Philippa Kaur

Bunse, C. & Pinhassi, J. Marine bacterioplankton seasonal succession dynamics. Trends Microbiol. 25, 494–505. https://doi.org/10.1016/j.tim.2016.12.013 (2017).CAS 
Article 
PubMed 

Google Scholar 
Mestre, M., Höfer, J., Sala, M. M. & Gasol, J. M. Seasonal variation of bacterial diversity along the marine particulate matter continuum. Front. Microbiol. 11, 1590. https://doi.org/10.3389/fmicb.2020.01590 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Teeling, H. et al. Substrate-controlled succession of marine bacterioplankton populations induced by a phytoplankton bloom. Science 336, 608–611. https://doi.org/10.1126/science.1218344 (2012).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Gilbert, J. A. et al. The seasonal structure of microbial communities in the Western English Channel. Environ. Microbiol. 11, 3132–3139. https://doi.org/10.1111/j.1462-2920.2009.02017.x (2009).CAS 
Article 
PubMed 

Google Scholar 
Sintes, E., Witte, H., Stodderegger, K., Steiner, P. & Herndl, G. J. Temporal dynamics in the free-living bacterial community composition in the coastal North Sea. FEMS Microbiol. Ecol. 83, 413–424. https://doi.org/10.1111/1574-6941.12003 (2013).CAS 
Article 
PubMed 

Google Scholar 
Lindh, M. V. et al. Disentangling seasonal bacterioplankton population dynamics by high-frequency sampling. Environ. Microbiol. 17, 2459–2476. https://doi.org/10.1111/1462-2920.12720 (2015).Article 
PubMed 

Google Scholar 
El-Swais, H., Dunn, K. A., Bielawski, J. P., Li, W. K. W. & Walsh, D. A. Seasonal assemblages and short-lived blooms in coastal north-west Atlantic Ocean bacterioplankton. Environ. Microbiol. 17, 3642–3661. https://doi.org/10.1111/1462-2920.12629 (2015).CAS 
Article 
PubMed 

Google Scholar 
Ward, C. S. et al. Annual community patterns are driven by seasonal switching between closely related marine bacteria. ISME J. 11, 1412–1422. https://doi.org/10.1038/ismej.2017.4 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Teeling, H. et al. Recurring patterns in bacterioplankton dynamics during coastal spring algae blooms. Elife 5, e11888. https://doi.org/10.7554/eLife.11888 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Tinta, T. et al. Bacterial community shift is induced by dynamic environmental parameters in a changing coastal ecosystem (northern Adriatic, northeastern Mediterranean Sea) – a 2-year time-series study. Environ. Microbiol. 17, 3581–3596. https://doi.org/10.1111/1462-2920.12519 (2015).CAS 
Article 
PubMed 

Google Scholar 
Salter, I. et al. Seasonal dynamics of active SAR11 ecotypes in the oligotrophic Northwest Mediterranean Sea. ISME J. 9, 347–360. https://doi.org/10.1038/ismej.2014.129 (2015).CAS 
Article 
PubMed 

Google Scholar 
Gilbert, J. A. et al. Defining seasonal marine microbial community dynamics. ISME J. 6, 298–308. https://doi.org/10.1038/ismej.2011.107 (2012).CAS 
Article 
PubMed 

Google Scholar 
Alonso-Sáez, L. et al. Seasonality in bacterial diversity in north-west Mediterranean coastal waters: Assessment through clone libraries, fingerprinting and FISH. FEMS Microbiol. Ecol. 60, 98–112. https://doi.org/10.1111/j.1574-6941.2006.00276.x (2007).CAS 
Article 
PubMed 

Google Scholar 
Alonso-Sáez, L., Díaz-Pérez, L. & Morán, X. A. G. The hidden seasonality of the rare biosphere in coastal marine bacterioplankton. Environ. Microbiol. 17, 3766–3780. https://doi.org/10.1111/1462-2920.12801 (2015).Article 
PubMed 

Google Scholar 
Needham, D. M. & Fuhrman, J. A. Pronounced daily succession of phytoplankton, archaea and bacteria following a spring bloom. Nat. Microbiol. 1, 1–7. https://doi.org/10.1038/nmicrobiol.2016.5 (2016).CAS 
Article 

Google Scholar 
Fuhrman, J. A., Cram, J. A. & Needham, D. M. Marine microbial community dynamics and their ecological interpretation. Nat. Rev. Microbiol. 13, 133–146. https://doi.org/10.1038/nrmicro3417 (2015).CAS 
Article 
PubMed 

Google Scholar 
Najdek, M. et al. Dynamics of environmental conditions during the decline of a Cymodocea nodosa meadow. Biogeosciences 17, 3299–3315. https://doi.org/10.5194/bg-17-3299-2020 (2020).ADS 
CAS 
Article 

Google Scholar 
Najdek, M. et al. Effects of the invasion of Caulerpa cylindracea in a Cymodocea nodosa meadow in the Northern Adriatic Sea. Front. Mar. Sci. 7, 602055. https://doi.org/10.3389/fmars.2020.602055 (2020).Article 

Google Scholar 
Ladau, J. et al. Global marine bacterial diversity peaks at high latitudes in winter. ISME J. 7, 1669–1677. https://doi.org/10.1038/ismej.2013.37 (2013).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
García, F. C., Alonso-Sáez, L., Morén, X. A. G. & López-Urrutia, Á. Seasonality in molecular and cytometric diversity of marine bacterioplankton: The re-shuffling of bacterial taxa by vertical mixing. Environ. Microbiol. 17, 4133–4142. https://doi.org/10.1111/1462-2920.12984 (2015).Article 
PubMed 

Google Scholar 
Reinthaler, T., Winter, C. & Herndl, G. J. Relationship between bacterioplankton richness, respiration, and production in the southern North Sea. Appl. Environ. Microbiol. 71, 2260–2266. https://doi.org/10.1128/AEM.71.5.2260-2266.2005 (2005).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Mozetič, P. et al. Recent trends towards oligotrophication of the Northern Adriatic: Evidence from chlorophyll a time series. Estuaries Coast 33, 362–375. https://doi.org/10.1007/s12237-009-9191-7 (2010).CAS 
Article 

Google Scholar 
Manna, V., De Vittor, C., Giani, M., Del Negro, P. & Celussi, M. Long-term patterns and drivers of microbial organic matter utilization in the northernmost basin of the Mediterranean Sea. Mar. Environ. Res. 164, 105245. https://doi.org/10.1016/j.marenvres.2020.105245 (2021).CAS 
Article 
PubMed 

Google Scholar 
Ivančić, I. et al. Long-term changes in heterotrophic prokaryotes abundance and growth characteristics in the northern Adriatic Sea. J. Mar. Syst. 82, 206–216. https://doi.org/10.1016/j.jmarsys.2010.05.008 (2010).Article 

Google Scholar 
Bowman, J. P. The family Cryomorphaceae. In The Prokaryotes: Other Major Lineages of Bacteria and the Archaea (eds Rosenberg, E. et al.) (Springer, New York, 2014). https://doi.org/10.1007/978-3-642-38954-2_135.Chapter 

Google Scholar 
Ngugi, D. K. & Stingl, U. High-quality draft single-cell genome sequence of the NS5 marine group from the coastal Red Sea. Genome Announc. 6, e00565-18. https://doi.org/10.1128/genomeA.00565-18 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Korlević, M., Pop Ristova, P., Garić, R., Amann, R. & Orlić, S. Bacterial diversity in the South Adriatic Sea during a strong, deep winter convection year. Appl. Environ. Microbiol. 81, 1715–1726; https://doi.org/10.1128/AEM.03410-14 (2015).Korlević, M. et al. Bacterial diversity across a highly stratified ecosystem: A salt-wedge Mediterranean estuary. Syst. Appl. Microbiol. 39, 398–408. https://doi.org/10.1016/j.syapm.2016.06.006 (2016).Article 
PubMed 

Google Scholar 
Hoarfrost, A. et al. Global ecotypes in the ubiquitous marine clade SAR86. ISME J. 14, 178–188. https://doi.org/10.1038/s41396-019-0516-7 (2020).CAS 
Article 
PubMed 

Google Scholar 
Šilović, T., Balagué, V., Orlić, S. & Pedrós-Alió, C. Picoplankton seasonal variation and community structure in the northeast Adriatic coastal zone. FEMS Microbiol. Ecol. 82, 678–691. https://doi.org/10.1111/j.1574-6941.2012.01438.x (2012).CAS 
Article 
PubMed 

Google Scholar 
Palenik, B. et al. The genome of a motile marine Synechococcus. Nature 424, 1037–1042. https://doi.org/10.1038/nature01943 (2003).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Spring, S. & Riedel, T. Mixotrophic growth of bacteriochlorophyll a-containing members of the OM60/NOR5 clade of marine gammaproteobacteria is carbon-starvation independent and correlates with the type of carbon source and oxygen availability. BMC Microbiol. 13, 117. https://doi.org/10.1186/1471-2180-13-117 (2013).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Durham, B. P. et al. Draft genome sequence of marine alphaproteobacterial strain HIMB11, the first cultivated representative of a unique lineage within the Roseobacter clade possessing an unusually small genome. Stand. Genomic Sci. 9, 632–645. https://doi.org/10.4056/sigs.4998989 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Carlson, C. A. et al. Seasonal dynamics of SAR11 populations in the euphotic and mesopelagic zones of the northwestern Sargasso Sea. ISME J. 3, 283–295. https://doi.org/10.1038/ismej.2008.117 (2009).CAS 
Article 
PubMed 

Google Scholar 
Vergin, K. L. et al. High-resolution SAR11 ecotype dynamics at the Bermuda Atlantic Time-series study site by phylogenetic placement of pyrosequences. ISME J. 7, 1322–1332. https://doi.org/10.1038/ismej.2013.32 (2013).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kim, J.-G. et al. Distinct temporal dynamics of planktonic archaeal and bacterial assemblages in the bays of the Yellow Sea. PLoS One 14, e0221408. https://doi.org/10.1371/journal.pone.0221408 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Bayer, B. et al. Nitrosopumilus adriaticus sp. nov. and Nitrosopumilus piranensis sp. nov., two ammonia-oxidizing archaea from the Adriatic Sea and members of the class Nitrososphaeria. Int. J. Syst. Evol. Microbiol. 69, 1892–1902. https://doi.org/10.1099/ijsem.0.003360 (2019).CAS 
Article 
PubMed 

Google Scholar 
Strickland, J. D. H. & Parsons, T. R. A Practical Handbook of Seawater Analysis vol. 167 (Fisheries Research Board of Canada, 1972).Holm-Hansen, O., Lorenzen, C. J., Holmes, R. W. & Strickland, J. D. H. Fluorometric determination of chlorophyll. ICES J. Mar. Sci. 30, 3–15. https://doi.org/10.1093/icesjms/30.1.3 (1965).CAS 
Article 

Google Scholar 
Porter, K. G. & Feig, Y. S. The use of DAPI for identifying and counting aquatic microflora. Limnol. Oceanogr. 25, 943–948. https://doi.org/10.4319/lo.1980.25.5.0943 (1980).ADS 
Article 

Google Scholar 
Massana, R., Murray, A. E., Preston, C. M. & DeLong, E. F. Vertical distribution and phylogenetic characterization of marine planktonic Archaea in the Santa Barbara Channel. Appl. Environ. Microbiol. 63, 50–56. https://doi.org/10.1128/aem.63.1.50-56.1997 (1997).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Korlević, M., Markovski, M., Zhao, Z., Herndl, G. J. & Najdek, M. Selective DNA and protein isolation from marine macrophyte surfaces. Front. Microbiol. 12, 665999. https://doi.org/10.3389/fmicb.2021.665999 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Caporaso, J. G. et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 6, 1621–1624. https://doi.org/10.1038/ismej.2012.8 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Apprill, A., McNally, S., Parsons, R. & Weber, L. Minor revision to V4 region SSU rRNA 806R gene primer greatly increases detection of SAR11 bacterioplankton. Aquat. Microb. Ecol. 75, 129–137. https://doi.org/10.3354/ame01753 (2015).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, 1403–1414. https://doi.org/10.1111/1462-2920.13023 (2016).CAS 
Article 
PubMed 

Google Scholar 
Korlević, M., Markovski, M., Zhao, Z., Herndl, G. J. & Najdek, M. Seasonal dynamics of epiphytic microbial communities on marine macrophyte surfaces. Front. Microbiol. 12, 671342. https://doi.org/10.3389/fmicb.2021.671342 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Schloss, P. D. et al. Introducing mothur: Open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541. https://doi.org/10.1128/AEM.01541-09 (2009).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kozich, J. J., Westcott, S. L., Baxter, N. T., Highlander, S. K. & Schloss, P. D. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Appl. Environ. Microbiol. 79, 5112–5120. https://doi.org/10.1128/AEM.01043-13 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596. https://doi.org/10.1093/nar/gks1219 (2013).CAS 
Article 
PubMed 

Google Scholar 
Yilmaz, P. et al. The SILVA and “All-species Living Tree Project (LTP)” taxonomic frameworks. Nucleic Acids Res. 42, D643–D648. https://doi.org/10.1093/nar/gkt1209 (2014).CAS 
Article 
PubMed 

Google Scholar 
Schloss, P. D., Jenior, M. L., Koumpouras, C. C., Westcott, S. L. & Highlander, S. K. Sequencing 16S rRNA gene fragments using the PacBio SMRT DNA sequencing system. PeerJ 4, e1869. https://doi.org/10.7717/peerj.1869 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
R Core Team. R: A language and environment for statistical computing (R Foundation for Statistical Computing, 2021).Oksanen, J. et al. vegan: Community ecology package (2020).Wickham, H. et al. Welcome to the tidyverse. J. Open Source Softw. 4, 1686; https://doi.org/10.21105/joss.01686 (2019)McKinnon Edwards, S. lemon: Freshing up your ’ggplot2’ plots (2020).Wilke, C. O. cowplot: Streamlined plot theme and plot annotations for ’ggplot2’ (2020).Neuwirth, E. RColorBrewer: ColorBrewer palettes (2014).Zhu, H. kableExtra: Construct complex table with ’kable’ and pipe syntax (2021).Allaire, J. et al. rmarkdown: Dynamic documents for R (2021).Xie, Y., Allaire, J. J. & Grolemund, G. R Markdown: The Definitive Guide (Chapman and Hall/CRC, New York, 2018).Book 

Google Scholar 
Xie, Y., Dervieux, C. & Riederer, E. R Markdown Cookbook (Chapman and Hall/CRC, New York, 2020).Book 

Google Scholar 
Xie, Y. knitr: A general-purpose package for dynamic report generation in R (2021).Xie, Y. & knitr, A comprehensive tool for reproducible research in R. In Implementing Reproducible Computational Research (eds Stodden, V. et al.) (Chapman and Hall/CRC, New York, 2014).Xie, Y. Dynamic Documents with R and knitr (Chapman and Hall/CRC, New York, 2015).
Google Scholar 
Xie, Y. tinytex: Helper functions to install and maintain TeX Live, and compile LaTeX documents (2021).Xie, Y. TinyTeX: A lightweight, cross-platform, and easy-to-maintain LaTeX distribution based on TeX Live. TUGboat 40, 30–32 (2019).CAS 

Google Scholar 
Jost, L. Entropy and diversity. Oikos 113, 363–375. https://doi.org/10.1111/j.2006.0030-1299.14714.x (2006).Article 

Google Scholar 
Borcard, D., Gillet, F. & Legendre, P. Numerical Ecology with R (Springer, New York, 2018). https://doi.org/10.1007/978-3-319-71404-2.Book 
MATH 

Google Scholar 
Legendre, P. & Legendre, L. Numerical Ecology (Elsevier, Amsterdam, 2012).MATH 

Google Scholar  More

Data collectionBetween June 27 and August 25, 2016, 6600 km of simultaneous visual and passive acoustic line transect surveys were completed on the National Oceanic and Atmospheric Administration (NOAA) ship Henry B. Bigelow5. Survey effort was distributed along saw tooth track lines spanning the continental slope from Virginia (US) to the southern tip of Nova Scotia (Canada) (36–42 N) and on several larger track lines over the abyssal plain. Two teams of visual observers independently recorded sightings of marine mammals using high-powered Fujinon binoculars (25 × 150; Fujifilm, Valhalla, NY) as well as environmental conditions (e.g. sea state) every 30 min.The speed of sound in water was collected three times each day (morning, noon, evening) by measuring conductivity, temperature, and depth (CTD) at specific intervals in the water column. The sound speed closest to the depth of the towed hydrophone array was extracted. On alternating survey days, Simrad EK60 single beam scientific echosounders operating at frequencies of 18, 38, 70, 120 and 200 kHz were used to collect active acoustic data.When possible during daylight hours (06:00–18:00 ET), passive acoustic data were collected continuously using a custom-built linear array composed of eight hydrophone elements and a depth sensor (Keller America Inc. PA7FLE, Newport News, VA) within two oil-filled modular sections separated by 30 m of cable (Fig. 1). The array was towed 300 m behind the vessel at approximately 5–10 m depth while the vessel was in waters more than 100 m deep and underway at speeds of 16–20 km/h. For more details see DeAngelis et al.31, with the only change being that two APC hydrophones and one Reson hydrophone in the aft section were replaced with HTI-96-Min hydrophones (High Tech, Inc., Long Beach, MS). The HTI’s had a flat frequency response from 1 to 30 kHz (− 167 dB re V/uPa ± 1.5 dB). Recordings were made using the acoustical software PAMGuard (v.1.15.02)34. This analysis used the data recorded by the last two 192 kHz sampled hydrophones in the array (MF5 and MF6).Figure 1The linear towed array included eight hydrophone elements and a depth sensor within two oil-filled modular sections separated by 30 m of cable. Six hydrophones sampled at 192 kHz (MF1–MF6) and two sampled at 500 kHz. The hydrophones were connected to two National Instruments sound cards (NI-USB-6356). A high pass filter of 1 kHz was applied by the recording system to reduce the amount of vessel noise in the recordings. This analysis used the passive acoustic data from MF5 and MF6. The schematic is not to scale.Full size imageClick detection and 2D event localizationThe passive acoustic data were filtered using a Butterworth band pass filter (4th order) between 2 and 20 kHz and decimated to 96 kHz to improve sperm whale click resolution. Clicks were automatically detected using the PAMGuard (v.2.01.03) general sperm whale click detector with a trigger threshold of 12 dB.Using PAMGuard’s bearing time display, all detections were reviewed to classify click types and mark click trains as “events” based on consistent changes in bearing, audible sound, ICI and spectral characteristics. Each event was marked to an individual level, tracking a whale from the first to the last detected click15,35. All events containing usual clicks were localized with PAMGuard’s Target Motion Analysis (TMA) module’s 2D simplex optimization algorithm. For further analysis, events were truncated at a slant range of 6500 m (Supplementary Fig. S1).Echosounder analysisA regression analysis was run using the R package MASS36. To account for overdispersion, a negative binomial generalized linear model (GLM) with a log link function was applied to a dataset of the daily acoustic detections33. Echosounder state (active versus passive), month (June, July, August), and habitat type (slope or abyssal) were included as covariates, with the total number of daily detections as the response variable. The track line distance covered per day was used as an offset for effort. The best fitting model was selected based on backwards stepwise selection using Akaike’s information criterion (AIC) and the single-term deletion method using Chi-squared goodness-of-fit tests37.3D localizationExtracting a .wav clip for each click and attributing metadataAn automated process was developed using the R package PAMpal38 (v. 0.14.0) to extract the time of each click in the marked events from PAMGuard databases, generate a .wav clip for each click, and attribute all metadata (e.g., event 2D localization, array depth, radial distance, sea state, and sound speed) necessary for estimating the click depth.Slant delayUsing the methods established by DeAngelis et al.31 and custom Matlab R2021a (MathWorks Inc., Natick, NA) scripts, the multipath arrival of clicks and surface reflected echoes were used to mathematically convert the linear array into a 2D planar array and estimate 3D localizations. Using the .wav clips exported from PAMPal, the time delay between the click and the corresponding surface reflected echo, known as the slant delay, was measured via autocorrelation. Within the autocorrelation solution’s envelope of correlation values, the optimal slant delay was measured using the peak with the highest correlation value above a threshold of 0.02 and within an expected time window after the direct click of 0.0005–0.015 s. Although theoretically a surface reflected echo could have arrived less than a millisecond ( 5 min) were categorized as U shaped, and as shallow ( 1600 m) based on the maximum click depth (Fig. 3).Figure 3Example of click depths (m) over time (min) for events categorized as (a) U shaped and shallow ( 1600 m).Full size imageClick depths were then binned at 400 m intervals to account for an animal’s unknown horizontal movement over time as well as uncertainty in the estimated click depths, and the total time an animal spent within each depth bin was calculated. For each event with a U shaped click depth pattern, the depth bin in which the bottom phase occurred6 was determined. Finally, to assess if a whale was diving in the water column or close to the seafloor, the depth bin in which the 90th percentile of the click depths was recorded was compared to the bin including the seafloor depth. If the whale was more than 400 m above the seafloor, it was determined to be diving in the water column.Distance samplingDepth-corrected average horizontal perpendicular distancesFor each event, a depth-corrected average horizontal perpendicular distance was calculated using the TMA derived perpendicular slant range and the average depth or an assumed depth in the Pythagorean theorem19,31. The weighted mean, first quartile, and third quartile of the average depths were tested as assumed depths for events excluded from 3D localization. If depth was greater than or equal to the slant range the perpendicular distance was coerced to 0, indicating the whale was diving directly below the track line. The resulting distribution of depth-corrected perpendicular distances that aligned most with distance sampling theory was used in the final distance analysis.Acoustic density and abundance estimationThe R package Distance41,42 (v.1.0.4) was used to estimate two separate detection functions based on the uncorrected slant ranges and the depth-corrected perpendicular distances. Half-normal, uniform, and hazard rate key functions were tested with cosine, simple polynomial, and Hermite polynomial adjustment terms. The best fitting models were selected based on the AIC, the Kolmogorov–Smirnov (K–S) test, the Cramer-von Mises (CvM) test, quantile–quantile plots, and visual review of the fitted models43. The probability of detection, abundance, and effective strip half width (ESW) were then estimated for foraging sperm whales.Permitting authorityData used in this manuscript were collected during surveys that were completed under U.S. Marine Mammal Protection Act permit numbers 17355 and 21371 issued to the Northeast Fisheries Science Center. More

Attention combination mechanismDue to the difficulty in extracting features from target areas in images, the high computational effort of the model and the low accuracy of detection are addressed. As shown in Fig. 3, we introduce a lightweight feedforward convolutional attention module CBAM after the backbone network Focus module of the YOLOv5s network model. The SE-Net (Squeeze and Excitation Networks) channel attention module is posted at the end of the backbone network. We propose an attention combination mechanism based on the YOLOv5s network model and name the improved network model YOLOv5s-CS. Where the CBAM module has a channel number of 128, a convolutional kernel size of 3 and a step size of 2, the SELayer has a channel number of 1024 and a step size of 4.Figure 3YOLOv5 backbone network structure before and after improvement.Full size imageConvolutional block attention module networkIn 2018, Woo et al.25 proposed the lightweight feedforward convolutional attention module CBAM. The CBAM module focuses on feature information from both channels and space dimensions and combines feature information to some extent to obtain more comprehensive reliable attentional information26. CBAM consists of two submodules, the channel attention module (CAM) and spatial attention module (SAM), and its overall module structure is shown in Fig. 4a.Figure 4Principle of CBAM.Full size imageThe working principle of the CAM is shown in Fig. 4b. First, the feature map F is input at the input entrance. Second, the global maximum pooling operation and the global average pooling operation are applied to the width and height of the feature map respectively to obtain two feature maps of the same size. Third, two feature maps of the same size are input to the shared parameter network MLP at the same time. Finally, the new feature map output from the shared parameter network is subjected to a summation operation and a sigmoid activation function to obtain the channel attention features ({M}_{c}).The channel attention module CAM is calculated as shown in Formula (1):$${text{M}}_{rm{c}}({text{F}}){=sigma}({text{MLP (AvgPool (F))}}+ {text MLP (MaxPool (F)))}{=sigma}({rm{W}}_{1}({text{W}}_{0}({text{F}}_{{{rm{avg}}}^{rm{c}}}))+{rm{W}}_{0}({rm{W}}_{1}({rm{F}}_{{{rm{max}}}^{rm{c}}})))$$
(1)
where σ represents the sigmoid function, MLP represents the shared parameter network, ({text{W}}_{0}) and ({text{W}}_{1}) represent the shared weights, ({text{F}}_{text{avg}}^{text{c}}) is the result of feature map F after global average pooling, and ({text{F}}_{text{max}}^{text{c}}) is the result of feature map F after global maximum pooling.The working principle of SAM is shown in Fig. 4c. The feature map F’ is regarded as the input of the SAM. F’ is obtained by multiplying the input of SAM with the output of CAM. First, the global maximum pooling operation and the global average pooling operation are applied to the channels of the feature map to obtain two feature maps of the same size. Second, two feature maps that have completed the pooling operation are stitched at the channels and the feature channels are dimensioned down using the convolution operation to obtain a new feature map. Finally, spatial attention features ({text{M}}_{text{s}}) are generated using the sigmoid activation function.The spatial attention module (SAM) is calculated, as shown in Formula (2):$${text{M}}_{text{s}}left({text{F}}right) {=sigma}left({text{f}}^{7 times 7}left(left[{text{AvgPool}}left({text{F}}right)text{;MaxPool}left({text{F}}right)right]right)right) {=sigma}left({text{f}}^{7 times 7}left(left[{text{F}}_{text{avg}}^{text{s}} ; {text{F}}_{text{max}}^{text{s}}right]right)right)$$
(2)
where σ is the sigmoid function, ({text{f}}^{7 times 7}) denotes the convolution operation with a filter size of 7 × 7, ({text{F}}_{text{avg}}^{text{s}}) is the result of the feature map after global average pooling, and ({text{F}}_{text{max}}^{text{s}}) is the result of the feature map after global maximum pooling.Squeeze and excitation networkIn 2018, Hu et al.27 proposed a single-path attention network structure SE-Net. SE-Net uses the idea of an attention mechanism to analyze the relationship feature maps by modeling and adaptively learning to obtain the importance of each feature map28 and then assigns different weights to the original feature map for updating according to the importance. In this way, SE-Net pays more attention to the features that are useful for the target task while suppressing useless feature information and allocates computational resources rationally to different channels to train the model to achieve better results.The SE-Net attention module is mainly composed of two parts: the squeeze operation and excitation operation. The structure of the SE-Net module is shown in Fig. 5.Figure 5The SE-Net module structure.Full size imageThe squeeze operation uses global average pooling to encode all spatial features on the channel as local features. Second, each feature map is compressed into a real number that has global information on the feature maps. Finally, the squeeze results of each feature map are combined into a vector as the weights of each group of feature maps. It is calculated as shown in Eq. (3):$${text{Z}}_{text{c}}={text{F}}_{text{sq}}left({text{u}}_{text{c}}right)=frac{1}{text{H} times {text{W}}}sum_{text{i=1}}^{text{H}}sum_{text{j=1}}^{text{W}}{{text{u}}}_{text{c}}left(text{i,j}right) , , , $$
(3)
where H is the height of the feature map, W is the feature map width, u is the result after convolution, z is the global attention information of the corresponding feature map, and the subscript c indicates the number of channels.After completing the squeeze operation to obtain the channel information, the feature vector is subjected to the excitation operation. First, it passes through two fully connected layers. Second, it uses the sigmoid function. Finally, the output weights are assigned to the original features. It is calculated as follows:$$text{s} = {text{F}}_{text{ex}}left(text{z,W}right){=sigma}left({text{g}}left(text{z,W}right)right){=sigma}left({text{W}}_{2}{delta}left({text{W}}_{1}{text{z}}right)right)$$
(4)
$$widetilde{{text{x}}_{rm{c}}}={text{F}}_{rm{scale}}left({text{u}}_{rm{c}}, {text{s}}_{rm{c}}right)={text{s}}_{rm{c}}{{text{u}}}_{rm{c}}$$
(5)
where σ is the ReLU activation function, δ represents the sigmoid activation function, and ({text{W}}_{1}) and ({text{W}}_{2}) represent two different fully connected layers. The vector s represents the set of feature mapping weights obtained through the fully connected layer and the activation function. (widetilde{{x}_{c}}) is the feature mapping of the x feature channel, ({text{s}}_{text{c}}) is a weight, and ({text{u}}_{text{c}}) is a two-dimensional matrix.Target detection layerThe garbage in rural areas is a smaller target and has fewer pixel characteristics, such as capsule, button butteries. Therefore, we insert a small target detection layer to improve the head network structure based on the original YOLOv5s network model for detecting objects with small targets to optimize the problem of missed detection in the original network model. The YOLOv5s network structure with the addition of the small target detection layer is shown in Fig. 6 and named YOLOv5s-STD.Figure 6The YOLOv5s-STD network structure.Full size imageIn the seventeenth layer of the neck network, operations such as upsampling are performed on the feature maps so that the feature maps continue to expand. Meanwhile, in the twentieth layer, the feature maps obtained from the neck network are fused with the feature maps extracted from the backbone network. We insert a detection layer capable of predicting small targets in the thirty-first layer. To improve the detection accuracy, we use a total of four detection layers for the output feature maps, which are capable of detecting smaller target objects. In addition to the three initial anchor values based on the original model, an additional set of anchor values is added as a way to detect smaller targets. The anchor values of the improved YOLOv5s network model are set to [5, 6, 8, 14, 15, 11], [10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119] and [116, 90, 156, 198, 373, 326].Bounding box regression loss functionThe loss function is an important indicator of the generalization ability of a model. In 2016, Yu et al.29 proposed a new joint intersection loss function IoU for bounding box prediction. IoU stands for intersection over union, which is a frequently used metric in target detection. It is used not only to determine the positive and negative samples, but also to determine the similarity between the predicted bounding box and the ground truth bounding box. It can be described as shown in the Eq. (6):$$text{IoU} = frac{left|text{A} capleft.{text{B}}right|right.}{left|{text{A}} cupleft.{text{B}}right|right.}$$
(6)
where the value domain of IoU ranges from [0,1]. A and B are the areas of arbitrary regions. Additionally, when IoU is used as a loss function, it has to scale invariance, as shown in Eq. (7):$$text{IoU_Loss} = 1-frac{left|text{A} cap left.{text{B}}right|right.}{left|{text{A}} cup left.{text{B}}right|right.}$$
(7)
However, when the prediction bounding box and the ground truth bounding box do not intersect, namely IoU = 0, the distance between the arbitrary region area of A and B cannot be calculated. The loss function at this point is not derivable and cannot be used to optimize the two disjoint bounding boxes. Alternatively, when there are different intersection positions, where the overlapping parts are the same but in different overlapping directions, the IoU loss function cannot be predicted.To address these issues, the idea of GIoU (Generalized Intersection over Union)30, in which a minimum rectangular Box C of A and B is added, was proposed in 2019 by Rezatofighi et al. Suppose the prediction bounding box is B, the ground truth bounding box is A, the area where A and B intersect is D, and the area containing two bounding boxes is C, as shown in Fig. 7.Figure 7GIoU evaluation chart.Full size imageThen, the GIoU calculation, as shown in Formula (8), is:$$text{GIoU}= text{IoU}-frac{text{|C}-left({text{A}} cup {text{B}}right)text{|}}{text{|C|}}$$
(8)
The GIoU_Loss is calculated as (9):$$text{GIoU_Loss=1}-{text{IoU}}-frac{text{|C}-left({text{A}} cup {text{B}}right)text{|}}{text{|C|}}$$
(9)
The original YOLOv5 algorithm uses GIoU_Loss as the loss function. Comparing Eqs. (6) and (8), it can be seen that GIoU is a new penalty term (frac{text{|C}-left({text{A}} cup {text{B}}right)text{|}}{text{|C|}}) that is added to IoU and is clearly represented by Fig. 7.Although the GIoU loss function solves the problem that the gradient of the IoU loss function cannot be updated in time and the prediction bounding box, the direction of the ground truth bounding box is not consistent when predicting, but there are still disadvantages, as shown in Fig. 8.Figure 8Comparsion of loss values.Full size imageFigure 8 shows three different position relationships formed when the predicted bounding box and the ground truth bounding box overlap exactly. Among them, the ratio of the length to width of the green grounding truth bounding box is 1:2, and the red predicted bounding box has the same aspect ratio as the ground truth bounding box, but the size is only one-half of the green ground truth bounding box. When the prediction bounding box and the ground truth bounding box completely overlap, the GIoU degenerates to the IoU, and the GIoU value and IoU value for the three different position cases are 0.45 at this time. The GIoU loss function does not directly reflect the distance between the prediction bounding box and the ground truth bounding box. Therefore, we introduce the CIoU (Complete Intersection over Union)31 loss function to replace the original GIoU loss function in the YOLOv5 algorithm and continue to optimize the prediction bounding box.Therefore, the CIoU is calculated as (10):$$text{GIoU_Loss}=1-text{IoU}-frac{text{|C}-left({text{A}} cup {text{B}}right)text{|}}{text{|C|}}$$
(10)
where b and ({text{b}}^{text{gt}}) denote the centroids of the prediction bounding box and the ground truth bounding box, respectively, ({rho}) is the Euclidean distance between the two centroids, and c is the diagonal length of the minimum closed area formed by the prediction bounding box and the ground truth bounding box.(alpha) is the parameter used to balance the scale, and v is the scale consistency used to measure the aspect ratio between the prediction bounding box and the ground truth bounding box, as shown in Eqs. (11) and (12).$$alpha =frac{text{v}}{left(1-text{IoU}right)+{text{v}}^{{prime}}}$$
(11)
$$text{v} = frac{4}{{pi}^{2}}{left({text{arctan}}frac{{omega}^{text{gt}}}{{text{h}}^{text{gt}}}- text{arctan}frac{{omega}^{text{p}}}{{text{h}}^{text{p}}}right)}^{2}$$
(12)
Therefore, the expression of CIoU_Loss can be obtained according to Eqs. (10), (11) and (12).$$text{CIoU_Loss} =1-text{CIoU}=1-text{IoU}+frac{{rho}^{2}left(text{b,}{text{b}}^{text{gt}}right)}{{text{c}}^{2}}{+ alpha v }$$
(13)
Optimization algorithmAfter optimizing the loss function of the network model, the next step is to optimize the hyperparameters of the network model. The function of the optimizer is to adjust the hyperparameters to the most appropriate values while making the loss function converge as much as possible32. In the target detection algorithm, the optimizer is mainly used to calculate the gradient of the loss function and to iteratively update the parameters.The optimizer used in YOLOv5 is stochastic gradient descent (SGD). Since a large number of problems in deep learning satisfy the strict saddle function, all the local optimal solutions obtained are almost as ideal. Therefore, SGD algorithm is not trapped in the saddle point and has strong generality. However, the slow convergence speed and the number of iterations of SGD algorithm are still problems that need to be improved. Adam algorithm has both the first-order momentum in the SGD algorithm and combines the second-order momentum in AdaGrad algorithm and AdaDelta algorithm, Adaptive&Momentum. Adam formula can be described as follows:$${m}_{t}={beta }_{1}{m}_{t-1}+left(1-{beta }_{1}right){g}_{t}$$
(14)
$${v}_{t}={beta }_{2}{v}_{t-1}+left(1-{beta }_{2}right){g}_{t}^{2}$$
(15)
$${widehat{m}}_{t}=frac{{m}_{t}}{1-{beta }_{1}^{t}}$$
(16)
$${widehat{v}}_{t}=frac{{v}_{t}}{1-{beta }_{2}^{t}}$$
(17)
where ({beta }_{1}) and ({beta }_{2}) parameters are hyperparameters and g is the current gradient value of the error function, ({m}_{t}) is the gradient of the first-order momentum and ({v}_{t}) is the gradient of the second-order momentum.Adam is an adaptive one-step random objective function optimization algorithm based on a low-order moment. It can replace the traditional first-order optimization algorithm for the stochastic gradient descent process. It is able to update the weights of the neural network adaptively based on the data trained during the iterative process. The Adam optimizer occupies fewer memory resources during the training process and is suitable for solving the problems of sparse gradients and large fluctuations in loss values33. Therefore, we use the Adam optimization algorithm instead of the SGD optimization algorithm to train the network model based on the YOLOv5s network model. The calculation is shown in Table 3.Table 3 Computing method of the Adam optimizer.Full size tablewhere ({alpha}) is a factor controlling the learning rate of the network, ({beta}^{{prime}}) is the exponential decay rate of the first-order moment estimate, ({beta}^{{primeprime}}) is the exponential decay rate of the second-order moment estimate, and ({varepsilon}) is a constant that tends to zero infinitely as the denominator. More

Abascal F, Zardoya R, Telford MJ (2010) TranslatorX: Multiple alignment of nucleotide sequences guided by amino acid translations. Nucleic Acids Res 38:7–13Article 

Google Scholar 
Akashi HD, Cádiz Díaz A, Shigenobu S, Makino T, Kawata M (2016) Differentially expressed genes associated with adaptation to different thermal environments in three sympatric Cuban Anolis lizards. Mol Ecol 25:2273–2285CAS 
PubMed 
Article 

Google Scholar 
Allen JA (1877) The influence of Physical conditions in the genesis of species. Radic Rev 1:108–140
Google Scholar 
Barghi N, Hermisson J, Schlötterer C (2020) Polygenic adaptation: A unifying framework to understand positive selection. Nat Rev Genet 21:769–781CAS 
PubMed 
Article 

Google Scholar 
Blem CR (1990) Avian energy storage. Curr Ornithol 7:59–113
Google Scholar 
Blix AS (2016) Adaptations to polar life in mammals and birds. J Exp Biol 219:1093–1105PubMed 
Article 

Google Scholar 
Boyle EA, Li YI, Pritchard JK (2017) An expanded view of complex traits: From polygenic to omnigenic. Cell 169:1177–1186CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Cannon B, Nedergaard J (2010) Thyroid hormones: Igniting brown fat via the brain. Nat Med 16:965–967CAS 
PubMed 
Article 

Google Scholar 
Castruita JAS, Westbury MV, Lorenzen ED (2020) Analyses of key genes involved in Arctic adaptation in polar bears suggest selection on both standing variation and de novo mutations played an important role. BMC Genom 21:1–8
Google Scholar 
Cherel Y, Gilles J, Handrich Y, Le Maho Y (1994) Nutrient reserve dynamics and energetics during long-term fasting in the king penguin (Aptenodytes patagonicus). J Zool 234:1–12Article 

Google Scholar 
Colles A, Liow LH, Prinzing A (2009) Are specialists at risk under environmental change? Neoecological, paleoecological and phylogenetic approaches. Ecol Lett 12:849–863PubMed 
PubMed Central 
Article 

Google Scholar 
Cristofari R, Bertorelle G, Ancel A, Benazzo A, Le Maho Y, Ponganis PJ et al. (2016) Full circumpolar migration ensures evolutionary utility in the Emperor penguin. Nat Commun 7:1–9Article 

Google Scholar 
Cristofari R, Liu X, Bonadonna F, Cherel Y, Pistorius P, Le Maho Y et al. (2018) Climate-driven range shifts of the king penguin in a fragmented ecosystem. Nat Clim Change 8:245–251Article 

Google Scholar 
Descamps S, Aars J, Fuglei E, Kovacs KM, Lydersen C, Pavlova O et al. (2017) Climate change impacts on wildlife in a High Arctic archipelago-Svalbard, Norway. Glob Change Biol 23:490–502Article 

Google Scholar 
Díaz-Franulic I, Raddatz N, Castillo K, González-Nilo FD, Latorre R (2020) A folding reaction at the C-terminal domain drives temperature sensing in TRPM8 channels. PNAS 117:20298–20304PubMed 
PubMed Central 
Article 

Google Scholar 
Duchamp C, Marmonier F, Denjean F, Lachuer J, Eldershaw TPD, Rouanet JL et al. (1999) Regulatory, cellular and molecular aspects of avian muscle non-shivering thermogenesis. Ornis Fennica 76:151–165
Google Scholar 
Elliott KH, Welcker J, Gaston AJ, Hatch SA, Palace V, Hare JF et al. (2013) Thyroid hormones correlate with resting metabolic rate, not daily energy expenditure, in two charadriiform seabirds. Biol Open 2:580–586CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Frost PGH, Siegfried WR, Greenwood PJ (1975) Arterio-venous heat exchange systems in the Jackass penguin Spheniscus demersus. J Zool 175:231–241Article 

Google Scholar 
Fu J, Gaetani S, Oveisi F, Lo Verme J, Serrano A, Rodríguez de Fonseca F et al. (2003) Oleylethanolamide regulates feeding and body weight through activation of the nuclear receptor PPAR-alpha. Nature 425:90–93CAS 
PubMed 
Article 

Google Scholar 
Gavryushkina A, Heath TA, Ksepka DT, Stadler T, Welch D, Drummond AJ (2017) Bayesian total-evidence dating reveals the recent crown radiation of penguins. Syst Biol 66:57–73PubMed 

Google Scholar 
Geering K, Kraehenbuhl JP, Rossier BC (1987) Maturation of the catalytic alpha unit of Na, K-ATPase during intracellular transport. J Cell Biol 105:2613–2619CAS 
PubMed 
Article 

Google Scholar 
Gilg O, Kovacs KM, Aars J, Fort J, Gauthier G, Grémillet D et al. (2012) Climate change and the ecology and evolution of Arctic vertebrates. Ann N. Y Acad Sci 1249:166–190PubMed 
Article 

Google Scholar 
Goldsmith R, Sladen WJ (1961) Temperature regulation of some Antarctic penguins. J Physiol 157:251–262CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Groscolas R (1990) Metabolic adaptations to fasting in emperor and king penguins. In Penguin Biology (LS Davis & JT Darby ed.), 269-296 San Diego: Academic PressGroscolas R, Robin JP (2001) Long-term fasting and re-feeding in penguins. Comp Biochem Physiol Part A Mol Integr Physiol 128:645–655CAS 

Google Scholar 
Halaas JL, Gajiwala KS, Maffei M, Cohen SL, Chait BT, Rabinowitz D et al. (1995) Weight-reducing effects of the plasma protein encoded by the obese gene. Science 269:543–546CAS 
PubMed 
Article 

Google Scholar 
Han MV, Demuth JP, McGrath CL, Casola C, Hahn MW (2009) Adaptive evolution of young gene duplicates in mammals. Genome Res 19:859–867CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hao Y, Xiong Y, Cheng Y, Song G, Jia C, Qu Y et al. (2019) Comparative transcriptomics of 3 high-altitude passerine birds and their low-altitude relatives. PNAS 116:11851–11856CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hunt BG, Ometto L, Wurm Y, Shoemaker D, Soojin VY, Keller L et al. (2011) Relaxed selection is a precursor to the evolution of phenotypic plasticity. PNAS 108:15936–15941CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Iannello S, Milazzo P, Belfiore F (2007) Animal and human tissue Na,K-ATPase in normal and insulin-resistant states: regulation, behaviour and interpretative hypothesis on NEFA effects. Obes Rev 8:231–251CAS 
PubMed 
Article 

Google Scholar 
Ishii S, Amano I, Koibuchi N (2021) The role of thyroid hormone in the regulation of cerebellar development. Endocrinol Metab 36:703–716CAS 
Article 

Google Scholar 
Jarvis ED, Mirarab S, Aberer AJ, Li B, Houde P, Li C et al. (2014) Whole genome analyses resolve early branches in the tree of life of modern birds. Science 346:1320–1331CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kajimura S, Spiegelman BM, Seale P (2015) Brown and beige fat: Physiological roles beyond heat generation. Cell Metab 22:546–559CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kooyman GL, Gentry RL, Bergman WP, Hammel HT (1976) Heat loss in penguins during immersion and compression. Comp Bioch Physiol Part A 54:75–80CAS 
Article 

Google Scholar 
Kumar V, Kutschera VE, Nilsson MA (2015) Genetic signatures of adaptation revealed from transcriptome sequencing of Arctic and red foxes. BMC Genom 16:1–13Article 

Google Scholar 
Kumar S, Stecher G, Tamura K (2016) MEGA7: Molecular evolutionary genetics analysis version 70 for bigger datasets. Mol Biol Evol 33:1870–1874CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lahti DC, Johnson NA, Ajie BC, Otto SP, Hendry AP, Blumstein DT et al. (2009) Relaxed selection in the wild. Trends Ecol Evol 24:487–496PubMed 
Article 

Google Scholar 
Lemberger T, Saladin R, Vazquez M, Assimacopoulos F, Staels B, Desvergne B et al. (1996) Expression of the peroxisome proliferator-activated receptor alpha gene is stimulated by stress and follows a diurnal rhythm. J Biol Chem 271:1764–1769CAS 
PubMed 
Article 

Google Scholar 
Li FG, Li H (2019) A time-dependent genome-wide SNP-SNP interaction analysis of chicken body weight. BMC Genom 20:1–9Article 

Google Scholar 
Li C, Zhang Y, Li J, Kong L, Hu H, Pan H et al. (2014) Two Antarctic penguin genomes reveal insights into their evolutionary history and molecular changes related to the Antarctic environment. Gigascience 3:2047–2217XArticle 

Google Scholar 
Lin Z, Chen L, Chen X, Zhong Y, Yang Y, Xia W et al. (2019) Biological adaptations in the Arctic cervid, the reindeer (Rangifer tarandus). Science 364:eaav6312CAS 
PubMed 
Article 

Google Scholar 
Liu S, Lorenzen ED, Fumagalli M, Li B, Harris K, Xiong Z et al. (2014) Population genomics reveal recent speciation and rapid evolutionary adaptation in polar bears. Cell 157:785–794CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Liu S, Westbury MV, Dussex N, Mitchell KJ, Sinding MH, Heintzman PD et al. (2021) Ancient and modern genomes unravel the evolutionary history of the rhinoceros family. Cell 184:4874–4885CAS 
PubMed 
Article 

Google Scholar 
Lowell BB, Spiegelman BM (2000) Towards a molecular understanding of adaptive thermogenesis. Nature 404:652–660CAS 
PubMed 
Article 

Google Scholar 
Löytynoja A (2013) Phylogeny-aware alignment with PRANK. Methods Mol Biol 1079:155–170Article 

Google Scholar 
Lynch VJ, Bedoya-Reina OC, Ratan A, Sulak M, Drautz-Moses DI, Perry GH et al. (2015) Elephantid genomes reveal the molecular bases of woolly mammoth adaptations to the Arctic. Cell Rep 12:217–228CAS 
PubMed 
Article 

Google Scholar 
Madeira F, Park YM, Lee J, Buso N, Gur T, Madhusoodanan N et al. (2019) The EMBL-EBI search and sequence analysis tools APIs in 2019. Nucleic Acids Res 47:636–641Article 

Google Scholar 
Matos-Cruz V, Schneider ER, Mastrotto M, Merriman DK, Bagriantsev SN, Gracheva EO (2017) Molecular prerequisites for diminished cold sensitivity in ground squirrels and hamsters. Cell Rep 21:3329–3337CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Monera OD, Sereda TJ, Zhou NE, Kay CM, Hodges RS (1995) Relationship of sidechain hydrophobicity and α-helical propensity on the stability of the single-stranded amphipathic α-helix. J Pept Sci 1:319–329CAS 
PubMed 
Article 

Google Scholar 
Myers BR, Sigal YM, Julius D (2009) Evolution of Thermal Response Properties in a Cold-Activated TRP Channel. PloS one 4:e5741PubMed 
PubMed Central 
Article 

Google Scholar 
Ng CS, Chen CK, Fan WL, Wu P, Wu SM, Chen JJ et al. (2015) Transcriptomic analyses of regenerating adult feathers in chicken. BMC Genom 16:1–16CAS 
Article 

Google Scholar 
Ohno H, Shinoda K, Spiegelman BM, Kajimura S (2012) PPARg agonists induce a white-to-brown fat conversion through stabilisation of PRDM16 protein. Cell Metab 15:395–404CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Oldham MC, Horvath S, Geschwind DH (2006) Conservation and evolution of gene coexpression networks in human and chimpanzee brains. PNAS 103:17973–17978CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pan H, Cole TL, Bi X, Fang M, Zhou C, Yang Z et al. (2019) High-coverage genomes to elucidate the evolution of penguins. GigaScience 8:1–17Article 

Google Scholar 
Pelleymounter MA, Cullen MJ, Baker MB, Hecht R, Winters D, Boone T et al. (1995) Effects of the obese gene product on body weight regulation in ob/ob mice. Science 269:540–543CAS 
PubMed 
Article 

Google Scholar 
Poirier H, Niot I, Monnot MC, Braissant O, Meunier-Durmort C, Costet P et al. (2001) Differential involvement of peroxi-some-proliferator-activated receptors alpha and delta in fibrate and fatty-acid-mediated inductions of the gene encoding liver fatty-acid-binding protein in the liver and the small intestine. Biochem J 355:481–488CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pond SK, Frost S, Muse SV (2005) HyPhy: hypothesis testing using phylogenies. Bioinform 21:676–679CAS 
Article 

Google Scholar 
R Core Team (2013) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria
Google Scholar 
Ramasamy S, Ometto L, Crava CM, Revadi S, Kaur R, Horner DS et al. (2016) The evolution of olfactory gene families in Drosophila and the genomic basis of chemical-ecological adaptation in Drosophila suzukii. Genome Biol Evol 8:2297–2311PubMed 
PubMed Central 
Article 

Google Scholar 
Ramos B, González-Acuña D, Loyola DE, Johnson WE, Parker PG, Massaro M et al. (2018) Landscape genomics: natural selection drives the evolution of mitogenome in penguins. BMC Genom 19:1–17Article 

Google Scholar 
Raudvere U, Kolberg L, Kuzmin I, Arak T, Adler P, Peterson H et al. (2019) g:Profiler: a web server for functional enrichment analysis and conversions of gene lists. Nucleic Acids Res 47:191–198Article 

Google Scholar 
Rey B, Roussel D, Romestaing C, Belouze M, Rouanet JL, Desplanches D et al. (2010) Up-regulation of avian uncoupling protein in cold-acclimated and hyperthyroid ducklings prevents reactive oxygen species production by skeletal muscle mitochondria. BMC Physiol 10:1–12Article 

Google Scholar 
Roussel D, Le Coadic M, Rouanet JL, Duchamp C (2020) Skeletal muscle metabolism in sea-acclimatised king penguins I Thermogenic mechanisms. J Exp Biol 223:pjeb233668Article 

Google Scholar 
Rowland LA, Bal NC, Periasamy M (2015) The role of skeletal‐muscle‐based thermogenic mechanisms in vertebrate endothermy. Biol Rev 90:1279–1297PubMed 
Article 

Google Scholar 
Savini G, Scolari F, Ometto L, Rota-Stabelli O, Carraretto D, Gomulski LM et al. (2021) Viviparity and habitat restrictions may influence the evolution of male reproductive genes in tsetse fly (Glossina) species. BMC Biol 19:1–13Article 

Google Scholar 
Scholander PF (1955) Evolution of climatic adaptation in homeotherms. Evolution 9:15–26Article 

Google Scholar 
Schymkowitz J, Borg J, Stricher F, Nys R, Rousseau F, Serrano L (2005) The FoldX web server: an online force field. Nuc Acids Res 33:382–388Article 

Google Scholar 
Seale P, Kajimura S, Yang W, Chin S, Rohas LM, Uldry M et al. (2007) Transcriptional control of brown fat determination by PRDM16. Cell Metab 6:38–54CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Seale P, Conroe HM, Estall J, Kajimura S, Frontini A, Ishibashi J et al. (2011) Prdm16 determines the thermogenic program of subcutaneous white adipose tissue in mice. J Clin Investig 121:96–105CAS 
PubMed 
Article 

Google Scholar 
Smith MD, Wertheim JO, Weaver S (2015) Less is more: An adaptive branch-site random effects model for efficient detection of episodic diversifying selection. Mol Biol Evol 32:1342–1353CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Sonna LA, Fujita J, Gaffin SL, Lilly CM (2002) Effects of heat and cold stress on mammalian gene expression. J Appl Physiol 92:1725–1742CAS 
PubMed 
Article 

Google Scholar 
Stelzer G, Rosen N, Plaschkes I, Zimmerman S, Twik M, Fishilevich S et al. (2016) The genecards suite: From gene data mining to disease genome sequence analysis. Curr Protoc Bioinform 54:1–30Article 

Google Scholar 
Storey KB, Storey JM (1992) Natural freeze tolerance in ectothermic vertebrates. Annu Rev Physiol 54:619–637CAS 
PubMed 
Article 

Google Scholar 
Storey JD, Bass AJ, Dabney A, Robinson D (2017) qvalue: Q-value estimation for false discovery rate control R package version 2150Supek F, Bošnjak M, Škunca N, Šmuc T (2011) REVIGO summarises and visualises long lists of gene ontology terms. PloS one 6:e21800CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Talbot DA, Duchamp C, Rey B, Hanuise N, Rouanet JL, Sibille B et al. (2004) Uncoupling protein and ATP/ADP carrier increase mitochondrial proton conductance after cold adaptation of king penguins. J Physiol 558:123–135CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Tattersall GJ, Sinclair BJ, Withers PC, Fields PA, Seebacher F, Cooper CE et al. (2012) Coping with thermal challenges: Physiological adaptations to environmental temperatures. Compr Physiol 2:2151–2202PubMed 
Article 

Google Scholar 
Teulier L, Rouanet JL, Letexier D, Romestaing C (2010) Cold-acclimation-induced non-shivering thermogenesis in birds is associated with upregulation of avian UCP but not with innate uncoupling or altered ATP efficiency. J Exp Biol 213:2476–2482CAS 
PubMed 
Article 

Google Scholar 
Thomas DB, Fordyce RE (2008) The heterothermic loophole exploited by penguins. Aust J Zool 55:317–321Article 

Google Scholar 
Tigano A, Reiertsen TK, Walters JR, Friesen VL (2018) A complex copy number variant underlies differences in both colour plumage and cold adaptation in a dimorphic seabird. BioRxiv 507384. https://doi.org/10.1101/507384Toyomizu M, Ueda M, Sato S, Seki Y, Sato K, Akiba Y (2002) Cold-induced mitochondrial uncoupling and expression of chicken UCP and ANT mRNA in chicken skeletal muscle. FEBS Lett 529:313–318CAS 
PubMed 
Article 

Google Scholar 
Trucchi E, Gratton P, Whittington JD, Cristofari R, Le Maho Y, Stenseth NC et al. (2014) King penguin demography since the last glaciation inferred from genome-wide data. Proc R Soc B 281:20140528PubMed 
PubMed Central 
Article 

Google Scholar 
Trucchi E, Cristofari R, Le Bohec C (2019) Reply to: The role of ocean dynamics in king penguin range estimation. Nat Clim Change 9:122–122Article 

Google Scholar 
Vermillion KL, Anderson KJ, Hampton M, Andrews MT (2015) Gene expression changes controlling distinct adaptations in the heart and skeletal muscle of a hibernating mammal. Physiol Genom 47:58–74CAS 
Article 

Google Scholar 
Vézina F, Gustowska A, Jalvingh KM, Chastel O, Piersma T (2015) Hormonal correlates and thermoregulatory consequences of moulting on metabolic rate in a northerly wintering shorebird. Physiol Biochem Zool 82:129–142Article 

Google Scholar 
Vianna JA, Fernandes FA, Frugone MJ, Figueiró HV, Pertierra LR, Noll D et al. (2020) Genome-wide analyses reveal drivers of penguin diversification. PNAS 117:22303–22310CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wang Q, Tan X, Jiao S (2014) Analyzing cold tolerance mechanism in transgenic Zebrafish (Danio rerio). PloS one 9:e102492PubMed 
PubMed Central 
Article 

Google Scholar 
Wertheim JO, Murrell B, Smith MD, Kosakovsky Pond SL, Scheffler K (2015) RELAX: Detecting relaxed selection in a phylogenetic framework. Mol Biol Evol 32:820–832CAS 
PubMed 
Article 

Google Scholar 
Wollenberg Valero KC, Pathak R, Prajapati I, Bankston S, Thompson A, Usher J et al. (2014) A candidate multimodal functional genetic network for thermal adaptation. PeerJ 2:e578PubMed 
PubMed Central 
Article 

Google Scholar 
Yang Z (2007) PAML 4: A program package for phylogenetic analysis by maximum likelihood. Mol Biol Evol 24:1586–1591CAS 
PubMed 
Article 

Google Scholar 
Yang J, Bromage TG, Zhao Q, Xu BH, Gao WL, Tian HF et al. (2011) Functional evolution of leptin of Ochotona curzoniae in adaptive thermogenesis driven by cold environmental stress. PloS one 6:e19833CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Yang S, Lu X, Wang Y, Xu L, Chen X, Yang F et al. (2020) A paradigm of thermal adaptation in penguins and elephants by tuning cold activation in TRPM8. PNAS 117:8633–8638CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Yin Y, Wu M, Zubcevic L, Borschel WF, Lander GC, Lee SY (2018) Structure of the cold- and menthol-sensing ion channel TRPM8. Science 359:237–241CAS 
PubMed 
Article 

Google Scholar 
Yudin NS, Larkin DM, Ignatieva EV (2017) A compendium and functional characterization of mammalian genes involved in adaptation to Arctic or Antarctic environments. BMC Genet 18:33–43Article 

Google Scholar 
Zelcer N, Sharpe LJ, Loregger A, Kristiana I, Cook EC, Phan L et al. (2014) The E3 ubiquitin ligase MARCH6 degrades squalene monooxygenase and affects 3-hydroxy-3-methyl-glutaryl coenzyme A reductase and the cholesterol synthesis pathway. Mol Cell Biol 34:1262–1270PubMed 
PubMed Central 
Article 

Google Scholar 
Zhang Y, Proenca R, Maffei M, Barone M, Leopold L, Friedman JM (1994) Positional cloning of the mouse obese gene and its human homologue. Nature 372:425–432CAS 
PubMed 
Article 

Google Scholar 
Zhang G, Li C, Li Q, Li B, Larkin DM, Lee C et al. (2014) Comparative genomics reveals insights into avian genome evolution and adaptation. Science 346:1311–1320CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More

Luiselli, L., Akani, G. & Capizzi, D. Food resource partitioning of a community of snakes in a swamp rainforest of south-eastern Nigeria. J. Zool. 246(2), 125–133. https://doi.org/10.1111/j.1469-7998.1998.tb00141.x (1998).Article 

Google Scholar 
Rouag, R., Djilali, H., Gueraiche, H. & Luiselli, L. Resource partitioning patterns between two sympatric lizard species from Algeria. J. Arid Environ. 69, 158–168. https://doi.org/10.1016/j.jaridenv.2006.08.008 (2007).ADS 
Article 

Google Scholar 
Bergeron, R. & Blouin-Demers, G. Niche partitioning between two sympatric lizards in the Chiricahua Mountains of Arizona. Copeia 108(3), 570–577. https://doi.org/10.1643/CH-19-268 (2020).Article 

Google Scholar 
Lucek, K., Butlin, R. K. & Patsiou, T. Secondary contact zones of closely-related Erebia butterflies overlap with narrow phenotypic and parasitic clines. J. Evol. Biol. 33(9), 1152–1163. https://doi.org/10.1111/jeb.13669 (2020).CAS 
Article 
PubMed 

Google Scholar 
Freeman, B. G. Competitive interaction upon secondary contact drive elevational divergence in tropical birds. Am. Nat. 186(4), 470–479. https://doi.org/10.5061/dryad.6qg3g (2015).Article 
PubMed 

Google Scholar 
Schoener, T. W. Resource partitioning in ecological communities. Science 185(4145), 27–39 (1974).ADS 
CAS 
Article 

Google Scholar 
Rivas, L. R. A Reinterpretation of the concepts “sympatric” and “allopatric” with proposal of the additional terms “syntopic” and “allotopic”. Syst. Zool. 13(1), 42 (1964).Article 

Google Scholar 
Macarthur, R. & Levins, R. The limiting similarity, convergence, and divergence of coexisting species. Am. Nat. 101(921), 377–385 (1967).Article 

Google Scholar 
Dayan, T. & Simberloff, D. Ecological and community-wide character displacement: The next generation. Ecol. Lett. 8(8), 875–894. https://doi.org/10.1111/j.1461-0248.2005.00791.x (2005).Article 

Google Scholar 
Holomuzki, J. R., Feminella, J. W. & Power, M. E. Biotic interactions in freshwater benthic habitats. J. N. Am. Benthol. Soc. 29(1), 220–244. https://doi.org/10.1899/08-044.1 (2010).Article 

Google Scholar 
Ferretti, F. et al. Competition between wild herbivores: Reintroduced red deer and Apennine chamois. Behav. Ecol. 26(2), 550–559. https://doi.org/10.1093/beheco/aru226 (2015).Article 

Google Scholar 
Takada, H., Yano, R., Katsumata, A., Takatsuki, S. & Minami, M. Diet compositions of two sympatric ungulates, the Japanese serow (Capricornis crispus) and the sika deer (Cervus nippon), in a montane forest and an alpine grassland of Mt. Asama central, Japan. Mamm. Biol. 101, 681–694. https://doi.org/10.1007/s42991-021-00122-5 (2021).Article 

Google Scholar 
Hubbel, S. P. The Unified Neutral Theory of Biodiversity and Biogeography (Princeton University Press, 2001) (ISBN 9780691021287).
Google Scholar 
Bell, G. Neutral macroecology. Science 293, 2413–2418. https://doi.org/10.1126/science.293.5539.2413 (2001).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Rosindell, J., Hubbel, S. P. & Etienne, R. S. The unified neutral theory of biodiversity and biogeography at age ten. Trends Ecol. Evol. 26(7), 340–348. https://doi.org/10.1016/j.tree.2011.03.024 (2011).Article 
PubMed 

Google Scholar 
Cowie, R. H. & Holland, B. S. Dispersal is fundamental to biogeography and the evolution of biodiversity on oceanic islands. J. Biogeogr. 33, 193–198. https://doi.org/10.1111/j.1365-2699.2005.01383.x (2006).Article 

Google Scholar 
Amarasekare, P. & Nisbet, R. M. Spatial heterogeneity, source-sink dynamics, and the local coexistence of competing species. Am. Nat. 158(6), 572–584. https://doi.org/10.1086/323586 (2001).CAS 
Article 
PubMed 

Google Scholar 
Kumar, K., Gentile, G. & Grant, T. D. Conolophus subcristatus. The IUCN Red List of Threatened Species 2020, e.T5240A3014082 (2020). https://doi.org/10.2305/IUCN.UK.2020-2.RLTS.T5240A3014082.enGentile, G. Conolophus marthae. The IUCN Red List of Threatened Species 2012, e. T174472A1414375 (2012). https://doi.org/10.2305/IUCN.UK.2012-1.RLTS.T174472A1414375.enGentile, G., Marquez, C., Snell, H. L., Tapia, W. & Izurieta, A. Conservation of a New Flagship Species: The Galápagos Pink Land Iguana (Conolophus marthae Gentile and Snell, 2009). In Problematic Wildlife: A Cross-Disciplinary Approach (ed. Angelici, F. M.) 315–336 (Springer International Publishing, 2016). https://doi.org/10.1007/978-3-319-22246-2_15.Chapter 

Google Scholar 
Gentile, G. & Snell, H. L. Conolophus marthae sp. Nov. (Squamata, iguanidae), a new species of land iguana from the Galápagos Archipelago. Zootaxa 2201, 1–10 (2009).Article 

Google Scholar 
Colosimo, G. et al. Chemical signatures of femoral pore secretions in two syntopic but reproductively isolated species of Galápagos land iguanas (Conolophus marthae and C. subcristatus). Sci. Rep. 10(1), 14314. https://doi.org/10.1038/s41598-020-71176-7 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Jackson, M. Galápagos: A Natural History, Revised and Expanded (University of Calgary Press, 1994).
Google Scholar 
Traveset, A. et al. Galápagos land iguana (Conolophus subcristatus) as a seed disperser. Integr. Zool. 11(3), 207–213. https://doi.org/10.1111/1749-4877.12187 (2016).Article 
PubMed 

Google Scholar 
Di Giambattista, L. et al. Molecular data exclude current hybridization between iguanas Conolophus marthae and C. subcristatus on Wolf volcano (Galápagos islands). Conserv. Genet. 19(6), 1461–1469. https://doi.org/10.1007/s10592-018-1114-3 (2018).Article 

Google Scholar 
MacLeod, A. et al. Hybridization masks speciation in the evolutionary history of the Galápagos marine iguana. Proc. R. Soc. B 282, 1–9. https://doi.org/10.1098/rspb.2015.0425 (2015).Article 

Google Scholar 
Gause, G. F. The Struggle for Existence (Williams and Wilkins Company, 1934).Book 

Google Scholar 
Hardin, G. The competitive exclusion principle. Science 131(3409), 1292–1297 (1960).ADS 
CAS 
Article 

Google Scholar 
Ashrafi, S., Beck, A., Rutishauser, M., Arlettaz, R. & Bontadina, F. Trophic niche partitioning of cryptic species of long-eared bats in Switzerland: Implications for conservation. Eur. J. Wildl. Res. 57, 843–849. https://doi.org/10.1007/s10344-011-0496-z (2011).Article 

Google Scholar 
Bleyhl, B. et al. Assessing niche overlap between domestic and threatened wild sheep to identify conservation priority areas. Divers. Distrib. 25(1), 129–141. https://doi.org/10.1111/ddi.12839 (2019).Article 

Google Scholar 
Newsome, S. D., del Rio, C. M., Bearhop, S. & Phillips, D. L. A niche for isotopic ecology. Front. Ecol. Environ. 5(8), 429–436. https://doi.org/10.1890/060150.1 (2007).Article 

Google Scholar 
Riera, P., Stal, L. J. & Nieuwenhuize, J. δ13C versus δ15N of co-occurring mollusks within a community dominated by Crassostrea gigas and Crepidula ornicate (Oossterschelde, The Netherlands). Mar. Ecol. Prog. Ser. 240, 291–295 (2002).ADS 
Article 

Google Scholar 
Page, B., McKenzie, J. & Goldsworthy, S. D. Dietary resources partitioning among sympatric New Zealand and Australian fur seals. Mar. Ecol. Prog. Ser. 293, 283–302 (2005).ADS 
Article 

Google Scholar 
DeNiro, M. J. & Epstein, S. Influence of diet on the distribution of carbon isotopes in animals. Geochim. Cosmochim. Acta 42(5), 495–506 (1978).ADS 
CAS 
Article 

Google Scholar 
DeNiro, M. J. & Epstein, S. Influence of diet on the distribution of nitrogen isotopes in animals. Geochim. Cosmochim. Acta 45(3), 341–351 (1981).ADS 
CAS 
Article 

Google Scholar 
Post, D. M. Using stable isotopes to estimate trophic position: Models, methods, and assumptions. Ecology 83(3), 703–718. https://doi.org/10.1890/0012-9658(2002)083[0703:USITET]2.0.CO;2 (2002).Article 

Google Scholar 
Crawford, K., McDonald, R. A. & Bearhop, S. Applications of stable isotope techniques to the ecology of mammals. Mammal. Rev. 38(1), 87–107. https://doi.org/10.1111/j.1365-2907.2008.00120.x (2008).Article 

Google Scholar 
Trueman, M. & d’Ozouville, N. Characterizing the Galápagos terrestrial climate in the face of global climate change. Gala Res. 67, 26–37 (2010).
Google Scholar 
Paltán, H. A. et al. Climate and sea surface trends in the Galápagos Islands. Sci. Rep. 11(1), 1–13. https://doi.org/10.1038/s41598-021-93870-w (2021).CAS 
Article 

Google Scholar 
Rivas-Torres, G. F., Benítez, F. L., Rueda, D., Sevilla, C. & Mena, C. F. A methodology for mapping native and invasive vegetation coverage in archipelagos: An example from the Galápagos islands. Prog. Phys. Geogr. 42(1), 83–111. https://doi.org/10.1177/0309133317752278 (2018).Article 

Google Scholar 
Gentile, G., Ciambotta, M. & Tapia, W. Illegal wildlife trade in Galápagos: Molecular tools help taxonomic identification and guide rapid repatriation of confiscated iguanas. Conserv. Genet. Resour. 5, 867–872. https://doi.org/10.1007/s12686-013-9915-7 (2013).Article 

Google Scholar 
Stephens, R. B., Ouimette, A. P., Hobbie, E. A. & Rowe, R. J. Re-evaluating trophic discrimination factors (Δδ13C and Δδ15N) for diet reconstruction. Ecol. Mono 92, e1525. https://doi.org/10.1002/ecm.1525 (2022).CAS 
Article 

Google Scholar 
Hobson, K. A. & Clark, R. G. Assessing avian diets using stable isotopes I: Turnover of 13C in tissues. The Condor 94(1), 181–188. https://doi.org/10.2307/1368807 (1992).Article 

Google Scholar 
Li, C.-H., Roth, J. D. & Detwiler, J. T. Isotopic turnover rates and diet-tissue discrimination depend on feeding habits of freshwater snails. PLoS ONE 13(7), e0199713. https://doi.org/10.1371/journal.pone.0199713 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Steinitz, R., Lemm, J., Pasachnik, S. & Kurle, C. Diet-tissue stable isotope (δ13C and δ15N) discrimination factors for multiple tissues from terrestrial reptiles. Rapid Commun. Mass Spectrom. 30(1), 9–21. https://doi.org/10.1002/rcm.7410 (2016).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Ethier, D. M., Kyle, C. J., Kyser, T. K. & Nocera, J. J. Variability in the growth patterns of the cornified claw sheath among vertebrates: Implications for using biogeochemistry to study animal movement. Can. J. Zool. 88(11), 1043–1051. https://doi.org/10.1139/Z10-073 (2010).Article 

Google Scholar 
Aresco, M. J. & James, F. C. Ecological relationships of turtles in northern Florida lakes: A study of omnivory and the structure of a lake food web. Florida Fish and Wildlife Conservation Commission (2005). https://www.semanticscholar.org/paper/ECOLOGICAL-RELATIONSHIPS-OF-TURTLES-IN-NORTHERN-A-A-Aresco-James/f6d59265eb6494aa19cfde7d2d80bb165e6432acLourenço, P. M., Granadeiro, J. P., Guilherme, J. L. & Catry, T. Turnover rates of stable isotopes in avian blood and toenails: Implications for dietary and migration studies. J. Exp. Mar. Biol. Ecol. 472, 89–96. https://doi.org/10.1016/j.jembe.2015.07.006 (2015).CAS 
Article 

Google Scholar 
Jackson, A. L., Inger, R., Parnell, A. C. & Bearhop, S. Comparing isotopic niche widths among and within communities: SIBER—Stable isotope Bayesian ellipses in r. J. Animal Ecol. 80(3), 595–602. https://doi.org/10.1111/j.1365-2656.2011.01806.x (2011).Article 

Google Scholar 
Wikelski, M. & Romero, L. M. Body size, performance and fitness in Galápagos marine iguanas. Integr Comp Biol 43(3), 376–386. https://doi.org/10.1093/icb/43.3.376 (2003).Article 
PubMed 

Google Scholar 
Iverson, J., Smith, G. & Pieper, L. Factors Affecting Long-Term Growth of the Allen Cays Rock Iguana in the Bahamas. In Iguanas: Biology and Conservation (eds Alberts, A. et al.) 176–192 (University of California Press, 2004). https://doi.org/10.1525/9780520930117-018.Chapter 

Google Scholar 
Smith, G. R. & Iverson, J. B. Effects of tourism on body size, growth, condition, and demography in the Allen Cay Iguana. Herpetol. Conserv. Biol. 11, 214–221 (2016).
Google Scholar 
Wikelski, M., Carrillo, V. & Trillmich, F. Energy limits to body size in a grazing reptile, the Galápagos Marine Iguana. Ecology 78(7), 2204–2217. https://doi.org/10.2307/2265956 (1997).Article 

Google Scholar 
Bulakhova, N. A. et al. Inter-observer and intra-observer differences in measuring body length: A test in the common lizard, Zootoca vivipara. Amphibia-Reptilia 32(4), 477–484. https://doi.org/10.1163/156853811X601636 (2011).Article 

Google Scholar 
R Development Core Team. R: A language and environment for statistical computing (2021). https://cran.r-project.orgGoslee, S. C. & Urban, D. L. The ecodist package for dissimilarity-based analysis of ecological data. J. Stat. Softw. 22(7), 1–19. https://doi.org/10.18637/jss.v022.i07 (2007).Article 

Google Scholar 
Randin, C. F., Jaccard, H., Vittoz, P., Yoccoz, N. G. & Guisan, A. Land use improves spatial predictions of mountain plant abundance but not presence–absence. J. Veg. Sci. 20, 996–1008. https://doi.org/10.1111/j.1654-1103.2009.01098.x (2009).Article 

Google Scholar 
Broennimann, O., Di Cola, V. & Guisan, A. ecospat: Spatial Ecology Miscellaneous Methods. R package version 3.2.1 (2022) https://CRAN.R-project.org/package=ecospatBorcard, D., Legendre, P. & Drapeau, P. Partialling out the spatial component of ecological variation. Ecology 73(3), 1045–1055. https://doi.org/10.2307/1940179 (1992).Article 

Google Scholar 
Wood, S. N. Generalized Additive Models: An Introduction with R 2nd edn. (Chapman and Hall/CRC, 2017). https://doi.org/10.1201/9781315370279.Book 
MATH 

Google Scholar 
Van Marken Lichtenbelt, W. D. Optimal foraging of a herbivorous lizard, the green iguana in a seasonal environment. Oecologia 95, 246–256. https://doi.org/10.1007/BF00323497 (1993).ADS 
Article 
PubMed 

Google Scholar 
Pasachnik, S. A. & Martin-Velez, V. An evaluation of the diet of Cyclura iguanas in the Dominican Republic. Herpetol. Bull. 140, 6–12 (2017).
Google Scholar 
Cerling, T. E. et al. Global vegetation change through the Miocene/Pliocene boundary. Nature 389(6647), 153–158. https://doi.org/10.1038/38229 (1997).ADS 
CAS 
Article 

Google Scholar 
O’Leary, M. H. Carbon isotopes in photosynthesis. Bioscience 38(5), 328–336. https://doi.org/10.2307/1310735 (1988).Article 

Google Scholar 
Snell, H. L. & Tracy, C. R. Behavioral and morphological adaptations by Galapagos land iguanas (Conolophus subcristatus) to water and energy requirements of eggs and neonates. Am. Zool. 25(4), 1009–1018. https://doi.org/10.1093/icb/25.4.1009 (1985).Article 

Google Scholar 
Christian, K., Tracy, C. R. & Porter, W. P. Diet, digestion, and food preferences of Galápagos land iguanas. Herpetologica 40(2), 205–212 (1984).
Google Scholar 
Mallona, I., Egea-Cortines, M. & Weiss, J. Conserved and divergent rhythms of crassulacean acid metabolism-related and core clock gene expression in the cactus Opuntia ficus-indica. Plant Physiol. 156, 1978–1989. https://doi.org/10.1104/pp.111.179275 (2011).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
San Sebastián, O., Navarro, J., Llorente, G. A. & Richter-Boix, Á. Trophic strategies of a non-native and a native amphibian species in shared ponds. PLoS ONE 10(6), 1–17. https://doi.org/10.1371/journal.pone.0130549 (2015).CAS 
Article 

Google Scholar 
Perga, M. E. & Grey, J. Laboratory measures of isotope discrimination factors: Comments on Caut, Angulo & Courchamp (2008, 2009). J. Appl. Ecol. 47(4), 942–947. https://doi.org/10.1111/j.1365-2664.2009.01730.x (2010).CAS 
Article 

Google Scholar 
Freeman, B. Sexual niche partitioning in two species of new Guinean Pachycephala whistlers. J. Field Ornithol. 85(1), 23–30. https://doi.org/10.1111/jofo.12046 (2014).Article 

Google Scholar 
Werner, D. I. Social Organization and Ecology of Land Iguanas, Conolophus subcristatus, on Isla Fernandina, Galápagos. In Iguanas of the World: Their Behavior, Ecology, and Conservation (eds Burghardt, G. M. & Rand, A. S.) 342–365 (Noyes Publications, 1982).
Google Scholar 
Doi, H., Akamatsu, F. & González, A. L. Starvation effects on nitrogen and carbon stable isotopes of animals: An insight from meta-analysis of fasting experiments. R. Soc. Open Sci. 4(8), 170633. https://doi.org/10.1098/rsos.170633 (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Persaud, A., Dillon, P., Molot, L. & Hargan, K. Relationships between body size and trophic position of consumers in temperate freshwater lakes. Aquat. Sci. 74(1), 203–212. https://doi.org/10.1007/s00027-011-0212-9 (2012).Article 

Google Scholar 
Keppeler, F. W. et al. Body size, trophic position, and the coupling of different energy pathways across a saltmarsh landscape. Limnol. Oceanogr. Lett. 6(6), 360–368. https://doi.org/10.1002/lol2.10212 (2021).Article 

Google Scholar 
Hanson, J. O. et al. Feeding across the food web: The interaction between diet, movement and body size in estuarine crocodiles (Crocodylus porosus). Austral. Ecol. 40(3), 275–286. https://doi.org/10.1111/aec.12212 (2015).Article 

Google Scholar 
Gustavino, B., Terrinoni, S., Paglierani, C. & Gentile, G. Conolophus marthae vs. Conolophus subcristatus: Does the skin pigmentation pattern exert a protective role against DNA damaging effect induced by UV light exposure? Analysis of blood smears through the micronucleus test. Paper presented at the Galápagos Land and Marine Iguanas Workshop, IUCN SSC Iguana Specialist Group Meeting, Puerto Ayora, 28–29 October 2014.Di Giacomo, C. et al. 25–Hydroxivitamin D plasma levels in natural populations of pigmented and partially pigmented land iguanas from Galápagos (Conolophus spp.). Hind 2022, 1–9. https://doi.org/10.1155/2022/7741397 (2022).CAS 
Article 

Google Scholar 
Percie du Sert, N. et al. Reporting animal research: Explanation and elaboration for the ARRIVE guidelines 2.0. PLoS Biol. 18(7), e3000411. https://doi.org/10.1371/journal.pbio.3000411 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar  More

Pörtner, H. O. et al. IPCC Special Report on the Ocean and Cryosphere in a Changing Cimate (2019).Genevier, L. G. C., Jamil, T., Raitsos, D. E., Krokos, G. & Hoteit, I. Marine heatwaves reveal coral reef zones susceptible to bleaching in the Red Sea. Glob. Chang. Biol. 25, 2338–2351 (2019).ADS 
PubMed 
Article 

Google Scholar 
Hughes, T. P. et al. Spatial and temporal patterns of mass bleaching of corals in the Anthropocene. Science (80-.) 359, 80–83 (2018).ADS 
CAS 
Article 

Google Scholar 
Morris, L. A., Voolstra, C. R., Quigley, K. M., Bourne, D. G. & Bay, L. K. Nutrient availability and metabolism affect the stability of coral–symbiodiniaceae symbioses. Trends Microbiol. 27, 678–689 (2019).CAS 
PubMed 
Article 

Google Scholar 
Suggett, D. J. & Smith, D. J. Coral bleaching patterns are the outcome of complex biological and environmental networking. Glob. Chang. Biol. 26, 68–79 (2020).ADS 
PubMed 
Article 

Google Scholar 
Baker, A. C., Glynn, P. W. & Riegl, B. Climate change and coral reef bleaching: an ecological assessment of long-term impacts, recovery trends and future outlook. Estuar. Coast. Shelf Sci. 80, 435–471 (2008).ADS 
Article 

Google Scholar 
Brown, B. E., Dunne, R. P., Scoffin, T. P. & Le Tissier, M. D. A. Solar damage in intertidal corals. Mar. Ecol. Prog. Ser. 105, 219–230 (1994).ADS 
Article 

Google Scholar 
Suggett, D. J. & Smith, D. J. Interpreting the sign of coral bleaching as friend vs. foe. Glob. Chang. Biol. 17, 45–55 (2011).ADS 
Article 

Google Scholar 
Maynard, J. A., Anthony, K. R. N., Marshall, P. A. & Masiri, I. Major bleaching events can lead to increased thermal tolerance in corals. Mar. Biol. 155, 173–182 (2008).Article 

Google Scholar 
Weis, V. M. The susceptibility and resilience of corals to thermal stress: adaptation, acclimatization or both?: NEWS and VIEWS. Mol. Ecol. 19, 1515–1517 (2010).PubMed 
Article 

Google Scholar 
Meyer, E., Aglyamova, G. V. & Matz, M. V. Profiling gene expression responses of coral larvae (Acropora millepora) to elevated temperature and settlement inducers using a novel RNA-Seq procedure. Mol. Ecol. 20, 3599–3616 (2011).CAS 
PubMed 

Google Scholar 
Dixon, G. B. et al. Genomic determinants of coral heat tolerance across latitudes. Science (80-.) 348, 1460–1462 (2015).ADS 
CAS 
Article 

Google Scholar 
Grottoli, A. G. et al. Increasing comparability among coral bleaching experiments. Ecol. Appl. 31, 1–17 (2021).Article 

Google Scholar 
Evensen, N. et al. Empirically derived thermal thresholds of four coral species along the Red Sea using a portable and standardized experimental approach. Coral Reefs 41, 239–252 (2022).Article 

Google Scholar 
Song, M. et al. The impact of acute thermal stress on the metabolome of the black rockfish (Sebastes schlegelii). PLoS ONE 14, 1–23 (2019).Article 

Google Scholar 
Kim, K. S. et al. Physiological responses to short-term thermal stress in mayfly (Neocloeon triangulifer) larvae in relation to upper thermal limits. J. Exp. Biol. 220, 2598–2605 (2017).PubMed 
Article 

Google Scholar 
Juárez, O. E. et al. Transcriptomic and metabolic response to chronic and acute thermal exposure of juvenile geoduck clams Panopea globosa. Mar. Genomics 42, 1–13 (2018).PubMed 
Article 

Google Scholar 
Pallarés, S., Arribas, P., Céspedes, V., Millán, A. & Velasco, J. Lethal and sublethal behavioural responses of saline water beetles to acute heat and osmotic stress. Ecol. Entomol. 37, 508–520 (2012).Article 

Google Scholar 
Qin, G. et al. Temperature-induced physiological stress and reproductive characteristics of the migratory seahorse Hippocampus erectus during a thermal stress simulation. Biol. Open 7, 1–7 (2018).CAS 

Google Scholar 
Zanuzzo, F. S., Bailey, J. A., Garber, A. F. & Gamperl, A. K. Comparative Biochemistry and Physiology, Part A The acute and incremental thermal tolerance of Atlantic cod (Gadus morhua) families under normoxia and mild hypoxia ☆. Comp. Biochem. Physiol. Part A 233, 30–38 (2019).CAS 
Article 

Google Scholar 
Cunning, R. et al. Census of heat tolerance among Florida ’ s threatened staghorn corals finds resilient individuals throughout existing nursery populations. (2021).Evensen, N. R., Fine, M., Perna, G., Voolstra, C. R. & Barshis, D. J. Remarkably high and consistent tolerance of a Red Sea coral to acute and chronic thermal stress exposures. Limnol. Oceanogr. https://doi.org/10.1002/lno.11715 (2021).Article 

Google Scholar 
Morikawa, M. K. & Palumbi, S. R. Using naturally occurring climate resilient corals to construct bleaching-resistant nurseries. Proc. Natl. Acad. Sci. U. S. A. 116, 10586–10591 (2019).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rose, N. H., Bay, R. A., Morikawa, M. K. & Palumbi, S. R. Polygenic evolution drives species divergence and climate adaptation in corals. Evolution (N. Y.) 72, 82–94 (2018).
Google Scholar 
Thomas, L. et al. Mechanisms of thermal tolerance in reef-building corals across a fine-grained environmental mosaic: lessons from Ofu, American Samoa. Front. Mar. Sci. 4, 1–14 (2018).CAS 
Article 

Google Scholar 
Voolstra, C. R. et al. Standardized short-term acute heat stress assays resolve historical differences in coral thermotolerance across microhabitat reef sites. Glob. Chang. Biol. 26, 4328–4343 (2020).ADS 
PubMed 
Article 

Google Scholar 
Klepac, C. N. & Barshis, D. J. High-resolution in situ thermal metrics coupled with acute heat stress experiments reveal differential coral bleaching susceptibility. Coral Reefs https://doi.org/10.1007/s00338-022-02276-1 (2022).Article 

Google Scholar 
Gardner, S. G. et al. A multi-trait systems approach reveals a response cascade to bleaching in corals. BMC Biol. 15, 117 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Madin, J. S. et al. A trait-based approach to advance coral reef science. Trends Ecol. Evol. 31, 419–428 (2016).PubMed 
Article 

Google Scholar 
Suggett, D. J. et al. Toward bio-optical phenotyping of reef-forming corals using light-induced fluorescence transient-fast repetition rate fluorometry. Limnol. Oceanogr. Methods https://doi.org/10.1002/lom3.10479 (2022).Article 

Google Scholar 
Krueger, T. et al. Differential coral bleaching-contrasting the activity and response of enzymatic antioxidants in symbiotic partners under thermal stress. Comp. Biochem. Physiol. Part A Mol. Integr. Physiol. 190, 15–25 (2015).CAS 
Article 

Google Scholar 
Leggat, W. et al. Differential responses of the coral host and their algal symbiont to thermal stress. PLoS ONE 6, e26687 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Nitschke, M. R. et al. Utility of photochemical traits as diagnostics of thermal tolerance amongst great barrier reef corals. Front. Mar. Sci. 5, 1–18 (2018).Article 

Google Scholar 
Warner, M. E., Fittt, W. K. & Schmidt, G. W. Damage to photosystem II in symbiotic dinoflagellates: a determinant of coral bleaching. Proc. Natl. Acad. Sci. 96, 8007–8012 (1999).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Fitt, W. K., Brown, B. E., Warner, M. E. & Dunne, R. P. Coral bleaching: Interpretation of thermal tolerance limits and thermal thresholds in tropical corals. Coral Reefs 20, 51–65 (2001).Article 

Google Scholar 
Tolosa, I., Treignier, C., Grover, R. & Ferrier-Pagès, C. Impact of feeding and short-term temperature stress on the content and isotopic signature of fatty acids, sterols, and alcohols in the scleractinian coral Turbinaria reniformis. Coral Reefs 30, 763–774 (2011).ADS 
Article 

Google Scholar 
Grottoli, A. G. et al. Coral physiology and microbiome dynamics under combined warming and ocean acidification. PLoS ONE 13, e0191156 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Chow, M. H., Tsang, R. H. L., Lam, E. K. Y. & Ang, P. Quantifying the degree of coral bleaching using digital photographic technique. J. Exp. Mar. Bio. Ecol. 479, 60–68 (2016).Article 

Google Scholar 
Nielsen, J. J. V. et al. Physiological effects of heat and cold exposure in the common reef coral Acropora millepora. Coral Reefs 39, 259–269 (2020).Article 

Google Scholar 
McLachlan, R. H., Price, J. T., Solomon, S. L. & Grottoli, A. G. Thirty years of coral heat-stress experiments: a review of methods. Coral Reefs 39, 885–902 (2020).Article 

Google Scholar 
Edmunds, P. J. & Burgess, S. C. Correction: Size-dependent physiological responses of the branching coral Pocillopora verrucosa to elevated temperature and PCO2 (J. Exp. Biol. (2016) 219 (3896-3906) doi: 10.1242/jeb.146381). J. Exp. Biol. 221, 3896–3906 (2018).Article 

Google Scholar 
Madin, J. S., Baird, A. H., Dornelas, M. & Connolly, S. R. Mechanical vulnerability explains size-dependent mortality of reef corals. Ecol. Lett. 17, 1008–1015 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Pausch, R. E., Williams, D. E. & Miller, M. W. Impacts of fragment genotype, habitat, and size on outplanted elkhorn coral success under thermal stress. Mar. Ecol. Prog. Ser. 592, 109–117 (2018).ADS 
Article 

Google Scholar 
Shenkar, N., Fine, M. & Loya, Y. Size matters: bleaching dynamics of the coral Oculina patagonica. Mar. Ecol. Prog. Ser. 294, 181–188 (2005).ADS 
Article 

Google Scholar 
Middlebrook, R., Anthony, K. R. N., Hoegh-Guldberg, O. & Dove, S. Heating rate and symbiont productivity are key factors determining thermal stress in the reef-building coral Acropora formosa. J. Exp. Biol. 213, 1026–1034 (2010).CAS 
PubMed 
Article 

Google Scholar 
Hoey, A. et al. Recent advances in understanding the effects of climate change on coral reefs. Diversity 8, 12 (2016).Article 

Google Scholar 
Marhoefer, S. R. et al. Signatures of adaptation and acclimatization to reef flat and slope habitats in the coral pocillopora damicornis. Front. Mar. Sci. https://doi.org/10.3389/fmars.2021.704709 (2021).Article 

Google Scholar 
Cornwell, B. et al. Widespread variation in heat tolerance and symbiont load are associated with growth tradeoffs in the coral acropora hyacinthus in palau. Elife 10, 1–15 (2021).Article 

Google Scholar 
McClanahan, T. R. et al. Large geographic variability in the resistance of corals to thermal stress. Glob. Ecol. Biogeogr. 29, 2229–2247 (2020).Article 

Google Scholar 
Magozzi, S. & Calosi, P. Integrating metabolic performance, thermal tolerance, and plasticity enables for more accurate predictions on species vulnerability to acute and chronic effects of global warming. Glob. Chang. Biol. 21, 181–194 (2015).ADS 
PubMed 
Article 

Google Scholar 
Drury, C., Manzello, D. & Lirman, D. Genotype and local environment dynamically influence growth, disturbance response and survivorship in the threatened coral, Acropora cervicornis. PLoS ONE 12, 1–21 (2017).Article 

Google Scholar 
McLachlan, R. H., Dobson, K. L., Schmeltzer, E. R., Thurber, R. V. & Grottoli, A. G. A review of coral bleaching specimen collection, preservation, and laboratory processing methods. PeerJ 9, 1–21 (2021).Article 

Google Scholar 
Okubo, N., Motokawa, T. & Omori, M. When fragmented coral spawn? Effect of size and timing on survivorship and fecundity of fragmentation in Acropora formosa. Mar. Biol. 151, 353–363 (2007).Article 

Google Scholar 
Bruno, J. F. Fragmentation in Madracis mirabilis (Duchassaing and Michelotti): How common is size-specific fragment survivorship in corals?. J. Exp. Mar. Bio. Ecol. 230, 169–181 (1998).Article 

Google Scholar 
Suggett, D. J. et al. Optimizing return-on-effort for coral nursery and outplanting practices to aid restoration of the Great Barrier Reef. Restor. Ecol. 27, 683–693 (2019).Article 

Google Scholar 
Howlett, L., Camp, E. F., Edmondson, J., Henderson, N. & Suggett, D. J. Coral growth, survivorship and return-on-effort within nurseries at high-value sites on the Great Barrier Reef. PLoS ONE 16, 1–15 (2021).Article 

Google Scholar 
Veal, C. J., Carmi, M., Fine, M. & Hoegh-Guldberg, O. Increasing the accuracy of surface area estimation using single wax dipping of coral fragments. Coral Reefs 29, 893–897 (2010).ADS 
Article 

Google Scholar 
Voolstra, C. R. et al. Contrasting heat stress response patterns of coral holobionts across the Red Sea suggest distinct mechanisms of thermal tolerance. Mol. Ecol. 30, 4466–4480 (2021).CAS 
PubMed 
Article 

Google Scholar 
Dove, S. et al. Response of holosymbiont pigments from the scleractinian coral Montipora monasteriata to short-term heat stress. Limnol. Oceanogr. 51, 1149–1158 (2006).ADS 
Article 

Google Scholar 
Traylor-Knowles, N., Rose, N. H., Sheets, E. A. & Palumb, S. Early tracriptional responses during heat stress in the coral Acropora hyacinthus. Biol. Bull. 232, 91–100 (2017).CAS 
PubMed 
Article 

Google Scholar 
Schuback, N. et al. Single-turnover variable chlorophyll fluorescence as a tool for assessing phytoplankton photosynthesis and primary productivity: opportunities, caveats and recommendations. Front. Mar. Sci. https://doi.org/10.3389/fmars.2021.690607 (2021).Article 

Google Scholar 
Macadam, A., Nowell, C. J. & Quigley, K. Machine learning for the fast and accurate assessment of fitness in coral early life history. Remote Sens. 13, 1–17 (2021).Article 

Google Scholar 
Teague, J., Willans, J., Allen, M. J., Scott, T. B. & Day, J. C. C. Applied marine hyperspectral imaging; coral bleaching from a spectral viewpoint. Spectrosc. Eur. 31, 13–17 (2019).CAS 

Google Scholar 
Davies, S. W., Ries, J. B., Marchetti, A. & Castillo, K. D. Symbiodinium functional diversity in the Coral Siderastrea siderea Is influenced by thermal stress and reef environment, but not ocean acidification. Front. Mar. Sci. 5, 1–14 (2018).Article 

Google Scholar 
Tang, J. et al. Increased ammonium assimilation activity in the scleractinian coral pocillopora damicornis but not its symbiont after acute heat stress. Front. Mar. Sci. 7, 1–10 (2020).ADS 
Article 

Google Scholar 
Sweet, M. et al. Species-specific variations in the metabolomic profiles of Acropora hyacinthus and Acropora millepora mask acute temperature stress effects in adult coral colonies. Front. Mar. Sci. 8, 1–15 (2021).Article 

Google Scholar 
Newton, J. R., Smith-Keune, C. & Jerry, D. R. Thermal tolerance varies in tropical and sub-tropical populations of barramundi (Lates calcarifer) consistent with local adaptation. Aquaculture 308, S128–S132 (2010).Article 

Google Scholar 
Waltham, N. J. & Sheaves, M. Acute thermal tolerance of tropical estuarine fish occupying a man-made tidal lake, and increased exposure risk with climate change. Estuar. Coast. Shelf Sci. 196, 173–181 (2017).ADS 
Article 

Google Scholar 
Iwabuchi, B. L. & Gosselin, L. A. Implications of acute temperature and salinity tolerance thresholds for the persistence of intertidal invertebrate populations experiencing climate change. Ecol. Evol. 10, 7739–7754 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Cox, J., Schubert, A. M., Travisano, M. & Putonti, C. Adaptive evolution and inherent tolerance to extreme thermal environments. BMC Evol. Biol. https://doi.org/10.1186/1471-2148-10-75 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
Quigley, K. M., Bay, L. K. & Willis, B. L. Temperature and water quality-related patterns in sediment-associated Symbiodinium communities impact symbiont uptake and fitness of juveniles in the genus Acropora. Front. Mar. Sci. 4, 1–17 (2017).Article 

Google Scholar 
Voolstra, C. R. et al. Extending the natural adaptive capacity of coral holobionts. Nat. Rev. Earth Environ. https://doi.org/10.1038/s43017-021-00214-3 (2021).Article 

Google Scholar 
Cocciardi, J. M. et al. Adjustable temperature array for characterizing ecological and evolutionary effects on thermal physiology. Methods Ecol. Evol. 2019, 1339–1346 (2019).Article 

Google Scholar 
Smith, G. & Spillman, C. New high-resolution sea surface temperature forecasts for coral reef management on the Great Barrier Reef. Coral Reefs 38, 1039–1056 (2019).ADS 
Article 

Google Scholar 
Bainbridge, S. J. Temperature and light patterns at four reefs along the Great Barrier Reef during the 2015–2016 austral summer: understanding patterns of observed coral bleaching. J. Oper. Oceanogr. 10, 16–29 (2017).
Google Scholar 
Siebeck, U. E., Marshall, N. J., Klüter, A. & Hoegh-Guldberg, O. Monitoring coral bleaching using a colour reference card. Coral Reefs 25, 453–460 (2006).ADS 
Article 

Google Scholar 
Palumbi, S. R., Barshis, D. J., Traylor-Knowles, N. & Bay, R. A. Mechanisms of reef coral resistance to future climate change. Science (80-.) 344, 895–899 (2014).ADS 
CAS 
Article 

Google Scholar 
Saxby, T., Dennison, W. C. & Hoegh-Guldberg, O. Photosynthetic responses of the coral Montipora digitata to cold temperature stress. Mar. Ecol. Prog. Ser. 248, 85–97 (2003).ADS 
Article 

Google Scholar 
Deschaseaux, E. S. M., Deseo, M. A., Shepherd, K. M., Jones, G. B. & Harrison, P. L. Air blasting as the optimal approach for the extraction of antioxidants in coral tissue. J. Exp. Mar. Bio. Ecol. 448, 146–148 (2013).CAS 
Article 

Google Scholar 
Holmes, G., Ortiz, J., Kaniewska, P. & Johnstone, R. Using three-dimensional surface area to compare the growth of two Pocilloporid coral species. Mar. Biol. 155, 421–427 (2008).Article 

Google Scholar 
Naumann, M. S., Niggl, W., Laforsch, C., Glaser, C. & Wild, C. Coral surface area quantification-evaluation of established techniques by comparison with computer tomography. Coral Reefs 28, 109–117 (2009).ADS 
Article 

Google Scholar 
Ritchie, R. J. Consistent sets of spectrophotometric chlorophyll equations for acetone, methanol and ethanol solvents. Photosynth. Res. 89, 27–41 (2006).CAS 
PubMed 
Article 

Google Scholar 
Licthenthaler, H. K. Chlorophylls and carotenoids – pigments of photosynthetic biomembranes. Methods Enzymol. 148, 350–382 (1987).Article 

Google Scholar 
R Core Team. R: a language and environment for statistical computing. (2020).Hartig, F. & Lohse, L. Package ‘DHARMa’ residual diangonstics for hierarchical (multi-level/mixed) regression models (2021).Brooks, M. E. et al. glmmTMB balances speed and flexibility among packaages for zero-inflated generalized linear mixed modelling. R Journal 9, 378–400 (2017).Article 

Google Scholar 
Harrison, X. A. et al. A brief introduction to mixed effects modelling and multi-model inference in ecology. PeerJ 2018, 1–32 (2018).
Google Scholar 
Nakagawa, S. & Schielzeth, H. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol. Evol. 4, 133–142 (2013).Article 

Google Scholar 
Oksanen, J. et al. Vegan (2020).Sarkar, D. Lattice: Multivariate Data Visualization with R (Springer, 2008).MATH 
Book 

Google Scholar  More

Serpentinite-hosted systems are rare and extreme habitats in which a hydrothermal process, serpentinization, alters ultramafic mantle rocks and yields hyperalkaline fluid rich in molecular hydrogen (H2) and reduced one-carbon compounds [1,2,3,4,5,6,7,8]. These fluids are often electron acceptor depleted—oxygen, nitrate, sulfate, etc. are absent (i.e., anoxic) and even the least favorable exogenous acceptor, carbon dioxide (CO2), is limiting due to the high alkalinity. Though previous studies explore the diversity of organisms in serpentinite-hosted systems, we have little insight into how indigenous H2-utilizing microorganisms combat the unique metabolic challenges in situ. One recent study shows strategies that methane-generating archaea employ to oxidize H2 in situ [9], but how other microorganisms (i.e., H2-utilizing anaerobic bacteria) overcome the electron acceptor limitation is poorly understood. Further, given that life is theorized to have emerged as H2-utilizing lithotrophs in early Earth serpentinite-hosted systems [10,11,12,13,14,15,16,17,18,19,20,21,22,23,24], modern lithotrophs inhabiting such ecosystems may represent valuable extant windows into the metabolism of primordial organisms. In this study, we pair metagenomics and thermodynamics to characterize uncultured putative anaerobic H2 utilizers inhabiting alkaline H2-rich serpentinite-hosted systems (Hakuba Happo hot springs in Hakuba, Japan, and The Cedars springs in California, USA; pH ~10.9 and ~11.9, respectively [25,26,27]) and elucidate novel, potentially ancient, lithotrophic strategies.Thermodynamics and geochemistryThe two primary strategies for utilizing H2 under anoxic conditions without favorable exogenous electron acceptors are methanogenesis and homoacetogenesis. As bacteria were detected in both Hakuba and The Cedars, yet archaea were absent in Hakuba, we focused our analyzes on metabolic strategies supporting bacterial H2 utilization (i.e., homoacetogenesis). To evaluate whether homoacetogenesis is viable in situ, we examined the in situ geochemical environment and the thermodynamics of H2/formate utilization and homoacetogenesis. The spring waters of both Hakuba and The Cedars contained H2 (e.g., 201–664 μM in Hakuba [27]). Formate, another compound thought to be abiotically generated through serpentinization, was also detected in Hakuba (8 μM in drilling well #3 [28]) and The Cedars (6.9 µM in GPS1). Acetate has also been detected in situ (4 μM in Hakuba [28] and 69.3 µM in The Cedars GPS1), suggesting these ecosystems may host novel H2- and/or formate-utilizing homoacetogens. Thermodynamic calculations using newly measured and published geochemical data (Tables S1 and S2) confirmed that H2 and formate are reductants in situ (i.e., H2 = 2H+ + 2e−/Formate− = H+ + CO2 + 2e−): the Gibbs free energy yields (∆G) for oxidation (coupled with physiological electron carriers NADP+, NAD+, and ferredoxin) are less than −4.78 kJ per mol H2 and −24.92 kJ per mol formate in Hakuba, and −10.73 and −22.03 in The Cedars respectively (H2 concentration was not available for The Cedars so the highest concentration observed in Hakuba [664 µM] was used; see Supplementary Results). However, serpentinite-hosted systems impose a unique challenge to homoacetogenesis—a key substrate, CO2, is at extremely low concentrations due to the high alkalinity. We estimate that the aqueous CO2 concentration is below 0.0006 nM in Hakuba (pH 10.7 and  +24.92 or +22.02 kJ per mol formate). Thus, catabolic reduction of CO2 to acetate is thermodynamically challenging in situ and may only run if investing ATP (e.g., Calvin–Benson–Bassham cycle [−6 ATP; ∆G of −361.68 kJ per mol acetate in Hakuba] or reductive tricarboxylic acid [−1 ATP; −61.68 kJ per mol]). Under CO2 limitation, autotrophs are known to accelerate CO2 uptake through HCO3− dehydration to CO2 (carbonic anhydrase) or carbonate mineral dissolution, but both only modify kinetics and are not effective in changing the maximum CO2 concentration (determined by equilibrium with carbonate species). In addition, in Hakuba, the CO32− concentration is too low (84.7 nM CO32−) to cause carbonate mineral precipitation (e.g., [CO32−] must exceed 38.5 µM given Ks of 5 ×  10−9 for CaCO3 and [Ca2+] of 0.13 mM).Based on thermodynamic calculations, the energy obtainable from H2/CO2-driven homoacetogenesis is too small to support life in many serpentinite-hosted systems, yet acetate is detected in some of these ecosystems (Fig. S2 and Table S2; note that we cannot exclude the possibility that acetate may be produced abiotically by water–rock reactions [30]). Thus, CO2-independent electron-disposing metabolism may have been necessary for extremophilic organisms to gain energy from H2 in the hyperalkaline fluids of hydrothermal systems. Here, we explore the metabolic capacities of organisms living in serpentinite-hosted systems to gain insight into potential metabolic strategies for utilizing H2 under the extreme conditions in situ.Diverse putative H2- and formate-utilizing organismsThrough metagenomic exploration of the two serpentinite-hosted systems (Table S3), we discover a plethora of phylogenetically novel organisms encoding genes for H2 and formate metabolism (19 bins with 73.2–94.8% completeness and 0.0–8.1% contamination [86.1% and 3.8% on average respectively]; available under NCBI BioProject PRJNA453100) despite challenges in acquisition of genomic DNA (15.7 and 18.9 ng of DNA from 233 and 720 L of filtered Hakuba Happo spring water, respectively; RNA was below the detection limit). We find metagenome-assembled genomes (MAGs) affiliated with lineages of Firmicutes (e.g., Syntrophomonadaceae and uncultured family SRB2), Actinobacteria, and candidate division NPL-UPA2 [31] (Fig. S3). We also recovered MAGs for a novel lineage, herein referred to as “Ca. Lithacetigenota”, that inhabits both Hakuba and The Cedars and, to our knowledge, no other ecosystems (Figs. 1, 2a, S3, and S4). The average amino acid identity (AAI) between Ca. Lithacetigenota and neighboring phyla (Coprothermobacterota, Dictyoglomi, Thermodesulfobiota [GTDB-defined phylum], Thermotogae, and Caldiserica) was comparable to the average interphylum AAI among the neighboring phyla (45.33 ± 0.86% vs 45.17 ± 0.99%), suggesting that Ca. Lithacetigenota represents a novel phylum-level lineage (Fig. 2a, b). These genomes encode enzymes for oxidizing H2 and formate (i.e., hydrogenases and formate dehydrogenases [32,33,34,35,36,37,38,39]; see Supplementary Results), suggesting that organisms in situ can employ H2 and formate as electron donors.Fig. 1: Ribosomal protein tree including high-quality MAGs from 74 GTDB-defined phylum-level lineages.Representative genomes (highest quality based on a score defined as completeness – 5*contamination, both estimated by CheckM) were chosen for bacterial classes that contain at least one genome the meet the following criteria: (i) cultured organisms with ≥90% completeness, ≤5% contamination (as estimated by CheckM), and ≤ 20 contigs; (ii) uncultured organisms with ≥85% completeness, ≤3% contamination, and ≤20 contigs; and (iii) Ca. Patescibacteria with ≥60% completeness and ≤1 contig. Universally conserved ribosomal proteins were collected from each genome, aligned with MAFFT v7.394, trimmed with BMGE v1.12 (-m BLOSUM30 -g 0.67 -b 3), and concatenated. A maximum likelihood tree was calculated using IQ-TREE v2.1.3 with the UDM0064LCLR model (-m Poisson+UDM0064LCLR), ultrafast bootstrap approximation, and SH-like approximate likelihood ratio test (-B 1000 -alrt 1000; bootstrap values are recalculated with BOOSTER using the -tbe option). Branches with ≥90% ultrafast bootstrap support and ≥80% SH-alrt support are indicated with black circles. Archaeal and eukaryotic genomes were used as an outgroup. The inter-domain branch was shortened with a break to 1/10 of the calculated length for illustrative purposes. Phylogenetic groups corresponding to “Gracilicutes” and “Terrabacteria” are indicated yellow and blue respectively. Ca. Lithacetigenota are highlighted (magenta). See Supplementary Fig. S4 for full tree.Full size imageFig. 2: “Ca. Lithacetigenota” phylogeny, lithotrophic acetate generation pathways, and comparative genomics with neighboring phyla.a A maximum likelihood tree was calculated for a concatenated alignment of universally conserved ribosomal protein sequences from representative genomes of individual phyla (aligned with MAFFT v7.394 [default parameters] and trimmed with BMGE v1.12 (−m BLOSUM30 −g 0.67 −b 3) using IQ-TREE v2.1.3 with the UDM0064LCLR model (-m Poisson+UDM0064LCLR), ultrafast bootstrap approximation, and SH-like approximate likelihood ratio test (-B 1000 -alrt 1000; bootstrap values are recalculated with BOOSTER using the -tbe option). Branches with ≥90% ultrafast bootstrap support and ≥80% SH-alrt support are indicated with black circles. Phylum names are shown for NCBI taxonomy (italicized) or GTDB classification (*). b The average inter-phylum AAI (as calculated by CompareM) was calculated using GTDB species representatives. c Putative metabolic pathways potentially adapted to the CO2-limited hyperalkaline conditions encoded by “Ca. Lithacetigenota” members and others: formate- and glycine-reducing acetate generation. Arrow colors indicate oxidative (pink), reductive (blue), ATP-yielding (orange), and ATP-consuming (green) steps. d Venn diagram of COGs/NOGs (as predicted by eggnog-mapper) fully conserved across all members of each phylum (genomes included in GTDB release 95 with completeness ≥85% and contamination ≤5%). COGs/NOGs related to lithotrophy and alkaliphily are highlighted. * “COG” abbreviated.Full size image“Ca. Lithacetigenota” has unique site-adapted metabolismInspection of the serpentinite-hosted environment-exclusive phylum “Ca. Lithacetigenota” reveals specialization to H2-driven lithotrophy potentially suitable for the low-CO2 in situ conditions (Fig. 2c). We discover that The Cedars-inhabiting population (e.g., MAG BS5B28, 94.8% completeness and 2.9% contamination) harbors genes for H2 oxidation ([NiFe] hydrogenase Hox), a nearly complete Wood-Ljungdahl pathway, and an oxidoreductase often associated with acetogenesis—NADH:ferredoxin oxidoreductase Rnf [40, 41] (Tables S4 and S5). One critical enzyme, the formate dehydrogenase, is missing from all three “Ca. Lithacetigenota” MAGs from The Cedars (and unbinned contigs), indicating that these bacteria can neither perform H2/CO2-driven nor formate-oxidizing acetogenesis (Fig. 2c). However, even without the formate dehydrogenase, the genes present can form a coherent pathway that uses formate rather than CO2 as a starting point for the “methyl branch” of the Wood–Ljungdahl pathway (i.e., formate serves as an electron acceptor; Fig. 2c). This is a simple yet potentially effective strategy for performing homoacetogenesis while circumventing the unfavorable reduction of CO2 to formate. Coupling H2 oxidation with this formate-reducing pathway is thermodynamically viable as it halves the usage of CO2 (3H2 + Formate− + CO2 = Acetate− + 2H2O; ∆G of −29.62 kJ per mol acetate) and, as a pathway, is simply an intersection between the conventional H2/CO2-driven and formate-disproportionating acetogenesis (Fig. 2c and S5). Although use of formate as an electron acceptor for formate-oxidizing acetogenesis is quite common, no previous homoacetogens have been observed to couple H2 oxidation with acetogenesis from formate, likely because CO2 has a much higher availability than formate in most ecosystems.The Hakuba-inhabiting “Ca. Lithacetigenota” (HKB210 and HKB111) also encodes Hox for H2 oxidation but lacks genes for homoacetogenesis (no homologs closely related to The Cedars population genes were detected even in unbinned metagenomic contigs). We suspect that this population forgoes the above H2/formate-driven homoacetogenesis because the estimated energy yield of the net reaction in situ (∆G of −19.94 kJ per mol acetate) is extremely close to the thermodynamic threshold of microbial catabolism (slightly above −20 kJ per mol) and, depending on the actual threshold for “Ca. Lithacetigenota” and/or even slight changes in the surrounding conditions (e.g., ∆G increases by 1 kJ per mol if H2 decreases by 20 µM decreases in Hakuba), the metabolism may be unable to recover energy. Through searching the physicochemical environment for alternative exogenous electron acceptors and MAGs for electron-disposing pathways, we detected a low concentration of glycine in situ (5.4 ± 1.6 nM; Table S6) and found genes specific to catabolic glycine reduction (see next paragraph). We suspect that some portion of this glycine is likely geochemically generated in situ, given that (a) glycine is often detected as the most abundant amino acid produced by both natural and laboratory-based serpentinization (e.g., H2 + Formate = Formaldehyde ⇒ Formaldehyde + NH3 = Glycine) [10, 16, 42,43,44,45,46,47] and (b) no other amino acid was consistently detectable (if glycine was cell-derived, other amino acids ought to also be consistently detected).For utilization of the putatively abiotic glycine, the Hakuba “Ca. Lithacetigenota” encodes glycine reductases (Grd; Fig. 3 and S6; Tables S4 and S5)—a unidirectional selenoprotein for catabolic glycine reduction [48, 49]. Based on the genes available, this population likely specializes in coupling H2 oxidation and glycine reduction (H2 + Glycine− → Acetate− + NH3; Fig. 2c). Firstly, the genomes encode NADP-linked thioredoxin reductases (NADPH + Thioredoxinox → NADP+ + Thioredoxinred) that can bridge electron transfer from H2 oxidation (H2 + NADP → NADPH + H+) to glycine reduction (Glycine− + Thioredoxinred → Acetyl-Pi + NH3 + Thioredoxinox). Secondly, though glycine reduction is typically coupled with amino acid oxidation (i.e., Stickland reaction in Firmicutes and Synergistetes [48, 50]), similar metabolic couplings have been reported for some organisms (i.e., formate-oxidizing glycine reduction [via Grd] [51] and H2-oxidizing trimethylglycine reduction [via Grd-related betaine reductase] [52]). Thirdly, Grd is a rare catabolic enzyme, so far found in organisms that specialize in amino acid (or peptide) catabolism, many of which are reported to use glycine for the Stickland reaction (e.g., Peptoclostridium of Firmicutes and Aminobacterium of Synergistetes [53]). Lastly, the population lacks any discernable fermentative (propionate [methylmalonyl-CoA pathway], butyrate [reverse beta oxidation], lactate [lactate dehydrogenase], and alanine [alanine dehydrogenase]) and respiratory (aerobic [terminal oxidases], nitrate [nitrate reductase, nitrite reductase, nitric oxide reductase, nitrous oxide reductase], sulfate [dissimilatory sulfate reductase and sulfite reductase], other sulfurous compounds [molybdopterin-binding protein family sulfurous compound reductases], and metals [outer membrane cytochrome OmcB]) electron disposal pathways and oxidative organotrophy (Tables S4 and S5). Although the BS5B28 genome encodes a bifunctional alcohol/aldehyde dehydrogenase and aldehyde:ferredoxin oxidoreductase, no complete sugar or amino acid degradation pathways could be identified, suggesting that these genes have a physiological role unrelated to ethanol fermentation. Further, though formate and glycine transporters were absent in the genomes, a survey of transporters (annotated in UniProtKB 2021_03 [54]) revealed that no alkaliphiles (organisms with optimum pH ≥ 9.5 in the DSMZ BacDive database [55]) encoded known formate transporters (focA; TIGR04060) or amino acid permeases (PF00324) (ABC transporters were not considered as substrate specificity for these complexes cannot be annotated reliably), indicating that alkaliphiles likely employ unknown transport proteins. Reflecting the lack of other catabolic pathways, the Hakuba “Ca. Lithacetigenota” MAGs display extensive genome streamlining, comparable to that of Aurantimicrobium [56, 57], “Ca. Pelagibacter” [58], and Rhodoluna [59] in aquatic systems, as also reported for other organisms inhabiting serpentinite-hosted systems [60, 61] (Fig. S7). Thermodynamic calculations show that H2-oxidizing glycine reduction is favorable in situ (∆G°’ of −70.37 kJ per mol glycine [∆G of −85.84 in Hakuba]; Fig. S1). Further, based on the pathway identified, this putative metabolism is >10 times more efficient in recovering energy from H2 (1 mol ATP per mol H2) than acetogenesis utilizing H2/CO2 (0.075 mol ATP per mol H2 based on the pathway Acetobacterium woodii utilizes) or H2/formate (0.075 mol ATP per mol H2, assuming no energy recovery associated with the formate dehydrogenase). We also detect glycine reductases in The Cedars “Ca. Lithacetigenota”, indicating that it may also perform this metabolism (∆G of −76.87 in The Cedars, assuming 201 µM H2).Fig. 3: Evolution and distribution of glycine reductases.a Phylogeny of serpentinite-hosted microbiome glycine reductase subunit GrdBE homologs (Hakuba Happo hot spring*, The Cedars springs†, and other serpentinite-hosted system metagenomes#) and a brief scheme for evolutionary history of Grd. Grd-related COG1978 homologs were collected from the representative species genomes in GTDB, filtered using a GrdB motif conserved across members of phyla known to perform glycine-reducing Stickland reaction (see Methods and Supplementary Fig. S6) and clustered with 75% amino acid sequence similarity using CD-HIT (-c 0.75). GrdB-related sarcosine reductase subunits were excluded by identification of a GrdF motif conserved across sequences that form a distinct cluster around the biochemically characterized Peptoclostridium acidaminophilum GrdF. GrdE neighboring GrdB were collected. D-proline reductase subunits PrdBA (homologous to GrdB and GrdE respectively) was used as an outgroup. GrdB+PrdB and GrdE+PrdA were aligned (MAFFT v7.394) and trimmed (BMGE v1.12 -m BLOSUM30 -g 0.05) separately, then concatenated. A maximum likelihood tree was calculated using IQ-TREE v2.1.3 (-m LG+C20+G+F) and 1000 ultrafast bootstrap replicates (bootstrap values are recalculated with BOOSTER). Branches with ≥95% ultrafast bootstrap support are indicated with pink circles. Serpentinite-hosted system-derived sequences are shown in blue and taxa that may have gained GrdB through horizontal transfer are shown in green. Though the GrdB motif did not match, the closest (and only) detectable archaeal homolog (COG1978) identified in Ca. Bathyarchaeota is included. An axis break is used for the branch connecting GrdBE (and the Ca. Bathyarchaeota homolog) and outlier PrdBA for readability (10% of actual length). See Supplementary Fig. S6 for complete tree and full branch length between GrdBE and PrdBA. In the brief scheme of Grd evolution (top left), the cladogram topology is based on Fig. S4. Vertical transfer (red lines in cladogram) and horizontal transfer (black arrows) inferred from tree structures are shown. Phyla that may have acquired Grd vertically (red) and horizontally (gray) are indicated. GTDB phyla belonging to Firmicutes were grouped together. * GTDB-defined phylum-level lineage nomenclature. b Number of glycine reductase-encoding GTDB-defined species representatives (GTDB r95) associated with different environments. Only genomes with both GrdB and GrdE were included.Full size imageGiven the phylogenetic and metabolic uniqueness of these populations, we report provisional taxonomic assignment to “Ca. Lithacetigenota” phyl. nov., “Ca. Lithacetigena glycinireducens” gen. nov., sp. nov. (HKB111 and HKB210), and “Ca. Psychracetigena formicireducens” gen. nov., sp. nov. (BS525, BS5B28, and GPS1B18) (see Supplementary Results). Based on a concatenated ribosomal protein tree, this serpentinite-hosted ecosystem-associated candidate phylum is closely related to the deepest-branching group of bacterial phyla in “Terrabacteria”, one of the two major of lineages Bacteria (Fig. 1). Comparative genomics shows that “Ca. Lithacetigenota” shares 623 core functions (based on Bacteria-level COGs/NOGs predicted by eggnog-mapper shared by the two highest quality Hakuba and The Cedars MAGs HKB210 and BS5B28; Fig. 2d). When compared with the core functions of two closest related phyla (Caldiserica and Coprothermobacterota), 176 functions were unique to “Ca. Lithacetigenota”, including those for NiFe hydrogenases (and their maturation proteins), selenocysteine utilization (essential for Grd), and sodium:proton antiporter for alkaliphily. With Coprothermobacterota, 232 functions were shared, including Grd, thioredoxin oxidoreductase (essential for electron transfer to Grd), and additional proteins for NiFe hydrogenases and selenocysteine utilization, pointing toward importance of H2 metabolism and glycine reduction for these closely related phyla. More importantly, among bacterial phyla in the deep-branching group, “Ca. Lithacetigenota” represents the first lineage inhabiting hyperalkaliphilic serpentinite-hosted ecosystems, suggesting that these organisms may be valuable extant windows into potential physiologies of primordial organisms who are thought to have lived under hyperalkaline conditions (albeit with 4 billion years of evolution in between; see discussion regarding Grd below).Widespread glycine reduction in serpentinite-hosted systemsUncultured members of Chloroflexi (Chloroflexota) class Dehalococcoidia inhabiting The Cedars and Firmicutes (Firmicutes_D) class SRB2 in Hakuba and The Cedars also possess glycine reductases (Table S5). In addition, these populations encode hydrogenases and formate dehydrogenases, suggesting that they may also link H2 and formate metabolism to glycine reduction. Closely related glycine reductases were also detected in other studied serpentinite-hosted systems (47–94% amino acid similarity in Tablelands, Voltri Massif, and Coast Range Ophiolite) [1, 2, 7]. Phylogenetic analysis of the glycine-binding “protein B” subunits GrdB and GrdE reveals close evolutionary relationships between glycine reductases from distant/remote sites (Fig. 3a and S6). Note that Tablelands spring glycine reductase sequences were not included in the analysis as they were only detected in the unassembled metagenomic reads (4460690.3; 69.7–82.2% similarity to Hakuba SRB2). Overall, “Ca. Lithacetigenota”, Dehalococcoidia, and SRB2 glycine reductases are all detected in at least two out of the seven metagenomically investigated systems despite the diverse environmental conditions (e.g., temperature). Thus, we propose glycine as an overlooked thermodynamically and energetically favorable electron acceptor for H2 oxidation in serpentinite-hosted systems. We suspect that glycine reduction may be a valuable catabolic strategy as the pathway requires few genes/proteins (a hydrogenase, Grd, acetate kinase, and thioredoxin oxidoreductase) and conveniently provides acetate, ammonia, and ATP as basic forms of carbon, nitrogen, and energy.Phylogenetic analysis of glycine reductases (Fig. 3a and S6) shows that the novel homologs recovered from serpentinite-hosted systems represent deep-branching lineages distantly related from those detectable in published genomes (GTDB r95 species representatives). Further comparison of the topology with a ribosomal protein-based genome tree (Fig. 2a) indicates that the two deep-branching serpentinite-hosted system-affiliated lineages (Ca. Lithacetigenota and novel Chloroflexi family) and Firmicutes vertically inherited glycine reductases. Thus, catabolic glycine reduction can be traced back to the concestor of these three lineages, suggesting the metabolism at least dates back to the ancestor of “Terrabacteria”. We further identified an archaeal GRD homolog (in Miscellaneous Crenarchaeota Group [MCG] or Ca. Bathyarchaeota member BA-1; Fig. 3a and S6), but whether this gene functions as a glycine reductase (GrdB motif not fully conserved) and, further, truly belong to this clade (source is metagenome-assembled genome) remains to be verified. Reconstruction of the ancestral Grd sequence and estimation of its pH preference (via AcalPred) showed that the ancestral enzyme likely had good efficiency under alkaline conditions (pH  > 9; probability of 0.9973 and 0.9858 for GrdB and GrdE, respectively). Thus, the currently available data suggest that Grd (and catabolic glycine reduction) is an ancient bacterial catabolic innovation in an alkaline habitat, dating back to one of the deepest nodes in the bacterial tree.While we detect glycine reductases in many serpentinite-hosted systems, examination of genomes derived from other natural ecosystems shows that only 107 species (species representatives in GTDB r95; 0.35% of all GTDB species) inhabiting such habitats encode GrdBE (Fig. 3b and S6). This is a level comparable to rare artificial contaminant-degrading enzymes (e.g., tetrachloroethane dehalogenase pceA—65 species [encoding KEGG KO K21647 based on AnnoTree with GTDB r95 and default settings [62]]; dibenzofuran dioxygenase—258 species [K14599 and K14600]). Most glycine reductase homologs are found in species affiliated with host-associated (mostly human body and rumen) or artificial habitats (360 species), the majority of which belong to the phylum Firmicutes. We suspect that glycine reduction has low utility in most natural ecosystems (e.g., no excess glycine via abiotic generation and no severe nutrient/electron acceptor limitation) and has been repurposed by some anaerobes for the fermentative Stickland reaction in organic-rich ecosystems (e.g., host-associated ecosystems) where excess amino acids are available but access to favorable electron acceptors is limited (Fig. 3b) (notably, glycine is the dominant amino acid in collagen [ >30%], the most abundant protein in vertebrate bodies).Other characteristics of putative indigenous homoacetogensIn contrast with members of “Ca. Lithacetigenota”, several other putative homoacetogenic populations encode the complete Wood–Ljundgahl pathway (Tables S4 and S5), indicating that other forms of acetogenesis may also be viable in situ. One putative homoacetogen in The Cedars, NPL-UPA2, lacks hydrogenases but encodes formate dehydrogenases. Although the NPL-UPA2 population cannot perform H2/formate-driven acetogenesis, it may couple formate oxidation with formate-reducing acetogenesis—another thermodynamically viable metabolism (∆G of –50.90 kJ per mol acetate in The Cedars; Fig. S5). The pathway uses CO2 as a substrate but has lower CO2 consumption compared to H2/CO2 homoacetogenesis and can produce intracellular CO2 from formate. A recent study also points out that methanogens inhabiting serpentinite-hosted environments oxidize formate presumably to generate intracellular CO2 [9]. In Hakuba, an Actinobacteria population affiliated with the uncultured class UBA1414 (MAG HKB206) encodes hydrogenases and a complete Wood–Ljungdahl pathway (Table S5) and, thus, may be capable of H2/formate or the above formate-disproportionating acetogenesis (Fig. 2c and S5). Indeed, the UBA1414 population was enriched in Hakuba-derived cultures aiming to enrich acetogens using the H2 generated by the metallic iron–water reaction [63] (Fig. S8). Many populations encoding a complete Wood–Ljungdahl pathway possess monomeric CO dehydrogenases (CooS unassociated with CODH/ACS subunits; NPL-UPA2, Actinobacteria, Syntrophomonadaceae [Hakuba and The Cedars], and Dehalococcoidia [The Cedars]; Table S4). Although CO is below the detection limit in Hakuba (personal communication with permission from Dr. Konomi Suda), another study shows that CO metabolism takes place in an actively serpentinizing system with no detectable CO [64]. Given that CO is a known product of serpentinization [7, 64], it may be an important substrate for thermodynamically favorable acetogenesis in situ. However, further investigation is necessary to verify this (e.g., need to measure CO at multiple time points).Another interesting adaptation observed for all putative homoacetogens detected in Hakuba and The Cedars was possession of an unusual CODH/ACS complex. Although Bacteria and Archaea are known to encode structurally distinct forms of CODH/ACS (designated as Acs and Cdh respectively for this study), all studied Hakuba/The Cedars putative homoacetogens encode genes for a hybrid CODH/ACS that integrate archaeal subunits for the CO dehydrogenase (AcsA replaced with CdhAB) and acetyl-CoA synthase (AcsB replaced with CdhC) and bacterial subunits for the corrinoid protein and methyltransferase components (AcsCDE) (Table S4). The Firmicutes lineages also additionally encode the conventional bacterial AcsABCDE. Given that all of the identified putative homoacetogens encode this peculiar hybrid complex, we suspect that such CODH/ACS’s may have features adapted to the high-pH low-CO2 conditions (e.g., high affinity for CO2 and/or CO). In agreement, a similar hybrid CODH/ACS has also been found in the recently isolated “Ca. Desulforudis audaxviator” inhabiting an alkaline (pH 9.3) deep subsurface environment with a low CO2 concentration (below detection limit [65, 66]) [67].Implications for primordial biologyThe last universal common ancestor (LUCA) is hypothesized to have evolved within alkaline hydrothermal mineral deposits at the interface of serpentinization-derived fluid and ambient water (e.g., Hadean weakly acidic seawater) [22,23,24]. Although such interfaces no longer exist (i.e., ancient Earth lacked O2 but most water bodies contain O2 on modern Earth), modern anoxic terrestrial and oceanic ecosystems harboring active serpentinization [1,2,3,4,5,6,7,8] may hold hints for how primordial organisms utilized H2 under hyperalkaline CO2-depleted conditions (e.g., post-LUCA H2-utilizing organisms that ventured away from the interface towards the alkaline fluids). Our findings suggest that unconventional modes of lithotrophy that take advantage of geogenic reduced carbon compounds (e.g., formate and glycine) as exogenous electron acceptors may have been viable approaches to circumventing thermodynamic issues and obtaining energy from H2 oxidation in situ. The strategies we discover are largely exclusive to the bacterial domain (archaeal CO2 reduction does not involve formate as an intermediate and, to our knowledge, glycine reduction is limited to Bacteria) and originated deep in the bacterial tree, suggesting they may have been relevant in the divergence towards the bacterial and archaeal domains. Notably, the estimated alkaliphily of the ancestral Grd also points towards the relevance of this metabolism in ancient alkaline habitats. More

Funk, W. C., McKay, J. K., Hohenlohe, P. A. & Allendorf, F. W. Harnessing genomics for delineating conservation units. Trends Ecol. Evol. 27, 489–496. https://doi.org/10.1016/j.tree.2012.05.012 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Hohenlohe, P. A., Funk, W. C. & Rajora, O. P. Population genomics for wildlife conservation and management. Mol. Ecol. 30, 62–82. https://doi.org/10.1111/mec.15720 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Helyar, S. J. et al. Application of SNPs for population genetics of nonmodel organisms: New opportunities and challenges. Mol. Ecol. Resour. 11, 123–136. https://doi.org/10.1111/j.1755-0998.2010.02943.x (2011).ADS 
Article 
PubMed 

Google Scholar 
Allendorf, F. W. Genetics and the conservation of natural populations: Allozymes to genomes. Mol. Ecol. 26, 420–430. https://doi.org/10.1111/mec.13948 (2017).CAS 
Article 
PubMed 

Google Scholar 
Zimmerman, S. J., Aldridge, C. L. & Oyler-McCance, S. J. An empirical comparison of population genetic analyses using microsatellite and SNP data for a species of conservation concern. BMC Genomics 21, 38. https://doi.org/10.1186/s12864-020-06783-9 (2020).CAS 
Article 

Google Scholar 
Lemopoulos, A. et al. Comparing RADseq and microsatellites for estimating genetic diversity and relatedness—implications for brown trout conservation. Ecol. Evol. 9, 2106–2120. https://doi.org/10.1002/ece3.4905 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Kleinman-Ruiz, D. et al. Novel efficient genome-wide SNP panels for the conservation of the highly endangered Iberian lynx. BMC Genomics 18, 556. https://doi.org/10.1186/s12864-017-3946-5 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Geist, J. Strategies for the conservation of endangered freshwater pearl mussels (Margaritifera margaritifera L.): A synthesis of conservation genetics and ecology. Hydrobiologia 644, 69–88. https://doi.org/10.1007/s10750-010-0190-2 (2010).Lopes-Lima, M. et al. Conservation status of freshwater mussels in Europe: State of the art and future challenges. Biol. Rev. 92, 572–607. https://doi.org/10.1111/brv.12244 (2017).Article 
PubMed 

Google Scholar 
Outeiro, A., Ondina, P., Fernández, C., Amaro, R. & Miguel, E. S. Population density and age structure of the freshwater Pearl mussel, Margaritifera margaritifera, in two Iberian rivers. Freshw. Biol. 53, 485–496. https://doi.org/10.1111/j.1365-2427.2007.01913.x (2008).CAS 
Article 

Google Scholar 
Clements, E. A., Thomas, R. & Adams, C. E. An investigation of salmonid host utilisation by the endangered freshwater pearl mussel (Margaritifera margaritifera) in north-west Scotland. Aquat. Conserv.: Mar. Freshw. Ecosyst. 28, 764–768. https://doi.org/10.1002/aqc.2900 (2018).Taeubert, J-E. & Geist, J. The relationship between the Freshwater Pearl Mussel (Margaritifera margaritifera) and its hosts. Biol. Bull. 44, 67–73. https://doi.org/10.1134/S1062359017010149 (2017).Sousa, R. et al. Conservation status of the freshwater pearl mussel Margaritifera margaritifera in Portugal. Limnologica 50, 4–10. https://doi.org/10.1016/j.limno.2014.07.004 (2015).Article 

Google Scholar 
Almodóvar, A., Nicola, G. G., Ayllón, D. & Elvira, B. Global warming threatens the persistence of Mediterranean brown trout. Glob. Change Biol. 18, 1549–1560. https://doi.org/10.1111/j.1365-2486.2011.02608.x (2012).ADS 
Article 

Google Scholar 
Nicola, G. G., Elvira, B., Johnson, B., Ayllón, D. & Almodóvar, A. Local and global climatic drivers of Atlantic salmon decline in southern Europe. Fish. Res. 198, 78–85. https://doi.org/10.1016/j.fishres.2017.10.012 (2018).Article 

Google Scholar 
da Silva, J. P. et al. Predicting climatic threats to an endangered freshwater mussel in Europe: The need to account for fish hosts. Freshw. Biol. 00, 1–15. https://doi.org/10.1111/fwb.13885 (2022).Article 

Google Scholar 
Strayer, D. L., Geist, J., Haag, W. R., Jackson, J. K. & Newbold, J. D. Essay: Making the most of recent advances in freshwater mussel propagation and restoration. Conserv. Sci. Pract. 43, e53. https://doi.org/10.1111/csp2.53 (2009).Article 

Google Scholar 
Geist, J., Bayerl, H., Stoeckle, B. C. & Kuehn, R. Securing genetic integrity in freshwater pearl mussel propagation and captive breeding. Sci. Rep. 11, 16019. https://doi.org/10.1038/s41598-021-95614-2 (2021).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Gomes dos Santos, A. et al. The Crown Pearl: a draft genome assembly of the European freshwater pearl mussel Margaritifera margaritifera (Linnaeus, 1758). DNA Res. 28, dsab002. https://doi.org/10.1093/dnares/dsab002 (2021).Bouza, C. et al. Threatened freshwater pearl mussel Margaritifera margaritifera L. in NW Spain: low and very structured genetic variation in southern peripheral assessed using microsatellite markers. Conserv. Genet. 8: 937–948. https://doi.org/10.1007/s10592-006-9248-0 (2007).Stoeckle, B. C. et al. Strong genetic differentiation and low genetic diversity of the freshwater pearl mussel (Margaritifera margaritifera L.) in the southwestern European distribution range. Conserv. Genet. 18, 147–157. https://doi.org/10.1007/s10592-016-0889-3 (2017).Geist, J., Söderberg, H., Karllberg, A. & Kuehn, R. Drainage-independent genetic structure and high genetic diversity of endangered freshwater pearl mussels (Margaritifera margaritifera) in northern Europe. Conserv. Genet. 11, 1339–1350. https://doi.org/10.1007/s10592-009-9963-4 (2010).Article 

Google Scholar 
implications for conservation and management. Geist, J. & Kuehn, R. Genetic diversity and differentiation of central European freshwater pearl mussel (Margaritifera margaritifera L.) populations. Mol. Ecol. 14, 239–425. https://doi.org/10.1111/j.1365-294X.2004.02420.x (2005).CAS 
Article 

Google Scholar 
Farrington, S. J., King, R. W., Baker, J. A. & Gibbons, J. G. Population genetics of freshwater pearl mussel (Margaritifera margaritifera) in central Massachusetts and implications for conservation. Aquat. Conserv.: Mar. Freshw. Ecosyst. 30, 1945–1958. https://doi.org/10.1002/aqc.3439 (2020).Zanatta, D. T. et al. High genetic diversity and low differentiation in North American Margaritifera margaritifera (Bivalvia: Unionida: Margaritiferidae). Biol. J. Linn. Soc. Lond., 123, 850–863. https://doi.org/10.1093/biolinnean/bly010. (2018)Garrison, N. L., Johnson, P. D. & Whelan, N. V. Conservation genomics reveals low genetic diversity and multiple parentage in the threatened freshwater mussel Margaritifera hembeli. Conser. Genet. 22, 217–231. https://doi.org/10.1007/s10592-020-01329-8 (2021).Article 

Google Scholar 
Roe, K. & Kim, K. S. Genome-wide SNPs redefine species-boundaries and conservation units in the freshwater mussel genus Cyprogenia of North America. Sci. Rep. 11, 10752. https://doi.org/10.1038/s41598-021-90325-0 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Wingett, S. W. & Andrews, S. FastQ Screen: A tool for multi-genome mapping and quality control. F1000Res 7, 1338. https://doi.org/10.12688/f1000research.15931.2 (2018).Ewels, P., Magnusson, M., Lundin, S. & Käller, M. MultiQC: Summarize analysis results for multiple tools and samples in a single report. Bioinformatics 32, 3047–3048. https://doi.org/10.1093/bioinformatics/btw354 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Rochette, N. C., Rivera-Colón, A. G. & Catchen, J. M. Stacks 2: Analytical methods for paired-end sequencing improve RADseq-based population genomics. Mol. Ecol. 28, 4737–4754. https://doi.org/10.1111/mec.15253 (2019).CAS 
Article 
PubMed 

Google Scholar 
Paris, R. J., Stevens, J. R. & Catchen, J. M. Lost in parameter space: A road map for STACKS. Methods Ecol. Evol. 8, 1360–1373. https://doi.org/10.1111/2041-210X.12775 (2017).Article 

Google Scholar 
Limin, F., Niu, B., Zhu, Z., Wu, S. & Li, W. CD-HIT: Accelerated for clustering the next-generation sequencing data. Bioinformatics 28, 3150–3152. https://doi.org/10.1093/bioinformatics/bts565 (2012).CAS 
Article 

Google Scholar 
Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv:1303.3997v2. https://doi.org/10.48550/arXiv.1303.3997 (2013).Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079. https://doi.org/10.1093/bioinformatics/btp352 (2009).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Garrison E, Marth G. Haplotype-based variant detection from short-read sequencing. arXiv:1207.3907v2. https://doi.org/10.48550/arXiv.1207.3907 (2012)Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158. https://doi.org/10.1093/bioinformatics/btr330 (2011).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Excoffier, L. & Lischer, H. E. L. Arlequin suite ver 3.5: A new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resour. 10, 564–567. https://doi.org/10.1111/j.1755-0998.2010.02847.x (2010).Weir, B. S. & Cockerham, C. Estimating F-statistics for the analysis of population structure. Evol. 38, 1358–1370. https://doi.org/10.2307/2408641 (1984).CAS 
Article 

Google Scholar 
Frichot, E. & François, O. LEA: An R package for landscape and ecological association studies. Methods Ecol. Evol. 6, 925–929. https://doi.org/10.1111/2041-210X.12382 (2015).Article 

Google Scholar 
Alexander, D. H, Novembre, J. & Lange, K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 19, 1655–1664. https://doi.org/10.1101/gr.094052.109 (2009).Jakobsson, M. & Rosenberg, N. A. CLUMPP: A cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics 23, 1801–1806. https://doi.org/10.1093/bioinformatics/btm233 (2007).CAS 
Article 
PubMed 

Google Scholar 
Frankham, R. et al. A practical guide for genetic management of fragmented animal and plant populations. Oxford University Press, New York. 174. https://doi.org/10.1093/oso/9780198783411.001.0001 (2019).Wacker, S., Larson, B., Jakobsen, P. & Karlssona, S. Multiple paternity promotes genetic diversity in captive breeding of a freshwater mussel. Glob. Ecol. Cons. 17, e00564. https://doi.org/10.1016/j.gecco.2019.e00564 (2019).Article 

Google Scholar 
Cao, R. et al. Genetic structure and diversity of Australian freshwater crocodiles (Crocodylus johnstoni) from the Kimberley Western Australia. Conserv. Genet. 21, 421–429. https://doi.org/10.1007/s10592-020-01259-5 (2020).Article 

Google Scholar 
Escalante, M. A. et al. Genotyping-by-sequencing reveals the effects of riverscape, climate and interspecific introgression on the genetic diversity and local adaptation of the endangered Mexican Golden trout (Oncorhynchus chrysogaster). Conserv. Genet. 21, 907–926. https://doi.org/10.1371/journal.pone.0141775 (2020).CAS 
Article 

Google Scholar 
Bauer, G. Reproductive strategy of the freshwater pearl mussel Margaritifera margaritifera. J. Anim. Ecol. 56, 691–704. https://doi.org/10.2307/5077 (1987).Article 

Google Scholar 
Machordom, A., Araujo, R., Erpenbeck, D. & Ramos, M. A. Phylogeography and conservation genetics of endangered European Margaritiferidae (Bibalvia: Unionoidae). Biol. J. Linn. Soc. 78, 235–252. https://doi.org/10.1046/j.1095-8312.2003.00158.x (2003).Article 

Google Scholar 
Viveen, W., Schoorl, J. M., Veldkamp, A., van Balen, R. T. & Vidal-Romani, J. R. Fluvial terraces of the northwest Iberian lower Miño River. J. Maps 9, 513–522. https://doi.org/10.1080/17445647.2013.821096 (2013).Article 

Google Scholar 
Pérez-Granados, C., López-Iborra, G. & Seoane, J. A multi-scale analysis of habitat selection in peripheral populations of the endangered Dupont’s Lark Chersophilus duponti. Bird Conserv. Intern. 27, 398–413. https://doi.org/10.1017/S0959270916000356 (2017).Article 

Google Scholar 
Sanz Ball-Llosera, N., Garcìa-Marìn, J. & Pla, C. Managing fish populations under mosaic relationships. The case of brown trout (Salmo trutta) in peripheral Mediterranean populations. Conserv. Genet. 3, 385–400. https://doi.org/10.1023/A:1020527420654 (2002).Vila, M. et al. Phylogeography and Conservation Genetics of the Ibero-Balearic Three-Spined Stickleback (Gasterosteus aculeatus). PLoS One 12, e0170685. https://doi.org/10.1371/journal.pone.0170685 (2017)Hamed, Y. et al. Climate impacto n Surface and groundwater in North Africa: A global synthesis of findings and recommendations. Euro-Mediterr. J. Environ. Integr. 3, 25. https://doi.org/10.1007/s41207-018-0067-8 (2018).Article 

Google Scholar 
Krijgsman, W. et al. The Gibraltar Corridor: Watergate of the Messinian Salinity Crisis. Mar. Geol. 403, 238–246. https://doi.org/10.1016/j.margeo.2018.06.008 (2018).ADS 
Article 

Google Scholar 
Zanatta, D. T. & Wilson, C. C. Testing congruency of geographic and genetic population structure for a freshwater mussel Bivalvia: Unionoida) and its host fish. Biol. J. Linn. Soc. 102, 669–685. https://doi.org/10.1111/j.1095-8312.2010.01596.x (2011).Article 

Google Scholar 
Österling, E. M., Ferm, J. & Piccolo, J.J. Parasitic freshwater pearl mussel larvae (Margaritifera margaritifera L.) reduce the drift-feeding rate of juvenile brown trout (Salmo trutta L.). Environ. Biol. Fish. 97, 543–549. https://doi.org/10.1007/s10641-014-0251-x (2014).Geist, J. et al. Genetic structure of Irish freshwater pearl mussels (Margaritifera margaritifera and Margaritifera durrovensis): Validity of subspecies, roles of host fish, and conservation implications. Aquat. Conserv. Mar. Freshw. Ecosyst. 28, 923–933. https://doi.org/10.1002/aqc.2913 (2018)Wacker, S., Larsen, B. M., Karlsson, S. & Hindar, K. Host specificity drives genetic structure in a freshwater mussel. Sci. Rep. 9, 10409 (2019).Machordom, A., Suárez, J., Almodóvar, A. & Bautista, J. Mitochondrial haplotype variation and phylogeography of Iberian brown trout populations. Mol. Ecol. 9, 1325–1338. https://doi.org/10.1046/j.1365-294x.2000.01015.x (2000).CAS 
Article 

Google Scholar 
Suárez, J., Bautista, J. M., Almodóvar, A. & Machordom, A. Evolution of the mitocondrial control region in Paleartic brown trout (Salmo trutta) populations: The biogeographical role of the Iberian Peninsula. Heredity 87, 198–206. https://doi.org/10.1046/j.1365-2540.2001.00905.x (2001).Article 
PubMed 

Google Scholar 
Velasco, J. C. et al. Descubiertos algunos ejemplares de Margaritifera margaritifera (L.) (Bivalvia, Unionoida) en el alto Duero (Soria, España). Iberus 32(2), 97–104 (2014).Geist, J. & Kuehn, R. Host-parasite interactions in oligotrophic stream ecosystems: the roles of life history strategy and ecological niche. Mol. Ecol. 17, 997–1008. https://doi.org/10.1111/j.1365-294X.2007.03636.x. (2008)Ledoux, J.-B., et al. Gradients of genetic diversity and differentiation across the distribution range of a Mediterranean coral: Patterns, processes and conservation implications. Divers. Distrib. 27, 2104–2123 https://doi.org/10.1111/ddi.13382 (2021).Hervella F, & Caballero P. Inventario piscícola dos ríos galegos. Consellería de Medio Ambiente. Xunta de Galicia. Santiago de Compostela (1999).Saura, M., Caballero, P. & Morán, P. Are there Atlantic salmon in the River Tambre?. J. Fish Biol. 72, 1223–1229. https://doi.org/10.1111/j.1095-8649.2007.01782.x (2008).Article 

Google Scholar 
Hoban, S. et al. Genetic diversity targets and indicators in the CBD post-2020 global biodiversity framework must be improved. Biol. Conserv. 248, 108654. https://doi.org/10.1016/j.biocon.2020.108654 (2020).Article 

Google Scholar 
Rilov, G. et al. Adaptive marine conservation planning in the face of climate change: What can we learn from physiological, ecological and genetic studies?. Glob. Ecol. Conserv. 17, e00566. https://doi.org/10.1016/j.gecco.2019.e00566 (2019).Article 

Google Scholar 
Muniz, F. L. et al. Delimitation of evolutionary units in Cuvier’s dwarf caiman, Paleosuchus palpebrosus (Cuvier, 1807): Insights from conservation of a broadly distributed species. Conserv. Genet. 19, 599–610. https://doi.org/10.1007/s10592-017-1035-6 (2018).Article 

Google Scholar 
Gum, B., Lange, M. & Geist, J. A critical reflection on the success of rearing and culturing of juvenile freshwater mussels with a focus on the endangered freshwater pearl mussel (Margaritifera margaritifera L.). Aquat. Conserv. 21, 743–751. https://doi.org/10.1002/aqc.1222 (2011).Thomas, G. R., Taylor, J. & García de Leaniz, C. Captive breeding of the endangered freshwater Pearl mussel Margaritifera margaritifera. Endanger. Species Res. 12, 1–9. https://doi.org/10.3354/esr00286 (2010).Wilson, C. D. et al. The importance of population genetic information in formulating ex situ conservation strategies for the freshwater pearl mussel (Margaritifera margaritifera L.) in Northern Ireland. Anim. Conserv. 15, 595–602. https://doi.org/10.1111/j.1469-1795.2012.00553.x (2012).Pires, D., Reis, J., Benites, L. & Rodrigues, P. Minimizing dams impacts on biodiversity by way of translocations: the case of freshwater mussels. Impact Assess. Proj. Apprais. 39, 110–117. https://doi.org/10.1080/14615517.2020.1836710. (2021) More