Ecology

Kays, R. et al. Does hunting or hiking affect wildlife communities in protected areas? J. Appl. Ecol. 54, 242–252 (2016).Article 

Google Scholar 
Heberlein, T. A. & Ericsson, G. Ties to the countryside: accounting for urbanites attitudes toward hunting, wolves, and wildlife. Hum. Dimens. Wildl. 10, 213–227 (2006).Article 

Google Scholar 
Wong, J. Z., Etoh, S. & Sujang, A. B. Towards sustainable community-based fishery resources management: the tagal system of Sabah. Malaysia. SEAFDEC 7, 18–23 (2009).
Google Scholar 
Riley, E. P. The importance of human-macaque folklore for conservation in Lore Lindu National Park, Sulawesi, Indonesia. Oryx 44, 235–240 (2010).Article 

Google Scholar 
Gaynor, K. M. et al. War and wildlife: linking armed conflict to conservation. Front. Ecol. Environ. 14, 533–542 (2016).Article 

Google Scholar 
Kurz, D. et al. Transformation and endurance of Indigenous hunting: Kadazandusun-Murut bearded pig hunting practices amidst oil palm expansion and urbanization in Sabah, Malaysia. People. Nat. 3, 1078–1092 (2021).Article 

Google Scholar 
Karanth, K. K., Nichols, J. D., Hines, J. E., Karanth, K. U. & Christensen, N. L. Patterns and determinants of mammal species occurrence in India. J. Appl. Ecol. 46, 1189–1200 (2009).Article 

Google Scholar 
Bettigole, C. A., Donovan, T. M., Manning, R., Austin, J. & Long, R. Acceptability of residential development in a regional landscape: potential effects on wildlife occupancy patterns. Biol. Conserv. 169, 401–409 (2014).Article 

Google Scholar 
Stahlecker, D. W., Wallace, Z. P., Mikesic, D. G. & Smith, C. S. Does Hopi religious harvest of eaglets affect golden eagle territory occupancy and reproduction on the Navajo Nation? J. Raptor Res. 51, 305–318 (2017).Article 

Google Scholar 
Deith, M. C. M. & Brodie, J. F. Predicting defaunation: accurately mapping bushmeat hunting pressure over large areas. Proc. Royal Soc. B. https://doi.org/10.1098/rspb.2019.2677 (2020).Article 

Google Scholar 
Lischka, S. A. et al. A conceptual model for the integration of social and ecological information to understand human-wildlife interactions. Biol. Conserv. 225, 80–87 (2018).Article 

Google Scholar 
Guerrero, A. M. et al. Achieving the promise of integration in social-ecological research: a review and prospectus. Ecol. Soc. https://doi.org/10.5751/ES-10232-230338 (2018).Article 

Google Scholar 
Behr, D. M., Ozgul, A. & Cozzi, G. Combining human acceptance and habitat suitability in a unified socio-ecological suitability model: a case study of the wolf in Switzerland. J. Appl. Ecol. 54, 1919–1929 (2017).Article 

Google Scholar 
Bennett, E. L., Nyaoi, A. J. & Sompud, J. In Hunting for Sustainability in Tropical Forests (eds. Robinson, J. G. & Bennett, E. L.) 305–324 (Columbia University Press, 2000).Chin, C. Pig in the pot: comments on Sus barbatus in the hunting lifestyle of the Penan in Sarawak (Borneo). Asian Wild Pig News. 1, 10–12 (2001).
Google Scholar 
Yi, M. C. K. & Mohd-Azlan, J. Wildlife hunting and utilization in Ulu Baleh, Sarawak, Malaysian Borneo. Ethnobiol. Lett. 11, 76–84 (2020).Article 

Google Scholar 
Janowski, M. Pigs and people in the Kelabit Highlands, Sarawak. Indones. Malay World. 42, 88–112 (2014).Article 

Google Scholar 
Department of Statistics Malaysia. Taburan penduduk dan ciri-ciri asas demografi. https://www.mycensus.gov/my/index.php/ms/produk-banci/penerbitan/banci-2010/664-taburan-penduduk-dan-ciri-ciri-asas-demografi-2010 (2011).Harrisson, T., Hooijer, D. A. & Medway, L. An extinct giant pangolin and associated mammals from Niah Cave, Sarawak. Nature 189, 166 (1961).Article 

Google Scholar 
Yusof, N. M. Study of social interaction among students of Vision Schools in Malaysia. Asian Ethn. 13, 47–73 (2012).Article 

Google Scholar 
Luskin, M. S., Ke, A., Linkie, M. & Meijaard, E. Sus barbatus. The IUCN Red List of Threatened Species: e.T41772A10559190. https://www.iucnredlist.org/species/41772/123793370 (2017).Medway, L. Post-Pleistocene changes in the mammalian fauna of Borneo: archaeological evidence from the Niah caves. Studies Speleology. 1, 33–37 (1964).
Google Scholar 
Love, K. et al. Bearded pig (Sus barbatus) utilisation of a fragmented forest-oil palm landscape in Sabah, Malaysian Borneo. Wildl. Res. 44, 603–612 (2018).Article 

Google Scholar 
Curran, L. M. & Leighton, M. Vertebrate responses to spatiotemporal variation in seed production of mast-fruiting Dipterocarpaceae. Ecol. Monogr. 70, 101–128 (2000).Article 

Google Scholar 
Luskin, M. S. & Ke, A. In Ecology, Conservation and Management of Wild Pigs and Peccaries (eds. Melletti, M. & Meijaard, E.) 175–183 (Cambridge University Press, 2018).Granados, A., Bernard, H. & Brodie, J. F. The influence of logging on vertebrate responses to mast fruiting. J. Anim. Ecol. 88, 892–902 (2019).Article 

Google Scholar 
Kurz, D. J., Malim, P. & Goossens, B. In Wildlife Atlas of Sabah (ed. Davies, G.) 123–132. (WWF – Malaysia, 2022).Ke, A. & Luskin, M. S. Integrating disparate occurrence reports to map data-poor species ranges and occupancy: a case study of the Vulnerable bearded pig Sus barbatus. Oryx 53, 377–387 (2019).Article 

Google Scholar 
Gaveau, D. L. A. et al. Rapid conversions and avoided deforestation: examining four decades of industrial plantation expansion in Borneo. Sci. Rep. https://doi.org/10.1038/srep32017 (2016).Article 

Google Scholar 
Luskin, M. S. et al. Cross-boundary subsidy cascades from oil palm degrade distant tropical forests. Nat. Commun. https://doi.org/10.1038/s41467-017-01920-7 (2017).Article 

Google Scholar 
Davison, C. W., Chapman, P. M., Wearn, O. R., Bernard, H. & Ewers, R. M. Shifts in the demographics and behavior of bearded pigs (Sus barbatus) across a land-use gradient. Biotropica 51, 938–948 (2019).Article 

Google Scholar 
Alberti, M. et al. Integrating humans into ecology: opportunities and challenges for studying urban ecosystems. BioScience 53, 1169–1179 (2003).Article 

Google Scholar 
Kumar, N. et al. Habitat selection by an avian top predator in the tropical megacity of Delhi: human activities and socio-religious practices as prey-facilitating tools. Urban Ecosyst. 21, 339–349 (2018).
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).Article 
CAS 

Google Scholar 
Hudson, L. N. et al. The database of the PREDICTS (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems) project. Ecol. Evol. 7, 145–188 (2017).Article 

Google Scholar 
Van Houtan, K. S. Conservation as virtue: a scientific and social process for conservation ethics. Conserv. Biol. 20, 1367–1372 (2006).Article 

Google Scholar 
Hunt, K. M. & Ditton, R. B. Freshwater fishing participation patterns of racial and ethnic groups in Texas. N. Am. J. Fish. Manag. 22, 52–65 (2002).Article 

Google Scholar 
Brashares, J. S. et al. Wildlife decline and social conflict. Science 345, 376–378 (2014).Article 
CAS 

Google Scholar 
Bolton, J. M., M. R. C. S., L. R. C. P., D. T. M. & H., D. Obst. R. C. O. G. Food taboos among the Orang Asli in West Malaysia: a potential nutritional hazard. Am. J. Clin. Nutr. 25, 789–799 (1972).Wadley, R. L., Colfer, C. J. P. & Hood, I. G. Hunting primates and managing forests: the case of Iban forest farmers in Indonesian Borneo. Hum. Ecol. 25, 243–271 (1997).Article 

Google Scholar 
Golden, C. D. & Comaroff, J. Effects of social change on wildlife consumption taboos in northeastern Madagascar. Ecol. Soc. https://doi.org/10.5751/ES-07589-200241 (2015).Article 

Google Scholar 
Pieroni, A. & Sõukand, R. Ethnic and religious affiliations affect traditional wild plant foraging in Central Azerbaijan. Genet. Resour. Crop Evol. 66, 1495–1513 (2019).Article 
CAS 

Google Scholar 
Caldecott, J. O., Blouch, R. A. & Macdonald, A. A. In Pigs, Peccaries and Hippos: Status Survey and Conservation Action Plan. (ed. Oliver, W. L.) 136–145 (IUCN, 1993).Caldecott, J. & Caldecott, S. A horde of pork. New Sci. 110, 32–35 (1985).
Google Scholar 
Liu, J. et al. Prevalence of African Swine Fever in China, 2018-2019. J. Med. Virol. 92, 1023–1034 (2020).Article 
CAS 

Google Scholar 
Food and Agriculture Organization of the United Nations. Agriculture and Consumer Protection Department. ASF situation in Asia & Pacific update. http://www.fao.org/ag/againfo/programmes/en/empres/ASF/situation_update.html (2021).Luskin, M. S. & Potts, M. D. Microclimate and habitat heterogeneity through the oil palm lifecycle. Basic Appl. Ecol. 12, 540–551 (2011).Article 

Google Scholar 
Wong, S. T., Servheen, C., Ambu, L. & Norhayati, A. Impacts of fruit production cycles on Malayan sun bears and bearded pigs in lowland tropical forest of Sabah, Malaysian Borneo. J. Trop. Ecol. 21, 627–639 (2005).Article 

Google Scholar 
Corner, E. J. H. The complex of Ficus deltoidea; a recent invasion of the Sunda shelf. Philos. Trans. R. Soc. B: Biol. Sci. 256, 281–317 (1969).
Google Scholar 
Pothasin, P., Compton, S. G. & Wangpakapattanawong, P. Riparian Ficus tree communities: the distribution and abundance of riparian fig trees in Northern Thailand. PLoS ONE. https://doi.org/10.1371/journal.pone.0108945 (2014).Article 

Google Scholar 
Declaration on the Heart of Borneo Initiative. Brunei Darussalam, Indonesia, and Malaysia. https://wwf.panda.org/discover/knowledge_hub/where_we_work/borneo_forests/about_borneo_forests/declaration/ (2007).Keong, C. Y. & Onuma, A. Transboundary ecological conservation, environmental value, and environmental sustainability: lessons from the Heart of Borneo. Sustainability https://doi.org/10.3390/su13179727 (2021).Article 

Google Scholar 
Neumann, W. et al. Hunting as land use: understanding the spatial associations among hunting, agriculture, and forestry. Ecol. Soc. https://doi.org/10.5751/ES-12882-270102 (2022).Article 

Google Scholar 
Shaffer, C. A. et al. Integrating ethnography and hunting sustainability modeling for primate conservation in an Indigenous reserve in Guyana. Int. J. Primatol. 39, 945–968 (2018).Article 

Google Scholar 
Popp, J. N., Priadka, P. & Kozmik, C. The rise of moose co-management and integration of Indigenous knowledge. Hum. Dimens. of Wildl. 24, 159–167 (2019).Article 

Google Scholar 
Breton-Honeyman, K. Beluga whale stewardship and collaborative research practices among Indigenous peoples in the Arctic. Polar Res. https://doi.org/10.33265/polar.v40.5522 (2021).Article 

Google Scholar 
Wildlife Conservation Enactment 1997. No. 6 of 1997. State of Sabah. https://www.sabahlaw.com/WILDLIFE_ENACTMENT.pdf.Wild Life Protection Ordinance, 1998. Chapter 26. Laws of Sarawak. https://www.sarawakforestry.com/pdf/laws/wildlife_protection_ordinance98_chap26.pdf.Wilting, A., Fischer, F., Bakar, S. A. & Linsenmair, K. E. Clouded leopards, the secretive top carnivore of South-East Asian rainforests: their distribution, status and conservation needs in Sabah, Malaysia. BMC Ecol. https://doi.org/10.1186/1472-6785-6-16 (2006).Article 

Google Scholar 
Bernard, H. et al. Camera-trapping survey of mammals in and around Imbak Canyon Conservation Area in Sabah, Malaysian Borneo. Raffles Bull. Zool. 61, 861–870 (2013).
Google Scholar 
Mohd-Azlan, J., Yi, M. C. K., Lip, B. & Hon, J. Camera trapping of wildlife in the newly established Baleh National Park, Sarawak. J. Sustain. Sci. Manag. 14, 38–51 (2019).
Google Scholar 
Miettinen, J., Shi, C. & Liew, S. C. 2015 Land cover map of Southeast Asia at 250 m spatial resolution. Remote Sens. Lett. 7, 701–710 (2016).Article 

Google Scholar 
Pekel, J.-F., Cottam, A., Gorelick, N. & Belward, A. S. High-resolution mapping of global surface water and its long-term changes. Nature 540, 418–422 (2016).Article 
CAS 

Google Scholar 
Gaveau, D. L. A., Salim, M. & Arjasakusuma, S. Deforestation and industrial plantations development in Borneo. Center for International Forestry Research (CIFOR). https://doi.org/10.17528/CIFOR/DATA.00049.UNEP-WCMC & IUCN. Protected planet: the world database on protected areas (WDPA). https://developers.google.com/earth-engine/datasets/catalog/WCMC_WDPA_current_polygons.Farr, T. G. et al. The shuttle radar topography mission. Rev. Geophys. https://doi.org/10.1029/2005RG000183 (2007).Article 

Google Scholar 
Dimiceli, C. et al. MOD44B MODIS/Terra Vegetation Continuous Fields Yearly L3 Global 250m SIN Grid V006. NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/MODIS/MOD44B.006 (2015).Article 

Google Scholar 
Zuur, A., Ieno, E. N., Walker, N., Saveliev, A. A. & Smith, G. M. Mixed effects models and extensions in ecology with R. 574 (Springer, 2009).R Development Core Team. R: A Language and Environment for Statistical Computing. https://www.r-project.org/ (R Foundation for Statistical Computing, 2019).Fiske, I. & Chandler, R. unmarked: an R package for fitting hierarchical models of wildlife occurrence and abundance. J. Stat. Softw. 43, 1–23, (2011) http://www.jstatsoft.org/v43/i10/.Article 

Google Scholar 
MacKenzie, D. I. et al. Estimating site occupancy rates when detection probabilities are less than one. Ecology 83, 2248–2255 (2002).Article 

Google Scholar 
Petracca, L. S. et al. Robust inference on large-scale species habitat use with interview data: the status of jaguars outside protected areas in Central America. J. Appl. Ecol. 55, 723–734 (2018).Article 

Google Scholar 
Gould, M. J., Gould, W. R., Cain, J. W. III. & Roemer, G. W. Validating the performance of occupancy models for estimating habitat use and predicting the distribution of highly-mobile species: a case study using the American black bear. Biol. Conserv. 234, 28–36 (2019).Article 

Google Scholar 
Niedballa, J., Sollmann, R., Courtiol, A. & Wilting, A. camtrapR: an R package for efficient camera trap data management. Methods Ecol. Evol. 7, 1457–1462 (2016).Article 

Google Scholar 
Bartoń, K. 2020. MuMIn: multi-model inference. R package version 1.43.17. https://cran.r-project.org/web/packages/MuMIn/index.html.Cobox, M. E., Peterson, A. T., Barve, N. & Osorio-Olvera, L. kuenm: an R package for detailed development of ecological niche models using Maxent. PeerJ. https://doi.org/10.7717/peerj.6281 (2019).Article 

Google Scholar 
Wickham, H. & Chang, W. Package ‘ggplot2’. https://cran.microsoft.com/snapshot/2015-01-06/web/packages/ggplot2/ggplot2.pdf (2014).Mazerolle, M. J. Model selection and multimodel inference using the AICcmodavg package. https://mirror.marwan.ma/cran/web/packages/AICcmodavg/vignettes/AICcmodavg.pdf (2020). More

Ultra-small prokaryotes were prevalent across diverse aquifer lithologies and anoxic to oxic groundwatersWe used 16S rRNA gene amplicons to assess microbial community composition in 81 groundwater samples. Samples were collected from 59 wells over 10 aquifers in four geographic regions, separated by over a thousand kilometers, and encompassed wide-ranging aquifer chemistries and lithologies (Fig. 1a), comprising primarily shallow sandy-gravel aquifers, but also sand/silt, gravel/peat, volcanic (basalt, ignimbrite) and shell-bed aquifers (Table S1). A large portion of microbial community diversity comprised ultra-small groups of prokaryotes (Fig. 1b). Out of 52,553 OTUs, 21.8% (or 18.4% of 46,713 ASVs) were assigned to seven ultra-small microbial phyla (when considering CPR as the single Patescibacteria phylum). These comprised the bacterial phyla Patescibacteria and Dependentiae, and archaeal DPANN radiation. Altiarchaeota was included in DPANN as previously suggested [63, 64], although its taxonomic placement is uncertain due to genomic under-sampling [65, 66].Fig. 1: Distribution and abundance of ultra-small prokaryotes across groundwater sites.a Distribution of groundwater samples along DOC (0–26 g/m3), DO (0.37–7.5 g/m3) and nitrate-N (0.45–12.6 g/m3) concentrations scaled between 0 and 100. b Top plot: Richness of ultra-small prokaryote variants (rarefied ASVs) at each site. Middle plot: Proportion of ultra-small prokaryotes compared with the total microbial communities (black bars = OTUs, grey crosses = ASVs). Samples are ordered from least to most abundant. Lower plot: Phylum-level breakdown of amplicon based-relative abundance of Patescibacteria, Dependentiae and DPANN archaea (bottom). Symbol bars indicate aquifer lithology (top symbol bar), and oxygen content (lower symbol bar) with dark to light blue shading representing anoxic, suboxic, dysoxic to oxic groundwater. c Class-level rank abundance curve showing the average relative abundance of each genome across sites. The center line of each boxplot represents the median; the top and bottom lines are the first and third quartiles, respectively; and the whiskers show 1.5 times the interquartile range.Full size imageUltra-small prokaryotes were detected in all samples, regardless of lithology, chemistry or geography. They have also been reported from several aquifers and lithologies in the USA (sandy gravel, agriculturally-impacted river sediment, mixed marine sedimentary/metasedimentary rocks, plutonic rock, and sandstone [10, 18, 19, 22], and from a carbonate rock aquifer system in Germany [67, 68]. Collectively these findings demonstrate that ultra-small microorganisms are geographically widespread across diverse aquifer lithologies. Moreover, while ultra-small microorganisms have mostly been detected in anoxic environments [69,70,71,72] or cultivated under anoxic conditions [15, 25], we found representatives in all oxic groundwaters ( >3 mg/L DO) [73] (54/81 samples, Table S1). A few members of DPANN and Patescibacteria lineages have previously been detected in oxic environments [28, 67, 68, 74, 75], suggesting a degree of oxygen tolerance (genetic evidence presented below) or that these organisms are concentrated in anoxic niches within the aquifer substrate.The relative abundance of ultra-small microorganisms was highly variable across the studied aquifers, ranging from 0.04% to 22% of all bacterial and archaeal 16S rRNA gene sequences (7.2% average ±5.5% standard deviation; Fig. 1b). Samples with low relative abundances of ultra-small microorganisms (lower than the average) had overall lower alpha diversity (Shannon diversity indices and OTU or ASV richness) and were mostly from volcanic aquifer sites (Fig. 1b; Table S2). At the phylum level, Patescibacteria and Nanoarchaeota tended to dominate groundwater ultra-small communities (Fig. 1b). However, we found that ultra-small species level diversity overall was considerable with up to 1429 unique OTUs in a single groundwater sample (or up to 653 variants via the more conservative ASV method) (Table S2). Rarefaction curves show most variant diversity was captured across all samples, with curve slopes equaling zero (or approaching zero post rarefaction) (Fig. S1; Table S2). Finally, our results confirm the site specificity of ultra-small prokaryotes [10], with only 16 OTUs common across ≥50% of all 81 groundwater samples, or five ASVs across ≥20% of samples (three Parcubacteria, a Ca. Uhrbacteria, and a Woesearchaeales) (Table S2).High shared phylogenetic and genomic similarity to ultra-small prokaryotes from groundwaters elsewhereTo further assess the phylogeny and assess the genomic attributes and metabolic capacities of groundwater microbial communities, we reconstructed MAGs from 16 groundwater samples (eight wells over four sites and two aquifers). The dataset comprised 7,695 MAGs, including 539 unique MAGs ( >50% complete, 90% complete) (Table S3; Fig. S2). Based on phylogenetic analysis using GTDB [7, 76], MAGs represent 51 phyla, including five ultra-small microbial phyla (Table S3; Fig. S3). The ultra-small MAGs were found at all four sites and accounted for >1/3 of all unique MAGs (216 MAGs 50–100% complete, with 76 MAGs >90% complete). MAGs included 171 assigned to Patescibacteria, six to Dependentiae, and 39 to DPANN archaea (28 Nanoarchaeota, 10 Micrarchaeota, and one Altiarchaeota; Fig. 2a, b). The high representation of ultra-small prokaryotes in the MAG dataset further highlights the prevalence, diversity and abundance of these organisms in groundwater. Consistent with previous studies [6, 9, 77], genomes of ultra-small prokaryotes were small (1 ± 0.4 Mbp on average) with a tendency towards low GC contents (Figs. 3a, S2), and possessed limited metabolic capacities, which significantly differ between ultra-small bacterial and archaeal domains (results in Supplementary Materials; Figs. 3b, S2, S4).Fig. 2: Diversity of groundwater ultra-small microbial communities.Maximum likelihood phylogenetic trees of 177 unique ultra-small bacterial MAGs (a) and 39 unique ultra-small archaeal MAGs (b) recovered in this study. Outer rings indicate the site characteristics where MAGs were enriched. Enrichment factors were calculated as (average relative abundance in oxic and planktonic ultra-small microbial communities, respectively)/(average relative abundance in anoxic-to-dysoxic or sediment-enriched microbial communities, respectively). Trees are based on either 120 concatenated bacterial marker genes or 122 concatenated archaeal marker genes from GTDB-Tk, and were rooted to other groundwater bacterial and archaeal MAGs, respectively (Table S3). Scale bars indicate the number of substitutions per site. Branch background shading denotes Patescibacteria classes (clockwise): Gracilibacteria, Saccharimonadia, UBA1384, Dojkabacteria, Microgenomatia, Doudnabacteria, ABY1, Paceibacteria_A and Paceibacteria. c Proportion of ultra-small microbial OTUs (top) and MAGs (bottom) enriched in low and high oxygen groundwater, and in planktonic and sediment-enriched samples (Table S1). Enrichment factors were calculated as described above.Full size imageFig. 3: Estimated genome size, metabolic content and novelty of groundwater ultra-small prokaryotes.a Estimated genome size of groundwater MAGs calculated as (bin size – (bin size * contamination)) / (completeness), as described by Castelle et al. [9]. Genomes of ultra-small prokaryotes are colored by phylum-level. Other microbial genomes are shown in grey. b Principal Component Analysis (PCA) based on the composition of COG metabolic categories in recovered ultra-small MAGs. c (Right) Range of all pairwise AAI values (grey) and maximum AAI values (blue) between ultra-small prokaryote MAGs recovered in this study and GTDB representative genomes for a given phylum. Red dashed lines represent the AAI range defining the same family of organisms (45–65%) [74]. The number of genomes included in this analysis is indicated for each phylum in brackets. (Left) Proportion of ultra-small prokaryotic MAGs reconstructed in this study classified at each taxonomic level using GTDB-Tk.Full size imageCompared to reference genomes (GTDB species representatives), all recovered ultra-small MAGs are predicted to be novel species [78], and almost half were novel groundwater genera (Fig. 3c, results in Supplementary Materials). Most shared the highest affinity matches with other ultra-small genomes derived from aquifers elsewhere (e.g., in the USA), indicating niche adaptation within these lineages (although ultra-small MAGs from these groundwater ecosystems are over-represented in the GTDB database). Niche-specific phylogenetic conservation among geographically distant microorganisms in groundwater has likewise been reported among geographically distant anammox bacteria in groundwater [30].Ultra-small microbial communities were structured by geography, lithology, and dissolved oxygen concentrationsWhile ultra-small prokaryotes were ubiquitous in groundwater, and overall highly similar to those found in groundwater at different global locations, community compositions varied across sites. To investigate environmental factors (Table S1) influencing ultra-small community composition, we performed distance-based redundancy analysis (Fig. 4a). DO, pH, nitrate-N, sulfate, and DOC were significantly associated with differences in the distribution of 16S rRNA gene amplicon sequences annotated as Patescibacteria, Dependentiae and DPANN (permutation test, p  More

Croft, M. T., Lawrence, A. D., Raux-Deery, E., Warren, M. J. & Smith, A. G. Algae acquire vitamin B12 through a symbiotic relationship with bacteria. Nature https://doi.org/10.1038/nature04056 (2005).Article 
PubMed 

Google Scholar 
Kazamia, E. et al. Mutualistic interactions between vitamin B12-dependent algae and heterotrophic bacteria exhibit regulation. Environ. Microbiol. https://doi.org/10.1111/j.1462-2920.2012.02733.x (2012).Article 
PubMed 

Google Scholar 
Bunbury, F. et al. Exploring the onset of B12-based mutualisms using a recently evolved Chlamydomonas auxotroph and B12-producing bacteria. Environ. Microbiol. 24, 3134–3147 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Butler, A. Acquisition and utilization of transition metal ions by marine organisms. Science https://doi.org/10.1126/science.281.5374.207 (1998).Article 
PubMed 

Google Scholar 
Amin, S. A. et al. Photolysis of iron-siderophore chelates promotes bacterial-algal mutualism. Proc. Natl. Acad. Sci. U. S. A. https://doi.org/10.1073/pnas.0905512106 (2009).Article 
PubMed 
PubMed Central 

Google Scholar 
Amin, S. A. et al. Interaction and signalling between a cosmopolitan phytoplankton and associated bacteria. Nature https://doi.org/10.1038/nature14488 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Mayali, X. & Azam, F. Algicidal bacteria in the sea and their impact on algal blooms. J. Eukaryot. Microbiol. https://doi.org/10.1111/j.1550-7408.2004.tb00538.x (2004).Article 
PubMed 

Google Scholar 
Bagwell, C. E. et al. Discovery of bioactive metabolites in biofuel microalgae that offer protection against predatory bacteria. Front. Microbiol. https://doi.org/10.3389/fmicb.2016.00516 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Hoeger, A. L., Jehmlich, N., Kipping, L., Griehl, C. & Noll, M. Associated bacterial microbiome responds opportunistic once algal host Scenedesmus vacuolatus is attacked by endoparasite Amoeboaphelidium protococcarum. Sci. Rep. https://doi.org/10.1038/s41598-022-17114-1 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Mars Brisbin, M., Mitarai, S., Saito, M. A. & Alexander, H. Microbiomes of bloom-forming Phaeocystis algae are stable and consistently recruited, with both symbiotic and opportunistic modes. ISME J. https://doi.org/10.1038/s41396-022-01263-2 (2022).Article 
PubMed 

Google Scholar 
Milici, M. et al. Co-occurrence analysis of microbial taxa in the Atlantic ocean reveals high connectivity in the free-living bacterioplankton. Front. Microbiol. 7, 1–20 (2016).Article 

Google Scholar 
Barberán, A., Bates, S. T., Casamayor, E. O. & Fierer, N. Using network analysis to explore co-occurrence patterns in soil microbial communities. ISME J. 6, 343–351 (2012).Article 
PubMed 

Google Scholar 
Tucker, A. E. & Brown, S. P. Sampling a gradient of red snow algae bloom density reveals novel connections between microbial communities and environmental features. Sci. Rep. 12, 1–15 (2022).Article 

Google Scholar 
Faust, K. & Raes, J. Microbial interactions: From networks to models. Nat. Rev. Microbiol. 10, 538–550 (2012).Article 
CAS 
PubMed 

Google Scholar 
Chun, S. J. et al. Network analysis reveals succession of microcystis genotypes accompanying distinctive microbial modules with recurrent patterns. Water Res. https://doi.org/10.1016/j.watres.2019.115326 (2020).Article 
PubMed 

Google Scholar 
Huang, S. Back to the biology in systems biology: What can we learn from biomolecular networks?. Briefings Funct. Genom. Proteom. https://doi.org/10.1093/bfgp/2.4.279 (2004).Article 

Google Scholar 
Ma’ayan, A. Introduction to network analysis in systems biology. Sci. Signal. https://doi.org/10.1126/scisignal.2001965 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
Williams, R. J., Howe, A. & Hofmockel, K. S. Demonstrating microbial co-occurrence pattern analyses within and between ecosystems. Front. Microbiol. 5, 1–10 (2014).Article 

Google Scholar 
Fuhrman, J. A., Cram, J. A. & Needham, D. M. Marine microbial community dynamics and their ecological interpretation. Nat. Rev. Microbiol. https://doi.org/10.1038/nrmicro3417 (2015).Article 
PubMed 

Google Scholar 
Chafee, M. et al. Recurrent patterns of microdiversity in a temperate coastal marine environment. ISME J. 12, 237–252 (2018).Article 
PubMed 

Google Scholar 
Zamkovaya, T., Foster, J. S., de Crécy-Lagard, V. & Conesa, A. A network approach to elucidate and prioritize microbial dark matter in microbial communities. ISME J. 15, 228–244 (2021).Article 
PubMed 

Google Scholar 
Lima-Mendez, G. et al. Determinants of community structure in the global plankton interactome. Science https://doi.org/10.1126/science.1262073 (2015).Article 
PubMed 

Google Scholar 
Bennke, C. M. et al. The distribution of phytoplankton in the Baltic Sea assessed by a prokaryotic 16S rRNA gene primer system. J. Plankton Res. 40, 244–254 (2018).Article 
CAS 

Google Scholar 
Berry, D. & Widder, S. Deciphering microbial interactions and detecting keystone species with co-occurrence networks. Front. Microbiol. https://doi.org/10.3389/fmicb.2014.00219 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Needham, D. M. & Fuhrman, J. A. Pronounced daily succession of phytoplankton, archaea and bacteria following a spring bloom. Nat. Microbiol. https://doi.org/10.1038/nmicrobiol.2016.5 (2016).Article 
PubMed 

Google Scholar 
Thompson, L. R. et al. A communal catalogue reveals Earth’s multiscale microbial diversity. Nature https://doi.org/10.1038/nature24621 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Gonzalez, A. et al. Qiita: Rapid, web-enabled microbiome meta-analysis. Nat. Methods https://doi.org/10.1038/s41592-018-0141-9 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl. Acad. Sci. U. S. A. https://doi.org/10.1073/pnas.1000080107 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
Caporaso, J. G. et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. https://doi.org/10.1038/ismej.2012.8 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. https://doi.org/10.1038/s41587-019-0209-9 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. J. https://doi.org/10.14806/ej.17.1.200 (2011).Article 

Google Scholar 
Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods https://doi.org/10.1038/nmeth.3869 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Bokulich, N. A. et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome https://doi.org/10.1186/s40168-018-0470-z (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Decelle, J. et al. PhytoREF: A reference database of the plastidial 16S rRNA gene of photosynthetic eukaryotes with curated taxonomy. Mol. Ecol. Resour. 15, 1435–1445 (2015).Article 
CAS 
PubMed 

Google Scholar 
Major oceanic 16S/18S databases in qiime2 format. https://github.com/ndu-invitae/Oceanic_database/tree/master/PhytoRef.Hemprich-Bennett, D. R., Oliveira, H. F. M., Le Comber, S. C., Rossiter, S. J. & Clare, E. L. Assessing the impact of taxon resolution on network structure. Ecology https://doi.org/10.1002/ecy.3256 (2021).Article 
PubMed 

Google Scholar 
Watts, S. C., Ritchie, S. C., Inouye, M. & Holt, K. E. FastSpar: Rapid and scalable correlation estimation for compositional data. Bioinformatics https://doi.org/10.1093/bioinformatics/bty734 (2019).Article 
PubMed 

Google Scholar 
Friedman, J. & Alm, E. J. Inferring correlation networks from genomic survey data. PLoS Comput. Biol. https://doi.org/10.1371/journal.pcbi.1002687 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Csardi, G., & Nepusz, T. The igraph software package for complex network research. InterJournal, Complex Systems. igraph Softw. Packag. (2006).Shannon, P. et al. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. https://doi.org/10.1101/gr.1239303 (2003).Article 
PubMed 
PubMed Central 

Google Scholar 
Assenov, Y., Ramírez, F., Schelhorn, S. E. S. E., Lengauer, T. & Albrecht, M. Computing topological parameters of biological networks. Bioinformatics https://doi.org/10.1093/bioinformatics/btm554 (2008).Article 
PubMed 

Google Scholar 
Barabási, A. L. Scale-free networks: A decade and beyond. Science https://doi.org/10.1126/science.1173299 (2009).Article 
MathSciNet 
PubMed 
PubMed Central 
MATH 

Google Scholar 
Morris, J. H. et al. ClusterMaker: A multi-algorithm clustering plugin for cytoscape. BMC Bioinform. https://doi.org/10.1186/1471-2105-12-436 (2011).Article 

Google Scholar 
Van Dongen, S. & Abreu-Goodger, C. Using MCL to extract clusters from networks. Methods Mol. Biol. https://doi.org/10.1007/978-1-61779-361-5_15 (2012).Article 
PubMed 

Google Scholar 
Raivo, K. Pheatmap: Pretty heatmaps. R Pacakage Version (2012).Albert, R., Jeong, H. & Barabási, A. L. Diameter of the world-wide web. Nature https://doi.org/10.1038/43601 (1999).Article 

Google Scholar 
Eisenberg, E. & Levanon, E. Y. Preferential attachment in the protein network evolution. Phys. Rev. Lett. https://doi.org/10.1103/PhysRevLett.91.138701 (2003).Article 
PubMed 

Google Scholar 
Ma, B. et al. Earth microbial co-occurrence network reveals interconnection pattern across microbiomes. Microbiome 8, 82 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wang, H. et al. Combined use of network inference tools identifies ecologically meaningful bacterial associations in a paddy soil. Soil Biol. Biochem. 105, 227–235 (2017).Article 
CAS 

Google Scholar 
Goecke, F., Thiel, V., Wiese, J., Labes, A. & Imhoff, J. F. Algae as an important environment for bacteria—Phylogenetic relationships among new bacterial species isolated from algae. Phycologia https://doi.org/10.2216/12-24.1 (2013).Article 

Google Scholar 
Krohn-Molt, I. et al. Metagenome survey of a multispecies and alga-associated biofilm revealed key elements of bacterial-algal interactions in photobioreactors. Appl. Environ. Microbiol. https://doi.org/10.1128/AEM.01641-13 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
Woebken, D. et al. Fosmids of novel marine Planctomycetes from the Namibian and Oregon coast upwelling systems and their cross-comparison with planctomycete genomes. ISME J. https://doi.org/10.1038/ismej.2007.63 (2007).Article 
PubMed 

Google Scholar 
Faria, M. et al. Planctomycetes attached to algal surfaces: Insight into their genomes. Genomics https://doi.org/10.1016/j.ygeno.2017.10.007 (2018).Article 
PubMed 

Google Scholar 
Barbeyron, T., L’Haridon, S., Corre, E., Kloareg, B. & Potin, P. Zobellia galactanovorans gen. nov., sp. nov., a marine species of Flavobacteriaceae isolated from a red alga, and classification of [Cytophaga] uliginosa (ZoBell and Upham 1944) Reichenbach 1989 as Zobellia uliginosa gen. nov., comb. Nov.. Int. J. Syst. Evol. Microbiol. https://doi.org/10.1099/00207713-51-3-985 (2001).Article 
PubMed 

Google Scholar 
Nedashkovskaya, O. I. et al. Mesonia algae gen. nov., sp. nov., a novel marine bacterium of the family Flavobacteriaceae isolated from the green alga Acrosiphonia sonderi (Kütz) Kornm. Int. J. Syst. Evol. Microbiol. https://doi.org/10.1099/ijs.0.02626-0 (2003).Article 
PubMed 

Google Scholar 
Kim, B. H., Ramanan, R., Cho, D. H., Oh, H. M. & Kim, H. S. Role of Rhizobium, a plant growth promoting bacterium, in enhancing algal biomass through mutualistic interaction. Biomass Bioenerg. https://doi.org/10.1016/j.biombioe.2014.07.015 (2014).Article 

Google Scholar 
Rivas, M. O., Vargas, P. & Riquelme, C. E. Interactions of Botryococcus braunii cultures with bacterial biofilms. Microb. Ecol. https://doi.org/10.1007/s00248-010-9686-6 (2010).Article 
PubMed 

Google Scholar 
Cole, J. J. Interactions between bacteria and algae in aquatic ecosystems. Annu. Rev. Ecol. Syst. https://doi.org/10.1146/annurev.es.13.110182.001451 (1982).Article 

Google Scholar 
Fulbright, S. P. et al. Bacterial community changes in an industrial algae production system. Algal Res. https://doi.org/10.1016/j.algal.2017.09.010 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Zhou, J. et al. Microbial community structure and associations during a marine dinoflagellate bloom. Front. Microbiol. https://doi.org/10.3389/fmicb.2018.01201 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Simon, M., Glöckner, F. O. & Amann, R. Different community structure and temperature optima of heterotrophic picoplankton in various regions of the Southern Ocean. Aquat. Microb. Ecol. https://doi.org/10.3354/ame018275 (1999).Article 

Google Scholar 
Brussaard, C. P. D., Mari, X., Van Bleijswijk, J. D. L. & Veldhuis, M. J. W. A mesocosm study of Phaeocystis globosa (Prymnesiophyceae) population dynamics: II. Significance for the microbial community. Harmful Algae https://doi.org/10.1016/j.hal.2004.12.012 (2005).Article 

Google Scholar 
Janse, I., Zwart, G., Van der Maarel, M. J. E. C. & Gottschal, J. C. Composition of the bacterial community degrading Phaeocystis mucopolysaccharides in enrichment cultures. Aquat. Microb. Ecol. https://doi.org/10.3354/ame022119 (2000).Article 

Google Scholar 
Grossart, H. P., Levold, F., Allgaier, M., Simon, M. & Brinkhoff, T. Marine diatom species harbour distinct bacterial communities. Environ. Microbiol. https://doi.org/10.1111/j.1462-2920.2005.00759.x (2005).Article 
PubMed 

Google Scholar 
Sapp, M. et al. Species-specific bacterial communities in the phycosphere of microalgae?. Microb. Ecol. https://doi.org/10.1007/s00248-006-9162-5 (2007).Article 
PubMed 

Google Scholar 
Sapp, M., Wichels, A. & Gerdts, G. Impacts of cultivation of marine diatoms on the associated bacterial community. Appl. Environ. Microbiol. https://doi.org/10.1128/AEM.02274-06 (2007).Article 
PubMed 
PubMed Central 

Google Scholar 
Milici, M. et al. Bacterioplankton biogeography of the Atlantic ocean: A case study of the distance-decay relationship. Front. Microbiol. 7, 1–15 (2016).Article 

Google Scholar 
Martin, K. et al. The biogeographic differentiation of algal microbiomes in the upper ocean from pole to pole. Nat. Commun. https://doi.org/10.1038/s41467-021-25646-9 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Martinez-Garcia, M. et al. Capturing single cell genomes of active polysaccharide degraders: An unexpected contribution of verrucomicrobia. PLoS ONE https://doi.org/10.1371/journal.pone.0035314 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Dell’Anno, F. et al. Highly contaminated marine sediments can host rare bacterial taxa potentially useful for bioremediation. Front. Microbiol. https://doi.org/10.3389/fmicb.2021.584850 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Newton, R. J. et al. Genome characteristics of a generalist marine bacterial lineage. ISME J. https://doi.org/10.1038/ismej.2009.150 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
Sañudo-Wilhelmy, S. A., Gómez-Consarnau, L., Suffridge, C. & Webb, E. A. The role of B vitamins in marine biogeochemistry. Ann. Rev. Mar. Sci. https://doi.org/10.1146/annurev-marine-120710-100912 (2014).Article 
PubMed 

Google Scholar 
Durham, B. P. et al. Cryptic carbon and sulfur cycling between surface ocean plankton. Proc. Natl. Acad. Sci. U. S. A. https://doi.org/10.1073/pnas.1413137112 (2015).Article 
PubMed 

Google Scholar 
Proulx, S. R., Promislow, D. E. L. & Phillips, P. C. Network thinking in ecology and evolution. Trends Ecol. Evol. https://doi.org/10.1016/j.tree.2005.04.004 (2005).Article 
PubMed 

Google Scholar 
Coelho, F. J. R. C. et al. Integrated analysis of bacterial and microeukaryotic communities from differentially active mud volcanoes in the Gulf of Cadiz. Sci. Rep. 6, 1–10 (2016).Article 

Google Scholar 
Queiroz, L. L. et al. Bacterial diversity in deep-sea sediments under influence of asphalt seep at the São Paulo Plateau. Antonie van Leeuwenhoek. Int. J. Gen. Mol. Microbiol. 8, 9. https://doi.org/10.1007/s10482-020-01384-8 (2020).Article 
CAS 

Google Scholar 
de Voogd, N. J., Cleary, D. F. R., Polónia, A. R. M. & Gomes, N. C. M. Bacterial community composition and predicted functional ecology of sponges, sediment and seawater from the thousand islands reef complex, West Java, Indonesia. FEMS Microbiol. Ecol. https://doi.org/10.1093/femsec/fiv019 (2015).Article 
PubMed 

Google Scholar 
Vigneron, A. et al. Multiple strategies for light-harvesting, photoprotection, and carbon flow in high latitude microbial mats. Front. Microbiol. https://doi.org/10.3389/fmicb.2018.02881 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Pushpakumara, B. L. D. U., Tandon, K., Willis, A. & Verbruggen, H. The bacterial microbiome of the coral skeleton algal symbiont Ostreobium shows preferential associations and signatures of phylosymbiosis. bioRxiv https://doi.org/10.1101/2022.12.13.520198 (2022).Article 

Google Scholar 
Lage, O. M. & Bondoso, J. Planctomycetes diversity associated with macroalgae. FEMS Microbiol. Ecol. https://doi.org/10.1111/j.1574-6941.2011.01168.x (2011).Article 
PubMed 

Google Scholar 
Longford, S. R. et al. Comparisons of diversity of bacterial communities associated with three sessile marine eukaryotes. Aquat. Microb. Ecol. https://doi.org/10.3354/ame048217 (2007).Article 

Google Scholar 
Bengtsson, M. M. & Øvreås, L. Planctomycetes dominate biofilms on surfaces of the kelp Laminaria hyperborea. BMC Microbiol. https://doi.org/10.1186/1471-2180-10-261 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
Ludington, W. B. et al. Assessing biosynthetic potential of agricultural groundwater through metagenomic sequencing: A diverse anammox community dominates nitrate-rich groundwater. PLoS ONE https://doi.org/10.1371/journal.pone.0174930 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Vidal-Melgosa, S. et al. Diatom fucan polysaccharide precipitates carbon during algal blooms. Nat. Commun. 12, 1–13 (2021).Article 

Google Scholar 
Walker, A. M., Leigh, M. B. & Mincks, S. L. Patterns in benthic microbial community structure across environmental gradients in the beaufort sea shelf and slope. Front. Microbiol. https://doi.org/10.3389/fmicb.2021.581124 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Morris, R. M., Longnecker, K. & Giovannoni, S. J. Pirellula and OM43 are among the dominant lineages identified in an Oregon coast diatom bloom. Environ. Microbiol. https://doi.org/10.1111/j.1462-2920.2006.01029.x (2006).Article 
PubMed 

Google Scholar 
Sichert, A. et al. Verrucomicrobia use hundreds of enzymes to digest the algal polysaccharide fucoidan. Nat. Microbiol. https://doi.org/10.1038/s41564-020-0720-2 (2020).Article 
PubMed 

Google Scholar 
Bohórquez, J. et al. Different types of diatom-derived extracellular polymeric substances drive changes in heterotrophic bacterial communities from intertidal sediments. Front. Microbiol. https://doi.org/10.3389/fmicb.2017.00245 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Landa, M. et al. Phylogenetic and structural response of heterotrophic bacteria to dissolved organic matter of different chemical composition in a continuous culture study. Environ. Microbiol. https://doi.org/10.1111/1462-2920.12242 (2014).Article 
PubMed 

Google Scholar 
Orsi, W. D. et al. Diverse, uncultivated bacteria and archaea underlying the cycling of dissolved protein in the ocean. ISME J. https://doi.org/10.1038/ismej.2016.20 (2016).Article 
PubMed 
PubMed Central 

Google Scholar  More

Peterson, A. T. & Vieglais, D. A. Predicting species invasions using ecological niche modeling: New approaches from bioinformatics attack a pressing problem. Bioscience 51, 363–371 (2001).Article 

Google Scholar 
Atkinson, I. A. E. Introduced mammals and models for restoration. Biol. Conserv. 99, 81–96 (2001).Article 

Google Scholar 
Parkes, J. P. & Panetta, F. D. Eradication of invasive species: progress and emerging issues in the 21st century. In Invasive Species Management: A Handbook of Principles and Techniques (eds Clout, M. N. & Williams, P. A.) (Oxford University Press, 2009).
Google Scholar 
Baker, C. M., Hodgson, J. C., Tartaglia, E. & Clarke, R. H. Modelling tropical fire ant (Solenopsis geminata) dynamics and detection to inform an eradication project. Biol. Invasions 19, 2959–2970 (2017).Article 

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

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

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

Google Scholar 
Sanchirico, J. N., Albers, H. J., Fischer, C. & Coleman, C. Spatial Management of invasive species: Pathways and policy options. Environ. Resour. Econ. 45, 517–535 (2010).Article 

Google Scholar 
Caplat, P., Hui, C., Maxwell, B. D. & Peltzer, D. A. Cross-scale management strategies for optimal control of trees invading from source plantations. Biol. Invasions 16, 677–690 (2014).Article 

Google Scholar 
Long, Y., Van der Merwe, J., Thomas, M. L., McKirdy, S. & Kompas, T. Biosecurity for valuable environmental island assets: Spatial post-border surveillance for early detection. Ecol. Econ. forthcoming (2022).Kroetz, K. & Sanchirico, J. N. The bioeconomics of spatial-dynamic systems in natural resource management. Annu. Rev. Resour. Econ. 7, 189–207 (2015).Article 

Google Scholar 
Liu, Y., Wang, P., Thomas, M. L., Zheng, D. & McKirdy, S. J. Cost-effective surveillance of invasive species using info-gap theory. Sci. Rep. 11, 22828 (2021).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Homans, F. & Horie, T. Optimal detection strategies for an established invasive pest. Ecol. Econ. 70, 1129–1138 (2011).Article 

Google Scholar 
Mehta, S. V., Haight, R. G., Homans, F. R., Polasky, S. & Venette, R. C. Optimal detection and control strategies for invasive species management. Ecol. Econ. 61, 237–245 (2007).Article 

Google Scholar 
Moffitt, L. J., Stranlund, J. K. & Osteen, C. D. Robust detection protocols for uncertain introductions of invasive species. J. Environ. Manage. 89, 293–299 (2008).Article 
PubMed 

Google Scholar 
Yokomizo, H., Possingham, H. P., Hulme, P. E., Grice, A. C. & Buckley, Y. M. Cost-benefit analysis for intentional plant introductions under uncertainty. Biol. Invasions 14, 839–849 (2011).Article 

Google Scholar 
Ben-Haim, Y. Info-gap Decision Theory: Decisions Under Severe Uncertainty 2nd edn. (Academic Press, 2006).
Google Scholar 
Knight, F. H. Risk, Uncertainty, and Profit (Houghton Mifflin Company, 1921).
Google Scholar 
Regan, H. M. et al. Robust decision-making under severe uncertainty for conservation management. Ecol. Appl. 15, 1471–1477 (2005).Article 

Google Scholar 
Ben-Haim, Y. Uncertainty, probability and information-gaps. Reliab. Eng. Syst. Saf. 85, 249–266 (2004).Article 

Google Scholar 
Ben-Haim, Y. & Demertzis, M. Decision making in times of Knightian uncertainty: An info-gap perspective. Economics 10, 1 (2016).Article 

Google Scholar 
Lever, C. Naturalized Reptiles and Amphibians of the World (Oxford University Press, 2003).
Google Scholar 
Wilson, J. R. U., Dormontt, E. E., Prentis, P. J., Lowe, A. J. & Richardson, D. M. Something in the way you move: Dispersal pathways affect invasion success. Trends Ecol. Evol. 24, 136–144 (2009).Article 
PubMed 

Google Scholar 
Torres-Carvajal, O. On the origin of South American populations of the common house gecko (Gekkonidae: Hemidactylus frenatus). NeoBiota 27, 69–79 (2015).Article 

Google Scholar 
Hoskin, C. J. The invasion and potential impact of the Asian House Gecko (Hemidactylus frenatus) in Australia. Austral Ecol. 36, 240–251 (2011).Article 

Google Scholar 
Barnett, L. K. Understanding Range Expansion of Asian House Geckos (Hemidactylus frenatus) in Natural Environments (James Cook University, 2017).
Google Scholar 
Norval, G. & Mao, J.-J. An instance of a house gecko (Hemidactylus frenatus Schlegel, 1836) utilizing an electrical timer for thermoregulation. IRCF Reptil. Amphib. 22, 76–78 (2015).Article 

Google Scholar 
Greenslade, P., Burbidge, A. A. & Lynch, A. J. J. Keeping Australias islands free of introduced rodents Barrow Island. Pac. Conserv. Biol. 19, 284–294 (2013).Article 

Google Scholar 
Perella, C. D. & Behm, J. E. Understanding the spread and impact of exotic geckos in the greater Caribbean region. Biodivers. Conserv. 29, 1109–1134 (2020).Article 

Google Scholar 
Davis, M. A. Invasion biology. In Encyclopedia of Biological Invasions (eds Simberloff, D. & RejmÁNek, M.) 364–369 (University of California Press, 2011).
Google Scholar 
García-Díaz, P., Ross, J. V., Vall-llosera, M. & Cassey, P. Low detectability of alien reptiles can lead to biosecurity management failure: A case study from Christmas Island (Australia). NeoBiota. 45, 75–92 (2019).Article 

Google Scholar 
Koopman, B. O. Search and Screening. Operations Evaluation Group (OEG) Report. (1946).Grasinger, M., O’Malley, D., Vesselinov, V. & Karra, S. Decision analysis for robust CO2 injection: Application of Bayesian-Information-Gap Decision Theory. Int. J. Greenh. Gas Control 49, 73–80 (2016).Article 
CAS 

Google Scholar 
MathWorks. MATLAB R2018b. (MathWorks, 2018).Commonwealth Government of Australia. Approval—Gorgon Gas Development (EPBC Reference: 2008/4178). (2009).Kalaris, T. et al. The role of surveillance methods and technologies in plant biosecurity. In The Handbook of Plant Biosecurity: Principles and Practices for the Identification, Containment and Control of Organisms that Threaten Agriculture and the Environment Globally (eds Gordh, G. & McKirdy, S.) 309–337 (Springer, 2014).Chapter 

Google Scholar 
Sharma, S., Mckirdy, S. & Macbeth, F. The biosecurity continuum and trade: Tools for post-border biosecurity. In The Handbook of Plant Biosecurity: Principles and Practices for the Identification, Containment and Control of Organisms that Threaten Agriculture and the Environment Globally (eds Gordh, G. & McKirdy, S.) 189–206 (Springer, 2014).Chapter 

Google Scholar 
Epanchin-Niell, R. S. Economics of invasive species policy and management. Biol. Invasions 19, 3333–3354 (2017).Article 

Google Scholar 
Gregg, H. et al. Invasive rodent eradication on islands. Conserv. Biol. 21, 1258–1268 (2007).Article 

Google Scholar 
Parkes, J. Feasibility plan to eradicate Common mynas (Acridotheres tristis) from Mangaia Island, Cook Islands. Landcare Research Contract Report LC0506/184. (2006).Barun, A. & Simberloff, D. Carnivores. In Encyclopedia of Biological Invasions (eds Simberloff, D. & RejmÁNek, M.) 95–100 (University of California Press, 2011).
Google Scholar 
Pluess, T. et al. When are eradication campaigns successful? A test of common assumptions. Biol. Invasions 14, 1365–1378 (2012).Article 

Google Scholar 
Epanchin-Niell, R. S., Haight, R. G., Berec, L., Kean, J. M. & Liebhold, A. M. Optimal surveillance and eradication of invasive species in heterogeneous landscapes. Ecol. Lett. 15, 803–812 (2012).Article 
PubMed 

Google Scholar 
Rout, T. M., Thompson, C. J. & McCarthy, M. A. Robust decisions for declaring eradication of invasive species. J. Appl. Ecol. 46, 782–786 (2009).Article 

Google Scholar 
Hauser, C. E. & McCarthy, M. A. Streamlining “search and destroy”: Cost-effective surveillance for invasive species management. Ecol. Lett. 12, 683–692 (2009).Article 
PubMed 

Google Scholar 
Epanchin-Niell, R. S. & Hastings, A. Controlling established invaders: Integrating economics and spread dynamics to determine optimal management. Ecol. Lett. 13, 528–541 (2010).Article 
PubMed 

Google Scholar 
Moore, J. L. et al. Protecting islands from pest invasion: Optimal allocation of biosecurity resources between quarantine and surveillance. Biol. Conserv. 143, 1068–1078 (2010).Article 

Google Scholar 
Rout, T. M., Moore, J. L., Possingham, H. P. & McCarthy, M. A. Allocating biosecurity resources between preventing, detecting, and eradicating island invasions. Ecol. Econ. 71, 54–62 (2011).Article 

Google Scholar  More

Regional contextTo understand the variation in salt marsh geomorphology and mussel coverage across the South Atlantic Bight (SAB), we assessed density and areal coverage of1 tidal creekheads and2 mussel aggregations with a combination of published data and new field surveys across the region. First, to assess creekhead density, we selected 10 sites ranging from Cape Romain (SC) to Amelia Island (FL). Given that all of our experiments were conducted on Sapelo Island and the surrounding marsh islands, we selected three sites on Sapelo Island for comparison with four sites to the north and three to the south61. At each site, we scored the total number of tidal creekheads in a 1 km2 contiguous marsh area using Google Earth. Assuming each tidal creekhead constitutes approximately 0.0025 km2, we calculate the creekhead areal coverage to be:$$Creekhead,Areal,Coverage,(%)=frac{(0.0025k{m}^{2})times {{{{{rm{Creekhead}}}}}},{{{{{rm{Density}}}}}}(#k{m}^{-2})}{{{{{{rm{Marsh}}}}}},{{{{{rm{Creekshed}}}}}},{{{{{rm{Area}}}}}},(1k{m}^{2})}times 100%$$
(1)
Differences across northern, Sapelo Island, and southern sites were assessed with a one-way ANOVA with location as the main factor.To next test the hypothesis that creekhead mussel coverage is similar at sites across the SAB, we conducted surveys of mussel aggregations at 12 sites across the region from Edisto Beach (SC) to Amelia Island (FL). Previous work14 has shown that mussel aggregations decrease in size and density with increasing distance from the tidal creekhead, so we focused our measurements at three distances from one tidal creekhead onto the marsh platform: 0 m, 20 m, and 40 m. We note that at all sites, mussel aggregations extended >40 m from the tidal creekhead. Sites were again distributed across the region, and included 3 sites to the north, 3 sites to the south, and 6 sites on Sapelo and its back barrier marsh islands. At each site, we selected one representative creek 100–175 m in length and ensured that the tidal creekhead did not overlap spatially with a tidal creekhead of an adjacent creek. At each distance from creekhead, we established one 50 m x 1 m transect. Walking the transect line, we scored each mussel aggregation, counting the total number of mussels and measuring the mound dimensions (L x W x H). We then calculated the areal coverage of mussels within each transect (50 m2) and took the mean value across the three distances as the measure for the site. All data was collected between May and August in 2016 and 2017. Differences across northern, Sapelo Island, and southern sites were assessed with a one-way ANOVA with location as the main factor. Finally, we calculated creekshed mussel areal coverage in the three sub-regions, as the product of the percent of creekshed occupied by creekheads (sub-region mean, %) and the proportion of creekhead area occupied by mussels at each site.Landscape assays of sediment deposition over seasons and tidal phasesTo quantify the relative rates of sediment deposition across marsh landscapes, we deployed 9-cm diameter filter papers (Whatman Quantitative Filter Paper, Grade 42 Circles, Ashless, 90 mm; 57) at 13 location types across 3 sites. Locations included: 1) outer marsh levee (‘outer levee’), 2) marsh platform 10 m inland from outer marsh levee (‘outer levee-adjacent’), 3) inner tidal creek levee (‘inner levee’), 4) marsh platform 10 m inland from inner tidal creek levee (‘inner levee-adjacent’), 5) non-mussel marsh platform ( >50 m from mussel creekhead), 6,7) ridge/runnel area at tidal creekhead (‘ridge’ and ‘runnel’), 8,9) mussel aggregations and adjacent non-mussel marsh areas at the tidal creekhead (‘0 m ON mussel mound’ and ‘0 m OFF mound’), 10,11) mussel aggregations and adjacent marsh areas 10 m onto marsh platform from tidal creekhead (‘10 m ON mussel mound’ and ‘10 m OFF mound’), and 12,13) mussel aggregations and adjacent marsh areas 20 m onto marsh platform from tidal creekhead (‘20 m ON mussel mound’ and ‘20 m OFF mound’). At each location type, we used 15 replicate filters, spaced 1–2 m apart. Each pre-weighed and labeled filter paper was deployed attached to a Polystyrene Petri Dish (100 × 15 mm) using 1.5 mm steel wire. After 24 h in the field, all filters were harvested, dried in an oven at 60 °C, and reweighed. Filter papers were deployed at four tides: Summer Spring (August 2017, +2.5 m), Summer Neap (August 2017, +2.1 m), Winter Spring (February 2018, +2.5 m), and Winter Neap (February 2018, +2.0 m).To quantify the total and percent inorganic and organic material that was deposited on the marsh surface over a 24 h period, we deployed 8 replicate 4.7 cm diameter filter papers (Whatman Glass Microfiber Filter Paper, Grade GF/F Circles, 47 mm) across five marsh locations at one site. Locations included: 1) outer marsh levee (‘outer levee’), 2) marsh platform 10 m inland from outer marsh levee (‘outer levee-adjacent’), 3–4) mussel aggregations and adjacent non-mussel marsh areas at the tidal creekhead (‘0 m ON mussel mound’ and ‘0 m OFF mound’), and 5) non-mussel marsh platform. Prior to deployment, filter papers were combusted in a 450 °C furnace for 4 h and stored in aluminum foil packets. Packeted filter papers were then labeled and pre-weighed. Once in the field, filter papers were removed from their packet with forceps, placed on a petri dish inserted into the marsh sediment during a Summer Spring low tide (+2.5 m), and secured with 1.5 mm steel wire.After 24 h, the filter papers were collected with forceps and inserted back in their corresponding packet. Upon transport back to the lab, the packeted filter papers were dried in a 60 °C oven until constant mass was obtained and re-weighed. The change in weight between pre- and post-deployment was used to calculate total dry weight. Packeted filter papers were combusted again in a 450 °C furnace for 4 h and re-weighed. The total dry weight and the weight lost from the second combustion were then used to calculate total inorganic and organic dry weight and percent organic material for each filter paper.To calculate the organic and inorganic material in persistent in marsh sediment layers, 5-cm cores were collected from the sediment layer using a 60 mL syringe with a 2.5 cm diameter. Cores were taken at same five location types: levee crest, levee-adjacent, on-mound, mound-adjacent, and non-mussel marsh platforms. Eight cores, 1–2 m apart, were collected from each location and placed into pre-weighed foil packets. Cores were dried at 60 °C in an oven until constant mass was obtained, weighed, and combusted in a 450 °C furnace for 4 h. The cores were then reweighed, and the weight loss after combustion was used to calculate the percent organic (and inorganic) material.The mass of both organic and inorganic material deposited on each mussel aggregation filter was far greater (0.11 g and 0.50 g, organic and inorganic sediment, respectively, here and below) than that deposited on levee crests (0.02 g and 0.06 g), levee-adjacent (0.04 g and 0.15 g), and non-mussel marsh platforms (0.04 g and 0.19 g; F4,38 = 9.5; p  0.20), with all locations exhibiting 13–14% organic content (Fig. S3).Field experiment 1: fate of mussel biodepositsTo assess the distribution of sediment supplemented by mussels via local biodeposition and, in turn, their contribution to sediment supply across the broader marsh landscape, we measured the transport of previously settled biodeposits as well as those actively deposited over one tidal cycle. For each process, we selected 6 mussel mounds in two marsh zones where mussels commonly aggregate: 1) the creekhead and 2) 20 meters away from the creekhead on the marsh platform. All focal mounds were at least 5 meters apart to avoid mixing of biodeposits. We addressed the transport of previously settled biodeposits by first removing 2 cm of each mound’s biodeposit layer, homogenizing it with fluorescent chalk (Irwin Straight-Line Fluorescent Orange Marking Chalk) at a 2:1 ratio (biodepost:chalk), and evenly distributing the mixture back on the mounds. We then revisited the mounds at night after one tide had flooded over the mounds (max tidal height +2.2 m) and traced the distribution of fluorescent material through black light detection. We measured the maximum distance fluorescent material traveled in each direction to quantify transport of previously settled biodeposits across the marsh landscape.To account for the distribution of biodeposits ejected by actively filter-feeding mussels, we collected 10 mussels from each mound, transported them back to University of Georgia Marine Institute’s wet lab, depurated them in saltwater (Instant Ocean, 28 ppt) for 24 h, and allowed them to feed on a mixture of seawater and fluorescent chalk for 2 h. We then rinsed the mussels to remove any loose fluorescent material from their shells before transplanting them back into the focal mounds at low tide. We then revisited the mounds at night after one tide had flooded over the mounds and traced the distribution of fluorescent material through black light detection. We measured the maximum distance fluorescent material traveled in each direction to quantify transport of actively ejected biodeposits across the marsh landscape.Field experiment 2: local scale depositional effects of mussels and cordgrassThe second experimental study was conducted at Airport Marsh on Sapelo Island, Georgia, USA. At this site, the experiment was deployed at two zones: the marsh platform >85 m from the nearest tidal creek (31°25’25.3“N 81°17’29.8“W) and the creekhead, where the tidal creek enters onto the marsh platform and tidal water first floods the marsh (31°25’28.1“N 81°17’30.2“W). Within each zone, we deployed seven experimental treatments (n = 5 replicates per treatment per zone) in which we varied mussel (M) presence and density, as well as cordgrass (C) presence. The full set of seven treatments included: 1) no-mussel, no-cordgrass controls (0 M, 0 C); 2) cordgrass-only controls (0 M, C + ); 3) 1-mussel (1 M, 0 C) blocks; 4) small mussel aggregations (20 M, 0 C); 5) intermediate size mussel aggregations (50 M, 0 C); 6) intermediate size mussel aggregations plus cordgrass (50 M, C + ); and 7) large mussel aggregations (80 M, 0 C; Fig. S5).In July 2017, we harvested 70 blocks of marsh peat (50 cm x 50 cm x 20 cm) from the experimental site using flat-edge shovels. We selected 30 blocks of standardized cordgrass density (48.9 ± 9.0 g dry biomass per block; mean ± SD) from non-mussel areas, 10 blocks containing small mussel aggregations (~20 mussels), 20 blocks of intermediate-size mussel aggregations (~50 mussels), and 10 blocks of large mussel aggregations (~80 mussels). All marsh blocks were transported back to the lab where they were washed completely clean of all surface sediment. With the exception of 10 non-mussel blocks and 10 intermediate-size mussel aggregation blocks, all cordgrass was clipped to the marsh surface. For the 1-mussel treatments, we harvested 10 mussels (6–8 cm in length) from the experimental site and individually inserted them in the center of the marsh block so that they were 40–50% below the marsh surface.After cleaning and cordgrass removal, all blocks were cut to new dimensions (36 cm x 36 cm x 16 cm) and placed within plastic-encased bins of the same dimensions. Bins containing marsh blocks were then centrally placed and fitted within an additional larger bin (61 cm x 61 cm x 8 cm), with the top of each box flush to the same height. The outside bin was filled with 64, 5 cm diameter PVC poles and 32, 2.5 cm diameter PVC poles (both 8 cm in height) so that all bin edges were held upright and PVC was rigidly filling all space within the outer box (Fig. S4). PVC poles were oriented in this way to capture all deposited sediment and minimize resuspension by substantially decreasing the fetch within the catchment bins. These sediment catchment units were then transported back to the experimental site where recipient holes were dug to the exact dimensions, so that the top of the marsh block (along with the top of each PVC pole) was exactly flush with the marsh surface sediment. We stapled 1-cm hardware cloth mesh (66 cm x 66 cm, with central 36 cm x 36 cm cutout) above PVC and flush to the marsh surface to allow invertebrate access to and from mussel aggregations and to limit the amount of disturbance to and resuspension of the settled material. Finally, to minimize mussel mortality in the absence of cordgrass, we built shades using 2 layers of 5-cm Aquamesh, attached these shades to four bamboo stakes, and inserted them above each plot at a height of ~1 m. The experiment ran for one month, from July 18 to August 18, 2017.After one month in the field, all experimental units and their contents were returned to the lab, rinsed into recipient aluminum tins, dried, and weighed. The contents of the central bins and sediment on plant tissue were dislodged and collected using spatulas, scraper tools, and a Waterpik Flosser device. After all sediment was collected, each mussel was removed from the aggregation, measured for length, and weighed for biomass. Finally, from treatments containing vegetation, all aboveground cordgrass biomass was harvested, dried, and weighed (Fig. S6).Delft3D ModelTo evaluate the contribution of mussel mounds to marsh accretion, we performed numerical simulations using the Delft3D-FLOW model63,64. We first modified the source code by adding a bivalve module (Delft3D-BIVALVES) to simulate sediment filtration and deposition processes that lead to mussel mound formations. In building this module, we assumed that mussels remove sediments from the water column because of filtration, and expel them as very cohesive pseudofeces, which are attached to the mounds, increasing their elevation. These processes are simulated by adding, in the computational cells containing the mussel mounds, a depositional term due to mussel filtration that reads:$${triangle z}_{{FILT}}={rho }_{{MM}}cdot {f}_{{MM}}cdot {C}_{{sed}}cdot {dt}cdot {{rho }_{{sed},{dry}}}^{-1},$$
(2)
where ({rho }_{{MM}}) is the density of mussels in the mounds [mussel m−2], set equal to 177 mussel m−214, and ({f}_{{MM}}) is the volume of water filtered by each mussel per unit of time [m3 s−1 mussel−1], set equal to 0.115 m3 s−1 mussel−1. ({C}_{{sed}}) is the sediment concentration in the water column above each mussel mound [kg m−3], ({dt}) is the simulation time step [s], set equal to 0.6 s, and ({rho }_{{sed},{dry}}) is the dry density of the sediments [kg m−3], set equal to 800 kg m−373. The volume of sediments correspondent to the mussel filtration depositional term obtained from Eq 2. is removed from the lower computational layer of the water column above the mussel aggregation by adding the following sink term in the advection-diffusion equation:$${SINK}={rho }_{{MM}}cdot {f}_{{MM}}cdot {C}_{{sed}}cdot {A}_{{cell}},$$
(3)
where ({A}_{{cell}}) is the area of the computational cell [m2]. Numerically, the term is implemented implicitly to prevent the appearance of negative concentrations. For settling velocity, we used a value of 0.1 mm s−1. This value provides the best fit of the Total Suspended Sediment (TSS) concentration we surveyed in a creek, on the adjacent Little Sapelo Island, with an error of 0.022 ± 0.025 kg m−3 (Fig. S8, MAE + RMSE). The fit was obtained by using the exponential decay formulation that reads:$${C}_{s}={C}_{s0}{e}^{-{tcdot w}_{s}/h},$$
(4)
where ({w}_{s}) is the settling velocity in [m s−1], (h) is the slow depth in [m], ({C}_{s0}) is the initial sediment concentration in [kg m−3], and (t) is the time in [s]. We set ({C}_{s0}) equal to 0.10 g m−3, which approximates the average value measured during flood tide, at the same location and tidal cycle. In addition, we set h equal to 0.30 m, which is the local mean annual high tide, calculated for 2018. To assess the sensitivity of the results to settling velocity, we ran a simulation in which we increased settling velocity by 50% (i.e., settling velocity equal to 0.15 mm s−1), and extra deposition due to mussel mounds varied by only approximately 6.5% of the original value.We next established a rectangular model domain to describe our study area in a simplified fashion (Fig. 5a). Within the model domain, the marsh platform is connected to the main channel by a tidal creek. The domain extends for 50 m and 207 m in the long-shore and landward directions, respectively. It is discretized using a rectangular grid constituted of 50 cm × 50 cm cells at the creek head and 50 cm × 100 cm cells elsewhere. In our model domain, mussel aggregations occupy only the creekhead, which is the 50 m × 50 m area between the creek and the upper part of the domain. We assign that each mussel mound has an area of 0.25 m2, corresponding to a mound diameter of ~0.5 m. At our resolution, a mound occupies a single cell. A sensitivity analysis using cells of 0.25 m and 0.125 m showed negligible changes in the results. The main channel occupies the lower 20 m of the domain, and its depth goes from 0 m AMSL at the marsh edge to −6 m at the seaward boundary. The tidal creek is located in the middle of the marsh platform and stops 50 m from the landward boundary of the domain. It is 2 m wide, and its depth goes from 0.79 m AMSL at the creek head to −1 m where it connects to the main channel. The marsh system consists of four subareas: (i) the levees (0.94 m AMSL), which are 5 m wide cordons separating the marsh platform from the channel and the creek (except at the creek head) and are vegetated by tall-form cordgrass; (ii) the levee adjacent areas (0.79 m AMSL), which are 10 m wide and vegetated by intermediate size cordgrass, (iii) mussel aggregations, which occupy a set proportion of the creek head (0, 10, or 20%), are vegetated by short-form cordgrass, and form a regular array (0.79 m AMSL, a newly formed mound); and (iv) the marsh platform, all remaining area consisting of short-form cordgrass and located at a uniform elevation of 0.79 m AMSL (Table S2).We used the Delft3D “trachytopes” functionality to impose vegetation resistance on flow propagating through the model domain. At every time step, a Chézy friction coefficient ((C)) is calculated for the vegetation, using a formulation developed by83. The formula is based on the unvegetated bed roughness (({C}_{b})), the drag coefficient (({C}_{D})), the vegetation height (({h}_{v})), and the vegetation density ((n)), expressed as the number of stems per square meter ((m)) times the stem diameter (({D}_{S})). In our model, only cells with an elevation higher than 0 m above MSL are vegetated. We considered four vegetation zones, as described above (Table S2; see details for collection of cordgrass and mussel parameters below). For each vegetation type, we used the same ({C}_{b}) and ({C}_{D}), equal to 45 m1/2s−1 and 1.65, respectively84. The vegetation properties, for each class, are based on local surveys and are reported in Table S1. For each of the three mussel scenarios analyzed, we considered two vegetation distributions. The first one sticks with the description of the vegetation zones we report above. In the second scenario, the vegetation is absent from the entire domain.To compute the sediment deposition in our numerical model, we simulated deposition from October 6th to October 22nd, 2018. This period contains the most representative spring and neap tides of the year and was obtained using the following procedure. First, we reconstructed the astronomic signal for 2018 using the tidal components of the NOAA station “Daymark #156, Head of Mud River, GA” # 8674975”, which is the closest to our study area. We then calculated the tidal ranges in 2018 using consecutive low and high tide levels extrapolated from the astronomic tidal signal. Next, we classified the tidal ranges using the 25th and 75th quantiles of their distribution (i.e., Q25 and Q75): ranges lower than the 25th quartile were neap tides and ranges greater than the 75th were spring tides. The 2018 astronomic tide was then divided into periods containing a spring and a consecutive neap tide. For each period, we identified the tidal ranges associated with spring and neap tides by using Q25 and Q75. Finally, for each period, we calculated the average tidal range for neap and spring tide, the difference between these average values and the yearly average, and the sum of these two differences. The period with the lowest value of this sum contains the most representative spring and neap tides of 2018. For this date range, we then ran our model under six scenarios: mussel cover at 0, 10, and 20%, but with and without vegetation present. We report both sediment deposition and annual accretion in the five location types (i.e., levee crest, levee-adjacent, mussel aggregation, aggregation-adjacent, and non-mussel marsh platform) at local (1 m2), creekhead (2500 m2) and entire domain scales (10,350-m2).Field experiment 3: creekshed mussel manipulationTo assess the effects of mussel presence and population size on marsh accretion at the creekshed scale, we first selected a marsh creekshed with three adjacent tidal creeks of similar length, structure, associated mussel populations, and marsh platform characteristics (i.e., size, elevation, and cordgrass characteristics). For each of the three tidal creeks, we first delineated a 50 m by 50 m creekhead area, oriented perpendicular to the direction of the tidal creek entry into the marsh, and located with midpoint of the front edge positioned at the point of tidal creek entry into the marsh. We then delineated a larger creekshed area associated with each creek of ≥10,000 m2 within which we would deploy our experimental treatments. To quantify initial mussel and cordgrass cover, we set up three 50 m2 transects (50 m long, 1 m wide) within the creekhead area, located at 0 m, 20 m, and 40 m distance from the tidal creek point of entry (and oriented perpendicular to the direction of flow). Within each transect, we counted each mussel aggregation, scoring each individual mussel as well as the length, width, and height from marsh platform of each mussel aggregation structure.For a subset of 20 mussel aggregations randomly selected within each transect (3 transects per creek, 180 aggregations total), we scored the total number of cordgrass tillers on each aggregation. For a subset of 5 randomly selected tillers on each aggregation, we measured both length and width. To assess the differences in cordgrass characteristics between mussel aggregations and aggregation-adjacent areas, we also measured cordgrass stem density, height, and diameter in non-aggregation areas (1 m2) located 1m away from each mussel aggregation.After all initial data was collected, we removed and transplanted approximately 200,000 mussels from one tidal creekhead to another. To do so, we initially flagged approximately 4000 mussel aggregations within the creekshed area of the “Removal” creek, encompassing both the 2500 m2 creekhead area as well as the surrounding ≥10,000 m2 creekshed extent. Mussel individuals were removed by hand over the course of 16 weeks, with all field personnel taking care to leave all pseudofeces in place and cordgrass intact. Field crews were split between the mussel removal and mussel addition creek, such that mussels were re-transplanted within 24 h of removal to minimize mortality. Due to logistical and permitting constraints, it was not feasible to replicate the treatments across multiple sites; instead, the three plots occupied a single contiguous creekshed (Fig. 6a, b).To assess changes in marsh elevation, we first quantified initial creekhead elevation (mean m AMSL in 2500-ft2 area perpendicular to point of entry) using two metrics: 1) Real Time Kinematic (RTK) elevation datapoints (Trimble R6 GNSS System) distributed across the creekshed; and 2) measurements of mussel mound heights throughout each transect at set distances from the point of water entry. For the RTK datapoints, we collected 86 total points across the creekshed in June 2017. Elevation datapoints were randomly selected in each 2500 m2 creekhead zone (minimum of 20 points per creekhead; Fig. S7). However, given the low number of RTK points across a large area, we additionally utilized mussel mound height calculations to provide a second estimate of initial elevation across the creekshed. Mussel aggregations and other bivalves, such as oysters, exhibit a height ceiling of growth, above which survivorship and growth are hypothesized to decrease. Previous work on Sapelo Island marshes reported the height ceiling to be +0.84 ± 0.004 m AMSL (mean ± SE). Therefore, assuming mature mussel aggregations (i.e., with tops at the aforementioned height ceiling), then mussel aggregation height (i.e., the distance between the marsh platform and the topmost point of the mussel aggregation mounded structure) will inform our knowledge of the marsh platform elevation by the following equation: Marsh Elevation (m AMSL) = Mussel Height Ceiling (+0.84 m AMSL) – Mussel Aggregation Height (m AMSL). For each distance from creekhead from which we conducted a 50 m2 transect (0, 20, and 40 m), we estimated mean platform elevation using each of the measured mussel aggregation heights. We then took the mean value of marsh elevation across the three distances (0 m, 20 m, and 40 m) as a measure of creekhead elevation in 2017 for each of our experimental creeks ( >60 mounds per creekhead; 250 total).To assess elevation three years after treatment deployment, we compared creekhead elevation using a 2020 Digital Elevation Model (DEM) of the creekshed. To build the DEM, we flew a DJI Matrice 600 Pro drone carrying a custom build Lidar payload in August 2020. The payload consisted of a Velodyne Puck Lite VLP16, paired with a Novatel Stim300 Inertial Measurement Unit. The point clouds from the drone were orthorectified from GPS data continuously measured on the drone (see the procedure described in 85,86). To remove the vegetation and any other surface perturbations (i.e., from digital surface model to digital elevation model), we used the CloudCompare software (https://github.com/cloudcompare/cloudcompare). The cloth Simulation Filter (CSF; 87) was applied twice to the dataset, which successfully removed the vegetation data. The point cloud of the marsh surface was then exported to ArcGIS 10.7 where the DEM was generated by raster interpolation. Once completed, the mean elevation within each 2500 m2 creekhead location was calculated using the Zonal Statistics tool in ArcGIS 10.7.Statistical analysesTo quantify the effects of season, tidal phase, and location type on short-term deposition, we first square root transformed short-term sediment deposition (i.e., filter paper results) to meet the assumptions of parametric statistics. We then conducted a three-way fully factorial ANOVA, with main effects season, tidal phase, and location type. Post-hoc analyses were conducted with Tukey HSD test, with Bonferroni-corrected p-values (STATA v 15.1). We further analyzed the effects of site, season, tide, and marsh location on short-term sediment deposition using regression tree analysis (rpart, R version 3.1.0). Over-fitted trees were pruned using k fold cross-validation. To next assess the effects of marsh location type on total organic material deposited over 24 h (surface) and percent organic material (surface and to 5 cm depth), we ran three separate one-way ANOVAs. Post-hoc analyses were again conducted with Tukey HSD tests, with Bonferroni-corrected p-values (STATA v 15.1). For Experiment 1, we assessed the fate of mussel biodeposits, both previously settled and newly ejected, with a one-way ANOVA with location (creekhead versus platform) as the main effect. Finally, for Experiment 2, to assess whether cordgrass and mussel aggregations significantly affected sediment deposition over the one-month experimental deployment, we used multiple regression analysis with cordgrass biomass and mussel biomass as predictor variables for sediment biomass collected in each zone (STATA v 15.1; Table S1).Reporting summaryFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article. More

Bjornlund, V., Bjornlund, H. & Van Rooyen, A. F. Int. J. Water Resour. Dev. 36 (Suppl. 1), S20–S53 (2020).World Population Prospects: the 2017 Revision (United Nations Department of Economic and Social Affairs Population Division, 2017).Affokpon, A. et al. In 69th International Symposium on Crop Protection (2017).Adesiyan, S. O. & Odihirin, R. A. Nematologica 24, 132–134a (1978).Article 

Google Scholar 
Gao, Q. K. Chinese Vegetables 5, 24–25 (1992).
Google Scholar 
Pirzada, T. et al. Nat. Food https://doi.org/10.1038/s43016-023-00695-z (2023).Article 

Google Scholar 
Hague, N. G. M. Nematodes, The Unseen Enemy: A Guide to Nematode Damage (Du Pont, 1980).Zasada, I. A. et al. Annu. Rev. Phytopathol. 48, 311–328 (2010).Article 
CAS 

Google Scholar 
Ochola, J. et al. Nat. Sustain. 5, 425–433 (2022).Article 

Google Scholar 
Pirzada, T. et al. ACS Sustain. Chem. Eng. 8, 6590–6600 (2020).Article 
CAS 

Google Scholar 
Cao, J. et al. Cellulose 23, 673–687 (2016).Article 
CAS 

Google Scholar  More

Gopalaswamy, A. M. et al. Nat. Ecol. Evol. 6, 1794–1795 (2022).Article 
PubMed 

Google Scholar 
Sandom, C., Donlan, C. J., Svenning, J. C. & Hansen, D. in Key Topics In Conservation Biology 2 (eds MacDonald, D. W. & Willis, K. J.) 430–451 (2013).Ripple, W. J. et al. Science 343, 1241484 (2014).Article 
PubMed 

Google Scholar 
Jhala, Y. V., Ranjitsinh, M. K., Bipin, C. M. & Yadav, S. P. Action Plan For Introduction Of Cheetah In India (2021).Divyabhanusinh & Kazami J. Bombay Nat. Hist. Soc. 116, 22–43 (2019).
Google Scholar 
IUCN/SSG. Guidelines For Reintroductions And Other Conservation Translocations IUCN. Ecological Applications 20 (IUCN Species Survival Commission, 2013).Prost, S. et al. Mol. Ecol. 31, 4208–4223 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Buk, K. G., van der Merwe, V. C., Marnewick, K. & Funston, P. J. Conservation Of Severely Fragmented Populations: Lessons From The Transformation Of Uncoordinated Reintroductions Of Cheetahs (Acinonyx jubatus) Into A Managed Metapopulation With Self-Sustained Growth. Biodiversity And Conservation 27 (Springer Netherlands, 2018).Scientific Authority of South Africa. Gov. Gaz. Repub. South Africa 677, 1–4 (2021).Walker, E. H., Verschueren, S., Schmidt-Küntzel, A. & Marker, L. Oryx 56, 495–504 (2022).Article 

Google Scholar 
Tordiffe, A. S. W. et al. Disease Risk Analysis For Introduction Of Cheetahs (Acinonyx jubatus) To India (2022).Brugière, D., Chardonnet, B. & Scholte, P. Trop. Conserv. Sci. 8, 513–527 (2015).Article 

Google Scholar 
Jhala, Y. V. et al. Front. Ecol. Evol. 7, https://doi.org/10.3389/fevo.2019.00312 (2019).Jhala, Y. et al. People Nat. 3, 281–293 (2021).Article 

Google Scholar 
Hayward, M. W., O’Brien, J. & Kerley, G. I. H. Biol. Conserv. 139, 219–229 (2007).Article 

Google Scholar 
Ogutu, J. O., Owen-Smith, N., Piepho, H. P. & Said, M. Y. J. Zool. 285, 99–109 (2011).Article 

Google Scholar 
Houser, A. M., Somers, M. J. & Boast, L. K. J. Zool. 278, 108–115 (2009).Article 

Google Scholar  More

Ware, I. M. et al. Climate-driven reduction of genetic variation in plant phenology alters soil communities and nutrient pools. Glob. Change Biol. 25, 1514–1528 (2019).Article 
ADS 

Google Scholar 
Ware, I. M. et al. Climate-driven divergence in plant-microbiome interactions generates range-wide variation in bud break phenology. Commun. Biol. 4, 748 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Bayliss, S. L. J., Mueller, L. O., Ware, I. M., Schweitzer, J. A. & Bailey, J. K. Plant genetic variation drives geographic differences in atmosphere–plant–ecosystem feedbacks. Plant Environ. Int. 1, 166–180 (2020).Article 

Google Scholar 
Van Nuland, M. E. et al. Intraspecific trait variation across elevation predicts a widespread tree species’ climate niche and range limits. Ecol. Evol. 10, 3856–3867 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Davis, M. B. & Shaw, R. G. Range shifts and adaptive responses to quaternary climate change. Science 292, 673–679 (2001).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Hendry, A. P. Eco-Evolutionary Dynamics (Princeton University Press, 2017).Book 

Google Scholar 
Anstett, D. N., Branch, H. A. & Angert, A. L. Regional differences in rapid evolution during severe drought. Evol. Lett. 5, 130–142 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Grainger, T. N., Rudman, S. M., Schmidt, P. & Levine, J. M. Competitive history shapes rapid evolution in a seasonal climate. PNAS 118, e2015772118 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bokhorst, S., Bjerke, J. W., Street, L. E., Callaghan, T. V. & Phoenix, G. K. Impacts of multiple extreme winter warming events on sub-Arctic heathland: Phenology, reproduction, growth, and CO2 flux responses. Glob. Change Biol. 17, 2817–2830 (2011).Article 
ADS 

Google Scholar 
Anderson, J. T., Perera, N., Chowdhury, B. & Mitchell-Olds, T. Microgeographic patterns of genetic divergence and adaptation across environmental gradients in Boechera stricta (Brassicaceae). Am. Nat. 186, S60–S73 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Wooliver, R., Tittes, S. B. & Sheth, S. N. A resurrection study reveals limited evolution of thermal performance in response to recent climate change across the geographic range of the scarlet monkeyflower. Evolution 74, 1699–1710 (2020).Article 
PubMed 

Google Scholar 
McCormack, J. E., Huang, H. & Knowles, L. L. Sky Islands. in Encyclopedia of Islands (eds. Gillespie, R. G. & Clague, D. A.) 839–843 (2009).Knowles, J. F., Scott, R. L., Minor, R. L. & Barron-Gafford, G. A. Ecosystem carbon and water cycling from a sky island montane forest. Agric. For. Meteorol. 281, 107835 (2020).Article 
ADS 

Google Scholar 
Heald, W. Sky Islands (Van Nostrand, 1967).
Google Scholar 
DeBano, L. H. et al. Biodiversity and management of the Madrean Archipelago: The Sky Islands of southwestern United States and northwestern Mexico: 1994 September 19–23; Tucson, AZ. Gen Tech Rep RM-GTR-264. Fort Collins, CO: US Dep Agric For Serv, Rocky Mt For Range Exp Stn. 669 p. (1995).Pérez-Alquicira, J. et al. The role of historical factors and natural selection in the evolution of breeding systems of Oxalis alpina in the Sonoran desert ‘Sky Islands’. J. Evol. Biol. 23, 2163–2175 (2010).Article 
PubMed 

Google Scholar 
Wiens, J. J. et al. Climate change, extinction, and Sky Island biogeography in a montane lizard. Mol. Ecol. 28, 2610–2624 (2019).Article 
PubMed 

Google Scholar 
Pielou, E. C. After the Ice Age. The return of Life to Glaciated North America (The University of Chicago Press, 1991).Book 

Google Scholar 
Hosner, P. A., Nyári, Á. S. & Moyle, R. G. Water barriers and intra-island isolation contribute to diversification in the insular Aethopyga sunbirds (Aves: Nectariniidae). J. Biogeogr. 40, 1094–1106 (2013).Article 

Google Scholar 
Favé, M.-J. et al. Past climate change on Sky Islands drives novelty in a core developmental gene network and its phenotype. Bmc Evol. Biol. 15, 183 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Yanahan, A. D. & Moore, W. Impacts of 21st-century climate change on montane habitat in the Madrean Sky Island Archipelago. Divers. Distrib. 25, 1625–1638 (2019).Article 

Google Scholar 
Oline, D. K., Mitton, J. B. & Grant, M. C. Population and subspecific genetic differentiation in the Foxtail Pine (Pinus balfouriana). Evolution 54, 1813–1819 (2000).CAS 
PubMed 

Google Scholar 
Barrowclough, G. F., Groth, J. G., Mertz, L. A. & Gutiérrez, R. J. Genetic structure of Mexican spotted owl (Strix Occidentalis Lucida) populations in a fragmented landscape. Auk 123, 1090–1102 (2006).
Google Scholar 
Atwood, T. C. et al. Modeling connectivity of black bears in a desert sky island archipelago. Biol. Conserv. 144, 2851–2862 (2011).Article 

Google Scholar 
Halbritter, D. A., Storer, C. G., Kawahara, A. Y. & Daniels, J. C. Phylogeography and population genetics of pine butterflies: Sky islands increase genetic divergence. Ecol. Evol. 9, 13389–13401 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
DeChaine, E. G. & Martin, A. P. Marked genetic divergence among sky island populations of Sedum lanceolatum (Crassulaceae) in the Rocky Mountains. Am. J. Bot. 92, 477–486 (2005).Article 
CAS 
PubMed 

Google Scholar 
Baker, A. J. Islands in the sky: The impact of Pleistocene climate cycles on biodiversity. J. Biol. 7, 32 (2008).Article 
PubMed 
PubMed Central 

Google Scholar 
Robin, V. V., Sinha, A. & Ramakrishnan, U. Ancient geographical gaps and paleo-climate shape the phylogeography of an endemic bird in the sky islands of southern India. PLoS ONE 5, e13321 (2010).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Manthey, J. D. & Moyle, R. G. Isolation by environment in White-breasted Nuthatches (Sitta carolinensis) of the Madrean Archipelago sky islands: A landscape genomics approach. Mol. Ecol. 24, 3628–3638 (2015).Article 
CAS 
PubMed 

Google Scholar 
Vásquez, D. L. A., Balslev, H., Hansen, M. M., Sklenář, P. & Romoleroux, K. Low genetic variation and high differentiation across sky island populations of Lupinus alopecuroides (Fabaceae) in the northern Andes. Alpine Bot. 126, 135–142 (2016).Article 

Google Scholar 
Mairal, M. et al. Geographic barriers and Pleistocene climate change shaped patterns of genetic variation in the Eastern Afromontane biodiversity hotspot. Sci. Rep. 7, 45749 (2017).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kidane, Y. O., Steinbauer, M. J. & Beierkuhnlein, C. Dead end for endemic plant species? A biodiversity hotspot under pressure. Glob. Ecol. Conserv. 19, e00670 (2019).Article 

Google Scholar 
Williamson, J. L. et al. Ecology, not distance, explains community composition in parasites of sky-island Audubon’s Warblers. Int. J. Parasitol. 49, 437–448 (2019).Article 
PubMed 

Google Scholar 
Knowles, L. L. & Richards, C. L. Importance of genetic drift during Pleistocene divergence as revealed by analyses of genomic variation. Mol. Ecol. 14, 4023–4032 (2005).Article 
PubMed 

Google Scholar 
Woolbright, S. A., Whitham, T. G., Gehring, C. A., Allan, G. J. & Bailey, J. K. Climate relicts and their associated communities as natural ecology and evolution laboratories. Trends Ecol. Evol. 29, 406–416 (2014).Article 
PubMed 

Google Scholar 
Evans, L. M., Allan, G. J., Meneses, N., Max, T. L. & Whitham, T. G. Herbivore host-associated genetic differentiation depends on the scale of plant genetic variation examined. Evol. Ecol. 27, 65–81 (2013).Article 

Google Scholar 
Kooyers, N. J., Greenlee, A. B., Colicchio, J. M., Oh, M. & Blackman, B. K. Replicate altitudinal clines reveal that evolutionary flexibility underlies adaptation to drought stress in annual Mimulus guttatus. New Phytol. 206, 152–165 (2015).Article 
PubMed 

Google Scholar 
Price, E. A. C. & Marshall, C. Clonal plants and environmental heterogeneity—An introduction to the proceedings. Plant Ecol. 141, 3–7 (1999).Article 

Google Scholar 
Matsuo, A. et al. Female and male fitness consequences of clonal growth in a dwarf bamboo population with a high degree of clonal intermingling. Ann. Bot. Lond. 114, 1035–1041 (2014).Article 
CAS 

Google Scholar 
Barrett, S. C. H. Influences of clonality on plant sexual reproduction. PNAS 112, 8859–8866 (2015).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bittebiere, A.-K., Benot, M.-L. & Mony, C. Clonality as a key but overlooked driver of biotic interactions in plants. Persp. Plant Ecol. Evol. Syst. 43, 125510 (2020).Article 

Google Scholar 
King, D. & Roughgarden, J. Multiple switches between vegetative and reproductive growth in annual plants. Theor. Popul. Biol. 21, 194–204 (1982).Article 
MathSciNet 
MATH 

Google Scholar 
LaDeau, S. L. & Clark, J. S. Rising CO2 levels and the fecundity of forest trees. Science 292, 95–98 (2001).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Qiu, T. et al. Is there tree senescence? The fecundity evidence. PNAS 118, e2106130118 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Oddou-Muratorio, S. et al. Crown defoliation decreases reproduction and wood growth in a marginal European beech population. Ann. Bot. Lond. 128, 193–204 (2021).Article 

Google Scholar 
Knops, J. M. H., Koenig, W. D. & Carmen, W. J. Negative correlation does not imply a tradeoff between growth and reproduction in California oaks. PNAS 104, 16982–16985 (2007).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Nakamura, I. et al. Phenotypic and genetic differences in a perennial herb across a natural gradient of CO2 concentration. Oecologia 165, 809–818 (2011).Article 
ADS 
PubMed 

Google Scholar 
Robinson, E. A., Ryan, G. D. & Newman, J. A. A meta-analytical review of the effects of elevated CO2 on plant–arthropod interactions highlights the importance of interacting environmental and biological variables. New Phytol. 194, 321–336 (2012).Article 
CAS 
PubMed 

Google Scholar 
Chen, X. Spatiotemporal Processes of Plant Phenology, Simulation and Prediction (Springer, 2017).Book 

Google Scholar 
Bradshaw, H. D. & Stettler, R. F. Molecular genetics of growth and development in Populus. IV. Mapping QTLs with large effects on growth, form, and phenology traits in a forest tree. Genetics 139, 963–973 (1995).Article 
CAS 
PubMed 

Google Scholar 
Rae, A. M. et al. QTL for yield in bioenergy Populus: Identifying G×E interactions from growth at three contrasting sites. Tree Genet. Genom. 4, 97–112 (2008).Article 

Google Scholar 
Rae, A. M., Street, N. R., Robinson, K. M., Harris, N. & Taylor, G. Five QTL hotspots for yield in short rotation coppice bioenergy poplar: The poplar biomass loci. Bmc Plant Biol. 9, 23 (2009).Article 
PubMed 
PubMed Central 

Google Scholar 
Allwright, M. R. et al. Biomass traits and candidate genes for bioenergy revealed through association genetics in coppiced European Populus nigra (L.). Biotechnol. Biofuels 9, 195 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Badmi, R. et al. A new calmodulin-binding protein expresses in the context of secondary cell wall biosynthesis and impacts biomass properties in Populus. Front. Plant Sci. 9, 1669 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Dai, A. Drought under global warming: a review. Wiley Interdiscip. Rev. Clim. Change 2, 45–65 (2011).Article 

Google Scholar 
IPCC. Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. (2021).Hughes, L., Hughes, L. & Hughes, L. Biological consequences of global warming: Is the signal already apparent?. Trends Ecol. Evol. 15, 56–61 (2000).Article 
CAS 
PubMed 

Google Scholar 
Walther, G.-R. et al. Ecological responses to recent climate change. Nature 416, 389–395 (2002).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Parmesan, C. & Yohe, G. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421, 37–42 (2003).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Parmesan, C. Ecological and evolutionary responses to recent climate change. Annu. Rev. Ecol. Evol. Syst. 37, 637–669 (2006).Article 

Google Scholar 
Chen, I.-C., Hill, J. K., Ohlemüller, R., Roy, D. B. & Thomas, C. D. Rapid range shifts of species associated with high levels of climate warming. Science 333, 1024–1026 (2011).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Fuller, A. et al. Physiological mechanisms in coping with climate change. Physiol. Biochem. Zool. 83, 713–720 (2010).Article 
PubMed 

Google Scholar 
Zhu, K., Woodall, C. W. & Clark, J. S. Failure to migrate: Lack of tree range expansion in response to climate change. Glob. Change Biol. 18, 1042–1052 (2012).Article 
ADS 

Google Scholar 
Zavaleta, E. et al. Ecosystem responses to community disassembly. Ann. NY. Acad. Sci. 1162, 311–333 (2009).Article 
ADS 
PubMed 

Google Scholar 
Bertel, C. et al. Natural selection drives parallel divergence in the mountain plant Heliosperma pusillum s.l. Oikos 127, 1355–1367 (2018).Article 

Google Scholar 
Knotek, A. et al. Parallel alpine differentiation in Arabidopsis arenosa. Front. Plant Sci. 11, 561526 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Tusiime, F. M. et al. Afro-alpine flagships revisited: Parallel adaptation, intermountain admixture and shallow genetic structuring in the giant senecios (Dendrosenecio). PLoS ONE 15, e0228979 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Cooke, J. E. K. & Rood, S. B. Trees of the people: The growing science of poplars in Canada and worldwide. Botany 85, 1103–1110 (2007).
Google Scholar 
Evans, L. M. et al. Geographical barriers and climate influence demographic history in narrowleaf cottonwoods. Heredity 114, 387–396 (2015).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Braatne, J. H., Rood, S. B. & Heilman, P. E. Life history, ecology, and conservation of riparian cottonwoods in North America. 57–86 (1996).Schweitzer, J. A., Martinsen, G. D. & Whitham, T. G. Cottonwood hybrids gain fitness traits of both parents: A mechanism for their long-term persistence?. Am. J. Bot. 89, 981–990 (2002).Article 
PubMed 

Google Scholar 
Moore, W. et al. Introduction to the Arizona Sky Island Arthropod Project (ASAP): Systematics, biogeography, ecology, and population genetics of arthropods of the Madrean Sky Islands. Proc. RMRS 2013, 144–168 (2013).PubMed 
PubMed Central 

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 
Van Nuland, M. E., Bailey, J. K. & Schweitzer, J. A. Divergent plant–soil feedbacks could alter future elevation ranges and ecosystem dynamics. Nat. Ecol. Evol. 1, 0150 (2017).Article 

Google Scholar 
Tuskan, G. A. et al. Characterization of microsatellites revealed by genomic sequencing of Populus trichocarpa. Can. J. For. Res. 34, 85–93 (2004).Article 
CAS 

Google Scholar 
Tuskan, G. A. et al. The genome of black cottonwood, Populus trichocarpa (Torr. & Gray). Science 313, 1596–1604 (2006).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Peakall, R. & Ssmouse, P. E. genalex 6: Genetic analysis in Excel. Population genetic software for teaching and research. Mol. Ecol. Notes 6, 288–295 (2006).Article 

Google Scholar 
Peakall, R. & Smouse, P. E. GenAlEx 6.5: Genetic analysis in Excel. Population genetic software for teaching and research—An update. Bioinformatics 28, 2537–2539 (2012).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. Arxiv https://doi.org/10.48550/arxiv.1406.5823 (2014).Article 

Google Scholar 
Schielzeth, H. & Nakagawa, S. Nested by design: Model fitting and interpretation in a mixed model era. Methods Ecol. Evol. 4, 14–24 (2013).Article 

Google Scholar 
Price, A. L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006).Article 
CAS 
PubMed 

Google Scholar 
Fox, J. et al. Package ‘car’: Companion to Applied Regression. R package version 3.0–10 (2020). More