Ecology

Plant materialsAn ALS-inhibitor-resistant johnsongrass (resistant to nicosulfuron) obtained from the University of Nebraska-Lincoln (source credit: Dr. John Lindquist) was used as the pollen source (male parent), and the natural johnsongrass population present in the experimental field at the Texas A&M University Farm, Somerville (Burleson County), Texas (30° 32′ 15.4″ N 96° 25′ 49.2″ W) with no history of ALS-inhibitor resistance was used as the pollen recipient (female parent). Prior to the initiation of the field experiment, the susceptibility to nicosulfuron of the natural johnsongrass population was verified by spraying Accent Q at the labeled field rate of 63 g ai ha−1 [mixed with 0.25% v/v Crop Oil Concentrate (COC)] on 10 randomly selected 1 m2 johnsongrass patches across the field area at 15–30 cm tall seedling stage. For this purpose, a CO2 pressurized backpack sprayer was calibrated to deliver 140 L ha−1 of spray volume at an operating speed of 4.8 kmph. The natural johnsongrass population was determined to be completely susceptible to nicosulfuron.During spring 2018, the seeds of AR johnsongrass were planted in pots (14-cm diameter and 12-cm tall) filled with potting soil mixture (LC1 Potting Mix, Sungro Horticulture Inc., Agawam, MA, USA) at the Norman Borlaug Center for Southern Crop Improvement Greenhouse Research Facility at Texas A&M University. The environmental conditions were set at 26/22 °C day/night temperature regime and a 14-h photoperiod. In each pot, 5 seeds were planted and thinned to one healthy seedling at 1-leaf stage. Seedlings were supplied with sufficient water and nutrients (Miracle-Gro Water Soluble All Purpose Plant Food, Scotts Miracle-Gro Products Inc., 14111 Scottslawn Road, Marysville, OH 43041). A total of 50 seedlings were established in the greenhouse and were maintained until they reached about 10 cm tall, at which point they were sprayed with 2× the field rate of nicosulfuron (63 × 2 = 126 g ai ha−1) (mixed with 0.25% v/v COC). The herbicide was applied using a track-sprayer (Research Track Sprayer, DeVries, Hollandale, MN) fitted with a flat fan nozzle (TeeJet XR110015) that was calibrated to deliver a spray volume of 140 L ha−1 at 276 kPa pressure, and at an operating speed of 4.8 kmph. All treated seedlings that survived the herbicide application at 21 days after treatment (DAT) were then used as the pollen donor in the field gene flow experiment. All plant materials were handled in accordance with relevant guidelines and regulations. No permissions or licenses were required for collecting the johnsongrass samples from the experimental fields.Dose–response assaysThe degree of resistance/susceptibility to nicosulfuron of the AR and AS johnsongrass biotypes were determined using a classical dose–response experiment. The assay consisted of seven rates (0, 0.0625, 0.125, 0.25, 0.5, 1, and 2×) for the AS population and nine rates (0, 0.25, 0.5, 1, 2, 4, 8, 16, and 32×) for the AR population [1 × (field recommended rate) = 63 g ai ha−1 of Accent Q]. The experimental units were arranged in a completely randomized design with four replications. Seeds of AR and AS plants were planted in plastic trays (25 × 25 cm) filled with commercial potting-soil mix (LC1 Potting Mix, Sungro Horticulture Inc., Agawam, MA, USA) and maintained at 26/22 °C day/night cycle with a 14-h photoperiod in the greenhouse. Seedlings at 1–2 leaf stage were thinned to 20 seedlings per tray; four replications each of 20 seedlings per dose were considered. The seedlings were watered and fertilized as needed. The assay was conducted twice, thus a total of 160 seedlings were screened for each dose.The established seedlings were sprayed with the appropriate herbicide dose at the 10–15 cm tall seedling stage. The herbicide was applied using a track sprayer calibrated to deliver a spray volume of 140 L ha−1 at 4.8 kmph operating speed. Survival (%) and injury (%) were assessed at 28 DAT. Any plant that failed to grow out of the herbicide impact was considered dead. Plant injury was rated for each plot (i.e. on the 20 seedlings per rep) on a scale of 0–100%, where 0 indicates no visible impact compared to the nontreated control and 100 indicates complete death of all plants in the tray. Immediately after the visual ratings were completed, shoot biomass produced by the 20 plants from each tray was determined by harvesting all the tissues at the soil level and drying them in an oven at 60 °C for 72 h. Seedling mortality data were used for fitting dose–response curves that allowed for determining the lethal dose that caused 100% mortality of the susceptible biotype. This dose was used as a discriminant dose to distinguish between a hybrid (that confers resistance to nicosulfuron as a result of gene flow) and a selfed progeny (susceptible to nicosulfuron) in the field gene flow study.Field experimental location and set-upThe field experiment was conducted across two ENVs in 2018 (summer and fall) and one in 2019 (fall) at the Texas A&M University Farm, Somerville (Burleson County), Texas (30° 32′ 15.4″ N 96° 25′ 49.2″ W). The study site is characterized by silty clay loam soil with an average annual rainfall of 98.2 cm. The field experiment followed the Nelder-wheel design40, i.e. concentric donor-receptor design, a widely used method for gene flow studies, wherein the pollen-donors are surrounded by the pollen-receptors (Fig. 1). In this study, the AR plants (planted in the central block of the wheel) served as the pollen-donors, whereas the AS plants (present in the spokes) served as the pollen-receptors.Figure 1Aerial view of the experimental arrangement that was used to quantify pollen-mediated gene flow from ALS-inhibitor resistant (AR) to -susceptible (AS) johnsongrass at the Texas A&M University Research Farm near College Station, Texas. AR johnsongrass plants were transplanted in the pollen-donor block of 5 m diameter at the center of the field. The surrounding pollen-receptor area was divided into four cardinal (N, E, S, W) and four ordinal (NE, SE, SW, NW) directional blocks where naturally-existing AS johnsongrass plants were used as the pollen-recipients. AS panicles exhibiting flowering synchrony with AR plants were tagged at specific distances (5–50 m, at 5 m increments) along the eight directional arms. A tall-growing biomass sorghum border was established in the perimeter of the experimental site to prevent pollen inflow from outside areas.Full size imageThe center of the wheel was 5 m in diameter, and each spoke was 50 m long starting at the periphery of the central circular block. Thirty AR plants (pollen-donors) were transplanted in four concentric rings of 1, 5, 9, and 15 plants in the 5 m diameter central block, surrounded by the pollen-receptors (i.e. AS plants) (Fig. 1). The AR plants were contained within the central block during the 2 years of the field experiment by harvesting and removing all mature seeds and removing any expanding rhizomatous shoots. Further, field cultivation was completely avoided in the central block throughout the study period. Any newly emerging johnsongrass plants (seedling/rhizomatous) other than the transplanted AR plants in the central block were removed periodically by manual uprooting.The wheel consisted of eight spokes (i.e. directional blocks) arranged in four cardinal (N, E, S, W) and four ordinal (NE, SE, NW, SW) directions (Fig. 1). The plots to quantify gene flow frequency were arranged at 0 (border of the central block), 5, 10, 15, 20, 25, 30, 35, 40, 45, and 50 m distances from the central block in all eight directions (Fig. 1). Each plot measured 3 × 2 m and the area surrounding the plots was shredded prior to the booting stage with a Rhino® RC flail shredder (RHINOAG, INC., Gibson City, IL 60936).A tall-growing biomass sorghum border (6 m wide) was established surrounding the experimental area in all directions to prevent potential inflow of pollen from other Sorghum spp. in the nearby areas. Additionally, prevailing weather conditions, specifically wind direction, wind speed, relative humidity, and air temperature measured at 5-min intervals were obtained from a nearby weather station located within the Texas A&M research farm (http://afs102.tamu.edu/). The field did not require any specific agronomic management in terms of irrigation, fertilization, or pest management.Flowering synchrony, tagging, and seed harvestingAt peak flowering, when  > 50% of the plants in the AR block started anther dehiscence (i.e., pollen shedding), ten AS panicles (five random plants × 2 panicles per plant) that showed flowering synchrony with AR plants and displayed protruded, receptive stigma were tagged using colored ribbons at each distance and direction. At seed maturity, the tagged AS panicles were harvested separately for each distance and direction. Panicles were threshed, seeds were cleaned manually, and stored under room conditions until used in the herbicide resistance screening to facilitate after-ripening and dormancy release.Resistance screeningThe hybrid progeny produced on AS plants as a result of outcrossing with AR plants would be heterozygous for the allele harboring nicosulfuron resistance, and would exhibit survival upon exposure to the herbicide applied at the discriminant dose at which all wild type (AS) plants would die. The discriminant dose was determined using the dose–response study described above. Thus, the frequency of resistant plants in the progeny would represent outcrossing/gene flow (%).To effectively detect the levels of gene flow from AR to AS biotypes especially at low frequencies, the minimum sample size required for resistance screening was determined based on the following formula (Eq. 1)41:$${text{N }} = {text{ ln}}left( {{1} – P} right)/{text{ln}}left( {{1} – p} right),$$
(1)
where P is the probability of detecting a resistant progeny in the least frequent class and p is the probability of the least frequent class. Based on this formula, a minimum of 298 to as high as 916 plants were screened for each distance within each direction, allowing for a 1% detection level (p = 0.01) with a 95% (P = 0.95) confidence interval.Approximately one-year old progeny seeds harvested from the AS plants were scarified using a sandpaper for 15–20 s to release dormancy. The seeds for each distance within each direction were planted in four replicates of plastic trays (50 × 25 cm) filled with potting soil mixture (LC1 Potting Mix, Sungro Horticulture Inc., Agawam, MA, USA). The plants were raised at the Norman Borlaug Center for Southern Crop Improvement Greenhouse Research Facility at Texas A&M University. The greenhouse was maintained at 28/24 °C day/night temperature regime and a 14-h photoperiod. About 10–15 cm tall seedlings were sprayed with the discriminant dose of the ALS-inhibitor nicosulfuron (Accent Q, 95 g ai ha−1) using a spray chamber (Research Track Sprayer, DeVries, Hollandale, MN) fitted with a flat fan nozzle (TeeJet XR110015) that was calibrated to deliver a spray volume of 140 L ha−1 at 276 kPa pressure, operating at a speed of 4.8 kmph. At 28 DAT, percent seedling survival was determined based on the number of plants that survived the herbicide application out of the total number of plants sprayed. The number of plants in each tray was counted before spraying.Molecular confirmation of hybridsLeaf tissue samples were collected from thirty random surviving plants (putative resistant) in the herbicide resistance screening study for each of the three field ENVs, thus totaling 90 samples. Genomic DNA was extracted from 100 mg of young leaf tissue using the modified CTAB protocol42. The concentration of DNA was determined using a Nanodrop 1000 UV–Vis spectrophotometer (DeNovix DS-II spectrophotometer, DeNovix Inc., Wilmington, DE 19810, USA). DNA was then diluted to a concentration of 20 ng/µl for PCR assay. The nicosulfuron-resistant johnsongrass from Nebraska used in this study possessed the Trp574Leu mutation39. Hence, single nucleotide polymorphism (SNP) primers targeting a unique short-range haplotype of Inzen® sorghum (Val560Ile + Trp574Leu) were performed using the PCR Allele Competitive Extension (PACE) platform to confirm the resistant plants43. The SNP primers and the PACE genotyping master mix were obtained from Integrated DNA Technologies (IDT) Inc. (Coralville, IA) and 3CR Bioscience (Harlow CM20 2BU, United Kingdom), respectively. In addition to the two no-template controls (NTCs), two nicosulfuron-resistant johnsongrass, one wild-type johnsongrass, and one Inzen® sorghum were also used in the PCR.The PCR was performed according to the manufacturer’s protocol (Bio-Rad Laboratories, Inc., Hercules, CA), with denaturation for 15 min at 94 °C, followed by 10 cycles of denaturation at 94 °C for 20 s, annealing and extension at 65–57 °C for 60 s, 30 cycles of denaturation for 20 s at 94 °C, and annealing and extension for 60 s at 57 °C. Fluorescence of the reaction products were detected using a BMG PHERAStar plate reader that uses the FAM (fluorescein amidite) and HEX (hexachloro-fluorescein) fluorophores.Data analysisFor the dose–response assay, three-parameter sigmoidal curves (Eq. 2) were fit on the seedling mortality data for the AS and AR biotypes (with log of herbicide doses), using SigmaPlot version 14.0 (Systat Software Inc., San Jose, CA).$$y=b/[1+{exp}^{left(-(x-eright)/c)}],$$
(2)
where, y is the mortality (%), x is the herbicide dose (g ai ha−1), b is the slope around e, c is the lower limit (theoretical minimum for y normalized to 0%), and e = LD50 (inflection point, mid-point or estimated herbicide dose when y = 50%). Windrose plots that represented wind speed and frequency during the flowering window in each of the eight directions were created using a macro in Microsoft Excel. Progeny seedling survival (%) that represents gene flow (%) was determined using Eq. (3).$${text{PMGF }}left( {text{%}} right){ } = { }left( frac{X}{Y} right)_{{i,j{ }}} times { }100,$$
(3)
where, X is the number of plants that survived the herbicide application, Y is the total number of plants sprayed for ith distance in jth direction.To test whether gene flow frequencies varied among the directions, ANOVA was conducted using JMP PRO v.14 (SAS Institute, Cary, NC, USA), based on the average gene flow frequency values in each direction; ENVs were considered as replicates in this analysis. A non-linear regression analysis for gene flow rate, describing an exponential decay function (Eq. 4), was fit using SigmaPlot based on the gene flow frequencies observed at different distances pooled across the directions and ENVs.$$y=y0+left[atimes {exp}^{left(-btimes xright)}right],$$
(4)
where, y is the PMGF (%), x is the distance (m) from pollen source, y0 is the lower asymptote (theoretical minimum for y normalized to 0%), a is the inflection point, mid-point or estimated distance when y = 50%, and b is the slope around a.A Pearson correlation analysis was conducted to determine potential association between PMGF [overall PMGF, short-distance PMGF (5 m), and long-distance PMGF (50 m)] and the environmental parameters temperature, relative humidity, and dew point. Further, a correlation analysis was also conducted to understand the association between PMGF frequencies and specific wind parameters such as wind frequency, wind speed, and gust speed. The molecular data were analyzed using KlusterCaller 1.1 software (KBioscience). More

Diomedenema dinarctos, a parasitic worm that infests penguins, is named after the Greek deinos, meaning terrible, and arktos, or bear, because of its resemblance to a menacing teddy bear.Credit: Bronwen Presswell and Jerusha Bennett

What scientists choose to name parasitic worms could say more about the researchers than the organism they are studying.A study1 examining the names of nearly 3,000 species of parasitic worm discovered in the past 20 years reveals a markedly higher proportion named after male scientists than after female scientists — and a growing appetite for immortalizing friends and family members in scientific names.The analysis uncovers ongoing biases in taxonomy — the classification of organisms — and could be used as a jumping-off point for rethinking how scientists name species, says study co-author Robert Poulin, an ecological parasitologist at the University of Otago in Dunedin, New Zealand.“When you name something, it is now named forever. I think it’s worth giving some thought to what names we choose,” he says. The research was published on 11 May in Proceedings of the Royal Society B.As the worm turnsSpecies names often describe how an organism looks or where it was found. But since the nineteenth century, they have also been used to immortalize scientists. The parasite that causes the intestinal disease giardiasis, for instance, was named after French zoologist Alfred Giard.Wondering how naming practices had changed, Poulin and his colleagues combed through papers published between 2000 and 2020 that describe roughly 2,900 new species of parasitic worm. The team found that well over 1,500 species were named after their host organism, where they were found or a prominent feature of their anatomy.Many others were named after people, ranging from technical assistants to prominent politicians (Baracktrema obamai, a species found in Malaysian freshwater turtles, was named after former US president Barack Obama). But just 19% of the 596 species named after eminent scientists were named after women, a percentage that essentially didn’t budge over the decades (see ‘Parasite name game’).

Source: Ref. 1

This could be because of a historical dearth of female figures in the field, says Janine Caira, a parasite taxonomist at the University of Connecticut in Storrs. But another possibility is that the work of past female scientists often goes unrecognized, says Tanapan Sukee, a parasitologist at the University of Melbourne in Australia.Sukee has named two species of parasitic worm after now-deceased Australian biologist Patricia Mawson, who was a key player in the characterization of marsupial parasites. For most of her career, Mawson worked part-time as a technician, and she was often designated second author on papers describing species she had discovered, Sukee says. Similar situations could explain why so few parasites are named after women.Poulin and his colleagues also noticed an upward trend in the number of parasites named after friends and family members of the scientists who formally described them. Some researchers even name species after pets: Rhinebothrium corbatai is a freshwater stingray parasite named after the first author’s Welsh terrier, Corbata.Poulin says this should be discouraged. Species are almost never named after the person who described them, and Poulin argues that names honouring parents, children or spouses could be seen as a way to get around this convention.And besides, “I don’t have any friends or family who want a parasite named after them!” says Sukee. More

Singh, B. K., Bardgett, R. D., Smith, P. & Reay, D. S. Microorganisms and climate change: terrestrial feedbacks and mitigation options. Nat. Rev. Microbiol. 8, 779–790 (2010).CAS 
PubMed 
Article 

Google Scholar 
Fierer, N. Embracing the unknown: disentangling the complexities of the soil microbiome. Nat. Rev. Microbiol. 15, 579–590 (2017).CAS 
PubMed 
Article 

Google Scholar 
Guerra, C. A. et al. Tracking, targeting, and conserving soil biodiversity. Science 371, 239–241 (2021).CAS 
PubMed 
Article 

Google Scholar 
Fanin, N. et al. Consistent effects of biodiversity loss on multifunctionality across contrasting ecosystems. Nat. Ecol. Evol. 2, 269–278 (2018).PubMed 
Article 

Google Scholar 
Delgado-Baquerizo, M. et al. Multiple elements of soil biodiversity drive ecosystem functions across biomes. Nat. Ecol. Evol. 4, 210–220 (2020).PubMed 
Article 

Google Scholar 
Chen, W. et al. Fertility-related interplay between fungal guilds underlies plant richness-productivity relationships in natural grasslands. New Phytol. 226, 1129–1143 (2020).PubMed 
Article 

Google Scholar 
Semchenko, M. et al. Fungal diversity regulates plant–soil feedbacks in temperate grassland. Sci. Adv. 4, eaau4578 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kohli, M. et al. Stability of grassland production is robust to changes in the consumer food web. Ecol. Lett. 22, 707–716 (2019).PubMed 
Article 

Google Scholar 
Liang, M. et al. Soil microbes drive phylogenetic diversity–productivity relationships in a subtropical forest. Sci. Adv. 5, eaax5088 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Tilman, D., Reich, P. B. & Knops, J. M. H. Biodiversity and ecosystem stability in a decade-long grassland experiment. Nature 441, 629–632 (2006).CAS 
PubMed 
Article 

Google Scholar 
Yang, G. W., Wagg, C., Veresoglou, S. D., Hempel, S. & Rillig, M. C. How soil biota drive ecosystem stability. Trends Plant Sci. 23, 1057–1067 (2018).CAS 
PubMed 
Article 

Google Scholar 
de Vries, F. T., Griffiths, R. I., Knight, C. G., Nicolitch, O. & Williams, A. Harnessing rhizosphere microbiomes for drought-resilient crop production. Science 368, 270–274 (2020).PubMed 
Article 
CAS 

Google Scholar 
Pörtner, H.O. et al. Scientific outcome of the IPBES-IPCC co-sponsored workshop on biodiversity and climate change (IPBES, 2021).Gessner, M. O. et al. Diversity meets decomposition. Trends Ecol. Evol. 25, 372–380 (2010).PubMed 
Article 

Google Scholar 
Bardgett, R. D. & van der Putten, W. H. Belowground biodiversity and ecosystem functioning. Nature 515, 505–511 (2014).CAS 
PubMed 
Article 

Google Scholar 
Anthony, M. A. et al. Forest tree growth is linked to mycorrhizal fungal composition and function across Europe. ISME J. https://doi.org/10.1038/s41396-021-01159-7 (2022).Jia, Y. Y., van der Heijden, M. G. A., Wagg, C., Feng, G. & Walder, F. Symbiotic soil fungi enhance resistance and resilience of an experimental grassland to drought and nitrogen deposition. J. Ecol. 109, 3171–3181 (2020).Article 
CAS 

Google Scholar 
Delgado-Baquerizo, M. et al. The proportion of soil-borne pathogens increases with warming at the global scale. Nat. Clim. Change 10, 550–554 (2020).Article 

Google Scholar 
Tedersoo, L., Bahram, M. & Zobel, M. How do mycorrhizal associations drive plant population and community biology? Science 367, eaba1223 (2020).CAS 
PubMed 
Article 

Google Scholar 
Guo, X. et al. Climate warming leads to divergent succession of grassland microbial communities. Nat. Clim. Change 8, 813–818 (2018).Article 

Google Scholar 
Põlme, S. et al. FungalTraits: a user-friendly traits database of fungi and fungus-like stramenopiles. Fungal Divers. 105, 1–16 (2020).Article 

Google Scholar 
Egidi, E. et al. A few Ascomycota taxa dominate soil fungal communities worldwide. Nat. Commun. 10, 2369 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Tedersoo, L. et al. Global diversity and geography of soil fungi. Science 346, 1078–1088 (2014).CAS 
Article 

Google Scholar 
Delgado-Baquerizo, M. et al. The influence of soil age on ecosystem structure and function across biomes. Nat. Commun. 11, 4721 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Isbell, F. et al. Biodiversity increases the resistance of ecosystem productivity to climate extremes. Nature 526, 574–577 (2015).CAS 
PubMed 
Article 

Google Scholar 
Steidinger, B. S. et al. Climatic controls of decomposition drive the global biogeography of forest-tree symbioses. Nature 569, 404–408 (2019).CAS 
PubMed 
Article 

Google Scholar 
Wagg, C. et al. Diversity and asynchrony in soil microbial communities stabilizes ecosystem functioning. Elife 10, 3207 (2021).Article 

Google Scholar 
Yang, G. W., Wagg, C., Veresoglou, S. D., Hempel, S. & Rillig, M. C. Plant and soil biodiversity have non-substitutable stabilizing effects on biomass production. Ecol. Lett. 24, 1582–1593 (2021).PubMed 
Article 

Google Scholar 
Chen, L. T. et al. Above- and belowground biodiversity jointly drive ecosystem stability in natural alpine grasslands on the Tibetan Plateau. Glob. Ecol. Biogeogr. https://doi.org/10.1111/geb.13307 (2021).Garcia-Palacios, P., Gross, N., Gaitan, J. & Maestre, F. T. Climate mediates the biodiversity-ecosystem stability relationship globally. Proc. Natl Acad. Sci. USA 115, 8400–8405 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Valencia, E. et al. Synchrony matters more than species richness in plant community stability at a global scale. Proc. Natl Acad. Sci. USA 117, 24345–24351 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Craven, D. et al. Multiple facets of biodiversity drive the diversity-stability relationship. Nat. Ecol. Evol. 2, 1579–1587 (2018).PubMed 
Article 

Google Scholar 
Naeem, S. & Li, S. B. Biodiversity enhances ecosystem reliability. Nature 390, 507–509 (1997).CAS 
Article 

Google Scholar 
Hautier, Y. et al. Eutrophication weakens stabilizing effects of diversity in natural grasslands. Nature 508, 521–525 (2014).CAS 
PubMed 
Article 

Google Scholar 
Jousset, A., Schmid, B., Scheu, S. & Eisenhauer, N. Genotypic richness and dissimilarity opposingly affect ecosystem performance. Ecol. Lett. 14, 537–624 (2011).CAS 
PubMed 
Article 

Google Scholar 
Jiang, L., Pu, Z. & Nemergut, D. R. On the importance of the negative selection effect for the relationship between biodiversity and ecosystem functioning. Oikos 117, 488–493 (2008).Article 

Google Scholar 
Ratzke, C., Barrere, J. & Gore, J. Strength of species interactions determines biodiversity and stability in microbial communities. Nat. Ecol. Evol. 4, 376–383 (2020).PubMed 
Article 

Google Scholar 
Lekberg, Y. et al. Nitrogen and phosphorus fertilization consistently favor pathogenic over mutualistic fungi in grassland soils. Nat. Commun. 12, 3484 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bastida, F. et al. Soil microbial diversity–biomass relationships are driven by soil carbon content across global biomes. ISME J. 15, 2081–2091 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Paruelo, J., Epstein, H. E., Lauenroth, W. K. & Burke, I. C. ANPP estimates from NDVI for the central grassland region of the United States. Ecology 78, 953–958 (1997).Article 

Google Scholar 
Jobbágy, E. G., Sala, O. E. & Paruelo, J. M. Patterns and controls of primary production in the Patagonian steppe: a remote sensing approach. Ecology 83, 307–319 (2002).
Google Scholar 
Oehri, J., Schmid, B., Schaepman-Strub, G. & Niklaus, P. A. Biodiversity promotes primary productivity and growing season lengthening at the landscape scale. Proc. Natl Acad. Sci. USA 114, 10160–10165 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bastos, A., Running, S. W., Gouveia, C. & Trigo, R. M. The global NPP dependence on ENSO: La Niña and the extraordinary year of 2011. J. Geophys. Res. Biogeosci. 118, 1247–1255 (2013).Article 

Google Scholar 
Orwin, K. H. & Wardle, D. A. New indices for quantifying the resistance and resilience of soil biota to exogenous disturbances. Soil Biol. Biochem. 36, 1907–1912 (2004).CAS 
Article 

Google Scholar 
Frankenberg, C. et al. Prospects for chlorophyll fluorescence remote sensing from the Orbiting Carbon Observatory-2. Remote Sens. Environ. 147, 1–12 (2014).Article 

Google Scholar 
Sun, Y. et al. Overview of solar-induced chlorophyll fluorescence (SIF) from the Orbiting Carbon Observatory-2: retrieval, cross-mission comparison, and global monitoring for GPP. Remote Sens. Environ. 209, 808–823 (2018).Article 

Google Scholar 
Zhang, Y., Joiner, J., Alemohammad, S. H., Zhou, S. & Gentine, P. A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks. Biogeosciences 15, 5779–5800 (2018).CAS 
Article 

Google Scholar 
Running, S. W. et al. A continuous satellite-derived measure of global terrestrial primary production. Bioscience 54, 547–560 (2004).Article 

Google Scholar 
Beguería, S. et al. Standardized precipitation evapotranspiration index (SPEI) revisited: parameter fitting, evapotranspiration models, tools, datasets and drought monitoring. Int. J. Climatol. 34, 3001–3023 (2014).Article 

Google Scholar 
Chen, C. et al. China and India lead in greening of the world through land-use management. Nat. Sustain. 2, 122–129 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Luo, H. et al. Contrasting responses of planted and natural forests to drought intensity in Yunnan, China. Remote Sens. 8, 635 (2016).Article 

Google Scholar 
Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. Updated high-resolution grids of monthly climatic observations—the CRU TS3. 10 Dataset. Int. J. Climatol. 34, 623–642 (2014).Article 

Google Scholar 
Allen, R. G. et al. Crop Evapotranspiration: Guidelines for Computing Crop Water Requirements (FAO, 1998); https://www.fao.org/3/x0490e/x0490e00.htmOksanen, J. et al. Vegan: Community Ecology Package (R Foundation for Statistical Computing, 2013).Legendre, P. et al. Studying beta diversity: ecological variation partitioning by multiple regression and canonical analysis. J. Plant Ecol. 1, 3–8 (2008).Article 

Google Scholar 
Grömping, U. Relative importance for linear regression in R: the package relaimpo. J. Stat. Softw. 17, 1–27 (2006).Article 

Google Scholar 
Lefcheck., J. S. piecewiseSEM: piecewise structural equation modelling in R for ecology, evolution, and systematics. Methods Ecol. Evol. 7, 573–579.Bates, D. et al. lme4: linear mixed-effects models using Eigen and S4. J. Stat. Soft. 67, 1–48 (2014).
Google Scholar  More

This work formulates a general framework of the WFE Nexus at the national level, which includes all pertinent interactions between water, food, and energy sources and demands. Figure 1 depicts the feedbacks involving resource availability and consumption. The causal loops of the developed model for national-scale assessment are shown in Fig. 2. The model depicted in Fig. 2 proposes reducing consumption to reduce the water crisis to the extent possible. By reducing water use and pollution the environmental water requirement can be reduced, thus alleviating the water crisis. This paper’s objective is sustainable management by reducing per capita water use (in the residential section) and per capita energy use (in the domestic, public, and commercial section). The WFE nexus is modeled as a dynamic system for demand management applied to the stocks of energy, surface water, and groundwater resources to calculate their input and output rates (flows) at the national level while providing for environmental flow requirements (Fig. 3). The national modeling approach is of the lumped type, meaning that inputs and outputs to the stocks of water and energy represent totals over an entire country (in the case study, Iran); therefore, the models does not consider intra-country regional variations. The units of water resources and energy resources are expressed in cubic meters and MWh, respectively.Figure 1Feedbacks between resources and uses in the WFE nexus taking into account environmental considerations.Full size imageFigure 2The causal loops of the model developed for simulating the WFE nexus.Full size imageFigure 3Flow diagram of the WFE Nexus system.Full size imageBalance of water resourcesThe study of water exchanges in a country is based on the law of conservation of matter. The following sections present calculations pertinent to the annual balance of surface and groundwater resources.Surface water resourcesThe national runoff generated in a country’s high-elevation areas (or high terrain) and low-elevation areas (plains) is quantified with the following equations:$${preheight}_{t}=HeightCotimes {Precipitation}_{t}$$
(1)

in which ({preheight}_{t}) = volume of precipitation that falls in high-elevation areas during period t, (HeightCo) = the percentage of total precipitation that falls in high-elevation areas, and ({Precipitation}_{t}) = volume of precipitation during period t.$${preplain}_{t}=PlainCotimes {Precipitation}_{t}$$
(2)

in which ({preplain}_{t}) = volume of precipitation that falls in the plains during period t, and (PlainCo) = the percentage of total precipitation that falls in plains (low elevation areas).$${SInflow}_{t}=HeighSInflowCotimes {preheight}_{t}+PlainSInflowCotimes {preplain}_{t}+{OutCSW}_{t}+{Dr}_{t}$$
(3)

in which ({SInflow}_{t}) = the total volume of surface flows during period t, (HeighSInflowCo) = the runoff coefficient in high-elevation areas, (PlainSInflowCo) = the runoff coefficient in the plains, ({OutCSW}_{t}) = the difference between the volume of surface inflow and outflow through a country’s border during period t; and ({Dr}_{t}) = the flow of groundwater resources to surface water resources (i.e., baseflow) during period t.It is possible to calculate the water use after calculating the annual surface water originating by precipitation. Some of the water use by the agricultural, industrial, and municipal sectors becomes return flows. Equations (4) through (9) show how to calculate the surface water use and the water return flows to the surface water sources.$${DomWD}_{t}={Population}_{t}times PerCapitaWatertimes 365$$
(4)

in which ({DomWD}_{t}) = the volume of water use in the municipal sector during period t, ({Population}_{t}) = the population of the country during period t, and (PerCapitaWater) = per capita drinking water use (cubic meters per person per day).$${IndDomWD}_{t}={DomWD}_{t}+{IndWD}_{t}$$
(5)

in which ({IndDomWD}_{t}) = the volume of water use in the municipal and industrial sectors during period t, and ({IndWD}_{t}) = the volume of water use in the industrial sector during period t.The water use by the agricultural sector accounts for the water footprint of agricultural products, which measures their water use per mass of produce, and adjusting the water use by including water losses and agricultural return flows. A separate sub-agent (AGR agent) is introduced to perform the calculations related to the agricultural sector to simplify the dynamic-system model (main model), and the required outputs (BWAgr, GWAgr) of the dynamic system model are called by the agent in the main model (see Figs. 3 and 4). The BWAgr is given by the expression within parentheses in Eq. (6).Figure 4Agricultural subsystem modeled in the AGR agent (shows how to calculate the blue and gray water footprints of agricultural products).Full size image$${AgrWD}_{t}=left(sum_{iin A}{BW}_{i}times {Product}_{i,t}right)times frac{1}{{E}_{Agr}}+OtherAgrWD$$
(6)

in which ({AgrWD}_{t}) = the volume of agricultural water use during period t, ({BW}_{i}) = blue water footprint of agricultural product i (cubic meters per ton), ({Product}_{i,t}) = the amount of production of agricultural product i during period t (tons), ({E}_{Agr}) = the overall irrigation efficiency, (OtherAgrWD) = the volume of water consumed by agricultural products not included in the set A of agricultural products (in cubic meters). The set A includes those agricultural products with the largest yields and shares of the national food basket.$${AgrReW}_{t}={AgrWD}_{t}times AgrReCo$$
(7)

in which ({AgrReW}_{t}) = the volume of water returned from agricultural water use during the period t, and (AgrReCo) = the coefficient of water returned from agricultural water use.$${IndDomReW}_{t}={IndDomWD}_{t}times IndDomReCo$$
(8)

in which ({IndDomReW}_{t}) = the volume of water returned from industrial and municipal water use during period t, and (IndDomReCo) = the coefficient of water returned from industrial and municipal water uses.$${ReSW}_{t}=IndDomReSWCotimes {IndDomReW}_{t}+AgrReSWCotimes {AgrReW}_{t}$$
(9)

in which ({ReSW}_{t}) = the volume of water returned from water uses to surface water resources during period t, (IndDomReSWCo) = the percentage of water returned from municipal and industrial water use to surface water resources, and (AgrReSWCo) = the percentage of water returned from agricultural water use to surface water resources.Water is applied to produce energy, and Eqs. (10) through (15) perform the related calculations. The ({WEIF}_{t}) variable in Eq. (14) is necessary to account for the volume of water saved as a result of the energy savings. A PR model is introduced to account for such water savings (see Fig. 3).$${Diff}_{t} ={OutputE}_{t}-{OutputE}_{t}^{P}$$
(10)

in which ({Diff}_{t})= the difference between the energy used in the main model during period t and the energy used in period t in the PR model, ({OutputE}_{t}) = the sum of energy uses during period t in the main model (the method of calculating ({OutputE}_{t}) is described in detail in “Energy uses”), and ({OutputE}_{t}^{P}) = the sum of energy uses during period t in the PR model. Equations (11) and (12) account for the case when energy use exceeds energy production under current conditions, in which case energy exports are reduced. This prevents additional energy production to meet excess demand, and, consequently, there would not be increases in water use.$${Diff}_{t} le 0,,,{if,,func}_{t}=0$$
(11)
$${Diff}_{t} >0,,,{ if,,func}_{t}={Diff}_{t}$$
(12)

in which ({ iffunc}_{t}) = the amount of energy saved during period t.Equation (13) calculates the water required to produce energy:$${{TotalWE}_{t}=Coal}_{t}times ENwateruseC+{Gas}_{t}times ENwateruseG+{OilPetroleumP}_{t }times ENwateruseO+{Nuclear}_{t}times ENwateruseN+{Elec}_{t}times ENwateruseE$$
(13)

in which ({TotalWE}_{t}) = the volume of water required to produce the energy demand during period t,({Elec}_{t}) = the amount of electricity production during period t (MWh), and (ENwateruseE) = the water required per unit of energy generated by electricity (cubic meters per MWh), all other terms were previously defined.Equation (14) calculates the water savings:$${WEIF}_{t}=sum_{t=1}^{T}frac{{TotalWE}_{t}}{{OutputE}_{t}^{0}}times {if,,func}_{t}$$
(14)

in which ({WEIF}_{t})= the volume of water saved as a result of the energy saved during period t, T = the number of periods of simulation (T = 5 years).Part of the water used to produce energy from coal, oil, petroleum products, and nuclear fuel is accounted for in the industrial sector water use. For this reason, the volume of water to produce energy calculated with Eq. (15) is reduced by that part of water already accounted for in the industrial water use to avoid double accounting.$${WE}_{t}={Coal}_{t}times ENwateruseC+{Gas}_{t}times ENwateruseG+{OilPetroleumP}_{t }times ENwateruseO+{Nuclear}_{t}times ENwateruseN-INDEtimes {IndWD}_{t}-{WEIF}_{t}$$
(15)

in which ({WE}_{t}) = the volume of water required to produce different types of energy (except those included in the industrial sector) during period t, ({Coal}_{t}) = the energy produced with coal during period t (MWh), (ENwateruseC) = the water required per unit of energy produced with coal (cubic meters per MWh),({Gas}_{t}) = the amount of energy produced with natural gas during period t (MWh), (ENwateruseG) = the water required per unit of energy produced with natural gas (cubic meters per MWh), ({OilPetroleumP}_{t}) = the amount of energy produced with crude oil and other petroleum products during period t (MWh), (ENwateruseO) = the water required per unit of energy produced with crude oil and petroleum products (cubic meters per MWh),({Nuclear}_{t}) = the amount of nuclear energy produced during period t (MWh), (ENwateruseN) = the water required per unit of nuclear energy produced (cubic meters per MWh), and (INDE) = the percentage of industrial water use already accounted for in Eq. (5) (which pertains to water used in the coke coal, oil refineries, and nuclear fuel industries).Part of the discharge of springs enters the surface water sources, and this enters the calculation of the input to the surface water-resources stock in Eq. (16):$${InputSW}_{t}= SInflow+{ReSW}_{t}{+ Fountain}_{t}$$
(16)

in which ({InputSW}_{t}) = the volume of inflow water to surface water sources during period t, and ({Fountain}_{t}) = discharge of springs to surface water sources during period t, other terms previously defined.The output of the surface water resources includes water use and the infiltration of surface water into groundwater, the latter calculated with Eq. (17):$${SInflowInf}_{t}={SInflow}_{t}times SInflowInfCo$$
(17)

in which ({SInflowInf}_{t}) = the infiltration volume of surface water during period t, and (SInflowInfCo) = the infiltration coefficient of surface water.The output of the surface water resources stock is calculated using Eq. (18):$${OutputSW}_{t}={AgrSWDCo}_{t}times {AgrWD}_{t}+{IndSWDCo}_{t}times {IndWD}_{t}+{DomSWDCo}_{t}times {DomWD}_{t}+{mathrm{ WE}}_{t}+{SInflowInf}_{t}-{EvSwSea}_{t}$$
(18)

in which ({OutputSW}_{t}) = the output volume of surface water during period t, ({AgrSWDCo}_{t}) = the percentage of gross agricultural water use from surface water resources during period t, ({IndSWDCo}_{t}) = the percentage of industrial water use from surface water resources during period t, ({DomSWDCo}_{t})= the percentage of gross drinking water consumption from surface water sources during period t, and ({EvSwSea}_{t}) = the total volume of evaporation from surface water plus the discharge of surface water to the sea during period t.The balance of surface water resources is calculated based on Eq. (19):$$SWaterleft(tright)=underset{{t}_{0}}{overset{t}{int }}left[{InputSW}_{t}left(Sright)-{OutputSW}_{t}(S)right]dt+SWater(0)$$
(19)

in which (SWaterleft(tright)) = the stock of surface water resources at time t, (SWater(0)) denotes the stock of surface water at the initial time (t = 0).Groundwater resourcesGroundwater resources gain water from deep infiltration of precipitation in the plains and elevated areas from (1) inflows from outside of the study area, (2) infiltration from surface flows and return waters. Groundwater output factors also include the discharge of groundwater resources (wells, springs, and aqueducts), groundwater flow that moves outside the study area and evaporation. Infiltration of precipitation in the plains and in high terrain into groundwater resources is calculated with Eq. (20):$${Inf}_{t}=PrePInfCotimes {preplain}_{t}+PreHInfCotimes {preheight}_{t}$$
(20)

in which ({Inf}_{t}) = the volume of water entering groundwater sources through infiltration of precipitation during period t, (PrePInfCo) = the infiltration coefficient of precipitation in the plains, and (PreHInfCo) = the infiltration coefficient of rainfall in high terrain.Equation (21) calculates the volume of return water that accrues to groundwater resources:$${ReGW}_{t}=IndDomReGWCotimes {IndDomReW}_{t}+AgrReGWCotimes {AgrReW}_{t}$$
(21)

in which ({ReGW}_{t}) = the volume of water returned from water use that accrues to groundwater resources during period t, (IndDomReGWCo) = the percentage of water returned from municipal and industrial water use accruing to groundwater resources, and (AgrReGWCo) = the percentage of water returned from agricultural water use accruing to groundwater resources.The volume of groundwater input is calculated with Eq. (22):$${InputGW}_{t}={Inf}_{t}+{ReGW}_{t}+{SInflowInf}_{t}+{OutCGw }_{t}$$
(22)

in which ({InputGW}_{t}) = the volume of groundwater input during period t, and ({OutCGw }_{t}) = the difference between the volume of groundwater leaving and that entering the country during period t.The volume of groundwater output is calculated with Eq. (23):$${OutputGW}_{t}={AgrGWDCo}_{t}times {AgrWD}_{t}+IndGWDCotimes {IndWD}_{t}+DomGWDCotimes {DomWD}_{t}+{EvGwDr}_{t}$$
(23)

in which ({OutputGW}_{t}) = the volume of groundwater output during period t, ({AgrGWDCo}_{t}) = the percentage of gross agricultural water use from groundwater resources during period t, IndGWDCo = the percentage of industrial water use from groundwater resources during period t, DomGWDCo = the percentage of municipal water use from groundwater resources during period t, and ({EvGwDr }_{t}) = the total volume of evaporation from groundwater plus the drainage of groundwater resources to surface water resources at time t.Equation (24) calculates the annual balance of groundwater resources:$$GWaterleft(tright)=underset{{t}_{0}}{overset{t}{int }}left[{InputGW}_{t}left(Sright)-{OutputGW}_{t}left(Sright)right]dt+GWater(0)$$
(24)

in which GWater(t) = the groundwater resources stock at time t, (GWater(0)) denotes the stock of groundwater at the initial time (t = 0).Energy usesEnergy uses are calculated with Eqs. (25)–(27). The total national energy use includes the agricultural, industrial, transportation, and exports sectors’ energy demands. The energy uses by these sectors do not change during the implementation of the policy, and, consequently do not change the WFE Nexus in that period; therefore, they are not included in the calculations.$${WDTP}_{t}={DomWD}_{t}times {CEIntensity}_{t}$$
(25)

in which ({WDTP}_{t}) = the energy used in the extraction, transmission, distribution, and treatment of water in the water and wastewater system during period t, and ({CEIntensity}_{t}) = the energy intensity in the extraction, transmission, distribution, and treatment of water in water and wastewater systems during the period t (MWh per cubic meter).$${ResComPubED}_{t}=ResComPubPerCapitatimes {Population}_{t}$$
(26)

in which ({ResComPubED}_{t}) = the energy use by the domestic, commercial, and public sectors during period t, and (ResComPubPerCapita) = the per capita energy consumption by the domestic, commercial, and public sectors (MWh per person per year).$${OutputE}_{t}={ResComPubED}_{t}+{WDTP}_{t}$$
(27)
Environmental water needsThe gray water footprint is defined as the volume of freshwater that is required to assimilate the load of pollutants based on natural background concentrations and existing ambient water quality standards. The estimation of the gray water footprint associated with discharges from agricultural production is based on the load of nitrogen fertilizers, which are pervasive in agriculture. The gray water footprint in terms of nitrogen concentration has been estimated by Mekonnen and Hoekstra24,25, as written in Eq. (28):$${GW}_{t}^{Agr}=sum_{iin A}{GW}_{i}times {Product}_{i,t}$$
(28)

in which ({GW}_{t}^{Agr})= the volume of gray water in the agricultural sector during period t, and ({GW}_{i}) = the volume of gray water associated with the production of one ton of agricultural product i (cubic meters per ton)(.)There are no accurate estimates of the concentrations of pollutants per unit of industrial production, or of the concentration of pollutants in municipal wastewater. Therefore, the conservative dilution factor (DF), which is equal to 1 for untreated returned water from the municipal and industrial sectors, is applied in this work. Equation (29) is a simplified equation of the gray water footprint26. The fraction appearing on the right-hand side of Eq. (29) is equal to the DF.$${GW}_{t}^{IndDom}= frac{{C}_{eff}-{C}_{nat}}{{C}_{max}-{C}_{nat}}times {IndDomReW}_{t}times IndDomReUT$$
(29)

in which ({GW}_{t}^{IndDom}) = the gray water footprint of the municipal and industrial sectors during period t, ({C}_{eff}) = the nitrogen concentration in return water (mg/L), ({C}_{nat}) = the natural concentrations of contaminant in surface water (mg/L), ({C}_{max}) = the maximum allowable concentration contaminant in surface water (mg/L), and (IndDomReUT) = the percentage of untreated returned water from the municipal and industrial sectors.The total gray water footprint is obtained by summing the footprints associated with the municipal/industrial and agricultural sectors:$${TotalGW}_{mathrm{t}}={GW}_{t}^{IndDom}+{GW}_{t}^{Agr}$$
(30)

in which ({TotalGW}_{mathrm{t}}) = the volume of gray water from all sectors during period t.This work considers qualitative and quantitative environmental water needs. Equation (31) is used to calculate the total environmental water need. The Tennant method for calculating the riverine environmental flow requirement (or instream flow) stipulates that, based on the conditions of each basin, between 10 to 30% of the average long-term flow of rivers represents the environmental flow requirement27. The sum of these requirements across all the basins equals the environmental requirement of the entire region or country. Yet, by providing 10 to 30% of the average long-term flow of rivers the riverine ecosystem barely emerges from critical conditions, and is far from optimal ecologic functioning. The total environmental water need is equal to the sum of the environmental flow requirement plus the volume of water needed to dilute the contaminants entering the surface water sources:$${ENV}_{t}={TotalGW}_{t}+Tennant$$
(31)

in which ({ENV}_{t}) = the environmental flow requirement during period t, and Tennant = the environmental flow requirement calculated by the Tennant (1976) method.The policy evaluation indexThe available renewable water is calculated with Eq. (32):$${IN}_{t}={OutCGW }_{t}+ {SInflow }_{t}+{ Inf}_{t}-{EvGwDr}_{t}$$
(32)

in which ({IN}_{t})= the renewable water available before the application of environmental constraints during period t.The volume of manageable water is calculated with Eq. (33):$$REWleft(tright)=underset{{t}_{0}}{overset{t}{int }}left[INleft(tright)-ENVleft(tright)right]dt$$
(33)

in which REW (t) = the (cumulative) manageable and exploitable renewable water in the period t-t0.Equation (34) calculates the total water withdrawals by the agricultural, industrial, municipal, and energy production sectors:$${WDW}_{t}={OutputSW }_{t}+ {OutputGW}_{t}- {cheshmeh}_{t}$$
(34)

in which ({WDW}_{t}) = the sum of the withdrawals by the agricultural, industrial, municipal, and energy production sectors during period t.The cumulative water withdrawals are calculated with Eq. (35):$$withdleft(tright)=underset{{t}_{0}}{overset{t}{int }}WDWleft(tright)dt$$
(35)

in which (withdleft(tright)) = the sum of the withdrawals by the agricultural, industrial, municipal and energy production sectors in the horizon t-t0.Equation (36) calculates the water stress index:$${index}_{{t}_{f}}^{MRW}=frac{withd({t}_{f})}{REWleft({t}_{f}right)}times 100$$
(36)

in which ({index}_{{t}_{f}}^{MRW}) = the renewable water stress index at the end of the study period, and ({t}_{f}) = the period marking the end of the study horizon.Once the water and energy model is developed it must be calibrated with observational data prior to its use in predictions, as shown below. More

Crops and their landrace study areasFood crops whose genetic resources are researched and conserved by CGIAR international agricultural research centres or by the CePaCT of the SPC were included in this study. Crop landrace distributions were modelled and conservation analyses conducted within recognized primary and, for some crops, secondary regions of diversity, where these crops were domesticated and/or have been cultivated for very long periods, and where they are, thus, expected to feature high genetic diversity and adaptation to local environmental and cultural factors (Supplementary Tables 1 and 2)9,13. These regions were identified through literature review (Supplementary Information) and confirmed by crop experts.Occurrence dataOur crop landrace group distribution modelling and conservation gap analysis rely on occurrence data, including coordinates of locations where landraces were previously collected for ex situ conservation and reference sightings. For ex situ conservation records, occurrences marked as landraces were retrieved from two major online databases: the Genesys Plant Genetic Resources portal33 and the World Information and Early Warning System on Plant Genetic Resources for Food and Agriculture (WIEWS) of the Food and Agriculture Organization of the United Nations34. Occurrences were also obtained directly from individual international genebank information systems: AfricaRice, the International Transit Centre and Musa Germplasm Information System of Bioversity International35, CePaCT, International Center for Tropical Agriculture (CIAT), International Maize and Wheat Improvement Center (CIMMYT), International Potato Center (CIP), International Center for Agricultural Research in the Dry Areas (ICARDA), International Crops Research Institute for the Semi-arid Tropics (ICRISAT), International Institute of Tropical Agriculture (IITA) and International Rice Research Institute (IRRI), as well as from the United States Department of Agriculture (USDA) Genetic Resources Information Network (GRIN)–Global36 and the Comisión Nacional para el Conocimiento y Uso de la Biodiversidad (CONABIO)37. Occurrences were compiled from the Global Biodiversity Information Facility (GBIF), with ‘living specimen’ records classified as ex situ conservation records and the remaining serving as reference sightings for use in distribution modelling. Reference occurrences were also drawn from published literature (Supplementary Information). Duplicated observations within or between data sources were eliminated, with a preference to utilize the most original data. Coordinates were corrected or removed when latitude and longitude were equal to zero or inverted, located in water bodies or in the wrong country or had poor resolution ( 10 (ref. 60). The predictors and whether they were selected for the modelling of each landrace group are presented in Supplementary Table 4.We generated a random sample of pseudo-absences as background points in areas that (1) were within the same ecological land units61 as the occurrence points, (2) were deemed potentially suitable according to a support vector machine classifier that uses all occurrences and predictor variables and (3) were farther than 5 km from any occurrence62. The number of pseudo-absences generated per crop group was ten times its number of unique occurrences.MaxEnt models were fitted through five-fold (K = 5) cross-validation with 80% training and 20% testing. For each fold, we calculated the area under the receiving operating characteristic curve (AUC), sensitivity, specificity and Cohen’s kappa as measures of model performance. To create a single prediction that represents the probability of occurrence for the landrace group, we computed the median across K models. Geographic areas in the form of pixels with probability values above the maximum sum of sensitivity and specificity were treated as the final area of predicted presence13.Ex situ conservation status and gapsThree separate but complementary metrics were developed to compare the geographic and environmental diversity in current ex situ conservation collections to the total geographic and environmental variation across the crop landrace group distribution model and, thus, to identify and quantify ex situ conservation gaps13.A connectivity gap score (SCON) was calculated for each 2.5-arc-minute pixel within the distribution model by drawing a triangle63,64 around each pixel using the three closest genebank accession occurrence locations as vertices and then deriving normalized values for the pixel based on distance to the triangle centroid and vertices13. The SCON of a pixel is high—closer to 1 on a scale of 0–1—when its corresponding triangle is large, when the pixel is close to the centroid of the triangle or when the distance to the vertices is large. A high SCON represents a greater probability of the pixel location being a gap in existing ex situ collections.An accessibility gap score (SACC) was calculated for each 2.5-arc-minute pixel in the distribution model by computing travel time from each pixel to its nearest genebank accession occurrence location based both on distance and the speed of travel, defined by a friction surface13,45. Travel time scores were normalized by dividing pixel values by the longest travel time within the distribution model, with the final score ranging from 0 to 1. A high SACC value for a pixel reflects long travel times from existing genebank collection occurrences and, thus, represents a higher probability of the pixel location being a gap in existing ex situ collections.An environmental gap score (SENV) was calculated for each 2.5-arc-minute pixel in the distribution model by conducting a hierarchical clustering analysis using Ward’s method with all the predictor variables from the distribution modelling. The Mahalanobis distance between each pixel and the environmentally closest genebank accession occurrence location was then computed13. Environmental distance scores were normalized between 0 and 1. A high SENV value for a pixel reflects a large distance to areas with similar environments where landraces have previously been collected for genebank conservation and, thus, represents a higher probability of the pixel location being a gap in existing ex situ collections.Spatial ex situ conservation gaps were determined from the conservation gap scores using a cross-validation procedure to derive a threshold for each score. We created synthetic gaps by removing existing genebank occurrences in five randomly chosen circular areas with a 100 km radius within the distribution model. We then tested whether these artificial gaps could be predicted by our gap analysis, identifying the threshold value of each score that would maximize the prediction of these synthetic gaps. Performance for each of the five gap areas was assessed using AUC, sensitivity and specificity. The average cross-area threshold value was calculated for each score to discern pixels with a high likelihood of finding ex situ conservation gaps and that, thus, were higher priority for further field sampling. These were pixels with combined gap scores above the threshold, assigned a value of 1, as opposed to the relatively well-conserved areas below the threshold, which were assigned a value of 0.The three binary conservation gap scores were then mapped in combination, resulting in pixels across the distribution model with gap values ranging from 0 to 3. Pixels with a value of 0 display no connectivity, accessibility or environmental gaps and are considered well represented ex situ. Pixels with a value of 1 indicate a conservation gap in connectivity, accessibility or the environment; we consider these ‘low-confidence’ gaps. Pixels with a value of 2 indicate gaps in two metrics or ‘medium-confidence’ gaps, and values of 3 indicate gaps across all metrics or ‘high-confidence’ gaps. High-confidence gap areas are displayed on crop-conservation-gap maps (Fig. 2b and Supplementary Information) and conservation hotspot maps across crops (Fig. 4 and Extended Data Figs. 5–8).The representation of crop landrace groups in current ex situ conservation collections was calculated based on the final 1–3 value conservation-gap maps. The complement of the proportion of the modelled distribution considered as a potential conservation gap by any single gap score represents the minimum estimate of current representation; the complement of the proportion considered by all three scores as a gap, which is to say high-confidence gap areas, represents the maximum estimate (Supplementary Tables 1 and 2).While distribution modelling and conservation gap analyses were conducted at the crop landrace group level and results are presented in full in the Supplementary Information, for ease of comparison of results across crops, and to avoid bias towards crops with many landrace groups, we also calculated summary results at the crop level. Crops that had been assessed with geographic differentiations, including maize in Africa and Latin America and yams in the New World and the Old World, were also combined. For spatial results, the pixels in crop landrace group models were summed—that is, constituent landrace group models were combined. The minimum and maximum current conservation representation estimations at the crop level were then calculated based on combined spatial models.GBIF occurrence downloadsThe following occurrence downloads from the Global Biodiversity Information Facility (GBIF; https://www.gbif.org/, 2017−2021) were used: 10.15468/dl.rrntfr, 10.15468/dl.2f2v4h, 10.15468/dl.2ywlb7, 10.15468/dl.lnfelh, 10.15468/dl.ryrmfj, 10.15468/dl.8adf61, 10.15468/dl.nff5ys, 10.15468/dl.erxs6e, 10.15468/dl.vbfgho, 10.15468/dl.mjjk3x, 10.15468/dl.uppz1n, 10.15468/dl.938bgm, 10.15468/dl.hr87hm, 10.15468/dl.k1va80, 10.15468/dl.coqpu2, 10.15468/dl.lkoo9u, 10.15468/dl.e998mp, 10.15468/dl.vfbmm7, 10.15468/dl.tnp478, 10.15468/dl.6zxsea, 10.15468/dl.0lray8, 10.15468/dl.5sjgsw, 10.15468/dl.wkju6h, 10.15468/dl.7xzfvc, 10.15468/dl.autlf5, 10.15468/dl.fe2amw, 10.15468/dl.2zblvz, 10.15468/dl.ddplkj, 10.15468/dl.jbzejg, 10.15468/dl.ej5bha, 10.15468/dl.905pxd, 10.15468/dl.pim1vs, 10.15468/dl.vdridc, 10.15468/dl.b43gyv, 10.15468/dl.nnw3z7, 10.15468/dl.bnt9jc, 10.15468/dl.f5x2cg, 10.15468/dl.ub7zbg, 10.15468/dl.sggf2v, 10.15468/dl.ath5ve, 10.15468/dl.23k3ug, 10.15468/dl.cym376, 10.15468/dl.53bwzk, 10.15468/dl.fsad7h and 10.15468/dl.fm6p7z.Reporting SummaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. 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Smith, J., Nicholas, A., & Beresford. Chernobyl-Catastrophe and Consequences. Springer (published in association with Praxis publishing, UK), ISBN 3–540–23866–2 Springer (2005)Rosenthal, H. L. Content of stable strontium in man and animal biota. In C Skoryna (4): Handbook of Common Strontium. New York Plenum, pp. 503–514 (1981)Ujwal, P. Studies on transfer factors and transfer coefficients of cesium and strontium in soil-grass-milk pathway and estimation and radiation dose in the environment of Kaiga. Ph D thesis, Mangalore University. http://hdl.handle.net/10603/131678 (2012).World Health Organization (WHO). Concise international chemical assessment document 77 (strontium and strontium compounds). http://apps.who.int/iris/bitstream/10665/44280/1/9789241530774_ eng.pdf (2010).Jones, S. Wind scale and Kyshtym: a double anniversary. J. Environ. Radioact. 99(1), 1–6. https://doi.org/10.1016/j.jenvrad.2007.10.002 (2008).CAS 
Article 
PubMed 

Google Scholar 
United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR). 2000. Vol. I, Annex A (2000)Nabeshi, et al. Surveillance of Strontium-90 in Foods after the Fukushima Daiichi Nuclear Power Plant Accident. Shokuhin Eiseigaku Zasshi. 56(4), 133–143. https://doi.org/10.3358/shokueishi.56.133 (2015).CAS 
Article 
PubMed 

Google Scholar 
Abu –Khadra et al. Transfer Factor of Radioactive Cs and Sr from Egyptian Soils to Roots and Leaves of Wheat Plant. Radiation Physics & Protection Conference, 15–19 November 2008, Nasr City – Cairo, Egypt (2008)Alexakhin, R. et al. Fluxes of radionuclides in agricultural environments: Main results and still unsolved problems. In The radiological consequences of the Chernobyl Accident (eds Karaoglou, A. et al.) 39–47 (European Commission, 1996).
Google Scholar 
International Atomic Energy Agency (IAEA). Handbook of parameter values for the prediction of radionuclide transfer in terrestrial and freshwater environments. Technical Reports Series (TRS) No. 472 (IAEA-TRS-472). IAEA, Vienna (2010).International Atomic Energy Agency (IAEA). Handbook of parameter values for the prediction of radionuclide transfer in temperate environments. Technical Report Series (TRS) No. 364. IAEA, Vienna (1994).Howard, B. J. et al. Improving the quantity, quality and transparency of data used to derive radionuclide transfer parameters for animal products. 2. Cow milk. J. Environ. Radioact. 167, 254–268 (2017).CAS 
Article 

Google Scholar 
Tagami, et al. Chapter 5 – Terrestrial Radioecology in Tropical Systems, Editor(s): John R. Twining, Radioactivity in the Environment, Elsevier, Vol 18, pp 155–230 (2012).Voigt, G. et al. Measurements of transfer coefficients for 137Cs, 60Co, 54Mn, 22Na, 131I, and 95mTc from feed into milk and beef. Radiat. Environ. Biophys. 27, 143–152. https://doi.org/10.1007/BF01214604 (1988).CAS 
Article 
PubMed 

Google Scholar 
Popplewell, D. S. & Ham, G. J. Transfer factors for 137Cs and 90Sr from grass to bovine milk under field conditions. J. Radio. Prot. 9(3), 189–193 (1989).CAS 
Article 

Google Scholar 
Schuller, P. et al. 137Cs concentration in soil, prairie plants, and milk from sites in southern Chile. Health Phy. 64(2), 157–161 (1993).CAS 
Article 

Google Scholar 
Kirchner, G. Transport of iodine and cesium via the grass-cow-milk pathway after the Chernobyl accident. Health Phys. 66(6), 653–665. https://doi.org/10.1097/00004032-199406000-00005 (1994).CAS 
Article 
PubMed 

Google Scholar 
Assimakopoulos, P. A. et al. Variation of the transfer coefficient for radiocaesium transport to sheep’s milk during a complete lactation period. J. Environ. Radioact. 22, 63–75 (1994).Article 

Google Scholar 
Wang, C. J. et al. Transfer of radionuclides from soil to grass in Northern Taiwan. Appl. Radiat. Isot. 48(2), 301–303 (1997).CAS 
Article 

Google Scholar 
Zhu, Y.-G. & Smolders, E. Plant uptake of radiocaesium: A review of mechanisms, regulation and application. J. Exp. Bot. 51, 1635–1645 (2000).CAS 
Article 

Google Scholar 
Beresford, N. A. et al. The transfer of 137Cs and 90Sr to dairy cattle fed fresh herbage collected 35 km from the Chernobyl nuclear power plant. J. Environ. Radioact. 47, 157–170 (2000).CAS 
Article 

Google Scholar 
Beresford, N. A. Does size matter? In: International conference on the protection of the environment from the effects of ionizing radiation, Stockholm, International Atomic Energy Agency, Vienna, IAEA-CN-109, 182–185 (2003).Howard, B. J. and Beresford, N. A. Advances in animal radioecology. In: Brechignac F, Howard, B.J., (Eds) Proceedings of international symposium in Aix-en-Provence, France, 3–7. EDP Science, Les Ulis, pp. 187–207 (2001).Solecki, J. & Chibowski, S. Determination of transfer factors for 137Cs and 90Sr isotopes in soil-plant system. J. Radioanal. Nucl. Chem. 252(1), 89–93 (2002).CAS 
Article 

Google Scholar 
Strebl, F. et al. Radiocaesium contamination of meadow vegetation-time-dependent variability and influence of soil characteristics at grassland sites in Austria. J. Environ. Radioact. 58, 143–161 (2002).CAS 
Article 

Google Scholar 
Tsukada, H. S. et al. Transfer of 137Cs and stable Cs in soil–grass–milk pathway in Aomori, Japan. J. Radioanal. Nucl. Chem. 255(3), 455–458 (2003).CAS 
Article 

Google Scholar 
Toki, H. et al. Relationship between environmental radiation and radioactivity and childhood thyroid cancer found in Fukushima health management survey. Sci. Rep. 10, 4074. https://doi.org/10.1038/s41598-020-60999-z (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kubo, K. et al. Variations in radioactive cesium accumulation in wheat germplasm from fields affected by the 2011 Fukushima nuclear power plant accident. Sci. Rep. 10(3744), 2020. https://doi.org/10.1038/s41598-020-60716-w (2020).CAS 
Article 

Google Scholar 
Saito, R. et al. Relationship between radiocaesium in muscle and physicochemical fractions of radiocaesium in the stomach of wild boar. Sci. Rep. 10, 6796. https://doi.org/10.1038/s41598-020-63507-5 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Joshy, P. J. et al. Soil to leaf transfer factor for the radionuclides 226Ra, 40K, 137Cs and 90Sr at Kaiga region. India. J. Environ. Radioact. 102, 1070–1077 (2011).Article 

Google Scholar 
Joshi, R. M. et al. Baseline radioactivity levels in Kaiga site soil and its migration to biosphere. J. Radioanal. Nucl. Chem. 247(3), 571–574 (2001).CAS 
Article 

Google Scholar 
Sachdev, P. et al. The classification of Indian soils on the basis of transfer factors of radionuclides from soil to reference plants (IAEA-TECDOC–1497). International Atomic Energy Agency (IAEA) (2006)Geetha, P. V. et al. Determination of concentration of iodine in grass and cow milk by NAA methods using reactor neutrons. J. Radioanal. Nucl. Chem. 294, 435–438 (2012).CAS 
Article 

Google Scholar 
Geetha, P. V. et al. Grass to cow milk transfer coefficient (Fm) of iodine for equilibrium and emergency situations. Radiat. Prot. Environ. 37(1), 14–20 (2014).Article 

Google Scholar 
Karunakara, N. et al. Studies on the soil to grass transfer factor (Fv) and grass to milk transfer coefficient (Fm) for cesium in Kaiga region. J. Environ. Radioact. 124, 101–112. https://doi.org/10.1016/j.jenvrad.2013.03.008 (2013).CAS 
Article 
PubMed 

Google Scholar 
Karunakara, N. et al. Soil to rice transfer factors for 226Ra, 228Ra, 210Pb, 40K and 137Cs: a study on rice grown in India. J. Environ. Radioact. 2013(118), 80–92. https://doi.org/10.1016/j.jenvrad.2012.11.002 (2013).CAS 
Article 

Google Scholar 
Ujwal, P. et al. Estimation of grass to milk transfer coefficient for cesium for emergency situations. Radiat Prot Environ [serial online] [cited 2021 Sep 23]; 34: 210–2. Available from: https://www.rpe.org.in/text.asp?2011/34/3/210/101727 (2011).International Atomic Energy Agency (IAEA). Soil–Plant Transfer of Radionuclides in Non-temperate Environments. IAEA-TECDOC No. 1979, IAEA, Vienna (2021a).Iurian, A.-R. et al. Transfer parameters and processes in arid or humid warm climates. J. Environ. Radioact https://doi.org/10.1016/j.jenvrad.2021.106692 (2021).Article 
PubMed 

Google Scholar 
Doering, et al. A revised IAEA data compilation for estimating the soil to plant transfer of radionuclides in tropical environments. J. Environ. Radioact., 232, 106570, ISSN 0265–931X, https://doi.org/10.1016/j.jenvrad.2021.106570 (2021).Rout et al. Transfer of radionuclides from soil to selected tropical plants of Indian Subcontinent: A review. J. Environ. Radioact., 235–236, 106652, ISSN 0265–931X. https://doi.org/10.1016/j.jenvrad.2021.106652 (2021a).Rout et al. A review of soil to rice transfer of radionuclides in tropical regions of Indian subcontinent. J. Environ. Radioact. 234: 106631. https://doi.org/10.1016/j.jenvrad.2021.106631 (2021b).Twining, J. R. et al. Soil-water distribution coefficients and plant transfer factors for 134Cs, 85Sr and 65Zn under field conditions in tropical Australia. J. Environ. Radioact. 71(2004), 71 (2004).CAS 
Article 

Google Scholar 
Twining, J. R. et al. Transfer of radioactive caesium, strontium and zinc from soil to sorghum and mung beans under field conditions in tropical northern Australia. Classification of Soil Systems on the Basis of Transfer Factors from Soil to Reference Plants, IAEA-TECDOC-1497, IAEA, Vienna (2006)Mollah, A. et al. Determination of soil-to-plant transfer factors of 137Cs and 90Sr in the tropical environment of Bangladesh. Radiat. Environ. Biophys. 37, 125–128. https://doi.org/10.1007/s004110050104 (1998).CAS 
Article 
PubMed 

Google Scholar 
Nguyen, H. Q. The classification of soil systems on the basis of transfer factors from soil to reference plants, Classification of Soil Systems on the Basis of Transfer Factors from Soil to Reference Plants, IAEA-TECDOC1497 (IAEA, 2006).
Google Scholar 
Mahfuza, S., Sultana et al. Transfer of heavy metals and radionuclides from soil to vegetables and plants in Bangladesh, Soil Remediation and Plants, Elsevier. https://doi.org/10.1016/B978-0-12-799937-1.00012-7 (2015)Nguyen, T. B. et al. Radionuclide transfer factors from air, soil and freshwater to the food chain of man in monsoon tropical condition of Vietnam, IAEA CRP Transfer of Radionuclides from Air, Soil and Fresh Water to the Food chain of Man in Tropical and Subtropical Environments, Annex VIII to this publication (2021).Robison, W.L. & Conrado, C.L. Concentration ratios for foods grown on Bikini Island at Bikini atoll, IAEA CRP Transfer of Radionuclides from Air, Soil and Fresh Water to the Food chain of Man in Tropical and Subtropical Environments, Annex X to this publication9 (2021).Doering, C. & Bollhöfer, A. A database of radionuclide activity and metal concentrations for the Alligator Rivers Region uranium province. J. Environ. Radioact. 162–163, 154 (2016).Article 

Google Scholar 
Tenpe, S. P. & Parwate, D. V. Evaluation of elemental uptake of Citrus reticulata by nuclear analytical techniques. Int. J. Innov. Res. Sci. Eng. Technol. 4(2015), 2754 (2015).
Google Scholar 
International Atomic Energy Agency (IAEA). Approaches for Modelling of Radioecological Data to Identify Key Radionuclides and Associated Parameter Values for Human and Wildlife. Exposure Assessments. IAEA-TECDOC No. 1950, IAEA, Vienna (2021b).Johansen, M. P. & Twining, J. R. Radionuclide concentration ratios in Australian terrestrial wildlife and livestock: Data compilation and analysis. Radiat. Environ. Biophys. 49(4), 603–611. https://doi.org/10.1007/s00411-010-0318-9 (2010).CAS 
Article 
PubMed 

Google Scholar 
Sotiropoulou, M., & Florou, H. Measurement and calculation of radionuclide concentration ratios from soil to grass in semi-natural terrestrial habitats in Greece, J. Environ. Radioact., 237, 2021, 106666, ISSN 0265–931X, https://doi.org/10.1016/j.jenvrad.2021.106666 (2021).Howard, B. J. et al. Updating animal product transfer parameter values for cow and goat milk. In: Soil-pant transfer of radionuclides in non-temperate environments, IAEA-TECDOC-1950, IAEA, Vienna (2021)Musatovová, O. & Vavrová, M. Transfer of 137Cs and 90Sr to some Animal Products in the site of Previewed Nuclear Power Plant Construction. Isotopenpraxis Isotopes Environ. Health Stud. 27(7), 339–341. https://doi.org/10.1080/10256019108622561 (1991).Article 

Google Scholar 
International Atomic Energy Agency (IAEA). Quantification of radionuclide transfer in terrestrial and freshwater environments for radiological assessments, IAEA-TECDOC-No. 1616. IAEA, Vienna (2009).Karunakara, N. et al. Studies on transfer Factors of Iodine, Cesium and Strontium in air→ grass→ cow→ milk pathway and estimation of radiation dose specific to Kaiga region. Final report of the research project, Nuclear Power Corporation of India Ltd. (NPCIL). Grant No. Kaiga–3&4/00000/SD/2007/S/343 dated 27.12.2007, Kaiga –3&4/00000/SD/2007/S/343 (2012).Karunakara, N. et al. Estimation of air-to-grass mass interception factors for iodine, J. Environ. Radioact., 186, 71–77. ISSN 0265–931X, https://doi.org/10.1016/j.jenvrad.2017.06.018 (2018).Nayak, R. S. et al. Experimental database on water equivalent factor (WEQp) and organically bound tritium activity for tropical monsoonal climate region of South West Coast of India. Appl. Radiat. Isotopes, https://doi.org/10.1016/j.apradiso.2020.109390 (2020).Karunakara, N. et al. 137Cs concentration in environment of Kaiga in the South-West Coast of India. Health Phys. 81(2), 148–155 (2001).CAS 
Article 

Google Scholar 
Karunakara, N. et al. 226Ra, 40K and 7Be activity concentrations in plants in the environment of Kaiga of south-west Coast of India. J. Environ. Radioact. 65, 255–266 (2003).CAS 
Article 

Google Scholar 
International Atomic Energy Agency (IAEA). Measurement of radionuclides in food and the environment, a guide book. Technical report series No. 295. IAEA, Vienna (1989).Environmental Measurements Laboratory, procedures manual. U.S. Department of Energy. Ed. 26 (1983).Uchida, S. & Tagami, K. Soil-to-plant transfer factors of fallout Cs-137 and native Cs-133 in various crops collected in Japan. J. Radioanal. Nucl. Chem. 273, 205–210 (2007).CAS 
Article 

Google Scholar 
Gavlak, R. D. et al. Plant, soil and water reference methods for the Western Region. Western Regional Extension Publication (WREP) 125, WERA-103 Technical Committee, http://www.naptprogram.org/files/napt/western-states-method-manual-2005.pdf (2005).Nuclear Power Corporation of India Ltd. (NPCIL). Environmental impact assessment for Kaiga atomic power project (Kaiga unit 5 & 6), 2 x 700 MWe PHWRs at Kaiga, Karnataka volume – I : Main report. NPCIL, Mumbai, India (2018).Siddappa, K. et al. Distribution of natural and artificial radioactivity components in the environs of coastal Karnataka, Kaiga and Goa (1991–94). Final Project Report to Board of Research in Nuclear Sciences (BRNS), Govt. of India, Mangalore University, Mangalore, India (1994).Radhakrishna, A. P. et al. Distribution of some natural and artificial radionuclides in mangalore environment of South India. J. Environ. Radioact. 30(1), 31–54 (1996).CAS 
Article 

Google Scholar 
Patra, A. K. et al. Influence of site characteristics on soil to plant transfer of Strontium. National Symposium on Environment, 2004. pp. 475–480 (2004).Ross, et al. Milk minerals in cow milk with special reference to elevated calcium and its radiological implications. Radiat. Protect. Environ., 35(2) 64–68, DOI https://doi.org/10.4103/0972-0464.112340 (2012).National Research Council (NRC), Nutrient requirements of dairy cattle. 5th revised edition, National Academic Press; Washington D.C (1978).Patra, A. K. Studies on The Biological Translocation of Major and Trace elements in Kaiga Environment, Ph.D. Thesis, Mangalore University (2005).Ehlken, S. & Kirchner, G. Seasonal variations in soil to grass transfer of fallout Strontium and Cesium and of Potassium in North German soils. J. Environ. Radioact. 33(2), 147–181 (1996).CAS 
Article 

Google Scholar 
International Union of Radioecology (IUR). 6th report of the working group soil-plant transfer factors. Report of the working group meeting in Guttannen, Grimselpass, Switzerland, May (1989).Lu, et al. The investigation of 137Cs and 90Sr background radiation levels in soil and plant around Tianwan NPP, China. Journal of Environmental Radioactivity 90(2), 89–99 (2006).Bergeijk, K. E. et al. Influence of pH, Soil Organic Matter Content on Soil-to-Plant Transfer of Radiocesium and Strontium as Analyzed by a Non-Parametric Method. J. of Environ. Radioactivity 15, 265–276 (1992).Article 

Google Scholar 
Anderson, R. R. Comparison of trace elements in milk of four species. J. Dairy Sci. 75, 3050–3055 (1992).CAS 
Article 

Google Scholar 
Hurley, W. L. Lactation Biology. Minerals and Vitamins. Ed. by Univ. Urbana. Illinois USA. (1997).Hingorani, S. B. et al. Sr-90 measurements in milk and composite diet samples in India. J. Sci. Indust. Res. 35, 557–579 (1976).CAS 

Google Scholar 
Lettner, H. A. et al. 137Cs and 90Sr transfer to milk in Austrian alpine agriculture. J. Environ. Radioact. 98, 69–84 (2007).CAS 
Article 

Google Scholar 
Klemola, S. et al. Monitoring of Radionuclides in the Environs of the Finnish Nuclear Power Stations in 1988. Supplement 3 to Annual Report STUK-A89, Helsinki (1991)Abukawa, J. et al. A Survey of 90Sr and 137Cs Activity Levels of Retail Foods in Japan. J. Environ. Radioact. 41 (3), 287–305. (1998)Green, N. et al. The transfer of Cs and Sr along the soil-pasture-cow’s milk pathway in an area of land reclaimed from the Sea. J. Environ. Radioact. 23, 151–170 (1994).CAS 
Article 

Google Scholar 
Green, N. et al. Factors affecting the transfer of radionuclides to sheep grazing on pastures reclaimed from the Sea. J. Environ. Radioact. 30(2), 173–183 (1996).CAS 
Article 

Google Scholar 
Beresford, N. A. et al. The transfer of radiocaesium to ewes through a breeding cycle: An illustration of the pitfalls of the transfer coefficient. J. Environ. Radioact. 98, 24–35 (2007).CAS 
Article 

Google Scholar 
Bobovnikova, et al. Chemical forms of occurrence of long-lived radionuclides and their alteration in soils near the Chernobyl Nuclear Power Station. Soviet Soil Sci. 23, 52–57. (1991).Kashparov, V. A. et al. Kinetics of fuel particle weathering and 90Sr mobility in the Chernobyl 30 km exclusion zone. Health Phys. 76, 251–299 (1999).CAS 
Article 

Google Scholar 
Joshy, P. J. Studies on Environmental Transportation of Natural Radionuclides in Kaiga Region. Ph D Thesis, Mangalore University, pp. 105 (2007). More

Our model focused on the trophic interactions among CoTS and two groups of coral within a feedback loop with natural and anthropogenic forcing. Our model draws on accepted features of the published dynamics described by Morello et al.37, Condie et al.28 and Condie et al.17, but is a substantial advance in terms of adding spatial structure and coupling with climate variables. Here we have resolved a fine spatiotemporal model structure, developed a novel recruitment formulation for CoTS, integrated tactical management control dynamics and incorporated the impact of broad-scale drivers upon the population dynamics of corals and CoTS at the local scale. Our model is formally fitted to a subset of the CoTS control program data described by Westcott et al.12. We operationalised our model as a tactical and strategic tool to inform how CoTS management strategies interact with alternative disturbance and ecological realisations at the sub-reef scale, the scale at which management operates.DataWe fitted our model to a subset of four reefs from the dataset described by Westcott et al.12, which were consistently and intensively managed (for a map with reef locations see Fig. 2). We restricted our focus to a subset to avoid parametrisation of reef and management site dynamics. Thus, ~39% of site visits were concentrated over the 13 management sites we considered, with a mean of 20.73 ± 5.5 (mean ± standard deviation) visits across the time series relative to a mean visitation rate of 12.23 ± 4.7 (mean ± standard deviation) for the rest of the sites. Each reef in the subset contained two or more management sites where each site was visited at least 18 times. The subset was used because it contained sufficient data for estimating the 11 model parameters for each management site. Across included sites were a range of CoTS densities, coral abundances and disturbance histories12,72,73. Given the intensity with which these sites were managed, they therefore provided us with a valuable opportunity to formally fit the interactions between management intervention, coral abundance and CoTS dynamics in the presence of regional sequential bleaching events.Model spatial structure and ecological componentsSpatially, we considered a circular 300 km region of the Great Barrier Reef centred between Cairns and Cape Tribulation, and resolved at a daily timescale and a sub-reef spatial scale, matching the scale at which observed data were resolved12,19. Reefs were randomly generated as points to capture possible spatial correlation in disturbance impacts between nearby reefs, as well as to allow variability in reef locations. Coral, CoTS and disturbance dynamics within the management sites of each reef were resolved relative to a 1 ha focal region. That is, each management site was captured as a 1 ha area representative of the whole site. In the Pacific, Acanthaster spp. disproportionally target faster-growing corals, predominantly Acropora, Pocillopora and Montipora22. Coral taxa characterised by slow growth rates and massive morphologies, such as Porites, are generally consumed less than expected based on their abundance22 and are thus non-preferred prey. The two modelled coral groups were the fast-growing favoured prey items of CoTS, and the slower-growing non-preferred prey. Processes resolved in the model included reproduction, density dependence, the effect of bleaching and cyclonic disturbances on corals and the impact of manual control (culling) upon CoTS and coral dynamics.CoTS population structureWe used an age-structured approach to model CoTS population dynamics. We defined our age classes to encapsulate plausible size-at-age variation due to plastic growth. This was achieved through linking catch size classes of the management control program19 to age classes through size-age relationships developed from observations spanning multiple environmental realisations, manipulated scenarios and methodologies55,70,74,75. Delayed growth in juvenile CoTS due to deferral of their switch to coral prey or composition of their pre-coral diet, may induce variability in the size-at-age of juveniles52,53. However, the population-level consequences of prolonged juvenile phases are not easily observed nor understood. For example, juveniles are subject to high mortality rates in situ, delayed growth may reduce lifetime fitness and there have been no observations of juveniles during spawning periods that would indicate protracted juvenile phases55,56,57. Consequently, suggests size-at-age is—due to an early life history mortality bottleneck or otherwise—predominantly concordant with growth curves of the literature55,70,74,75 and the size classes we have used here. Age classes comprised annual 0, 1, 2 and 3+ groups, with 3+ being an absorbing class – once there, they stay there. Age-0 ( ; 32.5)). This induced a slope change in the relationship between maximum wind velocity and its radius at a wind velocity of 32.5 m.s−1 (≥ category 3 intensities). However, whilst maximum wind velocity was modelled to determine ({d}_{{{{{{rm{m}}}}}}}), the overall size of the cyclone was uncorrelated with its intensity. The overall size was uniformly sampled from 130 to 460 km diameter which allowed for the potential of complete focal area coverage and for a range of intensity-size relationships to be captured. Given a cyclone footprint of radius ({d}_{0}) (km), wind velocity, (V) (m.s−1), at a distance, (d) (km), was interpolated104 through:$$Vleft(dright)=left{begin{array}{c}{V}_{0}+left({V}_{{{{{{rm{m}}}}}}}-{V}_{0}right){left(frac{sqrt{{d}_{0}}-sqrt{d}}{sqrt{{d}_{0}}-sqrt{{d}_{{{{{{rm{m}}}}}}}}}right)}^{alpha },,dge {d}_{{{{{{rm{m}}}}}}}\ {V}_{{{{{{rm{m}}}}}}}, , d ; < ; {d}_{{{{{{rm{m}}}}}}}end{array}right.$$ (32) The distance from the cyclone centre to the reef perimeter, (D) (km), is calculated through:$$D=sqrt{{left({x}_{{{{{{rm{rf}}}}}}}-{r}_{1}-{x}_{{{{{{rm{cyc}}}}}}}right)}^{2}+{left({x}_{{{rm{rf}}}}-{r}_{1}-{y}_{{{{{{rm{cyc}}}}}}}right)}^{2}}$$ (33) Thus, given a reef strike occurs ((sqrt{{d}_{0}}-sqrt{d}ge 0) required from non-integer (alpha)), the wind velocity experienced at said reef due to the tropical cyclone was calculated as (Vleft(Dright)). Wind velocity was subsequently categorised and damage to reef zone corals calculated as per Supplementary Table 4.We resolved stochasticity in cyclone dynamics in projection scenarios. In projected scenarios cyclone arrivals, locations and intensities were probabilistically sampled and their inflicted damage upon coral communities sampled from damage ranges. Cyclone locations, their footprints, intensity ranges and corresponding damage ranges were sampled from uniform distributions. Cyclone arrivals were sampled from a Poisson distribution and considered in scenarios from 2018 to 2029. Projections were averaged over 80 simulations to capture mean dynamics and bound trajectory uncertainty due to said stochasticity.Our cyclone model was calibrated to parameters sourced from the literature (Supplementary Tables 4-5). This was necessary since our data time series did not encompass a cyclone event and/or impacts upon a reef and cyclone-induced mortality is typically a key coral mortality source30. Consequently, we were unable to validate the impacts of cyclones through formal estimation in our model. However, our endeavours to source parameters from empirical and modelling studies in conjunction with our formulation allowed us to plausibly capture the cumulative outcomes of a cyclone event at discrete locations. Our cyclone model offers a limited complexity approach that is empirically grounded to simply resolve cyclone impacts in local-scale models without the need to be coupled to a regional-scale model.Cyclones, induced thermal stress and tactical managementThe occurrence of cyclone events was modelled to directly interact with both management interventions and thermal stress events. Cyclones were assumed to realistically preclude co-occurring co-located management interventions. This was such that a management site control visit was abandoned if a cyclone preceded or was forecast within five days of a control voyage. The later interaction of cyclones with thermal stress events operated through an induced thermal cooling of sea surface temperatures (SST) at impacted locations.In the case of the overlapping cyclone and thermally induced bleaching events, we first accounted for cyclone impacts. This was because, in addition to physical damage to corals, cyclones have the potential for regional-scale cooling of SST which can reduce coral bleaching43,107. To capture this interaction, we resolved the duration108,109 and amplitude107 of tropical cyclone-induced cooling. We captured this interaction through Degree Heating Weeks (DHW) which is a useful metric for the accumulated thermal stress experienced by corals94.The duration of tropical cyclone-induced cooling was modelled through a temporal-SST response curve consistent with the work of Lloyd and Vecchi108 and Vincent et al.109. Cooling rapidly occurs once a tropical cyclone arrives at a location and decays in an asymptotic manner over a period of ~40–60 days108,109. Temperatures however do not return to pre-cyclone levels and plateau at ~1/4 of the cooling signal amplitude below pre-cyclone levels108,109. We expressed this cooling response curve as it related to bleaching-induced coral mortality through DHWs.We based the average expected DHW cooling signal on the work of Carrigan and Puotinen107. This was achieved through scaling the difference in amplitude of overlapping thermal stress-tropical cyclone events and thermal stress only events—a cooling signal amplitude of ({{{{{{rm{DHW}}}}}}}_{{{{{{rm{Amp}}}}}}} sim 1.5) DHW. Consistent with the model of Carrigan and Puotinen107, we then resolved cooling within the radius of gale-force winds (category 1, 17 m.s−1) to model tropical cyclone-induced cooling. Depending on the size of the tropical cyclone, this meant that an individual cyclone would not necessarily cool all reefs within the model region. However, the culmination of multiple cyclones may have limited bleaching exposure for corals across the region107.We did not treat the cooling consequences of multiple cyclones additively nor the complex interplay of oceanic feedbacks upon cyclone intensity and cooling. Such processes were beyond the scope of our study and model. If multiple cyclones occurred within our model, then the cooling signal timeline was re-initialised at impacted reefs for the last tropical cyclone at said location. Non-impacted reefs maintained the timeline for the decay of the cooling signal originating from their previous tropical cyclone interaction.Once a tropical cyclone impacted a reef, the duration of the induced cooling signal was modelled. Price et al.110 found that cooling decays exponentially which is reflective of the recovery of SST following tropical cyclones as demonstrated by Lloyd and Vecchi108 and Vincent et al.109. We operationalised the exponential functional form in conjunction with the decay timelines of Lloyd and Vecchi108 and Vincent et al.109 and the DHW amplitude of Carrigan and Puotinen107. We modelled the level of cooling ({{{{{{rm{DHW}}}}}}}_{{{{{{rm{cool}}}}}}}) after ({d}_{{{{{{rm{postTC}}}}}}}) days post-cyclone event by:$${{{{{{rm{DHW}}}}}}}_{{{{{{rm{cool}}}}}}}left({d}_{{{{{{rm{postTC}}}}}}}right)=frac{1}{4}{{{{{{rm{DHW}}}}}}}_{{{{{{rm{Amp}}}}}}}+frac{frac{3}{4}{{{{{{rm{DHW}}}}}}}_{{{{{{rm{Amp}}}}}}}}{{e}^{{d}_{{{{{{rm{postTC}}}}}}}/10}}$$ (34) This ensured that once a reef experienced a tropical cyclone event, the cooling signal initialised at ({{{{{{rm{DHW}}}}}}}_{{{{{{rm{Amp}}}}}}}) and decayed to (sim frac{1}{4}{{{{{{rm{DHW}}}}}}}_{{{{{{rm{Amp}}}}}}}) after 40–60 days108,109. The rate of decay was given by the e-folding time (days required for the cooling signal to be reduced by a factor of (e)) which we took to be 10. This is consistent with the results of Price et al.110, Lloyd and Vecchi108 and Vincent et al.109 who found e-folding times ranging from 5 through to 20 days. Thermally induced bleaching mortality of corals was computed after cyclone physical damage and cooling had been accounted for.Formal model fittingWe formally fitted our coral-CoTS model simultaneously to coral cover data, catch-per-unit-effort data and catch numbers obtained from the management control program with dive effort (minutes) treated as an input (visits summarised in Supplementary Table 7)12. Simultaneously fitting CoTS and coral dynamics at concurrent locations was useful here as it allowed for coral cover trajectories to help inform local CoTS abundance (sensu CoTS feeding vs. coral trajectories63,79 and local site fidelity24). Our model also used Long Term Monitoring Program (LTMP) data (based on manta tows and provided by the Australian Institute of Marine Science) which provides an independent index of relative abundance of CoTS. This was such that our model here was developed and parametrised based on an earlier version37,111 which did not use CPUE information but was fitted to the LTMP data on CoTS relative abundance, as well as the corresponding coral cover, to estimate a number of CoTS-coral interaction parameters used in the present model (Supplementary Table 3).Fitting and estimation of our model were achieved through Maximum Likelihood Estimation (MLE). Our objective function was the outcome of combining the negative log-likelihood contributions arising from fitting the model to multiple sets of location-specific data, across a range of environmental and ecological realisations, in conjunction with penalty terms. Specifically, we fitted coral cover (data series ({x}^{{{{{{rm{Coral}}}}}}})) and CoTS CPUEs (data series ({x}^{{{{{{rm{CoTS}}}}}}})) at each management site which contained ({n}_{{{{{{rm{Coral}}}}}}}) and ({n}_{{{{{{rm{CoTS}}}}}}}) data points respectively. This involved fitting parameters that were specific to management sites (e.g. thermal stress - DHW), reefs (e.g. recruitment variability) as well as those that were common amongst reefs (e.g. CoTS consumption rates). A parametrisation that optimised one contribution was unlikely to optimise all contributions and hence we obtained a parametrisation across all reefs and sub-regions. For a modelled catch of (N) (sum of catches across age classes), a catchability coefficient (a constant of proportionality) of ({q}_{{{{{{rm{LL}}}}}}}^{{{{{{rm{prop}}}}}}}), and data standard deviation of ({sigma }_{{{{{{rm{LL}}}}}}}) our likelihood contribution arising from a management site CPUEs was given by:$$-{{log }}{{{{{rm{L}}}}}}left({q}_{{{{{{rm{LL}}}}}}}^{{{{{{rm{prop}}}}}}}N,{{sigma }_{{{{{{rm{LL}}}}}}}}^{2}{{{{{rm{|}}}}}}{x}_{i}^{{{{{{rm{CoTS}}}}}}}right) = {n}_{{{{{{rm{CoTS}}}}}}},{{{{{rm{ln}}}}}}left({sigma }_{{{{{{rm{LL}}}}}}}right)+{sum }_{i=1}^{{n}_{{{{{{rm{CoTS}}}}}}}}frac{{left({{{{{rm{ln}}}}}}left({x}_{i}^{{{{{{rm{CoTS}}}}}}}right)-{{{{{rm{ln}}}}}}left({q}_{{{{{{rm{LL}}}}}}}^{{{{{{rm{prop}}}}}}}{N}_{i}right)right)}^{2}}{2{{sigma }_{{{{{{rm{LL}}}}}}}}^{2}}$$ (35) From which the data series variance and catchability coefficient were computed for the maximum likelihood estimate. The derived variance and the catchability were respectively computed as per:$${sigma }_{{{{{{rm{LL}}}}}}}=sqrt{frac{1}{{n}_{{{{{{rm{CoTS}}}}}}}}{sum }_{i=1}^{{n}_{{{{{{rm{CoTS}}}}}}}}{left({{{{{rm{ln}}}}}}left({x}_{i}^{{{{{{rm{CoTS}}}}}}}right)-{{{{{rm{ln}}}}}}left({q}_{{{{{{rm{LL}}}}}}}^{{{{{{rm{prop}}}}}}}right)right)}^{2}}$$ (36) and$${q}_{{{{{{rm{LL}}}}}}}^{{{{{{rm{prop}}}}}}}=frac{1}{{n}_{{{{{{rm{CoTS}}}}}}}}{sum }_{i=1}^{{n}_{{{{{{rm{CoTS}}}}}}}}left({{{{{rm{ln}}}}}}left({x}_{i}^{{{{{{rm{CoTS}}}}}}}right)-{{{{{rm{ln}}}}}}left({N}_{i}right)right)$$ (37) Similarly, the likelihood contribution arising from fitting to a management site coral cover with standard deviation ({sigma }_{{Coral}}) was described by:$$-{{log }}{{{{{rm{L}}}}}}left(frac{{C}_{y,d}^{{{{{{rm{f}}}}}}}+{C}_{y,d}^{{{{{{rm{s}}}}}}}}{{K}^{{{{{{rm{coral}}}}}}}},{{sigma }_{{{{{{rm{Coral}}}}}}}}^{2}{{{{{rm{|}}}}}}{x}_{i}^{{{{{{rm{Coral}}}}}}}right) = {n}_{{{{{{rm{Coral}}}}}}},{{{{{rm{ln}}}}}}left({sigma }_{{{{{{rm{Coral}}}}}}}right)+{sum }_{i=1}^{{n}_{{{{{{rm{Coral}}}}}}}}frac{{left({ln}left({x}_{i}^{{{{{{rm{Coral}}}}}}}right)-left(frac{{C}_{y,d}^{{{{{{rm{f}}}}}},i}+{C}_{y,d}^{{{{{{rm{s}}}}}},i}}{{K}^{{{{{{rm{coral}}}}}}}}right)right)}^{2}}{2{{sigma }_{{{{{{rm{Coral}}}}}}}}^{2}}$$ (38) Where the standard deviation was given by:$${sigma }_{{{{{{rm{Coral}}}}}}}=sqrt{frac{1}{{n}_{{{{{{rm{Coral}}}}}}}}{sum }_{i=1}^{{n}_{{{{{{rm{Coral}}}}}}}}{left({{{{{rm{ln}}}}}}left({x}_{i}^{{{{{{rm{Coral}}}}}}}right)-{{{{{rm{ln}}}}}}left(frac{{C}_{y,d}^{{{{{{rm{f}}}}}},i}+{C}_{y,d}^{{{{{{rm{s}}}}}},i}}{{K}^{{{{{{rm{coral}}}}}}}}right)right)}^{2}}$$ (39) We computed the negative log-likelihood objective function by summing the contributions from all management sites across considered reefs.Fitting was conducted through the modelling language Automatic Differentiation Model Builder (ADMB) which implements a Quasi-Newton optimisation algorithm for estimation of parameters and provides Hessian based estimation of standard errors112. Penalty terms were added to our likelihood function to integrate a prior understanding of system dynamics and to reduce model variability. Penalty terms encompassed recruitment variability and the magnitude of catches observed in the data.Recruitment was expressed through recruitment deviations, ({r}_{y}), given a standard deviation of ({sigma }_{{{{{{rm{R}}}}}}}) about underlying modelled recruitment (sum of self-recruitment and immigration sources described previously). The recruitment variability negative log-likelihood penalty contribution was given by:$$-{{log }}{{{{{rm{L}}}}}}left(0,{sigma }_{{{{{{rm{R}}}}}}}^{2}{{{{{rm{|}}}}}}{r}^{{{{{{rm{rec}}}}}}}right)={sum }_{y=1}^{{{{{{rm{#Years}}}}}}}{sum }_{{{{{{rm{reef}}}}}}=1}^{{{{{{rm{#Reefs}}}}}}}{r}_{y,{{{{{rm{reef}}}}}}}^{{rec}}/2{sigma }_{{{{{{rm{R}}}}}}}^{2}$$ (40) An additional penalty term for model deviations from the magnitude of observed catches was encompassed. This was such that a constant of proportionality relating modelled catches to observed catches tended to one. For an allowed standard deviation of ({sigma }_{{{{{{rm{CM}}}}}}}), the likelihood function was penalised for deviations from unity proportionality, ({r}^{{{{{{rm{CM}}}}}}}), through:$$-{{log }}{{{{{rm{L}}}}}}left(0,{sigma }_{{{{{{rm{CM}}}}}}}^{2}{{{{{rm{|}}}}}}{r}^{{{{{{rm{CM}}}}}}}right)={sum }_{{{{{{rm{zone}}}}}}=1}^{{{{{{rm{#Zones}}}}}}}{r}_{{{{{{rm{zone}}}}}}}^{{{{{{rm{CM}}}}}}}/2{sigma }_{{{{{{rm{CM}}}}}}}^{2}$$ (41) Model simulations were conducted in ADMB with output analysis and visualisation conducted in MATLAB.Sensitivity to CoTS controlTo test whether our projected scenarios were consistent with the period over which data were collected, we conducted a model-based before and after comparison to the impact of control. Specifically, we used the fitted trajectory for sites, including both the coral data and CoTS control data (voyages and time spent), and compared this to the model-suggested coral trajectories if CoTS control had not taken place. These were modelled over the fitted period (2013–2018) and, unlike the projected scenarios (2019–2029), were variable in terms of the timing of control (amount of time between visits was variable), the amount of time spent at sites (not a consistent number of dive minutes per visit), CoTS dynamics (recruitment was fitted and hence different annually and between reefs), and in the level of thermal stress they experienced (different sites experienced different effective levels and some sites experience back-to-back events).Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

Scheffers, B. R. et al. The broad footprint of climate change from genes to biomes to people. Science 354, aaf7671 (2016).PubMed 
Article 
CAS 

Google Scholar 
Pecl, G. T. et al. Biodiversity redistribution under climate change: Impacts on ecosystems and human well-being. Science 355, eaai214 (2017).Article 
CAS 

Google Scholar 
Nogués-Bravo, D. et al. Cracking the code of biodiversity responses to past climate change. Trends Ecol. Evol. 33, 765–776 (2018).PubMed 
Article 

Google Scholar 
Forsman, A., Betzholtz, P.-E. & Franzén, M. Faster poleward range shifts in moths with more variable colour patterns. Sci. Rep. 6, 36265 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Voelkl, B. et al. Reproducibility of animal research in light of biological variation. Nat. Rev. Neurosci. 21, 384–393 (2020).CAS 
PubMed 
Article 

Google Scholar 
Rödder, D., Schmitt, T., Gros, P., Ulrich, W. & Habel, J. C. Climate change drives mountain butterflies towards the summits. Sci. Rep. 11, 14382 (2021).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Habel, J. C., Teucher, M., Gros, P., Schmitt, T. & Ulrich, W. Land use and climate change affects butterfly diversity across northern Austria. Landscape Ecol. 36, 1741–1754 (2021).Article 

Google Scholar 
Hill, J. K. et al. Responses of butterflies to twentieth century climate warming: implications for future ranges. Proc. Biol. Sci. 269, 2163–2171 (2002).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chen, I. C. et al. Elevation increases in moth assemblages over 42 years on a tropical mountain. Proc. Natl. Acad. Sci. 106, 1479–1483 (2009).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Cohen, J. M., Lajeunesse, M. J. & Rohr, J. R. A global synthesis of animal phenological responses to climate change. Nat. Clim. Change 8, 224–228 (2018).ADS 
Article 

Google Scholar 
Bell, J. R. et al. Spatial and habitat variation in aphid, butterfly, moth and bird phenologies over the last half century. Glob. Change Biol. 25, 1982–1994 (2019).ADS 
Article 

Google Scholar 
Hällfors, M. H. et al. Shifts in timing and duration of breeding for 73 boreal bird species over four decades. Proc. Natl. Acad. Sci. 117, 18557–18565 (2020).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Pruett, J. E. & Warner, D. A. Spatial and temporal variation in phenotypes and fitness in response to developmental thermal environments. Funct. Ecol. 35, 2635–2646 (2021).Article 

Google Scholar 
Hall, M., Nordahl, O., Larsson, P., Forsman, A. & Tibblin, P. Intra-population variation in reproductive timing covaries with thermal plasticity of offspring performance in perch Perca fluviatilis. J. Animal Ecol 90, 2236–2347 (2021).Article 

Google Scholar 
Ehrlich, P. R. & Hanski, I. On the Wings of Checkerspots: A Model System for Population Biology (Oxford University Press, 2004).
Google Scholar 
Warren, M. S. et al. The decline of butterflies in Europe: Problems, significance, and possible solutions. Proc. Natl. Acad. Sci. 118, e2002551117 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kristensen, N. P. Lepidoptera: Moths and Butterflies. 1. Evolution, Systematics, and Biogeography. Handbook of Zoology Vol. IV, Part 35 (De Gruyter, 1999).
Google Scholar 
Forsman, A. & Wennersten, L. Inter-individual variation promotes ecological success of populations and species: Evidence from experimental and comparative studies. Ecography 39, 630–648 (2016).Article 

Google Scholar 
Zografou, K. et al. Species traits affect phenological responses to climate change in a butterfly community. Sci. Rep. 11, 3283 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Stevens, C. J. et al. Nitrogen deposition threatens species richness of grasslands across Europe. Environ. Pollut. 158, 2940–2945 (2010).CAS 
PubMed 
Article 

Google Scholar 
Heinrich, B. The Thermal Warriors (Harvard University Press, 2013).
Google Scholar 
Bladon, A. J. et al. How butterflies keep their cool: Physical and ecological traits influence thermoregulatory ability and population trends. J. Anim. Ecol. 89, 2440–2450 (2020).PubMed 
Article 

Google Scholar 
Tsai, C.-C. et al. Physical and behavioral adaptations to prevent overheating of the living wings of butterflies. Nat. Commun. 11, 551 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ahnesjö, J. & Forsman, A. Differential habitat selection by pygmy grasshopper color morphs; interactive effects of temperature and predator avoidance. Evol. Ecol. 20, 235–257 (2006).Article 

Google Scholar 
Ma, C.-S., Ma, G. & Pincebourde, S. Survive a warming climate: Insect responses to extreme high temperatures. Annu. Rev. Entomol. 66, 163–184 (2021).CAS 
PubMed 
Article 

Google Scholar 
Forsman, A. Rethinking phenotypic plasticity and its consequences for individuals, populations and species. Heredity 115, 276–284 (2015).CAS 
PubMed 
Article 

Google Scholar 
Hill, G. M., Kawahara, A. Y., Daniels, J. C., Bateman, C. C. & Scheffers, B. R. Climate change effects on animal ecology: Butterflies and moths as a case study. Biol. Rev. Camb. Philos. Soc. 96, 2113–2126 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Gilbert, A. L. & Miles, D. B. Natural selection on thermal preference, critical thermal maxima and locomotor performance. Proc. R. Soc. B Biol. Sci. 284, 20170536 (2017).Article 

Google Scholar 
Eliasson, C. U., Ryrholm, N., Holmér, M., Gilg, K. & Gärdenfors, U. Nationalnyckeln till Sveriges flora och fauna. Fjärilar: Dagfjärilar. Hesperidae – Nymphalidae. (ArtDatabanken, SLU, 2005).Thomas, J. A. & Wardlaw, J. C. The capacity of a Myrmica ant nest to support a predacious species of Maculinea butterfly. Oecologia 91, 101–109 (1992).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Vilbas, M. et al. Habitat use of the endangered parasitic butterfly Phengaris arion close to its northern distribution limit. Insect Conserv. Divers. 8, 252–260 (2015).Article 

Google Scholar 
Johansson, V., Kindvall, O., Askling, J. & Franzén, M. Extreme weather affects colonization–extinction dynamics and the persistence of a threatened butterfly. J. Appl. Ecol. 57, 1068–1077 (2020).Article 

Google Scholar 
Johansson, V., Kindvall, O., Askling, J. & Franzén, M. Intense grazing of calcareous grasslands has negative consequences for the threatened marsh fritillary butterfly. Biol. Cons. 239, 108280 (2019).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical. R version 4.1.1. (2021).Eubank, R. L. & Speckman, P. Curve fitting by polynomial-trigonometric regression. Biometrika 77, 1–9 (1990).MathSciNet 
MATH 
Article 

Google Scholar 
Allen, J. C. A modified sine wave method for calculating degree days. Environ. Entomol. 5, 388–396 (1976).Article 

Google Scholar 
Wickham, H. & Wickham, M. H. The ggplot package. Google Scholar. http://ftp.uni-bayreuth.de/math/statlib/R/CRAN/doc/packages/ggplot.pdf, (2007).Lüdecke, D. ggeffects: Tidy data frames of marginal effects from regression models. J. Open Source Softw. 3, 772 (2018).ADS 
Article 

Google Scholar 
Forsman, A. Some like it hot: Intra-population variation in behavioral thermoregulation in color-polymorphic pygmy grasshoppers. Evol. Ecol. 14, 25–38 (2000).Article 

Google Scholar 
Forsman, A., Ringblom, K., Civantos, E. & Ahnesjo, J. Coevolution of color pattern and thermoregulatory behavior in polymorphic pygmy grasshoppers Tetrix undulata. Evolution 56, 349–360 (2002).PubMed 
Article 

Google Scholar 
Ahnesjö, J. & Forsman, A. Correlated evolution of colour pattern and body size in polymorphic pygmy grasshoppers, Tetrix undulata. J. Evol. Biol. 16, 1308–1318 (2003).PubMed 
Article 

Google Scholar 
Zeuss, D., Brandl, R., Brändle, M., Rahbek, C. & Brunzel, S. Global warming favours light-coloured insects in Europe. Nat. Commun. 5, 3874 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Heidrich, L. et al. The dark side of Lepidoptera: colour lightness of geometrid moths decreases with increasing latitude. Glob. Ecol. Biogeogr. 27, 407–416 (2018).MathSciNet 
Article 

Google Scholar 
Porter, K. Basking behaviour in larvae of the butterfly Euphydryas aurinia. Oikos 38, 308–312 (1982).Article 

Google Scholar 
Rolff, J., Johnston, P. R. & Reynolds, S. Complete metamorphosis of insects. Philos. Trans. R. Soc. B 374, 20190063 (2019).Article 

Google Scholar 
Thomas, J. A., Simcox, D. J. & Clarke, R. T. Successful conservation of a threatened Maculinea butterfly. Science 325, 80–83 (2009).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Nilsson, M. & Forsman, A. Evolution of conspicuous colouration, body size and gregariousness: A comparative analysis of lepidopteran larvae. Evol. Ecol. 17, 51–66 (2003).Article 

Google Scholar 
Mappes, J., Kokko, H., Ojala, K. & Lindström, L. Seasonal changes in predator community switch the direction of selection for prey defences. Nat. Commun. 5, 5016 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Bale, J. S. et al. Herbivory in global climate change research: direct effects of rising temperature on insect herbivores. Glob. Change Biol. 8, 1–16 (2002).ADS 
Article 

Google Scholar 
Otaki, J. M., Hiyama, A., Iwata, M. & Kudo, T. Phenotypic plasticity in the range-margin population of the lycaenid butterfly Zizeeria maha. BMC Evol. Biol. 10, 1–13 (2010).Article 

Google Scholar 
Galarza, J. A. et al. Evaluating responses to temperature during pre-metamorphosis and carry-over effects at post-metamorphosis in the wood tiger moth (Arctia plantaginis). Philos. Trans. R. Soc. B 374, 20190295 (2019).CAS 
Article 

Google Scholar 
Kingsolver, J. G. The well-temperatured biologist: (American Society of Naturalists Presidential Address). Am. Nat. 174, 755–768 (2009).PubMed 
Article 

Google Scholar 
Lafuente, E. & Beldade, P. Genomics of developmental plasticity in animals. Front. Genet. 10, 720 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Angilletta, M. J. Jr., Niewiarowski, P. H. & Navas, C. A. The evolution of thermal physiology in ectotherms. J. Therm. Biol 27, 249–268 (2002).Article 

Google Scholar 
Posledovich, D., Toftegaard, T., Wiklund, C., Ehrlén, J. & Gotthard, K. Phenological synchrony between a butterfly and its host plants: Experimental test of effects of spring temperature. J. Anim. Ecol. 87, 150–161 (2018).PubMed 
Article 

Google Scholar 
Adams, A. Succisa pratensis Moench. J. Ecol. 43, 709–718 (1955).Article 

Google Scholar 
Lawton, J. H. & Strong, D. J. Community patterns and competition in folivorous insects. Am. Nat. 118, 317–338 (1981).Article 

Google Scholar 
Forsman, A. Effects of genotypic and phenotypic variation on establishment are important for conservation, invasion, and infection biology. Proc. Natl. Acad. Sci. 111, 302–307 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Forsman, A., Betzholtz, P.-E. & Franzén, M. Variable coloration is associated with dampened population fluctuations in noctuid moths. Proc. R. Soc. B 282, 1–9 (2015).Article 

Google Scholar 
Betzholtz, P. E., Franzén, M. & Forsman, A. Colour pattern variation can inform about extinction risk in moths. Anim. Conserv. 20, 72–79 (2017).Article 

Google Scholar 
Klemme, I. & Hanski, I. Heritability of and strong single gene (Pgi) effects on life-history traits in the Glanville fritillary butterfly. J. Evol. Biol. 22, 1944–1953 (2009).CAS 
PubMed 
Article 

Google Scholar 
Mattila, A. L. Thermal biology of flight in a butterfly: genotype, flight metabolism, and environmental conditions. Ecol. Evol. 5, 5539–5551 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Russell, B. D. et al. Predicting ecosystem shifts requires new approaches that integrate the effects of climate change across entire systems. Biol. Let. 8, 164–166 (2012).Article 

Google Scholar 
van Bergen, E. et al. The effect of summer drought on the predictability of local extinctions in a butterfly metapopulation. Conserv. Biol. 34, 1503–1511 (2020).PubMed 
Article 

Google Scholar 
Thomas, J. A., Clarke, R. T., Elmes, G. W. & Hochberg, M. E. in Insect Populations in theory and in practice: 19th Symposium of the Royal Entomological Society 10–11 September 1997 at the University of Newcastle (eds J. P. Dempster & I. F. G. McLean) 261–290 (Springer Netherlands, 1998).Nakonieczny, M., Kedziorski, A. & Michalczyk, K. Apollo butterfly (Parnassius apollo L.) in Europe—Its history, decline and perspectives of conservation. Funct. Ecosyst. Communities 1, 56–79 (2007).
Google Scholar 
Schweiger, O., Harpke, A., Wiemers, M. & Settele, J. CLIMBER: Climatic niche characteristics of the butterflies in Europe. ZooKeys 367, 65–84 (2014).Article 

Google Scholar 
Ashton, S., Gutierrez, D. & Wilson, R. J. Effects of temperature and elevation on habitat use by a rare mountain butterfly: Implications for species responses to climate change. Ecological Entomology 34, 437–446 (2009).Article 

Google Scholar 
Klockmann, M. & Fischer, K. Effects of temperature and drought on early life stages in three species of butterflies: Mortality of early life stages as a key determinant of vulnerability to climate change?. Ecol. Evol. 7, 10871–10879 (2017).PubMed 
PubMed Central 
Article 

Google Scholar  More