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    Retrieving zinc concentrations in topsoil with reflectance spectroscopy at Opencast Coal Mine sites

    1.Jiang, X., Lu, W. X., Zhao, H. Q., Yang, Q. C. & Yang, Z. P. Potential ecological risk assessment and prediction of soil heavy-metals pollution around coal gangue dump. Nat. Hazard. Earth Syst. 2, 1977–2010 (2014).
    Google Scholar 
    2.Wang, Q. & Li, R. Decline in China’s coal consumption: An evidence of peak coal or a temporary blip?. Energ. Policy 108, 696–701 (2017).Article 

    Google Scholar 
    3.Li, W. et al. Addressing the Co2 emissions of the world’s largest coal producer and consumer: Lessons from the Haishiwan coalfield, China. Energy 80, 400–413 (2015).Article 

    Google Scholar 
    4.Luo, P. et al. Water quality trend assessment in Jakarta: A rapidly growing Asian megacity. Plos One 14, e219009 (2019).
    Google Scholar 
    5.Luo, P. et al. Flood inundation assessment for the Hanoi Central Area, Vietnam under historical and extreme rainfall conditions. Sci. Rep. Uk. 8, 12623 (2018).ADS 
    Article 
    CAS 

    Google Scholar 
    6.Guo, B. et al. Determining the effects of socioeconomic and environmental determinants on chronic obstructive pulmonary disease (Copd) mortality using geographically and temporally weighted regression model across Xi’an During 2014–2016. Sci. Total Environ. 756, 143869 (2021).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    7.Pei, L., Wang, X., Guo, B., Guo, H. & Yu, Y. Do air pollutants as well as meteorological factors impact corona virus disease 2019 (Covid-19)? Evidence from China based on the geographical perspective. Environ. Sci. Pollut. R. 28, 35584–35596 (2021).CAS 
    Article 

    Google Scholar 
    8.Chen, T., Chang, Q., Liu, J., Clevers, J. G. P. W. & Kooistra, L. Identification of soil heavy metals sources and improvement in spatial mapping based on soil spectral information: A Case Study in Northwest China. Sci. Total Environ. 565, 155–164 (2016).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    9.Li, Z., Ma, Z., Kuijp, T. J. V. D., Yuan, Z. & Huang, L. A review of soil heavy metals pollution from mines in China: Pollution and health risk assessment. Sci. Total Environ. 468, 843–853 (2014).ADS 
    PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    10.Wang, L. et al. A comprehensive mitigation strategy for heavy metals contamination of farmland around mining areas—screening of low accumulated cultivars, soil remediation and risk assessment. Environ. Pollut. 245, 820–828 (2019).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    11.Siddiqui, A. U., Jain, M. K. & Masto, R. E. Pollution evaluation, spatial distribution, and source apportionment of trace metals around coal mines soil: The Case Study of Eastern India. Environ. Sci. Pollut. R. 27, 10822–10834 (2020).Article 
    CAS 

    Google Scholar 
    12.Guo, D., Bai, Z., Shangguan, T., Shao, H. & Qiu, W. Impacts of coal mining on the aboveground vegetation and soil quality: A case study of Qinxin Coal Mine in Shanxi Province, China. Clean Soil Air Water. 39, 219–225 (2011).CAS 
    Article 

    Google Scholar 
    13.Woodworth, M. D. Frontier Boomtown Urbanism in Ordos, Inner Mongolia Autonomous Region. Cross Curr. East Asian Hist. Cult. Rev. 1, 74–101 (2012).
    Google Scholar 
    14.Zeng, X., Liu, Z., He, C., Ma, Q. & Wu, J. Quantifying Surface coal-mining patterns to promote regional sustainability in Ordos, Inner Mongolia. Sustain. Basel. 10, 1135 (2018).Article 

    Google Scholar 
    15.Bu, Q. et al. Concentrations, spatial distributions, and sources of heavy metals in surface soils of the Coal Mining City Wuhai, China. J. Chem. Ny. 2020, 1–10 (2020).Article 
    CAS 

    Google Scholar 
    16.Hou, L., Li, X. & Li, F. Hyperspectral-based inversion of heavy metals content in the soil of coal mining areas. J. Environ. Qual. 48, 57–63 (2019).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    17.Liu, X., Bai, Z., Zhou, W., Cao, Y. & Zhang, G. Changes in soil properties in the soil profile after mining and reclamation in an opencast coal mine on the loess plateau, China. Ecol. Eng. 98, 228–239 (2017).Article 

    Google Scholar 
    18.Liu, X., Shi, H., Bai, Z., Zhou, W. & He, Y. Heavy metals concentrations of soils near the large opencast coal mine pits in China. Chemosphere. 244, 125360 (2019).ADS 
    PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    19.Gabriel, et al. Amending potential of organic and industrial by-products applied to heavy metals-rich mining soils. Ecotox. Environ. Safe. 162, 581–590 (2018).Article 
    CAS 

    Google Scholar 
    20.Zhai, X. et al. Remediation of multiple heavy metals-contaminated soil through the combination of soil washing and in situ immobilization. Sci. Total Environ. 635, 92–99 (2018).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    21.Wang, F., Gao, J. & Zha, Y. Hyperspectral sensing of heavy metals in soil and vegetation: Feasibility and challenges. ISPRS J. Photogramm. 136, 73–84 (2018).Article 

    Google Scholar 
    22.Shi, T., Chen, Y., Liu, Y. & Wu, G. Visible and near-infrared reflectance spectroscopy—an alternative for monitoring soil contamination by heavy metals. J. Hazard. Mater. 265, 166–176 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    23.Zou, B., Jiang, X., Feng, H., Tu, Y. & Tao, C. Multisource spectral-integrated estimation of cadmium concentrations in soil using a direct standardization and spiking algorithm. Sci. Total Environ. 701, 134890 (2020).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    24.Guan, Q. et al. Source apportionment of heavy metals in agricultural soil based on Pmf: A case study in Hexi Corridor, Northwest China. Chemosphere 193, 189–197 (2017).ADS 
    PubMed 
    Article 
    CAS 

    Google Scholar 
    25.Horta, A. et al. Potential of integrated field spectroscopy and spatial analysis for enhanced assessment of soil contamination: A prospective review. Geoderma 241, 180–209 (2015).ADS 
    Article 
    CAS 

    Google Scholar 
    26.Saqib, et al. Efficiency and surface characterization of different plant derived biochar for cadmium (Cd) mobility, bioaccessibility and bioavailability to Chinese cabbage in highly contaminated soil. Chemosphere 211, 632–639 (2018).Article 
    CAS 

    Google Scholar 
    27.Wei, L. et al. An improved gradient boosting regression tree estimation model for soil heavy metals (arsenic) pollution monitoring using hyperspectral remote sensing. Appl. Sci. Basel. 9, 1943 (2019).CAS 
    Article 

    Google Scholar 
    28.Ngole-Jeme, V. M. Heavy metals in soils along unpaved roads in south west Cameroon: Contamination levels and health risks. Ambio 3, 374–386 (2016).Article 
    CAS 

    Google Scholar 
    29.Huang, Y. et al. Heavy metals pollution and health risk assessment of agricultural soils in a typical Peri-Urban area in Southeast China. J. Environ. Manage. 207, 159–168 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    30.Bruce, P. et al. Low-level lead exposure and mortality in Us adults: A population-based cohort study. Lancet Public Health. 3, 177–184 (2018).Article 

    Google Scholar 
    31.Harari, F. et al. Blood lead levels and decreased kidney function in a population-based cohort. Am. J. Kidney Dis. 72, 381–389 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    32.Sun, W., Zhang, X., Sun, X., Sun, Y. & Cen, Y. Predicting Nickel concentration in soil using reflectance spectroscopy associated with organic matter and clay minerals. Geoderma 327, 25–35 (2018).ADS 
    CAS 
    Article 

    Google Scholar 
    33.Guan, Q. et al. Prediction of heavy metals in soils of an arid area based on multi-spectral data. J. Environ. Manag. 243, 137–143 (2019).CAS 
    Article 

    Google Scholar 
    34.Lin, X. et al. Geographically weighted regression effects on soil zinc content hyperspectral modeling by applying the fractional-order differential. Remote Sens. Basel. 11, 636 (2019).ADS 
    Article 

    Google Scholar 
    35.Leenaers, H., Okx, J. P. & Burrough, P. A. Employing elevation data for efficient mapping of soil pollution on floodplains. Soil Use Manag. 6, 105–114 (2010).Article 

    Google Scholar 
    36.De Jesus, A., Zmozinski, A. V., Damin, I. C. F., Silva, M. M. & Vale, M. G. R. Determination of arsenic and cadmium in crude oil by direct sampling graphite furnace atomic absorption spectrometry. Spectrochim. Acta B 71, 86–91 (2012).ADS 
    Article 
    CAS 

    Google Scholar 
    37.Zhang, X., Sun, W., Cen, Y., Zhang, L. & Wang, N. Predicting cadmium concentration in soils using laboratory and field reflectance spectroscopy. Sci. Total Environ. 650, 321–334 (2019).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    38.Harun, C., Mursit, T. M. & Esen, C. Simultaneous preconcentration and determination of Ni and Pb in water samples by solid-phase extraction and flame atomic absorption spectrometry. J. Aoac Int. 96, 875–879 (2013).Article 
    CAS 

    Google Scholar 
    39.Gholizadeh, A., Saberioon, M., Ben-Dor, E. & Borůvka, L. Monitoring of selected soil contaminants using proximal and remote sensing techniques: background, state-of-the-art and future perspectives. Crit. Rev. Env. Sci. Technol. 48, 243–278 (2018).CAS 
    Article 

    Google Scholar 
    40.Wei, L., Yuan, Z., Yu, M., Huang, C. & Cao, L. Estimation of arsenic content in soil based on laboratory and field reflectance spectroscopy. Sensors-Basel. 19, 3904 (2019).ADS 
    CAS 
    PubMed Central 
    Article 
    PubMed 

    Google Scholar 
    41.Chen, T., Chang, Q., Clevers, J. G. P. W. & Kooistra, L. Rapid identification of soil cadmium pollution risk at regional scale based on visible and near-infrared spectroscopy. Environ. Pollut. 206, 217–226 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    42.Liu, G. et al. Partitioning and geochemical fractions of heavy metals from geogenic and anthropogenic sources in various soil particle size fractions. Geoderma 312, 104–113 (2018).ADS 
    CAS 
    Article 

    Google Scholar 
    43.Meng, X. et al. Regional soil organic carbon prediction model based on a discrete wavelet analysis of hyperspectral satellite data. Int. J. Appl. Earth Obs. 89, 102111 (2020).Article 

    Google Scholar 
    44.Hong, Y. et al. Exploring the potential of airborne hyperspectral image for estimating topsoil organic carbon: Effects of fractional-order derivative and optimal band combination algorithm. Geoderma. 365, 114228 (2020).ADS 
    CAS 
    Article 

    Google Scholar 
    45.Hong, Y. et al. Estimating lead and zinc concentrations in Peri-Urban agricultural soils through reflectance spectroscopy: Effects of fractional-order derivative and random forest. Sci. Total Environ. 651, 1969–1982 (2019).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    46.Wang, J. et al. Prediction of low heavy metals concentrations in agricultural soils using visible and near-infrared reflectance spectroscopy. Geoderma 216, 1–9 (2014).ADS 
    Article 
    CAS 

    Google Scholar 
    47.Jiang, Q., Liu, M., Wang, J. & Liu, F. Feasibility of using visible and near-infrared reflectance spectroscopy to monitor heavy metals contaminants in Urban lake sediment. CATENA 162, 72–79 (2018).CAS 
    Article 

    Google Scholar 
    48.Khosravi, V., Doulati Ardejani, F., Yousefi, S. & Aryafar, A. Monitoring soil lead and zinc contents via combination of spectroscopy with extreme learning machine and other data mining methods. Geoderma 318, 29–41 (2018).ADS 
    CAS 
    Article 

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

    Google Scholar 
    50.Zhang, S. et al. Hyperspectral inversion of heavy metals content in reclaimed soil from a mining wasteland based on different spectral transformation and modeling methods. Spectrochim. Acta A 211, 393–400 (2019).ADS 
    CAS 
    Article 

    Google Scholar 
    51.Gholizadeh, A., Saberioon, M., Carmon, N., Boruvka, L. & Ben-Dor, E. Examining the performance of Paracuda-Ii data-mining engine versus selected techniques to model soil carbon from reflectance spectra. Remote Sens.-Basel. 10, 1172 (2018).ADS 
    Article 

    Google Scholar 
    52.Tian, S. et al. Hyperspectral prediction model of metals content in soil based on the genetic ant colony algorithm. Sustainability-Basel. 11, 3197 (2019).CAS 
    Article 

    Google Scholar 
    53.Xu, S., Zhao, Y., Wang, M. & Shi, X. Comparison of multivariate methods for estimating selected soil properties from intact soil cores of paddy fields by Vis–Nir spectroscopy. Geoderma 310, 29–43 (2018).ADS 
    CAS 
    Article 

    Google Scholar 
    54.Tao, C. et al. A transferable spectroscopic diagnosis model for predicting arsenic contamination in soil. Sci. Total Environ. 669, 964–972 (2019).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    55.Lu, Q. et al. Rapid inversion of heavy metals concentration in Karst grain producing areas based on hyperspectral bands associated with soil components. Microchem. J. 148, 404–411 (2019).CAS 
    Article 

    Google Scholar 
    56.Tan, K. et al. Estimation of the spatial distribution of heavy metals in agricultural soils using airborne hyperspectral imaging and random forest. J. Hazard. Mater. 382, 120987 (2020).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    57.Chen, S. et al. Fine resolution map of top- and subsoil carbon sequestration potential in France. Sci. Total Environ. 630, 389–400 (2018).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    58.Tan, K. et al. Estimating the distribution trend of soil heavy metals in mining area from hymap airborne hyperspectral imagery based on ensemble learning. J. Hazard. Mater. 401, 123288 (2021).CAS 
    PubMed 
    Article 

    Google Scholar 
    59.Mao, X., Meng, J. & Xiang, Y. Cellular automata-based model for developing land use ecological security patterns in semi-arid areas: A case study of Ordos, Inner Mongolia, China. Environ. Earth Sci. 70, 269–279 (2013).Article 

    Google Scholar 
    60.Ramirez-Lopez, L. et al. Sampling optimal calibration sets in soil infrared spectroscopy. Geoderma 226, 140–150 (2014).ADS 
    Article 
    CAS 

    Google Scholar 
    61.Liu, W., Zhao, J., Ouyang, Z., Söderlund, L. & Liu, G. Impacts of sewage irrigation on heavy metals distribution and contamination in Beijing, China. Environ. Int. 31, 805–812 (2005).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    62.Keshavarzi, A. & Kumar, V. Ecological risk assessment and source apportionment of heavy metals contamination in agricultural soils of Northeastern Iran. Int. J. Environ. Heal. R. 29, 544–560 (2018).Article 
    CAS 

    Google Scholar 
    63.Salminen, R. et al. Geochemical mapping field manual, Espoo, Finland Geological Survey of Finland. Geol. Surv. Den. Greenl. 38, 1–20 (1998).
    Google Scholar 
    64.Sun, W., Skidmore, A. K., Wang, T. & Zhang, X. Heavy metals pollution at mine sites estimated from reflectance spectroscopy following correction for skewed data. Environ. Pollut. 252, 1117–1124 (2019).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    65.Guo, B. et al. Ecological risk evaluation and source apportionment of heavy metals in park playgrounds: A case study in Xi’an, Shaanxi Province, a Northwest City of China. Environ. Sci. Pollut. R. 27, 24400–24412 (2020).CAS 
    Article 

    Google Scholar 
    66.Guo, B. et al. Contamination, Distribution and health riskassessment of risk elements in topsoil foramusement parks in Xi’an, China. Pol. J. Environ. Stud. 30, 601–617 (2021).CAS 
    Article 

    Google Scholar 
    67.Hong, Y. et al. Comparing laboratory and airborne hyperspectral data for the estimation and mapping of topsoil organic carbon: Feature selection coupled with random forest. Soil Tillage Res. 199, 104589 (2020).Article 

    Google Scholar 
    68.Kursa, M. B. & Rudnicki, W. R. Feature selection with the Boruta package. J. Stat. Softw. 36, 1–13 (2010).Article 

    Google Scholar 
    69.Rudnicki, W. R., Wrzesien, M. & Paja, W. All relevant feature selection methods and applications. Comput. Intell.Us. 584, 11–28 (2015).MathSciNet 

    Google Scholar 
    70.Liu, Z. et al. Estimation of soil heavy metals content using hyperspectral data. Remote Sens. Basel. 11, 1464 (2019).ADS 
    Article 

    Google Scholar 
    71.Bellon-Maurel, V., Fernandez-Ahumada, E., Palagos, B. & Roger, J. Critical review of chemometric indicators commonly used for assessing the quality of the prediction of soil attributes by Nir spectroscopy. Trac. Trend. Anal. Chem. 29, 1073–1081 (2010).CAS 
    Article 

    Google Scholar 
    72.Wold, S., Martens, H. & Wold, H. The multivariate calibration problem in chemistry solved by the PLS method. Lect. Notes Math. 973, 286–293 (1983).MATH 
    Article 

    Google Scholar 
    73.Shi, T., Wang, J., Chen, Y. & Wu, G. Improving the prediction of arsenic contents in agricultural soils by combining the reflectance spectroscopy of soils and rice plants. Int. J. Appl. Earth Obs. 52, 95–103 (2016).Article 

    Google Scholar 
    74.Dotto, A. C., Dalmolin, R. S. D., Caten, A. T. & Grunwald, S. A systematic study on the application of scatter-corrective and spectral-derivative preprocessing for multivariate prediction of soil organic carbon by Vis–Nir spectra. Geoderma 314, 262–274 (2018).ADS 
    CAS 
    Article 

    Google Scholar 
    75.Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).MATH 
    Article 

    Google Scholar 
    76.Douglas, R. K. et al. Evaluation of Vis–Nir reflectance spectroscopy sensitivity to weathering for enhanced assessment of oil contaminated soils. Sci. Total Environ. 626, 1108–1120 (2018).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    77.Guo, B. et al. Estimating socio-economic parameters via machine learning methods using Luojia1-01 Nighttime Light remotely sensed images at multiple scales of China in 2018. IEEE Access. 9, 34352–34365 (2021).Article 

    Google Scholar 
    78.Tan, K., Ma, W., Wu, F. & Du, Q. Random forest-based estimation of heavy metals concentration in agricultural soils with hyperspectral sensor data. Environ. Monit. Assess. 191, 446 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    79.Guo, B. et al. Estimating Pm2.5 concentrations via random forest method using satellite, auxiliary, and ground-level station dataset at multiple temporal scales across China in 2017. Sci. Total Environ. 778, 146288 (2021).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    80.Ou, D. et al. Semi-supervised Dnn regression on airborne hyperspectral imagery for improved spatial soil properties prediction. Geoderma. 385, 114875 (2021).ADS 
    CAS 
    Article 

    Google Scholar 
    81.Gholizadeh, A., Žižala, D., Saberioon, M. & Borůvka, L. Soil organic carbon and texture retrieving and mapping using proximal, airborne and sentinel-2 spectral imaging. Remote Sens. Environ. 218, 89–103 (2018).ADS 
    Article 

    Google Scholar 
    82.Guo, B. et al. Identifying the spatiotemporal dynamic of Pm 2.5 concentrations at multiple scales using geographically and temporally weighted regression model across China during 2015–2018. Sci. Total Environ. 751, 141765 (2021).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    83.Guo, B. et al. Detecting spatiotemporal dynamic of regional electric consumption using Npp–Viirs Nighttime stable light data—a Case Study of Xi’an, China. IEEE Access 8, 171694–171702 (2020).Article 

    Google Scholar 
    84.Guo, B. et al. A land use regression application into simulating spatial distribution characteristics of particulate matter (Pm2.5) concentration in city of Xi’an, China. Pol. J. Environ. Stud. 29, 4065–4076 (2020).Article 

    Google Scholar 
    85.Malley, D. F. & Williams, P. C. Use of near-infrared reflectance spectroscopy in prediction of heavy metals in freshwater sediment by their association with organic matter. Environ. Sci. Technol. 31, 3461–3467 (1997).ADS 
    CAS 
    Article 

    Google Scholar 
    86.Pyo, J., Hong, S. M., Kwon, Y. S., Kim, M. S. & Cho, K. H. Estimation of heavy metals using deep neural network with visible and infrared spectroscopy of soil. Sci. Total Environ. 741, 140162 (2020).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    87.Sun, W. & Zhang, X. Estimating soil zinc concentrations using reflectance spectroscopy. Int. J. Appl. Earth Obs. 58, 126–133 (2017).Article 

    Google Scholar 
    88.Chao, T. et al. A transferable spectroscopic diagnosis model for predicting arsenic contamination in soil. Sci. Total Environ. 669, 964–972 (2019).ADS 
    Article 
    CAS 

    Google Scholar 
    89.Rossel, R. A. V., Walvoort, D. J. 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, 59–75 (2005).Article 
    CAS 

    Google Scholar 
    90.Rossel, R. A. V. et al. A global spectral library to characterize the world’s soil. Earth Sci. Rev. 155, 198–230 (2016).ADS 
    Article 

    Google Scholar 
    91.Chakraborty, S. et al. Development of a hybrid proximal sensing method for rapid identification of petroleum contaminated soils. Sci. Total Environ. 514, 399–408 (2015).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    92.Boker, A., Brownell, L. & Donen, N. The Amsterdam preoperative anxiety and information scale provides a simple and reliable measure of preoperative anxiety. Can. J. Anesth. 49, 792–798 (2002).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    93.Piñeiro, G., Perelman, S., Guerschman, J. P. & Paruelo, J. M. How to evaluate models: Observed vs. predicted or predicted vs. observed?. Ecol. Model. 216, 316–322 (2008).Article 

    Google Scholar 
    94.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 
    95.Ji, W., Rossel, R. A. V. & Shi, Z. Accounting for the effects of water and the environment on proximally sensed Vis–Nir soil spectra and their calibrations. Eur. J. Soil Sci. 66, 555–565 (2015).Article 

    Google Scholar 
    96.Altunkaynak, A. & Ozger, M. Comparison of discrete and continuous wavelet—Multilayer perceptron methods for daily precipitation prediction. J. Hydrol. Eng. 21, 04016014 (2016).Article 

    Google Scholar 
    97.Buddenbaum, H., Steffens, M. & Rossel, R. V. The effects of spectral pretreatments on chemometric analyses of soil profiles using laboratory imaging spectroscopy. Appl. Environ. Soil Sci. 2012, 1–12 (2012).Article 
    CAS 

    Google Scholar 
    98.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 
    99.Kuang, B. & Mouazen, A. M. Calibration of visible and near infrared spectroscopy for soil analysis at the field scale on three European farms. Eur. J. Soil Sci. 62, 629–636 (2011).CAS 
    Article 

    Google Scholar 
    100.Sipos, P., Németh, T., Kis, V. K. & Mohai, I. Association of individual soil mineral constituents and heavy metals as studied by sorption experiments and analytical electron microscopy analyses. J. Hazard. Mater. 168, 1512–1520 (2009).CAS 
    PubMed 
    Article 

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

    Google Scholar  More

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    Population viability analysis of the endangered Dupont’s Lark Chersophilus duponti in Spain

    1.Powers, R. P. & Jetz, W. Global habitat loss and extinction risk of terrestrial vertebrates under future land-use-change scenarios. Nat. Clim. Chang. 9, 323–329 (2019).Article 
    ADS 

    Google Scholar 
    2.Pardini, R., Nichols, E. & Püttker, T. Biodiversity response to habitat loss and fragmentation. Encycl. Anthropocene https://doi.org/10.1016/B978-0-12-809665-9.09824-4 (2017).Article 

    Google Scholar 
    3.Fahrig, L. et al. Functional landscape heterogeneity and animal biodiversity in agricultural landscapes. Ecol. Lett. 14(2), 101–112. https://doi.org/10.1111/j.1461-0248.2010.01559.x (2011).Article 
    PubMed 

    Google Scholar 
    4.Moilanen, A. & Hanski, I. Metapopulation dynamics: Effects of hábitat quality and landscape structure. Ecology 79, 2503–2515 (1998).Article 

    Google Scholar 
    5.Fahrig, L. Effects of habitat fragmentation on biodiversity. Annu. Rev. Ecol. Evol. Syst. 34(1), 487–515 (2003).Article 

    Google Scholar 
    6.Cornelius, C., Awade, M., Candia-Gallardo, C., Sieving, K. E. & Metzger, J. P. Habitat fragmentation drives inter-population variation in dispersal behavior in a Neotropical rainforest bird. Perspect. Ecol. Conserv. 15, 3–9 (2017).
    Google Scholar 
    7.Xu, Y. et al. Loss of functional connectivity in migration networks induces population decline in migratory birds. Ecol. Appl. 29(7), e01960. https://doi.org/10.1002/eap.1960 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    8.Hens, H., Pakanen, V., Jäkäläniemi, A., Tuomi, J. T. & Kvist, L. Low population viability in small endangered orchid populations: Genetic variation, seedling recruitment and stochasticity. Biol. Cons. 210, 174–183 (2017).Article 

    Google Scholar 
    9.Silva, J. P. et al. EU protected area network did not prevent a country wide population decline in a threatened grassland bird. PeerJ 6, e4284 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    10.Gaget, E., Fay, R., Audiron, S., Villers, A. & Bretagnolle, V. Long-term decline despite conservation efforts questions Eurasian Stone-curlew population viability in intensive farmlands. Ibis 161, 359–371 (2019).Article 

    Google Scholar 
    11.van Oosten, H. H. et al. Hatching failure and accumulation of organic pollutants through the terrestrial food web of a declining songbird in Western Europe. Sci. Total Environ. 650, 1547–1553 (2019).PubMed 
    Article 
    ADS 
    CAS 
    PubMed Central 

    Google Scholar 
    12.Brambilla, M. et al. Sixty years of habitat decline: impact of land-cover changes in northern Italy on the decreasing ortolan bunting Emberiza hortulana. Reg. Environ. Change 17, 323–333 (2017).Article 

    Google Scholar 
    13.Heldbjerg, H., Sunde, P. & Fox, A. D. Continuous Population Declines for Specialist Farmland Birds 1987–2014 in Denmark Indicates No Halt in Biodiversity Loss in Agricultural Habitats 278–292 (Bird Conservation International, 2018).
    Google Scholar 
    14.Traba, J. & Morales, M. B. The decline of farmland birds in Spain is strongly associated with the loss of fallowland. Sci. Rep. 9, 9473 (2019).PubMed 
    PubMed Central 
    Article 
    ADS 
    CAS 

    Google Scholar 
    15.Reif, J. & Vermouzek, Z. Collapse of farmland bird populations in an Eastern European country following its EU accession. Conserv. Lett. https://doi.org/10.1111/conl.12585 (2018).Article 

    Google Scholar 
    16.Levins, R. Extinction. In: Some mathematical problems in biology. Mathematical Society of America, Providence, R.I. Pages 77–107 (1970).17.Hanski, I. Metapopulation ecology (Oxford University Press, 1999).
    Google Scholar 
    18.Johnson, M. D. Measuring habitat quality: A review. Condor 109, 489–504 (2007).Article 

    Google Scholar 
    19.Vögeli, M., Serrano, D., Pacios, F. & Tella, J. L. The relative importance of patch habitat quality and landscape attributes on a declining steppe-bird metapopulation. Biol. Cons. 143, 1057–1067 (2010).Article 

    Google Scholar 
    20.Traba, J., Sastre, P. & Morales, M. B. Factors determining species richness and composition of steppe bird communities in peninsular Spain: grass-steppe vs. shrub-steppe bird species. In Steppe Ecosystems (eds Morales, M. B. & Traba, J.) (Nova Science, 2013).
    Google Scholar 
    21.Burfield, I. J. The conservation status of steppic birds in Europe. In Ecology and Conservation of Steppe-Land Birds (eds Bota, G. et al.) 69–102 (Lynx Edicions, 2005).
    Google Scholar 
    22.Donald, P. F., Sanderson, F. J., Burfield, I. J. & van Bommel, F. P. J. Further evidence of continent-wide impacts of agricultural intensification on European farmland birds, 1990–2000. Agr. Ecosyst. Environ. 116(3–4), 189–196 (2006).Article 

    Google Scholar 
    23.Burfield, I. & van Bommel, F. Birds in Europe: Population Estimates, Trends and Conservation Status (Birdlife International, 2004).
    Google Scholar 
    24.Benton, T. G., Vickery, J. A. & Wilson, J. D. Farmland biodiversity: is habitat heterogeneity the key?. Trends Ecol. Evol. 18(4), 182–188 (2003).Article 

    Google Scholar 
    25.Santos, T. & Suárez, F. Biogeography and population trends of iberian steppe bird. In Ecology and Conservation of Steppe-Land Birds (eds Bota, G. et al.) 69–102 (Lynx Edicions, 2005).
    Google Scholar 
    26.Gómez-Catasús, J., Garza, V. & Traba, J. Wind farms affect the occurrence, abundance and population trends of small passerine birds: The case of the Dupont’s Lark. J. Appl. Ecol. 55, 2033–2042 (2018).Article 

    Google Scholar 
    27.Donald, P. F., Green, R. & Heath, M. F. Agricultural intensification and the collapse of Europe’s farmland bird populations. Proc. R. Soc. Ser. B. 155, 39–43 (2001).
    Google Scholar 
    28.Brotons, L., Mañosa, S. & Estrada, J. Modelling the effects of irrigation schemes on the distribution of steppe birds in Mediterranean farmland. Biodivers. Conserv. 13, 1039–1058 (2004).Article 

    Google Scholar 
    29.Madroño, A. et al. (eds) Libro rojo de las aves de España (SEO/BirdLife y Dirección General para la Biodiversidad, 2004).
    Google Scholar 
    30.Concepción, E. D. & Díaz, M. Medidas agroambientales y conservación de la biodiversidad: Limitaciones y perspectivas de futuro. Ecosistemas 22(1), 44–49. https://doi.org/10.7818/ECOS.2013.22-1.08 (2013).Article 

    Google Scholar 
    31.Traba, J. Intensificación agrícola y efectos sobre las aves. Revista de la Sociedad Cordobesa de Historia Natural 3, 39–50 (2020).
    Google Scholar 
    32.Prévosto, B. et al. Impacts of land abandonment on vegetation: Successional pathways in European habitats. Folia Geobot 46, 303–325. https://doi.org/10.1007/s12224-010-9096-z (2011).Article 

    Google Scholar 
    33.García-Tejero, S., Taboada, A., Tárrega, R. & Salgado, J. M. Land use changes and ground dwelling beetle conservation in extensive grazing dehesa systems of north-west Spain. Biol. Cons. 161, 58–66 (2013).Article 

    Google Scholar 
    34.Dennis, P. et al. The effects of livestock grazing on foliar arthropods associated with bird diet in upland grasslands of Scotland. J. Appl. Ecol. 45(1), 279–287 (2008).Article 

    Google Scholar 
    35.BirdLife International. European Red List of Birds (Office for Official Publications of the European Communities, 2015).
    Google Scholar 
    36.Gómez-Catasús, J. et al. European population trends and current conservation status of an endangered steppe-bird species: the Dupont’s Lark Chersophilus duponti. PeerJ 6, e5627. https://doi.org/10.7717/peerj.5627 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    37.de Juana, E. & Suárez, F. Dupont’s Lark (Chersophilus duponti), version 1.0. In Birds of the World (eds del Hoyo, J. et al.) (Cornell Lab of Ornithology, 2020).
    Google Scholar 
    38.García, J. T. et al. Genetic and phenotypic variation among geographically isolated populations of the globally threatened Dupont’s Lark Chersophilus duponti. Mol. Phylogenet. Evol. 46(1), 237–251 (2008).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    39.Méndez, M., Tella, J. L. & Godoy, J. A. Restricted gene flow and genetic drift in recently fragmented populations of an endangered steppe bird. Biol. Cons. 144, 2615–2622 (2011).Article 

    Google Scholar 
    40.Méndez, M., Vögeli, M., Tella, J. L. & Godoy, J. A. Joint effects of population size and isolation on genetic erosion in fragmented populations: Finding fragmentation thresholds for management. Evol. Appl. 7, 506–518 (2014).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    41.Garza, V. & Suárez, F. Distribución, población y selección de hábitat de la Alondra de Dupont (Chersophilus duponti) en la Península Ibérica. Ardeola 37, 3–12 (1990).
    Google Scholar 
    42.Martín-Vivaldi, M., Marín, J. M., Archila, F., López, E. & De Manuel, L. C. Caracterización de una nueva población reproductora de Alondra de Dupont (Chersophilus duponti) (Passeriformes, Alaudidae) en el Sureste Ibérico. Zool. Baetica 10, 185–192 (1999).
    Google Scholar 
    43.Garza, V. et al. Home range, territoriality and habitat selection by the Dupont’s Lark Chersophilus duponti during the breeding and postbreeding periods. Ardeola 52, 133–146 (2005).
    Google Scholar 
    44.Seoane, J. et al. Habitat-suitability modelling to assess the effects of land-use changes on Dupont’s Lark Chersophilus duponti: A case study in the Layna Important Bird Area. Biol. Cons. 128, 241–252 (2006).Article 

    Google Scholar 
    45.Nogués-Bravo, D. & Agirre, A. Patrón y modelos de distribución espacial de la alondra ricotí Chersophilus duponti durante el periodo reproductor en el LIC de Ablitas (Navarra). Ardeola 53, 55–68 (2006).
    Google Scholar 
    46.García, J. T. et al. Assessing the distribution, habitat, and population size of the threatened Dupont’s Lark Chersophilus duponti in Morocco: Lessons for conservation. Oryx 42, 592–599 (2008).Article 

    Google Scholar 
    47.Pérez-Granados, C., López-Iborra, G. M. & Seoane, J. A multi-scale analysis of habitat selection in peripheral populations of the endangered Dupont’s Lark Chersophilus duponti. Bird Conserv. Int. 27, 398–413 (2017).Article 

    Google Scholar 
    48.García-Antón, A., Garza, V., Hernández-Justribó, J. & Traba, J. Factors affecting Dupont’s Lark distribution and range regression in Spain. PLoS ONE 14, e0211549 (2019).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    49.García-Antón, A., Garza, V. & Traba, J. (in press). Connectivity in Spanish metapopulation of Dupont’s Lark may be maintained by dispersal over medium 3 distances and stepping stones. PeerJ.50.Traba, J., Garza, V., García-Antón, A., Gómez-Catasús, J., Zurdo, J., Pérez-Granados, C., Morales, M. B., Oñate, J. J., Herranz, J., Malo, J. Criterios para la gestión y conservación de la población española de alondra ricotí Chersophilus duponti. Fundación Biodiversidad, Ministerio de Agricultura, Alimentación y Medio Ambiente. Madrid. (2019).51.Vögeli, M., Serrano, D., Tella, J. L., Méndez, M. & Godoy, J. A. Sex determination of Dupont´s lark Chersophilus duponti using molecular sexing and discriminant functions. Ardeola 54, 69–79 (2007).
    Google Scholar 
    52.Suárez, F. et al. Sex-ratios of an endangered lark after controlling for a male-biased sampling. Ardeola 56, 113–118 (2009).
    Google Scholar 
    53.Garza, V., Suárez, F., Tella, J. L. Alondra de Dupont, Chersophilus duponti. In: Madroño, A., González, C., Atienza, J. C. (eds). Libro Rojo de las Aves de España. Madrid: Dirección General para la Biodiversidad-SEO/BirdLife pp 309–312. (2004).54.Íñigo, A., Garza, V., Tella, J. L., Laiolo, P., Suárez, F., Barov, B. Action Plan for the Dupont’s Lark Chersophilus duponti in the European Union. SEO/Birdlife – BirdLife International –Comisión Europea. (2008).55.Gómez-Catasús, J. et al. Hierarchical habitat-use by an endangered steppe bird in fragmented landscapes is associated with large connected patches and high food availability. Sci Rep 9, 19010 (2019).PubMed 
    PubMed Central 
    Article 
    ADS 
    CAS 

    Google Scholar 
    56.Reverter, M. et al. Interactions in shrub-steppes: Implications for the maintenance of a threatened bird. Ecosistemas 28, 69–77 (2019).Article 

    Google Scholar 
    57.Serrano, D. et al. Renewables in Spain threaten biodiversity. Science 370, 1282–1283 (2020).PubMed 
    Article 
    ADS 
    CAS 
    PubMed Central 

    Google Scholar 
    58.Pe’er, G. et al. A greener path for the EU common agricultural policy. Science 365(6452), 449–451 (2019).CAS 
    PubMed 
    Article 
    ADS 
    PubMed Central 

    Google Scholar 
    59.Bland, L. M., Keith, D. A., Miller, R. M., Murray, N. J. & Rodríguez, J. P. (eds.) Guidelines for the application of IUCN Red List of Ecosystems Categories and Criteria, Version 1.1. Gland, Switzerland: IUCN. ix + 99pp. (2017).60.Flather, C. H., Hayward, G. D., Beissinger, S. R. & Stephens, P. A. Minimum viable populations: Is there a “magic number” for conservation practitioners?. Trends Ecol. Evol. 26(6), 307–316 (2011).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    61.Carvajal, M. A., Alaniz, A. J., Smith-Ramírez, C. & Sieving, K. E. Assessing habitat loss and fragmentation and their effects on population viability of forest specialist birds: Linking biogeographical and population approaches. Divers. Distrib. 24, 820–830 (2018).Article 

    Google Scholar 
    62.Trask, A. E. et al. Evaluating the efficacy of independent versus simultaneous management strategies to address ecological and genetic threats to population viability. J. Appl. Ecol. 56, 2264–2273 (2019).Article 

    Google Scholar 
    63.Akçakaya, H. R. & Sjögren-Gulve, P. Population viability analyses in conservation planning: An overview. Ecol. Bull. 48, 9–21 (2000).
    Google Scholar 
    64.Frankham, R., Ballou, J., Briscoe, D. & McInnes, K. Introduction to Conservation Genetics (Cambridge University Press, 2002).Book 

    Google Scholar 
    65.Pulliam, H. R. Sources, sinks, and population regulation. Am. Nat. 132(5), 652–661 (1988).Article 

    Google Scholar 
    66.Bota, G., Giralt, D. & Guixé, D. L. Alondra Ricotí en Cataluña: evolución histórica de una población en el límite del área de distribución (II Meeting of the Dupont’s Lark Experts Group, 2016).
    Google Scholar 
    67.Pérez-Granados, C., Bota, G., Giralt, D. & Traba, J. A cost-effective protocol for monitoring birds using autonomous recording units: A case study with a night-time singing passerine. Bird Study 65(3), 338–345 (2018).Article 

    Google Scholar 
    68.García-Antón, A., Garza, V. & Traba, J. Dispersión de más de 30 km en un macho de primer año de alondra ricotí (Chersophilus duponti) en el Sistema Ibérico. I Workshop Nacional de la Alondra ricotí Chersophilus duponti: Estrategias Futuras. Estación Ornitológica de Padul, Granada. 13 junio (2015).69.Kauhala, K., Helle, P. & Helle, E. Predator control and the density and reproductive success of grouse populations in Finland. Ecography 23, 161–168 (2000).Article 

    Google Scholar 
    70.Fletcher, K., Aebischer, N. J., Baines, D., Foster, R. & Hoodless, A. N. Changes in breeding success and abundance of ground-nesting moorland birds in relation to the experimental deployment of legal predator control. J. Appl. Ecol. 47, 263–272 (2010).Article 

    Google Scholar 
    71.Banks, P. B., Dickman, C. R. & Newsome, A. E. Ecological costs of feral predator control: Foxes and rabbits. J. Wildl. Manag. 62(2), 766–772 (1998).Article 

    Google Scholar 
    72.Bolton, M., Tyler, G., Smith, K. & Bamford, R. The impact of predator control on lapwing Vanellus vanellus breeding success on wet grassland nature reserves. J. Appl. Ecol. 44, 534–544 (2007).Article 

    Google Scholar 
    73.Walsh, J. C., Wilson, K. A., Benshemesh, J. & Possingham, H. P. Unexpected outcomes of invasive predator control: The importance of evaluating conservation management actions. Anim. Conserv. 15, 319–328 (2012).Article 

    Google Scholar 
    74.Oro, D., Margalida, A., Carrete, M., Heredia, R. & Donázar, J. A. Testing the goodness of supplementary feeding to enhance population viability in an endangered vulture. PLoS ONE 3(12), e4084 (2008).PubMed 
    PubMed Central 
    Article 
    ADS 
    CAS 

    Google Scholar 
    75.Ruffino, L. et al. Reproductive responses of birds to experimental food supplementation: A meta-analysis. Front. Zool. 11, 80 (2014).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    76.Cuesta, D., Taboada, A., Calvo, L. & Salgado, J. M. Short- and medium-term effects of experimental nitrogen fertilization on arthropods associated with Calluna vulgaris heathlands in north-west Spain. Environ. Pollut. 152, 394–402 (2008).CAS 
    PubMed 
    Article 

    Google Scholar 
    77.Estrada, A., Delgadom, M. P., Arroyo, B., Traba, J. & Morales, M. B. Forecasting large-scale habitat suitability of European bustards under climate change: The role of environmental and geographic variables. PLoS ONE 11(3), e0149810 (2016).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    78.Zhang, X., Johnston, E. R., Li, L., Konstantinidis, K. T. & Han, X. Experimental warming reveals positive feedbacks to climate change in the Eurasian Steppe. ISME J. 11, 885–895 (2017).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    79.Laiolo, P., Vögeli, M., Serrano, D. & Tella, J. L. Song diversity predicts the viability of fragmented bird populations. PLoS ONE 3(3), e1822 (2008).PubMed 
    PubMed Central 
    Article 
    ADS 
    CAS 

    Google Scholar 
    80.Traba, J., García de la Morena, E. L. & Garza, V. Análisis de Viabilidad de Poblaciones como herramienta para el diseño de Parques Eólicos. El caso de las poblaciones de alondra ricotí (Chersophilus duponti) del sur de Soria. I Congreso Ibérico sobre Energía Eólica y Conservación de Fauna. Jerez de la Frontera, Cádiz (2011).81.Suárez, F. & Carriles, E. Análisis de viabilidad poblacional. In: Suárez, F. (ed.) La alondra ricotí (Chersophilus duponti), pp. 319–326. Dirección General para la Biodiversidad. Ministerio de Medio Ambiente y Medio Rural y Marino. Madrid (2010).82.Pérez-Granados, C., López-Iborra, G. M., Garza, V. & Traba, J. Breeding biology of the endangered Dupont’s Lark Chersophilus duponti in two separate Spanish shrub-steppes. Bird Study 64(3), 328–338 (2017).Article 

    Google Scholar 
    83.Pérez-Granados, C. & López-Iborra, G. M. ¿Por qué la alondra ricotí debe catalogarse como “En peligro de Extinción”?. Quercus 337, 18–25 (2014).
    Google Scholar 
    84.Pérez-Granados, C. & López-Iborra, G. M. Census of breeding birds and population trends of the Dupont’s Lark Chersophilus duponti in eastern Spain. Ardeola 60, 143–150 (2013).Article 

    Google Scholar 
    85.Suárez, F. La alondra ricotí (Chersophilus duponti). Dirección General para la Biodiversidad. Ministerio de Medio Ambiente y Medio Rural y Marino Medio Rural y Marino, Madrid. 525 pp (2010).86.Lacy, R. C., & Pollak, J. P. Vortex: A Stochastic Simulation of the Extinction Process. Version 10.2.9. Chicago Zoological Society, Brookfield, Illinois, USA (2017).87.Lacy, R. C. Considering Threats to the Viability of Small Populations Using Individual-Based Models. Ecol. Bull. 48, 39–51 (2000).
    Google Scholar 
    88.Ogrady, J. J. et al. Realistic levels of inbreeding depression strongly affect extinction risk in wild populations. Biol. Conserv. 133(1), 42–51 (2006).Article 

    Google Scholar 
    89.Laiolo, P., Vögeli, M., Serrano, D. & Tella, J. L. Testing acoustic versus physical marking: Two complementary methods for individual-based monitoring of elusive species. J. Avian Biol. 38, 672–681 (2007).Article 

    Google Scholar 
    90.Vögeli, M., Laiolo, P., Serrano, D. & Tella, J. L. Who are we sampling? Apparent survival differs between methods in a secretive species. Oikos 117(12), 1816–1823 (2008).Article 

    Google Scholar 
    91.Chambers, J. M. (1992) Linear models. Chapter 4 of Statistical Models in eds J. M. Chambers and T. J. Hastie, Wadsworth & Brooks/Cole.92.Pérez-Granados, C. & López-Iborra, G. M. Baja dispersión adulta y baja tasa de recaptura juvenil de la alondra ricotí (Chersophilus duponti) en el Rincón de Ademuz (Valencia). XX Iberian Ringing Congress (2015).93.Briefer, E., Rybak, F. & Aubin, T. When to be a dear enemy: flexible acoustic relationships of neighbouring skylarks Alauda arvensis. Anim. Behav. 76, 1319–1325 (2008).Article 

    Google Scholar 
    94.Delius, J. D. A population study of Skylarks Alauda arvensis. Ibis 107, 466–492 (1965).Article 

    Google Scholar 
    95.Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).Article 

    Google Scholar  More

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    Deterring non-target birds from toxic bait sites for wild pigs

    Candidate bird deterrentsWe identified four candidate bird deterrents that were suitable for deployment within a SN-toxic baiting program (Fig. 2). Specifically, we searched published studies and vendor websites to identify candidate bird deterrents that had a proven record of deterring birds, or features that we expected would deter all birds after a deployment of SN-toxic bait while not deterring wild pigs. These features included: (1) not deterring wild pigs (i.e., user programmable operating hours for after wild pigs visits or being bird-specific), (2) aversive to birds (i.e., erratic movements or irritating to birds), and (3) remotely operated (i.e., battery operated or effects lasting ~ 12 h if user applied).Figure 2Examples of potential bird deterrents tested in in north-central Colorado, USA during April–May 2020, including (A) control, no deterrent, (B) 7.5% concentration of methyl anthranilate, (C) a metal grate, (D), an inflatable scarecrow, and (E) a scare dancer. Photos property of USDA.Full size imageWe selected two frightening devices that offered visual and auditory stimuli, were battery-powered, and programmable to have a user-specified start time. The first frightening device was a 1.8 m inflatable scare dancer (Snake 6 ft Cordless Inflatable Scarecrow, AirCrow LLC, Lake Charles, LA, USA). The scare dancer was a yellow nylon tube shaped like a snake and inflated by a small fan and control unit powered by a 12 V battery connected to a programmable control panel. If using the scare dancer for SN-toxic bait deployment, our strategy would be to program the device to operate continuously starting 1 h before first light the morning after toxic bait was deployed. Our expectation would be that wild pigs would have already visited bait sites and consumed SN-toxic bait prior to scare dancer activation. Once activated, the scare dancer would deter non-targets away from any spilled SN-toxic bait during the morning after toxic baiting until operators arrived to clean the site.The second frightening device was an inflatable scarecrow called the Scarey Man Birdscarer (Clarratts Ltc, United Kingdom). This device was also powered by a small fan using a 12 V battery, activated by a timer, and inflated for 25 s every 18 min accompanied by an audible 112 db siren. The timing of the inflation could not be altered. The blaze-orange inflatable scarecrow bobbed up and down as it inflated and deflated, and emitted a siren wail. Our strategy with the inflatable scarecrow, following SN-toxic bait deployment, would be the same as the scare dancer, except the inflatable scarecrow could not be programmed to operate continuously.For the physical barrier treatment, we constructed a metal grate using a 2.4 m × 1.2 m sheet of #13-gauge steel diamond-shaped expanded metal. The maximum openings of the expanded metal were 1.0 cm and were raised (i.e., tapered upwards) to facilitate bait falling through the grate. We constructed the grate to sit 9.0 cm above ground using a frame of standard construction lumber. We also tapered the top of the wooden frame to reduce surface area and facilitate bait falling through the grate. If using the grate for SN-toxic bait deployment, our strategy would be to put the bait station on top of the grate. Our expectation would be that wild pigs would stand on the grate to access the bait station, and spilled particles would fall under the grate and be inaccessible to non-target animals.The chemical repellent treatment we tested was Avian Migrate™ Goose and Bird Repellent (Avian Enterprises, Jupiter, FL, USA) which contained 14.5% methyl anthranilate. Avian Migrate required dilution with water for all applications. We followed the label instructions for spot repelling, and used the strongest dilution recommended at 50:50 Avian Migrate and water, resulting in 7.5% methyl anthranilate. We used a hand-pump-pressurized garden sprayer to apply 500 ml of the mixture to a 3 × 3 m area which resulted in an even and thorough coating of the area. Aversion to methyl anthranilate may be a learned behavior as an irritant for birds36, therefore would need to be applied daily for 1–2 days prior to SN-toxic bating. If using the repellent for SN-toxic bait deployment, our strategy would be to spray the ground immediately surrounding bait stations for 2 nights prior to deploying toxic bait, and the night of toxic baiting. Our expectation would be that by the 3rd night of application non-target birds would be repelled from consuming particles of spilled bait that fell on the treated ground; after which, we could safely deploy SN-toxic bait.Field study on deterrent effectiveness for birdsWe initially selected and pre-baited ~ 60 sites in north-central CO using 5 kg of bird seed (Deluxe Blend Bird Seed, Wild Birds Unlimited, Fort Collins, CO, USA). Sites were selected in diverse land covers that were likely to hold small passerine birds, such as thickets, wind rows, near water sources, or along shelter belts; and based on distance to nearby sites (i.e., goal of  > 500 m to nearest site). We cleared sites of tall grass and debris to ease discovery and access to the bird seed by smaller birds. We visited sites every 2–3 days to replenish and maintain ~ 2 kg of bait at the sites. We pre-baited sites for ~ 4 weeks to ensure birds were well-acclimated to visiting sites daily.We monitored visitation to sites using remote cameras (RECONYX PC900, RECONYX Inc, Holmen, WI, USA) mounted on T-post approximately 5 m from the bait pile, 1.5 m above ground, and angled down at 70° to provide a consistent field of view at each site. Cameras were programmed to record time-lapse imagery every 2 min (i.e., 720 images/day) which was used to calculate indices of species visitation. We used the Colorado Parks and Wildlife Photo Database to process all time-lapse imagery (Ivan and Newkirk 2016). For each image, a single observer recorded presence and count of each unique species present. We selected the best 20 sites (Fig. 1) based on the greatest rates of bird visitation, greatest diversity of bird species visiting, and lowest presence of other species that consumed large quantities of the bird seed (e.g., raccoons, deer, skunks).For the trial, we randomly assigned a deterrent treatment (i.e., inflatable scarecrow, metal grate, methyl anthranilate) or control (i.e., no deterrent method) to five sites each. We re-used the control sites to test the scare dancer after testing the initial four treatments, because the scare dancers were received later than first three treatments. We visited bait sites daily and weighed the amount of bird seed remaining to calculate the amount consumed with digital scales (MeasureTek GGS_42964, MeasureTek Scale Co, Ltd, Vancouver, BC, Canada). We replenished each site to ensure ~ 2 kg of fresh bird seed was available each day.The trials were seven consecutive days (Table 1). We focused on species visitation from 1 h before first light (~ 0500 h) to midday (1200 h) each day, because this time period represented the critical hours in which hazards occurred at toxic bait sites22,24. We visited the bait sites between 1200 and 1400 h each day to replenish bait and prepare sites for the following day. The 7-day trial consisted of:

    Days 1–2 = Pre-baiting days. No deterrent deployed.

    Day 3 = Acclimation day. We deployed the deterrent devices but did not activate. Scare dancers were installed on a t-post 1.5 m above the bait sites. Inflatable scarecrows were placed on the ground 3 m away from the bait sites. Metal grates were deployed 3 m away from the bait sites. Methyl anthranilate was sprayed for first time in the 3 × 3 m area surrounding bait sites to initiate the learned repellency.

    Day 4 = Pre-treatment day. This was the day we collected pre-treatment data (i.e., consumption and remote camera data) for comparison with treatment and post-treatment below. All deterrent devices remained inactive as described for acclimation day. The methyl anthranilate was sprayed in the same manner as before for the second time.

    Day 5 = Treatment day. Both frightening devices were activated at 1 h prior to first light. The metal grate was installed over the bird seed. Methyl anthranilate was sprayed in the same manner as before for the third and final time.

    Day 6 = Post-treatment day. All deterrent devices were inactivated but left in place similar to the pre-treatment day. The metal grate was moved 3 m away from the bait site. No methyl anthranilate was sprayed.

    Day 7 = Removal day. We removed all our cameras and deterrent devices and ceased re-baiting at all sites.

    Table 1 Strategies used to evaluate effectiveness of bird deterrents during a 7-day trial in north-central Colorado, USA during April–May 2020.Full size tableFor each site, we calculated an index of the number of passerine birds observed in each two-min time-lapse image (rate = average number of birds/two mins) during morning hours (i.e., 0500–1200) for the morning of pre-treatment, treatment, and post-treatment. We compared indices among each of the 3 days and five treatments using negative binomial mixed models and log-links with package glmmTMB37 in Program R v3.6.338. We used offsets of the number of hours monitored and site ID as a random effect to account for repeated (i.e., daily) measures taken at each site. We did not analyze for other species (i.e., predatory birds and mammals) because visitations were rare. For all analyses we calculated and examined the 95% confidence intervals (CIs) surrounding the regression coefficients (β) for non-overlap of zero to indicate statistical and biological differences.Effects of deterrents on captive wild pigsWe evaluated whether the deterrents influenced feeding behaviors of captive wild pigs. Specifically, we evaluated how wild pigs responded to the metal grate and methyl anthranilate, because these deterrent strategies would need to be in place as wild pigs visited bait sites, and we wanted to ensure wild pigs would not be deterred from feeding. Contrarily, neither of the deterrent devices should be encountered by wild pigs because these devices would be operated on a timer and set to activate after wild pigs visited toxic baiting sites. Therefore, we did not evaluate those treatments with captive wild pigs.For testing methyl anthranilate, we randomly selected and placed three captive wild pigs from the larger holding pen (i.e., two males and one female) into three 0.02 ha pens, respectively. We replicated this design twice, for a total of six pens (n = 18 wild pigs) tested. The wild pigs in each pen were acclimated for one night to the new pens and to feeding from two identical feed troughs (1.8 × 0.3 × 0.1 m) placed 3.2 m apart. Each night we fed ~ 10 kg of whole kernel corn in each trough and weighed any remaining corn the following morning. A 2-choice feeding test was conducted on nights two, three, and four, where we applied methyl anthranilate to a 3 × 3 m area surrounding one of the troughs using the same mixture as described above in CO. For the other trough, we did not apply methyl anthranilate to the surrounding soil. We applied the methyl anthranilate and whole kernel corn each evening of the 3-day treatment period.For testing the metal grate, we randomly selected and placed four captive wild pigs from the larger holding pen into two 0.2 ha pens, respectively. We replicated this design twice, for a total of four pens (n = 16 wild pigs) tested. A single feed trough (1.8 × 0.3 × 0.1 m) was placed in each pen. We placed the metal grate under the trough in one pen where it remained for the three nights of study. Two kg of pelleted sow ration were fed in each pen on night 1. On night two, ~ 10 kg of a placebo SN-toxic bait (i.e., HOGGONE without SN) and 1 kg of pelleted sow ration were fed in each pen. On night three we offered just 10 kg of placebo bait to evaluate whether spilled particles of the peanut paste-based bait16 would stick to the metal grate. We ceased testing the metal grate after the second replicate because we observed that wild pigs spilled small particles of the placebo bait which stuck to the top of the metal grate in the first replicate, followed by 100% aversion by wild pigs to the metal grate in the second replicate, rendering the metal grate a non-viable option for operational use.For the methyl anthranilate, we compared proportions of whole-kernel corn consumed in the 2-choice test using a linear model in Program R. We evaluated the interaction of treatment × night to determine if the application of methyl anthranilate influenced the amount of corn wild pigs consumed over time. We also tested the reduced model without the interaction to best interpret the unconditional main effects39. We did not analyze data from the metal grate treatment because the evaluation was stopped early, and the results were clear.Field evaluation of deterrent with toxic baitFor the final phase of this study, we evaluated the most effective deterrent identified in the first phase of the study (i.e., scare dancer deterrent device, see results) and implemented this deterrent device into a SN-toxic toxic baiting program for wild pigs in north-central TX. We followed methodologies established in previous studies (Table 2) to initiate a SN-baiting program24,40,41,42. Specifically, we initially deployed ~ 30 bait sites by placing ~ 11 kg of whole-kernel corn on the ground at locations with recent sign of wild pigs (e.g., fresh tracks, feces, wallowing, rooting). We installed one remote camera on a t-post 5 m away from each bait site, 1.5 m above ground, and angled down at 70°. We programmed cameras to capture time-lapse images every 5 min (i.e., 288 images/day). We revisited bait sites every day for 5 days to refresh bait (i.e., maintain 11 kg of corn) and view camera images for wild pigs. After day 5, we selected the 10 best sites (Fig. 1) using the highest ranked sites from this ranking system: (1) consistent wild pig visitation (i.e., ≥ 2 days in a row), (2) consistent visitation by a family group of wild pigs (i.e., ≥ 1 female with multiple juveniles or piglets), (3) consistent visitation by multiple family groups (4) consistent visitation of independent family groups not visiting other sites42. We also made sure to select bait sites that were  > 500 m apart to maintain independence among the groups of pigs visiting each site41,43.Table 2 Baiting strategy to locate, pre-bait, and train wild pigs to use bait stations and consume SN-toxic bait used in north-central Texas, USA during July 2020.Full size tableWe deployed wild pig-specific bait stations20 with ~ 13 kg of magnetic resistance on the lids21 at the 10 final sites and initiated a series of conditioning phases to acclimate wild pigs to open and consume bait from inside the bait stations (Table 2). We deployed two bait stations at sites with ≥ 10 wild pigs to ensure all wild pigs had sufficient access to bait. We deployed bait stations 10–30 m away from initial pre-baiting sites (where we originally placed corn on the ground) to reduce visitation by non-target animals that may be attracted to residual particles of corn. Where cattle were present, we also constructed 3-strand barbed-wire fences around the site to exclude them from accessing SN-toxic bait.We randomly selected five sites to deploy the deterrent devices, and five sites as controls (no deterrent devices). Three days prior to deploying SN-toxic bait, we deployed the deterrent devices but left them inactive to condition wild pigs to the presence of the devices. We mounted the deterrent devices on T-posts approximately 1.8 m above ground directly over each bait station with the battery box secured at the base of the T-post (Fig. 3). When we deployed SN-toxic bait, we programmed the deterrent devices to activate at 0520 h the next morning (i.e., 1 h before first-light). We waited until 0900–1200 h the next morning before visiting bait sites to allow ample testing time of the deterrent devices to deter birds, and to simulate realistic use in an operational setting. When we arrived at the bait site, we deactivated the deterrent devices and cleaned the surrounding area of any remaining spilled bait. We collected and weighed all spilled bait we could locate and turned over the soil surrounding the bait station to bury any small particles of spilled bait we could not collect.Figure 3Example of activated deterrent devices (scare dancers) mounted above bait stations containing a sodium nitrite toxic bait in north-central Texas, USA during July 2020. Photo property of USDA.Full size imageWe conducted systematic carcass searches along transects following the SN-toxic bait deployment. Specifically, we searched 400 m × 400 m transect grids centered on the bait sites every 50 m, walking transects oriented North/South the first day and East/West the second day. We generated the transects in ArcGIS (v10.8.1, Environmental Systems Research Institute, Redlands, CA, USA), and uploaded them to handheld devices (i.e., mobile phones or tablets) using ArcGIS Explorer (v20.0.1) to navigate along the transects. Additionally, we searched a smaller 50 m × 50 m transect grid centered on the bait sites every 5 m for three consecutive days, again switching between North/South, East/West, and North/South orientation each day, respectively. Transect spacing and distances were based on locations of carcasses found in a previous study with SN-toxic bait24. We searched transects for multiple days to ensure any carcasses were located and to determine if any animals succumbed to consuming spilled SN-toxic bait that may have been missed during our clean-up process days after deployment.We recorded sex, age based on tooth eruption44, weight, location, and evidence of SN-toxic bait consumption of any dead wild pigs that we located. Bait consumption was determined by observing bait in the mouth or stomach, or based on the percentage of methemoglobin in the blood by comparing the red-color-value of a drop of blood on a white laminated card to a standard curve45. For any non-target animals found dead, we recorded species, location, and evidence of SN-toxic bait consumption (as described above).We processed all time-lapse imagery from each bait station using the Colorado Parks and Wildlife Photo Database46. For each image, a single observer recorded the count of each species present. We did not include cattle because they were excluded from bait sites. We used two indices from the images for comparing the rates of visitation by different species. First, we used an index of the count of non-target animals/image during the hours that the deterrent devices were operating (0520–1200 h). We compared this index among the days of pre-, during, and post-activation periods of the deterrent devices to assess if the devices influenced the rate of visitation using linear models in program R. We analyzed sites with and without the deterrent devices separately to assess the effects of each treatment throughout the days independently.For the second index, we estimated rates of the number of wild pigs, non-target mammals, and non-target birds, respectively, observed per hour that visited bait sites. We followed methodology established by22, and used negative binomial generalized mixed models with package glmmTMB37 to compare rates of visitation between periods of pre- and post-SN-toxic bait deployment to assess changes relative to toxic baiting. We considered the change in rates of visitation to be attributed to lethality from SN-toxic bait for the populations of animals visiting the bait sites. We expect this methodology met the assumption that detection of animals remained consistent47 at bait sites because pre- and post-toxic periods were only separated by a single 24-h period when the toxic bait was deployed, and we refreshed the bait daily. We also compared the indices between treatments (with vs without deterrents) and the interaction of period × treatment. The models examined for each group of species were: rate of hourly visitation ~ period + treatment + period × treatment. We also used Site ID as random effects to account for repeated measures taken at each bait site.For the transect analysis, we calculated descriptive summaries of sexes, ages, and distances from carcass to nearest bait station for wild pigs that succumbed to the SN-toxic bait. We also summarized any non-target deaths and distances from the nearest bait site. All research methods for all phases of this study were approved under the USDA National Wildlife Research Center, Institutional Animal Care and Use Committee (protocol QA-3068), and performed and reported in accordance with ARRIVE guidelines and US EPA regulations. More

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    Potential impacts of polymetallic nodule removal on deep-sea meiofauna

    1.Hein, J. R., Mizell, K., Koschinsky, A. & Conrad, T. A. Deep-ocean mineral deposits as a source of critical metals for high- and green-technology applications: Comparison with land-based resources. Ore Geol. Rev. 51, 1–14 (2013).Article 

    Google Scholar 
    2.Petersen, S. et al. News from the seabed—Geological characteristics and resource potential of deep-sea mineral resources. Mar. Policy 70, 175–187 (2016).Article 

    Google Scholar 
    3.Dutkiewicz, A., Judge, A. & Müller, R. D. Environmental predictors of deep-sea polymetallic nodule occurrence in the global ocean. Geology 48, 293–297 (2020).ADS 
    CAS 
    Article 

    Google Scholar 
    4.Verlaan, P. A. & Cronan, D. S. Origin and variability of resource-grade marine ferromanganese nodules and crusts in the Pacific Ocean: A review of biogeochemical and physical controls. Geochemistry https://doi.org/10.1016/j.chemer.2021.125741 (2021).Article 

    Google Scholar 
    5.Radziejewska, T. & Stoyanova, V. Abyssal epibenthic megafauna of the Clarion-Clipperton area (NE Pacific): Changes in time and space versus anthropogenic environmental disturbance. Oceanol. Stud. 29, 83–101 (2000).
    Google Scholar 
    6.Vanreusel, A., Hilario, A., Ribeiro, P. A., Menot, L. & Arbizu, P. M. Threatened by mining, polymetallic nodules are required to preserve abyssal epifauna. Sci. Rep. 6, 1–6 (2016).Article 
    CAS 

    Google Scholar 
    7.Simon-Lledó, E. et al. Ecology of a polymetallic nodule occurrence gradient: Implications for deep-sea mining. Limnol. Oceanogr. 64, 1883–1894 (2019).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    8.Washburn, T. W. et al. Patterns of macrofaunal biodiversity across the Clarion-Clipperton zone: An area targeted for seabed mining. Front. Mar. Sci. 8, 626571 (2021).Article 

    Google Scholar 
    9.Bonifácio, P., Martinez Arbizu, P. & Menot, L. Alpha and beta diversity patterns of polychaete assemblages across the nodule province of the eastern Clarion-Clipperton Fracture Zone (equatorial Pacific). Biogeosciences 17, 865–886 (2020).ADS 
    Article 

    Google Scholar 
    10.Ansari, Z. A. Distribution of deep-sea benthos in the proposed mining area of Central Indian Basin. Mar. Georesour. Geotechnol. 18, 201–207 (2000).Article 

    Google Scholar 
    11.Pasotti, F. et al. A local scale analysis of manganese nodules influence on the Clarion-Clipperton Fracture Zone macrobenthos. Deep Sea Res. Part Oceanogr. Res. Pap. 168 (2021).12.Hauquier, F. et al. Geographic distribution of free-living marine nematodes in the Clarion-Clipperton Zone: Implications for future deep-sea mining scenarios. Biogeosciences 16, 3475–3489 (2019).ADS 
    CAS 
    Article 

    Google Scholar 
    13.Kuhn, T., Uhlenkott, K., Vink, A., Rühlemann, C. & Martinez Arbizu, P. Manganese nodule fields from the Northeast Pacific as benthic habitats. In Seafloor Geomorphology as Benthic Habitat 2nd edn (eds Harris, P. T. & Baker, E.) 933–947 (Elsevier, 2020). https://doi.org/10.1016/B978-0-12-814960-7.00058-0.Chapter 

    Google Scholar 
    14.Miljutina, M. A., Miljutin, D. M., Mahatma, R. & Galéron, J. Deep-sea nematode assemblages of the Clarion-Clipperton Nodule Province (Tropical North-Eastern Pacific). Mar. Biodivers. 40, 1–15 (2010).Article 

    Google Scholar 
    15.Mahatma, R. Meiofauna Communities of the Pacific Nodule Province: Abundance, Diversity and Community Structure (University of Oldenburg, 2009).
    Google Scholar 
    16.Singh, R. et al. Nematode communities inhabiting the soft deep-sea sediment in polymetallic nodule fields: Do they differ from those in the nodule-free abyssal areas?. Mar. Biol. Res. 12, 1–15 (2016).Article 

    Google Scholar 
    17.Thiel, H., Schriever, G., Bussau, C. & Borowski, C. Manganese nodule crevice fauna. Deep Sea Res. Part Oceanogr. Res. Pap. 40, 419–423 (1993).ADS 
    Article 

    Google Scholar 
    18.Bussau, C., Schriever, G. & Thiel, H. Evaluation of abyssal metazoan meiofauna from a manganese nodule area of the Eastern South Pacific. Vie Milieu 45, 39–48 (1995).
    Google Scholar 
    19.Oebius, H. U., Becker, H. J., Rolinski, S. & Jankowski, J. A. Parametrization and evaluation of marine environmental impacts produced by deep-sea manganese nodule mining. Deep Sea Res. Part II Top. Stud. Oceanogr. 48, 3453–3467 (2001).ADS 
    CAS 
    Article 

    Google Scholar 
    20.Levin, L. A. et al. Defining “serious harm” to the marine environment in the context of deep-seabed mining. Mar. Policy 74, 245–259 (2016).Article 

    Google Scholar 
    21.Global Sea Mineral Resources. Environmental Impact Statement—Small-scale testing of nodule collector components on the seafloor of the Clarion-Clipperton Fracture Zone and its environmental impact. 337 (2018).22.Durden, J. M. et al. A procedural framework for robust environmental management of deep-sea mining projects using a conceptual model. Mar. Policy 84, 193–201 (2017).Article 

    Google Scholar 
    23.Jones, D. O. B. et al. Biological responses to disturbance from simulated deep-sea polymetallic nodule mining. PLoS One 12, e0171750 (2017).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    24.Jones, D. O. B., Ardron, J. A., Colaço, A. & Durden, J. M. Environmental considerations for impact and preservation reference zones for deep-sea polymetallic nodule mining. Mar. Policy https://doi.org/10.1016/j.marpol.2018.10.025 (2018).Article 

    Google Scholar 
    25.Boschen, R. E. et al. A primer for use of genetic tools in selecting and testing the suitability of set-aside sites protected from deep-sea seafloor massive sulfide mining activities. Ocean Coast. Manag. 122, 37–48 (2016).Article 

    Google Scholar 
    26.Boucher, G. & Lambshead, P. J. D. Ecological biodiversity of marine nematodes in samples from temperate, tropical and deep-sea regions. Conserv. Biol. 9, 1594–1604 (1995).Article 

    Google Scholar 
    27.Ramirez-Llodra, E. et al. Deep, diverse and definitely different: Unique attributes of the world’s largest ecosystem. Biogeosciences 7, 2851–2899 (2010).ADS 
    Article 

    Google Scholar 
    28.Rex, M. A. & Etter, R. J. Deep-Sea Biodiversity: Pattern and Scale (Harvard University Press, 2010).
    Google Scholar 
    29.Paterson, G. L. J. et al. Biogeography and connectivity in deep-sea habitats with mineral resource potential: A gap analysis. Deliverable 4.2. MIDAS (2014).30.Christodoulou, M., O’Hara, T. D., Hugall, A. F. & Arbizu, P. M. Dark ophiuroid biodiversity in a prospective abyssal mine field. Curr. Biol. 29, 3909–3912 (2019).PubMed 
    CAS 
    Article 

    Google Scholar 
    31.Amon, D. J. et al. Insights into the abundance and diversity of abyssal megafauna in a polymetallic-nodule region in the eastern Clarion-Clipperton Zone. Sci. Rep. 6, 30492 (2016).ADS 
    PubMed 
    PubMed Central 
    CAS 
    Article 

    Google Scholar 
    32.Goineau, A. & Gooday, A. J. Diversity and spatial patterns of foraminiferal assemblages in the eastern Clarion-Clipperton zone (abyssal eastern equatorial Pacific). Deep Sea Res. Part Oceanogr. Res. Pap. 149, 103036 (2019).Article 

    Google Scholar 
    33.Macheriotou, L., Rigaux, A., Derycke, S. & Vanreusel, A. Phylogenetic clustering and rarity imply risk of local species extinction in prospective deep-sea mining areas of the Clarion-Clipperton Fracture Zone. Proc. R. Soc. B Biol. Sci. 287, 20192666 (2020).Article 

    Google Scholar 
    34.Błażewicz, M., Jóźwiak, P., Menot, L. & Pabis, K. High species richness and unique composition of the tanaidacean communities associated with five areas in the Pacific polymetallic nodule fields. Prog. Oceanogr. 176, 102141 (2019).Article 

    Google Scholar 
    35.Janssen, A. et al. A reverse taxonomic approach to assess macrofaunal distribution patterns in abyssal pacific polymetallic nodule fields. PLoS One 10, e0117790 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    36.Soetaert, K. & Heip, C. Sample-size dependence of diversity indexes and the determination of sufficient sample size in a high-diversity deep-sea environment. Mar. Ecol. Prog. Ser. 59, 305–307 (1990).ADS 
    Article 

    Google Scholar 
    37.Rose, A. et al. A method for comparing within-core alpha diversity values from repeated multicorer samplings, shown for abyssal Harpacticoida (Crustacea: Copepoda) from the Angola Basin. Org. Divers. Evol. 5, 3–17 (2005).Article 

    Google Scholar 
    38.George, K. H. et al. Community structure and species diversity of Harpacticoida (Crustacea: Copepoda) at two sites in the deep sea of the Angola Basin (Southeast Atlantic). Org. Divers. Evol. 14, 57–73 (2014).Article 

    Google Scholar 
    39.Mouillot, D. et al. Rare species support vulnerable functions in high-diversity ecosystems. PLoS Biol. 11, e1001569 (2013).PubMed 
    PubMed Central 
    CAS 
    Article 

    Google Scholar 
    40.Naeem, S. Species redundancy and ecosystem reliability. Conserv. Biol. 12, 39–45 (1998).Article 

    Google Scholar 
    41.Turner, P. J., Campbell, L. M. & Van Dover, C. L. Stakeholder perspectives on the importance of rare-species research for deep-sea environmental management. Deep Sea Res. Part Oceanogr. Res. Pap. 125, 129–134 (2017).ADS 
    Article 

    Google Scholar 
    42.Drury, W. H. Rare species. Biol. Conserv. 6, 162–169 (1974).Article 

    Google Scholar 
    43.Anderson, M. J., Gorley, R. N. & Clarke, K. R. PERMANOVA+ for PRIMER: Guide for Software and Statistical Methods (Primer-E Ltd, 2008).
    Google Scholar 
    44.Gollner, S. et al. Resilience of benthic deep-sea fauna to mining activities. Mar. Environ. Res. https://doi.org/10.1016/j.marenvres.2017.04.010 (2017).Article 
    PubMed 

    Google Scholar 
    45.Glover, A. G. et al. Polychaete species diversity in the central Pacific abyss: Local and regional patterns, and relationships with productivity. Mar. Ecol. Prog. Ser. 240, 157–170 (2002).ADS 
    Article 

    Google Scholar 
    46.Rosli, N., Leduc, D., Rowden, A. & Robert, K. Review of recent trends in ecological studies of deep-sea meiofauna, with focus on patterns and processes at small to regional spatial scales. Mar. Biodivers. 18, 13–34 (2018).Article 

    Google Scholar 
    47.Gallucci, F., Moens, T. & Fonseca, G. Small-scale spatial patterns of meiobenthos in the Arctic deep sea. Mar. Biodivers. 39, 9–25 (2009).Article 

    Google Scholar 
    48.Wieser, W. Die Beziehung zwischen Mundhöhlengestalt, Ernährungsweise und Vorkommen bei freilebenden marinen Nematoden Eine ökologisch-morphologische Studie. Ark. För Zool. 4, 439–483 (1953).
    Google Scholar 
    49.Leduc, D. Description of Oncholaimus moanae sp. nov. (Nematoda: Oncholaimidae), with notes on feeding ecology based on isotopic and fatty acid composition. J. Mar. Biol. Assoc. U. K. 89, 337–344 (2008).Article 
    CAS 

    Google Scholar 
    50.Pape, E., van Oevelen, D., Moodley, L., Soetaert, K. & Vanreusel, A. Nematode feeding strategies and the fate of dissolved organic matter carbon in different deep-sea sedimentary environments. Deep Sea Res. Part Oceanogr. Res. Pap. 80, 94–110 (2013).ADS 
    CAS 
    Article 

    Google Scholar 
    51.Schuelke, T., Pereira, T. J., Hardy, S. M. & Bik, H. M. Nematode-associated microbial taxa do not correlate with host phylogeny, geographic region or feeding morphology in marine sediment habitats. Mol. Ecol. 27, 1930–1951 (2018).PubMed 
    Article 

    Google Scholar 
    52.Tully, B. J. & Heidelberg, J. F. Microbial communities associated with ferromanganese nodules and the surrounding sediments. Extreme Microbiol. 4, 161 (2013).
    Google Scholar 
    53.Blöthe, M. et al. Manganese-cycling microbial communities inside deep-sea manganese nodules. Environ. Sci. Technol. 49, 7692–7700 (2015).ADS 
    PubMed 
    Article 
    CAS 

    Google Scholar 
    54.Maybury, C. Crevice Foraminifera from abyssal South East Pacific manganese nodules. In Microfossils and Oceanic Environments (eds Moguilevsky, A. & Whatley, R.) (University of Wales, 1996).
    Google Scholar 
    55.Pape, E., Bezerra, T. N., Hauquier, F. & Vanreusel, A. Limited spatial and temporal variability in meiofauna and nematode communities at distant but environmentally similar sites in an area of interest for deep-sea mining. Front. Mar. Sci. 4, 205 (2017).Article 

    Google Scholar 
    56.Uhlenkott, K., Vink, A., Kuhn, T. & Arbizu, P. M. Meiofauna in a potential deep-sea mining area—Influence of temporal and spatial variability on small-scale abundance models. Diversity 13, 3 (2021).CAS 
    Article 

    Google Scholar 
    57.Veillette, J., Juniper, S. K., Gooday, A. J. & Sarrazin, J. Influence of surface texture and microhabitat heterogeneity in structuring nodule faunal communities. Deep Sea Res. Part Oceanogr. Res. Pap. 54, 1936–1943 (2007).ADS 
    Article 

    Google Scholar 
    58.Tilot, V., Ormond, R., Moreno Navas, J. & Catalá, T. S. The Benthic Megafaunal Assemblages of the CCZ (Eastern Pacific) and an approach to their management in the face of threatened anthropogenic impacts. Front. Mar. Sci. 5, 7 (2018).Article 

    Google Scholar 
    59.ISA. Recommendations for the guidance of contractors for the assessment of the possible environmental impacts arising from exploration for marine minerals in the Area (2020).60.ISA. Draft regulations on exploitation of mineral resources in the Area (2019).61.ISA. Environmental Management Plan for the Clarion-Clipperton Zone (2011).62.Wedding, L. M. et al. From principles to practice: A spatial approach to systematic conservation planning in the deep sea. Proc. R. Soc. B Biol. Sci. 280, 20131684 (2013).CAS 
    Article 

    Google Scholar 
    63.ISA. Deep CCZ Biodiversity Synthesis Workshop Report. 206 (2020).64.McQuaid, K. A. et al. Using habitat classification to assess representativity of a protected area network in a large, data-poor area targeted for deep-sea mining. Front. Mar. Sci. 7, 558860 (2020).Article 

    Google Scholar 
    65.Mullineaux, L. S. The role of settlement in structuring a hard-substratum community in the deep sea. J. Exp. Mar. Biol. Ecol. 120, 247–261 (1988).Article 

    Google Scholar 
    66.Cuvelier, D. et al. Potential mitigation and restoration actions in ecosystems impacted by seabed mining. Front. Mar. Sci. 5, 467 (2018).Article 

    Google Scholar 
    67.De Smet, B. et al. The community structure of deep-sea macrofauna associated with polymetallic nodules in the eastern part of the Clarion-Clipperton fracture zone. Front. Mar. Sci. 4, 103 (2017).
    Google Scholar 
    68.Bezerra, T. N. et al. Nemys: World Database of Nematodes. http://nemys.ugent.be. https://doi.org/10.14284/366 (2021).69.George, K.-H. Gemeinschaftsanalytische Untersuchungen der Harpacticoidenfauna der Magellanregion, sowie erste similaritätsanalytische Vergleiche mit Assoziationen aus der Antarktis = Community analysis of the harpacticoid fauna of the Magellan Region, as well as first comparisons with antarctic associations, based on similarity analyses. Berichte Zur Polarforsch. Rep. Polar Res. 327, 1–187 (1999).
    Google Scholar 
    70.Moens, T. & Vincx, M. Observations on the feeding ecology of estuarine nematodes. J. Mar. Biol. Assoc. U. K. 77, 211–227 (1997).Article 

    Google Scholar 
    71.Guilini, K., Van Oevelen, D., Soetaert, K., Middelburg, J. J. & Vanreusel, A. Nutritional importance of benthic bacteria for deep-sea nematodes from the Arctic ice margin: Results of an isotope tracer experiment. Limnol. Oceanogr. 55, 1977–1989 (2010).ADS 
    CAS 
    Article 

    Google Scholar 
    72.Clarke, K. & Gorley, R. PRIMER v6: User Manual/Tutorial (Primer-E Ltd, 2006).
    Google Scholar 
    73.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).
    Google Scholar 
    74.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).MATH 
    Book 

    Google Scholar 
    75.Wilke, C. O. cowplot: Streamlined Plot Theme and Plot Annotations for ‘ggplot2’ (2019).76.Oksanen, J. et al. vegan: Community Ecology Package (2019).77.Martinez Arbizu, P. M. pairwiseAdonis: Pairwise Multilevel Comparison using Adonis (2017).78.Chao, A. et al. Rarefaction and extrapolation with Hill numbers: A framework for sampling and estimation in species diversity studies. Ecol. Monogr. 84, 45–67 (2014).Article 

    Google Scholar 
    79.Hsieh, T. C. & Chao, A. Package iNEXT 2.0.19: Interpolation and extrapolation of species diversity (2019).80.Schenker, N. & Gentleman, J. F. On judging the significance of differences by examining the overlap between confidence intervals. Am. Stat. 55, 182–186 (2001).MathSciNet 
    Article 

    Google Scholar 
    81.Gehlenborg, N. UpSetR: A More Scalable Alternative to Venn and Euler Diagrams for Visualizing Intersecting Sets (2019).82.Simpson, G. L. permute: Functions for Generating Restricted Permutations of Data (2019).83.Baselga, A., Orme, D., Villeger, S., Bortoli, J. D. & Leprieur, F. betapart: Partitioning Beta Diversity into Turnover and Nestedness Components (2018). More

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    The genome of Shorea leprosula (Dipterocarpaceae) highlights the ecological relevance of drought in aseasonal tropical rainforests

    Sequencing of Shorea leprosula genomeSample collectionLeaf samples of S. leprosula were obtained from a reproductively mature (diameter at breast height, 50 cm) diploid tree B1_19 (DNA ID 214) grown in the Dipterocarp Arboretum, Forest Research Institute Malaysia (FRIM).DNA extractionGenomic DNA was extracted from leaf samples using the 2% cetyltrimethylammonium bromide (CTAB) method90 and purified using a High Pure PCR Template Purification kit (Roche).Library preparation and sequencingPaired-end (170, 500, and 800 bp) and mate-pair (2 kb) genomic libraries were prepared using a TruSeq DNA Library Preparation kit (Illumina) and a Mate Pair Library Preparation kit (Illumina), respectively. Mate-pair libraries with larger insert sizes were constructed using a Nextera Mate Pair Library Preparation kit (Illumina). Ten micrograms of genomic DNA were tagmented in a 400 μl reaction and fractionated using SageELF, with the recovery of 11 fractions with 3–16+ kb. Each fraction was circularized and fragmented with a Covaris S2. Biotin-containing fragments were purified using Dynabeads M-280 streptavidin beads. Sequencing adapters (KAPA TruSeq Adapter kit) were attached using a KAPA Hyper Prep kit. The libraries were amplified for 10–13 cycles and purified with 0.8× AMpure XP. DNA libraries were then sequenced (~388× coverage) using Illumina HiSeq2000 (TruSeq libraries) and HiSeq2500 (Nextera libraries) at the Functional Genomics Center Zurich (FGCZ), University of Zurich, Switzerland (Supplementary Table 1).Genome assemblyAdapters and low-quality bases for all paired-end and mate-pair reads were removed using Trimmomatic91. The filtered paired-end reads of the 170 bp library were used to identify the genome size using k-mer distribution generated by Jellyfish92 that was implemented in the scripts by Joseph Ryan42. The raw R1 reads from paired-end 170 and 800 bp libraries (clipped at 95 bp, representing about 70 genome equivalents) were used to estimate the heterozygosity using KAT43 with a k-mer size of 23 nt. De novo genome assembly of all reads was performed using ALLPATHSLG assembler v5248840.Assembly verification and assessment of the assembled genomeAssembly validationTo validate the genome assembly, we mapped (i) the short reads used for the genome assembly, (ii) scanned the assembly for the presence of single-copy orthologs, and (iii) mapped transcriptome sequences obtained from seven organs.Assembly verification by mapping of short readsFor each library used for genome assembly, all trimmed reads were aligned to the assembled S. leprosula genome using Burrows–Wheeler Aligner (BWA) v0.7.1293. Then, mapping ratio was calculated for each BAM file using Samtools94 with “flagstat” command.Identification of highly conserved single-copy orthologsBUSCO v3.1.042 was run with the Embryophyta dataset and Arabidopsis as the species for AUGUSTUS prediction (see subsection below “Protein-coding gene prediction”).Assembly verification by mapping transcriptome sequencesFor mapping transcriptome sequences, samples of seven organs (leaf bud, flower bud, flower, inner bark, small seed, large seed, and calyx) were obtained from the S. leprosula individual used for the genome sequencing (Supplementary Table 2). Total RNA was extracted from each sample using RNeasy Plant Mini Kit (Qiagen) and it was treated with Turbo DNase I (Takara). Library preparation was carried out using a TruSeq RNA Library Preparation kit (Illumina). Paired-end sequencing was conducted for all the libraries using Illumina HiSeq2000 at the FGCZ, University of Zurich, Switzerland. Adapters and low-quality bases for all paired-end reads were removed using Trimmomatic. The trimmed sequences of each library were mapped onto the assembled genome using STAR aligner v2.4.2a95, and mapping ratio was obtained from the output file of STAR.Genome annotationRepeat sequence analysisBoth homology-based and de novo prediction analyses were used to identify the repeat content in the S. leprosula assembly. For the homology-based analysis, we used Repbase (version 20120418) to perform a TE search with RepeatMasker (4.0.5) and the WuBlast search engine. For the de novo prediction analysis, we used RepeatModeler to construct a TE library. Elements within the library were then classified by homology to Repbase sequences (see subsection below “Preparation of repeat sequences for evidence-based gene prediction”).Protein-coding gene predictionS. leprosula protein-coding genes were predicted by AUGUSTUS v3.245. For ab initio gene prediction, we used a pre-trained A. thaliana metaparameter implemented in AUGUSTUS. For the evidence-based gene prediction, we used the information of exon, intron and repeat sequences of S. leprosula as hints for the AUGUSTUS gene prediction. The details of the preparation of the hints were described in the following subsections.Preparation of repeat sequences for evidence-based gene predictionWe used RepeatModeler to construct a de novo library of repeated sequences in the S. leprosula assembly. Then, using RepeatMasker, we generated a file containing the information of the positions of repeat sequences in the S. leprosula genome based on the RepeatModeler library. Elements within the library were then classified by homology to Repbase sequences. Finally, the hint file for repeat sequences in GFF format was prepared using the two scripts, “10_makeGffRm.pl” and “12_makeTeHints.pl”, stored in https://gitlab.com/rbrisk/ahalassembly.Preparation of the exon and intron information for evidence-based gene predictionTo obtain the exon and intron hints, we used the mapping data of RNA-seq obtained from seven organs of the sequenced S. leprosula individual as described above. First, we merged all the mapping data stored in different BAM files into a single BAM file using SAMtools. Then, we prepared the intron hint file in GFF format using the, “bam2hints” script of AUGUSTUS. The exon hint file was also generated from the merged BAM file using the two AUGUSTUS scripts, “bam2wig” and “wig2hints.pl”. To conduct evidence-based gene prediction with AUGUSTUS, the three hint files (repeat sequences, intron and exon) described above were merged into a single file in GFF format.BUSCO analysisGenome annotation completeness were assessed with BUSCO v3.1.044 using the Embryophyta odb9 dataset composed of 1440 universal Embryophyta single-copy genes. We referred to these 1440 genes as core genes in the main text.Comparison with the proteome of Theobroma cacao
    T. cacao’s gene models18 were downloaded from Phytozome 11 (https://phytozome.jgi.doe.gov/pz/portal.html). Then, comparison was conducted with BLASTP96 using the T. cacao proteomes as the BLAST database (E-value cutoff: 1.0E-10). Only the best hit was stored for each gene. We considered these best hits of the T. cacao genes as orthologs of the S. leprosula genes. When the T. cacao orthologs were identified by the BLASTP search, the orthologs of A. thaliana were defined based on the T. cacao-A. thaliana orthologous information provided by Phytozome 11 (Supplementary Table 4). When the T. cacao orthologs were not identified, the orthologs of A. thaliana were searched by BLASTP (E-value cutoff: 1.0E-10) using the A. thaliana proteomes obtained from TAIR 10 (https://www.arabidopsis.org) as the BLAST database.Synteny analysisBased on the result of the above BLASTP searches, we assessed synteny between the S. leprosula scaffolds and the T. cacao chromosomes using MCScanX97. Genome information of T. cacao in GFF format was also obtained from Phytozome 11 as described above, which was used as an input file for MCScanX.Assessment of the genome assemblyPopulation data and other dipterocarp speciesTo assess whether the genome assembly could be used as a reference for the S. leprosula individuals from various populations, we checked mapping ratio, SNP positions, and admixture using the distribution-wide S. leprosula samples. Similarly, to assess whether the S. leprosula assembly could be used as a reference for aligning data from closely related species and determining their mapping ratios. For interspecific analysis, the following three Dipterocarpoideae species: S. platycarpa, D. aromatica, and N. heimii were used (Supplementary Table 7).Sample collection and DNA extractionLeaf samples of 19 S. leprosula individuals from different populations and three other dipterocarp species (S. platycarpa, D. aromatica, and N. heimii) were used as described in Supplementary Tables 6 and 7. Genomic DNA was extracted using the same method as described above.Library preparation and sequencingPaired-end genomic libraries (200 bp) were prepared using a TruSeq DNA Library Preparation kit (Illumina). DNA libraries were then sequenced (~16× coverage each) using Illumina HiSeq2000.Mapping and SNP callingAdapters and low-quality bases from resequencing reads were removed using Trimmomatic. All trimmed reads were then mapped and aligned to the S. leprosula assembly using BWA. Variants were called using GATK v3.598. Duplicated reads were marked using Picard 2.6.0. Within GATK, HaplotypeCaller was used to identify variants for each sample by generating an intermediate genomic variant call format (gVCF). Subsequently, gVCF files were merged using GenotypeGVCFs to produce a raw VCF file containing SNPs and INDELs. Low-quality variants were removed from the raw VCF file by applying the hard filters implemented in GATK. Variants with genotype quality (GQ)  More

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    Cenozoic climatic changes drive evolution and dispersal of coastal benthic foraminifera in the Southern Ocean

    1.Thomson, M. R. A. Geological and palaeoenvironmental history of the Scotia Sea region as a basis for biological interpretation. Deep Sea Res. II 51, 1467–1487 (2004).Article 
    ADS 

    Google Scholar 
    2.Maldonado, A. et al. A model of oceanic development by ridge jumping: Opening of the Scotia Sea. Glob. Planet. Change 123, 152–173 (2014).Article 
    ADS 

    Google Scholar 
    3.Crame, J. A. Key stages in the evolution of the Antarctic marine fauna. J. Biogeogr. 45, 986–994 (2018).Article 

    Google Scholar 
    4.Scher, H. D. & Martin, E. E. Timing and climatic consequences of the opening of the Drake Passage. Science 312, 428–430 (2006).CAS 
    PubMed 
    Article 
    ADS 

    Google Scholar 
    5.Eagles, G., Livermore, R. & Morris, P. Small basins in the Scotia Sea: the Eocene Drake passage gateway. Earth Planet. Sci. Lett. 242, 343–353 (2006).CAS 
    Article 
    ADS 

    Google Scholar 
    6.De Conto, R. M. & Pollard, D. Rapid Cenozoic glaciation of Antarctica induced by declining atmospheric CO2. Nature 421, 245–249 (2003).Article 
    ADS 
    CAS 

    Google Scholar 
    7.Anderson, J. B. et al. Progressive Cenozoic cooling and the demise of Antarctica’s last refugium. Proc. Natl. Acad. Sci. USA. 108, 11356–11360 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar 
    8.Arntz, W. E. Magellan-Antarctic: ecosystems that drifted apart. Summary review. Sci. Mar. 3(Suppl. 1), 503–511 (1999).Article 

    Google Scholar 
    9.Zachos, J., Pagani, M., Sloan, L., Thomas, E. & Billups, K. Trends, rhythms, and Aberrations in global climate 65 Ma to present. Science 292, 686–693 (2001).CAS 
    PubMed 
    Article 
    ADS 

    Google Scholar 
    10.Dalziel, I. W. D. et al. A potential barrier to deep Antarctic circumpolar flow until the Late Miocene?. Geology 41, 947–950 (2013).CAS 
    Article 
    ADS 

    Google Scholar 
    11.Anderson, J. B. et al. Ross Sea paleo-ice sheet drainage and deglacial history during and since the LGM. Quat. Sci. Rev. 100, 31–54 (2014).Article 
    ADS 

    Google Scholar 
    12.Klages, J. P. et al. Limited grounding-line advance onto the West Antarctic continental shelf in the easternmost Amundsen Sea Embayment during the last glacial period. PLoS ONE 12, e0181593 (2017).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    13.Thatje, S., Hillenbrand, C. D. & Larter, R. On the origin of Antarctic marine benthic community structure. Trends Ecol. Evol. 20, 534–540 (2005).PubMed 
    Article 

    Google Scholar 
    14.Fraser, C., Terauds, A., Smellie, J. L., Convey, P. & Chown, S. L. Geothermal activity helps life survive glacial cycles. Proc. Natl. Acad. Sci. USA. 111, 5634–5639 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar 
    15.Lau, S. C. Y., Wilson, N. G., Silva, C. N. S. & Strugnell, J. M. Detecting glacial refugia in the Southern Ocean. Ecography 43, 1639–1656 (2020).Article 

    Google Scholar 
    16.Naish, T. et al. Obliquity-paced Pliocene West Antarctic ice sheet oscillations. Nature 458, 322–328 (2009).CAS 
    PubMed 
    Article 
    ADS 

    Google Scholar 
    17.Clarke, A., Crame, J. A., Stromberg, J.-O. & Barker, P. F. The Southern Ocean benthic fauna and climate change: A historical perspective [and discussion]. Phil. Trans. R. Soc. B 338, 299–309 (1992).Article 
    ADS 

    Google Scholar 
    18.Clarke, A. & Crame, J. A. Evolutionary dynamics at high latitudes: speciation and extinction in polar marine faunas. Phil. Trans. R. Soc. B 365, 3655–3666 (2010).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    19.Barnes, D. K. A. & Conlan, K. E. Disturbance, colonization and development of Antarctic benthic communities. Philos. Trans. R. Soc. B 362, 11–38 (2007).Article 

    Google Scholar 
    20.Crame, J. A. An evolutionary perspective on marine faunal connections between southernmost South America and Antarctica. Sci. Mar. 63(Suppl 1), 1–14 (1999).Article 

    Google Scholar 
    21.Aronson, R. B. & Blake, D. B. Global climate change and the origin of modern benthic communities in Antarctica. Am. Zool. 41, 27–39 (2001).
    Google Scholar 
    22.Clarke, A., Aronson, R. B., Crame, A., Gili, J. M. & Blake, D. B. Evolution and diversity of the benthic fauna of the Southern Ocean continental shelf. Antarct. Sci. 16, 559–568 (2004).Article 
    ADS 

    Google Scholar 
    23.Aronson, R. B. et al. Climate change and trophic response of the Antarctic Bottom Fauna. PLoS ONE 4, e4385 (2009).PubMed 
    PubMed Central 
    Article 
    ADS 
    CAS 

    Google Scholar 
    24.Brandt, A. et al. First insights into the biodiversity and biogeography of the Southern Ocean deep sea. Nature 447, 307–311 (2007).CAS 
    PubMed 
    Article 
    ADS 

    Google Scholar 
    25.Orsi, A. H., Whitworth, T. W. & Nowlin, W. D. On the meridional extent and fronts of the Antarctic Circumpolar Current. Deep-Sea Res. I(42), 641–673 (1995).Article 

    Google Scholar 
    26.Mikhalevich, V. I. The general aspects of the distribution of Antarctic foraminifera. Micropaleontology 50, 179–194 (2004).
    Google Scholar 
    27.Gooday, A. J., Rothe, N., Bowser, S. S. & Pawlowski, J. Benthic foraminifera. Biogeographic atlas of the Southern Ocean (ed. De Broyer, C. et al.) 74–82 (SCAR Publications, 2014).28.Heron-Allen, E. & Earland, A. Foraminifera. Part I. The ice-free area of the Falkland Islands and adjacent seas. Discov. Rep. 4, 291–460 (1932).
    Google Scholar 
    29.Earland, A. Foraminifera, Part II, South Georgia. Discov. Rep. 7, 27–138 (1933).
    Google Scholar 
    30.Herb, R. Distribution of recent benthonic foraminifer in the Drake Passage. AGU Antarct. Res. Ser. 17, 251–300 (1971).
    Google Scholar 
    31.Thompson, L. Distribution of living benthic foraminifera, Isla de los Estados, Tierra del Fuego, Argentina. J. Foraminiferal Res. 8, 241–257 (1978).Article 
    ADS 

    Google Scholar 
    32.Dejardin, R. et al. “Live” stained) benthic foraminiferal living depths, stable isotopes, and taxonomy offshore South Georgia, Southern Ocean: Implications for calcification depths. J. Micropalaeontol. 37, 25–71 (2018).Article 
    ADS 

    Google Scholar 
    33.Arellano, F., Quezada, L. & Olave, C. Familia Cassidulinidae (Protozoa: Foraminiferida) en canales y fiordos patagónicos chilenos. An. Inst. Patagon. 39, 47–65 (2011).Article 
    CAS 

    Google Scholar 
    34.Hald, M. & Korsun, S. Distribution of modern benthic foraminifera from fjords of Svalbard, European Artic. J. Foraminiferal Res. 27, 101–122 (1997).Article 

    Google Scholar 
    35.Majewski, W., Bart, P. J. & McGlannan, A. J. Foraminiferal assemblages from ice-proximal paleo-settings in the Whales Deep Basin, eastern Ross Sea, Antarctica. Palaeogeogr. Palaeoclimatol. Palaeoecol. 493, 64–81 (2018).Article 

    Google Scholar 
    36.Majewski, W., Prothro, L. O., Simkins, L. M., Demianiuk, E. J. & Anderson, J. B. Foraminiferal patterns in deglacial sediment in the western Ross Sea, Antarctica: Life near grounding lines. Paleoceanogr. Paleoclimatol. 35, 003716 (2020).Article 

    Google Scholar 
    37.Majewski, W. & Anderson, J. B. Holocene foraminiferal assemblages from Firth of Tay, Antarctic Peninsula: Paleoclimate implications. Mar. Micropaleontol. 73, 135–147 (2009).Article 
    ADS 

    Google Scholar 
    38.Kilfeather, A. A. et al. Ice-stream retreat and ice-shelf history in Marguerite Trough, Antarctic Peninsula: Sedimentological and foraminiferal signatures. Geol. Soc. Am. Bull. 123, 997–1015 (2011).CAS 
    Article 
    ADS 

    Google Scholar 
    39.Hillenbrand, C. D. et al. West antarctic ice sheet retreat driven by Holocene warm water incursions. Nature 547, 43–48 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar 
    40.Leckie, R. M. & Webb, P. N. Late Paleogene and early Neogene foraminifers of deep sea drilling project site 270, Ross Sea, Antarctica. Initial Reports of the Deep Sea Drilling Project. Leg 90 (ed. Kennett, J. P. et al.) 1093–1118 (US Government Printing Office, 1986).41.Coccioni, R. & Galeotti, S. Foraminiferal biostratigraphy and paleoecology of the CIROS-1 core from McMurdo Sound (Ross Sea, Antarctica). Terra Antartica 4, 103–117 (1997).
    Google Scholar 
    42.Webb, P.-N. & Strong, C. P. Recycled Pliocene foraminifera from the CRP-1 Quaternary succession. Terra Antartica 5, 473–478 (1998).
    Google Scholar 
    43.Patterson, M. O. & Ishman, S. E. Neogene benthic foraminiferal assemblages and paleoenvironmetal record for McMurdo Sound, Antarctica. Geosphere 8, 1331–1341 (2012).Article 

    Google Scholar 
    44.Gaździcki, A. & Webb, P. N. Foraminifera from the Pecten Conglomerate (Pliocene) of Cockburn Island, Antarctic Peninsula. Palaeontol. Pol. 55, 147–174 (1996).
    Google Scholar 
    45.Gaździcki, A. & Majewski, W. Foraminifera from the Eocene La Meseta Formation of Isla Marambio (Seymour Island), Antarctic Peninsula. Antarct. Sci. 24, 408–416 (2012).Article 
    ADS 

    Google Scholar 
    46.Caramés, A. & Concheyro, A. Late cenozoic foraminifera from diamictites of Cape Lamb, Vega Island, Antarctic Peninsula. Ameghiniana 50, 114–135 (2013).Article 

    Google Scholar 
    47.Majewski, W. & Gaździcki, A. Shallow water benthic foraminifera from the Polonez Cove Formation (lower Oligocene) of King George Island, West Antarctica. Mar. Micropaleontol. 111, 1–14 (2014).Article 
    ADS 

    Google Scholar 
    48.Quilty, P. G. Reworked Paleocene and Eocene Foraminifera, Mac. Robertson Shelf, East Antarctica paleoenvironmental implications. J. Foraminiferal Res. 31, 369–384 (2001).Article 

    Google Scholar 
    49.Quilty, P. G. Foraminifera from late Pliocene sediments of Heidemann Valley, Vestfold Hills, East Antarctica. J. Foraminiferal Res. 40, 193–205 (2010).Article 

    Google Scholar 
    50.Majewski, W., Tatur, A., Witkowski, J. & Gaździcki, A. Rich shallow-water benthic ecosystem in Late Miocene East Antarctica (Fisher Bench Fm, Prince Charles Mountains). Mar. Micropaleontol. 133, 40–49 (2017).Article 
    ADS 

    Google Scholar 
    51.Pawlowski, J., Holzmann, M. & Tyszka, J. New supraordinal classification of Foraminifera: Molecules meet morphology. Mar. Micropaleontol. 100, 1–10 (2013).Article 
    ADS 

    Google Scholar 
    52.Pawlowski, J. & Holzmann, M. A plea for DNA barcoding of foraminifera. Mar. Biodivers. 44, 213–221 (2014).Article 

    Google Scholar 
    53.Roberts, A. et al. A New integrated approach to taxonomy: The fusion of molecular and morphological systematics with type material in Benthic Foraminifera. PLoS ONE 11, e0158754 (2016).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    54.Holzmann, M. & Pawlowski, J. An updated classification of rotaliid foraminifera based on ribosomal DNA phylogeny. Mar. Micropaleontol. 132, 18–34 (2017).Article 
    ADS 

    Google Scholar 
    55.Majewski, W. & Pawlowski, J. Morphologic and molecular diversity of the foraminiferal genus Globocassidulina in Admiralty Bay, West Antarctica. Antarct. Sci. 22, 271–281 (2010).Article 
    ADS 

    Google Scholar 
    56.Majewski, W., Bowser, S. S. & Pawlowski, J. Widespread intra-specific genetic homogeneity of coastal Antarctic benthic foraminifera. Polar Biol. 38, 1–12 (2015).Article 

    Google Scholar 
    57.Majda, A. et al. Variable dispersal histories across the Drake Passage: The case of coastal benthic Foraminifera. Mar. Micropaleontol. 140, 81–94 (2018).Article 
    ADS 

    Google Scholar 
    58.Gschwend, F., Majda, A., Majewski, W. & Pawlowski, J. Psammophaga fuegia sp. nov., a new monothalamid foraminifer from the Beagle Channel, South America. Acta Protozool. 55, 101–110 (2016).CAS 

    Google Scholar 
    59.Pawlowski, J. Introduction to the molecular systematics of foraminifera. Micropaleontology 46(Suppl 1), 1–12 (2000).
    Google Scholar 
    60.Gouy, M., Guindon, S. & Gascuel, O. SeaView version 4: A multiplatform graphical user interface for sequence alignment and phylogenetic tree building. Mol. Biol. Evol. 27, 221–224 (2010).CAS 
    Article 

    Google Scholar 
    61.Puillandre, N., Lambert, A., Brouillet, S. & Achaz, G. ABGD, Automatic barcode gap discovery for primary species delimitation. Mol. Ecol. 21, 1864–1877 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    62.Fujisawa, T. & Barraclough, T. G. Delimiting species using single-locus data and the generalized mixed yule coalescent (GMYC) Approach: A revised method and evaluation on simulated datasets. Syst. Biol. 62, 707–724 (2013).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    63.Zhang, J., Kapli, P., Pavlidis, P. & Stamatakis, A. A general species delimitation method with applications to phylogenetic placements. Bioinformatics 29, 2869–2876 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    64.Kapli, P. et al. Multi-rate Poisson tree processes for single-locus species delimitation under maximum likelihood and Markov chain Monte Carlo. Bioinformatics 33, 1630–1638 (2017).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    65.Bouckaert, R. et al. BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis. PLoS Comput. Biol. 15, e1006650 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    66.Ezard, T., Fujisawa, T. & Barraclough, T. G. SPLITS: SPecies’ LImits by Threshold Statistics. R package version 1.0-18/r45, http://R-Forge.R-project.org/projects/splits/ (2009).67.R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, Vienna). http://www.R-project.org/ (2020).68.Stamatakis, A. RAxML Version 8: A tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    69.Hall, T. A. BioEdit: A user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucleic Acids Symp. Ser. 41, 95–98 (1999).CAS 

    Google Scholar 
    70.Leigh, J. W. & Bryant, D. PopART: Full-feature software for haplotype network construction. Methods Ecol. Evol. 6, 1110–1116 (2015).Article 

    Google Scholar 
    71.Bandelt, H., Forster, P. & Röhl, A. Median-joining networks for inferring intraspecific phylogenies. Mol. Biol. Evol. 16, 37–48 (1999).CAS 
    PubMed 
    Article 

    Google Scholar 
    72.Tajima, F. Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics 123, 585–595 (1989).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    73.Fu, Y. X. New statistical tests of neutrality for DNA samples from a population. Genetics 143, 557–570 (1996).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    74.Rozas, J. et al. DnaSP 6: DNA sequence polymorphism analysis of large data sets. Mol. Biol. Evol. 34, 3299–3302 (2017).CAS 
    PubMed 
    Article 

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

    Google Scholar 
    76.Schweizer, M., Pawlowski, J., Kouwenhoven, T. J., Guiard, J. & van der Zwaan, G. J. Molecular phylogeny of Rotaliida (Foraminifera) based on complete small subunit rDNA sequences. Mar. Micropaleontol. 66, 233–246 (2008).Article 
    ADS 

    Google Scholar 
    77.Schweizer, M., Pawlowski, J., Kouwenhoven, T. & Van Der Zwaan, B. Molecular phylogeny of common Cibicidids and related rotaliida (Foraminifera) based on small subunit rDNA sequences. J. Foraminiferal Res. 39, 300–315 (2009).Article 

    Google Scholar 
    78.Schweizer, M. Evolution and molecular phylogeny of Cibicides and Uvigerina (Rotaliid, Foraminifera). Geol. Ultraiectina 261, 1–167 (2006).
    Google Scholar 
    79.Bouckaert, R. R. & Drummond, A. J. bModelTest: Bayesian phylogenetic site model averaging and model comparison. BMC Evol. Biol. 17, 42 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    80.Rambaut, A., Drummond, A. J., Xie, D., Baele, G. & Suchard, M. A. Posterior summarisation in Bayesian phylogenetics using Tracer 1.7. Syst. Biol. 67, 901–904 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    81.Loeblich, A. R. & Tappan, H. Foraminiferal Genera and their Classification (Van Nostrand Reinhold, 1987).
    Google Scholar 
    82.D’haenens, S., Bornemann, A., Stassen, P. & Speijer, R. Multiple early Eocene benthic foraminiferal assemblages and δ13C fluctuations at DSDP Site 401 (Bay of Biscay: NE Atlantic). Mar. Micropaleontol. 88–89, 15–35 (2012).Article 
    ADS 

    Google Scholar 
    83.Cushman, J. A. & Stone, B. Foraminifera from the Eocene, Chacra Formation, of Peru. Cont. Cushman Lab. Foram. Res. 25, 49–58 (1949).
    Google Scholar 
    84.Arreguin-Rodriguez, G. J., Thomas, E., Dhaenens, S., Speijer, R. P. & Alegret, L. Early eocene deep-sea benthic foraminiferal faunas: Recovery from the paleocene eocene thermal maximum extinction in a greenhouse world. PLoS ONE 13, e0193167 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    85.Proto Decima, F. & Ferasin, F. Nuove specie di foraminiferi nell’Eocene del Monte Ceva (Colli Euganei). Riv. Ital. Paleont. Strat. 60, 247–252 (1954).
    Google Scholar 
    86.Cushman, J. A. A rich foraminiferal fauna from the Cocoa Sand of Alabama. Cushman Lab. Foram. Res. Spec. Pub. 16, 1–40 (1946).
    Google Scholar 
    87.Heron-Allen, E. & Earland, A. Protozoa, Part 2. Foraminifera. Nat. Hist. Rep. Br. Antarct. Exp. 6, 25–268 (1922).
    Google Scholar 
    88.Shevenell, A. E., Kennett, J. P. & Lea, D. W. Middle Miocene ice sheet dynamics, deep-sea temperatures, and carbon cycling: A Southern Ocean perspective. Geochem. Geophys. Geosy. 9, Q02006 (2008).Article 
    ADS 
    CAS 

    Google Scholar 
    89.Lawver, L. A. & Gahagan, L. M. Evolution of Cenozoic seaways in the circum-Antarctic region. Palaeogeogr. Palaeoclimatol. Palaeoecol. 198, 11–37 (2003).Article 

    Google Scholar 
    90.Lewis, A. R. et al. Mid-Miocene cooling and the extinction of tundra in continental Antarctica. Proc. Natl. Acad. Sci. USA 105, 10676–10680 (2008).CAS 
    PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar 
    91.Verducci, M. et al. The Middle Miocene climatic transition in the Southern Ocean: Evidence of paleoclimatic and hydrographic changes at Kerguelen plateau from planktonic foraminifers and stable isotopes. Palaeogeogr. Palaeoclimatol. Palaeoecol. 280, 371–386 (2009).Article 

    Google Scholar 
    92.Majewski, W. & Bohaty, S. Surface−water cooling and salinity decrease during the Middle Miocene Climate Transition at Southern Ocean ODP Site 747 (Kerguelen Plateau). Mar. Micropaleontol. 74, 1–14 (2010).Article 
    ADS 

    Google Scholar 
    93.Cheng, C. H. C. & Detrich, H. W. Molecular ecophysiology of Antarctic notothenioid fishes. Philos. Trans. R. Soc. B 362, 2215–2232 (2007).CAS 
    Article 

    Google Scholar 
    94.Barco, A., Schiaparelli, S., Houart, R. & Oliverio, M. Cenozoic evolution of Muricidae (Mollusca, Neogastropoda) in the Southern Ocean, with the description of a new subfamily. Zool. Scr. 41, 596–616 (2012).Article 

    Google Scholar 
    95.González-Wevar, C. A., Nakano, T., Canete, J. I. & Poulin, E. Molecular phylogeny and historical biogeography of Nacella (Patellogastropoda: Nacellidae) in the Southern Ocean. Mol. Phylogen. Evol. 56, 115–124 (2010).Article 

    Google Scholar 
    96.González-Wevar, C. A. et al. Following the Antarctic Circumpolar Current: Patterns and processes in the biogeography of the limpet Nacella (Mollusca: Patellogastropoda) across the Southern Ocean. J. Biogeogr. 44, 861–874 (2017).Article 

    Google Scholar 
    97.González-Wevar, C. A. et al. Cryptic speciation in Southern Ocean Aequiyoldia eightsii (Jay, 1839): Mio-Pliocene trans-Drake separation and diversification. Prog. Oceanogr. 174, 44–54 (2019).Article 
    ADS 

    Google Scholar 
    98.Strugnell, J. M., Rogers, A. D., Prodohl, P. A., Collins, M. A. & Allcock, A. L. The thermohaline expressway: The Southern Ocean as a centre of origin for deep-sea octopuses. Cladistics 24, 853–860 (2008).Article 

    Google Scholar 
    99.Feakins, S., Warny, S. & Lee, J. E. Hydrologic cycling over Antarctica during the middle Miocene warming. Nat. Geosci. 5, 557–560 (2012).CAS 
    Article 
    ADS 

    Google Scholar 
    100.Malumián, N. Foraminíferos bentónicos de la localidad tipo de la Formación La Despedida (Eoceno, Isla Grande de Tierra del Fuego) Part I: Textulariina y Miliolina. Ameghiniana 25, 341–356 (1989).
    Google Scholar 
    101.Scarpa, R. & Malumián, N. Foraminíferos del Oligoceno inferior de los Andes Fueguinos, Argentina: Su significado tectónico-ambiental. Ameghiniana 45, 361–376 (2008).
    Google Scholar 
    102.Galeotti, S., Cita, M. B. & Coccioni, R. Foraminiferal biostratigraphy and palaeoecology from two intervals of the CRP2/2A drilhole. Terra Antartica 7, 473–478 (2000).
    Google Scholar 
    103.Malumián, N. & El Olivero, E. B. Grupo Cabo Domingo, Tierra del Fuego: Bioestratigrafía, paleoambientes y acontecimientos del Eoceno-Mioceno marino. Rev. Asoc. Geol. Argent. 61, 139–160 (2006).
    Google Scholar 
    104.Li, B., Yoon, H. I. & Park, B. K. Foraminiferal assemblages and CaCO3 dissolution since the last deglaciation in the Maxwell Bay King George Island, Antarctica. Mar. Geol. 169, 239–257 (2000).CAS 
    Article 
    ADS 

    Google Scholar 
    105.Majewski, W. Benthic foraminiferal communities: Distribution and ecology in Admiralty Bay, King George Island, West Antarctica. Pol. Polar Res. 26, 159–214 (2005).
    Google Scholar 
    106.Corliss, B. Size variation in the deep-sea benthonic foraminifer Globocassidulina subglobosa (Brady) in the Southeast Indian Ocean. J. Foraminiferal Res. 9, 50–60 (1979).Article 

    Google Scholar 
    107.Wright, J. D. & Miller, K. G. Southern ocean influences on late eocene to miocene deepwater circulation. Antarct. Res. Ser. 60, 1–25 (1993).Article 

    Google Scholar 
    108.Colleoni, F. et al. Past continental shelf evolution increased Antarctic ice sheet sensitivity to climatic conditions. Sci. Rep. 8, 11323 (2018).PubMed 
    PubMed Central 
    Article 
    ADS 
    CAS 

    Google Scholar 
    109.Strugnell, J. M. et al. The Southern ocean: Source and sink?. Deep-Sea Res. II 58, 196–204 (2011).CAS 
    Article 
    ADS 

    Google Scholar 
    110.Verheye, M. L., Backeljau, T. & d’Udekem d’Acoz, C. Locked in the icehouse: Evolution of an endemic Epimeria (Amphipoda, Crustacea) species flock on the Antarctic shelf. Mol. Phylogenet. Evol. 114, 14–33 (2017).PubMed 
    Article 

    Google Scholar 
    111.Galeotti, S. & Coccioni, R. Foraminiferal analysis of the Miocenc CRP-l core (Ross Sea, Antarctica). Terra Antartica 5, 521–526 (1998).
    Google Scholar 
    112.Pillet, L., Fontaine, D. & Pawlowski, J. Intra-genomic ribosomal RNA polymorphism and morphological variation in Elphidium macellum suggests inter-specific hybridization in Foraminifera. PLoS ONE 7, e32373 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar 
    113.Darling, J. Interspecific hybridization and mitochondrial introgression in invasive Carcinus shore crabs. PLoS ONE 6, e17828 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar 
    114.Dietz, L. et al. Regional differentiation and extensive hybridization between mitochondrial clades of the Southern Ocean giant sea spider Colossendeis megalonyx. R. Soc. Open Sci. 2, 140424 (2015).PubMed 
    PubMed Central 
    Article 
    ADS 
    CAS 

    Google Scholar 
    115.Ruiz, M. B., Taverna, A., Servetto, N., Sahade, R. & Held, C. Hidden diversity in Antarctica: Molecular and morphological evidence of two different species within one of the most conspicuous ascidian species. Ecol. Evol. 10, 8127–8143 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    116.Fraser, C. I. et al. Antarctica’s ecological isolation will be broken by storm-driven dispersal and warming. Nat. Clim. Change 8, 704–708 (2018).Article 
    ADS 

    Google Scholar 
    117.Avila, C. et al. Invasive marine species discovered on non–native kelp rafts in the warmest Antarctic island. Sci. Rep. 10, 1639 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar 
    118.Alve, E. & Goldstein, S. T. Propagule transport as a key method of dispersal in benthic Foraminifera (Protista). Limnol. Oceanogr. 48, 2163–2170 (2003).Article 
    ADS 

    Google Scholar 
    119.Alve, E. & Goldstein, S. T. Dispersal, survival and delayed growth of benthic foraminiferal propagules. J. Sea Res. 63, 36–51 (2010).Article 
    ADS 

    Google Scholar 
    120.Burke, K. D. et al. Pliocene and Eocene provide best analogs for near-future climates. Proc. Natl. Acad. Sci. USA. 115, 13288–13293 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar 
    121.Carter, A., Curtis, M. & Schwanenthal, J. Cenozoic tectonic history of the South Georgia microcontinent and potential as a barrier to Pacific-Atlantic through flow. Geology 42, 299–302 (2014).CAS 
    Article 
    ADS 

    Google Scholar 
    122.Clarke, A., Barnes, D. K. A. & Hodgson, D. A. How isolated is Antarctica?. Trends Ecol. Evol. 20, 1–3 (2005).PubMed 
    Article 

    Google Scholar 
    123.Glorioso, P. D., Piola, A. R. & Leben, R. R. Mesoscale eddies in the Subantarctic Front: Southwest Atlantic. Sci. Mar. 69(Suppl 2), 7–15 (2012).
    Google Scholar 
    124.Bart, P. J. & Iwai, M. The overdeepening hyphothesis: how erosional modification of the marine-scape during the early Pliocene altered glacial dynamics on the Antarctic Peninsula’s Pacific margin. Palaeogeogr. Palaeoclimatol. Palaeoecol. 335–336, 42–51 (2012).Article 

    Google Scholar 
    125.González-Wevar, C. A., Díaz, A., Gerard, K., Caňete, J. I. & Poulin, E. Divergence time estimations and contrasting patterns of genetic diversity between Antarctic and southern South America benthic invertebrates. Rev. Chil. Hist. Nat. 85, 445–456 (2012).Article 

    Google Scholar 
    126.Poulin, E., González-Wevar, C., Díaz, A., Gérard, K. & Hüne, M. Divergence between Antarctic and South American marine invertebrates: what molecular biology tells us about the Scotia Arc geodynamics and the intensification of the Antarctic Circumpolar Current. Glob. Planet. Change. 123, 392–399 (2014).Article 
    ADS 

    Google Scholar 
    127.McKay, R. et al. Pleistocene variability of Antarctic ice sheet extent in the Ross embayment. Quat. Sci. Rev. 34, 93–112 (2012).Article 
    ADS 

    Google Scholar 
    128.Pollard, D. & DeConto, R. M. Modelling West Antarctic ice sheet growth and collapse through the past five million years. Nature 458, 329–332 (2009).CAS 
    PubMed 
    Article 
    ADS 

    Google Scholar 
    129.Allcock, A. L. & Strugnell, J. M. Southern Ocean diversity: New paradigms from molecular ecology. Trends Ecol. Evol. 278, 520–528 (2012).Article 

    Google Scholar 
    130.Wilson, N. G., Schrödl, M. & Halanych, K. M. Ocean barriers and glaciation: Evidence for explosive radiation of mitochondrial lineages in the Antarctic sea slug Doris kerguelenensis (Mollusca, Nudibranchia). Mol. Ecol. 18, 965–984 (2009).PubMed 
    Article 

    Google Scholar 
    131.Arango, C. P., Soler-Membrives, A. & Miller, K. J. Genetic differentiation in the circum-Antarctic sea spider Nymphon australe (Pycnogonida; Nymphonidae). Deep Sea Res. II 58, 212–219 (2011).CAS 
    Article 
    ADS 

    Google Scholar 
    132.Fraser, C. I., Nikula, R., Ruzzante, D. E. & Waters, J. M. Poleward bound: Biological impacts of Southern Hemisphere glaciation. Trends Ecol. Evol. 27, 462–471 (2012).PubMed 
    Article 

    Google Scholar 
    133.Darling, K. F., Kucera, M., Pudsey, C. J. & Wade, C. M. Molecular evidence links cryptic diversification in polar planktonic protists to quaternary climate dynamics. Proc. Natl. Acad. Sci. USA. 101, 7657–7662 (2004).CAS 
    PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar 
    134.Quilty, P. G. Neogene foraminifers and accessories, ODP Leg 188, Sites 1165, 1166, and 1167, Prydz Bay, Antarctica. Proc. Ocean Drill. Prog. Sci. Results 188, 1–41 (2003).
    Google Scholar 
    135.Díaz, A. et al. Genetic structure and demographic inference of the regular sea urchin Sterechinus neumayeri (Meissner, 1900) in the Southern Ocean: The role of the last glaciation. PLoS ONE 13, e0197611 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    136.Brey, T., Dahm, C., Gorny, M., Stiller, M. & Arntz, W. E. Do Antarctic benthic invertebrates show extended levels of eurybathy?. Ant. Sci. 8, 3–6 (1996).Article 

    Google Scholar 
    137.Dambach, J., Thatje, S., Rödder, D., Basher, Z. & Raupach, M. J. Effects of Late-Cenozoic glaciation on habitat availability in Antarctic benthic shrimps (Crustacea: Decapoda: Caridea). PLoS ONE 7, e4628 (2012).Article 
    CAS 

    Google Scholar 
    138.Soler-Membrives, A., Linse, K., Miller, K. J. & Arango, C. P. Genetic signature of Last Glacial Maximum regional refugia in a circum-Antarctic sea spider. R. Soc. Open Sci. 4, 170615 (2017).PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar 
    139.Holbourn, A., Henderson, A. & McLeod, N. Atlas of Benthic Foraminifera (Wiley-Blackwell, 2013).Book 

    Google Scholar 
    140.Gooday, A. J. & Jorissen, F. J. Benthic foraminiferal biogeography: Controls on global distribution patterns in deep-water settings. Ann. Rev. Mar. Sci. 4, 237–262 (2012).PubMed 
    Article 

    Google Scholar 
    141.Melis, R. & Salvi, G. Late Quaternary foraminiferal assemblages from western Ross Sea (Antarctica) in relation to the main glacial and marine lithofacies. Mar. Micropaleontol. 70, 39–53 (2009).Article 
    ADS 

    Google Scholar 
    142.Majewski, W., Wellner, J. S. & Anderson, J. B. Environmental connotations of benthic foraminiferal assemblages from coastal West Antarctica. Mar. Micropaleontol. 124, 1–15 (2016).Article 
    ADS 

    Google Scholar 
    143.Majewski, W., Stolarski, J. & Bart, P. J. Two rare pustulose/sponose morphotypes of benthic foraminifera from eastern Ross Sea. J. Foraminiferal Res. 49, 405–422 (2019).Article 

    Google Scholar 
    144.Davies, B. J. et al. The evolution of the Patagonian Ice Sheet from 35 ka to the present day (PATICE). Earth Sci. Rev. 204, 103152 (2020).Article 

    Google Scholar 
    145.González-Wevar, C. A. et al. Phylogeography in Galaxias maculatus (Jenyns, 1848) along two biogeographical provinces in the Chilean coast. PLoS ONE 10, e0131289 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    146.Ocaranza-Barrera, P., González Wevar, C. A., Guillemin, M.-L., Rosenfeld, S. & Mansilla, A. Molecular divergence between Iridaea cordata (Turner) Bory de Saint-Vincent from the Antarctic Peninsula and the Magellan Region. J. Appl. Phycol. 31, 939–949 (2019).CAS 
    Article 

    Google Scholar 
    147.Yasuhara, M., Tittensor, D. P., Hillebrand, H. & Worm, B. Combining marine macroecology and palaeoecology in understanding biodiversity: Microfossils as a model. Biol. Rev. 92, 199–215 (2017).PubMed 
    Article 

    Google Scholar 
    148.Yasuhara, M. et al. Time machine biology: Cross-timescale integration of ecology, evolution, and oceanography. Oceanography 33, 16–28 (2020).Article 

    Google Scholar 
    149.Meredith, M. P. & King, J. C. Rapid climate change in the ocean west of the Antarctic Peninsula during the second half of the 20th century. Geophys. Res. Lett. 32, L19604 (2005).ADS 

    Google Scholar 
    150.Convey, P. & Peck, L. S. Antarctic environmental change and biological responses. Sci. Adv. 11, 0888 (2019).ADS 

    Google Scholar 
    151.Ingels, J. et al. Possible effects of global environmental changes on Antarctic benthis: A synthesis across five major taxa. Ecol. Evol. 2, 453–485 (2012).PubMed 
    PubMed Central 
    Article 

    Google Scholar  More

  • in

    An evaluation of multi-species empirical tree mortality algorithms for dynamic vegetation modelling

    1.Allen, C. D., Breshears, D. D. & McDowell, N. G. On underestimation of global vulnerability to tree mortality and forest die-off from hotter drought in the Anthropocene. Ecosphere 6, 1–55. https://doi.org/10.1890/Es15-00203.1 (2015).Article 

    Google Scholar 
    2.Allen, C. D. et al. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For. Ecol. Manag. 259, 660–684. https://doi.org/10.1016/j.foreco.2009.09.001 (2010).Article 

    Google Scholar 
    3.Anderegg, W. R. L., Kane, J. M. & Anderegg, L. D. L. Consequences of widespread tree mortality triggered by drought and temperature stress. Nat Clim Change 3, 30–36 (2013).ADS 
    Article 

    Google Scholar 
    4.Taccoen, A. et al. Background mortality drivers of European tree species: climate change matters. Proc R Soc B-Biol Sci 286, 1–10. https://doi.org/10.1098/rspb.2019.0386 (2019).Article 

    Google Scholar 
    5.Hartmann, H. et al. Research frontiers for improving our understanding of drought-induced tree and forest mortality. New Phytol. 218, 15–28. https://doi.org/10.1111/nph.15048 (2018).Article 
    PubMed 

    Google Scholar 
    6.Trugman, A. T., Anderegg, L. D. L., Anderegg, W. R. L., Das, A. J. & Stephenson, N. L. Why is tree drought mortality so hard to predict? Trends Ecol. Evol., 1–13. https://doi.org/10.1016/j.tree.2021.02. (2021).7.McDowell, N. G. et al. Evaluating theories of drought-induced vegetation mortality using a multimodel-experiment framework. New Phytol. 200, 304–321. https://doi.org/10.1111/nph.12465 (2013).CAS 
    Article 
    PubMed 

    Google Scholar 
    8.Keane, R. E. et al. Tree mortality in gap models: application to climate change. Clim. Change 51, 509–540. https://doi.org/10.1023/A:1012539409854 (2001).Article 

    Google Scholar 
    9.Bircher, N., Cailleret, M. & Bugmann, H. The agony of choice: different empirical mortality models lead to sharply different future forest dynamics. Ecol. Appl. 25, 1303–1318. https://doi.org/10.1890/14-1462.1 (2015).Article 
    PubMed 

    Google Scholar 
    10.Bugmann, H. et al. Tree mortality submodels drive long term forest dynamics: an assessment across 15 models from the stand to the global scale. Ecosphere 10, 1–22. https://doi.org/10.1002/ecs2.2616 (2019).Article 

    Google Scholar 
    11.Friend, A. D. et al. Carbon residence time dominates uncertainty in terrestrial vegetation responses to future climate and atmospheric CO2. Proc Natl Acad Sci USA 111, 3280–3285. https://doi.org/10.1073/pnas.1222477110 (2014).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    12.Lines, E. R., Coomes, D. A. & Purves, D. W. Influences of forest structure, climate and species composition on tree mortality across the Eastern US. PLoS ONE 5, 1–12. https://doi.org/10.1371/journal.pone.0013212 (2010).CAS 
    Article 

    Google Scholar 
    13.Purves, D. & Pacala, S. Predictive models of forest dynamics. Science 320, 1452–1453. https://doi.org/10.1126/science.1155359 (2008).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    14.Cailleret, M., Bircher, N., Hartig, F., Hülsmann, L. & Bugmann, H. Bayesian calibration of a growth-dependent tree mortality model to simulate the dynamics of European temperate forests. Ecol. Appl. 30, 1–17. https://doi.org/10.1002/eap.2021 (2020).Article 

    Google Scholar 
    15.Rowland, L., Martinez-Vilalta, J. & Mencuccini, M. Hard times for high expectations from hydraulics: predicting drought-induced forest mortality at landscape scales remains a challenge. New Phytol. 230, 1685–1687. https://doi.org/10.1111/nph.17317 (2021).Article 
    PubMed 

    Google Scholar 
    16.Cailleret, M. et al. A synthesis of radial growth patterns preceding tree mortality. Glob. Change Biol. 23, 1675–1690. https://doi.org/10.1111/gcb.13535 (2017).ADS 
    Article 

    Google Scholar 
    17.Bigler, C. & Bugmann, H. Growth-dependent tree mortality models based on tree rings. Can. J. For. Res. 33, 210–221. https://doi.org/10.1139/X02-180 (2003).Article 

    Google Scholar 
    18.Hülsmann, L., Bugmann, H., Cailleret, M. & Brang, P. How to kill a tree: empirical mortality models for 18 species and their performance in a dynamic forest model. Ecol. Appl. 28, 522–540. https://doi.org/10.1002/eap.1668 (2018).Article 
    PubMed 

    Google Scholar 
    19.Weiskittel, A. R., Hann, D. W., Kershaw, J. A. & Vanclay, J. K. in Forest Growth and Yield Modeling Ch. 8, 139–155 (Wiley, 2011).20.Holzwarth, F., Kahl, A., Bauhus, J. & Wirth, C. Many ways to die – partitioning tree mortality dynamics in a near-natural mixed deciduous forest. J. Ecol. 101, 220–230. https://doi.org/10.1111/1365-2745.12015 (2013).Article 

    Google Scholar 
    21.Dobbertin, M. Tree growth as indicator of tree vitality and of tree reaction to environmental stress: a review. Eur. J. For. Res. 124, 319–333. https://doi.org/10.1007/s10342-005-0085-3 (2005).Article 

    Google Scholar 
    22.Thrippleton, T., Hülsmann, L., Cailleret, M. & Bugmann, H. Projecting forest dynamics across Europe: potentials and pitfalls of empirical mortality algorithms. Ecosystems 23, 188–203. https://doi.org/10.1007/s10021-019-00397-3 (2020).Article 

    Google Scholar 
    23.Adams, H. D. et al. Empirical and process-based approaches to climate-induced forest mortality models. Front Plant Sci 4, 1–5. https://doi.org/10.3389/fpls.2013.00438 (2013).ADS 
    CAS 
    Article 

    Google Scholar 
    24.Archambeau, J. et al. Similar patterns of background mortality across Europe are mostly driven by drought in European beech and a combination of drought and competition in Scots pine. Agric. For. Meteorol. 280, 1–12. https://doi.org/10.1016/j.agrformet.2019.107772 (2020).Article 

    Google Scholar 
    25.Luo, Y. & Chen, H. Y. H. Competition, species interaction and ageing control tree mortality in boreal forests. J. Ecol. 99, 1470–1480. https://doi.org/10.1111/j.1365-2745.2011.01882.x (2011).Article 

    Google Scholar 
    26.Brzeziecki, B. & Kienast, F. Classifying the life-history strategies of trees on the basis of the grimian model. For. Ecol. Manage. 69, 167–187. https://doi.org/10.1016/0378-1127(94)90227-5 (1994).Article 

    Google Scholar 
    27.Valladares, F. & Niinemets, U. Shade tolerance, a key plant feature of complex nature and consequences. Annu. Rev. Ecol. Evol. Syst. 39, 237–257. https://doi.org/10.1146/annurev.ecolsys.39.110707.173506 (2008).Article 

    Google Scholar 
    28.Kobe, R. K. & Coates, K. D. Models of sapling mortality as a function of growth to characterize interspecific variation in shade tolerance of eight tree species of northwestern British Columbia. Can. J. For. Res. 27, 227–236. https://doi.org/10.1139/x96-182 (1997).Article 

    Google Scholar 
    29.Wyckoff, P. H. & Clark, J. S. The relationship between growth and mortality for seven co-occurring tree species in the southern Appalachian Mountains. J. Ecol. 90, 604–615. https://doi.org/10.1046/j.1365-2745.2002.00691.x (2002).Article 

    Google Scholar 
    30.Anderegg, L. D. L. & HilleRisLambers, J. Drought stress limits the geographic ranges of two tree species via different physiological mechanisms. Glob. Change Biol. 22, 1029–1045. https://doi.org/10.1111/gcb.13148 (2016).ADS 
    Article 

    Google Scholar 
    31.Clark, J. S. et al. The impacts of increasing drought on forest dynamics, structure, and biodiversity in the United States. Glob. Change Biol. 22, 2329–2352. https://doi.org/10.1111/gcb.13160 (2016).ADS 
    Article 

    Google Scholar 
    32.Etzold, S. et al. One century of forest monitoring data in Switzerland reveals species- and site-specific trends of climate-induced tree mortality. Front Plant Sci 10, 1–19. https://doi.org/10.3389/fpls.2019.00307 (2019).Article 

    Google Scholar 
    33.Schuldt, B. et al. A first assessment of the impact of the extreme 2018 summer drought on Central European forests. Basic Appl. Ecol. 45, 86–103. https://doi.org/10.1016/j.baae.2020.04.003 (2020).Article 

    Google Scholar 
    34.Vanoni, M., Cailleret, M., Hülsmann, L., Bugmann, H. & Bigler, C. How do tree mortality models from combined tree-ring and inventory data affect projections of forest succession?. For. Ecol. Manage. 433, 606–617. https://doi.org/10.1016/j.foreco.2018.11.042 (2019).Article 

    Google Scholar 
    35.Huber, N., Bugmann, H. & Lafond, V. Capturing ecological processes in dynamic forest models: why there is no silver bullet to cope with complexity. Ecosphere 11, 1–34. https://doi.org/10.1002/ecs2.3109 (2020).Article 

    Google Scholar 
    36.Bugmann, H. A simplified forest model to study species composition along climate gradients. Ecology 77, 2055–2074. https://doi.org/10.2307/2265700 (1996).Article 

    Google Scholar 
    37.Hülsmann, L., Bugmann, H. & Brang, P. How to predict tree death from inventory data – lessons from a systematic assessment of European tree mortality models. Can. J. For. Res. 47, 890–900. https://doi.org/10.1139/cjfr-2016-0224 (2017).Article 

    Google Scholar 
    38.Eid, T. & Tuhus, E. Models for individual tree mortality in Norway. For. Ecol. Manag. 154, 69–84. https://doi.org/10.1016/S0378-1127(00)00634-4 (2001).Article 

    Google Scholar 
    39.Monserud, R. A. & Sterba, H. Modeling individual tree mortality for Austrian forest species. For. Ecol. Manag. 113, 109–123. https://doi.org/10.1016/S0378-1127(98)00419-8 (1999).Article 

    Google Scholar 
    40.Dursky, J. Modellierung der Absterbeprozesse in Rein- und Mischbeständen aus Fichte und Buche. Allg. Forst- u. Jagdztg. 168, 131–134 (1997).
    Google Scholar 
    41.Trasobares, A., Pukkala, T. & Muna, J. Growth and yield model for uneven-aged mixtures of Pinus sylvestris L. and Pinus nigra Arn. in Catalonia, north-east Spain. Ann. For. Sci. 61, 9–24, doi:https://doi.org/10.1051/forset:2003080 (2004).42.Crecente-Campo, F., Soares, P., Tome, M. & Dieguez-Aranda, U. Modelling annual individual-tree growth and mortality of Scots pine with data obtained at irregular measurement intervals and containing missing observations. For. Ecol. Manage. 260, 1965–1974. https://doi.org/10.1016/j.foreco.2010.08.044 (2010).Article 

    Google Scholar 
    43.Palahi, M., Pukkala, T., Miina, J. & Montero, G. Individual-tree growth and mortality models for Scots pine (Pinus sylvestris L.) in north-east Spain. Ann. For. Sci. 60, 1–10, https://doi.org/10.1051/forest:2002068 (2003).44.Bravo-Oviedo, A., Sterba, H., del Rio, M. & Bravo, F. Competition-induced mortality for Mediterranean Pinus pinaster Ait. and P-sylvestris L. For. Ecol. Manag. 222, 88–98, doi:https://doi.org/10.1016/j.foreco.2005.10.016 (2006).45.Fridman, J. & Ståhl, G. A three-step approach for modelling tree mortality in Swedish forests. Scand. J. For. Res. 16, 455–466. https://doi.org/10.1080/02827580152632856 (2001).Article 

    Google Scholar 
    46.Wunder, J. et al. Growth-mortality relationships as indicators of life-history strategies: a comparison of nine tree species in unmanaged European forests. Oikos 117, 815–828. https://doi.org/10.1111/j.0030-1299.2008.16371.x (2008).Article 

    Google Scholar 
    47.Das, A., Battles, J., Stephenson, N. L. & van Mantgem, P. J. The contribution of competition to tree mortality in old-growth coniferous forests. For. Ecol. Manage. 261, 1203–1213. https://doi.org/10.1016/j.foreco.2010.12.035 (2011).Article 

    Google Scholar 
    48.Bigler, C. & Bugmann, H. Predicting the time of tree death using dendrochronological data. Ecol. Appl. 14, 902–914. https://doi.org/10.1890/03-5011 (2004).Article 

    Google Scholar 
    49.Larocque, G. R., Archambault, L. & Delisle, C. Development of the gap model ZELIG-CFS to predict the dynamics of North American mixed forest types with complex structures. Ecol. Model. 222, 2570–2583. https://doi.org/10.1016/j.ecolmodel.2010.08.035 (2011).Article 

    Google Scholar 
    50.Timofeeva, G. et al. Long-term effects of drought on tree-ring growth and carbon isotope variability in Scots pine in a dry environment. Tree Physiol. 37, 1028–1041. https://doi.org/10.1093/treephys/tpx041 (2017).CAS 
    Article 
    PubMed 

    Google Scholar 
    51.Neumann, M., Mues, V., Moreno, A., Hasenauer, H. & Seidl, R. Climate variability drives recent tree mortality in Europe. Glob. Change Biol. 23, 4788–4797. https://doi.org/10.1111/gcb.13724 (2017).ADS 
    Article 

    Google Scholar 
    52.Levesque, M. et al. Drought response of five conifer species under contrasting water availability suggests high vulnerability of Norway spruce and European larch. Glob. Change Biol. 19, 3184–3199. https://doi.org/10.1111/gcb.12268 (2013).ADS 
    Article 

    Google Scholar 
    53.Rigling, A. et al. Driving factors of a vegetation shift from Scots pine to pubescent oak in dry Alpine forests. Glob. Change Biol. 19, 229–240. https://doi.org/10.1111/gcb.12038 (2013).ADS 
    Article 

    Google Scholar 
    54.Eyvindson, K., Repo, A. & Mönkkönen, M. Mitigating forest biodiversity and ecosystem service losses in the era of bio-based economy. Forest Policy Econ 92, 119–127. https://doi.org/10.1016/j.forpol.2018.04.009 (2018).Article 

    Google Scholar 
    55.Mina, M. et al. Future ecosystem services from European mountain forests under climate change. J. Appl. Ecol. 54, 389–401. https://doi.org/10.1111/1365-2664.12772 (2017).Article 

    Google Scholar 
    56.Thom, D., Rammer, W. & Seidl, R. The impact of future forest dynamics on climate: interactive effects of changing vegetation and disturbance regimes. Ecol. Monogr. 87, 665–684. https://doi.org/10.1002/ecm.1272 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    57.Blattert, C., Lemm, R., Thees, O., Lexer, M. J. & Hanewinkel, M. Management of ecosystem services in mountain forests: review of indicators and value functions for model based multi-criteria decision analysis. Ecol Indic 79, 391–409. https://doi.org/10.1016/j.ecolind.2017.04.025 (2017).Article 

    Google Scholar 
    58.Haeler, E. et al. Saproxylic species are linked to the amount and isolation of dead wood across spatial scales in a beech forest. Landscape Ecol. 36, 89–104. https://doi.org/10.1007/s10980-020-01115-4 (2021).Article 

    Google Scholar 
    59.Das, A. J., Stephenson, N. L. & Davis, K. P. Why do trees die? Characterizing the drivers of background tree mortality. Ecology 97, 2616–2627. https://doi.org/10.1002/ecy.1497 (2016).Article 
    PubMed 

    Google Scholar 
    60.Franklin, J. F., Shugart, H. H. & Harmon, M. E. Tree death as an ecological process. Bioscience 37, 550–556. https://doi.org/10.2307/1310665 (1987).Article 

    Google Scholar 
    61.Huber, N., Bugmann, H. & Lafond, V. Global sensitivity analysis of a dynamic vegetation model: model sensitivity depends on successional time, climate and competitive interactions. Ecol. Model. 368, 377–390. https://doi.org/10.1016/j.ecolmodel.2017.12.013 (2018).Article 

    Google Scholar 
    62.Portier, J. et al. “Latent reserves”: a hidden treasure in National Forest Inventories. J. Ecol. 109, 369–383. https://doi.org/10.1111/1365-2745.13487 (2021).Article 

    Google Scholar 
    63.Kunstler, G. et al. Demographic performance of European tree species at their hot and cold climatic edges. J. Ecol. 109, 1041–1054. https://doi.org/10.1111/1365-2745.13533 (2021).Article 

    Google Scholar 
    64.Gutierrez, A. G., Snell, R. S. & Bugmann, H. Using a dynamic forest model to predict tree species distributions. Glob. Ecol. Biogeogr. 25, 347–358. https://doi.org/10.1111/geb.12421 (2016).Article 

    Google Scholar 
    65.Botkin, D. B., Janak, J. F. & Wallis, J. R. Some ecological consequences of a computer model of forest growth. J. Ecol. 60, 849–872. https://doi.org/10.2307/2258570 (1972).Article 

    Google Scholar 
    66.Bugmann, H. A review of forest gap models. Clim. Change 51, 259–305. https://doi.org/10.1023/A:1012525626267 (2001).Article 

    Google Scholar 
    67.Watt, A. S. Pattern and process in the plant community. J. Ecol. 35, 1–22. https://doi.org/10.2307/2256497 (1947).Article 

    Google Scholar 
    68.Shugart, H. H. & Smith, T. M. A review of forest patch models and their application to global change research. Clim. Change 34, 131–153. https://doi.org/10.1007/BF00224626 (1996).ADS 
    Article 

    Google Scholar 
    69.Monserud, R. A. Simulation of forest tree mortality. Forest Science 22, 438–444. https://doi.org/10.1093/forestscience/22.4.438 (1976).Article 

    Google Scholar 
    70.IPCC. Climate Change 2014: Impacts, adaptation, and vulnerability, Pt A: global and sectoral aspects. Climate Change 2014: Impacts, Adaptation, and Vulnerability, Pt A: Global and Sectoral Aspects, 1-1131, doi:https://doi.org/10.1017/CBO9781107415379 (2014).71.Manusch, C., Bugmann, H., Heiri, C. & Wolf, A. Tree mortality in dynamic vegetation models: a key feature for accurately simulating forest properties. Ecol. Model. 243, 101–111. https://doi.org/10.1016/j.ecolmodel.2012.06.008 (2012).Article 

    Google Scholar 
    72.R: A language and environment for statistical computing (R Foundation for Statistical Computing, Vienna, Austria, 2020). More

  • in

    Spatiotemporal origin of soil water taken up by vegetation

    1.Graven, H. D. et al. Enhanced seasonal exchange of CO2 by Northern ecosystems since 1960. Science 341, 1085–1089 (2013).ADS 
    CAS 
    Article 

    Google Scholar 
    2.Humphrey, V. et al. Sensitivity of atmospheric CO2 growth rate to observed changes in terrestrial water storage. Nature 560, 628–631 (2018).ADS 
    CAS 
    Article 

    Google Scholar 
    3.Bonan, G. B. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320, 1444–1449 (2008).ADS 
    CAS 
    Article 

    Google Scholar 
    4.Schlesinger, W. H. & Jasechko, S. Agricultural and forest meteorology transpiration in the global water cycle. Agric. For. Meteorol. 189–190, 115–117 (2014).ADS 
    Article 

    Google Scholar 
    5.Dawson, T. E. & Pate, J. S. Seasonal water uptake and movement in root systems of Australian phraeatophytic plants of dimorphic root morphology: a stable isotope investigation. Oecologia 107, 13–20 (1996).ADS 
    Article 

    Google Scholar 
    6.Voltas, J., Devon, L., Maria Regina, C. & Juan Pedro, F. Intraspecific variation in the use of water sources by the circum-Mediterranean conifer Pinus halepensis. New Phytol. 208, 1031–1041 (2015).Article 

    Google Scholar 
    7.Grossiord, C. et al. Prolonged warming and drought modify belowground interactions for water among coexisting plants. Tree Physiol. 39, 55–63 (2018).Article 

    Google Scholar 
    8.Rempe, D. M. & Dietrich, W. E. Direct observations of rock moisture, a hidden component of the hydrologic cycle. Proc. Natl Acad. Sci. USA 115, 2664–2669 (2018).ADS 
    CAS 
    Article 

    Google Scholar 
    9.Querejeta, J. I., Estrada-Medina, H., Allen, M. F. & Jiménez-Osornio, J. J. Water source partitioning among trees growing on shallow karst soils in a seasonally dry tropical climate. Oecologia 152, 26–36 (2007).ADS 
    Article 

    Google Scholar 
    10.Evaristo, J. & McDonnell, J. J. Prevalence and magnitude of groundwater use by vegetation: a global stable isotope meta-analysis. Sci Rep. 7, 44110 (2017).ADS 
    Article 

    Google Scholar 
    11.Barbeta, A. & Peñuelas, J. Relative contribution of groundwater to plant transpiration estimated with stable isotopes. Sci Rep. 7, 10580 (2017).ADS 
    Article 

    Google Scholar 
    12.Jobbágy, E. G., Nosetto, M. D., Villagra, P. E. & Jackson, R. B. Water subsidies from mountains to deserts: their role in sustaining groundwater-fed oases in a sandy landscape. Ecol. Appl. 21, 678–694 (2011).Article 

    Google Scholar 
    13.Fan, Y., Miguez-Macho, G., Jobbágy, E. G., Jackson, R. B. & Otero-Casal, C. Hydrologic regulation of plant rooting depth. Proc. Natl Acad. Sci. USA 114, 10572–10577 (2017).ADS 
    CAS 
    Article 

    Google Scholar 
    14.Ellsworth, P. Z. & Sternberg, L. S. L. Seasonal water use by deciduous and evergreen woody species in a scrub community is based on water availability and root distribution. Ecohydrology 551, 538–551 (2015).Article 

    Google Scholar 
    15.Sohel, S. Spatial and Temporal Variation of Sources of Water Across Multiple Tropical Rainforest Trees. PhD thesis, Univ. Queensland (2019).16.Williams, D. G. & Ehleringer, J. R. Intra- and interspecific variation for summer precipitation use in pinyon-juniper woodlands. Ecol. Monogr. 70, 517–537 (2000).
    Google Scholar 
    17.Allen, S. T., Kirchner, J. W., Braun, S., Siegwolf, R., T. W. & Goldsmith, G. R. Seasonal origins of soil water used by trees. Hydrol. Earth Syst. Sci. 23, 1199–1210 (2019).ADS 
    CAS 
    Article 

    Google Scholar 
    18.David, T. S. et al. Water-use strategies in two co-occurring Mediterranean evergreen oaks: surviving the summer drought. Tree Physiol. 27, 793–803 (2007).CAS 
    Article 

    Google Scholar 
    19.Zencich, S. J., Froend, R. H., Turner, J. V. & Gailitis, V. Influence of groundwater depth on the seasonal sources of water accessed by Banksia tree species on a shallow, sandy coastal aquifer. Oecologia 131, 8–19 (2002).ADS 
    Article 

    Google Scholar 
    20.Naumburg, E., Mata-Gonzalez, R., Hunter, R. G. & Martin, D. W. Phreatophytic vegetation and groundwater fluctuations: a review of current research and application of ecosystem response modeling with an emphasis on Great Basin vegetation. Environ. Manage. 35, 726–740 (2005).Article 

    Google Scholar 
    21.Snyder, K. A. & Williams, D. G. Water sources used by riparian trees varies among stream types on the San Pedro River, Arizona. Agric. For. Meteorol. 105, 227–240 (2000).ADS 
    Article 

    Google Scholar 
    22.Kottek, M., Grieser, J., Beck, C., Rudolf, B. & Rubel, F. World map of the Köppen–Geiger climate classification updated. Meteorol. Zeitschrift 15, 259–263 (2006).ADS 
    Article 

    Google Scholar 
    23.Eleringer J. R. & Dawson T. Water uptake by plants: perspectives from stable isotope composition. Plant Cell Environ. 1073–1082 (1992).24.Dawson, T. E., Mambelli, S., Plamboeck, A. H., Templer, P. H. & Tu, K. P. Stable isotopes in plant ecology. Annu. Rev. Ecol. Syst. 33, 507–559 (2002).Article 

    Google Scholar 
    25.Rothfuss, Y. & Javaux, M. Reviews and syntheses: isotopic approaches to quantify root water uptake: a review and comparison of methods. Biogeosciences 14, 2199–2224 (2017).ADS 
    CAS 
    Article 

    Google Scholar 
    26.Orlowski, N. et al. Inter-laboratory comparison of cryogenic water extraction systems for stable isotope analysis of soil water. Hydrol. Earth Syst. Sci. 22, 3619–3637 (2018).ADS 
    CAS 
    Article 

    Google Scholar 
    27.Chen, Y. et al. Stem water cryogenic extraction biases estimation in deuterium isotope composition of plant source water. Proc. Natl Acad. Sci. USA 117, 33345–33350 (2021).ADS 
    Article 

    Google Scholar 
    28.Pastorello, G., Trotta, C., Canfora, E. & Al., E. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Sci. Data 7, 225 (2020).Article 

    Google Scholar 
    29.Zhao, Y. & Wang, L. Plant water use strategy in response to spatial and temporal variation in precipitation patterns in China: a stable isotope analysis. Forests 9, 1–21 (2018).
    Google Scholar 
    30.Miguez-Macho, G. & Fan, Y. The role of groundwater in the Amazon water cycle: 2. Influence on seasonal soil moisture and evapotranspiration. J. Geophys. Res. Atmos. 117, (2012).31.Poulter, B. et al. Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle. Nature 509, 600–603 (2014).ADS 
    CAS 
    Article 

    Google Scholar 
    32.Ahlstrom, A. et al. The dominant role of semi-arid ecosystems in the trend and variability of the land CO2 sink. Science 348, 895–899 (2015).ADS 
    Article 

    Google Scholar  More