Philippa Kaur

(ECHA), E. C. A. Know more about the effects of the chemicals we use in Europe (ECHA/PR/16/01). https://echa.europa.eu/de/-/know-more-about-the-effects-of-the-chemicals-we-use-in-europe (2016).Liu, W. J., Nie, H. X., Liang, D. P., Bai, Y. & Liu, H. W. Phospholipid imaging of zebrafish exposed to fipronil using atmospheric pressure matrix-assisted laser desorption ionization mass spectrometry. Talanta https://doi.org/10.1016/j.talanta.2019.120357 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Sparvero, L. J. et al. Mapping of phospholipids by MALDI imaging (MALDI-MSI): Realities and expectations. Chem. Phys. Lipid. 165, 545–562. https://doi.org/10.1016/j.chemphyslip.2012.06.001 (2012).CAS 
Article 

Google Scholar 
Koizumi, S. et al. Imaging mass spectrometry revealed the production of lyso-phosphatidylcholine in the injured ischemic rat brain. Neuroscience 168(1), 219–225. https://doi.org/10.1016/j.neuroscience.2010.03.056 (2010).CAS 
Article 
PubMed 

Google Scholar 
Hankin, J. A. et al. MALDI mass spectrometric imaging of lipids in rat brain injury models. J. Am. Soc. Mass Spectrom. 22(6), 1014–1021. https://doi.org/10.1007/s13361-011-0122-z (2011).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Zhao, C. et al. MALDI-MS imaging reveals asymmetric spatial distribution of lipid metabolites from bisphenol s-induced nephrotoxicity. Anal. Chem. 90(5), 3196–3204. https://doi.org/10.1021/acs.analchem.7b04540 (2018).CAS 
Article 
PubMed 

Google Scholar 
Barbacci, D. C. et al. Mass spectrometric imaging of ceramide biomarkers tracks therapeutic response in traumatic brain injury. ACS Chem. Neurosci. 8(10), 2266–2274. https://doi.org/10.1021/acschemneuro.7b00189 (2017).CAS 
Article 
PubMed 

Google Scholar 
Rompp, A. et al. Histology by mass spectrometry: Label-free tissue characterization obtained from high-accuracy bioanalytical imaging. Angew. Chem. Int. Ed. 49, 3834–3838. https://doi.org/10.1002/anie.200905559 (2010).CAS 
Article 

Google Scholar 
Zemski Berry, K. A. et al. MALDI imaging of lipid biochemistry in tissues by mass spectrometry. Chem. Rev. 111, 6491–6512. https://doi.org/10.1021/cr200280p (2011).CAS 
Article 

Google Scholar 
Cornett, D. S., Reyzer, M. L., Chaurand, P. & Caprioli, R. M. MALDI imaging mass spectrometry: Molecular snapshots of biochemical systems. Nat. Methods 4, 828–833. https://doi.org/10.1038/nmeth1094 (2007).CAS 
Article 
PubMed 

Google Scholar 
Römpp, A. & Spengler, B. Mass spectrometry imaging with high resolution in mass and space. Histochem. Cell Biol. 139, 759–783. https://doi.org/10.1007/s00418-013-1097-6 (2013).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Monroe, E. B. et al. SIMS and MALDI MS imaging of the spinal cord. Proteomics 8(18), 3746-3754. https://doi.org/10.1002/pmic.200800127 (2008).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Chaurand, P., Cornett, D. S., Angel, P. M. & Caprioli, R. M. From whole-body sections down to cellular level, multiscale imaging of phospholipids by MALDI mass spectrometry. Mol. Cell. Proteom. https://doi.org/10.1074/mcp.O110.004259 (2011).Article 

Google Scholar 
Lee, H.-B. & Peart, T. E. Determination of bisphenol A in sewage effluent and sludge by solid-phase and supercritical fluid extraction and gas chromatography/mass spectrometry. J. AOAC Int. 83, 290–298. https://doi.org/10.1093/jaoac/83.2.290 (2000).CAS 
Article 
PubMed 

Google Scholar 
Desbenoit, N., Walch, A., Spengler, B., Brunelle, A. & Römpp, A. Correlative mass spectrometry imaging, applying time-of-flight secondary ion mass spectrometry and atmospheric pressure matrix-assisted laser desorption/ionization to a single tissue section. Rapid Commun. Mass Spectrometry 32, 159–166. https://doi.org/10.1002/rcm.8022 (2018).ADS 
CAS 
Article 

Google Scholar 
Meding, S. et al. Tumor classification of six common cancer types based on proteomic profiling by MALDI imaging. J. Proteome Res. 11, 1996–2003. https://doi.org/10.1021/pr200784p (2012).CAS 
Article 
PubMed 

Google Scholar 
Ritschar, S. et al. Classification of target tissues of Eisenia fetida using sequential multimodal chemical analysis and machine learning. Histochem. Cell Biol. https://doi.org/10.1007/s00418-021-02037-1 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Altshuler, I. et al. An integrated multi-disciplinary approach for studying multiple stressors in freshwater ecosystems: Daphnia as a model organism. Integr. Comp. Biol. 51(4), 623–633. https://doi.org/10.1093/icb/icr103 (2011).CAS 
Article 
PubMed 

Google Scholar 
Bambino, K. & Chu, J. in Zebrafish at the Interface of Development and Disease Research Vol. 124 Current Topics in Developmental Biology (ed K. C. Sadler) 331–367 (2017).Seda, J. & Petrusek, A. Daphnia as a model organism in limnology and aquatic biology: Introductory remarks. J. Limnol. 70, 337–344. https://doi.org/10.4081/jlimnol.2011.337 (2011).Article 

Google Scholar 
de Souza Anselmo, C., Sardela, V. F., de Sousa, V. P. & Pereira, H. M. G. Zebrafish (Danio rerio): A valuable tool for predicting the metabolism of xenobiotics in humans? Comp. Biochem. Physiol. Part C: Toxicol. Pharmacol. 212, 34–46. https://doi.org/10.1016/j.cbpc.2018.06.005 (2018).CAS 
Article 

Google Scholar 
Panula, P. et al. The comparative neuroanatomy and neurochemistry of zebrafish CNS systems of relevance to human neuropsychiatric diseases. Neurobiol. Dis. 40, 46–57. https://doi.org/10.1016/j.nbd.2010.05.010 (2010).CAS 
Article 
PubMed 

Google Scholar 
Korn, H. & Faber, D. S. The Mauthner cell half a century later: A neurobiological model for decision-making?. Neuron 47, 13–28. https://doi.org/10.1016/j.neuron.2005.05.019 (2005).CAS 
Article 
PubMed 

Google Scholar 
Schirmer, E., Schuster, S. & Machnik, P. Bisphenols exert detrimental effects on neuronal signaling in mature vertebrate brains. Commun. Biol. https://doi.org/10.1038/s42003-021-01966-w (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Flößner, D. Book review: Cladocera: The genus Daphnia (including Daphniopsis). Int. Rev. Hydrobiol. 90, 637. https://doi.org/10.1002/iroh.200590003 (2005).Article 

Google Scholar 
OECD. Test No. 211: Daphnia magna Reproduction Test. (2012).Muyssen, B. T. A. & Janssen, C. R. Multigeneration zinc acclimation and tolerance in Daphnia magna: Implications for water-quality guidelines and ecological risk assessment. Environ. Toxicol. Chem. 20, 2053–2060. https://doi.org/10.1002/etc.5620200926 (2001).CAS 
Article 
PubMed 

Google Scholar 
Blewett, T. A. et al. Sublethal and reproductive effects of acute and chronic exposure to flowback and produced water from hydraulic fracturing on the water flea Daphnia magna. Environ. Sci. Technol. 51, 3032–3039. https://doi.org/10.1021/acs.est.6b05179 (2017).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Yang, J. H., Kim, H. J., Lee, S. M., Kim, B. M. & Seo, Y. R. Cadmium-induced biomarkers discovery and comparative network analysis in Daphnia magna. Mol. Cell. Toxicol. 13, 327–336. https://doi.org/10.1007/s13273-017-0036-3 (2017).CAS 
Article 

Google Scholar 
Ferain, A. et al. Body lipid composition modulates acute cadmium toxicity in Daphnia magna adults and juveniles. Chemosphere 205, 328–338. https://doi.org/10.1016/j.chemosphere.2018.04.091 (2018).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Ritschar, S., Narayana, V. K. B., Rabus, M. & Laforsch, C. Uncovering the chemistry behind inducible morphological defences in the crustacean Daphniamagna via micro-Raman spectroscopy. Sci. Rep. 10(1), 22408. https://doi.org/10.1038/s41598-020-79755-4 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Machnik, P., Schirmer, E., Glück, L. & Schuster, S. Recordings in an integrating central neuron provide a quick way for identifying appropriate anaesthetic use in fish. Sci. Rep. 8, 17541. https://doi.org/10.1038/s41598-018-36130-8 (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Luzio, A. et al. Copper induced upregulation of apoptosis related genes in zebrafish (Danio rerio) gill. Aquat. Toxicol. 128, 183–189. https://doi.org/10.1016/j.aquatox.2012.12.018 (2013).CAS 
Article 
PubMed 

Google Scholar 
Macirella, R. & Brunelli, E. Morphofunctional alterations in zebrafish (Danio rerio) gills after exposure to mercury chloride. Int. J. Mol. Sci. https://doi.org/10.3390/ijms18040824 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Mansouri, B. & Johari, S. A. Effects of short-term exposure to sublethal concentrations of silver nanoparticles on histopathology and electron microscope ultrastructure of zebrafish (Danio rerio) gills. IJT 10, 15–20. https://doi.org/10.32598/IJT.10.1.60.4 (2016).CAS 
Article 

Google Scholar 
Perez, C. J., Tata, A., de Campos, M. L., Peng, C. & Ifa, D. R. Monitoring toxic ionic liquids in zebrafish (Danio rerio) with desorption electrospray ionization mass spectrometry imaging (DESI-MSI). J. Am. Soc. Mass Spectrom. 28, 1136–1148. https://doi.org/10.1007/s13361-016-1515-9 (2017).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Stutts, W. L. et al. Methods for cryosectioning and mass spectrometry imaging of whole-body zebrafish. J. Am. Soc. Mass Spectrom. 31, 768–772. https://doi.org/10.1021/jasms.9b00097 (2020).CAS 
Article 
PubMed 

Google Scholar 
Purves, D. & Williams, S. M. Neuroscience. 2nd edition. Vol. Chapter 11, Vision: The Eye (Sinauer Associates, 2001).
Google Scholar 
Strungaru, S. A. et al. Toxicity and chronic effects of deltamethrin exposure on zebrafish (Danio rerio) as a reference model for freshwater fish community. Ecotoxicol. Environ. Saf. 171, 854–862. https://doi.org/10.1016/j.ecoenv.2019.01.057 (2019).CAS 
Article 
PubMed 

Google Scholar 
Mishra, A. & Devi, Y. Histopathological alterations in the brain (optic tectum) of the fresh water teleost Channa punctatus in response to acute and subchronic exposure to the pesticide Chlorpyrifos. Acta Histochem. 116, 176–181. https://doi.org/10.1016/j.acthis.2013.07.001 (2014).CAS 
Article 
PubMed 

Google Scholar 
Jia, W., Mao, L., Zhang, L., Zhang, Y. & Jiang, H. Effects of two strobilurins (azoxystrobin and picoxystrobin) on embryonic development and enzyme activities in juveniles and adult fish livers of zebrafish (Danio rerio). Chemosphere 207, 573–580. https://doi.org/10.1016/j.chemosphere.2018.05.138 (2018).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Seyoum, A., Pradhan, A., Jass, J. & Olsson, P. E. Perfluorinated alkyl substances impede growth, reproduction, lipid metabolism and lifespan in Daphnia magna. Sci. Total Environ. https://doi.org/10.1016/j.scitotenv.2020.139682 (2020).Article 
PubMed 

Google Scholar 
Scanlan, L. D. et al. Gene transcription, metabolite and lipid profiling in eco-indicator Daphnia magna indicate diverse mechanisms of toxicity by legacy and emerging flame-retardants. Environ. Sci. Technol. 49, 7400–7410. https://doi.org/10.1021/acs.est.5b00977 (2015).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Heinlaan, M. et al. Changes in the Daphnia magna midgut upon ingestion of copper oxide nanoparticles: A transmission electron microscopy study. Water Res. 45, 179–190. https://doi.org/10.1016/j.watres.2010.08.026 (2011).CAS 
Article 
PubMed 

Google Scholar 
Abe, T., Saito, H., Niikura, Y., Shigeoka, T. & Nakano, Y. Embryonic development assay with Daphnia magna: Application to toxicity of aniline derivatives. Chemosphere 45, 487–495. https://doi.org/10.1016/s0045-6535(01)00049-2 (2001).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Sengupta, N., Gerard, P. D. & Baldwin, W. S. Perturbations in polar lipids, starvation survival and reproduction following exposure to unsaturated fatty acids or environmental toxicants in Daphnia magna. Chemosphere 144, 2302–2311. https://doi.org/10.1016/j.chemosphere.2015.11.015 (2016).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Huber, K. et al. Approaching cellular resolution and reliable identification in mass spectrometry imaging of tryptic peptides. Anal. Bioanal. Chem. 410, 5825–5837. https://doi.org/10.1007/s00216-018-1199-z (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
White, R. M. et al. Transparent adult zebrafish as a tool for in vivo transplantation analysis. Cell Stem Cell 2, 183–189. https://doi.org/10.1016/j.stem.2007.11.002 (2008).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Nagayoshi, S. et al. Insertional mutagenesis by the Tol2 transposon-mediated enhancer trap approach generated mutations in two developmental genes: tcf7 and synembryn-like. Development 135, 159–169. https://doi.org/10.1242/dev.009050 (2008).CAS 
Article 
PubMed 

Google Scholar 
Perciedu Sert, N. et al. The ARRIVE guidelines 2.0: Updated guidelines for reporting animal research. Exp. Physiol. 105, 1459–1466. https://doi.org/10.1113/EP088870 (2020).Article 

Google Scholar 
Elendt, B. P. Selenium deficiency in Crustacea. Protoplasma 154, 25–33. https://doi.org/10.1007/BF01349532 (1990).CAS 
Article 

Google Scholar 
Sud, M. et al. LMSD: LIPID MAPS structure database. Nucleic Acids Res. 35, D527–D532. https://doi.org/10.1093/nar/gkl838 (2007).CAS 
Article 
PubMed 

Google Scholar 
Race, A. M., Styles, I. B. & Bunch, J. Inclusive sharing of mass spectrometry imaging data requires a converter for all. J. Proteom. 75, 5111–5112. https://doi.org/10.1016/j.jprot.2012.05.035 (2012).CAS 
Article 

Google Scholar 
Robichaud, G., Garrard, K. P., Barry, J. A. & Muddiman, D. C. MSiReader: An open-source interface to view and analyze high resolving power MS imaging files on Matlab platform. J. Am. Soc. Mass Spectrom. 24, 718–721. https://doi.org/10.1007/s13361-013-0607-z (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar  More

Ryther, J. H. Photosynthesis and fish production in the sea. Sci. (80-.) 166, 72–76 (1969).ADS 
CAS 
Article 

Google Scholar 
Follows, M. J., Dutkiewicz, S., Grant, S. & Chisholm, S. W. Emergent biogeography of microbial communities in a model ocean. Sci. (80-.). 315, 1843–1846 (2007).ADS 
CAS 
Article 

Google Scholar 
Edwards, K. F., Litchman, E. & Klausmeier, C. A. Functional traits explain phytoplankton community structure and seasonal dynamics in a marine ecosystem. Ecol. Lett. 16, 56–63 (2013).PubMed 
Article 

Google Scholar 
Nemergut, D. R. et al. Patterns and processes of microbial community assembly. Microbiol. Mol. Biol. Rev. 77, 342–356 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Villarino, E. et al. Large-scale ocean connectivity and planktonic body size. Nat. Commun. 9, 142 (2018).Collins, S., Rost, B. & Rynearson, T. A. Evolutionary potential of marine phytoplankton under ocean acidification. Evol. Appl. 7, 140–155 (2014).CAS 
PubMed 
Article 

Google Scholar 
Rusch, D. B. et al. The Sorcerer II global ocean sampling expedition: Northwest Atlantic through Eastern Tropical Pacific. PLOS Biol. 5, e77 (2007).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
de Vargas, C. et al. Eukaryotic plankton diversity in the sunlit ocean. Sci. (80-.). 348, 1261605–1/11 (2015).Sunagawa, S. et al. Structure and function of the global ocean microbiome. Sci. (80-.) 348, 1–10 (2015).Article 
CAS 

Google Scholar 
Fuhrman, J. A. et al. A latitudinal diversity gradient in planktonic marine bacteria. Proc. Natl Acad. Sci. 105, 7774–7778 (2008).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Righetti, D., Vogt, M., Gruber, N., Psomas, A. & Zimmermann, N. E. Global pattern of phytoplankton diversity driven by temperature and environmental variability. Sci. Adv. 5, 1–11 (2019).Article 

Google Scholar 
Cermeño, P. et al. The role of nutricline depth in regulating the ocean carbon cycle. PNAS 105, 20344–20349 (2008).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Barton, A. D., Dutkiewicz, S., Flierl, G., Bragg, J. & Follows, M. J. Patterns of diversity in marine phytoplankton. Sci. (80-.) 327, 1509–1511 (2010).ADS 
CAS 
Article 

Google Scholar 
Mantyla, A. W., Venrick, E. L. & Hayward, T. L. Primary production and chlorophyll relationships, derived from ten year of CalCOFI measurements. Calif. Cooperative Ocean. Fish. Investig. Rep. 36, 159–166 (1995).
Google Scholar 
Hayward, T. L. & Venrick, E. L. Nearsurface pattern in the California Current: Coupling between physical and biological structure. Deep. Res. Part II Top. Stud. Oceanogr. https://doi.org/10.1016/S0967-0645(98)80010-6 (1998).Article 

Google Scholar 
Venrick, E. L. Floral patterns in the California Current: The coastal-offshore boundary zone. J. Mar. Res. 67, 89–111 (2009).Article 

Google Scholar 
Powell, J. R. & Ohman, M. D. Covariability of zooplankton gradients with glider-detected density fronts in the Southern California Current System. Deep Sea Res. Part II Top. Stud. Oceanogr. 112, 79–90 (2015).ADS 
CAS 
Article 

Google Scholar 
Taylor, A. G., Landry, M. R., Selph, K. E. & Wokuluk, J. J. Temporal and spatial patterns of microbial community biomass and composition in the Southern California Current Ecosystem. Deep. Res. Part II Top. Stud. Oceanogr. 112, 117–128 (2015).Catlett, D. et al. Diagnosing seasonal to multi-decadal phytoplankton group dynamics in a highly productive coastal ecosystem. Prog. Oceanogr. 197, 102637 (2021).Article 

Google Scholar 
Lilly, L. E. & Ohman, M. D. CCE IV: El Niño-related zooplankton variability in the southern California Current System. Deep. Res. Part I Oceanogr. Res. Pap. 140, 36–51 (2018).ADS 
Article 

Google Scholar 
Richardson, A. J. et al. Using continuous plankton recorder data. Prog. Oceanogr. 68, 27–74 (2006).ADS 
Article 

Google Scholar 
Wang, Z. et al. Microbial communities across nearshore to offshore coastal transects are primarily shaped by distance and temperature. Environ. Microbiol. 1462–2920.14734. https://doi.org/10.1111/1462-2920.14734 (2019).Wang, Y. et al. Patterns and processes of free-living and particle-associated bacterioplankton and archaeaplankton communities in a subtropical river-bay system in South China. Limnol. Oceanogr. 65, S161–S179 (2020).Ibarbalz, F. M. et al. Global Trends in Marine Plankton Diversity across Kingdoms of Life. Cell 1084–1097. https://doi.org/10.1016/j.cell.2019.10.008 (2019).Fuhrman, J. A., Cram, J. A. & Needham, D. M. Marine microbial community dynamics and their ecological interpretation. Nat. Rev. Microbiol. 13, 133–146 (2015).CAS 
PubMed 
Article 

Google Scholar 
Gilbert, J. A. et al. Defining seasonal marine microbial community dynamics. ISME J. 6, 298–308 (2012).CAS 
PubMed 
Article 

Google Scholar 
Karl, D. M. & Lukas, R. The Hawaii Ocean Time-series (HOT) program: background, rationale and field implementation. Deep. Res. Part II Top. Stud. Oceanogr. 43, 129–156 (1996).ADS 
CAS 
Article 

Google Scholar 
Steinberg, D. K. et al. Overview of the US JGOFS Bermuda Atlantic Time-series Study (BATS): A decade-scale look at ocean biology and biogeochemistry Overview of the US JGOFS Bermuda Atlantic Time-series Study (BATS): a decade-scale look at ocean biology and biogeochemistry. Deep. Res. Part II Top. Stud. Oceanogr. 48, 1405–1447 (2015).ADS 
Article 

Google Scholar 
Needham, D. M. & Fuhrman, J. A. Pronounced daily succession of phytoplankton, archaea and bacteria following a spring bloom. Nat. Microbiol. 1, 16005 (2016).Zhu, Z. et al. Understanding the blob bloom: Warming increases toxicity and abundance of the harmful bloom diatom Pseudo-nitzschia in California coastal waters. Harmful Algae 67, 36–43 (2017).CAS 
PubMed 
Article 

Google Scholar 
Mcclatchie, S. et al. State of the California Current 2015–16: Comparisons with the 1997–98 El Niño. Calif. Cooperative Ocean. Fish. Investig. Rep. 57, (2016).Walker, H. J. Jr et al. Unusual occurrences of fishes in the Southern California Current System during the warm water period of 2014–2018. Estuar. Coast. Shelf Sci. 236, 106634 (2020).Article 

Google Scholar 
Kahru, M., Jacox, M. G. & Ohman, M. D. CCE1: Decrease in the frequency of oceanic fronts and surface chlorophyll concentration in the California Current System during the 2014–2016 northeast Pacific warm anomalies. Deep. Res. Part I Oceanogr. Res. Pap. 140, 4–13 (2018).ADS 
Article 

Google Scholar 
Azam, F. et al. The Ecological Role of Water-Column Microbes in the Sea. Mar. Ecol. Prog. Ser. 10, 257–263 (1983).ADS 
Article 

Google Scholar 
Calbet, A. & Landry, M. R. Phytoplankton growth, microzooplankton grazing, and carbon cycling in marine systems. Limnol. Oceanogr. 49, 51–57 (2004).ADS 
CAS 
Article 

Google Scholar 
Buchan, A., LeCleir, G. R., Gulvik, C. A. & González, J. M. Master recyclers: features and functions of bacteria associated with phytoplankton blooms. Nat. Rev. Microbiol. 12, 686–698 (2014).CAS 
PubMed 
Article 

Google Scholar 
Kohonen, T. Exploration of very large databases by self-organizing maps. IEEE Int. Conf. Neural Networks – Conf. Proc. 1, (1997).Istvánovics, V. Eutrophication of Lakes and Reservoirs. Encycl. Inl. Waters 157–165 https://doi.org/10.1016/B978-012370626-3.00141-1 (2009).Partensky, F., Blanchot, J. & Vaulot, D. Differential distribution and ecology of Prochlorococcus and Synechococcus in oceanic waters: a review. Bull. Oceanogr. Monaco 19, 457–475 (1999).
Google Scholar 
Laws, E. A., Falkowski, P. G., Smith, W. O., Ducklow, H. & McCarthy, J. J. Temperature effects on export production in the open ocean. Global Biogeochem. Cycles 14, (2000).Grover, J. P. Resource Competition in a Variable Environment: Phytoplankton Growing According to Monod’s Model. Am. Nat. 136, 771–789 (1990).Article 

Google Scholar 
Benincá, E. et al. Chaos in a long-term experiment with a plankton community. Nature 451, 822–825 (2008).ADS 
PubMed 
Article 
CAS 

Google Scholar 
Williams, R. G. & Follows, M. J. Ocean Dynamics and the Carbon Cycle: Principles and Mechanisms. Book (2011).Lindegren, M., Checkley, D. M., Ohman, M. D., Koslow, J. A. & Goericke, R. Resilience and stability of a pelagic marine ecosystem. Proc. R. Soc. B Biol. Sci. 283, (2016).Vallina, S. M. et al. Global relationship between phytoplankton diversity and productivity in the ocean. Nat. Commun. 1–10 https://doi.org/10.1038/ncomms5299 (2014).Chase, J. M. & Leibold, M. A. Spatial scale dictates the productivity-biodiversity relationship. Nature 416, 427–430 (2002).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Jacox, M. G., Edwards, C. A., Hazen, E. L. & Bograd, S. J. Coastal Upwelling Revisited: Ekman, Bakun, and Improved Upwelling Indices for the U.S. West Coast. J. Geophys. Res. Ocean. 123, 7332–7350 (2018).ADS 
Article 

Google Scholar 
Zaba, K. D. & Rudnick, D. L. The 2014-2015 warming anomaly in the Southern California Current System observed by underwater gliders. Geophys. Res. Lett. 43, 1241–1248 (2016).ADS 
Article 

Google Scholar 
Weber, E. D. et al. State of the California Current 2019–2020: Back to the Future With Marine Heatwaves? Front. Mar. Sci. 8, (2021).Closset, I. et al. Diatom response to alterations in upwelling and nutrient dynamics associated with climate forcing in the California Current System. Limnol. Oceanogr. 1–16. https://doi.org/10.1002/lno.11705 (2021).Kenitz, K. M. et al. Environmental drivers of population variability in colony-forming marine diatoms. Limnol. Oceanogr. 65, 2515–2528 (2020).ADS 
Article 

Google Scholar 
Mullin, M. M. Biomasses of large-celled phytoplankton and their relation to the nitricline and grazing in the California current system off Southern California, 1994–1996. Calif. Cooperative Ocean. Fish. Investig. Rep. 39, 117–123 (1998).
Google Scholar 
Rykaczewski, R. R. & Checkley, D. M. Influence of ocean winds on the pelagic ecosystem in upwelling regions. PNAS 105, 1965–1970 (2007).ADS 
Article 

Google Scholar 
Grzymski, J. J. & Dussaq, A. M. The significance of nitrogen cost minimization in proteomes of marine microorganisms. ISME J. 6, 71–80 (2012).Margalef, R. Life-forms of phytoplankton as survival alternatives in an unstable environment. Ocean. Acta 1, (1978).Falkowski, P. G. & Oliver, M. J. Mix and match: How climate selects phytoplankton. Nat. Rev. Microbiol. 5, 813–819 (2007).Mende, D. R. et al. Environmental drivers of a microbial genomic transition zone in the ocean’s interior. Nat. Microbiol. 2, 1367–1373 (2017).Phoma, B. S. & Makhalanyane, T. P. Depth-dependent variables shape community structure and functionality in the Prince Edward Islands. Microb. Ecol. 81, 396–409 (2021).Kahru, M. & Mitchell, B. G. Seasonal and nonseasonal variability of satellite-derived chlorophyll and colored dissolved organic matter concentration in the California Current. J. Geophys. Res. Ocean. 106, 2517–2529 (2001).ADS 
CAS 
Article 

Google Scholar 
Barth, A., Walter, R. K., Robbins, I. & Pasulka, A. Seasonal and interannual variability of phytoplankton abundance and community composition on the Central Coast of California. Mar. Ecol. Prog. Ser. 637, (2020).Powell, J. R. & Ohman, M. D. Changes in zooplankton habitat, behavior, and acoustic scattering characteristics across glider-resolved fronts in the Southern California Current System. Prog. Oceanogr. 134, 77–92 (2015).ADS 
Article 

Google Scholar 
Taylor, A. G. & Landry, M. R. Phytoplankton biomass and size structure across trophic gradients in the southern California Current and adjacent ocean ecosystems. Mar. Ecol. Prog. Ser. 592, 1–17 (2018).ADS 
CAS 
Article 

Google Scholar 
Dutkiewicz, S., Follows, M. J. & Bragg, J. G. Modeling the coupling of ocean ecology and biogeochemistry. Glob. Biogeochem. Cycles 23, 1–15 (2009).Article 
CAS 

Google Scholar 
D’Ovidio, F., De Monte, S., Alvain, S., Dandonneau, Y. & Lévy, M. Fluid dynamical niches of phytoplankton types. Proc. Natl Acad. Sci. U. S. A. 107, 18366–18370 (2010).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Clayton, S., Dutkiewicz, S., Jahn, O. & Follows, M. J. Dispersal, eddies, and the diversity of marine phytoplankton. Limnol. Oceanogr. Fluids Environ. 3, 182–197 (2013).Article 

Google Scholar 
Moisan, T. A., Rufty, K. M., Moisan, J. R. & Linkswiler, M. A. Satellite observations of phytoplankton functional type spatial distributions, phenology, diversity, and ecotones. Front. Mar. Sci. 4, 1–24 (2017).Article 

Google Scholar 
Combes, V. et al. Cross-shore transport variability in the California Current: Ekman upwelling vs. eddy dynamics. Prog. Oceanogr. 109, 78–89 (2013).ADS 
Article 

Google Scholar 
Chenillat, F., Rivière, P., Capet, X., Franks, P. J. S. & Blanke, B. California coastal upwelling onset variability: cross-shore and bottom-up propagation in the planktonic ecosystem. PLoS ONE 8, (2013).Chenillat, F., Franks, P. J. S. & Combes, V. Biogeochemical properties of eddies in the California Current System. Geophys. Res. Lett. 43, 5812–5820 (2016).ADS 
CAS 
Article 

Google Scholar 
Edwards, K. F., Thomas, M. K., Klausmeier, C. A. & Litchman, E. Allometric scaling and taxonomic variation in nutrient utilization traits and maximum growth rate of phytoplankton. Limnol. Oceanogr. 57, 554–566 (2012).ADS 
Article 

Google Scholar 
Wells, B. K. et al. State of the California Current 2016–17: Still anything but ‘normal’ in the north. Calif. Cooperative Ocean. Fish. Investig. Rep. 58 (2017).Thompson, A. R. et al. State of the California Current 2017–18: Still not quite normal in the north and getting interesting in the south. Calif. Cooperative Ocean. Fish. Investig. Rep. 59 (2018).Ward, C. S. et al. Annual community patterns are driven by seasonal switching between closely related marine bacteria. ISME J. 11, 1412–1422 (2017).Bograd, S. J., Schroeder, I. D. & Jacox, M. G. A water mass history of the Southern California current system. Geophys. Res. Lett. 46, 6690–6698 (2019).ADS 
Article 

Google Scholar 
Parada, A. E., Needham, D. M. & Fuhrman, J. A. Every base matters: Assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ. Microbiol. 18 (2016).Amaral-Zettler, L. A., McCliment, E. A., Ducklow, H. W. & Huse, S. M. A method for studying protistan diversity using massively parallel sequencing of V9 hypervariable regions of small-subunit ribosomal RNA Genes. PLoS ONE 4, (2009).Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.J. 17, (2011).Callahan, B. J., Mcmurdie, P. J., Rosen, M. J., Han, A. W. & A, A. J. DADA2: High resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bokulich, N. A. et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome 6 (2018).Pedregosa, F. et al. Scikit-learn: Machine learning in Python. J. Mach. Learn. Res. 12 (2011).Pruesse, E. et al. SILVA: A comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Res. 35 (2007).Guillou, L. et al. The Protist Ribosomal Reference database (PR2): A catalog of unicellular eukaryote Small Sub-Unit rRNA sequences with curated taxonomy. Nucleic Acids Res. 41 (2013).McMurdie, P. J. & Holmes, S. Waste Not, Want Not: Why Rarefying Microbiome Data Is Inadmissible. PLoS Comput. Biol. 10 (2014).Gloor, G. B., Wu, J. R., Pawlowsky-Glahn, V. & Egozcue, J. J. It’s all relative: analyzing microbiome data as compositions. Ann. Epidemiol. 26 (2016).Cameron, E. S., Schmidt, P. J., Tremblay, B. J. M., Emelko, M. B. & Müller, K. M. To rarefy or not to rarefy: Enhancing microbial community analysis through next-generation sequencing. bioRxiv. https://doi.org/10.1101/2020.09.09.290049 (2020).Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5-7. (2020).Bowman, J. S., Amaral-zettler, L. A., Rich, J. J., Luria, C. M. & Ducklow, H. W. Bacterial community segmentation facilitates the prediction of ecosystem function along the coast of the western Antarctic Peninsula. Nat. Publ. Gr. 11, 1460–1471 (2017).
Google Scholar 
Boelaert, J., Bendhaiba, L., Olteanu, M. & Villa-Vialaneix, N. SOMbrero: An R package for numeric and non-numeric self-organizing maps. Adv. Intell. Syst. Comput 295, 219–228 (2014).
Google Scholar 
Johnson, J. B. & Omland, K. S. Model selection in ecology and evolution. Trends Ecol. Evol. 19, 101–108 (2004).PubMed 
Article 

Google Scholar 
James, C. C. et al. Influence of nutrient supply on plankton microbiome biodiversity and distribution in a coastal upwelling region. https://doi.org/10.5281/zenodo.6359865 (2022).Legendre, P. & Legendre, L. Numerical ecology (Elsevier, 2012). More

Study siteThe study area covers the campus of Suranaree University of Technology (SUT) and its surrounding landscape in Muang, Nakhon Ratchasima, Thailand (14.879° N, 102.018° E; Fig. 1). The university campus covers about 11.2 km2, and comprises a matrix of human modified lands interspersed with mixed deciduous forest fragments (at the onset of this study we identified there were 37 mixed deciduous forest fragments on campus, mean = 7.36 ± 1.48 ha, range = 0.45–45.6 ha [note, “±” is used for standard error throughout the text]). More than 15,000 students are enrolled at SUT, and there are numerous multi-story classrooms, laboratory and workshop buildings, residential housing, parking areas, eating and sports facilities, an elementary school, and a large hospital on the university campus. During the first term of the 2019 school year, 7622 students, as well as numerous SUT staff, lived in on-campus residential areas. The landscape surrounding the university is primarily dominated by agriculture, though there are also patches of less-disturbed areas as well as several densely populated villages and suburban housing divisions among the monoculture plots of upland crops (e.g., cassava, maize, and eucalyptus).Figure 1Study site map illustrating the land-use types spanning the area where the Malayan kraits (Bungarus candidus) were tracked in Muang Nakhon Ratchasima, Nakhon Ratchasima province, Thailand. Map created using QGIS v.3.8.2 (https://qgis.org/) in combination with Inkscape v.1.1.0 (https://inkscape.org/).Full size imageThe study site is located within the Korat Plateau region with an altitude range of 205–285 m above sea level. Northeast Thailand has a tropical climate, and the average daily temperature from 1 January 2018 to 31 December 2020 in Muang Nakhon Ratchasima was 28.29 °C, with daily averages ranging from 19.3 to 34.1 °C38. The region receives an average annual rainfall ranging from 1270 to 2000 mm39. There are three distinct seasons in northeast Thailand: cold, wet, and hot, each are classified by annual changes in temperature and rainfall. Cold season is typically between mid-October and mid-February, hot season is generally from mid-February to May, while the highly unpredictable rainfall of the wet season is predominantly concentrated between the months May to October39,40.Due to the representation of agriculture, semi-urban, and suburban areas with patches of more natural areas all within a relatively small area, we determined the university campus provided an ideal setting to examine how land-use features and human activity influence the movements of B. candidus. Additionally, past studies have indicated northeast Thailand hosts the most bites by B. candidus in Thailand29,33, making sites like ours ideal.Study animalsWe opportunistically sampled Malayan kraits captured as a result of notifications from locals and ad-hoc encounters during transit due to low detectability in visual encounter surveys, in addition to those discovered through unstandardized visual encounter surveys. Upon capture, we collected morphometric data, including snout-vent length (SVL), tail length (TL), mass, and sex (Table 1, Supp. Table 1). We measured body lengths with a tape measure, measured body mass with a digital scale, and determined sex via cloacal probing, all while the snakes were anesthetized via inhaling vaporized isoflurane. We then housed individuals with an SVL > 645 mm and mass > 50 g in plastic boxes (with refugia and water) prior to surgical transmitter implantation by a veterinarian from the Nakhon Ratchasima Zoo. We attempted to minimize the time snakes were in captivity awaiting implantation; however, delays arose due to the veterinarian’s availability, the snake being mid-ecdysis, or the snake having a bolus that needed to pass through the digestive tract before implantation (n = 21 implantations, mean = 5.02 ± 0.61 days, range = 0.60–13.02 days). The Nakhon Ratchasima Zoo veterinarian implanted radio transmitters (1.8 g BD-2 or 3.6 g SB-2 Holohil Inc, Carp, Canada) into the coelomic cavity using procedures described by Reinert and Cundall41, while the snake was anesthetized. We assigned each individual an ID according to sex and individual detection number (e.g., M02 = a male was the second B. candidus individual documented during the study). We released the implanted individuals as close as possible to their capture locations (mean = 65.31 m ± 13.7 m, range = 0–226.42 m), though on six occasions we moved individuals ≥ 100 m because the individual came from either residential areas or a busy road (all but one were moved  800 mm; thus, nine of the males were adults and four were juveniles (though two of the males had an SVL > 720 mm, and therefore likely sub-adults). The single telemetered female was an adult.Individual tracking durations varied (mean = 106.46 ± 15.36 days, range = 28.5–222.77 days; Supp. Fig. 1), as many individuals were lost due to unexpected premature transmitter failures (n = 5) or unsuccessful recapture efforts due to individuals sheltering under large buildings as the transmitter reached the end of its battery life (n = 4). We only recorded one confirmed mortality in the study, M01, who was killed by a motorized vehicle when crossing a road (n = 1). Another three individuals were lost due to unknown reasons, which may have been due to premature transmitter failure, mortality, or the animal moving beyond radio signal despite extensive search efforts. Thus, we only successfully recaptured and re-implanted five individuals (M01 once, M02 twice, M07 once, M27 once, and M33 twice). Transmitter batteries generally lasted approximately 90–110 days, so we aimed to replace transmitters after ≥ 90 days of use. At the end of the study, only one individual was successfully recaptured to remove the transmitter.Data collectionWe used very high frequency radio-telemetry to locate each telemetered individual on average every 24.20 h (SE ± 0.41, 0.17–410.0 h; see Supp. Fig. 2 for distribution of tracking time lags). We aimed to locate each individual’s shelter locations once each day during the daylight (06:00–18:00 h); however, we were occasionally (n = 34 days) unable to locate a snake for several consecutive days when we were unable to obtain radio signal due to an individual having moved far away or deep underneath a large structure. There were also a few occasions where we were unable to track snakes due to prolonged and heavy rainfall (n = 4 days), as the moisture damages equipment, or other reasons (n = 4 days). We additionally located snakes nocturnally (18:00–06:00 h) ad hoc and in an attempt to observe nocturnal behaviors and movement pathways when animals were active. We defined fixes as any time a telemetered individual was located, and relocations (i.e., moves) as the occasions where we located an individual > 5 m from its previous known location.Each day we manually honed in on signal via a radio receiver to locate individuals (as described by Amelon et al.42, and recorded locations in Universal Transverse Mercator (UTM; 47 N World Geodetic System 84) coordinate reference system with a handheld global positioning system (GPS) unit (Garmin 64S GPS, Garmin International, Inc., Olathe, Kansas) directly above the sheltered snake. We generally approached within one meter of sheltering snakes during daylight to precisely record shelter locations and identify shelter type. Since we could not visually confirm snake locations, we methodically eliminated all possible locations where the snake could possibly be while at close range with the minimum possible gain on the radio receiver.Telemetered kraits tended to be inactive and sheltering underground during the daylight, thus we were confident that our diurnal location checks would not affect their movements. However, in some cases we resorted to determining an individual’s location via triangulation, where multiple lines cast from different vantage points towards the snake intersect on the snake’s location on the GPS, allowing us to determine the animal’s coordinate location from approximately 10–30 m away. This helped ensure that we recorded locations with greater accuracy when snakes sheltered underneath large buildings, as it allowed us to move away from large structures that hindered the GPS accuracy. This technique was also implemented during some nocturnal location checks when a snake was believed to be active among dense vegetation, in an attempt to prevent disturbance of the animals’ natural behavior. While we did hope to gain visual observations of active individuals during the night, we exercised more caution during nocturnal location checks, typically maintaining a minimum distance of approximately 5 m in attempt to lessen the chances of disturbing an active individual’s behavior. If the animal was active we recorded the animal’s observed behavioral state (i.e., moving, feeding, or foraging). When the radio signal was stable and the individual was not visible, we recorded the animal’s behavior as “sheltering”. We strived for an accuracy of  5 m difference), and land-use type (e.g., mixed deciduous forest, human-settlement, semi-natural area, agriculture, plantation; see Supp. Figs. 3 and 4 for photos of land-use types), behavior (e.g., sheltering, moving, foraging, or feeding), and shelter type (e.g., anthropogenic, burrow, or unknown, note we also recorded if we suspected the shelter to be part of a termite tunnel complex due to a close proximity to a visible termite mound; Supp. Fig. 5).During each location check we recorded the straight-line distance between the current and previous locations (distance moved/step length) with the GPS device. We then used step-lengths to summarize their movements by estimating the mean daily displacement (MDD; the total distance moved divided by the number of days the snake was located) and mean movement distance (MMD; the mean relocation distance, excludes distances ≤ 5). In order to limit biases due to some snakes being located multiple times within a given day/night, we limited our sample for estimating MMD and MDD to only include a single location per day. This was accomplished by manually removing “extra” nocturnal location checks that occurred within the same day, making sure to have all shelter relocations present within the dataset. When calculating MDD, we used the total number of daily location checks rather than the number of days between the individual’s tracking start and stop date since there were some days where individuals were not tracked. We also used the same one location check per day dataset to calculate movement/relocation probabilities and to examine each individual’s MMD, MDD, and relocation probability for the overall tracking duration as well as for each season.When feasible, we positioned a Bushnell (Bushnell Corporation, Overland Park, Kansas) time lapse field camera (Trophy Cam HD Essential E3, Model:119837) with infrared night capability on a tripod spaced 2–5 m from occupied shelter sites. We positioned the cameras so that we may gather photos of the focal snake as it exited the shelter site and/or behaviors exhibited near the shelter. We programmed the cameras using a combined setting, including field scan, which continuously captured one photo every minute, along with a motion sensor setting, which took photos upon movement trigger outside of the regular 1-min intervals.Space use and site fidelityAll analyses and most visualizations were done in R v.4.0.5 using RStudio v.1.4.1106 43,44. We attempted to estimate home ranges for the telemetered B. candidus individuals using autocorrelated kernel density estimates (AKDEs) using R package ctmm v.0.6.045,46 in order to better understand the spatial requirements of B. candidus. However, examination of the variograms revealed that the majority of the variograms had not fully stabilized (i.e., limited evidence of range stability in our sample), and many individuals had extremely low effective sample sizes (21.82 ± 9.75, range = 1.49–135.75; Supp. Table 4). Therefore, we do not report home ranges in this text, as the AKDE estimates would violate the assumption of range residency and either underestimate or misrepresent B. candidus spatial requirements. We also examined the speed estimates resulting from fitted movement models. Resulting variograms and tentative home range estimates are included in a supplementary file for viewing only (Supp. Fig. 6, Supp. Table 4). The original code is from Montaño et al.47.Since our data was not sufficient to estimate home range size for the telemetered B. candidus, we instead used Dynamic Brownian Bridge Movement Models (dBBMMs) with the R package move v.4.0.648 to estimate within study occurrence distributions. We caution readers that these are not home range estimates but instead modeling the potential movement pathways animals could have traversed49. Use of dBBMMs not only allows us to estimate occurrence distributions for each individual, thus helping us better understand the animal’s movement pathways and resource use, but it also allows us to examine movement patterns through dBBMM derived motion variance50,51. We selected a window size of 19 and margin size of 5, to catch short resting periods with the margin, while the window size of 19 is long enough to get a valid estimate of motion variance when the animals exhibit activity/movement. Contours however are somewhat arbitrary; therefore, we used three different contours levels (90%, 95%, 99%) to estimate dBBMM occurrence distributions (using R packages adehabitatHR v.0.4.19, and rgeos v.0.5.5), and show the sensitivity to contour choice52,53.All movement data, either including initial capture locations or beginning with the first location check ~ 24 h post release, was used for production of both the AKDEs and dBBMMs for each individual. We also estimated dBBMM occurrence distributions for each telemetered individual with the exception of M29, which only made three small moves within a burrow complex during the short time he was radio-tracked before transmitter failure.We compared space use estimates to two previously published B. candidus tracking datasets34,36, and one unpublished dataset shared on the Zenodo data repository54, all originating from the Sakaerat Biosphere Reserve (approximately 41 km to the south of our study site): two adult males from within the forested area of the reserve [one tracked every 27.8 ± 0.99 h over a period of 103 days, the other tracked every 38.63 ± 11.2 h over a period of 30.58 days]34,54, and a juvenile male from agriculture on a forest boundary [tracked every 50.19 ± h for 66.91 days]36. The previous studies on B. candidus only tracked the movements of a single individual each, had coarser tracking regimes, and used traditional—fundamentally flawed methods55,56—to estimate space use34,36. Therefore, we ran dBBMMs with these previous datasets using the same window (19) and margin size (5).To quantify site reuse and time spent at sites (residency time) we used recursive analysis with the R package recurse v.1.1.257. We defined each site as a circular area with a radius of 5 m around each unique location (matching the targeted GPS accuracy). Then we calculated each individual’s overall number of relocations, each individual’s total number of relocations to each site, and each individual’s site revisit frequency and residency time at each unique site. Then we plotted revisited locations on a land-use map with space use estimates (95% and 99% dBBMM) in an attempt to help identify and highlight activity centers for telemetered individuals (see Supp. Figs. 7–13). All maps were created using Quantum Geographic Information System (QGIS v.3.8.2).Habitat selectionWe used Integrated Step Selection Function models (ISSF) to examine the influence of land-use features on the movements of B. candidus at both the individual and population levels. We included movement data from all male individuals that used more than one habitat feature in our ISSF analysis. Therefore, we excluded F16 and M29 who both only used settlement habitat. Excluding M29 was justified by the individual having been tracked for the shortest duration (19 days) and had the fewest number of moves (n = 3), thus there were not enough relocations for ISSF models to work effectively. Using modified code from Smith et al.51 that used ISSF with Burmese python radio-telemetry data, we used the package amt v.0.1.458 to run ISSF for each individual, with Euclidean distance to particular land-use features (natural areas, agriculture, settlement, buildings, and roads) to determine association or avoidance of features. Cameron Hodges created all land-use shape files in QGIS by digitizing features from satellite imagery and verified all questionable satellite land-use types via on-ground investigation.The semi-natural areas, plantations, mixed deciduous forest and water bodies (such as irrigation canals and ponds which have densely vegetated edges) were all combined into a single layer of less-disturbed habitats which we refer to as “natural areas”. All feature raster layers were then converted into layers with a gradient of continuous values of Euclidean distances to the land-use features, and were inverted in order to avoid zero-inflation of distance to feature values and to make the resulting model directional effects easier to more intuitive. We were able to generate 200 random steps per each observed step (following Smith et al.51), due to the coarse temporal resolution of manually collected radio-telemetry data (i.e., we were not computational limited when deciding the number of random locations). Higher numbers of random steps are preferable as they can aid in detecting smaller effects and rarer landscape features59.To investigate individual selection, we created nine different models testing for association to habitat features, with one being a null model which solely incorporated step-length and turning angle to predict movement60, five examining land-use features individually (agriculture, buildings, settlement, natural areas, roads), and the other three being multi-factor models. Each model considers distance to a land-use variable, step-length, and turn-angle as an aspect of the model. After running each of the nine models for each individual, we then examined the AIC for each model, point estimates (with lower and upper confidence intervals), and p-values in order to identify the best models for each individual and determine the strongest relationships and trends among the samples. We considered models with ∆ AIC  More

Wäckers, F. L. & van Rijn, P. C. J. Food for Protection: An Introduction. In Plant-Provided Food for Carnivorous Insects: A Protective Mutualism and its Applications (eds Wäckers, F. L. et al.) 1–14 (Cambridge University Press, 2005). https://doi.org/10.1017/CBO9780511542220.002.Chapter 

Google Scholar 
Benelli, G. et al. The impact of adult diet on parasitoid reproductive performance. J. Pest Sci. 90, 807–823. https://doi.org/10.1007/s10340-017-0835-2 (2017).Article 

Google Scholar 
Wäckers, F. Assessing the suitability of flowering herbs as parasitoid food sources: Flower attractiveness and nectar accessibility. Biol. Control. 29, 307–314. https://doi.org/10.1016/j.biocontrol.2003.08.005 (2004).Article 

Google Scholar 
Heimpel, G. E. & Jervis, M. A. Does Floral Nectar Improve Biological Control by Parasitoids? In Plant-Provided Food for Carnivorous Insects: A Protective Mutualism and its Applications (eds Wäckers, F. L. et al.) 267–304 (Cambridge University Press, 2009). https://doi.org/10.1017/CBO9780511542220.010.Chapter 

Google Scholar 
Wäckers, F. L. Suitability of (extra-)Floral Nectar, Pollen, and Honeydew as Insect Food Sources. In Plant-Provided Food for Carnivorous Insects: A Protective Mutualism and its Applications (eds Wäckers, F. L. et al.) 17–74 (Cambridge University Press, 2005). https://doi.org/10.1017/CBO9780511542220.003.Chapter 

Google Scholar 
Wäckers, F. L., van Rijn, P. C. & Heimpel, G. E. Honeydew as a food source for natural enemies: Making the best of a bad meal?. Biol. Control. 45, 176–184. https://doi.org/10.1016/j.biocontrol.2008.01.007 (2008).Article 

Google Scholar 
Jervis, M. A., Ellers, J. & Harvey, J. A. Resource acquisition, allocation, and utilization in parasitoid reproductive strategies. Annu. Rev. Entomol. 53, 361–385. https://doi.org/10.1146/annurev.ento.53.103106.093433 (2008).CAS 
Article 

Google Scholar 
Rosenheim, J. A. An evolutionary argument for egg limitation. Evolution 50, 2089–2094 (1996).Article 

Google Scholar 
Rosenheim, J. A. The relative contributions of time and eggs to the cost of reproduction. Evolution 53, 376–385 (1999).Article 

Google Scholar 
Rosenheim, J. A., Jepsen, S. J., Matthews, C. E., Smith, D. S. & Rosenheim, M. R. Time limitation, egg limitation, the cost of oviposition, and lifetime reproduction by an insect in nature. Am. Nat. 172, 486–496 (2008).Article 

Google Scholar 
Rosenheim, J. A., Heimpel, G. E. & Mangel, M. Egg maturation, egg resorption and the costliness of transient egg limitation in insects. Proc. Royal Soc London. Ser. B Biol. Sci. 267, 1565–1573 (2000).CAS 
Article 

Google Scholar 
Takasu, K. & Hirose, Y. Host searching behavior in the parasitoid Ooencyrtus nezarae Ishii (Hymenoptera: Encyrtidae) as influenced by non-host food deprivation. Appl. Entomol. Zool. 26, 415–417. https://doi.org/10.1303/aez.26.415 (1991).Article 

Google Scholar 
Sisterson, M. S. & Averill, A. L. Costs and benefits of food foraging for a braconid parasitoid. J. Insect Behav. 15, 571–588. https://doi.org/10.1023/A:1016389402543 (2002).Article 

Google Scholar 
Jacob, H. S. & Evans, E. W. Influence of food deprivation on foraging decisions of the parasitoid Bathyplectes curculionis (Hymenoptera: Ichneumonidae). Ann. Entomol. Soc. Am. 94, 605–611. https://doi.org/10.1603/0013-8746(2001)094[0605:iofdof]2.0.co;2 (2001).Article 

Google Scholar 
Siekmann, G., Keller, M. A. & Tenhumberg, B. The sweet tooth of adult parasitoid cotesia rubecula: Ignoring hosts for nectar?. J. Insect Behav. 17, 459–476. https://doi.org/10.1023/b:joir.0000042535.76279.c7 (2004).Article 

Google Scholar 
Williams, L., Deschodt, P., Pointurier, O. & Wyckhuys, K. A. Sugar concentration and timing of feeding affect feeding characteristics and survival of a parasitic wasp. J. Insect Physiol. 79, 10–18. https://doi.org/10.1016/j.jinsphys.2015.05.004 (2015).CAS 
Article 

Google Scholar 
Talamas, E. J. et al. A maximalist approach to the systematics of a biological control agent: Gryon aetherium Talamas, sp. nov. (Hymenoptera, Scelionidae). J. Hymenopt. Res. 87, 323–480. https://doi.org/10.3897/jhr.87.72842 (2021).Article 

Google Scholar 
Straser, R. K., Daane, K. M., Talamas, E. & Wilson, H. Evaluation of egg parasitoid Hadronotus pennsylvanicus as a prospective biocontrol agent of the leaffooted bug Leptoglossus zonatus. Biocontrol https://doi.org/10.1007/s10526-022-10131-z (2022).Article 

Google Scholar 
Mitchell, P. L. & Mitchell, F. L. Parasitism and predation of leaffooted bug (Hemiptera: Heteroptera: Coreidae) eggs. Ann. Entomol. Soc. Am. 79, 854–860. https://doi.org/10.1093/aesa/79.6.854 (1986).Article 

Google Scholar 
Yasuda, K. Function of the male pheromone of the leaf-footed plant bug, Leptoglossus australis (Fabricius) (Heteroptera: Coreidae) and its kairomonal effect. Jpn. Agric. Res. Q. 32, 161 (1998).CAS 

Google Scholar 
Bates, S. L. & Borden, J. H. Parasitoids of Leptoglossus occidentalis Heidemann (Heteroptera: Coreidae) in British Columbia. J. Entomol. Soc. Br. Columbia 101, 143–144 (2004).
Google Scholar 
Maltese, M., Caleca, V., Guerrieri, E. & Strong, W. B. Parasitoids of Leptoglossus occidentalis Heidemann (Heteroptera: Coreidae) recovered in western North America and first record of its egg parasitoid Gryon pennsylvanicum (Ashmead) (Hymenoptera: Platygastridae) in California. The Pan-Pacific Entomol. 88, 347–355. https://doi.org/10.3956/2012-23.1 (2012).Article 

Google Scholar 
Roversi, P. F. et al. Pre-release risk assessment of the egg-parasitoid Gryon pennsylvanicum for classical biological control of Leptoglossus occidentalis. J. Appl. Entomol. 138, 27–35. https://doi.org/10.1111/jen.12062 (2013).Article 

Google Scholar 
Nechols, J. R., Tracy, J. L. & Vogt, E. A. Comparative ecological studies of indigenous egg parasitoids (Hymenoptera: Scelionidae: Encyrtidae) of the squash bug, Anasa tristis (Hemiptera: Coreidae). J. Kansas Entomol. Soc. 62, 177–188 (1989).
Google Scholar 
Cornelius, M. L., Buffington, M. L., Talamas, E. J. & Gates, M. W. Impact of the egg parasitoid, Gryon pennsylvanicum (Hymenoptera: Scelionidae), on sentinel and wild egg masses of the squash bug (Hemiptera: Coreidae) in Maryland. Environ. Entomol. 45, 367–375. https://doi.org/10.1093/ee/nvv228 (2016).Article 

Google Scholar 
Cornelius, M. L., Hu, J. S. & Vinyard, B. T. Comparative study of egg parasitism by Gryon pennsylvanicum (Hymenoptera: Scelionidae) on two squash bug species Anasa tristis and Anasa armigera (Hemiptera: Coreidae). Environ. Entomol. https://doi.org/10.1093/ee/nvy145 (2018).Article 

Google Scholar 
Daane, K. M. et al. Stink bugs and leaffooted bugs. Pistachio Prod. Man. Publ. 3545, 225–238 (2016).
Google Scholar 
Joyce, A. L., Higbee, B. S., Haviland, D. R. & Brailovsky, H. Genetic variability of two leaffooted bugs, Leptoglossus clypealis and Leptoglossus zonatus (Hemiptera: Coreidae) in the Central Valley of California. J. Econ. Entomol. 110, 2576–2589. https://doi.org/10.1093/jee/tox222 (2017).CAS 
Article 

Google Scholar 
Zalom, F. G., Haviland, D. R., Symmes, E. T. & Tollerup, K. Almonds: Insects and Mites. University of California, Agriculture and Natural Resources, Oakland, CA, USA, University of California IPM Pest Management Guidelines, Publication 3431 ed. (2018).Michailides, T. J., Rice, R. E. & Ogawa, J. M. Succession and significance of several hemipterans attacking a pistachio orchard. J. Econ. Entomol. 80, 398–406. https://doi.org/10.1093/jee/80.2.398 (1987).Article 

Google Scholar 
Michailides, T. The ‘Achilles heel’of pistachio fruit. Calif. Agric. 43, 10–11 (1989).
Google Scholar 
Michailides, T. J. & Morgan, D. P. Association of botryosphaeria panicle and shoot blight of pistachio with injuries of fruit caused by hemiptera insects and birds. Plant Dis. 100, 1405–1413. https://doi.org/10.1094/pdis-09-15-1077-re (2016).Article 

Google Scholar 
Daane, K. et al. Large bugs damage pistachio nuts most severely during midseason. Calif. Agric. 59, 95–102 (2005).Article 

Google Scholar 
Haviland, D., Bentley, W., Beede, R. & Daane, K. Pistachios: Insects and mites. Univ. California IPM Pest Manag. Guidel. Publ. 3461 (2018).Joyce, A. L., Barman, A. K., Doll, D. & Higbee, B. S. Assessing feeding damage from two leaffooted bugs, Leptoglossus clypealis Heidemann and Leptoglossus zonatus (Dallas) (Hemiptera: Coreidae), on four almond varieties. Insects 10, 333. https://doi.org/10.3390/insects10100333 (2019).Article 

Google Scholar 
Stahl, J. M., Scaccini, D., Pozzebon, A. & Daane, K. M. Comparing the feeding damage of the invasive brown marmorated stink bug to a native stink bug and leaffooted bug on California pistachios. Insects 11, 688. https://doi.org/10.3390/insects11100688 (2020).Article 

Google Scholar 
Olson, D. L. & Nechols, J. R. Effects of squash leaf trichome exudates and honey on adult feeding, survival, and fecundity of the squash bug (Heteroptera: Coreidae) egg parasitoid Gryon pennsylvanicum (Hymenoptera: Scelionidae). Environ. Entomol. 24, 454–458. https://doi.org/10.1093/ee/24.2.454 (1995).Article 

Google Scholar 
Sabbatini Peverieri, G., Furlan, P., Simoni, S., Strong, W. & Roversi, P. Laboratory evaluation of Gryon pennsylvanicum (Ashmead) (Hymenoptera: Platygastridae) as a biological control agent of Leptoglossus occidentalis Heidemann (Heteroptera: Coreidae). Biol. Control. 61, 104–111. https://doi.org/10.1016/j.biocontrol.2012.01.005 (2012).Article 

Google Scholar 
Cornelius, M. L., Vinyard, B. T., Mowery, J. D. & Hu, J. S. Ovipositional behavior of the egg parasitoid Gryon pennsylvanicum (Hymenoptera: Scelionidae) on two squash bug species Anasa tristis (Hemiptera: Coreidae) and Anasa armigera: Effects of parasitoid density, nutrition, and host egg chorion on parasitism rates. Environ. Entomol. 49, 1307–1315. https://doi.org/10.1093/ee/nvaa118 (2020).CAS 
Article 

Google Scholar 
Vogt, E. & Nechols, J. The influence of host deprivation and host source on the reproductive biology and longevity of the squash bug egg parasitoid Gryon pennsylvanicum (Ashmead) (Hymenoptera: Scelionidae). Biol. Control. 3, 148–154. https://doi.org/10.1006/bcon.1993.1022 (1993).Article 

Google Scholar 
Olson, D., Fadamiro, H., Lundgren, J. & Heimpel, G. E. Effects of sugar feeding on carbohydrate and lipid metabolism in a parasitoid wasp. Physiol. Entomol. 25, 17–26 (2000).CAS 
Article 

Google Scholar 
Jervis, M. A., Heimpel, G. E., Ferns, P. N., Harvey, J. A. & Kidd, N. A. C. Life-history strategies in parasitoid wasps: A comparative analysis of “ovigeny”. J. Animal Ecol. 70, 442–458. https://doi.org/10.1046/j.1365-2656.2001.00507.x (2001).Article 

Google Scholar 
Jervis, M. A. & Ferns, P. N. The timing of egg maturation in insects: Ovigeny index and initial egg load as measures of fitness and of resource allocation. Oikos 107, 449–461 (2004).Article 

Google Scholar 
Lee, J. C. & Heimpel, G. E. Effect of floral nectar, water, and feeding frequency on cotesia glomerata longevity. Biocontrol 53, 289–294 (2008).Article 

Google Scholar 
Wu, H., Meng, L. & Li, B. Effects of feeding frequency and sugar concentrations on lifetime reproductive success of Meteorus pulchricornis (Hymenoptera: Braconidae). Biol. Control. 45, 353–359. https://doi.org/10.1016/j.biocontrol.2008.01.017 (2008).CAS 
Article 

Google Scholar 
King, B. H. Offspring sex ratios in parasitoid wasps. Q. Rev. Biol. 62, 367–396. https://doi.org/10.1086/415618 (1987).Article 

Google Scholar 
Berndt, L. A. & Wratten, S. D. Effects of alyssum flowers on the longevity, fecundity, and sex ratio of the leafroller parasitoid Dolichogenidea tasmanica. Biol. Control. 32, 65–69. https://doi.org/10.1016/j.biocontrol.2004.07.014 (2005).Article 

Google Scholar 
Sabbatini Peverieri, G. et al. Host egg age of Leptoglossus occidentalis (Heteroptera: Coreidae) and parasitism by Gryon pennsylvanicum (Hymenoptera: Platygastridae). J. Econ. Entomol. 106, 633–640. https://doi.org/10.1603/ec12344 (2013).Article 

Google Scholar 
Abram, P. K., Brodeur, J., Urbaneja, A. & Tena, A. Nonreproductive effects of insect parasitoids on their hosts. Annu. Rev. Entomol. 64(1), 259–276 (2019).CAS 
Article 

Google Scholar 
Lewis, W. & Takasu, K. Use of learned odours by a parasitic wasp in accordance with host and food needs. Nature 348, 635–636 (1990).ADS 
Article 

Google Scholar 
Takasu, K. & Lewis, W. Importance of adult food sources to host searching of the larval parasitoid Microplitis croceipes. Biol. Control 5, 25–30 (1995).Article 

Google Scholar 
Wäckers, F. The effect of food deprivation on the innate visual and olfactory preferences in the parasitoid Cotesia rubecula. J. Insect Physiol. 40, 641–649 (1994).Article 

Google Scholar 
Lightle, D., Ambrosino, M. & Lee, J. C. Sugar in moderation: Sugar diets affect short-term parasitoid behaviour. Physiol. Entomol. 35, 179–185 (2010).CAS 
Article 

Google Scholar 
Varennes, Y.-D., Gonzalez Chang, M., Boyer, S. & Wratten, S. Nectar feeding increases exploratory behaviour in the aphid parasitoid Diaeretiella rapae (Mcintosh). J. Appl. Entomol. 140, 479–483 (2016).Article 

Google Scholar 
Takano, S. & Takasu, K. Food deprivation increases reproductive effort in a parasitoid wasp. Biol. Control. 133, 75–80. https://doi.org/10.1016/j.biocontrol.2019.03.010 (2019).Article 

Google Scholar 
Landis, D. A., Wratten, S. D. & Gurr, G. M. Habitat management to conserve natural enemies of arthropod pests in agriculture. Annu. Rev. Entomol. 45(1), 175–201 (2000).CAS 
Article 

Google Scholar 
Masner, L. A revision of gryon haliday in North America (Hymenoptera: Proctotrupoidea: Scelionidae). Can. Entomol. 115, 123–174. https://doi.org/10.4039/ent115123-2 (1983).Article 

Google Scholar 
Vogt, E. A. & Nechols, J. R. Diel activity patterns of the squash bug egg parasitoid Gryon pennsylvanicum (Hymenoptera: Scelionidae). Ann. Entomol. Soc. Am. 84, 303–308. https://doi.org/10.1093/aesa/84.3.303 (1991).Article 

Google Scholar 
Wiedemann, L. M., Canto-Silva, C. R., Romanowski, H. P. & Redaelli, L. R. Oviposition behavior of Gryon gallardoi (Hym.: Scelionidae) on eggs of Spartocera dentiventris (Hem.: Coreidae). Braz. J. Biol. 63, 133 (2003).CAS 
Article 

Google Scholar 
Friard, O. & Gamba, M. BORIS: a free, versatile open-source event-logging software for video/audio coding and live observations. Methods Ecol. Evol. 7, 1325–1330. https://doi.org/10.1111/2041-210x.12584 (2016).Article 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria. https://www.r-project.org/ (2019).Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48. https://doi.org/10.18637/jss.v067.i01 (2015).Article 

Google Scholar 
Hothorn, T., Bretz, F. & Westfall, P. Simultaneous inference in general parametric models. Biom. J. 50, 346–363 (2008).MathSciNet 
Article 

Google Scholar  More

Brown, R., Derksen, C, & Wang, L. A multi‐data set analysis of variability and change in Arctic spring snow cover extent, 1967–2008. J. Geophys. Res. Atmos. 115(D16), 1–16 (2010).Tan, A., Adam J. C., & Lettenmaier, D. P. Change in spring snowmelt timing in Eurasian Arctic rivers. J. Geophys. Rese. Atmos. 116(D3), 1–12 (2011).St. Jacques, J. M., & Sauchyn, D. J. Increasing winter baseflow and mean annual streamflow from possible permafrost thawing in the Northwest Territories, Canada. Geophys. Res. Lett. 36(1), 1–6 (2009).Liljedahl, A. K. et al. Pan-Arctic ice-wedge degradation in warming permafrost and its influence on tundra hydrology. Nat. Geosci. 9(4), 312–318 (2016).ADS 
CAS 
Article 

Google Scholar 
Farquharson, L. M. et al. Climate change drives widespread and rapid thermokarst development in very cold permafrost in the Canadian High Arctic. Geophys. Res. Lett. 46(12), 6681–6689 (2019).ADS 
Article 

Google Scholar 
Nitze, I. et al. Remote sensing quantifies widespread abundance of permafrost region disturbances across the Arctic and Subarctic. Nat. Commun. 9(1), 1–11 (2018).Article 

Google Scholar 
Lewkowicz, A. G. & Way, R. G. Extremes of summer climate trigger thousands of thermokarst landslides in a High Arctic environment. Nat. Commun. 10(1), 1–11 (2019).CAS 
Article 

Google Scholar 
Jones, M. K. W., Pollard, W. H. & Jones, B. M. Rapid initialization of retrogressive thaw slumps in the Canadian high Arctic and their response to climate and terrain factors. Environ. Res. Lett. 14(5), 055006 (2019).ADS 
Article 

Google Scholar 
Schuur, E. A. G. et al. Climate change and the permafrost carbon feedback. Nature 520(7546), 171–179 (2015).ADS 
CAS 
Article 

Google Scholar 
Sturm, M., Racine, C. & Tape, K. D. Increasing shrub abundance in the Arctic. Nature 411(6837), 546–547 (2001).ADS 
CAS 
Article 

Google Scholar 
Berner, L. T. et al. Summer warming explains widespread but not uniform greening in the Arctic tundra biome. Nat. Commun. 11(1), 1–12 (2020).ADS 
MathSciNet 
Article 

Google Scholar 
Tape, K. D. et al. Range expansion of moose in Arctic Alaska linked to warming and increased shrub habitat. PLoS ONE 11(4), e0152636 (2016).Article 

Google Scholar 
Ward, D. H. et al. Multi-decadal trends in spring arrival of avian migrants to the central Arctic coast of Alaska: Effects of environmental and ecological factors. J. Avian Biol. 47(2), 197–207 (2016).Article 

Google Scholar 
Tape, K. D. et al. Tundra be dammed: beaver colonization of the Arctic. Glob. Change Biol. 24(10), 4478–4488 (2018).ADS 
Article 

Google Scholar 
Whitfield, C. J. et al. Beaver-mediated methane emission: the effects of population growth in Eurasia and the Americas. Ambio 44(1), 7–15 (2015).CAS 
Article 

Google Scholar 
Westbrook, C. J., Cooper, D. J., & Baker, B. W. Beaver dams and overbank floods influence groundwater–surface water interactions of a Rocky Mountain riparian area. Water Resour. Res. 42(6), 1–12 (2006).
Bunn, S. E. & Arthington, A. H. Basic principles and ecological consequences of altered flow regimes for aquatic biodiversity. Environ. Manage 30(4), 492–507 (2002).Article 

Google Scholar 
Naiman, R. J. & Rogers, K. H. Large animals and system-level characteristics in river corridors. Bioscience 47(8), 521–529 (1997).Article 

Google Scholar 
Kemp, P. S. et al. Qualitative and quantitative effects of reintroduced beavers on stream fish. Fish Fish. 13(2), 158–181 (2012).Article 

Google Scholar 
Furbearer Reports, various authors. Alaska Department of Fish & Game, Division of Wildlife Conservation. Juneau, Alaska (1965–2017)Young, S.B., et al. The Environment of the Noatak River basin, Alaska. Center For Northern Studies. Wolcott, VT (1974)Melchior, H. R. ed. Terrestrial Mammals of the Chukchi-Imuruk Area. In Biological Survey of the Bering Land Bridge National Monument: Revised Final Report. Biology and Resource Management Program, Alaska Cooperative Park Studies Unit, University of Alaska (1979)Georgette, S. & Shiedt, A. Whitefish: traditional ecological knowledge and subsistence fishing in the Kotzebue Sound Region, Alaska. No. 290. Alaska Department of Fish and Game, Division of Subsistence (2005).Brubaker, M. et al. Climate change and health effects in Northwest Alaska. Glob. Health Action 4(1), 8445 (2011).MathSciNet 
Article 

Google Scholar 
Rabung, S. & Norton sound bering straight regional planning team. Norton Sound Bering Straight Regional Comprehensive Salmon Plan: Phase II. Alaska Department of Fish & Game, 1–217 (2015)Jones, B. M. et al. Increase in beaver dams controls surface water and thermokarst dynamics in an Arctic tundra region, Baldwin Peninsula, northwestern Alaska. Environ. Res. Lett. 15(7), 075005 (2020).ADS 
Article 

Google Scholar 
Brewer, M. C. The thermal regime of an arctic lake. EOS Trans. Am. Geophys. Union 39(2), 278–284 (1958).Article 

Google Scholar 
Jorgenson, M. T. et al. Resilience and vulnerability of permafrost to climate change. Can. J. For. Res. 40(7), 1219–1236 (2010).Article 

Google Scholar 
Lachenbruch, A. H. et al. Temperatures in permafrost. Temp. Meas. Control Sci. Ind. 1, 791 (1962).
Google Scholar 
Smith, M. W. Microclimatic influences on ground temperatures and permafrost distribution, Mackenzie Delta, Northwest Territories. Can. J. Earth Sci. 12(8), 1421–1438 (1975).ADS 
Article 

Google Scholar 
Langer, M. et al. Rapid degradation of permafrost underneath waterbodies in tundra landscapes—toward a representation of thermokarst in land surface models. J. Geophys. Res. Earth Surf. 121(12), 2446–2470 (2016).ADS 
Article 

Google Scholar 
Jones, B. M. et al. Identification of unrecognized tundra fire events on the north slope of Alaska. J. Geophys. Res. Biogeosci. 118(3), 1334–1344 (2013).Article 

Google Scholar 
Kantner, S. Swallowed by the Great Land: And Other Dispatches from Alaska’s Frontier. Mountaineers Books (2015)Pastick, N. J. et al. Distribution of near-surface permafrost in Alaska: Estimates of present and future conditions. Remote Sens. Environ. 168, 301–315 (2015).ADS 
Article 

Google Scholar 
Obu, J. et al. Northern Hemisphere permafrost map based on TTOP modelling for 2000–2016 at 1 km2 scale. Earth Sci. Rev. 193, 299–316 (2019).ADS 
Article 

Google Scholar 
Willby, N. J. et al. Rewilding wetlands: beaver as agents of within-habitat heterogeneity and the responses of contrasting biota. Philos. Trans. R. Soc. B Biol. Sci. 373(1761), 20170444 (2018).Article 

Google Scholar 
Kivinen, S., Nummi, P. & Kumpula, T. Beaver-induced spatiotemporal patch dynamics affect landscape-level environmental heterogeneity. Environ. Res. Lett. 15(9), 094065 (2020).ADS 
Article 

Google Scholar 
Pollock, M. M. et al. The importance of beaver ponds to coho salmon production in the Stillaguamish River basin, Washington, USA. North Am. J. Fish. Manag. 24(3), 749–760 (2004).Article 

Google Scholar 
Weber, N. et al. Alteration of stream temperature by natural and artificial beaver dams. PLoS ONE 12(5), e0176313 (2017).Article 

Google Scholar 
Nicieza, A. G. & Metcalfe, N. B. Growth compensation in juvenile Atlantic salmon: responses to depressed temperature and food availability. Ecology 78(8), 2385–2400 (1997).Article 

Google Scholar 
Deegan, L. A. et al. Influence of environmental variability on the growth of age-0 and adult Arctic grayling. Trans. Am. Fish. Soc. 128(6), 1163–1175 (1999).Article 

Google Scholar 
Jones, B. M. et al. Multi-dimensional remote sensing analysis documents beaver-induced permafrost degradation, Seward Peninsula, Alaska. Remote Sens. 13(23), 4863 (2021).ADS 
Article 

Google Scholar 
Turetsky, M. R. et al. Permafrost collapse is accelerating carbon release. Nature 569, 32–34 (2019).ADS 
CAS 
Article 

Google Scholar 
Jung, T. S. et al. Colonization of the Beaufort coastal plain by Beaver (Castor canadensis): a response to shrubification of the Tundra?. Can. Field-Naturalist 130(4), 332–335 (2016).Article 

Google Scholar 
Halley, D. J., Saveljev, A. P. & Rosell, F. Population and distribution of beavers Castor fiber and Castor canadensis in Eurasia. Mammal Rev. 51(1), 1–24 (2021).Article 

Google Scholar 
Ecke, F. et al. Meta-analysis of environmental effects of beaver in relation to artificial dams. Environ. Res. Lett. 12(11), 113002 (2017).ADS 
Article 

Google Scholar 
Raynolds, M. K. et al. A raster version of the Circumpolar Arctic Vegetation Map (CAVM). Remote Sens. Environ. 232, 111297 (2019).ADS 
Article 

Google Scholar  More

Cooke, S. J. et al. Knowledge co-production: A pathway to effective fisheries management, conservation, and governance. Fisheries 46, 89–97 (2021).Article 

Google Scholar 
Kobluk, H. M. et al. Indigenous knowledge of key ecological processes confers resilience to a small-scale kelp fishery. People Nat. 3, 723–739 (2021).Article 

Google Scholar 
Lee, L. C. et al. Drawing on indigenous governance and stewardship to build resilient coastal fisheries: People and abalone along Canada’s northwest coast. Mar. Policy 109, 103701 (2019).Article 

Google Scholar 
Reid, A. J. et al. “Two-Eyed Seeing”: An Indigenous framework to transform fisheries research and management. Fish. Fish. 22, 243–261 (2021).Article 

Google Scholar 
Toniello, G., Lepofsky, D., Lertzman-Lepofsky, G., Salomon, A. K. & Rowell, K. 11,500 y of human–clam relationships provide long-term context for intertidal management in the Salish Sea, British Columbia. Proc. Natl Acad. Sci. 116, 22106–22114 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ahn, J. E. & Ronan, A. D. Development of a model to assess coastal ecosystem health using oysters as the indicator species. Estuar., Coast. Shelf Sci. 233, 106528 (2020).CAS 
Article 

Google Scholar 
Skilbeck, C. G., Heap, A. D. & Woodroffe, C. D. Geology and sedimentary history of modern estuaries. in Applications of Paleoenvironmental Techniques in Estuarine Studies (eds. Weckström, K., Saunders, K. M., Gell, P. A. & Skilbeck, C. G.) 45–74 (Springer Netherlands, 2017). https://doi.org/10.1007/978-94-024-0990-1_3.Durham, S. R., Gillikin, D. P., Goodwin, D. H. & Dietl, G. P. Rapid determination of oyster lifespans and growth rates using LA-ICP-MS line scans of shell Mg/Ca ratios. Palaeogeogr., Palaeoclimatol., Palaeoecol. 485, 201–209 (2017).Article 

Google Scholar 
Lockwood, R. & Mann, R. A conservation palaeobiological perspective on Chesapeake Bay oysters. Philos. Trans. R. Soc. B 374, 20190209 (2019).CAS 
Article 

Google Scholar 
Rick, T. C. et al. Millennial-scale sustainability of the Chesapeake Bay native American oyster fishery. Proc. Natl Acad. Sci. 113, 6568–6573 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Thompson, V. D. et al. Ecosystem stability and Native American oyster harvesting along the Atlantic Coast of the United States. Sci. Adv. 6, eaba9652 (2020).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zimmt, J. B., Lockwood, R., Andrus, C. F. T. & Herbert, G. S. Sclerochronological basis for growth band counting: A reliable technique for life-span determination of Crassostrea virginica from the mid-Atlantic United States. Palaeogeogr. Palaeoclimatol. Palaeoecol. 516, 54–63 (2019).Article 

Google Scholar 
Alleway, H. K. & Connell, S. D. Loss of an ecological baseline through the eradication of oyster reefs from coastal ecosystems and human memory. Conserv Biol. 29, 795–804 (2015).PubMed 
Article 

Google Scholar 
Beck, M. W. et al. Oyster reefs at risk and recommendations for conservation, restoration, and management. Bioscience 61, 107–116 (2011).Article 

Google Scholar 
Kirby, M. X. Fishing down the coast: Historical expansion and collapse of oyster fisheries along continental margins. Proc. Natl Acad. Sci. 101, 13096 (2004).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lotze, H. K. et al. Depletion, degradation, and recovery potential of estuaries and coastal seas. Science 312, 1806 (2006).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Zu Ermgassen, P. S. et al. Historical ecology with real numbers: Past and present extent and biomass of an imperilled estuarine habitat. Proc. R. Soc. B: Biol. Sci. 279, 3393–3400 (2012).Article 

Google Scholar 
Carranza, A., Defeo, O. & Beck, M. Diversity, conservation status and threats to native oysters (Ostreidae) around the Atlantic and Caribbean coasts of South America. Aquat. Conserv.: Mar. Freshw. Ecosyst. 19, 344–353 (2009).Article 

Google Scholar 
Pluckhahn, T. J. & Thompson, V. D. Woodland-period mound building as historical tradition: Dating the mounds and monuments at Crystal River (8CI1). J. Archaeological Sci.: Rep. 15, 73–94 (2017).Article 

Google Scholar 
Waselkov, G. A. Shellfish gathering and shell midden archaeology. Adv. Archaeol. Method Theory 10, 93–210 (1987).Article 

Google Scholar 
McNiven, I. J. Ritualized middening practices. J. Archaeol. Method Theory 20, 552–587 (2013).Article 

Google Scholar 
Hawkes, A. D. et al. Relative sea-level change in northeastern Florida (USA) during the last ~8.0 ka. Quat. Sci. Rev. 142, 90–101 (2016).ADS 
Article 

Google Scholar 
Kelley, J. T., Belknap, D. F. & Claesson, S. Drowned coastal deposits with associated archaeological remains from a sea-level “slowstand”: Northwestern Gulf of Maine, USA. Geology 38, 695–698 (2010).ADS 
Article 

Google Scholar 
Khan, N. S. et al. Drivers of Holocene sea-level change in the Caribbean. Quat. Sci. Rev. 155, 13–36 (2017).ADS 
Article 

Google Scholar 
Love, R. et al. The contribution of glacial isostatic adjustment to projections of sea-level change along the Atlantic and Gulf coasts of North America. Earth’s Future 4, 440–464 (2016).ADS 
Article 

Google Scholar 
Shugar, D. H. et al. Post-glacial sea-level change along the Pacific coast of North America. Quat. Sci. Rev. 97, 170–192 (2014).ADS 
Article 

Google Scholar 
Dougherty, A. J. et al. Redating the earliest evidence of the mid-Holocene relative sea-level highstand in Australia and implications for global sea-level rise. PLoS ONE. 14, e0218430 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bailey, G. N. The role of molluscs in coastal economies: The results of midden analysis in Australia. J. Archaeol. Sci. 2, 45–62 (1975).Article 

Google Scholar 
Habu, J., Matsui, A., Yamamoto, N. & Kanno, T. Shell midden archaeology in Japan: Aquatic food acquisition and long-term change in the Jomon culture. Quat. Int. 239, 19–27 (2011).Article 

Google Scholar 
Hale, J. C. et al. Submerged landscapes, marine transgression and underwater shell middens: Comparative analysis of site formation and taphonomy in Europe and North America. Quat. Sci. Rev. 258, 106867 (2021).Article 

Google Scholar 
Erlandson, J. M. et al. Shellfish, geophytes, and sedentism on Early Holocene Santa Rosa Island, Alta California, USA. J. Isl. Coast. Archaeol. 15, 504–524 (2020).Article 

Google Scholar 
Rick, T. C. Early to Middle Holocene estuarine shellfish collecting on the islands and mainland coast of the Santa Barbara Channel, California, USA. Open Quaternary 6, 9 (2020).Sanger, D. & Sanger, M. J. Boom and bust on the river: The story of the Damariscotta oyster shell heaps. Archaeol. East. North Am. 14, 65–78 (1986).
Google Scholar 
Moss, M. L. Shellfish gender, and status on the Northwest Coast: Reconciling archaeological, ethnographic, and ethnohistoric records of the Tlingit. Am. Anthropologist 95, 631–652 (1993).Article 

Google Scholar 
Cannon, A., Burchell, M. & Bathurst, R. Trends and strategies in shellfish gathering on the Pacific Northwest Coast of North America. in Early Human Impact on Megamolluscs (eds. Antczak, A. & Cipriani, R.) 7–22 (Archaeopress, 2008).Grier, C., Angelbeck, B. & McLay, E. Terraforming and monumentality as long-term social practice in the Salish Sea region of the Northwest Coast of North America. Hunt. Gatherer Res. 3, 107–132 (2017).Article 

Google Scholar 
Pluckhahn, T. J. & Thompson, V. D. New Histories of Village Life at Crystal River. (University Press of Florida, 2018).Thompson, V. D. et al. Ancient engineering of fish capture and storage in southwest Florida. Proc. Natl Acad. Sci. 117, 8374–8381 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Sassaman, K. E. Complex hunter–gatherers in evolution and history: A North American perspective. J. Archaeol. Res. 12, 227–280 (2004).Article 

Google Scholar 
Luby, E. M. & Gruber, M. F. The dead must be fed: Symbolic meanings of the shellmounds of the San Francisco Bay area. Camb. Archaeol. J. 9, 95–108 (1999).Article 

Google Scholar 
Lightfoot, K. G. & Luby, E. M. Mound building by California hunter-gatherers. in The Oxford Handbook of North American Archaeology (ed. Pauketat, T.) 212–223 (Oxford University Press, 2012).Smith, A. D. T. Archaeological expressions of Holocene cultural and environmental change in coastal Southeast Queensland. (The University of Queensland, 2016).Reeder-Myers, L., Rick, T., Lowery, D., Wah, J. & Henkes, G. Human ecology and coastal foraging at Fishing Bay, Maryland, USA. J. Ethnobiol. 36, 595–616 (2016).Article 

Google Scholar 
Petrie, C. C. Tom Petrie’s reminiscences of Early Queensland (dating from 1837). (Watson, Ferguson & Company, 1904).Eipper, C. Statement of the Origin, Condition and Prospects, of the German Mission to the Aborigines at Moreton Bay, etc. (James Reading, 1841).Watkins, G. Notes on the Aboriginals of Stradbroke and Moreton Islands. Proc. R. Soc. Qld. 8, 40–50 (1891).
Google Scholar 
Ross, A. & with members of the Quandamooka Aboriginal Land Council. Aboriginal approaches to cultural heritage management: A Quandamooka case study. in Australian Archaeology ’95: Proceedings of the 1995 Australian Archaeological Association Annual Conference (eds. Ulm, S., Lilley, I. & Ross, A.) vol. Tempus 6 107–112 (Anthropology Museum, University of Queensland, 1996).Jenkins, J. A. & Gallivan, M. D. Shell on earth: Oyster harvesting, consumption, and deposition practices in the Powhatan Chesapeake. J. Isl. Coast. Archaeol. 15, 384–406 (2020).Article 

Google Scholar 
Hatch, M. B. A. & Wyllie-Echeverria, S. Historic distribution of Ostrea lurida (Olympia oyster) in the San Juan Archipelago. Wash. State Tribal Coll. Univ. Res. J. 1, 38–45 (2016).
Google Scholar 
Swanton, J. R. Social Organization and Social Usages of the Indians of the Creek Confederacy. (Bureau of American Ethnology, 1928).Hening, W. W. The Statutes at Large of Virginia. (1809).Wharton, J. The Bounty of the Chesapeake: Fishing in Colonial Virginia. (Virginia 350th Anniversary Celebration Corporation, 1957).Denys, N. Description géographique et historique des Costes de l’Amérique Septentrionale. Avec l’Histoire naturelle du Pais. (Chez Claude Barbin, 1672).Nicolar, J. The Life and Traditions of the Red Man. (Duke University Press, 2007 Print, 1893).Speck, F. G. Penobscot Man: The Life History of a Forest Tribe in Maine. (University of Pennsylvania Press, 1940).Washburn, K. Passamaquoddy tribe conducts oyster project. Bangor Daily News (1979).Kennedy, V. S. Shifting Baselines in the Chesapeake Bay: An Environmental History. (Johns Hopkins University Press, 2018).de Charlevoix, P. F. X. Journal of a Voyage to North America, Vollume II. Translated by Louise Phelps Kellogg. (The Caxton Club, 1923).Ingersoll, E. The Oyster Industry. (United States Bureau of Fisheries, United States Census Office, Government Printing Office, 1881).Brice, J. J. Report on the fish and fisheries of the coastal waters of Florida. in Report of the Commissioner for the Year Ending June 30, 1896 263–242 (U.S. Commission of Fish and Fisheries, U.S. Government Printing Office, 1896).Blake, B. & Zu Ermgassen, P. S. E. The history and decline of Ostrea lurida in Willapa Bay, Washington. J. Shellfish Res. 34, 273–280 (2015).Article 

Google Scholar 
Thurstan, R. H. et al. Charting two centuries of transformation in a coastal social-ecological system: A mixed methods approach. Global Environmental Change 61, 102058 (2020).Schulte, D. M. History of the Virginia oyster fishery, Chesapeake Bay, USA. Front. Mar. Sci. 4, 127 (2017).Fletcher, M.-S., Hamilton, R., Dressler, W. & Palmer, L. Indigenous knowledge and the shackles of wilderness. Proc. Natl Acad. Sci. 118, e2022218118 (2021).Ross, A., Coghill, S. & Coghill, B. Discarding the evidence: The place of natural resources stewardship in the creation of the Peel Island Lazaret Midden, Moreton Bay, southeast Queensland. Quat. Int. 385, 177–190 (2015).Article 

Google Scholar 
Reeder-Myers, L. A. & Rick, T. C. Kayak surveys in estuarine environments: addressing sea level rise and climate change. Antiquity 93, 1040–1051 (2019).Article 

Google Scholar 
Savarese, M., Walker, K. J., Stingu, S., Marquardt, W. H. & Thompson, V. The effects of shellfish harvesting by aboriginal inhabitants of Southwest Florida (USA) on productivity of the eastern oyster: Implications for estuarine management and restoration. Anthropocene 16, 28–41 (2016).Article 

Google Scholar 
Lulewicz, I. H., Thompson, V. D., Cramb, J. & Tucker, B. Oyster paleoecology and native American subsistence practices on Ossabaw Island, Georgia, USA. J. Archaeol. Sci.: Rep. 15, 282–289 (2017).
Google Scholar 
Hesterberg, S. G. et al. Prehistoric baseline reveals substantial decline of oyster reef condition in a Gulf of Mexico conservation priority area. Biol. Lett. 16, 20190865 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Cannarozzi, N. R. & Kowalewski, M. Seasonal oyster harvesting recorded in a Late Archaic period shell ring. PloS ONE. 14, e0224666 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Cook-Patton, S. C., Weller, D., Rick, T. C. & Parker, J. D. Ancient experiments: Forest biodiversity and soil nutrients enhanced by Native American middens. Landsc. Ecol. 29, 979–987 (2014).Article 

Google Scholar 
Stalter, R. & Kincaid, D. The vascular flora of five Florida shell middens. J. Torre. Botanical Soc. 131, 93–103 (2004).Article 

Google Scholar 
Kirby, M. X. & Miller, H. M. Response of a benthic suspension feeder (Crassostrea virginica Gmelin) to three centuries of anthropogenic eutrophication in Chesapeake Bay. Estuar. Coast. Shelf Sci. 62, 679–689 (2005).ADS 
Article 

Google Scholar 
Suttles, W. Variation in habitat and culture on the Northwest Coast. in Coastal Salish Essays 26–44 (University of Washington Press, 1987).Bliege Bird, R. & Nimmo, D. Restore the lost ecological functions of people. Nat. Ecol. Evolution 2, 1050–1052 (2018).Article 

Google Scholar 
Berkes, F. Indigenous ways of knowing and the study of environmental change. J. R. Soc. N.Z. 39, 151–156 (2009).Article 

Google Scholar 
Tengö, M., Malmer, P., Elmqvist, T. & Brondizio, E. S. A Framework for Connecting Indigenous, Local and Scientific Knowledge Systems. (2012).Ellis, E. C. et al. People have shaped most of terrestrial nature for at least 12,000 years. Proc. Natl Acad. Sci.118, e2023483118 (2021).Roberts, P. et al. Reimagining the relationship between Gondwanan forests and Aboriginal land management in Australia’s “Wet Tropics”. Iscience 24, 102190 (2021).Ogburn, D. M., White, I. & McPhee, D. P. The disappearance of oyster reefs from eastern Australian estuaries—impact of colonial settlement or mudworm invasion? Coast. Manag. 35, 271–287 (2007).Article 

Google Scholar 
Diggles, B. K. Historical epidemiology indicates water quality decline drives loss of oyster (Saccostrea glomerata) reefs in Moreton Bay, Australia. N.Z. J. Mar. Freshw. Res. 47, 561–581 (2013).CAS 
Article 

Google Scholar 
Pritchard, C., Shanks, A., Rimler, R., Oates, M. & Rumrill, S. The Olympia oyster Ostrea lurida: Recent advances in natural history, ecology, and restoration. J. Shellfish Res. 34, 259–271 (2015).Article 

Google Scholar 
Trimble, A. C., Ruesink, J. L. & Dumbauld, B. R. Factors preventing the recovery of a historically overexploited shellfish species, Ostrea lurida Carpenter 1864. J. Shellfish Res. 28, 97–106 (2009).Article 

Google Scholar 
White, J., Ruesink, J. L. & Trimble, A. C. The nearly forgotten oyster: Ostrea lurida Carpenter 1864 (Olympia oyster) history and management in Washington State. J. Shellfish Res. 28, 43–49 (2009).Article 

Google Scholar 
Harding, J. M., Spero, H. J., Mann, R., Herbert, G. S. & Sliko, J. L. Reconstructing early 17th century estuarine drought conditions from Jamestown oysters. Proc. Natl Acad. Sci. 107, 10549–10554 (2010).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mann, R., Harding, J. M. & Southworth, M. J. Reconstructing pre-colonial oyster demographics in the Chesapeake Bay, USA. Estuar., Coast. Shelf Sci. 85, 217–222 (2009).ADS 
Article 

Google Scholar 
Bayne, B. L. Biology of Oysters. (Elsevier Science & Technology, 2017).Galtsoff, P. S. The American Oyster Crassostrea virginica Gmelin. (United States Government Printing Office, 1964).Kennedy, V. S., Newell, R. I. E. & Eble, A. F. The Eastern Oyster: Crassostrea virginica. (University of Maryland Sea Grant Publications, 1996).Grabowski, J. H., Powers, S. P., Peterson, C. H., Gaskill, D. & Summerson, H. C. Growth and survivorship of non-native (Crassostrea gigas and Crassostrea ariakensis) versus native eastern (Crassostrea virginica) oysters. J. Shellfish Res. 23, 781–793 (2004).
Google Scholar 
Shumway, S. Natural environmental factors. in The eastern oyster Crassostrea virginica (eds. Kennedy, V., Newell, R. & Eble, A.) 467–513 (Maryland Sea Grant, 1996).Lyman, R. L. Paleoenvironmental reconstruction from faunal remains: Ecological basics and analytical assumptions. J. Archaeol. Res. 25, 315–371 (2017).MathSciNet 
Article 

Google Scholar 
Claasen, C. Shells. (Cambridge University Press, 1990).Giovas, C. M. The shell game: Analytic problems in archaeological mollusc quantification. J. Archaeol. Sci. 36, 1557–1564 (2009).Article 

Google Scholar 
Peltier, W. R., Argus, D. F. & Drummond, R. Space geodesy constrains ice age terminal deglaciation: The global ICE-6G_C (VM5a) model: Global Glacial Isostatic Adjustment. J. Geophys. Res.: Solid Earth 120, 450–487 (2015).ADS 
Article 

Google Scholar  More

Argonema gen. nov. Skoupý et Dvořák.Type species: Argonema galeatum.Morphology: Filamentous cyanobacterium, colonies macroscopic, growing in round bulbs and tufts. The filaments are dark green to blue-green, grey-green or brown-green in color. Cells are wider than they are long. Filaments sheathed, sheaths are colorless to light brown, distinct, and variable in length. The filament can protrude from the sheath or the sheath can exceed filament. Trichomes are cylindrical, not attenuated to slightly attenuated towards the end, slightly or not constricted at cell walls. The apical cell can be concave, dark brown, purple-brown to almost black. Cell content often granulated. Necridic cells present, reproduction by hormogonia. The morphological description was based on both culture and fresh material.Etymology: The genus epithet (Argonema) is derived from greek Argo – slow, latent (αργός) and nema – thread (νήμα).A. galeatum sp. nov. Skoupý et Dvořák.Morphology: The cells of A. galeatum are 6.5–9.1 µm (mean 7.81 µm) wide and 1.1–2.5 µm (mean 1.83 µm) long (Figs. 1–5). Filaments are straight, blue-green to gray-green in color. The sheaths are colorless to light brown, distinct, and variable in length. The filament can protrude from the sheath or the sheath can exceed filament. No true branching was observed. Trichomes are cylindrical, not attenuated or slightly attenuated towards the end, slightly or not constricted at cell walls. Some filaments have a concave apical cell that is dark brown, purple-brown to almost black (Fig. 11b). Cell content often granulated. Reproduction by necridic cells and subsequent breaking of the filaments into hormogonia (Fig. 11a,c). The morphological description was based on both culture and fresh material.Figures 1-8Microphotographs of Argonema galeatum (Figs 1–5) and Argonema antarcticum (Figs. 6–8) Trichomes of A. galeatum appear more straight (Fig 2), while trichomes of A. antarcticum form waves (Fig 6) and loops (Fig 7). Scale = 10 µm, wide arrow = necridic cells, arrowhead = granules, asterisk = colored apical cell, circle = empty sheath.Full size imageFigures 9 and 10Histograms of cell dimensions constructed using PAST software. Fig. 9 – Histogram of cell width frequencies in A. galeatum (blue) and A. antarcticum (red). Fig. 10 – Histogram of cell length frequencies in A. galeatum (blue) and A. antarcticum (red).Full size imageHolotype: 38,057, Herbarium of the Department of Botany (OL), Palacký University Olomouc, Czech Republic.Reference strain: Argonema galeatum A003/A1.Type locality: James Ross Island, Western Antarctica, 63.80589S, 57.92147 W.Habitat: Well-developed soil crust.Etymology: Species epithet A. galeatum was derived from latin galea – helmet.A. antarcticum sp. nov. Skoupý et Dvořák.Morphology: The cells are 7.6–9.2 µm (mean 8.52 µm) wide and 1.2–2.8 µm (mean 1.72 µm) long (Figs. 5–8). Filaments are wavy, gray-green to brown-green in color. The sheaths are colorless to light brown, distinct, and variable in length. The filament can protrude from the sheath or the sheath can exceed filament. No true branching was observed. Trichomes are cylindrical, not attenuated or slightly attenuated towards the end with a concave apical cell, slightly or not constricted at cell walls (Fig. 11d). Necridic cells present (Fig. 11e), reproduction by hormogonia. The morphological description was based on both culture and fresh material.Holotype: 38,058, Herbarium of the Department of Botany (OL), Palacký University, Olomouc, Czech Republic.Reference strain: Argonema antarcticum A004/B2.Type locality: James Ross Island, Western Antarctica, 63.89762S, 57.79743 W.Habitat: Well-developed soil crust.Etymology: Species epithet A. antarcticum was derived from the original sampling site.Morphological variabilityWe used light microscopy to assess the morphology of Argonema from soil crust samples and cultured strains. Argonema is morphologically similar to other Oscillatoriales, such as Lyngbya, Phormidium, and Oscillatoria. In culture, the morphology of A. galeatum and A. antarcticum differed slightly. Filaments of A. antarcticum are wider than cells of A. galeatum, averaging at 8.52 µm (A. galeatum – 7.81 µm). The average cell width/length ratio is 4.54 for A.galeatum and 4.89 for A. antarcticum. The cell width was significantly different between the two species (Nested ANOVA, p  More

Study sites, sample collection and preparationAdult female mosquitoes used in this study had previously been collected from three areas: Kerio Valley (Baringo county), Rabai (Kilifi county) and Nguruman (Kajiado county) (Fig. 1), as part of vector-borne disease surveillance project and stored at – 80 °C at the International Centre of Insect Physiology and Ecology (icipe). The mosquitoes were surveyed between August 2019 and May 2020. Nguruman is an agropastoral area located in Kajiado county at the southern end of the Kenyan Rift Valley bordering Tanzania. The area has a semi-arid climate characterized by erratic rains, extreme temperatures, and cyclic and prolonged droughts30. The vegetation is dominated by bushland, grassland and open woodlands along seasonal river valleys. Specific indicator data for malaria is not available for Nguruman except for estimates pertaining to the larger Kajiado county which as of 2019 indicates a malaria incidence rate of 5 per 1000 population31. Collections in Kerio Valley (Baringo county within the Rift Valley) were conducted in Kapluk and Barwesa, both agro-pastoral areas with arid and semi-arid ecology. Malaria is a major vector-borne disease in the areas with report of perennially occurrence in neighboring riverine areas32. Rabai is one of the seven administrative sub-counties of Kilifi county in the coastal region of Kenya where malaria is endemic. The main economic activities in the area include subsistence agriculture, casual labor, crafts and petty trading. The weather patterns at the sites during the sampling period were as follows: Kerio Valley (mean daily temperature: 21.2 °C, mean daily rainfall: 4.1 mm, mean relative humidity: 73.4%); Rabai (mean daily temperature: 26.4 °C, mean daily rainfall: 2.1 mm; mean relative humidity: 78.1%) and Nguruman (mean daily temperature: 22.5 °C, mean daily rainfall: 0.9 mm, mean relative humidity: 61.2%).Mosquito survey and processingHost seeking mosquitoes were trapped using CDC light traps baited with dry ice (carbon dioxide) attractive to several mosquitoes. Traps were set outdoors about 10–15 m away from randomly selected homesteads from 18:00 h to 06:00 h. After collection, the mosquitoes were anesthetized with trimethylamine and temporarily stored in liquid nitrogen before transportation to the Emerging Infectious Disease (EID) laboratory at icipe and later stored at − 80 °C. Anopheline mosquitoes were morphologically identified to species level using published taxonomic keys15,33.DNA extraction and Anopheles species discriminationDNA was extracted from the head/thorax of individual mosquitoes using ISOLATE II Genomic DNA Extraction kit (Bioline, UK) following the manufacturer’s instructions and used for species discrimination and screening for P. falciparum infection and Gste2 mutations (described below).Cryptic sibling species of the Anopheles funestus and Anopheles gambiae complexes were identified using conventional PCR34,35 and/or sequencing. PCR for An. funestus complex in a 15 µl reaction volume comprised 0.5 µM of each primer targeting: Anopheles funestus s.s, Anopheles vaneedeni, Anopheles rivulorum, Anopheles parensis, Anopheles leesoni, Anopheles longipalpis A and Anopheles longipalpis C, 3 µl of 5X HOT FIREPol Blend Master Mix Ready to Load (Solis BioDyne, Estonia) and 2 µl of DNA template. The cycling conditions were initial denaturation at 95 °C for 15 min, and then 30 cycles of denaturation at 95 °C for 30 s, annealing at 46 °C for 30 s and extension at 72 °C for 40 s and final extension at 72 °C for 10 min. Size fragments of each species were scored after separation in 1.5% agarose gel electrophoresis stained with ethidium bromide against a 1 Kb DNA ladder (HyperLadder, Bioline, London, UK).For An. gambiae s.l., PCR in a 10 µl volume consisted of 2 µl of 5X Evagreen HRM Master Mix (Solis BioDyne, Estonia), 1 µl of DNA template and 10 µM concentration of each primer targeting An. gambiae s.s and An. arabiensis. The thermal cycling conditions included initial denaturation for 15 min at 95 °C followed by 40 cycles of denaturation at 95 °C for 20 s, annealing at 61 °C for 15 s and extension at 72 °C for 20 s followed by final extension at 72 °C for 7 min.A subset of An. funestus s.l. samples that failed to amplify using the established protocol, was further amplified and sequenced targeting the internal transcribed spacer 2 (ITS2) region of the ribosomal DNA (rDNA)36. This target has shown utility in discriminating closely related mosquito species including anophelines12 and sequences from diverse species for this marker are well represented in reference databases (e.g. GenBank). PCR volumes for rDNA ITS2 were 15 µl containing 0.5 µM of the forward and reverse primers, 3 µl of 5X HOT FIREPol Blend Master Mix Ready to Load (Solis BioDyne, Estonia) and 2 µl of DNA template. The cycling conditions were initial denaturation at 95 °C for 15 min, followed by 40 cycles of denaturation at 95 °C for 30 s, annealing at 60 °C for 30 s and extension at 72 °C for 45 s and final extension at 72 °C for 7 min. ExoSAP IT rapid cleanup kit (Affymetrix Inc., Santa Clara, CA, USA) was used to clean the PCR product as per the manufacturer’s guideline, and then outsourced for bidirectional Sanger sequencing to Macrogen, South Korea.Detection of malaria parasitesPlasmodium falciparum sporozoites in individual mosquitoes (head/thorax) were detected by analyzing high resolution melting (HRM) profiles generated from real time PCR products of non-coding mitochondrial sequence (ncMS)37. A P. falciparum DNA from National Institute for Biological Standards and Control (NIBSC; London, UK) was used as a reference positive control. PCR was carried out in a 10 µl volume consisting of 2 µl of 5X Evagreen HRM Master Mix (Solis BioDyne, Estonia), 1 µl of DNA template and 10 µM of each primer. PCR cycling conditions were initial denaturation for 15 min at 95 °C followed by 40 cycles of denaturation at 95 °C for 20 s, annealing at 61 °C for 15 s and extension at 72 °C for 20 s followed by final extension at 72 °C for 7 min. A fraction of RT-PCR-HRM positive samples were further analyzed using conventional PCR in a 10 µl volume consisting of 2 µl of 5X HOT FIREPol Blend Master Mix Ready to Load (Solis BioDyne, Estonia), 1 µl of DNA template and 10 µM of each primer. The cycling conditions comprised initial denaturation for 15 min at 95 °C followed by 40 cycles of denaturation at 95 °C for 20 s, annealing at 61 °C for 15 s and extension at 72 °C for 20 s followed by final extension at 72 °C for 7 min. PCR product of samples positive by RT-PCR were purified using ExoSAP- IT (USB Corporation, Cleveland, OH, USA) and outsourced for sequencing to Macrogen, South Korea. All sporozoite-positive mosquitoes were molecularly identified to species by PCR of the ITS2 region as described above.Genotyping for L119F-GSTe2 mutation and sequencingTwo outer and two inner primers in a PCR assay were used to genotype the L119F-GSTe2 mutations that confer resistance of An. funestus mosquitoes to pyrethroids/DDT19 as described previously28. Thus, only An. funestus s.l. was screened using this assay. Briefly, PCR in a 15 µl reaction volume consisted of 10 µM of each primer, 3 µl of 5X HOT FIREPol Blend Master Mix Ready to Load (Solis BioDyne, Estonia), and 2 µl of DNA template. The cycling conditions were initial denaturation at 95 °C for 15 min, followed by 40 cycles of denaturation at 95 °C for 30 s, annealing at 59 °C for 30 s and extension at 72 °C for 40 s and final extension at 72 °C for 7 min. Amplicons were resolved in a 1.5% agarose gel stained with ethidium bromide (Sigma-Aldrich, GmbH, Germany) against a 1 Kb DNA ladder (HyperLadder, Bioline, London, UK). The amplicons were scored as either homozygous susceptible (SS) at 312 bp, homozygous resistant (RR) at 523 bp or heterozygous (RS) when both bands were visualized.Representative GSTe2 allele positive samples were sequenced for the GSTe2 gene using the Gste2F and Gste2R primers as described previously38. PCR comprised a reaction volume of 15 µl in MyTaq DNA Polymerase Kit (Bioline, London, UK) containing 10 µM of each primer, 5X My Taq reaction buffer, 2 µl of My taq DNA polymerase and 1 µl of DNA template. PCR conditions were: initial denaturation of 5 min at 95 °C, followed by 30 cycles of 94 °C for 30 s, 58 °C for 30 s and 72 °C for 1 min, with a final extension at 72 °C for 10 min. Cleaning and sequencing of amplicons were performed as described above.Sequence and polymorphism analysisSequences (mosquito, P. falciparum, GSTe2) were viewed and cleaned in Geneious Prime39 and queried in GenBank using Basic Local Alignment Search Tool (BLastn). Parasite sequences were assigned as P. falciparum after  > 98% percentage identity. MAFFT in Geneious Prime39 was used to perform multiple sequence alignments with default parameters. Maximum likelihood (ML) trees were inferred for mosquito ITS2 sequences using the best fit model of sequence evolution with nodal support for different groupings evaluated through 1000 bootstrap replications. GSTe2 gene polymorphism analysis was performed in Geneious Prime and ML tree reconstructed from MAFFT alignment using PhyML v. 2.2.4. Haplotype distribution network was constructed using Templeton-Crandall Sing (TCS) program v. 1.2140.Statistical analysisRelative abundance was used to estimate the outdoor composition of the anopheline mosquitoes. Daily counts of female mosquito/trap/night for An. funestus s.l. and An. gambiae s.l. were compared for each area using generalized linear models (GLM) with negative binomial error structure based on best-fit model residuals. The mean catches/trap/night was computed for each of the species complexes. The P. falciparum sporozoite infection rates (Pfsp) were expressed as the number of positive specimens of the total number of specimens examined. The distribution of L119F-GSTe2 mutations was assessed by determining allelic frequencies in different species. Infection status among the resistant mosquitoes was compared using the Fisher’s Exact Test. Data were analyzed using R v 4.1.0 software at 95% confidence limit.Ethical considerationsEthical review and approval of the study was granted by the Scientific and Ethical Review Unit (SERU) of the Kenya Medical Research Institute (KEMRI) (Protocol No. SSC 2787). Prior to data collection, the purpose of the study, procedures and associated benefits/risks were provided to the local leadership at county and community levels. Additionally, informed verbal consent to trap mosquitoes around homesteads was obtained from household heads. More