Ecology

Ristaino, J. B. et al. The persistent threat of emerging plant disease pandemics to global food security. Proc. Natl. Acad. Sci. USA 118, e2022239118 (2021).Blackburn, T. M. et al. A proposed unified framework for biological invasions. Trends Ecol. Evol. 26, 333–339 (2011).PubMed 
Article 

Google Scholar 
Chapman, D., Purse, B. V., Roy, H. E. & Bullock, J. M. Global trade networks determine the distribution of invasive non-native species. Glob. Ecol. Biogeogr. 26, 907–917 (2017).Article 

Google Scholar 
Liebhold, A. M. et al. Plant diversity drives global patterns of insect invasions. Sci. Rep. 8, 1–5 (2018).CAS 
Article 

Google Scholar 
Bradshaw, C. J. A. et al. Massive yet grossly underestimated global costs of invasive insects. Nat. Commun. 7, 1–8 (2016).Article 
CAS 

Google Scholar 
Wyckhuys, K. A. G. et al. Biological control of an invasive pest eases pressures on global commodity markets. Environ. Res. Lett. 13, 094005 (2018).Article 
CAS 

Google Scholar 
Leung, B., Finnoff, D., Shogren, J. F. & Lodge, D. Managing invasive species: rules of thumb for rapid assessment. Ecol. Econ. 55, 24–36 (2005).Article 

Google Scholar 
Reed, C. et al. Novel framework for assessing epidemiologic effects of influenza epidemics and pandemics. Emerg. Infect. Dis. 19, 85 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Qualls, N. et al. Community mitigation guidelines to prevent pandemic influenza—United States, 2017. MMWR Recomm. Rep. 66, 1 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Grarock, K., Lindenmayer, D. B., Wood, J. T. & Tidemann, C. R. Using invasion process theory to enhance the understanding and management of introduced species: a case study reconstructing the invasion sequence of the common myna (Acridotheres tristis). J. Environ. Manag. 129, 398–409 (2013).Article 

Google Scholar 
Nuñez, M. A., Pauchard, A. & Ricciardi, A. Invasion science and the global spread of SARS-CoV-2. Trends Ecol. Evol. 35, 642–645 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Ogden, N. H. et al. Emerging infectious diseases and biological invasions: a call for a one health collaboration in science and management. R. Soc. Open Sci. 6, 181577 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Hatcher, M. J., Dick, J. T. A. & Dunn, A. M. Disease emergence and invasions. J. Ecol. 26, 1275–1287 (2016).
Google Scholar 
Bright, C. Invasive species: pathogens of globalization. Foreign Policy 1, 50–64 (1999).Article 

Google Scholar 
Simberloff, D., Meyerson, L. & Fefferman, N. Invasive species policy and COVID-19. The Ecological Society of America https://www.esa.org/about/esa-covid-19/invasive-species-policy-and-covid-19/ (2020).Comizzoli, P., Pagenkopp Lohan, K. M., Muletz-Wolz, C., Hassell, J. & Coyle, B. The interconnected health initiative: a Smithsonian framework to extend one health research and education. Front. Vet. Sci. 8, 629410 (2021).Katella, K. Our new COVID-19 vocabulary—what does it all mean? Stories at Yale Medicine. Yale Medicine https://www.yalemedicine.org/stories/covid-19-glossary/ (2020).Parra, G., Moylett, H. & Bulluck, R. USDA-APHIS-PPQ-CPHST Technical working group summary report spotted lanternfly, Lycorma delicatula (White, 1845) (2018).Floerl, O., Inglis, G. J., Dey, K. & Smith, A. The importance of transport hubs in stepping-stone invasions. J. Appl. Ecol. 46, 37–45 (2009).Article 

Google Scholar 
Barringer, L. E., Donovall, L. R., Spichiger, S.-E., Lynch, D. & Henry, D. The first New World record of Lycorma delicatula (Insecta: Hemiptera: Fulgoridae). Entomol. N. 125, 20–23 (2015).Article 

Google Scholar 
Urban, J. M. Perspective: shedding light on spotted lanternfly impacts in the USA. Pest Manag. Sci. 76, 10–17 (2020).CAS 
PubMed 
Article 

Google Scholar 
Nixon, L. J. et al. Survivorship and development of the invasive Lycorma delicatula (Hemiptera: Fulgoridae) on wild and cultivated temperate host plants. Environ. Entomol. 51, 222–228 https://doi.org/10.1093/ee/nvab137 (2022).Urban, J. M., Calvin, D. & Hills-Stevenson, J. Early response (2018–2020) to the threat of spotted lanternfly, Lycorma delicatula (Hemiptera: Fulgoridae) in Pennsylvania. Ann. Entomol. Soc. Am. 114, 709–718 (2021).Article 

Google Scholar 
Du, Z. et al. Global phylogeography and invasion history of the spotted lanternfly revealed by mitochondrial phylogenomics. Evol. Appl. 14, 915–930 https://doi.org/10.1111/eva.13170 (2020).Lee, J.-E. et al. Feeding behavior of Lycorma delicatula (Hemiptera: Fulgoridae) and response on feeding stimulants of some plants. Korean. J. Appl. Entomol. 48, 467–477 (2009).Article 

Google Scholar 
Lee, D.-H., Park, Y.-L. & Leskey, T. C. A review of biology and management of Lycorma delicatula (Hemiptera: Fulgoridae), an emerging global invasive species. J. Asia-Pac. Entomol. 22, 589–596 (2019).Article 

Google Scholar 
Roush, R. How we can contain the spotted lanternfly—maybe the worst invasive pest in generations | Opinion https://www.inquirer.com (2018).Imbler, S. The dreaded lanternfly, scourge of agriculture, spreads in New Jersey. The New York Times (2020).Morrison, R. Invasive insects: The top 4 ‘most wanted’ list. Entomology Today https://entomologytoday.org/2018/06/21/invasive-insects-the-top-4-most-wanted-list/ (2018).Murman, K. et al. Distribution, survival, and development of spotted lanternfly on host plants found in North America. Environ. Entomol. 49, 1270–1281 (2020).PubMed 
Article 

Google Scholar 
Derstine, N. T. et al. Plant volatiles help mediate host plant selection and attraction of the spotted lanternfly (Hemiptera: Fulgoridae): a generalist with a preferred host. Environ. Entomol. 49, 1049–1062 (2020).PubMed 
Article 

Google Scholar 
Dechaine, A. C. et al. Phenology of Lycorma delicatula (Hemiptera: Fulgoridae) in Virginia, USA. Environ. Entomol. 50, 1267–1275 https://doi.org/10.1093/ee/nvab107 (2021).Uyi, O. et al. Spotted lanternfly (Hemiptera: Fulgoridae) can complete development and reproduce without access to the Ppreferred host, Ailanthus altissima. Environ. Entomol. 49, 1185–1190 https://doi.org/10.1093/ee/nvaa083 (2020).Park, M., Kim, K.-S. & Lee, J.-H. Genetic structure of Lycorma delicatula (Hemiptera: Fulgoridae) populations in Korea: Implication for invasion processes in heterogeneous landscapes. Bull. Entomol. Res. 103, 414–424 (2013).CAS 
PubMed 
Article 

Google Scholar 
Dara, S. K., Barringer, L. & Arthurs, S. P. Lycorma delicatula (Hemiptera: Fulgoridae): a new invasive pest in the United States. J. Integr. Pest Manag. 6, 1–6 (2015).Article 

Google Scholar 
Leach, H. & Leach, A. Seasonal phenology and activity of spotted lanternfly (Lycorma delicatula) in Eastern U.S. vineyards. J. Pest Sci. 93, 1215–1224 (2020).Article 

Google Scholar 
International Organisation of Vine and Wine. 2019 Statistical Report on World Vitiviniculture. 23 (2019).California Department of Food and Agriculture. Pest Detection Advisory No. PD17-2020 Spotted Lanternfly PD/EP Activity Summary 2020. 1–7 (2020).Oak Ridge National Lab. Freight analysis framework version 4. http://faf.ornl.gov/fafweb/ (2017).U.S. Census Bureau. U.S.A. Trade Online. https://usatrade.census.gov/index.php?do=login (2019).Derived dataset GBIF.org. Filtered export of GBIF occurrence data. https://doi.org/10.15468/DD.KS6ACS (2021).Jung, J.-M., Jung, S., Byeon, D. & Lee, W.-H. Model-based prediction of potential distribution of the invasive insect pest, spotted lanternfly Lycorma delicatula (Hemiptera: Fulgoridae), by using CLIMEX. J. Asia-Pac. Biodivers. 10, 532–538 (2017).Article 

Google Scholar 
Wakie, T. T., Neven, L. G., Yee, W. L. & Lu, Z. The establishment risk of Lycorma delicatula (Hemiptera: Fulgoridae) in the United States and globally. J. Econ. Entomol. 113, 306–314 (2020).PubMed 

Google Scholar 
Lewkiewicz, S. M., De Bona, S., Helmus, M. R. & Seibold, B. Temperature sensitivity of pest reproductive numbers in age-structured PDE models, with a focus on the invasive spotted lanternfly. Preprint at ArXiv211211448 Q-Bio (2021).Maino, J. L., Schouten, R., Lye, J. C., Umina, P. A. & Reynolds, O. L. Mapping the life-history, development, and survival of spotted lantern fly in occupied and uninvaded ranges. InReview 1–18 https://doi.org/10.21203/rs.3.rs-400798/v1 (2021).FAOSTAT. FAOSTAT statistical database. http://www.fao.org/faostat/en/#data/QC (2019).USDA National Agricultural Statistics Service. National agricultural statistics service – quick stats. https://quickstats.nass.usda.gov/ (2019).U.S. Alcohol and Tobacco Tax and Trade Bureau. Wine statistics. https://www.ttb.gov/wine/wine-stats.shtml (2019).Crowe, J. Spotted lanternfly control program in the Mid-Atlantic region environmental assessment. USDA APHIS Rep. 46 (2018).US Animal and Plant Health Inspection Service. USDA provides $7.1 million to Pennsylvania to support projects that protect agriculture and natural resources. https://www.aphis.usda.gov/wcm/connect/APHIS_Content_Library/SA_Newsroom/SA_News/SA_By_Date/SA-2019/pennsylvania-funding?presentationtemplate=APHIS_Design_Library%2FPT_Print_Friendly_News_release (2019).Jones, C. M. et al. Iteratively forecasting biological invasions with PoPS and a little help from our friends. Front. Ecol. Environ. 19, 411–418 https://doi.org/10.1002/fee.2357 (2021).Smyers, E. C. et al. Spatio-temporal model for predicting spring hatch of the spotted lanternfly (Hemiptera: Fulgoridae). Environ. Entomol. 50, 126–137 (2021).CAS 
PubMed 
Article 

Google Scholar 
Brooks, R. K., Wickert, K. L., Baudoin, A., Kasson, M. T. & Salom, S. Field-inoculated Ailanthus altissima stands reveal the biological control potential of Verticillium nonalfalfae in the Mid-Atlantic region of the United States. Biol. Control 148, 104298 (2020).CAS 
Article 

Google Scholar 
Commonwealth of Pennsylvania. Pennsylvania Bulletin. 49, 2705–2902 (2019).Barringer, L. & Ciafré, C. M. Worldwide feeding host plants of spotted lanternfly, with significant additions from North America. Environ. Entomol. 49, 999–1011 (2020).PubMed 
Article 

Google Scholar 
Leach, H., Biddinger, D. J., Krawczyk, G., Smyers, E. & Urban, J. M. Evaluation of insecticides for control of the spotted lanternfly, Lycorma delicatula, (Hemiptera: Fulgoridae), a new pest of fruit in the Northeastern U.S. Crop Prot. 124, 104833 (2019).CAS 
Article 

Google Scholar 
Francese, J. A. et al. Developing traps for the spotted lanternfly, Lycorma delicatula (Hemiptera: Fulgoridae). Environ. Entomol. 49, 269–276 (2020).PubMed 
Article 

Google Scholar 
Penn State Extension. Spotted lanternfly management in vineyards. https://extension.psu.edu/spotted-lanternfly-management-in-vineyards (2021).Nixon, L. J. et al. Development of behaviorally based monitoring and biosurveillance tools for the invasive spotted lanternfly (Hemiptera: Fulgoridae). Environ. Entomol. 49, 1117–1126 (2020).PubMed 
Article 

Google Scholar 
Liu, H. & Mottern, J. An old remedy for a new problem? Identification of Ooencyrtus kuvanae (Hymenoptera: Encyrtidae), an egg parasitoid of Lycorma delicatula (Hemiptera: Fulgoridae) in North America. J. Insect Sci. 17, 1–6 (2017).Article 

Google Scholar 
Yang, Z.-Q., Choi, W.-Y., Cao, L.-M., Wang, X.-Y. & Hou, Z.-R. A new species of Anastatus (Hymenoptera: Eulpelmidae) from China, parasitizing eggs of Lycorma delicatula (Homoptera: Fulgoridae). Zool. Syst. 40, 290–302 (2015).
Google Scholar 
Clifton, E. H. et al. Applications of Beauveria bassiana (Hypocreales: Cordycipitaceae) to control populations of spotted lanternfly (Hemiptera: Fulgoridae), in semi-natural landscapes and on grapevines. Environ. Entomol. 49, 854–864 (2020).PubMed 
Article 

Google Scholar 
Hogan, M. J. & Pardi, N. mRNA vaccines in the COVID-19 pandemic and beyond. Annu. Rev. Med. 73, 17–39 (2022).PubMed 
Article 
CAS 

Google Scholar 
Whyard, S., Singh, A. D. & Wong, S. Ingested double-stranded RNAs can act as species-specific insecticides. Insect Biochem. Mol. Biol. 39, 824–832 (2009).CAS 
PubMed 
Article 

Google Scholar 
Ordish, G. The Great Wine Blight (Charles Scribner’s Sons, 1972).About the Council. https://www.doi.gov/invasivespecies/about-nisc (2016).Invasive Species Advisory Committee Products. https://www.doi.gov/invasivespecies/isac-resources (2015).Simberloff, D. et al. U.S. action lowers barriers to invasive species. Science 367, 636–636 (2020).PubMed 
Article 
CAS 

Google Scholar 
Exec. Order No. 14048, A. of J. R. B., Jr. Executive Order on Continuance or Reestablishment of Certain Federal Advisory Committees and Amendments to Other Executive Orders (2021).Zhu, G., Illan, J. G., Looney, C. & Crowder, D. W. Assessing the ecological niche and invasion potential of the Asian giant hornet. Proc. Natl Acad. Sci. USA 117, 24646–24648 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Freitas, A. R. R. et al. Assessing the severity of COVID-19. Epidemiol. E Serviços. Saúde. 29, 1–5 (2020).
Google Scholar 
Prevent Epidemics. COVID-19 Key COVID-19 Metrics Based on the Latest Available Science. https://preventepidemics.org/wp-content/uploads/2020/09/COVID-19-Science-Metrics_2020Sept18.pdf (2020).Lockwood, J. L., Hoopes, M. F. & Marchetti, M. P. Invasion Ecology (Wiley-Blackwell, 2013).Ehler, L. E. Invasion biology and biological control. Biol. Control 13, 127–133 (1998).Article 

Google Scholar 
Ludsin, S. A. & Wolfe, A. D. Biological invasion theory: Darwin’s contributions from The Origin of Species. BioScience 51, 780 (2001).Article 

Google Scholar 
Schulz, A. N., Lucardi, R. D. & Marsico, T. D. Strengthening the ties that bind: an evaluation of cross-disciplinary communication between invasion ecologists and biological control researchers in entomology. Ann. Entomol. Soc. Am. 114, 163–174 (2021).CAS 
Article 

Google Scholar 
Lockwood, J. L., Cassey, P. & Blackburn, T. The role of propagule pressure in explaining species invasions. Trends Ecol. Evol. 20, 223–228 (2005).PubMed 
Article 

Google Scholar 
Liu, H. Oviposition substrate selection, egg mass characteristics, host preference, and life history of the spotted lanternfly (Hemiptera: Fulgoridae) in North America. Environ. Entomol. 48, 1452–1468 (2019).PubMed 
Article 
CAS 

Google Scholar 
Liu, H. Seasonal development, cumulative growing degree-days, and population density of spotted lanternfly (Hemiptera: Fulgoridae) on selected hosts and substrates. Environ. Entomol. 49, 1171–1184 (2020).PubMed 
Article 

Google Scholar 
Phillips, S. J., Anderson, R. P., Dudík, M., Schapire, R. E. & Blair, M. E. Opening the black box: an open‐source release of Maxent. Ecography 40, 887–893 (2017).Article 

Google Scholar 
Araújo, M. B. & New, M. Ensemble forecasting of species distributions. Trends Ecol. Evol. 22, 42–47 (2007).PubMed 
Article 

Google Scholar 
Araújo, M. B. et al. Standards for distribution models in biodiversity assessments. Sci. Adv. 5, eaat4858 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).Article 

Google Scholar 
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).Article 

Google Scholar 
Weiss, D. J. et al. A global map of travel time to cities to assess inequalities in accessibility in 2015. Nature 553, 333–336 (2018).CAS 
PubMed 
Article 

Google Scholar 
Simard, M., Pinto, N., Fisher, J. B. & Baccini, A. Mapping forest canopy height globally with spaceborne lidar. J. Geophys. Res. Biogeosciences 116 (2011).Sladonja, B., Sušek, M. & Guillermic, J. Review on invasive tree of heaven (Ailanthus altissima (Mill.) Swingle) conflicting values: assessment of its ecosystem services and potential biological threat. Environ. Manag. 56, 1009–1034 (2015).Article 

Google Scholar 
Fielding, A. H. & Bell, J. F. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ. Conserv. 24, 38–49 (1997).Article 

Google Scholar 
Pearson, R. G., Raxworthy, C. J., Nakamura, M. & Peterson, A. T. Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. J. Biogeogr. 34, 102–117 (2007).Article 

Google Scholar 
Anderson, R. P. & Gonzalez, I. Species-specific tuning increases robustness to sampling bias in models of species distributions: an implementation with Maxent. Ecol. Model. 222, 2796–2811 (2011).Article 

Google Scholar 
AVCALC. Density of alcoholic beverage, wine, table, all (food). https://www.aqua-calc.com/page/density-table/substance/alcoholic-blank-beverage-coma-and-blank-wine-coma-and-blank-table-coma-and-blank-all (2019).U.S. Alcohol and Tobacco Tax and Trade Bureau. Established AVAs. https://www.ttb.gov/wine/established-avas (2019).Wikipedia. https://en.wikipedia.org/wiki/List_of_wine-producing_regions. (2020).Allison, P. D. Multiple Regression: A Primer (Pine Forge Press, 1999).Ponti, L. et al. Biological invasion risk assessment of Tuta absoluta: Mechanistic versus correlative methods. Biol. Invasions 23, 3809–3829 (2021).Article 

Google Scholar 
Briscoe, N. J. et al. Forecasting species range dynamics with process-explicit models: matching methods to applications. Ecol. Lett. 22, 1940–1956 (2019).PubMed 
Article 

Google Scholar 
Wang, C.-J. et al. Risk assessment of insect pest expansion in alpine ecosystems under climate change. Pest Manag. Sci. 77, 3165–3178 (2021).CAS 
PubMed 
Article 

Google Scholar 
Keena, M. A. & Nielsen, A. L. Comparison of the hatch of newly laid Lycorma delicatula (Hemiptera: Fulgoridae) eggs from the United States after exposure to different temperatures and durations of low temperature. Environ. Entomol. 50, 410–417 https://doi.org/10.1093/ee/nvaa177 (2021).Xin, B. et al. Exploratory survey of spotted lanternfly (Hemiptera: Fulgoridae) and its natural enemies in China. Environ. Entomol. 50, 36–45 (2020).Article 
CAS 

Google Scholar 
Leach, A. & Leach, H. Characterizing the spatial distributions of spotted lanternfly (Hemiptera: Fulgoridae) in Pennsylvania vineyards. Sci. Rep. 10, 1–9 (2020).Article 
CAS 

Google Scholar 
Granett, J., Walker, M. A., Kocsis, L. & Omer, A. D. Biology and management of grape phylloxera. Annu. Rev. Entomol. 46, 387–412 (2001).CAS 
PubMed 
Article 

Google Scholar  More

Wild animals construct various types of structures that are adaptive to their life and reproduction. For example, termites that inhabit the African savanna use soil to construct a huge mound that reaches 10 m in height; they produce hollows and holes in these mounds to allow air ventilation, thereby keeping the internal temperature constant1. In addition, prairie dogs inhabiting the North American prairie dig vertically and horizontally extending burrows in the ground that they use for shelter and rearing offspring; these burrows have multiple entrances, some of which are chimney-shaped to improve ventilation efficiency2. In the field of biomimetics, researchers apply the principles of animal-created structures in applications useful to humans3.The white-spotted pufferfish Torquigener albomaculosus (Pisces: Tetraodontidae) is a relatively small species that grows to ~10 cm in total total length (Fig. 1). Male T. albomaculosus individuals construct an intricate geometric circular structure, known as the “mystery circle,” with a diameter of 2 m in the sand of the seabed;4 the discovery of these structures has fascinated researchers and the general public worldwide. The male pufferfish digs the sand on the seabed with its fins and body while swimming straight ahead toward the centre from different directions, and a circular structure composed of radially aligned peaks and valleys was constructed. Finally, the male creates a maze-like pattern by flapping its anal fin on the bottom of the central zone4. Thus, the male completes the circular structure by himself. Furthermore, we discovered that the earliest stage of the mystery circle is composed of dozens of irregular depressions, which might function as landmarks for the formation of the radial patterns5. By accumulating observations of pufferfish behaviour, we were able to conduct a computer simulation including the swimming trajectory of the pufferfish extracted from video images wherein they constructed the circular structure. This simulation revealed that an elaborate circular geometric pattern is inevitably formed if the pufferfish repeats the digging behavior on the seabed using simple rules6. We also observed the reproductive behaviour of the pufferfish and found that they consistently breed in a semilunar cycle from spring to summer. Each male constructs a mystery circle and spawns with multiple females on the nest, and the male cares for the eggs alone until they hatch. Some of the elements of the circular structure, i.e., its size, symmetry, ornaments, and maze-like pattern, might be important factors in terms of female mate choice4,7.Fig. 1The white-spotted pufferfish Torquigener albomaculosus. Lateral view of a male (a), and male digging behaviour on the seabed while rolling up fine sand particles (b).Full size imageAlthough data on the reproductive ecology and circle-construction behaviour of these pufferfish have been collected, many questions remain. Our interdisciplinary research currently has two themes: (i) theoretical studies on the logic of 3D-structure formation of the circular structure and (ii) ethological studies on the relationship between female mate choice and the features of the structure. To advance these studies, it is essential to collect quantitative data on the circular structure. Thus, we reconstructed 3D models of six completed mystery circles using a “structure from motion” (SfM) algorithm (Fig. 2).Fig. 2“Mystery circle” constructed by a white-spotted pufferfish (Torquigener albomaculosus). 3D model displayed on a computer (a), one of the video frames used to reconstruct the 3D model (b), and a Styrofoam model output in full size created using a 3D printer and the 3D data (c) for a specific mystery circle 20160615_K13.Full size imageOn the other hand, the mystery circle constructed by the pufferfish may have potential applications in biomimetics similar to the structures constructed by termites and prairie dogs. To support the importance of its structural characteristics, it has been observed that the water passing through the valley upstream always gathers in the center of the structure, regardless of the direction of water flow4. Furthermore, particle size analysis of the sand forming the mystery circle has revealed that it has the function of extracting fine-grained sand particles from the valleys arranged radially to the outside and directing them to the center (Kawase, in prep.). The field of computational fluid dynamics, which makes full use of fluid dynamics technology, engineering knowledge, and computers, will logically clarify the characteristics of the 3D structure of the mystery circle we have reconstructed here. Shameem et al. reconstructed a 3D model of a mystery circle to explore the flow features with 2D computational fluid dynamic simulations8. Since our model has already been quantified as 3D data, computational fluid analysis can be immediately performed using this data, and the structural features of the mystery circle are expected to be applied in a wide range of fields, such as architecture and engineering, via biomimetics. More

Ryan, M. J. Darwin, sexual selection, and the brain. Proc. Natl Acad. Sci. USA 118, e2008194118 (2021).CAS 
Article 

Google Scholar 
Le Moëne, O. & Ågmo, A. The neuroendocrinology of sexual attraction. Front. Neuroendocrinol. 51, 46–67 (2018).Article 

Google Scholar 
Witchel, S. F. Disorders of sex development. Best Pract. Res. Clin. Obstet. Gynaecol. 48, 90–102 (2018).Article 

Google Scholar 
Mäkelä, J., Koskenniemi, J. J., Virtanen, H. E. & Toppari, J. Testis development. Endocr. Rev. 40, 857–905 (2019).Article 

Google Scholar 
Bilen, J., Atallah, J., Azanchi, R., Levine, J. D. & Riddiford, L. M. Regulation of onset of female mating and sex pheromone production by juvenile hormone in Drosophila melanogaster. Proc. Natl Acad. Sci. USA 110, 18321–18326 (2013).CAS 
Article 

Google Scholar 
Auer, T. O. & Benton, R. Sexual circuitry in Drosophila. Curr. Opin. Neurobiol. 38, 18–26 (2016).CAS 
Article 

Google Scholar 
Schal, C., Fan, Y. & Blomquist, G. J. in Insect Pheromone Biochemistry and Molecular Biology (eds Blomquist, G. J. & Vogt, R. G.) 283–322 (Elsevier Academic Press, 2003).Eliyahu, D., Nojima, S., Mori, K. & Schal, C. New contact sex pheromone components of the German cockroach, Blattella germanica, predicted from the proposed biosynthetic pathway. J. Chem. Ecol. 34, 229–237 (2008).CAS 
Article 

Google Scholar 
Nojima, S., Schal, C., Webster, F. X., Santangelo, R. G. & Roelofs, W. L. Identification of the sex pheromone of the German cockroach, Blattella germanica. Science 307, 1104–1106 (2005).CAS 
Article 

Google Scholar 
Mori, K. Synthesis of all the six components of the female-produced contact sex pheromone of the German cockroach, Blattella germanica (L.). Tetrahedron 64, 4060–4071 (2008).CAS 
Article 

Google Scholar 
Pei, X.-J. et al. Modulation of fatty acid elongation in cockroaches sustains sexually dimorphic hydrocarbons and female attractiveness. PLoS Biol. 19, e3001330 (2021).CAS 
Article 

Google Scholar 
Nishida, R., Fukami, H. & Ishii, S. Sex pheromone of the German cockroach (Blattella germanica L.) responsible for male wing-raising: 3,11-dimethyl-2-nonacosanone. Experientia 30, 978–979 (1974).CAS 
Article 

Google Scholar 
Chase, J., Touhara, K., Prestwich, G. D., Schal, C. & Blomquist, G. J. Biosynthesis and endocrine control of the production of the German cockroach sex pheromone 3,11-dimethylnonacosan-2-one. Proc. Natl Acad. Sci. USA 89, 6050–6054 (1992).CAS 
Article 

Google Scholar 
Harrison, M. C. et al. Hemimetabolous genomes reveal molecular basis of termite eusociality. Nat. Ecol. Evol. 2, 557–566 (2018).Article 

Google Scholar 
Gu, X., Quilici, D., Juarez, P., Blomquist, G. J. & Schal, C. Biosynthesis of hydrocarbons and contact sex pheromone and their transport by lipophorin in females of the German cockroach (Blattella germanica). J. Insect Physiol. 41, 257–267 (1995).CAS 
Article 

Google Scholar 
Chen, N., Pei, X., Li, S., Fan, Y.-L. & Liu, T.-X. Involvement of integument-rich CYP4G19 in hydrocarbon biosynthesis and cuticular penetration resistance in Blattella germanica (L.). Pest Manag. Sci. 76, 215–226 (2020).CAS 
Article 

Google Scholar 
Roy, S., Saha, T. T., Zou, Z. & Raikhel, A. S. Regulatory pathways controlling female insect reproduction. Annu. Rev. Entomol. 63, 489–511 (2018).CAS 
Article 

Google Scholar 
Li, S. et al. The genomic and functional landscapes of developmental plasticity in the American cockroach. Nat. Commun. 9, 1008 (2018).Article 

Google Scholar 
Zhu, S. et al. Insulin/IGF signaling and TORC1 promote vitellogenesis via inducing juvenile hormone biosynthesis in the American cockroach. Development. 147, dev188805 (2020).CAS 
Article 

Google Scholar 
Luo, W. et al. Juvenile hormone signaling promotes ovulation and maintains egg shape by inducing expression of extracellular matrix genes. Proc. Natl Acad. Sci. USA 118, e2014461118 (2021).
Google Scholar 
Tillman, J. A., Seybold, S. J., Jurenka, R. A. & Blomquist, G. J. Insect pheromones—an overview of biosynthesis and endocrine regulation. Insect Biochem. Mol. Biol. 29, 481–514 (1999).CAS 
Article 

Google Scholar 
Jindra, M., Palli, S. R. & Riddiford, L. M. The juvenile hormone signaling pathway in insect development. Annu. Rev. Entomol. 58, 181–241 (2013).CAS 
Article 

Google Scholar 
Piulachs, M. D., Maestro, J. L. & Belles, X. Juvenile hormone production and accessory gland development during sexual maturation of male Blattella germanica (L.) (Dictyoptera: Blattellidae). Comp. Biochem. Physiol. A 102, 477–480 (1992).Article 

Google Scholar 
Ferveur, J. F. et al. Genetic feminization of pheromones and its behavioral consequences in Drosophila males. Science 276, 1555–1558 (1997).CAS 
Article 

Google Scholar 
Shirangi, T., Dufour, H., Williams, T. & Carroll, S. Rapid evolution of sex pheromone-producing enzyme expression in Drosophila. PLoS Biol. 7, e1000168 (2009).Article 

Google Scholar 
Verhulst, E. C., de Zande, L. & Beukeboom, L. W. Insect sex determination: it all evolves around transformer. Curr. Opin. Genet. Dev. 20, 376–383 (2010).CAS 
Article 

Google Scholar 
Yamamoto, D. & Koganezawa, M. Genes and circuits of courtship behaviour in Drosophila males. Nat. Rev. Neurosci. 14, 681–692 (2013).CAS 
Article 

Google Scholar 
Wexler, J. et al. Hemimetabolous insects elucidate the origin of sexual development via alternative splicing. eLife 8, e47490 (2019).CAS 
Article 

Google Scholar 
Clynen, E., Ciudad, L., Belles, X. & Piulachs, M.-D. Conservation of fruitless’ role as master regulator of male courtship behaviour from cockroaches to flies. Dev. Genes Evol. 221, 43–48 (2011).Article 

Google Scholar 
Defelipea, L. A. et al. Juvenile hormone synthesis: ‘esterify then epoxidize’ or ‘epoxidize then esterify’? Insights from the structural characterization of juvenile hormone acid methyltransferase. Insect Biochem. Mol. Biol. 41, 228–235 (2011).Article 

Google Scholar 
Fan, Y., Zurek, L., Dykstra, M. J. & Schal, C. Hydrocarbon synthesis by enzymatically dissociated oenocytes of the abdominal integument of the German cockroach, Blattella germanica. Naturwissenschaften 90, 121–126 (2003).CAS 
Article 

Google Scholar 
Beach, F. A. Sexual attractivity, proceptivity, and receptivity in female mammals. Horm. Behav. 7, 105–138 (1976).CAS 
Article 

Google Scholar 
Lozano, J. & Belles, X. Conserved repressive function of Krüppel homolog 1 on insect metamorphosis in hemimetabolous and holometabolous species. Sci. Rep. 1, 163 (2011).Article 

Google Scholar 
Lozano, J. & Belles, X. Role of methoprene-tolerant (Met) in adult morphogenesis and in adult ecdysis of Blattella germanica. PLoS ONE 9, e103614 (2014).Article 

Google Scholar 
Tian, L. et al. 20-hydroxyecdysone upregulates Atg genes to induce autophagy in the Bombyx fat body. Autophagy 9, 1172–1187 (2013).CAS 
Article 

Google Scholar 
Jia, Q. et al. Juvenile hormone and 20-hydroxyecdysone coordinately control the developmental timing of matrix metalloproteinase-induced fat body cell dissociation. J. Biol. Chem. 292, 21504–21516 (2017).CAS 
Article 

Google Scholar 
Eliyahu, D., Nojima, S., Mori, K. & Schal, C. Jail baits: how and why nymphs mimic adult females of the German cockroach, Blattella germanica. Anim. Behav. 78, 1097–1105 (2009).Article 

Google Scholar 
Schal, C., Burns, E. L., Jurenka, R. A. & Blomquist, G. J. A new component of the female sex pheromone of Blattella germanica (L.) (Dictyoptera: Blattellidae) and interaction with other pheromone components. J. Chem. Ecol. 16, 1997–2008 (1990).CAS 
Article 

Google Scholar  More

Post, E. et al. The polar regions in a 2 °C warmer world. Sci. Adv. 5, eaaw9883 (2019).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pearson, R. G. et al. Shifts in Arctic vegetation and associated feedbacks under climate change. Nat. Clim. Chang. 3, 673–677 (2013).ADS 
Article 

Google Scholar 
Wang, J. A. et al. Extensive land cover change across Arctic-Boreal Northwestern North America from disturbance and climate forcing. Glob. Chang. Biol. 26, 807–822 (2020).ADS 
PubMed 
Article 

Google Scholar 
Mekonnen, Z. A. et al. Arctic tundra shrubification: a review of mechanisms and impacts on ecosystem carbon balance. Environ. Res. Lett. 16, 053001 (2021).ADS 
CAS 
Article 

Google Scholar 
Elmendorf, S. C. et al. Plot-scale evidence of tundra vegetation change and links to recent summer warming. Nat. Clim. Chang. 2, 453–457 (2012).ADS 
Article 

Google Scholar 
Sturm, M., Racine, C. & Tape, K. Climate change. Increasing shrub abundance in the Arctic. Nature 411, 546–547 (2001).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Tape, K., Sturm, M. & Racine, C. The evidence for shrub expansion in Northern Alaska and the Pan-Arctic. Glob. Chang. Biol. 12, 686–702 (2006).ADS 
Article 

Google Scholar 
Forbes, B. C., Fauria, M. M. & Zetterberg, P. Russian Arctic warming and ‘greening’ are closely tracked by tundra shrub willows. Glob. Chang. Biol. 16, 1542–1554 (2010).ADS 
Article 

Google Scholar 
Myers-Smith, I. H. et al. Complexity revealed in the greening of the Arctic. Nat. Clim. Chang. 10, 106–117 (2020).ADS 
Article 

Google Scholar 
Chapin, F. S. 3rd et al. Role of land-surface changes in Arctic summer warming. Science 310, 657–660 (2005).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Swann, A. L., Fung, I. Y., Levis, S., Bonan, G. B. & Doney, S. C. Changes in Arctic vegetation amplify high-latitude warming through the greenhouse effect. Proc. Natl Acad. Sci. USA 107, 1295–1300 (2010).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bonfils, C. J. W. et al. On the influence of shrub height and expansion on northern high latitude climate. Environ. Res. Lett. 7, 015503 (2012).ADS 
Article 

Google Scholar 
Natali, S. M. et al. Large loss of CO2 in winter observed across the northern permafrost region. Nat. Clim. Chang. 9, 852–857 (2019).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Paradis, M., Lévesque, E. & Boudreau, S. Greater effect of increasing shrub height on winter versus summer soil temperature. Environ. Res. Lett. 11, 085005 (2016).ADS 
Article 

Google Scholar 
Post, E. et al. Ecological dynamics across the Arctic associated with recent climate change. Science 325, 1355–1358 (2009).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Chen, Y. et al. Future increases in Arctic lightning and fire risk for permafrost carbon. Nat. Clim. Chang. 11, 404–410 (2021).ADS 
Article 

Google Scholar 
Mack, M. C. et al. Carbon loss from boreal forest wildfires offset by increased dominance of deciduous trees. Science 372, 280–283 (2021).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Keenan, T. F. & Riley, W. J. Greening of the land surface in the world’s cold regions consistent with recent warming. Nat. Clim. Chang. 8, 825–828 (2018).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Büntgen, U. et al. Temperature-induced recruitment pulses of Arctic dwarf shrub communities. J. Ecol. 103, 489–501 (2015).Article 

Google Scholar 
Myers-Smith, I. H. & Hik, D. S. Climate warming as a driver of tundra shrubline advance. J. Ecol. 106, 547–560 (2018).Article 

Google Scholar 
Tape, K. D., Hallinger, M., Welker, J. M. & Ruess, R. W. Landscape heterogeneity of shrub expansion in Arctic Alaska. Ecosystems 15, 711–724 (2012).CAS 
Article 

Google Scholar 
Myers-Smith, I. H. et al. Climate sensitivity of shrub growth across the tundra biome. Nat. Clim. Chang. 5, 887–891 (2015).ADS 
Article 

Google Scholar 
Berner, L. T. et al. Summer warming explains widespread but not uniform greening in the Arctic tundra biome. Nat. Commun. 11, 4621 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Campbell, T. K. F., Lantz, T. C., Fraser, R. H. & Hogan, D. High Arctic vegetation change mediated by hydrological conditions. Ecosystems 24, 106–121 (2021).CAS 
Article 

Google Scholar 
Chen, Y., Hu, F. S. & Lara, M. J. Divergent shrub-cover responses driven by climate, wildfire, and permafrost interactions in Arctic tundra ecosystems. Glob. Chang. Biol. 27, 652–663 (2021).ADS 
PubMed 
Article 

Google Scholar 
Martin, A. C., Jeffers, E. S., Petrokofsky, G., Myers-Smith, I. & Macias-Fauria, M. Shrub growth and expansion in the Arctic tundra: an assessment of controlling factors using an evidence-based approach. Environ. Res. Lett. 12, 085007 (2017).ADS 
Article 

Google Scholar 
Blois, J. L., Williams, J. W., Fitzpatrick, M. C., Jackson, S. T. & Ferrier, S. Space can substitute for time in predicting climate-change effects on biodiversity. Proc. Natl Acad. Sci. USA 110, 9374–9379 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Svenning, J.-C. & Sandel, B. Disequilibrium vegetation dynamics under future climate change. Am. J. Bot. 100, 1266–1286 (2013).PubMed 
Article 

Google Scholar 
Damgaard, C. A critique of the space-for-time substitution practice in community ecology. Trends Ecol. Evol. 34, 416–421 (2019).PubMed 
Article 

Google Scholar 
Klesse, S. et al. Continental-scale tree-ring-based projection of Douglas-fir growth: testing the limits of space-for-time substitution. Glob. Chang. Biol. 26, 5146–5163 (2020).ADS 
PubMed 
Article 

Google Scholar 
Nathan, R. et al. Mechanisms of long-distance seed dispersal. Trends Ecol. Evol. 23, 638–647 (2008).PubMed 
Article 

Google Scholar 
Rogers, H. S. et al. The total dispersal kernel: a review and future directions. AoB Plants 11, lz042 (2019).Article 

Google Scholar 
Bullock, J. M. et al. Modelling spread of British wind-dispersed plants under future wind speeds in a changing climate. J. Ecol. 100, 104–115 (2012).Article 

Google Scholar 
Shipley, B. R. et al. megaSDM: integrating dispersal and time‐step analyses into species distribution models. Ecography 2022, e05450 (2022).Article 

Google Scholar 
Anadon‐Rosell, A., Talavera, M., Ninot, J. M., Carrillo, E. & Batllori, E. Seed production and dispersal limit treeline advance in the Pyrenees. J. Veg. Sci. 31, 981–994 (2020).Article 

Google Scholar 
Standish, R. J., Cramer, V. A., Wild, S. L. & Hobbs, R. J. Seed dispersal and recruitment limitation are barriers to native recolonization of old-fields in western Australia. J. Appl. Ecol. 44, 435–445 (2007).Article 

Google Scholar 
Kunstler, G. et al. Tree colonization of sub-Mediterranean grasslands: effects of dispersal limitation and shrub facilitation. Can. J. Res. 37, 103–115 (2007).Article 

Google Scholar 
Reid, J. L., Holl, K. D. & Zahawi, R. A. Seed dispersal limitations shift over time in tropical forest restoration. Ecol. Appl. 25, 1072–1082 (2015).PubMed 
Article 

Google Scholar 
van Breugel, M. et al. Soil nutrients and dispersal limitation shape compositional variation in secondary tropical forests across multiple scales. J. Ecol. 107, 566–581 (2019).Article 

Google Scholar 
Münzbergová, Z. & Herben, T. Seed, dispersal, microsite, habitat and recruitment limitation: identification of terms and concepts in studies of limitations. Oecologia 145, 1–8 (2005).ADS 
PubMed 
Article 

Google Scholar 
Alsos, I. G. et al. Frequent long-distance plant colonization in the changing Arctic. Science 316, 1606–1609 (2007).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Makoto, K. & Wilson, S. D. When and where does dispersal limitation matter in primary succession? J. Ecol. 107, 559–565 (2019).Article 

Google Scholar 
Flannigan, M., Stocks, B., Turetsky, M. & Wotton, M. Impacts of climate change on fire activity and fire management in the circumboreal forest. Glob. Chang. Biol. 15, 549–560 (2009).ADS 
Article 

Google Scholar 
Higuera, P. E. et al. Frequent fires in ancient shrub tundra: implications of paleorecords for arctic environmental change. PLoS ONE 3, e0001744 (2008).ADS 
PubMed 
Article 

Google Scholar 
Mekonnen, Z. A., Riley, W. J., Randerson, J. T., Grant, R. F. & Rogers, B. M. Expansion of high-latitude deciduous forests driven by interactions between climate warming and fire. Nat. Plants 5, 952–958 (2019).PubMed 
Article 

Google Scholar 
Johnstone, J. F., Hollingsworth, T. N., Chapin, F. S. III & Mack, M. C. Changes in fire regime break the legacy lock on successional trajectories in Alaskan boreal forest. Glob. Chang. Biol. 16, 1281–1295 (2010).ADS 
Article 

Google Scholar 
Bret-Harte, M. S. et al. The response of Arctic vegetation and soils following an unusually severe tundra fire. Philos. Trans. R. Soc. Lond. B Biol. Sci. 368, 20120490 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Klupar, I., Rocha, A. V. & Rastetter, E. B. Alleviation of nutrient co-limitation induces regime shifts in post-fire community composition and productivity in Arctic tundra. Glob. Chang. Biol. 27, 3324–3335 (2021).PubMed 
Article 

Google Scholar 
Racine, C., Jandt, R., Meyers, C. & Dennis, J. Tundra fire and vegetation change along a hillslope on the Seward Peninsula, Alaska, USA Arct. Antarct. Alp. Res. 36, 1–10 (2004).Article 

Google Scholar 
Narita, K. et al. Vegetation and permafrost thaw depth 10 years after a tundra fire in 2002, Seward Peninsula, Alaska. Arct. Antarct. Alp. Res. 47, 547–559 (2015).Article 

Google Scholar 
Iwahana, G. et al. Geomorphological and geochemistry changes in permafrost after the 2002 tundra wildfire in Kougarok, Seward Peninsula, Alaska. J. Geophys. Res. Earth Surf. 121, 1697–1715 (2016).ADS 
CAS 
Article 

Google Scholar 
CAVM Team. Circumpolar Arctic Vegetation (1:7,500,000 scale), Conservation of Arctic Flora and Fauna (CAFF) Map No. 1 (U.S. Fish and Wildlife Service, 2003).Myers-Smith, I. H. et al. Shrub expansion in tundra ecosystems: dynamics, impacts and research priorities. Environ. Res. Lett. 6, 045509 (2011).ADS 
Article 

Google Scholar 
Lantz, T. C., Marsh, P. & Kokelj, S. V. Recent shrub proliferation in the MacKenzie delta uplands and microclimatic implications. Ecosystems 16, 47–59 (2013).Article 

Google Scholar 
Wilson, S. D. & Nilsson, C. Arctic alpine vegetation change over 20 years. Glob. Chang. Biol. 15, 1676–1684 (2009).ADS 
Article 

Google Scholar 
Mielke, K. P. et al. Disentangling drivers of spatial autocorrelation in species distribution models. Ecography 43, 1741–1751 (2020).Article 

Google Scholar 
Mack, M. C. et al. Carbon loss from an unprecedented Arctic tundra wildfire. Nature 475, 489–492 (2011).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Ims, R. A. & Henden, J.-A. Collapse of an arctic bird community resulting from ungulate-induced loss of erect shrubs. Biol. Conserv. 149, 2–5 (2012).Article 

Google Scholar 
IPCC. Global warming of 1.5 C. An IPCC Special Report on the Impacts of Global Warming of 1.5 °C Above Pre-industrial Levels and Related Global Greenhouse Gas Emission Pathways, in the Context of Strengthening the Global Response to the Threat of Climate Change, Sustainable Development, and Efforts to Eradicate Poverty. (Intergovernmental Panel on Climate Change, 2018).Engler, R. et al. Predicting future distributions of mountain plants under climate change: does dispersal capacity matter? Ecography 32, 34–45 (2009).Article 

Google Scholar 
Travis, J. M. J. et al. Dispersal and species’ responses to climate change. Oikos 122, 1532–1540 (2013).Article 

Google Scholar 
Fricke, E. C., Ordonez, A., Rogers, H. S. & Svenning, J.-C. The effects of defaunation on plants’ capacity to track climate change. Science 375, 210–214 (2022).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Graae, B. J. et al. Strong microsite control of seedling recruitment in tundra. Oecologia 166, 565–576 (2011).ADS 
PubMed 
Article 

Google Scholar 
Frei, E. R. et al. Biotic and abiotic drivers of tree seedling recruitment across an alpine treeline ecotone. Sci. Rep. 8, 10894 (2018).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Huebner, D. C. & Bret-Harte, M. S. Microsite conditions in retrogressive thaw slumps may facilitate increased seedling recruitment in the Alaskan Low Arctic. Ecol. Evol. 9, 1880–1897 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Hargreaves, A. L. et al. Seed predation increases from the Arctic to the Equator and from high to low elevations. Sci. Adv. 5, eaau4403 (2019).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rupp, T. S., Starfield, A. M. & Chapin, F. S. A frame-based spatially explicit model of subarctic vegetation response to climatic change: comparison with a point model. Landsc. Ecol. 15, 383–400 (2000).Article 

Google Scholar 
Veraverbeke, S. et al. Lightning as a major driver of recent large fire years in North American boreal forests. Nat. Clim. Chang. 7, 529–534 (2017).ADS 
Article 

Google Scholar 
Camac, J. S., Williams, R. J., Wahren, C.-H., Hoffmann, A. A. & Vesk, P. A. Climatic warming strengthens a positive feedback between alpine shrubs and fire. Glob. Chang. Biol. 23, 3249–3258 (2017).ADS 
PubMed 
Article 

Google Scholar 
McDowell, N. G. et al. Pervasive shifts in forest dynamics in a changing world. Science 368, eaaz9463 (2020).CAS 
PubMed 
Article 

Google Scholar 
Angers-Blondin, S., Myers-Smith, I. H. & Boudreau, S. Plant–plant interactions could limit recruitment and range expansion of tall shrubs into alpine and Arctic tundra. Polar Biol. 41, 2211–2219 (2018).Article 

Google Scholar 
Mekonnen, Z. A., Riley, W. J. & Grant, R. F. Accelerated nutrient cycling and increased light competition will lead to 21st century shrub expansion in North American Arctic Tundra. J. Geophys. Res. Biogeosci. 123, 1683–1701 (2018).CAS 
Article 

Google Scholar 
Scherrer, D., Vitasse, Y., Guisan, A., Wohlgemuth, T. & Lischke, H. Competition and demography rather than dispersal limitation slow down upward shifts of trees’ upper elevation limits in the Alps. J. Ecol. 108, 2416–2430 (2020).Article 

Google Scholar 
Kunstler, G. et al. Plant functional traits have globally consistent effects on competition. Nature 529, 204–207 (2016).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Bjorkman, A. D. et al. Plant functional trait change across a warming tundra biome. Nature 562, 57–62 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Myers-Smith, I. H., Thomas, H. J. D. & Bjorkman, A. D. Plant traits inform predictions of tundra responses to global change. N. Phytol. 221, 1742–1748 (2019).Article 

Google Scholar 
Wang, J. A. et al. ABoVE: landsat-derived annual dominant land cover across ABoVE core domain, 1984–2014. ORNL DAAC. https://doi.org/10.3334/ORNLDAAC/1691 (2019).Wang, T., Hamann, A., Spittlehouse, D. & Carroll, C. Locally downscaled and spatially customizable climate data for historical and future periods for North America. PLoS ONE 11, e0156720 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Schwalm, C. R., Glendon, S. & Duffy, P. B. RCP8.5 tracks cumulative CO2 emissions. Proc. Natl Acad. Sci. USA 117, 19656–19657 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team. ASTER Global Digital Elevation Model V003. (2019).Barnes, R. RichDEM: Terrain Analysis Software. (2016).Loboda, T. V., Chen, D., Hall, J. V. & He, J. ABoVE: Landsat-derived Burn Scar dNBR across Alaska and Canada, 1985–2015. ORNL DAAC. https://doi.org/10.3334/ORNLDAAC/1564 (2018).James, G., Witten, D., Hastie, T. & Tibshirani, R. An Introduction to Statistical Learning: With Applications in R (Springer, 2013).Thuiller, W., Georges, D., Gueguen, M., Engler, R. & Breiner, F. biomod2: Ensemble Platform for Species Distribution Modeling. https://CRAN.R-project.org/package=biomod2 (2013).R Core Team. R: A Language and Environment for Statistical Computing. http://www.R-project.org/ (2013).Bullock, J. M. et al. A synthesis of empirical plant dispersal kernels. J. Ecol. 105, 6–19 (2017).Article 

Google Scholar 
Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Finley, A. O., Banerjee, S. & Gelfand, A. E. spBayes for Large univariate and multivariate point-referenced spatio-temporal data models. J. Stat. Softw. 63, 1–28 (2015).Article 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria. https://www.R-project.org/ (2021).Banerjee, S., Carlin, B. P. & Gelfand, A. E. Hierarchical Modeling and Analysis for Spatial Data. (Chapman and Hall/CRC, 2003).Liu, Y. et al. Dataset: dispersal and fire limit Arctic shrub expansion. Figshare https://doi.org/10.6084/m9.figshare.20097104.v1 (2022).Article 

Google Scholar 
Liu, Y. et al. Code: dispersal and fire limit Arctic shrub expansion. Zenodo https://doi.org/10.5281/zenodo.6672698 (2022).Article 

Google Scholar  More

Butchart, S. H. M. et al. Global biodiversity: Indicators of recent declines. Science 328(5982), 1164–1168. https://doi.org/10.1126/science.1187512 (2010).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Dirzo, R. et al. Defaunation in the anthropocene. Science 345(6195), 401–406. https://doi.org/10.1126/science.1251817 (2014).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Bradshaw, C. J. A. et al. Underestimating the challenges of avoiding a ghastly future. Front. Conserv. Sci. https://doi.org/10.3389/fcosc.2020.615419 (2021).Article 

Google Scholar 
Barnosky, A. D. et al. Has the Earth’s sixth mass extinction already arrived?. Nature 471(7336), 51–57. https://doi.org/10.1038/nature09678 (2011).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Ceballos, G. et al. Accelerated modern human-induced species losses: Entering the sixth mass extinction. Sci. Adv. https://doi.org/10.1126/sciadv.1400253 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Ceballos, G., Ehrlich, P. R. & Raven, P. H. Vertebrates on the brink as indicators of biological annihilation and the sixth mass extinction. Proc. Natl. Acad. Sci. U.S.A. 117(24), 13596–13602. https://doi.org/10.1073/pnas.1922686117 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
McGowan, P. J., Traylor-Holzer, K. & Leus, K. IUCN guidelines for determining when and how ex situ management should be used in species conservation. Conserv. Lett. 10(3), 361–366. https://doi.org/10.1111/conl.12285 (2016).Article 

Google Scholar 
Clout, M. N. & Merton, D. V. Saving the Kakapo: The conservation of the world’s most peculiar parrot. Bird Conserv. Int. 8(3), 281–296. https://doi.org/10.1017/s0959270900001933 (1998).Article 

Google Scholar 
Milinkovitch, M. C. et al. Genetic analysis of a successful repatriation programme: Giant Galápagos tortoises. Proc. R. Soc. B Biol. Sci. 271(1537), 341–345. https://doi.org/10.1098/rspb.2003.2607 (2004).CAS 
Article 

Google Scholar 
Ryder, O. A. & Wedemeyer, E. A. A cooperative breeding programme for the Mongolian wild horse Equus przewalskii in the United States. Biol. Conserv. 22(4), 259–271. https://doi.org/10.1016/0006-3207(82)90021-0 (1982).Article 

Google Scholar 
Mallinson, J. J. C. Conservation breeding programmes: An important ingredient for species survival. Biodivers. Conserv. 4(6), 617–635. https://doi.org/10.1007/bf00222518 (1995).Article 

Google Scholar 
Seddon, P. J., Armstrong, D. P. & Maloney, R. F. Developing the science of reintroduction biology. Conserv. Biol. 21(2), 303–312. https://doi.org/10.1111/j.1523-1739.2006.00627.x (2007).Article 
PubMed 

Google Scholar 
Bowkett, A. E. Recent captive-breeding proposals and the return of the ark concept to global species conservation. Conserv. Biol. 23(3), 773–776. https://doi.org/10.1111/j.1523-1739.2008.01157.x (2009).Article 
PubMed 

Google Scholar 
Shan, L. et al. Large-scale genetic survey provides insights into the captive management and reintroduction of giant pandas. Mol. Biol. Evol. 31(10), 2663–2671. https://doi.org/10.1093/molbev/msu210 (2014).CAS 
Article 
PubMed 

Google Scholar 
Fischer, J. & Lindenmayer, D. An assessment of the published results of animal relocations. Biol. Conserv. 96(1), 1–11. https://doi.org/10.1016/s0006-3207(00)00048-3 (2014).Article 

Google Scholar 
Christie, M. R., Marine, M. L., French, R. A. & Blouin, M. S. Genetic adaptation to captivity can occur in a single generation. Proc. Natl. Acad. Sci. U.S.A. 109(1), 238–242. https://doi.org/10.1073/pnas.1111073109 (2011).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Fraser, D. J. et al. Population correlates of rapid captive-induced maladaptation in a wild fish. Evol. Appl. 12(7), 1305–1317. https://doi.org/10.1111/eva.12649 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Ralls, K., Brugger, K. & Ballou, J. Inbreeding and juvenile mortality in small populations of ungulates. Science 206(4422), 1101–1103. https://doi.org/10.1126/science.493997 (1979).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Charlesworth, D. & Charlesworth, B. Inbreeding depression and its evolutionary consequences. Annu. Rev. Ecol. Evol. Syst. 18(1), 237–268. https://doi.org/10.1146/annurev.es.18.110187.001321 (1987).Article 

Google Scholar 
Ralls, K., Ballou, J. D. & Templeton, A. Estimates of lethal equivalents and the cost of inbreeding in mammals. Conserv. Biol. 2(2), 185–193. https://doi.org/10.1111/j.1523-1739.1988.tb00169.x (1988).Article 

Google Scholar 
Hedrick, P. W. & Kalinowski, S. T. Inbreeding depression in conservation biology. Annu. Rev. Ecol. Evol. Syst. 31(1), 139–162. https://doi.org/10.1146/annurev.ecolsys.31.1.139 (2000).Article 

Google Scholar 
Frankham, R. Introduction to Conservation Genetics 2nd edn. (Cambridge University Press, 2010).Book 

Google Scholar 
Laikre, L. Conservation genetics of Nordic carnivores: Lessons from zoos. Hereditas 130(3), 203–216. https://doi.org/10.1111/j.1601-5223.1999.00203.x (2004).Article 

Google Scholar 
Gomendio, M., Cassinello, J. & Roldan, E. R. S. A comparative study of ejaculate traits in three endangered ungulates with different levels of inbreeding: Fluctuating asymmetry as an indicator of reproductive and genetic stress. Proc. R. Soc. B Biol. Sci. 267(1446), 875–882. https://doi.org/10.1098/rspb.2000.1084 (2000).CAS 
Article 

Google Scholar 
Swinnerton, K. J., Groombridge, J. J., Jones, C. G., Burn, R. W. & Mungroo, Y. Inbreeding depression and founder diversity among captive and free-living populations of the endangered pink pigeon Columba mayeri. Anim. Conserv. 7(4), 353–364. https://doi.org/10.1017/s1367943004001556 (2004).Article 

Google Scholar 
Farquharson, K. A., Hogg, C. J. & Grueber, C. E. Offspring survival changes over generations of captive breeding. Nat. Commun. https://doi.org/10.1038/s41467-021-22631-0 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Kleiman, D. G., Thompson, K. V. & Baer, C. K. Wild Mammals in Captivity: Principles and Techniques for Zoo Management 2nd edn. (University of Chicago Press, 2021).
Google Scholar 
Ralls, K. & Ballou, J. D. Captive breeding and reintroduction. In Encyclopedia of Biodiversity (ed. Levin, S. A.) 662–667 (Academic Press, 2013). https://doi.org/10.1016/b978-0-12-384719-5.00268-9.Chapter 

Google Scholar 
Reed, D. H. & Frankham, R. Correlation between fitness and genetic diversity. Conserv. Biol. 17(1), 230–237. https://doi.org/10.1046/j.1523-1739.2003.01236.x (2003).Article 

Google Scholar 
Spielman, D., Brook, B. W. & Frankham, R. Most species are not driven to extinction before genetic factors impact them. Proc. Natl. Acad. Sci. U.S.A. 101(42), 15261–15264. https://doi.org/10.1073/pnas.0403809101 (2003).ADS 
CAS 
Article 

Google Scholar 
Willi, Y., van Buskirk, J. & Hoffmann, A. A. Limits to the adaptive potential of small populations. Annu. Rev. Ecol. Evol. Syst. 37(1), 433–458. https://doi.org/10.1146/annurev.ecolsys.37.091305.110145 (2006).Article 

Google Scholar 
Habel, J. C., Husemann, M., Finger, A., Danley, P. D. & Zachos, F. E. The relevance of time series in molecular ecology and conservation biology. Biol. Rev. 89(2), 484–492. https://doi.org/10.1111/brv.12068 (2013).Article 
PubMed 

Google Scholar 
Araki, H., Cooper, B. & Blouin, M. S. Genetic effects of captive breeding cause a rapid, cumulative fitness decline in the wild. Science 318(5847), 100–103. https://doi.org/10.1126/science.1145621 (2007).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Purohit, D. et al. Genetic effects of long-term captive breeding on the endangered pygmy hog. PeerJ 9, e12212. https://doi.org/10.7717/peerj.12212 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Hahn, E. E. & Culver, M. Genetic diversity and structure in Arizona pronghorn following conservation efforts. Conserv. Sci. Pract. https://doi.org/10.1111/csp2.498 (2021).Article 

Google Scholar 
Charlesworth, B. Effective population size and patterns of molecular evolution and variation. Nat. Rev. Gen. 10(3), 195–205. https://doi.org/10.1038/nrg2526 (2009).CAS 
Article 

Google Scholar 
Wang, J., Santiago, E. & Caballero, A. Prediction and estimation of effective population size. Heredity 117(4), 193–206. https://doi.org/10.1038/hdy.2016.43 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
O’Gara, W., Yoakum, J. D. & McCabe, R. E. Pronghorn: Ecology and Managment (University Press of Colorado, 2004).
Google Scholar 
Janis, C. M., Scott, K. M. & Jacobs, L. L. Evolution of Tertiary Mammals of North America: Terrestrial Carnivores, Ungulates, and Ungulate like Mammals Vol. 1 (Cambridge University Press, 2005).
Google Scholar 
Nelson, E. W. Status of the Pronghorn Antelope, 1922–1924 (U.S Department Agriculture Bulletin, 1925).Book 

Google Scholar 
O’Gara, B. W. & McCabe, R. E. From exploitation to conservation. In Pronghorn: Ecology and Management (eds O’Gara, B. W. & Yoakum, J. D.) 41–73 (University Press Colorado, 2004).
Google Scholar 
Cancino, J., Ortega-Rubio, A. & Sanchez-Pacheco, J. A. Status of an endangered subspecies: The peninsular pronghorn at Baja California. J. Arid Environ. 32(4), 463–467. https://doi.org/10.1006/jare.1996.0039 (1996).ADS 
Article 

Google Scholar 
Laliberte, A. S. & Ripple, W. J. Range contractions of North American carnivores and ungulates. Bioscience 54(2), 123–138. https://doi.org/10.1641/0006-3568 (2004).Article 

Google Scholar 
Medellín, R. A. et al. History, ecology, and conservation of the pronghorn antelope, bighorn sheep, and black bear in Mexico. In Biodiversity, Ecosystems, and Conservation in Northern Mexico (eds Cartron, J.-L. et al.) 387–405 (Oxford University Press, 2005).
Google Scholar 
Lee, T. E., Bickham, J. W. & Scott, M. D. Mitochondrial DNA and allozyme analysis of North American pronghorn populations. J. Wildl. Manag. 58(2), 307–318. https://doi.org/10.2307/3809396 (1994).Article 

Google Scholar 
IUCN SSC Antelope Specialist Group. Antilocapra americana ssp. peninsularis. The IUCN Red List of Threatened Species 2021: e.T1679A200726719. https://doi.org/10.2305/IUCN.UK.2021-2.RLTS.T1679A200726719.en (2021).SEMARNAT. Norma Oficial Mexicana NOM-059-SEMARNAT-2010, Protección ambiental– Especies nativas de México de flora y fauna silvestres– Categorías de riesgo y especificaciones para su inclusión, exclusión o cambio– Lista de especies en riesgo. Diario Oficial de la Federación 30 diciembre (2010).U. S. Fish and Wildlife Service. Recovery Plan for the Sonoran pronghorn (Antilocapra americana sonoriensis), Second Revision. (U.S. Fish and Wildlife Service, Southwest Region, Albuquerque, 2016).Cancino, J., Sanchez-Sotomayor, V. & Castellanos, R. From the field: Capture, hand-raising, and captive management of peninsular pronghorn. Wildl. Soc. Bull. 33(1), 61–65. https://doi.org/10.2193/0091-7648 (2005).Article 

Google Scholar 
Horne, J. S., Hervert, J. J., Woodruff, S. P. & Mills, L. S. Evaluating the benefit of captive breeding and reintroductions to endangered Sonoran pronghorn. Biol. Conserv. 196, 133–146. https://doi.org/10.1016/j.biocon.2016.02.005 (2016).Article 

Google Scholar 
CONANP. Programa de Acción para la Conservación de la Especie: Berrendo (Antilocapra americana), 2009 año del berrendo. Secretaria del Medio Ambiente y Recursos Naturales (SEMARNAT). www.conanp.gob.mx (2009).Cancino, J., Rodríguez-Estrella, R. & Miller, P. Using population viability analysis for management recommendations of the endangered endemic peninsular pronghorn. Acta Zool. Mex. 26(1), 173–189 (2010).
Google Scholar 
Danoff-Burg, J. A. & Mulroe, K. Peninsular Pronghorn Species Action Plan (2021) (in press).Stephen, C. L. et al. Population genetic analysis of sonoran pronghorn (Antilocapra americana sonoriensis). J. Mammal. 86(4), 782–792. https://doi.org/10.1644/1545-1542 (2005).Article 

Google Scholar 
Stephen, C. L., Whittaker, D. G., Gillis, D., Cox, L. L. & Rhodes, O. E. Genetic consequences of reintroductions: An example from oregon pronghorn antelope (Antilocapra americana). J. Wildl. Manag. 69(4), 1463–1474. https://doi.org/10.2193/0022-541x (2005).Article 

Google Scholar 
Barnow-Meyer, K. & Byers, J. Genetic diversity and gene flow in Yellowstone Basin pronghorn (Antilocapra americana). UW Natl. Parks Serv. Res. Station Annu. Rep. 31, 65–72. https://doi.org/10.13001/uwnpsrc.2008.3705 (2008).Article 

Google Scholar 
LaCava, M. E. F. et al. Pronghorn population genomics show connectivity in the core of their range. J. Mammal. 101(4), 1061–1071. https://doi.org/10.1093/jmammal/gyaa054 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Klimova, A., Munguia-Vega, A., Hoffman, J. I. & Culver, M. Genetic diversity and demography of two endangered captive pronghorn subspecies from the Sonoran Desert. J. Mammal. 95(6), 1263–1277. https://doi.org/10.1644/13-mamm-a-321 (2014).Article 

Google Scholar 
Hahn, E. E., Klimova, A., Munguía-Vega, A., Clark, K. B. & Culver, M. Use of museum specimens to refine historical pronghorn subspecies boundaries. J. Wildl. Manag. 84(3), 524–533. https://doi.org/10.1002/jwmg.21810 (2020).Article 

Google Scholar 
Axelrod, D. I. The evolution of desert vegetation in western North America. Carnegie Instit. Wash. Publ. 590, 215–306 (1950).
Google Scholar 
Dolby, G. A., Bennett, S. E. K., Lira-Noriega, A., Wilder, B. T. & Munguía-Vega, A. Assessing the geological and climatic forcing of biodiversity and evolution Surrounding the Gulf of California. J. Southwest. 57, 391–455. https://doi.org/10.1353/jsw.2015.0005 (2015).Article 

Google Scholar 
Gedir, J. V., Cain, J. W., Harris, G. & Turnbull, T. T. Effects of climate change on long-term population growth of pronghorn in an arid environment. Ecosphere 6(10), art189. https://doi.org/10.1890/es15-00266.1 (2015).Article 

Google Scholar 
Cornuet, J. M. et al. DIYABC v2.0: A software to make approximate Bayesian computation inferences about population history using single nucleotide polymorphism, DNA sequence and microsatellite data. Bioinformatics 30(8), 1187–1189. https://doi.org/10.1093/bioinformatics/btt763 (2014).CAS 
Article 
PubMed 

Google Scholar 
Islas-Espinoza, M. & de las Heras, A. Peninsular pronghorn conservation: Too many paradigms, too few indicators. In Sustainability Indicators in Practice (eds Latawiec, A. & Agol, D.) 126–145 (De Gruyter Open Poland, 2015). https://doi.org/10.1515/9783110450507-012.Chapter 

Google Scholar 
Willoughby, J. R. et al. The impacts of inbreeding, drift and selection on genetic diversity in captive breeding populations. Mol. Ecol. 24(1), 98–110. https://doi.org/10.1111/mec.13020 (2014).CAS 
Article 
PubMed 

Google Scholar 
Crow, J. F. & Kimura, M. An Introduction in Population Genetics Theory (Harper and Row, 1970).MATH 

Google Scholar 
Falconer, D. S. Introduction to Quantitative Genetics 3rd edn. (Longman Scientific and Technical, 1989).
Google Scholar 
Ballou, J. D. Strategies for maintaining genetic diversity in captive populations through reproductive technology. Zoo Biol. 3(4), 311–323. https://doi.org/10.1002/zoo.1430030404 (1984).Article 

Google Scholar 
Ballou, J. D. & Lacy, R. C. Identifying genetically important individuals for management of genetic diversity in pedigreed populations. In Population Management for Survival and Recovery (eds Ballou, J. D. et al.) 76–111 (Columbia Press, 1995).
Google Scholar 
Montgomery, M. E. et al. Minimizing kinship in captive breeding programs. Zoo Biol. 16(5), 377–389. https://doi.org/10.1002/(sici)1098-2361 (1997).Article 

Google Scholar 
Dunn, S. J., Clancey, E., Waits, L. P. & Byers, J. A. Inbreeding depression in pronghorn (Antilocapra americana) fawns. Mol. Ecol. 20(23), 4889–4898. https://doi.org/10.1111/j.1365-294x.2011.05327.x (2011).Article 
PubMed 

Google Scholar 
Hoffman, J. I. et al. High-throughput sequencing reveals inbreeding depression in a natural population. Proc. Natl. Acad. Sci. U.S.A. 111(10), 3775–3780. https://doi.org/10.1073/pnas.1318945111 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kardos, M. et al. The crucial role of genome-wide genetic variation in conservation. Proc. Natl. Acad. Sci. U.S.A. 118(48), e2104642118. https://doi.org/10.1073/pnas.2104642118 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Zoonomia Consortium. A comparative genomics multitool for scientific discovery and conservation. Nature 587(7833), 240–245. https://doi.org/10.1038/s41586-020-2876-6 (2020).ADS 
CAS 
Article 

Google Scholar 
Ceballos, F. C., Joshi, P. K., Clark, D. W., Ramsay, M. & Wilson, J. F. Runs of homozygosity: Windows into population history and trait architecture. Nat. Rev. Genet. 19(4), 220–234. https://doi.org/10.1038/nrg.2017.109 (2018).CAS 
Article 
PubMed 

Google Scholar 
Supple, M. A. & Shapiro, B. Conservation of biodiversity in the genomics era. Genome Biol. https://doi.org/10.1186/s13059-018-1520-3 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Hohenlohe, P. A. & Rajora, O. P. Population Genomics: Wildlife (Springer, 2020).
Google Scholar 
Chalmers, G. A. & Barrett, M. W. Capture myopathy in pronghorns in Alberta, Canada. J. Am. Vet. Med. Assoc. 171(9), 918–923 (1977).CAS 
PubMed 

Google Scholar 
Sotelo-Gallardo, H., Contreras Balderas, A. J. & Espinosa Treviño, A. Comparación de dos métodos de liberación del berrendo, Antilocapra americana (Artiodactyla: Antilocapridae) en Coahuila, México. Rev. Biol. Trop. 65(3), 1208. https://doi.org/10.15517/rbt.v65i3.29447 (2017).Article 

Google Scholar 
Breed, D. et al. Conserving wildlife in a changing world: Understanding capture myopathy—A malignant outcome of stress during capture and translocation. Conserv. Physiol. https://doi.org/10.1093/conphys/coz027 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Snyder, N. F. et al. Limitations of captive breeding in endangered species recovery. Conserv. Biol. 10(2), 338–348. https://doi.org/10.1046/j.1523-1739.1996.10020338.x (1996).Article 

Google Scholar 
Bonebrake, T. C., Christensen, J., Boggs, C. L. & Ehrlich, P. R. Population decline assessment, historical baselines, and conservation. Conserv. Lett. 3(6), 371–378. https://doi.org/10.1111/j.1755-263x.2010.00139.x (2010).Article 

Google Scholar 
Grismer, L. L. & McGuire, J. A. The oases of central Baja California, Mexico. Part I. A preliminary account of the relict mesophilic herpetofauna and the status of the oases. Bull. South. Calif. Acad. Sci. 92, 2–24 (1993).
Google Scholar 
Welsh, H. H., Clark, W. H., Franco-Vizcaíno, E. & Valdéz-Villavicencio, J. H. Herpetofauna associated with palm oases across the Californian-Sonoran transition in Northern Baja California, Mexico. Southwest. Nat. 55(4), 581–585. https://doi.org/10.1894/pas-15.1 (2010).Article 

Google Scholar 
Mann, D. H., Groves, P., Gaglioti, B. V. & Shapiro, B. A. Climate-driven ecological stability as a globally shared cause of Late Quaternary megafaunal extinctions: The Plaids and Stripes Hypothesis. Biol. Rev. 94(1), 328–352. https://doi.org/10.1111/brv.12456 (2018).Article 

Google Scholar 
Brown, D. E., Warnecke, D. & McKinney, T. Effects of midsummer drought on mortality of doe pronghorn (Antilocapra americana). Southwest. Nat. 51(2), 220–225. https://doi.org/10.1894/0038-4909 (2006).Article 

Google Scholar 
Simpson, D. C., Harveson, L. A., Brewer, C. E., Walser, R. E. & Sides, A. R. Influence of precipitation on pronghorn demography in Texas. J. Wildl. Manag. 71(3), 906–910. https://doi.org/10.2193/2005-753 (2007).Article 

Google Scholar 
McKinney, T., Brown, D. E. & Allison, L. Winter precipitation and recruitment of pronghorns in Arizona. Southwest. Nat. 53(3), 319–325. https://doi.org/10.1894/cj-147.1 (2008).Article 

Google Scholar 
Otte, A. Partners save the Sonoran pronghorn. Endang. Species Bull. 31, 22–23 (2006).
Google Scholar 
McCullough, D. R. & Barrett, R. H. Wildlife 2001: Populations (Springer, 1992).Book 

Google Scholar 
Percie Du Sert, N. et al. Reporting animal research: Explanation and elaboration for the ARRIVE guidelines 20. PLOS Biol. 18(7), e3000411. https://doi.org/10.1371/journal.pbio.3000411 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Carling, M. D., Passavant, C. W. & Byers, J. A. DNA microsatellites of pronghorn (Antilocapra americana). Mol. Ecol. Not. 3(1), 10–11. https://doi.org/10.1046/j.1471-8286.2003.00334.x (2002).Article 

Google Scholar 
Dunn, S. J. et al. Ten polymorphic microsatellite markers for pronghorn (Antilocapra americana). Conserv. Genet. Resour. 2(1), 81–84. https://doi.org/10.1007/s12686-009-9166-9 (2010).Article 

Google Scholar 
Munguia-Vega, A., Klimova, A. & Culver, M. New microsatellite loci isolated via next-generation sequencing for two endangered pronghorn from the Sonoran Desert. Conserv. Genet. Resour. 5(1), 125–127. https://doi.org/10.1007/s12686-012-9749-8 (2012).Article 

Google Scholar 
Boutin-Ganache, I., Raposo, M., Raymond, M. & Deschepper, C. F. M13-Tailed primers improve the readability and usability of microsatellite analyses performed with two different allele-sizing methods. Biotechniques 31(1), 25–28. https://doi.org/10.2144/01311bm02 (2001).Article 

Google Scholar 
Amos, W. et al. Automated binning of microsatellite alleles: Problems and solutions. Mol. Ecol. Not. 7(1), 10–14. https://doi.org/10.1111/j.1471-8286.2006.01560.x (2006).CAS 
Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021). https://www.R-project.org/.Jombart, T. & Ahmed, I. Adegenet 1.3–1: New tools for the analysis of genome-wide SNP data. Bioinformatics 27(21), 3070–3071. https://doi.org/10.1093/bioinformatics/btr521 (2011).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kamvar, Z. N., Tabima, J. F. & Grünwald, N. J. Poppr: An R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction. PeerJ 2, e281. https://doi.org/10.7717/peerj.281 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Adamack, A. T. & Gruber, B. PopGenReport: Simplifying basic population genetic analyses in R. Methods Ecol. Evol. 5(4), 384–387. https://doi.org/10.1111/2041-210x.12158 (2014).Article 

Google Scholar 
Agapow, P. M. & Burt, A. Indices of multilocus linkage disequilibrium. Mol. Ecol. Not. 1(1–2), 101–102. https://doi.org/10.1046/j.1471-8278.2000.00014.x (2001).CAS 
Article 

Google Scholar 
Paradis, E. pegas: An R package for population genetics with an integrated-modular approach. Bioinformatics 26(3), 419–420. https://doi.org/10.1093/bioinformatics/btp696 (2010).CAS 
Article 
PubMed 

Google Scholar 
Goudet, J. hierfstat, a package for r to compute and test hierarchical F-statistics. Mol. Ecol. Not. 5(1), 184–186. https://doi.org/10.1111/j.1471-8286.2004.00828.x (2005).Article 

Google Scholar 
Aparicio, J. M., Ortego, J. & Cordero, P. J. What should we weigh to estimate heterozygosity, alleles or loci?. Mol. Ecol. 15(14), 4659–4665. https://doi.org/10.1111/j.1365-294x.2006.03111.x (2006).CAS 
Article 
PubMed 

Google Scholar 
Alho, J. S., Välimäki, K. & Merilä, J. Rhh: An R extension for estimating multilocus heterozygosity and heterozygosity–heterozygosity correlation. Mol. Ecol. Res. 10(4), 720–722. https://doi.org/10.1111/j.1755-0998.2010.02830.x (2010).Article 

Google Scholar 
Stoffel, M. A. et al. inbreedR: An R package for the analysis of inbreeding based on genetic markers. Methods Ecol. Evol. 7(11), 1331–1339. https://doi.org/10.1111/2041-210x.12588 (2016).Article 

Google Scholar 
Wang, J. Coancestry: A program for simulating, estimating and analysing relatedness and inbreeding coefficients. Mol. Ecol. Res. 11(1), 141–145. https://doi.org/10.1111/j.1755-0998.2010.02885.x (2010).ADS 
Article 

Google Scholar 
Wang, J. Triadic IBD coefficients and applications to estimating pairwise relatedness. Genet. Res. 89(3), 135–153. https://doi.org/10.1017/s0016672307008798 (2007).CAS 
Article 
PubMed 

Google Scholar 
Marshall, T. C. et al. Estimating the prevalence of inbreeding from incomplete pedigrees. Proc. R. Soc. B Biol. Sci. 269(1500), 1533–1539. https://doi.org/10.1098/rspb.2002.2035 (2002).CAS 
Article 

Google Scholar 
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. https://doi.org/10.18637/jss.v067.i01 (2015).Article 

Google Scholar 
Beaumont, M. A., Zhang, W. & Balding, D. J. Approximate Bayesian computation in population genetics. Genetics 162(4), 2025–2035. https://doi.org/10.1093/genetics/162.4.2025 (2002).Article 
PubMed 
PubMed Central 

Google Scholar 
Bertorelle, G., Benazzo, A. & Mona, S. ABC as a flexible framework to estimate demography over space and time: Some cons, many pros. Mol. Ecol. 19(13), 2609–2625. https://doi.org/10.1111/j.1365-294x.2010.04690.x (2010).CAS 
Article 
PubMed 

Google Scholar 
Fagundes, N. J. R. et al. Statistical evaluation of alternative models of human evolution. Proc. Natl. Acad. Sci. U.S.A. 104(45), 17614–17619. https://doi.org/10.1073/pnas.0708280104 (2007).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar  More

Ronce O. How does it feel to be like a rolling stone? Ten questions about dispersal evolution. Annu Rev Ecol Evol Syst. 2007;38:231–53.Article 

Google Scholar 
Shmida A, Wilson MV. Biological determinants of species diversity. J Biogeogr. 1985;12:1–20.Article 

Google Scholar 
Vellend M. Conceptual synthesis in community ecology. Q Rev Biol. 2010;85:183–206.PubMed 
Article 

Google Scholar 
Slatkin M. Gene flow and the geographic structure of natural populations. Science. 1987;236:787–92.CAS 
PubMed 
Article 

Google Scholar 
Baas-Becking, LGM. Geobiology or introduction to environmental science (Translated from Dutch). The Hague: W.P. Van Stockum & Zoon; 1934.Martiny JBH, Bohannan BJM, Brown JH, Colwell RK, Fuhrman JA, Green JL, et al. Microbial biogeography: putting microorganisms on the map. Nat Rev Microbiol. 2006;4:102–12.CAS 
PubMed 
Article 

Google Scholar 
Peay KG, Schubert MG, Nguyen NH, Bruns TD. Measuring ectomycorrhizal fungal dispersal: macroecological patterns driven by microscopic propagules. Mol Ecol. 2012;21:4122–36.PubMed 
Article 

Google Scholar 
Andam CP, Doroghazi JR, Campbell AN, Kelly PJ, Choudoir MJ, Buckley DH. A latitudinal diversity gradient in terrestrial bacteria of the genus Streptomyces. mBio. 2016;7:e02200–15.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Choudoir MJ, Barberán A, Menninger HL, Dunn RR, Fierer N. Variation in range size and dispersal capabilities of microbial taxa. Ecology. 2018;99:322–34.PubMed 
Article 

Google Scholar 
Hanson CA, Müller AL, Loy A, Dona C, Appel R, Jørgensen BB, et al. Historical factors associated with past environments influence the biogeography of thermophilic endospores in Arctic marine sediments. Front Microbiol. 2019;10:245.PubMed 
PubMed Central 
Article 

Google Scholar 
Albright MBN, Martiny JBH. Dispersal alters bacterial diversity and composition in a natural community. ISME J. 2018;12:296–9.PubMed 
Article 

Google Scholar 
Evans SE, Bell-Dereske LP, Dougherty KM, Kittredge HA. Dispersal alters soil microbial community response to drought. Environ Microbiol. 2020;22:905–16.CAS 
PubMed 
Article 

Google Scholar 
Svoboda P, Lindström ES, Ahmed Osman O, Langenheder S. Dispersal timing determines the importance of priority effects in bacterial communities. ISME J. 2018;12:644–6.PubMed 
Article 

Google Scholar 
Cevallos-Cevallos JM, Danyluk MD, Gu G, Vallad GE, van Bruggen AHC. Dispersal of Salmonella typhimurium by rain splash onto tomato plants. J Food Prot. 2012;75:472–9.PubMed 
Article 

Google Scholar 
Lindström ES, Langenheder S. Local and regional factors influencing bacterial community assembly. Environ Microbiol Rep. 2012;4:1–9.PubMed 
Article 

Google Scholar 
Rime T, Hartmann M, Frey B. Potential sources of microbial colonizers in an initial soil ecosystem after retreat of an alpine glacier. ISME J. 2016;10:1625–41.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lindström ES, Östman Ö. The importance of dispersal for bacterial community composition and functioning. PLoS ONE. 2011;6:e25883.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Declerck SAJ, Winter C, Shurin JB, Suttle CA, Matthews B. Effects of patch connectivity and heterogeneity on metacommunity structure of planktonic bacteria and viruses. ISME J. 2013;7:533–42.PubMed 
Article 

Google Scholar 
Souffreau C, Pecceu B, Denis C, Rummens K, De Meester L. An experimental analysis of species sorting and mass effects in freshwater bacterioplankton. Freshw Biol. 2014;59:2081–95.Article 

Google Scholar 
Comte J, Langenheder S, Berga M, Lindström ES. Contribution of different dispersal sources to the metabolic response of lake bacterioplankton following a salinity change. Environ Microbiol. 2017;19:251–60.CAS 
PubMed 
Article 

Google Scholar 
Albright MBN, Sevanto S, Gallegos-Graves LV, Dunbar J. Biotic interactions are more important than propagule pressure in microbial community invasions. mBio. 2020;11:e02089–20.PubMed 
PubMed Central 
Article 

Google Scholar 
Galès A, Latrille E, Wéry N, Steyer JP, Godon JJ. Needles of Pinus halepensis as biomonitors of bioaerosol emissions. PLoS ONE. 2014;9:e112182.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Bell E, Blake LI, Sherry A, Head IM, Hubert CRJ. Distribution of thermophilic endospores in a temperate estuary indicate that dispersal history structures sediment microbial communities. Environ Microbiol. 2018;20:1134–47.PubMed 
PubMed Central 
Article 

Google Scholar 
Leung MHY, Wilkins D, Li EKT, Kong FKF, Lee PKH. Indoor-air microbiome in an urban subway network: diversity and dynamics. Appl Environ Microbiol. 2014;80:6760–70.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Maignien L, DeForce EA, Chafee ME, Murat Eren A, Simmons SL. Ecological succession and stochastic variation in the assembly of Arabidopsis thaliana phyllosphere communities. mBio. 2014;5:e00682–13.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Bell T. Experimental tests of the bacterial distance-decay relationship. ISME J. 2010;4:1357–65.PubMed 
Article 

Google Scholar 
Kaneko R, Kaneko S. The effect of bagging branches on levels of endophytic fungal infection in Japanese beech leaves. For Pathol. 2004;34:65–78.Article 

Google Scholar 
Vannette RL, Fukami T. Dispersal enhances beta diversity in nectar microbes. Ecol Lett. 2017;20:901–10.PubMed 
Article 

Google Scholar 
Satou M, Kubota M, Nishi K. Measurement of horizontal and vertical movement of Ralstonia solanacearum in soil. J Phytopathol. 2006;154:592–7.CAS 
Article 

Google Scholar 
Veen GF, Snoek BL, Bakx-Schotman T, Wardle DA, van der Putten WH. Relationships between fungal community composition in decomposing leaf litter and home-field advantage effects. Funct Ecol. 2019;33:1524–35.Article 

Google Scholar 
Liu G, Cornwell WK, Pan X, Ye D, Liu F, Huang Z, et al. Decomposition of 51 semidesert species from wide-ranging phylogeny is faster in standing and sand-buried than in surface leaf litters: implications for carbon and nutrient dynamics. Plant Soil. 2015;396:175–87.CAS 
Article 

Google Scholar 
Kimball S, Goulden ML, Suding KN, Parker S. Altered water and nitrogen input shifts succession in a southern California coastal sage community. Ecol Appl. 2014;24:1390–404.PubMed 
Article 

Google Scholar 
Finks SS, Weihe C, Kimball S, Allison SD, Martiny AC, Treseder KK, et al. Microbial community response to a decade of simulated global changes depends on the plant community. Elementa. 2021;9:124.
Google Scholar 
Khalili B, Weihe C, Kimball S, Schmidt KT, Martiny JBH. Optimization of a method to quantify soil bacterial abundance by flow cytometry. mSphere. 2019;4:e00435–19.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lane DJ, Pace B, Olsen GJ, Stahl DA, Sogin ML, Pace NR. Rapid determination of 16S ribosomal RNA sequences for phylogenetic analyses. Proc Natl Acad Sci USA. 1985;82:6955–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Huntley J, Fierer N, et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 2012;6:1621–4.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Looby CI, Maltz MR, Treseder KK. Belowground responses to elevation in a changing cloud forest. Ecol Evol. 2016;6:1996–2009.PubMed 
PubMed Central 
Article 

Google Scholar 
Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37:852–7.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Nilsson RH, Larsson KH, Taylor AFS, Bengtsson-Palme J, Jeppesen TS, Schigel D, et al. The UNITE database for molecular identification of fungi: handling dark taxa and parallel taxonomic classifications. Nucleic Acids Res. 2019;47:D259–D264.CAS 
PubMed 
Article 

Google Scholar 
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–D596.CAS 
PubMed 
Article 

Google Scholar 
Smith DJ, Ravichandar JD, Jain S, Griffin DW, Yu H, Tan Q, et al. Airborne bacteria in Earth’s lower stratosphere resemble taxa detected in the troposphere: results from a new NASA Aircraft Bioaerosol Collector (ABC). Front Microbiol. 2018;9:1752.PubMed 
PubMed Central 
Article 

Google Scholar 
Bryan NC, Christner BC, Guzik TG, Granger DJ, Stewart MF. Abundance and survival of microbial aerosols in the troposphere and stratosphere. ISME J. 2019;13:2789–99.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Matulich KL, Weihe C, Allison SD, Amend AS, Berlemont R, Goulden ML, et al. Temporal variation overshadows the response of leaf litter microbial communities to simulated global change. ISME J. 2015;9:2477–89.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kim N, Zabaloy MC, Villamil MB, Riggins CW, Rodríguez-Zas S. Microbial shifts following five years of cover cropping and tillage practices in fertile agroecosystems. Microorganisms. 2020;8:1773.CAS 
PubMed Central 
Article 

Google Scholar 
Gurfield N, Grewal S, Cua LS, Torres PJ, Kelley ST. Endosymbiont interference and microbial diversity of the Pacific coast tick, Dermacentor occidentalis, in San Diego County, California. PeerJ. 2017;5:e3202.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Knights D, Kuczynski J, Charlson ES, Zaneveld J, Mozer MC, Collman RG, et al. Bayesian community-wide culture-independent microbial source tracking. Nat Methods. 2011;8:761–3.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bertolini V, Gandolfi I, Ambrosini R, Bestetti G, Innocente E, Rampazzo G, et al. Temporal variability and effect of environmental variables on airborne bacterial communities in an urban area of Northern Italy. Appl Microbiol Biotechnol. 2013;97:6561–70.CAS 
PubMed 
Article 

Google Scholar 
Voříšková J, Baldrian P. Fungal community on decomposing leaf litter undergoes rapid successional changes. ISME J. 2013;7:477–86.PubMed 
Article 
CAS 

Google Scholar 
Rastogi G, Coaker GL, Leveau JHJ. New insights into the structure and function of phyllosphere microbiota through high-throughput molecular approaches. FEMS Microbiol Lett. 2013;348:1–10.CAS 
PubMed 
Article 

Google Scholar 
Lindow SE, Leveau JHJ. Phyllosphere microbiology. Curr Opin Biotechnol. 2002;13:238–43.CAS 
PubMed 
Article 

Google Scholar 
Purahong W, Wubet T, Lentendu G, Schloter M, Pecyna MJ, Kapturska D, et al. Life in leaf litter: novel insights into community dynamics of bacteria and fungi during litter decomposition. Mol Ecol. 2016;25:4059–74.CAS 
PubMed 
Article 

Google Scholar 
Austin AT, Vivanco L. Plant litter decomposition in a semi-arid ecosystem controlled by photodegradation. Nature. 2006;442:555–8.CAS 
PubMed 
Article 

Google Scholar 
Glassman SI, Weihe C, Li J, Albright MBN, Looby CI, Martiny AC, et al. Decomposition responses to climate depend on microbial community composition. Proc Natl Acad Sci USA. 2018;115:11994–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Punnapayak H, Sudhadham M, Prasongsuk S, Pichayangkura S. Characterization of Aureobasidium pullulans isolated from airborne spores in Thailand. J Ind Microbiol Biotechnol. 2003;30:89–94.CAS 
PubMed 
Article 

Google Scholar 
Elmassry MM, Ray N, Sorge S, Webster J, Merry K, Caserio A, et al. Investigating the culturable atmospheric fungal and bacterial microbiome in West Texas: implication of dust storms and origins of the air parcels. FEMS Microbes. 2020;1:xtaa009.Article 

Google Scholar 
Van Diepen LTA, Frey SD, Landis EA, Morrison EW, Pringle A. Fungi exposed to chronic nitrogen enrichment are less able to decay leaf litter. Ecology. 2017;98:5–11.PubMed 
Article 

Google Scholar 
Du X, Guo Q, Gao X, Ma K. Seed rain, soil seed bank, seed loss and regeneration of Castanopsis fargesii (Fagaceae) in a subtropical evergreen broad-leaved forest. Ecol Manag. 2007;238:212–9.Article 

Google Scholar 
Work TT, Buddle CM, Korinus LM, Spence JR. Pitfall trap size and capture of three taxa of litter-dwelling arthropods: implications for biodiversity studies. Environ Entomol. 2002;31:438–48.Article 

Google Scholar 
Leibold MA, Holyoak M, Mouquet N, Amarasekare P, Chase JM, Hoopes MF, et al. The metacommunity concept: a framework for multi-scale community ecology. Ecol Lett. 2004;7:601–13.Article 

Google Scholar 
Evans S, Martiny JBH, Allison SD. Effects of dispersal and selection on stochastic assembly in microbial communities. ISME J. 2017;11:176–85.PubMed 
Article 

Google Scholar 
Cadotte MW. Dispersal and species diversity: a meta-analysis. Am Nat. 2006;167:913–24.PubMed 
Article 

Google Scholar 
Schmidt SK, Nemergut DR, Darcy JL, Lynch R. Do bacterial and fungal communities assemble differently during primary succession? Mol Ecol. 2014;23:254–8.CAS 
PubMed 
Article 

Google Scholar 
Baker NR, Khalili B, Martiny JBH, Allison SD. Microbial decomposers not constrained by climate history along a Mediterranean climate gradient in southern California. Ecology. 2018;99:1441–52.PubMed 
Article 

Google Scholar 
Martiny JBH, Martiny AC, Weihe C, Lu Y, Berlemont R, Brodie EL, et al. Microbial legacies alter decomposition in response to simulated global change. ISME J. 2017;11:490–9.PubMed 
Article 

Google Scholar 
Santander MV, Mitts BA, Pendergraft MA, Dinasquet J, Lee C, Moore AN, et al. Tandem fluorescence measurements of organic matter and bacteria released in sea spray aerosols. Environ Sci Technol. 2021;55:5171–9.CAS 
PubMed 
Article 

Google Scholar 
Hobbie SE. Plant species effects on nutrient cycling: revisiting litter feedbacks. Trends Ecol Evol. 2015;30:357–63.PubMed 
Article 

Google Scholar  More

Szathmáry E. Toward major evolutionary transitions theory 2.0. Proc Natl Acad Sci USA. 2015;112:10104–11. https://doi.org/10.1073/pnas.1421398112CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Niklas KJ, Newman SA. The origins of multicellular organisms. Evol Dev. 2013;15:41–52. https://doi.org/10.1111/ede.12013Article 
PubMed 

Google Scholar 
Pfeiffer T, Bonhoeffer S. An evolutionary scenario for the transition to undifferentiated multicellularity. Proc Natl Acad Sci USA. 2003;100:1095–8. https://doi.org/10.1073/pnas.0335420100CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Fisher RM, Regenberg B, Multicellular group formation in Saccharomyces cerevisiae. Proc Royal Soc B: Biol Sci. 2019;286. https://doi.org/10.1098/rspb.2019.1098Umen JG. Green algae and the origins of multicellularity in the plant kingdom. Cold Spring Harb Perspect Biol. 2014;6:a016170 https://doi.org/10.1101/cshperspect.a016170Article 
PubMed 
PubMed Central 

Google Scholar 
Knoll AH. The multiple origins of complex multicellularity. Annu Rev Earth Planet Sci. 2011;39:217–39. https://doi.org/10.1146/annurev.earth.031208.100209CAS 
Article 

Google Scholar 
Bonner JT. The origins of multicellularity. Integr Biol Issues N. Rev. 1998;1:27–36.Article 

Google Scholar 
Tarnita CE, Taubes CH, Nowak MA. Evolutionary construction by staying together and coming together. J Theor Biol. 2013;320:10–22. https://doi.org/10.1016/j.jtbi.2012.11.022Article 
PubMed 

Google Scholar 
Ratcliff WC, Denison RF, Borrello M, Travisano M. Experimental evolution of multicellularity. Proc Natl Acad Sci USA. 2012;109:1595–1600. https://doi.org/10.1073/pnas.1115323109Article 
PubMed 
PubMed Central 

Google Scholar 
Koschwanez JH, Foster KR, Murray AW. Sucrose utilization in budding yeast as a model for the origin of undifferentiated multicellularity. PLoS Biol. 2011;9:e1001122 https://doi.org/10.1371/journal.pbio.1001122CAS 
Article 
PubMed 

Google Scholar 
Kuzdzal-Fick JJ, Chen L, Balázsi G. Disadvantages and benefits of evolved unicellularity versus multicellularity in budding yeast. Ecol Evol. 2019;9:8509–23. https://doi.org/10.1002/ece3.5322Article 
PubMed 
PubMed Central 

Google Scholar 
Brückner S, Schubert R, Kraushaar T, Hartmann R, Hoffmann D, Jelli E, et al. Kin discrimination in social yeast is mediated by cell surface receptors of the flo11 adhesin family. eLife 2020;9. https://doi.org/10.7554/eLife.55587Smukalla S, Caldara M, Pochet N, Beauvais A, Guadagnini S, Yan C, et al. FLO1 is a variable green beard gene that drives biofilm-like cooperation in budding yeast. Cell. 2008;135:726–37. https://doi.org/10.1016/j.cell.2008.09.037CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Driscoll WW, Travisano M, Synergistic cooperation promotes multicellular performance and unicellular free-rider persistence. Nat Commun. 2017;8. https://doi.org/10.1038/ncomms15707Pentz JT, Márquez-Zacarías P, Bozdag GO, Burnetti A, Yunker PJ, Libby E, et al. Ecological advantages and evolutionary limitations of aggregative multicellular development. Curr Biol. 2020;30:4155–.e6. https://doi.org/10.1016/j.cub.2020.08.006.CAS 
Article 
PubMed 

Google Scholar 
Goossens K, Willaert R. Flocculation protein structure and cell-cell adhesion mechanism in Saccharomyces cerevisiae. Biotechnol Lett. 2010;32:1571–85. https://doi.org/10.1007/s10529-010-0352-3CAS 
Article 
PubMed 

Google Scholar 
Di Gianvito P, Tesnière C, Suzzi G, Blondin B, Tofalo R. FLO5 gene controls flocculation phenotype and adhesive properties in a Saccharomyces cerevisiae sparkling wine strain. Sci Rep. 2017;7:1–12. https://doi.org/10.1038/s41598-017-09990-9CAS 
Article 

Google Scholar 
Veelders M, Brückner S, Ott D, Unverzagt C, Mösch HU, Essen LO. Structural basis of flocculin-mediated social behavior in yeast. Proc Natl Acad Sci USA. 2010;107:22511–6. https://doi.org/10.1073/pnas.1013210108Article 
PubMed 
PubMed Central 

Google Scholar 
Verstrepen KJ, Jansen A, Lewitter F, Fink GR. Intragenic tandem repeats generate functional variability. Nat Genet. 2005;37:986–90. https://doi.org/10.1038/ng1618CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Verstrepen KJ, Klis FM. Flocculation, adhesion and biofilm formation in yeasts. Mol Microbiol. 2006;60:5–15. https://doi.org/10.1111/j.1365-2958.2006.05072.xCAS 
Article 
PubMed 

Google Scholar 
Verstrepen KJ, Reynolds TB, Fink GR. Origins of variation in the fungal cell surface. Nat Rev Microbiol. 2004;2:533–40. https://doi.org/10.1038/nrmicro927CAS 
Article 
PubMed 

Google Scholar 
Kraushaar T, Brückner S, Veelders M, Rhinow D, Schreiner F, Birke R, et al. Interactions by the fungal Flo11 adhesin depend on a fibronectin type III-like adhesin domain girdled by aromatic bands. Structure. 2015;23:1005–17. https://doi.org/10.1016/j.str.2015.03.021CAS 
Article 
PubMed 

Google Scholar 
Chen L, Noorbakhsh J, Adams RM, Samaniego-Evans J, Agollah G, Nevozhay D, et al. Two-dimensionality of yeast colony expansion accompanied by pattern formation. PLoS Comput Biol. 2014;10. https://doi.org/10.1371/journal.pcbi.1003979Oppler ZJ, Parrish ME, Murphy HA, Variation at an adhesin locus suggests sociality in natural populations of the yeast saccharomyces cerevisiae. Proc Royal Soc B: Biol Sci. 2019;286. https://doi.org/10.1098/rspb.2019.1948Lo WS, Dranginis AM. The cell surface flocculin Flo11 is required for pseudohyphae formation and invasion by Saccharomyces cerevisiae. Mol Biol Cell. 1998;9:161–71. https://doi.org/10.1091/mbc.9.1.161CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
El-Kirat-Chatel S, Beaussart A, Vincent SP, Abellán Flos M, Hols P, Lipke PN, et al. Forces in yeast flocculation. Nanoscale. 2015;7:1760–7. https://doi.org/10.1039/c4nr06315eCAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kobayashi O, Hayashi N, Kuroki R, Sone H. Region of Flo1 proteins responsible for sugar recognition. J Bacteriol. 1998;180:6503–10. https://doi.org/10.1128/jb.180.24.6503-6510.1998CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kapsetaki SE, West SA. The costs and benefits of multicellular group formation in algae. Evolution. 2019;73:1296–308. https://doi.org/10.1111/evo.13712Article 
PubMed 

Google Scholar 
Quintero-Galvis JF, Paleo-López R, Solano-Iguaran JJ, Poupin MJ, Ledger T, Gaitan-Espitia JD, et al. Exploring the evolution of multicellularity in Saccharomyces cerevisiae under bacteria environment: An experimental phylogenetics approach. Ecol Evol. 2018;8:4619–30. https://doi.org/10.1002/ece3.3979Article 
PubMed 
PubMed Central 

Google Scholar 
Goossens KV, Ielasi FS, Nookaew I, Stals I, Alonso-Sarduy L, Daenen L, et al. Molecular mechanism of flocculation self-recognition in yeast and its role in mating and survival. mBio. 2015;6:1–16. https://doi.org/10.1128/mBio.00427-15CAS 
Article 

Google Scholar 
Hamilton WD. The genetical evolution of social behaviour. I. J Theor Biol. 1964;7:1–16. https://doi.org/10.1016/0022-5193(64)90038-4CAS 
Article 
PubMed 

Google Scholar 
Queller DC, Ponte E, Bozzaro S, Strassmann JE. Single-gene greenbeard effects in the social amoeba Dictyostelium discoideum. Science. 2003;299:105–6. https://doi.org/10.1126/science.1077742CAS 
Article 
PubMed 

Google Scholar 
Foty RA, Steinberg MS. The differential adhesion hypothesis: A direct evaluation. Dev Biol. 2005;278:255–63. https://doi.org/10.1016/j.ydbio.2004.11.012CAS 
Article 
PubMed 

Google Scholar 
Nowak MA. Five rules for the evolution of cooperation. Science. 2006;314:1560–3. https://doi.org/10.1126/science.1133755Article 
PubMed 
PubMed Central 

Google Scholar 
Nadell CD, Foster KR, Xavier JB. Emergence of spatial structure in cell groups and the evolution of cooperation. PLoS Comput Biol. 2010;6:e1000716 https://doi.org/10.1371/journal.pcbi.1000716CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Drescher K, Nadell CD, Stone HA, Wingreen NS, Bassler BL. Solutions to the public goods dilemma in bacterial biofilms. Curr Biol. 2014;24:50–55. https://doi.org/10.1016/j.cub.2013.10.030CAS 
Article 
PubMed 

Google Scholar 
Liu CG, Li ZY, Hao Y, Xia J, Bai FW, Mehmood MA, Computer simulation elucidates yeast flocculation and sedimentation for efficient industrial fermentation. Biotechnol J. 2018;13. https://doi.org/10.1002/biot.201700697Boraas ME, Seale DB, Boxhorn JE. Phagotrophy by flagellate selects for colonial prey: A possible origin of multicellularity. Evol Ecol. 1998;12:153–64. https://doi.org/10.1023/A:1006527528063Article 

Google Scholar 
Staps M, van Gestel J, Tarnita CE. Emergence of diverse life cycles and life histories at the origin of multicellularity. Nat Ecol Evol. 2019;3:1197–205. https://doi.org/10.1038/s41559-019-0940-0Article 
PubMed 

Google Scholar 
De Vargas Roditi L, Boyle KE, Xavier JB. Multilevel selection analysis of a microbial social trait. Mol Syst Biol. 2013;9:684 https://doi.org/10.1038/msb.2013.42Article 
PubMed 
PubMed Central 

Google Scholar 
Damore JA, Gore J. Understanding microbial cooperation. J Theor Biol. 2012;299:31–41. https://doi.org/10.1016/j.jtbi.2011.03.008Article 
PubMed 

Google Scholar 
Denoth Lippuner A, Julou T, Barral Y. Budding yeast as a model organism to study the effects of age. FEMS Microbiol Rev. 2014;38:300–25. https://doi.org/10.1111/1574-6976.12060CAS 
Article 
PubMed 

Google Scholar 
Janssens GE, Veenhoff LM. The natural variation in lifespans of single yeast cells is related to variation in cell size, ribosomal protein, and division time. PLoS ONE. 2016;11:e0167394 https://doi.org/10.1371/journal.pone.0167394CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Ross-Gillespie A, Gardner A, West SA, Griffin AS. Frequency dependence and cooperation: Theory and a test with bacteria. Am Nat. 2007;170:331–42. https://doi.org/10.1086/519860Article 
PubMed 

Google Scholar 
Healey D, Axelrod K, Gore J. Negative frequency-dependent interactions can underlie phenotypic heterogeneity in a clonal microbial population. Mol Syst Biol. 2016;12:877 https://doi.org/10.15252/msb.20167033CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Harrow GL, Lees JA, Hanage WP, Lipsitch M, Corander J, Colijn C, et al. Negative frequency-dependent selection and asymmetrical transformation stabilise multi-strain bacterial population structures. ISME J. 2021;15:1523–38. https://doi.org/10.1038/s41396-020-00867-wCAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Avilés L. Solving the freeloaders paradox: Genetic associations and frequency-dependent selection in the evolution of cooperation among nonrelatives. Proc Natl Acad Sci USA. 2002;99:14268–73. https://doi.org/10.1073/pnas.212408299CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Fisher RM, Cornwallis CK, West SA. Group formation, relatedness, and the evolution of multicellularity. Curr Biol. 2013;23:1120–5. https://doi.org/10.1016/j.cub.2013.05.004CAS 
Article 
PubMed 

Google Scholar 
Pentz JT, Travisano M, Ratcliff WC, Clonal development is evolutionarily superior to aggregation in wild-collected Saccharomyces cerevisiae. In Artificial Life 14 – Proceedings of the 14th International Conference on the Synthesis and Simulation of Living Systems, ALIFE 2014, 2014;550–4. 10.7551/978-0-262-32621-6-ch088.Melbinger A, Cremer J, Frey E, The emergence of cooperation from a single mutant during microbial life cycles. J Royal Soc Interface. 2015;12. https://doi.org/10.1098/rsif.2015.0171 More

Krause, M. & Robins, K. Charismatic species and beyond: How cultural schemas and organisational routines shape conservation. Conserv. Soc. 15, 313–321 (2017).
Google Scholar 
Marshall, K. N., Stier, A. C., Samhouri, J. F., Kelly, R. P. & Ward, E. J. Conservation challenges of predator recovery. Conserv. Lett. 9, 70–78 (2016).
Google Scholar 
Bearzi, G., Holcer, D. & Di Sciara, G. N. The role of historical dolphin takes and habitat degradation in shaping the present status of northern Adriatic cetaceans. Aquat. Conserv. Mar. Freshw. Ecosyst. 14, 363–379 (2004).
Google Scholar 
Lavigne, D. M. Marine mammals and fisheries: The role of science in the culling debate. In Marine Mammals: Fisheries Tourism and Management Issues (eds Gales, N. et al.) 31–47 (CSIRO Publishing, 2003).
Google Scholar 
Bowen, W. D. & Lidgard, D. Marine mammal culling programs: Review of effects on predator and prey populations. Mamm. Rev. 43, 207–220 (2013).
Google Scholar 
Svanbäck, R. & Persson, L. Individual diet specialization, niche width and population dynamics: Implications for trophic polymorphisms. J. Anim. Ecol. 73, 973–982 (2004).
Google Scholar 
Butler, J. R. A. et al. The Moray Firth Seal Management Plan: An adaptive framework for balancing the conservation of seals, salmon, fisheries and wildlife tourism in the UK. Aquat. Conserv. Mar. Freshw. Ecosyst. 18, 1025–1038 (2008).
Google Scholar 
Graham, I. M., Harris, R. N., Matejusová, I. & Middlemas, S. J. Do ‘rogue’ seals exist? Implications for seal conservation in the UK. Anim. Conserv. 14, 587–598 (2011).
Google Scholar 
Linnell, J. D. C., Aanes, R., Swenson, J. E., Odden, J. & Smith, M. E. Large carnivores that kill livestock: Do ‘problem individuals’ really exist?. Wildl. Soc. Bull. 27, 698–705 (1999).
Google Scholar 
Tidwell, K. S., van der Leeuw, B. K., Magill, L. N., Carrothers, B. A. & Wertheimer, R. H. Evaluation of pinniped predation on adult salmonids and other fish in the Bonneville Dam tailrace (2017).Guillemette, M. & Brousseau, P. Does culling predatory gulls enhance the productivity of breeding common terns?. J. Appl. Ecol. 38, 1–8 (2001).
Google Scholar 
Rudolf, V. H. W. & Rasmussen, N. L. Population structure determines functional differences among species and ecosystem processes. Nat. Commun. 4, 2318 (2013).ADS 
PubMed 

Google Scholar 
Harmon, L. J. et al. Evolutionary diversification in stickleback affects ecosystem functioning. Nature 458, 1167–1170 (2009).ADS 
CAS 
PubMed 

Google Scholar 
Adams, J. et al. A century of Chinook salmon consumption by marine mammal predators in the Northeast Pacific Ocean. Ecol. Inform. 34, 44–51 (2016).
Google Scholar 
Chasco, B. et al. Competing tradeoffs between increasing marine mammal predation and fisheries harvest of Chinook salmon. Sci. Rep. 7, 1–14 (2017).CAS 

Google Scholar 
Bearhop, S. et al. Stable isotopes indicate sex-specific and long-term individual foraging specialisation in diving seabirds. Mar. Ecol. Prog. Ser. 311, 157–164 (2006).ADS 

Google Scholar 
Thiemann, G. W., Iverson, S. J., Stirling, I. & Obbard, M. E. Individual patterns of prey selection and dietary specialization in an Arctic marine carnivore. Oikos 120, 1469–1478 (2011).
Google Scholar 
Königson, S., Fjälling, A., Berglind, M. & Lunneryd, S. G. Male gray seals specialize in raiding salmon traps. Fish. Res. 148, 117–123 (2013).
Google Scholar 
Sih, A., Sinn, D. L. & Patricelli, G. L. On the importance of individual differences in behavioural skill. Anim. Behav. 155, 307–317 (2019).
Google Scholar 
Bjorkland, R. H. et al. Stable isotope mixing models elucidate sex and size effects on the diet of a generalist marine predator. Mar. Ecol. Prog. Ser. 526, 213–225 (2015).ADS 

Google Scholar 
Schwarz, D. et al. Large-scale molecular diet analysis in a generalist marine mammal reveals male preference for prey of conservation concern. Ecol. Evol. 8, 9889–9905 (2018).PubMed 
PubMed Central 

Google Scholar 
Tinker, M. T., Costa, D. P., Estes, J. A. & Wieringa, N. Individual dietary specialization and dive behaviour in the California sea otter: Using archival time-depth data to detect alternative foraging strategies. Deep. Res. Part II Top. Stud. Oceanogr. 54, 330–342 (2007).ADS 

Google Scholar 
Voelker, M. R., Schwarz, D., Thomas, A., Nelson, B. W. & Acevedo-Gutiérrez, A. Large-scale molecular barcoding of prey DNA reveals predictors of intrapopulation feeding diversity in a marine predator. Ecol. Evol. 10, 9867–9885 (2020).PubMed 
PubMed Central 

Google Scholar 
Bolnick, D. I. et al. The ecology of individuals: Incidence and implications of individual specialization. Am. Nat. 161, 1–28 (2003).MathSciNet 
PubMed 

Google Scholar 
Harcourt, R. Individual variation in predation on fur seals by southern sea lions (Otaria byronia) in Peru. Can. J. Zool. 71, 1908–1911 (1993).
Google Scholar 
Marine Mammal Commission. Marine Mammal Protection Act. Marine Mammal Protection Act Amendment 1–56 (U.S. Fish and Wildlife Service, 2004). https://doi.org/10.1002/tcr.201190008.Book 

Google Scholar 
National Marine Fisheries Service. Willamette Falls Pinniped-Fishery Interaction Task Force Marine Mammal Protection Act, Section 120 (National Marine Fisheries Service, 2018).
Google Scholar 
Jefferson, T. A., Smultea, M. A., Ward, E. J. & Berejikian, B. Estimating the stock size of harbor seals (Phoca vitulina richardii) in the inland waters of Washington State using line-transect methods. PLoS ONE 16, e0241254 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Jeffries, S., Huber, H., Calambokidis, J. & Laake, J. Trends and status of harbor seals in Washington State: 1978–1999. J. Wildl. Manag. 67, 208–219 (2003).
Google Scholar 
Thomas, A. C., Lance, M. M., Jeffries, S. J., Miner, B. G. & Acevedo-Gutiérrez, A. Harbor seal foraging response to a seasonal resource pulse, spawning Pacific herring. Mar. Ecol. Prog. Ser. 441, 225–239 (2011).ADS 

Google Scholar 
Chasco, B. et al. Estimates of chinook salmon consumption in Washington State inland waters by four marine mammal predators from 1970 to 2015. Can. J. Fish. Aquat. Sci. 74, 1173–1194 (2017).
Google Scholar 
Farrer, J. & Acevedo-Gutiérrez, A. Use of haul-out sites by harbor seals (Phoca vitulina) in Bellingham: Implications for future development. Northwest. Nat. 91, 74–79 (2010).
Google Scholar 
Steingass, S., Jeffries, S., Hatch, D. & Dupont, J. Field report: 2020 pinniped research and management activities at Bonneville Dam (2020).Tidwell, K. S., Carrothers, B. A., Blumstein, D. T. & Schakner, Z. A. Steller sea lion (Eumetopias jubatus) response to non-lethal hazing at Bonneville Dam. Front. Conserv. Sci. 2, 1–9 (2021).
Google Scholar 
Hiruki, L. M., Schwartz, M. K. & Boveng, P. L. Hunting and social behaviour of leopard seals (Hydrurga leptonyx) at Seal Island, South Shetland Islands, Antarctica. J. Zool. 249, 97–109 (1999).
Google Scholar 
Ainley, D. G., Ballard, G., Karl, B. J. & Dugger, K. M. Leopard seal predation rates at penguin colonies of different size. Antarct. Sci. 17, 335–340 (2005).ADS 

Google Scholar 
Páez-Rosas, D. et al. Hunting and cooperative foraging behavior of Galapagos sea lion: An attack to large pelagics. Mar. Mammal Sci. 36, 386–391 (2020).
Google Scholar 
Macneale, K. H., Kiffney, P. M. & Scholz, N. L. Pesticides, aquatic food webs, and the conservation of Pacific salmon. Front. Ecol. Environ. 8, 475–482 (2010).
Google Scholar 
Roni, P., Anders, P. J., Beechie, T. J. & Kaplowe, D. J. Review of tools for identifying, planning, and implementing habitat restoration for Pacific salmon and steelhead. North Am. J. Fish. Manag. 38, 355–376 (2018).
Google Scholar 
Morissette, L., Christensen, V. & Pauly, D. Marine mammal impacts in exploited ecosystems: Would large scale culling benefit fisheries?. PLoS ONE 7, 1–18 (2012).
Google Scholar 
Thompson, D., Coram, A. J., Harris, R. N. & Sparling, C. E. Review of non-lethal seal control options to limit seal predation on salmonids in rivers and at finfish farms. Scott. Mar. Freshw. Sci. 12, 137 (2021).
Google Scholar 
Dickinson, J. L., Zuckerberg, B. & Bonter, D. N. Citizen science as an ecological research tool: Challenges and benefits. Annu. Rev. Ecol. Evol. Syst. 41, 149–172 (2010).
Google Scholar 
Fairbanks, C. & Penttila, D. Bellingham Bay Forage Fish Spawning Assessment (2016).Madsen, S. W. & Nightengale, T. Whatcom Creek Ten-Years After: Summary Report (Department of Public Works, 2009). https://doi.org/10.2307/j.ctt20krzd7.7.Book 

Google Scholar 
Martin, P. & Bateson, P. Measuring Behaviour: An Introductory Guide (Cambridge University Press, 2007).
Google Scholar 
Bolger, D. T., Morrison, T. A., Vance, B., Lee, D. & Farid, H. A computer-assisted system for photographic mark-recapture analysis. Methods Ecol. Evol. 3, 813–822 (2012).
Google Scholar 
Harrison, P. J. et al. Incorporating movement into models of grey seal population dynamics. J. Anim. Ecol. 75, 634–645 (2006).PubMed 

Google Scholar 
Thompson, P. M. & Wheeler, H. Photo-ID-based estimates of reproductive patterns in female harbor seals. Mar. Mammal Sci. 24, 138–146 (2008).
Google Scholar 
Washington Department of Fish and Wildlife. Whatcom Creek Hatchery (WDFW, 2019).
Google Scholar 
R Core Team. R: A language and environment for statistical computing (R Core Team, 2020).
Google Scholar 
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. https://doi.org/10.18637/jss.v067.i01 (2015).Article 

Google Scholar 
Lloyd-Smith, J. O. Maximum likelihood estimation of the negative binomial dispersion parameter for highly overdispersed data, with applications to infectious diseases. PLoS ONE 2, 1–8 (2007).
Google Scholar 
Zhang, D. rsq: R-Squared and Related Measures. R package version 2.1 (2020).Lüdecke, D., Ben-Shachar, M., Patil, I., Waggoner, P. & Makowski, D. Performance: An R package for assessment, comparison and testing of statistical models. J. Open Source Softw. 6, 3139 (2021).ADS 

Google Scholar 
Bolker, B. M. et al. Generalized linear mixed models: A practical guide for ecology and evolution. Trends Ecol. Evol. 24, 127–135 (2009).PubMed 

Google Scholar 
Zuur, A. F., Ieno, E. N., Walker, N., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R (Springer, 2009). https://doi.org/10.1007/978-0-387-87458-6.Book 
MATH 

Google Scholar  More