Philippa Kaur

Sample procurement and data analysisTo establish a diachronic dataset of Galápagos tortoise dietary stable isotope ecology, we selected samples from five sources (see Supplemental Text): the American Museum of Natural History, New York, New York, (2) the California Academy of Sciences, San Francisco, California, (3) the Natural History Museum, London, England, (4) the National Museum of Natural History, Smithsonian Institution, Washington, D.C., and (5) the Thompson’s Cove (CA-SFR-186H) archaeological site in San Francisco, California. We provide details regarding sample provenience information and date-of-death as supplemental information. From these collections, we obtained single or multiple isotope samples from a total of 57 individual tortoises representing the following subspecies (n = 10) and islands: five C. n. abingdonii (Pinta Island), one C. n. becki (Volcán Wolf, Isabela Island), five C. n. chathamensis (San Cristóbal Island), four C. n. darwini (Santiago Island), thirteen C. n. duncanensis (Pinzón Island), four C. n. guentheri (Sierra Nega, Isabela Island), six C. n. hoodensis (Española Island), one C. n. microphyes (Volcán Darwin, Isabela Island), four C. n. niger (Floreana Island), nine C. n. porteri (Western Santa Cruz Island), one C. n. vicina (Cerro Azul, Isabela Island), one unknown Isabela Island tortoise, two C. n. vicina tortoises which were transported, lived and collected on Rabida Island, and one unknown tortoise (Chelonoidis niger ssp.; unknown Island—the San Francisco Gold Rush sample). The two earliest collected tortoises in our sample date to1833 and the latest tortoise is from 1967, representing a period of 134 years.To understand tissue-specific isotopic variation and fractionation for the purposes of reconstructing long-term dietary ecology, we sampled tortoise bone collagen (n = 57), bone apatite (n = 23), scute keratin (n = 8) and skin (n = 2) for carbon (δ13Ccollagen and δ13Capatite), nitrogen (δ15N), hydrogen (δD) and oxygen (δ18Oapatite) stable isotopes. All samples were drilled or cut using a Dremel rotary tool with either a blade or diamond spherical bit attachment and were transported to the University of New Mexico, Center for Stable Isotopes (UNM-CSI), Albuquerque, NM, for preparation and analysis. All statistical and metric data analysis and visualization occurred in R (4.0.4) and RStudio (2022.02.4). We provide reproducible source code supplemental to the text35.Bone collagen δ13C, δ15N and δDAnalysis of bone collagen, skin and scute keratin for carbon, nitrogen and hydrogen stable isotopes followed standardized protocols (e.g., see36). For bone collagen, we cut and demineralized a small portion of bulk bone in 0.5 N hydrochloric acid (HCl) at 5 °C for 24 h prior to rinsing all samples to neutrality using deionized water. For lipid extraction, we immersed the samples in a solution of 2:1 chloroform:methanol (C2H5Cl3) for 24 h (repeated three times) while also sonicating samples for 15 min to ensure complete chemical saturation. Preparation of skin and scute keratin samples was only included this during the later lipid extraction step (i.e., no demineralization required). After 72 h we rinsed all samples to neutrality and lyophilized the tortoise samples for another 24 h. We then measured approximately 0.5–0.6 mg of bone collagen/skin/scute tissue into tin capsules for carbon (δ13Ccollagen) and nitrogen (δ15N) stable isotope analysis. We also measured approximately 0.2–0.3 mg of bone collagen/skin/scute tissue into silver capsules for hydrogen (δD) isotope analysis. We report isotope values in delta (δ) notation, calculated as: ((Rsample/Rstandard) − 1) × 1000, where Rsample and Rstandard are the ratios (e.g., 13C/12C, 15N/14N) of the unknown and standard material, respectively. Delta values are reported as parts per thousand (‰).Carbon and nitrogen samples were measured on a Costech 4010 elemental analyzer (Valencia, California, USA) coupled to a Scientific Delta V Plus isotope ratio mass spectrometer by a Conflo IV, and hydrogen samples were measured on a Finnigan high-temperature conversion elemental analyzer (TC/EA) coupled to a Thermo Scientific Delta V Plus mass spectrometer by a Conflo IV at UNM-CSI (see37 for details on the high temperature conversion method for hydrogen analysis). All nitrogen and carbon isotope data are reported relative to atmospheric N2 and V-PDB, respectively. The data were corrected using lab standards with values of δ15 N = 6.4‰ and δ13C =  − 26.5‰ (casein protein), and of δ15N = 13.3‰ and δ13C =  − 16.7‰ (tuna muscle) that have been calibrated relative to the universally accepted standards: IAEA-N1, USGS 24, IAEA 600, USGS 63, and USGS 40.To ensure equilibrium between the exchangeable hydrogen in tissue samples and local atmosphere38, we weighed hydrogen standards and samples into silver capsules and allowed both to sit in the laboratory for at least 2 weeks before analysis. Hydrogen data were corrected using three UNM-CSI laboratory keratin standards (δDnon-ex =  − 174‰, − 93‰, and − 54‰) of which the δDnon-ex values were previously determined through a series of atmospheric exchange experiments. These standards were also calibrated to USGS standards CBS and KHS values of − 178.8‰ and − 47.5‰, respectively (see39,40 for details and updated values). To quantitate any error imparted to our collagen data through correction with keratin standards, a UNM-CSI cow (Bos taurus) bone collagen standard was analyzed in every run over a 6-month period (July 2017–January 2018) and gave an inter-run standard deviation of 3.9‰, suggesting the difference in percent exchangeable hydrogen between collagen and keratin tissues did not significantly impact our results. All hydrogen isotope data are reported relative to Vienna-Standard Mean Ocean Water (V-SMOW). The H3 factor was between 8 and 8.5 for all runs.Collagen precision (standard deviation; SD) for within-run analyses is  More

Takoutsing, B. et al. Assessment of soil health indicators for sustainable production of maize in smallholder farming systems in the highlands of Cameroon. Geoderma 276, 64–73 (2016).Article 
ADS 
CAS 

Google Scholar 
Wenzel, W. W. et al. Soil and land use factors control organic caron status and accumulation in agricultural soils of Lower Austria. Geoderma 409, 115595. https://doi.org/10.1016/j.geoderma.2021.115595 (2022).Article 
ADS 
CAS 

Google Scholar 
Rinot, O., Levy, G. J., Steinberger, Y., Svoray, T. & Eshel, G. Soil health assessment: A critical review of current methodologies and a proposed new approach. Sci. Total. Environ. 648, 1484–1491 (2019).Article 
ADS 
CAS 

Google Scholar 
Veum, K. S., Sudduth, K. A., Kremer, R. J. & Kitchen, R. (2017) Sensor data fusion for soil health assessment. Geoderma 305, 53–61 (2017).Article 
ADS 

Google Scholar 
Nunes, M. R., Van Es, H. M., Schindelbeck, R., Ristow, A. J. & Ryan, M. No-till and cropping system diversification improve soil health and crop yield. Geoderma 328, 30–43 (2018).Article 
ADS 
CAS 

Google Scholar 
Chhipaa, V., Stein, A., Shankar, H., George, K. J. & Alidoost, F. Assessing and transferring soil health information in a hilly terrain. Geoderma 343, 130–138 (2019).Article 
ADS 

Google Scholar 
Oliver, D. P., Bramley, R. G. V., Riches, D., Porter, I. & Edwards, J. A review of soil physical and chemical properties as indicators of soil quality in Australian viticulture. Aust. J. Grape Wine Res. 19, 129–139 (2013).Article 
CAS 

Google Scholar 
Riches, D. et al. Review: soil biological properties as indicators of soil quality in Australian viticulture. Aust. J. Grape Wine Res. 19, 311–323 (2013).CAS 

Google Scholar 
Ritz, K., Black, H. I. J., Campbell, C. D., Harris, J. A. & Wood, C. Selecting biological indicators for monitoring soils: a framework for balancing scientific opinion to assist policy development. Ecol. Ind. 9, 1212–1221 (2009).Article 
CAS 

Google Scholar 
Zhuo, Z., Kirchner, I., Pfahl, S. & Cubasch, U. Climate impact of volcanic eruptions: the sensitivity to eruption season and latitude in MPI-ESM ensemble experiments. Atmos. Chem. Phys. 21, 13425–13442 (2021).Article 
ADS 
CAS 

Google Scholar 
Griffiths, B. S., Bonkowski, M., Roy, J. & Ritz, K. Functional stability, substrate utilisation and biological indicators of soils following environmental impacts. Appl. Soil Ecol. 16(1), 49–61 (2001).Article 

Google Scholar 
Avidano, L., Gamalero, E., Cossa, G. P. & Carraro, E. Characterization of soil health in an Italian polluted site by using microorganisms as bioindicators. Appl. Soil Ecol. 30(1), 21–33 (2005).Article 

Google Scholar 
Pattison, A. B. et al. Development of key soil health indicators for the Australian banana industry. Appl. Soil Ecol. 40(1), 155–164 (2008).Article 

Google Scholar 
Damsma, K. M., Rose, M. T. & Cavagnaro, T. R. Landscape scale survey of indicators of soil health in grazing systems. Soil Res. 53(2), 154–167 (2015).Article 

Google Scholar 
Fine, A. K., van Es, H. M. & Schindelbeck, R. R. Statistics, scoring functions, and regional analysis of a comprehensive soil health database. Soil Sci. Soc. Am. J. 81(3), 589–601 (2016).Article 

Google Scholar 
Roper, W. R., Osmond, D. L., Heitman, J. L., Wagger, M. G. & Reberg-Horton, S. C. Soil Health indicators do not differentiate among agronomic management systems in North Carolina soils. Soil Sci. Soc. Am. J. 81(4), 828–843 (2016).Article 

Google Scholar 
Li, Z. et al. Rapid diagnosis of agricultural soil health: A novel soil health index based on natural soil productivity and human management. J. Environ. Manage. 277, 111402. https://doi.org/10.1016/j.jenvman.2020.111402 (2021).Article 

Google Scholar 
Oren, A. & Steinberger, Y. Catabolic profiles of soil fungal communities along a geographic climatic gradient in Israel. Soil Biol. Biochem. 40, 2578–2587 (2008).Article 
CAS 

Google Scholar 
Yu, J., Glazer, N. & Steinberger, Y. Carbon utilization, microbial biomass, and respiration in biological soil crusts in the Negev Desert. Biol. Fert. Soils 50, 285–293 (2014).Article 
CAS 

Google Scholar 
Van der Putten, W. H. et al. Plant–soil feedbacks: the past, the present and future challenges. J. Ecol. 101, 265–276 (2013).Article 

Google Scholar 
Sherman, C. & Steinberger, Y. Microbial functional diversity associated with plant litter decomposition along a climatic gradient. Microb. Ecol. 64, 399–415 (2012).Article 
CAS 

Google Scholar 
Dwivedi, V. & Soni, P. A review on the role of soil microbial biomass in eco-restoration of degraded ecosystem with special reference to mining areas. J. Appl. Nat. Sci. 3(1), 151–158 (2011).Article 

Google Scholar 
Barreiro, A., Martín, A., Carballas, T. & Díaz-Raviña, M. Long-term response of soil microbial communities to fire and fire-fighting chemicals. Biol. Fertil. Soils 52, 963–975 (2016).Article 
CAS 

Google Scholar 
Soil Science Division Staff. Soil survey manual. In USDA Handbook 18 (ed. Ditzler, C., Scheffe, K. & Monger, H.C.). (Washington, G. P. O., 2017).Campbell, C. D., Chapman, S. J., Cameron, C. M., Davidson, M. S. & Potts, J. M. A rapid microtiter plate method to measure carbon dioxide evolved from carbon substrate amendments so as to determine the physiological profiles of soil microbial communities by using whole soil. Appl. Environ. Microbiol. 69, 3593–3599 (2003).Article 
ADS 
CAS 

Google Scholar 
Anderson, J. P. E. & Domsch, K. H. Physiological method for quantitative measurement of microbial biomass in soils. Soil Biol. Biochem. 10, 215–221 (1978).Article 
CAS 

Google Scholar 
Creamer, R. E., Stone, D., Berry, P. & Kuiper, I. Measuring respiration profiles of soil microbial communities across Europe using MicroResp™ method. Appl. Soil Ecol. 97, 36–43 (2016).Article 

Google Scholar 
Oren, A. & Steinberger, Y. Coping with artifacts induced by CaCO3–CO2–H2O equilibria in substrate utilization profiling of calcareous soils. Soil Biol. Biochem. 40, 2569–2577 (2008).Article 
CAS 

Google Scholar 
Zak, J. C., Willig, M. R., Howard, D. L. & Wildman, G. Functional diversity of microbial communities: A quantitative approach. Soil Biol. Biochem. 26(9), 1101–1108 (1994).Article 

Google Scholar 
Hotelling, H. The most predictable criterion. J. Educ. Psychol. 26, 139–142 (1935).Article 

Google Scholar 
Morrison, D. F. Multivariate Statistical Methods 2nd edn. (McGraw-Hill, 1976).MATH 

Google Scholar 
Rencher, A. C. Methods of Multivariate Analysis (Wiley, Uk, 1995).MATH 

Google Scholar 
IBM Corp. Released 2020. IBM SPSS Statistics for Windows, Version 27.0. (Armonk, NY: IBM Corp., 2020)R Core Team. A language and environment for statistical computing (R Foundation for Statistical Computing, 2021).
Google Scholar 
Bartoń K. MuMIn: Multi-Model Inference. R package version 1.46.0, https://CRAN.R-project.org/package=MuMIn, 2022.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).Book 
MATH 

Google Scholar 
Johnson, J. B. & Omland, K. S. Model selection in ecology and evolution. Trends Ecol. Evol. 19, 101–108 (2004).Article 

Google Scholar 
Kiryushin, V. I. The management of soil fertility and productivity of agrocenoses in adaptive-landscape farming systems. Eurasian Soil Sci. 52, 1137–1145 (2019).Article 
ADS 
CAS 

Google Scholar 
Hermans, S. M. et al. Using soil bacterial communities to predict physic-chemical variables and soil quality. Microbiome 8, 79 (2020).Article 
CAS 

Google Scholar 
Fierer, N. & Jackson, R. B. The diversity and biogeography of soil bacterial communities. Proc. Natl. Acad. Sci. U. S. A. 103, 626–631 (2006).Article 
ADS 
CAS 

Google Scholar 
Frey, S. D., Drijber, R., Smith, H. & Melillo, J. M. Microbial biomass, functional capacity, and community structure after 12 years of soil warming. Soil Biol. Biochem. 40, 2904–2907 (2008).Article 
CAS 

Google Scholar 
Powlson, D. S., Brookes, P. C. & Christensen, B. T. Measurement of soil microbial biomass provides an early indication of changes in total soil organic matter due to straw incorporation. Soil Biol. Biochem. 19, 159–164 (1987).Article 
CAS 

Google Scholar 
Brookes, P. C. The use of microbial parameters in monitoring soil pollution by heavy metals. Biol. Fertil. Soils. 19, 269–279 (1995).Article 
CAS 

Google Scholar 
Hermans, S. M. et al. Bacteria as emerging indicators of soil condition. Appl. Environ. Microbiol. 83, e02826-e2916. https://doi.org/10.1128/AEM.02826-16 (2016).Article 

Google Scholar 
Liddicoat, C. et al. Can bacterial indicators of a grassy woodland restoration inform ecosystem assessment and microbiota-mediated human health?. Environ. Int. 129, 105–117 (2019).Article 

Google Scholar 
Jeanne, T., Parent, S. -É. & Hogue, R. Using a soil bacterial species balance index to estimate potato crop productivity. PLoS ONE 14, e0214089. https://doi.org/10.1371/journal.pone.0214089 (2019).Article 
CAS 

Google Scholar 
Taylor, B. R. & Parkinson, D. Respiration and mass loss rates of aspen and pine leaf litter decomposing in laboratory microcosms. Can. J. Bot. 66, 1948–1959 (1988).Article 

Google Scholar 
Wardle, D. A. & Parkinson, D. Response of the soil microbial biomass to glucose, and selective inhibitors, across a soil moisture gradient. Soil Biol. Biochem. 22, 825–834 (1990).Article 
CAS 

Google Scholar 
Baldrian, P., Merhautová, V., Petránková, M., Cajthaml, T. & Snajdr, J. Distribution of microbial biomass and activity of extracellular enzymes in a hardwood forest soil reflect soil moisture content. Appl. Soil Ecol. 46, 177–182 (2010).Article 

Google Scholar 
Holland, T. C. et al. The response of soil biota to water availability in vineyards. Pedobiol. Int. J. Soil Biol. 56, 9–14 (2013).
Google Scholar 
Yu, J. & Steinberger, Y. Vertical distribution of microbial-community functionality under the canopies of Zygophyllum dumosum and Hammada scoparia in the Negev Desert. Microb. Ecol. 62, 218–227 (2011).Article 

Google Scholar 
Wardle, D. A. & Parkinson, D. Interaction between microclimatic variables and the soil microbial biomass. Biol. Fertil. Soils. 9, 273–280 (1990).Article 

Google Scholar  More

Monte Carlo analysisSeaweed production costs and net costs of climate benefits were estimated on the basis of outputs of the biophysical and technoeconomic models described below. The associated uncertainties and sensitivities were quantified by repeatedly sampling from uniform distributions of plausible values for each cost and economic parameter (n = 5,000 for each nutrient scenario from the biophysical model, for a total of n = 10,000 simulations; see Supplementary Figs. 14 and 15)47,48,49,50,51,52. Parameter importance across Monte Carlo simulations (Fig. 3 and Supplementary Fig. 9) was determined using decision trees in LightGBM, a gradient-boosting machine learning framework.Biophysical modelG-MACMODS is a nutrient-constrained, biophysical macroalgal growth model with inputs of temperature, nitrogen, light, flow, wave conditions and amount of seeded biomass30,53, that we used to estimate annual seaweed yield per area (either in tons of carbon or tons of dry weight biomass per km2 per year)33,34. In the model, seaweed takes up nitrogen from seawater, and that nitrogen is held in a stored pool before being converted to structural biomass via growth54. Seaweed biomass is then lost via mortality, which includes breakage from variable ocean wave intensity. The conversion from stored nitrogen to biomass is based on the minimum internal nitrogen requirements of macroalgae, and the conversion from biomass to units of carbon is based on an average carbon content of macroalgal dry weight (~30%)55. The model accounts for farming intensity (sub-grid-scale crowding) and employs a conditional harvest scheme, where harvest is optimized on the basis of growth rate and standing biomass33.The G-MACMODS model is parameterized for four types of macroalgae: temperate brown, temperate red, tropical brown and tropical red. These types employed biophysical parameters from genera that represent over 99.5% of present-day farmed macroalgae (Eucheuma, Gracilaria, Kappahycus, Sargassum, Porphyra, Saccharina, Laminaria, Macrocystis)39. Environmental inputs were derived from satellite-based and climatological model output mapped to 1/12-degree global resolution, which resolves continental shelf regions. Nutrient distributions were derived from a 1/10-degree resolution biogeochemical simulation led by the National Center for Atmospheric Research (NCAR) and run in the Community Earth System Model (CESM) framework35.Two nutrient scenarios were simulated with G-MACMODS and evaluated using the technoeconomic model analyses described below: the ‘ambient nutrient’ scenario where seaweed growth was computed using surface nutrient concentrations without depletion or competition, and ‘limited nutrient’ simulations where seaweed growth was limited by an estimation of the nutrient supply to surface waters (computed as the flux of deep-water nitrate through a 100 m depth horizon). For each Monte Carlo simulation in the economic analysis, the technoeconomic model randomly selects either the 5th, 25th, 50th, 75th or 95th percentile G-MACMODS seaweed yield map from a normal distribution to use as the yield map for that simulation. Figures and numbers reported in the main text are based on the ambient-nutrient scenario; results based on the limited-nutrient scenario are shown in Supplementary Figures.Technoeconomic modelAn interactive web tool of the technoeconomic model is available at https://carbonplan.org/research/seaweed-farming.We estimated the net cost of seaweed-related climate benefits by first estimating all costs and emissions related to seaweed farming, up to and including the point of harvest at the farm location, then estimating costs and emissions related to the transportation and processing of harvested seaweed, and finally estimating the market value of seaweed products and either carbon sequestered or GHG emissions avoided.Production costs and emissionsSpatially explicit costs of seaweed production ($ tDW−1) and production-related emissions (tCO2 tDW−1) were calculated on the basis of ranges of capital costs ($ km−2), operating costs (including labour, $ km−2), harvest costs ($ km−2) and transport emissions per distance travelled (tCO2 km−1) in the literature (Table 1, Supplementary Tables 1 and 2); annual seaweed biomass (tDW km−2, for the preferred seaweed type in each grid cell), line spacing and number of harvests (species-dependent) from the biophysical model; as well as datasets of distances to the nearest port (km), ocean depth (m) and significant wave height (m).Capital costs were calculated as:$$c_{cap} = c_{capbase} + left( {c_{capbase} times left( {k_d + k_w} right)} right) + c_{sl}$$
(1)
where ccap is the total annualized capital costs per km2, ccapbase is the annualized capital cost per km2 (for example, cost of buoys, anchors, boats, structural rope) before applying depth and wave impacts, kd and kw are the impacts of depth and waviness on capital cost, respectively, each expressed as a multiplier between 0 and 1 modelled using our Monte Carlo method and applied only to grid cells with depth >500 m and/or significant wave height >3 m, respectively, and csl is the total annual cost of seeded line calculated as:$$c_{sl} = c_{slbase} times p_{sline}$$
(2)
where cslbase is the cost per metre of seeded line, and psline is the total length of line per km2, based on the optimal seaweed type grown in each grid cell.Operating and maintenance costs were calculated as:$$c_{op} = c_{ins} + c_{lic} + c_{lab} + c_{opbase}$$
(3)
where cop is the total annualized operating and maintenance costs per km2, cins is the annual insurance cost per km2, clic is the annual cost of a seaweed aquaculture license per km2, clab is the annual cost of labour excluding harvest labour, and copbase is all other operating and maintenance costs.Harvest costs were calculated as:$$c_{harv} = c_{harvbase} times n_{harv}$$
(4)
where charv is the total annual costs associated with harvesting seaweed per km2, charvbase is the cost per harvest per km2 (including harvest labour but excluding harvest transport), and nharv is the total number of harvests per year.Costs associated with transporting equipment to the farming location were calculated as:$$c_{eqtrans} = c_{transbase} times m_{eq} times d_{port}$$
(5)
where ceqtrans is total annualized cost of transporting equipment, ctransbase is the cost to transport 1 ton of material 1 km on a barge, meq is the annualized equipment mass in tons and dport is the ocean distance to the nearest port in km.The total production cost of growing and harvesting seaweed was therefore calculated as:$$c_{prod} = frac{{left( {c_{cap}} right) + left( {c_{op}} right) + left( {c_{harv}} right) + (c_{eqtrans})}}{{s_{dw}}}$$
(6)
where cprod is total annual cost of seaweed production (growth + harvesting), ccap is as calculated in equation (1), cop is as calculated in equation (3), charv is as calculated in equation (4), ceqtrans is as calculated in equation (5) and sdw is the DW of seaweed harvested annually per km2.Emissions associated with transporting equipment to the farming location were calculated as:$$e_{eqtrans} = e_{transbase} times m_{eq} times d_{port}$$
(7)
where eeqtrans is the total annualized CO2 emissions in tons from transporting equipment, etransbase is the CO2 emissions from transporting 1 ton of material 1 km on a barge, meq is the annualized equipment mass in tons and dport is the ocean distance to the nearest port in km.Emissions from maintenance trips to/from the seaweed farm were calculated as:$$e_{mnt} = left( {left( {2 times d_{port}} right) times e_{mntbase} times left( {frac{{n_{mnt}}}{{a_{mnt}}}} right)} right) + (e_{mntbase} times d_{mnt})$$
(8)
where emnt is total annual CO2 emissions from farm maintenance, dport is the ocean distance to the nearest port in km, nmnt is the number of maintenance trips per km2 per year, amnt is the area tended to per trip, dmnt is the distance travelled around each km2 for maintenance and emntbase is the CO2 emissions from travelling 1 km on a typical fishing maintenance vessel (for example, a 14 m Marinnor vessel with 2 × 310 hp engines) at an average speed of 9 knots (16.67 km h−1), resulting in maintenance vessel fuel consumption of 0.88 l km−1 (refs. 28,56).Total emissions from growing and harvesting seaweed were therefore calculated as:$$e_{prod} = frac{{(e_{eqtrans}) + (e_{mnt})}}{{s_{dw}}}$$
(9)
where eprod is total annual emissions from seaweed production (growth + harvesting), eeqtrans is as calculated in equation (7), emnt is as calculated in equation (8) and sdw is the DW of seaweed harvested annually per km2.Market value and climate benefits of seaweedFurther transportation and processing costs, economic value and net emissions of either sinking seaweed in the deep ocean for carbon sequestration or converting seaweed into usable products (biofuel, animal feed, pulses, vegetables, fruits, oil crops and cereals) were calculated on the basis of ranges of transport costs ($ tDW−1 km−1), transport emissions (tCO2-eq t−1 km−1), conversion cost ($ tDW−1), conversion emissions (tCO2-eq tDW−1), market value of product ($ tDW−1) and the emissions avoided by product (tCO2-eq tDW−1) in the literature (Table 1). Market value was treated as globally homogeneous and does not vary by region. Emissions avoided by products were determined by comparing estimated emissions related to seaweed production to emissions from non-seaweed products that could potentially be replaced by seaweed (including non-CO2 greenhouse gas emissions from land use)24. Other parameters used are distance to nearest port (km), water depth (m), spatially explicit sequestration fraction (%)57 and distance to optimal sinking location (km; cost-optimized for maximum emissions benefit considering transport emissions combined with spatially explicit sequestration fraction; see ‘Distance to sinking point calculation’ below). Each Monte Carlo simulation calculated the cost of both CDR via sinking seaweed and GHG emissions mitigation via seaweed products.For seaweed CDR, after the seaweed is harvested, it can either be sunk in the same location that it was grown, or be transported to a more economically favourable sinking location where more of the seaweed carbon would remain sequestered for 100 yr (see ‘Distance to optimal sinking point’ below). Immediately post-harvest, the seaweed still contains a large amount of water, requiring a conversion from dry mass to wet mass for subsequent calculations33:$$s_{ww} = frac{{s_{dw}}}{{0.1}}$$
(10)
where sww is the annual wet weight of seaweed harvested per km2 and sdw is the annual DW of seaweed harvested per km2.The cost to transport harvested seaweed to the optimal sinking location was calculated as:$$c_{swtsink} = c_{transbase} times d_{sink} times s_{ww}$$
(11)
where cswtsink is the total annual cost to transport harvested seaweed to the optimal sinking location, ctransbase is the cost to transport 1 ton of material 1 km on a barge, dsink is the distance in km to the economically optimized sinking location and sww is the annually harvested seaweed wet weight in t km−2 as in equation (10).The costs associated with transporting replacement equipment (for example, lines, buoys,anchors) to the farming location and hauling back used equipment at the end of its assumed lifetime (1 yr for seeded line, 5–20 yr for capital equipment by equipment type) in the sinking CDR pathway were calculated as:$$c_{eqtsink} = left( {c_{transbase} times left( {2 times d_{sink}} right) times m_{eq}} right) + (c_{transbase} times d_{port} times m_{eq})$$
(12)
where ceqtsink is the total annualized cost to transport both used and replacement equipment, ctransbase is the cost to transport 1 ton of material 1 km on a barge, meq is the annualized equipment mass in tons, dsink is the distance in km to the economically optimized sinking location and dport is the ocean distance to the nearest port in km. We assumed that the harvesting barge travels from the farming location directly to the optimal sinking location with harvested seaweed and replaced (used) equipment in tow (including used seeded line and annualized mass of used capital equipment), sinks the harvested seaweed, returns to the farm location and then returns to the nearest port (see Supplementary Fig. 16). These calculations assumed the shortest sea-route distance (see Distance to optimal sinking point).The total value of seaweed that is sunk for CDR was therefore calculated as:$$v_{sink} = frac{{left( {v_{cprice} – left( {c_{swtsink} + c_{eqtsink}} right)} right)}}{{s_{dw}}}$$
(13)
where vsink is the total value (cost, if negative) of seaweed farmed for CDR in $ tDW−1, vcprice is a theoretical carbon price, cswtsink is as calculated in equation (11), ceqtsink is as calculated in equation (12) and sdw is the annually harvested seaweed DW in t km−2. We did not assume any carbon price in our Monte Carlo simulations (vcprice is equal to zero), making vsink negative and thus representing a net cost.To calculate net carbon impacts, our model included uncertainty in the efficiency of using the growth and subsequent deep-sea deposition of seaweed as a CDR method. The uncertainty is expected to include the effects of reduced phytoplankton growth from nutrient competition, the relationship between air–sea gas exchange and overturning circulation (hereafter collectively referred to as the ‘atmospheric removal fraction’) and the fraction of deposited seaweed carbon that remains sequestered for at least 100 yr. The total amount of atmospheric CO2 removed by sinking seaweed was calculated as:$$e_{seqsink} = k_{atm} times k_{fseq} times frac{{tC}}{{tDW}} times frac{{tCO_2}}{{tC}}$$
(14)
where eseqsink is net atmospheric CO2 sequestered annually in t km−2, katm is the atmospheric removal fraction and kfseq is the spatially explicit fraction of sunk seaweed carbon that remains sequestered for at least 100 yr (see ref. 57).The emissions from transporting harvested seaweed to the optimal sinking location were calculated as:$$e_{swtsink} = e_{transbase} times d_{sink} times s_{ww}$$
(15)
where eswtsink is the total annual CO2 emissions from transporting harvested seaweed to the optimal sinking location in tCO2 km−2, etransbase is the CO2 emissions (tons) from transporting 1 ton of material 1 km on a barge (tCO2 per t-km), dsink is the distance in km to the economically optimized sinking location and sww is the annually harvested seaweed wet weight in t km−2 as in equation (10). Since the unit for etransbase is tCO2 per t-km, the emissions from transporting seaweed to the optimal sinking location are equal to (e_{mathrm{transbase}} times d_{mathrm{sink}} times s_{mathrm{ww}}), and the emissions from transporting seaweed from the optimal sinking location back to the farm are equal to 0 (since the seaweed has already been deposited, the seaweed mass to transport is now 0). Note that this does not yet include transport emissions from transport of equipment post-seaweed-deposition (see equation 16 below and Supplementary Fig. 16).The emissions associated with transporting replacement equipment (for example, lines, buoys, anchors) to the farming location and hauling back used equipment at the end of its assumed lifetime (1 yr for seeded line, 5–20 yr for capital equipment by equipment type)28,41 in the sinking CDR pathway were calculated as:$$e_{eqtsink} = left( {e_{transbase} times left( {2 times d_{sink}} right) times m_{eq}} right) + (e_{transbase} times d_{port} times m_{eq})$$
(16)
where eeqtsink is the total annualized CO2 emissions in tons from transporting both used and replacement equipment, etransbase is the CO2 emissions from transporting 1 ton of material 1 km on a barge, meq is the annualized equipment mass in tons, dsink is the distance in km to the economically optimized sinking location and dport is the ocean distance to the nearest port in km. We assumed that the harvesting barge travels from the farming location directly to the optimal sinking location with harvested seaweed and replaced (used) equipment in tow (including used seeded line and annualized mass of used capital equipment), sinks the harvested seaweed, returns to the farm location and then returns to the nearest port. These calculations assumed the shortest sea-route distance (see Distance to optimal sinking point).Net CO2 emissions removed from the atmosphere by sinking seaweed were thus calculated as:$$e_{remsink} = frac{{left( {e_{seqsink} – left( {e_{swtsink} + e_{eqtsink}} right)} right)}}{{s_{dw}}}$$
(17)
where eremsink is the net atmospheric CO2 removed per ton of seaweed DW, eseqsink is as calculated in equation (14), eswtsink is as calculated in equation (15), eeqtsink is as calculated in equation (16) and sdw is the annually harvested seaweed DW in t km−2.Net cost of climate benefitsSinkingTo calculate the total net cost and emissions from the production, harvesting and transport of seaweed for CDR, we combined the cost and emissions from the sinking-pathway cost and value modules. The total net cost of seaweed CDR per DW ton of seaweed was calculated as:$$c_{sinknet} = c_{prod} – v_{sink}$$
(18)
where csinknet is the total net cost of seaweed for CDR per DW ton harvested, cprod is the net production cost per DW ton as calculated in equation (6) and vsink is the net value (or cost, if negative) per ton seaweed DW as calculated in equation (13).The total net CO2 emissions removed per DW ton of seaweed were calculated as:$$e_{sinknet} = e_{remsink} – e_{prod}$$
(19)
where esinknet is the total net atmospheric CO2 removed per DW ton of seaweed harvested annually (tCO2 tDW−1 yr−1), eremsink is the net atmospheric CO2 removed via seaweed sinking annually as calculated in equation (17) and eprod is the net CO2 emitted from production and harvesting of seaweed annually as calculated in equation (9). For each Monte Carlo simulation, locations where esinknet is negative (that is, net emissions rather than net removal) were not included in subsequent calculations since they would not be contributing to CDR in that location under the given scenario. Note that these net emissions cases only occur in areas far from port in specific high-emissions scenarios. Even in such cases, most areas still contribute to CO2 removal (negative emissions), hence costs from locations with net removal were included.Total net cost was then divided by total net emissions to get a final value for cost per ton of atmospheric CO2 removed:$$c_{pertonsink} = frac{{c_{sinknet}}}{{e_{sinknet}}}$$
(20)
where cpertonsink is the total net cost per ton of atmospheric CO2 removed via seaweed sinking ($ per tCO2 removed), csinknet is total net cost per ton seaweed DW harvested as calculated in equation (18) ($ tDW−1) and esinknet is the total net atmospheric CO2 removed per ton seaweed DW harvested as calculated in equation (19) (tCO2 tDW−1).GHG emissions mitigationInstead of sinking seaweed for CDR, seaweed can be used to make products (including but not limited to food, animal feed and biofuels). Replacing convention products with seaweed-based products can result in ‘avoided emissions’ if the emissions from growing, harvesting, transporting and converting seaweed into products are less than the total greenhouse gas emissions (including non-CO2 GHGs) embodied in conventional products that seaweed-based products replace.When seaweed is used to make products, we assumed it is transported back to the nearest port immediately after being harvested. The annualized cost to transport the harvested seaweed and replacement equipment (for example, lines, buoys, anchors) was calculated as:$$c_{transprod} = frac{{left( {c_{transbase} times d_{port} times left( {s_{ww} + m_{eq}} right)} right)}}{{s_{dw}}}$$
(21)
where ctransprod is the annualized cost per ton seaweed DW to transport seaweed and equipment back to port from the farm location, ctransbase is the cost to transport 1 ton of material 1 km on a barge, meq is the annualized equipment mass in tons, dport is the ocean distance to the nearest port in km, sww is the annual wet weight of seaweed harvested per km2 as calculated in equation (10) and sdw is the annual DW of seaweed harvested per km2.The total value of seaweed that is used for seaweed-based products was calculated as:$$v_{product} = v_{mkt} – left( {c_{transprod} + c_{conv}} right)$$
(22)
where vproduct is the total value (cost, if negative) of seaweed used for products ($ tDW−1), vmkt is how much each ton of seaweed would sell for, given the current market price of conventional products that seaweed-based products replace ($ tDW−1), ctransprod is as calculated in equation (21) and cconv is the cost to convert each ton of seaweed to a usable product ($ tDW−1).The annualized CO2 emissions from transporting harvested seaweed and equipment back to port were calculated as:$$e_{transprod} = frac{{left( {e_{transbase} times d_{port} times left( {s_{ww} + m_{eq}} right)} right)}}{{s_{dw}}}$$
(23)
where etransprod is the annualized CO2 emissions per ton seaweed DW to transport seaweed and equipment back to port from the farm location, etransbase is the CO2 emissions from transporting 1 ton of material 1 km on a barge, meq is the annualized equipment mass in tons, dport is the ocean distance to the nearest port in km, sww is the annual wet weight of seaweed harvested per km2 as calculated in equation (10) and sdw is the annual DW of seaweed harvested per km2.Total emissions avoided by each ton of harvested seaweed DW were calculated as:$$e_{avprod} = e_{subprod} – left( {e_{transprod} + e_{conv}} right)$$
(24)
where eavprod is total CO2-eq emissions avoided per ton of seaweed DW per year (including non-CO2 GHGs using a GWP time period of 100 yr), esubprod is the annual CO2-eq emissions avoided per ton seaweed DW by replacing a conventional product with a seaweed-based product, etransprod is as calculated in equation (23) and econv is the annual CO2 emissions per ton seaweed DW from converting seaweed into usable products. esubprod was calculated by converting seaweed DW to caloric content58 for food/feed and comparing emissions intensity per kcal to agricultural products24, or by converting seaweed DW into equivalent biofuel content with a yield of 0.25 tons biofuel per ton DW59 and dividing the CO2 emissions per ton fossil fuel by the seaweed biofuel yield.To calculate the total net cost and emissions from the production, harvesting, transport and conversion of seaweed for products, we combined the cost and emissions from the product-pathway cost and value modules. The total net cost of seaweed for products per ton DW was calculated as:$$c_{prodnet} = c_{prod} – v_{product}$$
(25)
where cprodnet is the total net cost per ton DW of seaweed harvested for use in products, cprod is the net production cost per ton DW as calculated in equation (6) and vproduct is the net value (or cost, if negative) per ton DW as calculated in equation (22).The total net CO2-eq emissions avoided per ton DW of seaweed used in products were calculated as:$$e_{prodnet} = e_{avprod} – e_{prod}$$
(26)
where eprodnet is the total net CO2-eq emissions avoided per ton DW of seaweed harvested annually (tCO2 tDW−1 yr−1), eavprod is the net CO2-eq emissions avoided by seaweed products annually as calculated in equation (24) and eprod is the net CO2 emitted from production and harvesting of seaweed annually as calculated in equation (9). For each Monte Carlo simulation, locations where eprodnet is negative (that is, net emissions rather than net emissions avoided) were not included in subsequent calculations since they would not be avoiding any emissions in that scenario.Total net cost was then divided by total net emissions avoided to get a final value for cost per ton of CO2-eq emissions avoided:$$c_{pertonprod} = frac{{c_{prodnet}}}{{e_{prodnet}}}$$
(27)
where cpertonprod is the total net cost per ton of CO2-eq emissions avoided by seaweed products ($ per tCO2-eq avoided), cprodnet is total net cost per ton seaweed DW harvested for products as calculated in equation (25) ($ tDW−1) and eprodnet is total net CO2-eq emissions avoided per ton seaweed DW harvested for products as calculated in equation (26) (tCO2 tDW−1).Parameter ranges for Monte Carlo simulationsFor technoeconomic parameters with two or more literature values (see Supplementary Table 1), we assumed that the maximum literature value reflected the 95th percentile and the minimum literature value represented the 5th percentile of potential costs or emissions. For parameters with only one literature value, we added ±50% to the literature value to represent greater uncertainty within the modelled parameter range. Values at each end of parameter ranges were then rounded before Monte Carlo simulations as follows: capital costs, operating costs and harvest costs to the nearest $10,000 km−2, labour costs and insurance costs to the nearest $1,000 km−2, line costs to the nearest $0.05 m−1, transport costs to the nearest $0.05 t−1 km−1, transport emissions to the nearest 0.000005 tCO2 t−1 km−1, maintenance transport emissions to the nearest 0.0005 tCO2 km−1, product-avoided emissions to the nearest 0.1 tCO2-eq tDW−1, conversion cost down to the nearest $10 tDW−1 on the low end of the range and up to the nearest $10 tDW−1 on the high end of the range, and conversion emissions to the nearest 0.01 tCO2 tDW−1.We extended the minimum range values of capital costs to $10,000 km−2 and transport emissions to 0 to reflect potential future innovations, such as autonomous floating farm setups that would lower capital costs and net-zero emissions boats that would result in 0 transport emissions. To calculate the minimum value of $10,000 km−2 for a potential autonomous floating farm, we assumed that the bulk of capital costs for such a system would be from structural lines and flotation devices, and we therefore used the annualized structural line (system rope) and buoy costs from ref. 41 rounded down to the nearest $5,000 km−2. The full ranges used for our Monte Carlo simulations and associated literature values are shown in Supplementary Table 1.Distance to optimal sinking pointDistance to the optimal sinking point was calculated using a weighted distance transform (path-finding algorithm, modified from code in ref. 60) that finds the shortest ocean distance from each seaweed growth pixel to the location at which the net CO2 removed is maximized (including impacts of both increased sequestration fraction and transport emissions for different potential sinking locations) and the net cost is minimized. This is not necessarily the location in which the seaweed was grown, since the fraction of sunk carbon that remains sequestered for 100 yr is spatially heterogeneous (see ref. 57). For each ocean grid cell, we determined the cost-optimal sinking point by iteratively calculating equations (11–20) and assigning dsink as the distance calculated by weighted distance transform to each potential sequestration fraction (0.01–1.00) in increments of 0.01. Except for transport emissions, the economic parameter values used for these calculations were the averages of unrounded literature value ranges; we assumed that the maximum literature value reflected the 95th percentile and the minimum literature value represented the 5th percentile of potential costs or emissions, or for parameters with only one literature value, we added ±50% to the literature value to represent greater uncertainty within the modelled parameter range. For transport and maintenance transport emissions, we extended the minimum values of the literature ranges to zero to reflect potential net-zero emissions transport options and used the mean values of the resulting ranges. The dsink that resulted in minimum net cost per ton CO2 for each ocean grid cell was saved as the final dsink map, and the associated sequestration fraction value that the seaweed is transported to via dsink was assigned to the original cell where the seaweed was farmed and harvested (Supplementary Fig. 19). If the cost-optimal location to sink using this method was the same cell where the seaweed was harvested, then dsink was 0 km and the sequestration fraction was not modified from its original value (Supplementary Fig. 18).Comparison of gigaton-scale sequestration area to previous estimatesPrevious related work estimating the ocean area suitable for macroalgae cultivation13 and/or the area that might be required to reach gigaton-scale carbon removal via macroalgae cultivation13,19,36 has yielded a wide range of results, primarily due to differences in modelling methods. For example, Gao et al. (2022)36 estimate that 1.15 million km2 would be required to sequester 1 GtCO2 annually when considering carbon lost from seaweed biomass/sequestered as particulate organic carbon (POC) and refractory dissolved organic carbon (rDOC), and assume that the harvested seaweed is sold as food such that the carbon in the harvested seaweed is not sequestered. The area (0.31 million km2) required to sequester 1 GtCO2 in our study assumes that all harvested seaweed is sunk to the deep ocean to sequester carbon.Additionally, Wu et al.19 estimates that roughly 12 GtCO2 could be sequestered annually via macroalgae cultivation in approximately 20% of the world ocean area (that is, 1.67% ocean area per GtCO2), which is a much larger area per GtCO2 than our estimate of 0.085% ocean area. This notable difference arises for several reasons (including differences in yields, which in Wu et al. are around 500 tDW yr−1 in the highest-yield areas, whereas yields in our cheapest sequestration areas from G-MACMODS average 3,400 tDW km−2 yr−1) that arise from differences in model methodology. First, Wu et al. model temperate brown seaweeds, while our study considers different seaweed types, many of which have higher growth rates, and uses the most productive seaweed type for each ocean grid cell. The G-MACMODS seaweed growth model we use also has a highly optimized harvest schedule, includes luxury nutrient uptake (a key feature of macroalgal nutrient physiology) and does not directly model competition with phytoplankton during seaweed growth. Finally, tropical red seaweeds (the seaweed type in our cheapest sequestration areas) grow year-round, while others, such as the temperate brown seaweeds modelled by Wu et al., only grow seasonally. These differences all contribute to higher productivity in our model, leading to a smaller area required for gigaton-scale CO2 sequestration compared with Wu et al.Conversely, the ocean areas we model for seaweed-based CO2 sequestration or GHG emissions avoided are much larger than the 48 million km2 that Froehlich et al.13 estimate to be suitable for macroalgae farming globally. Although our maps show productivity and costs everywhere, the purpose of our modelling was to evaluate where different types of seaweed grow best and how production costs and product values vary over space, to highlight the lowest-cost areas (which are often the highest-producing areas) under various technoeconomic assumptions.Comparison of seaweed production costs to previous estimatesAlthough there are not many estimates of seaweed production costs in the scientific literature, our estimates for the lowest-cost 1% area of the ocean ($190–$2,790 tDW−1) are broadly consistent with previously published results: seaweed production costs reported in the literature range from $120 to $1,710 tDW−1 (refs. 40,41,61,62), but are highly dependent on assumed seaweed yields. For example, Camus et al.41 calculate a cost of $870 tDW−1 assuming a minimum yield of 12.4 kgDW m−1 of cultivation line (equivalent to 8.3 kgDW m−2 using 1.5 m spacing between lines). Using the economic values from Camus et al. but with our estimates of average yield for the cheapest 1% production cost areas (2.6 kgDW m−2) gives a much higher average cost of $2,730 tDW−1. Contrarily, van den Burg et al.40 calculate a cost of $1,710 tDW−1 using a yield of 20 tDW ha−1 (that is, 2.0 kg m−2). Instead assuming the average yield to be that from our lowest-cost areas (that is, 2.6 kgDW m−2 or 26 tDW ha−1) would decrease the cost estimated by van den Burg et al. (2016) to $1,290 tDW−1. Most recently, Capron et al.62 calculate an optimistic scenario cost of $120 tDW−1 on the basis of an estimated yield of 120 tDW ha−1 (12 kg m−2; over 4.5 times higher than the average yield in our lowest-cost areas). Again, instead assuming the average yield to be that in our lowest-cost areas would raise Capron et al.’s production cost to $540 tDW−1 (between the $190–$880 tDW−1 minimum to median production costs in the cheapest 1% areas from our model; Fig. 1a,b).Data sourcesSeaweed biomass harvestedWe used spatially explicit data for seaweed harvested globally under both ambient and limited-nutrient scenarios from the G-MACMODS seaweed growth model33.Fraction of deposited carbon sequestered for 100 yrWe used data from ref. 57 interpolated to our 1/12-degree grid resolution.Distance to the nearest portWe used the Distance from Port V1 dataset from Global Fishing Watch (https://globalfishingwatch.org/data-download/datasets/public-distance-from-port-v1) interpolated to our 1/12-degree grid resolution.Significant wave heightWe used data for annually averaged significant wave height from the European Center for Medium-range Weather Forecasts (ECMWF) interpolated to our 1/12-degree grid resolution.Ocean depthWe used data from the General Bathymetric Chart of the Oceans (GEBCO).Shipping lanesWe used data of Automatic Identification System (AIS) signal count per ocean grid cell, interpolated to our 1/12-degree grid resolution. We defined a major shipping lane grid cell as any cell with >2.25 × 108 AIS signals, a threshold that encompasses most major trans-Pacific and trans-Atlantic shipping lanes as well as major shipping lanes in the Indian Ocean, the North Sea, and coastal routes worldwide.Marine protected areas (MPAs)We used data from the World Database on Protected Areas (WDPA) and defined an MPA as any ocean MPA >20 km2.Reporting summaryFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article. More

Seto, K. C., Guneralp, B. & Hutyra, L. R. Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proc. Natl Acad. Sci. USA 109, 16083–16088 (2012).Article 
ADS 
CAS 

Google Scholar 
McDonald, R. I., Marcotullio, P. J. & Güneralp, B. Urbanization and global trends in biodiversity and ecosystem services. in Urbanization, Biodiversity and Ecosystem Services: Challenges And Opportunities, 31–52 (Springer, 2013).McDonald, R. I., Kareiva, P. & Forman, R. T. T. The implications of current and future urbanization for global protected areas and biodiversity conservation. Biol. Conserv. 141, 1695–1703 (2008).Article 

Google Scholar 
Müller, N., Ignatieva, M., Nilon, C. H., Werner, P. & Zipperer, W. C. Patterns and trends in urban biodiversity and landscape design. In Urbanization, Biodiversity and Ecosystem Services: Challenges And Opportunities, 123–174 (Springer, 2013).Lahr, E. C., Dunn, R. R. & Frank, S. D. Getting ahead of the curve: Cities as surrogates for global change. Proc. R. Soc. B. 285, 20180643 (2018).Article 

Google Scholar 
Cadotte, M. W., Yasui, S. L. E., Livingstone, S. & MacIvor, J. S. Are urban systems beneficial, detrimental, or indifferent for biological invasion?. Biol. Invasions. 19, 3489–3503 (2017).Article 

Google Scholar 
Thompson, K. A., Rieseberg, L. H. & Schluter, D. Speciation and the city. Trends Ecol. Evol. 33, 815–826 (2018).Article 

Google Scholar 
Borden, J. B. & Flory, S. L. Urban evolution of invasive species. Front. Ecol. Environ. 19, 184–191 (2021).Article 

Google Scholar 
Melliger, R. L., Braschler, B., Rusterholz, H. P. & Baur, B. Diverse effects of degree of urbanisation and forest size on species richness and functional diversity of plants, and ground surface-active ants and spiders. PLoS ONE 13, e0199245 (2018).Article 

Google Scholar 
McKinney, M. L. Urbanization, biodiversity, and conservation: The impacts of urbanization on native species are poorly studied, but educating a highly urbanized human population about these impacts can greatly improve species conservation in all ecosystems. Bioscience 52, 883–890 (2002).Article 

Google Scholar 
Roshnath, R. & Sinu, P. A. Nesting tree characteristics of heronry birds of urban ecosystems in peninsular India: Implications for habitat management. Curr. Zool. 63, 599–605 (2017).Article 

Google Scholar 
Roshnath, R., Athira, K. & Sinu, P. A. Does predation pressure drive heronry birds to nest in the urban landscape?. J. Asia Pac. Biodivers. 12, 311–315 (2019).Article 

Google Scholar 
Fenoglio, M. S., Rossetti, M. R. & Videla, M. Negative effects of urbanization on terrestrial arthropod communities: A meta-analysis. Glob. Ecol. Biogeogr. 29, 1412–1429 (2020).Article 

Google Scholar 
Saari, S. et al. Urbanization is not associated with increased abundance or decreased richness of terrestrial animals-dissecting the literature through meta-analysis. Urban Ecosyst. 19, 1251–1264 (2016).Article 

Google Scholar 
Lessard, J. P. & Buddle, C. M. The effects of urbanization on ant assemblages (Hymenoptera: Formicidae) associated with the Molson Nature Reserve. Quebec. Can. Entomol. 137, 215–225 (2005).Article 

Google Scholar 
Uno, S., Cotton, J. & Philpott, S. M. Diversity, abundance, and species composition of ants in urban green spaces. Urban Ecosyst. 13, 425–441 (2010).Article 

Google Scholar 
Fortel, L. et al. Decreasing abundance, increasing diversity and changing structure of the wild bee community (Hymenoptera: Anthophila) along an urbanization gradient. PLoS ONE 9, e104679 (2014).Article 
ADS 

Google Scholar 
Baldock, K. C. et al. Where is the UK’s pollinator biodiversity? The importance of urban areas for flower-visiting insects. Proc. R. Soc. B. 282, 20142849 (2015).Article 

Google Scholar 
Baldock, K. C. R. et al. A systems approach reveals urban pollinator hotspots and conservation opportunities. Nat. Ecol. Evol. 3, 363–373 (2019).Article 

Google Scholar 
Rocha, E. A. & Fellowes, M. D. Urbanisation alters ecological interactions: Ant mutualists increase and specialist insect predators decrease on an urban gradient. Sci. Rep. 10, 1–8 (2020).Article 
ADS 

Google Scholar 
Theodorou, P. et al. Urban areas as hotspots for bees and pollination but not a panacea for all insects. Nat. Commun. 11, 1–13 (2020).Article 

Google Scholar 
Carvalho, R. L. et al. Understanding what bioindicators are actually indicating: Linking disturbance responses to ecological traits of dung beetles and ants. Ecol. Indic. 108, 105764 (2020).Article 

Google Scholar 
Asha, G., Manoj, K., Megha, P. P. & Sinu, P. A. Spatiotemporal effects on dung beetle activities in island forests-home garden matrix in a tropical village landscape. Sci. Rep. 11, 1–13 (2021).Article 

Google Scholar 
Correa, C. M. A., da Silva, P. G., Ferreira, K. R. & Puker, A. Residential sites increase species loss and cause high temporal changes in functional diversity of dung beetles in an urbanized Brazilian Cerrado landscape. J. Insect Conserv. 25, 417–428 (2021).Article 

Google Scholar 
Correa, C. M. A., Ferreira, K. R., Puker, A., Audino, L. D. & Korasaki, V. Greenspace sites conserve taxonomic and functional diversity of dung beetles in an urbanized landscape in the Brazilian Cerrado. Urban Ecosyst. 24, 1023–1034 (2021).Article 

Google Scholar 
Beiroz, W. et al. Spatial and temporal shifts in functional and taxonomic diversity of dung beetle in a human-modified tropical forest landscape. Ecol. Indic. 95, 418–526 (2018).Article 

Google Scholar 
Fuzessy, L. F. et al. Identifying the anthropogenic drivers of declines in tropical dung beetle communities and functions. Biol. Conserv. 256, 109063 (2021).Article 

Google Scholar 
Barragan, F., Moreno, C. E., Escobar, F., Halffter, G. & Navarrete, D. Negative impacts of human land use on dung beetle functional diversity. PLoS ONE 6, e17976 (2011).Article 
ADS 
CAS 

Google Scholar 
Salomão, R. P. et al. Urbanization effects on dung beetle assemblages in a tropical city. Ecol. Indic. 103, 665–675 (2019).Article 

Google Scholar 
Filgueiras, B. K. C., Liberal, C. N., Aguiar, C. D. M., Hernández, M. I. M. & Iannuzzi, L. Attractivity of omnivore, carnivore and herbivore mammalian dung to Scarabaeinae (Coleoptera: Scarabaeidae) in a tropical Atlantic rainforest remnant. Rev. Bras. Entomol. 53, 422–427 (2009).Article 

Google Scholar 
Ramírez-Restrepo, L. & Halffter, G. Copro-necrophagous beetles (Coleoptera: Scarabaeinae) in urban areas: A global review. Urban Ecosyst. 19, 1179–1195 (2016).Article 

Google Scholar 
Krell, F. T. et al. Human influence on the dung fauna in Afrotropical grasslands (Insecta: Coleoptera). In African Biodiversity: Molecules Organisms Ecosystems (eds Huber, B. A. et al.) 133–139 (Springer, 2005).Chapter 

Google Scholar 
Jiménez-Ferbans, L., Mendieta-Otálora, W., García, H. & Amat-García, G. Notes on dung beetles (Coleoptera: Scarabaeinae) in dry environments of the Santa Marta region, Colombia. Acta Biol. Colomb. 13, 203–208 (2008).
Google Scholar 
Costa, F. C. et al. What is the importance of open habitat in a predominantly closed forest area to the dung beetle (Coleoptera, Scarabaeinae) assemblage?. Rev. Bras. Entomol. 57, 329–334 (2013).Article 

Google Scholar 
Korasaki, V., Lopes, J., Gardner, B. G. & Louzada, J. Using dung beetles to evaluate the effects of urbanization on Atlantic Forest biodiversity. Insect Sci. 20, 393–406 (2013).Article 

Google Scholar 
Audino, L., Louzada, J. & Comita, L. Dung beetles as indicators of tropical forest restoration success: Is it possible to recover species and functional diversity?. Biol. Conserv. 169, 248–257 (2014).Article 

Google Scholar 
Gómez-Cifuentez, A., Munevar, A., Gimenez, V. C., Gatti, M. G. & Zurita, G. A. Influence of land use on the taxonomic and functional diversity of dung beetles (Coleoptera: Scarabaeinae) in the southern Atlantic Forest of Argentina. J. Insect. Conserv. 21, 147–156 (2017).Article 

Google Scholar 
Gómez-Cifuentes, A., Gómez, V. C. G., Moreno, C. E. & Zurita, G. A. Tree retention in cattle ranching systems partially preserves dung beetle diversity and functional groups in the semideciduous Atlantic forest: The role of microclimate and soil conditions. Basic Appl. Ecol. 34, 64–74 (2019).Article 

Google Scholar 
Magnano, L. F. S. et al. Functional attributes change but functional richness is unchanged after fragmentation of Brazilian Atlantic forests. J. Ecol. 102, 475–485 (2014).Article 

Google Scholar 
GiménezGómez, V. C., Verdú, J. R., Casanoves, F. & Zurita, G. A. Functional responses to anthropogenic disturbance and the importance of selected traits: a study case using dung beetles. Ecol. Entomol. 1, 1–12 (2022).
Google Scholar 
Lobo, J. M. Decline of roller dung beetle (Scarabaeinae) populations in the Iberian Peninsula during the 20th century. Biol. Conserv. 97, 43–50 (2001).Article 

Google Scholar 
Ballullaya, U. P. et al. Stakeholder motivation for the conservation of sacred groves in south India: An analysis of environmentalperceptions of rural and urban neighbourhood communities. Land Use Policy 89, 104213 (2019).Article 

Google Scholar 
Lowman, M. D. & Sinu, P. A. Can the spiritual values of forests inspire effective conservation?. Bioscience 67, 688–690 (2017).Article 

Google Scholar 
Bhagwat, S. A., Kushalappa, C. G., Williams, P. H. & Brown, N. D. The role of informal protected areas in maintaining biodiversity in the Western Ghats of India. Ecol. Soc 10, 108 (2005).Article 

Google Scholar 
Rajesh, T. P., Prashanth Ballullaya, U., Unni, A. P., Parvathy, S. & Sinu, P. A. Interactive effects of urbanization and year on invasive and native ant diversity of sacred groves of South India. Urban Ecosyst. 23, 1335–1348 (2020).Article 

Google Scholar 
Asha, G., Navya, K. K., Rajesh, T. P. & Sinu, P. A. Roller dung beetles of dung piles suggest habitats are alike, but that of guarding pitfall traps suggest habitats are different. J. Trop. Ecol. 37, 209–213 (2021).Article 

Google Scholar 
Arrow, G. J. The Fauna Of British India Including Ceylon And Burma, Coleoptera: Lamellicornia (Coprinae) (Taylor and Francis, 1931).
Google Scholar 
Hsieh, T. C., Ma, K. H. & Chao, A. iNEXT: Interpolation and extrapolation for species diversity. R package version 2.0.20. http://chao.stat.nthu.edu.tw/wordpress/software-download/ (2020).Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
Fox, J. et al. Package ‘car’, Vol. 16, (R Foundation for Statistical Computing, 2012).Barton, K. MuMIn: Multi-Model Inference. R package version 1.43.17. https://CRAN.R-project.org/package=MuMIn (2020).Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5-7. https://CRAN.R-project.org/package=vegan (2020).Hartig, F. & Hartig, M. F. Package ‘DHARMa’. R package (2017).Warnes, G. R. et al. gplots: Various R Programming Tools for Plotting Data. R package version 3.1.1. https://CRAN.R-project.org/package=gplots (2020).R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. https://www.R-project.org/ (2021).Venugopal, K. S., Thomas, S. K. & Flemming, A. T. Diversity and community structure of dung beetles (Coleoptera: Scarabaeinae) associated with semi-urban fragmented agricultural land in the Malabar coast in southern India. J. Threat. Taxa. 4, 2685–2692 (2012).Article 

Google Scholar 
Sabu, T. K. & Nithya, S. Comparison of the arboreal dung beetles (Coleoptera: Scarabaeidae: Scarabaeinae) of the wet and dry forests of the western Ghats. India. Coleopt. Bull. 70, 144–148 (2016).Article 

Google Scholar 
Sabu, T. K., Vinod, K. V. & Vineesh, P. J. Guild structure, diversity and succession of dung beetles associated with Indian elephant dung in South Western Ghats forests. J. Insect Sci. 6, 6–17 (2006).Article 

Google Scholar 
Rodrigues, M. M., Uchôa, M. A. & Ide, S. Dung beetles (Coleoptera: Scarabaeoidea) in three landscapes in Mato Grosso do Sul, Brazil. Braz. J. Biol. 73, 211–220 (2013).Article 
CAS 

Google Scholar 
Rios-Diaz, C. L. et al. Sheep herding in small grasslands promotes dung beetle diversity in a mountain forest landscape. J. Insect. Conserv. 25, 13–26 (2020).Article 

Google Scholar 
Carrión-Paladines, V. et al. Effects of land-use change on the community structure of the dung beetle (Scarabaeinae) in an altered ecosystem in Southern Ecuador. Insects. 12, 306 (2021).Article 

Google Scholar 
Gómez, V. C. G., Verdú, J. R. & Zurita, G. A. Thermal niche helps to explain the ability of dung beetles to exploit disturbed habitats. Sci. Rep. 10, 1–14 (2020).ADS 

Google Scholar 
Slade, E. M., Mann, D. J., Villanueva, J. F. & Lewis, O. T. Experimental evidence for the effects of dung beetle functional group richness and composition on ecosystem function in a tropical forest. J. Anim. Ecol. 76, 1094–1104 (2007).Article 

Google Scholar 
Vinod, K. V. & Sabu, T. K. Species composition and community structure of dung beetles attracted to dung of gaur and elephant in the moist forests of South Western Ghats. J. Insect. Sci. 7, 1–14 (2007).Article 
CAS 

Google Scholar 
Milotić, T. et al. Functionally richer communities improve ecosystem functioning: Dung removal and secondary seed dispersal by dung beetles in the Western Palaearctic. J. Biogeogr. 46, 70–82 (2019).Article 

Google Scholar 
Braga, R. F., Korasaki, V., Andresen, E. & Louzada, J. Dung beetle community and functions along a habitat-disturbance gradient in the amazon: A rapid assessment of ecological functions associated to biodiversity. PLoS ONE 8, e57786 (2013).Article 
ADS 
CAS 

Google Scholar 
Nichols, E. et al. Trait-dependent response of dung beetle populations to tropical forest conversion at local and regional scales. Ecology 94, 180–189 (2013).Article 

Google Scholar 
Gardner, T. A. et al. The cost-effectiveness of biodiversity surveys in tropical forests. Ecol. Lett. 11, 139–150 (2008).Article 

Google Scholar  More

Gorosito, I., Benitez, A. & Busch, M. Home range variability, spatial aggregation, and excursions of Akodon azarae and Oligoryzomys flavescens in Pampean agroecosystems. Integr. Zool. 15, 401–415 (2020).Article 

Google Scholar 
Christy, M. T., Savidge, J. A., Adams, A. A. Y., Gragg, J. E. & Rodda, G. H. Experimental landscape reduction of wild rodents increases movements in the invasive brown treesnake (Boiga irregularis). Manag. Biol. Invasion. 8, 455–467 (2017).Article 

Google Scholar 
Cutrera, A. P., Antinuchi, C. D., Mora, M. S. & Vassallo, A. I. Home-range and activity patterns of the south American subterranean rodent Ctenomys talarum. J. Mammal. 87, 1183–1191 (2006).Article 

Google Scholar 
Tisell, H. B., Degrassi, A. L., Stephens, R. B. & Rowe, R. J. Influence of field technique, density, and sex on home range and overlap of the southern red-backed vole (Myodes gapperi). Can. J. Zool. 97, 1101–1108 (2019).Article 

Google Scholar 
Vieira, E. M., Baumgarten, L. C., Paise, G. & Becker, R. G. Seasonal patterns and influence of temperature on the daily activity of the diurnal neotropical rodent Necromys lasiurus. Can. J. Zool. 88, 259–265 (2010).Article 

Google Scholar 
Burt, W. H. Territoriality and home range concepts as applied to mammals. J. Mammal. 24, 346–352 (1943).Article 

Google Scholar 
Samuel, M. D., Pierce, D. & Garton, E. O. Identifying areas of concentrated use within the home range. J. Anim. Ecol. 54, 711–719 (1985).Article 

Google Scholar 
Worton, B. Kernel methods for estimating the utilization distribution in home-range studies. Ecology 70, 164–168 (1989).Article 

Google Scholar 
Powell, R. A. & Mitchell, M. S. What is a home range?. J. Mammal. 93, 948–958 (2012).Article 

Google Scholar 
White, G. C. & Garrott, R. A. Analysis of Wildlife Radio-tracking Data (Academic Press, 1990).
Google Scholar 
Lee, E. J., Rhim, S. J. & Lee, W. S. Seasonal movements and home range sizes of Korean field mouse Apodemus peninsulae in unburned and post-fire pine planted stands within a pine forest. J. Anim. Vet. Adv. 11, 3834–3839 (2012).
Google Scholar 
Thompson, R. L., Chambers, C. L. & McComb, B. C. Home range and habitat of western red-backed voles in the Oregon Cascades. Northwest Sci. 83, 46–56 (2009).Article 

Google Scholar 
Tu, C. L., He, T. B., Lu, X. H., Luo, Y. & Smith, P. Extent to which pH and topographic factors control soil organic carbon level in dry farming cropland soils of the mountainous region of Southwest China. CATENA 163, 204–209 (2018).Article 
CAS 

Google Scholar 
Khandelwal, S., Goyal, R., Kaul, N. & Mathew, A. Assessment of land surface temperature variation due to change in elevation of area surrounding Jaipur, India. Egypt. J. Remote Sens. 21, 87–94 (2018).
Google Scholar 
Hatfield, J. L. & Prueger, J. H. Temperature extremes: Effect on plant growth and development. Weather Clim. Extrem. 10, 4–10 (2015).Article 

Google Scholar 
Fang, J. Y. et al. Precipitation patterns alter growth of temperate vegetation. Geophys. Res. Lett. 32, L21411 (2005).Article 
ADS 

Google Scholar 
Palmer, M. S., Fieberg, J., Swanson, A., Kosmala, M. & Packer, C. A “dynamic” landscape of fear: Prey responses to spatiotemporal variations in predation risk across the lunar cycle. Ecol. Lett. 20, 1364–1373 (2017).Article 
CAS 

Google Scholar 
Lee, J. K., Hwang, H. S., Eom, T. K. & Rhim, S. J. Influence of tree thinning on abundance and survival probability of small rodents in a natural deciduous forest. Turk. J. Zool. 42, 323–329 (2018).
Google Scholar 
Navarro-Castilla, A. & Barja, I. Stressful living in lower-quality habitats? Body mass, feeding behavior and physiological stress levels in wild wood mouse populations. Integr. Zool. 14, 114–126 (2019).Article 

Google Scholar 
Casula, P., Luiselli, L. & Amori, G. Which population density affects home ranges of co-occurring rodents?. Basic Appl. Ecol. 34, 46–54 (2019).Article 

Google Scholar 
D’Elia, G., Fabre, P. H. & Lessa, E. P. Rodent systematics in an age of discovery: Recent advances and prospects. J. Mammal. 100, 852–871 (2019).Article 

Google Scholar 
Lee, J. K., Eom, T. K., Bae, H. K., Lee, D. H. & Rhim, S. J. Responsive strategies of three sympatric small rodents to the altitudinal effects on microhabitats. Anim. Biol. 72, 63–77 (2022).Article 

Google Scholar 
Lee, J. K., Hwang, H. S., Eum, T. K., Bae, H. K. & Rhim, S. J. Cascade effects of slope gradient on ground vegetation and small-rodent populations in a forest ecosystem. Anim. Biol. 70, 203–213 (2020).Article 

Google Scholar 
Orrock, J. L. & Connolly, B. M. Changes in trap temperature as a method to determine timing of capture of small mammals. PLoS ONE 11, e0165710 (2016).Article 

Google Scholar 
Endries, M. J. & Adler, G. H. Spacing patterns of a tropical forest rodent, the spiny rat (Proechimys semispinosus), in Panama. J. Zool. 265, 147–155 (2005).Article 

Google Scholar 
Kawata, M. & Saitoh, T. The effect of introduced males on spatial patterns of initially introduced red-backed voles. Acta Theriol. 33, 585–588 (1988).Article 

Google Scholar 
Desy, E., Batzli, G. & Liu, J. Effects of food and predation on behaviour of prairie voles: A field experiment. Oikos 58, 159–168 (1990).Article 

Google Scholar 
Attuquayefio, D., Gorman, M. & Wolton, R. Home range sizes in the wood mouse Apodemus sylvaticus: Habitat, sex and seasonal differences. J. Zool. 210, 45–53 (1986).Article 

Google Scholar 
Lovari, S., Sforzi, A. & Mori, E. Habitat richness affects home range size in a monogamous large rodent. Behav. Process. 99, 42–46 (2013).Article 

Google Scholar 
Puckey, H., Lewis, M., Hooper, D. & Michell, C. Home range, movement and habitat utilisation of the Carpentarian rock-rat (Zyzomys palatalis) in an isolated habitat patch. Wildlife Res. 31, 327–337 (2004).Article 

Google Scholar 
Jones, E. N. Effects of forage availability on home range and population density of Microtus pennsylvanicus. J. Mammal. 71, 382–389 (1990).Article 

Google Scholar 
Chun, J. H., Ali, A. & Lee, C. B. Topography and forest diversity facets regulate overstory and understory aboveground biomass in a temperate forest of South Korea. Sci. Total. Environ. 744, 140783 (2020).Article 
ADS 
CAS 

Google Scholar 
Koskela, E., Mappes, T. & Ylonen, H. Territorial behaviour and reproductive success of bank vole Clethrionomys glareolus females. J. Anim. Ecol. 66, 341–349 (1997).Article 

Google Scholar 
Vlasata, T. et al. Daily activity patterns in the giant root rat (Tachyoryctes macrocephalus), a fossorial rodent from the Afro-alpine zone of the Bale Mountains, Ethiopia. J. Zool. 302, 157–163 (2017).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2013).
Google Scholar 
Calenge, C. The package “adehabitat” for the R software: A tool for the analysis of space and habitat use by animals. Ecol. Model. 197, 516–519 (2006).Article 

Google Scholar 
Rhim, S. J., Kim, K. J., Son, S. H. & Hwang, H. S. Effect of forest road on stand structure and small mammals in temperate forests. J. Anim. Vet. Adv. 11, 2540–2547 (2012).Article 

Google Scholar 
Carrilho, M., Teixeira, D., Santos-Reis, M. & Rosalino, L. M. Small mammal abundance in Mediterranean Eucalyptus plantations: How shrub cover can really make a difference. For. Ecol. Manag. 391, 256–263 (2017).Article 

Google Scholar 
Emsens, W. J. et al. Effects of food availability on space and refuge use by a beotropical scatterhoarding rodent. Biotropica 45, 88–93 (2013).Article 

Google Scholar 
Malo, A. F. et al. Positive effects of an invasive shrub on aggregation and abundance of a native small rodent. Behav. Ecol. 24, 759–767 (2013).Article 

Google Scholar 
Johnson, M. D. & De Leon, Y. L. Effect of an invasive plant and moonlight on rodent foraging behavior in a coastal dune ecosystem. PLoS ONE 10, e0117903 (2015).Article 

Google Scholar 
Mori, E., Sangiovanni, G. & Corlatti, L. Gimme shelter: The effect of rocks and moonlight on occupancy and activity pattern of an endangered rodent, the garden dormouse Eliomys quercinus. Behav. Process. 170, 103999 (2020).Article 

Google Scholar 
Prugh, L. R. & Golden, C. D. Does moonlight increase predation risk? Meta-analysis reveals divergent responses of nocturnal mammals to lunar cycles. J. Anim. Ecol. 83, 504–514 (2014).Article 

Google Scholar 
Orrock, J. L., Danielson, B. J. & Brinkerhoff, R. J. Rodent foraging is affected by indirect, but not by direct, cues of predation risk. Behav. Ecol. 15, 433–437 (2004).Article 

Google Scholar 
Penteriani, V., Delgado, M. D. M., Campioni, L. & Lourenco, R. Moonlight makes owls more chatty. PLoS ONE 5, e8696 (2010).Article 
ADS 

Google Scholar 
Penteriani, V., Kuparinen, A., del Mar Delgado, M., Lourenço, R. & Campioni, L. Individual status, foraging effort and need for conspicuousness shape behavioural responses of a predator to moon phases. Anim. Behav. 82, 413–420 (2011).Article 

Google Scholar 
Reher, S., Dausmann, K. H., Warnecke, L. & Turner, J. M. Food availability affects habitat use of Eurasian red squirrels (Sciurus vulgaris) in a semi-urban environment. J. Mammal. 97, 1543–1554 (2016).Article 

Google Scholar 
Lee, J. K., Hwang, H. S., Eom, T. K., Lee, D. H. & Rhim, S. J. Slope gradient effect on microhabitat and small rodents in a tree thinned Japanese larch plantation. Pak. J. Zool. 54, 2213–2220 (2022).Article 

Google Scholar 
Heroldova, M., Bryja, J., Janova, E., Suchomel, J. & Homolka, M. Rodent damage to natural and replanted mountain forest regeneration. Sci. World J. 2012, 872536 (2012).Article 

Google Scholar 
Jo, Y. S., Baccus, J. T. & Koprowski, J. L. Mammals of Korea (National Institute of Biological Resources, 2018).
Google Scholar 
Bondrup-Nielsen, S. Investigation of spacing behavior of Clethrionomys gapperi by experimentation. J. Anim. Ecol. 55, 269–279 (1986).Article 

Google Scholar 
Ylonen, H., Kojola, T. & Viitala, J. Changing female spacing behavior and demography in an enclosed breeding population of Clethrionomys glareolus. Holarctic Ecol. 11, 286–292 (1988).
Google Scholar 
Vander Wall, S. B. Seed harvest by scatter-hoarding yellow pine chipmunks (Tamias amoenus). J. Mammal. 100, 531–536 (2019).Article 

Google Scholar 
Lee, E. J. & Rhim, S. J. Seasonal home ranges and activity of three rodent species in a post-fire planted stand. Folia Zool. 65, 101–106 (2016).Article 

Google Scholar 
Bondrup-Nielsen, S. & Ims, R. A. Reproduction and spacing behavior of females in a peak density population of Clethrionomys glareolus. Holarctic Ecol. 9, 109–112 (1986).
Google Scholar 
Bujalska, G. & Grum, L. Social organization of the bank vole (Clethrionomys glareolus, Schreber 1780) and its demographic consequences: A model. Oecologia 80, 70–81 (1989).Article 
ADS 
CAS 

Google Scholar 
Henttonen, H. Importance of demography in understanding disease ecology in small mammals. Therya 13, 33–38 (2022).Article 

Google Scholar 
Rezende, E. L., Cortes, A., Bacigalupe, L. D., Nespolo, R. F. & Bozinovic, F. Ambient temperature limits above-ground activity of the subterranean rodent Spalacopus cyanus. J. Arid Environ. 55, 63–74 (2003).Article 
ADS 

Google Scholar 
Guiden, P. W. & Orrock, J. L. Seasonal shifts in activity timing reduce heat loss of small mammals during winter. Anim. Behav. 164, 181–192 (2020).Article 

Google Scholar  More

Odum, E. P. The strategy of ecosystem development. Science 164, 262–270 (1969).Article 
ADS 
CAS 

Google Scholar 
Gorham, E., Vitousek, P. M. & Reiners, W. A. The regulation of element budgets over the course of terrestrial ecosystem succession. Annu. Rev. Ecol. Syst. 10, 53–84 (1979).Article 
CAS 

Google Scholar 
Corman, J. R. et al. Foundations and frontiers of ecosystem science: Legacy of a classic paper (Odum 1969). Ecosystems 22, 1160–1172. https://doi.org/10.1007/s10021-018-0316-3 (2019).Article 

Google Scholar 
Santín, C. et al. Towards a global assessment of pyrogenic carbon from vegetation fires. Glob. Change Biol. 22, 76–91. https://doi.org/10.1111/gcb.12985 (2016).Article 
ADS 

Google Scholar 
Kominoski, J. S., Gaiser, E. E. & Baer, S. G. Advancing theories of ecosystem development through long-term ecological research. Bioscience 68, 554–562. https://doi.org/10.1093/biosci/biy070 (2018).Article 

Google Scholar 
Balch, J. K., Bradley, B. A., D’Antonio, C. M. & Gómez-Dans, J. Introduced annual grass increases regional fire activity across the arid western USA (1980–2009). Glob. Change Biol. 19, 173–183. https://doi.org/10.1111/gcb.12046 (2013).Article 
ADS 

Google Scholar 
Abatzoglou, J. T. & Kolden, C. A. Climate change in Western US deserts: Potential for increased wildfire and invasive annual grasses. Rangeland Ecol. Manag. 64(5), 471–478 (2011).Article 

Google Scholar 
Shi, H. et al. Historical cover trends in a sagebrush steppe ecosystem from 1985 to 2013: Links with climate, disturbance, and management. Ecosystems 21, 913–929. https://doi.org/10.1007/s10021-017-0191-3 (2018).Article 

Google Scholar 
Seyfried, M. S. & Wilcox, B. P. Scale and the nature of spatial variability: Field examples having implications for hydrologic modeling. Water Resour. Res. 31, 173–184. https://doi.org/10.1029/94WR02025 (1995).Article 
ADS 

Google Scholar 
Gasch, C. K., Huzurbazar, S. V. & Stahl, P. D. Description of vegetation and soil properties in sagebrush steppe following pipeline burial, reclamation, and recovery time. Geoderma 265, 19–26. https://doi.org/10.1016/j.geoderma.2015.11.013 (2016).Article 
ADS 

Google Scholar 
Huber, D. P. et al. Vegetation and precipitation shifts interact to alter organic and inorganic carbon storage in desert soils. Ecosphere 10, e02655. https://doi.org/10.1002/ecs2.2655 (2019).Article 

Google Scholar 
Knight, D. H., Jones, G. P., Reiners, W. A. & Romme, W. H. Mountains and Plains: The Ecology of Wyoming Landscapes (Yale University Press, 2014).
Google Scholar 
Patton, N. R., Lohse, K. A., Seyfried, M. S., Godsey, S. E. & Parsons, S. Topographic controls on soil organic carbon on soil mantled landscapes. Sci. Rep. 9, 6390. https://doi.org/10.1038/s41598-019-42556-5 (2019).Article 
ADS 
CAS 

Google Scholar 
Schwabedissen, S. G., Lohse, K. A., Reed, S. C., Aho, K. A. & Magnuson, T. S. Nitrogenase activity by biological soil crusts in cold sagebrush steppe ecosystems. Biogeochemistry 134, 57–76. https://doi.org/10.1007/s10533-017-0342-9 (2017).Article 
CAS 

Google Scholar 
You, Y. et al. Biological soil crust bacterial communities vary along climatic and shrub cover gradients within a sagebrush steppe ecosystem. Front. Microbiol. 12, 2365. https://doi.org/10.3389/fmicb.2021.569791 (2021).Article 

Google Scholar 
Burke, I. C., Reiners, W. A. & Olson, R. K. Topographic control of vegetation in a mountain big sagebrush steppe. Vegetation 84, 77–86 (1989).Article 

Google Scholar 
Poulos, M. J., Pierce, J. L., Flores, A. N. & Benner, S. G. Hillslope asymmetry maps reveal widespread, multi-scale organization. Geophys. Res. Lett. 39, 6. https://doi.org/10.1029/2012GL051283 (2012).Article 

Google Scholar 
Smith, T. & Bookhagen, B. Climatic and biotic controls on topographic asymmetry at the global scale. J. Geophys. Res.: Earth Surf. 126, e2020JF005692. https://doi.org/10.1029/2020JF005692Received22 (2021).Article 
ADS 

Google Scholar 
Seyfried, M., Link, T., Marks, D. & Murdock, M. Soil temperature variability in complex terrain measured using fiber-optic distributed temperature sensing. Vadose Zone J. 15, 6. https://doi.org/10.2136/vzj2015.09.0128 (2016).Article 

Google Scholar 
Chambers, J. C. et al. Resilience and resistance of sagebrush ecosystems: Implications for state and transition models and management treatments. Rangel. Ecol. Manage. 67, 440–454. https://doi.org/10.2111/REM-D-13-00074.1 (2014).Article 

Google Scholar 
Chambers, J. C. et al. Operationalizing resilience and resistance concepts to address invasive grass-fire cycles. Front. Ecol. Evol. 7, 2369. https://doi.org/10.3389/fevo.2019.00185 (2019).Article 

Google Scholar 
Boehm, A. R. et al. Slope and aspect effects on seedbed microclimate and germination timing of fall-planted seeds. Rangel. Ecol. Manage. 75, 58–67. https://doi.org/10.1016/j.rama.2020.12.003 (2021).Article 

Google Scholar 
Sankey, J. B., Germino, M. J., Sankey, T. T. & Hoover, A. N. Fire effects on the spatial patterning of soil properties in sagebrush steppe, USA: A meta-analysis. Int. J. Wildl. Fire 21, 545–556. https://doi.org/10.1071/WF11092 (2012).Article 

Google Scholar 
Fellows, A., Flerchinger, G., Seyfried, M. S. & Lohse, K. A. Rapid recovery of mesic mountain big sagebrush gross production and respiration following prescribed fire. Ecosystems 21, 1283–1294. https://doi.org/10.1007/s10021-017-0218-9 (2018).Article 

Google Scholar 
Vega, S. P. et al. Interaction of wind and cold-season hydrologic processes on erosion from complex topography following wildfire in sagebrush steppe. Earth Surf. Process. Landforms https://doi.org/10.1002/esp.4778 (2019).Article 

Google Scholar 
Xie, J., Li, Y., Zhai, C., Li, C. & Lan, Z. CO2 absorption by alkaline soils and its implication to the global carbon cycle. Environ. Geol. 56, 953–961 (2009).Article 
ADS 
CAS 

Google Scholar 
Stanbery, C., Pierce, J. L., Benner, S. G. & Lohse, K. On the rocks: Quantifying storage of inorganic soil carbon on gravels and determining pedon-scale variability. CATENA 157, 436–442. https://doi.org/10.1016/j.catena.2017.06.011 (2017).Article 
CAS 

Google Scholar 
Stanbery, C. et al. Controls on the presence and concentration of soil inorganic carbon in a semi-arid watershed. CATENA https://doi.org/10.2139/ssrn.4267018 (2023).Article 

Google Scholar 
Cerling, T. E. & Quade, J. Stable carbon and oxygen isotopes in soil carbonates. Geophys. Monogr. 78, 217–231 (1993).ADS 

Google Scholar 
Tappa, D. J., Kohn, M. J., McNamara, J. P., Benner, S. G. & Flores, A. N. Isotopic composition of precipitation in a topographically steep, seasonally snow-dominated watershed and implications of variations from the global meteoric water line. Hydrol. Process. 30, 4582–4592. https://doi.org/10.1002/hyp.10940 (2016).Article 
ADS 
CAS 

Google Scholar 
Salomons, W., Goudie, A. & Mook, W. G. Isotopic composition of calcrete deposits from Europe, Africa and India. Earth Surf. Process. 3, 43–57. https://doi.org/10.1002/esp.3290030105 (1978).Article 
CAS 

Google Scholar 
Salomons, W. & Mook, W. G. In Handbook of Environmental Isotope Geochemistry (eds P. Fritz & J. Fontes) Ch. 6, 241–269 (Elsevier, 1986).Bodí, M. B. et al. Wildland fire ash: Production, composition and eco-hydro-geomorphic effects. Earth Sci. Rev. 130, 103–127. https://doi.org/10.1016/j.earscirev.2013.12.007 (2014).Article 
ADS 
CAS 

Google Scholar 
Kéraval, B. et al. Soil carbon dioxide emissions controlled by an extracellular oxidative metabolism identifiable by its isotope signature. Biogeosciences 13, 6353–6362. https://doi.org/10.5194/bg-13-6353-2016 (2016).Article 
ADS 
CAS 

Google Scholar 
Goforth, B. R., Graham, R. C., Hubbert, K. R., Zanner, C. W. & Minnich, R. A. Spatial distribution and properties of ash and thermally altered soils after high-severity forest fire, southern California. Int. J. Wildland Fire 14, 343–354 (2005).Article 

Google Scholar 
Glossner, K. L. et al. Long-term suspended sediment and particulate organic carbon yields from the Reynolds Creek Experimental Watershed and Critical Zone Observatory. Hydrol. Process. 36, e14484. https://doi.org/10.1002/hyp.14484 (2022).Article 
CAS 

Google Scholar 
Seyfried, M. S. et al. Reynolds creek experimental watershed and critical zone observatory. Vadoze Zone J. 17, 180129. https://doi.org/10.2136/vzj2018.07.0129 (2018).Article 
CAS 

Google Scholar 
McIntyre, D. H. Cenozoic geology of the Reynolds Creek Experimental Watershed, Owyhee County, Idaho (Idaho Bureau of Mines and Geology, 1972).Earth Resources Observation and Science (EROS) Center, U. Image of the week: Burned Area Analysis for the Soda Fire, Idaho, https://eros.usgs.gov/media-gallery/image-of-the-week/burned-area-analysis-the-soda-fire-idaho (2015).Jenny, H. Factors of Soil Formation (McGraw-Hill, 1941).Book 

Google Scholar 
Kormos, P. R. et al. 31 years of hourly spatially distributed air temperature, humidity, and precipitation amount and phase from Reynolds Critical Zone Observatory. Earth Syst. Sci. Data 10, 1197–1205. https://doi.org/10.5194/essd-10-1197-2018 (2018).Article 
ADS 

Google Scholar 
Thomas, G. W. In Methods in Soil Analysis. Part 3. Chemical Methods (ed Sparks, D. L. ) (Soil Science Society of America and American Society of Agronomy, 1996).Brodie, C. R. et al. Evidence for bias in C and N concentrations and δ13C composition of terrestrial and aquatic organic materials due to pre-analysis acid preparation methods. Chem. Geol. 282, 67–83. https://doi.org/10.1016/j.chemgeo.2011.01.007 (2011).Article 
ADS 
CAS 

Google Scholar 
Patton, N. P., Lohse, K. A., Seyfried, M. S., Will, R. & Benner, S. G. Lithology and coarse fraction adjusted bulk density estimates for determining total organic carbon stocks in dryland soils. Geoderma 337, 844–852. https://doi.org/10.1016/j.geoderma.2018.10.036 (2019).Article 
ADS 
CAS 

Google Scholar 
McGuire, L. A., Rasmussen, C., Youberg, A. M., Sanderman, J. & Fenerty, B. Controls on the Spatial distribution of near-surface pyrogenic carbon on hillslopes 1 year following wildfire. J. Geophys. Res.: Earth Surf. 126, e2020JF005996. https://doi.org/10.1029/2020JF005996 (2021).Article 
ADS 

Google Scholar 
Jiménez-González, M. A. et al. Spatial distribution of pyrogenic carbon in Iberian topsoils estimated by chemometric analysis of infrared spectra. Sci. Total Env. 790, 148170. https://doi.org/10.1016/j.scitotenv.2021.148170 (2021).Article 
CAS 

Google Scholar 
Baldock, J. A. et al. Quantifying the allocation of soil organic carbon to biologically significant fractions. Soil Res. 51, 561–576. https://doi.org/10.1071/SR12374 (2013).Article 
CAS 

Google Scholar 
Sanderman, J. et al. Soil organic carbon fractions in the Great Plains of the United States: An application of mid-infrared spectroscopy. Biogeochemistry 156, 97–114. https://doi.org/10.1007/s10533-021-00755-1 (2021).Article 
CAS 

Google Scholar 
Sherrod, L. A., Dunn, G., Peterson, G. A. & Kolberg, R. L. Inorganic carbon analysis by modified pressure-calcimeter method. Soil Sci. Soc. Am. J. 66, 299–305 (2002).Article 
ADS 
CAS 

Google Scholar 
Mikutta, R., Kleber, M., Kaiser, K. & Jahn, R. Review. Soil Sci. Soc. Am. J. 69, 120–135. https://doi.org/10.2136/sssaj2005.0120 (2005).Article 
ADS 
CAS 

Google Scholar 
Risk, D., Nickerson, N., Creelman, C., McArthur, G. & Owens, J. Forced Diffusion soil flux: A new technique for continuous monitoring of soil gas efflux. Agric. For. Meteorol. 151, 1622–1631. https://doi.org/10.1016/j.agrformet.2011.06.020 (2011).Article 
ADS 

Google Scholar 
Fierer, N. & Schimel, J. P. Effects of drying–rewetting frequency on soil carbon and nitrogen transformations. Soil Biol. Biochem. 34, 777–787. https://doi.org/10.1016/S0038-0717(02)00007-X (2002).Article 
CAS 

Google Scholar 
Dane, J. H., Topp, G. C. & Campbell, G. S. In Methods of Soil Analysis: Physical Methods. Vol. 4 (ed SSSA) 721–738 (2002). More

Stokland, J. N., Siitonen, J. & Jonsson, B. G. Biodiversity in Dead Wood (Cambridge Univ. Press, 2012).Turetsky, M. R. et al. Nat. Geosci. 8, 11–14 (2014).Article 

Google Scholar 
Wenger, S. J., Subalusky, A. L. & Freeman, M. C. Food Webs 18, e00106 (2019).Article 

Google Scholar 
Tomatsuri, M. & Kon, K. Hydrobiologia 790, 225–232 (2017).Article 

Google Scholar 
Henry, L. A. & Roberts, J. M. in Marine Animal Forests (eds Rossi, S. et al.) 235–256 (Springer, 2017).Walton, M. E. M. et al. Sci. Total Environ. 820, 153191 (2022).Article 
CAS 

Google Scholar 
Wolfe, K., Kenyon, T. M. & Mumby, P. J. Coral Reefs 40, 1769–1806 (2021).Article 

Google Scholar 
Kim, H. et al. Glob. Change Biol. 28, 6180–6193 (2022).Jackson, R. B. et al. Annu. Rev. Ecol. Evol. Syst. 48, 419–445 (2017).Article 

Google Scholar 
Pan, Y. et al. Science 333, 988–993 (2011).Article 
CAS 

Google Scholar 
Hedges, J. I., Keil, R. G. & Benner, R. Org. Geochem. 27, 195–212 (1997).Article 
CAS 

Google Scholar 
Lønborg, C. et al. Front. Mar. Sci. 7, 466 (2020).Article 

Google Scholar 
Harden, J. W. et al. Glob. Change Biol. 6, 174–184 (2000).Davidson, E. A. & Janssens, I. A. Nature 440, 165–173 (2006).Article 
CAS 

Google Scholar 
Hugelius, G. et al. Proc. Natl Acad. Sci. USA 117, 20438–20446 (2020).Article 
CAS 

Google Scholar 
Hennige, S. J. et al. Front. Mar. Sci. https://doi.org/10.3389/fmars.2020.00668 (2020).Article 

Google Scholar 
Wolfram, U. et al. Sci. Rep. 12, 8052 (2022).Article 
CAS 

Google Scholar 
Roberts, J. M., Wheeler, A. J. & Freiwald, A. Science 312, 543–547 (2006).Article 
CAS 

Google Scholar 
Mortensen, P. B. & Fosså, J. H. Species diversity and spatial distribution of invertebrates on deep-water Lophelia reefs in Norway. In Proc. 10th Int. Coral Reef Symp. 1849–1868 (ICRS, 2006).Maier, S. R. et al. Deep Sea Res. I 175, 103574 (2021).. More

Rice, D. W. Marine mammals of the world: systematics and distribution. In The Society for Marine Mammalogy (ed. Rice, D. W.) 231 (Allen Press, 1998).
Google Scholar 
Best, P. B. External characters of southern minke whales and the existence of a diminutive form. Sci. Rep. Whales Res. Inst. 36, 1–33 (1985).
Google Scholar 
Acevedo, J. et al. Occurrence of dwarf minke whales (Balaenoptera acutorostrata subsp.) around the Antarctic Peninsula. Polar Biol. 34, 313–318 (2011).Article 

Google Scholar 
Risch, D., Norris, T., Curnock, M. & Friedlaender, A. Common and Antarctic minke whales: Conservation status and future research directions. Front. Mar. Sci. 6, 247. https://doi.org/10.3389/fmars.2019.00247 (2019).Article 

Google Scholar 
International Whaling Commission (IWC). Report of the scientific committee. J. Cetacean Res. Manag. 14, 102 (2013).
Google Scholar 
Matsuoka, K. et al. Overview of minke whale sightings surveys conducted on IWC/IDCR and SOWER Antarctic cruises from 1978/79 to 2000/01. J. Cetacean Res. Manag. 5, 173–201 (2003).
Google Scholar 
Glover, K. A. et al. Migration of Antarctic minke whales to the Arctic. PLoS One 5, e15197. https://doi.org/10.1371/journal.pone.0015197 (2010).Article 
ADS 
CAS 

Google Scholar 
Williams, R., Brierley, A., Friedlaender, A. & Scheidat, M. Densitiy of Antarctic minke whales in Weddell Sea from helicopter survey data. Ecology 63, IA14 (2011).
Google Scholar 
Williams, R. et al. Counting whales in a challenging, changing environment. Sci. Rep. 4, 4170. https://doi.org/10.1038/srep04170 (2014).Article 
CAS 

Google Scholar 
Shabangu, F. W., Findlay, K. & Stafford, K. M. Seasonal acoustic occurrence, diel vocalizing patterns and bioduck call-type composition of Antarctic minke whales off the west coast of South Africa and the Maud Rise Antarctica. Mar. Mamm. Sci. 36, 658–675 (2019).Article 

Google Scholar 
Kasamatsu, F., Nishiwaki, S. & Ishikawa, H. Breeding areas and southbound migrations of southern minke whales Balaenoptera acutorostrata. Mar. Ecol. Prog. Ser. 119, 1–10 (1995).Article 
ADS 

Google Scholar 
Tamura, T. & Konishi, K. Food habit and prey consumption of Antarctic minke whale Balaenoptera bonaerensis in the JARPA research area. J. Northwest Atl. Fish. Sci. 42, 13–25 (2009).Article 

Google Scholar 
Perrin, W. F., Mallette, S. D. & Brownell, R. L. Minke whales. In Encyclopedia of Marine Mammals (eds Perrin, W. F. et al.) 608–613 (Academic Press, 2018).Chapter 

Google Scholar 
Taylor, R. J. F. An unusual record of three species of whale being restricted to pools in Antarctic sea-ice. Proc. R. Soc. Lond. 129, 325–331 (1957).
Google Scholar 
Ensor, P. H. Minke whales in the pack ice zone, East Antarctica, during the period of maximum annual ice extent. Rep. Int. Whal. Commn 39, 219–225 (1989).
Google Scholar 
Scheidat, M. et al. Cetacean surveys in the Southern Ocean using icebreaker-supported helicopters. Polar Biol. 34, 1513–1522 (2011).Article 

Google Scholar 
Meirelles, A. C. O. & Furtado-Neto, M. A. A. Stranding of an Antarctic minke whale, Balaenoptera bonaerensis Burmeister, 1867, on the northern coast of South America. Lat. Am. J. Aquat. Mamm. 3, 81–82 (2004).Article 

Google Scholar 
Juri, E., Valdivia, M., Simoes-Lopes, P. C. & Le Bas, A. A note on minke whales (Cetacea: Balaenopteridae) in Uruguay: Strandings review. JCRM 21, 135–140 (2020).Article 

Google Scholar 
Williamson, G. R. Minke whales off Brazil. Sci. Rep. Whales Res. Inst. 27, 37–59 (1975).
Google Scholar 
Pastene, L. A. & Goto, M. Genetic characterization and population genetic structure of the Antarctic minke whale Balaenoptera bonaerensis in the Indo-Pacific region of the Southern Ocean. Fish Sci. 82, 873–886 (2016).Article 
CAS 

Google Scholar 
Balbuena, J. A., Aznar, F. J., Fernández, M. & Raga, J. A. Parasites as indicators of social structure and stock identity of marine mammals. Dev. Mar. Biol. 4, 133–139 (1995).
Google Scholar 
Kuramochi, T., Araki, J., Uchida, Moriyama, N., Takeda, Y., Hayashi, N., Wakao, H., Machida, M. & Nagasawa, K. Summary of parasite and epizoit investigations during JARPN surveys 1994–1999, with reference to stock structure analysis for the western North Pacific minke whales. IWC Scientific Committee Workshop to Review the Japanese Whaling Programme under Special Permit for North Pacific Minke Whales (JARPN) SC/F2K/J19 (2000).Kaliszewska, Z. A. et al. Population histories of right whales (Cetacea: Eubalaena) inferred from mitochondrial sequence diversities and divergences of their whale lice (Amphipoda: Cyamus). Mol. Ecol. 14, 3439–3456 (2005).Article 
CAS 

Google Scholar 
Ólafsdóttir, D. & Shinn, A. P. Epibiotic macrofauna on common minke whales, Balaenoptera acutorostrata Lacépède, 1804 Icelandic waters. Parasit. Vectors 6, 1–10 (2013).Article 

Google Scholar 
Matthews, C. J., Ghazal, M., Lefort, K. J. & Inuarak, E. Epizoic barnacles on Arctic killer whales indicate residency in warm waters. Mar. Mamm. Sci. 36, 1010–1014 (2020).Article 

Google Scholar 
Flach, L., Van Bressem, M. F., Pitombo, F. & Aznar, F. J. Emergence of the epibiotic barnacle Xenobalanus globicipitis in Guiana dolphins after a morbillivirus outbreak in Sepetiba Bay Brazil. Estuar. Coast. Shelf Sci. 263, 107632. https://doi.org/10.1016/j.ecss.2021.107632 (2021).Article 

Google Scholar 
Ten, S., Raga, J. A. & Aznar, F. J. Epibiotic fauna on cetaceans worldwide: A systematic review of records and indicator potential. Front. Mar. Sci. 9, 846558. https://doi.org/10.3389/fmars.2022.846558 (2022).Article 

Google Scholar 
Liouville, J. Cétacés de l’Antarctique. Paris: Deuxième Expédition Antarctique Française (1908–1910) (1913).Ohsumi, S., Masaki, Y. & Kawamura, A. Stock of the Antarctic minke whale. Sci. Rep. Whales Res. Inst. 22, 75–125 (1970).
Google Scholar 
Ohsumi, S. Find of marlin spear from the Antarctic minke whales. Sci. Rep. Whales Res. Inst. 25, 237–239 (1973).
Google Scholar 
Ivashin, M. V. External Parasites on Lesser Rorquals in the Antarctic 125–127 (Naukova Dumka, 1975).
Google Scholar 
Berzin, A. A. & Vlasova, L. P. Fauna of the Cetacea Cyamidae (Amphipoda) of the world ocean. Investig. Cet. 13, 149–164 (1982).
Google Scholar 
Best, P. B. Seasonal abundance, feeding, reproduction, age and growth in minke whales off Durban (with incidental observations from the Antarctic). Rep. Int. Whal. Commn 32, 759–786 (1982).
Google Scholar 
Avdeev, V. V. Parasitic amphipods of the family Cyamidae and the problem of Cetacea origin. Biol. Morja 4, 27–33 (1989).
Google Scholar 
Bushuev, S. G. A study of the population structure of the southern minke whale (Balaenoptera acutorostrata Lacepede) based on morphological and ecological variability. Rep. Int. Whal. Commn 40, 317–324 (1990).
Google Scholar 
Sedlak-Weinstein, E. Preliminary report of parasitic infestation of the minke whale Balaenoptera acutorostrata taken during the 1988/89 Antarctic expedition. Unpublished paper (1990).Dailey, M. D. & Vogelbein, W. Parasite fauna of 3 species of Antarctic whales with reference to their use as potential stock indicators. Fish. Bull. 89, 355–365 (1991).
Google Scholar 
Nemoto, T., Best, P. B., Ishimaru, K. & Takano, H. Diatom films on whales in South African waters. Sci. Rep. Whales Res. Inst. 32, 97–103 (1980).
Google Scholar 
Donovan, G. A review of IWC stock boundaries. Rep. Int. Whal. Commn 13, 39–68 (1991).
Google Scholar 
Lester, R. J. G. & MacKenzie, K. The use and abuse of parasites as stock markers for fish. Fish. Res. 97, 1–2 (2009).Article 

Google Scholar 
Ten, S. et al. Epibiotic barnacles of sea turtles as indicators of habitat use and fishery interactions: an analysis of juvenile loggerhead sea turtles, Caretta caretta, in the western Mediterranean. Ecol. Indic. 107, 105672. https://doi.org/10.1016/j.ecolind.2019.105672 (2019).Article 

Google Scholar 
Calman, W. T. A whale-barnacle of the genus Xenobalanus from Antarctic Seas. Ann. Mag. Nat. Hist. 6, 165–166 (1920).Article 

Google Scholar 
Kato, H., Hiroyama, H., Fujise, Y. & Ono, K. Preliminary report of the 1987/88 Japanese feasibility study of the special permit proposal for Southern Hemisphere Minke Whales. Rep. int. Whal. Commn 39, 235–248 (1989).
Google Scholar 
International Whaling Commission (IWC). Report of the Intersessional Workshop to review data and results from special permit research on minke whales in the Antarctic, Tokyo, 7–8 December 2006. J. Cetacean Res. Manag. 10, 411–445 (2008).
Google Scholar 
Bush, A. O., Lafferty, K. D., Lotz, J. M. & Shostak, A. W. Parasitology meets ecology on its own terms: Margolis et al. revisited. J. Parasitol. 83, 575–583 (1997).Article 
CAS 

Google Scholar 
Kim, H., Chan, B., Kang, C., Kim, H. & Kim, W. How do whale barnacles live on their hosts? Functional morphology and mating-group sizes of Coronula diadema (Linnaeus, 1767) and Conchoderma auritum (Linnaeus, 1767) (Cirripedia: Thoracicalcarea). J. Crustac. Biol. 40, 808–824 (2020).Article 

Google Scholar 
Reiczigel, J. Confidence intervals for the binomial parameter: Some new considerations. Stat. Med. 22, 611–621 (2003).Article 

Google Scholar 
Kato, H. Migration strategy of southern minke whales to maintain high reproductive rate. Dev. Mar. Biol. 4, 465–480 (1995).
Google Scholar 
Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. A. & Smith, G. M. Mixed effects models and extensions in ecology with R. In Statistics for Biology and Health (ed. Gail, M.) (Springer, 2009).MATH 

Google Scholar 
Fransen, C. H. J. M. & Smeenk, C. Whale-lice (Amphipoda: Cyamidae) recorded from The Netherlands. Zool. Meded. 65, 393–405 (1991).
Google Scholar 
Barton, N. A., Farewell, T. S. & Hallett, S. H. Using generalized additive models to investigate the environmental effects on pipe failure in clean water networks. NPJ Clean Water 3, 31. https://doi.org/10.1038/s41545-020-0077-3 (2020).Article 

Google Scholar 
Schindelin, J. et al. Fiji: An open-source platform for biological-image analysis. Nat. Methods 9, 676–682. https://doi.org/10.1038/nmeth.2019 (2012).Article 
CAS 

Google Scholar 
Kane, E. A., Olson, P. A., Gerrodette, T. & Fiedler, P. Prevalence of the commensal barnacle Xenobalanus globicipitis on cetacean species in the eastern tropical Pacific Ocean, and a review of global occurrence. Fish. Bull. 106, 395–404 (2008).
Google Scholar 
Aznar, F. J., Balbuena, J. A. & Raga, J. A. Are epizoites biological indicators of a western Mediterranean striped dolphin die-off?. Dis. Aquat. Organ. 18, 159–163 (1994).Article 

Google Scholar 
Carrillo, J. M., Overstreet, R. M., Raga, J. A. & Aznar, F. J. Living on the edge: Settlement patterns by the symbiotic barnacle Xenobalanus globicipitis on small cetaceans. PLoS One 10, e0127367. https://doi.org/10.1371/journal.pone.0127367 (2015).Article 
CAS 

Google Scholar 
Moreno-Colom, P., Ten, S., Raga, J. A. & Aznar, F. J. Spatial distribution and aggregation of Xenobalanus globicipitis on the flukes of striped dolphins, Stenella coeruleoalba: An indicator of host hydrodynamics?. Mar. Mamm. Sci. 36, 897–914 (2020).Article 

Google Scholar 
Aznar, F. J. et al. Changes in epizoic crustacean infestations during cetacean die-offs: The mass mortality of Mediterranean striped dolphins Stenella coeruleoalba revisited. Dis. Aquat. Org. 67, 239–247 (2005).Article 
CAS 

Google Scholar 
Wood, S. N. & Augustin, N. H. GAMs with integrated model selection using penalized regression splines and applications to environmental modelling. Ecol. Modell. 157, 157–177 (2002).Article 

Google Scholar 
Wood, S. N. Generalized Additive Models: An Introduction with R (Chapman and Hall/CRC, 2017).Book 
MATH 

Google Scholar 
Bloch, D. et al. Short-term movements of long-finned pilot whales Globicephala melas around the Faroe Islands. Wildl. Biol. 9, 47–58 (2003).Article 

Google Scholar 
Beasley, I. et al. Stomach contents of long-finned pilot whales, Globicephala melas mass-stranded in Tasmania. PLoS One 14, e0206747. https://doi.org/10.1371/journal.pone.0206747 (2019).Article 
CAS 

Google Scholar 
Ohno, M. & Fujino, K. Biological investigation on the whales caught by the Japanese Antarctic whaling fleets, season 1950/51. Sci. Rep. Whales Res. Inst. 7, 125–188 (1952).
Google Scholar 
Clarke, R. The stalked barnacle Conchoderma, ectoparasitic on whales. Norsk Hvalfangst-Tidende 55, 153–168 (1966).
Google Scholar 
Christensen, I. First record of gooseneck barnacles (Conchoderma auritum) on a minke whale (Balaenoptera acutorostrata). ICES C. M. 1985/N:9 (1985).Bertulli, C. G., Cecchetti, A., Van Bressem, M. F. & Van Waerebeek, K. Skin disorders in common minke whales and white-beaked dolphins off Iceland, a photographic assessment. J. Mar. Anim. Ecol. 5, 29–40 (2012).
Google Scholar 
Knowlton, N. Sibling species in the sea. Annu. Rev. Ecol. Evol. Syst. 24, 189–216 (1993).Article 

Google Scholar 
Trontelj, P. & Fišer, C. Perspectives: Cryptic species diversity should not be trivialised. Syst. Biodivers. 7, 1–3 (2009).Article 

Google Scholar 
Norris, R. & Hull, P. The temporal dimension of marine speciation. Evol. Ecol. 26, 393–415 (2011).Article 

Google Scholar 
Rawson, P., Macnamee, R., Frick, M. & Williams, K. Phylogeography of the coronulid barnacle, Chelonibia testudinaria, from loggerhead sea turtles Caretta caretta. Mol. Ecol. 12, 2697–2706 (2003).Article 
CAS 

Google Scholar 
Cabezas, M. P., Cabezas, P., Machordom, A. & Guerra-García, J. M. Hidden diversity and cryptic speciation refute cosmopolitan distribution in Caprella penantis (Crustacea: Amphipoda: Caprellidae). J. Zool. Syst. Evol. 51, 85–99 (2013).Article 

Google Scholar 
Boyd, L. L., Zardus, J. D., Knauer, C. M. & Wood, L. D. Evidence for host selectivity and specialization by epizoic Chelonibia barnacles between hawksbill and green sea turtles. Front. Ecol. Evol. 9, 807237. https://doi.org/10.3389/fevo.2021.807237 (2021).Article 

Google Scholar 
Schell, D., Rowntree, V. & Pfeiffer, C. Stable-isotope and electron-microscopic evidence that cyamids (Crustacea: Amphipoda) feed on whale skin. Can. J. Zool. 78, 721–727 (2000).Article 

Google Scholar 
Iwasa-Arai, T. & Serejo, C. S. Phylogenetic analysis of the family Cyamidae (Crustacea: Amphipoda): A review based on morphological characters. Zool. J. Linn. Soc. 184, 66–94 (2018).Article 

Google Scholar 
Fraija-Fernández, N. et al. Living in a harsh habitat: Epidemiology of the whale louse, Syncyamus aequus (Cyamidae), infecting striped dolphins in the Western Mediterranean. J. Zool. 303, 199–206 (2017).Article 

Google Scholar 
Angot, M. Rapport scientifique sur les expeditions baleinieres autour de Madagascar (saisons 1949 et 1950). Mem. Inst. Sci. Madag. Ser. A 6, 439–486 (1951).
Google Scholar 
Newman, W. A. & Abbott, D. P. Cirripedia: The barnacles. In Intertidal Invertebrates of California (eds Morris, R. H. et al.) 504–535 (Stanford University Press, 1980).
Google Scholar 
Nogata, Y. & Matsumura, K. Larval development and settlement of a whale barnacle. Biol Lett. 2, 92–93 (2006).Article 

Google Scholar 
Hiro, F. The fauna of Akkeshi Bay. II. Cirripedia. J. Fac. Sci. Hokkaido Univ. 4, 213–229 (1935).
Google Scholar 
Rice, D. W. Progress report on biological studies of the larger Cetacea in the waters off California. Norsk Hvalfangst-Tid 52, 181–187 (1963).
Google Scholar 
Klinkhart, E. G. The beluga whale in Alaska. State Alsk. Dep. Fish 7, 11 (1966).
Google Scholar 
Nilsson-Cantell, C. A. Cirripedia Thoracica and Acrothoracica. MIOS 5, 1–133 (1978).
Google Scholar 
Scarff, J. E. Occurrence of the barnacles Coronula diadema, C. reginae and Cetopirus complanatus (Cirripedia) on right whales. Sci. Rep. Whales Res. Inst. 37, 129–153 (1986).
Google Scholar 
Kakuwa, Z., Kawakami, T. & Iguchi, K. Biological investigation on the whales caught by the Japanese Antarctic whaling fleets in the 1951–52 season. Sci. Rep. Whales Res. Inst. 8, 147–213 (1953).
Google Scholar 
Nishiwaki, M. Humpback whales in Ryukyuan waters. Sci. Rep. Whales Res. Inst. 14, 49–87 (1959).
Google Scholar 
Best, P. B. The presence of coronuline barnacles on a southern right whale Eubalaena australis. S. Afr. J. Mar. Sci. 11, 585–587 (1991).Article 

Google Scholar 
Mackintosh, N. A. & Wheeler, J. F. G. Southern blue and fin whales. Disc. Rep. 1, 257–540 (1929).
Google Scholar 
Nilsson-Cantell, C. A. Thoracic cirripedes collected in 1925–1927. Disc. Rep. 2, 223–260 (1930).
Google Scholar 
Nishiwaki, M. & Hayashi, K. Biological survey of fin and blue whales taken in the Antarctic season 1947–48 by the Japanese fleet. Sci. Rep. Whales Res. Inst. 3, 132–190 (1950).
Google Scholar 
Mizue, K. & Murata, T. Biological investigation on the whales caught by the Japanese Antarctic whaling fleets season 1949–50. Sci. Rep. Whales Res. Inst. 6, 73–131 (1951).
Google Scholar 
Nishiwaki, M. & Oye, T. Biological investigation on blue whales (Balaenoptera musculus) and Fin Whales (Balaenoptera physalus) caught by the Japanese Antarctic Whaling Fleets. Sci. Rep. Whales Res. Inst. 5, 91–167 (1951).
Google Scholar 
Tomilin, A. G. Cetacea. In Mammals of the U.S.S.R. and Adjacent Countries Vol. 9 (ed. Tomilin, A. G.) 717 (Akademii Nauk SSSR, 1957).
Google Scholar 
Cockrill, W. R. Pathology of the cetacea. A veterinary study on whales. Br. Vet. J. 116, 1–28 (1960).
Google Scholar 
Kawamura, A. Some consideration on the stock unit of sei whales by the aspect of ectoparasitic organisms on the body. Bull. Jpn. Soc. Fish. Oceanogr. 14, 38–43 (1969).
Google Scholar 
Fraija-Fernández, N., Hernández-Hortelano, A., Ahuir-Baraja, A. E., Raga, J. A. & Aznar, F. J. Taxonomic status and epidemiology of the mesoparasitic copepod Pennella balaenoptera in cetaceans from the western Mediterranean. Dis. Aquat. Org. 128, 249–258 (2018).Article 

Google Scholar 
Foster, B. A. & Willan, R. C. Foreign barnacles transported to New Zealand on an oil platform. N. Z. J. Mar. Freshw. Res. 13, 143–149 (1979).Article 

Google Scholar 
González, J. et al. Cirripedia of the Canary islands: Distribution and ecological notes. J. Mar. Biol. Assoc. U.K. 92, 129–141 (2012).Article 

Google Scholar 
Zettler, M. L. An example for transatlantic hitchhiking by macrozoobenthic organisms with a research vessel. Helgol. Mar. Res. 75, 4. https://doi.org/10.1186/s10152-021-00549-w (2021).Article 

Google Scholar 
Matthews, L. H. The humpback whale Megaptera novaeangliae. Disc. Rep. 17, 7–92 (1937).
Google Scholar 
Scheffer, V. B. Organisms collected from whales in the Aleutian Islands. Murrelet 20, 67–69 (1939).Article 

Google Scholar 
Symons, H. W. & Weston, R. D. Studies on the humpback whale (Megaptera nodosa) in the Bellinghausen Sea. Norsk Hvalfangsttid 47, 53–81 (1958).
Google Scholar 
Van Waerebeek, K., Reyes, J. C. & Alfaro, J. Helminth parasites and phoronts of dusky dolphins Lagenorhynchus obscurus (Gray, 1828) from Peru. Aquat. Mamm. 19, 159–169 (1993).
Google Scholar 
Fertl, D. Barnacles. In Encyclopedia of Marine Mammals (eds Perrin, W. F. et al.) 75–78 (Academic Press, 2002).
Google Scholar 
Cornwall, I. E. The barnacles of british Columbia. Br. Col. Prov. Mus. Dept. 7, 5–69 (1955).
Google Scholar 
Abaunza, P., Arroyo, N. L. & Preciado, I. A contribution to the knowledge on the morphometry and the anatomical characters of Pennella balaenopterae (Copepoda, Ciphonostomatoida, Pennellidae), with special reference to the buccal complex. Crustaceana 74, 193–210 (2001).Article 

Google Scholar 
Marcer, F. et al. Parasitological and pathological findings in fin whales Balaenoptera physalus stranded along Italian coastlines. Dis. Aquat. Org. 133, 25–37 (2019).Article 
CAS 

Google Scholar 
Turner, W. On Pennella balænopteræ: A crustacean, parasitic on a finner whale, Balaenoptera musculus. Earth. Environ. Sci. Trans. R. Soc. Edinb. 41, 409–434 (1905).Article 

Google Scholar 
Walker, W. A. & Hanson, M. B. Biological observations on Stejneger’s beaked whale, Mesoplodon stejnegeri, from strandings on Adak Alaska. Mar. Mamm. Sci. 15, 1314–1329 (1999).Article 

Google Scholar 
Delaney, M. A., Ford, J. K. B., Tang, K. & Gaydos, J. K. Mesoparasitic copepod (Pennella balaenopterae) infestation of a stranded offshore orca (Orcinus orca) in Southeast Alaska: Review of significance as a health indicator in cetaceans. In IAAAM 21–26 (2016).Suyama, S., Kakehi, S., Yanagimoto, T. & Chow, S. Infection of the pacific saury Cololabis saira (Brevoort, 1856) (Teleostei: Beloniformes: Scomberesocidae) by Pennella sp. (Copepoda: Siphonostomatoida: Pennellidae) south of the Subarctic Front. J. Crust. Biol. 40, 384–389 (2020).Article 

Google Scholar 
Rowntree, V. J. Feeding, distribution and reproductive behavior of cyamids (Crustacea: Amphipoda) living on humpback and right whales. Can. J. Zool. 74, 103–109 (1996).Article 

Google Scholar 
Leung, Y. M. Life cycle of Cyamus scammoni (Amphipoda: Cyamidae), ectoparasite of gray whale, with a remark on the associated species. Sci. Rep. Whales Res. Inst. 28, 153–160 (1976).
Google Scholar 
MacIntyre, R. J. Rapid growth in stalked barnacles. Nature 212, 637–638 (1966).Article 
ADS 

Google Scholar 
Rasmussen, T. Notes on the biology of the shipfouling gooseneck barnacle Conchoderma auritum Linnaeus, 1776 (Cirripedia; Lepadomorpha). Biol. Mar. 2, 37–44 (1980).
Google Scholar 
Dalley, R. & Crisp, D. J. Conchoderma: A fouling hazard to ships underway. Mar. Biol. Lett. 2, 141–152 (1981).
Google Scholar 
Dalley, R. The larval stages of the oceanic, pedunculate barnacle Conchoderma auritum (L) (Cirripedia, Thoracica). Crustaceana 46, 39–54 (1984).Article 

Google Scholar 
Foskolos, I., Provata, M. T. & Frantzis, A. First record of Conchoderma auritum (Cirripedia: Lepadidae) on Ziphius cavirostris (Cetacea: Ziphiidae) in Greece. Ann. Ser. Hist. 27, 29–34 (2017).
Google Scholar 
Lee, J. F., Friedlaender, A. S., Oliver, M. J. & DeLiberty, T. L. Behavior of satellite-tracked Antarctic minke whales (Balaenoptera bonaerensis) in relation to environmental factors around the western Antarctic Peninsula. Anim. Biotelem. 5, 23. https://doi.org/10.1186/s40317-017-0138-7 (2017).Article 

Google Scholar 
Darwin, C. A Monograph on the Subclass Cirripedia Vol. 1 (The Ray Society, 1851).
Google Scholar 
Tsikhon-Lukanina, V. A., Soldatova, I. N., Kuznetsova, I. A. & Il’in, I. I. Macrofouling community in the Strait of Tunisia (Sicily). Oceanology 16, 519–522 (1977).
Google Scholar 
Nilsson-Cantell, C. A. Cirripedien von der Stewart Insel und von Südgeorgien. Senckenbergiana 12, 210–213 (1930).
Google Scholar 
Slijper, E. J. Whales (Hutchinson, 1962).
Google Scholar 
Kaufman, G. D. & Forestell, P. H. Hawaii’s humpback whales, a complete whalewatching guide (Pacific Whale Foundation Press, 1986).
Google Scholar 
Dawbin, W. H. Baleen whales. In Whales, Dolphins and Porpoises (eds Harrison, R. & Bryden, M.) 44–65 (Facts on File, 1988).
Google Scholar 
Félix, F., Bearson, B. & Falconí, J. Epizoic barnacles removed from the skin of a humpback whale after a period of intense surface activity. Mar. Mamm. Sci. 22, 979–984 (2006).Article 

Google Scholar 
Towers, J. R. et al. Seasonal movements and ecological markers as evidence for migration of common minke whales photo-identified in the eastern North Pacific. J. Cetacean Res. Manag. 13, 221–229 (2013).
Google Scholar 
Iwasa-Arai, T. et al. The host-specific whale louse (Cyamus boopis) as a potential tool for interpreting humpback whale (Megaptera novaeangliae) migratory routes. J. Exp. Mar. Biol. Ecol. 505, 45–51 (2018).Article 

Google Scholar 
Lehnert, K. et al. Whale lice (Isocyamus deltobranchium & Isocyamus delphinii; Cyamidae) prevalence in odontocetes off the German and Dutch coasts – Morphological and molecular characterization and health implications. Int. J. Parasitol. 15, 22–30 (2021).
Google Scholar 
Dreyer, N. et al. How whale and dolphin barnacles attach to their hosts and the paradox of remarkably versatile attachment structures in cypris larvae. Org. Divers. Evol. 20, 233–249 (2020).Article 

Google Scholar 
Visser, I. N., Cooper, T. E. & Grimm, H. Duration of pseudo-stalked barnacles (Xenobalanus globicipitis) on a New Zealand Pelagic ecotype orca (Orcinus orca), with comments on cookie cutter shark bite marks (Isistius sp.); can they be used as biological tags?. Biol. Divers. 11, 1067–1086 (2020).
Google Scholar 
Van Waerebeek, K. & Reyes, J. C. A note on incidental fishery mortality of southern minke whales off western South America. Rep. Int. Whal. Commn 15, 521–523 (1994).
Google Scholar 
Félix, F. & Haase, B. A note on the northernmost record of the Antarctic minke whale (Balaenoptera bonaerensis) in the Eastern Pacific. J. Cetacean Res. Manag. 13, 191–194 (2013).
Google Scholar 
Esposito, C., Bichet, O. & Petit, M. First sightings of Antarctic minke whale (Balaenoptera bonaerensis) mother–calf pairs in French Polynesia. Aquat. Mamm. 47, 175–180 (2021).Article 

Google Scholar 
Karaa, S., Insacco, G., Bradai, M. N. & Scaravelli, D. Records of Xenobalanus globicipitis on Balaenoptera physalus and Stenella coeruleoalba in Tunisian and Sicilian waters. Nat. Rerum 1, 55–59 (2011).
Google Scholar 
Oliveira, J. B., Morales, J. A., González-Barrientos, R. C., Hernández-Gamboa, J. & Hernández-Mora, G. Parasites of cetaceans stranded on the Pacific Coast of Costa Rica. Vet. Parasitol. 182, 319–328. https://doi.org/10.1016/j.vetpar.2011.05.014 (2011).Article 
CAS 

Google Scholar 
Dı́az-Gamboa, R. E. Varamiento de orcas pigmeas (Feresa attenuata Gray 1874) en Yucatán: Reporte de caso. Bioagrociencias 8, 36–43 (2015).
Google Scholar 
IJsseldijk, L. L. et al. Beached bachelors: An extensive study on the largest recorded sperm whale Physeter macrocephalus mortality event in the north sea. PloS One 13, e0201221. https://doi.org/10.1371/journal.pone.0201221 (2018).Article 
CAS 

Google Scholar 
Guerrero-Ruiz, M. & Urbán, J. R. First report of remoras on two killer whales (Orcinus orca) in the Gulf of California Mexico. Aquat. Mamm. 26, 148–150 (2000).
Google Scholar 
Kautek, G., Van Bressem, M. F. & Ritter, F. External body conditions in cetaceans from La Gomera, Canary Islands Spain. J. Marine Anim. Ecol. 11, 4–17 (2008).
Google Scholar 
Bearzi, M. & Patonai, K. Occurrence of the barnacle (Xenobalanus globicipitis) on coastal and offshore common bottlenose dolphins (Tursiops truncatus) in Santa Monica Bay and adjacent areas California. Bull. S. Calif. Acad. Sci. 109, 37–44. https://doi.org/10.3160/0038-3872-109.2.37 (2010).Article 

Google Scholar 
Foote, A. D. et al. Genetic differentiation among North Atlantic killer whale populations. Mol. Ecol. 20, 629–641. https://doi.org/10.1111/j.1365-294X.2010.04957.x (2011).Article 

Google Scholar 
Toth, J. L., Hohn, A. A., Able, K. W. & Gorgone, A. M. Defining bottlenose dolphin (Tursiops truncatus) stocks based on environmental, physical and behavioral characteristics. Mar. Mamm. Sci. 28, 461–478. https://doi.org/10.1111/j.1748-7692.2011.00497.x (2012).Article 

Google Scholar 
Urian, K. W., Kaufmann, R., Waples, D. M. & Read, A. J. The prevalence of ectoparasitic barnacles discriminates stocks of Atlantic common bottlenose dolphins (Tursiops truncatus) at risk of entanglement in coastal gill net fisheries. Mar. Mamm. Sci. 35, 290–299. https://doi.org/10.1111/mms.12522 (2019).Article 

Google Scholar 
Siciliano, S. et al. Epizoic barnacle (Xenobalanus globicipitis) infestations in several cetacean species in South-Eastern Brazil. Mar. Biol. Res. 16, 1–13. https://doi.org/10.1080/17451000.2020.1783450 (2020).Article 

Google Scholar 
Whitehead, T. O., Rollinson, D. P. & Reisinger, R. R. Pseudostalked barnacles Xenobalanus globicipitis attached to killer whales Orcinus orca in South African waters. Mar. Biodivers. Rec. 45, 873–876. https://doi.org/10.1007/s12526-014-0296-2 (2014).Article 

Google Scholar 
Methion, S. & Dı́az López, B. First record of atypical pigmentation pattern in fin whale Balaenoptera physalus in the Atlantic ocean. Dis. Aquat. Org. 135, 121–125. https://doi.org/10.3354/dao03385 (2019).Article 

Google Scholar 
Herr, H., Burkhardt-Holm, P., Heyer, K., Siebert, U. & Selling, J. Injuries, malformations and epidermal conditions in cetaceans of the strait of Gibraltar. Aquat. Mamm. 46, 215–235. https://doi.org/10.1578/AM.46.2.2020.215 (2020).Article 

Google Scholar 
Herr, H. et al. Return of large fin whale feeding aggregations to historical whaling grounds in the southern ocean. Sci. Rep. 12, 9458. https://doi.org/10.1038/s41598-022-13798-7 (2022).Article 
ADS 
CAS 

Google Scholar 
Gruvel, J. A. Cirrhipèdes Provenant Des Campagnes Scientifiques De S.A.S. Le Prince De Monaco, (1885– 1913). In Résultas Des Campagnes Scientifiques Accomplies Sur Son Yacht Par Albert Ler (Monaco: Prince Souverain de Monaco) 1-88 (1920).Annandale, N. The rate of growth in Conchoderma and Lepas. Rec. Indian Mus. 3, 295 (1909).
Google Scholar 
Il’in, I. I., Kuznetsova, L. A. & Starostin, I. V. Oceanic fouling in the equatorial Atlantic. Oceanology 18, 597–599 (1978).
Google Scholar 
Eckert, K. L. & Eckert, S. A. Growth rate and reproductive condition of the barnacle Conchoderma virgatum on gravid leatherback sea turtles in Caribbean waters. J. Crust. Biol. 7, 682–690. https://doi.org/10.2307/1548651 (1987).Article 

Google Scholar 
Arroyo, N. L., Abaunza, P. & Preciado, I. The first naupliar stage of Pennella balaenopterae Koren and Danielssen 1877 (Copepoda: Siphonostomatoida, Pennellidae). Sarsia 87, 333–337. https://doi.org/10.1080/0036482021000155785 (2002).Article 

Google Scholar  More