Ecology

We make the first report of Microsporidia MB in An. gambiae s.s and An. coluzzii following identification of the symbiont in An. arabiensis. This does not only demonstrate the existence of the microsporidian in another predominant malaria vector species in Africa but also extends its incidence from East to West Africa. The prevalence of MB-positive mosquitoes was estimated to be 1.8%, which is within the rate of  More

1.Aryal, D. R., De Jong, B. H., Ochoa-Gaona, S., Esparza-Olguin, L. & Mendoza-Vega, J. Carbon stocks and changes in tropical secondary forests of southern Mexico. Agr. Ecosyst. Environ. 195, 220–230 (2014).Article 

Google Scholar 
2.Aryal, D. R., De Jong, B. H., Ochoa-Gaona, S., Mendoza-Vega, J. & Esparza-Olguin, L. Successional and seasonal variation in litterfall and associated nutrient transfer in semi-evergreen tropical forests of SE Mexico. Nutr. Cycl. Agroecosys. 103(1), 45–60 (2015).CAS 
Article 

Google Scholar 
3.Aryal, D. R. et al. Soil organic carbon depletion from forests to grasslands conversion in Mexico: A review. Trop. Agric. 8, 181 (2018).CAS 

Google Scholar 
4.Gao, W., Yang, J., Ren, S. R. & Liu, H. L. The trend of soil organic carbon, total nitrogen, and wheat and maize productivity under different long-term fertilizations in the upland fluvo-aquic soil of North China. Nutr. Cycl. Agroecosys. 103, 61–73 (2015).CAS 
Article 

Google Scholar 
5.Qi, H., Paz-Kagan, T., Karnieli, A., Jin, X. & Li, S. Evaluating calibration methods for predicting soil available nutrients using hyperspectral VNIR data. Soil Till Res. 175, 267–275 (2018).Article 

Google Scholar 
6.Dong, X., Tian, J., Zhang, R., He, D. & Chen, Q. Study on the relationship between soil emissivity spectra and content of soil element. Spectrosc. Spect. Anal. 37(02), 557–564 (2017).CAS 

Google Scholar 
7.Kemper, T. & Sommer, S. Estimate of heavy metal contamination in soils after a mining accident using reflectance spectroscopy. Environ. Sci. Technol. 36(12), 2742–2747 (2002).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
8.Panigrahi, N. & Das, B. S. Canopy spectral reflectance as a predictor of soil water potential in rice. Water Resour. Res. 54(4), 2544–2560 (2018).ADS 
Article 

Google Scholar 
9.Peddle, D. R., White, H. P., Soffer, R. J., Miller, J. R. & LeDrew, E. F. Reflectance processing of remote sensing spectroradiometer data. Comput. Geoences. 27(2), 203–213 (2001).ADS 

Google Scholar 
10.Ben-Dor, E. et al. Using imaging spectroscopy to study soil properties. Remote Sens. Environ. 113, S38–S55 (2009).Article 

Google Scholar 
11.Rossel, R. A., Walvoort, D. J., Mcbratney, A. B., Janik, L. J. & Skjemstad, J. O. Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma 131(1), 59–75 (2006).ADS 
Article 
CAS 

Google Scholar 
12.Cheng, H. et al. Estimating heavy metal concentrations in suburban soils with reflectance spectroscopy. Geoderma 336, 59–67 (2019).ADS 
CAS 
Article 

Google Scholar 
13.Ding, J., Yang, A., Wang, J., Sagan, V. & Yu, D. Machine-learning-based quantitative estimation of soil organic carbon content by VIS/NIR spectroscopy. PeerJ 6(3), e5714 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
14.Gobrecht, A., Bendoula, R., Roger, J.-M. & Bellon-Maurel, V. A new optical method coupling light polarization and vis–NIR spectroscopy to improve the measurement of soil carbon content. Soil Till Res. 155, 461–470 (2016).Article 

Google Scholar 
15.Gu, X., Wang, Y., Song, X. & Xu, X. The Inversion Model of Soil Organic Matter of Cultivated Land Based on Hyperspectral Technology. Remote Sensing for Agriculture, Ecosystems, and Hydrology XVII (International Society for Optics and Photonics, 2015).
Google Scholar 
16.Nawar, S., Buddenbaum, H., Hill, J., Kozak, J. & Mouazen, A. M. Estimating the soil clay content and organic matter by means of different calibration methods of vis-NIR diffuse reflectance spectroscopy. Soil Till Res. 155, 510–522 (2016).Article 

Google Scholar 
17.Yu, X., Liu, Q., Wang, Y., Liu, X. & Liu, X. Evaluation of MLSR and PLSR for estimating soil element contents using visible/near-infrared spectroscopy in apple orchards on the Jiaodong peninsula. CATENA 137, 340–349 (2016).CAS 
Article 

Google Scholar 
18.Ji, W. J., Li, X., Li, C. X., Zhou, Y. & Shi, Z. Using different data mining algorithes to predict soil organic matter based on visible-near infrared spectroscopy. Spectrosc. Spect. Anal. 32(09), 2393–2397 (2012).CAS 

Google Scholar 
19.Douglas, R. K., Nawar, S., Alamar, M. C., Mouazen, A. M. & Coulon, F. Rapid prediction of total petroleum hydrocarbons concentration in contaminated soil using vis-NIR spectroscopy and regression techniques. Sci. Total Environ. 616, 147–155 (2018).ADS 
PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
20.Mouazen, A. M. & Al-Asadi, R. A. Influence of soil moisture content on assessment of bulk density with combined frequency domain reflectometry and visible and near infrared spectroscopy under semi field conditions. Soil Till Res. 176, 95–103 (2018).Article 

Google Scholar 
21.Rossel, R. A. & Behrens, T. Using data mining to model and interpret soil diffuse reflectance spectra. Geoderma 158(1), 46–54 (2010).ADS 
CAS 
Article 

Google Scholar 
22.Nawar, S. & Mouazen, A. M. Comparison between random forests, artificial neural networks and gradient boosted machines methods of on-line vis-NIR spectroscopy measurements of soil total nitrogen and total carbon. Sensors. 17(10), 2428 (2017).ADS 
PubMed Central 
Article 
CAS 

Google Scholar 
23.Wang, J., Chen, Y., Chen, F., Shi, T. & Wu, G. Wavelet-based coupling of leaf and canopy reflectance spectra to improve the estimation accuracy of foliar nitrogen concentration. Agr. Forest Meteorol. 248, 306–315 (2018).ADS 
Article 

Google Scholar 
24.Hong, Y. et al. Combining fractional order derivative and spectral variable selection for organic matter estimation of homogeneous soil samples by vis–NIR spectroscopy. Remote Sens. 10(3), 479 (2018).ADS 
Article 

Google Scholar 
25.Sorenson, P. T. et al. Monitoring organic carbon, total nitrogen, and pH for reclaimed soils using field reflectance spectroscopy. Can J Soil Sci. 97(2), 241–248 (2017).CAS 
Article 

Google Scholar 
26.Gomez, C., Rossel, R. A. V. & Mcbratney, A. B. Soil organic carbon prediction by hyperspectral remote sensing and field vis-NIR spectroscopy: An Australian case study. Geoderma 146(3–4), 403–411 (2008).ADS 
CAS 
Article 

Google Scholar 
27.Shi, T. Z. et al. Comparison of multivariate methods for estimating soil total nitrogen with visible/near-infrared spectroscopy. Plant Soil. 366(1–2), 363–375 (2013).CAS 
Article 

Google Scholar 
28.Stenberg, B., Rossel, R. A. V., Mouazen, A. M. & Wetterlind, J. Chapter five-visible and near infrared spectroscopy in soil science. Adv. Agron. 107, 163–215 (2010).CAS 
Article 

Google Scholar 
29.Uddin, M. P., Mamun, M. A. & Hossain, M. A. PCA-based feature reduction for hyperspectral remote sensing image classification. IETE Tech. Rev. 5, 1–21 (2020).
Google Scholar 
30.Cambule, A. H., Rossiter, D. G., Stoorvogel, J. J. & Smaling, E. M. A. Building a near infrared spectral library for soil organic carbon estimation in the Limpopo National Park Mozambique. Geoderma 183, 41–48 (2012).ADS 
Article 
CAS 

Google Scholar 
31.Kawamura, K. et al. Vis-NIR spectroscopy and PLS regression with waveband selection for estimating the total C and N of paddy soils in Madagascar. Remote Sens. 9(10), 1081 (2017).ADS 
Article 

Google Scholar 
32.Leone, A. P., Viscarra-Rossel, R. A., Amenta, P. & Buondonno, A. Prediction of soil properties with PLSR and vis-NIR spectroscopy: Application to mediterranean soils from southern Italy. Curr. Anal. Chem. 8(2), 283–299 (2012).CAS 
Article 

Google Scholar 
33.Wang, S., Chen, Y., Wang, M., Zhao, Y. & Li, J. SPA-based methods for the quantitative estimation of the soil salt content in saline-alkali land from field spectroscopy data: A case study from the Yellow River irrigation regions. Remote Sens. 11(8), 967 (2019).ADS 
CAS 
Article 

Google Scholar 
34.Barnes, E. M. et al. Remote- and ground-based sensor techniques to map soil properties. Photogramm. Eng Rem S. 69(6), 619–630 (2003).Article 

Google Scholar 
35.Priori, S. et al. Field-scale mapping of soil carbon stock with limited sampling by coupling gamma-ray and vis-NIR spectroscopy. Soil Sci Soc Am J. 80(4), 954–964 (2016).ADS 
CAS 
Article 

Google Scholar 
36.Amin, I., Fikrat, F., Mammadov, E. & Babayev, M. Soil organic carbon prediction by vis-NIR spectroscopy: Case study the Kur-Aras plain Azerbaijan. Commun. Soil Sci. Plan. 51(6), 726–734 (2020).CAS 
Article 

Google Scholar 
37.Yu, L. et al. Hyperspectral estimation of soil organic matter content based on partial least squares regression. Trans. CSAE. 31(14), 103–109 (2015).
Google Scholar 
38.Liu, Y. F., Lu, Y. N., Guo, L., Xiao, F. T. & Chen, Y. Y. Construction of calibration set based on the land use types in visible and near-infrared (VIS-NIR)model for soil organic matter estimation. Acta Pedol. Sin. 53, 332–341 (2016).
Google Scholar 
39.Zhou, X. M. & Zhang, T. Analysis of the April 2019 atmospheric circulation and weather. Meteor. Mon. 45(7), 1028–1036 (2019).
Google Scholar 
40.Guan, L. & Zhang, T. Analysis of the May 2019 atmospheric circulation and weather. Meteor. Mon. 45(8), 1181–1188 (2019).
Google Scholar 
41.Li, X., He, Y. & Wu, C. Non-destructive discrimination of paddy seeds of different storage age based on Vis/NIR spectroscopy. J. Stored Prod. Res. 44(3), 264–268 (2008).Article 

Google Scholar 
42.Boško, M. & Bensa, A. Prediction of soil organic carbon using VIS-NIR spectroscopy: Application to Red Mediterranean soils from Croatia. Eurasian J. Soil Sci. 6(4), 365–373 (2017).
Google Scholar 
43.McCarty, G. W., Reeves, J. B. III., Reeves, V. B., Follett, R. F. & Kimble, J. M. Mid-infrared and near-infrared diffuse reflectance spectroscopy for soil carbon measurement. Soil Sci. Soc. Am. J. 66(2), 640–646 (2002).ADS 
CAS 
Article 

Google Scholar 
44.Gholizadeh, A. et al. Comparing different data preprocessing methods for monitoring soil heavy metals based on soil spectral features. Soil Water Res. 10(4), 218–227 (2015).CAS 
Article 

Google Scholar 
45.Wang, X., Xue, L., He, X. W. & Liu, M. H. Vitamin C content estimation of chilies using Vis/NIR spectroscopy. Int. Conf. Electr. Inf. Control Eng. 2011, 1894–1897 (2011).
Google Scholar 
46.Lee, K. S. et al. Wavelength identification and diffuse reflectance estimation for surface and profile soil properties. Am. Soc. Agric. Biol. Eng. 52(3), 683–695 (2009).CAS 

Google Scholar  More

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

Download PDF

Here at the Natural History Museum at Tring, UK, I’m in our nest collection, which numbers just over 4,000. Behind me are 67 metal cabinets with nests arranged in taxonomic order. Each nest is labelled with the date and place of collection, and the collector’s name. Next to me is a 1928 mud nest from Argentina that was made by the rufous hornero (Furnarius rufus), known for its large, globular nests that shield eggs and young from predators.I’m the senior curator of birds’ eggs and nests. I ensure that specimens are stored appropriately to prevent damage and are well catalogued, so we know exactly what we have and where. Our nest and egg collections are the most comprehensive archive of information on bird breeding in the world. When I came here about 20 years ago, the nest collection was rarely used and we didn’t know how many examples of extinct and endangered species we had. I’ve spent a lot of time and effort cataloguing and understanding these particular 129 nests, 40 of which belong to extinct birds such as the Laysan crake (Zapornia palmeri) and the Aldabra brush warbler (Nesillas aldabrana).We have up to 300,000 sets of eggs. I am holding four dunlin (Calidris alpina) eggshells, collected in 1952 in Ireland. They were donated to the Wildfowl & Wetlands Trust, a UK conservation charity, which gave them to us as part of a larger collection.I have been interested in birds and natural history since childhood, and my mother used to take me to the Royal Museum of Scotland (now the National Museum of Scotland) in Edinburgh. After graduating in biological sciences from Edinburgh Napier University, I volunteered at the museum before getting my first paid museum job.When researchers want to access the collections, I check that we have specimens relevant to their research, discuss exactly what they intend to do and work with them to minimize the risk of damage. Although I want our collections to result in robust science, they must be preserved.

Nature 597, 586 (2021)
doi: https://doi.org/10.1038/d41586-021-02529-z

Related Articles

The bird librarian

Sharing space with 34 million dead insects

To look after these birds is to ‘fall in love’ with them

Subjects

Careers

Animal behaviour

Ecology

Latest on:

Careers

Cash boost looms for historically Black US colleges and universities
Career News 20 SEP 21

Australian funder backflips on controversial preprint ban
News 20 SEP 21

Stop undervaluing smaller institutions
Career Column 17 SEP 21

Animal behaviour

No hands, no problem: clever parrots craft and wield tools
Research Highlight 03 SEP 21

Australia’s cane toads evolved as cannibals with frightening speed
News 25 AUG 21

Clever orangutans invent nutcrackers from scratch
Research Highlight 18 AUG 21

Ecology

A spotlight on seafood for global human nutrition
News & Views 15 SEP 21

Widespread phytoplankton blooms triggered by 2019–2020 Australian wildfires
Article 15 SEP 21

Preventing spillover as a key strategy against pandemics
Correspondence 14 SEP 21

Jobs

Senior Laboratory Research Scientist

Francis Crick Institute
London, United Kingdom

Bioinformatics Officer

Francis Crick Institute
London, United Kingdom

Laboratory Research Scientist – Human Embryo and Stem Cell Unit

Francis Crick Institute
London, United Kingdom

Postdoc Position on Nitrous Oxide Turnover in the Ocean

University of Southern Denmark (SDU)
Odense M, Denmark More