Ecology

Godfray, H. C. J. et al. Science 327, 812–818 (2010).ADS 
CAS 
Article 

Google Scholar 
Pimm, S. L. et al. Science 344, 1246752 (2014).CAS 
Article 

Google Scholar 
Chung, M. G. & Liu, J. Nat. Food https://doi.org/10.1038/s43016-022-00499-7 (2022).Myers, N., Mittermeier, R. A., Mittermeier, C. G., Da Fonseca, G. A. & Kent, J. Nature 403, 853–858 (2000).ADS 
CAS 
Article 

Google Scholar 
A complex prairie ecosystem. National Park Service https://www.nps.gov/tapr/learn/nature/a-complex-prairie-ecosystem.htm (2022)Davalos, L. M. et al. Environ. Sci. Technol. 45, 1219–1227 (2011).ADS 
CAS 
Article 

Google Scholar 
Vijay, V., Pimm, S. L., Jenkins, C. N. & Smith, S. J. PLoS ONE 11, e0159668 (2016).Article 

Google Scholar 
Liu, J. et al. Ecol. Soc. 18, 26 (2013).CAS 
Article 

Google Scholar 
Liu, J. Consumption patterns and biodiversity. The Royal Society https://go.nature.com/3M19vup (2020).Xu, Z. et al. Nat. Sustain. 3, 964–971 (2020).Article 

Google Scholar 
Dou, Y., da Silva, R. F. B., Yang, H. & Liu, J. J. Geogr. Sci. 28, 1715–1732 (2018).Article 

Google Scholar  More

Schindler, D. W. Lakes as sentinels and integrators for the effects of climate change on watersheds, airsheds, and landscapes. Limnol. Oceanogr. 54, 2349–2358 (2009).CAS 
Article 

Google Scholar 
Steffen, W., Crutzen, P. J. & McNeill, J. R. The Anthropocene: are humans now overwhelming the great forces of nature. AMBIO J. Hum. Environ. 36, 614–621 (2007).Steffen, W., Broadgate, W., Deutsch, L., Gaffney, O. & Ludwig, C. The trajectory of the anthropocene: The great acceleration. Anthropoc. Rev. 2, 81–98 (2015).Article 

Google Scholar 
Richardson, D. et al. Transparency, geomorphology and mixing regime explain variability in trends in lake temperature and stratification across Northeastern North America (1975–2014). Water 9, 442 (2017).Article 

Google Scholar 
Jane, S. F. et al. Widespread deoxygenation of temperate lakes. Nature 594, 66–70 (2021).CAS 
PubMed 
Article 

Google Scholar 
Adrian, R. et al. Lakes as sentinels of climate change. Limnol. Oceanogr. 54, 2283–2297 (2009).PubMed 
PubMed Central 
Article 

Google Scholar 
Smol, J. P. Pollution of lakes and rivers: a paleoenvironmental perspective. (Blackwell Pub, 2008).Bennion, H., Simpson, G. L. & Goldsmith, B. J. Assessing degradation and recovery pathways in lakes impacted by eutrophication using the sediment record. Front. Ecol. Evol. 3, (2015).Arseneau, K. M. A., Driscoll, C. T., Cummings, C. M., Pope, G. & Cumming, B. F. Adirondack (NY, USA) reference lakes show a pronounced shift in chrysophyte species composition since ca. 1900. J. Paleolimnol. 56, 349–364 (2016).Ellegaard, M. et al. Dead or alive: sediment DNA archives as tools for tracking aquatic evolution and adaptation. Commun. Biol. 3, 169 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Coolen, M. J. L. et al. Evolution of the plankton paleome in the Black Sea from the Deglacial to Anthropocene. Proc. Natl. Acad. Sci. 110, 8609–8614 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Capo, E., Domaizon, I., Maier, D., Debroas, D. & Bigler, C. To what extent is the DNA of microbial eukaryotes modified during burying into lake sediments? A repeat-coring approach on annually laminated sediments. J. Paleolimnol. 58, 479–495 (2017).Article 

Google Scholar 
Capo, E. et al. Tracking a century of changes in microbial eukaryotic diversity in lakes driven by nutrient enrichment and climate warming: Long-term dynamics of microbial eukaryotes. Environ. Microbiol. 19, 2873–2892 (2017).CAS 
PubMed 
Article 

Google Scholar 
Capo, E. et al. Lake sedimentary DNA research on past terrestrial and aquatic biodiversity: Overview and recommendations. Quaternary 4, 6 (2021).Article 

Google Scholar 
Domaizon, I., Winegardner, A., Capo, E., Gauthier, J. & Gregory-Eaves, I. DNA-based methods in paleolimnology: New opportunities for investigating long-term dynamics of lacustrine biodiversity. J. Paleolimnol. 58, 1–21 (2017).Article 

Google Scholar 
Domaizon, I. et al. DNA from lake sediments reveals the long-term dynamics and diversity of Synechococcus assemblages. Biogeosciences 10, 3817–3838 (2013).Article 

Google Scholar 
Zhang, H. et al. Climate and nutrient-driven regime shifts of cyanobacterial communities in low-latitude plateau lakes. Environ. Sci. Technol. 55, 3408–3418 (2021).CAS 
PubMed 
Article 

Google Scholar 
Keck, F. et al. Assessing the response of micro-eukaryotic diversity to the Great acceleration using lake sedimentary DNA. Nat. Commun. 11, 3831 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Cockrell, C. The value of microorganisms. Environ. Ethics 27, 375–390 (2005).Article 

Google Scholar 
Sagova-Mareckova, M. et al. Expanding ecological assessment by integrating microorganisms into routine freshwater biomonitoring. Water Res. 191, 116767 (2021).CAS 
PubMed 
Article 

Google Scholar 
Likens, G. Plankton of Inland Waters a derivative of Encyclopedia of Inland Waters. in (Elsevier Science & Technology Books, 2010).Weisse, T. Functional diversity of aquatic ciliates. Eur. J. Protistol. 61, 331–358 (2017).PubMed 
Article 

Google Scholar 
Finlay, B. J. & Esteban, G. F. Freshwater protozoa: Biodiversity and ecological function. Biodivers. Conserv. 7, 1163–1186 (1998).Article 

Google Scholar 
Stoecker, D. K. & Lavrentyev, P. J. Mixotrophic plankton in the polar seas: A pan-arctic review. Front. Mar. Sci. 5, 292 (2018).Article 

Google Scholar 
Bick, H. Ciliated protozoa : an illustrated guide to the species used as biological indicators in freshwater biology. (World Health Organisation, 1972).Curds, C. R. An illustrated key to the British Freshwater Ciliated Protozoa commonly found in activated sludge. (Her Majesty’s Stationary Office, 1969).Pitsch, G. et al. Seasonality of planktonic freshwater ciliates: Are analyses based on V9 regions of the 18S rRNA gene correlated with morphospecies counts?. Front. Microbiol. 10, 248 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Lynn, D. H. The Ciliated Protozoa. (Springer, 2010).Adl, S. M. et al. The new higher level classification of eukaryotes with emphasis on the taxonomy of protists. J. Eukaryot. Microbiol. 52, 399–451 (2005).PubMed 
Article 

Google Scholar 
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Ibrahim, A. et al. Anthropogenic impact on the historical phytoplankton community of Lake Constance reconstructed by multimarker analysis of sediment-core environmental DNA. Mol. Ecol. 30, 3040–3056 (2021).CAS 
PubMed 
Article 

Google Scholar 
Mosher, J. J. & Findlay, R. H. Direct and indirect influence of parental bedrock on streambed microbial community structure in forested streams. Appl. Environ. Microbiol. 77, 7681–7688 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bennion, H., Monteith, D. & Appleby, P. Temporal and geographical variation in lake trophic status in the English Lake District: evidence from (sub)fossil diatoms and aquatic macrophytes. Freshw. Biol. 45, 394–412 (2000).Article 

Google Scholar 
Hornung, M. et al. The sensitivity of surface waters of Great Britain to acidification predicted from catchment characteristics. Environ. Pollut. 87, 207–214 (1995).CAS 
PubMed 
Article 

Google Scholar 
Gámez-Virués, S. et al. Landscape simplification filters species traits and drives biotic homogenization. Nat. Commun. 6, 8568 (2015).PubMed 
Article 

Google Scholar 
Nielsen, T. F., Sand-Jensen, K., Dornelas, M. & Bruun, H. H. More is less: Net gain in species richness, but biotic homogenization over 140 years. Ecol. Lett. 22, 1650–1657 (2019).Article 

Google Scholar 
Magurran, A. E., Dornelas, M., Moyes, F., Gotelli, N. J. & McGill, B. Rapid biotic homogenization of marine fish assemblages. Nat. Commun. 6, 8405 (2015).CAS 
PubMed 
Article 

Google Scholar 
Petsch, D. K. Causes and consequences of biotic homogenization in freshwater ecosystems: Biotic homogenization of freshwater systems. Internat. Rev. Hydrobiol. 101, 113–122 (2016).Article 

Google Scholar 
Perga, M.-E. et al. High-resolution paleolimnology opens new management perspectives for lakes adaptation to climate warming. Front. Ecol. Evol. 3, (2015).Rioual, P. Limnological characteristics of 25 lakes of the French Massif Central. Ann. Limnol. Int. J. Lim. 38, 311–327 (2002).Article 

Google Scholar 
Belle, S. et al. Increase in benthic trophic reliance on methane in 14 French lakes during the Anthropocene. Freshw. Biol. 61, 1105–1118 (2016).CAS 
Article 

Google Scholar 
Télesphore, S.-N. Population dynamics of autotrophic picoplankton in relation to environmental factors in a productive lake. Aquat. Sci. 57, 91–105 (1995).Article 

Google Scholar 
Esteban, G. F., Fenchel, T. & Finlay, B. J. Mixotrophy in Ciliates. Protist 161, 621–641 (2010).CAS 
PubMed 
Article 

Google Scholar 
Woelfl, S. & Geller, W. Chlorella-bearing ciliates dominate in an oligotrophic North Patagonian lake (Lake Pirehueico, Chile). Freshw. Biol. 47, 231–242 (2002).Article 

Google Scholar 
Berninger, U.-G., Finlay, B. J. & Canter, H. M. The spatial distribution and ecology of Zoochlorellae-bearing ciliates in a productive pond. J. Protozool. 33, 557–563 (1986).Article 

Google Scholar 
Haraguchi, L., Jakobsen, H. H., Lundholm, N. & Carstensen, J. Phytoplankton community dynamic: A driver for ciliate trophic strategies. Front. Mar. Sci. 5, 272 (2018).Article 

Google Scholar 
Staehr, P. A., Testa, J. & Carstensen, J. Decadal changes in water quality and net productivity of a shallow danish estuary following significant nutrient reductions. Estuaries Coasts 40, 63–79 (2017).CAS 
Article 

Google Scholar 
Jeppesen, E., Pierson, D. & Jennings, E. Effect of extreme climate events on lake ecosystems. Water 13, 282 (2021).Article 

Google Scholar 
Sonntag, B., Strüder-Kypke, M. C. & Summerer, M. Uroleptus willii nov. sp., a euplanktonic freshwater ciliate (Dorsomarginalia, Spirotrichea, Ciliophora) with algal symbionts: morphological description including phylogenetic data of the small subunit rRNA gene sequence and ecological notes. Denisia 23, 279–288 (2008).Mitra, A. et al. The role of mixotrophic protists in the biological carbon pump. Biogeosciences 11, 995–1005 (2014).Article 

Google Scholar 
Munawar, M., Niblock, H., Fitzpatrick, M. & Lorimer, J. Ciliate ecology in the eutrophic Bay of Quinte, Lake Ontario: Community structure and feeding characteristics. Aquat. Ecosyst. Health Manage. 23, 35–44 (2020).Article 

Google Scholar 
Carrick, H. J. An under-appreciated component of biodiversity in plankton communities: The role of protozoa in Lake Michigan (a case study). Hydrobiologia 551, 17–32 (2005).Article 

Google Scholar 
Beaver, J. R. & Crisman, T. L. The role of ciliated protozoa in pelagic freshwater ecosystems. Microb. Ecol. 17, 111–136 (1989).CAS 
PubMed 
Article 

Google Scholar 
Carrias, J.-F., Thouvenot, A., Amblard, C. & Sime-Ngando, T. Dynamics and growth estimates of planktonic protists during early spring in Lake Pavin France. Aquat. Microb. Ecol. 24, 163–174 (2001).Article 

Google Scholar 
Sherr, E. B. & Sherr, B. F. Significance of predation by protists in aquatic microbial food webs. Antonie Van Leeuwenhoek 81, 293–308 (2002).CAS 
PubMed 
Article 

Google Scholar 
Van Wichelen, J. et al. Planktonic ciliate community structure in shallow lakes of lowland Western Europe. Eur. J. Protistol. 49, 538–551 (2013).PubMed 
Article 

Google Scholar 
Posch, T. et al. Network of interactions between ciliates and phytoplankton during spring. Front. Microbiol. 6, (2015).DeNicola, D. M. & Kelly, M. Role of periphyton in ecological assessment of lakes. Freshw. Sci. 33, 619–638 (2014).Article 

Google Scholar 
Hao, B. et al. Warming effects on periphyton community and abundance in different seasons are influenced by nutrient state and plant type: A shallow lake mesocosm study. Front. Plant Sci. 11, 404 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Schindler, D. E. Warmer climate squeezes aquatic predators out of their preferred habitat. Proc. Natl. Acad. Sci. USA 114, 9764–9765 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Millet, L. et al. Diagnostic fonctionnel des systèmes lacustres de Gérardmer, Longemer et RetournemerUne approche combinée limnologie/paléolimnologie. 38 (2015).Sabart, M. Projet DIVERSITOX (DIVERSIté des cyanoTOXines dans différents milieux aquatiques ligériens et relation avec la biodiversité microbienne). 28 (2018).Jenny, J.-P. et al. A spatiotemporal investigation of varved sediments highlights the dynamics of hypolimnetic hypoxia in a large hard-water lake over the last 150 years. Limnol. Oceanogr. 58, 1395–1408 (2013).CAS 
Article 

Google Scholar 
Nogués-Bravo, D., Araújo, M. B., Romdal, T. & Rahbek, C. Scale effects and human impact on the elevational species richness gradients. Nature 453, 216–219 (2008).PubMed 
Article 

Google Scholar 
Hayden, C. J. & Beman, J. M. Microbial diversity and community structure along a lake elevation gradient in Yosemite National Park, California, USA: Lake microbial ecology along an elevation gradient. Environ. Microbiol. 18, 1782–1791 (2016).PubMed 
Article 

Google Scholar 
Catalan, J. et al. Global change revealed by palaeolimnological records from remote lakes: A review. J. Paleolimnol. 49, 513–535 (2013).Article 

Google Scholar 
Novotny, A., Zamora-Terol, S. & Winder, M. DNA metabarcoding reveals trophic niche diversity of micro and mesozooplankton species. Proc. R. Soc. B. 288, 20210908 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lei, Y., Stumm, K., Wickham, S. A. & Berninger, U. Distributions and biomass of benthic ciliates, foraminifera and amoeboid protists in marine, brackish, and freshwater sediments. J. Eukaryot. Microbiol. 61, 493–508 (2014).CAS 
PubMed 
Article 

Google Scholar 
Foissner, W. & Berger, H. A user-friendly guide to the ciliates (Protozoa, Ciliophora) commonly used by hydrobiologists as bioindicators in rivers, lakes, and waste waters, with notes on their ecology. Freshw. Biol. 35, 375–482 (1996).Article 

Google Scholar 
Posch, T. et al. Size selective feeding in Cyclidium glaucoma (Ciliophora, Scuticociliatida) and its effects on bacterial community structure: A study from a continuous cultivation system. Microb. Ecol. 42, 217–227 (2001).PubMed 
Article 

Google Scholar 
Pawlowski, J. et al. The future of biotic indices in the ecogenomic era: Integrating (e)DNA metabarcoding in biological assessment of aquatic ecosystems. Sci. Total Environ. 637–638, 1295–1310 (2018).PubMed 
Article 

Google Scholar 
Ogram, Andrew., Sayler, G. S., Gustin, Denise. & Lewis, R. J. DNA adsorption to soils and sediments. Environ. Sci. Technol. 22, 982–984 (1988).Parducci, L. et al. Shotgun environmental DNA, pollen, and macrofossil analysis of lateglacial lake sediments from southern Sweden. Front. Ecol. Evol. 7, 189 (2019).Article 

Google Scholar 
Pedersen, M. W. et al. Ancient and modern environmental DNA. Phil. Trans. R. Soc. B 370, 20130383 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Epp, L. S. A global perspective for biodiversity history with ancient environmental DNA. Mol. Ecol. 28, 2456–2458 (2019).PubMed 
Article 

Google Scholar 
Puitika, T., Kasahara, Y., Miyoshi, N., Sato, Y. & Shimano, S. A taxon-specific oligonucleotide primer set for PCR-based detection of soil ciliate. Microb. Environ. 22, 78–81 (2007).Article 

Google Scholar 
Dopheide, A., Lear, G., Stott, R. & Lewis, G. Molecular characterization of ciliate diversity in stream biofilms. Appl. Environ. Microbiol. 74, 1740–1747 (2008).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mangot, J.-F. et al. Short-term dynamics of diversity patterns: evidence of continual reassembly within lacustrine small eukaryotes. Environ. Microbiol. 15, 1745–1758 (2013).CAS 
PubMed 
Article 

Google Scholar 
Schloss, P. D. et al. Introducing mothur: open-source, platform independent, community-supported software for describing and comparing microbial communities. AEM 75, 7537–7541 (2009).CAS 
Article 

Google Scholar 
Vaulot, D. pr2database/pr2database: PR2 version 4.12.0. (Zenodo, 2019). 10.5281/ZENODO.3362765.Stoeck, T. et al. A morphogenetic survey on ciliate plankton from a mountain lake pinpoints the necessity of lineage-specific barcode markers in microbial ecology. Environ. Microbiol. 16, 430–444 (2014).CAS 
PubMed 
Article 

Google Scholar 
Gao, F. et al. The all-data-based evolutionary hypothesis of ciliated protists with a revised classification of the Phylum Ciliophora (Eukaryota, Alveolata). Sci. Rep. 6, 24874 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Foissner, W., Chao, A. & Katz, L. A. Diversity and geographic distribution of ciliates (Protista: Ciliophora). Biodivers Conserv 17, 345–363 (2008).Article 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. (R Foundation for Statistical Computing, 2020).Oksanen, J. et al. vegan: Community Ecology PackageJari. (2020).Therneau, T. & Atkinson, B. rpart: Recursive Partitioning and Regression Trees. (2019).Shapiro, S. S. & Wilk, M. B. An analysis of variance test for normality (complete samples). Biometrika 52, 591–611 (1965).MathSciNet 
MATH 
Article 

Google Scholar 
Conover, W. J., Johnson, M. E. & Johnson, M. M. A comparative study of tests for homogeneity of variances, with applications to the outer continental shelf bidding data. Technometrics 23, 351–361 (1981).Article 

Google Scholar 
Kruskal, W. H. & Wallis, W. A. Use of ranks in one-criterion variance analysis. J. Am. Stat. Assoc. 48, 907–911 (1952).MATH 
Article 

Google Scholar 
Benjamini, Y. & Hochberg, Y. Controlling the False Discovery Rate: a practical and powerful approach to multiple testing. J. Roy. Stat. Soc.: Ser. B (Methodol.) 57, 289–300 (1995).MathSciNet 
MATH 

Google Scholar 
Clarke, K. R. & Gorley, R. N. PRIMER v6: User manual/tutorial. (PRIMER-E, 2006).QGIS Development Team. QGIS Geographic Information System. (QGIS Association, 2021).Wickham, H. ggplot2: elegant graphics for dada analysis. (Springer-Verlag, 2016).Pedersen, T. L. & Crameri, F. scico: colour palettes based on the scientific colour-maps. (2020).Crameri, F., Shephard, G. E. & Heron, P. J. The misuse of colour in science communication. Nat. Commun. 11, 5444 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More

Cockroach strainsAll cockroaches were maintained on rodent diet (Purina 5001, PMI Nutrition International, St. Louis, MO) and distilled water at 27 °C, ~40% RH, and a 12:12 h L:D cycle. The WT colony (Orlando Normal) was collected in Florida in 1947 and has served as a standard insecticide-susceptible strain. The GA colony (T-164) was collected in 1989, also in Florida, and shown to be aversive to glucose; continued artificial selection with glucose-containing toxic bait fixed the homozygous GA trait in this population (approximately 150 generations as of 2020).Generating recombinant lines and life history dataTo homogenize the genetic backgrounds of the WT and GA strains, two recombinant colonies were initiated in 2013 by crossing 10 pairs of WT♂ × GA♀ and 10 pairs of GA♂ × WT♀ (Fig. 3a). At the F8 generation (free bulk mating without selection), 400 cockroaches were tested in two-choice feeding assays (see below) that assessed their initial response to tastants, as described in previous studies11,26. The cockroaches were separated into glucose-accepting and glucose-rejecting groups by the rapid Acceptance-Rejection assay (described in Feeding Bioassays). These colonies were bred for three more generations, and 200 cockroaches from each group were assayed in the F11 generation and backcrossed to obtain homozygous glucose-accepting (aa) and glucose-averse (AA) lines. Similar results were obtained in both directions of the cross, confirming previous findings of no sex linkage of the GA trait27. These two lines were defined as WT_aa (homozygotes, glucose-accepting) and GA_AA (homozygotes, glucose-averse). To obtain heterozygous GA cockroaches, GA_Aa, a single intercross group was generated from crosses of 10 pairs of WT_aa♂ × GA_AA♀ and 10 pairs of GA_AA♂ × WT_aa♀.The GA trait follows Mendelian inheritance. Therefore, we used backcrosses, guided by two-choice feeding assays and feeding responses in Acceptance-rejection assays, to determine the homozygosity of WT and GA cockroaches. The cross of WT♂ × WT♀ produced homozygous F1 cockroaches showing maximal glucose-acceptance. The cross of GA♂ × GA♀ produced homozygous F1 cockroaches showing maximal glucose-aversion. The cross of WT × GA produced F1 heterozygotes with intermediate glucose-aversion. When the F1 heterozygotes were backcrossed with WT cockroaches, they produced F2 cockroaches with a 1:1 ratio of WT and GA phenotypes.The two-choice feeding assay assessed whether cockroaches accepted or rejected glucose (binary: yes-no). Insects were held for 24 h without water, or starved without food and water. Either 10 adults or 2 day-old first instar siblings (30–40) were placed in a Petri dish (either 90 mm or 60 mm diameter × 15 mm height). Each Petri dish contained two agar discs: one disc contained 1% agar and 1 mmol l−1 red food dye (Allura Red AC), and the second disc contained 1% agar, 0.5 mmol l−1 blue food dye (Erioglaucine disodium salt) and either 1000 mmol l−1 or 3000 mmol l−1 glucose. The assay duration was 2 h during the dark phase of the insects’ L:D cycle. After each assay, the color of the abdomen of each cockroach was visually inspected under a microscope to infer the genotype.We assessed whether the recombinant colonies had different traits from the parental WT and GA lines. We paired single newly eclosed females (day 0) with single 10–12 days-old males of the same line in a Petri dish (90 mm diameter, 15 mm height) with fresh distilled water in a 1.5 ml microcentrifuge tube and a pellet of rodent food, and monitored when they mated. When females formed egg cases, each gravid female was placed individually in a container (95 × 95 × 80 mm) with food and water until the eggs hatched. After removing the female, her offspring were monitored until adult emergence. We recorded the time to egg hatch, first appearance of each nymphal stage, first appearance of adults and the end of adult emergence. The first instar nymphs and adults in each cohort were counted to obtain measures of survivorship. Although there were significant differences in some of these parameters across all four strains, we found no significant differences between the two recombinant lines, except mating success, which was significantly lower in GA_AA♀ than WT_aa♀ (Supplementary Table 11).Mating bioassaysAll mating sequences were recorded using an infra-red-sensitive camera (Polestar II EQ610, Everfocus Electronics, New Taipei City, Taiwan) coupled to a data acquisition board and analyzed by searchable and frame-by-frame capable software (NV3000, AverMedia Information) at 27 °C, ~40% RH and a 12:12 h L:D cycle. For behavioral analysis, tested pairs were classified into two groups: mated (successful courtship) and not-mated (failed courtship). Four distinct behavioral events (Fig. 1c, Contact, Wing raising, Nuptial feeding, and Copulation) were analyzed using seven behavioral parameters as shown in Supplementary Table 2.We extracted behavioral data from successful courtship sequences, defined as courtship that led to Copulation. For failed courtship sequences, we extracted the behavioral data from the first courtship of both mated and not-mated groups, because most pairs in both groups failed to copulate in their first encounter, and there were no significant differences in behavioral parameters between the two groups.To assay female choice, we conducted two-choice mating assays (Fig. 1a). A single focal WT♀ or GA♀ and two males, one WT and one GA, were placed in a Petri dish (90 mm diameter, 15 mm height) with fresh distilled water in a 1.5 ml microcentrifuge tube and a pellet of rodent food (n = 25 WT♀ and 27 GA♀). To assay male choice, a single focal WT♂ or GA♂ was given a choice of two females, one WT♀ and one GA♀ (n = 27 WT♂ and 18 GA♂). Experiments were started using 0 day-old sexually unreceptive females and 10–12 days-old sexually mature males. Newly emerged (0 day-old) females were used to avoid the disruption of introducing a sexually mature female into the bioassay. B. germanica females become sexually receptive at 5–7 days of age, so the mating behavior of the focal insect was video-recorded for several days until they mated. Fertility of mated females was evaluated by the number of offspring produced. We assessed the gustatory phenotype of nymphs (either WT-type or GA-type) to determine which of the two adult cockroaches mated with the focal insect. Each gravid female was maintained individually in a container (95 × 95 × 80 mm) with food and water until the eggs hatched. Two day-old first instar nymphs were starved for one day without water and food, and then they were tested in Two-choice feeding assays using 1000 mmol l−1 glucose-containing agar with 0.5 mmol l−1 blue food dye vs. plain sugar-free agar with 1 mmol l−1 red food dye. If all the nymphs chose the glucose-containing agar, their parents were considered WT♂ and WT♀. When all the nymphs showed glucose-aversion, they were raised to the adult stage. Newly emerged adults were backcrossed with WT cockroaches, and their offspring were tested in the Two-choice assay. When the parents were both GA, 100% of the offspring exhibited glucose-aversion. When the parents were WT and GA, the offspring showed a 1:1 ratio of glucose-accepting and glucose-aversive behavior. Mate choice, mating success ratio and the number of offspring were analyzed statistically.We conducted no-choice mating assay using the WT and GA strains (Fig. 1b, d). A female and a male were placed in a Petri dish with fresh water and a piece of rodent food and video-recorded for 24 h. The females were 5–7 days-old and males were 10–12 days-old. Four treatment pairs were tested: WT♂ × WT♀ (n = 20, 18 and 14 pairs for 5, 6 and 7 day-old females, respectively); GA♂ × GA♀ (n = 23, 22 and 35 pairs); GA♂ × WT♀ (n = 21, 14 and 17 pairs); and WT♂ × GA♀ (n = 33, 19 and 15 pairs).To confirm that gustatory stimuli guide nuptial feeding, we artificially augmented the male nuptial secretion and assessed whether the duration of nuptial feeding and mating success of GA♀ were affected (Fig. 2c). Before starting the mating assay with 5 day-old GA♀, 10–12 days-old WT♂ were separated into three groups: A control group did not receive any augmentation; A water control group received distilled water with 1 mmol l−1 blue dye (+Blue); A fructose group received 3000 mmol l−1 fructose solution with blue dye (+Blue+Fru). Approximately 50 nl of the test solution was placed into the tergal gland reservoirs using a glass microcapillary. No-choice mating assays were carried out for 24 h. n = 20–25 pairs for each treatment.We evaluated the association of short nuptial feeding (Fig. 1c) and the GA trait we conducted no-choice mating assays using females from the recombinant lines (Fig. 3c). Before starting each mating assay with 4 day-old females from the WT, GA and recombinant lines (WT_aa, GA_AA and GA_Aa), the EC50 for glucose was obtained by the instantaneous Acceptance-Rejection assay using 0, 10, 30, 100, 300, 1000 and 3000 mmol l−1 glucose (WT♀ and WT_aa♀, non-starved; GA♀, GA_AA♀ and GA_Aa♀, 1-day starved). After the Acceptance-Rejection assay, GA_Aa♀ were separated into two groups according to their sensitivity for rejecting glucose; the GA_Aa_high sensitivity group rejected glucose at 100 and 300 mmol l−1, whereas the GA_Aa_low sensitivity group rejected glucose at 1000 and 3000 mmol l−1. We paired these females with 10–12 days-old WT♂ (n = 15 WT_aa♀, n = 20 GA_AA♀, n = 20 GA_Aa_high♀ and n = 17 GA_Aa_low♀).Feeding bioassayWe conducted two feeding assays: Acceptance-Rejection assay and Consumption assay. The Acceptance-Rejection assay assessed the instantaneous initial responses (binary: yes-no) of cockroaches to tastants, as previously described7,22,27. Briefly, acceptance means that the cockroach started drinking. Rejection means that the cockroach never initiated drinking. The percentage of positive responders was defined as the Number of insects accepting tastants/Total number of insects tested. The effective concentration (EC50) for each tastant was obtained from dose-response curves using this assay. The Consumption assay was previously described27. Briefly, we quantified the amount of test solution females ingested after they started drinking. Females were observed until they stopped drinking, and we considered this a single feeding bout.We used the Acceptance-Rejection assay and Consumption assay, respectively, to assess the sensitivity of 5 day-old WT♀ and GA♀ for accepting and consuming the WT♂ nuptial secretion (Fig. 2a, b). The secretion was diluted with HPLC-grade water to 0.001, 0.01, 0.03, 0.1, 0.3 and 1 male-equivalents/µl (n = 20 non-starved females each). The amount of nuptial secretion consumed was tested at 0.1 male-equivalents/µl in the Consumption assay (n = 10 each).The Acceptance-Rejection assay was used to calculate the effective concentration (EC50) of glucose for females in the WT, GA and recombinant lines (Fig. 3a, b). A glucose concentration series of 0.1, 1, 10, 100 and 1000 mmol l−1 was tested with one-day starved 4-day old females (n = 65 GA_Aa♀, n = 50 GA_AA♀ and n = 50 GA♀) and non-starved females (n = 50 WT_aa♀ and n = 16 WT♀).The effects of female saliva on feeding responses of 5 day-old WT♀ and GA♀ were tested using the Acceptance-Rejection assay (Fig. 4a). Freshly collected saliva of WT♀ and GA♀ was immediately used in experiments. Assays were prepared as follows: 3 µl of 200 mmol l−1 maltose or maltotriose were mixed with 3 µl of either HPLC-grade water or saliva of WT♀ or GA♀. The final concentration of each sugar was 100 mmol l−1 in a total volume of 6 µl. This concentration represented approximately the acceptance EC70 for WT♀ and GA♀27. Nuptial secretion (1 µl representing 10 male-equivalents) was mixed with 1 µl of either HPLC-grade water or saliva from WT♀ or GA♀, and 8 µl of HPLC-grade water was added to the mix. The final concentration of the nuptial secretion was 1 male-equivalent/µl in a total volume of 10 µl. This concentration also represented approximately the acceptance EC70 for WT♀ and GA♀ (Fig. 2a). The mix of saliva and either sugar or nuptial secretion was incubated for 300 s at 25 °C. Additionally, we tested the effect of only saliva in the Acceptance-Rejection assay. Either 1-day starved or non-starved females were tested with water only and then a 1:1 mixture of saliva and water. Saliva alone did not affect acceptance or rejection of stimuli. n = 20–33 females from each strain.To evaluate whether salivary enzymes are involved in the hydrolysis of oligosaccharides, the contribution of salivary glucosidases was tested using the glucosidase inhibitor acarbose in the Acceptance-Rejection assay (Fig. 4b), as previously described27. We first confirmed that the range of 0–125 mmol l−1 acarbose in HPLC-grade water did not disrupt the acceptance and rejection of tastants. Test solutions were prepared as follows: 2 µl of either HPLC-grade water or saliva of GA♀ was mixed with 1 µl of either 250 µmol l−1 of acarbose or HPLC-grade water, then the mixture was added to 1 µl of 400 mmol l−1 of either maltose or maltotriose solution. The total volume was 4 µl, with the final concentration of sugar being 100 mmol l−1. For assays with nuptial secretion, 1 µl of either HPLC-grade water or saliva from 5 day-old GA♀ was mixed with 0.5 µl of either 250 µmol l−1 of acarbose or HPLC-grade water. This mixture was added to 0.5 µl of 10 male-equivalents of nuptial secretion (i.e., 20 male-equivalents/µl). HPLC-grade water was added for a total volume of 10 µl and a final concentration of 1 male-equivalent/µl. The mix of saliva and either sugars or nuptial secretion was incubated for 5 min at 25 °C. All test solutions contained blue food dye. Test subjects were 5 day-old GA♀ and 20–25 females were tested in each assay.Nuptial secretion and saliva collectionsThe nuptial secretion of WT♂ was collected by the following method: Five 10–12 days-old males were placed in a container (95 × 95 × 80 mm) with 5 day-old GA♀. After the males displayed wing-raising courtship behavior toward the females, individual males were immediately decapitated and the nuptial secretion in their tergal gland reservoirs was drawn into a calibrated borosilicate glass capillary (76 × 1.5 mm) under the microscope. The nuptial secretions from 30 males were pooled in a capillary and stored at −20 °C until use. Saliva from 5 day-old WT♀ and GA♀ was collected by the following method: individual females were briefly anesthetized with carbon dioxide under the microscope and the side of the thorax was gently squeezed. A droplet of saliva that accumulated on the mouthparts was then collected into a microcapillary (10 µl, Kimble Glass). Fresh saliva was immediately used in experiments.GC-MS procedures for analysis of sugarsStandards of D-( + )-glucose (Sigma-Aldrich), D-( + )-maltose (Fisher Scientific) and maltotriose (Sigma-Aldrich) were diluted in HPLC-grade water (Fisher Scientific) at 10, 50, 100, 500 and 1000 ng/µl to generate calibration curves. Samples were vortexed for 20 s and a 10 μl aliquot of each sample was transferred to a Pyrex reaction vial containing a 10 μl solution of 5 ng/μl sorbitol (≥98%) in HPLC-grade water as internal standard and dried under a gentle flow of N2 for 20 min.Samples containing degradation products from nuptial secretions were prepared by adding 15 μl of HPLC-water to each sample in a 1.5 ml Eppendorf tube, vortexed for 30 s and centrifuged at 8000 rpm (5223 RCF) for 5 min to separate lipids from the water layer. The water phase was transferred to a reaction vial using a glass capillary. This procedure was repeated with the remaining lipid layer and the water layers were combined in the same reaction vial containing 10 μl of a solution of 5 ng/μl sorbitol and dried under N2 for 20 min.For derivatization of sugars and samples, each reaction vial received 12 μl of anhydrous pyridine under a constant N2 flow, then vortexed and incubated at 90 °C for 5 min. Three μl of N-methyl-N-(trimethylsilyl)trifluoroacetamide (MSTFA; Sigma-Aldrich) was added to each reaction vial and centrifuged at 1000 rpm (118 RCF) for 2 min. Vials were incubated in a heat block at 90 °C for 1.5 hr and vortexed every 10 min for the first 30 min of incubation.The total volume of sample was ~10 μl, and 1 μl was injected into the GC-MS (6890 GC coupled to a 5975 MS, Agilent Technologies, Palo Alto, CA). The inlet was operated in splitless mode (17.5 psi) at 290 °C. The GC was equipped with a DB-5 column (30 m, 0.25 mm, 0.25 μm, Agilent), and helium was used as the carrier gas at an average velocity of 50 cm/s. The oven temperature program started at 80 °C for 1 min, increased at 10 °C/min to 180 °C, then increased at 5 °C/min to 300 °C, and held for 10 min. The transfer line was set at 250 °C for 24 min, ramped at 5 °C/min to 300 °C and held until the end of program. The ion source operated at 70 eV and 230 °C, while the MS quadrupole was maintained at 200 °C. The MSD was operated in scan mode, starting after 9 min (solvent delay time) with a mass range of 33–650 AMU.For GC-MS data analysis, the sorbitol peak area was obtained from the extracted ion chromatograms with m/z = 205, the sorbitol base peak. The area of peaks of glucose, maltose and maltotriose were obtained from the extracted ion chromatograms using m/z = 204, the base peak of the three sugars. The most abundant peaks of each sugar were selected for quantification36, and these peaks did not coelute with other peaks. Then, the peak areas of the three sugars were divided by the area of the respective sorbitol peak in each sample to normalize the data and to correct technical variability during sample processing. This procedure was performed to obtain the calibration curves and quantification of sugars in our experiments.The results of sugar analysis using GC-MS are reported in Supplementary Figs. 1–4.Analysis of nuptial secretionsWe focused the GC-MS analysis on glucose, maltose and maltotriose in WT♂ nuptial secretion (Fig. 4c). To quantify the time-course of saliva-catalyzed hydrolysis of WT♂ nuptial secretion to glucose, 1 µl of GA♀ saliva was mixed with 1 µl of 10 male-equivalents/µl. We incubated the mixtures for 0, 5, 10 and 300 s at 25 °C, and added 4 µl of methanol to stop the enzyme activity (n = 5 each treatment). Each sample contained the nuptial secretions of 5 males to obtain enough detectable amount of sugars. For the statistical analysis, the amounts of sugars were divided by 5 to obtain the amount of sugars in 1 male (1 male-equivalent). These amounts were also used for generating Fig. 4c and Supplementary Table 9. In calculations of the concentration of the three sugars (mmol l−1), the mass and volume of the nuptial secretion were measured using 70–130 male-equivalents of undiluted secretion of each strain (n = 3). The mass and volume of the nuptial secretion/male, including both lipid and aqueous layers, were approximately 30–50 µg and 40–50 nl. Because it was difficult to separate the lipid layer from the water layer at this small scale, we roughly estimated that the tergal reservoirs of the four cockroach lines had 30 nl of aqueous layer that contained sugars.To quantify the time-course of saliva-catalyzed hydrolysis of maltose and maltotriose to glucose, 1 µl of GA♀ saliva was mixed with 1 µl of 200 mmol l−1 of either maltose or maltotriose (Fig. 4d, e). Incubation time points were 0, 5, 10 and 300 s at 25 °C and methanol was used to stop the enzyme activity. Controls without saliva were also prepared using HPLC-grade water instead of saliva and 300 s incubations. n = 5 for each treatment.PhotomicroscopyThe photographs of the tergal glands and mouthparts (Fig. 5) were obtained using an Olympus Digital camera attached to an Olympus CX41 microscope (Olympus America, Center Valley, PA).Statistics and reproducibilityThe sample size and number of replicates for each experiment are noted in the respective section describing the experimental details. In summary, the samples sizes were: Mating bioassays, n = 18–80; Feeding assays, n = 16–65; Sugar analysis, n = 5; Life history parameters, n  > 14. All statistical analyses were conducted in R Statistical Software (v4.1.0; R Core Team 2021) and JMP Pro 15.2 software (SAS Institute Inc., Carey, NC). For bioassay data and sugar analysis data, we calculated the means and standard errors, and we used the Chi-square test with Holm’s method for post hoc comparisons, t-test, and ANOVA followed by Tukey’s HSD test (all α = 0.05), as noted in each section describing the experimental details, results, and in Supplementary Tables 1–11.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

Sustainable Development Goals (United Nations, 2015); https://sustainabledevelopment.un.orgNilsson, M., Griggs, D. & Visbeck, M. Policy: map the interactions between Sustainable Development Goals. Nature 534, 320–322 (2016).ADS 
PubMed 

Google Scholar 
Lu, Y., Nakicenovic, N., Visbeck, M. & Stevance, A.-S. Policy: five priorities for the UN Sustainable Development Goals. Nature 520, 432–433 (2015).ADS 
PubMed 

Google Scholar 
Xu, Z. et al. Impacts of international trade on global sustainable development. Nat. Sustain. https://doi.org/10.1038/s41893-020-0572-z (2020).Xu, Z. et al. Assessing progress towards sustainable development over space and time. Nature 577, 74–78 (2020).ADS 
CAS 
PubMed 

Google Scholar 
Liu, J. An integrated framework for achieving Sustainable Development Goals around the world. Ecol. Econ. Soc. 1, 11–17 (2018).
Google Scholar 
Zhao, Z. et al. Synergies and tradeoffs among Sustainable Development Goals across boundaries in a metacoupled world. Sci. Total Environ. https://doi.org/10.1016/j.scitotenv.2020.141749 (2020).Liu, J. in The International Encyclopedia of Geography: People, the Earth, Environment, and Technology (eds Richardson, D. et al.) 1–8 (John Wiley & Sons, 2020).Foley, J. A. et al. Solutions for a cultivated planet. Nature 478, 337–342 (2011).ADS 
CAS 
PubMed 

Google Scholar 
Carole, D. & Ignacio, R.-I. Environmental impacts of food trade via resource use and greenhouse gas emissions. Environ. Res. Lett. 11, 035012 (2016).
Google Scholar 
Crist, E., Mora, C. & Engelman, R. The interaction of human population, food production, and biodiversity protection. Science 356, 260–264 (2017).ADS 
CAS 
PubMed 

Google Scholar 
Wiedmann, T. & Lenzen, M. Environmental and social footprints of international trade. Nat. Geosci. 11, 314–321 (2018).ADS 
CAS 

Google Scholar 
Delzeit, R., Zabel, F., Meyer, C. & Václavík, T. Addressing future trade-offs between biodiversity and cropland expansion to improve food security. Reg. Environ. Change 17, 1429–1441 (2017).
Google Scholar 
Marques, A. et al. Increasing impacts of land use on biodiversity and carbon sequestration driven by population and economic growth. Nat. Ecol. Evol. 3, 628–637 (2019).PubMed 
PubMed Central 

Google Scholar 
Porkka, M., Kummu, M., Siebert, S. & Varis, O. From food insufficiency towards trade dependency: a historical analysis of global food availability. PLoS ONE 8, e82714 (2013).ADS 
PubMed 
PubMed Central 

Google Scholar 
Wood, S. A., Smith, M. R., Fanzo, J., Remans, R. & DeFries, R. S. Trade and the equitability of global food nutrient distribution. Nat. Sustain. 1, 34–37 (2018).
Google Scholar 
MacDonald, G. K. et al. Rethinking agricultural trade relationships in an era of globalization. Bioscience 65, 275–289 (2015).
Google Scholar 
Godfray, H. C. J. et al. Food security: the challenge of feeding 9 billion people. Science 327, 812–818 (2010).ADS 
CAS 
PubMed 

Google Scholar 
DeFries, R. S., Rudel, T., Uriarte, M. & Hansen, M. Deforestation driven by urban population growth and agricultural trade in the twenty-first century. Nat. Geosci. 3, 178–181 (2010).ADS 
CAS 

Google Scholar 
Moran, D. & Kanemoto, K. Identifying species threat hotspots from global supply chains. Nat. Ecol. Evol. 1, 0023 (2017).
Google Scholar 
Lenzen, M. et al. International trade drives biodiversity threats in developing nations. Nature 486, 109–112 (2012).ADS 
CAS 
PubMed 

Google Scholar 
Dalin, C., Wada, Y., Kastner, T. & Puma, M. J. Groundwater depletion embedded in international food trade. Nature 543, 700–704 (2017).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Chaudhary, A. & Kastner, T. Land use biodiversity impacts embodied in international food trade. Glob. Environ. Change 38, 195–204 (2016).
Google Scholar 
Tilman, D. et al. Future threats to biodiversity and pathways to their prevention. Nature 546, 73–81 (2017).ADS 
CAS 
PubMed 

Google Scholar 
Green, J. M. H. et al. Linking global drivers of agricultural trade to on-the-ground impacts on biodiversity. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.1905618116 (2019).Pimm, S. L. et al. The biodiversity of species and their rates of extinction, distribution, and protection. Science 344, 1246752 (2014).CAS 
PubMed 

Google Scholar 
Myers, N., Mittermeier, R. A., Mittermeier, C. G., da Fonseca, G. A. B. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).ADS 
CAS 
PubMed 

Google Scholar 
Hoffman, M., Koenig, K., Bunting, G., Costanza, J. & Williams, K. J. Biodiversity hotspots (version 2016.1). Zenodo https://doi.org/10.5281/zenodo.3261807 (2016).World Development Indicators (World Bank, 2020); https://data.worldbank.orgLiu, J. et al. Framing sustainability in a telecoupled world. Ecol. Soc. 18, 26 (2013).CAS 

Google Scholar 
Hull, V. & Liu, J. Telecoupling: a new frontier for global sustainability. Ecol. Soc. https://doi.org/10.5751/ES-10494-230441 (2018).Kapsar, K. E. et al. Telecoupling research: the first five years. Sustainability 11, 1033 (2019).
Google Scholar 
Richards, D. R. & Friess, D. A. Rates and drivers of mangrove deforestation in Southeast Asia, 2000–2012. Proc. Natl Acad. Sci. USA 113, 344–349 (2016).ADS 
CAS 
PubMed 

Google Scholar 
Chung, M. G., Kapsar, K., Frank, K. A. & Liu, J. The spatial and temporal dynamics of global meat trade networks. Sci. Rep. 10, 16657 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Soterroni, A. C. et al. Expanding the soy moratorium to Brazil’s Cerrado. Sci. Adv. 5, eaav7336 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
Phalan, B., Onial, M., Balmford, A. & Green, R. E. Reconciling food production and biodiversity conservation: land sharing and land sparing compared. Science 333, 1289–1291 (2011).ADS 
CAS 
PubMed 

Google Scholar 
Bardgett, R. D. et al. Combatting global grassland degradation. Nat. Rev. Earth Environ. 2, 720–735 (2021).ADS 

Google Scholar 
Dengler, J., Janišová, M., Török, P. & Wellstein, C. Biodiversity of Palaearctic grasslands: a synthesis. Agric. Ecosyst. Environ. 182, 1–14 (2014).
Google Scholar 
Wimberly, M. C., Narem, D. M., Bauman, P. J., Carlson, B. T. & Ahlering, M. A. Grassland connectivity in fragmented agricultural landscapes of the north-central United States. Biol. Conserv. 217, 121–130 (2018).
Google Scholar 
Wu, W. et al. Global cropping intensity gaps: increasing food production without cropland expansion. Land Use Policy 76, 515–525 (2018).
Google Scholar 
Dou, Y., da Silva, R. F. B., Yang, H. & Liu, J. Spillover effect offsets the conservation effort in the Amazon. J. Geogr. Sci. 28, 1715–1732 (2018).
Google Scholar 
Sun, J. et al. Importing food damages domestic environment: evidence from global soybean trade. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.1718153115 (2018).Liu, J. et al. Spillover systems in a telecoupled Anthropocene: typology, methods, and governance for global sustainability. Curr. Opin. Environ. Sustain. 33, 58–69 (2018).
Google Scholar 
Díaz, S. et al. Pervasive human-driven decline of life on Earth points to the need for transformative change. Science 366, eaax3100 (2019).PubMed 

Google Scholar 
Post-2020 Global Biodiversity Framework: Discussion Paper Adopted by the Conference of the Parties CBD/POST2020/PREP/1/1 (UNEP, 2019); https://www.cbd.int/doc/c/d0f3/aca0/d42fa469029f5a4d69f4da8e/post2020-prep-01-01-en.pdfEhrlich, P. R. & Harte, J. Food security requires a new revolution. Int. J. Environ. Stud. 72, 908–920 (2015).
Google Scholar 
Redford, K. H. et al. Mainstreaming biodiversity: conservation for the twenty-first century. Front. Ecol. Evol. 3, 137 (2015).
Google Scholar 
Pe’er, G. et al. EU agricultural reform fails on biodiversity. Science 344, 1090–1092 (2014).ADS 
PubMed 

Google Scholar 
Liu, J. Consumption Patterns and Biodiversity (Biodiversity Programme of the Royal Society, 2020); https://royalsociety.org/topics-policy/projects/biodiversity/consumption-patterns-and-biodiversityLiu, J., Daily, G. C., Ehrlich, P. R. & Luck, G. W. Effects of household dynamics on resource consumption and biodiversity. Nature 421, 530–533 (2003).ADS 
CAS 
PubMed 

Google Scholar 
World Population Prospects (United Nations Department of Economic and Social Affairs Population Division, 2019); https://population.un.org/wpp/Download/Standard/Population/FAOSTAT Statistics Database (UN FAO, 2020); https://www.fao.org/faostatR Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021); https://www.r-project.orgKastner, T., Kastner, M. & Nonhebel, S. Tracing distant environmental impacts of agricultural products from a consumer perspective. Ecol. Econ. 70, 1032–1040 (2011).
Google Scholar 
Liu, J. Forest sustainability in China and implications for a telecoupled world. Asia Pac. Policy Stud. 1, 230–250 (2014).
Google Scholar 
Torres-Reyna, O. Getting Started in Fixed/Random Effects Models Using R (Data & Statistical Services, Princeton Univ., 2010).Chung, M. G., Dietz, T. & Liu, J. Global relationships between biodiversity and nature-based tourism in protected areas. Ecosyst. Serv. 34, 11–23 (2018).
Google Scholar 
O’Brien, R. A caution regarding rules of thumb for variance inflation factors. Qual. Quant. 41, 673–690 (2007).
Google Scholar  More

There are 23 international conventions related to protecting the marine environment and biodiversity, with five of these requiring the implementation of marine protected areas27. Targets for the effective protection of marine habitats that conserve nature and secure nature’s contributions to people are increasingly seen as critical in ensuring progress toward meeting treaty commitments. Aichi Target 11 and the Sustainable Development Goals Target 14.5 aim to conserve at least 10% of marine and coastal areas by 2020, reflecting a shift to a more target-driven conservation policy at the international level, although this is hotly debated. Warm-water corals, mangroves, and saltmarshes all have more than 30% of their extent within PCAs, with seagrasses and cold-water corals approaching 30%, which reveals a dedicated effort to their conservation of these critical habitats. However, the protection of the total global ocean area is still at 7.92%, with only 1.18% of ABNJ covered by PCAs, falling short of the 10% of Aichi Target 11 previously set for 202016.Standardized and open source tools and platforms are needed to allow robust monitoring of progress towards international targets. While tools are available to measure advancement in some targets24,25,26, fully replicable workflows that guide the user from data preparation to index calculations have been lacking. The workflow presented here provides one of the first steps to fill this gap. The indexes also give a global context for the conservation of the habitats, highlighting ecological representation and individual jurisdictions’ potential to contribute to future conservation efforts. Combining our indexes with other tools, such as spatial conservation planning, allows policymakers to balance tradeoffs with different priorities, such as climate mitigation and resource extraction (e.g.13).While our indexes do not measure the “equitably managed” component of the Aichi target 11, it is critical that a holistic, human rights-based approach is taken in meeting any targets set and efforts to improve biodiversity outcomes. The consideration of human rights of local communities and indigenous people and inclusion of their voices is absolutely necessary in the decision-making process28.Interpretation and Usefulness of the Workflow and IndexesThe LPHPI and GPHPI are consistent ways of measuring progress in establishing protected areas that have the potential to conserve habitats and biodiversity. Additionally, the completely open access workflow described in Fig. 5 is highly adaptable and can include a wide range of habitats as data become available, or it can be applied to different conservation features like species distributions. The workflow could also be adapted to calculate the amount of key biodiversity areas within PCAs per jurisdiction and globally, or human threats (e.g., pollution or heatwaves) when geospatial data is available. Notably, the workflow can also measure progress towards targets in the draft post-2020 global biodiversity framework (as of August 2020).Fig. 5A flow chart describing the key steps of the indexes calculations. We also connect each step to the R script available at: https://github.com/jkumagai96/Marine_Habitat_protection where a more detailed explanation on how to replicate the workflow is available.Full size imageSpecifically, the workflow and resulting LPHPI dataset can directly monitor the marine components T2.1 and T2.3 of Target 2 of the draft monitoring framework (reproduced in Table 1 for convenience). The workflow can also be easily adapted to calculate the freshwater and terrestrial aspects of Target 2 – component T2.1 and component T2.2. The Protected Area Representativeness Index and Species Protection Index currently proposed for T2.3 do not account for marine regions or species. We provide more data directly on the other indicator mentioned (Proportion of terrestrial, freshwater, and marine ecological areas within PCAs) for marine areas in a FAIR workflow. Our workflow and indexes are useful resources that monitor Target 2 of the draft monitoring framework for the post-2020 global biodiversity framework. Additionally, the inclusion of ABNJ in the indexes is extremely important given current discussions on a new implementing agreement for the United Nations Convention on the Law of the Sea to protect marine biodiversity in areas beyond national jurisdiction and thus the whole ocean29.Table 1 Subset of the draft monitoring framework for the post-2020 global biodiversity framework available online (https://www.cbd.int/sbstta/sbstta-24/post2020-monitoring-en.pdf).Full size tableThe GPHPI is a valuable index that reveals the protection status of habitats distributed globally. The index highlights that not all countries have the same amount of habitat, and international effort is needed to conserve biodiversity worldwide, aspects that the LPHPI does not readily show. It is valuable to understand where habitats are covered by protected areas and where further efforts need to be placed. For example, Norway has a relatively low LPHPI (0.168) and simultaneously a relatively high GPHPI (top 11%) because of the total area of mapped habitats within their jurisdiction and their efforts to conserve them. If they can improve their LPHPI to 0.3 (30%), their GPHPI would also increase since they have a large area of habitats. But even with less than 30% of these habitats in PCAs, the protection Norway has established, or other countries have in a similar situation, substantially contributes to the global effort.Jurisdictions have direct control over their LPHPI. Increasing the protected area coverage of their marine and coastal habitats will directly increase the index score. Small countries and territories with a limited area may see large improvements in their LPHPI through a few additional protected areas, while their GPHPI score will not increase much from this effort. For these jurisdictions, international strategies need to be implemented to promote the conservation of marine and coastal habitats. The GPHPI also reveals that each jurisdiction may physically contribute only a small percentage. However, when combined, these could provide the overall coverage of PCAs distributed around the world that is ecologically advisable to promote overall biodiversity.Within the targeted analysis of the global proportion of habitats protected, any jurisdiction that protects more than 30% of its habitat extent can move from a negative to a positive score; thus, it is relative to each jurisdiction. However, the targeted analysis also reflects the absolute contribution of each jurisdiction. In particular, the targeted analysis can be interpreted to reveal jurisdictions that have the highest opportunity to conserve the most habitat, if they can reach the 30% target. Thus, this informs part of goal D of the post-2020 biodiversity framework, which requires understanding where to prioritize effort. The jurisdictions that rank the lowest in the analysis, currently ABNJ, Norway, Papua New Guinea, Nigeria, and Iraq (Fig. 4), represent a great opportunity to further expand PCAs to 30% coverage of marine habitats within their territorial waters and coast, as these would contribute the most added area. The jurisdictions that score highest have the opportunity to monitor and improve the effectiveness of their PCAs to adequately protect these marine habitats and reduce surrounding pressures, especially since they contribute significantly to the total global extent of these habitats.LimitationsOne limitation of our indexes is that they do not distinguish between areas that are readily protected (e.g., due to remoteness) and those that most urgently need protection (e.g., highly threatened biodiverse locations)30,31. Additionally, the analysis presented here is sensitive to the choice of coastal and marine habitats included in the indexes. We selected these six habitats based on the availability of high-quality spatially explicit global data recognized by the scientific community. Each habitat dataset is published in a peer-reviewed journal and available online (https://data.unep-wcmc.org/datasets) within the UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC) website and follows their data standards. The data represent the known and mapped distribution of habitats; thus, there are inherent knowledge gaps between the actual extent and available data. For example, it is likely that significant portions of cold-water corals, particularly in the ABNJ, are still unknown. Over time, the workflow will be updated and improved yearly to strengthen data coverage, and if additional high-quality data on habitats emerge, these will be included ensuring the indexes stay up to date and relevant. The original analysis with the same habitats will also be repeated to ensure a consistent time series of the indexes is provided.An important consideration when using these indexes is that habitat extent that spatially aligns with a PCA does not necessarily mean that a particular habitat is protected. For example, some PCAs enforce regulations on the water area (e.g., fishing exclusion), but do not prevent mangrove deforestation. Additionally, because of the buffering of points within the workflow, some of the habitats that are counted as protected may fall near a PCA but not within it. Nevertheless, our analysis assumes that habitats that fall within a PCA will be better conserved than habitats not within a PCA, as the primary purpose of protected areas is conservation. Similarly, we assume that other effective area-based conservation measures provide some conservation benefit and are often sustainably managed by local communities and indigenous peoples who live on them32,33.The LPHPI and GPHPI indexes report detailed information for policymakers, the scientific community, and stakeholders to understand the state of protection for marine and coastal habitats at both global and local levels. Simple metrics like these indexes that the public and politicians understand help communicate the plight of ocean health and efforts to improve it. The workflow, based on open-source programming and datasets, is reproducible and scalable and was developed to allow other scientists and data providers to calculate the indexes for any areas or habitats of interest and repeat and adapt our analysis for any target. The indexes will be updated annually to ensure continued relevance and the provision of a time series to track how the world is advancing towards the goals defined by global policy, such as aspects of the Sustainable Development Goal 14, therefore bringing to the forefront the importance and status of conserving critical marine and coastal habitats. Ultimately, transparency in protection efforts, effectiveness, and representation must be improved so policymakers can grasp the current conditions, possible scenarios, and make informed decisions to meet international policy commitments34. More

Mace, G. M., Norris, K. & Fitter, A. H. Biodiversity and ecosystem services: a multilayered relationship. Trends Ecol. Evol. 27, 19–26 (2012).Article 

Google Scholar 
Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES, 2019).Williams, B. A. et al. A robust goal is needed for species in the Post-2020 Global Biodiversity Framework. Conserv. Lett. 14, e12778 (2021).Article 

Google Scholar 
Rodrigues, A. S. L. et al. Effectiveness of the global protected area network in representing species diversity. Nature 428, 640–643 (2004).CAS 
Article 

Google Scholar 
Watson, J. E. M., Dudley, N., Segan, D. B. & Hockings, M. The performance and potential of protected areas. Nature 515, 67–73 (2014).CAS 
Article 

Google Scholar 
McCarthy, D. P. et al. Financial costs of meeting global biodiversity conservation targets: current spending and unmet needs. Science 338, 946–949 (2012).CAS 
Article 

Google Scholar 
Pe’er, G. et al. Action needed for the EU Common Agricultural Policy to address sustainability challenges. People Nat. 2, 305–316 (2020).Article 

Google Scholar 
McDonald, R. I. et al. Research gaps in knowledge of the impact of urban growth on biodiversity. Nat. Sustain. 3, 16–24 (2020).Article 

Google Scholar 
Rosenzweig, M. L. Reconciliation ecology and the future of species diversity. Oryx 37, 194–205 (2003).Article 

Google Scholar 
Dunn, R. R., Gavin, M. C., Sanchez, M. C. & Solomon, J. N. The pigeon paradox: dependence of global conservation on urban nature. Conserv. Biol. 20, 1814–1816 (2006).Article 

Google Scholar 
Callaghan, C. T. et al. How to build a biodiverse city: environmental determinants of bird diversity within and among 1581 cities. Biodivers. Conserv. 30, 217–234 (2021).Article 

Google Scholar 
Ives, C. D. et al. Cities are hotspots for threatened species. Glob. Ecol. Biogeogr. 25, 117–126 (2016).Article 

Google Scholar 
Soanes, K. & Lentini, P. E. When cities are the last chance for saving species. Front. Ecol. Environ. 17, 225–231 (2019).Article 

Google Scholar 
Luck, G. W., Davidson, P., Boxall, D. & Smallbone, L. Relations between urban bird and plant communities and human well-being and connection to nature. Conserv. Biol. 25, 816–826 (2011).Article 

Google Scholar 
Maller, C., Mumaw, L. & Cooke, B. in Rewilding (eds Pettorelli, N. et al.) Ch. 9 (Cambridge, Univ. Press, 2019).Jiang, L. & O’Neill, B. C. Global urbanization projections for the Shared Socioeconomic Pathways. Glob. Environ. Change 42, 193–199 (2017).Article 

Google Scholar 
Prévot, A.-C., Cheval, H., Raymond, R. & Cosquer, A. Routine experiences of nature in cities can increase personal commitment toward biodiversity conservation. Biol. Conserv. 226, 1–8 (2018).Article 

Google Scholar 
Berthon, K., Thomas, F. & Bekessy, S. The role of ‘nativeness’ in urban greening to support animal biodiversity. Landsc. Urban Plan. 205, 103959 (2021).Article 

Google Scholar 
van Heezik, Y., Freeman, C., Davidson, K. & Lewis, B. Uptake and engagement of activities to promote native species in private gardens. Environ. Manag. 66, 42–55 (2020).Article 

Google Scholar 
Jorgensen, A. & Keenan, R. Urban Wildscapes (Routledge, 2012).Majewska, A. A. & Altizer, S. Planting gardens to support insect pollinators. Conserv. Biol. 34, 15–25 (2020).Article 

Google Scholar 
Tallamy, D. W. Bringing Nature Home: How You Can Sustain Wildlife with Native Plants (Timber Press, 2007).Burghardt, K. T., Tallamy, D. W. & Gregory Shriver, W. Impact of native plants on bird and butterfly biodiversity in suburban landscapes. Conserv. Biol. 23, 219–224 (2009).Article 

Google Scholar 
Kraljevic, A. & Mitlacher, G. Barometer on CBD’s Target for International Resource Mobilization (WWF, 2020).Eichenberg, D. et al. Widespread decline in Central European plant diversity across six decades. Glob. Change Biol. 27, 1097–1110 (2020).Article 

Google Scholar 
Lughadha, E. N. et al. Extinction risk and threats to plants and fungi. Plants People Planet 2, 389–408 (2020).Article 

Google Scholar 
Metzing, D., Hofbauer, N., Ludwig, G. & Matzke-Hajek, G. Rote Liste Gefährdeter Tiere, Pflanzen und Pilze Deutschlands: Pflanzen/Redaktion: Detlev Metzing, Natalie Hofbauer, Gerhard Ludwig und Günter Matzke-Hajek (Bundesamt für Naturschutz, 2018).Kalusová, V. et al. Naturalization of European plants on other continents: the role of donor habitats. Proc. Natl Acad. Sci. USA 114, 13756–13761 (2017).Article 
CAS 

Google Scholar 
Staude, I. R. et al. Directional turnover towards larger-ranged plants over time and across habitats. Ecol. Lett. 25, 466–482 (2021).Article 

Google Scholar 
Galloway, J. N. et al. The nitrogen cascade. Bioscience 53, 341–356 (2003).Article 

Google Scholar 
Lundholm, J. T. & Richardson, P. J. Mini-Review: Habitat analogues for reconciliation ecology in urban and industrial environments. J. Appl. Ecol. 47, 966–975 (2010).Article 

Google Scholar 
Ellenberg, H. Gefahrdung wildlebender Pflanzenarten in der Bundesrepublik Deutschland, Versuch einer okologischen Betrachtung. Forstarchiv 57, 127–133 (1983).
Google Scholar 
Deeb, M. et al. Using constructed soils for green infrastructure—challenges and limitations. Soil 6, 413–434 (2020).CAS 
Article 

Google Scholar 
BuGG-Marktreport Gebäudegrün 2020 Dach-, Fassaden-und Innenraumbegrünung Deutschland Neu begrünte Flächen Bestand und Potenziale Kommunale Förderung (BuGG, 2020).Reichard, S. H. & White, P. Horticulture as a pathway of invasive plant introductions in the United States: most invasive plants have been introduced for horticultural use by nurseries, botanical gardens, and individuals. Bioscience 51, 103–113 (2001).Article 

Google Scholar 
der Lippe, M. & Kowarik, I. Do cities export biodiversity? Traffic as dispersal vector across urban—rural gradients. Divers. Distrib. 14, 18–25 (2008).Article 

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

Google Scholar 
Goddard, M. A., Dougill, A. J. & Benton, T. G. Scaling up from gardens: biodiversity conservation in urban environments. Trends Ecol. Evol. 25, 90–98 (2010).Article 

Google Scholar 
Sharrock, S. Plant Conservation Report 2020: A Review of Progress Towards the Global Strategy for Plant Conservation 2011–2020 CBD Technical Series No. 95 (Convention on Biological Diversity, 2020).Ismail, S. A., Pouteau, R., van Kleunen, M., Maurel, N. & Kueffer, C. Horticultural plant use as a so-far neglected pillar of ex situ conservation. Conserv. Lett. 14, e12825 (2021).Article 

Google Scholar 
Wüstemann, H., Kalisch, D. & Kolbe, J. Access to urban green space and environmental inequalities in Germany. Landsc. Urban Plan. 164, 124–131 (2017).Article 

Google Scholar 
Kleingärten im Wandel—Innovationen für verdichtete Räume (BBSR, 2018).Rudd, H., Vala, J. & Schaefer, V. Importance of backyard habitat in a comprehensive biodiversity conservation strategy: a connectivity analysis of urban green spaces. Restor. Ecol. 10, 368–375 (2002).Article 

Google Scholar 
Kowarik, I. & von der Lippe, M. Plant population success across urban ecosystems: a framework to inform biodiversity conservation in cities. J. Appl. Ecol. 55, 2354–2361 (2018).Article 

Google Scholar 
du Toit, M. J., Shackleton, C. M., Cilliers, S. S. & Davoren, E. in Urban Ecology in the Global South (eds Shackleton, C. M. et al.) 433–461 (Springer, 2021).Sawyer, J. Saving threatened native plant species in cities—from traffic islands to real islands. In Greening the City: Bringing Biodiversity Back Into the Urban Environment: Proc. (ed Dawson, M. I.) 111–117 (Royal New Zealand Institute of Horticulture, 2005).Webb, E. L. A Guide to the Native Ornamental Trees of American Samoa (National Univ. Singapore, 2011).Pan, K. et al. Urban green spaces as potential habitats for introducing a native endangered plant, Calycanthus chinensis. Urban For. Urban Green. 46, 126444 (2019).Article 

Google Scholar 
Gardening sales value worldwide from 2015 to 2020, with a forecast up to 2024. Statista statista.com/statistics/1220222/global-gardening-sales-value/ (2021).Warenstromanalyse 2018: Blumen, Zierpflanzen & Gehölze (AMI, 2020).Nature Awareness Study (Bundesamt für Naturschutz (BfN), 2019).Abbandonato, H., Pedrini, S., Pritchard, H. W., De Vitis, M. & Bonomi, C. Native seed trade of herbaceous species for restoration: a European policy perspective with global implications. Restor. Ecol. 26, 820–826 (2018).Article 

Google Scholar 
Hancock, N., Gibson-Roy, P., Driver, M. & Broadhurst, L. The Australian Native Seed Survey Report (Australian Network for Plant Conservation, 2020).Wilkinson, D. M. Is local provenance important in habitat creation? J. Appl. Ecol. 38, 1371–1373 (2001).Article 

Google Scholar 
Pedrini, S. & Dixon, K. W. International principles and standards for native seeds in ecological restoration. Restor. Ecol. 28, S286–S303 (2020).
Google Scholar 
De Vitis, M. et al. The European native seed industry: characterization and perspectives in grassland restoration. Sustainability 9, 1682 (2017).Article 

Google Scholar 
Westwood, M., Cavender, N., Meyer, A. & Smith, P. Botanic garden solutions to the plant extinction crisis. Plants People Planet 3, 22–32 (2021).Article 

Google Scholar 
Mounce, R., Smith, P. & Brockington, S. Ex situ conservation of plant diversity in the world’s botanic gardens. Nat. Plants 3, 795–802 (2017).Article 

Google Scholar 
Pedrini, S. et al. Collection and production of native seeds for ecological restoration. Restor. Ecol. 28, S228–S238 (2020).
Google Scholar 
Groves, R. H. Can Australian native plants be weeds. Plant Prot. Q. 16, 114–117 (2001).
Google Scholar 
Brummitt, R. K., Pando, F., Hollis, S. & Brummitt, N. A. World Geographic Scheme for Recording Plant Distributions 2nd edn (Hunt Institute for Botanical Documentation, 2001).Davis, M. A. et al. Don’t judge species on their origins. Nature 474, 153–154 (2011).CAS 
Article 

Google Scholar 
Mumaw, L. & Bekessy, S. Wildlife gardening for collaborative public—private biodiversity conservation. Australas. J. Environ. Manag. 24, 242–260 (2017).Article 

Google Scholar 
Mumaw, L. Transforming urban gardeners into land stewards. J. Environ. Psychol. 52, 92–103 (2017).Article 

Google Scholar 
Abeli, T. et al. Ex situ collections and their potential for the restoration of extinct plants. Conserv. Biol. 34, 303–313 (2020).Article 

Google Scholar 
Ladouceur, E. et al. Native seed supply and the restoration species pool. Conserv. Lett. 11, e12381 (2018).Article 

Google Scholar 
Hyvärinen, M.-T. Rubus humulifolius rescued by narrowest possible margin, conserved ex situ, and reintroduced in the wild. J. Nat. Conserv. 55, 125819 (2020).Article 

Google Scholar 
Holz, H., Segar, J., Valdez, J. & Staude, I. R. Assessing extinction risk across the geographic ranges of plant species in Europe. Plants People Planet https://doi.org/10.1002/ppp3.10251 (2022).Brodie, J. F. et al. Global policy for assisted colonization of species. Science 372, 456–458 (2021).CAS 
Article 

Google Scholar 
Gann, G. D. et al. International principles and standards for the practice of ecological restoration. Restor. Ecol. 27, S1–S46 (2019).Article 

Google Scholar 
Bower, A. D., Clair, J. B. S. & Erickson, V. Generalized provisional seed zones for native plants. Ecol. Appl. 24, 913–919 (2014).Article 

Google Scholar 
Goddard, M. A., Dougill, A. J. & Benton, T. G. Why garden for wildlife? Social and ecological drivers, motivations and barriers for biodiversity management in residential landscapes. Ecol. Econ. 86, 258–273 (2013).Article 

Google Scholar 
Ignatieva, M. & Ahrné, K. Biodiverse green infrastructure for the 21st century: from “green desert” of lawns to biophilic cities. J. Archit. Urban. 37, 1–9 (2013).Article 

Google Scholar 
van Heezik, Y. M., Dickinson, K. J. M. & Freeman, C. Closing the gap: communicating to change gardening practices in support of native biodiversity in urban private gardens. Ecol. Soc. 17, 34 (2012).
Google Scholar 
Shaw, A. E. & Miller, K. K. Preaching to the converted? Designing wildlife gardening programs to engage the unengaged. Appl. Environ. Educ. Commun. 15, 214–224 (2016).Article 

Google Scholar 
Mumaw, L. M. & Raymond, C. M. A framework for catalysing the rapid scaling of urban biodiversity stewardship programs. J. Environ. Manag. 292, 112745 (2021).Article 

Google Scholar 
Niemiec, R., Jones, M. S., Lischka, S. & Champine, V. Efficacy-based and normative interventions for facilitating the diffusion of conservation behavior through social networks. Conserv. Biol. 35, 1073–1085 (2021).Article 

Google Scholar 
Haywood, B. K., Parrish, J. K. & Dolliver, J. Place-based and data-rich citizen science as a precursor for conservation action. Conserv. Biol. 30, 476–486 (2016).Article 

Google Scholar 
Lerman, S. B., Turner, V. K. & Bang, C. Homeowner associations as a vehicle for promoting native urban biodiversity. Ecol. Soc. 17, 45 (2012).Article 

Google Scholar 
Nassauer, J. I. Messy ecosystems, orderly frames. Landsc. J. 14, 161–170 (1995).Article 

Google Scholar 
Cavender, N., Smith, P. & Marfleet, K. BGCI Technical Review: The Role of Botanic Gardens in Urban Greening and Conserving Urban Biodiversity (BGCI, 2019). More

Allendorf, F. W. & Lundquist, L. L. Introduction: Population biology, evolution, and control of invasive species. Conserv. Biol. 17, 24–30 (2003).Article 

Google Scholar 
Kopf, R. K. et al. Confronting the risks of large-scale invasive species control. Nat. Ecol. Evol. 1, 0172 (2017).Article 

Google Scholar 
Luque, G. M. et al. The 100th of the world’s worst invasive alien species. Biol. Invasions 16, 981–985 (2014).Article 

Google Scholar 
Genovesi, P. Eradications of invasive alien species in Europe: A review. Biol. Invasions 7, 127–133 (2005).Article 

Google Scholar 
Simberloff, D. How much information on population biology is needed to manage introduced species?. Conserv. Biol. 17, 83–92 (2003).Article 

Google Scholar 
Takeshita, F. & Maekawa, T. Paratectonatica tigrina (Gastropoda: Naticidae) adjusts its predation tactics depending on the chosen prey and their shell weight relative to its own. J. Mar. Biol. Assoc. U. K. 100, 921–926 (2020).Article 

Google Scholar 
Wiltse, W. I. Effects of Polinices duplicatus (Gastropoda: Naticidae) on infaunal community structure at Barnstable Harbor, Massachusetts, USA. Mar. Biol. 56, 301–310 (1980).Article 

Google Scholar 
Commito, J. A. Effects of Lunatia heros predation on the population dynamics of Mya arenaria and Macoma balthica in Maine, USA. Mar. Biol. 69, 187–193 (1982).Article 

Google Scholar 
Ansell, A. D. Experimental studies of a benthic predator–prey relationship. I. Feeding, growth, and egg-collar production in long-term cultures of the gastropod drill Polinices alderi (Forbes) feeding on the bivalve Tellina tenuis (da Costa). J. Exp. Mar. Biol. Ecol. 56, 235–255 (1982).Article 

Google Scholar 
Savazzi, E. & Reyment, R. A. Subaerial hunting behaviour in Natica gualteriana (naticid gastropod). Palaeogeogr. Palaeoclimatol. Palaeoecol. 74, 355–364 (1989).Article 

Google Scholar 
Gonor, J. J. Predator–prey reactions between two marine prosobranch gastropods. The Veliger 7, 228–232 (1969).
Google Scholar 
Hughes, R. N. Predatory behaviour of Natica unifasciata feeding intertidally on gastropods. J. Molluscan Stud. 51, 331–335 (1985).Article 

Google Scholar 
Pahari, A. et al. Subaerial naticid gastropod drilling predation by Natica tigrina on the intertidal molluscan community of Chandipur, Eastern Coast of India. Palaeogeogr. Palaeoclimatol. Palaeoecol. 451, 110–123 (2016).Article 

Google Scholar 
Sakai, K. Predation of the moon snail Neverita didyma, on the Manila clam Ruditapes philippinarum, at the culture ground in Mangoku-ura Inlet. Bull. Miyagi Prefect. Fish. Res. Dev. Cent. 16, 109–111 (2000) (in Japanese).
Google Scholar 
Tomiyama, T. et al. Unintentional introduction and the distribution of the nonindigenous moonsnail Euspira fortunei in Matsukawaura Lagoon, Japan. Nippon Suisan Gakkaishi 77, 1020–1026 (2011) (in Japanese with English abstract).Article 

Google Scholar 
Okoshi, K. Alien species introduced with imported clams: The clam-eating moon snail Euspira fortunei and other unintentionally introduced species. Japanese J. Benthol. 59, 74–82 (2004) (in Japanese with English abstract).
Google Scholar 
Okoshi, K. & Sato-Okoshi, W. Euspira fortunei: Biology and fisheries science of an invasive species (Kouseishakouseikaku Press, 2011).
Google Scholar 
Tomiyama, T. Lethal and non-lethal effects of an invasive naticid gastropod on the production of a native clam. Biol. Invasions 20, 2005–2014 (2018).Article 

Google Scholar 
Sato, S., Chiba, T. & Hasegawa, H. Long-term fluctuations in mollusk populations before and after the appearance of the alien predator Euspira fortunei on the Tona coast, Miyagi Prefecture, northern Japan. Fish. Sci. 78, 589–595 (2012).CAS 
Article 

Google Scholar 
Kinoshita, K., Sasaki, N., Seki, A., Matsumasa, M. & Takehara, A. Distribution of the invasive snail Laguncula pulchella and the effect of its predation on the mollusk populations in the Orikasa River Estuary after the 2011 off the Pacific Coast of Tohoku Earthquake. Japanese J. Benthol. 72, 61–70 (2018) (in Japanese with English abstract).
Google Scholar 
Sato, T., Iwasaki, T., Narita, K. & Matsumoto, I. Occurrence of the moonsnail Euspira fortunei after the earthquake in Matsukawaura Lagoon Japan. Bull. Fukushima Prefect. Fish. Exp. Stn. 79–82 (2016). (in Japanese).Chiba, T. & Sato, S. Size-selective predation and drillhole-site selectivity in Euspira fortunei (Gastropoda: Naticidae): Implications for ecological and palaeoecological studies. J. Molluscan Stud. 78, 205–212 (2012).Article 

Google Scholar 
Tanabe, T. Relationship between the shell height of the predatory moon snail Euspira fortunei and drilled hole diameter on the prey shell of manila clam Ruditapes philippinarum. Nippon Suisan Gakkaishi 78, 37–42 (2012) (in Japanese with English abstract).Article 

Google Scholar 
Hasegawa, H. & Sato, S. Predatory behaviour of the naticid Euspira fortunei: Why does it drill the left shell valve of Ruditapes philippinarum?. J. Molluscan Stud. 75, 147–151 (2009).Article 

Google Scholar 
Kingsley-Smith, P. R., Richardson, C. A. & Seed, R. Size-related and seasonal patterns of egg collar production in Polinices pulchellus (Gastropoda: Naticidae) Risso 1826. J. Exp. Mar. Biol. Ecol. 295, 191–206 (2003).Article 

Google Scholar 
Tomiyama, T. Timing and frequency of egg-collar production of the moonsnail Euspira fortunei. Fish. Sci. 79, 905–910 (2013).CAS 
Article 

Google Scholar 
Sakai, K. & Suto, A. Early development and behavior of the moon snail Neverita didyma. Miyagi Prefect. Rep. Fish. Sci. 5, 55–58 (2005) (in Japanese).
Google Scholar 
Cook, N. & Bendell-Young, L. Determining the ecological role of Euspira lewisii: Part I: Feeding ecology. J. Shellfish Res. 29, 223–232 (2010).Article 

Google Scholar 
Peitso, E., Hui, E., Hartwick, B. & Bourne, N. Predation by the naticid gastropod Polinices lewisii (Gould) on littleneck clams Protothaca staminea (Conrad) in British Columbia. Can. J. Zool. 72, 319–325 (1994).Article 

Google Scholar 
Johannesson, K., Saltin, S. H., Duranovic, I., Havenhand, J. N. & Jonsson, P. R. Indiscriminate males: Mating behaviour of a marine snail compromised by a sexual conflict?. PLoS ONE 5, e12005 (2010).ADS 
Article 

Google Scholar 
Vaughn, D., Turnross, O. R. & Carrington, E. Sex-specific temperature dependence of foraging and growth of intertidal snails. Mar. Biol. 161, 75–87 (2014).Article 

Google Scholar 
Watson, P. J., Arnqvist, G. & Stallmann, R. R. Sexual conflict and the energetic costs of mating and mate choice in water striders. Am. Nat. 151, 46–58 (1998).CAS 
Article 

Google Scholar 
Schlacher, T. A. & Wooldridge, T. H. Patterns of selective predation by juvenile, benthivorous fish on estuarine macrofauna. Mar. Biol. 125, 241–247 (1996).Article 

Google Scholar 
Sudo, H. & Azeta, M. Selective predation on mature male Byblis japonicus (Amphipoda: Gammaridea) by the barface cardinalfish, Apogon semilineatus. Mar. Biol. 114, 211–217 (1992).Article 

Google Scholar 
Vadas, R. L., Burrows, M. T. & Hughes, R. N. Foraging strategies of dogwhelks, Nucella lapillus (L.): Interacting effects of age, diet and chemical cues to the threat of predation. Oecologia 100, 439–450 (1994).ADS 
Article 

Google Scholar 
Donelan, S. C. & Trussell, G. C. Sex-specific differences in the response of prey to predation risk. Funct. Ecol. 34, 1235–1243 (2020).Article 

Google Scholar 
Yoshida, K., Sato, T., Narita, K. & Tomiyama, T. Abundance and body size of the moonsnail Laguncula pulchella in the Misuji River estuary, Seto Inland Sea, Japan: Comparison with a population in northern Japan. Plankt. Benthos Res. 12, 53–60 (2017).Article 

Google Scholar 
Sato, T., Ogata, Y., Nemoto, Y. & Shimamura, S. Status review and current concerns of the fishery for the short-neck clam, Ruditapes philippinarum, in Matsukawaura Lagoon, Fukushima Prefecture. Bull. Fukushima Prefect. Fish. Exp. Stn. 14, 57–67 (2007) (in Japanese).
Google Scholar 
Tomiyama, T. & Sato, T. Effects of translocation on the asari clam Ruditapes philippinarum at small spatial scales in Matsukawaura, Japan. Bull. Mar. Sci. 97, 647–664 (2021).Article 

Google Scholar 
Chiba, T. & Sato, S. Invasion of Laguncula pulchella (Gastropoda: Naticidae) and predator–prey interactions with bivalves on the Tona coast, Miyagi prefecture, northern Japan. Biol. Invasions 15, 587–598 (2013).Article 

Google Scholar  More

Doubleday, Z. A. et al. Global proliferation of cephalopods. Curr. Biol. 26, R406–R407 (2016).CAS 
PubMed 
Article 

Google Scholar 
Jereb, P. et al. Cephalopod biology and fisheries in Europe: II. Species Accounts. ICES Cooperative Research Report No vol. 325 (2015).ICES. ICES WGCEPH REPORT 2015 Interim Report of the Working Group on Cephalopod Fisheries and Life History (WGCEPH). 8–11 (2019).Quetglas, A. et al. Long-term spatiotemporal dynamics of cephalopod assemblages in the Mediterranean sea. Sci. Mar. 83, 33–42 (2019).Article 

Google Scholar 
Martins, H. R. Biological studies of the exploited stock of Loligo forbesi (Mollusca: Cephalopoda) in the Azores. J. Mar. Biol. Assoc. United Kingdom 62, 799–808 (1982).Article 

Google Scholar 
Guerra, A. & Rocha, F. The life history of Loligo vulgaris and Loligo forbesi (Cephalopoda: Loliginidae) in Galician waters (NW Spain). Fish. Res. 21, 43–69 (1994).Article 

Google Scholar 
Pierce, G. J. & Boyle, P. R. Empirical modelling of interannual trends in abundance of squid (Loligo forbesi) in Scottish waters. Fish. Res. 59, 305–326 (2003).Article 

Google Scholar 
Lishchenko, F. et al. A review of recent studies on the life history and ecology of European cephalopods with emphasis on species with the greatest commercial fishery and culture potential. Fish. Res. 236, 105847 (2021).Article 

Google Scholar 
Laptikhovsky, V. et al. Identification of benthic egg masses and spawning grounds in commercial squid in the English Channel and Celtic Sea: Loligo vulgaris vs L. forbesii. Fish. Res. 241, 106004 (2021).Article 

Google Scholar 
Souza, H. V. et al. Analysis of the mitochondrial COI gene and its informative potential for evolutionary inferences in the families Coreidae and Pentatomidae (Heteroptera). Genet. Mol. Res. 15, 1–14 (2016).CAS 

Google Scholar 
Brierley, A. S. et al. Genetic variation in the neritic squid Loligo forbesi (Myopsida: Loliginidae) in the northeast Atlantic Ocean. Mar. Biol. 122, 79–86 (1995).Article 

Google Scholar 
Shaw, P. W. et al. Subtle population structuring within a highly vagile marine invertebrate, the veined squid Loligo forbesi, demonstrated with microsatellite DNA markers. Mol. Ecol. 8, 407–417 (1999).CAS 
Article 

Google Scholar 
Ellegren, H. Microsatellites: Simple sequences with complex evolution. Nat. Rev. Genet. 5, 435–445 (2004).CAS 
PubMed 
Article 

Google Scholar 
Begg, G. A. & Waldman, J. R. An holistic approach to fish stock identification. Fish. Res. 43, 35–44 (1999).Article 

Google Scholar 
Shaw, P. W. Polymorphic microsatellite markers in a cephalopod: The veined squid Loligo forbesi. Mol. Ecol. 6, 297–298 (1997).CAS 
Article 

Google Scholar 
Emery, A. M. et al. New microsatellite markers for assessment of paternity in the squid Loligo forbesi (Mollusca: Cephalopoda). Mol. Ecol. 9, 110–112 (2000).CAS 
PubMed 
Article 

Google Scholar 
Butler, J. M. Advanced Topics in Forensic DNA Typing: Interpretation (Elsevier Academic Press, 2015).
Google Scholar 
Park, S. D. E. Trypanotolerance in West African Cattle and the Population Genetics Effects of Selection. Trinity Coll. (2001).Nei, M. Molecular Evolutionary Genetics (Columbia University Press, 1987).
Google Scholar 
Hedrick, P. W. Genetics of Populations (Science Books International, 1983).
Google Scholar 
Weir, B. S. & Cockerham, C. C. Estimating F statistics for Population Structure. Evolution 38, 1358–1370 (1984).CAS 
PubMed 

Google Scholar 
Raymond, M. & Rousset, F. GENEPOP (version 1.2): Population genetics software for exact tests and ecumenicism. J. Hered. 86, 248–249 (1995).Article 

Google Scholar 
Rousset, F. GENEPOP’007: A complete re-implementation of the GENEPOP software for Windows and Linux. Mol. Ecol. Resour. 8, 103–106 (2008).PubMed 
Article 

Google Scholar 
Kalinowski, S. T. HP-RARE 1.0: A computer program for performing rarefaction on measures of allelic richness. Mol. Ecol. Notes 5, 187–189 (2005).CAS 
Article 

Google Scholar 
Excoffier, L. et al. Arlequin (version 3.0): An integrated software package for population genetics data analysis. Evol. Bioinforma. 1, 117693430500100 (2005).Article 

Google Scholar 
Pritchard, J. K. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gilbert, K. J. et al. Recommendations for utilizing and reporting population genetic analyses: The reproducibility of genetic clustering using the program structure. Mol. Ecol. 21, 4925–4930 (2012).PubMed 
Article 

Google Scholar 
Porras-Hurtado, L. et al. An overview of STRUCTURE: Applications, parameter settings, and supporting software. Front. Genet. 4, 1–13 (2013).CAS 
Article 

Google Scholar 
Evanno, G. et al. Detecting the number of clusters of individuals using the software STRUCTURE: A simulation study. Mol. Ecol. 14, 2611–2620 (2005).CAS 
PubMed 
Article 

Google Scholar 
Kopelman, N. M. et al. Clumpak: A program for identifying clustering modes and packaging population structure inferences across K. Mol. Ecol. Resour. 15, 1179–1191 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Folmer, O. et al. DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Mol. Mar. Biol. Biotechnol. 3, 294–299 (1994).CAS 
PubMed 

Google Scholar 
Hall, T. A. BioEdit: A user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucleic Acids Symp. Ser. 41, 95–98 (1999).CAS 

Google Scholar 
Anderson, F. E. Phylogeny and historical biogeography of the loliginid squids (Mollusca: Cephalopoda) based on mitochondrial DNA sequence data. Mol. Phylogenet. Evol. 15, 191–214 (2000).CAS 
PubMed 
Article 

Google Scholar 
Gebhardt, K. & Knebelsberger, T. Identification of cephalopod species from the North and Baltic Seas using morphology, COI and 18S rDNA sequences. Helgol. Mar. Res. 69, 259–271 (2015).ADS 
Article 

Google Scholar 
Lobo, J. et al. Enhanced primers for amplification of DNA barcodes from a broad range of marine metazoans. BMC Ecol. 13, 1–8 (2013).Article 
CAS 

Google Scholar 
de Luna Sales, J. B. et al. New molecular phylogeny of the squids of the family Loliginidae with emphasis on the genus Doryteuthis Naef ,1912: Mitochondrial and nuclear sequences indicate the presence of cryptic species in the southern Atlantic Ocean. Mol. Phylogenet. Evol. 68, 293–299 (2013).Article 

Google Scholar 
Tatulli, G. et al. A rapid colorimetric assay for on-site authentication of cephalopod species. Biosensors 10, 3–10 (2020).Article 
CAS 

Google Scholar 
Velasco, A. et al. A new rapid method for the authentication of common octopus (Octopus vulgaris) in seafood products using recombinase polymerase amplification (rpa) and lateral flow assay (lfa). Foods 10, 1825 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Luz, A. & Keskin, E. Building Reference Library for Marine Fish Species of Azores Archipelago and Bio-monitoring via DNA Metabarcoding. https://www.ncbi.nlm.nih.gov/nuccore/MT491734 (2020).BoldSystems. https://boldsystems.org/index.php/Public_RecordView?processid=AZB030-20 (2018). (Accessed 2 May 2022).Tamura, K. et al. MEGA6: Molecular evolutionary genetics analysis version 6.0. Mol. Biol. Evol. 30, 2725–2729 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ronquist, F. & Huelsenbeck, J. P. MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinformatics 19, 1572–1574 (2003).CAS 
PubMed 
Article 

Google Scholar 
Rambaut, A. et al. Posterior summarization in Bayesian phylogenetics using Tracer 1.7. Syst. Biol. 67, 901–904 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bandelt, H.-J. et al. Median-joining networks for inferring intraspecific phylogenies. Mol. Biol. Evol. 16, 37–48 (2009).Article 

Google Scholar 
Librado, P. & Rozas, J. DnaSP v5: A software for comprehensive analysis of DNA polymorphism data. Bioinformatics 25, 1451–1452 (2009).CAS 
PubMed 
Article 

Google Scholar 
Schlitzer, R. Ocean Data View. (2013).Shaw, P. W. & Boyle, P. R. Multiple paternity within the brood of single females of Loligo forbesi (Cephalopoda: Loliginidae), demonstrated with microsatellite DNA markers. Mar. Ecol. Prog. Ser. 160, 279–282 (1997).ADS 
Article 

Google Scholar 
Emery, A. M. et al. Assignment of paternity groups without access to parental genotypes: Multiple mating and developmental plasticity in squid. Mol. Ecol. 10, 1265–1278 (2001).CAS 
PubMed 
Article 

Google Scholar 
Catarino, D. et al. The role of the Strait of Gibraltar in shaping the genetic structure of the Mediterranean Grenadier, Coryphaenoides mediterraneus, between the Atlantic and Mediterranean Sea. PLoS ONE 12, 1–24 (2017).
Google Scholar 
Gonzalez, E. G. & Zardoya, R. Relative role of life-history traits and historical factors in shaping genetic population structure of sardines (Sardina pilchardus). BMC Evol. Biol. 7, 1–12 (2007).Article 
CAS 

Google Scholar 
Reichow, D. & Smith, M. J. Microsatellites reveal high levels of gene flow among populations of the California squid Loligo opalescens. Mol. Ecol. 10, 1101–1109 (2001).CAS 
PubMed 
Article 

Google Scholar 
Shaw, P. W. et al. DNA markers indicate that distinct spawning cohorts and aggregations of Patagonian squid, Loligo gahi, do not represent genetically discrete subpopulations. Mar. Biol. 144, 961–970 (2004).CAS 
Article 

Google Scholar 
Göpel, A. Populationsgenetik und Phylogeographie des Nordischen Kalmars Loligo forbesii Steenstrup, 1856 in Europäischen Gewässern. Masterthesis, Univ. Rostock in German, 76pp (2020).Oesterwind, D. et al. Biology and meso-scale distribution patterns of North Sea cephalopods. Fish. Res. 106, 141–150 (2010).Article 

Google Scholar 
Sauer, W. H. H. et al. Tag recapture studies of the chokka squid Loligo vulgaris reynaudii d’Orbigny, 1845 on inshore spawning grounds on the south-east coast of South Africa. Fish. Res. 45, 283–289 (2000).ADS 
Article 

Google Scholar 
Knowlton, N. & Weigt, L. A. New dates and new rates for divergence across the Isthmus of Panama. Proc. R. Soc. B Biol. Sci. 265, 2257–2263 (1998).Article 

Google Scholar 
Pérez-Losada, M. et al. Testing hypotheses of population structuring in the Northeast Atlantic Ocean and Mediterranean Sea using the common cuttlefish Sepia officinalis. Mol. Ecol. 16, 2667–2679 (2007).PubMed 
Article 

Google Scholar 
O’Dor, R. K. Can understanding squid life-history strategies and recruitment improve management?. South African J. Mar. Sci. 7615, 193–206 (1998).Article 

Google Scholar 
Izquierdo, A. et al. Modelling in the Strait of Gibraltar: From operational oceanography to scale interactions. Fundam. i Prikl. Gidrofiz. 9, 15–24 (2016).
Google Scholar 
Clarke, M. & Hart, M. Treatise Online no. 102: Part M, Chapter 11: Statoliths and coleoid evolution. Treatise Online (2018).Hsü, K. J. et al. Late Miocene desiccation of the mediterranean. Nature 242, 240–244 (1973).ADS 
Article 

Google Scholar 
Garcia-Castellanos, D. et al. Catastrophic flood of the Mediterranean after the Messinian salinity crisis. Nature 462, 778–781 (2009).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Thunell, R. C. et al. Atlantic-mediterranean water exchange during the late neocene. Paleoceanography 2(6), 661 (1987).ADS 
Article 

Google Scholar 
Green, C. P. et al. Combining statolith element composition and fourier shape data allows discrimination of spatial and temporal stock structure of arrow squid (Nototodarus gouldi). Can. J. Fish. Aquat. Sci. 72, 1609–1618 (2015).Article 

Google Scholar  More