Philippa Kaur

Brown, J. S., Laundre, J. W. & Gurung, M. The ecology of fear: optimal foraging, game theory, and trophic interactions. J. Mammal. 80, 385–399 (1999).
Google Scholar 
Laundré, J. W., Hernández, L. & Altendorf, K. B. Wolves, elk, and bison: reestablishing the ‘landscape of fear’ in Yellowstone National Park, US.A. Can. J. Zool. 79, 1401–1409 (2001).
Google Scholar 
Atkins, J. L. et al. Cascading impacts of large-carnivore extirpation in an African ecosystem. Science 364, 173–177 (2019).CAS 

Google Scholar 
Laundre, J. W., Hernandez, L. & Ripple, W. J. The landscape of fear: ecological implications of being afraid. Open Ecol. J. 3, 1–7 (2010).
Google Scholar 
Loggins, A. A., Shrader, A. M., Monadjem, A. & McCleery, R. A. Shrub cover homogenizes small mammals’ activity and perceived predation risk. Sci. Rep. 9, 16857 (2019).
Google Scholar 
Whittingham, M. J. & Evans, K. L. The effects of habitat structure on predation risk of birds in agricultural landscapes. Ibis 146, 210–220 (2004).
Google Scholar 
Marshall, K. L. A., Philpot, K. E. & Stevens, M. Microhabitat choice in island lizards enhances camouflage against avian predators. Sci. Rep. 6, 19815 (2016).CAS 

Google Scholar 
Stevens, M., Troscianko, J., Wilson-Aggarwal, J. K. & Spottiswoode, C. N. Improvement of individual camouflage through background choice in ground-nesting birds. Nat. Ecol. Evol. 1, 1325–1333 (2017).
Google Scholar 
Wilson-Aggarwal, J. K., Troscianko, J. T., Stevens, M. & Spottiswoode, C. N. Escape distance in ground-nesting birds differs with individual level of camouflage. Am. Nat. 188, 231–239 (2016).
Google Scholar 
Troscianko, J., Wilson-Aggarwal, J., Stevens, M. & Spottiswoode, C. N. Camouflage predicts survival in ground-nesting birds. Sci. Rep. 6, 19966 (2016).CAS 

Google Scholar 
Gaston, K. J., Duffy, J. P., Gaston, S., Bennie, J. & Davies, T. W. Human alteration of natural light cycles: causes and ecological consequences. Oecologia 176, 917–931 (2014).
Google Scholar 
Gaston, K. J., Davies, T. W., Nedelec, S. L. & Holt, L. A. Impacts of artificial light at night on biological timings. Annu. Rev. Ecol. Evol. Syst. 48, 49–68 (2017).
Google Scholar 
Falchi, F. et al. The new world atlas of artificial night sky brightness. Sci. Adv. 2, e1600377 (2016).
Google Scholar 
Gaston, K. J. et al. Pervasiveness of biological impacts of artificial light at night. Integr. Comp. Biol. 61, 1098–1110 (2021).
Google Scholar 
Sanders, D., Frago, E., Kehoe, R., Patterson, C. & Gaston, K. J. A meta-analysis of biological impacts of artificial light at night. Nat. Ecol. Evol. 5, 74–81 (2021).
Google Scholar 
Kronfeld-Schor, N., Visser, M. E., Salis, L. & van Gils, J. A. Chronobiology of interspecific interactions in a changing world. Philos. Trans. R. Soc. B Biol. Sci. 372, 20160248 (2017).
Google Scholar 
Underwood, C. N., Davies, T. W. & Queir Os, A. M. Artificial light at night alters trophic interactions of intertidal invertebrates. J. Anim. Ecol. 86, 781–789 (2017).
Google Scholar 
Burger, J., Howe, M. A., Hahn, D. C. & Chase, J. Effects of tide cycles on habitat selection and habitat partitioning by migrating shorebirds. Auk 94, 743–758 (1977).
Google Scholar 
Granadeiro, J. P., Dias, M. P., Martins, R. C. & Palmeirim, J. M. Variation in numbers and behaviour of waders during the tidal cycle: implications for the use of estuarine sediment flats. Acta Oecologica 29, 293–300 (2006).
Google Scholar 
Lourenço, P. M. et al. The energetic importance of night foraging for waders wintering in a temperate estuary. Acta Oecologica 34, 122–129 (2008).
Google Scholar 
McNeil, R., Drapeau, P. & Goss-Custard, J. D. The occurrence and adaptive significance of nocturnal habits in waterfowl. Biol. Rev. 67, 381–419 (1992).
Google Scholar 
Martin, G. R. Visual fields and their functions in birds. J. Ornithol. 148, 547–562 (2007).
Google Scholar 
Martin, G. R. What is binocular vision for? A birds’ eye view. J. Vis. 9, 1–19 (2009).
Google Scholar 
Davies, T. W., Duffy, J. P., Bennie, J. & Gaston, K. J. The nature, extent, and ecological implications of marine light pollution. Front. Ecol. Environ. 12, 347–355 (2014).
Google Scholar 
Leopold, M. F., Philippart, C. J. M. & Yorio, P. Nocturnal feeding under artificial light conditions by Brown-hooded Gull (Larus maculipennis) in Puerto Madryn harbour (Chubut Province, Argentina). Hornero 25, 55–60 (2010).
Google Scholar 
Pugh, A. R. & Pawson, S. M. Artificial light at night potentially alters feeding behaviour of the native southern black-backed gull (Larus dominicanus). Notornis 63, 37–39 (2016).
Google Scholar 
Santos, C. D. et al. Effects of artificial illumination on the nocturnal foraging of waders. Acta Oecologica 36, 166–172 (2010).
Google Scholar 
Montevecchi, W. A. Influences of Artificial Light on Marine Birds. in Ecological Consequences of Artificial Night Lighting (eds. Rich, C. & Longcore, T.) 94–113 (Island Press, 2006).Dwyer, R. G., Bearhop, S., Campbell, H. A. & Bryant, D. M. Shedding light on light: benefits of anthropogenic illumination to a nocturnally foraging shorebird. J. Anim. Ecol. 82, 478–485 (2013).
Google Scholar 
Blumstein, D. T. Developing an evolutionary ecology of fear: how life history and natural history traits affect disturbance tolerance in birds. Anim. Behav. 71, 389–399 (2006).
Google Scholar 
Stankowich, T. & Blumstein, D. T. Fear in animals: a meta-analysis and review of risk assessment. Proc. R. Soc. B Biol. Sci. 272, 2627–2634 (2005).
Google Scholar 
Caro, T. Antipredator Defenses in Birds and Mammals. (University of Chicago Press, 2005).Tillmann, J. E. Fear of the dark: night-time roosting and anti-predation behaviour in the grey partridge (Perdix perdix L.). Behaviour 146, 999–1023 (2009).
Google Scholar 
IUCN. The IUCN Red List of Threatened Species. Version 2022-1. https://www.iucnredlist.org/species/22693190/117917038 (2022).Brown, D. et al. The Eurasian Curlew—the most pressing bird conservation priority in the UK? Br. Birds 108, 660–668 (2015).
Google Scholar 
Franks, S. E., Douglas, D. J. T., Gillings, S. & Pearce-Higgins, J. W. Environmental correlates of breeding abundance and population change of Eurasian Curlew Numenius arquata in Britain. Bird. Study 64, 393–409 (2017).
Google Scholar 
Desholm, M. & Kahlert, J. Avian collision risk at an offshore wind farm. Biol. Lett. 1, 296–298 (2005).
Google Scholar 
Clarke, J. A. Moonlight’s influence on predator/prey interactions between short-eared owls (Asio flammeus) and Deermice (Peromyscus maniculatus). Behav. Ecol. Sociobiol. 13, 205–209 (1983).
Google Scholar 
Mandelik, Y., Jones, M. & Dayan, T. Structurally complex habitat and sensory adaptations mediate the behavioural responses of a desert rodent to an indirect cue for increased predation risk. Evol. Ecol. Res. 5, 501–515 (2003).
Google Scholar 
Alexander, R. D. The Evolution of Social Behavior | Annual Review of Ecology, Evolution, and Systematics. Annu. Rev. Ecol. Syst. 5, 325–383 (1974).
Google Scholar 
Pulliam, H. R. On the advantages of flocking. J. Theor. Biol. 38, 419–422 (1973).CAS 

Google Scholar 
Barnard, C. J. Flock feeding and time budgets in the house sparrow (Passer domesticus L.). Anim. Behav. 28, 295–309 (1980).
Google Scholar 
Cooper, W. E. Jr. et al. Effects of risk, cost, and their interaction on optimal escape by nonrefuging Bonaire whiptail lizards, Cnemidophorus murinus. Behav. Ecol. 14, 288–293 (2003).
Google Scholar 
Lagos, P. A. et al. Flight initiation distance is differentially sensitive to the costs of staying and leaving food patches in a small-mammal prey. Can. J. Zool. 87, 1016–1023 (2009).
Google Scholar 
Ydenberg, R. C. & Dill, L. M. The economics of fleeing from predators. Adv. Study Behav. 16, 229–249 (1986).
Google Scholar 
Tucker, V. A., Tucker, A. E., Akers, K. & Enderson, J. H. Curved flight paths and sideways vision in peregrine falcons (Falco peregrinus). J. Exp. Biol. 203, 3755–3763 (2000).CAS 

Google Scholar 
Carr, J. M. & Lima, S. L. Wintering birds avoid warm sunshine: predation and the costs of foraging in sunlight. Oecologia 174, 713–721 (2014).
Google Scholar 
van den Hout, P. J. & Martin, G. R. Extreme head-tilting in shorebirds: predator detection and sun avoidance. Wader Study Group Bull. 118, 18–21 (2011).
Google Scholar 
Ferguson, J. W. H., Galpin, J. S. & de Wet, M. J. Factors affecting the activity patterns of black-backed jackals Canis mesomelas. J. Zool. 214, 55–69 (1988).
Google Scholar 
Pyke, G. H. Optimal foraging theory: a critical review. Annu. Rev. Ecol. Syst. 15, 523–575 (1984).
Google Scholar 
Stephens, D. W. & Krebs, J. R. Foraging Theory. (Princeton University Press, 1986).Mouritsen, K. N. Predator avoidance in night-feeding dunlins calidris alpina: a matter of concealment. Ornis Scand. 23, 195–198 (1992).
Google Scholar 
Blumstein, D. T. Flight-initiation distance in birds is dependent on intruder starting distance. J. Wildl. Manag. 67, 852–857 (2003).
Google Scholar 
Troscianko, J. OSpRad; an open-source, low-cost, high-sensitivity spectroradiometer (p. 2022.12.09.519768). bioRxiv https://doi.org/10.1101/2022.12.09.519768 (2022).Article 

Google Scholar 
Hartig, F. DHARMa: residual diagnostics for hierarchical (multi-level/mixed) regression models. R package version 0.4.4. http://florianhartig.github.io/DHARMa/ (2022).Core Team, R. R: a Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, Vienna, 2022).
Google Scholar  More

Methods for data analysis in figuresAll analyses in figures were performed using Mathematica 12.3 (Wolfram Research, Inc., Champaign, IL, USA).Analysis in Fig. 1
C&D. To calculate integrated abundances of E. huxleyi cells and EhV, we first selected days for which all the bags had a non-null value. Values were then summed up to obtain the integrated abundance.E&J. We computed a standard linear fit between the E. huxleyi total abundances and total EhV abundances for covered and uncovered bags separately. We followed the same procedure for the correlations in panel J and provide a comparison between different models in Supplementary Fig. 5.Analysis in Fig. 2
A. The ASVs that were selected appeared at a relative abundance of at least 2% in at least 4 samples for the 0.2–2 µm 16S sequences and at least in 8 samples for the 2–20 µm 18S sequences. Abundances were concatenated for each time point and normalized by row, to have maximum relative abundance of 1 across all samples. ASVs were sorted by the position of their individual center of mass ({t}_{{CM}}) defined by$${t}_{{CM}}=,frac{mathop{sum}limits_{i}{t}_{i}f({t}_{i})}{mathop{sum}limits_{i}f({t}_{i})}$$
(1)
with i representing the different time points and f(({t}_{i})) the relative abundance of the ASV. The same figure for the individual bags in shown in Supplementary Fig. 14 and Supplementary Fig. 15.B. We selected 18S ASVs with a maximum relative abundance of at least 2% and observed in at least five samples. We averaged relative abundance across bags and then smoothed the time series with a moving average filter (width 2). Then, we grouped all ASVs into clusters based on their cosine distance using Mathematica’s FindClusters function and the KMeans method. The number of possible clusters ranged from 2 to 12, and the final number of clusters was decided using the silhouette method71. Only silhouette scores for 2 and 6 clusters were positive (between-cluster distance minus within-cluster distance).D. We subset reads that map to either Flavobacteriales or Fhodobacterales, then renormalized within each class, taking the mean over bags. Results per bag are shown in Supplementary Fig. 9.F. The turnover time was defined by the exponential rate k at which the Bray-Curtis similarity ({BC}(t)) declined over time. To this end, for a given bag, we computed the Bray–Curtis similarity between the composition vector at a starting day t’ with all following days t, giving a curve that declined roughly exponentially. For earlier starting days (for which the similarity curves declined the furthest), we found that the Bray–Curtis similarity never reached 0 but instead leveled out around ({{BC}}_{infty }=0.05) (due to ASVs that are constantly present in all the samples and maintain a minimal level of similarity between bags). Thus, we imposed an offset at(,{{BC}}_{infty }) for all fits (using Mathematica’s FindFit function) with the function:$${BC}(t)=(1-{{BC}}_{{{infty }}}) times {e}^{-kleft({t}^{{prime} }-tright)}+{{BC}}_{{{infty }}}$$
(2)
The turnover is averaged over bags, showing the standard deviation as error bars in the figure.G. To find differentially abundant ASVs, we first selected a subset of ASVs that had a maximum abundance of at least 10%, and performed Mann–Whitney U-Tests between the relative abundance values of a given ASV in the focal bag and all the other bags over all timepoints of the bloom’s demise. Correcting for multiple testing, we found four 16S ASVs that were differentially abundant in any of the bags, three of which were specific to bag 7, shown in Fig. 2g; and five 18S ASVs, two specific to bags 5 and 6 (Rhizosolenia delicatula and Aplanochytrium), one specific to bag 4 (Pterosperma), and two specific to bag 7 (MAST-1C and Woloszynskia halophila, shown in Fig. 2g).H. The divergence between bags was calculated as follows: we first measured, for each bag, the Bray–Curtis distance between this given bag and all the other bags at the end of the experiment (Supplementary Fig. 13). In order to control for the existing differences between bags at the beginning of the bloom, Bray–Curtis distances were normalized according to the differences between bags at the starting day of the E. huxleyi bloom. As the exact starting days of the bloom is not clear, we normalized for starting days 11, 12, or 13. The plot shows averages with the standard deviation as error bars. For the 18S microbiome, we first removed reads that map to E. huxleyi to reduce bias toward bag 7 (which had by far the lowest E. huxleyi abundance, Fig. 1c).Analysis in Fig. 3
A. Functional annotation of dominant 18S ASVs was based on manual literature search for the 100 most abundant 18S ASVs. Automatic annotation using the functional database created by72 gave qualitatively identical results but contained fewer organisms (covering about 50% of reads). The relative abundance of each trait was obtained by summing up the relative abundance of all the species harboring a specific trait. We used the annotations from72 to further subdivide heterotrophs into osmotrophs, saprotrophs, and other types of heterotrophy (e.g., grazing), ignoring ASVs with missing annotations.D. Growth rates were computed by fitting a linear model to the log-transformed absolute abundances. For thraustochytrids, we measured growth rates until the abundances reached their maximum, i.e., for days indicated by solid lines in Fig. 3b. For bacteria in the 0.2–2 micron fraction, we measured growth rates during the bloom and demise of E. huxleyi, i.e., for the time period after day 15 until the final day, except for bag 4 (until day 22) and bag 7 (until day 18) to account for their different bloom and demise dynamics. For bacteria in the 2–20 micron fraction, we measured growth rates similarly, starting after day 10 until the final day, except bags 4 and 7 (until day 22).E. To quantify the rate of change k of the biomass ratio of thraustochytrids to bacteria we fit a linear function to the log of biomass ratio from day 10 to the time point t where the ratio was maximal; for bag 7, this was day 18, for all others, day 23. We thus have:$$,{{log }},{BR},(t)={kt},+,{{log }},{BR},(0)$$
(3)
Analysis in Fig. 4
C&D. Since TEP accumulates over time, it cannot be expressed as a weighted sum of phytoplankton abundances. Instead, we formulate the model as a recursive relation where TEP can be produced by E. huxleyi, naked nanophytoplankton, and picophytoplankton, and degraded or lost through sinking:$${TEP}left(tright)=left(1-dright){TEP}left(t-1right)+{a}_{E}Eleft(tright)+{a}_{N}Nleft(tright)+{a}_{P}Pleft(tright),$$
(4)
The amount of TEP at time t is given by the fraction (1-d) of TEP at time t-1, where d corresponds to the fraction of TEP that is degraded between time points, plus the amount of TEP produced by the phytoplankton cells present at time t (or time t-1, which gives equivalent results). E, N, and P correspond to E. huxleyi, naked nanophytoplankton, and picophytoplankton, respectively. The parameter ({a}_{E}) corresponds to the amount of TEP produced per E. huxleyi cell, reported in panel D. ({a}_{E}) is set to be fixed through time, and different for each bag. This recursion can be solved to give an explicit expression for TEP(t):$${TEP}left(tright)=mathop{sum }limits_{{t}^{{prime} }=0}^{t}{left(1-dright)}^{t-{t}^{{prime} }}[{a}_{E}Eleft({t}^{{prime} }right)+{a}_{N}Nleft({t}^{{prime} }right)+{a}_{P}Pleft({t}^{{prime} }right)].$$
(5)
This functional form was then used to perform a linear model fitting with the constraint ({a}_{i}ge 0) for various values of the parameter d. The best fit, defined by maximum ({R}^{2}) over the resulting linear model, was used to fix d = 0.12. Our model considers that the fraction of non-calcified E. huxleyi cells in the nanophytoplankton counts is small.Larger phytoplankton cells ( >40 μm) filtered out from flow-cytometry measurements can also be a major source of TEP, despite low cell density. In order to verify this, FlowCam data was analyzed. None of the identified classes of larger phytoplankton (such as Phaeocystis or Dinobryon) increased in a systematic manner toward later stages of the bloom, explaining why larger phytoplankton were not included in the TEP model (Supplementary Fig. 24 and Supplementary Fig. 25).E. Using the smFISH method that reports the proportion of infected E. huxleyi cells, we estimated the amount of TEP produced from infected cells. We first used the least infected uncovered bags (bags 1 and 3) as a baseline to fix model parameters such as how much TEP does a non-infected cell produce. We then split the E. huxleyi abundance into an uninfected subpopulation producing T TEP/cell as in the uninfected bags, and an infected subpopulation producing I×T TEP/cells. To define I, we combined the fixed model parameters (i.e., amount of TEP produced per cell from Fig. 4d for bags 1 and 3) with the measured fraction of infected cells. We adjusted the factor I = 4 to minimize deviation of the measure total TEP concentration from the model prediction including the two subpopulations. The same procedure was used for panel H, using the corresponding model for PIC.F&G. To model the amount of PIC produced per cell we assume that the measured PIC only increases via new E. huxleyi coccoliths. The equivalent model for PIC reads$${PIC}left(tright)=left(1-dright){PIC}left(t-1right)+{a}_{E}{{max }}left(Eleft(tright)-Eleft(t-1right)right).$$
(6)
Where ({a}_{E}) is the amount of PIC produced per cell, and displayed in panel G. Using the same procedure as for TEP, we obtain the best fit for d = 0.0075. Our PIC model assumes that all PIC production comes from E. huxleyi, supported by large occurrence of E. huxleyi cells observed in scanning electron microscopy (Supplementary Fig. 1).Methods for data collectionMesocosm core setupThe mesocosm experiment AQUACOSM VIMS-Ehux was carried out for 24 days between 24th May (day 0) and 16th June (day 23) 2018 in Raunefjorden at the University of Bergen’s Marine Biological Station Espegrend, Norway (60°16′11 N; 5°13′07E). The experiment consisted of seven enclosure bags made of transparent polyethylene (11 m3, 4 m deep and 2 m wide, permeable to 90% photosynthetically active radiation) mounted on floating frames and moored to a raft in the middle of the fjord. The bags were filled with surrounding fjord water (day −1; pumped from 5 m depth) and continuously mixed by aeration (from day 0 onwards). Each bag was supplemented with nutrients at a nitrogen to phosphorus ratio of 16:1 according to the optimal Redfield Ratio (1.6 µM NaNO3 and 0.1 µM KH2PO4 final concentration) on days 0–5 and 14–17, whereas on days 6, 7 and 13 only nitrogen was added to limit the growth of pico-eukaryotes and favor the growth of E. huxleyi that is more resistant to phosphate limited conditions. Silica was not added as a nutrient source in order to suppress the growth of diatoms and to enhance E. huxleyi proliferation. Bags 5, 6, 7 were covered to collect aerosols and guarantee minimal contamination while sampling for core variables. Bags 1, 2, 3, 4 were sampled for additional assays such as metabolomics, polysaccharides profiling, and vesicles, which increase sampling time and potential for contamination.Measurement of dissolved inorganic nutrientsUnfiltered seawater aliquots (10 mL) were collected from each bag and the surrounding fjord water in 12 mL polypropylene tubes and stored frozen at −20 °C. Dissolved inorganic nutrients were measured with standard segmented flow analysis with colorimetric detection73, using a Bran & Luebe autoanalyser. Data are available in ref. 74 and values for individual bags are plotted in Supplementary Fig. 26.Measurement of water temperature and salinityWater temperature and salinity were measured in each bag and the surrounding fjord water using a SD204 CTD/STD (SAIV A/S, Laksevag, Norway). Data points were averaged for 1–3 m depth (descending only). When this depth was not available, the available data points were taken. Data are missing for the fjord in days 0–1. Outliers were removed for the following samples: bag 1 at days 0, 4, 15; bag 7 at day 15. Data are available in ref. 74.Flow cytometry measurementsSamples for flow cytometric counts were collected twice a day, in the morning (7:00 a.m.) and evening (8:00–9:00 p.m.) from each bag and the surrounding fjord, which served as an environmental reference. Water samples were collected in 50 mL centrifugal tubes from 1 m depth, pre-filtered using 40 µm cell strainers, and immediately analyzed with an Eclipse iCyt (Sony Biotechology, Champaign, IL, USA) flow cytometer. A total volume of 300 µL with a flow rate of 150 µL/min was analyzed with the machine’s software ec800 v1.3.7. A threshold was applied based on the forward scatter signal to reduce the background noise.Phytoplankton populations were identified by plotting the autofluorescence of chlorophyll versus phycoerythrin and side scatter: calcified E. huxleyi (high side scatter and high chlorophyll), Synechococcus (high phycoerythrin and low chlorophyll), nano- and picophytoplankton (high and low chlorophyll, respectively). Chlorophyll fluorescence was detected by FL4 (excitation (ex): 488 nm and emission (em): 663–737 nm). Phycoerythrin was detected by FL3 (ex: 488 nm and em: 570–620 nm). Raw.fcs files were extracted and analyzed in R using ‘flowCore’ and ‘ggcyto’ packages and all data are available on Dryad74. In particular, the gating strategy was adapted to each day and each bag and individual plots for each days and each bag can be found in the Dryad link.For bacteria and viral counts, 200 µL of sample were fixed with 4 µL of 20% glutaraldehyde (final concentration of 0.5%) for 1 h at 4 °C and flash frozen. They were thawed and stained with SYBR gold (Invitrogen) that was diluted 1:10,000 in Tris-EDTA buffer, incubated for 20 min at 80 °C and cooled to room temperature. Bacteria and viruses were counted and analyzed using a Cytoflex and identified based on the Violet SSC-A versus FITC-A by comparing to reference samples containing fixed bacteria and viruses from lab cultures. A total volume of 60 µL with a flow rate of 10 µL/min was analyzed. A threshold was applied based on the forward scatter signal to reduce the background noise. For plotting bacteria (Fig. 1h), a moving average of three successive days was used.Enumeration of extracellular EhV abundance by qPCRDNA extracts from filters from the core sampling (see above) were diluted 100 times, and 1 µL was then used for qPCR analysis. EhV abundance was determined by qPCR for the major capsid protein (mcp) gene: 5′-acgcaccctcaatgtatggaagg-3′ (mcp1F) and 5′-rtscrgccaactcagcagtcgt -3′ (mcp94Rv). All reactions were carried out in technical triplicates using water as a negative control. For all reactions, Platinum SYBER Green qPCR SuperMix-UDG with ROX (Invitrogen, Carlsbad, CA, USA) was used as described by the manufacturer. Reactions were performed on a QuantStudio 5 Real-Time PCR System equipped with the QuantStudio Design and Analysis Software version 1.5.1 (Applied Biosystems, Foster City, CA, USA) as follows: 50 °C for 2 min, 95 °C for 5 min, 40 cycles of 95 °C for 15 s, and 60 °C for 30 s. Results were calibrated against serial dilutions of EhV201 DNA at known concentrations, enabling exact enumeration of viruses. Samples showing multiple peaks in melting curve analysis or peaks that were not corresponding to the standard curves were omitted. Data are available in ref. 74. A comparison of viral counts based on flow-cytometry and qPCR is shown in Supplementary Fig. 2.FlowCam analysisSamples for automated flow imaging microcopy were collected once a day in the morning (7:00 a.m.) from each bag and the surrounding fjord, which served as an environmental reference. Water samples were collected in 50 mL centrifugal tubes from 1 m depth, kept at 12 °C in darkness, and analyzed within 2 h of sampling, using a FlowCAM II (Fluid Imaging Technologies Inc., Scarborough, ME, USA) fitted with a 300 µm path length flow cell and a 4× microscope objective. Images were collected using auto-image mode at a rate of 7 frames/second. A sample volume of 10 mL was processed at a flow rate of 0.7 mL/min. Individual objects within each sample were clustered and annotated using the Ecotaxa platform75. Absolute counts for major groups, including the most abundant ciliate category Ciliophora U04, were then exported and normalized by the individual amount of water volume processed for each sample.Data are available under “Flowcam Composite Aquacosm_2018_VIMS-Ehux” project on Ecotaxa.Scanning electron microscopy50 ml of water samples from bags or fjord were collected on polycarbonate filters (0.2 µm pore size, 47 mm diameter, Millipore). The filters were air dried and stored on petri-slides (Millipore) at room temperature. Prior to observation, a small fraction of the filter was cut and coated with 2 nm of iridium using a Safematic CCU-010 coater (Safematic GMBH, Switzerland). Samples were observed on a Zeiss Ultra SEM that was set at a working distance of 6.2 ± 0.1 mm, an acceleration voltage of 3.0 kV and an aperture size of 30 mm. The secondary electron detector was used for image acquisition.Paired dilution experimentPhytoplankton growth and microzooplankton grazing rates were estimated using the dilution method76,77. A slightly modified version of the method was used with only one low dilution level (20%) and an undiluted treatment used78. Rates calculated using this method are considered conservative but accurate when compared with those using multiple dilution levels and a linear regression. Water from bags 1–4 was collected using a peristaltic pump at ~1 m depth and mixed into a 20 L clean carboy. Water was screened through a 200 µm mesh to remove larger mesozooplankton. The collected water was shaded with black plastic and returned to shore. Dilution experiments were set-up in a temperature-controlled room, set to ambient water temperature (±2 °C). Particle-free diluent (FSW) was prepared by gravity filtering whole seawater (WSW) through a 0.45 µm inline filter (PALL Acropak™ Membrane capsule) into a clean carboy. To the FSW, WSW was gently siphoned at a proportion of 20%. The 20% dilution and 100% WSW treatments were prepared in single carboys and then siphoned into triplicate 1.2 L Nalgene™ incubation bottles. To control for nutrient limitation, additional triplicate bottles of 100% WSW were incubated without added nutrients (10 µM nitrate and 1 µM phosphate). The incubation bottles were incubated for 24 h in an outdoor tank maintained at in-situ water temperatures by a flow-through system of ambient seawater. Bottles could float freely, and the seawater inflow caused gentle agitation throughout the 24 h period. A screen was used to mimic light conditions experienced within the mesocosm bags.To quantify viral mortality, we used the paired dilution method79 which involves setting up an extra low dilution level (20%) containing water filtered through a tangential flow filter (TFF) of 100 kDå to remove viral particles. During this experiment, TFF water was produced 1–2 days prior to the dilution experiment, to ensure the chemical composition of the water was as similar as possible, and experiments could be set up in a timely manner.At T0 hours and T24 hours from all dilution experiments, sub-samples were taken for the determination of chlorophyll-a and flow cytometry. For chlorophyll-a, 100–150 mL of seawater was filtered under low vacuum pressure through a 47 mm Whatman GF/F filters (effective pore size 0.7 µm), and then extracted in 7 mL of 97% methanol at 4 °C in the dark for 12 h. All chlorophyll readings were conducted on a Turner TD700 fluorometer80. Methanol blanks were included, and all samples were corrected for phaeophytin using a drop of 10% hydrochloric acid and then reading the sample again81.Water samples (2 × 1 mL) for flow cytometry were taken at T0 and T24 of dilution experiments for the determination of phytoplankton abundances. Water samples were taken in triplicate from T0, and from each bottle at T24. Samples were immediately fixed in 20 µL of glutaraldehyde (final concentration More

Daskalova, G. N. et al. Landscape-scale forest loss as a catalyst of population and biodiversity change. Science 368(6497), 1341–1347 (2020).ADS 
CAS 

Google Scholar 
Betts, M. G. et al. Extinction filters mediate the global effects of habitat fragmentation on animals. Science 366(6470), 1236–1239 (2019).ADS 
CAS 

Google Scholar 
Siddig, A. A., Ellison, A. M., Ochs, A., Villar-Leeman, C. & Lau, M. K. How do ecologists select and use indicator species to monitor ecological change? Insights from 14 years of publication in Ecological Indicators. Ecol. Ind. 60, 223–230 (2016).
Google Scholar 
Thancharoen, A. Well managed firefly tourism: A good tool for firefly conservation in Thailand. Lampyrid. 2, 142–148 (2012).
Google Scholar 
Hwang, Y. T., Moon, J., Lee, W. S., Kim, S. A. & Kim, J. Evaluation of firefly as a tourist attraction and resource using contingent valuation method based on a new environmental paradigm. J. Qual. Assur. Hosp. Tour. 21(3), 320–336 (2019).Carlson, A. D. & Copeland, J. Flash communication in fireflies. Q. Rev. Biol. 60(4), 415–436 (1985).
Google Scholar 
Evans, T. R., Salvatore, D., van de Pol, M. & Musters, C. J. M. Adult firefly abundance is linked to weather during the larval stage in the previous year. Ecol. Entomol. 44(2), 265–273 (2018).
Google Scholar 
Lewis, S. M. et al. A global perspective on firefly extinction threats. Bioscience 70(2), 157–167 (2020).
Google Scholar 
Cao, C. Q., Zhang, Y., Wang, Y. Z. & He, H. Progress in the research, protection, development and utilization of fireflies. J. Environ. Entomol.1–36 (2022).Myers, N., Mittermeier, R. A., Mittermeier, C. G., da Fonseca, G. A. B. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403(6772), 853–858 (2000).ADS 
CAS 

Google Scholar 
Thorn, J. S., Nijman, V., Smith, D. & Nekaris, K. A. I. Ecological niche modelling as a technique for assessing threats and setting conservation priorities for Asian slow lorises (Primates:Nycticebus). Divers. Distrib. 15(2), 289–298 (2009).
Google Scholar 
Elith, J. & Leathwick, J. R. Species distribution models: Ecological explanation and prediction across space and time. Annu. Rev. Ecol. Evol. Syst. 40(1), 677–697 (2009).
Google Scholar 
Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190(3–4), 231–259 (2006).
Google Scholar 
Hirzel, A. H., Hausser, J., Chessel, D. & Perrin, N. Ecological-Niche Factor Analysis: How to compute habitat-suitability maps without absence data?. Ecology 83(7), 2027–2036 (2002).
Google Scholar 
Nelder, J. A. & Wedderburn, R. W. Generalized linear models. J. R. Stat. Soc. Ser. A (General). 135(3), 370–384 (1972).
Google Scholar 
Hastie, T. J. Generalized additive models. Statistical models in S. Routledge. 249–307 (2017).Stockwell, D. R. & Noble, I. R. Induction of sets of rules from animal distribution data: A robust and informative method of data analysis. Math. Comput. Simul. 33(5–6), 385–390 (1992).
Google Scholar 
Beaumont, L. J., Hughes, L. & Poulsen, M. Predicting species distributions: use of climatic parameters in BIOCLIM and its impact on predictions of species’ current and future distributions. Ecol. Model. 186(2), 251–270 (2005).
Google Scholar 
Jung, J. M., Lee, W. H. & Jung, S. Insect distribution in response to climate change based on a model: Review of function and use of CLIMEX. Entomol. Res. 46(4), 223–235 (2016).
Google Scholar 
Phillips, S. J. & Dudík, M. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31(2), 161–175 (2008).
Google Scholar 
Moreno, R., Zamora, R., Molina, J. R., Vasquez, A. & Herrera, M. Á. Predictive modeling of microhabitats for endemic birds in South Chilean temperate forests using Maximum entropy (Maxent). Eco. Inform. 6(6), 364–370 (2011).
Google Scholar 
Wang, Z. et al. Prediction of potential distribution of the invasive Chrysanthemum Lace Bug, Corythucha marmorata in China based on Maxent. J. Environ. Entomol. 41(3), 626–633 (2019).
Google Scholar 
Li, A. et al. MaxEnt modeling to predict current and future distributions of Batocera lineolata (Coleoptera: Cerambycidae) under climate change in China. Ecoscience 27(1), 23–31 (2020).
Google Scholar 
Sutherland, L. N., Powell, G. S. & Bybee, S. M. Validating species distribution models to illuminate coastal fireflies in the South Pacific (Coleoptera: Lampyridae). Sci. Rep. 11(1), 1–12 (2021).ADS 

Google Scholar 
Fu, X. H., Ballantyne, L. A. & Lambkin, C. Emeia gen. nov., a new genus of Luciolinae fireflies from China (Coleoptera: Lampyridae) with an unusual trilobite-like larva, and a redescription of the genus Curtos Motschulsky. Zootaxa. 3403(1), 1–53 (2012).Idris, N. S. et al. The dynamics of landscape changes surrounding a firefly ecotourism area. Glob. Ecol. Conserv. 29, e01741 (2021).
Google Scholar 
Santiago-Blay, J. A. Silent Sparks: The Wondrous World of Fireflies. Life: The Excitement of Biology. (2016).Picchi, M. S., Avolio, L., Azzani, L., Brombin, O. & Camerini, G. Fireflies and land use in an urban landscape: the case of Luciola italica L.(Coleoptera: Lampyridae) in the city of Turin. J. Insect Conserv. 17(4), 797–805 (2013).Pearsons, K. A., Lower, S. E. & Tooker, J. F. Toxicity of clothianidin to common Eastern North American fireflies. PeerJ 9, e12495 (2021).
Google Scholar 
Madruga Rios, O. & Hernández Quinta, M. Larval Feeding Habits of the Cuban Endemic FireflyAlecton discoidalisLaporte (Coleoptera: Lampyridae). Psyche J. Entomol. 2010, 1–5 (2010).Roberge, J. M. & Angelstam, P. E. R. Usefulness of the umbrella species concept as a conservation tool. Conserv. Biol. 18(1), 76–85 (2004).
Google Scholar 
Bowen-Jones, E. & Entwistle, A. Identifying appropriate flagship species: The importance of culture and local contexts. Oryx 36(2), 189–195 (2002).
Google Scholar 
Walpole, M. J. & Leader-Williams, N. Tourism and flagship species in conservation. Biodivers. Conserv. 11(3), 543–547 (2002).Zhejiang Provincial Bureau of Statistics. Zhejiang physical geography profile, http://tjj.zj.gov.cn/col/col1525489/index.html (2022).Zhejiang Provincial Forestry Department. Announcement of Forest Resources and Their Ecological Function Value in Zhejiang Province. Zhejiang Daily. https://doi.org/10.38328/n.cnki.nzjrb.2016.002829 (2016).Boria, R. A., Olson, L. E., Goodman, S. M. & Anderson, R. P. Spatial filtering to reduce sampling bias can improve the performance of ecological niche models. Ecol. Model. 275, 73–77 (2014).
Google Scholar 
Brown, J. L. SDM toolbox: A python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses. Methods Ecol. Evol. 5(7), 694–700 (2014).
Google Scholar 
Fick, S. E. & Hijmans, R. J. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37(12), 4302–4315 (2017).
Google Scholar 
Elvidge, C. D., Zhizhin, M., Ghosh, T., Hsu, F.-C. & Taneja, J. Annual time series of global VIIRS nighttime lights derived from monthly averages: 2012 to 2019. Remote Sens. 13(5), 922 (2021).ADS 

Google Scholar 
WAN, J. et al. Predicting the potential geographic distribution of Bactrocera bryoniae and Bactrocera neohumeralis (Diptera: Tephritidae) in China using MaxEnt ecological niche modeling. J. Integr. Agric. 19(8), 2072–2082 (2020).Zhou, R. et al. Projecting the potential distribution of glossina morsitans (Diptera: Glossinidae) under climate change using the MaxEnt model. Biology. 10(11), 1150 (2021).
Google Scholar 
Hill, M. P., Hoffmann, A. A., McColl, S. A. & Umina, P. A. Distribution of cryptic blue oat mite species in Australia: current and future climate conditions. Agric. For. Entomol. 14(2), 127–137 (2011).
Google Scholar 
Su, H., Bista, M. & Li, M. Mapping habitat suitability for Asiatic black bear and red panda in Makalu Barun National Park of Nepal from Maxent and GARP models. Sci. Rep. 11(1), 1 (2021).ADS 
CAS 

Google Scholar 
Proosdij, A. J., Sosef, M., Wieringa, J. & Raes, N. Minimum required number of specimen records to develop accurate species distribution models. Ecography 39, 542–552 (2016).
Google Scholar 
Lobo, J. M., Jiménez-Valverde, A. & Real, R. AUC: A misleading measure of the performance of predictive distribution models. Glob. Ecol. Biogeogr. 17(2), 145–151 (2008).
Google Scholar 
Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models: Prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43(6), 1223–1232 (2006).
Google Scholar 
Liu, C., Newell, G. & White, M. On the selection of thresholds for predicting species occurrence with presence-only data. Ecol. Evol. 6(1), 337–348 (2016).
Google Scholar 
Swets, J. A. Measuring the accuracy of diagnostic systems. Science 240(4857), 1285–1293 (1988).ADS 
CAS 
MATH 

Google Scholar 
Pearce, J. & Ferrier, S. Evaluating the predictive performance of habitat models developed using logistic regression. Ecol. Model. 133(3), 225–245 (2000).
Google Scholar 
Gama, M., Crespo, D., Dolbeth, M. & Anastácio, P. M. Ensemble forecasting of Corbicula fluminea worldwide distribution: projections of the impact of climate change. Aquat. Conserv. Mar. Freshwat. Ecosyst. 27(3), 675–684 (2017).
Google Scholar 
Zhao, Y., Deng, X., Xiang, W., Chen, L. & Ouyang, S. Predicting potential suitable habitats of Chinese fir under current and future climatic scenarios based on Maxent model. Eco. Inform. 64, 101393 (2021).
Google Scholar 
Evans, T. R., Salvatore, D., van de Pol, M. & Musters, C. J. M. Adult firefly abundance is linked to weather during the larval stage in the previous year. Ecol. Entomol. 44(2), 265–273 (2018).Chettri, B., Bhupathy, S. & Acharya, B. K. Distribution pattern of reptiles along an eastern Himalayan elevation gradient India. Acta Oecol. 36(1), 16–22 (2010).ADS 

Google Scholar 
Brown, J. H. Mammals on mountainsides: elevational patterns of diversity. Global Ecol. Biogeogr. 10(1), 101–109 (2001).Gairola, S., Sharma, C. M., Ghildiyal, S. K. & Suyal, S. Tree species composition and diversity along an altitudinal gradient in moist tropical montane valley slopes of the Garhwal Himalaya India. For. Sci. Technol. 7(3), 91–102 (2011).
Google Scholar 
Pearson, R. G., Raxworthy, C. J., Nakamura, M. & Townsend Peterson, A. Predicting species distributions from small numbers of occurrence records: A test case using cryptic geckos in Madagascar. J. Biogeogr. 34(1), 102–117 (2007).Hernandez, P. A., Graham, C. H., Master, L. L. & Albert, D. L. The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography 29(5), 773–785 (2006).
Google Scholar 
Abe, N. Kansei estimation on luminescence of Firefly-Kansei information measurement and welfare utilization. J. Japan Soc. Kansei Eng. 3(2), 41–50 (2004).
Google Scholar 
Buckley, R. et al. Economic value of protected areas via visitor mental health. Nat. Commun. 10(1), 1 (2019).
Google Scholar 
Lewis, S. M. et al. Firefly tourism: Advancing a global phenomenon toward a brighter future. Conserv. Sci. Pract. 3(5), 1 (2021).
Google Scholar  More

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
and JavaScript. 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

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

Millard, J. et al. Global effects of land-use intensity on local pollinator biodiversity. Nat. Commun. 12, 2902 (2021).Article 
CAS 

Google Scholar 
Haddad, N. M. et al. Habitat fragmentation and its lasting impact on Earth’s ecosystems. Sci. Adv. 1, e1500052 (2015).Article 

Google Scholar 
Valiente-Banuet, A. et al. Beyond species loss: the extinction of ecological interactions in a changing world. Funct. Ecol. 29, 299–307 (2015).Article 

Google Scholar 
Rybicki, J., Abrego, N. & Ovaskainen, O. Habitat fragmentation and species diversity in competitive communities. Ecol. Lett. 23, 506–517 (2020).Article 

Google Scholar 
Chase, J. M., Blowes, S. A., Knight, T. M., Gerstner, K. & May, F. Ecosystem decay exacerbates biodiversity loss with habitat loss. Nature 584, 238–243 (2020).Article 
CAS 

Google Scholar 
Ewers, R. M. & Didham, R. K. Confounding factors in the detection of species responses to habitat fragmentation. Biol. Rev. 81, 117–142 (2006).Article 

Google Scholar 
Didham, R. K. Ecological consequences of habitat fragmentation. In Encyclopedia of Life Sciences (ed Jansson, R.), 61, 1–39 (Wiley, UK2010).Aizen, M. A., Sabatino, M. & Tylianakis, J. M. Specialization and rarity predict nonrandom loss of interactions from mutualist networks. Science 335, 1486–1489 (2012).Article 
CAS 

Google Scholar 
Spiesman, B. J. & Inouye, B. D. Habitat loss alters the architecture of plant-pollinator interaction networks. Ecology 94, 2688–2696 (2013).Article 

Google Scholar 
Aizen, M. A. et al. The phylogenetic structure of plant-pollinator networks increases with habitat size and isolation. Ecol. Lett. 19, 29–36 (2016).Article 

Google Scholar 
Emer, C. et al. Seed-dispersal interactions in fragmented landscapes-a metanetwork approach. Ecol. Lett. 21, 484–493 (2018).Article 

Google Scholar 
Fortuna, M. A. & Bascompte, J. Habitat loss and the structure of plant-animal mutualistic networks. Ecol. Lett. 9, 278–283 (2006).Article 

Google Scholar 
Grass, I., Jauker, B., Steffan-Dewenter, I., Tscharntke, T. & Jauker, F. Past and potential future effects of habitat fragmentation on structure and stability of plant-pollinator and host-parasitoid networks. Nat. Ecol. Evol. 2, 1408–1417 (2018).Article 

Google Scholar 
Glenn R. Matlack & John A. Litvaitis. Forest edges. In Maintaining Biodiversity in Forest Ecosystems (ed Hunter, M.) 6, 210–233 (Cambridge Univ. Press, 1999).Hadley, A. S. & Betts, M. G. The effects of landscape fragmentation on pollination dynamics: absence of evidence not evidence of absence. Biol. Rev. 87, 526–544 (2012).Article 

Google Scholar 
Ibanez, I., Katz, D. S. W., Peltier, D., Wolf, S. M. & Barrie, B. T. C. Assessing the integrated effects of landscape fragmentation on plants and plant communities: the challenge of multiprocess-multiresponse dynamics. J. Ecol. 102, 882–895 (2014).Article 

Google Scholar 
Morreale, L. L., Thompson, J. R., Tang, X., Reinmann, A. B. & Hutyra, L. R. Elevated growth and biomass along temperate forest edges. Nat. Commun. 12, 7181 (2021).Article 
CAS 

Google Scholar 
Martinez-Ramos, M., Alvarez-Buylla, E. & Sarukhan, J. Tree demography and gap dynamics in a tropical rain forest. Ecology 70, 555–558 (1989).Article 

Google Scholar 
Yamamoto, S. I. Forest gap dynamics and tree regeneration. J. For. Res. 5, 223–229 (2000).Article 

Google Scholar 
Schnitzer, S. A. & Carson, W. P. Treefall gaps and the maintenance of species diversity in a tropical forest. Ecology 82, 913–919 (2001).Article 

Google Scholar 
Kricher, J. A Shifting Mosaic: Rain Forest Development and Dynamics. In Tropical Ecology 6, 188–226 (Princeton Univ. Press, 2011).Gayer, C. et al. Flowering fields, organic farming and edge habitats promote diversity of plants and arthropods on arable land. J. Appl. Ecol. 58, 1155–1166 (2021).Article 

Google Scholar 
Bailey, S. et al. Distance from forest edge affects bee pollinators in oilseed rape fields. Ecol. Evol. 4, 370–380 (2014).Article 

Google Scholar 
Thebault, E. & Fontaine, C. Stability of ecological communities and the architecture of mutualistic and trophic networks. Science 329, 853–856 (2010).Article 
CAS 

Google Scholar 
Hagen, M. et al. Biodiversity, species interactions and ecological networks in a fragmented world. Adv. Ecol. Res. 46, 89–210 (2012).Article 

Google Scholar 
Traveset, A., Castro-Urgal, R., Rotllan-Puig, X. & Lazaro, A. Effects of habitat loss on the plant-flower visitor network structure of a dune community. Oikos 127, 45–55 (2018).Article 

Google Scholar 
Rezende, E. L., Lavabre, J. E., Guimaraes, P. R., Jordano, P. & Bascompte, J. Non-random coextinctions in phylogenetically structured mutualistic networks. Nature 448, 925–928 (2007).Article 
CAS 

Google Scholar 
Staddon, P., Lindo, Z., Crittenden, P. D., Gilbert, F. & Gonzalez, A. Connectivity, non-random extinction and ecosystem function in experimental metacommunities. Ecol. Lett. 13, 543–552 (2010).Article 

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

Google Scholar 
Sargent, R. D. & Ackerly, D. D. Plant-pollinator interactions and the assembly of plant communities. Trends Ecol. Evol. 23, 123–130 (2008).Article 

Google Scholar 
Bastolla, U. et al. The architecture of mutualistic networks minimizes competition and increases biodiversity. Nature 458, 1018–1020 (2009).Article 
CAS 

Google Scholar 
Rohr, R. P., Saavedra, S. & Bascompte, J. On the structural stability of mutualistic systems. Science 345, 1253497 (2014).Article 

Google Scholar 
Dunne, J. A., Williams, R. J. & Martinez, N. D. Network structure and biodiversity loss in food webs: robustness increases with connectance. Ecol. Lett. 5, 558–567 (2002).Article 

Google Scholar 
Pawar, S. Why are plant-pollinator networks nested? Science 345, 383–383 (2014).Article 
CAS 

Google Scholar 
Kaiser-Bunbury, C. N., Muff, S., Memmott, J., Muller, C. B. & Caflisch, A. The robustness of pollination networks to the loss of species and interactions: a quantitative approach incorporating pollinator behaviour. Ecol. Lett. 13, 442–452 (2010).Article 

Google Scholar 
Evans, D. M., Pocock, M. J. O. & Memmott, J. The robustness of a network of ecological networks to habitat loss. Ecol. Lett. 16, 844–852 (2013).Article 

Google Scholar 
Ponisio, L. C., Gaiarsa, M. P. & Kremen, C. Opportunistic attachment assembles plant-pollinator networks. Ecol. Lett. 20, 1261–1272 (2017).Article 

Google Scholar 
Wilson, M. C. et al. Habitat fragmentation and biodiversity conservation: key findings and future challenges. Landsc. Ecol. 31, 219–227 (2016).Article 

Google Scholar 
Zhong, L., Didham, R. K., Liu, J., Jin, Y. & Yu, M. Community re-assembly and divergence of woody plant traits in an island-mainland system after more than 50 years of regeneration. Divers. Distrib. 27, 1435–1448 (2021).Article 

Google Scholar 
Liu, J. et al. The asymmetric relationships of the distribution of conspecific saplings and adults in forest fragments. J. Plant Ecol. 13, 398–404 (2020).Article 
CAS 

Google Scholar 
Ewers, R. M., Bartlam, S. & Didham, R. K. Altered species interactions at forest edges: contrasting edge effects on bumble bees and their phoretic mite loads in temperate forest remnants. Insect Conserv. Divers. 6, 598–606 (2013).Article 

Google Scholar 
Wardhaugh, C. W. The spatial and temporal distributions of arthropods in forest canopies: uniting disparate patterns with hypotheses for specialisation. Biol. Rev. Camb. Philos. Soc. 89, 1021–1041 (2015).Article 

Google Scholar 
Lowman, M. Life in the treetops – an overview of forest canopy science and its future directions. Plants People Planet 3, 16–21 (2021).Article 

Google Scholar 
Nakamura, A. et al. Forests and their canopies: achievements and horizons in canopy science. Trends Ecol. Evol. 32, 438–451 (2017).Article 

Google Scholar 
Lennartsson, T. Extinction thresholds and disrupted plant-pollinator interactions in fragmented plant populations. Ecology 83, 3060–3072 (2002).
Google Scholar 
Aguilar, R., Ashworth, L., Galetto, L. & Aizen, M. A. Plant reproductive susceptibility to habitat fragmentation: review and synthesis through a meta-analysis. Ecol. Lett. 9, 968–980 (2006).Article 

Google Scholar 
Kremen, C. et al. Pollination and other ecosystem services produced by mobile organisms: a conceptual framework for the effects of land-use change. Ecol. Lett. 10, 299–314 (2007).Article 

Google Scholar 
Goulson, D., Nicholls, E., Botias, C. & Rotheray, E. L. Bee declines driven by combined stress from parasites, pesticides, and lack of flowers. Science 347, 1255957 (2015).Article 

Google Scholar 
Gathmann, A. & Tscharntke, T. Foraging ranges of solitary bees. J. Anim. Ecol. 71, 757–764 (2002).Article 

Google Scholar 
Winfree, R., Bartomeus, I. & Cariveau, D. P. Native pollinators in anthropogenic habitats. Annu. Rev. Entomol. 42, 1–22 (2011).
Google Scholar 
Torné-Noguera, A. et al. Determinants of spatial distribution in a bee community: nesting resources, flower resources, and body size. PLoS ONE 9, e97255 (2014).Article 

Google Scholar 
Roswell, M., Dushoff, J. & Winfree, R. A conceptual guide to measuring species diversity. Oikos 130, 321–338 (2021).Article 

Google Scholar 
Schoereder, J. H. et al. Should we use proportional sampling for species-area studies? J. Biogeogr. 31, 1219–1226 (2004).Article 

Google Scholar 
Jordano, P. Patterns of mutualistic interactions in pollination and seed dispersal: connectance, dependence asymmetries, and coevolution. Am. Nat. 129, 657–677 (1987).Article 

Google Scholar 
Devoto, M., Medan, D. & Montaldo, N. H. Patterns of interaction between plants and pollinators along an environmental gradient. Oikos 109, 461–472 (2005).Article 

Google Scholar 
Petanidou, T., Kallimanis, A. S., Tzanopoulos, J., Sgardelis, S. P. & Pantis, J. D. Long-term observation of a pollination network: fluctuation in species and interactions, relative invariance of network structure and implications for estimates of specialization. Ecol. Lett. 11, 564–575 (2008).Article 

Google Scholar 
Brodie, J. F. et al. Secondary extinctions of biodiversity. Trends Ecol. Evol. 29, 664–672 (2014).Article 

Google Scholar 
Vazquez, D. P. & Aizen, M. A. Asymmetric specialization: a pervasive feature of plant-pollinator interactions. Ecology 85, 1251–1257 (2004).Article 

Google Scholar 
Memmott, J., Waser, N. M. & Price, M. V. Tolerance of pollination networks to species extinctions. Proc. R. Soc. Lond. B 271, 2605–2611 (2004).
Google Scholar 
Malhi, Y., Gardner, T. A., Goldsmith, G. R., Silman, M. R. & Zelazowski, P. Tropical forests in the Anthropocene. Annu. Rev. Environ. Resour. 39, 125–159 (2014).Article 

Google Scholar 
Lewis, S. L., Edwards, D. P. & Galbraith, D. Increasing human dominance of tropical forests. Science 349, 827–832 (2015).Article 
CAS 

Google Scholar 
Fletcher, R. J. Jr et al. Is habitat fragmentation good for biodiversity? Biol. Conserv. 226, 9–15 (2018).Article 

Google Scholar 
Ren, P., Si, X. & Ding, P. Stable species and interactions in plant-pollinator networks deviate from core position in fragmented habitats. Ecography 2022, e06102 (2022).Article 

Google Scholar 
Fortuna, M. A. et al. Nestedness versus modularity in ecological networks: two sides of the same coin? J. Anim. Ecol. 79, 811–817 (2010).
Google Scholar 
Bascompte, J., Jordano, P., Melian, C. J. & Olesen, J. M. The nested assembly of plant-animal mutualistic networks. Proc. Natl Acad. Sci. USA 100, 9383–9387 (2003).Article 
CAS 

Google Scholar 
Almeida-Neto, M., Guimaraes, P., Guimaraes, P. R. Jr, Loyola, R. D. & Ulrich, W. A consistent metric for nestedness analysis in ecological systems: reconciling concept and measurement. Oikos 117, 1227–1239 (2008).Article 

Google Scholar 
Ulrich, W., Almeida-Neto, M. & Gotelli, N. J. A consumer’s guide to nestedness analysis. Oikos 118, 3–17 (2009).Article 

Google Scholar 
Dicks, L. V., Corbet, S. A. & Pywell, R. F. Compartmentalization in plant-insect flower visitor webs. J. Anim. Ecol. 71, 32–43 (2002).Article 

Google Scholar 
Beckett, S. J. Improved community detection in weighted bipartite networks. R. Soc. Open Sci. 3, 140536 (2016).Article 

Google Scholar 
Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5-7 (2020). https://CRAN.R-project.org/package=veganDormann, C. F. et al. bipartite: Visualising Bipartite Networks and Calculating Some (Ecological) Indices. R package version 2.16 (2021). https://CRAN.R-project.org/package=bipartitePocock, M. J. O., Evans, D. M. & Memmott, J. The robustness and restoration of a network of ecological networks. Science 335, 973–977 (2012).Article 
CAS 

Google Scholar 
Scherber, C. et al. Bottom-up effects of plant diversity on multitrophic interactions in a biodiversity experiment. Nature 468, 553–556 (2010).Article 
CAS 

Google Scholar 
Schleuning, M. et al. Ecological networks are more sensitive to plant than to animal extinction under climate change. Nat. Commun. 7, 13965 (2016).Article 
CAS 

Google Scholar 
Shipley, B. Confirmatory path analysis in a generalized multilevel context. Ecology 90, 363–368 (2009).Article 

Google Scholar 
Lefcheck, J. S. piecewiseSEM: piecewise structural equation modelling in R for ecology, evolution, and systematics. Methods Ecol. Evol. 7, 573–579 (2016).Article 

Google Scholar 
Grace, J. B., Scheiner, S. M. & Schoolmaster, D. R. Jr. Structural equation modeling: building and evaluating causal models. In Ecological Statistics: From Principles to Applications (eds Fox, G. A. et al.), 8, 168–199 (Oxford Univ. Press, 2015).Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2014).
Google Scholar 
Shipley, B. The AIC model selection method applied to path analytic models compared using a d-separation test. Ecology 94, 560–564 (2013).Article 

Google Scholar 
Murphy, M. semEff: Automatic Calculation of Effects for Piecewise Structural Equation Models. R package version 0.6.0 (2021). https://CRAN.R-project.org/package=semEffDudgeon, P. A comparative investigation of confidence intervals for independent variables in linear regression. Multivar. Behav. Res. 51, 139–153 (2016).Article 

Google Scholar 
Gotelli, N. J. & Graves, G. R. Null Models in Ecology (Smithsonian Inst. Press, 1996).Jung, V., Violle, C., Mondy, C., Hoffmann, L. & Muller, S. Intraspecific variability and trait-based community assembly. J. Ecol. 98, 1134–1140 (2010).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020). 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