Ecology

Field measurementsWe used two sets of field measurements of soil moisture, VPD, and stomatal conductance of maize at the daily scale to illustrate a proof-of-concept for the co-regulation of soil moisture and VPD on stomatal conductance.The first set was measurements from greenhouse experiments of maize (seed: Dekalb hybrid DKC52-04) at Colorado State University during the 2013 growing season (planted on June 10, 2013)49. There were two treatments (well-watered, WW, and water-stressed, WS) with five plants per treatment. The soil of the greenhouse experiments was the air-dried soilless substrate (8.8 kg) consisting of a 1:1.3 by volume ratio of Greens GradeTM, Turface® Quick Dry® and Fafard 2SV in 26 L pots49. The soil moisture measurements came from soil moisture sensors (Decagon5TM sensors) installed in the middle of the pots (~6 inches from top). The greenhouse measurements of leaf-level stomatal conductance and soil moisture were performed in approximately 2-week intervals beginning in the vegetative stage and continuing until plant senescence (DOY 198–199, 210–211, 217–218, 233–234, 247), with 11 replicates for each plant under two treatments (WW and WS). The environmental variables, such as relative humidity and air temperature, were continuously measured in minutes. Other detailed experimental setups can be found in Miner and Bauerle (2017)49.The second set was eddy-covariance measurements of maize cropping systems (seed: Pioneer 33P67/33B51) from 2001 to 2012 at three AmeriFlux sites (US-Ne1, Ne2, and Ne3). US-Ne1 and Ne2 were irrigated sites, with a continuous maize cropping system during 2001–2012 for US-Ne1 and with a maize-soybean rotation cropping system during 2001-2009 and then a continuous maize cropping system during 2010-2012 for US-Ne2. US-Ne3 was rainfed with a maize-soybean rotation cropping system during 2001–2012. The soil at the three AmeriFlux sites was a deep silty clay loam consisting of four soil series: Yutan, Tomek, Filbert, and Filmore. There are three replicates with the soil moisture sensors (theta probes: ML2, Dynamax Inc.) installed horizontally with the profile of soil depth (10, 25, 50, and 100 cm) in the US-Ne1 and US-Ne2, and four replicates with soil moisture sensors (theta probes: ML2, Dynamax Inc.) installed horizontally with the profile of soil depth (10, 25, 50, and 100 cm) in the US-Ne3 (http://csp.unl.edu/public/G_moist.htm). The soil moisture data used here was from the top soil layer (10–25 cm). The canopy-level stomatal conductance (Gs) was derived by inverting the Penman-Monteith equation50 (Equations 1 and 2) from the eddy-covariance measurements at the hourly scale18,24,51, and the averaged value near midday (from 12:00 to 14:00) was applied as the daily canopy-level stomatal conductance to remove the diurnal cycle. This inversion was only conducted during peak growing season (July and August) to avoid the impact of LAI24. The impact of evaporation from canopy interception and of low incoming shortwave radiation was removed by data filtering24, i.e., excluding the data within 2 days following every precipitation and irrigation event, and periods of low incoming shortwave radiation conditions ( More

Mineralogy and thermal propertiesThe bulk mineral composition of sapropels is detailed in Table 1. The XRD analysis indicates that Amara and Tekirghiol sapropels are enriched in silicates, i.e., quartz (30.8% and 29.1% respectively), plagioclase-albite (10.1% and 8.9%), carbonates, mainly calcite (6.8%) and aragonite (13.1%) in Amara, and calcite (8.7%) in Tekirghiol (Fig. 2). By contrast, Ursu sapropel contains lower concentrations of silicates, mainly quartz (15.4%), plagioclase (5.5% albite and 8% andesine), sulfides, i.e., pyrite (1.5%) and is enriched in halite (34.5%). The major clay components in the sapropels were 2:1 dioactahedral and 2:1 trioctahedral clays, representing 28.9%, 23.6% and 20.8% of clay minerals in Tekirghiol, Amara and Ursu samples, respectively. Muscovite was detected in similar concentrations in Tekirghiol (4.5%) and Amara (4.2%). Quantitative mineralogical clay composition of the fraction  90% in each sample), and kaolinite and chlorite as minor fractions (Table 2; Fig. 3).Table 1 Quantitative bulk mineralogical compositions of saline sapropels collected from Tekirghiol, Amara and Ursu lakes.Full size tableFigure 2X-ray diffraction patterns on the raw mud samples (upper image) collected from the three lakes. The main minerals that contribute to the most important reflections are indicated. Chl: Chlorite, M: Muscovite, K: Kaolinite Group minerals, Q: Quartz, A: Anatase, 2:1: 2:1 phyllosilicate (e.g., illite and smectite), Ca: Calcite, Pl: Plagioclase/Albite/Andesine, R: Rutile, P: Pyrite, Ar: Aragonite, H: Halite.Full size imageTable 2 Quantitative mineralogical clay composition of the fraction  More

1.Lobell, D. B. & Field, C. B. Global scale climate-crop yield relationships and the impacts of recent warming. Environ. Res. Lett. 2, 014002 (2007).ADS 
Article 

Google Scholar 
2.Lobell, D. B. et al. The critical role of extreme heat for maize production in the United States. Nat. Clim. Change 3, 497–501 (2013).ADS 
Article 

Google Scholar 
3.Zhao, C. et al. Temperature increase reduces global yields of major crops in four independent estimates. Proc. Natl Acad. Sci. USA 114, 9326–9331 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
4.Schlenker, W. & Roberts, M. J. Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change. Proc. Natl Acad. Sci. USA 106, 15594–15598 (2009).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
5.Vogel, E. et al. The effects of climate extremes on global agricultural yields. Environ. Res. Lett. 14, 054010 (2019).ADS 
Article 

Google Scholar 
6.Lobell, D. B., Bänziger, M., Magorokosho, C. & Vivek, B. Nonlinear heat effects on African maize as evidenced by historical yield trials. Nat. Clim. Change 1, 42–45 (2011).ADS 
Article 

Google Scholar 
7.Urban, D. W., Sheffield, J. & Lobell, D. B. The impacts of future climate and carbon dioxide changes on the average and variability of US maize yields under two emission scenarios. Environ. Res. Lett. 10, 045003 (2015).ADS 
Article 
CAS 

Google Scholar 
8.Prasad, P. V. V. et al. in Response of Crops to Limited Water: Understanding and Modeling Water Stress Effects on Plant Growth Processes (eds Ahuja, L. R. et al.) 301–356 (American Society of Agronomy, Crop Science Society of America, Soil Science Society of America, 2008); https://doi.org/10.2134/advagricsystmodel1.c119.Troy, T. J., Kipgen, C. & Pal, I. The impact of climate extremes and irrigation on US crop yields. Environ. Res. Lett. 10, 054013 (2015).ADS 
Article 

Google Scholar 
10.Carter, E. K., Melkonian, J., Riha, S. J. & Shaw, S. B. Separating heat stress from moisture stress: analyzing yield response to high temperature in irrigated maize. Environ. Res. Lett. 11, 094012 (2016).ADS 
Article 

Google Scholar 
11.Matiu, M., Ankerst, D. P. & Menzel, A. Interactions between temperature and drought in global and regional crop yield variability during 1961-2014. PLoS ONE 12, e0178339 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
12.Coffel, E. D. et al. Future hot and dry years worsen Nile Basin water scarcity despite projected precipitation increases. Earth’s Future 7, 967–977 (2019).ADS 
Article 

Google Scholar 
13.Rigden, A. J., Mueller, N. D., Holbrook, N. M., Pillai, N. & Huybers, P. Combined influence of soil moisture and atmospheric evaporative demand is important for accurately predicting US maize yields. Nat. Food 1, 127–133 (2020).Article 

Google Scholar 
14.Schauberger, B. et al. Consistent negative response of US crops to high temperatures in observations and crop models. Nat. Commun. 8, 13931 (2017).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
15.Ortiz-Bobea, A., Wang, H., Carrillo, C. M. & Ault, T. R. Unpacking the climatic drivers of US agricultural yields. Environ. Res. Lett. 14, 064003 (2019).16.Siebert, S., Webber, H., Zhao, G. & Ewert, F. Heat stress is overestimated in climate impact studies for irrigated agriculture. Environ. Res. Lett. 12, 044012 (2017).17.Lesk, C. & Anderson, W. Decadal variability modulates trends in concurrent heat and drought over global croplands. Environ. Res. Lett. 16 055024 (2021).18.Berg, A. et al. Interannual coupling between summertime surface temperature and precipitation over land: processes and implications for climate change. J. Clim. 28, 1308–1328 (2015).ADS 
Article 

Google Scholar 
19.Seneviratne, S. I. et al. Investigating soil moisture–climate interactions in a changing climate: a review. Earth Sci. Rev. 99, 125–161 (2010).ADS 
CAS 
Article 

Google Scholar 
20.Zscheischler, J. & Seneviratne, S. I. Dependence of drivers affects risks associated with compound events. Sci. Adv. 3, e1700263 (2017).21.Trenberth, K. E. & Shea, D. J. Relationships between precipitation and surface temperature. Geophys. Res. Lett. 32, 1–4 (2005).Article 

Google Scholar 
22.Seneviratne, S. I., Lüthi, D., Litschi, M. & Schär, C. Land–atmosphere coupling and climate change in Europe. Nature 443, 205–209 (2006).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
23.Horton, R. M., Mankin, J. S., Lesk, C., Coffel, E. & Raymond, C. A review of recent advances in research on extreme heat events. Curr. Clim. Change Rep. 2, 242–259 (2016).Article 

Google Scholar 
24.Berg, A. et al. Impact of soil moisture–atmosphere interactions on surface temperature distribution. J. Clim. 27, 7976–7993 (2014).ADS 
Article 

Google Scholar 
25.Miralles, D. G., Teuling, A. J., Van Heerwaarden, C. C. & De Arellano, J. V. G. Mega-heatwave temperatures due to combined soil desiccation and atmospheric heat accumulation. Nat. Geosci. 7, 345–349 (2014).ADS 
CAS 
Article 

Google Scholar 
26.Ray, D. K. et al. Climate change has likely already affected global food production. PLoS ONE 14, e0217148 (2019).27.Ray, D. K., Gerber, J. S., Macdonald, G. K. & West, P. C. Climate variation explains a third of global crop yield variability. Nat. Commun. 6, 5989 (2015).28.Liu, B. et al. Similar estimates of temperature impacts on global wheat yield by three independent methods. Nat. Clim. Change 6, 1130–1136 (2016).ADS 
Article 

Google Scholar 
29.Sánchez, B., Rasmussen, A. & Porter, J. R. Temperatures and the growth and development of maize and rice: a review. Glob. Change Biol. 20, 408–417 (2014).ADS 
Article 

Google Scholar 
30.Welch, J. R. et al. Rice yields in tropical/subtropical Asia exhibit large but opposing sensitivities to minimum and maximum temperatures. Proc. Natl Acad. Sci. USA 107, 14562–14567 (2010).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
31.Zhang, T., Lin, X. & Sassenrath, G. F. Current irrigation practices in the central United States reduce drought and extreme heat impacts for maize and soybean, but not for wheat. Sci. Total Environ. 508, 331–342 (2015).ADS 
CAS 
PubMed 
Article 

Google Scholar 
32.Mittler, R. Abiotic stress, the field environment and stress combination. Trends Plant Sci. 11, 15–19 (2006).CAS 
PubMed 
Article 

Google Scholar 
33.Swann, A. L. S. Plants and drought in a changing climate. Curr. Clim. Change Rep. 4, 192–201 (2018).Article 

Google Scholar 
34.Skinner, C. B., Poulsen, C. J. & Mankin, J. S. Amplification of heat extremes by plant CO2 physiological forcing. Nat. Commun. 9, 1–11 (2018).CAS 
Article 

Google Scholar 
35.Gates, D. M. Transpiration and leaf temperature. Annu. Rev. Plant Physiol. 19, 211–238 (1968).Article 

Google Scholar 
36.Crafts-Brandner, S. J. & Salvucci, M. E. Sensitivity of photosynthesis in a C4 plant, maize, to heat stress. Plant Physiol. 129, 1773–1780 (2002).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
37.Grossiord, C. et al. Plant responses to rising vapor pressure deficit. N. Phytol. 226, 1550–1566 (2020).Article 

Google Scholar 
38.Rosenzweig, C. et al. Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proc. Natl Acad. Sci. USA 111, 3268–3273 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
39.Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016).ADS 
Article 

Google Scholar 
40.Seth, A. et al. Monsoon responses to climate changes—connecting past, present and future. Curr. Clim. Change Rep. 5, 63–79 (2019).41.Orlowsky, B. & Seneviratne, S. I. Statistical analyses of land–atmosphere feedbacks and their possible pitfalls. J. Clim. 23, 3918–3932 (2010).ADS 
Article 

Google Scholar 
42.Lesk, C., Coffel, E. & Horton, R. Net benefits to US soy and maize yields from intensifying hourly rainfall. Nat. Clim. Change 10, 819–822 (2020).ADS 
Article 

Google Scholar 
43.Vogel, M. M. et al. Regional amplification of projected changes in extreme temperatures strongly controlled by soil moisture–temperature feedbacks. Geophys. Res. Lett. 44, 1511–1519 (2017).ADS 
Article 

Google Scholar 
44.Mueller, B. et al. Evaluation of global observations-based evapotranspiration datasets and IPCC AR4 simulations. Geophys. Res. Lett. 38, 1–7 (2011).
Google Scholar 
45.Pendergrass, A. G. et al. Flash droughts present a new challenge for subseasonal-to-seasonal prediction. Nat. Clim. Change 10, 191–199 (2020).ADS 
Article 

Google Scholar 
46.Mueller, N. D. et al. Global relationships between cropland intensification and summer temperature extremes over the last 50 years. J. Clim. 30, 7505–7528 (2017).ADS 
Article 

Google Scholar 
47.He, Y., Lee, E. & Mankin, J. S. Seasonal tropospheric cooling in northeast China associated with cropland expansion. Environ. Res. Lett. 15, 034032 (2020).48.Ainsworth, E. A. & Long, S. P. 30 years of free-air carbon dioxide enrichment (FACE): what have we learned about future crop productivity and its potential for adaptation? Glob. Change Biol. 27, 27–49 (2021).ADS 
Article 

Google Scholar 
49.Deryng, D. et al. Regional disparities in the beneficial effects of rising CO2 concentrations on crop water productivity. Nat. Clim. Change 6, 786–790 (2016).ADS 
Article 

Google Scholar 
50.Challinor, A. J., Koehler, A.-K., Ramirez-Villegas, J., Whitfield, S. & Das, B. Current warming will reduce yields unless maize breeding and seed systems adapt immediately. Nat. Clim. Change 6, 954–958 (2016).ADS 
Article 

Google Scholar 
51.Lobell, D. B., Deines, J. M. & Di Tommaso, S. Changes in the drought sensitivity of US maize yields. Nat. Food 1, 729–735 (2020).Article 

Google Scholar 
52.Bassu, S. et al. How do various maize crop models vary in their responses to climate change factors? Glob. Change Biol. 20, 2301–2320 (2014).ADS 
Article 

Google Scholar 
53.Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. Updated high-resolution grids of monthly climatic observations—the CRU TS3.10 dataset. Int. J. Clim. 34, 623–642 (2014).Article 

Google Scholar 
54.Rodell, M. et al. The Global Land Data Assimilation System. Bull. Am. Meteorol. Soc. 85, 381–394 (2004).ADS 
Article 

Google Scholar 
55.Sacks, W. J., Deryng, D. & Foley, J. A. Crop planting dates: an analysis of global patterns. Glob. Ecol. Biogeogr. 19, 607–620 (2010).
Google Scholar 
56.Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146, 1999–2049 (2020).ADS 
Article 

Google Scholar 
57.Vautard, R., Yiou, P. & Ghil, M. Singular-spectrum analysis: a toolkit for short, noisy chaotic signals. Physica D 58, 95–126 (1992).ADS 
Article 

Google Scholar  More

1.Descamps, S. et al. Climate change impacts on wildlife in a High Arctic archipelago–Svalbard, Norway. Glob. Change Biol. 23, 490–502 (2017).ADS 
Article 

Google Scholar 
2.Yletyinen, J. Arctic climate resilience. Nat. Clim. Change 9, 805–806 (2019).ADS 
Article 

Google Scholar 
3.Renaud, P. E. et al. Pelagic food-webs in a changing Arctic: A trait-based perspective suggests a mode of resilience. ICES J. Mar. Sci. 75, 1871–1881 (2018).Article 

Google Scholar 
4.Möller, E. F. & Nielsen, T. G. Borealization of Arctic zooplankton—smaller and less fat zooplankton species in Disko Bay, Western Greenland. Limnol. Oceanogr. 65, 1175–1188 (2020).ADS 
Article 

Google Scholar 
5.Dalpadado, P. et al. Climate effects on temporal and spatial dynamics of phytoplankton and zooplankton in the Barents Sea. Prog. Oceanogr. 185, 102320. https://doi.org/10.1016/j.pocean.2020.102320 (2020).Article 

Google Scholar 
6.Csapó, H. K., Grabowski, M. & Węsławski, J. M. Coming home – Boreal ecosystem claims Atlantic sector of the Arctic. Sci. Total. Environ. 771, 144817. https://doi.org/10.1016/j.scitotenv.2020.144817 (2021).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
7.Bauerfeind, E., Nöthig, E. M., Pauls, B., Kraft, A. & Beszczynska-Möller, A. Variability in pteropod sedimentation and corresponding aragonite flux at the Arctic deep-sea long-term observatory HAUSGARTEN in the eastern Fram Strait from 2000 to 2009. J. Mar. Syst. 132, 95–10 (2014).Article 

Google Scholar 
8.Weydmann, A. et al. Shift towards the dominance of boreal species in the Arctic: Inter-annual and spatial zooplankton variability in the West Spitsbergen Current. Mar. Ecol. Prog. Ser. 501, 41–52 (2014).ADS 
Article 

Google Scholar 
9.Gluchowska, M. et al. Zooplankton in Svalbard fjords on the Atlantic-Arctic boundary. Polar. Biol. 39, 1785–1802 (2016).Article 

Google Scholar 
10.Wassmann, P. et al. The contiguous domains of Arctic Ocean advection: Trails of life and death. Prog. Oceanogr. 139, 42–65 (2015).ADS 
Article 

Google Scholar 
11.Nielsen, T. G. & Andersen, C. Plankton community structure and production along a freshwater-influenced Norwegian fjord system. Mar. Biol. 141, 707–724 (2002).Article 

Google Scholar 
12.Lischka, S. & Hagen, W. Life histories of the copepods Pseudocalanus minutus, P. acuspes, (Calanoida) and Oithona similis (Cyclopoida) in the Arctic Kongsfjorden (Svalbard). Polar Biol. 28, 910–921 (2005).Article 

Google Scholar 
13.Arendt, K. E., Nielsen, T. G., Rysgaard, S. & Tönnesson, K. Differences in plankton community structure along the Godthåbsfjord, from the Greenland Ice Sheet to offshore waters. Mar. Ecol. Prog. Ser. 401, 49–62 (2010).ADS 
CAS 
Article 

Google Scholar 
14.Trudnowska, E., Stemmann, L., Błachowiak-Samołyk, K. & Kwasniewski, S. Taxonomic and size structures of zooplankton communities in the fjords along the Atlantic water passage to the Arctic. J. Mar. Sys. 204, 103306. https://doi.org/10.1016/j.jmarsys.2020.103306 (2020).Article 

Google Scholar 
15.Balazy, K., Trudnowska, E., Wichorowski, M. & Błachowiak-Samołyk, K. Large versus small zooplankton in relation to temperature in the Arctic shelf region. Polar. Res. 37, 1427409. https://doi.org/10.1080/17518369.2018.1427409 (2018).Article 

Google Scholar 
16.Turner, J. T. The importance of small planktonic copepods and their roles in pelagic marine food webs. Zool. Stud. 43, 255–266 (2004).
Google Scholar 
17.Turner, J. T. Planktonic copepods of Boston Harbor, Massachusetts Bay and Cape Cod Bay. Hydrobiologia 292(293), 405–413 (1994).Article 

Google Scholar 
18.Castellani, C., Robinson, C., Smith, T. & Lampitt, R. S. Temperature affects respiration rate of Oithona similis. Mar. Ecol. Prog. Ser. 285, 129–135 (2005).ADS 
Article 

Google Scholar 
19.Turner, J. T., Levinsen, H., Nielsen, T. G. & Hansen, B. W. Zooplankton feeding ecology: Grazing on phytoplankton and predation on protozoans by copepod and barnacle nauplii in Disko Bay, West Greenland. Mar. Ecol. Prog. Ser. 221, 209–219 (2001).ADS 
Article 

Google Scholar 
20.Boissonnot, L., Niehoff, B., Hagen, W., Søreide, J. E. & Graeve, M. Lipid turnover reflects life-cycle strategies of small-sized Arctic copepods. J. Plankton Res. 38, 1420–1432 (2016).CAS 

Google Scholar 
21.Błachowiak-Samołyk, K. et al. Winter Tales: The dark side of planktonic life. Polar Biol. 38, 23–36 (2015).Article 

Google Scholar 
22.Berge, J. et al. Zooplankton in the Polar Night in Polar Night Marine Ecology. In Advances in Polar Ecology Vol. 4 (eds Berge, J. et al.) (Springer, New York, 2020). https://doi.org/10.1007/978-3-030-33208-2_5.Chapter 

Google Scholar 
23.Hobbs, L., Banas, N. S., Cottier, F. R., Berge, J. & Daase, M. Eat or sleep: Availability of winter prey explains mid-winter and early-spring activity in an Arctic Calanus population. Front. Mar. Sci. 7, 744. https://doi.org/10.3389/fmars.2020.541564 (2020).Article 

Google Scholar 
24.Svensen, C., Seuthe, L., Vasilyeva, Y., Pasternak, A. & Hansen, E. Zooplankton distribution across Fram Strait in autumn: Are small copepods and protozooplankton important?. Prog. Oceanog. 91, 534–544 (2011).Article 

Google Scholar 
25.Węsławski, J. M., Kwasniewski, S. & Wiktor, J. Winter in Svalbard fjord ecosystem. Arctic 44, 115–123 (1991).Article 

Google Scholar 
26.Lischka, S., Giménez, L., Hagen, W. & Ueberschär, B. Seasonal changes in digestive enzyme (trypsin) activity of the copepods Pseudocalanus minutus (Calanoida) and Oithona similis (Cyclopoida) in the Arctic Kongsfjorden (Svalbard). Polar Biol. 30, 1331–1341 (2007).Article 

Google Scholar 
27.Lischka, S. & Hagen, W. Seasonal dynamics of mesozooplankton in the Arctic Kongsfjord (Svalbard) during year-round observations from August 1998 to July 1999. Polar Biol. 39, 1859–1878 (2016).Article 

Google Scholar 
28.Weydmann-Zwolicka, A. et al. Zooplankton and sediment flux in two contrasting fjords reveal Atlantification of the Arctic. Sci. Total. Environ. 773, 145599. https://doi.org/10.1016/j.scitotenv.2021.145599 (2021).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
29.Zamora-Terol, S., Nielsen, T. G. & Saiz, E. Plankton community structure and role of Oithona similis on the western coast of Greenland during the winter-spring transition. Mar. Ecol. Prog. Ser. 483, 85–102 (2013).ADS 
Article 

Google Scholar 
30.Zamora-Terol, S., Kjellerup, S., Swalethorp, R., Saiz, E. & Nielsen, T. G. Population dynamics and production of the small copepod Oithona spp. in a subarctic fjord of West Greenland. Polar. Biol. 37, 953–965 (2014).Article 

Google Scholar 
31.Dvoretsky, V. G. & Dvoretsky, A. G. Life cycle of Oithona similis (Copepoda: Cyclopoida) in Kola Bay (Barents Sea). Mar. Biol. 156, 1433–1446 (2009).Article 

Google Scholar 
32.Glad, P. Seasonal occurrence of Oithona similis (cyclopoida), Microsetella norvegica (harpacticoida) and Microcalanus spp. (calanoida), and productivity of O. similis, in three high-latitude Norwegian fjords. Master thesis (UiT The Arctic University of Norway, 2018).33.Kosobokova, K. & Hirche, H. J. Biomass of zooplankton in the eastern Arctic Ocean—a baseline study. Progr. Oceanogr. 82, 265–280 (2009).ADS 
Article 

Google Scholar 
34.Bluhm, B., Kosobokova, K. & Carmack, E. A tale of two basins: An integrated physical and biological perspective of the deep Arctic Ocean. Prog. Oceanog. 139, 89–121 (2015).Article 

Google Scholar 
35.Hop, H. et al. Zooplankton in Kongsfjorden (1996–2016) in Relation to Climate Change in The Ecosystem of Kongsfjorden, Svalbard. In Advances in Polar Ecology Vol. 2 (eds Hop, H. & Wiencke, C.) 10.1007/978-3-319-46425–1_7 (Springer, New York, 2019).
Google Scholar 
36.Böttger-Schnack, R., Schnack, D. & Hagen, W. Microcopepod community structure in the Gulf of Aqaba and northern Red Sea, with special reference to Oncaeidae. J. Plankton Res. 30, 529–550 (2008).Article 

Google Scholar 
37.Cornwell, L. E. et al. Seasonality of Oithona similis and Calanus helgolandicus reproduction and abundance: Contrasting responses to environmental variation at a shelf site. J. Plankton Res. 40, 295–310 (2018).Article 

Google Scholar 
38.Kubiszyn, A. M. et al. The annual planktonic protist community structure in an ice-free high Arctic fjord (Adventfjorden, West Spitsbergen). J. Mar. Syst. 169, 61–72 (2017).Article 

Google Scholar 
39.Kellogg, C. T. E., McClelland, J. W., Dunton, K. H. & Crump, B. C. Strong seasonality in Arctic estuarine microbial food webs. Front. Microbiol. 10, 2628. https://doi.org/10.3389/fmicb.2019.02628 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
40.Bhaskar, J. T., Parli, B. V. & Tripathy, S. C. Spatial and seasonal variations of dinoflagellates and ciliates in the Kongsfjorden. Svalbard. Mar. Ecol. 41, 1–12 (2020).Article 

Google Scholar 
41.Skogseth, R. et al. Variability and decadal trends in the Isfjorden (Svalbard) ocean climate and circulation–An indicator for climate change in the European Arctic. Prog. Oceanog. 187, 102394. https://doi.org/10.1016/j.pocean.2020.102394 (2020).Article 

Google Scholar 
42.Ward, P. & Hirst, A. G. Oithona similis in a high latitude ecosystem: Abundance, distribution and temperature limitation of fecundity rates in a sac spawning copepod. Mar. Biol. 151, 1099–1110 (2007).Article 

Google Scholar 
43.Nielsen, T. G. & Sabatini, M. Role of cyclopoid copepods Oithona spp. in North Sea plankton communities. Mar. Ecol. Prog. Ser. 139, 79–93 (1996).ADS 
Article 

Google Scholar 
44.Nilsen, F., Cottier, F., Skogseth, R. & Mattsson, S. Fjord–shelf exchanges controlled by ice and brine production: The interannual variation of Atlantic Water in Isfjorden, Svalbard. Cont. Shelf Res. 28, 1838–1853 (2008).ADS 
Article 

Google Scholar 
45.Cohen, J. H., Berge, J., Moline, M. A., Johnsen, G. & Zolich, A. P. Light in the Polar Night. In Polar Night Marine Ecology Advances in Polar Ecology Vol. 4 (eds Berge, J. et al.) (Springer, New York, 2020). https://doi.org/10.1007/978-3-030-33208-2_3.Chapter 

Google Scholar 
46.Wiedmann, I., Reigstad, M., Marquardt, M., Vader, A. & Gabrielsen, T. M. Seasonality of vertical flux and sinking particle characteristics in an ice-free high arctic fjord—different from subarctic fjords?. J. Mar. Syst. 154, 192–205 (2015).Article 

Google Scholar 
47.Holm-Hansen, O. & Riemann, B. Chlorophyll a determination: Improvements in methodology. Oikos 30, 438–447 (1978).CAS 
Article 

Google Scholar 
48.Stübner, E. I., Søreide, J. E., Reigstad, M., Marquardt, M. & Blachowiak-Samolyk, K. Year-round meroplankton dynamics in high-Arctic Svalbard. J. Plankton Res. 38, 522–536 (2016).Article 

Google Scholar 
49.Marquardt, M., Vader, A., Stübner, E. I., Reigstad, M. & Gabrielsen, T. M. Strong seasonality of marine microbial eukaryotes in a high-Arctic fjord (Isfjorden, West Spitsbergen). Appl. Environ. Microb. 82, 1868–1880 (2016).ADS 
CAS 
Article 

Google Scholar 
50.Trantner, D. J. & Fraser, H. Zooplankton sampling. Monographs on Oceanographic Methodology 2. (UNESCO, 1968).51.Harris, R., Wiebe, L., Lenz, J., Skjoldal, H. R. & Huntley, M. ICES Zooplankton Methodology Manual (Academic Press, Cambridge, 2000).
Google Scholar 
52.Espinasse, M. et al. Interannual phenological variability in two North-East Atlantic populations of Calanus finmarchicus. Mar. Biol. Res. 14, 752–767 (2018).Article 

Google Scholar 
53.Mackas, D. L., Batten, S. & Trudel, M. Effects on zooplankton of a warmer ocean: Recent evidence from the Northeast Pacific. Prog. Oceanogr. 75, 223–252 (2007).ADS 
Article 

Google Scholar 
54.Head, E. J. H., Melle, W., Pepin, P., Bagøien, E. & Broms, C. On the ecology of Calanus finmarchicus in the Subarctic North Atlantic: A comparison of population dynamics and environmental conditions in areas of the Labrador Sea-Labrador/Newfoundland Shelf and Norwegian Sea Atlantic and Coastal Waters. Prog. Oceanog. 114, 46–63 (2013).Article 

Google Scholar 
55.Kwasniewski, S. et al. Interannual changes in zooplankton on theWest Spitsbergen Shelf in relation to hydrography and their consequences for the diet of planktivorous seabirds. J. Mar. Sci. 69, 890–901 (2012).
Google Scholar 
56.Kiorboe, T. Sex, sex-ratios, and the dynamics of pelagic copepod populations. Oecol. 148, 40–50 (2006).ADS 
Article 

Google Scholar 
57.Thackeray, et al. Food web de-synchronization in England’s largest lake: An assessment based on multiple phenological metrics. Glob. Change Biol. 19, 3568–3580 (2013).ADS 
Article 

Google Scholar 
58.Anderson, M. J., Gorley, R. N. & Clarke, K. R. PERMANOVA+ for PRIMER: Guide to Software and Statistical Methods. (Primer-E Ltd., 2008).59.Clarke, K. R. & Gorley, R. N. Primer. (Primer-E Ltd., 2001).60.Anderson, M. J. & Braak, C. J. F. Permutation tests for multi-factorial analysis of variance. J. Stat. Comput. Simul. 73, 85–113 (2003).MathSciNet 
MATH 
Article 

Google Scholar 
61.Schlitzer, R. Ocean Data View; https://odv.awi.de, (2021).62.Walczowski, W., Piechura, J., Goszczko, I. & Wieczorek, P. Changes in Atlantic water properties: An important factor in the European Arctic marine climate. ICES J. Mar. Sci 69, 864–869 (2012).Article 

Google Scholar 
63.Wassman, P., Duarte, C. M., Agustí, S. & Sejr, M. L. Footprints of climate change in the Arctic marine ecosystem. Glob. Change Biol. 17, 1235–1249 (2010).ADS 
Article 

Google Scholar 
64.Andrews, A. J. et al. Boreal marine fauna from the Barents Sea disperse to Arctic Northeast Greenland. Sci Rep 9, 5799 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
65.Atkinson, D. & Sibly, R. M. Why are organisms usually bigger in colder environments? Making sense of life history puzzle. Trends Ecol. Evol. 12, 235–239 (1997).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
66.Beaugrand, G., Ibanez, F. & Reid, P. C. Spatial seasonal and long term fluctuations of plankton in relation to hydroclimatic features in the English Channel, Celtic Sea and Bay of Biscay. Mar. Ecol. Prog. Ser. 200, 93–102 (2000).ADS 
Article 

Google Scholar 
67.Beaugrand, G., Reid, P. C., Ibañez, F., Lindley, A. & Edwards, M. Reorganization of North Atlantic Marine Copepod Biodiversity and Climate. Science 31, 1692–1694 (2002).ADS 
Article 

Google Scholar 
68.Coyle, K. O. et al. Climate change in the southeastern Bering Sea: Impacts on pollock stocks and implications for the oscillating control hypothesis. Fisher. Oceanogr. 20, 139–156 (2011).Article 

Google Scholar 
69.Edwards, M. & Richardson, A. J. The impact of climate change on the phenology of the plankton community and trophic mismatch. Nature 430, 881–884 (2004).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
70.Stevens, G. C. The latitudinal gradient in geographical range: How so many species coexist in the tropics. Am. Nat. 133, 240–256 (1989).Article 

Google Scholar 
71.Kortsch, S., Primicerio, R., Fossheim, M., Dolgov, A. V. & Aschan, M. Climate change alters the structure of arctic marine food webs due to poleward shifts of boreal generalists. Proc. R. Soc. B. 282, 20151546 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
72.Richardson, A. J. In hot water: Zooplankton and climate change. ICES J. Mar. Sci. 65, 279–295 (2008).Article 

Google Scholar 
73.Kwasniewski, S. A note on zooplankton of the Hornsund Fjord and its seasonal changes. Oceanografia 12, 7–27 (1990).
Google Scholar 
74.Piwosz, K. et al. Comparison of productivity and phytoplankton in a warm (Kongsfjorden) and a cold (Hornsund) Spitsbergen fjord in midsummer 2002. Polar Biol. 32, 549–559 (2009).Article 

Google Scholar 
75.Trudnowska, E., Basedow, S. L. & Blachowiak-Samolyk, K. Mid-summer mesozooplankton biomass, its size distribution, and estimated production within a glacial Arctic fjord (Hornsund, Svalbard). J. Mar. Syst. 137, 55–66 (2014).Article 

Google Scholar 
76.Castellani, C., Licandro, P., Fileman, E., di Capua, I. & Mazzocchi, M. G. Oithona similis likes it cool: Evidence from two long-term time series. J. Plankton Res. 38, 703–717 (2016).CAS 
Article 

Google Scholar 
77.Cornwell, L. E. et al. Resilience of the copepod Oithona similis to climatic variability: Egg production, mortality, and vertical habitat partitioning. Front. Mar. Sci. https://doi.org/10.3389/fmars.2020.00029 (2020).Article 

Google Scholar 
78.Eiane, K. & Ohman, M. D. Stage-specific mortality of Calanus finmarchicus, Pseudocalanus elongatus and Oithona similis on Fladen Ground, North Sea, during a spring bloom. Mar. Ecol. Prog. Ser. 268, 183–193 (2004).ADS 
Article 

Google Scholar 
79.Thor, P. et al. Post-spring bloom community structure of pelagic copepods in the Disko Bay, Western Greenland. J. Plankton Res. 27, 341–356 (2005).CAS 
Article 

Google Scholar 
80.Dvoretsky, V. G. Seasonal mortality rates of Oithona similis (Cyclopoida) in a large Arctic fjord. Polar Sci. 6, 263–269 (2012).ADS 
Article 

Google Scholar 
81.Ussing, H. H. The biology of some important plankton animals in the fjords of east Greenland. Medd Grønland 100–108 (1938).
82.Lonsdale, D. J., Caron, D. A., Dennett, M. R. & Schaffner, R. Predation by Oithona spp on protozooplankton in the Ross Sea. Antarctica. Deep-Sea Res. II 47, 3273–3283 (2000).
Google Scholar 
83.Castellani, C., Irigoien, X., Harris, R. P. & Lampitt, R. S. Feeding and egg production of Oithona similis in the North Atlantic. Mar. Ecol. Prog. Ser. 288, 173–182 (2005).ADS 
Article 

Google Scholar 
84.Barth-Jensen, C. et al. Temperature-dependent egg production and egg hatching rates of small egg-carrying and broadcast-spawning copepods Oithona similis, Microsetella norvegica and Microcalanus pusillus. J. Plankton Res. 42, 564–580 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
85.Falk-Petersen, S., Pedersen, G., Kwasniewski, S., Hegseth, E. N. & Hop, H. Spatial distribution and life-cycle timing of zooplankton in the marginal ice zone of the Barents Sea during the summer melt season in 1995. J. Plankton Res. 21, 1249–1264 (1999).Article 

Google Scholar 
86.Gluchowska, M. et al. Interannual zooplankton variability in the main pathways of the Atlantic water flow into the Arctic Ocean (Fram Strait and Barents Sea branches). ICES J. Mar. Sci. 74, 1921–1936 (2017).Article 

Google Scholar 
87.Balazy, K., Trudnowska, E. & Błachowiak-Samołyk, K. Dynamics of Calanus copepodite structure during Little Auks’ breeding seasons in two different Svalbard locations. Water 11, 1405. https://doi.org/10.3390/w11071405 (2019).CAS 
Article 

Google Scholar 
88.Hop, H. et al. The marine ecosystem of Kongsfjorden, Svalbard. Polar Res. 21, 167–208 (2002).Article 

Google Scholar 
89.Poje, A. The relationship between plankton and water mass properties in high Arctic (Svalbard) fjords. Clark Honors College Theses, (University of Oregon, 2016).90.Falk-Petersen, S., Mayzaud, P., Kattner, G. & Sargent, J. R. Lipids and life strategy of Arctic Calanus. Mar. Biol. Res. 5, 18–39 (2009).Article 

Google Scholar 
91.Svensen, C. et al. Zooplankton communities associated with new and regenerated primary production in the Atlantic inflow North of Svalbard. Front. Mar. Sci. 6, 293. https://doi.org/10.3389/fmars.2019.00293 (2019).Article 

Google Scholar 
92.González, H. E. & Smetacek, V. The possible role of the cyclopoid copepod Oithona in retarding vertical flux of zooplankton faecal material. Mar. Ecol. Prog. Ser. 113, 233–246 (1994).ADS 
Article 

Google Scholar 
93.Berge, J. et al. Unexpected levels of biological activity during the polar night offer new perspectives on a warming Arctic. Curr. Biol. 25, 2555–2561 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
94.Berge, J. et al. In the dark: A review of ecosystem processes during the Arctic polar night. Progr. Oceanog. 139, 258–271 (2015).ADS 
Article 

Google Scholar 
95.Narcy, F. et al. Seasonal and individual variability of lipid reserves in Oithona similis (Cyclopoida) in an Arctic fjord. Polar Biol. 32, 233–242 (2009).Article 

Google Scholar 
96.Kattner, G. & Hagen, W. Lipids in marine copepods: Latitudinal characteristics and perspective to global warming. In Lipids in Aquatic Ecosystems (eds Kainz, M. et al.) 257–280 (Springer, New York, 2009).Chapter 

Google Scholar 
97.Rokkan Iversen, K. & Seuthe, L. Seasonal microbial processes in a high-latitude fjord (Kongsfjorden, Svalbard): I. Heterotrophic bacteria, picoplankton and nanoflagellates. Polar Biol. 34, 731–749 (2011).Article 

Google Scholar 
98.Auel, H. & Hagen, W. Mesozooplankton community structure, abundance and biomass in the central Arctic Ocean. Mar. Biol. 140, 1013–1021 (2002).Article 

Google Scholar 
99.Madsen, S., Nielsen, T. & Hansen, B. Annual population development and production by small copepods in Disko Bay, western Greenland. Mar. Biol. 155, 63–77 (2008).Article 

Google Scholar 
100.Corkett, C. J. & McLaren, I. A. The biology of Pseudocalanus. In Advances in Marine Biology Vol. 15 (eds Russell, F. S. & Yonge, M.) 1–231 (Academic Press, Cambridge, 1978).
Google Scholar 
101.Kwasniewski, S., Hop, H., Falk-Petersen, S. & Pedersen, G. Distribution of Calanus species in Kongsfjorden, a glacial fjord in Svalbard. J. Plankton Res. 2003(25), 1–20 (2003).Article 

Google Scholar 
102.Willis, K., Cottier, F., Kwasniewski, S., Wold, A. & Falk-Petersen, S. The influence of advection on zooplankton community composition in an Arctic fjord (Kongsfjorden, Svalbard). J. Mar. Syst. 61, 39–54 (2006).Article 

Google Scholar  More

1.Cardinale, B. J. et al. Effects of biodiversity on the functioning of trophic groups and ecosystems. Nature 443, 989–992 (2006).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
2.Hooper, D. U. et al. A global synthesis reveals biodiversity loss as a major driver of ecosystem change. Nature 486, 105–108 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
3.Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 489, 326–326 (2012).CAS 
Article 

Google Scholar 
4.Isbell, F. et al. Linking the influence and dependence of people on biodiversity across scales. Nature 546, 65–72 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
5.Mori, A. S., Isbell, F. & Seidl, R. β-Diversity, community assembly, and ecosystem functioning. Trends Ecol. Evol. 33, 549–564 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
6.Gonzalez, A. et al. Scaling‐up biodiversity–ecosystem functioning research. Ecol. Lett. 23, 757–776 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
7.Genung, M. A., Fox, J. & Winfree, R. Species loss drives ecosystem function in experiments, but in nature the importance of species loss depends on dominance. Glob. Ecol. Biogeogr. 29, 1531–1541 (2020).Article 

Google Scholar 
8.Duffy, J. E., Godwin, C. M. & Cardinale, B. J. Biodiversity effects in the wild are common and as strong as key drivers of productivity. Nature 549, 261–264 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
9.Wardle, D. A. Do experiments exploring plant diversity–ecosystem functioning relationships inform how biodiversity loss impacts natural ecosystems? J. Veg. Sci. 27, 646–653 (2016).Article 

Google Scholar 
10.Wardle, D. A., Bardgett, R. D., Callaway, R. M. & Van der Putten, W. H. Terrestrial ecosystem responses to species gains and losses. Science 332, 1273–1277 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
11.Hillebrand, H. & Matthiessen, B. Biodiversity in a complex world: consolidation and progress in functional biodiversity research. Ecol. Lett. 12, 1405–1419 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
12.Jochum, M. et al. The results of biodiversity–ecosystem functioning experiments are realistic. Nat. Ecol. Evol. 4, 1485–1494 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
13.van der Plas, F. Biodiversity and ecosystem functioning in naturally assembled communities. Biol. Rev. 94, 1220–1245 (2019).PubMed 
PubMed Central 

Google Scholar 
14.Bannar-Martin, K. H. et al. Integrating community assembly and biodiversity to better understand ecosystem function: the Community Assembly and the Functioning of Ecosystems (CAFE) approach. Ecol. Lett. 21, 167–180 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
15.Leibold, M. A., Chase, J. M. & Ernest, S. K. M. Community assembly and the functioning of ecosystems: how metacommunity processes alter ecosystems attributes. Ecology 98, 909–919 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
16.Tilman, D., Isbell, F. & Cowles, J. M. Biodiversity and ecosystem functioning. Annu. Rev. Ecol. Evol. Syst. 45, 471–493 (2014).Article 

Google Scholar 
17.Leibold, M. A. et al. The metacommunity concept: a framework for multi-scale community ecology. Ecol. Lett. 7, 601–613 (2004).Article 

Google Scholar 
18.HilleRisLambers, J., Adler, P. B., Harpole, W. S., Levine, J. M. & Mayfield, M. M. Rethinking community assembly through the lens of coexistence theory. Annu. Rev. Ecol. Evol. Syst. 43, 227–248 (2012).Article 

Google Scholar 
19.Stein, A., Gerstner, K. & Kreft, H. Environmental heterogeneity as a universal driver of species richness across taxa, biomes and spatial scales. Ecol. Lett. 17, 866–880 (2014).Article 

Google Scholar 
20.Grace, J. B. et al. Integrative modelling reveals mechanisms linking productivity and plant species richness. Nature 529, 390–393 (2016).CAS 
PubMed 
Article 

Google Scholar 
21.Harpole, W. S. et al. Addition of multiple limiting resources reduces grassland diversity. Nature 537, 93–96 (2016).CAS 
PubMed 
Article 

Google Scholar 
22.Legendre, P. & De Cáceres, M. Beta diversity as the variance of community data: dissimilarity coefficients and partitioning. Ecol. Lett. 16, 951–963 (2013).PubMed 
Article 

Google Scholar 
23.Legendre, P. Interpreting the replacement and richness difference components of beta diversity. Glob. Ecol. Biogeogr. 23, 1324–1334 (2014).Article 

Google Scholar 
24.Craven, D. et al. A cross‐scale assessment of productivity–diversity relationships. Glob. Ecol. Biogeogr. 29, 1940–1955 (2020).Article 

Google Scholar 
25.Barry, K. E. et al. A graphical null model for scaling biodiversity–ecosystem functioning relationships. J. Ecol. 109, 1549–1560 (2021).Article 

Google Scholar 
26.Winfree, R. et al. Species turnover promotes the importance of bee diversity for crop pollination at regional scales. Science 359, 791–793 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
27.Isbell, F. et al. Quantifying effects of biodiversity on ecosystem functioning across times and places. Ecol. Lett. 21, 763–778 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
28.Isbell, F. et al. High plant diversity is needed to maintain ecosystem services. Nature 477, 199–202 (2011).CAS 
PubMed 
Article 

Google Scholar 
29.Bell, T., Newman, J. A., Silverman, B. W., Turner, S. L. & Lilley, A. K. The contribution of species richness and composition to bacterial services. Nature 436, 1157–1160 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
30.Fox, J. W. & Kerr, B. Analyzing the effects of species gain and loss on ecosystem function using the extended Price equation partition. Oikos 121, 290–298 (2012).Article 

Google Scholar 
31.Peters, M. K. et al. Predictors of elevational biodiversity gradients change from single taxa to the multi-taxa community level. Nat. Commun. 7, 13736 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
32.Peters, M. K. et al. Climate–land-use interactions shape tropical mountain biodiversity and ecosystem functions. Nature 568, 88–92 (2019).CAS 
PubMed 
Article 

Google Scholar 
33.Winfree, R., Fox, J. W., Williams, N. M., Reilly, J. R. & Cariveau, D. P. Abundance of common species, not species richness, drives delivery of a real-world ecosystem service. Ecol. Lett. 18, 626–635 (2015).PubMed 
Article 

Google Scholar 
34.Garnier, E. et al. Plant functional markers capture ecosystem properties during secondary succession. Ecology 85, 2630–2637 (2004).Article 

Google Scholar 
35.Stepp, J. R., Castaneda, H. & Cervone, S. Mountains and biocultural diversity. Mt. Res. Dev. 25, 223–227 (2005).Article 

Google Scholar 
36.Balehegn, M. Unintended consequences: the ecological repercussions of land grabbing in sub-Saharan Africa. Environment 57, 4–21 (2015).
Google Scholar 
37.The IPBES Regional Assessment Report on Biodiversity and Ecosystem Services for Africa (Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, 2018); https://doi.org/10.5281/ZENODO.323617838.Maitima, J. et al. The linkages between land use change, land degradation and biodiversity across East Africa. Afr. J. Environ. Sci. Technol. 3, 310–325 (2009).
Google Scholar 
39.Clough, Y. et al. Combining high biodiversity with high yields in tropical agroforests. Proc. Natl Acad. Sci. USA 108, 8311–8316 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
40.Muhumuza, M. & Balkwill, K. Factors affecting the success of conserving biodiversity in national parks: a review of case studies from Africa. Int. J. Biodivers. 2013, 798101 (2013).Article 

Google Scholar 
41.Mbow, C., van Noordwijk, M., Prabhu, R. & Simons, T. Knowledge gaps and research needs concerning agroforestry’s contribution to Sustainable Development Goals in Africa. Curr. Opin. Environ. Sustain. 6, 162–170 (2014).Article 

Google Scholar 
42.Kangalawe, R. Y. M., Noe, C., Tungaraza, F. S. K., Naimani, G. & Mlele, M. Understanding of traditional knowledge and indigenous institutions on sustainable land management in Kilimanjaro Region, Tanzania. Open J. Soil Sci. 04, 469–493 (2014).Article 

Google Scholar 
43.Pretty, J., Toulmin, C. & Williams, S. Sustainable intensification in African agriculture. Int. J. Agric. Sustain. 9, 5–24 (2011).Article 

Google Scholar 
44.Mbow, C. et al. Agroforestry solutions to address food security and climate change challenges in Africa. Curr. Opin. Environ. Sustain. 6, 61–67 (2014).Article 

Google Scholar 
45.Ofori, D. A. et al. Developing more productive African agroforestry systems and improving food and nutritional security through tree domestication. Curr. Opin. Environ. Sustain. 6, 123–127 (2014).Article 

Google Scholar 
46.Munang, R. et al. Ecosystem Based Adaptation (EBA) for Food Security in Africa—Towards a Comprehensive Strategic Framework to Upscale and Out-scale EBA-Driven Agriculture in Africa (United Nations Environment Programme, 2015).47.Albrecht, A. & Kandji, S. T. Carbon sequestration in tropical agroforestry systems. Agric. Ecosyst. Environ. 99, 15–27 (2003).CAS 
Article 

Google Scholar 
48.van der Plas, F. et al. Biotic homogenization can decrease landscape-scale forest multifunctionality. Proc. Natl Acad. Sci. USA 113, 3557–3562 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
49.Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M. & West, G. B. Toward a metabolic theory of ecology. Ecology 85, 1771–1789 (2004).Article 

Google Scholar 
50.Allen, A. P. Global biodiversity, biochemical kinetics, and the energetic-equivalence rule. Science 297, 1545–1548 (2002).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
51.Nottingham, A. T. et al. Microbes follow Humboldt: temperature drives plant and soil microbial diversity patterns from the Amazon to the Andes. Ecology 99, 2455–2466 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
52.Hemp, A. Continuum or zonation? Altitudinal gradients in the forest vegetation of Mt. Kilimanjaro. Plant Ecol. 184, 27–42 (2006).Article 

Google Scholar 
53.Hemp, A. Vegetation of Kilimanjaro: hidden endemics and missing bamboo. Afr. J. Ecol. 44, 305–328 (2006).Article 

Google Scholar 
54.Appelhans, T. et al. Eco-meteorological characteristics of the southern slopes of Kilimanjaro. Tanzan. Int. J. Climatol. 36, 3245–3258 (2016).Article 

Google Scholar 
55.van Genuchten, M. T. H. A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Sci. Soc. Am. J. 44, 892–898 (1980).Article 

Google Scholar 
56.Gebert, F., Steffan-Dewenter, I., Moretto, P. & Peters, M. K. Climate rather than dung resources predict dung beetle abundance and diversity along elevational and land use gradients on Mt. Kilimanjaro. J. Biogeogr. 47, 371–381 (2019).Article 

Google Scholar 
57.Dunning, J. B. CRC Handbook of Avian Body Masses (CRC Press, 2008).58.Wilman, H. et al. EltonTraits 1.0: species-level foraging attributes of the world’s birds and mammals. Ecology 95, 2027 (2014).Article 

Google Scholar 
59.Kingdon, J. et al. Mammals of Africa (Bloomsbury, 2013).60.Kaspari, M. & Weiser, M. D. The size–grain hypothesis and interspecific scaling in ants. Funct. Ecol. 13, 530–538 (1999).Article 

Google Scholar 
61.Cane, J. H. Estimation of bee size using intertegular span (Apoidea). J. Kans. Entomol. Soc. 60, 145–147 (1987).
Google Scholar 
62.Classen, A., Steffan-Dewenter, I., Kindeketa, W. J. & Peters, M. K. Integrating intraspecific variation in community ecology unifies theories on body size shifts along climatic gradients. Funct. Ecol. 31, 768–777 (2017).Article 

Google Scholar 
63.Kendall, L. K. et al. Pollinator size and its consequences: robust estimates of body size in pollinating insects. Ecol. Evol. 9, 1702–1714 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
64.Hódar, J. A. The use of regression equations for estimation of arthropod biomass in ecological studies. Acta Oecol. 17, 421–433 (1996).
Google Scholar 
65.Ensslin, A. et al. Effects of elevation and land use on the biomass of trees, shrubs and herbs at Mount Kilimanjaro. Ecosphere 6, 45 (2015).Article 

Google Scholar 
66.Cheng, D.-L. & Niklas, K. J. Above- and below-ground biomass relationships across 1534 forested communities. Ann. Bot. 99, 95–102 (2007).PubMed 
Article 

Google Scholar 
67.Pabst, H., Gerschlauer, F., Kiese, R. & Kuzyakov, Y. Land use and precipitation affect organic and microbial carbon stocks and the specific metabolic quotient in soils of eleven ecosystems of Mt. Kilimanjaro, Tanzania. Land Degrad. Dev. 27, 592–602 (2016).Article 

Google Scholar 
68.Albrecht, J. et al. Plant and animal functional diversity drive mutualistic network assembly across an elevational gradient. Nat. Commun. 9, 3177 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
69.Classen, A. et al. Specialization of plant–pollinator interactions increases with temperature at Mt. Kilimanjaro. Ecol. Evol. 10, 2182–2195 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
70.Mayr, A. V. et al. Climate and food resources shape species richness and trophic interactions of cavity-nesting Hymenoptera. J. Biogeogr. 47, 854–865 (2020).Article 

Google Scholar 
71.Peters, M. K., Mayr, A., Röder, J., Sanders, N. J. & Steffan-Dewenter, I. Variation in nutrient use in ant assemblages along an extensive elevational gradient on Mt Kilimanjaro. J. Biogeogr. 41, 2245–2255 (2014).Article 

Google Scholar 
72.Genung, M. A. et al. The relative importance of pollinator abundance and species richness for the temporal variance of pollination services. Ecology 98, 1807–1816 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
73.Manly, B. F. J. Randomization, Bootstrap, and Monte Carlo Methods in Biology (Chapman & Hall/CRC, 2007).74.Gelman, A. Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper). Bayesian Anal. 1, 515–533 (2006).
Google Scholar 
75.Huang, A. & Wand, M. P. Simple marginally noninformative prior distributions for covariance matrices. Bayesian Anal. 8, 439–452 (2013).Article 

Google Scholar 
76.O’Hara, R. B. & Sillanpää, M. J. A review of Bayesian variable selection methods: what, how and which. Bayesian Anal. 4, 85–117 (2009).
Google Scholar 
77.Albrecht, J., Hagge, J., Schabo, D. G., Schaefer, H. M. & Farwig, N. Reward regulation in plant–frugivore networks requires only weak cues. Nat. Commun. 9, 4838 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
78.Grace, J. B., Johnson, D. J., Lefcheck, J. S. & Byrnes, J. E. K. Quantifying relative importance: computing standardized effects in models with binary outcomes. Ecosphere 9, e02283 (2018).Article 

Google Scholar 
79.Kass, R. E. & Raftery, A. E. Bayes factors. J. Am. Stat. Assoc. 90, 773–795 (1995).Article 

Google Scholar 
80.Levy, R. Bayesian data–model fit assessment for structural equation modeling. Struct. Equ. Modeling 18, 663–685 (2011).Article 

Google Scholar 
81.Nakagawa, S. & Schielzeth, H. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol. Evol. 4, 133–142 (2013).Article 

Google Scholar 
82.Nakagawa, S., Johnson, P. C. D. & Schielzeth, H. The coefficient of determination R2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded. J. R. Soc. Interface 14, 20170213 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
83.R Development Core Team. R: A Language and Environment for Statistical Computing v. 4.0.3 (R Foundation for Statistical Computing, 2020).84.Oksanen, J. et al. vegan: Community ecology package. R package version 2.5-7 http://cran.r-project.org/package=vegan (2020).85.Plummer, M. JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling http://mcmc-jags.sourceforge.net (2003)86.Plummer, M. rjags: Bayesian graphical models using MCMC. R package version 4-10 https://CRAN.R-project.org/package=rjags (2016)87.Statisticat, LLC. LaplacesDemon: Complete environment for Bayesian inference. R package version 16.1.4 https://CRAN.R-project.org/package=LaplacesDemon (2021)88.Plummer, M., Best, N., Cowles, K. & Vines, K. CODA: convergence diagnosis and output analysis for MCMC. R N. 6, 7–11 (2006).
Google Scholar 
89.Epskamp, S., Cramer, A. O. J., Waldorp, L. J., Schmittmann, V. D. & Borsboom, D. qgraph: network visualizations of relationships in psychometric data. J. Stat. Softw. 48, 1–18 (2012).Article 

Google Scholar 
90.Wood, S. N. Generalized Additive Models: An Introduction with R (CRC/Taylor & Francis, 2017).91.Chao, A. & Jost, L. Coverage-based rarefaction and extrapolation: standardizing samples by completeness rather than size. Ecology 93, 2533–2547 (2012).Article 

Google Scholar 
92.Hsieh, T. C., Ma, K. H. & Chao, A. iNEXT: an R package for rarefaction and extrapolation of species diversity (Hill numbers). Methods Ecol. Evol. 7, 1451–1456 (2016).Article 

Google Scholar 
93.Albrecht, J. et al. Data and code from ‘Species richness is more important for ecosystem functioning than species turnover along an elevational gradient’. Figshare https://doi.org/10.6084/m9.figshare.14544207 (2021). More

Alexander DH, Novembre J, Lange K (2009) Fast model-based estimation of ancestry in unrelated individuals. Genome Res 19:1655–1664CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Alexander DH, Shringarpure SS, Novembre J, Lange K (2015) Admixture 1.3 software manual. UCLA Hum Genet Softw Distrib, Los Angeles
Google Scholar 
Angert AL, Bontrager MG, Ågren J (2020) What do we really know about adaptation at range edges? Annu Rev Ecol Evol Syst 51:341–361Article 

Google Scholar 
Araújo MB, Pearson RG, Thuiller W, Erhard M (2005) Validation of species–climate impact models under climate change. Glob Change Biol 11:1504–1513Article 

Google Scholar 
Astle W, Balding DJ (2009) Population structure and cryptic relatedness in genetic association studies. Stat Sci 24:451–471Article 

Google Scholar 
Attard CRM, Beheregaray LB, Möller LM (2018) Genotyping-by-sequencing for estimating relatedness in nonmodel organisms: Avoiding the trap of precise bias. Mol Ecol Resour 18:381–390CAS 
PubMed 
Article 

Google Scholar 
Baird NA, Etter PD, Atwood TS, Currey MC, Shiver AL, Lewis ZA et al. (2008) Rapid SNP discovery and genetic mapping using sequenced RAD markers. PLoS One 3:1–7Article 
CAS 

Google Scholar 
Barbosa S, Mestre F, White TA, Paupério J, Alves PC, Searle JB (2018) Integrative approaches to guide conservation decisions: Using genomics to define conservation units and functional corridors. Mol Ecol 27:3452–3465PubMed 
Article 

Google Scholar 
Beever EA, Brussard PF, Berger J (2003) Patterns of apparent extirpation among isolated populations of pikas (Ochotona princeps) in the Great Basin. J Mammal 84:37–54Article 

Google Scholar 
Beever EA, Ray C, Mote PW, Wilkening JL (2010) Testing alternative models of climate-mediated extirpations. Ecol Appl 20:164–178PubMed 
Article 

Google Scholar 
Beever EA, Ray C, Wilkening JL, Brussard PF, Mote PW (2011) Contemporary climate change alters the pace and drivers of extinction. Glob Change Biol 17:2054–2070Article 

Google Scholar 
Beever EA, Perrine JD, Rickman T, Flores M, Clark JP, Waters C et al. (2016) Pika (Ochotona princeps) losses from two isolated regions reflect temperature and water balance, but reflect habitat area in a mainland region. J Mammal 97:1495–1511Article 

Google Scholar 
Bellard C, Bertelsmeier C, Leadley P, Thuiller W, Courchamp F (2012) Impacts of climate change on the future of biodiversity. Ecol Lett 15:365–377PubMed 
PubMed Central 
Article 

Google Scholar 
Blois JL, Williams JW, Fitzpatrick MC, Jackson ST, Ferrier S (2013) Space can substitute for time in predicting climate-change effects on biodiversity. Proc Natl Acad Sci USA 110:9374–9379CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Browning SR (2008) Missing data imputation and haplotype phase inference for genome-wide association studies. Hum Genet 124:439–450CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Calkins MT, Beever EA, Boykin KG, Frey JK, Andersen MC (2012) Not-so-splendid isolation: modeling climate-mediated range collapse of a montane mammal Ochotona princeps across numerous ecoregions. Ecography 35:780–791Article 

Google Scholar 
Carlson SM, Cunningham CJ, Westley PAH (2014) Evolutionary rescue in a changing world. Trends Ecol Evol 29:521–530PubMed 
Article 

Google Scholar 
Castillo JA, Epps CW, Davis AR, Cushman SA (2014) Landscape effects on gene flow for a climate-sensitive montane species, the American pika. Mol Ecol 23:843–856PubMed 
Article 

Google Scholar 
Castillo JA, Epps CW, Jeffress MR, Ray C, Rodhouse TJ, Schwalm D (2016) Replicated landscape genetic and network analyses reveal wide variation in functional connectivity for American pikas. Ecol Appl 26:1660–1676PubMed 
Article 
PubMed Central 

Google Scholar 
Ceballos G, Ehrlich PR, Raven PH (2020) Vertebrates on the brink as indicators of biological annihilation and the sixth mass extinction. Proc Natl Acad Sci USA 117:13596–13602CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ceballos G, Ehrlich PR, Barnosky AD, García A, Pringle RM, Palmer TM (2015) Accelerated modern human–induced species losses: entering the sixth mass extinction. Sci Adv 1:e1400253PubMed 
PubMed Central 
Article 

Google Scholar 
Chapman JA, Flux JE (1990) Rabbits, hares and pikas: status survey and conservation action plan. IUCN.Chypre M, Zaidi N, Smans K (2012) ATP-citrate lyase: a mini-review. Biochem Biophys Res Commun 422:1–4CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Cornuet JM, Luikart G (1996) Description and power analysis of two tests for detecting recent population bottlenecks from allele frequency data. Genetics 144:2001–2014CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA et al. (2011) The variant call format and VCFtools. Bioinformatics 27:2156–2158CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Diaz HF, Grosjean M, Graumlich L (2003) Climate variability and change in high elevation regions: past, present and future. Clim Change 59:1–4Article 

Google Scholar 
Eckert CG, Samis EK, Lougheed SC (2008) Genetic variation across species’ geographical ranges: the central-marginal hypothesis and beyond. Mol Ecol 17:1170–1188CAS 
PubMed 
Article 

Google Scholar 
Erb LP, Ray C, Guralnick R (2011) On the generality of a climate-mediated shift in the distribution of the American pika (Ochotona princeps). Ecology 92:1730–1735PubMed 
Article 

Google Scholar 
Excoffier L, Foll M, Petit RJ (2009) Genetic consequences of range expansions. Annu Rev Ecol Evol Syst 40:481–501Article 

Google Scholar 
Flanagan SP, Forester BR, Latch EK, Aitken SN, Hoban S (2018) Guidelines for planning genomic assessment and monitoring of locally adaptive variation to inform species conservation. Evol Appl 11:1035–1052PubMed 
Article 
PubMed Central 

Google Scholar 
Foll M, Gaggiotti O (2008) A genome-scan method to identify selected loci appropriate for both dominant and codominant markers: a Bayesian perspective. Genetics 180:977–993PubMed 
PubMed Central 
Article 

Google Scholar 
Franks SJ, Hoffmann AA (2012) Genetics of climate change adaptation. Annu Rev Genet 46:185–208CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Frichot E, François O (2015) LEA: an R package for landscape and ecological association studies. Methods Ecol Evol 6:925–929Article 

Google Scholar 
Frichot E, Schoville SD, Bouchard G, François O (2013) Testing for associations between loci and environmental gradients using latent factor mixed models. Mol Biol Evol 30:1687–1699CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Funk WC, McKay JK, Hohenlohe PA, Allendorf FW (2012) Harnessing genomics for delineating conservation units. Trends Ecol Evol 27:489–496PubMed 
PubMed Central 
Article 

Google Scholar 
Galbreath KE, Hafner DJ, Zamudio KR (2009) When cold is better: climate-driven elevation shifts yield complex patterns of diversification and demography in an Alpine specialist (American Pika, Ochotona Princeps). Evolution 63:2848–2863CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Gautier M (2015) Genome-wide scan for adaptive divergence and association with population-specific covariates. Genetics 201:1555–1579CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Götz S, García-Gómez JM, Terol J, Williams TD, Nagaraj SH, Nueda MJ et al. (2008) High-throughput functional annotation and data mining with the Blast2GO suite. Nucleic Acids Res 36:3420–3435PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Gradogna A, Gavazzo P, Boccaccio A, Pusch M (2017) Subunit‐dependent oxidative stress sensitivity of LRRC8 volume‐regulated anion channels. J Physiol 595:6719–6733CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hafner DJ, Sullivan RM (1995) Historical and ecological biogeography of Nearctic pikas (Lagomorpha: Ochotonidae). J Mammal 76:302–321Article 

Google Scholar 
Hafner DJ, Smith AT (2010) Revision of the subspecies of the American pika, Ochotona princeps (Lagomorpha: Ochotonidae). J Mammal 91:401–417Article 

Google Scholar 
Hanson JO, Marques A, Veríssimo A, Camacho-Sanchez M, Velo-Antón G, Martínez-Solano Í et al. (2020) Conservation planning for adaptive and neutral evolutionary processes. J Appl Ecol 57:2159–2169Article 

Google Scholar 
Harrisson KA, Pavlova A, Telonis-Scott M, Sunnucks P (2014) Using genomics to characterize evolutionary potential for conservation of wild populations. Evol Appl 7:1008–1025PubMed 
PubMed Central 
Article 

Google Scholar 
Heled J, Drummond AJ (2008) Bayesian inference of population size history from multiple loci. BMC Evol Biol 8:289PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Henry P, Russello MA (2013) Adaptive divergence along environmental gradients in a climate-change-sensitive mammal. Ecol Evol 3:3906–3917CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Henry P, Sim Z, Russello MA (2012) Genetic evidence for restricted dispersal along continuous altitudinal gradients in a climate change-sensitive mammal: the American pika. PLoS One 7:e39077CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hewitt G (2000) The genetic legacy of the Quaternary ice ages. Nature 405:907–913CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Hewitt GM (1996) Some genetic consequences of ice ages, and their role in divergence and speciation. Biol J Linn Soc 58:247–276Article 

Google Scholar 
Hinzpeter A, Lipecka J, Brouillard F, Baudoin-Legros M, Dadlez M, Edelman A et al. (2006) Association between Hsp90 and the ClC-2 chloride channel upregulates channel function. Am J Physiol Cell Physiol 290:C45–56CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Hoban S (2018) Integrative conservation genetics: Prioritizing populations using climate predictions, adaptive potential and habitat connectivity. Mol Ecol Resour 18:14–17PubMed 
Article 
PubMed Central 

Google Scholar 
Hoffmann AA, Sgrò CM (2011) Climate change and evolutionary adaptation. Nature 470:479–485CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Holbrook JD, DeYoung RW, Janecka JE, Tewes ME, Honeycutt RL, Young JH (2012) Genetic diversity, population structure, and movements of mountain lions (Puma concolor) in Texas. J Mammal 93:989–1000Article 

Google Scholar 
IPCC (2014) AR5 Synthesis Report: Climate Change 2014. IPCC.Jentsch TJ, Lutter D, Planells-Cases R, Ullrich F, Voss FK (2016) VRAC: molecular identification as LRRC8 heteromers with differential functions. Pflüg Arch Eur J Physiol 468:385–393CAS 
Article 

Google Scholar 
Johnston AN, Bruggeman JE, Beers AT, Beever EA, Christophersen RG, Ransom JI (2019) Ecological consequences of anomalies in atmospheric moisture and snowpack. Ecology 100:e02638PubMed 
Article 
PubMed Central 

Google Scholar 
Johnston KM, Freund KA, Schmitz OJ (2012) Projected range shifting by montane mammals under climate change: implications for Cascadia’s National Parks. Ecosphere 3:art97Article 

Google Scholar 
Kilham L (1958) Territorial behavior in pikas. J Mammal 39:307–307Article 

Google Scholar 
Kimura M (1971) Theoretical foundation of population genetics at the molecular level. Theor Popul Biol 2:174–208CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Klingler KB, Jahner JP, Parchman TL, Ray C, Peacock MM (2021) Genomic variation in the American pika: signatures of geographic isolation and implications for conservation. BMC Ecol Evol 21:2CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lambers JHR (2015) Extinction risks from climate change. Science 348:501–502CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Latch EK, Dharmarajan G, Glaubitz JC, Rhodes OE (2006) Relative performance of Bayesian clustering software for inferring population substructure and individual assignment at low levels of population differentiation. Conserv Genet 7:295–302Article 

Google Scholar 
Latch EK, Scognamillo DG, Fike JA, Chamberlain MJ, Rhodes Jr OE (2008) Deciphering ecological barriers to North American River Otter (Lontra canadensis) gene flow in the Louisiana landscape. J Hered 99:265–274CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Lee KM, Coop G (2017) Distinguishing among modes of convergent adaptation using population genomic data. Genetics 207:1591–1619PubMed 
PubMed Central 
Article 

Google Scholar 
Lee KM, Coop G (2019) Population genomics perspectives on convergent adaptation. Philos Trans R Soc B Biol Sci 374:20180236Article 

Google Scholar 
Lemay MA, Russello MA (2015) Genetic evidence for ecological divergence in kokanee salmon. Mol Ecol 24:798–811CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Li H (2013) Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. ArXiv13033997 Q-Bio.MacArthur RA, Wang LCH (1974) Behavioral thermoregulation in the pika Ochotona princeps: a field study using radiotelemetry. Can J Zool 52:353–358CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
McCain CM (2019) Assessing the risks to United States and Canadian mammals caused by climate change using a trait-mediated model. J Mammal 100:1808–1817
Google Scholar 
Meirmans PG, Van Tienderen PH (2004) GENOTYPE and GENODIVE: two programs for the analysis of genetic diversity of asexual organisms. Mol Ecol Notes 4:792–794Article 

Google Scholar 
Morin PA, Luikart G, Wayne RK, the SNP Workshop Group (2004) SNPs in ecology, evolution and conservation. Trends Ecol Evol 19:208–216Article 

Google Scholar 
Moritz C, Agudo R (2013) The future of species under climate change: resilience or decline? Science 341:504–508CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Moritz C, Patton JL, Conroy CJ, Parra JL, White GC, Beissinger SR (2008) Impact of a century of climate change on small-mammal communities in Yosemite National Park, USA. Science 322:261–264CAS 
PubMed 
Article 

Google Scholar 
Morrison SF, Hik DS (2007) Demographic analysis of a declining pika Ochotona collaris population: linking survival to broad-scale climate patterns via spring snowmelt patterns. J Anim Ecol 76:899–907PubMed 
Article 
PubMed Central 

Google Scholar 
Moyer‐Horner L, Mathewson PD, Jones GM, Kearney MR, Porter WP (2015) Modeling behavioral thermoregulation in a climate change sentinel. Ecol Evol 5:5810–5822PubMed 
PubMed Central 
Article 

Google Scholar 
Mussmann SM, Douglas MR, Chafin TK, Douglas ME (2019) BA3-SNPs: contemporary migration reconfigured in BayesAss for next-generation sequence data. Methods Ecol Evol 10:1808–1813Article 

Google Scholar 
Parmesan C (2006) Ecological and evolutionary responses to recent climate change. Annu Rev Ecol Evol Syst 37:637–669Article 

Google Scholar 
Paz-Vinas I, Loot G, Hermoso V, Veyssière C, Poulet N, Grenouillet G et al. (2018) Systematic conservation planning for intraspecific genetic diversity. Proc R Soc B Biol Sci 285:20172746Article 

Google Scholar 
Peacock MM (1997) Determining natal dispersal patterns in a population of North American pikas (Ochotona princeps) using direct mark-resight and indirect genetic methods. Behav Ecol 8:340–350Article 

Google Scholar 
Peacock MM, Smith AT (1997) Nonrandom mating in pikas Ochotona princeps: evidence for inbreeding between individuals of intermediate relatedness. Mol Ecol 6:801–811CAS 
PubMed 
Article 

Google Scholar 
Pew J, Muir PH, Wang J, Frasier TR (2015) related: an R package for analysing pairwise relatedness from codominant molecular markers. Mol Ecol Resour 15:557–561PubMed 
Article 

Google Scholar 
Pickett STA (1989) Space-for-time substitution as an alternative to long-term studies. In: Likens GE (ed) Long-term studies in ecology. Springer New York, New York, NY, p 110–135Chapter 

Google Scholar 
Piry S, Alapetite A, Cornuet J-M, Paetkau D, Baudouin L, Estoup A (2004) GENECLASS2: a software for genetic assignment and first-generation migrant detection. J Hered 95:536–539CAS 
PubMed 
Article 

Google Scholar 
R Core Team (2019) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, http://www.R-project.org/Rankin AM, Galbreath KE, Teeter KC (2017) Signatures of adaptive molecular evolution in American pikas (Ochotona princeps). J Mammal 98:1156–1167Article 

Google Scholar 
Rannala B, Mountain JL (1997) Detecting immigration by using multilocus genotypes. Proc Natl Acad Sci USA 94:9197–9201CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Razgour O, Taggart JB, Manel S, Juste J, Ibáñez C, Rebelo H et al. (2018) An integrated framework to identify wildlife populations under threat from climate change. Mol Ecol Resour 18:18–31PubMed 
Article 

Google Scholar 
Rellstab C, Gugerli F, Eckert AJ, Hancock AM, Holderegger R (2015) A practical guide to environmental association analysis in landscape genomics. Mol Ecol 24:4348–4370PubMed 
Article 

Google Scholar 
Ritland K (1996) Estimators for pairwise relatedness and individual inbreeding coefficients. Genet Res 67:175–185Article 

Google Scholar 
Robson KM, Lamb CT, Russello MA (2016) Low genetic diversity, restricted dispersal, and elevation-specific patterns of population decline in American pikas in an atypical environment. J Mammal 97:464–472Article 

Google Scholar 
Rochette NC, Rivera‐Colón AG, Catchen JM (2019) Stacks 2: analytical methods for paired-end sequencing improve RADseq-based population genomics. Mol Ecol 28:4737–4754CAS 
PubMed 
Article 

Google Scholar 
Rousset F (2008) GENEPOP’007: a complete re-implementation of the GENEPOP software for Windows and Linux. Mol Ecol Resour 8:103–106PubMed 
Article 

Google Scholar 
Rubidge EM, Patton JL, Lim M, Burton AC, Brashares JS, Moritz C (2012) Climate-induced range contraction drives genetic erosion in an alpine mammal. Nat Clim Change 2:285–288Article 

Google Scholar 
Russello MA, Waterhouse MD, Etter PD, Johnson EA (2015) From promise to practice: pairing non-invasive sampling with genomics in conservation. PeerJ 3:e1106PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Savolainen O, Lascoux M, Merilä J (2013) Ecological genomics of local adaptation. Nat Rev Genet 14:807–820CAS 
PubMed 
Article 

Google Scholar 
Sjodin BMF, Galbreath KE, Lanier HC, Russello MA (2021) Chromosome-level reference genome assembly for the American pika (Ochotona princeps). J Hered https://doi.org/10.1093/jhered/esab031Smith AT (1974b) The distribution and dispersal of pikas: influences of behavior and climate. Ecology 55:1368–1376Article 

Google Scholar 
Smith AT (1974a) The distribution and dispersal of pikas: consequences of insular population structure. Ecology 55:1112–1119Article 

Google Scholar 
Smith AT (2020) Conservation status of American pikas (Ochotona princeps). J Mammal 101:1466–1488Article 

Google Scholar 
Smith AT, Ivins BL (1983) Colonization in a pika population: dispersal vs philopatry. Behav Ecol Sociobiol 13:37–47Article 

Google Scholar 
Smith AT, Weston ML (1990) Ochotona princeps. Mamm Species 352:1–8.Article 

Google Scholar 
Smith AT, Millar CI (2018) American pika (Ochotona princeps) population survival in winters with low or no snowpack. West North Am Nat 78:126–132Article 

Google Scholar 
La Sorte FA, Jetz W (2010) Projected range contractions of montane biodiversity under global warming. Proc R Soc B Biol Sci 277:3401–3410Article 

Google Scholar 
Stern DL (2013) The genetic causes of convergent evolution. Nat Rev Genet 14:751–764CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Stewart JAE, Wright DH, Heckman KA (2017) Apparent climate-mediated loss and fragmentation of core habitat of the American pika in the Northern Sierra Nevada, California, USA. PLoS One 12:e0181834PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Stewart JAE, Perrine JD, Nichols LB, Thorne JH, Millar CI, Goehring KE et al. (2015) Revisiting the past to foretell the future: summer temperature and habitat area predict pika extirpations in California. J Biogeogr 42:880–890Article 

Google Scholar 
Varner J, Dearing MD (2014) The importance of biologically relevant microclimates in habitat suitability assessments. PLoS One 9:e104648PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Waldvogel A-M, Feldmeyer B, Rolshausen G, Exposito‐Alonso M, Rellstab C, Kofler R et al. (2020) Evolutionary genomics can improve prediction of species’ responses to climate change. Evol Lett 4:4–18PubMed 
PubMed Central 
Article 

Google Scholar 
Wang T, Hamann A, Spittlehouse DL, Murdock TQ (2012) ClimateWNA—high-resolution spatial climate data for Western North America. J Appl Meteorol Climatol 51:16–29Article 

Google Scholar 
Waterhouse MD, Erb LP, Beever EA, Russello MA (2018) Adaptive population divergence and directional gene flow across steep elevational gradients in a climate-sensitive mammal. Mol Ecol 27:2512–2528PubMed 
Article 
PubMed Central 

Google Scholar 
Weir BS, Cockerham CC (1984) Estimating F-statistics for the analysis of population structure. Evolution 38:1358–1370CAS 
PubMed 

Google Scholar 
Wiens JJ (2016) Climate-related local extinctions are already widespread among plant and animal species. PLoS Biol 14:e2001104PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Wilkening JL, Ray C (2016) Characterizing predictors of survival in the American pika (Ochotona princeps). J Mammal 97:1366–1375Article 

Google Scholar 
Wilkening JL, Ray C, Beever EA, Brussard PF (2011) Modeling contemporary range retraction in Great Basin pikas (Ochotona princeps) using data on microclimate and microhabitat. Quat Int 235:77–88Article 

Google Scholar 
Wilson GA, Rannala B (2003) Bayesian inference of recent migration rates using multilocus genotypes. Genetics 163:1177–1191PubMed 
PubMed Central 
Article 

Google Scholar 
Wogan GOU, Wang IJ (2018) The value of space-for-time substitution for studying fine-scale microevolutionary processes. Ecography 41:1456–1468Article 

Google Scholar 
Yandow LH, Chalfoun AD, Doak DF (2015) Climate tolerances and habitat requirements jointly shape the elevational distribution of the American pika (Ochotona princeps), with implications for climate change effects. PLoS One 10:e0131082PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Zgurski JM, Hik DS (2012) Polygynandry and even-sexed dispersal in a population of collared pikas, Ochotona collaris. Anim Behav 83:1075–1082Article 

Google Scholar 
Zhang D, Pan J, Cao J, Cao Y, Zhou H (2020) Screening of drought-resistance related genes and analysis of promising regulatory pathway in camel renal medulla. Genomics 112:2633–2639CAS 
PubMed 
Article 

Google Scholar 
Zhang Z, Schwartz S, Wagner L, Miller W (2000) A greedy algorithm for aligning DNA sequences. J Comput Biol 7:203–214CAS 
PubMed 
Article 

Google Scholar  More

1.Paerl, H. W. & Otten, T. G. Harmful cyanobacterial blooms: causes, consequences, and controls. Microb. Ecol. 65, 995–1010 (2013).CAS 
PubMed 
Article 

Google Scholar 
2.Visser, P. M. et al. How rising CO2 and global warming may stimulate harmful cyanobacterial blooms. Harmful Algae 54, 145–159 (2016).CAS 
PubMed 
Article 

Google Scholar 
3.Lürling, M., Mendes e Mello, M., van Oosterhout, F., de Senerpont Domis, L. & Marinho, M. M. Response of natural cyanobacteria and algae assemblages to a nutrient pulse and elevated temperature. Front. Microbiol. 9, 1851 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
4.Low-Décarie, E., Fussmann, G. F. & Bell, G. Aquatic primary production in a high-CO2 world. Trends Ecol. Evol. 29, 223–232 (2014).PubMed 
Article 

Google Scholar 
5.Stanley, S. M. Estimates of the magnitudes of major marine mass extinctions in earth history. PNAS 113, E6325–E6334 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
6.Sun, Y. D. et al. Lethally hot temperatures during the Early Triassic Greenhouse. Science 338, 366–370 (2012).ADS 
CAS 
PubMed 
Article 

Google Scholar 
7.Frank, T. D. et al. Pace, magnitude, and nature of terrestrial climate change through the end Permian extinction in southeastern Gondwana. Geology 49, https://doi.org/10.1130/G48795.1 (2021).8.Wu, Y. et al. Six-fold increase of atmospheric pCO2 during the Permian–Triassic mass extinction. Nat. Commun. 12, 2137 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
9.Burgess, S. D., Muirhead, J. D. & Bowring, S. A. Initial pulse of Siberian Traps sills as the trigger of the end-Permian mass extinction. Nat. Commun. 8, 164 (2017).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
10.Mays, C. et al. Refined Permian–Triassic floristic timeline reveals early collapse and delayed recovery of south polar terrestrial ecosystems. GSA Bull. 132, 1489–1513 (2020).CAS 
Article 

Google Scholar 
11.Chu, D. et al. Ecological disturbance in tropical peatlands prior to marine Permian-Triassic mass extinction. Geology 48, 288–292 (2020).ADS 
Article 

Google Scholar 
12.Retallack, G. J., Veevers, J. J. & Morante, R. Global coal gap between Permian–Triassic extinction and Middle Triassic recovery of peat-forming plants. GSA Bull. 108, 195–207 (1996).CAS 
Article 

Google Scholar 
13.Fielding, C. R. et al. Age and pattern of the southern high-latitude continental end-Permian extinction constrained by multiproxy analysis. Nat. Commun. 10, 385 (2019).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
14.Fielding, C. R. et al. Sedimentology of the continental end-Permian extinction event in the Sydney Basin, eastern Australia. Sedimentology 68, 30–62 (2021).CAS 
Article 

Google Scholar 
15.Metcalfe, I., Crowley, J. L., Nicoll, R. S. & Schmitz, M. High-precision U-Pb CA-TIMS calibration of Middle Permian to Lower Triassic sequences, mass extinction and extreme climate-change in eastern Australian Gondwana. Gondwana Res. 28, 61–81 (2015).ADS 
CAS 
Article 

Google Scholar 
16.Vajda, V. et al. End-Permian (252 Mya) deforestation, wildfires and flooding—an ancient biotic crisis with lessons for the present. Earth Planet. Sci. Lett. 529, 115875 (2020).CAS 
Article 

Google Scholar 
17.McLoughlin, S. et al. Dwelling in the dead zone—vertebrate burrows immediately succeeding the end-Permian extinction event in Australia. Palaios 35, 342–357 (2020).ADS 
Article 

Google Scholar 
18.Lamb, A. L., Wilson, G. P. & Leng, M. J. A review of coastal palaeoclimate and relative sea-level reconstructions using δ13C and C/N ratios in organic material. Earth-Sci. Rev. 75, 29–57 (2006).ADS 
CAS 
Article 

Google Scholar 
19.Mays, C., Vajda, V. & McLoughlin, S. Permian–Triassic non-marine algae of Gondwana—distributions, natural affinities and ecological implications. Earth-Sci. Rev. 212, 103382 (2021).CAS 
Article 

Google Scholar 
20.McLoughlin, S. et al. Age and paleoenvironmental significance of the Frazer Beach Member—a new lithostratigraphic unit overlying the end-Permian extinction horizon in the Sydney Basin, Australia. Front. Earth Sci. 8, 600976 (2021).Article 

Google Scholar 
21.Huber, J. K. A postglacial pollen and nonsiliceous algae record from Gegoka Lake, Lake County, Minnesota. J. Paleolimnol. 16, 23–35 (1996).ADS 
Article 

Google Scholar 
22.Woodward, C. A. & Shulmeister, J. A Holocene record of human induced and natural environmental change from Lake Forsyth (Te Wairewa), New Zealand. J. Paleolimnol. 34, 481–501 (2005).ADS 
Article 

Google Scholar 
23.Pacton, M., Gorin, G. & Fiet, N. Occurrence of photosynthetic microbial mats in a Lower Cretaceous black shale (central Italy): a shallow-water deposit. Facies 55, 401–419 (2009).Article 

Google Scholar 
24.Pacton, M., Gorin, G. E. & Vasconcelos, C. Amorphous organic matter—Experimental data on formation and the role of microbes. Rev. Palaeobot. Palynol. 166, 253–267 (2011).Article 

Google Scholar 
25.Tyson, R. V. Sedimentary Organic Matter: Organic Facies and Palynofacies (Chapman & Hall, 1995).26.Retallack, G. J. Earliest Triassic claystone breccias and soil-erosion crisis. J. Sediment. Res. 75, 679–695 (2005).ADS 
Article 

Google Scholar 
27.Augland, L. E. et al. The main pulse of the Siberian Traps expanded in size and composition. Sci. Rep. 9, 18723 (2019).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
28.Retallack, G. J. Post-apocalyptic greenhouse paleoclimate revealed by earliest Triassic paleosols in the Sydney Basin. Aust. GSA Bull. 111, 52–70 (1999).CAS 
Article 

Google Scholar 
29.Woodward, C., Shulmeister, J., Larsen, J., Jacobsen, G. E. & Zawadzki, A. The hydrological legacy of deforestation on global wetlands. Science 346, 844–847 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
30.Stevenson, R. J. & Smol, J. P. In Freshwater Algae of North America: Ecology and Classification (eds Wehr, J. D., Sheath, R. G. & Kociolek, P.) Ch. 21, 921–962 (Academic Press, 2015).31.Lindström, S., Bjerager, M., Alsen, P., Sanei, H. & Bojesen-Koefoed, J. The Smithian–Spathian boundary in North Greenland: implications for extreme global climate changes. Geol. Mag. 157, 1547–1567 (2020).ADS 
Article 
CAS 

Google Scholar 
32.de Leeuw, J. W., Versteegh, G. J. M. & van Bergen, P. F. in Plants and Climate Change, Plant Ecology (eds Rozema, J., Aerts, R. & Cornelissen, H.) Vol. 182, 209–233 (Springer, 2006).33.Baudelet, P.-H., Ricochon, G., Linder, M. & Muniglia, L. A new insight into cell walls of Chlorophyta. Algal Res 25, 333–371 (2017).Article 

Google Scholar 
34.Graham, L. E. & Gray, J. In Plants Invade the Land: Evolutionary and Environmental Perspectives (eds Gensel, P. G. & Edwards, D.) 140–158 (Columbia University Press, 2001).35.Demura, M., Ioki, M., Kawachi, M., Nakajima, N. & Watanabe, M. M. Desiccation tolerance of Botryococcus braunii (Trebouxiophyceae, Chlorophyta) and extreme temperature tolerance of dehydrated cells. J. Appl. Phycol. 26, 49–53 (2014).CAS 
PubMed 
Article 

Google Scholar 
36.Del Cortona, A. et al. Neoproterozoic origin and multiple transitions to macroscopic growth in green seaweeds. PNAS 117, 2551–2559 (2020).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
37.Wheeler, A., Van de Wetering, N., Esterle, J. S. & Götz, A. E. Palaeoenvironmental changes recorded in the palynology and palynofacies of a Late Permian Marker Mudstone (Galilee Basin, Australia). Palaeoworld 29, 439–452 (2020).Article 

Google Scholar 
38.Reynolds, C. S., Huszar, V., Kruk, C., Naselli-Flores, L. & Melo, S. Towards a functional classification of the freshwater phytoplankton. J. Plankton Res. 24, 417–428 (2002).Article 

Google Scholar 
39.Low-Décarie, E., Fussmann, G. F. & Bell, G. The effect of elevated CO2 on growth and competition in experimental phytoplankton communities. Glob. Change Biol. 17, 2525–2535 (2011).ADS 
Article 

Google Scholar 
40.von Alvensleben, N., Magnusson, M. & Heimann, K. Salinity tolerance of four freshwater microalgal species and the effects of salinity and nutrient limitation on biochemical profiles. J. Appl. Phycol. 28, 861–876 (2016).Article 
CAS 

Google Scholar 
41.Chu, D. et al. Microbial mats in the terrestrial Lower Triassic of North China and implications for the Permian–Triassic mass extinction. Palaeogeog. Palaeoclimatol. Palaeoecol. 474, 214–231 (2017).ADS 
Article 

Google Scholar 
42.Guo, W. et al. Secular variations of ichnofossils from the terrestrial Late Permian–Middle Triassic succession at the Shichuanhe section in Shaanxi Province, North China. Glob. Planet. Change 181, 102978 (2019).Article 

Google Scholar 
43.Lee, J. Y. et al. Future global climate: Scenario-based projections and near-term information. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (eds Masson-Delmotte, V., et al.) 195 pp. (Cambridge University Press, 2021).44.de Jersey, N. J. Palynology of the Permian-Triassic transition in the western Bowen Basin. Geol. Surv. Qld. Publ. 374, 1–39 (1979).
Google Scholar 
45.Lindström, S. & McLoughlin, S. Synchronous palynofloristic extinction and recovery after the end-Permian event in the Prince Charles Mountains, Antarctica: Implications for palynofloristic turnover across Gondwana. Rev. Palaeobot. Palynol. 145, 89–122 (2007).Article 

Google Scholar 
46.Grebe, H. Permian plant microfossils from the Newcastle Coal Measures/Narrabeen Group Boundary, Lake Munmorah, New South Wales. Rec. Geol. Surv. NSW 12, 125–136 (1970).
Google Scholar 
47.Mishra, S. et al. A new acritarch spike of Leiosphaeridia dessicata comb. nov. emend. from the Upper Permian and Lower Triassic sequence of India (Pranhita-Godavari Basin): its origin and palaeoecological significance. Palaeogeog. Palaeoclimatol. Palaeoecol. 567, 110274 (2021).ADS 
Article 

Google Scholar 
48.Grice, K. et al. Photic zone euxinia during the Permian-Triassic superanoxic event. Science 307, 706–709 (2005).ADS 
CAS 
PubMed 
Article 

Google Scholar 
49.Kershaw, S. et al. Microbialites and global environmental change across the Permian–Triassic boundary: a synthesis. Geobiology 10, 25–47 (2012).CAS 
PubMed 
Article 

Google Scholar 
50.Schneebeli-Hermann, E. et al. Palynofacies analysis of the Permian–Triassic transition in the Amb section (Salt Range, Pakistan): implications for the anoxia on the South Tethyan Margin. J. Asian Earth Sci. 60, 225–234 (2012).ADS 
Article 

Google Scholar 
51.van Soelen, E. E. & Kürschner, W. M. Late Permian to Early Triassic changes in acritarch assemblages and morphology in the Boreal Arctic: new data from the Finnmark Platform. Palaeogeog. Palaeoclimatol. Palaeoecol. 505, 120–127 (2018).ADS 
Article 

Google Scholar 
52.Spina, A., Cirilli, S., Utting, J. & Jansonius, J. Palynology of the Permian and Triassic of the Tesero and Bulla sections (Western Dolomites, Italy) and consideration about the enigmatic species Reduviasporonites chalastus. Rev. Palaeobot. Palynol. 218, 3–14 (2015).Article 

Google Scholar 
53.Thomas, B. M. et al. Unique marine Permian‐Triassic boundary section from Western Australia. Aust. J. Earth Sci. 51, 423–430 (2004).ADS 
Article 

Google Scholar 
54.Schneebeli-Hermann, E. & Bucher, H. Palynostratigraphy at the Permian-Triassic boundary of the Amb section, Salt Range, Pakistan. Palynology 39, 1–18 (2015).Article 

Google Scholar 
55.Lei, Y. et al. Phytoplankton (acritarch) community changes during the Permian-Triassic transition in South China. Palaeogeog. Palaeoclimatol. Palaeoecol. 519, 84–94 (2019).ADS 
Article 

Google Scholar 
56.Algeo, T. J. et al. Plankton and productivity during the Permian–Triassic boundary crisis: An analysis of organic carbon fluxes. Glob. Planet. Change 105, 52–67 (2013).ADS 
Article 

Google Scholar 
57.van Soelen, E. E., Twitchett, R. J. & Kürschner, W. M. Salinity changes and anoxia resulting from enhanced run-off during the late Permian global warming and mass extinction event. Climate 14, 441–453 (2018).
Google Scholar 
58.Kaiho, K. et al. Effects of soil erosion and anoxic–euxinic ocean in the Permian–Triassic marine crisis. Heliyon 2, e00137 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
59.Bond, D. P. G. & Grasby, S. E. On the causes of mass extinctions. Palaeogeog. Palaeoclimatol. Palaeoecol. 478, 3–29 (2017).ADS 
Article 

Google Scholar 
60.Lindström, S., Erlström, M., Piasecki, S., Nielsen, L. H. & Mathiesen, A. Palynology and terrestrial ecosystem change of the Middle Triassic to lowermost Jurassic succession of the eastern Danish Basin. Rev. Palaeobot. Palynol. 244, 65–95 (2017).Article 

Google Scholar 
61.Garel, S. et al. Paleohydrological and paleoenvironmental changes recorded in terrestrial sediments of the Paleocene–Eocene boundary (Normandy, France). Palaeogeog. Palaeoclimatol. Palaeoecol. 376, 184–199 (2013).ADS 
Article 

Google Scholar 
62.van de Schootbrugge, B. & Gollner, S. In Ecosystem Paleobiology and Geobiology, The Paleontological Society Papers (eds Bush, A. M., Pruss, S. B. & Payne, J. L.) 19, 87–114 (The Paleontological Society, 2013).63.Mata, S. A. & Bottjer, D. J. Microbes and mass extinctions: paleoenvironmental distribution of microbialites during times of biotic crisis. Geobiology 10, 3–24 (2012).CAS 
PubMed 
Article 

Google Scholar 
64.Peterffy, O., Calner, M. & Vajda, V. Early Jurassic microbial mats—a potential response to reduced biotic activity in the aftermath of the end-Triassic mass extinction event. Palaeogeog. Palaeoclimatol. Palaeoecol. 464, 76–85 (2016).ADS 
Article 

Google Scholar 
65.Schoene, B. et al. U-Pb constraints on pulsed eruption of the Deccan Traps across the end-Cretaceous mass extinction. Science 3636, 862–866 (2019).ADS 
Article 
CAS 

Google Scholar 
66.Hull, P. M. et al. On impact and volcanism across the Cretaceous-Paleogene boundary. Science 367, 266–272 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
67.Vajda, V., Ocampo, A., Ferrow, E. & Bender Koch, C. Nano particles as the primary cause for long-term sunlight suppression at high southern latitudes following the Chicxulub impact—evidence from ejecta deposits in Belize and Mexico. Gondwana Res. 27, 1079–1088 (2015).ADS 
CAS 
Article 

Google Scholar 
68.Sepúlveda, J., Wendler, J. E., Summons, R. E. & Hinrichs, K.-U. Rapid resurgence of marine productivity after the Cretaceous-Paleogene mass extinction. Science 326, 129–132 (2009).ADS 
PubMed 
Article 
CAS 

Google Scholar 
69.Bralower, T. J. et al. Origin of a global carbonate layer deposited in the aftermath of the Cretaceous-Paleogene boundary impact. Earth Planet. Sci. Lett. 548, 116476 (2020).CAS 
Article 

Google Scholar 
70.Schaefer, B. et al. Microbial life in the nascent Chicxulub crater. Geology 48, 328–332 (2020).ADS 
CAS 
Article 

Google Scholar 
71.Milligan, J. N., Royer, D. L., Franks, P. J., Upchurch, G. R. & McKee, M. L. No evidence for a large atmospheric CO2 spike across the Cretaceous‐Paleogene boundary. Geophys. Res. Lett. 46, 3462–3472 (2019).ADS 
CAS 
Article 

Google Scholar 
72.Strother, P. K. & Wellman, C. H. The Nonesuch Formation Lagerstätte: a rare window into freshwater life one billion years ago. J. Geol. Soc. 178, jgs2020–jgs2133 (2021).Article 

Google Scholar 
73.Sepkoski, J. J., Bambach, R. K. & Droser, M. L. In Cycles and Events in Stratigraphy (eds Einsele, G., Ricken, W. & Seilacher, A.) 298–312 (Springer-Verlag, 1991).74.Tyson, R. V. Calibration of hydrogen indices with microscopy: a review, reanalysis and new results using the fluorescence scale. Org. Geochem. 37, 45–63 (2006).CAS 
Article 

Google Scholar 
75.Benninghoff, W. S. Calculation of pollen and spore density in sediments by addition of exotic pollen in known quantities. Pollen et. Spores 4, 332–333 (1962).
Google Scholar 
76.Maher, L. J. Statistics for microfossil concentration measurements employing samples spiked with marker grains. Rev. Palaeobot. Palynol. 32, 153–191 (1981).Article 

Google Scholar 
77.Simpson, M. G. Plant Systematics (Academic Press, 2019).78.Evitt, W. R. A discussion and proposals concerning fossil dinoflagellates, hystrichospheres, and acritarchs, II. PNAS 49, 298–302 (1963).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
79.Rampino, M. R. & Eshet, Y. The fungal and acritarch events as time markers for the latest Permian mass extinction: an update. Geosci. Front. 9, 147–154 (2018).Article 

Google Scholar 
80.Combaz, A. Les palynofaciès. Rev. Micropaléontol. 7, 205–218 (1964).
Google Scholar 
81.Hammer, Ø., Harper, D. A. T. & Ryan, P. D. PAST: paleontological statistics software package for education and data analysis. Palaeontol. Electron. 4, 4 (2001).
Google Scholar 
82.Wei, W. & Algeo, T. J. Elemental proxies for paleosalinity analysis of ancient shales and mudrocks. Geochim. Cosmochim. Acta 287, 341–366 (2020).ADS 
CAS 
Article 

Google Scholar 
83.Rowe, H., Hughes, N. & Robinson, K. The quantification and application of handheld energy-dispersive x-ray fluorescence (ED-XRF) in mudrock chemostratigraphy and geochemistry. Chem. Geol. 324–325, 122–131 (2012).ADS 
Article 
CAS 

Google Scholar 
84.Blakey, R. C. Global paleogeography and tectonics in deep time. https://deeptimemaps.com/global-series-details/. Accessed 16 June 2020 (2016).85.Zhuravlev, A. Y. & Wood, R. A. Anoxia as the cause of the mid-Early Cambrian (Botomian) extinction event. Geology 24, 311–314 (1996).ADS 
CAS 
Article 

Google Scholar 
86.Zhang, W., Shi, X., Jiang, G., Tang, D. & Wang, X. Mass-occurrence of oncoids at the Cambrian Series 2–Series 3 transition: Implications for microbial resurgence following an Early Cambrian extinction. Gondwana Res. 28, 432–450 (2015).ADS 
Article 
CAS 

Google Scholar 
87.Vecoli, M. Fossil microphytoplankton dynamics across the Ordovician–Silurian boundary. Rev. Palaeobot. Palynol. 148, 91–107 (2008).Article 

Google Scholar 
88.Xie, S. et al. Contrasting microbial community changes during mass extinctions at the Middle/Late Permian and Permian/Triassic boundaries. Earth Planet. Sci. Lett. 460, 180–191 (2017).ADS 
CAS 
Article 

Google Scholar 
89.Eshet, Y., Rampino, M. R. & Visscher, H. Fungal event and palynological record of ecological crisis and recovery across the Permian-Triassic boundary. Geology 23, 967–970 (1995).ADS 
Article 

Google Scholar 
90.Richoz, S. et al. Hydrogen sulphide poisoning of shallow seas following the end-Triassic extinction. Nat. Geosci. 5, 662–667 (2012).ADS 
CAS 
Article 

Google Scholar 
91.Lindström, S. et al. No causal link between terrestrial ecosystem change and methane release during the end-Triassic mass extinction. Geology 40, 531–534 (2012).ADS 
Article 
CAS 

Google Scholar 
92.van de Schootbrugge, B. et al. End-Triassic calcification crisis and blooms of organic-walled “disaster species”. Palaeogeog. Palaeoclimatol. Palaeoecol. 244, 126–141 (2007).ADS 
Article 

Google Scholar 
93.Slater, S. M., Twitchett, R. J., Danise, S. & Vajda, V. Substantial vegetation response to Early Jurassic global warming with impacts on oceanic anoxia. Nat. Geosci. 12, 462–467 (2019).ADS 
CAS 
Article 

Google Scholar 
94.Polgári, M. et al. Mineral and chemostratigraphy of a Toarcian black shale hosting Mn-carbonate microbialites (Úrkút, Hungary). Palaeogeog. Palaeoclimatol. Palaeoecol. 459, 99–120 (2016).ADS 
Article 

Google Scholar 
95.Xu, W. et al. Carbon sequestration in an expanded lake system during the Toarcian oceanic anoxic event. Nat. Geosci. 10, 129–134 (2017).ADS 
CAS 
Article 

Google Scholar 
96.Kashiyama, Y. et al. Diazotrophic cyanobacteria as the major photoautotrophs during mid-Cretaceous oceanic anoxic events: nitrogen and carbon isotopic evidence from sedimentary porphyrin. Org. Geochem. 39, 532–549 (2008).CAS 
Article 

Google Scholar 
97.Jarvis, I. et al. Microfossil assemblages and the Cenomanian-Turonian (late Cretaceous) oceanic anoxic event. Cretac. Res 9, 3–103 (1988).Article 

Google Scholar 
98.Layeb, M., Ben Fadhel, M., Layeb-Tounsi, Y. & Ben Youssef, M. First microbialites associated to organic-rich facies of the Oceanic Anoxic Event 2 (Northern Tunisia, Cenomanian–Turonian transition). Arab. J. Geosci. 7, 3349–3363 (2014).CAS 
Article 

Google Scholar 
99.Pearce, M. A., Jarvis, I. & Tocher, B. A. The Cenomanian–Turonian boundary event, OAE2 and palaeoenvironmental change in epicontinental seas: new insights from the dinocyst and geochemical records. Palaeogeog. Palaeoclimatol. Palaeoecol. 280, 207–234 (2009).ADS 
Article 

Google Scholar 
100.Kuypers, M. M. M., Pancost, R. D., Nijenhuis, I. A. & Sinninghe Damsté, J. S. Enhanced productivity led to increased organic carbon burial in the euxinic North Atlantic basin during the late Cenomanian oceanic anoxic event. Paleoceanography 17, 1051 (2002).ADS 
Article 

Google Scholar 
101.Dodsworth, P., Eldrett, J. S. & Hart, M. B. Cretaceous Oceanic Anoxic Event 2 in eastern England: further palynological and geochemical data from Melton Ross. figshare https://doi.org/10.6084/m9.figshare.c.4987205.v3 (2020).102.Schwab, K. W., Bayliss, G. S., Smith, M. A. & Yoder, N. B. Mushroom and broccoli-head shaped algal fragments from the Eagle Ford Shale of south Texas and Coahuila, Mexico. Search and Discovery 70134 (2013).103.Lyson, T. R. et al. Exceptional continental record of biotic recovery after the Cretaceous–Paleogene mass extinction. Science 366, 977–983 (2019).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
104.Scasso, R. A. et al. A high-resolution record of environmental changes from a Cretaceous-Paleogene section of Seymour Island. Antarctica. Palaeogeog. Palaeoclimatol. Palaeoecol. 555, 109844 (2020).ADS 
Article 

Google Scholar 
105.Sosa-Montes de Oca, C. et al. Minor changes in biomarker assemblages in the aftermath of the Cretaceous-Paleogene mass extinction event at the Agost distal section (Spain). Palaeogeog. Palaeoclimatol. Palaeoecol. 569, 110310 (2021).ADS 
Article 

Google Scholar 
106.Sluijs, A. et al. Environmental precursors to rapid light carbon injection at the Palaeocene/Eocene boundary. Nature 450, 1218–1222 (2007).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
107.Junium, C. K., Dickson, A. J. & Uveges, B. T. Perturbation to the nitrogen cycle during rapid Early Eocene global warming. Nat. Commun. 9, 3186 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
108.Pagani, M. et al. Arctic hydrology during global warming at the Palaeocene/Eocene thermal maximum. Nature 442, 671–675 (2006).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
109.Kender, S. et al. Marine and terrestrial environmental changes in NW Europe preceding carbon release at the Paleocene–Eocene transition. Earth Planet. Sci. Lett. 353–354, 108–120 (2012).ADS 
Article 
CAS 

Google Scholar 
110.Huisman, J. et al. Cyanobacterial blooms. Nat. Rev. Microbiol. 16, 471–483 (2018).CAS 
PubMed 
Article 

Google Scholar 
111.Paerl, H. W., Hall, N. S. & Calandrino, E. S. Controlling harmful cyanobacterial blooms in a world experiencing anthropogenic and climatic-induced change. Sci. Tot. Environ. 409, 1739–1745 (2011).CAS 
Article 

Google Scholar  More

1.Darwin, C. On the origin of species. (D. Appleton and Co., 1871). https://doi.org/10.5962/bhl.title.28875.2.Thompson, J. N. Mutualistic webs of species. Science (80–) 312, 372–373 (2006).CAS 
Article 

Google Scholar 
3.Bronstein, J. L. The exploitation of mutualisms. Ecol. Lett. 4, 277–287 (2001).Article 

Google Scholar 
4.Bshary, R. & Grutter, A. S. Experimental evidence that partner choice is a driving force in the payoff distribution among cooperators or mutualists: The cleaner fish case. Ecol. Lett. 5, 130–136 (2002).Article 

Google Scholar 
5.Kiers, E. T., Rousseau, R. A., West, S. A. & Denison, R. F. Host sanctions and the legume–rhizobium mutualism. Nature 425, 78–81 (2003).ADS 
CAS 
Article 

Google Scholar 
6.Heil, M., Barajas-Barron, A., Orona-Tamayo, D., Wielsch, N. & Svatos, A. Partner manipulation stabilises a horizontally transmitted mutualism. Ecol. Lett. 17, 185–192 (2014).Article 

Google Scholar 
7.Hindsbo, O. Effects of Polymorphus (Acanthocephala) on colour and behaviour of Gammarus lacustris. Nature 238, 333 (1972).ADS 
Article 

Google Scholar 
8.Thomas, F., Renaud, F., de Meeus, T. & Poulin, R. Manipulation of host behaviour by parasites: Ecosystem engineering in the intertidal zone?. Proc. R. Soc. B Biol. Sci. 265, 1091–1096 (1998).Article 

Google Scholar 
9.Thomas, F. et al. Do hairworms (Nematomorpha) manipulate the water seeking behaviour of their terrestrial hosts?. J. Evol. Biol. 15, 356–361 (2002).Article 

Google Scholar 
10.Kadoya, E. Z., Ishii, H. S. & Williams, N. M. Host manipulation of bumble bee queens by Sphaerularia nematodes indirectly affects foraging of non-host workers. Ecology 96, 1361–1370 (2015).Article 

Google Scholar 
11.Hojo, M. K., Pierce, N. E. & Tsuji, K. Lycaenid caterpillar secretions manipulate attendant ant behavior. Curr. Biol. 25, 2260–2264 (2015).CAS 
Article 

Google Scholar 
12.Poulin, R., Brodeur, J. & Moore, J. Parasite manipulation of host behaviour: Should hosts always lose?. Oikos 70, 479 (1994).Article 

Google Scholar 
13.Heil, M. et al. Divergent investment strategies of Acacia myrmecophytes and the coexistence of mutualists and exploiters. Proc. Natl. Acad. Sci. USA. 106, 18091–18096 (2009).ADS 
CAS 
Article 

Google Scholar 
14.Watanabe, S., Murakami, T., Yoshimura, J. & Hasegawa, E. Color polymorphism in an aphid is maintained by attending ants. Sci. Adv. 2, (2016).15.Watanabe, S., Yoshimura, J. & Hasegawa, E. Ants improve the reproduction of inferior morphs to maintain a polymorphism in symbiont aphids. Sci. Rep. 8, (2018).16.Sakata, H. Density-dependent predation of the ant Lasius niger (Hymenoptera: Formicidae) on two attended aphids Lachnus tropicalis and Myzocallis kuricola (Homoptera: Aphididae). Res. Popul. Ecol. (Kyoto) 37, 159–164 (1995).Article 

Google Scholar 
17.Evans, P. D. Biogenic Amines in the insect nervous system. Adv. In Insect Phys. 15, 317–473 (1980).CAS 
Article 

Google Scholar 
18.Aonuma, H. & Watanabe, T. Octopaminergic system in the brain controls aggressive motivation in the ant Formica japonica. Acta Biol. Hung. 63, 63–68 (2012).Article 

Google Scholar 
19.Stevenson, P. A., Dyakonova, V., Rillich, J. & Schildberger, K. Octopamine and experience-dependent modulation of aggression in crickets. J. Neurosci. 25, 1431–1441 (2005).CAS 
Article 

Google Scholar 
20.Kostowski, W. & Tarchalska, B. The effects of some drugs affecting brain 5-HT on the aggressive behaviour and spontaneous electrical activity of the central nervous system of the ant Formica rufa. Brain Res. 38, 143–149 (1972).CAS 
Article 

Google Scholar 
21.Szczuka, A. et al. The effects of serotonin, dopamine, octopamine and tyramine on behavior of workers of the ant Formica polyctena during dyadic aggression tests. Acta Neurobiol. Exp. (Wars) 73, 495–520 (2013).
Google Scholar 
22.Way, M. J. Mutualism between ants and honeydew-producing homoptera. Annu. Rev. Entomol. 8, 307–344 (1963).Article 

Google Scholar 
23.Hafer-Hahmann, N. Behavior out of control: Experimental evolution of resistance to host manipulation. Ecol. Evol. 9, 7237–7245 (2019).Article 

Google Scholar 
24.Martinez, J., Fleury, F. & Varaldi, J. Heritable variation in an extended phenotype: The case of a parasitoid manipulated by a virus. J. Evol. Biol. 25, 54–65 (2012).Article 

Google Scholar 
25.Engelstädter, J. & Hurst, G. D. D. The ecology and evolution of microbes that manipulate host reproduction. Annu. Rev. Ecol. Evol. Syst. 40, 127–149 (2009).Article 

Google Scholar 
26.Rosenthal, G. G. & Servedio, M. R. Chase-away sexual selection: Resistance to ‘resistance’. Evolution (N.Y.) 53, 296 (1999).
Google Scholar 
27.Woodring, J., Wiedemann, R., Fischer, M. K., Hoffmann, K. H. & Völkl, W. Honeydew amino acids in relation to sugars and their role in the establishment of ant-attendance hierarchy in eight species of aphids feeding on tansy (Tanacetum vulgare). Physiol. Entomol. 29, 311–319 (2004).CAS 
Article 

Google Scholar 
28.Stadler, B. & Dixon, A. F. G. Ecology and evolution of aphid-ant interactions. Annu. Rev. Ecol. Evol. Syst. 36, 345–372 (2005).Article 

Google Scholar 
29.Tsuji, K. & Dobata, S. Social cancer and the biology of the clonal ant Pristomyrmex punctatus (Hymenoptera: Formicidae). Myrmecological News 15, 91–99 (2011).
Google Scholar 
30.Vellend, M. Conceptual synthesis in community ecology. Q. Rev. Biol. 85, 183–206 (2010).Article 

Google Scholar 
31.Agawa, H. & Kawata, M. The effect of color polymorphism on mortality in the aphid Macrosiphoniella yomogicola. Ecol. Res. 10, 301–306 (1995).Article 

Google Scholar 
32.Watanabe, S., Murakami, Y. & Hasegawa, E. Effects of attending ant species on the fate of colonies of an aphid, Macrosiphoniella yomogicola (Matsumura) (Homoptera: Aphididae), in an ant-aphid symbiosis. Entomol. News 128, 325 (2019).Article 

Google Scholar 
33.Wada-Katsumata, A., Yamaoka, R. & Aonuma, H. Social interactions influence dopamine and octopamine homeostasis in the brain of the ant Formica japonica. J. Exp. Biol. 214, 1707–1713 (2011).CAS 
Article 

Google Scholar 
34.Aonuma, H. & Watanabe, T. Changes in the content of brain biogenic amine associated with early colony establishment in the queen of the ant, formica japonica. PLoS One 7, (2012).35.Aonuma, H. Serotonergic control in initiating defensive responses to unexpected tactile stimuli in the trap-jaw ant Odontomachus kuroiwae. J. Exp. Biol. 223, jeb228874 (2020).Article 

Google Scholar 
36.R Core Team. R: A Language and Environment for Statistical Computing. (2020).37.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using {lme4}. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
38.Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S. (Springer, 2002).39.Hothorn, T. & Hornik, K. exactRankTests: Exact Distributions for Rank and Permutation Tests. (2019). More