google Scholar 
Wsaaie, A., Teketay, D. & Powell, N. Church forests in North Gondar Administative Zone, Northern Ethioopia. For. Trees Livelihoods 15, 349–373 (2005).
Google Scholar 
Lemenih, M. & Bongers, F. Dry forests of Ethiopia and their silviculture. In Silviculture in the Tropics, Tropical Forestry 8 (ed. S. G€unter et al.) 261–272 (Springer, Heidelberg, 2011). https://doi.org/10.1007/978-3-642-19986-8_17.Fernández, A., Sánchez, S., García, P. & Sánchez, J. Macrofungal diversity in an isolated and fragmented Mediterranean Forest ecosystem. Plant Biosyst. Int. J. Deal. Asp. Plant Biol. 154, 139–148 (2020).
Google Scholar 
Peay, K. G. & Bruns, T. D. Spore dispersal of basidiomycete fungi at the landscape scale is driven by stochastic and deterministic processes and generates variability in plant-fungal interactions. New Phytol. 204, 180–191 (2014).PubMed 

Google Scholar 
Burgess, N. D., Hales, J. D. A., Ricketts, T. H. & Dinerstein, E. Factoring species, non-species values and threats into biodiversity prioritisation across the ecoregions of Africa and its islands. Biol. Conserv. 127, 383–401 (2006).
Google Scholar 
Dejene, T., Oria-de-Rueda, J. A. & Martín-Pinto, P. Fungal community succession and sporocarp production following fire occurrence in Dry Afromontane forests of Ethiopia. For. Ecol. Manag. 398, 37–47 (2017).
Google Scholar 
Větrovský, T. et al. GlobalFungi, a global database of fungal occurrences from high-throughput-sequencing metabarcoding studies. Sci. Data 7, 1–14 (2020).
Google Scholar 
Tedersoo, L. et al. Global diversity and geography of soil fungi. Science (80-. ). 346 (2014).Hawksworth, D. L. Global species numbers of fungi: are tropical studies and molecular approaches contributing to a more robust estimate?. Biodivers. Conserv. 21, 2425–2433 (2012).
Google Scholar 
Crous, P. W. et al. How many species of fungi are there at the tip of Africa?. Stud. Mycol. 55, 13–33 (2006).PubMed 
PubMed Central 

Google Scholar 
Martínez, M. L. et al. Effects of land use change on biodiversity and ecosystem services in tropical montane cloud forests of Mexico. For. Ecol. Manag. 258, 1856–1863 (2009).
Google Scholar 
Phillips, H. et al. The effects of global change on soil faunal communities: a meta-analytic approach. Res. Ideas Outcomes 5 (2019).Riutta, T. et al. Experimental evidence for the interacting effects of forest edge, moisture and soil macrofauna on leaf litter decomposition. Soil Biol. Biochem. 49, 124–131 (2012).CAS 

Google Scholar 
Rantalainen, M., Haimi, J., Fritze, H., Pennanen, T. & Setala, T. Soil decomposer community as a model system in studying the effects of habitat fragmentation and habitat corridors. Soil Biol. Biochem. 40, 853–863 (2008).CAS 

Google Scholar 
Newsham, K. K. et al. Relationship between soil fungal diversity and temperature in the maritime Antarctic. Nat. Clim. Chang. 6, 182–186 (2016).ADS 

Google Scholar 
Bahram, M., Põlme, S., Kõljalg, U., Zarre, S. & Tedersoo, L. Regional and local patterns of ectomycorrhizal fungal diversity and community structure along an altitudinal gradient in the Hyrcanian forests of northern Iran. New Phytol. 193, 465–473 (2012).PubMed 

Google Scholar 
Rousk, J. et al. Soil bacterial and fungal communities across a pH gradient in an arable soil. ISME J. 4, 1340–1351 (2010).PubMed 

Google Scholar 
Krüger, C. et al. Plant communities rather than soil properties structure arbuscular mycorrhizal fungal communities along primary succession on a mine spoil. Front. Microbiol. 8, 1–16 (2017).
Google Scholar 
Bahram, M., Peay, K. G. & Tedersoo, L. Local-scale biogeography and spatiotemporal variability in communities of mycorrhizal fungi. New Phytol. 205, 1454–1463 (2015).CAS 
PubMed 

Google Scholar 
Li, P. et al. Spatial variation in soil fungal communities across paddy fields in Subtropical China. mSystems 5 (2020).Grilli, G., Urcelay, C. & Galetto, L. Forest fragment size and nutrient availability: Complex responses of mycorrhizal fungi in native–exotic hosts. Plant Ecol. 213, 155–165 (2012).
Google Scholar 
Fernández, C., Vega, J. A. & Fonturbel, T. Shrub Resprouting Response After Fuel Reduction Treatments: Comparison of Prescribed Burning, Clearing and Mastication (Elsevier, 2013).
Google Scholar 
Tedersoo, L., Sadam, A., Zambrano, M., Valencia, R. & Bahram, M. Low diversity and high host preference of ectomycorrhizal fungi in Western Amazonia, a neotropical biodiversity hotspot. ISME J. 4, 465–471 (2010).PubMed 

Google Scholar 
Glassman, S. I., Wang, I. J. & Bruns, T. D. Environmental filtering by pH and soil nutrients drives community assembly in fungi at fine spatial scales. Mol. Ecol. 26, 6960–6973 (2017).CAS 
PubMed 

Google Scholar 
Colwell, R. K. EstimateS: statistical estimation of species richness and shared species from samples. Version 9. User’s Guide and application published at: http://purl.oclc.org/estimates (2013).Purvis, A. & Hector, A. Getting the measure of biodiversity. Nature 405, 212–219 (2000).CAS 
PubMed 

Google Scholar 
Pan, W. et al. DNA polymerase preference determines PCR priming efficiency. BMC Biotechnol. 14, 10 (2014).PubMed 
PubMed Central 

Google Scholar 
Kirk, P. M., Cannon, P. F., Minter, D. W. & J.A, S. Dictionary of the Fungi (The Centre for Agriculture and Bioscience International (CABI), 2008).Rossman, A., Samuel, G., Rogerson, C. & Lowen, R. Genera of bionectriaceae, hypocreaceae and nectriaceae (hypocreales, ascomycetes). Stud. Mycol. 42, 1–260 (1999).
Google Scholar 
Samuels, G. Trichoderma: A review of biology and systematics of the genus. Mycol. Res. 923–935 (1996).Alem, D. et al. Soil fungal communities and succession following wildfire in Ethiopian dry Afromontane forests, a highly diverse underexplored ecosystem. For. Ecol. Manag. 474, 118328 (2020).
Google Scholar 
Muleta, D., Woyessa, D. & Teferi, Y. Mushroom consumption habits of Wacha Kebele residents, southwestern Ethiopia. Glob. Res. J. Agric. Biol. Sci. 4, 6–16 (2013).
Google Scholar 
Dejene, T., Oria-de-Rueda, J. A. & Martín-Pinto, P. Edible wild mushrooms of Ethiopia: Neglected non-timber forest products. Rev. Fitotec. Mex. 40, 391–397 (2017).
Google Scholar 
Tedersoo, L. et al. Disentangling global soil fungal diversity. Science (80-) 346, 1052–1053 (2014).
Google Scholar 
Dejene, T., Oria-de-Rueda, J. A. & Martín-Pinto, P. Fungal community succession and sporocarp production following fire occurrence in Dry Afromontane forests of Ethiopia. For. Ecol. Manag. 398 (2017).Dang, P. et al. Changes in soil fungal communities and vegetation following afforestation with Pinus tabulaeformis on the Loess Plateau. Ecosphere 9 (2018).Gilbert, G. S., Ferrer, A. & Carranza, J. Polypore fungal diversity and host density in a moist tropical forest. Biodivers. Conserv. 11, 947–957 (2002).
Google Scholar 
Kottke, I., Beck, A., Oberwinkler, F., Homeier, J. & Neill, D. Arbuscular endomycorrhizas are dominant in the organic soil of a neotropical montane cloud forest. J. Trop. Ecol. 20, 125–129 (2004).
Google Scholar 
Barnes, C. J., Van der Gast, C. J., Burns, C. A., McNamara, N. P. & Bending, G. D. Temporally variable geographical distance effects contribute to the assembly of root-associated fungal communities. Front. Microbiol. 7, 1–13 (2016).
Google Scholar 
Tian, J. et al. Environmental factors driving fungal distribution in freshwater lake sediments across the Headwater Region of the Yellow River, China. Sci. Rep. 8, 4–11 (2018).
Google Scholar 
Rosales-Castillo, J. et al. Fungal community and ligninolytic enzyme activities in Quercus deserticola Trel. litter from forest fragments with increasing levels of disturbance. Forests 9, 11 (2017).
Google Scholar 
Kuhar, F., Barroetaveña, C. & Rajchenberg, M. New species of Tomentella (Thelephorales) from the Patagonian Andes forests. Mycologia 108, 780–790 (2016).CAS 
PubMed 

Google Scholar 
Alem, D., Dejene, T., Oria-de-Rueda, J. A., Geml, J. & Martín-Pinto, P. Soil fungal communities under Pinus patula Schiede ex Schltdl. & Cham. Plantation forests of different ages in Ethiopia. Forests 11, 1109 (2020).
Google Scholar 
Tedersoo, L. et al. Terrestrial and lignicolous macrofungi. ISME J. 10, 1228–1239 (2016).
Google Scholar 
Ruiz, R., Decock, C., Saikawa, M., Gene, J. & Guarro, J. Polyschema obclaviformis sp. Nov., and some new records of hyphomycetes from Cuba. Cryptogam. Mycol. 21, 215–220 (2000).
Google Scholar 
Kaygusuz, O. New locality records of Trichoglossum hirsutum (Geoglossales: Geoglossaceae) based on molecular analyses, and prediction of its potential distribution in Turkey. Curr. Res. Environ. Appl. Mycol. 10, 443–456 (2020).
Google Scholar 
Mayer, P. M. Ecosystem and decomposer effects on litter dynamics along an old field to old-growth forest successional gradient. Acta Oecol. 33, 222–230 (2008).ADS 

Google Scholar 
Krishna, M. P. & Mohan, M. Litter decomposition in forest ecosystems: A review. Energy Ecol. Environ. 2, 236–249 (2017).
Google Scholar 
Kirschbaum, M. U. F. The temperature dependence of soil organic matter decomposition, and the effect of global warming on soil organic C storage. Soil Biol. Biochem. 27, 753–760 (1995).CAS 

Google Scholar 
Mayer, P. M., Tunnell, S. J., Engle, D. M., Jorgensen, E. E. & Nunn, P. Invasive grass alters litter decomposition by influencing macrodetritivores. Ecosystems 8, 200–209 (2005).
Google Scholar 
Epstein, H. E., Burke, I. C. & Lauenroth, W. K. Regional patterns of decomposition and primary production rates in the U.S. great plains. Ecology 83, 320 (2002).
Google Scholar 
Sharon, R., Degani, G. & Warburg, M. Comparing the soil macro-fauna in two oak-wood forests: Does community structure differ under similar ambient conditions?. Pedobiologia (Jena). 45, 355–366 (2001).
Google Scholar 
Clocchiatti, A., Hannula, S. E., van den Berg, M., Korthals, G. & de Boer, W. The hidden potential of saprotrophic fungi in arable soil: Patterns of short-term stimulation by organic amendments. Appl. Soil Ecol. 147, 103434 (2020).
Google Scholar 
Drenovsky, R., Vo, D., Graham, K. & Scow, K. Soil water content and organic carbon availability are major determinants of soil microbial community composition. Microb. Ecol. 48, 424–430 (2004).CAS 
PubMed 
PubMed Central 

Google Scholar 
Lauber, C., Hamady, M., Knigh, R. & Fierer, N. Pyrosequencing based assessment of soil pH as a predictor of soil bacterial community structure at the continental scale. Appl. Environ. Microbiol. 75, 5111–5120 (2009).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ullah, S. et al. The response of soil fungal diversity and community composition to long-term fertilization. Appl. Soil Ecol. 140, 35–41 (2019).
Google Scholar 
Bååth, E. & Anderson, T.-H. Comparison of soil fungal/bacterial ratios in a pH gradient using physiological and PLFA-based techniques. Soil Biol. Biochem. 35, 955–963 (2003).
Google Scholar 
Zhang, T., Wang, N.-F., Liu, H.-Y., Zhang, Y.-Q. & Yu, L.-Y. Soil pH is a key determinant of soil fungal community composition in the Ny-Ålesund Region, Svalbard (high arctic). Front. Microbiol. 7 (2016).Tian, D. et al. Effects of nitrogen deposition on soil microbial communities in temperate and subtropical forests in China. Sci. Total Environ. 607–608, 1367–1375 (2017).ADS 
PubMed 

Google Scholar 
Zhao, A. et al. Influences of canopy nitrogen and water addition on am fungal biodiversity and community composition in a mixed deciduous forest of China. Front. Plant Sci. 9 (2018).He, J. et al. Greater diversity of soil fungal communities and distinguishable seasonal variation in temperate deciduous forests compared with subtropical evergreen forests of eastern China. FEMS Microbiol. Ecol. 93, 1–12 (2017).
Google Scholar 
Shi, L. et al. Variation in forest soil fungal diversity along a latitudinal gradient. Fungal Divers. 64, 305–315 (2014).
Google Scholar 
Gebeyehu, G., Soromessa, T., Bekele, T. & Teketay, D. Plant diversity and communities along environmental, harvesting and grazing gradients in dry afromontane forests of Awi Zone, northwestern Ethiopia. Taiwania 64, 307–320 (2019).
Google Scholar 
Zegeye, H., Teketay, D. & Kelbessa, E. Diversity and regeneration status of woody species in Tara Gedam and Abebaye forests, northwestern Ethiopia. J. For. Res. 22, 315–328 (2011).
Google Scholar 
Abere, F., Belete, Y., Kefalew, A. & Soromessa, T. Carbon stock of Banja forest in Banja district, Amhara region, Ethiopia: An implication for climate change mitigation. J. Sustain. For. 36, 604–622 (2017).
Google Scholar 
Masresha, G., Soromessa, T. & Kelbessa, E. Status and species diversity of Alemsaga Forest, Northwestern Ethiopia 14 (2015).Rudolph, S., Maciá-Vicente, J. G., Lotz-Winter, H., Schleuning, M. & Piepenbring, M. Temporal variation of fungal diversity in a mosaic landscape in Germany. Stud. Mycol. 89, 95–104 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
De la Varga, H., Águeda, B., Martínez-Peña, F., Parladé, J. & Pera, J. Quantification of extraradical soil mycelium and ectomycorrhizas of Boletus edulis in a Scots pine forest with variable sporocarp productivity. Mycorrhiza 22, 59–68 (2012).PubMed 

Google Scholar 
Voříšková, J. & Baldrian, P. Fungal community on decomposing leaf litter undergoes rapid successional changes. ISME J. 7, 477–486 (2013).PubMed 

Google Scholar 
Reeuwijk, L. Procedures for Soil Analysis (International Soil Reference and Information Centre, 2002).
Google Scholar 
Walkley, A. & Black, I. A. An examination of the digestion method for determining soil organic matter and a proposed modification of the chromic acid titration method. Soil Sci. 34, 29–38 (1934).ADS 

Google Scholar 
Kim, J., Kreller, C. R. & Greenberg, M. M. Preparation and analysis of oligonucleotides containing the C4’-oxidized abasic site and related mechanistic probes. J. Org. Chem. 70, 8122–8129 (2005).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kim, H. T. Soil sampling, preparation and analysis. 139–145 (1996).Bouyoucos, G. H. A reclamation of the hydrometer for making mechanical analysis. Soil. Agro. J. 43, 434–438 (1951).CAS 

Google Scholar 
Ihrmark, K., Bödeker, I. & Cruz-Martinez, K. New primers to amplify the fungal ITS2 region—evaluation by 454-sequencing of artificial and natural communities. FEMS Microbiol. Ecol. 82, 666–677 (2012).CAS 
PubMed 

Google Scholar 
White, T. ., Bruns, S., Lee, S. & Taylor, J. Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics. in PCR Protocols: A Guide to Methods and Applications (eds. Innis, M. A., Gelfand, D. H., Sninsky, J. J. & White, T. J.) 315–322 (Academic Press, 1990).Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 10 (2011).
Google Scholar 
Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kõljalg, U. et al. Towards a unified paradigm for sequence-based identification of fungi. Mol. Ecol. 22, 5271–5277 (2013).PubMed 

Google Scholar 
Põlme, S. et al. FungalTraits: a user-friendly traits database of fungi and fungus-like stramenopiles. Fungal Divers. 105 (2020).Hedberg, I. & Edwards, S. Flora of Ethiopia and Eritria (1989).Collins, C. G., Stajich, J. E., Weber, S. E., Pombubpa, N. & Diez, J. M. Shrub range expansion alters diversity and distribution of soil fungal communities across an alpine elevation gradient. Mol. Ecol. 27, 2461–2476 (2018).PubMed 
PubMed Central 

Google Scholar 
Schön, M. E., Nieselt, K. & Garnica, S. Belowground fungal community diversity and composition associated with Norway spruce along an altitudinal gradient. PLoS ONE 13, e0208493 (2018).PubMed 
PubMed Central 

Google Scholar 
Castaño, C. et al. Changes in fungal diversity and composition along a chronosequence of Eucalyptus grandis plantations in Ethiopia. Fungal Ecol. 39, 328–335 (2019).
Google Scholar 
Shannon, C. E. & Weaver, W. The Mathematical Theory of Communication (University of Illinois Press, 1949).MATH 

Google Scholar 
Kent, M. & Coker, P. Vegetation Description and Analysis: A Practical Approach (Belhaven Press, 1993).
Google Scholar 
Magurran, A. E. Ecological Diversity and Its Measurement (Princeton University Press, 1988).
Google Scholar 
Jost, L., Chao, A. & Chazdon, R. Compositional similarity and β (beta) diversity. in Biological Diversity. Frontiers in Measurement and Assessment (eds. A.E., Magurran & B.J., M.) 66–84 (Oxford University Press, 2011).Kindt, R. & Coe, R. Tree diversity analysis. A manual and software for common statistical methods for ecological and biodiversity studies. (World Agroforestry Centre (ICRAF), 2005).R Core Team. A language and environment for statistical computing. (R Foundation for Statistical Computing, Vienna, Austria, 2020).Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & Team, R. C. Nlme: Linear and Nonlinear Mixed Effects Models. R Package Version 3.1-128. http://CRAN.R-project.org/package=nlme (2016).Tóthmérész, B. Comparison of different methods for diversity ordering. J. Veg. Sci. 6, 283–290 (1995).
Google Scholar 
Clarke, K. R., Gorley, R. N., Somerfield, P. J. & Warwick, R. M. Change in marine communities: an approach to statistical analysis and interpretation. (PRIMER-E, Plymouth, 2014).Hammer, Ø., Harper, D. A. T. & Ryan, P. D. PAST: Paleontological statistics software package for education and data analysis. Palaeontol. Electron. 4, 9 (2001).
Google Scholar  More

Climate change is affecting arctic ecosystems through temperature increase1, hydrological changes2, earlier snowmelt3,4, and the associated increase in growing season length5. Annual arctic air temperature has been increasing at more than double the magnitude of the global mean air temperature increase1, and terrestrial snow cover in June has decreased by 15.2% per decade from 1981 to 20194. Warming is the main driver of the earlier start of the growing season, and the greening of the Arctic6,7,8. Arctic greening is associated with enhanced vegetation height, biomass, cover, and abundance9. However, the complexity of arctic systems reveals an intricate patchwork of landscape greening and browning8,10,11, with browning linked to a variety of stresses to vegetation8 including water stress12,13. The interconnected changes in temperature, soil moisture, snowmelt timing, etc. can have important effects on the carbon sequestered by arctic ecosystems14. The reservoir of carbon in arctic soil and vegetation depends on the interaction of two main processes: (1) changes in net CO2 uptake by vegetation; and (2) increased net loss of CO2 (from vegetation and soil) to the atmosphere via respiration. Therefore, defining the response of both plant productivity and ecosystem respiration to environmental changes is needed to predict the sensitivity of the net CO2 fluxes of arctic systems to climate change.An earlier snowmelt, and a longer growing season, do not necessarily translate into more carbon sequestered by high latitude ecosystems5. There is a large disagreement on the response of plant productivity and the net CO2 uptake to early snowmelt in tundra ecosystems15,16,17,18,19. A warmer and longer growing season might not result in more net CO2 uptake if CO2 loss from respiration increases16, particularly later in the season, and surpasses the CO2 sequestered by enhanced plant productivity in northern ecosystems16,20. Moreover, snowmelt timing and the growing season length greatly affect hydrologic conditions of arctic soils21, as well as plant productivity22. Longer non-frozen periods earlier in the year23 and earlier vegetation greening can increase evapotranspiration (ET), resulting in lower summer soil moisture24,25,26. The complexity in the hydrology of tundra systems arises from the tight link between the water drainage and the presence and depth of permafrost. The presence of permafrost reduces vertical water losses, preventing soil drainage in northern wetlands during most of the summer despite low precipitation input27. Increasing rainfall28 and increased permafrost degradation can increase soil wetness in continuous permafrost regions2. Further permafrost degradation (e.g. ice-wedge melting) can increase hydrologic connectivity leading to increased lateral drainage of the landscape and subsequent soil drying2,29.Given the importance of soil moisture in affecting the carbon balance of arctic ecosystems, and its links with snowmelt timing, in this study, we investigated the correlation between summer fluxes of CO2 (i.e., net ecosystem exchange (NEE), gross primary productivity (GPP) ecosystem respiration (ER)), ET, and environmental drivers such as soil moisture and snowmelt timing, while controlling for the other most important drivers of photosynthesis and respiration (i.e. solar radiation and air temperature). We expected earlier snowmelt to be correlated with larger ET and lower soil moisture, particularly during peak and late season, consistent with drying associated with a longer growing season. The lower soil moisture with earlier snowmelt should result in a negative correlation between snowmelt timing and GPP, particularly during the peak and late season (when we expect the most water stress), and in a positive correlation between snowmelt timing and ER during the entire growing season. This soil moisture limitation to plant productivity should result in lower net cumulative CO2 sequestration during the entire summer, because of lower plant productivity if these ecosystems are water-limited due to lower soil moisture with earlier snowmelt.Testing the impact of snowmelt timing on the carbon dynamics and hydrology of tundra ecosystemsThe 11 sites were selected as among the longest-running tower sites in the circumpolar Arctic (including 6 to 19 years of fluxes per site and a total of 119 site-years of summer (June to August) eddy covariance CO2 flux data, Table S1). All sites lie in the zone of continuous permafrost. The sites are representative of dominant tundra vegetation classes (wetland, graminoids, and shrub tundra), together accounting for 31% of all tundra vegetation types (Fig. 130 and Supplementary Information). Given the complex interactions among different variables (many covarying together), we used a variety of statistical analyses to identify the association between standardized anomalies of NEE, GPP, ER, and ET, and standardized anomalies of the main environmental controls during different periods of the summer corresponding to various stages in seasonal phenology (early season: June, peak season: July, and late season: August). We used a partial correlation analysis to identify if the timing of the snowmelt associates with anomalies of ET, soil moisture, NEE, GPP, ER, atmospheric vapor pressure deficit (VPD), or the Bowen ratio (the ratio between Sensible Heat (H) and Latent Heat (LE)) while statistically controlling for the main meteorological forcing such as air temperature and solar radiation (Methods). Identifying the correlation between ET (and the Bowen ratio) and snowmelt timing is a way to assess water limitation to ecosystems (in addition to testing their response to soil moisture changes), as H, and therefore, the Bowen ratio, are expected to increase with surface drying31,32. To identify the association between snowmelt timing, the main environmental variables (i.e., air temperature and solar radiation), and NEE, GPP, ER, and ET over time, we performed a maximum covariance analysis (MCA) on the monthly median standardized anomalies from 2004 to 2019 (a time period when data for most of the sites were available). MCA allowed us to find patterns in two space–time datasets that are highly correlated using a cross-covariance matrix26. We retained sites as the unit of variation (i.e., by estimating the standardized anomalies by site for each of the indicated variables, see “Methods”), hence the results of the MCA integrated the site level relationships between each of the variables over time). The goal of this analysis was to identify the most important environmental drivers associated with NEE, GPP, and ER across all the sites over time. MCA is particularly appropriate for this study as it can handle data with gaps and unequal lengths in the datasets. We also tested the relative importance of the abovementioned environmental drivers on the monthly median GPP, ER, and NEE using a linear mixed effect model, including site as a random effect to account for the site-to-site variability. The MCA and the mixed model analyses were conducted to test the relative importance of snowmelt and other variables at different times of the season. Finally, to evaluate the water balance at different times of the season, we estimated the difference between Potential Evapotranspiration (PET) and the actual ET, and the difference between precipitation (PPT) and ET for each of the sites, years, and months (e.g. June, July, and August). This study did not attempt to describe the long-term temporal changes in the anomalies of snowmelt and carbon fluxes, given the short data record available for some of the sites (i.e. less than 10 years, Table S1), but instead focused on understanding the association between environmental variables and the carbon balance at different times of the season. More details of these analyses are included in the Methods.Figure 1Study sites. Locations of the 11 eddy covariance flux tower sites used in this study. Light blue regions delineate the total Circumpolar Arctic Vegetation Map (CAVM), green regions delineate the subset of CAVM vegetation types represented in this study (including all the vegetation types listed in Table S1). This map was created using QGIS.org, 2020, QGIS 3.10. Geographic Information System User Guide. QGIS Association: https://docs.qgis.org/3.10/en/docs/user_manual/index.html. The dataset used in the map was the CAVM map: CAVM Team. 2003. Circumpolar Arctic Vegetation Map. (1:7,500,000 scale), Conservation of Arctic Flora and Fauna (CAFF) Map No. 1. U.S. Fish and Wildlife Service, Anchorage, Alaska. ISBN: 0-9767525-0-6, ISBN-13: 978-0-9767525-0-9.Full size imageInfluence of snowmelt timing on NEE, GPP, ER, and hydrological status of tundra ecosystemsOnce statistically controlling for solar radiation and air temperature (in the partial correlation analysis, see “Methods”), we observed a significant positive relationship between the snowmelt timing anomalies and NEE anomalies (i.e. earlier snowmelt was associated with a higher net CO2 sequestration) in June and July, but a negative correlation in August (Fig. 2a, Table 1). A significant relationship was also found between snowmelt date anomalies and GPP anomalies, with more positive GPP anomalies (i.e. higher plant productivity) with earlier snowmelt in June and July, and more negative GPP anomalies with earlier snowmelt in August (Fig. 2b, Table 1). Earlier snowmelt was associated with significantly higher ER in both June and July, but there was no significant relationship in August (Fig. 2c, Table 1), suggesting that the late-season correlation between NEE and snowmelt timing was mostly driven by the lower GPP and with earlier snowmelt in August. The MCA analysis showed that the anomalies in snowmelt timing had the highest squared covariance fraction (SCF) with the monthly median anomalies of GPP, NEE, and ER in June and July, and the lowest in August over the 2004–2019 period (Fig. 3). A similar result was observed in the linear mixed effect model, which showed a significant relationship between snowmelt date and GPP, and NEE, in all summer months, higher ({R}_{m}^{2}) between the snowmelt date and GPP in June and July, and no significant relationship between snowmelt date and ER in August (Table S3). In late season, other environmental variables had a higher covariance with the GPP, NEE, and ER anomalies than the snowmelt timing (Fig. 3, Table S3).Figure 2Relationships between the indicated median monthly anomalies using partial correlation analysis accounting for solar radiation and air temperature anomalies (retaining site as the unit of variation). Given that the interaction term between “month” and snowmelt timing was significant, we included the correlation coefficients and P of the regressions for each of the indicated months separately in each panel (also included in Table 1). Negative values indicate CO2 uptake and positive values CO2 release into the atmosphere, and shaded areas are 95% confidence intervals.Full size imageTable 1 Significance (P) and Pearson’s correlation coefficient (r) of the relationships between the indicated monthly median standardized anomalies for June, July, and August retaining site as a unit of variation.Full size tableFigure 3Squared covariance fraction (SCF) of each couple of the indicated variables for the maximum covariance analysis (MCA) of the monthly median anomalies of GPP, ER, and NEE in June, July, and August. The first pair of singular vectors are the phase-space directions when projected that have the largest possible cross-covariance. The singular vectors describe the patterns in the anomalies that are linearly correlated. A higher SCF indicates a stronger association over time between the indicated variables.Full size imageOur results are consistent with the discrepancy between the observed increase in the maxNDVI over the last four decades and the time-integrated (TI) NDVI which instead has plateaued in the last two decades and even decreased over the last 10 years in several northern arctic ecosystems33. TI-NDVI considers the length of the growing season and phenological variations34 and, therefore, better integrates vegetation development during the entire growing season. Moisture was shown to be important for the NDVI trends33,35. Given the potential water limitation to summer carbon uptake in northern ecosystems12,23,24,25, we tested if an earlier snowmelt was associated with a decrease in soil moisture, which would affect GPP and NEE. We only observed a significant correlation between soil moisture anomalies and snowmelt date anomalies in June (i.e. higher soil moisture with earlier snowmelt, Fig. S1a, Table S2), but no significant correlation in July and August (Fig. S1a, Table S2). The higher soil moisture with earlier snowmelt in June is consistent with surface inundation after snowmelt36,37 and earlier soil thawing resulting in higher soil moisture (i.e., soil moisture is low while soils are frozen). A similar result was observed for the ET anomalies. Higher ET with earlier snowmelt in June (Fig. S1b) could be the result of surface inundation after snowmelt32. The standardized NEE anomalies were significantly correlated with the soil moisture anomalies in each of the summer months (Fig. S1d, Table S2). However, the relationship between the GPP (and ER anomalies) and soil moisture anomalies was only significant in June (Fig. S1e,f, Table S2) suggesting an earlier activation of the vegetation with earlier soil thaw (and the associated higher soil moisture). A higher water loss from ET in early season (Fig. S1b) could have resulted in the drying of the surface moss layer with the progression of the summer, which would have been consistent with the observed lower GPP and the lower net CO2 sequestration with earlier snowmelt observed in August (Fig. 2a,b, Table 1). A potential moisture limitation to plant productivity might have been consistent also with the higher SCF of NEE, or GPP and VPD anomalies in August than in June and July (Fig. 3). However, no significant relationship between ET (or soil moisture) and snowmelt date anomalies was observed in July and August (Fig. S1a,b) contrary to what would be expected if drying occurred following earlier snowmelt. No significant relationship was found between VPD anomalies and snowmelt date anomalies in any of the summer months (P = 0.14 in a partial correlation considering air temperature and solar radiation anomalies). Finally, surface drying should result in an increase in the Bowen ratio anomalies with the progression of the summer, given that H increases with a decrease in water table and surface drying32,38. However, the Bowen ratio showed no correlation with the standardized snowmelt date anomalies in any of the summer months (Fig. S1c, Table S2), and presented similar values in all the summer months (Fig. S2a). The lack of correlation between the soil moisture, VPD, Bowen ratio, and snowmelt date anomalies suggests that an earlier snowmelt did not result in significant surface drying in the sites of this study. The median PET-ET and PPT-ET for all years and sites included in this analysis (Fig.S2 b,c) was slightly higher in August, similar to reports by others for the Russian arctic tundra38,39, further supporting a lack of soil moisture limitation in late season. Although these analyses do not consider runoff, which can be significant21,26, overall our results do not suggest that an earlier snowmelt resulted in a water stress that significantly limited plant productivity in these arctic ecosystems over continuous permafrost.The correlation between the anomalies in the August GPP and snowmelt timing is consistent with earlier senescence in northern plant species (e.g. Eriophorum vaginatum, a dominant species across these tundra types) compared to southern species growing in the same location in a common garden experiment40. The phenotypic variation was shown to be persisting for decades41, and ecotypes may be unable to extend their effective growth period or take advantage of a longer growing season40. Several studies across different plant functional types have shown that once plant growth is initiated after the snowmelt in northern ecosystems, it continues only for a fixed number of days until the occurrence of senescence42,43,44. Therefore, the lower GPP in August with earlier snowmelt might not be linked to water limitation on photosynthesis later in the season, but rather to an earlier senescence arising from the endogenous rhythms of growth and senescence, that plant functional types living in these extreme conditions have developed over decades. On a broader scale, earlier senescence with an earlier start of the growing season after snowmelt in northern ecosystems is also consistent with an earlier spring zero-crossing date and an earlier autumn zero-crossing date of the mean detrended seasonal CO2 variations at Barrow, AK, USA (NOAA ESRL: https://www.esrl.noaa.gov/gmd/ccgg/obspack/) during 2013–2017 compared to 1980–19845. The spring and autumn zero-crossing date is the time when the detrended seasonal CO2 variations intersect the zero line in spring and autumn respectively and can be used as an indicator for the start and end of the net CO2 uptake by vegetation45,46. On the other hand, NDVI measurements show both an earlier start of the season and a later end of season for 2008–2012 compared to 1982–19865. The disagreement between the detrended seasonal atmospheric CO2 concentration showing an earlier autumn zero-crossing date and the NDVI measurements showing a later end of the season has been explained by the increase in respiration in the fall20. The disagreement between atmospheric CO2 concentration trends (showing an earlier autumn zero-crossing date), and NDVI (showing a later end of the season,5 may also be explained by the challenges in using NDVI as a proxy for plant productivity in these arctic systems. The relationship between NDVI and CO2 flux and plant productivity is highly variable and non-linear in arctic ecosystems47. While some arctic ecosystems have shown that NDVI was strongly correlated with GPP (explaining 75% of the variation in GPP48, other studies showed that NDVI was either not significantly correlated with GPP and NEE49 or was only able to explain a minor fraction (maximum of 25%) of the variation in NEE and GPP in some arctic tundra ecosystems after accounting for the seasonal variation50,51.In conclusion, earlier snowmelt was associated with greater net CO2 uptake and higher GPP in early and peak seasons, but with less net CO2 uptake and lower GPP later in the summer, in the studied arctic tundra ecosystems. We did not find evidence of a late-season water limitation to GPP with earlier snowmelt. Although several hypotheses can be forwarded to explain the link between snowmelt and late season declines in plant productivity and carbon uptake, the current literature does not provide a definitive explanation (schematic Fig. 4). Future studies should investigate the potential interaction of different processes explaining the response of the carbon dynamics in the Arctic to earlier snowmelt and reconstruct the temporal changes in the carbon balance from these systems. The link between the long-term changes in the CO2 fluxes and NDVI in tundra ecosystems needs closer examination. Studies should investigate if higher NDVI is definitively associated with higher net CO2 uptake. Greening of the Arctic might not necessarily translate into more net CO2 uptake, as early and peak season carbon gains might be offset by a late-season CO2 loss, and respiration might counterbalance the increase in plant productivity. A better understanding of the processes driving these temporal changes is a fundamental step in advancing our prediction of the response of the arctic CO2 balance to changing climate.Figure 4Schematic of the effect of earlier snowmelt on NEE, GPP, and ER at different times of the season. Earlier snowmelt results in an earlier activation of the vegetation, higher plant productivity, and higher net carbon uptake in June and July. This earlier activation could result in more carbon loss and lower plant productivity with earlier snowmelt in August, potentially related to either environmental stress, or to earlier senescence. Photo credit: Donatella Zona.Full size image More

Akins JR, Aubry KB, Sacks BN (2018) Genetic integrity, diversity, and population structure of the Cascade red fox. Conserv Genet 19:969–980
Google Scholar 
Allen SH, Sargeant AB (1993) Dispersal patterns of red foxes relative to population density. J Wildlife Manag 57(3):526–533Arbogast BS, Schumacher KI, Kerhoulas NJ, Bidlack AL, Cook JA, Kenagy GJ (2017) Genetic data reveal a cryptic species of New World flying squirrel: Glaucomys oregonensis. J Mammal 98(4):1027–41
Google Scholar 
Ashbrook FG (1928) Silver-Fox Farming. US Department of Agriculture. Washington D.C.Aubry KB (1984) The recent history and present distribution of the red fox in Washington. Northwest Sci 58(1):69–79
Google Scholar 
Aubry KB, Statham MJ, Sacks BN, Perrine JD, Wisely SM (2009) Phylogeography of the North American red fox: vicariance in Pleistocene forest refugia. Mol Ecol 18(12):2668–2686CAS 
PubMed 

Google Scholar 
Bailey V (1936a) The red fox in America. Nature 28:269–272
Google Scholar 
Bailey V (1936b) The Mammals and Life Zones of Oregon. U.S. Government Printing Office, Washington, D.C
Google Scholar 
Ball MC, Finnegan L, Manseau M, Wilson P (2010) Integrating multiple analytical approaches to spatially delineate and characterize genetic population structure: an application to boreal caribou (Rangifer tarandus caribou) in central Canada. Conserv Genet 11(6):2131–2143
Google Scholar 
Barrowclough GF, Groth JG, Mertz LA, Gutiérrez RJ (2004) Phylogeographic structure, gene flow and species status in blue grouse (Dendragapus obscurus). Mol Ecol 13(7):1911–22CAS 
PubMed 

Google Scholar 
Bell DA, Robinson ZL, Funk WC, Fitzpatrick SW, Allendorf FW, Tallmon DA et al. (2019) The exciting potential and remaining uncertainties of genetic rescue. Trends Ecol Evol 34(12):1070–1079PubMed 

Google Scholar 
Black KL, Petty SK, Radeloff VC, Pauli JN (2018) The Great Lakes Region is a melting pot for vicariant red fox (Vulpes vulpes) populations. J Mammal 99(5):1229–1236
Google Scholar 
Champagnon J, Elmberg J, Guillemain M, Gauthier-Clerc M, Lebreton JD (2012) Conspecifics can be aliens too: a review of effects of restocking practices in vertebrates. J Nat Conserv 20(4):231–41
Google Scholar 
Chen C, Durand E, Forbes F, François O (2007) Bayesian clustering algorithms ascertaining spatial population structure: a new computer program and a comparison study. Mol Ecol Notes 7(5):747–756
Google Scholar 
Cross PR, Sacks BN, Luikart G, Schwartz MK, Van Etten KW, Crabtree RL (2018) Red Fox Ancestry and Connectivity Assessments Reveal Minimal Fur Farm Introgression in Greater Yellowstone Ecosystem. J Fish Wildl Manag 9(2):519–530
Google Scholar 
Dalquest, WW (1948) Mammals of Washington, Vol II. University of Kansas Publications, Museum of Natural History, Lawrence, KansasDevenish-Nelson ES, Harris S, Soulsbury CD, Richards SA, Stephens PA (2013) Demography of a carnivore, the red fox, Vulpes vulpes: what have we learnt from 70 years of published studies? Oikos 122(5):705–16
Google Scholar 
Do C, Waples RS, Peel D, Macbeth G, Tillett BJ, Ovenden JR (2014) NeEstimator v2: re-implementation of software for the estimation of contemporary effective population size (Ne) from genetic data. Mol Ecol Resour 14(1):209–214CAS 
PubMed 

Google Scholar 
Durand E, Jay F, Gaggiotti OE, François O (2009) Spatial inference of admixture proportions and secondary contact zones. Mol Biol Evol 26(9):1963–1973CAS 
PubMed 

Google Scholar 
Earl DA, vonHoldt BM (2012) Structure Harvester: a website and program for visualizing Structure output and implementing the Evanno method. Conserv Genet Resour 4(2):359–361
Google Scholar 
Edmands S (2007) Between a rock and a hard place: evaluating the relative risks of inbreeding and outbreeding for conservation and management. Mol Ecol 6(3):463–75
Google Scholar 
Falush D, Stephens M, Pritchard JK (2003) Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics 164(4):1567–1587CAS 
PubMed 
PubMed Central 

Google Scholar 
Fichter E, Williams R (1967) Distribution and status of the red fox in Idaho. J Mammal 48(2):219–230
Google Scholar 
Fitzpatrick SW, Bradburd GS, Kremer CT, Salerno PE, Angeloni LM, Funk WC (2020) Genomic and fitness consequences of genetic rescue in wild populations. Curr Biol 30(3):517–22CAS 
PubMed 

Google Scholar 
Frankham R (1996) Relationship of genetic variation to population size in wildlife. Conserv Biol 10(6):1500–8
Google Scholar 
Frankham R (2015) Genetic rescue of small inbred populations: Meta-analysis reveals large and consistent benefits of gene flow. Mol Ecol 24(11):2610–2618PubMed 

Google Scholar 
Frankham R, Ballou JD, Eldridge MD, Lacy RC, Ralls K, Dudash MR, Fenster CB (2011) Predicting the probability of outbreeding depression. Conserv Biol 25(3):465–75PubMed 

Google Scholar 
Frankham R, Ballou JD, Ralls K, Eldridge MD, Dudash MR, Fenster CB et al. (2017) Genetic management of fragmented animal and plant populations. Oxford University Press, New York, NY
Google Scholar 
Fuhrmann RT (1998) Distribution, Morphology, and Habitat Use of the Red Fox in the Northern Yellowstone Ecosystem. MSc Thesis, Montana State University, Bozeman, MontanaGortázar C, Travaini A, Delibes M (2000) Habitat-related microgeographic body size variation in two Mediterranean populations of red fox (Vulpes vulpes). J Zool Lond 250:335–338
Google Scholar 
Gosselink TE, Piccolo KA, Van Deelen TR, Warner RE, Mankin PC (2010) Natal dispersal and philopatry of red foxes in urban and agricultural areas of Illinois. J Wildl Manag 74(6):1204–17
Google Scholar 
Goudet J (2005) Hierfstat, a package for R to compute and test hierarchical F-statistics. Mol Ecol Notes 5(1):184–186
Google Scholar 
Green GA, Sacks BN, Erickson LJ, Aubry KB (2017) Genetic characteristics of red foxes in northeastern Oregon. Northwest Naturalist 98(2):73–81
Google Scholar 
Grinnell J, Dixon JS, Linsdale JM (1937) Fur-Bearing Mammals of California, Vol II. University of California Press, Berkeley, California
Google Scholar 
Hall E, Kelson KR (1959) The Mammals of North America. 2 Vols. Ronald Press, New York, NY
Google Scholar 
Hampe A, Petit RJ (2005) Conserving biodiversity under climate change: the rear edge matters. Ecol Lett 8(5):461–7PubMed 

Google Scholar 
Harris K, Zhang Y, Nielsen R (2019) Genetic rescue and the maintenance of native ancestry. Conserv Genet 20(1):59–64
Google Scholar 
Hedrick PW, Peterson RO, Vucetich LM, Adams JR, Vucetich JA (2014) Genetic rescue in Isle Royale wolves: genetic analysis and the collapse of the population. Conserv Genet 15(5):1111–21
Google Scholar 
Hiller TL, McFadden-Hiller JE, Sacks BN (2015) Genetic and photographic detections document Sierra Nevada red fox in the Northern Cascades of Oregon. Northwest Sci 89(4):409–13
Google Scholar 
Hoffmann M, Sillero-Zubiri C (2021) Vulpes vulpes (amended version of 2016 assessment). IUCN Red List Threatened Species 2021:e.T23062A193903628. https://doi.org/10.2305/IUCN.UK.2021-1.RLTS.T23062A193903628.en. Accessed 23 September 2021Article 

Google Scholar 
Hoffmann RS, Wright PL, Newby FE (1969) The distribution of some mammals in Montana I. Mammals other than bats. J Mammal 50(3):579–604
Google Scholar 
Hope AG, Malaney JL, Bell KC, Salazar-Miralles F, Chavez AS, Barber BR et al. (2016) Revision of widespread red squirrels (genus: Tamiasciurus) highlights the complexity of speciation within North American forests. Mol Phylogenet Evol 100:170–82PubMed 

Google Scholar 
Jakobsson M, Rosenberg NA (2007) CLUMPP: a cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics 23(14):1801–1806CAS 
PubMed 

Google Scholar 
Jombart T (2008) adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics 24(11):1403–1405CAS 
PubMed 

Google Scholar 
Jombart T, Devillard S, Balloux F (2010) Discriminant analysis of principal components: a new method for the analysis of genetically structured populations. BMC Genet 11(1):94PubMed 
PubMed Central 

Google Scholar 
Kamler JF, Ballard WB (2002) A review of native and nonnative red foxes in North America. Wildlife Soc Bullet 30(2):370–379Kamler JF, Ballard WB (2003) Range expansion of red foxes in eastern Nevada and western Utah. J Arizona-Nevada Acad Sci 36(1):18–20Kardos M, Taylor HR, Ellegren H, Luikart G, Allendorf FW (2016) Genomics advances the study of inbreeding depression in the wild. Evolut Appl 9(10):1205–18
Google Scholar 
Kasprowicz AE, Statham MJ, Sacks BN (2016) Fate of the other redcoat: remnants of colonial British foxes in the eastern United States. J Mammal 97(1):298–309
Google Scholar 
Kuchler A (1964) Potential natural vegetation of the conterminous United States. Am Geogr Soc Spec Publ 36:1–116Kyriazis CC, Wayne RK, Lohmueller KE (2021) Strongly deleterious mutations are a primary determinant of extinction risk due to inbreeding depression. Evol Lett 5(1):33–47PubMed 

Google Scholar 
Laikre L, Schwartz MK, Waples RS, Ryman N, Group GW (2010) Compromising genetic diversity in the wild: unmonitored large-scale release of plants and animals. Trends Ecol Evol 25(9):520–529PubMed 

Google Scholar 
Larivière S, Pasitschniak-Arts M (1996) Vulpes vulpes. Mamm Species 537:1–11
Google Scholar 
Laut AC (1921) The fur trade of America. Macmillan Company, New York, NY
Google Scholar 
Leigh JW, Bryant D (2015) Popart: full-feature software for haplotype network construction. Methods Ecol Evol 6(9):1110–6
Google Scholar 
Lesica P, Allendorf FW (1995) When are peripheral populations valuable for conservation? Conserv Biol 9(4):753–60
Google Scholar 
Lewis JC, Sallee KL, Golightly Jr RT (1999) Introduction and range expansion of nonnative red foxes (Vulpes vulpes) in California. Am Midland Naturalist 142(2):372–381Long J (2003) Introduced mammals of the world: their history, distribution and influence. CSIRO Publishing, Melbourne, Australia
Google Scholar 
Lord KA, Larson G, Coppinger RP, Karlsson EK (2020) The history of farm foxes undermines the animal domestication syndrome. Trends Ecol Evol 35(2):125–136PubMed 

Google Scholar 
Love Stowell SM, Pinzone CA, Martin AP (2017) Overcoming barriers to active interventions for genetic diversity. Biodivers Conserv 26(8):1753–65
Google Scholar 
Lounsberry ZT, Quinn CB, Statham MJ, Angulo CL, Kalani TJ, Tiller E et al. (2017) Investigating genetic introgression from farmed red foxes into the wild population in Newfoundland, Canada. Conserv Genet 18(2):383–392
Google Scholar 
Mace RU (1970). Oregon’s Furbearing Animals. Oregon State Game Commission, Corvallis, OregonManthey JD, Klicka J, Spellman GM (2012) Is gene flow promoting the reversal of Pleistocene divergence in the Mountain Chickadee (Poecile gambeli)? PLOS ONE 7(11):e49218CAS 
PubMed 
PubMed Central 

Google Scholar 
Merson C, Statham MJ, Janecka JE, Lopez RR, Silvy NJ, Sacks BN (2017) Distribution of native and nonnative ancestry in red foxes along an elevational gradient in central Colorado. J Mammal 98(2):365–377
Google Scholar 
Miller JM, Cullingham CI, Peery RM (2020) The influence of a priori grouping on inference of genetic clusters: simulation study and literature review of the DAPC method. Heredity 125(5):269–280PubMed 
PubMed Central 

Google Scholar 
Moore M, Brown SK, Sacks BN (2010) Thirty-one short red fox (Vulpes vulpes) microsatellite markers. Mol Ecol Resour 10:404–8PubMed 

Google Scholar 
Moritz C (1994) Applications of mitochondrial DNA analysis in conservation: a critical review. Mol Ecol 3(4):401–11CAS 

Google Scholar 
Moritz C (2002) Strategies to protect biological diversity and the evolutionary processes that sustain it. Syst Biol 51(2):238–254PubMed 

Google Scholar 
Neel MC, McKelvey K, Ryman N, Lloyd MW, Bull RS, Allendorf FW et al. (2013) Estimation of effective population size in continuously distributed populations: there goes the neighborhood. Heredity 111(3):189–99CAS 
PubMed 
PubMed Central 

Google Scholar 
Nei M (1987) Molecular Evolutionary Genetics. Columbia University Press, New York, NY
Google Scholar 
Pebesma EJ (2004) Multivariable geostatistics in S: the gstat package. Comput Geosci 30(7):683–691
Google Scholar 
Paradis E (2010) pegas: an R package for population genetics with an integrated–modular approach. Bioinformatics 26(3):419–20CAS 
PubMed 

Google Scholar 
Perrine JD, Campbell LA, Green GA (2010) Sierra Nevada red fox (Vulpes vulpes necator): a conservation assessment. US Department of Agriculture, Vallejo, CaliforniaPerrine JD, Pollinger JP, Sacks BN, Barrett RH, Wayne RK (2007) Genetic evidence for the persistence of the critically endangered Sierra Nevada red fox in California. Conserv Genet 8(5):1083–1095
Google Scholar 
Petersen M (1914) The fur traders and fur bearing animals. Hammond Press, Buffalo, New York, NY
Google Scholar 
Petkova D, Novembre J, Stephens M (2016) Visualizing spatial population structure with estimated effective migration surfaces. Nat Genet 48(1):94–100CAS 
PubMed 

Google Scholar 
Petkova (2020) reemsplots2: Generate plots to inspect and visualize the results of EEMS. R package version 0.1.0. https://github.com/dipetkov/eemsPritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155(2):945–959CAS 
PubMed 
PubMed Central 

Google Scholar 
Pritchard JK, Wen W, Falush D (2003) Documentation for STRUCTURE Software: Version 2. https://web.stanford.edu/group/pritchardlab/software/readme_structure2.pdf Accessed 3 Dec 2020Quinn CB, Alden PB, Sacks BN (2019) Noninvasive sampling reveals short-term genetic rescue in an insular red fox population. J Heredity 110(5):559–576
Google Scholar 
Ralls K, Ballou JD, Dudash MR, Eldridge MD, Fenster CB, Lacy RC et al. (2018) Call for a paradigm shift in the genetic management of fragmented populations. Conserv Lett 11(2):e12412
Google Scholar 
Ralls K, Sunnucks P, Lacy RC, Frankham R (2020) Genetic rescue: a critique of the evidence supports maximizing genetic diversity rather than minimizing the introduction of putatively harmful genetic variation. Biol Conserv 251:108784
Google Scholar 
Rando HM, Stutchman JT, Bastounes ER, Johnson JL, Driscoll CA, Barr CS et al. (2017) Y-chromosome Markers for the Red Fox. J Heredity 108(6):678–685CAS 

Google Scholar 
Rhymer JM, Simberloff D (1996) Extinction by hybridization and introgression. Annu Rev Ecol Syst 27(1):83–109
Google Scholar 
Roest AI (1977) Taxonomic status of the red fox in California. State of California, The Resources Agency, Department of Fish and Game, California Polytechnic State University, San Luis Obispo, CaliforniaRoux C, Fraisse C, Romiguier J, Anciaux Y, Galtier N, Bierne N (2016) Shedding light on the grey zone of speciation along a continuum of genomic divergence. PLOS Biol 14(12):e2000234PubMed 
PubMed Central 

Google Scholar 
Sacks BN, Brazeal JL, Lewis JC (2016) Landscape genetics of the nonnative red fox of California. Ecol Evol 6(14):4775–4791PubMed 
PubMed Central 

Google Scholar 
Sacks B, Lounsberry Z, Rando H, Kluepfel K, Fain S, Brown S et al. (2021) Sequencing red fox Y chromosome fragments to develop phylogenetically informative SNP markers and glimpse male-specific trans-Pacific phylogeography. Genes 12(1):97CAS 
PubMed 
PubMed Central 

Google Scholar 
Sacks BN, Moore M, Statham MJ, Wittmer HU (2011) A restricted hybrid zone between native and introduced red fox (Vulpes vulpes) populations suggests reproductive barriers and competitive exclusion. Mol Ecol 20(2):326–341PubMed 

Google Scholar 
Sacks BN, Statham MJ, Perrine JD, Wisely SM, Aubry KB (2010) North American montane red foxes: expansion, fragmentation, and the origin of the Sacramento Valley red fox. Conserv Genet 11(4):1523–1539
Google Scholar 
Saunders G, Coman B, Kinnear J, Braysher M (1995) Managing vertebrate pests: foxes. Bureau of Resource Science and Australian Nature Conservation Agency, Commonwealth of Australia, Canberra
Google Scholar 
Sierra Nevada Red Fox Conservation Advisory Team [SCAT] (2022) A Conservation Strategy for the Sierra Nevada Red Fox. California Department of Fish and Wildlife, Sacramento, USA, In pressSeton E (1929) Lives of Game Animals. Doubleday, Doran and Co, New York, NY
Google Scholar 
Shaffer ML, Stein BA (2000) Safeguarding our precious heritage. In: Stein BA, Kutner LS, Adams JS (eds) Precious heritage: the status of biodiversity in the United States. Oxford University Press, Oxford, p 301–322
Google Scholar 
Shirk A, Cushman S (2011) sGD: software for estimating spatially explicit indices of genetic diversity. Mol Ecol Resour 11(5):922–934CAS 
PubMed 

Google Scholar 
Sikes RS, Gannon WL, Animal Care and Use Committee of the American Society of Mammalogists (2011) Guidelines of the American Society of Mammalogists for the use of wild mammals in research. J Mammal 92:235–253
Google Scholar 
Statham MJ, Murdoch J, Janecka J, Aubry KB, Edwards CJ, Soulsbury CD et al. (2014) Range-wide multilocus phylogeography of the red fox reveals ancient continental divergence, minimal genomic exchange and distinct demographic histories. Mol Ecol 23(19):4813–4830PubMed 

Google Scholar 
Statham MJ, Sacks BN, Aubry KB, Perrine JD, Wisely SM (2012) The origin of recently established red fox populations in the United States: translocations or natural range expansions? J Mammal 93(1):52–65
Google Scholar 
Statham MJ, Trut LN, Sacks BN, Kharlamova AV, Oskina IN, Gulevich RG et al. (2011) On the origin of a domesticated species: identifying the parent population of Russian silver foxes (Vulpes vulpes). Biol J Linn Soc 103(1):168–175
Google Scholar 
Szuma E (2008) Evolutionary and climatic factors affecting tooth size in the red fox Vulpes vulpes in the Holarctic. Mammal Res 53(4):289–332
Google Scholar 
U.S. Fish and Wildlife Service (2015) Endangered and threatened wildlife and plants; 12-month finding on a petition to list Sierra Nevada red fox as an endangered or threatened species. Fed Reg 80:60989–61028
Google Scholar 
U.S. Fish and Wildlife Service (2021) Endangered and threatened wildlife and plants; endangered status for the Sierra Nevada Distinct Population Segment of the Sierra Nevada red fox. Fed Reg 86:41743–41758
Google Scholar 
van der Valk T, de Manuel M, Marques-Bonet T, Guschanski K (2021) Estimates of genetic load suggest frequent purging of deleterious alleles in small populations. bioRxiv:696831Verts B, Carraway LN (1998) Land Mammals of Oregon. University of California Press, Berkeley
Google Scholar 
Volkmann LA, Statham MJ, Mooers AØ, Sacks BN (2015) Genetic distinctiveness of red foxes in the Intermountain West as revealed through expanded mitochondrial sequencing. J Mammal 96(2):297–307
Google Scholar 
Waples RS (2006) A bias correction for estimates of effective population size based on linkage disequilibrium at unlinked gene loci. Conserv Genet 7(2):167–184
Google Scholar 
Waples RS, Antao T, Luikart G (2014) Effects of overlapping generations on linkage disequilibrium estimates of effective population size. Genetics 197(2):769–780PubMed 
PubMed Central 

Google Scholar 
Waples RS, Do C (2008) LDNE: a program for estimating effective population size from data on linkage disequilibrium. Mol Ecol Resour 8(4):753–756PubMed 

Google Scholar 
Waples RS, Do C (2010) Linkage disequilibrium estimates of contemporary Ne using highly variable genetic markers: a largely untapped resource for applied conservation and evolution. Evolut Appl 3(3):244–262
Google Scholar 
Waples RS, England PR (2011) Estimating contemporary effective population size on the basis of linkage disequilibrium in the face of migration. Genetics 189(2):633–644PubMed 
PubMed Central 

Google Scholar 
Washington Department of Fish and Wildlife (2015) Washington’s State Wildlife Action Plan: 2015 Update. Washington Department of Fish and Wildlife, Olympia, Washington, USA
Google Scholar 
Wei W, Ayub Q, Xue Y, Tyler-Smith C (2013) A comparison of Y-chromosomal lineage dating using either resequencing or Y-SNP plus Y-STR genotyping. Forensic Sci Int- Genet 7:568–572CAS 
PubMed 
PubMed Central 

Google Scholar 
Weir BS, Cockerham CC (1984) Estimating F-statistics for the analysis of population structure. Evolution 36:1358–1370Whiteley AR, Fitzpatrick SW, Funk WC, Tallmon DA (2015) Genetic rescue to the rescue. Trends Ecol Evol 30(1):42–49PubMed 

Google Scholar 
Wilder AP, Navarro AY, King SN, Miller WB, Thomas SM, Steiner CC et al. (2020) Fitness costs associated with ancestry to isolated populations of an endangered species. Conserv Genet 21(3):589–601CAS 

Google Scholar 
Wright S (1946) Isolation by distance under diverse systems of mating. Genetics 31(1):39CAS 
PubMed 
PubMed Central 

Google Scholar 
Zabel CJ, Taggart SJ (1989) Shift in red fox, Vulpes vulpes, mating system associated with El Niño in the Bering Sea. Anim Behav 38(5):830–838
Google Scholar  More