Philippa Kaur

Vegetation and land-cover dataTo monitor vegetation at the global scale, we use three datasets: (1) vegetation optical depth (VOD, 0.25°, Ku-Band, daily 1987–201723) (Fig. 1A), (2) AVHRR GIMMSv3g normalized difference vegetation index (NDVI, 1/12°, bi-weekly 1981–201524) (Fig. 1B), and (3) MODIS MOD13 NDVI at 0.05° (16-day, 2000–202125). We correct for spurious values in the NDVI data (e.g., cloud contamination) using the method of Chen et al.43. We resample the VOD data using bi-weekly medians to agree with the NDVI data time sampling.For all three vegetation datasets, we remove seasonality and long-term trends using seasonal trend decomposition by Loess4,44 based on the proposed optimal parameters listed in Cleveland et al.44 (code available on Zenodo45). That is, we use a period of 24 (bi-monthly, 1 year), 47 for the trend smoother (just under 2 years) and 25 for low-pass (just over 1 year). We only use the STL residual—the de-seasoned and de-trended NDVI and VOD time series—in our analysis.To contextualize our understanding of vegetation resilience, we use MODIS MCD12Q1 land cover46 (Fig. 1C) as well as a global average aridity index based on WorldCLIM data31 (Fig. 1D). We exclude from our analysis anthropogenic and non-vegetated landscapes (e.g., permanent snow and ice, desert, urban), as well as any land covers which have changed (e.g., forest to grassland) during the period 2001–2020.Precipitation data and variability metricsTo measure precipitation at the global scale, we rely upon ERA5 data (~30 km, monthly, 1981–2021)33. We process global-scale precipitation metrics using the Google Earth Engine47 platform. We further use the sum of soil moisture from the surface down to 28 cm of depth (first two layers of the ECMWF Integrated Forecasting System soil moisture estimates) to quantify soil moisture means and inter-annual variability33.It is well-documented that vegetation resilience is responsive to the MAP of certain regions1. However, the role of precipitation variability in controlling vegetation resilience has not been well-studied. Here we examine precipitation variability in terms of both intra- and inter-annual patterns. Intra-annual precipitation variability is determined in terms of the Walsh-Lawler Seasonality index32 (Fig. 1D), calculated using monthly data from ERA533.Partly due to the fact that precipitation is non-negative, simple inter-annual variability metrics such as the standard deviation of annual precipitation sums are biased by the absolute precipitation sums; higher precipitation regions have a higher possible range of variability. To limit the influence of MAP, we hence investigate the standard deviation of annual precipitation sums normalized by the MAP, over the period 1981–2021, based on ERA5 data33 (Fig. 1F). We motivate our normalization by MAP with the strong linear relationship between MAP and MAP standard deviation (Supplementary Fig. S2). We further confirm our discovered relationships (Fig. 5) using only those regions where MAP was between the 40 and 60th percentile of MAP for a given land cover (Supplementary Figs. S11,S12). This serves as an additional check that our normalization of MAP standard deviation by MAP does not bias the inferred relationship between vegetation resilience and precipitation variability. Similarly, we generate a normalized inter-annual soil moisture variability by normalizing year-on-year soil moisture standard deviation (Supplementary Fig. S8) by long-term mean soil moisture (Supplementary Fig. S5).Empirical resilience estimationResilience is defined as the ability of a system to recover from perturbations, and can be quantified empirically by the speed of recovery to the previous state16,17. To measure resilience on the global scale, we employ a recently introduced methodology4 which we will briefly summarize in the following.We first identify sharp transitions in the vegetation time series using an 18-point (9 month) moving window to define local slopes throughout the time series48. We then identify slopes above the 99th percentile, and define connected regions as individual perturbations. The highest peak (largest instantaneous slope) within each connected region is then labeled as an individual disturbance.The employed approach does not delineate every rapid transition in a time series due to our reliance on percentiles; our dataset will be inherently biased towards the largest transitions. Furthermore, the same transitions are not guaranteed to be captured for both NDVI and VOD data in each location, as the percentiles will naturally vary between the datasets. Finally, our method will in some cases produce false positives, especially in cases where a given time series does not have any significant rapid transitions. To limit the influence of false positives on our results, we discard any perturbations where the time series does not drop significantly, and where the period before and after a given transition does not pass a two-sample Kolmogorov–Smirnov test4.Finally, using our global set of time-series transitions, we can identify each local vegetation (NDVI or VOD) minima, and use the five following years of data to fit an exponential function to the residual time series, assuming that the recovery after a perturbation to a vegetation state x0 follows approximately the equation$$x(t),approx ,{x}_{0}{e}^{rt}$$
(1)
where x(t) denotes the vegetation state at time t after the perturbation. Negative r indicates that the vegetation system will return to the original stable state at rate ∣r∣. For positive r, the initial perturbation would be amplified, suggesting a non-resilient vegetation state. Our empirical recovery rates are defined as the fitted exponent r, obtained for each detected transition in the NDVI and VOD residual time series. We finally use the coefficient of determination R2 to remove instances where the fitted exponential poorly matches the underlying data4.For the empirical estimate of the restoring rate obtained from fitting an exponential to the recovery after an abrupt negative deviation of VOD or NDVI, abrupt changes in the mean state induced by changing sensors rather than an actual vegetation shift may impact the results. However, all datasets used here are tightly cross-calibrated to eliminate mean-shifts when new instruments are introduced23,24. It is therefore unlikely that changes in the instrumentation of the various datasets unduly influence our empirical estimates of λ.Dynamical system metrics of resilienceThe lag-one autocorrelation (AC1) has previously been proposed to measure the stability of real-world dynamical systems in general, and the resilience of vegetation systems in particular1,19,20,21,49. Based on the concept of critical slowing down, the AC1 has, together with the variance, also been suggested as an early-warning indicator for forthcoming critical transitions50,51. Mathematically, the suitability of the variance and AC1 as resilience measures and early-warning indicators can be motivated as follows4,52,53. First, linearize the system around a given stable state x*:$$dbar{x}=lambda bar{x}dt+sigma dW$$
(2)
for (bar{x}: !!=x-{x}^{*}), assuming a Wiener Process W with standard deviation σ. The dynamics are stable for λ  More

Ecological sampling and structural complexity profilesThe ecological sampling consists of 10 surveys, taking place in 2005 and from 2008 to 2016, and documents changes in coral colony abundance and size distributions (i.e. width, length, and height) for the three most conspicuous taxa (i.e. Acropora, Pocillopora, and Porites) within a 10 m2 transect on the outer slope23. To quantify reef structural complexity, we built a 3D model of the coral assemblages distributed along a cross-section of the reef substrate separating the 20 m water depth from the reef crest, representing a 160 m stretch along the reef slope (Fig. 1). First, we take 200 overlapping high-resolution photos (300 dpi) of 10 individual corals from each species (i.e. n = 30 coral colonies) and built 3D models using the Agisoft Metashape software24, capturing intra- and inter-species morphological variability (Fig. 1). Then, we systematically and randomly select one of the ten 3D coral models for each taxon to add to the substrate until that the sum of the planar area for each 3D coral models match with the coral cover reported for each taxon and for each year23. We randomly place coral colonies along the 160 m reef cross-section going from 20 m depth to the reef crest (Fig. 1). The individual coral 3D models are resized in width, length, and height according to ecological surveys, and, randomly rotated between − π/2 and π/2 to ensure ecological variability. Finally, we estimated structural complexity of the 3D coral assemblage model using the function rumple_index of the LidR package25 in R 4.0.026. We repeat this approach 100 times for each year, resulting in a total of 1000 reef structural complexity profiles. Our estimates are consistent with previous reef structural complexity estimates at this location27.Figure 1(a) Representation of the three different coral species (Acropora hyacinthus in red, Pocillopora cf. verrucosa in yellow, and Porites lutea in blue). (b) A representaitive Ha’apiti reef cross-section simulation (one of 1000 total simulations) on the outer slope across a water depth range of 0–20 m.Full size imageHydrodynamic and topographic measurementsMo’orea (French Polynesia) is encircled by coral reefs, 500–700 m wide with a dominant swell direction coming from the southwest. In this study, we focus on Ha’apiti, a site with a southwest orientation that is considered as a high-energy site28. We extract 30-year offshore wave data (1980–2010) from a wave hindcast8,29 (Fig. 2a). We also collect high-frequency, in situ wave data using INW PT2X Aquistar and DHI SensorONE pressure transducers (PTs), which are logged at 4 Hz30. The sensors are installed at four locations along a cross-shelf gradient (Fig. 2b,c) covering a 250 m long stretch, including sections through the fore reef, reef crest, and reef flat. Pressure records are corrected for pressure attenuation with depth31 and are split into 15-min bursts30.Figure 2(a) Histogram of the offshore wave height (m) at Ha’apiti, Mo’orea (French Polynesia) in 2016. (b) Aerial view of Ha’apiti (WorldView-3 imagery) with an outline of the wave transect and sensor location. The ecological sampling took place near the S1 location c. Topographic cross-section of the wave transect and position of the sensors on the sea bottom.Full size imageThe beach profile and the reef morphology are measured using airborne bathymetric and topo-bathymetric lidar surveys conducted in June 2015 by the Service Hydrographique & Océanographique de la Marine (SHOM). The bathymetric data are defined by the combination of bathymetric laser (for the submerged part of the beach) and topo-bathymetric laser (for the subaerial beach). The data come at 1 m resolution and are available at https://diffusion.shom.fr.Hydraulic roughness vs structural complexitySpectral attenuation analysis of the water level measurements32,33 is used to estimate the Nikuradse (hydraulic; kn) roughness34 of the coral reef surface along the beach profile sections covered by the pressure transducers. The method is described in detail in the references provided above and uses the conservation of energy equations to obtain estimates of wave energy dissipation from friction. We obtain more than 300 kn estimates for each pair of sensors, each representing a different geomorphologic section. Since the field measurements took place in 2015, the kn outputs obtained from the fore reef section concur with the reef structural complexity estimates of that year (Fig. 3). Then, we define a coefficient factor according to the geomorphologic section as ⍺back reef = kn, back reef/kn, fore reef and ⍺reef crest = kn, reef crest/kn, fore reef. We carefully delineate the sandy section from the reef sections within the cross-shelf gradient (i.e. within the reef flat, lagoon section) and apply the following procedure. First, for the reef sections, we apply the relationship between the reef structural complexity and kn (Fig. 3) to convert our reef structural complexity estimates into continuous kn profiles through Monte Carlo simulations, using the coefficient factor of each geomorphologic section (e.g., forereef, reef crest, and back reef). Second, for the sandy section, we define kn on the grounds of the mean grain size (d50 = 63 μm). Applying this workflow (Fig. 3), we obtain 100 continuous kn profiles for each year (i.e. n = 1000 kn profiles in total).Figure 3Flow chart illustrating how the kn profiles have been obtained along the cross-section at Ha’apiti. The relationship between the Structural complexity (SC) and the Nikuradse roughness (kn) measurements can be described as kn = 0.01 × SC2.98.Full size imageHydrodynamic modelNearshore wave propagation is simulated using a nonlinear wave model based on the Boussinesq Equations35. The rationale of using a Boussinesq type model instead of other types of models (e.g. SWAN) is that the former is able to describe in detail (i.e. 1 m grid resolution) several hydrodynamic parameters (e.g. nearshore nonlinear wave propagation, shoaling, refraction, dissipation due to the bottom friction and breaking and run-up) in the swash zone. The model is defined as follows:$$frac{partial U}{partial t}+frac{1}{h}frac{partial {M}_{u}}{partial x}-frac{1}{h}Ufrac{partial left(Uhright)}{partial x}+gfrac{partialupzeta }{partial x}=frac{left({d}^{2}+2partialupzeta right)}{3}frac{{partial }^{3}U}{partial {x}^{2}partial t}+{d}_{x}hfrac{{partial }^{2}U}{partial xpartial t}+frac{{partial }^{2}}{3}left(Ufrac{{partial }^{3}U}{{partial x}^{3}}-frac{partial U}{partial x}frac{{partial }^{2}U}{partial {x}^{2}}right)+dfrac{partialupzeta }{partial mathrm{x}}frac{{partial }^{2}U}{partialupzeta partial mathrm{t}}+d{d}_{x}Ufrac{{partial }^{2}U}{partial {x}^{2}}+{d}_{x}frac{partialupzeta }{partial mathrm{x}}frac{partial mathrm{U}}{partial mathrm{t}}-dfrac{{partial }^{2}}{partial mathrm{x}partial mathrm{t}}left(delta frac{partial mathrm{U}}{partial mathrm{x}}right)+E-frac{{tau }_{b}}{rho h}+B{d}^{2}left(frac{{partial }^{3}U}{partial {x}^{2}}+gfrac{{partial }^{3}upzeta }{partial {x}^{3}}+frac{{partial }^{2}left(Ufrac{partial U}{partial x}right)}{partial {x}^{2}}right)+2Bd{d}_{x}left(frac{{partial }^{2}U}{partial xpartial t}+gfrac{{partial }^{2}upzeta }{partial {mathrm{x}}^{2}}right),$$
(1)
where, U is the mean over the depth horizontal velocity, ζ is the surface elevation, d is the water depth, uo is the near bottom velocity, h = d + ζ, ({M}_{u}=left(d+zeta right){u}_{0}^{2}+delta ({c}^{2}-{u}_{0}^{2})), δ is the roller thickness determined geometrically36, E is an eddy viscosity, τb is the bed friction term and B = 1/1535.In this work the wave breaking mechanism is based on the surface roller concept36. However, in the swash zone, surface roller is not present and the eddy viscosity concept is used to describe the breaking process. The term E in Eq. (1) is written:$${mathrm{E}}_{{mathrm{b}}_{mathrm{x}}}= {mathrm{B}}_{mathrm{b}}frac{1}{mathrm{h}+upeta }{left{{{mathrm{v}}_{e}left[left(mathrm{h}+upeta right)mathrm{U}right]}_{mathrm{x}}right}}_{mathrm{x}},$$
(2)
where ({v}_{e}) is the eddy viscosity coefficient:$${mathrm{v}}_{mathrm{e}}={{ell}}^{2}left|frac{partial {mathrm{U}}}{partial {mathrm{x}}}right|,$$
(3)
where ({ell}) is the mixing length ({ell}) = 3.5 h και Βb37.The width of the swash zone is assumed to extend from the run-down point (seaward boundary) up to the run-up point (landward boundary). We start from a first estimate of the run-up R using the Stockdon formula38 and the depths below R/4 are considered as the swash zone, using Eq. (2). The final wave run-up height R which comes as output is estimated by the model.The ‘dry bed’ boundary condition is used to simulate run-up35. The numerical solution is based on the fourth-order time predictor–corrector scheme39. Therefore, the bed friction term τb is calculated such as:$${tau }_{bx}=frac{1}{2}rho {f}_{w}Uleft|Uright|,$$
(4)
where fw is the bottom friction coefficient40, which is an explicit approximation to the implicit, semi-empirical formula given by Jonsson, 196741.$${f}_{mathrm{w}}=mathrm{exp}left[{5.213left(frac{{mathrm{k}}_{mathrm{n}}}{{mathrm{alpha }}_{0}}right)}^{0.194}-5.977right],$$
(5)
where αo is the amplitude of the near-bed wave orbital motion and kn is the Nikuradse roughness height.Simulations and post processingWe use our wave propagation model to assess how different coral reef states affect the impact waves have on the coast. We run an ensemble of 10,000 simulations that covers all the possible combinations of (i) 10 bottom roughness profiles expressing the different observed coral reef states (i.e. healthy vs. not unhealthy); and (ii) 1000 percentiles of wave conditions. The wave conditions are produced as follows: (i) from the weekly values, we estimate all significant wave height (Hs) percentiles from 0.1 to 100, with a step of 0.1; (ii) the resulting 1000 Hs values are linked to the corresponding peak wave period Tp using a copula expressing the dependence of the two variables42. The output of the simulations is the nearshore Hs and 2% exceedance run-up (R2%) height for each of the 1000 conditions and 10 coral reef states. To quantify how the coral reef states are altering wave propagation during extreme events, we apply extreme value analysis to estimate the R2% for different return periods43. We then compare how the return period curves changed from the two coral reef states and we define the change in frequency of extreme R2% under unhealthy coral reefs. It is important to highlight that the tidal range is  More

Srivastava, R. K., Mequanint, F., Chakraborty, A., Panda, R. K. & Halder, D. Augmentation of maize yield by strategic adaptation to cope with climate change for a future period in Eastern India. J. Clean. Prod. 339, 130599 (2022).
Google Scholar 
Pooniya, V. et al. Six years of conservation agriculture and nutrient management in maize–mustard rotation: Impact on soil properties, system productivity and profitability. Field Crops Res. 260, 108002 (2021).
Google Scholar 
Tsimba, R., Edmeades, G. O., Millner, J. P. & Kemp, P. D. The effect of planting date on maize grain yields and yield components. Field Crops Res. 150, 135–144 (2013).
Google Scholar 
Maresma, A., Ballesta, A., Santiveri, F. & Lloveras, J. Sowing date affects maize development and yield in irrigated mediterranean environments. Agriculture 9(3), 67 (2019).
Google Scholar 
Srivastava, R. K., Panda, R. K., Chakraborty, A. & Halder, D. Enhancing grain yield, biomass and nitrogen use efficiency of maize by varying sowing dates and nitrogen rate under rainfed and irrigated conditions. Field Crops Res. 221, 339–349 (2018).
Google Scholar 
Van Roekel, R. J. & Coulter, J. A. Agronomic responses of corn hybrids to row width and plant density. Agronomy J. 104(3), 612–620 (2012).
Google Scholar 
Santiveri, F., Royo, C. & Romagosa, I. Growth and yield responses of spring and winter triticale cultivated under Mediterranean conditions. Eur. J. Agron. 20(3), 281–292 (2004).
Google Scholar 
Masoni, A., Ercoli, L., Mariotti, M. & Arduini, I. Post-anthesis accumulation and remobilization of dry matter, nitrogen and phosphorus in durum wheat as affected by soil type. Eur. J. Agron. 26(3), 179–186 (2007).CAS 

Google Scholar 
Yang, W., Peng, S., Dionisio-Sese, M. L., Laza, R. C. & Visperas, R. M. Grain filling duration, a crucial determinant of genotypic variation of grain yield in field-grown tropical irrigated rice. Field Crops Res. 105, 221–227 (2008).
Google Scholar 
Wei, H. et al. Comparisons of grain yield and nutrient accumulation and translocation in high-yielding japonica/indica hybrids, indica hybrids, and japonica conventional varieties. Field Crops Res. 204, 101–109 (2017).
Google Scholar 
Wu, H. et al. Effects of post-anthesis nitrogen uptake and translocation on photosynthetic production and rice yield. Sci. Rep. 8(1), 1–11 (2018).ADS 

Google Scholar 
Laza, M. R., Peng, S., Akita, S. & Saka, H. Contribution of biomass partitioning and translocation to grain yield under sub-optimum growing conditions in irrigated rice. Plant Prod. Sci. 6(1), 28–35 (2003).
Google Scholar 
Gao, H. et al. Intercropping modulates the accumulation and translocation of dry matter and nitrogen in maize and peanut. Field Crops Res. 284, 108561 (2022).
Google Scholar 
Yang, Y. et al. Solar radiation effects on dry matter accumulations and transfer in maize. Front. Plant Sci. 12, 1927 (2021).
Google Scholar 
Jamshidi, A. & Javanmard, H. R. Evaluation of barley (Hordeum vulgare L.) genotypes for salinity tolerance under field conditions using the stress indices. Ain Shams Eng. J. 9(4), 2093–2099 (2018).
Google Scholar 
Tyagi, B. S. et al. Identification of wheat cultivars for low nitrogen tolerance using multivariable screening approaches. Agronomy 10(3), 417 (2020).CAS 

Google Scholar 
Fischer, R. A. & Maurer, R. Drought resistance in spring wheat cultivars. I. Grain yield responses. Aust. J. Agric. Res. 29(5), 897–912 (1978).
Google Scholar 
Fernandez, G. C. Effective selection criteria for assessing plant stress tolerance. In Proceeding of the International Symposium on Adaptation of Vegetables and other Food Crops in Temperature and Water Stress, Aug. 13–16, Shanhua, Taiwan. 257–270 (1992).Bouslama, M. & Schapaugh, W. T. Jr. Stress tolerance in soybeans. I. Evaluation of three screening techniques for heat and drought tolerance 1. Crop sci. 24(5), 933–937 (1984).
Google Scholar 
Ciampitti, I. A. & Vyn, T. J. Grain nitrogen source changes over time in maize: A review. Crop Sci. 53(2), 366–377 (2013).CAS 

Google Scholar 
Chen, Y. et al. Characterization of the plant traits contributed to high grain yield and high grain nitrogen concentration in maize. Field Crops Res. 159, 1–9 (2014).
Google Scholar 
Mi, G. et al. Nitrogen uptake and remobilization in maize hybrids differing in leaf senescence. J. plant nutr. 26(1), 237–247 (2003).CAS 

Google Scholar 
Tollenaar, M. & Lee, E. A. Dissection of physiological processes underlying grain yield in maize by examining genetic improvement and heterosis. Maydica 51(2), 399 (2006).
Google Scholar 
Samonte, S. O. P. et al. Nitrogen utilization efficiency: Relationships with grain yield, grain protein, and yield-related traits in rice. Agronomy J. 98(1), 168–176 (2006).CAS 

Google Scholar 
Qiao, J., Yang, L., Yan, T., Xue, F. & Zhao, D. Nitrogen fertilizer reduction in rice production for two consecutive years in the Taihu Lake area. Agric. Ecosyst. Environ. 146(1), 103–112 (2012).CAS 

Google Scholar 
Marschner, P. Marschner’s Mineral Nutrition of Higher Plants 3rd edn. (Academic Press, 2012).
Google Scholar 
Ning, P., Li, S., Yu, P., Zhang, Y. & Li, C. Post-silking accumulation and partitioning of dry matter, nitrogen, phosphorus and potassium in maize varieties differing in leaf longevity. Field Crops Res. 144, 19–27 (2013).
Google Scholar 
Hawkesford, M. et al. Functions of macronutrients. In Marschners Mineral Nutrition of Higher Plants 3rd edn (ed. Marschner, P.) 178–189 (Academic Press, 2012).
Google Scholar 
Palta, J. A. et al. Large root systems: Are they useful in adapting wheat to dry environments?. Funct. Plant Biol. 38(5), 347–354 (2011).
Google Scholar 
Pooniya, V., Palta, J. A., Chen, Y., Delhaize, E. & Siddique, K. H. Impact of the TaMATE1B gene on above and below-ground growth of durum wheat grown on an acid and Al3+-toxic soil. Plant Soil 447(1), 73–84 (2020).CAS 

Google Scholar 
Bonelli, L. E., Monzon, J. P., Cerrudo, A., Rizzalli, R. H. & Andrade, F. H. Maize grain yield components and source-sink relationship as affected by the delay in sowing date. Field Crops Res. 198, 215–225 (2016).
Google Scholar 
Sorensen, I., Stone, P. & Rogers, B. Effect of sowing time on yield of a short and a long season maize hybrid. Proc. Agron. Soc. NZ 30, 63–66 (2000).
Google Scholar 
Tsimba, R., Edmeades, G. O., Millner, J. P. & Kemp, P. D. The effect of planting date on maize: Phenology, thermal time durations and growth rates in a cool temperate climate. Field Crops Res. 150, 145–155 (2013).
Google Scholar 
Zhou, B. et al. Maize kernel weight responses to sowing date-associated variation in weather conditions. Crop J. 5(1), 43–51 (2017).
Google Scholar 
Cirilo, A. G. & Andrade, F. H. Sowing date and maize productivity: I. Crop growth and dry matter partitioning. Crop Sci. 34(4), 1039–1043 (1994).
Google Scholar 
Shi, Y. et al. Tillage practices affect dry matter accumulation and grain yield in winter wheat in the North China Plain. Soil Till. Res. 160, 73–81 (2016).
Google Scholar 
He, P., Zhou, W. & Jin, J. Carbon and nitrogen metabolism related to grain formation in two different senescent types of maize. J. Plant Nutrit. 27(2), 295–311 (2004).CAS 

Google Scholar 
Pommel, B. et al. Carbon and nitrogen allocation and grain filling in three maize hybrids differing in leaf senescence. Eur. J. Agron. 24(3), 203–211 (2006).CAS 

Google Scholar 
Clarke, J. M., Campbell, C. A., Cutforth, H. W., DePauw, R. M. & Winkleman, G. E. Nitrogen and phosphorus uptake, translocation, and utilization efficiency of wheat in relation to environment and cultivar yield and protein levels. Can. J. Plant Sci. 70(4), 965–977 (1990).CAS 

Google Scholar 
Mardeh, A. S. S., Ahmadi, A., Poustini, K. & Mohammadi, V. Evaluation of drought resistance indices under various environmental conditions. Field Crops Res. 98(2–3), 222–229 (2006).
Google Scholar 
Naderi, A., Majidi-Harvan, E., Hashemi-Dezfoli, A., Rezaei, A. & Normohamadi, G. Analysis of efficiency of drought tolerance indices in crop plants and introduction of a new criteria. Seed Plant 15(4), 390–402 (1999).
Google Scholar 
Zeng, W. et al. Comparative proteomics analysis of the seedling root response of drought-sensitive and drought-tolerant maize varieties to drought stress. Int. J. Mol. Sci. 20(11), 2793 (2019).CAS 

Google Scholar 
Hajibabaei, M. & Azizi, F. Evaluation of drought tolerance indices in some new hybrids of corn. Electron. J. Crop Prod. 3, 139–155 (2011).
Google Scholar 
Zhao, J. et al. Yield and water use of drought-tolerant maize hybrids in a semiarid environment. Field Crops Res. 216, 1–9 (2018).
Google Scholar 
Fageria, N. K. Nitrogen harvest index and its association with crop yields. J. Plant Nutri. 37(6), 795–810 (2014).CAS 

Google Scholar 
Raghuram, N., Sachdev, M. S. & Abrol, Y. P. Towards an integrative understanding of reactive nitrogen. In Agricultural Nitrogen Use & Its Environmental Implications (eds Abrol, Y. P. et al.) 1–6 (I.K. International Publishing House Pvt. Ltd., 2007).
Google Scholar 
Baligar, V. C., Fageria, N. K. & He, Z. L. Nutrient use efficiency in plants. Commun. Soil Sci. Plant Anal. 32(7–8), 921–950 (2001).CAS 

Google Scholar 
Foulkes, M. J. et al. Identifying traits to improve the nitrogen economy of wheat: Recent advances and future prospects. Field Crops Res. 114(3), 329–342 (2009).
Google Scholar 
Gaju, O. et al. Identification of traits to improve the nitrogen-use efficiency of wheat genotypes. Field Crops Res. 123(2), 139–152 (2011).
Google Scholar 
Ehdaie, B. A. H. M. A. N., Mohammadi, S. A. & Nouraein, M. QTLs for root traits at mid-tillering and for root and shoot traits at maturity in a RIL population of spring bread wheat grown under well-watered conditions. Euphytica 211(1), 17–38 (2016).
Google Scholar 
Piper, C. S. Soil and Plant Analysis (Adelaide University, 1950).
Google Scholar 
Subbiah, B. V. & Asija, G. L. A rapid method for the estimation of nitrogen in soil. Curr. Sci. 26, 259–260 (1956).
Google Scholar 
Olsen, S. R., Cole, C. V., Watanabe, F. S. & Dean, L. A. Estimation of Available Phosphorus in Soil by Extraction with Sodium Carbonate (USDA, 1954).
Google Scholar 
Hanway, J. J. & Heidel, H. Soil Analysis Methods as used in Iowa State College Soil Testing Laboratory, Bulletin 57 (Iowa State College of Agriculture, 1952).
Google Scholar 
Walkley, A. L. & Black, I. A. An examination of the Degtjareff method for determination of soil organic matter and a proposed modification of the chromic acid titration method. Soil Sci. 37, 29–38 (1934).ADS 
CAS 

Google Scholar 
Ntanos, D. A. & Koutroubas, S. D. Dry matter and N accumulation and translocation for Indica and Japonica rice under Mediterranean conditions. Field Crops Res. 74, 93–101 (2002).
Google Scholar 
Prasad, R., Shivay, Y. S., Kumar, D., & Sharma, S. N. Learning by doing exercises in soil fertility (A practical manual for soil fertility). Division of Agronomy, Indian Agricultural Research Institute, India, (2006).Jiang, L. et al. Characterizing physiological N-use efficiency as influenced by nitrogen management in three rice cultivars. Field Crops Res. 88, 239–250 (2004).
Google Scholar 
Dai, X. et al. Managing the seeding rate to improve nitrogen-use efficiency of winter wheat. Field Crops Res. 154, 100–109 (2013).
Google Scholar 
Liu, W. et al. Root growth, water and nitrogen use efficiencies in winter wheat under different irrigation and nitrogen regimes in North China Plain. Front. Plant Sci. 9, 1798 (2018).
Google Scholar 
Gomez, K. A. & Gomez, A. A. Statistical Procedures for Agricultural Research 2nd edn, 180–209 (Wiley, 1984).
Google Scholar  More

Habitat loss is one of main threats to biodiversity worldwide and in general is perceived as something to be avoided. However, the prevalence of negative effects of forest fragmentation is less clear. Fragmentation creates edges between once-pristine forest and the adjacent non-forest system or systems (for example, agricultural lands, cities or water reservoirs), but the effects of these edges on biodiversity are not always clear. By performing a robust study of the interaction between insect pollinators and flowering plants at forest edges and within the forest, Ren et al.1 add a new piece to this puzzle by showing that forest edges can have a positive buffering effect on interaction networks. More

Ceballos, G., Ehrlich, P. R. & Dirzo, R. Biological annihilation via the ongoing sixth mass extinction signaled by vertebrate population losses and declines. PNAS 114, E6089–E6096 (2017).ADS 
CAS 

Google Scholar 
Dirzo, R. et al. Defaunation in the Anthropocene. Science 345, 401–406 (2014).ADS 
CAS 

Google Scholar 
Ceballos, G., Ehrlich, P. R. & Raven, P. H. Vertebrates on the brink as indicators of biological annihilation and the sixth mass extinction. PNAS 117, 13596–13602 (2020).ADS 
CAS 

Google Scholar 
Butchart, S. H. et al. Global biodiversity: Indicators of recent declines. Science 328, 1164–1168 (2010).ADS 
CAS 

Google Scholar 
Excoffier, L., Foll, M. & Petit, R. J. Genetic consequences of range expansions. Annu. Rev. Ecol. Evol. Syst. 40, 481–501 (2009).
Google Scholar 
Arenas, M., Ray, N., Currat, M. & Excoffier, L. Consequences of range contractions and range shifts on molecular diversity. Mol. Biol. Evol. 29, 207–218 (2012).CAS 

Google Scholar 
Banks, S. C. et al. How does ecological disturbance influence genetic diversity?. Trends Ecol. Evol. 28, 670–679 (2013).
Google Scholar 
Branco, C., Ray, N., Currat, M. & Arenas, M. Influence of Paleolithic range contraction, admixture and long-distance dispersal on genetic gradients of modern humans in Asia. Mol. Ecol. 29, 2150–2159 (2020).
Google Scholar 
Lomolino, M. V. & Channell, R. Splendid isolation: Patterns of geographic range collapse in endangered mammals. J. Mammal. 76(2), 335–347 (1995).
Google Scholar 
Lomolino, M. V. & Channell, R. Range collapse, re-introductions, and biogeographic guidelines for conservation. Conserv. Biol. 12, 481–484 (1998).
Google Scholar 
Channell, R. & Lomolino, M. V. Dynamic biogeography and conservation of endangered species. Nature 403, 84–86 (2000).ADS 
CAS 

Google Scholar 
Channell, R. & Lomolino, M. V. Trajectories to extinction: Spatial dynamics of the contraction of geographical ranges. J. Biogeogr. 27, 169–179 (2000).
Google Scholar 
Laliberte, A. S. & Ripple, W. J. Range contractions of North American carnivores and ungulates. Bioscience 54, 123–138 (2004).
Google Scholar 
Donald, P. F. & Greenwood, J. J. Spatial patterns of range contraction in British breeding birds. Ibis 143, 593–601 (2001).
Google Scholar 
Boakes, E. H., Isaac, N. J., Fuller, R. A., Mace, G. M. & McGowan, P. J. Examining the relationship between local extinction risk and position in range. Conserv. Biol. 32, 229–239 (2018).
Google Scholar 
Spielman, D., Brook, B. W. & Frankham, R. Most species are not driven to extinction before genetic factors impact them. PNAS 101(42), 15261–15264 (2004).ADS 
CAS 

Google Scholar 
Hoelzel, A. R. et al. Elephant seal genetic variation and the use of simulation models to investigate historical population bottlenecks. J. Hered. 84, 443–449 (1993).CAS 

Google Scholar 
Amos, W. & Balmford, A. When does conservation genetics matter?. Heredity 87, 257–265 (2001).CAS 

Google Scholar 
Reed, D. H. & Frankham, R. Correlation between fitness and genetic diversity. Conserv. Biol. 17, 230–237 (2003).
Google Scholar 
Carvalho, C. D. S. et al. Habitat loss does not always entail negative genetic consequences. Front. Genet. 10, 1101 (2019).CAS 

Google Scholar 
Wheeler, B. A., Prosen, E., Mathis, A. & Wilkinson, R. F. Population declines of a long-lived salamander: A 20+-year study of hellbenders, Cryptobranchus alleganiensis. Biol. Cons. 109, 151–156 (2003).
Google Scholar 
Walkup, D. K., Leavitt, D. J. & Fitzgerald, L. A. Effects of habitat fragmentation on population structure of dune-dwelling lizards. Ecosphere 8, e01729 (2017).
Google Scholar 
Mikle, N., Graves, T. A., Kovach, R., Kendall, K. C. & Macleod, A. C. Demographic mechanisms underpinning genetic assimilation of remnant groups of a large carnivore. Proc. R. Soc. B Biol. Sci. 283, 20161467 (2016).
Google Scholar 
DeWoody, J. A., Harder, A. M., Mathur, S. & Willoughby, J. R. The long-standing significance of genetic diversity in conservation. Mol. Ecol. 30(17), 4147–4154 (2021).
Google Scholar 
Kardos, M., Armstrong, E. E., Fitzpatrick, S. W. & Funk, W. C. The crucial role of genome-wide genetic variation in conservation. PNAS 118(48), e210462118 (2021).
Google Scholar 
García-Dorado, A. & Caballero, A. Neutral genetic diversity as a useful tool for conservation biology. Conserv. Genet. 22, 541–545 (2021).
Google Scholar 
Charlesworth, B. Effective population size and patterns of molecular evolution and variation. Nat. Rev. Genet. 10, 195–205 (2009).CAS 

Google Scholar 
Eyre-Walker, A. & Keightley, P. D. The distribution of fitness effects of new mutations. Nat. Rev. Genet. 8, 610–618 (2007).CAS 

Google Scholar 
Haller, B. C., Galloway, J., Kelleher, J., Messer, P. W. & Ralph, P. L. Tree-sequence recording in SLiM opens new horizons forward-time simulation of whole genomes. Mol. Ecol. Resour. 19, 552–566 (2018).
Google Scholar 
Kelleher, J., Thornton, K. R., Ashander, J. & Ralph, P. L. Efficient pedigree recording for fast population genetics simulation. PLoS Comput. Biol. 14, e1006581 (2018).ADS 

Google Scholar 
Haller, B. C. & Messer, P. W. SLiM 3: Forward genetic simulations beyond the Wright–Fisher model. Mol. Biol. Evol. 36, 632–637 (2019).CAS 

Google Scholar 
Rodríguez, J. P. Range contraction in declining North American bird populations. Ecol. Appl. 12, 238–248 (2002).
Google Scholar 
Fisher, D. O. Trajectories from extinction: where are missing mammals rediscovered?. Glob. Ecol. Biogeogr. 20, 415–425 (2011).
Google Scholar 
Lino, A., Fonseca, C., Rojas, D., Fischer, E. & Pereira, M. J. R. A meta-analysis of the effects of habitat loss and fragmentation on genetic diversity in mammals. Mamm. Biol. 94, 69–76 (2019).
Google Scholar 
Vandergast, A. G., Bohonak, A. J., Weissman, D. B. & Fisher, R. N. Understanding the genetic effects of recent habitat fragmentation in the context of evolutionary history: Phylogeography and landscape genetics of a southern California endemic Jerusalem cricket (Orthoptera: Stenopelmatidae: Stenopelmatus). Mol. Ecol. 16, 977–992 (2007).CAS 

Google Scholar 
Young, A., Boyle, T. & Brown, T. The population genetic consequences of habitat fragmentation for plants. Trends Ecol. Evol. 11, 413–418 (1996).CAS 

Google Scholar 
Wilkins, J. F. & Wakeley, J. The coalescent in a continuous, finite, linear population. Genetics 161, 873–888 (2002).
Google Scholar 
Ringbauer, H., Coop, G. & Barton, N. H. Inferring recent demography from isolation by distance of long shared sequence blocks. Genetics 205, 1335–1351 (2017).
Google Scholar 
Bradburd, G. S. & Ralph, P. L. Spatial population genetics: It’s about time. Annu. Rev. Ecol. Evol. Syst. 50, 427–429 (2019).
Google Scholar 
Barton, N. H., Etheridge, A. M., Kelleher, J. & Véber, A. Inference in two dimensions: Allele frequencies versus lengths of shared sequence blocks. Theor. Popul. Biol. 87, 105–119 (2013).CAS 
MATH 

Google Scholar 
Aguillon, S. M. et al. Deconstructing isolation-by-distance: The genomic consequences of limited dispersal. PLoS Genet. 13, e1006911 (2017).
Google Scholar 
Blanco-Pastor, J. L., Fernández-Mazuecos, M. & Vargas, P. Past and future demographic dynamics of alpine species: Limited genetic consequences despite dramatic range contraction in a plant from the Spanish Sierra Nevada. Mol. Ecol. 22, 4177–4195 (2013).CAS 

Google Scholar 
Chen, N. et al. Allele frequency dynamics in a pedigreed natural population. PNAS 116, 2158–2164 (2019).ADS 
CAS 

Google Scholar 
Exposito-Alonso, M., Booker, T. A., Czech, L., Fukami, T., Gillespie, L., Hateley, S. et al. Quantifying the scale of genetic diversity extinction in the Anthropocene. bioRxiv (2021).Keller, I. & Largiadèr, C. R. Recent habitat fragmentation caused by major roads leads to reduction of gene flow and loss of genetic variability in ground beetles. Proc. R. Soc. B Biol. Sci. 270, 417–423 (2003).CAS 

Google Scholar 
Chan, L. M. et al. Phylogeographic structure of the dunes sagebrush lizard, an endemic habitat specialist. PLoS ONE 15, 0238194 (2020).
Google Scholar 
Wang, I. J. & Bradburd, G. S. Isolation by environment. Mol. Ecol. 23, 5649–5662 (2014).
Google Scholar 
Cayuela, H. et al. Demographic and genetic approaches to study dispersal in wild animal populations: A methodological review. Mol. Ecol. 27, 3976–4010 (2018).
Google Scholar 
Battey, C. J., Ralph, P. L. & Kern, A. D. Space is the place: Effects of continuous spatial structure on analysis of population genetic data. Genetics 215, 193–214 (2020).CAS 

Google Scholar 
Stubbs, D. & Swingland, I. R. The ecology of a Mediterranean tortoise (Testudo hermanni): A declining population. Can. J. Zool. 63, 169–180 (1985).
Google Scholar 
Channell, R. The conservation value of peripheral populations: The supporting science. in Proceedings of the Species at Risk 2004 Pathways to Recovery Conference. 1–17. (Species at Risk 2004 Pathways to Recovery Conference Organizing Committee, 2004).Brown, J. H. On the relationship between abundance and distribution of species. Am. Nat. 124(2), 255–279 (1984).
Google Scholar 
Brown, J. H. Macroecology (University of Chicago Press, 1995).
Google Scholar 
Brown, J. H., Stevens, G. C. & Kaufman, D. M. The geographic range: Size, shape, boundaries, and internal structure. Annu. Rev. Ecol. Syst. 27(1), 597–623 (1996).
Google Scholar 
Sagarin, R. D. & Gaines, S. D. The ‘abundant centre’distribution: To what extent is it a biogeographical rule?. Ecol. Lett. 5, 137–147 (2002).
Google Scholar 
Eckert, C. G., Samis, K. E. & Lougheed, S. C. Genetic variation across species’ geographical ranges: The central-marginal hypothesis and beyond. Mol. Ecol. 17, 1170–1188 (2008).CAS 

Google Scholar 
Yackulic, C. B., Sanderson, E. W. & Uriarte, M. Anthropogenic and environmental drivers of modern range loss in large mammals. PNAS 108, 4024–4029 (2011).ADS 
CAS 

Google Scholar 
Fitzgerald L.A., Walkup, D. Chyn, K. Buchholtz, E. Angeli, N. & Parker M. The future for reptiles: Advances and challenges in the Anthropocene. in Encyclopedia of the Anthropocene. (eds. Dellasala, D.A., & Goldstein, M.I.). 163–174 (Elsevier, 2018).Segelbacher, G., Höglund, J. & Storch, I. From connectivity to isolation: Genetic consequences of population fragmentation in capercaillie across Europe. Mol. Ecol. 12, 1773–1780 (2003).CAS 

Google Scholar 
Cegelski, C. C., Waits, L. P. & Anderson, N. J. Assessing population structure and gene flow in Montana wolverines (Gulo gulo) using assignment-based approaches. Mol. Ecol. 12, 2907–2918 (2003).CAS 

Google Scholar 
Proctor, M. F., McLellan, B. N., Strobeck, C. & Barclay, R. M. Genetic analysis reveals demographic fragmentation of grizzly bears yielding vulnerably small populations. Proc. R. Soc. B Biol. Sci. 272, 2409–2416 (2005).
Google Scholar 
Leavitt, D. J. & Fitzgerald, L. A. Disassembly of a dune–dwelling lizard community due to landscape fragmentation. Ecosphere 4, 97 (2013).
Google Scholar 
Fahrig, L. Effects of habitat fragmentation on biodiversity. Annu. Rev. Ecol. Evol. Syst. 34, 487–515 (2003).
Google Scholar 
Rogan, J.E., & Lacher Jr., T.E. Impacts of habitat loss and fragmentation on terrestrial biodiversity. in Reference Modules in Earth Systems and Environmental Sciences. 1–18 (Elsevier, 2018).Hurtado, L. A., Santamaria, C. A. & Fitzgerald, L. A. Conservation genetics of the critically endangered St. Croix ground lizard (Ameiva polops Cope 1863). Conserv. Genet. 13, 665–679 (2012).
Google Scholar 
Lawton, J. H. Range, population abundance and conservation. Trends Ecol. Evol. 8, 409–413 (1993).CAS 

Google Scholar 
Purvis, A., Gittleman, J. L., Cowlishaw, G. & Mace, G. M. Predicting extinction risk in declining species. Proc. R. Soc. B Biol. Sci. 267, 1947–1952 (2000).CAS 

Google Scholar 
Cardillo, M. et al. The predictability of extinction: Biological and external correlates of decline in mammals. Proc. R. Soc. B Biol. Sci. 275, 1441–1448 (2008).
Google Scholar 
Templeton, A. R. Coadaptation and outbreeding depression. in Conservation Biology: The Science of Scarcity and Diversity. (ed. Soulé, M.E.). 105–116 (Sinauer, 1986). Lomolino, M. V. & Smith, G. A. Dynamic biogeography of prairie dog (Cynomys ludovicianus) towns near the edge of their range. J. Mammal. 82, 937–945 (2001).
Google Scholar 
Wright, S. Isolation by distance. Genetics 28, 114 (1943).CAS 

Google Scholar 
Maruyama, T. Rate of decrease of genetic variability in a two-dimensional continuous population of finite size. Genetics 4(1), 639–651 (1972).
Google Scholar 
Wright, S. Coefficients of inbreeding and relationship. Am. Nat. 645, 330–338 (1922).
Google Scholar 
Kelleher, J. & EtheridgeMcVean, A. M. G. Efficient coalescent simulation and genealogical analysis for large sample sizes. PLoS Comput. Biol. 12, e1004842 (2016).
Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. http://www.R-project.org/ (R Foundation for Statistical Computing, 2019).Greenstein, B. J. & Pandolfi, J. M. Escaping the heat: Range shifts of reef coral taxa in coastal Western Australia. Glob. Change Biol. 14, 513–528 (2008).ADS 

Google Scholar 
Wilcove, D. S. & Terborgh, J. W. Patterns of population decline in birds. Am. Birds 38, 10–13 (1984).
Google Scholar 
Gabelli, F. M. et al. Range contraction in the Pampas meadowlark Sturnella defilippii in the southern Pampas grasslands of Argentina. Oryx 38, 164–170 (2004).
Google Scholar 
Pomara, L. Y., LeDee, O. E., Martin, K. J. & Zuckerberg, B. Demographic consequences of climate change and land cover help explain a history of extirpations and range contraction in a declining snake species. Glob. Change Biol. 20, 2087–2099 (2014).ADS 

Google Scholar 
Towns, D. R. & Daugherty, C. H. Patterns of range contractions and extinctions in the New Zealand herpetofauna following human colonisation. N. Z. J. Zool. 21, 325–339 (1994).
Google Scholar 
Rudolph, D. C., Burgdorf, S. J., Schaefer, R. R., Conner, R. N. & Maxey, R. W. Status of Pituophis ruthveni (Louisiana pine snake). Southeast. Nat. 5(3), 463–472 (2006).
Google Scholar 
Russell, R. W., Lipps, G. J. Jr., Hecnar, S. J. & Haffner, G. D. Persistent organic pollutants in Blanchard’s cricket frogs (Acris crepitans blanchardi) from Ohio. Ohio J. Sci. 102, 119–122 (2002).CAS 

Google Scholar 
Fellers, G. M. & Drost, C. A. Disappearance of the Cascades frog Rana cascadae at the southern end of its range, California, USA. Biol. Cons. 65, 177–181 (1993).
Google Scholar 
Franco, A. M. et al. Impacts of climate warming and habitat loss on extinctions at species’ low-latitude range boundaries. Glob. Change Biol. 12, 1545–1553 (2006).ADS 

Google Scholar 
Stewart, J. A., Wright, D. H. & Heckman, K. A. Apparent climate-mediated loss and fragmentation of core habitat of the American pika in the Northern Sierra Nevada, California, USA. PLoS ONE 12, e0181834 (2017).
Google Scholar 
Rodríguez, A. & Delibes, M. Internal structure and patterns of contraction in the geographic range of the Iberian lynx. Ecography 25, 314–328 (2002).
Google Scholar 
Kattan, G. et al. Range fragmentation in the spectacled bear Tremarctos ornatus in the northern Andes. Oryx 38(2), 155–163 (2004).
Google Scholar 
Jones, S. J., Lima, F. P. & Wethey, D. S. Rising environmental temperatures and biogeography: poleward range contraction of the blue mussel, Mytilus edulis L., in the western Atlantic. J. Biogeogr. 37, 2243–2259 (2010).
Google Scholar 
Smale, D. A. & Wernberg, T. Extreme climatic event drives range contraction of a habitat-forming species. P. R. Soc. B Biol. Sci. 280, 20122829 (2013).
Google Scholar  More

Ackerly, D. D. Community assembly, niche conservatism, and adaptive evolution in changing environments. Int. J. Plant Sci. 164, S165–S184 (2003).Article 

Google Scholar 
Kellermann, V., Van Heerwaarden, B., Sgrò, C. M. & Hoffmann, A. A. Fundamental evolutionary limits in ecological traits drive Drosophila species distributions. Science 325, 1244–1246 (2009).Article 
CAS 

Google Scholar 
Hansen, M. M., Olivieri, I., Waller, D. M. & Nielsen, E. E. Monitoring adaptive genetic responses to environmental change. Mol. Ecol. 21, 1311–1329 (2012).Article 

Google Scholar 
Aitken, S. N. & Whitlock, M. C. Assisted gene flow to facilitate local adaptation to climate change. Annu. Rev. Ecol. Evol. Syst. 44, 367–388 (2013).Article 

Google Scholar 
Becker, M. et al. Hybridization may facilitate in situ survival of endemic species through periods of climate change. Nat. Clim. Change 3, 1039–1043 (2013).Article 

Google Scholar 
Allendorf, F. W., Leary, R. F., Spruell, P. & Wenburg, J. K. The problems with hybrids: setting conservation guidelines. Trends Ecol. Evol. 16, 613–622 (2001).Article 

Google Scholar 
Todesco, M. et al. Hybridization and extinction. Evol. Appl. 9, 892–908 (2016).Article 
CAS 

Google Scholar 
Rhymer, J. M. & Simberloff, D. Extinction by hybridization and introgression. Annu. Rev. Ecol. Syst. 27, 83–109 (1996).Article 

Google Scholar 
Taylor, S. A. & Larson, E. L. Insights from genomes into the evolutionary importance and prevalence of hybridization in nature. Nat. Ecol. Evol. 3, 170–177 (2019).Article 

Google Scholar 
vonHoldt, B. M., Brzeski, K. E., Wilcove, D. S. & Rutledge, L. Y. Redefining the role of admixture and genomics in species conservation. Conserv. Lett. 11, e12371 (2018).Article 

Google Scholar 
Hamilton, J. A. & Miller, J. M. Adaptive introgression as a resource for management and genetic conservation in a changing climate. Conserv. Biol. 30, 33–41 (2016).Article 

Google Scholar 
Ralls, K., Sunnucks, P., Lacy, R. C. & Frankham, R. Genetic rescue: a critique of the evidence supports maximizing genetic diversity rather than minimizing the introduction of putatively harmful genetic variation. Biol. Conserv. 251, 108784 (2020).Article 

Google Scholar 
Capblancq, T., Fitzpatrick, M. C., Bay, R. A., Exposito-Alonso, M. & Keller, S. R. Genomic prediction of (mal) adaptation across current and future climatic landscapes. Annu. Rev. Ecol. Evol. Syst. 51, 245–269 (2020).Article 

Google Scholar 
Rellstab, C., Dauphin, B. & Exposito‐Alonso, M. Prospects and limitations of genomic offset in conservation management. Evol. Appl. 14, 1202–1212 (2021).Article 

Google Scholar 
Bay, R. A. et al. Genomic signals of selection predict climate-driven population declines in a migratory bird. Science 359, 83–86 (2018).Article 
CAS 

Google Scholar 
Rellstab, C. et al. Signatures of local adaptation in candidate genes of oaks (Quercus spp.) with respect to present and future climatic conditions. Mol. Ecol. 25, 5907–5924 (2016).Article 

Google Scholar 
Fitzpatrick, M. C. & Keller, S. R. Ecological genomics meets community-level modelling of biodiversity: mapping the genomic landscape of current and future environmental adaptation. Ecol. Lett. 18, 1–16 (2015).Article 

Google Scholar 
Exposito-Alonso, M. et al. Genomic basis and evolutionary potential for extreme drought adaptation in Arabidopsis thaliana. Nat. Ecol. Evol. 2, 352–358 (2018).Article 

Google Scholar 
Kindt, R. AlleleShift: an R package to predict and visualize population-level changes in allele frequencies in response to climate change. PeerJ 9, e11534 (2021).Article 

Google Scholar 
Gain, C. & François, O. LEA 3: factor models in population genetics and ecological genomics with R. Mol. Ecol. Resour. 21, 2738–2748 (2020).Article 

Google Scholar 
Aguirre-Liguori, J. A., Ramírez-Barahona, S. & Gaut, B. S. The evolutionary genomics of species’ responses to climate change. Nat. Ecol. Evol. 5, 1350–1360 (2021).Article 

Google Scholar 
Taylor, S. A., Larson, E. L. & Harrison, R. G. Hybrid zones: windows on climate change. Trends Ecol. Evol. 30, 398–406 (2015).Article 

Google Scholar 
Hoffmann, A. A. & Sgro, C. M. Climate change and evolutionary adaptation. Nature 470, 479–485 (2011).Article 
CAS 

Google Scholar 
McGuigan, K., Franklin, C. E., Moritz, C. & Blows, M. W. Adaptation of rainbow fish to lake and stream habitats. Evolution 57, 104–118 (2003).
Google Scholar 
Smith, S., Bernatchez, L. & Beheregaray, L. RNA-seq analysis reveals extensive transcriptional plasticity to temperature stress in a freshwater fish species. BMC Genomics 14, 375 (2013).Article 
CAS 

Google Scholar 
Smith, S. et al. Latitudinal variation in climate‐associated genes imperils range edge populations. Mol. Ecol. 29, 4337–4349 (2020).Article 
CAS 

Google Scholar 
Sandoval-Castillo, J. et al. Adaptation of plasticity to projected maximum temperatures and across climatically defined bioregions. Proc. Natl Acad. Sci. USA 117, 17112–17121 (2020).Article 
CAS 

Google Scholar 
Brauer, C., Unmack, P. J., Smith, S., Bernatchez, L. & Beheregaray, L. B. On the roles of landscape heterogeneity and environmental variation in determining population genomic structure in a dendritic system. Mol. Ecol. 27, 3484–3497 (2018).Article 
CAS 

Google Scholar 
Attard, C. R. et al. Fish out of water: genomic insights into persistence of rainbowfish populations in the desert. Evolution 76, 171–183 (2022).Article 

Google Scholar 
Gates, K. et al. Environmental selection, rather than neutral processes, best explain patterns of diversity in a tropical rainforest fish. Preprint at bioRxiv https://doi.org/10.1101/2022.1105.1113.491913 (2022).Article 

Google Scholar 
McCairns, R. J. S., Smith, S., Sasaki, M., Bernatchez, L. & Beheregaray, L. B. The adaptive potential of subtropical rainbowfish in the face of climate change: heritability and heritable plasticity for the expression of candidate genes. Evol. Appl. 9, 531–545 (2016).Article 
CAS 

Google Scholar 
McGuigan, K., Zhu, D., Allen, G. & Moritz, C. Phylogenetic relationships and historical biogeography of melanotaeniid fishes in Australia and New Guinea. Mar. Freshwat. Res. 51, 713–723 (2000).Article 

Google Scholar 
Unmack, P. J. et al. Malanda Gold: the tale of a unique rainbowfish from the Atherton Tablelands, now on the verge of extinction. Fish. Sahul. 30, 1039–1054 (2016).
Google Scholar 
Moritz, C. Strategies to protect biological diversity and the evolutionary processes that sustain it. Syst. Biol. 51, 238–254 (2002).Article 

Google Scholar 
Pope, L., Estoup, A. & Moritz, C. Phylogeography and population structure of an ecotonal marsupial, Bettongia tropica, determined using mtDNA and microsatellites. Mol. Ecol. 9, 2041–2053 (2000).Article 
CAS 

Google Scholar 
Hugall, A., Moritz, C., Moussalli, A. & Stanisic, J. Reconciling paleodistribution models and comparative phylogeography in the Wet Tropics rainforest land snail Gnarosophia bellendenkerensis (Brazier 1875). Proc. Natl Acad. Sci. USA 99, 6112–6117 (2002).Article 
CAS 

Google Scholar 
Moritz, C. et al. Identification and dynamics of a cryptic suture zone in tropical rainforest. Proc. R. Soc. B. 276, 1235–1244 (2009).Article 
CAS 

Google Scholar 
Phillips, B. L., Baird, S. J. & Moritz, C. When vicars meet: a narrow contact zone between morphologically cryptic phylogeographic lineages of the rainforest skink, Carlia rubrigularis. Evolution 58, 1536–1548 (2004).
Google Scholar 
Krosch, M. N., Baker, A. M., Mckie, B. G., Mather, P. B. & Cranston, P. S. Deeply divergent mitochondrial lineages reveal patterns of local endemism in chironomids of the Australian Wet Tropics. Austral Ecol. 34, 317–328 (2009).Article 

Google Scholar 
Williams, S. E., Bolitho, E. E. & Fox, S. Climate change in Australian tropical rainforests: an impending environmental catastrophe. Proc. R. Soc. B. 270, 1887–1892 (2003).Article 

Google Scholar 
Whitehead, P. et al. Temporal development of the Atherton Basalt Province, north Queensland. Aust. J. Earth Sci. 54, 691–709 (2007).Article 
CAS 

Google Scholar 
Moy, K. G., Unmack, P. J., Lintermans, M., Duncan, R. P. & Brown, C. Barriers to hybridisation and their conservation implications for a highly threatened Australian fish species. Ethology 125, 142–152 (2019).Article 

Google Scholar 
Alexander, D. H., Novembre, J. & Lange, K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 19, 1655–1664 (2009).Article 
CAS 

Google Scholar 
Buerkle, C. A. Maximum‐likelihood estimation of a hybrid index based on molecular markers. Mol. Ecol. Notes 5, 684–687 (2005).Article 
CAS 

Google Scholar 
Anderson, E. & Thompson, E. A model-based method for identifying species hybrids using multilocus genetic data. Genetics 160, 1217–1229 (2002).Article 
CAS 

Google Scholar 
Dorion, S. & Landry, J. Activation of the mitogen-activated protein kinase pathways by heat shock. Cell Stress Chaperones 7, 200 (2002).Article 
CAS 

Google Scholar 
Blumstein, M. et al. Protocol for projecting allele frequency change under future climate change at adaptive-associated loci. STAR Protoc. 1, 100061 (2020).Article 

Google Scholar 
Gougherty, A. V., Keller, S. R. & Fitzpatrick, M. C. Maladaptation, migration and extirpation fuel climate change risk in a forest tree species. Nat. Clim. Change 11, 166–171 (2021).Article 

Google Scholar 
Blumstein, M. et al. A new perspective on ecological prediction reveals limits to climate adaptation in a temperate tree species. Curr. Biol. 30, 1447–1453. e1444 (2020).Article 
CAS 

Google Scholar 
Razgour, O. et al. Considering adaptive genetic variation in climate change vulnerability assessment reduces species range loss projections. Proc. Natl Acad. Sci. USA 116, 10418–10423 (2019).Article 
CAS 

Google Scholar 
Goicoechea, P. G. et al. Adaptive introgression promotes fast adaptation in oaks marginal populations. Preprint available at bioRxiv https://doi.org/10.1101/731919 (2019).Lavergne, S. & Molofsky, J. Increased genetic variation and evolutionary potential drive the success of an invasive grass. Proc. Natl Acad. Sci. USA 104, 3883–3888 (2007).Article 
CAS 

Google Scholar 
De Carvalho, D. et al. Admixture facilitates adaptation from standing variation in the European aspen (Populus tremula L.), a widespread forest tree. Mol. Ecol. 19, 1638–1650 (2010).Article 

Google Scholar 
De-Kayne, R. et al. Genomic architecture of adaptive radiation and hybridization in Alpine whitefish. Nat. Commun. 13, 4479 (2022).Article 
CAS 

Google Scholar 
Baskett, M. L. & Gomulkiewicz, R. Introgressive hybridization as a mechanism for species rescue. Theor. Ecol. 4, 223–239 (2011).Article 

Google Scholar 
Meier, J. I. et al. The coincidence of ecological opportunity with hybridization explains rapid adaptive radiation in Lake Mweru cichlid fishes. Nat. Commun. 10, 1–11 (2019).Article 
CAS 

Google Scholar 
Svardal, H. et al. Ancestral hybridization facilitated species diversification in the Lake Malawi cichlid fish adaptive radiation. Mol. Biol. Evol. 37, 1100–1113 (2020).Article 
CAS 

Google Scholar 
Racimo, F., Sankararaman, S., Nielsen, R. & Huerta-Sánchez, E. Evidence for archaic adaptive introgression in humans. Nat. Rev. Genet. 16, 359–371 (2015).Article 
CAS 

Google Scholar 
Jeong, C. et al. Admixture facilitates genetic adaptations to high altitude in Tibet. Nat. Commun. 5, 1–7 (2014).Article 

Google Scholar 
Nolte, A. W., Freyhof, J., Stemshorn, K. C. & Tautz, D. An invasive lineage of sculpins, Cottus sp. (Pisces, Teleostei) in the Rhine with new habitat adaptations has originated from hybridization between old phylogeographic groups. Proc. R. Soc. B. 272, 2379–2387 (2005).Article 

Google Scholar 
Fitzpatrick, M. C., Chhatre, V. E., Soolanayakanahally, R. Y. & Keller, S. R. Experimental support for genomic prediction of climate maladaptation using the machine learning approach Gradient Forests. Mol. Ecol. Resour. 21, 2749–2765 (2021).Article 
CAS 

Google Scholar 
Schneider, C., Cunningham, M. & Moritz, C. Comparative phylogeography and the history of endemic vertebrates in the Wet Tropics rainforests of Australia. Mol. Ecol. 7, 487–498 (1998).Article 

Google Scholar 
Hewitt, G. M. Quaternary phylogeography: the roots of hybrid zones. Genetica 139, 617–638 (2011).Article 

Google Scholar 
Pfennig, K. S., Kelly, A. L. & Pierce, A. A. Hybridization as a facilitator of species range expansion. Proc. R. Soc. B. 283, 20161329 (2016).Article 

Google Scholar 
Soulé, M. E. What is conservation biology? A new synthetic discipline addresses the dynamics and problems of perturbed species, communities, and ecosystems. Bioscience 35, 727–734 (1985).
Google Scholar 
Biermann, C. & Havlick, D. Genetics and the question of purity in cutthroat trout restoration. Restor. Ecol. 29, e13516 (2021).Article 

Google Scholar 
Fredrickson, R. J. & Hedrick, P. W. Dynamics of hybridization and introgression in red wolves and coyotes. Conserv. Biol. 20, 1272–1283 (2006).Article 

Google Scholar 
Hirashiki, C., Kareiva, P. & Marvier, M. Concern over hybridization risks should not preclude conservation interventions. Conserv. Sci. Pract. 3, e424 (2021).
Google Scholar 
Unmack, P. J., Allen, G. R. & Johnson, J. B. Phylogeny and biogeography of rainbowfishes (Melanotaeniidae) from Australia and New Guinea. Mol. Phylogenet. Evol. 67, 15–27 (2013).Article 

Google Scholar 
Allen, G. Rainbowfishes in Nature and the Aquarium (Tetra Publications, 1995).Seehausen, O. Hybridization and adaptive radiation. Trends Ecol. Evol. 19, 198–207 (2004).Article 

Google Scholar 
Pusey, B., Kennard, M. J. & Arthington, A. H. Freshwater Fishes of North-eastern Australia (CSIRO Publishing, 2004).Zhu, D., Degnan, S. & Moritz, C. Evolutionary distinctiveness and status of the endangered Lake Eacham rainbowfish (Melanotaenia eachamensis). Conserv. Biol. 12, 80–93 (1998).Article 

Google Scholar 
McGuigan, K., Chenoweth, S. F. & Blows, M. W. Phenotypic divergence along lines of genetic variance. Am. Nat. 165, 32–43 (2005).Article 

Google Scholar 
Sunnucks, P. & Hales, D. F. Numerous transposed sequences of mitochondrial cytochrome oxidase I-II in aphids of the genus Sitobion (Hemiptera: Aphididae). Mol. Biol. Evol. 13, 510–524 (1996).Article 
CAS 

Google Scholar 
Peterson, B., Weber, J., Kay, E., Fisher, H. & Hoekstra, H. Double digest RADseq: an inexpensive method for de novo SNP discovery and genotyping in model and non-model species. PLoS ONE 7, e37135 (2012).Article 
CAS 

Google Scholar 
Catchen, J. M., Amores, A., Hohenlohe, P., Cresko, W. & Postlethwait, J. H. Stacks: building and genotyping loci de novo from short-read sequences. G3: Genes Genomes Genet. 1, 171–182 (2011).Article 
CAS 

Google Scholar 
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).Article 
CAS 

Google Scholar 
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357 (2012).Article 
CAS 

Google Scholar 
DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).Article 
CAS 

Google Scholar 
Danecek, P. et al. Twelve years of SAMtools and BCFtools. Gigascience 10, giab008 (2021).Article 

Google Scholar 
Goudet, J. Hierfstat, a package for R to compute and test hierarchical F‐statistics. Mol. Ecol. Notes 5, 184–186 (2005).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021).Bailey, R. ribailey/gghybrid: gghybrid R package for Bayesian hybrid index and genomic cline estimation. v2.0.0 https://doi.org/10.5281/zenodo.3676498 (2020).Wringe, B. hybriddetective: automates the process of detecting hybrids from genetic data. R package version 0.1.0.9000 https://github.com/bwringe/hybriddetective (2016).Pickrell, J. K. & Pritchard, J. K. Inference of population splits and mixtures from genome-wide allele frequency data. PLoS Genet. 8, e1002967 (2012).Article 
CAS 

Google Scholar 
Malinsky, M., Matschiner, M. & Svardal, H. Dsuite‐Fast D‐statistics and related admixture evidence from VCF files. Mol. Ecol. Resour. 21, 584–595 (2021).Article 

Google Scholar 
Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).Article 
CAS 

Google Scholar 
Green, R. E. et al. A draft sequence of the Neandertal genome. Science 328, 710–722 (2010).Article 
CAS 

Google Scholar 
Durand, E. Y., Patterson, N., Reich, D. & Slatkin, M. Testing for ancient admixture between closely related populations. Mol. Biol. Evol. 28, 2239–2252 (2011).Article 
CAS 

Google Scholar 
Malinsky, M. et al. Genomic islands of speciation separate cichlid ecomorphs in an East African crater lake. Science 350, 1493–1498 (2015).Article 
CAS 

Google Scholar 
Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).Article 
CAS 

Google Scholar 
Karger, D. N. et al. Climatologies at high resolution for the earth’s land surface areas. Sci. Data 4, 1–20 (2017).Article 

Google Scholar 
Karger, D. N. et al. CHELSA climatologies at high resolution for the Earth’s land surface areas (v.1.0). https://doi.org/10.1594/WDCC/CHELSA_v1 (2016).Ackerley, D. & Dommenget, D. Atmosphere-only GCM (ACCESS1.0) simulations with prescribed land surface temperatures. Geosci. Model Dev. 9, 2077–2098 (2016).Article 

Google Scholar 
Brown, J. L., Hill, D. J., Dolan, A. M., Carnaval, A. C. & Haywood, A. M. PaleoClim: high spatial resolution paleoclimate surfaces for global land areas. Sci. Data 5, 1–9 (2018).Article 

Google Scholar 
Fordham, D. A. et al. PaleoView: a tool for generating continuous climate projections spanning the last 21,000 years at regional and global scales. Ecography 40, 1348–1358 (2017).Article 

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

Google Scholar 
Lemus-Canovas, M., Lopez-Bustins, J. A., Martin-Vide, J. & Royé, D. synoptReg: an R package for computing a synoptic climate classification and a spatial regionalization of environmental data. Environ. Model. Softw. 118, 114–119 (2019).Article 

Google Scholar 
Hao, T., Elith, J., Guillera‐Arroita, G. & Lahoz‐Monfort, J. J. A review of evidence about use and performance of species distribution modelling ensembles like BIOMOD. Divers. Distrib. 25, 839–852 (2019).Article 

Google Scholar 
Galpern, P., Peres‐Neto, P. R., Polfus, J. & Manseau, M. MEMGENE: spatial pattern detection in genetic distance data. Methods Ecol. Evol. 5, 1116–1120 (2014).Article 

Google Scholar 
Peres‐Neto, P. R. & Galpern, P. memgene: spatial pattern detection in genetic distance data using Moran’s eigenvector maps. R package version 1.0.1 https://cran.r-project.org/web/packages/memgene/ (2019).Oksanen, J. et al. vegan: community ecology package. R package version 2.3–0 https://cran.r-project.org/web/packages/vegan/ (2015).Forester, B. R., Jones, M. R., Joost, S., Landguth, E. L. & Lasky, J. R. Detecting spatial genetic signatures of local adaptation in heterogeneous landscapes. Mol. Ecol. 25, 104–120 (2015).Article 

Google Scholar 
Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly 6, 80–92 (2012).Article 
CAS 

Google Scholar 
Szklarczyk, D. et al. The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 49, D605–D612 (2021).Article 
CAS 

Google Scholar 
Brauer, C. J. et al. Data for ‘Natural hybridisation reduces vulnerability to climate change’. figshare https://doi.org/10.6084/m9.figshare.21692918 (2022).Brauer, C. J. et al. Code for ‘Natural hybridisation reduces vulnerability to climate change’. GitHub https://github.com/pygmyperch/NER (2022). More

Siegert M, Ross N, Le Brocq A. Recent advances in understanding Antarctic subglacial lakes and hydrology. Philos Trans R Soc A-Math Phys Eng Sci. 2016;374:20140306.
Google Scholar 
Fricker H, Scambos T, Bindschadler R, Padman L. An active subglacial water system in West Antarctica mapped from space. Science. 2007;315:1544–8.CAS 

Google Scholar 
Livingstone S, Li Y, Rutishauser A, Sanderson R, Winter K, Mikucki J, et al. Subglacial lakes and their changing role in a warming climate. Nat Rev Earth Environ. 2022;3:106–24.
Google Scholar 
Tulaczyk S, Mikucki J, Siegfried M, Priscu J, Barcheck C, Beem L, et al. WISSARD at Subglacial Lake Whillans, West Antarctica: scientific operations and initial observations. Ann Glaciol. 2014;55:51–8.
Google Scholar 
Priscu J, Achberger A, Cahoon J, Christner B, Edwards R, Jones W, et al. A microbiologically clean strategy for access to the Whillans Ice Stream subglacial environment. Antarctitc Sci. 2013;25:637–47.
Google Scholar 
Christner BC, Priscu JC, Achberger AM, Barbante C, Carter SP, Christianson K, et al. A microbial ecosystem beneath the West Antarctic ice sheet. Nature. 2014;512:310–3.CAS 

Google Scholar 
Michaud A, Dore J, Achberger A, Christner B, Mitchell A, Skidmore M, et al. Microbial oxidation as a methane sink beneath the West Antarctic Ice Sheet. Nat Geosci. 2017;10:582–6.CAS 

Google Scholar 
Achberger A, Christner B, Michaud A, Priscu J, Skidmore M, Vick-Majors T, et al. Microbial community structure of Subglacial Lake Whillans, West Antarctica. Front Microbiol. 2016;7:1457.
Google Scholar 
Vick-Majors TJ, Mitchell AC, Achberger AM, Christner BC, Dore JE, Michaud AB, et al. Physiological ecology of microorganisms in Subglacial Lake Whillans. Front Microbiol. 2016;7:1705.
Google Scholar 
Vick‐Majors TJ, Michaud AB, Skidmore ML, Turetta C, Barbante C, Christner BC, et al. Biogeochemical connectivity between freshwater ecosystems beneath the West Antarctic Ice Sheet and the Sub‐Ice Marine Environment. Global Biogeochem Cycles. 2020;34:1–17.
Google Scholar 
Montross S, Skidmore M, Tranter M, Kivimaki A, Parkes R. A microbial driver of chemical weathering in glaciated systems. Geology. 2013;41:215–8.CAS 

Google Scholar 
Gill-Olivas B, Telling J, Tranter M, Skidmore M, Christner B, O’Doherty S, et al. Subglacial erosion has the potential to sustain microbial processes in Subglacial Lake Whillans, Antarctica. Commun Earth Environ. 2021;2:1–12.
Google Scholar 
Priscu JC, Kalin J, Winans J, Campbell T, Siegfried MR, Skidmore M, et al. Scientific access into Mercer Subglacial Lake: scientific objectives, drilling operations and initial observations. Ann Glaciol. 2021;62:340–52.
Google Scholar 
Fricker H, Scambos T. Connected subglacial lake activity on lower Mercer and Whillans Ice Streams, West Antarctica, 2003-2008. J Glaciol. 2009;55:303–15.
Google Scholar 
Carter S, Fricker H, Siegfried M. Evidence of rapid subglacial water piracy under Whillans Ice Stream, West Antarctica. J Glaciol. 2013;59:1147–62.
Google Scholar 
Venturelli RA, Boehman B, Davis C, Hawkings JR, Johnston SE, Gustafson CD, et al. Constraints on the timing and extent of deglacial grounding line retreat in West Antarctica from subglacial sediments. AGU Advances. 2022; (in review).Kingslake J, Scherer R, Albrecht T, Coenen J, Powell R, Reese R, et al. Extensive retreat and re-advance of the West Antarctic Ice Sheet during the Holocene. Nature. 2018;558:430–4.CAS 

Google Scholar 
Venturelli RA, Siegfried MR, Roush KA, Li W, Burnett J, Zook R, et al. Mid-Holocene Grounding Line Retreat and Readvance at Whillans Ice Stream, West Antarctica. Geophys Res Lett. 2020;47:e2020GL088476.
Google Scholar 
Scherer R, Aldahan A, Tulaczyk S, Possnert G, Engelhardt H, Kamb B. Pleistocene collapse of the West Antarctic ice sheet. Science. 1998;281:82–5.CAS 

Google Scholar 
Achberger A. Structure and functional potential of microbial communities in Subglacial Lake Whillans and at the Ross Ice Shelf Grounding Zone, West Antarctica: Louisiana State University; 2016.Blythe D, Duling D, Gibson D. Developing a hot-water drill system for the WISSARD project: 2. In situ water production. Ann Glaciol. 2014;55:298–310.
Google Scholar 
Burnett J, Rack FR, Blythe D, Swanson P, Duling D, Gibson D, et al. Developing a hot-water drill system for the WISSARD project: 3. Instrumentation and control systems. Ann Glaciol. 2014;55:303–10.
Google Scholar 
Rack F, Duling D, Blythe D, Burnett J, Gibson D, Roberts G, et al. Developing a hot-water drill system for the WISSARD project: 1. Basic drill system components and design. Ann Glaciol. 2014;55:285–97.
Google Scholar 
Michaud A, Vick-Majors T, Achberger A, Skidmore M, Christner B, Tranter M, et al. Environmentally clean access to Antarctic subglacial aquatic environments. Antarctic Sci. 2020;32:1–12.Kallmeyer J, Smith DC, Spivack AJ, D’Hondt S. New cell extraction procedure applied to deep subsurface sediments. Limnol Oceanogr Methods. 2008;6:236–45.
Google Scholar 
Pan D, Morono Y, Inagaki F, Takai K. An improved method for extracting viruses from sediment: detection of far more viruses in the subseafloor than previously reported. Front Microbiol. 2019;10:878.
Google Scholar 
Battin T, Wille A, Sattler B, Psenner R. Phylogenetic and functional heterogeneity of sediment biofilms along environmental gradients in a glacial stream. Appl Environ Microbiol. 2001;67:799–807.CAS 

Google Scholar 
Klock J-H, Wieland A, Seifert R, Michaelis W. Extracellular polymeric substances (EPS) from cyanobacterial mats: characterisation and isolation method optimisation. Marine Biol. 2007;152:1077–85.CAS 

Google Scholar 
Miyatake T, Moerdijk-Poortvliet T, Stal L, Boschker H. Tracing carbon flow from microphytobenthos to major bacterial groups in an intertidal marine sediment by using an in situ C-13 pulse-chase method. Limnol Oceanogr. 2014;59:1275–87.CAS 

Google Scholar 
Albalasmeh A, Berhe A, Ghezzehei T. A new method for rapid determination of carbohydrate and total carbon concentrations using UV spectrophotometry. Carbohydrate Polymers. 2013;97:253–61.CAS 

Google Scholar 
Lerotic M, Mak R, Wirick S, Meirer F, Jacobsen C. MANTiS: a program for the analysis of X-ray spectromicroscopy data. J Synchrotron Radiat. 2014;21:1206–12.CAS 

Google Scholar 
Bonneville S, Delpomdor F, Preat A, Chevalier C, Araki T, Kazemian M, et al. Molecular identification of fungi microfossils in a Neoproterozoic shale rock. Sci Adv. 2020;6:eaax7599.CAS 

Google Scholar 
Le Guillou C, Bernard S, De la Pena F, Le Brech Y. XANES-based quantification of carbon functional group concentrations. Anal Chem. 2018;90:8379–86.
Google Scholar 
Solomon D, Lehmann J, Kinyangi J, Liang B, Heymann K, Dathe L, et al. Carbon (1s) NEXAFS spectroscopy of biogeochemically relevant reference organic compounds. Soil Sci Soc Am J. 2009;73:1817–30.CAS 

Google Scholar 
Michaud A, Skidmore M, Mitchell A, Vick-Majors T, Barbante C, Turetta C, et al. Solute sources and geochemical processes in Subglacial Lake Whillans, West Antarctica. Geology. 2016;44:347–50.CAS 

Google Scholar 
Raiswell R, Hawkings J, Eisenousy A, Death R, Tranter M, Wadham J. Iron in glacial systems: speciation, reactivity, freezing behavior, and alteration during transport. Front Earth Sci. 2018;6:222.
Google Scholar 
Hyacinthe C, Bonneville S, Van Cappellen P. Reactive iron(III) in sediments: Chemical versus microbial extractions. Geochimica Et Cosmochimica Acta. 2006;70:4166–80.CAS 

Google Scholar 
Raiswell R, Benning L, Tranter M, Tulaczyk S. Bioavailable iron in the Southern Ocean: the significance of the iceberg conveyor belt. Geochem Trans. 2008;9:7.
Google Scholar 
Raiswell R, Vu H, Brinza L, Benning L. The determination of labile Fe in ferrihydrite by ascorbic acid extraction: Methodology, dissolution kinetics and loss of solubility with age and de-watering. Chem Geol. 2010;278:70–9.CAS 

Google Scholar 
Fossing H, Jorgensen B. Measurement of bacterial sulfate reduction in sediments—evaluation of a single-step chromium reduction method. Biogeochemistry. 1989;8:205–22.CAS 

Google Scholar 
Cline J. Spectrophotometric determination of hydrogen sulfide in natural waters. Limnol Oceanogr. 1969;14:454.CAS 

Google Scholar 
Kallmeyer J, Ferdelman T, Weber A, Fossing H, Jorgensen B. A cold chromium distillation procedure for radiolabeled sulfide applied to sulfate reduction measurements. Limnol Oceanogr Methods. 2004;2:171–80.
Google Scholar 
Roy H, Weber H, Tarpgaard I, Ferdelman T, Jorgensen B. Determination of dissimilatory sulfate reduction rates in marine sediment via radioactive S-35 tracer. Limnol Oceanogr Methods. 2014;12:196–211.
Google Scholar 
Caporaso J, Lauber C, Walters W, Berg-Lyons D, Huntley J, Fierer N, et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 2012;6:1621–4.CAS 

Google Scholar 
Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJ, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.CAS 

Google Scholar 
Button DK, Robertson BR. Determination of DNA content of aquatic bacteria by flow cytometry. Appl Environ Microbiol. 2001;67:1636–45.CAS 

Google Scholar 
Michaud AB, Priscu JC, the Salsa Science Team. Sediment oxygen consumption in Antarctic subglacial environments. Limnology and Oceanography. 2022. (In Review).Siegfried MR, Venturelli RA, Patterson MO, Arnuk W, Campbell TD, Gustafson CD, et al. The life and death of a subglacial lake in West Antarctica. Geology. 2023; in press; https://doi.org/10.1130/G50995.1.Vyse S, Herzschuh U, Pfalz G, Pestryakova L, Diekmann B, Nowaczyk N, et al. Sediment and carbon accumulation in a glacial lake in Chukotka (Arctic Siberia) during the Late Pleistocene and Holocene: combining hydroacoustic profiling and down-core analyses. Biogeosciences. 2021;18:4791–816.CAS 

Google Scholar 
Oliva-Urcia B, Moreno A, Leunda M, Valero-Garces B, Gonzalez-Samperiz P, Gil-Romera G, et al. Last deglaciation and Holocene environmental change at high altitude in the Pyrenees: the geochemical and paleomagnetic record from Marbor, Lake (N Spain). J Paleolimnol. 2018;59:349–71.
Google Scholar 
Davis C. Ecology of subglacial lake microbial communities in West Antarctica: University of Florida; 2022.Lanoil B, Skidmore M, Priscu JC, Han S, Foo W, Vogel SW, et al. Bacteria beneath the West Antarctic ice sheet. Environ Microbiol. 2009;11:609–15.CAS 

Google Scholar 
Boyd E, Hamilton T, Havig J, Skidmore M, Shock E. Chemolithotrophic Primary Production in a Subglacial Ecosystem. Appl Environ Microbiol. 2014;80:6146–53.
Google Scholar 
Sattley WM, Madigan MT. Isolation, characterization, and ecology of cold-active, chemolithotrophic, sulfur-oxidizing bacteria from perennially ice-covered Lake Fryxell, Antarctica. Appl Environ Microbiol. 2006;72:5562–8.CAS 

Google Scholar 
Dieser M, Broemsen E, Cameron KA, King GM, Achberger A, Choquette K, et al. Molecular and biogeochemical evidence for methane cycling beneath the western margin of the Greenland Ice Sheet. ISME J. 2014;8:2305–16.CAS 

Google Scholar 
Vaclavkova S, Schultz-Jensen N, Jacobsen O, Elberling B, Aamand J. Nitrate-controlled anaerobic oxidation of pyrite by thiobacillus cultures. Geomicrobiol J. 2015;32:412–9.CAS 

Google Scholar 
Gustafson C, Key K, Siegfried M, Winberry J, Fricker H, Venturelli R, et al. A dynamic saline groundwater system mapped beneath an Antarctic ice stream. Science. 2022;376:640–4.CAS 

Google Scholar 
Priscu JC, Tulaczyk S, Studinger M, Kennicutt M, Christner BC, Foreman CM. Antarctic subglacial water: origin, evolution and ecology. Polar lakes and rivers: limnology of Arctic and Antarctic aquatic ecosystems Oxford University Press, Oxford. 2008:119–35.Whitman W, Coleman D, Wiebe W. Prokaryotes: the unseen majority. Proc Natl Acad Sci USA 1998;95:6578–83.CAS 

Google Scholar 
Scherer R. Quaternary and tertiary microfossils from beneath Ice Stream-B—evidence for a dynamic West Antarctic ice-sheet history. Global Planet Change. 1991;90:395–412.
Google Scholar 
Haran T, Bohlander J, Scambos T, Painter T, Fahnestock M. MODIS Mosaic of Antarctica 2008–2009 (MOA2009) Image Map, Version 2. 2021; Boulder, Colorado USA NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/4ZL43A4619AF.Mouginot J, Rignot E, Scheuchl B. Continent‐Wide Interferometric SAR Phase Mapping of Antarctic Ice Velocity. Geophysical Research Letters. 2019;46:9710–8. https://doi.org/10.1029/2019GL083826.Depoorter MA, Bamber JL, Griggs JA, Lenaerts JTM, Ligtenberg SRM, van den Broeke MR, et al. Calving fluxes and basal melt rates of Antarctic ice shelves. Nature. 2013;502:89–92. https://doi.org/10.1038/nature12567. More

Davis, K. F., Downs, S. & Gephart, J. A. Towards food supply chain resilience to environmental shocks. Nat. Food 2, 54–65 (2021).Article 

Google Scholar 
Lempert, R. J. & Collins, M. T. Managing the risk of uncertain threshold responses: comparison of robust, optimum, and precautionary approaches. Risk Anal. 27, 1009–1026 (2007).Article 

Google Scholar 
Garnett, P., Doherty, B. & Heron, T. Vulnerability of the United Kingdom’s food supply chains exposed by COVID-19. Nat. Food 1, 315–318 (2020).Article 
CAS 

Google Scholar 
Abson, D. J. et al. Leverage points for sustainability transformation. Ambio 46, 30–39 (2017).Article 

Google Scholar 
Westley, F. et al. Tipping toward sustainability: emerging pathways of transformation. Ambio 40, 762–780 (2011).Article 

Google Scholar 
Steffen, W., Broadgate, W., Deutsch, L., Gaffney, O. & Ludwig, C. The trajectory of the Anthropocene: the Great Acceleration. Anthr. Rev. 2, 81–98 (2015).
Google Scholar 
Jouffray, J.-B., Blasiak, R., Norström, A. V., Österblom, H. & Nyström, M. The blue acceleration: the trajectory of human expansion into the ocean. One Earth 2, 43–54 (2020).Article 

Google Scholar 
Adger, W. N., Eakin, H. & Winkels, A. Nested and teleconnected vulnerabilities to environmental change. Front. Ecol. Environ. 7, 150–157 (2009).Article 

Google Scholar 
Nyström, M. et al. Anatomy and resilience of the global production ecosystem. Nature 575, 98–108 (2019).Article 

Google Scholar 
Mason, W. & Watts, D. J. Collaborative learning in networks. Proc. Natl Acad. Sci. USA 109, 764–769 (2012).Article 
CAS 

Google Scholar 
Helbing, D. Globally networked risks and how to respond. Nature 497, 51–59 (2013).Article 
CAS 

Google Scholar 
Worm, B. & Paine, R. T. Humans as a hyperkeystone species. Trends Ecol. Evol. 31, 600–607 (2016).Article 

Google Scholar 
Crutzen, P. J. & Stoermer, E. F. in The Future of Nature (eds Robin, L. et al.) 479–490 (Yale Univ. Press, 2017); https://doi.org/10.12987/9780300188479-041Ellis, E. C. Anthropogenic transformation of the terrestrial biosphere. Phil. Trans. R. Soc. A 369, 1010–1035 (2011).Article 

Google Scholar 
Senevirante, S. I. et al. in Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) 1513–1766 (IPCC, Cambridge Univ. Press, 2021).Frank, A. B. et al. Dealing with femtorisks in international relations. Proc. Natl Acad. Sci. USA 111, 17356–17362 (2014).Article 
CAS 

Google Scholar 
Folke, C. et al. Our future in the Anthropocene biosphere. Ambio 50, 834–869 (2021).Article 

Google Scholar 
Walker, B. & Salt, D. Resilience Practice: Building Capacity to Absorb Disturbance and Maintain Function (Island Press/Center for Resource Economics, 2012); https://doi.org/10.5822/978-1-61091-231-0Biggs, R., Schlüter, M. & Schoon, M. L. (eds) Principles for Building Resilience: Sustaining Ecosystem Services in Social–Ecological Systems (Cambridge Univ. Press, 2015); https://doi.org/10.1017/CBO9781316014240Cervantes Saavedra, M. de & Rutherford, J. Don Quixote: The Ingenious Hidalgo de la Mancha (Penguin, 2003).Coronese, M., Lamperti, F., Keller, K., Chiaromonte, F. & Roventini, A. Evidence for sharp increase in the economic damages of extreme natural disasters. Proc. Natl Acad. Sci. USA 116, 21450–21455 (2019).Article 
CAS 

Google Scholar 
Cottrell, R. S. et al. Food production shocks across land and sea. Nat. Sustain. 2, 130–137 (2019).Article 

Google Scholar 
Elmqvist, T. et al. Response diversity, ecosystem change, and resilience. Front. Ecol. Environ. 1, 488–494 (2003).Article 

Google Scholar 
Arrow, K. J. & Fisher, A. C. Environmental preservation, uncertainty, and irreversibility. Q. J. Econ. 88, 312–319 (1974).Article 

Google Scholar 
Dixit, A. K. & Pindyck, R. S. Investment under Uncertainty (Princeton Univ. Press, 1994).Markowitz, H. Portfolio selection. J. Finance 7, 77–91 (1952).
Google Scholar 
Sharpe, W. F. Capital asset prices: a theory of market equilibrium under conditions of risk. J. Finance 19, 425–442 (1964).
Google Scholar 
Cifdaloz, O., Regmi, A., Anderies, J. M. & Rodriguez, A. A. Robustness, vulnerability, and adaptive capacity in small-scale social–ecological systems: the Pumpa Irrigation System in Nepal. Ecol. Soc. 15, art39 (2010).Article 

Google Scholar 
Levin, S. A. et al. Governance in the face of extreme events: lessons from evolutionary processes for structuring interventions, and the need to go beyond. Ecosystems 25, 697–711 (2022).Article 

Google Scholar 
Peterson, G., Allen, C. R. & Holling, C. S. Ecological resilience, biodiversity, and scale. Ecosystems 1, 6–18 (1998).Article 

Google Scholar 
Nyström, M. Redundancy and response diversity of functional groups: implications for the resilience of coral reefs. Ambio 35, 30–35 (2006).Article 

Google Scholar 
Kummu, M. et al. Interplay of trade and food system resilience: gains on supply diversity over time at the cost of trade independency. Glob. Food Secur. 24, 100360 (2020).Article 

Google Scholar 
Hedblom, M., Andersson, E. & Borgström, S. Flexible land-use and undefined governance: from threats to potentials in peri-urban landscape planning. Land Use Policy 63, 523–527 (2017).Article 

Google Scholar 
Haldane, A. Rethinking the Financial Network—Speech by Andy Haldane (Bank of England, 2009); https://www.bankofengland.co.uk/speech/2009/rethinking-the-financial-networkHaldane, A. G. & May, R. M. Systemic risk in banking ecosystems. Nature 469, 351–355 (2011).Article 
CAS 

Google Scholar 
Carpenter, S. R., Brock, W. A., Folke, C., van Nes, E. H. & Scheffer, M. Allowing variance may enlarge the safe operating space for exploited ecosystems. Proc. Natl Acad. Sci. USA 112, 14384–14389 (2015).Article 
CAS 

Google Scholar 
Mouillot, D., Graham, N. A. J., Villéger, S., Mason, N. W. H. & Bellwood, D. R. A functional approach reveals community responses to disturbances. Trends Ecol. Evol. 28, 167–177 (2013).Article 

Google Scholar 
Leslie, P. & McCabe, J. T. Response diversity and resilience in social–ecological systems. Curr. Anthropol. 54, 114–143 (2013).Article 

Google Scholar 
Biggs, R. et al. Toward principles for enhancing the resilience of ecosystem services. Annu. Rev. Environ. Resour. 37, 421–448 (2012).Article 

Google Scholar 
Anderies, J. M. Managing variance: key policy challenges for the Anthropocene. Proc. Natl Acad. Sci. USA 112, 14402–14403 (2015).Article 
CAS 

Google Scholar 
Csete, M. E. & Doyle, J. C. Reverse engineering of biological complexity. Science 295, 1664–1669 (2002).Article 
CAS 

Google Scholar 
Carlson, J. M. & Doyle, J. Highly optimized tolerance: robustness and design in complex systems. Phys. Rev. Lett. 84, 2529–2532 (2000).Article 
CAS 

Google Scholar 
Kitano, H. Biological robustness. Nat. Rev. Genet. 5, 826–837 (2004).Article 
CAS 

Google Scholar 
Csete, M. & Doyle, J. Bow ties, metabolism and disease. Trends Biotechnol. 22, 446–450 (2004).Article 
CAS 

Google Scholar 
Anderies, J. M., Rodriguez, A. A., Janssen, M. A. & Cifdaloz, O. Panaceas, uncertainty, and the robust control framework in sustainability science. Proc. Natl Acad. Sci. USA 104, 15194–15199 (2007).Article 
CAS 

Google Scholar 
Rodriguez, A. A., Cifdaloz, O., Anderies, J. M., Janssen, M. A. & Dickeson, J. Confronting management challenges in highly uncertain natural resource systems: a robustness–vulnerability trade-off approach. Environ. Model. Assess. 16, 15–36 (2011).Article 

Google Scholar 
Charpentier, A. Insurability of climate risks. Geneva Pap. Risk Insur. Issues Pract. 33, 91–109 (2008).Article 

Google Scholar 
Alfieri, L., Feyen, L. & Di Baldassarre, G. Increasing flood risk under climate change: a pan-European assessment of the benefits of four adaptation strategies. Climatic Change 136, 507–521 (2016).Article 

Google Scholar 
Isakson, S. R. Derivatives for development? Small-farmer vulnerability and the financialization of climate risk management: small-farmer vulnerability and financialization. J. Agrar. Change 15, 569–580 (2015).Article 

Google Scholar 
Müller, B. & Kreuer, D. Ecologists should care about insurance, too. Trends Ecol. Evol. 31, 1–2 (2016).Article 

Google Scholar 
Walker, B. et al. Looming global-scale failures and missing institutions. Science 325, 1345–1346 (2009).Article 
CAS 

Google Scholar 
Berkes, F. et al. Globalization, roving bandits, and marine resources. Science 311, 1557–1558 (2006).Article 
CAS 

Google Scholar 
Walker, B. H., Langridge, J. L. & McFarlane, F. Resilience of an Australian savanna grassland to selective and non-selective perturbations. Austral Ecol. 22, 125–135 (1997).Article 

Google Scholar 
Polasky, S. et al. Corridors of clarity: four principles to overcome uncertainty paralysis in the Anthropocene. BioScience 70, 1139–1144 (2020).Article 

Google Scholar 
Engström, G. et al. Carbon pricing and planetary boundaries. Nat. Commun. 11, 4688 (2020).Article 

Google Scholar 
Sun, J. C., Ugolini, S. & Vivier, E. Immunological memory within the innate immune system. EMBO J. https://doi.org/10.1002/embj.201387651 (2014).Vély, F. et al. Evidence of innate lymphoid cell redundancy in humans. Nat. Immunol. 17, 1291–1299 (2016).Article 

Google Scholar 
Grimm, N., Cook, E., Hale, R. & Iwaniec, D. in The Routledge Handbook of Urbanization and Global Environmental Change (eds Seto, K. et al.) Ch. 14 (Routledge, 2015).Jiang, B., Mak, C. N. S., Zhong, H., Larsen, L. & Webster, C. J. From broken windows to perceived routine activities: examining impacts of environmental interventions on perceived safety of urban alleys. Front. Psychol. 9, 2450 (2018).Article 

Google Scholar 
Andersson, E. et al. Urban climate resilience through hybrid infrastructure. Curr. Opin. Environ. Sustain. 55, 101158 (2022).Article 

Google Scholar 
Douglas, M. & Wildavsky, A. Risk and Culture: An Essay on the Selection of Technological and Environmental Dangers (Univ. of California Press, 1983).Weber, E. U., Ames, D. R. & Blais, A.-R. ‘How do I choose thee? Let me count the ways’: a textual analysis of similarities and differences in modes of decision-making in China and the United States. Manage. Organ. Rev. 1, 87–118 (2005).Article 

Google Scholar 
Kunreuther, H. et al. in Climate Change 2014: Mitigation of Climate Change (eds Edenhofer, O. et al.) Ch. 2 (IPCC, Cambridge Univ. Press, 2014); https://www.ipcc.ch/site/assets/uploads/2018/02/ipcc_wg3_ar5_chapter2.pdfMeadows, D. H. Thinking in Systems: A Primer (Earthscan, 2009).Nyborg, K. et al. Social norms as solutions. Science 354, 42–43 (2016).Article 
CAS 

Google Scholar 
Hall, P. A. & Lamont, M. (eds) Social Resilience in the Neoliberal Era (Cambridge Univ. Press, 2013).Norström, A. V. et al. Principles for knowledge co-production in sustainability research. Nat. Sustain. 3, 182–190 (2020).Article 

Google Scholar 
United Nations Conference on Trade and Development Review of Maritime Transport 2017 (United Nations, 2017).United Nations Conference on Trade and Development Review of Maritime Transport 2018 (United Nations, 2019).Bailey, R. & Wellesley, L. Chatham House Report 2017: Chokepoints and Vulnerabilities in Global Food Trade (Energy, Environment and Resources Department, Chatham House, The Royal Institute of International Affairs, 2017); https://www.chathamhouse.org/sites/default/files/publications/research/2017-06-27-chokepoints-vulnerabilities-global-food-trade-bailey-wellesley-final.pdfKhoury, C. K. et al. Increasing homogeneity in global food supplies and the implications for food security. Proc. Natl Acad. Sci. USA 111, 4001–4006 (2014).Article 
CAS 

Google Scholar 
Hendrickson, M. K. Resilience in a concentrated and consolidated food system. J. Environ. Stud. Sci. 5, 418–431 (2015).Article 

Google Scholar 
Öborn, I. et al. Restoring rangelands for nutrition and health for humans and livestock. in The XXIV International Grassland Congress / XI International Rangeland Congress (Sustainable Use of Grassland and Rangeland Resources for Improved Livelihoods) (ed. National Organizing Committee of 2021 IGC/IRC Congress) (Kenya Agricultural and Livestock Research Organization, 2022).Vulnerable Supply Chains—Interim Report (Productivity Commission, Australian Government, 2021); https://www.pc.gov.au/inquiries/completed/supply-chains/interim More