Ecology

Simberloff, D. et al. Impacts of biological invasions: What’s what and the way forward. Trends Ecol. Evol. 28, 58–66 (2013).PubMed 
Article 

Google Scholar 
Pyšek, P. et al. Scientists’ warning on invasive alien species. Biol. Rev. 95(6), 1511–1534 (2020).PubMed 
Article 

Google Scholar 
Seebens, H. et al. No saturation in the accumulation of alien species worldwide. Nat. Commun. 8, 14435 (2017).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bailey, S. A. et al. Trends in the detection of aquatic non–indigenous species across global marine, estuarine and freshwater ecosystems: A 50–year perspective. Divers. Distrib. 26, 1780–1797 (2020).MathSciNet 
Article 

Google Scholar 
Ricciardi, A. Are modern biological invasions an unprecedented form of global change?. Conserv. Biol. 21, 329–336 (2007).PubMed 
Article 

Google Scholar 
Meyerson, M. Invasive alien species in an era of globalization. Front. Ecol. Environ. 5, 199–208 (2007).Article 

Google Scholar 
Hulme, P. E. Trade, transport and trouble: Managing invasive species pathways in an era of globalization. J. Appl. Ecol. 46, 10–18 (2009).Article 

Google Scholar 
Bonnamour, A., Gippet, J. M. & Bertelsmeier, C. Insect and plant invasions follow two waves of globalisation. Ecol. Lett. 24(11), 2418–2426 (2021).PubMed 
Article 

Google Scholar 
Piola, R. F. & Johnston, E. L. Pollution reduces native diversity and increases invader dominance in marine hard-substrate communities. Divers. Distrib. 14, 329–342 (2008).Article 

Google Scholar 
Rahel, F. J. & Olden, J. D. Assessing the effects of climate change on aquatic invasive species. Conserv. Biol. 22, 521–533 (2008).PubMed 
Article 

Google Scholar 
Kenworthy, J. M., Davoult, D. & Lejeusne, C. Compared stress tolerance to short-term exposure in native and invasive tunicates from the NE Atlantic: When the invader performs better. Mar. Biol. 165(10), 1–11 (2018).Article 

Google Scholar 
Gollasch, S., Galil, B. S., & Cohen, A. N. Bridging divides: Maritime canals as invasion corridors. In Bridging Divides: Maritime Canals as Invasion Corridors (Vol. 83). https://doi.org/10.1007/978-1-4020-5047-3 (2006).Galil, B. S. et al. ‘Double trouble’: The expansion of the Suez Canal and marine bioinvasions in the Mediterranean Sea. Biol. Invasions 17, 973–976 (2015).Article 

Google Scholar 
Jeschke, J. et al. Support for major hypotheses in invasion biology is uneven and declining. NeoBiota 14, 1–20 (2012).Article 

Google Scholar 
Lowry, E. et al. Biological invasions: A field synopsis, systematic review, and database of the literature. Ecol. Evol. 3, 182–196 (2012).PubMed 
Article 

Google Scholar 
Brockerhoff, A., & McLay, C. Human-Mediated Spread of Alien Crabs. In In the Wrong Place – Alien Marine Crustaceans: Distribution, Biology and Impacts (pp. 27–106). Springer Netherlands. https://doi.org/10.1007/978-94-007-0591-3_2 (2011).Hammock, B. G. et al. Low food availability narrows the tolerance of the copepod eurytemora affinis to salinity, but not to temperature. Estuar. Coasts 39, 189–200 (2016).CAS 
Article 

Google Scholar 
Rato, L. D., Crespo, D. & Lemos, M. F. L. Mechanisms of bioinvasions by coastal crabs using integrative approaches – A conceptual review. Ecol. Ind. 125, 107578 (2021).Article 

Google Scholar 
Weis, J. S. The role of behavior in the success of invasive crustaceans. Mar. Freshw. Behav. Physiol. 43, 83–98 (2010).Article 

Google Scholar 
Hänfling, B., Edwards, F. & Gherardi, F. Invasive alien Crustacea: Dispersal, establishment, impact and control. Biocontrol 56, 573–595 (2011).Article 

Google Scholar 
Kouba, A. et al. Identifying economic costs and knowledge gaps of invasive aquatic crustaceans. Sci. Total Environ. 813, 152325 (2022).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Geburzi, J. C., & McCarthy, M. L. How Do They Do It? – Understanding the Success of Marine Invasive Species. In YOUMARES 8 – Oceans Across Boundaries: Learning from each other (pp. 109–124). Springer International Publishing. https://doi.org/10.1007/978-3-319-93284-2_8 (2018).Casties, I. & Briski, E. Life history traits of aquatic non-indigenous species: Freshwater vs. marine habitats. Aquat. Invasions 14, 566–581 (2019).Article 

Google Scholar 
Grosholz, E. D. & Ruiz, G. M. Predicting the impact of introduced marine species: Lessons from the multiple invasions of the European green crab Carcinus maenas. Biol. Cons. 78, 59–66 (1996).Article 

Google Scholar 
Geburzi, J., Graumann, G., Köhnk, S. & Brandis, D. First record of the Asian crab Hemigrapsus takanoi Asakura & Watanabe, 2005 (Decapoda, Brachyura, Varunidae) in the Baltic Sea. BioInvasions Rec. 4, 103–107 (2015).Article 

Google Scholar 
Briski, E., Ghabooli, S., Bailey, S. A. & MacIsaac, H. J. Invasion risk posed by macroinvertebrates transported in ships’ ballast tanks. Biol. Invasions 14, 1843–1850 (2012).Article 

Google Scholar 
Wasserstraßen-und Schifffahrtsverwaltung des Bundes. Halbjahresbilanz Nord-Ostsee-Kanal 2021. www.wsv.de (2021).Nour, O. M., Stumpp, M., Morón Lugo, S. C., Barboza, F. R. & Pansch, C. Population structure of the recent invader Hemigrapsus takanoi and prey size selection on Baltic Sea mussels. Aquat. Invasions 15, 297–317 (2020).Article 

Google Scholar 
Andersson, A. et al. Projected future climate change and Baltic Sea ecosystem management. Ambio 44(Suppl 3), 345–356 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
BACC Author Team. Assessment of Climate Change for the Baltic Sea Basin. (2008).BACC Author Team. Second Assessment of Climate Change for the Baltic Sea Basin. (2015).Meier, H. E. M. et al. Modeling the combined impact of changing climate and changing nutrient loads on the Baltic Sea environment in an ensemble of transient simulations for 1961–2099. Clim. Dyn. 39, 2421–2441 (2012).Article 

Google Scholar 
Meier, H. E. M. et al. Climate change in the baltic sea region: A summary. Earth Syst. Dyn. Discuss. https://doi.org/10.5194/esd-2021-67 (2021).Article 

Google Scholar 
Ricciardi, A. et al. Four priority areas to advance invasion science in the face of rapid environmental change. Environ. Rev. 29, 119–141 (2021).Article 

Google Scholar 
Solomon, M. E. The natural control of animal populations. J. Anim. Ecol. 18, 1–35 (1949).Article 

Google Scholar 
Holling, C. S. Some characteristics of simple types of predation and parasitism. Can. Entomol. 91, 385–398 (1959).Article 

Google Scholar 
Dick, J. T. A. et al. Advancing impact prediction and hypothesis testing in invasion ecology using a comparative functional response approach. Biol. Invasions 16, 735–753 (2014).Article 

Google Scholar 
Laverty, C. et al. Assessing the ecological impacts of invasive species based on their functional responses and abundances. Biol. Invasions 19, 1653–1665 (2017).Article 

Google Scholar 
Anton, A. et al. Global ecological impacts of marine exotic species. Nat. Ecol. Evol. 3, 787–800 (2019).PubMed 
Article 

Google Scholar 
Crystal-Ornelas, R. & Lockwood, J. L. The ‘known unknowns’ of invasive species impact measurement. Biol. Invasions 22, 1513–1525 (2020).Article 

Google Scholar 
Boudreau, S. A. & Worm, B. Ecological role of large benthic decapods in marine ecosystems: A review. Mar. Ecol. Prog. Ser. 469, 195–213 (2012).ADS 
Article 

Google Scholar 
Dick, J. T. A. et al. Invader relative impact potential: A new metric to understand and predict the ecological impacts of existing, emerging and future invasive alien species. J. Appl. Ecol. 54, 1259–1267 (2017).Article 

Google Scholar 
Cornelius, A., Wagner, K. & Buschbaum, C. Prey preferences, consumption rates and predation effects of Asian shore crabs (Hemigrapsus takanoi) in comparison to native shore crabs (Carcinus maenas) in northwestern Europe. Mar. Biodivers. 51(5), 1–17 (2021).Article 

Google Scholar 
Elner, R. W. The influence of temperature, sex and chela size in the foraging strategy of the shore crab, Carcinus maenas (L.). Mar. Behav. Physiol. 7, 15–24 (1980).Article 

Google Scholar 
Brose, U. Body-mass constraints on foraging behaviour determine population and food-web dynamics. Funct. Ecol. 24, 28–34 (2010).Article 

Google Scholar 
Cuthbert, R. N. et al. Influence of intra- and interspecific variation in predator-prey body size ratios on trophic interaction strengths. Ecol. Evol. 10, 5946–5962 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Payne, A. & Kraemer, G. P. Morphometry and claw strength of the non-native asian shore crab, Hemigrapsus sanguineus. Northeast. Nat. 20, 478–492 (2013).Article 

Google Scholar 
Sedova, L. G. The effect of temperature on the rate of oxygen consumption in the sea urchin Strongylocentrotus intermedius. Russ. J. Mar. Biol. 26, 51–53 (2000).Article 

Google Scholar 
Saucedo, P. E., Ocampo, L., Monteforte, M. & Bervera, H. Effect of temperature on oxygen consumption and ammonia excretion in the Calafa mother-of-pearl oyster, Pinctada mazatlanica (Hanley, 1856). Aquaculture 229, 377–387 (2004).Article 

Google Scholar 
Nie, H. et al. Effects of temperature and salinity on oxygen consumption and ammonia excretion in different colour strains of the Manila clam, Ruditapes philippinarum. Aquac. Res. 48, 2778–2786 (2017).CAS 
Article 

Google Scholar 
Nguyen, K. D. T. et al. Upper Temperature limits of tropical marine ectotherms: Global warming implications. PLoS ONE 6, e29340 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Tattersall, G. J. et al. Coping with thermal challenges: Physiological adaptations to environmental temperatures. In Comprehensive Physiology 2151–2202 (Wiley, Hoboken, 2012).Chapter 

Google Scholar 
Barrios-O’Neill, D., Dick, J. T., Emmerson, M. C., Ricciardi, A. & MacIsaac, H. J. Predator-free space, functional responses and biological invasions. Funct. Ecol. 29(3), 377–384 (2015).Article 

Google Scholar 
Tattersall, G. J. et al. Coping with Thermal Challenges: Physiological Adaptations to Environmental Temperatures Vol. 2 (Wiley, Hoboken, 2012).
Google Scholar 
Bollache, L., Dick, J., Farnsworth, K. & Montgomery, I. Comparison of the functional responses of invasive and native amphipods. Biol. Lett. 4, 166–169 (2008).PubMed 
Article 

Google Scholar 
Dick, J. T. A. et al. Ecological impacts of an invasive predator explained and predicted by comparative functional responses. Biol. Invasions 15, 837–846 (2013).Article 

Google Scholar 
Cuthbert, R. N., Dickey, J. W. E., Coughlan, N. E., Joyce, P. W. S. & Dick, J. T. A. The functional response ratio (FRR): Advancing comparative metrics for predicting the ecological impacts of invasive alien species. Biol. Invasions 21, 2543–2547 (2019).Article 

Google Scholar 
Englund, G., Ohlund, G., Hein, C. L. & Diehl, S. Temperature dependence of the functional response. Ecol Lett 14, 914–921 (2011).PubMed 
Article 

Google Scholar 
Jeschke, J. M., Kopp, M. & Tollrian, R. Predator functional responses: Discriminating between handling and digesting prey. Ecol. Monogr. 72(1), 95–112 (2002).Article 

Google Scholar 
Dell, A. I., Pawar, S. & van Savage, M. Systematic variation in the temperature dependence of physiological and ecological traits. Proc. Natl. Acad. Sci. U.S.A 108, 10591–10596 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
South, J., Welsh, D., Anton, A., Sigwart, J. D. & Dick, J. T. A. Increasing temperature decreases the predatory effect of the intertidal shanny Lipophrys pholis on an amphipod prey. J. Fish Biol. 92, 150–164 (2018).CAS 
PubMed 
Article 

Google Scholar 
Pörtner, H.-O. & Knust, R. Climate change affects marine fishes through the oxygen limitation of thermal tolerance. Science 315, 95–97 (2007).ADS 
PubMed 
Article 
CAS 

Google Scholar 
Dickey, J. W. E. et al. Breathing space: Deoxygenation of aquatic environments can drive differential ecological impacts across biological invasion stages. Biol. Invasions 23, 2831–2847 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Watanabe, S., Wilder, M. N., Strüssmann, C. A. & Shinji, J. Short-term responses of the adults of the common Japanese intertidal crab, Hemigrapsus takanoi (Decapoda: Brachyura: Grapsoidea) at different salinities: Osmoregulation, oxygen consumption, and ammonia excretion. J. Crustac. Biol. 29, 269–272 (2009).Article 

Google Scholar 
Wasserman, R. J. et al. Using functional responses to quantify interaction effects among predators. Funct. Ecol. 30, 1988–1998 (2016).Article 

Google Scholar 
Murdoch, W. W. Switching in general predators: Experiments on predator specificity and stability of prey populations. Ecol. Monogr. 39, 335–354 (1969).Article 

Google Scholar 
Gonzalez, A., Lambert, A. & Ricciardi, A. When does ecosystem engineering cause invasion and species replacement?. Oikos 117, 1247–1257 (2008).Article 

Google Scholar 
King, J. R. & Tschinkel, W. R. Experimental evidence that human impacts drive fire ant invasions and ecological change. Proc. Natl. Acad. Sci. U.S.A 105, 20339–20343 (2008).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Asakura, A. & Watanabe, S. Hemigrapsus takanoi, new species, a sibling species of the common Japanese Intertidal Crab H. penicillatus (Decapoda: Brachyura: Grapsoidea). J. Crustac. Biol. 25, 279–292 (2005).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. (2021).Hartig, F. DHARMa: Residual Diagnostics for Hierarchical (Multi-Level/Mixed) Regression Models. R package version 0.4.3, https://CRAN.R-project.org/package=DHARMa (2021).Crawley, M. J. The R Book (Wiley, Hoboken, 2007).MATH 
Book 

Google Scholar 
Fox, J. & Weisberg, S. An R Companion to Applied Regression (Sage, Thousand Oaks, 2019).
Google Scholar 
Lenth, R. v. emmeans: Estimated Marginal Means, aka Least-Squares Means. R package version 1.6.2-1, https://CRAN.R-project.org/package=emmeans (2021).Pritchard, D. frair: Tools for Functional Response Analysis. R package version 0.5.100, https://CRAN.R-project.org/package=frair (2017).Juliano, S.A., Nonlinear Curve Fitting: Predation and Functional Response Curves. In: Cheiner, S.M. and Gurven, J., Eds., Design and Analysis of Ecological Experiments, 2nd Edition, Chapman and Hall, London, 178–196. (2001)Rogers, D. Random search and insect population models. J. Anim. Ecol. 41, 369 (1972).Article 

Google Scholar  More

Geospatial dataSampling locations were established, flagged, and recorded in June 2016, using a Trimble Geo7X global navigation satellite system (GNSS) receiver using the Trimble® VRS Now real-time kinematic (RTK) correction. Location accuracies were verified to within ±2 cm. Points were imported into a geodatabase using Esri ArcMap (Advanced license, Version 10.5) and projected using the Universal Transverse Mercator (UTM), Zone 17 North projection, with the 1983 North American datum (NAD83). Field investigators navigated to the flagged locations by visually locating them in the field or by using recreational grade GNSS receivers with the locations stored as waypoints.Plant tissue sampling and preparationMiscanthus x giganteus grows in clumps of bamboo-like canes. A single cane was cut at soil level from each of the five sample collection points in each circular plot, individually labelled, and brought to the lab for processing (Fig. 2). Each stem was measured from the cut at the base to the last leaf node, and the length was recorded. Green, fully expanded leaves were cut from each stem and leaves and stems from each plant were placed in separate paper bags and dried at 60 °C. The dry leaf and stem tissues were ground to pass a 1 mm screen (Wiley Mill Model 4, Thomas Scientific, Swedesboro, New Jersey, USA). Subsamples of the ground material were analyzed for total carbon (C) and nitrogen (N), acid-digested for the analysis of total macro- and micronutrients, and water-extracted for spectroscopic analysis and the characterization of the water extractable organic matter (WEOM) (Fig. 2).Fig. 2Images of field samples, and diagram of plant tissue processing. Center panel – flow chart outlining the procedures for plant tissue processing, the kinds of analyses performed, and the type of data generated. Upper left inset panel – ground level picture of Miscanthus x giganteus circular plots. Upper right inset panel – some plant samples on the day of collection.Full size imageTotal carbon and nitrogenDried and ground leaf and stem material (~4–6 mg) was analyzed for total C and N content by combustion (Vario EL III, Elementar Americas Inc., Mt. Laurel, New Jersey, USA). The instrument was calibrated using an aspartic acid standard (36.08% C ± 0.52% and 10.53% N ± 0.18%). Validation by inclusion of two aspartic acid samples as checks in each autosampler carousel (80 wells) resulted in a net positive bias of 1.44 and 1.68% for C and N, respectively. The mean C and N concentrations and standard deviations for the sample set are presented in Table 1.Table 1 Giant miscanthus composition including leaf (L) and stem (S) dry weight, length, and carbon (C) and nitrogen (N) concentrations (n = 165). Values are reported as means ± standard deviations.Full size tableMacro- and micronutrientsPlant tissue samples were analyzed for a suite of macro- and micronutrients including aluminum (Al), arsenic (As), boron (B), calcium (Ca), cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), iron (Fe), potassium (K), magnesium (Mg), manganese (Mn), molybdenum (Mo), sodium (Na), nickel (Ni), phosphorus (P), lead (Pb), sulfur (S), selenium (Se), silicon (Si), titanium (Ti), vanadium (V), and zinc (Zn) using Inductively Coupled Plasma with Optical Emission Spectroscopy (ICP-OES). Samples (0.5 g) were digested using 10 mL of trace metal grade nitric acid (HNO3) in a microwave digestion system (Mars 6, CEM, Matthews, North Carolina, USA). During the digestion procedure (CEM Mars 6 Plant Material Method), the oven temperature was increased from room temperature to 200 °C in 15 minutes and held at 200 °C for 10 minutes. The pressure limit of the digestion vessels was set to 800 psi although it was not monitored during individual runs. Sample digestates were transferred quantitatively to centrifuge tubes, diluted to 50 mL with 2% HNO3 (prepared with lab grade deionized water), and centrifuged at 2500 rpm for 10 min (Sorvall ST8 centrifuge, Thermo Fisher Scientific, San Jose, California, USA). The digestates were decanted into clean centrifuge tubes and analyzed using an iCAP 7400 ICP-OES Duo equipped with a Charge Injection Device detector (Thermo Fisher Scientific, San Jose, California, USA). An aliquot of digested sample was aspirated from the centrifuge tube using a CETAC ASX-520 autosampler (Teledyne CETAC Technologies, Omaha, Nebraska, USA) and passed through a concentric tube nebulizer. The resulting aerosol was then swept through the plasma using argon as the carrier gas with a flow rate of 0.5 L/min and a nebulizer gas flow rate of 0.7 L/min. Macro- and micronutrients were quantified by monitoring the emission wavelengths (Em λ) reported in Table 2.Table 2 Macro- and micronutrients measured, and emission wavelengths (Em λ) used to quantify them in the miscanthus leaves (L) and stems (S), the total number and percentage detected (n = 150 for leaves and 162 for stems), the mean detected concentration ± standard deviation, and the mean method detection limit (MDL) ± standard deviation.Full size tableCharacterization of the water extractable organic matter (WEOM)The WEOM of the giant miscanthus leaves and stems was isolated by extracting the plant material with deionized water at room temperature6. The water extractions were performed by mixing ~0.2 g of dry, ground leaves and stems with 100 mL of deionized water in 125 mL pre-washed brown Nalgene bottles. All brown Nalgene bottles used for these extractions were pre-washed by soaking them for 24 hours in a 10% hydrochloric acid solution followed by 24 hours in a 10% sodium hydroxide solution, and a thorough rinse with deionized water. The bottles containing the extraction solution were shaken on an orbital shaker at 180 rpm for 24 hours. The extract was vacuum filtered using 0.45 µm glass fibre filters (GF/F, Whatman) into pre-washed 60 mL brown Nalgene bottles. The filtered water extracts containing the WEOM were stored in the dark in a refrigerator (4 °C) until analysis by UV-Visible and fluorescence spectroscopy. Samples were visually inspected just prior to analysis to ensure no colloids or precipitates had formed during storage. Samples that had become visually cloudy were re-filtered.On the day of analysis, the water extracts were removed from the refrigerator and allowed to warm up to room temperature. Chemical characteristics of the WEOM were assessed through the analysis of optical properties on an Aqualog spectrofluorometer (Horiba Scientific, New Jersey, USA) equipped with a 150 W continuous output Xenon arc lamp. Excitation-emission matrix (EEM) scans were acquired in a 1 cm quartz cuvette with excitation wavelengths (Ex λ) scanned using a double-grating monochrometer from 240 to 621 nm at 3 nm intervals. Emission wavelengths (Em λ) were scanned from 246 to 693 nm at 2 nm intervals and emission spectra were collected using a Charge Coupled Device (CCD) detector. All fluorescence spectra were acquired in sample over reference ratio mode to account for potential fluctuations and wavelength dependency of the excitation lamp output. Samples were corrected for the inner filter effect7 and each sample EEM underwent spectral subtraction with a deionized water blank to remove the effects due to Raman scattering. Rayleigh masking was applied to remove the signal intensities for both the first and second order Rayleigh lines. Instrument bias related to wavelength-dependent efficiencies of the specific instrument’s optical components (gratings, mirrors, etc.) was automatically corrected by the Aqualog software after each spectral acquisition. The fluorescence intensities were normalized to the area under the water Raman peak collected on each day of analysis and are expressed in Raman-normalized intensity units (RU). All sample EEM processing was performed with the Aqualog software (version 4.0.0.86).The optical data obtained from the EEM scans were used to calculate several indices representative of WEOM chemical composition (Table 3) including the absorbance at 254 nm (Abs254), the ratio of the absorbance at 254 to 365 nm (Abs254:365), the ratio of the absorbance at 280 to 465 nm (Abs280:465), the spectral slope ratio (SR), the fluorescence index (FI), the humification index (HIX), the biological index (BIX), and the freshness index (β:α). The SR was calculated as the ratio of two spectral slope regions of the absorbance spectra (275–295 and 350–400 nm)8. The FI was calculated as the ratio of the emission intensities at Em λ 470 and 520 nm, at an Ex λ of 370 nm9. The HIX was calculated by dividing the emission intensity in the 435–480 nm region by the sum of emission intensities in the 300–345 and 435–480 nm regions, at an Ex λ of 255 nm10. The BIX was calculated as the ratio of emission intensities at 380 and 430 nm, at an Ex λ of 310 nm11. The freshness index β:α was calculated as the emission intensity at 380 nm divided by the maximum emission intensity between 420 and 432 nm, at an Ex λ of 310 nm12. To further characterize the giant miscanthus WEOM, the fluorescence intensity at specific excitation-emission pairs was also identified. The fluorescence peaks identified here have previously been reported for surface water samples and water extracts13 and include peak A (Ex λ 260, Em λ 450), peak C (Ex λ 340, Em λ 440), peak M (Ex λ 300, Em λ 390), peak B (Ex λ 275, Em λ 310), and peak T (Ex λ 275, Em λ 340). A brief description of these optical indices is provided in Table 3.Table 3 Description of the optical indices calculated from the excitation-emission matrix (EEM) fluorescence scans and used to analyze the WEOM composition of giant miscanthus leaves and stems.Full size table More

Rasmussen, R. S. & Morrissey, M. T. Application of DNA-based methods to identify fish and seafood substitution on the commercial market. Compr. Rev. Food Sci. Food Saf. 8, 118–154 (2009).CAS 
Article 

Google Scholar 
Chiu, M.-C., Huang, C.-G., Wu, W.-J. & Shiao, S.-F. A new horsehair worm, Chordodes formosanus sp. N. (Nematomorpha, Gordiida) from Hierodula mantids of Taiwan and Japan with redescription of a closely related species, Chordodes japonensis. ZooKeys 160, 1–22 (2011).Article 

Google Scholar 
Robins, J. H. et al. Phylogenetic species identification in Rattus highlights rapid radiation and morphological similarity of new Guinean species. PLoS One 9, e98002. https://doi.org/10.1371/journal.pone.0098002 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Sutherland, W. J., Roy, D. B. & Amano, T. An agenda for the future of biological recording for ecological monitoring and citizen science. Biol. J. Linn. Soc. 115, 779–784 (2015).Article 

Google Scholar 
Ho, J. K. I., Puniamoorthy, J., Srivathsan, A. & Meier, R. MinION sequencing of seafood in Singapore reveals creatively labelled flatfishes, confused roe, pig DNA in squid balls, and phantom crustaceans. Food Control 112, 107144. https://doi.org/10.1016/j.foodcont.2020.107144 (2020).CAS 
Article 

Google Scholar 
Elson, J. & Lightowlers, R. Mitochondrial DNA clonality in the dock: Can surveillance swing the case?. Trends Genet. 22, 603–607 (2006).CAS 
PubMed 
Article 

Google Scholar 
Bernt, M., Braband, A., Schierwater, B. & Stadler, P. F. Genetic aspects of mitochondrial genome evolution. Mol. Phylogenet. Evol. 69, 328–338 (2013).CAS 
PubMed 
Article 

Google Scholar 
Blaxter, M. L. The promise of a DNA taxonomy. Philos. Trans. R. Soc. Lond. B Biol. Sci. 359, 669–679 (2004).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Waugh, J. DNA barcoding in animal species: progress, potential and pitfalls. BioEssays 29, 188–197 (2007).CAS 
PubMed 
Article 

Google Scholar 
Grandjean, F. et al. Rapid recovery of nuclear and mitochondrial genes by genome skimming from Northern Hemisphere freshwater crayfish. Zool. Scr. 46, 718–728 (2017).Article 

Google Scholar 
Trevisan, B., Alcantara, D. M. C., Machado, D. J., Marques, F. P. L. & Lahr, D. J. G. Genome skimming is a low-cost and robust strategy to assemble complete mitochondrial genomes from ethanol preserved specimens in biodiversity studies. PeerJ 7, e7543. https://doi.org/10.7717/peerj.7543 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Franco-Sierra, N. D. & Díaz-Nieto, J. F. Rapid mitochondrial genome sequencing based on Oxford Nanopore Sequencing and a proxy for vertebrate species identification. Ecol. Evol. 10, 3544–3560 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Baeza, J. A. Yes, we can use it: a formal test on the accuracy of low-pass nanopore long-read sequencing for mitophylogenomics and barcoding research using the Caribbean spiny lobster Panulirus argus. BMC Genomics 21, 882. https://doi.org/10.1186/s12864-020-07292-5 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Phillips, A. R., Robertson, A. L., Batzli, J., Harris, M. & Miller, S. Aligning goals, assessments, and activities: An approach to teaching PCR and gel electrophoresis. CBE Life Sci. Educ. 7, 96–106 (2008).PubMed 
PubMed Central 
Article 

Google Scholar 
Dhorne-Pollet, S., Barrey, E. & Pollet, N. A new method for long-read sequencing of animal mitochondrial genomes: application to the identification of equine mitochondrial DNA variants. BMC Genomics 21, 785. https://doi.org/10.1186/s12864-020-07183-9 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Jain, M., Olsen, H. E., Paten, B. & Akeson, M. The Oxford Nanopore MinION: Delivery of nanopore sequencing to the genomics community. Genome Biol. 17, 239. https://doi.org/10.1186/s13059-016-1103-0 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Krehenwinkel, H. et al. Nanopore sequencing of long ribosomal DNA amplicons enables portable and simple biodiversity assessments with high phylogenetic resolution across broad taxonomic scale. GigaScience 8, giz006. https://doi.org/10.1093/gigascience/giz006 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Srivathsan, A. et al. ONTbarcoder and MinION barcodes aid biodiversity discovery and identification by everyone, for everyone. BMC Biol. 19, 217. https://doi.org/10.1186/s12915-021-01141-x (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Prost, S. et al. Education in the genomics era: Generating high-quality genome assemblies in university courses. GigaScience 9, giaa058. https://doi.org/10.1093/gigascience/giaa058 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Salazar, A. N. et al. An educational guide for nanopore sequencing in the classroom. PLoS Comput. Biol. 16, e1007314. https://doi.org/10.1371/journal.pcbi.1007314 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Watsa, M., Erkenswick, G. A., Pomerantz, A. & Prost, S. Portable sequencing as a teaching tool in conservation and biodiversity research. PLoS Biol. 18, e3000667. https://doi.org/10.1371/journal.pbio.3000667 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Egeter, B. et al. Speeding up the detection of invasive bivalve species using environmental DNA: A Nanopore and Illumina sequencing comparison. Mol. Ecol. Resour. https://doi.org/10.1111/1755-0998.13610 (2022).Article 
PubMed 

Google Scholar 
Oxford Nanopore. Flongle. https://nanoporetech.com/products/flongle. Last accessed 05 May 2022 (2022).Oxford Nanopore. MinION. https://nanoporetech.com/products/minion. Last accessed 05 May 2022 (2022).Baeza, J. A. & García-De León, F. J. Are we there yet? Benchmarking low-coverage nanopore long-read sequencing for the assembling of mitochondrial genomes using the vulnerable silky shark Carcharhinus falciformis. BMC Genomics 23, 320. https://doi.org/10.1186/s12864-022-08482-z (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Ghiselli, F. et al. Molluscan mitochondrial genomes break the rules. Philos. Trans. R. Soc. B Biol. Sci. 376, 20200159. https://doi.org/10.1098/rstb.2020.0159 (2021).Article 

Google Scholar 
Zhang, Z.-Q. Animal biodiversity: An introduction to higher-level classification and taxonomic richness. Zootaxa 3148, 7–12 (2011).Article 

Google Scholar 
Bouchet, P., Bary, S., Héros, V. & Marani, G. How many species of molluscs are there in the world’s oceans, and who is going to describe them? In Tropical Deep-Sea Benthos 29 (eds Héros, V. et al.) 9–24 (Muséum national d’histoire naturelle, 2016).
Google Scholar 
Reese, D. S. Palaikastro shells and bronze age purple-dye production in the Mediterranean Basin. Annu. Br. Sch. Athens 82, 201–206 (1987).Article 

Google Scholar 
Lardans, V. & Dissous, C. Snail control strategies for reduction of schistosomiasis transmission. Parasitol. Today 14, 413–417 (1998).CAS 
PubMed 
Article 

Google Scholar 
Baker, G. M. (ed.) Molluscs as Crop Pests. (CABI, 2002). https://doi.org/10.1079/9780851993201.0000Mannino, M. A. & Thomas, K. D. Depletion of a resource? The impact of prehistoric human foraging on intertidal mollusc communities and its significance for human settlement, mobility and dispersal. World Archaeol. 33, 452–474 (2002).Article 

Google Scholar 
Carter, R. The history and prehistory of pearling in the Persian Gulf. J. Econ. Soc. Hist. Orient 48, 139–209 (2005).Article 

Google Scholar 
Vilariño, M. L. et al. Assessment of human enteric viruses in cultured and wild bivalve molluscs. Int. Microbiol. Off. J. Span. Soc. Microbiol. 12, 145–151 (2009).
Google Scholar 
Tedde, T. et al. Toxoplasma gondii and other zoonotic protozoans in Mediterranean mussel (Mytilus galloprovincialis) and blue mussel (Mytilus edulis): A food safety concern?. J. Food Prot. 82, 535–542 (2019).CAS 
PubMed 
Article 

Google Scholar 
Clark, K., Karsch-Mizrachi, I., Lipman, D. J., Ostell, J. & Sayers, E. W. GenBank. Nucleic Acids Res. 44, D67–D72 (2016).CAS 
PubMed 
Article 

Google Scholar 
Grande, C., Templado, J. & Zardoya, R. Evolution of gastropod mitochondrial genome arrangements. BMC Evol. Biol. 8, 61. https://doi.org/10.1186/1471-2148-8-61 (2008).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Formenti, G. et al. Complete vertebrate mitogenomes reveal widespread repeats and gene duplications. Genome Biol. 22, 120. https://doi.org/10.1186/s13059-021-02336-9 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Buchfink, B., Reuter, K. & Drost, H.-G. Sensitive protein alignments at tree-of-life scale using DIAMOND. Nat. Methods 18, 366–368 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kolmogorov, M., Yuan, J., Lin, Y. & Pevzner, P. A. Assembly of long, error-prone reads using repeat graphs. Nat. Biotechnol. 37, 540–546 (2019).CAS 
PubMed 
Article 

Google Scholar 
Meng, G., Li, Y., Yang, C. & Liu, S. MitoZ: A toolkit for animal mitochondrial genome assembly, annotation and visualization. Nucleic Acids Res. 47, e63. https://doi.org/10.1093/nar/gkz173 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Bernt, M. et al. MITOS: Improved de novo metazoan mitochondrial genome annotation. Mol. Phylogenet. Evol. 69, 313–319 (2013).PubMed 
Article 

Google Scholar 
Chaisson, M. J. P., Wilson, R. K. & Eichler, E. E. Genetic variation and the de novo assembly of human genomes. Nat. Rev. Genet. 16, 627–640 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Alexander, J. & Valdés, A. The ring doesn’t mean a thing: Molecular data suggest a new taxonomy for two pacific species of sea hares (Mollusca: Opisthobranchia, Aplysiidae). Pac. Sci. 67, 283–294 (2013).Article 

Google Scholar 
WoRMS Editorial Board. World Register of Marine Species. https://www.marinespecies.org at VLIZ. Accessed 10 Jan 2022 (2022).Barco, A. et al. A molecular phylogenetic framework for the Muricidae, a diverse family of carnivorous gastropods. Mol. Phylogenet. Evol. 56, 1025–1039 (2010).CAS 
PubMed 
Article 

Google Scholar 
Houart, R. Description of eight new species and one new genus of Muricidae (Gastropoda) from the Indo-West Pacific. Novapex 18, 81–103 (2017).
Google Scholar 
Shao, K.-T. & Chung, K.-F. The National Checklist of Taiwan (Catalogue of Life in Taiwan, TaiCoL). GBIF. https://www.gbif.org/dataset/1ec61203-14fa-4fbd-8ee5-a4a80257b45a (2021).Gaitán-Espitia, J. D., González-Wevar, C. A., Poulin, E. & Cardenas, L. Antarctic and sub-Antarctic Nacella limpets reveal novel evolutionary characteristics of mitochondrial genomes in Patellogastropoda. Mol. Phylogenet. Evol. 131, 1–7 (2019).PubMed 
Article 
CAS 

Google Scholar 
Feng, J. et al. Comparative analysis of the complete mitochondrial genomes in two limpets from Lottiidae (Gastropoda: Patellogastropoda): rare irregular gene rearrangement within Gastropoda. Sci. Rep. 10, 19277. https://doi.org/10.1038/s41598-020-76410-w (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Xu, T., Qi, L., Kong, L. & Li, Q. Mitogenomics reveals phylogenetic relationships of Patellogastropoda (Mollusca, Gastropoda) and dynamic gene rearrangements. Zool. Scr. 51, 147–160 (2022).Article 

Google Scholar 
Ranjard, L. et al. Complete mitochondrial genome of the green-lipped mussel, Perna canaliculus (Mollusca: Mytiloidea), from long nanopore sequencing reads. Mitoch. DNA Part B 3, 175–176 (2018).Article 

Google Scholar 
Sun, J. et al. The Scaly-foot Snail genome and implications for the origins of biomineralised armour. Nat. Commun. 11, 1657. https://doi.org/10.1038/s41467-020-15522-3 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Dixit, B., Vanhoozer, S., Anti, N. A., O’Connor, M. S. & Boominathan, A. Rapid enrichment of mitochondria from mammalian cell cultures using digitonin. MethodsX 8, 101197. https://doi.org/10.1016/j.mex.2020.101197 (2021).Article 
PubMed 

Google Scholar 
Wanner, N., Larsen, P. A., McLain, A. & Faulk, C. The mitochondrial genome and Epigenome of the Golden lion Tamarin from fecal DNA using Nanopore adaptive sequencing. BMC Genomics 22, 726. https://doi.org/10.1186/s12864-021-08046-7 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Malukiewicz, J. et al. Genomic skimming and nanopore sequencing uncover cryptic hybridization in one of world’s most threatened primates. Sci. Rep. 11, 17279. https://doi.org/10.1038/s41598-021-96404-6 (2021).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kipp, E. J. et al. Nanopore adaptive sampling for mitogenome sequencing and bloodmeal identification in hematophagous insects. bioRxiv. https://doi.org/10.1101/2021.11.11.468279 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Sereika, M. et al. Oxford Nanopore R10.4 long-read sequencing enables near-perfect bacterial genomes from pure cultures and metagenomes without short-read or reference polishing. bioRxiv. https://doi.org/10.1101/2021.10.27.466057 (2021).Article 

Google Scholar 
Oxford Nanopore. Nanopore Community. https://nanoporetech.com/community. Last accessed 05 May 2022 (2022).Chen, S., Zhou, Y., Chen, Y. & Gu, J. fastp: An ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, i884–i890 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Li, H. Minimap2: Pairwise alignment for nucleotide sequences. Bioinformatics 34, 3094–3100 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Vaser, R., Sović, I., Nagarajan, N. & Šikić, M. Fast and accurate de novo genome assembly from long uncorrected reads. Genome Res. 27, 737–746 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Oxford Nanopore. medaka. https://github.com/nanoporetech/medaka. Last accessed 05 May 2022 (2022).Walker, B. J. et al. Pilon: An integrated tool for comprehensive microbial variant detection and genome assembly improvement. PLoS One 9, e112963. https://doi.org/10.1371/journal.pone.0112963 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Faust, G. G. & Hall, I. M. SAMBLASTER: Fast duplicate marking and structural variant read extraction. Bioinformatics 30, 2503–2505 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pedersen, B. S. & Quinlan, A. R. Mosdepth: Quick coverage calculation for genomes and exomes. Bioinformatics 34, 867–868 (2018).CAS 
PubMed 
Article 

Google Scholar 
Tsai, I. J. Genome skimming exercise (last updated 2022.04.14). https://introtogenomics.readthedocs.io/en/latest/emcgs.html (2022).Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Vaidya, G., Lohman, D. J. & Meier, R. SequenceMatrix: Concatenation software for the fast assembly of multi-gene datasets with character set and codon information. Cladistics 27, 171–180 (2011).PubMed 
Article 

Google Scholar 
Edler, D., Klein, J., Antonelli, A. & Silvestro, D. raxmlGUI 2.0: A graphical interface and toolkit for phylogenetic analyses using RAxML. Methods Ecol. Evol. 12, 373–377 (2021).Article 

Google Scholar 
Rabiee, M., Sayyari, E. & Mirarab, S. Multi-allele species reconstruction using ASTRAL. Mol. Phylogenet. Evol. 130, 286–296 (2019).PubMed 
Article 

Google Scholar 
Rambaut, A. FigTree, version 1.4.4. http://tree.bio.ed.ac.uk/software/figtree/ (2018).Hackl, T. & Ankenbrand, M. J. gggenomes: A Grammar of Graphics for Comparative Genomics. https://github.com/thackl/gggenomes (2022).Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).CAS 
PubMed 
Article 

Google Scholar 
Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: Molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More

Pourret, O. & Hursthouse, A. It’s time to replace the term “heavy metals” with “potentially toxic elements” when reporting environmental research. IJERPH 16, 4446 (2019).CAS 
PubMed Central 

Google Scholar 
Wuana, R. A. & Okieimen, F. E. Heavy metals in contaminated soils: A review of sources, chemistry, risks and best available strategies for remediation. ISRN Ecol. 2011, 1–20 (2011).
Google Scholar 
Mahar, A. et al. Challenges and opportunities in the phytoremediation of heavy metals contaminated soils: A review. Ecotoxicol. Environ. Saf. 126, 111–121 (2016).CAS 
PubMed 

Google Scholar 
Vangronsveld, J. et al. Phytoremediation of contaminated soils and groundwater: Lessons from the field. Environ. Sci. Pollut. Res. 16, 765–794 (2009).CAS 

Google Scholar 
Desjardins, D., Nissim, W. G., Pitre, F. E., Naud, A. & Labrecque, M. Distribution patterns of spontaneous vegetation and pollution at a former decantation basin in southern Québec, Canada. Ecol. Eng. 64, 385–390 (2014).
Google Scholar 
Marchiol, L. et al. Gentle remediation at the former “Pertusola Sud” zinc smelter: Evaluation of native species for phytoremediation purposes. Ecol. Eng. 53, 343–353 (2013).
Google Scholar 
van Oort, F. et al. Les pollutions métalliques d’un site industriel et des sols environnants : distributions hétérogènes des métaux et relations avec l’usage des sols. In: Contaminations métalliques des agrosystèmes et écosystèmes péri-urbains 15–44 (Editions Quae, 2009).Hodge, A. Plastic plants and patchy soils. J. Exp. Bot. 57, 401–411 (2006).CAS 
PubMed 

Google Scholar 
Huber-Sannwald, E. & Jackson, R. B. Heterogeneous soil-resource distribution and plant responses—from individual-plant growth to ecosystem functioning. In Progress in Botany Vol. 62 (eds Esser, K. et al.) 451–476 (Springer, 2001).
Google Scholar 
Loecke, T. D. & Philip Robertson, G. Soil resource heterogeneity in the form of aggregated litter alters maize productivity. Plant Soil 325, 231–241 (2009).CAS 

Google Scholar 
Reynolds, H. L., Hungate, B. A., Iii, F. S. C. & D’Antonio, C. M. Soil Heterogeneity and Plant Competition in an Annual Grassland. 16 (2021).Maestre, F. T., Cortina, J., Bautista, S., Bellot, J. & Vallejo, R. Small-scale environmental heterogeneity and spatiotemporal dynamics of seedling establishment in a semiarid degraded ecosystem. Ecosystems 6, 630–643 (2003).
Google Scholar 
Shutcha, M. N. et al. Three years of phytostabilisation experiment of bare acidic soil extremely contaminated by copper smelting using plant biodiversity of metal-rich soils in tropical Africa (Katanga, DR Congo). Ecol. Eng. 82, 81–90 (2015).
Google Scholar 
Testiati, E. et al. Trace metal and metalloid contamination levels in soils and in two native plant species of a former industrial site: Evaluation of the phytostabilization potential. J. Hazard. Mater. 248–249, 131–141 (2013).PubMed 

Google Scholar 
Cabrera, F., Clemente, L., Díaz Barrientos, E., López, R. & Murillo, J. M. Heavy metal pollution of soils affected by the Guadiamar toxic fiood. Sci. Total Environ. 242, 117–129 (1999).CAS 
PubMed 

Google Scholar 
Imperato, M. et al. Spatial distribution of heavy metals in urban soils of Naples city (Italy). Environ. Pollut. 124, 247–256 (2003).CAS 
PubMed 

Google Scholar 
Gallagher, F. J., Pechmann, I., Bogden, J. D., Grabosky, J. & Weis, P. Soil metal concentrations and vegetative assemblage structure in an urban brownfield. Environ. Pollut. 153, 351–361 (2008).CAS 
PubMed 

Google Scholar 
Gallagher, F. J., Pechmann, I., Holzapfel, C. & Grabosky, J. Altered vegetative assemblage trajectories within an urban brownfield. Environ. Pollut. 159, 1159–1166 (2011).CAS 
PubMed 

Google Scholar 
Heckenroth, A. et al. Selection of native plants with phytoremediation potential for highly contaminated Mediterranean soil restoration: Tools for a non-destructive and integrative approach. J. Environ. Manag. 183, 850–863 (2016).CAS 

Google Scholar 
Dickinson, N. M., Turner, A. P. & Lepp, N. W. How do trees and other long-lived plants survive in polluted environments?. Funct. Ecol. 5, 5 (1991).
Google Scholar 
Partida-Martínez, L. P. & Heil, M. The microbe-free plant: Fact or artifact?. Front. Plant Sci. 2, 100 (2011).PubMed 
PubMed Central 

Google Scholar 
Giller, K. E., Witter, E. & Mcgrath, S. P. Toxicity of heavy metals to microorganisms and microbial processes in agricultural soils: A review. Soil Biol. Biochem. 30, 1389–1414 (1998).CAS 

Google Scholar 
Kabata-Pendias, A. & Pendias, H. Trace Elements in Soils and Plants (CRC Press, 2001).
Google Scholar 
Tyler, G. Heavy metal pollution and mineralisation of nitrogen in forest soils. Nature 255, 701–702 (1975).CAS 

Google Scholar 
Seshadri, B., Bolan, N. S. & Naidu, R. Rhizosphere-induced heavy metal(loid) transformation in relation to bioavailability and remediation. J. Soil Sci. Plant Nutr. https://doi.org/10.4067/S0718-95162015005000043 (2015).Article 

Google Scholar 
Kidd, P. et al. Trace element behaviour at the root–soil interface: Implications in phytoremediation. Environ. Exp. Bot. 67, 243–259 (2009).CAS 

Google Scholar 
Rivera-Becerril, F. Cadmium accumulation and buffering of cadmium-induced stress by arbuscular mycorrhiza in three Pisum sativum L. genotypes. J. Exp. Bot. 53, 1177–1185 (2002).CAS 
PubMed 

Google Scholar 
Krupa, P. & Kozdrój, J. Ectomycorrhizal fungi and associated bacteria provide protection against heavy metals in inoculated pine (Pinus sylvestris L.) seedlings. Water Air Soil Pollut. 182, 83–90 (2007).CAS 

Google Scholar 
Janoušková, M., Pavlíková, D. & Vosátka, M. Potential contribution of arbuscular mycorrhiza to cadmium immobilisation in soil. Chemosphere 65, 1959–1965 (2006).PubMed 

Google Scholar 
Leyval, C., Turnau, K. & Haselwandter, K. Effect of heavy metal pollution on mycorrhizal colonization and function: Physiological, ecological and applied aspects. Mycorrhiza 7, 139–153 (1997).CAS 

Google Scholar 
Zhang, Y., Zhang, Y., Liu, M., Shi, X. & Zhao, Z. Dark septate endophyte (DSE) fungi isolated from metal polluted soils: Their taxonomic position, tolerance, and accumulation of heavy metals in vitro. J. Microbiol. 46, 624–632 (2008).PubMed 

Google Scholar 
Krumins, J. A., Goodey, N. M. & Gallagher, F. Plant–soil interactions in metal contaminated soils. Soil Biol. Biochem. 80, 224–231 (2015).CAS 

Google Scholar 
Glick, B. R. Phytoremediation: Synergistic use of plants and bacteria to clean up the environment. Biotechnol. Adv. 21, 383–393 (2003).CAS 
PubMed 

Google Scholar 
Heckenroth, A. et al. What are the potential environmental solutions for diffuse pollution ? In Pollution of Marseille’s Industrial Calanques—The Impact of the Past on the Present 291–328 (REF2C, 2016).Li, M. S. Ecological restoration of mineland with particular reference to the metalliferous mine wasteland in China: A review of research and practice. Sci. Total Environ. 357, 38–53 (2006).CAS 
PubMed 

Google Scholar 
Mendez, M. O. & Maier, R. M. Phytoremediation of mine tailings in temperate and arid environments. Rev. Environ. Sci. Biotechnol. 7, 47–59 (2008).CAS 

Google Scholar 
Yaalon, D. H. Soils in the Mediterranean region: What makes them different?. CATENA 28, 157–169 (1997).CAS 

Google Scholar 
Li, S. et al. A comprehensive survey on the horizontal and vertical distribution of heavy metals and microorganisms in soils of a Pb/Zn smelter. J. Hazard. Mater. 400, 123255 (2020).CAS 
PubMed 

Google Scholar 
Pérez-de-Mora, A. et al. Microbial community structure and function in a soil contaminated by heavy metals: Effects of plant growth and different amendments. Soil Biol. Biochem. 38, 327–341 (2006).
Google Scholar 
Keller, C. et al. Root development and heavy metal phytoextraction efficiency: Comparison of different plant species in the field. Plant Soil. 249, 67–81 (2003).CAS 

Google Scholar 
Lambrechts, T. et al. Comparative analysis of Cd and Zn impacts on root distribution and morphology of Lolium perenne and Trifolium repens: Implications for phytostabilization. Plant Soil 376, 229–244 (2014).CAS 

Google Scholar 
Pauwels, M., Frérot, H., Bonnin, I. & Saumitou-Laprade, P. A broad-scale analysis of population differentiation for Zn tolerance in an emerging model species for tolerance study: Arabidopsis halleri (Brassicaceae). J. Evol. Biol. 19, 1838–1850 (2006).CAS 
PubMed 

Google Scholar 
Padilla, F. M. & Pugnaire, F. I. The role of nurse plants in the restoration of degraded environments. Front. Ecol. Environ. 4, 196–202 (2006).
Google Scholar 
Robles, A. B., Allegretti, L. I. & Passera, C. B. Coronilla juncea is both a nutritive fodder shrub and useful in the rehabilitation of abandoned Mediterranean marginal farmland. J. Arid Environ. 50, 381–392 (2002).
Google Scholar 
Grime, J. P. Plant Strategies and Vegetation Processes (Wiley, 1979).
Google Scholar 
Laffont-Schwob, I. et al. Diffuse and widespread present-day pollution. In Pollution of Marseille’s industrial Calanques—The Impact of the Past on the Future 204–249 (REF2C, 2016).Gelly, R. et al. Lead, zinc, and copper redistributions in soils along a deposition gradient from emissions of a Pb-Ag smelter decommissioned 100 years ago. Sci. Total Environ. 665, 502–512 (2019).CAS 
PubMed 

Google Scholar 
Tóth, G. et al. Soils of the European Union. JRC Scientific and Technical Reports 85 (2008).IUSS Working Group WRB. Base de référence mondiale pour les ressources en sols 2014, Mise à jour 2015. Système international de classification des sols pour nommer les sols et élaborer des légendes de cartes pédologiques. Rapport sur les ressources en sols du monde. Vol. 106 (2015).Dias, T. et al. Ammonium as a driving force of plant diversity and ecosystem functioning: Observations based on 5 years’ manipulation of n dose and form in a Mediterranean ecosystem. PLoS ONE 9, e92517 (2014).PubMed 
PubMed Central 

Google Scholar 
Remon, E. et al. Soil characteristics, heavy metal availability and vegetation recovery at a former metallurgical landfill: Implications in risk assessment and site restoration. Environ. Pollut. 137, 316–323 (2005).CAS 
PubMed 

Google Scholar 
Baumberger, T. et al. Plant community changes as ecological indicator of seabird colonies’ impacts on Mediterranean Islands. Ecol. Ind. 15, 76–84 (2012).
Google Scholar 
Navas, M.-L., Roumet, C., Bellmann, A., Laurent, G. & Garnier, E. Suites of plant traits in species from different stages of a Mediterranean secondary succession: Plant traits and succession. Plant Biol. 12, 183–196 (2010).CAS 
PubMed 

Google Scholar 
Guillamot, F., Calvert, V., Millot, M.-V. & Criquet, S. Does antimony affect microbial respiration in Mediterranean soils? A microcosm experiment. Pedobiologia 57, 119–121 (2014).
Google Scholar 
Wang, A., He, M., Ouyang, W., Lin, C. & Liu, X. Effects of antimony (III/V) on microbial activities and bacterial community structure in soil. Sci. Total Environ. 789, 148073 (2021).CAS 
PubMed 

Google Scholar 
Oleńska, E. et al. Trifolium repens-associated bacteria as a potential tool to facilitate phytostabilization of zinc and lead polluted waste heaps. Plants 9, 1002 (2020).PubMed Central 

Google Scholar 
Stambulska, U. Y., Bayliak, M. M. & Lushchak, V. I. Chromium(VI) toxicity in legume plants: Modulation effects of rhizobial symbiosis. BioMed Res. Int. 2018, 1–13 (2018).
Google Scholar 
Karthika, K. S., Rashmi, I. & Parvathi, M. S. Biological functions, uptake and transport of essential nutrients in relation to plant growth. In Plant Nutrients and Abiotic Stress Tolerance 1–49 (Springer Singapore, 2018). https://doi.org/10.1007/978-981-10-9044-8_1.Dary, M., Chamber-Pérez, M. A., Palomares, A. J. & Pajuelo, E. “In situ” phytostabilisation of heavy metal polluted soils using Lupinus luteus inoculated with metal resistant plant-growth promoting rhizobacteria. J. Hazard. Mater. 177, 323–330 (2010).CAS 
PubMed 

Google Scholar 
Reichman, S. M. The potential use of the legume–rhizobium symbiosis for the remediation of arsenic contaminated sites. Soil Biol. Biochem. 39, 2587–2593 (2007).CAS 

Google Scholar 
Parraga-Aguado, I., Querejeta, J.-I., González-Alcaraz, M.-N., Jiménez-Cárceles, F. J. & Conesa, H. M. Usefulness of pioneer vegetation for the phytomanagement of metal(loid)s enriched tailings: Grasses vs. shrubs vs. trees. J. Environ. Manag. 133, 51–58 (2014).CAS 

Google Scholar 
Jones, C. G., Lawton, J. H. & Shachak, M. Organisms as ecosystem engineers. Oikos 69, 373 (1994).
Google Scholar 
Carrasco, L., Azcón, R., Kohler, J., Roldán, A. & Caravaca, F. Comparative effects of native filamentous and arbuscular mycorrhizal fungi in the establishment of an autochthonous, leguminous shrub growing in a metal-contaminated soil. Sci. Total Environ. 409, 1205–1209 (2011).CAS 
PubMed 

Google Scholar 
Padilla, F. M., Ortega, R., Sánchez, J. & Pugnaire, F. I. Rethinking species selection for restoration of arid shrublands. Basic Appl. Ecol. 10, 640–647 (2009).
Google Scholar 
Ilunga wa Ilunga, E. et al. Plant functional traits as a promising tool for the ecological restoration of degraded tropical metal-rich habitats and revegetation of metal-rich bare soils: A case study in copper vegetation of Katanga, DRC. Ecol. Eng. 82, 214–221 (2015).
Google Scholar 
Salducci, M.-D. et al. How can a rare protected plant cope with the metal and metalloid soil pollution resulting from past industrial activities? Phytometabolites, antioxidant activities and root symbiosis involved in the metal tolerance of Astragalus tragacantha. Chemosphere 217, 887–896 (2019).CAS 
PubMed 

Google Scholar 
Kachout, S. S. et al. Accumulation of Cu, Pb, Ni and Zn in the halophyte plant Atriplex grown on polluted soil. J. Sci. Food Agric. 92, 336–342 (2012).CAS 
PubMed 

Google Scholar 
Schaeffer, A. et al. The impact of chemical pollution on the resilience of soils under multiple stresses: A conceptual framework for future research. Sci. Total Environ. 568, 1076–1085 (2016).CAS 
PubMed 

Google Scholar 
Tosini, L. et al. Gain in biodiversity but not in phytostabilization after 3 years of ecological restoration of contaminated Mediterranean soils. Ecol. Eng. 157, 105998 (2020).
Google Scholar 
Michelaki, C. et al. An integrated phenotypic trait-network in thermo-Mediterranean vegetation describing alternative, coexisting resource-use strategies. Sci. Total Environ. 672, 583–592 (2019).CAS 
PubMed 

Google Scholar 
Affholder, M.-C. et al. Transfer of metals and metalloids from soil to shoots in wild rosemary (Rosmarinus officinalis L.) growing on a former lead smelter site: Human exposure risk. Sci. Total Environ. 454–455, 219–229 (2013).PubMed 

Google Scholar 
Affholder, M.-C. et al. As, Pb, Sb, and Zn transfer from soil to root of wild rosemary: Do native symbionts matter?. Plant Soil 382, 219–236 (2014).CAS 

Google Scholar 
Ellili, A. et al. Decision-making criteria for plant-species selection for phytostabilization: Issues of biodiversity and functionality. J. Environ. Manag. 201, 215–226 (2017).CAS 

Google Scholar 
Laffont-Schwob, I. et al. Insights on metal-tolerance and symbionts of the rare species Astragalus tragacantha aiming at phytostabilization of polluted soils and plant conservation. ecmed 37, 57–62 (2011).
Google Scholar 
Rabier, J. et al. Heavy metal and arsenic resistance of the halophyte Atriplex halimus L. along a gradient of contamination in a French Mediterranean spray zone. Water Air Soil Pollut. 225, 1993 (2014).
Google Scholar 
Quevauviller, Ph. et al. Interlaboratory comparison of EDTA and DTPA procedures prior to certification of extractable trace elements in calcareous soil. Sci. Total Environ. 178, 127–132 (1996).CAS 

Google Scholar 
Anderson, J. P. E. & Domsch, K. H. A physiological method for the quantitative measurement of microbial biomass in soils. Soil Biol. Biochem. 10, 215–221 (1978).CAS 

Google Scholar 
R Development Core Team.pdf.Dray, S., Dufour, A. B. & Chessel, D. The ade4 package—II: Two-table and K-table methods. R News 7, 6 (2007).
Google Scholar  More

Faisal, A. A., Selen, L. P. J. & Wolpert, D. M. Noise in the nervous system. Nat. Rev. Neurosci. 9, 292–303 (2008).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Tsetsos, K. et al. Economic irrationality is optimal during noisy decision making. Proc. Natl. Acad. Sci. 113, 3102–3107 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bushnell, P. J. Behavioral approaches to the assessment of attention in animals. Psychopharmacology 138, 231–259 (1998).CAS 
PubMed 
Article 

Google Scholar 
Katsuki, F. & Constantinidis, C. Bottom-up and top-down attention: Different processes and overlapping neural systems. Neuroscientist 20, 509–521 (2014).PubMed 
Article 

Google Scholar 
Moore, T. & Zirnsak, M. Neural mechanisms of selective visual attention. Annu. Rev. Psychol. 68, 47–72 (2017).CAS 
PubMed 
Article 

Google Scholar 
Ferguson, K. I. & Stiling, P. Non-additive effects of multiple natural enemies on aphid populations. Oecologia 108, 375–379 (1996).ADS 
PubMed 
Article 

Google Scholar 
Sih, A., Englund, G. & Wooster, D. Emergent impacts of multiple predators on prey. Trends Ecol. Evol. 13, 350–355 (1998).CAS 
PubMed 
Article 

Google Scholar 
Soluk, D. A. & Collins, N. C. Synergistic interactions between fish and stoneflies: Facilitation and interference among stream predators. Oikos. 52, 94–100 (1988).
Article 

Google Scholar 
Cooper, W. E., Pérez-Mellado, V. & Hawlena, D. Number, speeds, and approach paths of predators affect escape behavior by the Balearic lizard, Podarcis lilfordi. J. Herpetol. 41, 197–204 (2007).Article 

Google Scholar 
Relyea, R. A. How prey respond to combined predators: A review and an empirical test. Ecology 84, 1827–1839 (2003).Article 

Google Scholar 
Krupa, J. J. & Sih, A. Fishing spiders, green sunfish, and a stream-dwelling water strider: Male–female conflict and prey responses to single versus multiple predator environments. Oecologia 117, 258–265 (1998).ADS 
PubMed 
Article 

Google Scholar 
Nityananda, V. Attention-like processes in insects. Proc. R. Soc. B Biol. Sci. 283, 20161986 (2016).Article 

Google Scholar 
Amo, L., López, P. & Martín, J. in Annales Zoologici Fennici, 671–679 (JSTOR).Bagheri, Z. M., Donohue, C. G. & Hemmi, J. M. Evidence of predictive selective attention in fiddler crabs during escape in the natural environment. J. Exp. Biol. 223, 234963 (2020).Article 

Google Scholar 
Geist, C., Liao, J., Libby, S. & Blumstein, D. T. Does intruder group size and orientation affect flight initiation distance in birds?. Anim. Biodivers. Conserv. 28, 69–73 (2005).
Google Scholar 
McIntosh, A. R. & Peckarsky, B. L. Criteria determining behavioural responses to multiple predators by a stream mayfly. Oikos. 554–564 (1999).Hemmi, J. M. & Tomsic, D. The neuroethology of escape in crabs: From sensory ecology to neurons and back. Curr. Opin. Neurobiol. 22, 194–200 (2012).CAS 
PubMed 
Article 

Google Scholar 
Zeil, J. & Hemmi, J. M. The visual ecology of fiddler crabs. J. Comp. Physiol. A. 192, 1–25 (2006).ADS 
Article 

Google Scholar 
Nalbach, H.-O., Nalbach, G. & Forzin, L. Visual control of eye-stalk orientation in crabs: Vertical optokinetics, visual fixation of the horizon, and eye design. J. Comp. Physiol. A. 165, 577–587 (1989).Article 

Google Scholar 
Zeil, J. & Al-Mutairi, M. The variation of resolution and of ommatidial dimensions in the compound eyes of the fiddler crab Uca lactea annulipes (Ocypodidae, Brachyura, Decapoda). J. Exp. Biol. 199, 1569–1577 (1996).CAS 
PubMed 
Article 

Google Scholar 
Howard, J. & Snyder, A. W. Transduction as a limitation on compound eye function and design. Proc. R. Soc. Lond. Series B Biol. Sci. 217, 287–307 (1983).ADS 

Google Scholar 
Land, M. F. Visual acuity in insects. Annu. Rev. Entomol. 42, 147–177 (1997).CAS 
PubMed 
Article 

Google Scholar 
Land, M. F. & Nilsson, D.-E. Animal Eyes (OUP, 2012).Book 

Google Scholar 
Bagheri, Z. M. et al. A new method for mapping spatial resolution in compound eyes suggests two visual streaks in fiddler crabs. J. Exp. Biol. 223, 210195 (2020).Article 

Google Scholar 
Smolka, J. & Hemmi, J. M. Topography of vision and behaviour. J. Exp. Biol. 212, 3522–3532 (2009).PubMed 
Article 

Google Scholar 
Land, M. & Layne, J. The visual control of behaviour in fiddler crabs. J. Comp. Physiol. A. 177, 91–103 (1995).Article 

Google Scholar 
Layne, J., Land, M. & Zeil, J. Fiddler crabs use the visual horizon to distinguish predators from conspecifics: A review of the evidence. J. Mar. Biol. Assoc. UK. 77, 43–54 (1997).Article 

Google Scholar 
Hemmi, J. M. Predator avoidance in fiddler crabs: 1. Escape decisions in relation to the risk of predation. Animal Behav. 69, 603–614 (2005).Article 

Google Scholar 
Layne, J. E. Retinal location is the key to identifying predators in fiddler crabs (Uca pugilator). J. Exp. Biol. 201, 2253–2261 (1998).CAS 
PubMed 
Article 

Google Scholar 
Nalbach, H.-O. Frontiers in Crustacean Neurobiology 165–172 (Springer, 1990).Book 

Google Scholar 
Smolka, J., Zeil, J. & Hemmi, J. M. Natural visual cues eliciting predator avoidance in fiddler crabs. Proc. R. Soc. B Biol. Sci. 278, 3584–3592 (2011).Article 

Google Scholar 
Hemmi, J. M. Predator avoidance in fiddler crabs: 2. The visual cues. Animal Behav. 69, 615–625 (2005).Article 

Google Scholar 
Hemmi, J. M. & Pfeil, A. A multi-stage anti-predator response increases information on predation risk. J. Exp. Biol. 213, 1484–1489 (2010).PubMed 
Article 

Google Scholar 
Smolka, J., Raderschall, C. A. & Hemmi, J. M. Flicker is part of a multi-cue response criterion in fiddler crab predator avoidance. J. Exp. Biol. 216, 1219–1224 (2013).PubMed 

Google Scholar 
How, M. J., Pignatelli, V., Temple, S. E., Marshall, N. J. & Hemmi, J. M. High e-vector acuity in the polarisation vision system of the fiddler crab Uca vomeris. J. Exp. Biol. 215, 2128–2134 (2012).PubMed 
Article 

Google Scholar 
Paulk, A. C. et al. Selective attention in the honeybee optic lobes precedes behavioral choices. Proc. Natl. Acad. Sci. 111, 5006–5011 (2014).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Tang, S. & Juusola, M. Intrinsic activity in the fly brain gates visual information during behavioral choices. Nat. Precedings. 1–1 (2010).Bagheri, Z. M., Cazzolato, B. S., Grainger, S., O’Carroll, D. C. & Wiederman, S. D. An autonomous robot inspired by insect neurophysiology pursues moving features in natural environments. J. Neural Eng. 14, 046030 (2017).ADS 
PubMed 
Article 

Google Scholar 
Chancán, M., Hernandez-Nunez, L., Narendra, A., Barron, A. B. & Milford, M. A hybrid compact neural architecture for visual place recognition. IEEE Robot. Automat. Lett. 5, 993–1000 (2020).Article 

Google Scholar 
Colonnier, F., Ramirez-Martinez, S., Viollet, S. & Ruffier, F. A bio-inspired sighted robot chases like a hoverfly. Bioinspir. Biomim. 14, 036002 (2019).ADS 
PubMed 
Article 

Google Scholar 
Medan, V., Oliva, D. & Tomsic, D. Characterization of lobula giant neurons responsive to visual stimuli that elicit escape behaviors in the crab Chasmagnathus. J. Neurophysiol. 98, 2414–2428 (2007).PubMed 
Article 

Google Scholar 
Oliva, D. & Tomsic, D. Computation of object approach by a system of visual motion-sensitive neurons in the crab Neohelice. J. Neurophysiol. 112, 1477–1490 (2014).PubMed 
Article 

Google Scholar 
Oliva, D. & Tomsic, D. Object approach computation by a giant neuron and its relationship with the speed of escape in the crab Neohelice. J. Exp. Biol. 219, 3339–3352 (2016).PubMed 

Google Scholar 
Sztarker, J., Strausfeld, N. J. & Tomsic, D. Organization of optic lobes that support motion detection in a semiterrestrial crab. J. Comparat. Neurol. 493, 396–411 (2005).Article 

Google Scholar 
Medan, V., De Astrada, M. B., Scarano, F. & Tomsic, D. A network of visual motion-sensitive neurons for computing object position in an arthropod. J. Neurosci. 35, 6654–6666 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Tomsic, D. & Sztarker, J. in Oxford Research Encyclopedia of Neuroscience (2019).Sztarker, J. & Tomsic, D. Neuronal correlates of the visually elicited escape response of the crab Chasmagnathus upon seasonal variations, stimuli changes and perceptual alterations. J. Comp. Physiol. A. 194, 587–596 (2008).Article 

Google Scholar 
Tomsic, D., de Astrada, M. B. & Sztarker, J. Identification of individual neurons reflecting short-and long-term visual memory in an arthropodo. J. Neurosci. 23, 8539–8546 (2003).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Layne, J. E., Barnes, W. J. P. & Duncan, L. M. J. Mechanisms of homing in the fiddler crab Uca rapax 1. Spatial and temporal characteristics of a system of small-scale navigation. J. Exp. Biol. 206, 4413–4423 (2003).PubMed 
Article 

Google Scholar 
Dahmen, H., Wahl, V. L., Pfeffer, S. E., Mallot, H. A. & Wittlinger, M. Naturalistic path integration of Cataglyphis desert ants on an air-cushioned lightweight spherical treadmill. J. Exp. Biol. 220, 634–644 (2017).PubMed 
Article 

Google Scholar 
Hemmi, J. M. & Merkle, T. High stimulus specificity characterizes anti-predator habituation under natural conditions. Proc. R. Soc. B Biol. Sci. 276, 4381–4388 (2009).Article 

Google Scholar 
Scarano, F. & Tomsic, D. Escape response of the crab Neohelice to computer generated looming and translational visual danger stimuli. J. Physiol.-Paris 108, 141–147 (2014).PubMed 
Article 

Google Scholar 
Ryan, T. P. & Morgan, J. P. Modern experimental design. J. Stat. Theory Practice 1, 501–506 (2007).MATH 
Article 

Google Scholar 
Hemmi, J. M. & Zeil, J. Burrow surveillance in fiddler crabs I. Description of behaviour. J. Exp. Biol. 206, 3935–3950 (2003).PubMed 
Article 

Google Scholar 
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. (2014).emmeans: Estimated Marginal Means, aka Least-Squares Means. v. R package version 1.5.2-1. (2020).Cremers, J. Bpnreg: Bayesian projected normal regression models for circular data. R Package Version 1, 3 (2018).
Google Scholar 
Cremers, J. & Klugkist, I. One direction? A tutorial for circular data analysis using R with examples in cognitive psychology. Front. Psychol. 2040 (2018).Oliva, D., Medan, V. & Tomsic, D. Escape behavior and neuronal responses to looming stimuli in the crab Chasmagnathus granulatus (Decapoda: Grapsidae). J. Exp. Biol. 210, 865–880 (2007).PubMed 
Article 

Google Scholar 
Gabbiani, F., Krapp, H. G. & Laurent, G. Computation of object approach by a wide-field, motion-sensitive neuron. J. Neurosci. 19, 1122–1141 (1999).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Simultaneous Inference in General Parametric Models. v. R package version v1.4-10 (2019).Avargues-Weber, A., Deisig, N. & Giurfa, M. Visual cognition in social insects. Annu. Rev. Entomol. 56, 423–443 (2011).CAS 
PubMed 
Article 

Google Scholar 
Avarguès-Weber, A. & Giurfa, M. Conceptual learning by miniature brains. Proc. R. Soc. B Biol. Sci. 280, 20131907 (2013).Article 

Google Scholar 
De Bivort, B. L. & Van Swinderen, B. Evidence for selective attention in the insect brain. Curr. Opin. Insect Sci. 15, 9–15 (2016).PubMed 
Article 

Google Scholar 
Klapoetke, N. C. et al. Ultra-selective looming detection from radial motion opponency. Nature 551, 237–241 (2017).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Von Reyn, C. R. et al. A spike-timing mechanism for action selection. Nat. Neurosci. 17, 962–970 (2014).Article 
CAS 

Google Scholar 
Fotowat, H. & Gabbiani, F. Collision detection as a model for sensory-motor integration. Annu. Rev. Neurosci. 34, 1–19 (2011).CAS 
PubMed 
Article 

Google Scholar 
Strausfeld, N. J. & Olea-Rowe, B. Convergent evolution of optic lobe neuropil in Pancrustacea. Arthropod. Struct. Dev. 61, 101040 (2021).PubMed 
Article 

Google Scholar 
Tomsic, D. Visual motion processing subserving behavior in crabs. Curr. Opin. Neurobiol. 41, 113–121 (2016).CAS 
PubMed 
Article 

Google Scholar 
Giribet, G. & Edgecombe, G. D. The phylogeny and evolutionary history of arthropods. Curr. Biol. 29, R592–R602 (2019).CAS 
PubMed 
Article 

Google Scholar 
Christian, E. V. Sprung der Collembolen. Zoologische Jahrbucher. Abteilung fur Systematik, Okologie und Geographie der Tiere (1979).Brackenbury, J. Regulation of swimming in the Culex pipiens (Diptera, Culicidae) pupa: Kinematics and locomotory trajectories. J. Exp. Biol. 202, 2521–2529 (1999).CAS 
PubMed 
Article 

Google Scholar 
Domenici, P. & Blake, R. W. Escape trajectories in angelfish (Pterophyllum eimekei). J. Exp. Biol. 177, 253–272 (1993).Article 

Google Scholar 
Kimura, H. & Kawabata, Y. Effect of initial body orientation on escape probability of prey fish escaping from predators. Biol. Open. 7, bio023812 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Martín, J. & López, P. The escape response of juvenile Psammodromus algirus lizards. J. Comp. Psychol. 110, 187 (1996).Article 

Google Scholar 
Lancer, B. H., Evans, B. J. E., Fabian, J. M., O’Carroll, D. C. & Wiederman, S. D. A target-detecting visual neuron in the dragonfly locks on to selectively attended targets. J. Neurosci. 39, 8497–8509 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Nityananda, V. & Pattrick, J. G. Bumblebee visual search for multiple learned target types. J. Exp. Biol. 216, 4154–4160 (2013).PubMed 

Google Scholar 
Pollack, G. S. Selective attention in an insect auditory neuron. J. Neurosci. 8, 2635–2639 (1988).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rossel, S. Binocular vision in insects: How mantids solve the correspondence problem. Proc. Natl. Acad. Sci. 93, 13229–13232 (1996).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wiederman, S. D. & O’Carroll, D. C. Selective attention in an insect visual neuron. Curr. Biol. 23, 156–161 (2013).CAS 
PubMed 
Article 

Google Scholar 
Jackson, R. R. & Cross, F. R. Spider cognition. Adv. Insect Physiol. 41, 115–174 (2011).Article 

Google Scholar 
Jackson, R. R. & Li, D. One-encounter search-image formation by araneophagic spiders. Anim. Cogn. 7, 247–254 (2004).PubMed 
Article 

Google Scholar 
Guest, B. B. & Gray, J. R. Responses of a looming-sensitive neuron to compound and paired object approaches. J. Neurophysiol. 95, 1428–1441 (2006).PubMed 
Article 

Google Scholar 
Eliassen, S., Jørgensen, C., Mangel, M. & Giske, J. Quantifying the adaptive value of learning in foraging behavior. Am. Nat. 174, 478–489 (2009).PubMed 
Article 

Google Scholar 
Eliassen, S., Andersen, B. S., Jørgensen, C. & Giske, J. From sensing to emergent adaptations: Modelling the proximate architecture for decision-making. Ecol. Model. 326, 90–100 (2016).Article 

Google Scholar 
Gigerenzer, G. Why heuristics work. Perspect. Psychol. Sci. 3, 20–29 (2008).PubMed 
Article 

Google Scholar  More

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.This is a summary of: Wu, L. et al. Reduction of microbial diversity in grassland soil is driven by long-term climate warming. Nat. Microbiol. https://doi.org/10.1038/s41564-022-01147-3 (2022). More

Mu, Q. Y., Zhou, C. & Ng, C. W. W. Compression and wetting induced volumetric behavior of loess: Macro- and micro-investigations. Transp. Geotech. 23, 100345 (2020).Article 

Google Scholar 
Pan, L., Zhu, J. G. & Zhang, Y. F. Evaluation of structural strength and parameters of collapsible loess. Int. J. Geomech. 21, 04021066 (2021).Article 

Google Scholar 
He, S. X., Bai, H. B. & Xu, Z. W. Evaluation on tensile behavior characteristics of undisturbed loess. Energies 11, 1974 (2018).Article 
CAS 

Google Scholar 
He, S. X. & Bai, H. B. Elastic-plastic behavior of compacted loess under direct and cyclic tension. Adv. Mater. Sci. Eng. 2019, 1–12 (2019).
Google Scholar 
Wu, X. Y., Niu, F. J., Liang, Q. G. & Li, G. Y. Study on tensile strength and tensile-shear coupling mechanism of loess around Lanzhou and Yanan city in china by unconfined penetration test. KSCE J. Civ. Eng. 23, 1–12 (2019).Article 

Google Scholar 
You, Z. L., Zhang, M. Y., Liu, F. & Ma, Y. M. Numerical investigation of the tensile strength of loess using discrete element method. Eng. Fract. Mech. 247, 107610 (2021).Article 

Google Scholar 
Zhang, F. Y., Pei, X. J. & Yan, X. D. Physicochemical and mechanical properties of lime-treated loess. Geotech. Geol. Eng. 36, 685–696 (2018).Article 

Google Scholar 
Gu, K. & Chen, B. Loess stabilization using cement, waste phosphogypsum, fly ash and quicklime for self-compacting rammed earth construction. Constr. Build. Mater. 231, 117195–117195 (2020).CAS 
Article 

Google Scholar 
Xue, Z. F., Cheng, W. C., Wang, L. & Song, G. Y. Improvement of the shearing behaviour of loess using recycled straw fiber reinforcement. KSCE J. Civ. Eng. 25, 3319–3335 (2021).Article 

Google Scholar 
Chu, F., Luo, J. B. & Deng, G. H. Experimental study of dynamic deformation and strength properties and seismic subsidence characteristics of fiber yarn reinforced loess. J. Rock. Mech. Geotech. 39, 2306–2320 (2020).
Google Scholar 
Liu, W., Wang, Q., Lin, G. C. & Tian, X. X. Variations of suction and suction stress of unsaturated loess due to changes in lignin content and sample preparation method. J. Mt. Sci. Engl. 18, 16 (2021).
Google Scholar 
Wang, X. G., Liu, K. & Lian, B. Q. Experimental study on ring shear properties of fiber-reinforced loess. Bull. Eng. Geol. Environ. 80, 5021–5029 (2021).Article 

Google Scholar 
Lian, B. Q., Peng, J. B., Zhan, H. B. & Wang, X. G. Mechanical response of root-reinforced loess with various water contents. Soil. Tillage Res. 193, 85–94 (2019).Article 

Google Scholar 
Xu, J. et al. Triaxial shear behavior of basalt fiber-reinforced loess based on digital image technology. KSCE J. Civ. Eng. 1, 1–13 (2021).
Google Scholar 
Li, J. D. et al. Study on strength characteristics and mechanism of loess stabilized by F1 ionic soil stabilizer. Arab. J. Geosci. 14, 1162 (2021).Article 

Google Scholar 
Lv, Q. F., Chang, C. R., Zhao, B. H. & Ma, B. Loess soil stabilization by means of SiO2 nanoparticles. Soil Mech. Found. Eng. 54, 409–413 (2018).Article 

Google Scholar 
Ma, W. J., Wang, B. L., Wang, X., Jiang, D. J. & Li, Z. Y. Experimental study on mechanical properties of modified loess. Water. Resour. Hydropower Eng. 49, 150–156 (2018).
Google Scholar 
Hou, Y. F., Li, P. & Wang, J. D. Review of chemical stabilizing agents for improving the physical and mechanical properties of loess. Bull. Eng. Geol. Environ. 80, 9201–9215 (2021).Article 

Google Scholar 
Liu, X. J., Fan, J. Y., Yu, J. & Gao, X. Solidification of loess using microbial induced carbonate precipitation. J. Mt. Sci. Engl. 18, 265–274 (2021).Article 

Google Scholar 
Chang, I., Im, J. & Cho, G. C. Introduction of microbial biopolymers in soil treatment for future environmentally-friendly and sustainable geotechnical engineering. Sustainability 8, 251 (2016).Article 

Google Scholar 
Jang, J. A review of the application of biopolymers on geotechnical engineering and the strengthening mechanisms between typical biopolymers and soils. Adv. Mater. Sci. Eng. 2020, 1465709 (2020).Article 
CAS 

Google Scholar 
Chang, I., Lee, M., Tran, T., Lee, S. & Cho, G. C. Review on biopolymer-based soil treatment (BPST) technology in geotechnical engineering practices. Transp. Geotech. 24, 100385 (2020).Article 

Google Scholar 
Mendonça, A., Morais, P. V., Pires, A. C., Chung, A. P. & Oliveira, P. V. A review on the importance of microbial biopolymers such as xanthan gum to improve soil properties. Appl. Sci. 11, 170 (2020).Article 
CAS 

Google Scholar 
Rosalam, S. & England, R. Review of xanthan gum production from unmodified starches by Xanthomonas campestris sp. Microb. Technol. 39, 197–207 (2006).CAS 
Article 

Google Scholar 
Moghal, A. A. B. & Vydehi, K. V. State-of-the-art review on efficacy of xanthan gum and guar gum inclusion on the engineering behavior of soils. Innov. Infrastruct. Solut. 6, 1–14 (2021).Article 

Google Scholar 
Shimizu, Y. et al. Viscosity measurement of Xanthan–Poly(vinyl alcohol) mixture and its effect on the mechanical properties of the hydrogel for 3D modeling. Sci. Rep. 8, 16538 (2018).PubMed 
PubMed Central 
Article 
ADS 
CAS 

Google Scholar 
Kumar, S. A. & Sujatha, E. R. An Appraisal of the Hydro-mechanical behaviour of polysaccharides, xanthan gum, guar gum and β-glucan amended soil. Carbohyd. Polym. 265, 118083 (2021).Article 
CAS 

Google Scholar 
Chang, I., Prasidhi, A. K., Im, J., Shi, H. D. & Cho, G. C. Soil treatment using microbial biopolymers for anti-desertification purposes. Geoderma 253–254, 39–47 (2015).Article 
ADS 
CAS 

Google Scholar 
Fatehi, H., Ong, D. E. L., Yu, J. & Chang, I. Biopolymers as green binders for soil improvement in geotechnical applications: A review. Geosciences (Switzerland). 11, 291 (2021).CAS 
ADS 

Google Scholar 
Lee, S., Chang, I., Chung, M. K., Kim, Y. & Kee, J. Geotechnical shear behavior of xanthan gum biopolymer treated sand from direct shear testing. Geomech. Eng. 12, 831–847 (2017).Article 

Google Scholar 
Lee, S., Im, J., Cho, G. C. & Chang, I. Laboratory triaxial test behavior of xanthan gum biopolymer treated sands. Geomech. Eng. 17, 445–452 (2019).
Google Scholar 
Chang, I., Im, J., Prasidhi, A. K. & Cho, G. C. Effects of xanthan gum biopolymer on soil strengthening. Constr. Build. Mater. 74, 65–72 (2015).Article 

Google Scholar 
Liu, J. E. et al. The impact of natural polymer derivatives on sheet erosion on experimental loess hillslope. Soil. Tillage Res. 139, 23–27 (2014).Article 

Google Scholar 
Pu, S. et al. Stabilization behavior and performance of loess using a novel biomass-based polymeric soil stabilizer. Environ. Eng. Geosci. 25, 103–114 (2019).Article 

Google Scholar 
Zhang, X. C., Zhong, Y. J., Pei, X. J. & Duan, Y. Y. A cross-linked polymer soil stabilizer for hillslope conservation on the loess plateau. Front. Earth Sci. 9, 771316 (2021).Article 

Google Scholar 
Ni, J., Li, S. S., Ma, L. & Geng, X. Y. Performance of soils enhanced with eco-friendly biopolymers in unconfined compression strength tests and fatigue loading tests. Constr. Build. Mater. 263, 120039 (2020).CAS 
Article 

Google Scholar 
Kameda, J. & Yohei, H. Influence of biopolymers on the rheological properties of seafloor sediments and the runout behavior of submarine debris flows. Sci. Rep. 11, 1493 (2021).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Ramani, S., Atchaya, S., Sivasaran, A. & Keerdthe, R. S. Enhancing the geotechnical properties of soil using xanthan gum—An eco-friendly alternative to traditional stabilizers. Bull. Eng. Geol. Environ. 80, 1157–1167 (2020).
Google Scholar 
Cabalar, A. F., Awraheem, M. H. & Khalaf, M. M. Geotechnical properties of a low-plasticity clay with biopolymer. J. Mater. Civ. Eng. 30, 04018170 (2018).Article 

Google Scholar 
Reddy, J. J. & Varaprasad, B. J. S. Long-term and durability properties of xanthan gum treated dispersive soils—An eco-friendly material. Mater. Today. 44, 309–314 (2021).CAS 

Google Scholar 
Joga, J. R. & Varaprasad, B. J. S. Effect of xanthan gum biopolymer on dispersive properties of soils. J. Eng. Technol. 17, 563–571 (2020).CAS 

Google Scholar 
Muguda, S. et al. Mechanical properties of biopolymer-stabilised soil-based construction materials. Géotech. Lett. 7, 309–314 (2017).Article 

Google Scholar 
Muguda, S., et al. Cross-linking of biopolymers for stabilizing earthen construction materials. Build. Res. Inf. 1–13 (2021).Soldo, A., Miletić, M. & Auad, M. L. Biopolymers as a sustainable solution for the enhancement of soil mechanical properties. Sci. Rep. 10, 267 (2020).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Jiang, T., et al. Diametric splitting tests on loess based on PIV technique. Rock Soil Mech. 42, 2120–2126+2140 (2021).Zhang, J. R., Wang, L. J., Jiang, T., Ren, M. & Wei, M. Diametric splitting tests on unsaturated expansive soil with different dry densities based on the particle image velocimetry technique. Acta Geotech. Slov. 18, 15–27 (2021).Article 

Google Scholar 
Qureshi, M. U., Chang, I. & Al-Sadarani, K. Strength and durability characteristics of biopolymer-treated desert sand. Geomech. Eng. 12, 785–801 (2017).Article 

Google Scholar 
Ng, C. W. W. et al. Influence of biopolymer on gas permeability in compacted clay at different densities and water contents. Eng. Geol. 272, 105631 (2020).Article 

Google Scholar 
Kwon, Y. M., Ham, S. M., Kwon, T. H., Cho, G. C. & Chang, I. Surface-erosion behaviour of biopolymer-treated soils assessed by EFA. Géotech. Lett. 10, 106–112 (2020).Article 

Google Scholar 
Ramachandran, A. L., Dubey, A. A., Dhami, N. K. & Mukherjee, A. Multiscale study of soil stabilisation using bacterial biopolymers. J. Geotech. Geoenviron. Eng. 147, 04021074 (2021).CAS 
Article 

Google Scholar 
Nugent, R. A., Zhang, G. & Gambrell, R. P. Effect of exopolymers on the liquid limit of clays and its engineering implications. Transp. Res. Rec. 2101, 34–43 (2009).Article 

Google Scholar 
Wang, Y., Li, T. L., Zhao, C. X., Hou, X. K. & Zhang, Y. G. A study on the effect of pore and particle distributions on the soil water characteristic curve of compacted loess. Environ. Earth. Sci. 80, 764 (2021).Article 

Google Scholar 
Gao, Y., Sun, D. A., Zhu, Z. C. & Xu, Y. F. Hydromechanical behavior of unsaturated soil with different initial densities over a wide suction range. Acta. Geotech. 14, 417–428 (2018).Article 

Google Scholar 
Li, B. & Chen, Y. L. Influence of dry density on soil-water retention curve of unsaturated soils and its mechanism based on mercury intrusion porosimetry. Trans. Tianjin Univ. 22, 268–272 (2016).CAS 
Article 

Google Scholar 
Xu, W. S., Li, K. S., Chen, L. X., Kong, W. H. & Liu, C. X. The impacts of freeze-thaw cycles on saturated hydraulic conductivity and microstructure of saline-alkali soils. Sci. Rep. 11, 18655 (2021).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Li, Z. Q., Qi, Z. Y., Qi, S. W., Zhang, L. X. & Hou, X. H. Microstructural changes and micro-macro-relationships of an intact, compacted and remolded loess for land-creation project from the Loess Plateau. Environ. Earth. Sci. 80, 593 (2021).Article 

Google Scholar  More

Variable names, units and timestampsStreamflow is runoff routed along a drainage network, in m3/s, also known as discharge, which is the variable name used in the files. Water temperature is given in units of Kelvin. Filenames include the variable name, GCM, scenario (hist for historical, or one of the RCPs) and the time period (years). The timestamps in the files reflect the last date of the period over which the output was averaged, so the first timestamp of the weekly averages is January 7th 1976.Ecologically-relevant variablesThe ecologically-relevant streamflow and water temperature variables derived from the weekly values are established based on a combination of classification frameworks, i.e., indicators of hydrologic alteration19, terrestrial bioclimatic variables in the worldclim dataset20 as well as the CMCC-BioClimInd dataset21, aggregated accordingly: 1976–2005 (1979–2005 for E2O); 2021–2040; 2041–2060; 2061–2080; 2081–2099. The scripts used to compute these derived variables can be found under Code Availability.For files containing information on timing (see Tables 2–3), note that the counting is 0-indexed. So week numbers run from 0 through 51, months from 0 to 11. For timing of quarters, 0 is DJF, 1 is MAM, 2 is JJA, 3 is SON. The week number (for WT-wmin, WT-wmax, Q-wmin, Q-wmax) is determined as the mode, i.e. the most frequent week number within a period. For each period (20, 25 or 30 years) we looked for the week number in which the minimum or maximum water temperature or discharge occurs. If that happens most often in week X, that week number is stored. It can however occur that a certain minimum/maximum temperature or discharge occurs equally often in multiple weeks – then we assign a missing value.The variables Q-bfi and Q-vi are calculated according to Pastor et al.30. The baseflow index is an indicator of the importance of stored sources; a high index indicates that flow is mostly sustained by stored sources such as groundwater.Scripts used to create the derived variables are available through the FutureStreams GitHub repository (see Code Availability below).Multi-model set-upWe provide future scenarios for four RCPs (representative concentration pathways; 2.6, 4.5, 6.0 and 8.5 W/m2 in 2100) for the five ISI-MIP GCMs. Projections differ across RCPs due to differences in greenhouse gas forcing, and across GCMs due to differences in e.g model parameterization and resolution. Generally the spread across GCMs is larger than that across RCPs7,31. When interested in the general effect of climate change, users are advised to use the mean or median across the GCMs, rather than selecting a specific GCM. When interested in the spread across GCMs, users can explore or represent that in various ways, such as color intensity indicating agreement amongst models5, bar or violin plots7 etc.Warming levelsTo facilitate assessments and comparisons of streamflow and water temperature at a certain air temperature rise rather than specific years5,7, we provide a table with the years in which each GCM/RCP reaches the global mean temperature rises 1.5°, 2.0°, 3.2°, 4.5° compared to pre-industrial temperatures (as used by Barbarossa et al.7) with our scripts (see Code Availability). These years represent the central value of a 30-year running mean, so users should evaluate the 30-year mean (or other statistic) of discharge or water temperature centered around the year that a certain warming level is reached, which is specific to each RCP and GCM combination. For instance, if 1.5° warming is reached in 2040, the 30-year period 2025–2054 should be considered.GCMs, bias-correction and reanalysis dataThe majority of our simulations are forced with meteorological time series from GCMs. Those are bias-corrected27 before being applied to impact models such as PCR-GLOBWB, which corrects for systematic deviations of the simulated historical data from observations. For instance, for temperature the offset in average temperature in the historical GCM simulation with respect to observations is subtracted from temperatures in all scenarios of that GCM. The bias-corrected GCM forcing should thus well represent climatology, but not necessarily timing of actual events such as floods and droughts. Reanalysis data is created by assimilating observations into weather models, to obtain consistent and globally complete time series. The output of the simulation forced with meteorological time series from the (E2O) reanalysis data should therefore reflect not only the average streamflow and water temperatures, but also timing of actual events such as droughts.If users want to check for themselves how the GCM-forced historical simulations discussed here deviate from reanalysis-forced simulations, they can use the output from the E2O-forced simulation provided here, the monthly output linked to Wanders et al.13 (see also Code Availability) or the daily output of those simulations which are available from Niko Wanders upon request. The latter are forced with ERA-40/ERA-Interim reanalysis data.Notes of cautionBeware of temperature in grid cells where streamflow is low, which can cause temperatures to become unrealistically high due to strong fluctuations in the water level. The computational timesteps currently implemented in DynWat are not sufficiently small to provide stable solutions for these conditions. For some lakes and reservoirs we observe a similar problem when lakes expand or shrink as a result of water levels changes. These locations can be masked and we can assume that water temperature follows the air temperature for these very shallow water layers. A file with locations of lakes and reservoirs is provided in the data repository (under indicators/mask) so users can mask these if desired.Furthermore, we provide masks for each GCM-RCP-period which users can apply to the derived variables if desired. These masks are based on Q-mean and WT-mean and thresholds of 10 m3/s and 350 K, respectively. They can be found in the data repository (i.e. indicators/waterTemperature/WT-mask). The scripts used to create these masks are provided through the FutureStreams GitHub repository (see Code Availability below), which can be used to create masks with different thresholds. These scripts are called mask_unrealistic_values.py and maskFunctions.py.We also provide scripts to mask out unrealistic values directly in the weekly Q and WT files, these scripts are mask_unrealistic_values_weekly.py and maskFunctions_weekly.py. In all these scripts the threshold for discharge is set to 10 m3/s and for water temperature to 350 K, but users can change those to their preferred values. The threshold value will be included in the resulting output file name.Furthermore, we encountered spin-up issues in some pixels for the future RCP simulations. Instead of following the temperatures from the end of the historical simulation, temperatures drop at the beginning of the future simulation, so the first few weeks of 2006 temperatures can be unrealistically low. In Fig. 2, output of the year 2007 is used for the year 2006 .Fig. 2Water temperature [°C] anomaly. The maps show the difference between the mean water temperature over the period 2070–2099 (RCP8p5) and the historical period 1975–2005. The map shows values only for rivers with streamflow greater than 50 m3/s and the width of the rivers is scaled based on the streamflow values for clarity of representation. Insets below the map show the original gridded resolution at 5 arcminute for cells with streamflow values greater than 10 m3/s. The bottom insets show water temperature time series sampled at specific grid-cell locations (white crosses in the insets) for the Amazon (−57.2083° longitude, −2.625° latitude), Danube (20.125° lon, 45.2083° lat) and Ganges (88.375° lon, 24.375° lat). Time series are represented for each GCM and RCP available within FutureStreams; thin lines represent weekly values, thick lines represent 10 year rolling means.Full size imageFig. 3Streamflow [m3/s] anomaly. The maps show the difference between the log10 transformed mean streamflow over the period 2070–2099 (RCP8p5) and the log10 transformed mean streamflow over historical period 1975–2005. The map shows values only for rivers with streamflow values greater than 50 m3/s and the width of the rivers is scaled based on the streamflow values for clarity of representation. Insets below the map show the original gridded resolution at 5 arcminute for cells with streamflow values greater than 10 m3/s. The bottom insets show water temperature time series sampled at specific grid-cell locations (white crosses in the insets) for the Amazon (−57.2083° longitude, −2.625° latitude), Danube (20.125° lon, 45.2083° lat) and Ganges (88.375° lon, 24.375° lat). Time series are represented for each GCM and RCP available within FutureStreams; thin lines represent weekly values and thick lines represent 10 year rolling means.Full size imageFig. 4Anomalies for selected ecologically relevant derived variables (bioclimatic indicators) for the same areas in the Amazone (left), Danube (middle) and Ganges (right) basins as used in Figs. 2 and 3. Differences are shown between RCP8.5 2080–2099 and 1976–2005. WT-cq is the water temperature of the coldest quarter, WT-range is temperature range, Q-max is maximum streamflow, Q-dm is streamflow of the driest month (see also Tables 2 and 3 below). For streamflow we show the difference between log10-transformed flow.Full size image More