Ecology

Allen, R. G. et al. Crop evapotranspiration-guidelines for computing crop water requirements-FAO irrigation and drainage paper 56. FAO, Rome 300, D05109 http://www.fao.org/docrep/X0490E/X0490E00.htm (1998).
Google Scholar 
Trenberth, K. E., Fasullo, J. T. & Kiehl, J. Earth’s global energy budget. Bulletin of the American Meteorological Society 90, 311–324, https://doi.org/10.1175/2008BAMS2634.1 (2009).ADS 
Article 

Google Scholar 
Wang, K. & Dickinson, R. E. A review of global terrestrial evapotranspiration: Observation, modeling, climatology, and climatic variability. Reviews of Geophysics 50, https://doi.org/10.1029/2011RG000373 (2012).Liu, W. Evaluating remotely sensed monthly evapotranspiration against water balance estimates at basin scale in the Tibetan Plateau. Hydrology Research 49, 1977–1990, https://doi.org/10.2166/nh.2018.008 (2018).Article 

Google Scholar 
Valipour, M., Bateni, S. M., Gholami Sefidkouhi, M. A., Raeini-Sarjaz, M. & Singh, V. P. Complexity of forces driving trend of reference evapotranspiration and signals of climate change. Atmosphere 11, 1081, https://doi.org/10.1007/s41748-021-00252-3 (2020).ADS 
Article 

Google Scholar 
Anwar, S. A., Mamadou, O., Diallo, I. & Sylla, M. B. On the influence of vegetation cover changes and vegetation-runoff systems on the simulated summer potential evapotranspiration of Tropical Africa using RegCM4. Earth Systems and Environment 5, 883–897, https://doi.org/10.3390/atmos11101081 (2021).ADS 
Article 

Google Scholar 
Tomas-Burguera, M., Vicente-Serrano, S. M., Beguera, S., Reig, F. & Latorre, B. Reference crop evapotranspiration database in Spain (1961–2014). Earth System Science Data 11, 1917–1930, https://doi.org/10.5194/essd-11-1917-2019 (2019).ADS 
Article 

Google Scholar 
Córdova, M., Carrillo-Rojas, G., Crespo, P., Wilcox, B. & Célleri, R. Evaluation of the Penman-Monteith (FAO 56 PM) Method for Calculating Reference Evapotranspiration Using Limited Data. Mountain Research and Development 35, 230–239, https://doi.org/10.1659/MRD-JOURNAL-D-14-0024.1 (2015).Article 

Google Scholar 
Valle Júnior, L. C. G. d. et al. Evaluation of FAO-56 procedures for estimating reference evapotranspiration using missing climatic data for a Brazilian tropical savanna. Water 13, 1763, https://doi.org/10.3390/w13131763 (2021).Article 

Google Scholar 
Djaman, K., Irmak, S. & Futakuchi, K. Daily reference evapotranspiration estimation under limited data in Eastern Africa. Journal of Irrigation and Drainage Engineering 143, 06016015, https://doi.org/10.1061/(ASCE)IR.1943-4774.0001154 (2017).Article 

Google Scholar 
Čadro, S., Uzunović, M., Žurovec, J. & Žurovec, O. Validation and calibration of various reference evapotranspiration alternative methods under the climate conditions of Bosnia and Herzegovina. International Soil and Water Conservation Research 5, 309–324, https://doi.org/10.1016/j.iswcr.2017.07.002 (2017).Article 

Google Scholar 
Vicente-Serrano, S. M. et al. Recent changes in monthly surface air temperature over Peru, 1964–2014. International Journal of Climatology 38, 283–306, https://doi.org/10.1002/joc.5176 (2018).ADS 
Article 

Google Scholar 
Huerta, A., Aybar, C. & Lavado-Casimiro, W. PISCO temperatura versión 1.1 (PISCOt v1. 1). Lima, Peru: National Meteorology and Hydrology Service of Peru (SENAMHI) https://iridl.ldeo.columbia.edu/SOURCES/.SENAMHI/.HSR/.PISCO/.Temp/ (2018).Lavado Casimiro, W. S., Labat, D., Guyot, J. L. & Ardoin-Bardin, S. Assessment of climate change impacts on the hydrology of the Peruvian Amazon–Andes basin. Hydrological Processes 25, 3721–3734, https://doi.org/10.1002/hyp.8097 (2011).ADS 
Article 

Google Scholar 
Rau, P. et al. Assessing multidecadal runoff (1970–2010) using regional hydrological modelling under data and water scarcity conditions in Peruvian Pacific catchments. Hydrological Processes 33, 20–35, https://doi.org/10.1002/hyp.13318 (2019).ADS 
Article 

Google Scholar 
Olsson, T. et al. Downscaling climate projections for the Peruvian coastal Chancay-Huaral basin to support river discharge modeling with WEAP. Journal of Hydrology: Regional Studies 13, 26–42, https://doi.org/10.1016/j.ejrh.2017.05.011 (2017).Article 

Google Scholar 
Lavado-Casimiro, W., Lhomme, J. P., Labat, D. & Loup, J. Estimating reference evapotranspiration (FAO 56 Penman Monteith) with limited climatic data in the Peruvian Amazon-Andes basin. Revista Peruana Geo-Atmosferica 4, 31–43 (2015).
Google Scholar 
Hargreaves, G. H. & Samani, Z. A. Reference crop evapotranspiration from temperature. Applied engineering in agriculture 1, 96–99, https://doi.org/10.13031/2013.26773 (1985).Article 

Google Scholar 
Laqui, W. et al. Can artificial neural networks estimate potential evapotranspiration in Peruvian highlands? Modeling Earth Systems and Environment 5, 1911–1924, https://doi.org/10.1007/s40808-019-00647-2 (2019).Article 

Google Scholar 
Baigorria, G. A., Villegas, E. B., Trebejo, I., Carlos, J. F. & Quiroz, R. Atmospheric transmissivity: distribution and empirical estimation around the central Andes. International Journal of Climatology 24, 1121–1136, https://doi.org/10.1002/joc.1060 (2004).ADS 
Article 

Google Scholar 
Huerta, A. PISCO potential evapotranspiration, https://iridl.ldeo.columbia.edu/SOURCES/.SENAMHI/.HSR/.PISCO/.PET/.Oudin, L., Michel, C. & Anctil, F. Which potential evapotranspiration input for a lumped rainfall-runoff model?: Part 1—can rainfall-runoff models effectively handle detailed potential evapotranspiration inputs? Journal of Hydrology 303, 275–289, https://doi.org/10.1016/j.jhydrol.2004.08.025 (2005).ADS 
Article 

Google Scholar 
Xiang, K., Li, Y., Horton, R. & Feng, H. Similarity and difference of potential evapotranspiration and reference crop evapotranspiration – a review. Agricultural Water Management 232, 106043, https://doi.org/10.1016/j.agwat.2020.106043 (2020).Article 

Google Scholar 
Harris, I., Osborn, T. J., Jones, P. & Lister, D. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Scientific data 7, 1–18, https://doi.org/10.1038/s41597-020-0453-3 (2020).Article 

Google Scholar 
Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A. & Hegewisch, K. C. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Scientific data 5, 1–12, https://doi.org/10.1038/sdata.2017.191 (2018).Article 

Google Scholar 
Kalnay, E. et al. The NCEP/NCAR 40-year reanalysis project. Bulletin of the American Meteorological Society 77, 437–472, 10.1175/1520-0477(1996)077  2.0.CO;2 (1996).Hersbach, H. et al. The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society 146, 1999–2049, https://doi.org/10.1002/qj.3803 (2020).ADS 
Article 

Google Scholar 
Muñoz Sabater, J. et al. ERA5-Land: a state-of-the-art global reanalysis dataset for land applications. Earth System Science Data 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021 (2021).ADS 
Article 

Google Scholar 
Aybar, C. et al. Construction of a high-resolution gridded rainfall dataset for Peru from 1981 to the present day. Hydrological Sciences Journal 65, 770–785, https://doi.org/10.1080/02626667.2019.1649411 (2020).Article 

Google Scholar 
Llauca, H., Lavado-Casimiro, W., Montesinos, C., Santini, W. & Rau, P. PISCO_HyM_GR2M: A model of monthly water balance in Peru (1981–2020). Water 13, 1048, https://doi.org/10.3390/w13081048 (2021).Article 

Google Scholar 
Gubler, S. et al. The influence of station density on climate data homogenization. International Journal of Climatology 37, 4670–4683, https://doi.org/10.1002/joc.5114 (2017).ADS 
Article 

Google Scholar 
Hunziker, S. et al. Identifying, attributing, and overcoming common data quality issues of manned station observations. International Journal of Climatology 37, 4131–4145, https://doi.org/10.1002/joc.5037 (2017).ADS 
Article 

Google Scholar 
Hunziker, S. et al. Effects of undetected data quality issues on climatological analyses. Climate of the Past 14, 1–20, https://doi.org/10.5194/cp-14-1-2018 (2018).ADS 
Article 

Google Scholar 
Paredes, P., Pereira, L., Almorox, J. & Darouich, H. Reference grass evapotranspiration with reduced data sets: Parameterization of the FAO Penman-Monteith temperature approach and the Hargeaves-Samani equation using local climatic variables. Agricultural Water Management 240, 106210, https://doi.org/10.1016/j.agwat.2020.106210 (2020).Article 

Google Scholar 
Irmak, S., Kabenge, I., Skaggs, K. E. & Mutiibwa, D. Trend and magnitude of changes in climate variables and reference evapotranspiration over 116-yr period in the Platte River Basin, central Nebraska–USA. Journal of Hydrology 420, 228–244, https://doi.org/10.1016/j.jhydrol.2011.12.006 (2012).ADS 
Article 

Google Scholar 
Tomas-Burguera, M., Vicente-Serrano, S. M., Grimalt, M. & Beguera, S. Accuracy of reference evapotranspiration (ETo) estimates under data scarcity scenarios in the Iberian Peninsula. Agricultural water management 182, 103–116, https://doi.org/10.1016/j.agwat.2016.12.013 (2017).Article 

Google Scholar 
Mardikis, M., Kalivas, D. & Kollias, V. Comparison of interpolation methods for the prediction of reference evapotranspiration—an application in Greece. Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) 19, 251–278, https://doi.org/10.1007/s11269-005-3179-2 (2005).Article 

Google Scholar 
McVicar, T. R. et al. Spatially distributing monthly reference evapotranspiration and pan evaporation considering topographic influences. Journal of Hydrology 338, 196–220, https://doi.org/10.1016/j.jhydrol.2007.02.018 (2007).ADS 
Article 

Google Scholar 
Robinson, E. L., Blyth, E. M., Clark, D. B., Finch, J. & Rudd, A. C. Trends in atmospheric evaporative demand in Great Britain using high-resolution meteorological data. Hydrology and Earth System Sciences 21, 1189–1224, https://doi.org/10.5194/hess-21-1189-2017 (2017).ADS 
Article 

Google Scholar 
Mohammadi, B. & Moazenzadeh, R. Performance analysis of daily global solar radiation models in Peru by regression analysis. Atmosphere 12, https://doi.org/10.3390/atmos12030389 (2021).Vicente-Serrano, S. M., Beguería, S., López-Moreno, J. I., García-Vera, M. A. & Stepanek, P. A complete daily precipitation database for northeast Spain: reconstruction, quality control, and homogeneity. International Journal of Climatology 30, 1146–1163, https://doi.org/10.1002/joc.1850 (2010).ADS 
Article 

Google Scholar 
Lanzante, J. R. Resistant, robust and non-parametric techniques for the analysis of climate data: Theory and examples, including applications to historical radiosonde station data. International Journal of Climatology: A Journal of the Royal Meteorological Society 16, 1197–1226, 10.1002/(SICI)1097-0088(199611)16:11 < 1197::AID-JOC89 > 3.0.CO;2-L (1996).ADS 
Article 

Google Scholar 
Wood, W. H., Marshall, S. J., Whitehead, T. L. & Fargey, S. E. Daily temperature records from a mesonet in the foothills of the Canadian Rocky Mountains, 2005–2010. Earth System Science Data 10, 595–607, https://doi.org/10.5194/essd-10-595-2018 (2018).ADS 
Article 

Google Scholar 
Guentchev, G., Barsugli, J. J. & Eischeid, J. Homogeneity of gridded precipitation datasets for the Colorado River basin. Journal of Applied Meteorology and Climatology 49, 2404–2415, https://doi.org/10.1175/2010JAMC2484.1 (2010).ADS 
Article 

Google Scholar 
Oyler, J. W., Ballantyne, A., Jencso, K., Sweet, M. & Running, S. W. Creating a topoclimatic daily air temperature dataset for the conterminous United States using homogenized station data and remotely sensed land skin temperature. International Journal of Climatology 35, 2258–2279, https://doi.org/10.1002/joc.4127 (2015).ADS 
Article 

Google Scholar 
McAfee, S. A., McCabe, G. J., Gray, S. T. & Pederson, G. T. Changing station coverage impacts temperature trends in the Upper Colorado River basin. International Journal of Climatology 39, 1517–1538, https://doi.org/10.1002/joc.5898 (2019).ADS 
Article 

Google Scholar 
Beguería, S., Vicente-Serrano, S. M., Tomás-Burguera, M. & Maneta, M. Bias in the variance of gridded data sets leads to misleading conclusions about changes in climate variability. International Journal of Climatology 36, 3413–3422, https://doi.org/10.1002/joc.4561 (2016).ADS 
Article 

Google Scholar 
Thevakaran, A. & Sonnadara, D. Estimating missing daily temperature extremes in Jaffna, Sri Lanka. Theoretical and applied climatology 132, 145–152, https://doi.org/10.1007/s00704-017-2082-0 (2018).ADS 
Article 

Google Scholar 
Hubbard, K. Spatial variability of daily weather variables in the high plains of the USA. Agricultural and Forest Meteorology 68, 29–41, https://doi.org/10.1016/0168-1923(94)90067-1 (1994).ADS 
Article 

Google Scholar 
Camargo, M. B. & Hubbard, K. G. Spatial and temporal variability of daily weather variables in sub-humid and semi-arid areas of the United States high plains. Agricultural and forest meteorology 93, 141–148, https://doi.org/10.1016/S0168-1923(98)00122-1 (1999).ADS 
Article 

Google Scholar 
Brugnara, Y., Good, E., Squintu, A. A., van der Schrier, G. & Brönnimann, S. The EUSTACE global land station daily air temperature dataset. Geoscience Data Journal 6, 189–204, https://doi.org/10.5285/7925ded722d743fa8259a93acc7073f2 (2019).ADS 
Article 

Google Scholar 
Gonzalez-Hidalgo, J. C., Peña-Angulo, D., Brunetti, M. & Cortesi, N. MOTEDAS: a new monthly temperature database for mainland Spain and the trend in temperature (1951–2010). International Journal of Climatology 35, 4444–4463, https://doi.org/10.1002/joc.4298 (2015).ADS 
Article 

Google Scholar 
Gudmundsson, L., Bremnes, J. B., Haugen, J. E. & Engen-Skaugen, T. Technical note: Downscaling RCM precipitation to the station scale using statistical transformations – a comparison of methods. Hydrology and Earth System Sciences 16, 3383–3390, https://doi.org/10.5194/hess-16-3383-2012 (2012).ADS 
Article 

Google Scholar 
Stanley, T., Kirschbaum, D. B., Huffman, G. J. & Adler, R. F. Approximating long-term statistics early in the global precipitation measurement era. Earth Interactions 21, 1–10, https://doi.org/10.1175/EI-D-16-0025.1 (2017).Article 

Google Scholar 
Haimberger, L. Homogenization of radiosonde temperature time series using innovation statistics. Journal of Climate 20, 1377–1403, https://doi.org/10.1175/JCLI4050.1 (2007).ADS 
Article 

Google Scholar 
Menne, M. J. & Williams, C. N. Homogenization of temperature series via pairwise comparisons. Journal of Climate 22, 1700–1717, https://doi.org/10.1175/2008JCLI2263.1 (2009).ADS 
Article 

Google Scholar 
Vincent, L. A., Zhang, X., Bonsal, B. R. & Hogg, W. D. Homogenization of daily temperatures over Canada. Journal of Climate 15, 1322–1334, 10.1175/1520-0442(2002)015 < 1322:HODTOC > 2.0.CO;2 (2002).ADS 
Article 

Google Scholar 
Jin, M. & Dickinson, R. E. Land surface skin temperature climatology: Benefitting from the strengths of satellite observations. Environmental Research Letters 5, 044004, https://doi.org/10.1088/1748-9326/5/4/044004 (2010).ADS 
Article 

Google Scholar 
Wan, Z., Hook, S. & Hulley, G. MOD11A2 MODIS/Terra land surface temperature/emissivity 8-day l3 global 1 km SIN grid v006. NASA EOSDIS Land Processes DAAC 10, https://doi.org/10.5067/MODIS/MOD11A2.006 (2015).Wilson, A. M. & Jetz, W. Remotely sensed high-resolution global cloud dynamics for predicting ecosystem and biodiversity distributions. PLoS biology 14, e1002415, https://doi.org/10.1371/journal.pbio.1002415 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Danielson, J. J. & Gesch, D. B. Global multi-resolution terrain elevation data 2010 (GMTED2010) (US Department of the Interior, US Geological Survey Washington, DC, USA, 2011).Holden, Z. A., Abatzoglou, J. T., Luce, C. H. & Baggett, L. S. Empirical downscaling of daily minimum air temperature at very fine resolutions in complex terrain. Agricultural and Forest Meteorology 151, 1066–1073, https://doi.org/10.1016/j.agrformet.2011.03.011 (2011).ADS 
Article 

Google Scholar 
Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. International journal of climatology 37, 4302–4315, https://doi.org/10.1002/joc.5086 (2017).ADS 
Article 

Google Scholar 
Willmott, C. J. & Robeson, S. M. Climatologically aided interpolation (CAI) of terrestrial air temperature. International Journal of Climatology 15, 221–229, https://doi.org/10.1002/joc.3370150207 (1995).ADS 
Article 

Google Scholar 
Hunter, R. D. & Meentemeyer, R. K. Climatologically aided mapping of daily precipitation and temperature. Journal of Applied Meteorology 44, 1501–1510, https://doi.org/10.1175/JAM2295.1 (2005).ADS 
Article 

Google Scholar 
Parmentier, B. et al. Using multi-timescale methods and satellite-derived land surface temperature for the interpolation of daily maximum air temperature in Oregon. International Journal of Climatology 35, 3862–3878, https://doi.org/10.1002/joc.4251 (2015).ADS 
Article 

Google Scholar 
Hengl, T., Heuvelink, G. B. & Rossiter, D. G. About regression-kriging: From equations to case studies. Computers & Geosciences 33, 1301–1315, https://doi.org/10.1016/j.cageo.2007.05.001. Spatial Analysis (2007).Sun, X.-L., Yang, Q., Wang, H.-L. & Wu, Y.-J. Can regression determination, nugget-to-sill ratio and sampling spacing determine relative performance of regression kriging over ordinary kriging? CATENA 181, 104092, https://doi.org/10.1016/j.catena.2019.104092 (2019).Article 

Google Scholar 
Trajkovic, S. & Gocic, M. Evaluation of three wind speed approaches in temperature-based ET 0 equations: a case study in Serbia. Arabian Journal of Geosciences 14, 1–8, https://doi.org/10.1007/s12517-020-06331-5 (2021).Article 

Google Scholar 
Fotheringham, A. S., Brunsdon, C. & Charlton, M. Geographically weighted regression: the analysis of spatially varying relationships (John Wiley & Sons, 2003).Comber, A. et al. A route map for successful applications of geographically weighted regression. Geographical Analysis https://doi.org/10.1111/gean.12316 (2021).Li, X., Zhou, Y., Asrar, G. R. & Zhu, Z. Developing a 1 km resolution daily air temperature dataset for urban and surrounding areas in the conterminous United States. Remote Sensing of Environment 215, 74–84, https://doi.org/10.1016/j.rse.2018.05.034 (2018).ADS 
Article 

Google Scholar 
Wu, J., Zhong, B., Tian, S., Yang, A. & Wu, J. Downscaling of urban land surface temperature based on multi-factor geographically weighted regression. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12, 2897–2911, https://doi.org/10.1109/JSTARS.2019.2919936 (2019).ADS 
Article 

Google Scholar 
Zhang, G., Zhu, S., Zhang, N., Zhang, G. & Xu, Y. Downscaling hourly air temperature of WRF simulations over complex topography: A case study of Chongli District in Hebei Province, China. Journal of Geophysical Research: Atmospheres 127, e2021JD035542, https://doi.org/10.1029/2021JD035542 (2022).ADS 
Article 

Google Scholar 
Callañaupa Gutierrez, S. et al. Seasonal variability of daily evapotranspiration and energy fluxes in the Central Andes of Peru using eddy covariance techniques and empirical methods. Atmospheric Research 261, 105760, https://doi.org/10.1016/j.atmosres.2021.105760 (2021).Article 

Google Scholar 
Zotarelli, L., Dukes, M. D., Romero, C. C., Migliaccio, K. W. & Morgan, K. T. Step by step calculation of the Penman-Monteith Evapotranspiration (FAO-56 Method). Institute of Food and Agricultural Sciences. University of Florida https://edis.ifas.ufl.edu/pdf/AE/AE45900.pdf (2010).Huerta, A. PISCOeo_pm, a reference evapotranspiration gridded database based on FAO Penman-Monteith in Peru. figshare https://doi.org/10.6084/m9.figshare.c.5633182.v3 (2021).Willmott, C. J., Robeson, S. M. & Matsuura, K. A refined index of model performance. International Journal of climatology 32, 2088–2094, https://doi.org/10.1002/joc.2419 (2012).ADS 
Article 

Google Scholar 
Legates, D. R. & McCabe, G. J. A refined index of model performance: a rejoinder. International Journal of Climatology 33, 1053–1056, https://doi.org/10.1002/joc.3487 (2013).ADS 
Article 

Google Scholar 
Durre, I., Menne, M. J., Gleason, B. E., Houston, T. G. & Vose, R. S. Comprehensive automated quality assurance of daily surface observations. Journal of Applied Meteorology and Climatology 49, 1615–1633, https://doi.org/10.1175/2010JAMC2375.1 (2010).ADS 
Article 

Google Scholar 
Huerta, A. & Lavado-Casimiro, W. Atlas de Zonas Áridas del Perú: una evaluación presente y futura (Servicio Nacional de Meteorología e Hidrología del Perú, Lima, Perú, 2021).Singer, M. B. et al. Hourly potential evapotranspiration at 0.1° resolution for the global land surface from 1981-present. Scientific Data 8, 1–13, https://doi.org/10.1038/s41597-021-01003-9 (2021).Article 

Google Scholar 
Pelosi, A. & Chirico, G. Regional assessment of daily reference evapotranspiration: Can ground observations be replaced by blending ERA5-Land meteorological reanalysis and CM-SAF satellite-based radiation data? Agricultural Water Management 258, 107169, https://doi.org/10.1016/j.agwat.2021.107169 (2021).Article 

Google Scholar 
McCuen, R. H. A sensitivity and error analysis cf procedures used for estimating evaporation. JAWRA Journal of the American Water Resources Association 10, 486–497, https://doi.org/10.1111/j.1752-1688.1974.tb00590.x (1974).ADS 
Article 

Google Scholar 
Coleman, G. & DeCoursey, D. G. Sensitivity and model variance analysis applied to some evaporation and evapotranspiration models. Water Resources Research 12, 873–879, https://doi.org/10.1029/WR012i005p00873 (1976).ADS 
Article 

Google Scholar 
Beven, K. A sensitivity analysis of the Penman-Monteith actual evapotranspiration estimates. Journal of Hydrology 44, 169–190, https://doi.org/10.1016/0022-1694(79)90130-6 (1979).ADS 
Article 

Google Scholar 
Hupet, F. & Vanclooster, M. Effect of the sampling frequency of meteorological variables on the estimation of the reference evapotranspiration. Journal of Hydrology 243, 192–204, https://doi.org/10.1016/S0022-1694(00)00413-3 (2001).ADS 
Article 

Google Scholar 
Ali, M. et al. Sensitivity of Penman–Monteith estimates of reference evapotranspiration to errors in input climatic data. Journal of Agrometeorology 11, 1–8, https://journal.agrimetassociation.org/index.php/jam/article/view/1214 (2009).
Google Scholar 
Field, C. B., Jackson, R. B. & Mooney, H. A. Stomatal responses to increased CO2: implications from the plant to the global scale. Plant, Cell & Environment 18, 1214–1225, https://doi.org/10.1111/j.1365-3040.1995.tb00630.x (1995).Article 

Google Scholar 
Roderick, M. L., Greve, P. & Farquhar, G. D. On the assessment of aridity with changes in atmospheric CO2. Water Resources Research 51, 5450–5463, https://doi.org/10.1002/2015WR017031 (2015).ADS 
CAS 
Article 

Google Scholar 
Swann, A. L., Hoffman, F. M., Koven, C. D. & Randerson, J. T. Plant responses to increasing CO2 reduce estimates of climate impacts on drought severity. Proceedings of the National Academy of Sciences 113, 10019–10024, https://doi.org/10.1073/pnas.1604581113 (2016).ADS 
CAS 
Article 

Google Scholar 
Milly, P. C. & Dunne, K. A. Potential evapotranspiration and continental drying. Nature Climate Change 6, 946–949, https://doi.org/10.1038/nclimate3046 (2016).ADS 
Article 

Google Scholar 
Greve, P., Roderick, M. L. & Seneviratne, S. I. Simulated changes in aridity from the last glacial maximum to 4xCO2. Environmental Research Letters 12, 114021, https://doi.org/10.1088/1748-9326/aa89a3 (2017).ADS 
CAS 
Article 

Google Scholar 
Scheff, J. Drought indices, drought impacts, CO2, and warming: a historical and geologic perspective. Current Climate Change Reports 4, 202–209, https://doi.org/10.1007/s40641-018-0094-1 (2018).Article 

Google Scholar 
Swann, A. L. Plants and drought in a changing climate. Current Climate Change Reports 4, 192–201, https://doi.org/10.1007/s40641-018-0097-y (2018).Article 

Google Scholar 
Lemordant, L., Gentine, P., Swann, A. S., Cook, B. I. & Scheff, J. Critical impact of vegetation physiology on the continental hydrologic cycle in response to increasing CO2. Proceedings of the National Academy of Sciences 115, 4093–4098, https://doi.org/10.1073/pnas.1720712115 (2018).ADS 
CAS 
Article 

Google Scholar 
Yang, Y., Roderick, M. L., Zhang, S., McVicar, T. R. & Donohue, R. J. Hydrologic implications of vegetation response to elevated CO2 in climate projections. Nature Climate Change 9, 44–48, https://doi.org/10.1038/s41558-018-0361-0 (2019).ADS 
CAS 
Article 

Google Scholar 
Greve, P., Roderick, M., Ukkola, A. & Wada, Y. The aridity index under global warming. Environmental Research Letters 14, 124006, https://doi.org/10.1088/1748-9326/ab5046 (2019).ADS 
CAS 
Article 

Google Scholar 
Huerta, A. & Gutierrez, L. PISCOeo_pm. figshare https://doi.org/10.6084/m9.figshare.19686738.v1 (2022). More

Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G., Davis, A., Dean, J., Devin, M., Ghemawat, S. & Zheng, X. TensorFlow: Large-scale machine learning on heterogeneous systems. Software available from tensorflow.org. (2015).Alberdi, A., Aizpurua, O., Gilbert, M. T. P. & Bohmann, K. Scrutinizing key steps for reliable metabarcoding of environmental samples. Methods Ecol. Evol. 9, 134–147 (2018).Article 

Google Scholar 
Albert, J. S. & Reis, R. E. One. Introduction to Neotropical freshwaters. In Historical biogeography of Neotropical freshwater fishes (pp. 3-20). University of California Press. (2011).Allard, L., Popée, M., Vigouroux, R. & Brosse, S. Effect of reduced impact logging and small-scale mining disturbances on Neotropical stream fish assemblages. Aquat. Sci. 78, 315–325 (2016).Article 

Google Scholar 
Berry, O. et al. Making environmental DNA (eDNA) biodiversity records globally accessible. Environ. DNA 3(4), 699–705 (2020).Article 

Google Scholar 
Bohmann, K. et al. Environmental DNA for wildlife biology and biodiversity monitoring. Trends Ecol. Evol. 29(6), 358–367 (2014).PubMed 
Article 

Google Scholar 
Bolyen, E. et al. QIIME 2: Reproducible, interactive, scalable, and extensible microbiome data science. Nat. Biotechnol. 32, 852–857 (2019).Article 
CAS 

Google Scholar 
Bonder, M. J., Abeln, S., Zaura, E. & Brandt, B. W. Comparing clustering and pre-processing in taxonomy analysis. Bioinformatics 28(22), 2891–2897 (2012).CAS 
PubMed 
Article 

Google Scholar 
Boussarie, G. et al. Environmental DNA illuminates the dark diversity of sharks. Sci. Adv. 4, eaap9661 (2018).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Boyer, F. et al. obitools: A unix-inspired software package for DNA metabarcoding. Mol. Ecology Resour. 16(1), 176–182 (2016).CAS 
Article 

Google Scholar 
Brandt, M.I., Trouche, B., Quintric, L., Günther, B., Wincker, P., Poulain, J. & Arnaud-Haond, S. Bioinformatic pipelines combining denoising and clustering tools allow for more comprehensive prokaryotic and eukaryotic metabarcoding. Molecular Ecology Resources. Accepted (2021).Brosse, S., Melki, F. & Vigouroux, R. Fishes from the Mitaraka mountains (French Guiana). Zoosystema 41, 131–151 (2019).Article 

Google Scholar 
Brown, E. A., Chain, F. J., Crease, T. J., MacIsaac, H. J. & Cristescu, M. E. Divergence thresholds and divergent biodiversity estimates: can metabarcoding reliably describe zooplankton communities?. Ecol. Evol. 5(11), 2234–2251 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Busia, K., George, D. E., Fannjiang, C., Alexander, D.H., Dorfman, E., Poplin, R., Chang, P., & DePris, M. A deep learning approach to pattern recognition for short DNA sequences. BioRxiv (2020).Bylemans, J., Gleeson, D. M., Hardy, C. M. & Furlan, E. Toward an ecoregion scale evaluation of eDNA metabarcoding primers: A case study for the freshwater fish biodiversity of the Murray-Darling Basin (Australia). Ecol. Evol. 8(17), 8697–8712 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Calderón-Sanou, I., Münkemüller, T., Boyer, F., Zinger, L. & Thuiller, W. From environmental DNA sequences to ecological conclusions: How strong is the influence of methodological choices?. J. Biogeogr. 47(1), 193–206 (2020).Article 

Google Scholar 
Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13(7), 581–583 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Cantera, I., Coutant, O., Jézéuel, C., Decotte, J.B., Dejean, T., Vigouroux, R., Valentini, A. Murienne, J. & Brosse S. Slight deforestation causes harsh biodiversity decline in Amazonian rivers (submitted)Cantera, I., Decotte, J. B., Dejean, T., Murienne, J., Vigouroux, R., Valentini, A., & Brosse, S. Characterizing the spatial signal of environmental DNA in river systems using a community ecology approach. BioRxiv (2020).Cantera, I. et al. Optimizing environmental DNA sampling effort for fish inventories in tropical streams and rivers. Sci. Rep. 9(1), 1–1 (2019).CAS 
Article 

Google Scholar 
Cardoso, Y. P. & Montoya-Burgos, J. I. Unexpected diversity in the catfish Pseudancistrus brevispinis reveals dispersal routes in a Neotropical center of endemism: The Guyanas Region. Mol. Ecol. 18, 947–964 (2009).CAS 
PubMed 
Article 

Google Scholar 
Cilleros, K. et al. Unlocking biodiversity and conservation studies in high-diversity environments using environmental DNA (eDNA): A test with Guianese freshwater fishes. Mol. Ecol. Resour. 19(1), 27–46 (2019).CAS 
PubMed 
Article 

Google Scholar 
Collen, B., Ram, M., Zamin, T. & McRae, L. The tropical biodiversity data gap: Addressing disparity in global monitoring. Trop. Conserv. Sci. 1(2), 75–88 (2008).Article 

Google Scholar 
Cordier, T., Lanzén, A., Apothéloz-Perret-Gentil, L., Stoeck, T. & Pawlowski, J. Embracing environmental genomics and machine learning for routine biomonitoring. Trends Microbiol. 27(5), 387–397 (2019).CAS 
PubMed 
Article 

Google Scholar 
Cordier, T. et al. Ecosystems monitoring powered by environmental genomics: A review of current strategies with an implementation roadmap. Mol. Ecol. 30(13), 2937–2958 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Coutant, O. et al. Detecting fish assemblages with environmental DNA: Does protocol matter? Testing eDNA metabarcoding method robustness. Environ. DNA 3(3), 619–630 (2020).Article 

Google Scholar 
Deiner, K. et al. Environmental DNA metabarcoding: Transforming how we survey animal and plant communities. Mol. Ecol. 26(21), 5872–5895 (2017).PubMed 
Article 

Google Scholar 
Deneu, B., Servajean, M., Bonnet, P., Botella, C., Munoz, F., & Joly, A. Convolutional neural networks improve species distribution modelling by capturing the spatial structure of the environment. PLoS Comput. Biol. (in press) (2021).de Mérona, B., Tejerina-Garro, F. L. & Vigouroux, R. Fish-habitat relationships in French Guiana rivers: A review. Cybium 36, 7–15 (2012).
Google Scholar 
DiBattista, J. D. et al. Environmental DNA can act as a biodiversity barometer of anthropogenic pressures in coastal ecosystems. Sci. Rep. 10(1), 1–15 (2020).Article 
CAS 

Google Scholar 
Dornelas, M., Madin, E. M., Bunce, M., DiBattista, J. D., Johnson, M., Madin, J. S., Magurran, A. E., McGill, B. J., Pettorelli, N., Pizarro, O. & Williams, S. B. Towards a macroscope: Leveraging technology to transform the breadth, scale and resolution of macroecological data. Glob. Ecol. Biogeogr. (2019).Dufresne, Y., Lejzerowicz, F., Perret-Gentil, L. A., Pawlowski, J. & Cordier, T. SLIM: A flexible web application for the reproducible processing of environmental DNA metabarcoding data. BMC Bioinform. 20(1), 1–6 (2019).Article 

Google Scholar 
Ficetola, G. F., Miaud, C., Pompanon, F. & Taberlet, P. Species detection using environmental DNA from water samples. Biol. Lett. 4(4), 423–425 (2008).PubMed 
PubMed Central 
Article 

Google Scholar 
Ficetola, G. F., Taberlet, P. & Coissac, E. How to limit false positives in environmental DNA and metabarcoding?. Mol. Ecol. Resour. 16(3), 604–607 (2016).CAS 
PubMed 
Article 

Google Scholar 
Ficetola, G. F. et al. Replication levels, false presences and the estimation of the presence/absence from eDNA metabarcoding data. Mol. Ecology Resour. 15(3), 543–556 (2015).CAS 
Article 

Google Scholar 
Flynn, J. M., Brown, E. A., Chain, F. J., MacIsaac, H. J. & Cristescu, M. E. Toward accurate molecular identification of species in complex environmental samples: Testing the performance of sequence filtering and clustering methods. Ecol. Evol. 5(11), 2252–2266 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Gold, Z. et al. eDNA metabarcoding bioassessment of endangered fairy shrimp (Branchinecta spp.). Conserv. Genet. Resour. 12, 685–690 (2020).Article 

Google Scholar 
Grünig, M., Razavi, E., Calanca, P., Mazzi, D., Wegner, J. D., & Pellissier, L. Applying deep neural networks to predict incidence and phenology of plant pests and diseases. Ecosphere (accepted) (2021).Helaly, M. A., Rady, S., & Aref, M. M. Convolutional neural networks for biological sequence taxonomic classification: A comparative study. In International Conference on Advanced Intelligent Systems and Informatics (pp. 523–533). Springer, Cham (2019).Holman, L. E. et al. Animals, protists and bacteria share marine biogeographic patterns. Nat. Ecol. Evol. 5(6), 738–746 (2021).PubMed 
Article 

Google Scholar 
Iknayan, K. J., Tingley, M. W., Furnas, B. J. & Beissinger, S. R. Detecting diversity: Emerging methods to estimate species diversity. Trends Ecol. Evol. 29(2), 97–106 (2014).PubMed 
Article 

Google Scholar 
Jarman, S. N., Berry, O. & Bunce, M. The value of environmental DNA biobanking for long-term biomonitoring. Nat. Ecol. Evol. 2(8), 1192–1193 (2018).PubMed 
Article 

Google Scholar 
Juhel, J. B., Utama, R. S., Marques, V., Vimono, I. B., Sugeha, H. Y., Kadarusman, Pouyaud, L., Dejean, T., Mouillot, D. & Hocdé, R. Accumulation curves of environmental DNA sequences predict coastal fish diversity in the coral triangle. Proc. R. Soc. B 287(1930), 20200248 (2020).Kopp, W., Monti, R., Tamburrini, A., Ohler, U. & Akalin, A. Deep learning for genomics using Janggu. Nat. Commun. 11(1), 1–7 (2020).Article 
CAS 

Google Scholar 
Le Bail, P. Y. et al. Updated checklist of the freshwater and estuarine fishes of French Guiana. Cybium 36(1), 293–319 (2012).
Google Scholar 
LeCun, Y. et al. Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541–551 (1989).Article 

Google Scholar 
Li, W. et al. Validating eDNA measurements of the richness and abundance of anurans at a large scale. J. Anim. Ecol. 90(6), 1466–1479 (2021).PubMed 
Article 

Google Scholar 
Lopes, C. M. et al. eDNA metabarcoding: A promising method for anuran surveys in highly diverse tropical forests. Mol. Ecol. Resour. 17(5), 904–914 (2017).CAS 
PubMed 
Article 

Google Scholar 
Makiola, A. et al. Key questions for next-generation biomonitoring. Front. Environ. Sci. 7, 197 (2020).Article 

Google Scholar 
Marques, V. et al. Blind assessment of vertebrate taxonomic diversity across spatial scales by clustering environmental DNA metabarcoding sequences. Ecography 43(12), 1779–1790 (2020).Article 

Google Scholar 
Marques, V. et al. GAPeDNA: Assessing and mapping global species gaps in genetic databases for eDNA metabarcoding. Divers. Distrib. 27(10), 1880–1892 (2020).Article 

Google Scholar 
Mathon, L. et al. Benchmarking bioinformatic tools for fast and accurate eDNA metabarcoding species identification. Mol. Ecol. Resour. 21(7), 2565–2579 (2021).CAS 
PubMed 
Article 

Google Scholar 
McGee, K. M., Robinson, C. & Hajibabaei, M. Gaps in DNA-based biomonitoring across the globe. Front. Ecol. Evol. 7, 337 (2019).Article 

Google Scholar 
Murienne, J. et al. Aquatic eDNA for monitoring French Guiana biodiversity. Biodivers. Data J. 7, e37518 (2019).Nugent, C. M. & Adamowicz, S. J. Alignment-free classification of COI DNA barcode data with the Python package Alfie. Metabarcoding Metagenomics 4, e55815 (2020).Pagni, M. et al. Density-based hierarchical clustering of pyro-sequences on a large scale-the case of fungal ITS1. Bioinformatics 29(10), 1268–1274 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Papa, Y., Le Bail, P. Y. & Covain, R. Genetic landscape clustering of a large DNA barcoding dataset reveals shared patterns of genetic divergence among freshwater fishes of the Maroni Basin. Authorea Preprints (2020).Piro, V. C., Dadi, T. H., Seiler, E., Reinert, K. & Renard, B. Y. ganon: Precise metagenomics classification against large and up-to-date sets of reference sequences. Bioinformatics 36(Supplement 1), i12–i20 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Polanco Fernández, A., Marques, V., Fopp, F., Juhel, J. B., Borrero-Pérez, G. H., Cheutin, M. C., Eme, D. & Pellissier, L. Comparing environmental DNA metabarcoding and underwater visual census to monitor tropical reef fishes. Environ. DNA 3, 142–156 (2021).Polanco, A. et al. Comparing the performance of 12S mitochondrial primers for fish environmental DNA across ecosystems. Environ. DNA 3(6), 1113–1127 (2021).Article 

Google Scholar 
Polanco Fernández, A., Martinezguerra, M. M., Marques, V., Francisco Villa-Navarro, Borrero-Pérez, G. H., Cheutin, M. C., Dejean, T., Hocdé, R., Juhel, J. B., Maire, E., Manel, S. & Pellissier, L. Recovering aquatic and terrestrial biodiversity in a tropical estuary using environmental DNA. Biotropica 53(6), 1606–1619 (2021).Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahé, F. VSEARCH: A versatile open source tool for metagenomics. PeerJ 4, 1–22 (2016).Article 

Google Scholar 
Rojahn, J., Gleeson, D. M., Furlan, E., Haeusler, T. & Bylemans, J. Improving the detection of rare native fish species in environmental DNA metabarcoding surveys. Aquat. Conserv. Mar. Freshw. Ecosyst. 31(4), 990–997 (2021).Article 

Google Scholar 
Ruppert, K. M., Kline, R. J. & Rahman, M. S. Past, present, and future perspectives of environmental DNA (eDNA) metabarcoding: A systematic review in methods, monitoring, and applications of global eDNA. Glob. Ecol. Conserv. 17, e00547 (2019).Article 

Google Scholar 
Sato, Y., Miya, M., Fukunaga, T., Sado, T. & Iwasaki, W. MitoFish and MiFish pipeline: A mitochondrial genome database of fish with an analysis pipeline for environmental DNA metabarcoding. Mol. Biol. Evol. 35(6), 1553–1555 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Schirmer, M. et al. Insight into biases and sequencing errors for amplicon sequencing with the Illumina MiSeq platform. Nucleic Acids Res. 43(6), e37 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Schnell, I. B., Bohmann, K. & Gilbert, M. T. P. Tag jumps illuminated–reducing sequence-to-sample misidentifications in metabarcoding studies. Mol. Ecol. Resour. 15(6), 1289–1303 (2015).CAS 
PubMed 
Article 

Google Scholar 
Sepulveda, A. J., Nelson, N. M., Jerde, C. L. & Luikart, G. Are environmental DNA methods ready for aquatic invasive species management?. Trends Ecol. Evol. 35, 668–678 (2020).PubMed 
Article 

Google Scholar 
Shokralla, S., Spall, J. L., Gibson, J. F. & Hajibabaei, M. Next-generation sequencing technologies for environmental DNA research. Mol. Ecol. 21(8), 1794–1805 (2012).CAS 
PubMed 
Article 

Google Scholar 
Shorten, C. & Khoshgoftaar, T. A survey on image data augmentation for deep learning. J. Big Data 6, 60 (2019).Article 

Google Scholar 
Singer, G. A. C., Fahner, N. A., Barnes, J. G., McCarthy, A. & Hajibabaei, M. Comprehensive biodiversity analysis via ultra-deep patterned flow cell technology: A case study of eDNA metabarcoding seawater. Sci. Rep. 9(1), 1–12 (2019).ADS 
CAS 
Article 

Google Scholar 
Su, G. et al. Human impacts on global freshwater fish biodiversity. Science 371(6531), 835 (2021).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Taberlet, P., Bonin, A., Coissac, E. & Zinger, L. Environmental DNA: For Biodiversity Research and Monitoring (Oxford University Press, Oxford, 2018).Book 

Google Scholar 
Taberlet, P., Coissac, E., Pompanon, F., Brochmann, C. & Willerslev, E. Towards next-generation biodiversity assessment using DNA metabarcoding. Mol. Ecol. 21(8), 2045–2050 (2012).CAS 
PubMed 
Article 

Google Scholar 
Thomsen, P. F. & Willerslev, E. Environmental DNA-An emerging tool in conservation for monitoring past and present biodiversity. Biol. Conserv. 183, 4–18 (2015).Article 

Google Scholar 
Thuiller, W., Lafourcade, B., Engler, R. & Araújo, M. B. BIOMOD–A platform for ensemble forecasting of species distributions. Ecography 32(3), 369–373 (2009).Article 

Google Scholar 
Valentini, A. et al. Next-generation monitoring of aquatic biodiversity using environmental DNA metabarcoding. Mol. Ecol. 25(4), 929–942 (2016).CAS 
PubMed 
Article 

Google Scholar 
West, K. et al. Large-scale eDNA metabarcoding survey reveals marine biogeographic break and transitions over tropical north-western Australia. Divers. Distrib. 27(10), 1942–1957 (2021).Article 

Google Scholar  More

Basset, A., Elliott, M., West, R. J. & Wilson, J. G. Estuarine and lagoon biodiversity and their natural goods and services. Estuar. Coast. Shelf Sci. 132, 1–4 (2013).CAS 

Google Scholar 
Newton, A. et al. Assessing, quantifying and valuing the ecosystem services of coastal lagoons. J. Nat. Conserv. 44, 50–65 (2018).
Google Scholar 
Heck, K. L., Able, K. W., Roman, C. T. & Fahay, M. P. Composition, abundance, biomass, and production of macrofauna in a New England estuary: Comparisons among eelgrass meadows and other nursery habitats. Estuaries 18, 379–389 (1995).
Google Scholar 
Franco, A. et al. Use of shallow water habitats by fish assemblages in a Mediterranean coastal lagoon. Estuar. Coast. Shelf Sci. 66, 67–83 (2006).
Google Scholar 
Barbosa, F. A. R., Scarano, F. R., Sabará, M. & Esteves, F. A. Brazilian LTER: Ecosystem and biodiversity information in support of decision-making. Environ. Monit. Assess. 90, 121–133 (2004).CAS 
PubMed 

Google Scholar 
Esteves, F. et al. Neotropical coastal lagoons: An appraisal of their biodiversity, functioning, threats and conservation management. Braz. J. Biol. 68, 967–981 (2008).CAS 
PubMed 

Google Scholar 
Kjerfve, B. Coastal lagoons. Elsevier Oceanogr. Ser. 60, 1–8 (1994).
Google Scholar 
Whitfield, A. K. Coastal lagoons—Critical habitats of environmental change. Mar. Biol. Res. 7, 416–417 (2011).
Google Scholar 
Obolewski, K. et al. Patterns of salinity regime in coastal lakes based on structure of benthic invertebrates. PLoS ONE 13, 1–19 (2018).
Google Scholar 
Schallenberg, M., Hall, C. J. & Burns, C. W. Consequences of climate-induced salinity increases on zooplankton abundance and diversity in coastal lakes. Mar. Ecol. Prog. Ser. 251, 181–189 (2003).
Google Scholar 
Broman, E. et al. Salinity drives meiofaunal community structure dynamics across the Baltic ecosystem. Mol. Ecol. 28, 3813–3829 (2019).PubMed 
PubMed Central 

Google Scholar 
Bird, E. C. F. Physical setting and geomorphology of coastal lagoons. Elsevier Oceanogr. Ser. 60, 9–39 (1994).
Google Scholar 
Barnes, N., Bamber, R. N., Moncrieff, C. B., Sheader, M. & Ferrero, T. J. Estuarine, Coastal and Shelf Science Meiofauna in closed coastal saline lagoons in the United Kingdom: Structure and biodiversity of the nematode assemblage. Estuar. Coast. Shelf Sci. 79, 328–340 (2008).
Google Scholar 
Frühe, L. et al. Supervised machine learning is superior to indicator value inference in monitoring the environmental impacts of salmon aquaculture using eDNA metabarcodes. Mol. Ecol. 00, 1–19 (2020).
Google Scholar 
Cordier, T. et al. Multi-marker eDNA metabarcoding survey to assess the environmental impact of three offshore gas platforms in the North Adriatic Sea (Italy). Mar. Environ. Res. 146, 24–34 (2019).CAS 
PubMed 

Google Scholar 
Balzano, S., Abs, E. & Leterme, S. C. Protist diversity along a salinity gradient in a coastal lagoon. Aquat. Microb. Ecol. 74, 263–277 (2015).
Google Scholar 
Polinski, J. M., Bucci, J. P., Gasser, M. & Bodnar, A. G. Metabarcoding assessment of prokaryotic and eukaryotic taxa in sediments from Stellwagen Bank National Marine Sanctuary. Sci. Rep. 9, 14820 (2019).PubMed 
PubMed Central 

Google Scholar 
López-Escardó, D. et al. Metabarcoding analysis on European coastal samples reveals new molecular metazoan diversity. Sci. Rep. 8, 1–14 (2018).
Google Scholar 
Günther, B., Knebelsberger, T., Neumann, H., Silke, L. & Arbizu, P. M. Metabarcoding of marine environmental DNA based on mitochondrial and nuclear genes. Sci. Rep. 8, 1–13 (2018).
Google Scholar 
Park, D. S. & Razafindratsima, O. H. Anthropogenic threats can have cascading homogenizing effects on the phylogenetic and functional diversity of tropical ecosystems. Ecography (Cop.) 42, 148–161 (2019).
Google Scholar 
Pan, Y., Yang, J., McManus, G. B., Lin, S. & Zhang, W. Insights into protist diversity and biogeography in intertidal sediments sampled across a range of spatial scales. Limnol. Oceanogr. 65, 1103–1115 (2020).
Google Scholar 
Wangensteen, O. S., Palacín, C., Guardiola, M. & Turon, X. DNA metabarcoding of littoral hard-bottom communities: High diversity and database gaps revealed by two molecular markers. PeerJ 6, e4705 (2018).PubMed 
PubMed Central 

Google Scholar 
Polanco Fernández, A. et al. Comparing environmental DNA metabarcoding and underwater visual census to monitor tropical reef fishes. Environ. DNA 3, 1–15 (2020).
Google Scholar 
Armeli Minicante, S. et al. Habitat heterogeneity and connectivity: Effects on the planktonic protist community structure at two adjacent coastal sites (the lagoon and the Gulf of Venice, Northern Adriatic Sea, Italy) revealed by metabarcoding. Front. Microbiol. 10, 1–16 (2019).
Google Scholar 
Alves-De-Souza, C. et al. Does environmental heterogeneity explain temporal β diversity of small eukaryotic phytoplankton? Example from a tropical eutrophic coastal lagoon. J. Plankton Res. 39, 698–714 (2017).
Google Scholar 
Grzebyk, D. et al. Insights into the harmful algal flora in northwestern Mediterranean coastal lagoons revealed by pyrosequencing metabarcodes of the 28S rRNA gene. Harmful Algae 68, 1–16 (2017).CAS 
PubMed 

Google Scholar 
Lallias, D. et al. Environmental metabarcoding reveals heterogeneous drivers of microbial eukaryote diversity in contrasting estuarine ecosystems. ISME J. 9, 1208–1221 (2015).PubMed 

Google Scholar 
Avó, A. P. et al. DNA barcoding and morphological identification of benthic nematodes assemblages of estuarine intertidal sediments: Advances in molecular tools for biodiversity assessment. Front. Mar. Sci. 4, 1–16 (2017).
Google Scholar 
Behera, P. et al. Salinity and macrophyte drive the biogeography of the sedimentary bacterial communities in a brackish water tropical coastal lagoon. Sci. Total Environ. 595, 472–485 (2017).CAS 
PubMed 

Google Scholar 
Alsaffar, Z. et al. The role of seagrass vegetation and local environmental conditions in shaping benthic bacterial and macroinvertebrate communities in a tropical coastal lagoon. Sci. Rep. 10, 1–17 (2020).
Google Scholar 
Spalding, M. D. et al. Marine ecoregions of the world: A bioregionalization of coastal and shelf areas. Bioscience 57, 573–583 (2007).
Google Scholar 
Lara-Lara, J. Los ecosistemas marinos. Cap. Nat. Méx. 1, 135–159 (2008).
Google Scholar 
García-Grajales, J. & Buenrostro-Silva, A. E. Parque Nacional Lagunas de Chacahua, Oaxaca: Perspectivas a sus 75 años. Cienc. Ergo Sum. 21, 149–153 (2014).
Google Scholar 
Zamorano, P., Barrientos-Luján, N. A. & Ahumada-Sempoal, M. Á. Moluscos bentónicos de dos sistemas lagunares de la costa chica de Oaxaca, México y su relación con parámetros fisicoquímicos. Cienc. y Mar. 14, 13–28 (2012).
Google Scholar 
Sanay-González, R., MonrealGómez, M. A. & de León, D. A. S. Simulación de la circulación en el sistema lagunar Chacahua-Pastoría, Oaxaca, México. Cienc. y Mar. 10, 3–16 (2006).
Google Scholar 
Comisión Nacional de Acuacultura y Pesca. Obras de dragado y escolleras en Boca de Oro, laguna de Corralero, Oaxaca (2010).Sánchez-Meraz, B. & Martínez-Vega, J. A. Inmigración de Postlarvas de Camarón Litopenaeus sp. y Farfantepenaeus sp. a través la Boca El Oro del Sistema Lagunar Corralero-Alotengo, Oaxaca. Cienc. y Mar. 4, 29–46 (2000).
Google Scholar 
Angel-Pérez, C., Serrano-Guzmán, S. J. & Ahumada-Sempoal, M. A. Ciclo reproductivo del molusco Atrina maura (Pterioidea: Pinnidae) en un sistema lagunar costero, al sur del Pacífico tropical mexicano. Rev. Biol. Trop. 55, 839–852 (2007).PubMed 

Google Scholar 
Sánchez Méndez, E., Urbano Alonso, B., Sierra Hernández, S. & Garcés Salazar, J. L. Características malacológicas y sociales de la pesquería artesanal de moluscos en la Laguna de Chacahua, Oaxaca, México. Cienc. y Mar. 19, 3–11 (2015).
Google Scholar 
Cowart, D. A. et al. Metabarcoding is powerful yet still blind: A comparative analysis of morphological and molecular surveys of seagrass communities. PLoS ONE 10, 1–26 (2015).
Google Scholar 
Holman, L. E. et al. Detection of novel and resident marine species using environmental DNA metabarcoding of sediment and water. Sci. Rep. https://doi.org/10.1038/s41598-019-47899-7 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Bojorges-Baños, J. C. Riqueza y diversidad de especies de aves asociadas a manglar en tres sistemas lagunares en la región costera de Oaxaca, México. Rev. Mex. Biodivers. 82, 205–215 (2011).
Google Scholar 
Ahumada-Sempoal, M. Á. & Ruiz-García, N. Características fisicoquímicas de la Laguna Pastoría, Oaxaca, México. Cienc. y Mar. 12, 3–17 (2008).
Google Scholar 
Aylagas, E., Mendibil, I., Borja, Á. & Rodríguez-ezpeleta, N. Marine sediment sample pre-processing for macroinvertebrates metabarcoding: Mechanical enrichment and homogenization. Front. Mar. Sci. 3, 1–8 (2016).
Google Scholar 
Hestetun, J. T., Lanzén, A., Skaar, K. S. & Dahlgren, T. G. The impact of DNA extract homogenization and replication on marine sediment metabarcoding diversity and heterogeneity. Environ. DNA 3, 997–1006 (2021).
Google Scholar 
Comeau, M., Li, W. K. W., Carmack, E. C. & Lovejoy, C. Arctic ocean microbial community structure before and after the 2007 record sea ice minimum. PLoS ONE 6, 1–12 (2011).
Google Scholar 
Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).CAS 
PubMed 

Google Scholar 
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 10–12 (2011).
Google Scholar 
Edgar, R. C. UPARSE: Highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10, 996–998 (2013).CAS 
PubMed 

Google Scholar 
Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C. & Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27, 2194–2200 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, 590–596 (2013).
Google Scholar 
Ratnasingham, S. & Hebert, P. D. N. BOLD: The barcode of life data system. Mol. Ecol. Notes 7, 355–364 (2007).CAS 
PubMed 
PubMed Central 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (2018).Anderson, M. J. Permutational multivariate analysis of variance (PERMANOVA). Wiley StatsRef Stat. Ref. Online. https://doi.org/10.1002/9781118445112.stat07841 (2017).Article 

Google Scholar 
Ter Braak, C. J. F. Canonical correspondence analysis: A new eigenvector technique for multivariate direct gradient analysis. Ecology 67, 1167–1179 (1986).
Google Scholar 
Ter Braak, C. J. F. The analysis of vegetation-environment relationships by canonical correspondence analysis*. Vegetatio 69, 69–77 (1987).
Google Scholar 
Hammer, Ø., Harper, D. A. T. & Ryan, P. D. PAST: Paleontological statisticssofware package for education and data analysis. Palaeontol. Electron. 4, 1–9 (2001).
Google Scholar 
Coan, E. V. & Valentich-Scott, P. Bivalve Seashells of Tropical West America. Marine Bivalve Mollusks from Baja California to Northern Peru (Santa Barbara Museum of Natural History, 2012).
Google Scholar 
MolluscaBase. MolluscaBase. Mytella strigata (Hanley, 1843) (2022).Aylagas, E., Borja, Á., Muxika, I. & Rodríguez-ezpeleta, N. Adapting metabarcoding-based benthic biomonitoring into routine marine ecological status assessment networks. Ecol. Indic. 95, 194–202 (2018).
Google Scholar 
Cronin-O’Reilly, S. et al. Limited congruence exhibited across microbial, meiofaunal and macrofaunal benthic assemblages in a heterogeneous coastal environment. Sci. Rep. 8, 1–10 (2018).
Google Scholar 
Forster, D. et al. Benthic protists: The under-charted majority. FEMS Microbiol. Ecol. 92, 1–11 (2016).
Google Scholar 
Kim, H., Kim, H., Hwang, H. S. & Kim, W. Metagenomic analysis of the marine coastal invertebrates of South Korea as assessed by Ilumina MiSeq. Anim. Cells Syst. (Seoul) 21, 37–44 (2017).
Google Scholar 
Brannock, P. M., Wang, L., Ortmann, A. C., Waits, D. S. & Halanych, K. M. Genetic assessment of meiobenthic community composition and spatial distribution in coastal sediments along northern Gulf of Mexico. Mar. Environ. Res. 119, 166–175 (2016).CAS 
PubMed 

Google Scholar 
Guardiola, M. et al. Spatio-temporal monitoring of deep-sea communities using metabarcoding of sediment DNA and RNA. PeerJ 4, e2807 (2016).PubMed 
PubMed Central 

Google Scholar 
Barnes, M. A. & Turner, C. R. The ecology of environmental DNA and implications for conservation genetics. Conserv. Genet. 17, 1–17 (2016).CAS 

Google Scholar 
Bastida-Zavala, J. R. et al. Marine and coastal biodiversity of Oaxaca, Mexico. Check List 9, 329–390 (2013).
Google Scholar 
Nascimento, F. J. A., Lallias, D., Bik, H. M. & Creer, S. Sample size effects on the assessment of eukaryotic diversity and community structure in aquatic sediments using high-throughput sequencing. Sci. Rep. https://doi.org/10.1038/s41598-018-30179-1 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
In the Wrong Place: Alien Marine Crustaceans: Distribution, Biology and Impacts, Vol. 6 (2011).Rodríguez-Almaraz, G. A. & García-Madrigal, M. D. S. Crustáceos exóticos invasores. Especies Acuáticas Invasoras en México 347–371 (2014).Gómez, S., Fleeger, J. W., Rocha, A. & Foltz, D. Four new species of Cletocamptus Schmankewitsch, 1875, closely related to Cletocamptus deitersi (Richard) (Copepoda: Harpacticoida). J. Nat. Hist. 38, 2669. https://doi.org/10.1080/0022293031000156240 (2004).Article 

Google Scholar 
Ciros Pérez, J., Silva Briano, M. & Elías Gutierrez, M. A new species of Macrothrix (Anomopoda: Macrothricidae) from Central Mexico. Hydrobiologia 319, 159–166 (1996).
Google Scholar 
Fuentes-Reines, J. M., De Roa, E. Z., Morón, E., Gámez, D. & López, C. Conocimiento de la fauna de cladocera (Crustacea: Branchiopoda) de la ciénaga grande de Santa Marta, Colombia. Bol. Investig. Mar. y Costeras 41, 121–164 (2012).
Google Scholar 
Thakur, R. K., Jindal, R., Singh, U. B. & Ahluwalia, A. S. Plankton diversity and water quality assessment of three freshwater lakes of Mandi (Himachal Pradesh, India) with special reference to planktonic indicators. Environ. Monit. Assess. 185, 8355–8373 (2013).CAS 
PubMed 

Google Scholar 
Band-Schmidt, C. J., Bustillos-Guzmán, J. J., López-Cortés, D. J., Núñez-Vázquez, E. & Hernández-Sandoval, F. E. The actual state of the study of harmful algal blooms in Mexico. Hidrobiológica 21, 381–413 (2011).
Google Scholar 
Maciel-Baltazar, E. Dinoflagelados (Dinoflagellata) tóxicos de la costa de Chiapas, México, Pacífico centro oriental. UNED Res. J. 7, 39–48 (2015).
Google Scholar 
Okolodkov, Y. B. & Gárete-Izárraga, I. An annotated checklist od dinoflagellates (Dinophyceae) from the Mexican Pacific. Acta Bot. Mex. 74, 1–154 (2006).
Google Scholar 
Murray, S. A. et al. A fish kill associated with a bloom of Amphidinium carterae in a coastal lagoon in Sydney, Australia. Harmful Algae 49, 19–28 (2015).PubMed 
PubMed Central 

Google Scholar 
Gárate-Lizárraga, I. et al. Seasonality of the dinoflagellate Amphidinium cf. carterae (Dinophyceae: Amphidiniales) in Bahía de la Paz, Gulf of California. Mar. Pollut. Bull. 146, 532–541 (2019).PubMed 

Google Scholar 
Varona-Cordero, F. & Gutiérrez, J. Seasonal phytoplankton composition of two coastal lagoons of the tropical Pacific. Hidrobiológica 16, 159–174 (2006).
Google Scholar 
Hyeon, S. & Jin, H. Gyrodinium jinhaense n. sp., a new heterotrophic unarmored dinoflagellate from the coastal waters of Korea. J. Eukaryot. Microbiol. 66, 821–835 (2019).
Google Scholar 
Onuma, R., Watanabe, K. & Horiguchi, T. Pellucidodinium psammophilum gen. & sp. nov. and Nusuttodinium desymbiontum sp. nov. (Dinophyceae), two novel heterotrophs closely related to kleptochloroplastidic dinoflagellates. Phycologia 54, 192–209 (2015).
Google Scholar 
Elliott, M. & Whitfield, A. K. Challenging paradigms in estuarine ecology and management. Estuar. Coast. Shelf Sci. 94, 306–314 (2011).
Google Scholar 
Sreenivasulu, G., Jayaraju, N. & Sundara Raja, R. Physico-chemical parameters of coastal water from Tupilipalem coast, Southeast coast of India. J. Coast. Sci. 2, 34–39 (2015).
Google Scholar 
Landa-Jaime, V. Benthic mollusc assemblage of the Agua Dulce / El Ermitaño lagoon estuarine system, Jalisco, Mexico. Ciencias Mar. 29, 169–184 (2003).
Google Scholar 
Smyth, K. & Elliott, M. Effects of changing salinity on the ecology of the marine environment. In Stressors in the Marine Environment: Physiological and Ecological Responses (eds Solan, M. & Whiteley, N.) 384 (Societal Implications. Oxford University Press, 2016).
Google Scholar 
Rivera-Velázquez, G., Soto, L. A., Salgado-Ugarte, I. H. & Naranjo, E. J. Growth, mortality and migratory pattern of white shrimp (Litopenaeus vannamei, Crustacea, Penaeidae) in the Carretas-Pereyra coastal lagoon system, Mexico. Rev. Biol. Trop. 56, 523–533 (2008).PubMed 

Google Scholar 
Gainey, L. F. & Greenberg, M. J. Physiological basis of the species abundance-salinity relationship in molluscs: A speculation*. Mar. Biol. 40, 41–49 (1977).CAS 

Google Scholar 
Baqueiro-Cárdenas, E. R., Borabe, L. & Goldaracena-Islas, C. G. Mollusks and pollution. A review. Rev. Mex. Biodivers. 78, 1–7 (2007).
Google Scholar 
Purcell, J. E., Uye, S. & Lo, W. Anthropogenic causes of jellyfish blooms and their direct consequences for humans: A review. Mar. Ecol. Prog. Ser. 350, 153–174 (2007).
Google Scholar 
Nemcová, Y., Pusztai, M., Skaloudová, M. & Neustupa, J. Silica-scaled chrysophytes (Stramenopiles, Ochrophyta) along a salinity gradient: A case study from the Gulf of Bothnia western shore (northern Europe). Hydrobiologia 764, 187–197 (2016).
Google Scholar 
Li, R., Jiao, N., Warren, A. & Xu, D. Changes in community structure of active protistan assemblages from the lower Pearl River to coastal Waters of the South China Sea. Eur. J. Protistol. 63, 72–82 (2018).PubMed 

Google Scholar 
Kataoka, T. & Kondo, R. Estuarine, coastal and shelf science protistan community composition in anoxic sediments from three salinity-disparate Japanese lakes ☆. Estuar. Coast. Shelf Sci. 224, 34–42 (2019).CAS 

Google Scholar 
Sun, P. et al. Marked seasonality and high spatial variation in estuarine ciliates are driven by exchanges between the ‘abundant’ and ‘intermediate’ biospheres. Sci. Rep. https://doi.org/10.1038/s41598-017-10308-y (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Contreras, E. F. O., Castañeda, L. R., Torres, A. & Pérez, M. A. H. Problemática sobre las lagunas costeras mexicanas V, Pesquerías. ContactoSS 25, 36–46 (1998).
Google Scholar 
Reizopoulou, S. & Nicolaidou, A. Benthic diversity of coastal brackish-water lagoons in western Greece. Aquat. Conserv. Mar. Freshw. Ecosyst. 14, 93–102 (2004).
Google Scholar 
Zamorano, P., Barrientos-luján, N. A. & Ramírez-luna, S. Malacofauna del infralitoral rocoso de Agua Blanca, Santa Elena Cozoaltepec, Oaxaca. Cienc. y Mar. 12, 19–33 (2008).
Google Scholar 
Chávez-lópez, Y. & Cruz-gómez, C. New records of polychaetes (Annelida: Polychaeta) from three locations of Oaxaca. Mexico. 67, 157–168 (2019).
Google Scholar 
Thomsen, P. F. et al. Monitoring endangered freshwater biodiversity using environmental DNA. Mol. Ecol. 21, 2565–2573 (2012).CAS 
PubMed 

Google Scholar 
Thomsen, P. F. & Willerslev, E. Environmental DNA—An emerging tool in conservation for monitoring past and present biodiversity. Biol. Conserv. 183, 4–18 (2015).
Google Scholar 
Miller, S. E., Hausmann, A., Hallwachs, W. & Janzen, D. H. Advancing taxonomy and bioinventories with DNA barcodes. Philos. Trans. R. Soc. B Biol. Sci. 371, 20150339 (2016).
Google Scholar  More

Dunham, A. E. & Miles, D. B. Patterns of covariation in life history traits of squamate reptiles: The effects of size and phylogeny reconsidered. Am. Nat. 126, 231–257 (1985).Article 

Google Scholar 
Dobson, F. S. & Oli, M. K. Fast and slow life histories of mammals. Ecoscience 14, 292–299 (2007).Article 

Google Scholar 
Sæther, B. E. Pattern of covariation between life-history traits of European birds. Nature 1, 616–617 (1988).ADS 
Article 

Google Scholar 
Promislow, D. E. L. & Harvey, P. H. Living fast and dying young: A comparative analysis of life-history variation among mammals. J. Zool. 220, 417–437 (1990).Article 

Google Scholar 
De Magalhaes, J. P. & Costa, J. A database of vertebrate longevity records and their relation to other life—history traits. J. Evol. Biol. 22, 1770–1774 (2009).PubMed 
Article 

Google Scholar 
Fisher, D. O., Dickman, C. R., Jones, M. E. & Blomberg, S. P. Sperm competition drives the evolution of suicidal reproduction in mammals. Proc. Natl. Acad. Sci. USA 44, 17910–17914 (2013).ADS 
Article 

Google Scholar 
Blanco, M. A. & Sherman, P. W. Maximum longevities of chemically protected and non-protected fishes, reptiles, and amphibians support evolutionary hypotheses of aging. Mech. Ageing Dev. 126, 794–803 (2005).CAS 
PubMed 
Article 

Google Scholar 
Shine, R. & Charnov, E. L. Patterns of survival, growth, and maturation in snakes and lizards. Am. Nat. 139, 1257–1269 (1992).Article 

Google Scholar 
Pedrono, M. et al. Using a surviving lineage of Madagascar’s vanished megafauna for ecological restoration. Biol. Cons. 159, 501–506 (2013).Article 

Google Scholar 
Karsten, K. B., Andriamandimbiarisoa, L. N., Fox, S. F. & Raxworthy, C. J. A unique life history among tetrapods: An annual chameleon living mostly as an egg. Proc. Natl. Acad. Sci. USA 105, 8980–8984 (2008).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Uetz, P., Freed, P. & Hošek, J. (eds) The reptile database. http://www.reptile-database.org (2020).Glaw, F. & Vences, M. A Field Guide to the Amphibians and Reptiles of Madagascar (Vences and Glaw, 2007).
Google Scholar 
Anderson, C. V. Off like a shot: Scaling of ballistic tongue projection reveals extremely high performance in small chameleons. Sci. Rep. 6, 18625 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Keren-Rotem, T., Levy, N., Wolf, L., Bouskila, A. & Geffen, E. Male preference for sexual signalling over crypsis is associated with alternative mating tactics. Anim. Behav. 117, 43–49 (2016).Article 

Google Scholar 
Keren-Rotem, T., Levy, N., Wolf, L., Bouskila, A. & Geffen, E. Alternative mating tactics in male chameleons (Chamaeleo chamaeleon) are evident in both long-term body color and short-term courtship pattern. PLoS ONE 11, e0159032 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Ligon, R. A. & McGraw, K. J. Chameleons communicate with complex colour changes during contests: Different body regions convey different information. Biol. Lett. 9, 20130892 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Prötzel, D. et al. Widespread bone-based fluorescence in chameleons. Sci. Rep. 8, 698 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Tolley, K. A. & Herrel, A. (eds) The Biology of Chameleons (University of California Press, 2014).
Google Scholar 
Andreone, F., Guarino, F. M. & Randrianirina, J. E. Life history traits, age profile, and conservation of the panther chameleon, Furcifer pardalis (Cuvier 1829), at Nosy Be, NW Madagascar. Trop. Zool. 18, 209–225 (2005).Article 

Google Scholar 
Tessa, G., Glaw, F. & Andreone, F. Longevity in Calumma parsonii, the World’s largest chameleon. Exp. Geront. 89, 41–44 (2017).Article 

Google Scholar 
Karsten, K. B., Andriamandimbiarisoa, L. N., Fox, S. F. & Raxworthy, C. J. Sexual selection on body size and secondary sexual characters in 2 closely related, sympatric chameleons in Madagascar. Behav. Ecol. 20, 1079–1088 (2009).Article 

Google Scholar 
Eckhardt, F., Kappeler, P. M. & Kraus, C. Highly variable lifespan in an annual reptile, Labord’s chameleon (Furcifer labordi). Sci. Rep. 7, 11397 (2017).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Eckhardt, F., Kraus, C. & Kappeler, P. M. Life histories, demographies and population dynamics of three sympatric chameleon species (Furcifer spp.) from western Madagascar. Amphibia-Reptilia 40, 41–54 (2018).Article 

Google Scholar 
Karsten, K. B., Andriamandimbiarisoa, L. N., Fox, S. F. & Raxworthy, C. J. Social behavior of two species of chameleons in Madagascar: Insights into sexual selection. Herpetologica 65, 54–69 (2009).Article 

Google Scholar 
Emlen, S. T. & Oring, L. W. Ecology, sexual selection, and the evolution of mating systems. Science 197, 215–223 (1977).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Chiaverano, L. M., Wright, M. J. & Holland, B. S. Movement behavior is habitat dependent in invasive Jackson’s chameleons in Hawaii. J. Herpetol. 48, 471–479 (2014).Article 

Google Scholar 
Smith, D. et al. Observations on nesting and clutch size in Furcifer oustaleti (Oustalet’s chameleon) in South Florida. Southeast Nat. 15, 75–88 (2016).Article 

Google Scholar 
Van Kleeck, M. J., Smith, T. A. & Holland, B. S. Paedophagic cannibalism, resource partitioning, and ontogenetic habitat use in an invasive lizard. Ethol. Ecol. Evol. 30, 497–514 (2018).Article 

Google Scholar 
Tolley, K. A., Raw, R. N., Altwegg, R. & Measey, J. G. Chameleons on the move: Survival and movement of the Cape dwarf chameleon, Bradypodion pumilum, within a fragmented urban habitat. Afr. Zool. 45, 99–106 (2010).Article 

Google Scholar 
Cuadrado, M. The influence of female size on the extent and intensity of mate guarding by males in Chamaeleo chamaeleon. J. Zool. 246, 351–358 (1998).Article 

Google Scholar 
Cuadrado, M. Mating asynchrony favors no assortative mating by size and serial-type polygyny in common chameleons, Chamaeleo chamaeleon. Herpetologica 55, 523–530 (1999).
Google Scholar 
Cuadrado, M. Influence of female’s sexual stage and number of available males on the intensity of guarding behavior by male common chameleons: A test of different predictions. Herpetologica 56, 387–393 (2000).
Google Scholar 
Cuadrado, M. Mate guarding and social mating system in male common chameleons (Chamaeleo chamaeleon). J. Zool. 255, 425–435 (2001).Article 

Google Scholar 
Kauffmann, J. L. D., Brady, L. D. & Jenkins, R. K. B. Behavioural observations of the chameleon Calumma oshaughnessyi oshaughnessyi in Madagascar. Herpetol. J. 7, 77–80 (1997).
Google Scholar 
Greenwood, P. J. Mating systems, philopatry and dispersal in birds and mammals. Anim. Behav. 28, 1140–1162 (1980).Article 

Google Scholar 
Kappeler, P. M. Intrasexual selection in Mirza coquereli: Evidence for scramble competition polygyny in a solitary primate. Behav. Ecol. Sociobiol. 41, 115–127 (1997).Article 

Google Scholar 
Croft, D. P. et al. Sex-biased movement in the guppy (Poecilia reticulata). Oecologia 137, 62–68 (2003).ADS 
PubMed 
Article 

Google Scholar 
Glaudas, X. & Rodriguez-Robles, J. A. Vagabond males and sedentary females: Spatial ecology and mating system of the speckled rattlesnake (Crotalus mitchellii). Biol. J. Linn. Soc. 103, 681–695 (2011).Article 

Google Scholar 
Taborsky, M. & Brockmann, H. J. Alternative reproductive tactics and life history phenotypes. In Animal Behaviour: Evolution and Mechanisms (ed. Kappeler, P. M.) 537–586 (Springer, 2010).Chapter 

Google Scholar 
Tolley, K. A., Chauke, L. F., Jackson, J. C. & Feldheim, K. A. Multiple paternity and sperm storage in the Cape dwarf chameleon (Bradypodion pumilum). Afr. J. Herpetol. 63, 47–56 (2014).Article 

Google Scholar 
Rebelo, A. D., Altwegg, R., Katz, E. M. & Tolley, K. A. Out on a limb: Female chameleons (Bradypodion pumilum) position themselves to minimise detection, whereas males maximise mating opportunity. Afr. J. Herpetol https://doi.org/10.1080/21564574.2021.1998233 (2022).Article 

Google Scholar 
Dollion, A. Y., Herrel, A., Marquis, O., Leroux-Coyau, M. & Meylan, S. The colour of success: Does female mate choice rely on male colour change in the chameleon Furcifer pardalis?. J. Exp. Biol. 223, jeb224550 (2020).PubMed 
Article 

Google Scholar 
Dollion, A. Y., Meylan, S., Marquis, O., Leroux-Coyau, M. & Herrel, A. Do male panther chameleons use different aspects of color change to settle disputes?. Sci. Nat. 109, 13 (2022).CAS 
Article 

Google Scholar 
Shine, R. Reproductive strategies in snakes. Proc. R. Soc. Lond. B 270, 995–1004 (2003).Article 

Google Scholar 
Andrews, R. M. & Karsten, K. B. Evolutionary innovations of squamate reproductive and developmental biology in the family Chamaeleonidae. Biol. J. Linn. Soc. 100, 656–668 (2010).Article 

Google Scholar 
Sever, D. M. & Hamlett, W. C. Female sperm storage in reptiles. J. Exp. Zool. 292, 187–199 (2002).PubMed 
Article 

Google Scholar 
Friesen, C. R., Kahrl, A. F. & Olsson, M. Sperm competition in squamate reptiles. Philos. Trans. R. Soc. Lond. B Biol. Sci. 375, 20200079 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Parker, G. A. & Birkhead, T. R. Polyandry: The history of a revolution. Philos. Trans. R. Soc. Lond. B Biol. Sci. 368, 20120335 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Székely, T., Weissing, F. J. & Komdeur, J. Adult sex ratio variation: Implications for breeding system evolution. J. Evol. Biol. 27, 1500–1512 (2014).PubMed 
Article 

Google Scholar 
Kokko, H. & Jennions, M. D. Parental investment, sexual selection and sex ratios. J. Evol. Biol. 21, 919–948 (2008).PubMed 
Article 

Google Scholar 
Holleley, C. E., Dickman, C. R., Crowther, M. S. & Oldroyd, B. P. Size breeds success: Multiple paternity, multivariate selection and male semelparity in a small marsupial, Antechinus stuartii. Mol. Ecol. 15, 3439–3448 (2006).CAS 
PubMed 
Article 

Google Scholar 
Kappeler, P. M. & Fichtel, C. A 15-year perspective on the social organization and life history of sifaka in Kirindy forest. In Long-Term Field Studies of Primates (eds Kappeler, P. M. & Watts, D. P.) 101–121 (Springer, 2012).Chapter 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. https://www.R-project.org/ (2020).RStudio Team. RStudio: Integrated development for R. RStudio, PBC, Boston, MA. http://www.rstudio.com/ (2020).Wickham, H. The split-apply-combine strategy for data analysis. J. Stat. Softw. 40, 1–29 (2011).
Google Scholar 
Wickham, H., François, R., Henry, L. & Müller, K. dplyr: A grammar of data manipulation. R package version 102. https://cran.r-project.org/package=dplyr (2020).Grolemund, G. & Wickham, H. Dates and times made easy with lubridate. J. Stat. Softw. 40, 1–25 (2011).Article 

Google Scholar 
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).MATH 
Book 

Google Scholar 
Wilke, C. O. cowplot: Streamlined plot theme and plot annotations for ggplot2. R package version 100. https://cran.r-project.org/package=cowplot (2019).Revelle, W. psych: Procedures for personality and psychological research, Northwestern University, Evanston, IL. https://cran.r-project.org/package=psych (2020).Wickham, H. modelr: Modelling functions that work with the pipe. R package version 018. https://cran.r-project.org/package=modelr (2020).Fox, J. & Weisberg, S. An R Companion to Applied Regression 3rd edn. (Sage, 2019).
Google Scholar 
Ara, T. brunnermunzel: (Permuted) Brunner–Munzel Test R Package Version 133 (2019).Crane, M., Silva, I., Marshall, B. M. & Strine, C. T. Lots of movement, little progress: A review of reptile home range literature. PeerJ 9, e11742 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Signer, J. & Fieberg, J. A fresh look at an old concept: Home-range estimation in a tidy world. PeerJ 9, e11031 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Laver, P. N. & Kelly, M. J. A critical review of home range studies. J. Wildl. Manag. 72, 290–298 (2008).Article 

Google Scholar 
Signer, J., Fieberg, J. & Avgar, T. Animal movement tools (amt): R package for managing tracking data and conducting habitat selection analyses. Ecol. Evol. 9, 880–890 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
QGIS Development Team. QGIS Geographic Information System Open Source Geospatial Foundation Project. http://qgis.osgeo.org (2020).Getz, W. M. & Wilmers, C. C. A local nearest-neighbor convex-hull construction of home ranges and utilization distributions. Ecography 27, 489–505 (2004).Article 

Google Scholar 
Worton, B. J. Kernel methods for estimating the utilization distribution in homerange studies. Ecology 70, 1641–1668 (1989).Article 

Google Scholar 
Yagi, K. T. & Green, D. M. Performance and movement in relation to postmetamorphic body size in a pond-breeding amphibian. J. Herpetol. 51, 482–489 (2017).Article 

Google Scholar  More

Kangaroo Island (~ 4400 km2, KI hereafter) is the third largest island in Australia. It underwent substantial land clearing, and consequent fragmentation of the natural bushland habitat, after World War II1,2. Relatively intact western KI was eventually identified as a key biodiversity hotspot3, home to several endangered and endemic native species including the KI dunnart.Dunnarts (Sminthopsis spp.) are small insectivorous dasyurid marsupials. The KI dunnart is distinguished from the other 17 dunnart species found in Australia by morphological features, including manus, pes, and penis shape4. This endangered species is the only dasyurid found on the island, exclusively resident in ~ 342 km2 before 20205, and found nowhere else in the world2. The species is rarely recorded, with only 28 individuals found during  > 33,000 trap-nights pre-20195. With a low number of individuals restricted to a small geographic area, the KI dunnart is exceptionally vulnerable to stochastic events. Predation by feral cats (Felis catus) is likely to be another source of pressure on the KI dunnart. Cats were introduced to KI during European settlement and quickly became apex predators, reaching higher relative abundance than adjacent mainland6 with an estimated density of 0.37 ± 0.15 cat/km25. Cat predation has been the cause for extinction or near-extinction of several native species around the globe7, with the extinction risk becoming increasingly acute in insular islands like KI. Cat predation on islands has contributed to  > 13% of globally recorded extinction events, accounting for  > 8% of instances within these taxa of species being pushed to critically endangered status8. A recent meta-analysis found evidence of cat predation for three critically endangered species and four endangered species in Australia on the IUCN Red List of Threatened Species7.Australian bushfires in 2019–2020 burnt ~ 97,000 km2 of vegetation9,10, with damage overlapping with habitats of  > 100 threatened species. Dry lightning storms in the remote and vegetated northwest of the Island started the bushfire in the KI. The bushfire eventually spread easterly, burning approximately 98% of the known and predicted habitat of the KI dunnart10.In this study, we have analysed the diet of feral cats humanely euthanized in designated areas of local conservation interest immediately after the 2019 KI bushfire. More

FAO. Global Forest Resource Assessment 2020—Key Findings (FAO, 2020).
Google Scholar 
Rasmussen, L. V. et al. A combination of methods needed to assess the actual use of provisioning ecosystem services. Ecosyst. Serv. 17, 75–86 (2016).Article 

Google Scholar 
Pan, Y. et al. A large and persistent carbon sink in the World’s Forests. Science 333, 988 (2011).ADS 
CAS 
Article 

Google Scholar 
Gao, J., Tang, X. G., Lin, S. Q. & Bian, H. Y. The influence of land use change on key ecosystem services and their relationships in a mountain region from past to future (1995–2050). Forests 12, 616 (2021).Article 

Google Scholar 
Rodríguez-Echeverry, J., Echeverría, C., Oyarzún, C. & Morales, L. Impact of land-use change on biodiversity and ecosystem services in the Chilean temperate forests. Landsc. Ecol. 33(3), 439–453 (2018).Article 

Google Scholar 
Hoque, M. Z., Islam, I., Ahmed, M., Hasan, S. S. & Prodhan, F. A. Spatio-temporal changes of land use land cover and ecosystem service values in coastal Bangladesh. Egypt. J. Remote Sens. Space Sci. 25(1), 173–180 (2022).
Google Scholar 
Costanza, R. et al. Changes in the global value of ecosystem services. Glob. Environ. Change 26, 152–158 (2014).Article 

Google Scholar 
Sil, Â. et al. Analysing carbon sequestration and storage dynamics in a changing mountain landscape in Portugal: Insights for management and planning. Int. J. Biodivers. Sci. Ecosyst. Serv. Manage. 13(2), 82–104 (2017).Article 

Google Scholar 
Xu, Y., Tang, H., Wang, B. & Chen, J. Effects of land-use intensity on ecosystem services and human well-being: A case study in Huailai County, China. Environ. Earth. Sci. 75(5), 416 (2016).Article 

Google Scholar 
Liang, Y., Liu, L. & Huang, J. Integrating the SD-CLUE-S and InVEST models into assessment of oasis carbon storage in northwestern China. PLoS ONE 12(2), e0172494 (2017).Article 

Google Scholar 
Zhao, M. et al. Assessing the effects of ecological engineering on carbon storage by linking the CA-Markov and InVEST models. Ecol. Indic. 98, 29–38 (2019).Article 

Google Scholar 
Leh, M. D., Matlock, M. D., Cummings, E. C. & Nalley, L. L. Quantifying and mapping multiple ecosystem services change in West Africa. Agric. Ecosyst. Environ. 165, 6–18 (2013).Article 

Google Scholar 
Zhao, Z. et al. Assessment of carbon storage and its influencing factors in Qinghai-Tibet Plateau. Sustainability 10(6), 1864 (2018).Article 

Google Scholar 
Fu, Q. et al. Scenario analysis of ecosystem service changes and interactions in a mountain-oasis-desert system: A case study in Altay Prefecture, China. Sci. Rep. 8(1), 1–13 (2018).ADS 

Google Scholar 
Li, Z., Cheng, X. & Han, H. Future impacts of land use change on ecosystem services under different scenarios in the ecological conservation area, Beijing, China. Forests 11(5), 584 (2020).CAS 
Article 

Google Scholar 
Liu, H., Xiao, W., Li, Q., Tian, Y. & Zhu, J. Spatio-temporal change of multiple ecosystem services and their driving factors: A case study in Beijing, China. Forests 13(2), 260 (2022).CAS 
Article 

Google Scholar 
Nizami, S. M. The inventory of the carbon stocks in sub tropical forests of Pakistan for reporting under Kyoto Protocol. J. For. Res. 23(3), 377–384 (2012).CAS 
Article 

Google Scholar 
Ghafoor, G. Z., Sharif, F., Khan, A. U., Shahzad, L. & Hayyat, M. U. Assessment of tree biomass carbon stock of subtropical scrub forest, Soan valley Pakistan. App. Ecol. Environ. Res. 18(2), 2231–2245 (2020).Article 

Google Scholar 
Siddiq, Z. et al. Models to estimate the above and below ground carbon stocks from a subtropical scrub forest of Pakistan. Glob. Ecol. Conserv. 27, e01539 (2021).Article 

Google Scholar 
Ali, A., Ashraf, M. I., Gulzar, S. & Akmal, M. Estimation of forest carbon stocks in temperate and subtropical mountain systems of Pakistan: Implications for REDD+ and climate change mitigation. Environ. Monit. Assess. 192(3), 1–13 (2020).Article 

Google Scholar 
Mannan, A. et al. Application of land-use/land cover changes in monitoring and projecting forest biomass carbon loss in Pakistan. Glob. Ecol. Conserv. 17, e00535 (2019).Article 

Google Scholar 
Khan, A. U. et al. Piloting restoration initiatives in subtropical scrub forest: Specifying areas asserting adaptive management. Environ. Monit. Assess. 191(11), 675 (2019).Article 

Google Scholar 
Mohajane, M. et al. Land use/land cover (LULC) using Landsat data series (MSS, TM, ETM+ and OLI) in Azrou Forest, in the Central Middle Atlas of Morocco. Environments 5(12), 131 (2018).Article 

Google Scholar 
Brown, J. NDVI, the foundation for remote sensing phenology. In USGS Remote Sensing Phenology: Vegetation Indices (2015).Liping, C., Yujun, S. & Saeed, S. Monitoring and predicting land use and land cover changes using remote sensing and GIS techniques—A case study of a hilly area, Jiangle, China. PLoS ONE 13(7), e0200493 (2018).Article 

Google Scholar 
Ricke, K., Drouet, L., Caldeira, K. & Tavoni, M. Country-level social cost of carbon. Nat. Clim. Change 8(10), 895–900 (2018).ADS 
CAS 
Article 

Google Scholar 
Salehi, M. H., Beni, O. H., Harchegani, H. B., Borujeni, I. E. & Motaghian, H. R. Refining soil organic matter determination by loss-on-ignition. Pedosphere 21(4), 473–482 (2011).Article 

Google Scholar 
Tivet, F. et al. Soil carbon inventory by wet oxidation and dry combustion methods: Effects of land use, soil texture gradients, and sampling depth on the linear model of C-equivalent correction factor. Soil Sci. Soc. Am. J. 76(3), 1048–1059 (2012).ADS 
CAS 
Article 

Google Scholar 
Government of Punjab. The Punjab Forest (Amendment) Act, 2010 (Government of the Punjab, 2010).
Google Scholar 
Kamwi, J. M., Kaetsch, C., Graz, F. P., Chirwa, P. & Manda, S. Trends in land use and land cover change in the protected and communal areas of the Zambezi Region, Namibia. Environ. Monit. Assess. 189(5), 242 (2017).Article 

Google Scholar 
Negassa, M. D., Mallie, D. T. & Gemeda, D. O. Forest cover change detection using Geographic Information Systems and remote sensing techniques: A spatio-temporal study on Komto Protected forest priority area, East Wollega Zone, Ethiopia. Environ. Syst. Res. 9(1), 1 (2020).Article 

Google Scholar 
Government of Punjab. Punjab Development Statistics 2007. Burreau of Statistics (Government of the Punjab, 2007).
Google Scholar 
Government of Punjab. Punjab Development Statistics 2013. Burreau of Statistics (Government of the Punjab, 2013).
Google Scholar 
Government of Punjab. Punjab Development Statistics 2019. Burreau of Statistics (Government of the Punjab, 2019).
Google Scholar 
Dunn, R. J. H., Stanitski, D. M., Gobron, N. & Willett, K. M. State of the climate in 2019: Global climate. Special online supplement to the B. Am. Meteorol. Soc. 101(8), S9. https://doi.org/10.1175/BAMS-D-20-0104.1 (2020).Article 

Google Scholar 
Gray, S. B. & Brady, S. M. Plant developmental responses to climate change. Dev. Biol. 419(1), 64–77 (2016).CAS 
Article 

Google Scholar 
Ghafoor, G. Z. et al. Effect of climate warming on seedling growth and biomass accumulation of Acacia modesta and Olea ferruginea in a subtropical scrub forest of Pakistan. Écoscience 29, 1–14 (2021).
Google Scholar 
Bibi, S., Sultana, J., Sultana, H. & Malik, R. N. Ethnobotanical uses of medicinal plants in the highlands of Soan valley, salt range, Pakistan. J. Ethnopharmacol. 155(1), 352–361 (2014).Article 

Google Scholar 
Chaudhry, Q. U. Z. Climate Change Profile of Pakistan (Asian Development Bank, 2017).
Google Scholar 
Shaheen, H. et al. Carbon stocks assessment in subtropical forest types of Kashmir Himalayas. Pak. J. Bot. 48, 2351–2357 (2016).CAS 

Google Scholar 
Arunyawat, S. & Shrestha, R. P. Assessing land use change and its impact on ecosystem services in Northern Thailand. Sustainability 8(8), 768 (2016).Article 

Google Scholar 
Sing, L., Metzger, M. J., Paterson, J. S. & Ray, D. A review of the effects of forest management intensity on ecosystem services for northern European temperate forests with a focus on the UK. For. Int. J. For. Res. 91(2), 151–164 (2018).
Google Scholar  More

Suding, K. Understanding successes and failures in restoration ecology. Annu. Rev. Ecol. Evol. Syst. 42, (2011).Brudvig, L. A. et al. Interpreting variation to advance predictive restoration science. J. Appl. Ecol. 54, 1018–1027 (2017).Article 

Google Scholar 
Germino, M. J. et al. Thresholds and hotspots for shrub restoration following a heterogeneous megafire. Landsc. Ecol. 33, 1177–1194 (2018).Article 

Google Scholar 
Shriver, R. K. et al. Transient population dynamics impede restoration and may promote ecosystem transformation after disturbance. Ecol. Lett. 22, 1357–1366 (2019).PubMed 
Article 

Google Scholar 
Chambers, J. C. et al. Resilience and resistance of sagebrush ecosystems: implications for state and transition models and management treatments. Rangel. Ecol. Manag. 67, 440–454 (2014).Article 

Google Scholar 
Pilliod, D. S., Welty, J. L. & Toevs, G. R. Seventy-five years of vegetation treatments on public rangelands in the great basin of North America. Rangelands 39, 1–9 (2017).Article 

Google Scholar 
Applestein, C., Germino, M. J., Pilliod, D. S., Fisk, M. R. & Arkle, R. S. Appropriate sample sizes for monitoring burned pastures in sagebrush steppe: how many plots are enough, and can one size fit all? Rangel. Ecol. Manag. 71, 721–726 (2018).Article 

Google Scholar 
Homer, C. et al. Completion of the 2011 National Land Cover Database for the Conterminous United States-Representing a Decade of Land Cover Change Information Landsat-based mapping project. Photogramm. Eng. Remote Sens. 81, 345–354 (2015).
Google Scholar 
Homer, C. G., Aldridge, C. L., Meyer, D. K. & Schell, S. J. Multi-scale remote sensing sagebrush characterization with regression trees over Wyoming, USA: Laying a foundation for monitoring. Int. J. Appl. Earth Obs. Geoinf. 14, 233–244 (2012).ADS 

Google Scholar 
Tredennick, A. T. et al. Forecasting climate change impacts on plant populations over large spatial extents. Ecosphere 7, 1–16 (2016).Article 

Google Scholar 
Rigge, M. et al. Quantifying western U.S. rangelands as fractional components with multi-resolution remote sensing and in situ data. Remote Sens. 12, 1–26 (2020).Article 

Google Scholar 
Shi, H., Homer, C., Rigge, M., Postma, K. & Xian, G. Analyzing vegetation change in a sagebrush ecosystem using long-term field observations and Landsat imagery in Wyoming. Ecosphere 11, 1–20 (2020).Article 

Google Scholar 
Williamson, M. A., Schwartz, M. W. & Lubell, M. N. Spatially explicit analytical models for social–ecological systems. Bioscience 68, 885–895 (2018).
Google Scholar 
Reid, J. L., Fagan, M. E. & Zahawi, R. A. Positive site selection bias in meta-analyses comparing natural regeneration to active forest restoration. Sci. Adv. 4, 1–4 (2018).Article 

Google Scholar 
Joppa, L. N. & Pfaff, A. High and far: biases in the location of protected areas. PLoS One 4, 1–6 (2009).Article 
CAS 

Google Scholar 
Prach, K., Šebelíková, L., Řehounková, K. & del Moral, R. Possibilities and limitations of passive restoration of heavily disturbed sites. Landsc. Res. 45, 247–253 (2020).Article 

Google Scholar 
Andam, K. S., Ferraro, P. J., Pfaff, A., Sanchez-Azofeifa, G. A. & Robalino, J. A. Measuring the effectiveness of protected area networks in reducing deforestation. Proc. Natl Acad. Sci. USA 105, 16089–16094 (2008).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Jones, K. W. & Lewis, D. J. Estimating the counterfactual impact of conservation programs on land cover outcomes: The role of matching and panel regression techniques. PLoS One 10, 1–22 (2015).
Google Scholar 
Christie, A. P. et al. Simple study designs in ecology produce inaccurate estimates of biodiversity responses. J. Appl. Ecol. 56, 2742–2754 (2019).Article 

Google Scholar 
Larsen, A. E., Meng, K. & Kendall, B. E. Causal analysis in control–impact ecological studies with observational data. Methods Ecol. Evol. 10, 924–934 (2019).Article 

Google Scholar 
Parkhurst, T., Prober, S. M., Hobbs, R. J. & Standish, R. J. Global meta-analysis reveals incomplete recovery of soil conditions and invertebrate assemblages after ecological restoration in agricultural landscapes. J. Appl. Ecol. 1–15. https://doi.org/10.1111/1365-2664.13852. (2021)Crouzeilles, R. et al. A global meta-Analysis on the ecological drivers of forest restoration success. Nat. Commun. 7, 1–8 (2016).Article 
CAS 

Google Scholar 
Kettenring, K. M. & Adams, C. R. Lessons learned from invasive plant control experiments: a systematic review and meta-analysis. J. Appl. Ecol. 48, 970–979 (2011).Article 

Google Scholar 
Atkinson, J. & Bonser, S. P. “Active” and “passive” ecological restoration strategies in meta-analysis. Restor. Ecol. 28, 1032–1035 (2020).Article 

Google Scholar 
Rosenbaum, P. R. & Rubin, D. B. The central role of the propensity score in observational studies for causal effects. Biometrika 170–184. https://doi.org/10.1017/CBO9780511810725.016. (1983)Angrist, J. D., & Pischke, J. S. Mostly harmless econometrics. (Princeton University Press, 2009).Bernes, C. et al. How are biodiversity and dispersal of species affected by the management of roadsides? A systematic map. Environ. Evid. 6, 1–16 (2017).Article 

Google Scholar 
França, F. et al. Do space-for-time assessments underestimate the impacts of logging on tropical biodiversity? An Amazonian case study using dung beetles. J. Appl. Ecol. 53, 1098–1105 (2016).Article 

Google Scholar 
Davies, K. W. et al. Saving the sagebrush sea: an ecosystem conservation plan for big sagebrush plant communities. Biol. Conserv. 144, 2573–2584 (2011).Article 

Google Scholar 
Miller, R. F. et al. Characteristics of Sagebrush Habitats and Limitations to Long-term Conservation. Greater sage-grouse: ecology and conservation of a landscape species and its habitats. USGS Adm. Rep. (2011).Pierson, F. B. et al. Hydrologic and erosion responses of sagebrush steppe following juniper encroachment, wildfire, and tree cutting. Rangel. Ecol. Manag. 66, 274–289 (2013).Article 

Google Scholar 
Wijayratne, U. C. & Pyke, D. A. Burial increases seed longevity of two Artemisia tridentata (Asteraceae) subspecies. Am. J. Bot. 99, 438–447 (2012).PubMed 
Article 

Google Scholar 
Pyke, D. A., Wirth, T. A. & Beyers, J. L. Does seeding after wildfires in rangelands reduce erosion or invasive species? Restor. Ecol. 21, 415–421 (2013).Article 

Google Scholar 
Knutson, K. C. et al. Long-term effects of seeding after wildfire on vegetation in Great Basin shrubland ecosystems. J. Appl. Ecol. 51, 1414–1424 (2014).Article 

Google Scholar 
Shriver, R. K. et al. Adapting management to a changing world: Warm temperatures, dry soil, and interannual variability limit restoration success of a dominant woody shrub in temperate drylands. Glob. Chang. Biol. 24, 4972–4982 (2018).PubMed 
Article 
ADS 

Google Scholar 
Eiswerth, M. E., Krauter, K., Swanson, S. R. & Zielinski, M. Post-fire seeding on Wyoming big sagebrush ecological sites: Regression analyses of seeded nonnative and native species densities. J. Environ. Manag. 90, 1320–1325 (2009).Article 

Google Scholar 
Arkle, R. S. et al. Quantifying restoration effectiveness using multi-scale habitat models: Implications for sage-grouse in the Great Basin. Ecosphere 5, 1–32 (2014).Article 

Google Scholar 
Davies, K. W. & Bates, J. D. Restoring big sagebrush after controlling encroaching western juniper with fire: aspect and subspecies effects. Restor. Ecol. 25, 33–41 (2017).Article 

Google Scholar 
Davies, K. W., Bates, J. D. & Boyd, C. S. Postwildfire seeding to restore native vegetation and limit exotic annuals: an evaluation in juniper-dominated sagebrush steppe. Restor. Ecol. 27, 120–127 (2019).Article 

Google Scholar 
Davies, K. W., Boyd, C. S., Madsen, M. D., Kerby, J. & Hulet, A. Evaluating a seed technology for Sagebrush restoration across an elevation gradient: support for Bet Hedging. Rangel. Ecol. Manag. 71, 19–24 (2018).Article 

Google Scholar 
Rinella, M. J. et al. High precipitation and seeded species competition reduce seeded shrub establishment during dryland restoration. Ecol. Appl. 25, 1044–1053 (2015).Davies, K. W., Boyd, C. S. & Nafus, A. M. Restoring the sagebrush component in crested wheatgrass-dominated communities. Rangel. Ecol. Manag. 66, 472–478 (2013).Article 

Google Scholar 
United States General Accounting. WILDLAND FIRES: Better Information Needed on Effectiveness of Emergency Stabilization and Rehabilitation Treatments. Report to Congressional Requesters. https://doi.org/10.1089/blr.2006.9996. (2003)Requena-Mullor, J. M., Maguire, K. C., Shinneman, D. J. & Caughlin, T. T. Integrating anthropogenic factors into regional-scale species distribution models—A novel application in the imperiled sagebrush biome. Glob. Chang. Biol. 00, 1–15 (2019).
Google Scholar 
Pyke, D. A. et al. Restoration handbook for sagebrush steppe ecosystems with emphasis on greater sage-grouse habitat—Part 3. Site level restoration decisions. U.S. Geological Survey Circular 1426 (2017).Chambers, J. C. et al. Science framework for conservation and restoration of the sagebrush biome: Linking the department of the interior’s integrated rangeland fire management strategy to long-term strategic conservation actions. USDA . Serv. – Gen. Tech. Rep. RMRS-GTR 2017, 1–217 (2017).
Google Scholar 
US-BLM. Burned Area Emergency Stabilization and Rehabilitation – BLM Handbook H-1742-1. 2, (2007).Pilliod, D. S. & Welty, J. L. Land Treatment Digital Library. Data Series. https://doi.org/10.3133/ds806. (2013)Bradley, B. A. et al. Cheatgrass (Bromus tectorum) distribution in the intermountain Western United States and its relationship to fire frequency, seasonality, and ignitions. Biol. Invasions 20, 1493–1506 (2018).Article 

Google Scholar 
Fusco, E. J., Finn, J. T., Balch, J. K., Chelsea Nagy, R. & Bradley, B. A. Invasive grasses increase fire occurrence and frequency across US ecoregions. Proc. Natl Acad. Sci. USA 116, 23594–23599 (2019).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
O’Connor, R. C. et al. Small-scale water deficits after wildfires create long-lasting ecological impacts. Environ. Res. Lett. 15, 044001 (2020).Applestein, C., Caughlin, T. T. & Germino, M. J. Weather affects post‐fire recovery of sagebrush‐steppe communities and model transferability among sites. Ecosphere 12, (2021).Cameron, A. C. & Miller, D. L. A. Practitioner’ s Guide to Cluster-Robust Inference. J. Human Resources. 50, 317–372 (2015).Oshchepkov, A. & Shirokanova, A. Bridging the gap between multilevel modeling and economic methods. Soc. Sci. Res. in press, (2022).Aldridge, C. L. & Boyce, M. S. Linking occurrence and fitness to persistence: habitat-based approach for endangered Greater Sage-Grouse. Ecol. Appl. 17, 508–526 (2007).PubMed 
Article 

Google Scholar 
Allen-Diaz, B. & Bartolome, J. W. Sagebrush-grass vegetation dynamics: Comparing Classical and State-Transition models. Ecol. Appl. 8, 795–804 (1998).
Google Scholar 
Schlaepfer, D. R., Lauenroth, W. K. & Bradford, J. B. Natural regeneration processes in big sagebrush (Artemisia tridentata). Rangel. Ecol. Manag. 67, 344–357 (2014).Article 

Google Scholar 
Melgoza, G., Nowak, R. S. & Tausch, R. J. Soil water exploitation after fire: competition between Bromus tectorum (cheatgrass) and two native species. Oecologia 83, 7–13 (1990).PubMed 
Article 
ADS 

Google Scholar 
Williamson, M. A. et al. Fire, livestock grazing, topography, and precipitation affect occurrence and prevalence of cheatgrass (Bromus tectorum) in the central Great Basin, USA. Biol. Invasions 22, 663–680 (2020).Article 

Google Scholar 
Groves, A. M., Bauer, J. T. & Brudvig, L. A. Lasting signature of planting year weather on restored grasslands. Sci. Rep. 10, 1–10 (2020).Article 
CAS 

Google Scholar 
Groves, A. M. & Brudvig, L. A. Interannual variation in precipitation and other planting conditions impacts seedling establishment in sown plant communities. Restor. Ecol. 27, 128–137 (2019).Article 

Google Scholar 
Werner, C. M., Stuble, K. L., Groves, A. M. & Young, T. P. Year effects: Interannual variation as a driver of community assembly dynamics. Ecology 0, 1–8 (2020).
Google Scholar 
Stuble, K. L., Fick, S. E. & Young, T. P. Every restoration is unique: testing year effects and site effects as drivers of initial restoration trajectories. J. Appl. Ecol. 54, 1051–1057 (2017).Article 

Google Scholar 
Stuble, K. L., Zefferman, E. P., Wolf, K. M., Vaughn, K. J. & Young, T. P. Outside the envelope: rare events disrupt the relationshipbetween climate factors and species interactions. Ecology 98, 1623–1630 (2017).PubMed 
Article 

Google Scholar 
Hardegree, S. P. et al. Weather-Centric Rangeland Revegetation Planning. Rangel. Ecol. Manag. 71, 1–11 (2018).Article 

Google Scholar 
Allison, B., Cara, S-W. & Applestein, M. J., Germino Interannual variation in climate contributes to contingency in post‐fire restoration outcomes in seeded sagebrush steppe. Conservation Science and Practice https://doi.org/10.1111/csp2.12737.Callaway, B. & Sant’Anna, P. H. C. Difference-in-Differences with multiple time periods. J. Econom. 225, 200–230 (2021).MathSciNet 
MATH 
Article 

Google Scholar 
Goodman-Bacon, A. Difference-in-differences with variation in treatment timing. J. Econom. 225, 254–277 (2021).MathSciNet 
MATH 
Article 

Google Scholar 
Starrs, C. F., Butsic, V., Stephens, C. & Stewart, W. The impact of land ownership, firefighting, and reserve status on fire probability in California. Environ. Res. Lett. 13, (2018).Ferraro, P. J. & Miranda, J. J. Panel data designs and estimators as substitutes for randomized controlled trials in the evaluation of public programs. J. Assoc. Environ. Resour. Econ. 4, 281–317 (2017).
Google Scholar 
Schlaepfer, D. R., Lauenroth, W. K. & Bradford, J. B. Modeling regeneration responses of big sagebrush (Artemisia tridentata) to abiotic conditions. Ecol. Modell. 286, 66–77 (2014).Article 

Google Scholar 
Kleinhesselink, A. R. & Adler, P. B. The response of big sagebrush (Artemisia tridentata) to interannual climate variation changes across its range. Ecology 99, 1139–1149 (2018).PubMed 
Article 

Google Scholar 
Brabec, M. M., Germino, M. J. & Richardson, B. A. Climate adaption and post-fire restoration of a foundational perennial in cold desert: insights from intraspecific variation in response to weather. J. Appl. Ecol. 54, 293–302 (2017).Article 

Google Scholar 
Eidenshink, J. C. et al. A project for monitoring trends in burn severity. Fire Ecol. 3, 3–21 (2007).Article 

Google Scholar 
Pebesma, E. J. & Bivand, R. S. Classes and methods for spatial data in R. R News 5. http://cran.r-project.org/doc/Rnews/ (2005).Applestein, C. & Germino, M. J. Detecting shrub recovery in sagebrush steppe: comparing Landsat-derived maps with field data on historical wildfires. Fire Ecol. 17, (2021).Rigge, M. et al. Rangeland fractional components across the western United States from 1985 to 2018. Remote Sens. 13, 1–26 (2021).Article 

Google Scholar 
Hijmans, R. J. & van Etten, J. raster: Geographic analysis and modeling with raster data. (2012).U.S. Geological, S. 1/3rd arc-second Digital Elevation Models (DEMs)–USGS National Map 3DEP Downloadable Data Collection. (2017).Walkinshaw, Mike, A. T. O’Geen, D. E. B. Soil Properties. California Soil Resource Lab,McCune, B. & Keon, D. Equations for potential annual direct incident radiation and heat load. J. Veg. Sci. 13, 603–606 (2002).Article 

Google Scholar 
Abatzoglou, J. T. Development of gridded surface meteorological data for ecological applications and modelling. Int. J. Climatol. 33, 121–131 (2013).Article 

Google Scholar 
Ferraro, P. J. & Hanauer, M. M. Advances in measuring the environmental and social impacts of environmental programs. Annu. Rev. Environ. Resour. 39, 495–517 (2014).Article 

Google Scholar 
Butsic, V., Lewis, D. J., Radeloff, V. C., Baumann, M. & Kuemmerle, T. Quasi-experimental methods enable stronger inferences from observational data in ecology. Basic Appl. Ecol. 19, 1–10 (2017).Article 

Google Scholar 
Ho, D., Imai, K., King, G. & Stuart, E. MatchIt: nonparametric preprocessing for parametric causal inference. J. Stat. Softw. 42, 1–28, https://www.jstatsoft.org/v42/i08/ (2011).Article 

Google Scholar 
Guo, S. & Fraser, M. Propensity score analysis: statistical methods and applications. (Sage Publications, 2010).Puhani, P. A. The treatment effect, the cross difference, and the interaction term in nonlinear “difference-in-differences” models. Econ. Lett. 115, 85–87 (2012).MathSciNet 
MATH 
Article 

Google Scholar 
Schlaepfer, D. R., Lauenroth, W. K. & Bradford, J. B. Effects of ecohydrological variables on current and future ranges, local suitability patterns, and model accuracy in big sagebrush. Ecography (Cop.). 35, 374–384 (2012).Article 

Google Scholar 
Stan Development Team. RStan: the R interface to Stan. R package version 2.16.2. http://mc-stan.org (2020).Bürkner, P. C. brms: An R package for Bayesian multilevel models using Stan. J. Stat. Softw. 80, (2017).Mahr, T. & Gabry, J. bayesplot: Plotting for Bayesian Models. https://mc-stan.org/bayesplot/ R package version (2021).Kay, M. tidybayes: Tidy Data and Geoms for Bayesian Models. https://doi.org/10.5281/zenodo.1308151 R package version 3.0.1. (2021).Simler-Williamson, A. & Germino, M. J. Data associated with “Statistical consideration of nonrandom treatment applications reveal region-wide benefits of widespread post-fire restoration action”. https://doi.org/10.25338/B8W63R (2022).Simler‐Williamson, A. B. R code associated with “Statistical consideration of nonrandom treatment applications reveal region-wide benefits of widespread post-fire restoration action”. https://doi.org/10.5281/zenodo.6565074 (2022). More

The biotic induction of OAE-2The rapid proliferation of select microbial communities at 427.54 mcd likely represents a pre-OAE biotic perturbation (pre-OAE BP) presaging the protracted period of widespread marine deoxygenation during OAE-2, and progressive deoxygenation predating the +CIE7 (Fig. 4). At the beginning of the pre-OAE BP (427.54 mcd), abruptly elevated tetrapyrroles and crenarchaeol concentrations signify an abrupt increase in primary production by photoautotrophs and chemoautotrophs residing above the chemocline. Increased volumes of precipitating biogenic snow concordantly consumed oxygen, expanding the preexisting OMZ as anaerobic bacteria thrived based on accelerated obGDGTs synthesis. Euxinia did not penetrate the photic zone at the outset of the productivity bloom as isorenieratane was not detected and heightened rates of microbial sulfate reduction were seemingly transient, inferred from the DAGEs profile, and limited to pre-OAE BP initiation. The lack of a well-stratified water column, evinced by absent to low concentrations of halophilic archaeal lipids (i.e., extended archaeols), relatively low rates of microbial sulfate reduction, and a dense oxygenic microbial plate likely precluded the development of PZE initially.Establishing a definitive causal mechanism for the pre-OAE BP is difficult, but the concomitance of LIP activity with the productivity spike is intriguing. Application of a linear sedimentation rate from OAE-2 to the pre-OAE BP interval following previous works6,7 approximated the pre-OAE BP occurring 220 ± 4 kyr before OAE-2, lasting for ~100 kyr (427.54–426.88 mcd; see Estimating the duration of the pre-OAE BP in Supplementary Information for rationale and calculation). Significantly, this was roughly coincident with the onset of LIP activity (~200–300 kyr before OAE-2) inferred from marine osmium isotope stratigraphy27. Similarities in the modern planktonic community response, such as elevated productivity and compositional changes, between the 2018 Kilauea eruption28 and the pre-OAE BP reinforce inference of a potential magmatic trigger for this event (see Evidence for LIP trigger of the pre-OAE biotic perturbation in Supplementary Information for additional details).A constant, yet overall lower, nutrient and trace metal inventory6 (Fig. S4) combined with a redox-driven shift in fixed N species (from NO3− to NH4+)15, potentially leading to a fixed N shortage29 via intensified denitrification and annamox reactions30, were probable culprits in the failure to sustain prolific rates of primary production beyond 100 kyr at the Demerara Rise. The gradual decline in biomass production, indicated by decreasing tetrapyrrole and crenarchaeol profiles (Fig. 4), was accompanied by a notable shift in deep water communities. Sulfate-reducing bacteria exerted increasing predominance over methanogenic archaea, a trend coeval with the primary productivity spike and extending well into the OAE (Fig. 3). A collapse of autotrophic communities to pre-perturbation levels was concordant with the progressive shoaling of H2S-laden waters. Continued vertical migration of the chemocline intruded the photic zone, producing PZE that enabled anoxygenic photosynthesis by Chlorobiaceae (Fig. 4). Unlike the overall oscillatory character of PZE throughout the studied section, this protracted phase of PZE was sustained until the onset of OAE-2 (426.43–426.00 mcd, Figs. 3 and 4) and is approximately contemporaneous with a thallium (Tl) isotope excursion7 (426.40–426.30 mcd).The positive Tl isotope excursion represents the progressive expansion of bottom water anoxia predating OAE-2 by 43 ± 11 kyr6,7. However, evidence for a causal mechanism of pre-OAE deoxygenation remains indeterminate. Our comprehensive biomarker inventory provides an interpreted sequence of events culminating in the regional to global expansion of anoxia predating OAE-2. A protracted phase of enhanced primary productivity began ~220 ± 4 kyr prior to OAE-2, increasing localized production and export of organic carbon at Demerara Rise. Similar productivity spikes likely occurred in settings of comparable paleogeographic configuration (e.g., equatorial, continental margins/shelves), seeding the oceans with fixed carbon. Continued scavenging of marine oxygen via organic carbon remineralization resulted in OMZ expansion locally, and likely initiated oxygen drawdown in much of the proto-North Atlantic Ocean. Stratigraphic records of sulfur isotopes of pyrite (δ34Spyrite) from the proto-North Atlantic and Tethys Oceans11 validate the areal extrapolation of our interpretations. A gradual decline in δ34Spyrite values at Demerara Rise begins at 427.50 mcd, nearly identical to the onset of the pre-OAE BP (427.54 mcd, Fig. 4). Correlation of δ34Spyrite in a global transect (Western Interior Seaway, proto-North Atlantic, Tethys) revealed consistent behavior in δ34Spyrite prior to the +CIE, indicating increasingly expansive marine deoxygenation on a global scale11. Over ~100 kyr, increased regional biomass production induced pervasive marine anoxia, inhibiting Mn-oxide formation, producing the observed positive Tl isotope excursion, and ultimately, the globally observed +CIE reflecting enhanced organic carbon burial signaling the onset of OAE-2. Thus, the local biotic signal recorded at ODP Site 1258 underlines the crucial role the Demerara Rise, and similar undocumented settings, served in initiating deoxygenation of the global ocean.Microbial ecological dynamics during and after OAE-2Changes in microbial community compositions during OAE-2 were apparent, signified by a shift in the normalized total biomarker pool (Fig. 3) and variations in the absolute concentrations of individual biomarkers (Fig. 4). In general, OAE-2 was defined by an expansion and diversification of intermediate and deep water communities (426.00–423.07 mcd), followed by a period of instability leading to the termination of the OAE (423.07–422.00 mcd). Photo- and chemoautotrophs residing above the chemocline were adversely affected, evinced by relatively low, invariant tetrapyrrole and crenarchaeol profiles (Fig. 4). Based on these observations, we divided OAE-2 into two periods defined by contrasting paleoenvironmental conditions modulating the microbial inhabitants of Demerara Rise.The first period of OAE-2 (426.00–423.07 mcd, Fig. 4) was marked by the intrusion of a euxinic OMZ into the photic zone. Elevated, yet fluctuating isorenieratane concentrations suggest relatively persistent PZE of varying vertical extent, in agreement with previous investigations using biomarkers and nitrogen isotopes at nearby sites12,13,31. During this interval, microbial sulfate reduction was likely active as DAGEs continually increased, aligning with estimates of expanded seafloor euxinia32. The co-occurrence of abundant extended archaeols and isorenieratane intimates the role that density stratification served in maintaining the protracted PZE of OAE-2, substantiating concurrent findings based on neodymium33 and oxygen isotopes34. Vertical nutrient advection via upwelling35 led to preferential exposure to expanding intermediate water communities tolerant to sulfidic conditions in the OMZ. Scavenging of a potentially limited fixed N inventory30, depleted in NO3− and predominated by NH4+[ 15,29, and inhibition of efficient nutrient transfer by pronounced density stratification likely induced severe N deficiency in surface water communities, explaining the relatively muted productivity of oxygenic photoautotrophs (i.e., tetrapyrroles) and chemoautotrophs (i.e., crenarchaeol) observed (Fig. 4). The concentration and predominant utilization of fixed N in the OMZ led to the proliferation and diversification of intermediate and deep water microbial taxa, while a shoaling chemocline led to increased nutrient (i.e., fixed N) competition between photoautotrophs and retreating Thaumarchaeota as highlighted by our biomarker inventory and the nitrogen isotopic record31. These findings challenge previous interpretations of highly productive, predominantly eukaryotic primary producers reliant on the upwelling of isotopically depleted NH4+ during OAE-215. Instead, the decline of C30-17-nor-DPEP (Fig. S5; Supplementary Data 3), a source-specific tetrapyrrole diagenetically derived from algal chlorophyll-c36, and reconstructed water column conditions during OAE-2 indirectly support a rise in cyanobacteria, diazotrophs able to fix N2, in oxygenated, nutrient-depleted shallow waters. Increased cyanobacterial contribution is further supported by C and N stable isotopes16,37, as well as the prominence of potentially phylum-specific biomarkers across OAE-2 (e.g., 2-methylhopanoids6,14).Fig. 5: Contrasting biogeochemical conditions between the pre-OAE BP and OAE-2.a, b Microbial ecology and water column conditions during the pre-OAE BP, reflecting high primary production of organic carbon (a) and OAE-2, characterized by relatively lower organic carbon production, but substantially enhanced biomass preservation (b). c, d Averaged fractional abundances of individual biomarkers throughout the pre-OAE BP (c) and OAE-2 (d). Biomarker source organisms are abbreviated as follows: phytoplankton (P), ammonia oxidizing archaea (AOA), sulfur oxidizing bacteria (SOB), unknown anaerobic bacteria (UAB), sulfate reducing bacteria (SRB), halophilic archaea (HA), methanogenic archaea (MA).Full size imageA reversal from the formerly outlined conditions typified the second period of OAE-2 (423.07–421.99 mcd, Fig. 4). Destabilization of the stratified water column and reduced production of H2S led to deepening and contraction of the euxinic OMZ. The observed decline in halophilic archaea, coincident with an overall decline in Chlorobiaceae populations, is roughly coeval with positive neodymium isotopic excursions observed across the proto-North Atlantic33 attributed to the enhanced latitudinal commingling of proto-North Atlantic water masses38. Although detrimental to sustained PZE, the persistence of a well-developed anaerobic bacterial community (i.e., obGDGTs) suggests the lasting presence of a non-euxinic OMZ despite improved bottom water circulation. A premature recovery of the chemoautotrophic Thaumarchaeota, inhabiting the base of the photic zone, relative to the shallower dwelling obligately oxygenic phototrophs (Fig. 3) likely reflects reduced toxicity associated with retreating euxinic waters, lessened resource competition with [primarily] Chlorobiaceae, and a competitive advantage tied to preferential exposure to upwelled nutrients and tolerance to low O2 conditions.The termination of OAE-2 was marked by the temporary re-establishment of microbial community compositions mirroring those observed prior to the pre-OAE BP (Figs. 3 and 4). Contraction of the OMZ led to a deep chemocline, with PZE restricted to the basal photic zone as the production of reduced sulfide species diminished. The Thaumarchaeota continued the recovery initiated towards the latter half of OAE-2, accompanied by the rebounding oxygenic photoautotrophs. However, the recovery of shallow autotrophic communities was halted by an episode of PZE (421.19–421.04 mcd) based on abrupt increases in isorenieratane concentrations (Fig. 4). Temporary development of pronounced density stratification likely facilitated the accumulation of H2S in the lower to intermediate photic zone, producing the short-lived PZE episode. Interestingly, covariant responses observed in additional biomarker profiles (e.g., obGDGTs) to PZE during OAE-2 were not evident across this post-OAE interval, possibly due to the transient nature of PZE at this time. For example, the initial increase in isorenieratane concentrations at the onset of OAE-2 was not immediately accompanied by shifts in other biomarker classes (e.g., obGDGTs; Fig. 4), suggesting frequent recurrences of PZE may be required to illicit a major microbial ecological response as observed later during the OAE. Still, this brief episode of post-OAE PZE (421.19–421.04 mcd) coincides with a positive organic carbon isotope excursion9 (Fig. S5), trace metal drawdown6 (Fig. S4), and minor positive Tl isotope excursion7 at the Demerara Rise. Prior study7 tentatively attributed this interval to enhanced carbon burial during a post-OAE deoxygenation event of smaller magnitude, with subsequent work revealing continued pyrite burial post-OAE 211. Our biomarker inventory revealed some environmental consistencies (e.g., PZE) between this interval and OAE-2, but the overall biotic response to this post-OAE geochemical perturbation was relatively subdued and requires additional sampling and investigation to properly constrain.Broader implicationsThe recognition of the pre-OAE BP and evolving water column conditions at Demerara Rise highlights additional complexities of a dynamic ocean relevant to interpretations of OAE-2 and the +CIE. Enhanced, sustained, and widespread carbon burial is required to produce the +CIE used to define OAE-28,10. Still, the principal forcing, productivity or preservation, remains enigmatic as evidence for the former mounts12,39.Based on the tetrapyrrole profiles (Fig. 4) primary production was greatest during the pre-OAE BP and relatively muted throughout OAE-2 at Demerara Rise, assuming minimal alteration to the genetic tetrapyrrole stratigraphic signal. Biomass preservation was presumedly enhanced during OAE-2 through sulfurization11, as the OMZ transitioned from anoxic to euxinic and penetrated the photic zone, yet low tetrapyrrole concentrations persist. Previous work noted a similar discrepancy between preservation potential and porphyrin abundance, postulating a paucity of trace metals to chelate with the free-base porphyrins induced poor preservation as desulfurization did not reveal additional porphyrin content16. However, both the pre-OAE BP and OAE-2 were characterized by relatively depleted trace metal inventories6 (Fig. S4), yet exhibit contrasting tetrapyrrole profiles, suggesting relative changes in primary production were the predominate control on the stratigraphic distribution of tetrapyrroles across the studied interval at the Demerara Rise. The strong covariance between tetrapyrrole and crenarchaeol concentrations reinforces the interpretation tetrapyrroles faithfully reflect primary production (Fig. S6). Crenarchaeol, a biosynthetic product of chemoautotrophic archaea (Thaumarchaeota) comprising up to 20% of all archaea and bacteria in the modern ocean40, is structurally distinct from the tetrapyrroles making it likely that diagenetic alteration of the two biomarkers is not consistent in rate or form. Thus, the positive correlation between key proxies for major contributors to primary production, the photoautotrophs and chemoautotrophs, minimizes concern for the integrity of the biotic signal at Demerara Rise (see Tetrapyrroles as a record of primary production in Supplementary Information for additional details).These findings provide direct evidence for a causal mechanism resulting in both the Tl isotope excursion and +CIE as previously described. It is highly probable the pre-OAE BP was not exclusive to the Demerara Rise based on the immense and presently unconstrained organic carbon burial required to produce the +CIE10. Further characterization of comparable localities to Demerara Rise may reveal similar high productivity events, as primed, highly productive settings likely capitalized on exogenous nutrient delivery via efficient upwelling to the photic zone prior to stratification during OAE-2. Hence, OAE-2 and the +CIE were not coincident with heightened surface water productivity relative to the pre-OAE BP at the Demerara Rise. Rather, antecedent increases in primary production locally facilitated the initiation of the OAE as a mechanism to consume marine oxygen and subsequently enhance organic carbon preservation globally. This highlights how OAE-2, and perhaps other OAEs in the geologic record, were not instantaneously induced but rather a gradual transition stemming from sustained forcing(s). In addition, the occurrence of the pre-OAE BP well before the established onset of OAE-2 reveals how fluctuations in primary production can be linked to marine deoxygenation but may not necessarily be concurrent. As shown here, OAE-2 at the Demerara Rise was preceded by elevated primary production that progressively attenuated towards event onset. While the hallmark features of an OAE are well-established, further identification and refinement of trends preceding widespread anoxia in the past will improve our understanding of how marine deoxygenation develops, as well as our ability to assess planetary health today.A shift from a productivity- to preservation-dominant system during OAE-2 at Demerara Rise, and possibly similar paleogeographic settings experiencing the pre-OAE BP, facilitated substantial organic carbon burial producing the +CIE. Distinct shifts in water column chemistry and structure from the pre-OAE BP to OAE-2 imparted considerable changes on microbial life, which altered the primary driver governing biomass sequestration (Fig. 5). Yet, both intervals reveal relatively comparable carbonate-corrected total organic carbon values6 (Fig. S5), signifying enhanced preservation as a critical component of organic carbon burial during OAE-2 at Demerara Rise. Consequently, this work suggests that sustained increases in primary production prior to OAE-2 initiated and regulated pre-OAE deoxygenation, resulting in a progressive shift to preservation as the primary control on organic carbon accumulation in sediments. Expanding euxinia and attendant changes to biogeochemical cycling adversely affected primary producers while simultaneously enhancing organic matter preservation via sulfurization11. Flourishment of Thaumarchaeota in oligotrophic settings in the modern open ocean41, and lack thereof during OAE-2 based on diminished crenarchaeol concentrations, underscores the scarcity of bioessential elements (e.g., fixed N) caused by microbial utilization of electron acceptors further down the redox ladder due to intensified marine anoxia, ultimately limiting primary production. The switch from a productivity to preservation model, reconstructed using biomarkers (Fig. 5) and initially suggested based on drawdown of the trace metal inventory6, was also concomitant with relative warming4. Simulated projections of the marine microbial response to continued global warming in the future revealed similar biotic trends (e.g., decreased primary productivity) to warming-induced oceanographic changes42 (e.g., intensified stratification) observed during OAE-2. Thus, an abundance of proxy- and model-based results paired with conceptual evidence suggest relatively low production and enhanced preservation of organic carbon throughout OAE-2 at the equatorial Demerara Rise.The pre-OAE BP may foreshadow greater regional trends observed during OAE-2. Equatorial upwelling centers, like Demerara Rise, are spatially restricted and represent regions of already high primary production before OAE-2. Climatic shifts concurrent with OAE-2 may have produced favorable conditions for elevated primary productivity in regions unable to capitalize on or exposed to allochthonous nutrient delivery prior to the +CIE. While the pre-OAE BP offers a causal mechanism for the Tl isotope excursion and +CIE initiation, areal expansion of organic carbon preservation and production is necessary to sustain enhanced organic carbon burial for the duration of the +CIE.Continued development of preexisting proxies is critical to extract and clarify current understandings of major climatic events in Earth history. Although reliant on excellent preservation of the microbial signal, the analytical and interpretative approach used here enables simultaneous examination of a wide array of biomarkers, producing a more holistic reconstruction of oceanographic changes inferred from microbial ecological variations spanning the surface to the sediment. This is timely, as investigations of the sedimentary archives become increasingly valuable analogs to understand the response of modern oceans to natural and anthropogenic forcings. Similarities between the pre-OAE BP and modern, climate-driven marine deoxygenation are concerning, while particular attention to preexisting highly productive settings may hold the key to forecasting the geologically rapid transition to a global OAE. Even though natural processes are currently beyond our control, stifling anthropogenic catalysts of climate change may decelerate the unfortunate, progressive suitability of OAEs as climate analogs in the future. More