Philippa Kaur

All Alteromonas strains support long-term survival of Prochlorococcus under N starvationPrevious research showed that Prochlorococcus, and to some extent Synechococcus depend on co-occurring heterotrophic bacteria to survive various types of stress, including nitrogen starvation [33, 34, 42, 43]. At the first encounter between previously axenic Prochlorococcus and Alteromonas (E1), all co-cultures and axenic controls grew exponentially (Fig. 1B, C). However, all axenic cultures showed a rapid and mostly monotonic decrease in fluorescence starting shortly after the cultures stopped growing, reaching levels below the limit of detection after ~20–30 days. None of the axenic Prochlorococcus cultures were able to re-grow when transferred into fresh media after 60 days (Fig. 1C). In contrast, the decline of co-cultures rapidly slowed, and in some cases was interrupted by an extended “plateau” or second growth stage (Fig. 1B). Across multiple experiments, 92% of the co-cultures contained living Prochlorococcus cells for at least 140 days, meaning that they could be revived by transfer into fresh media. Thus, the ability of Alteromonas to support long-term N starvation in Prochlorococcus was conserved in all analyzed strains.Fig. 1: Experimental designs and overview of the dynamics of Prochlorococcus-Alteromonas co-cultures from first encounter and over multiple transfers.A Schematic illustration of the experimental design. One ml from Experiment E1 was transferred into 20 ml fresh media after 100 days, starting experiment E2. Experiment E2 was similarly transferred into fresh media after 140 days, starting experiment E3. Additional experiments replicating these transfers are described in Supplementary Fig. S1. B Overview of the growth curves of the 25 Prochlorococcus-Alteromonas co-cultures over three transfers spanning ~1.2 years (E1, E2 and E3). Results show mean + standard error from biological triplicates, colored by Prochlorococcus strain as in panel D. C Axenic Prochlorococcus grew exponentially in E1 but failed to grow when transferred into fresh media after 60, 100, or 140 days. Axenic Alteromonas cultures were counted after 60 and 100 days, as their growth cannot be monitored sensitively and non-invasively using fluorescence (optical density is low at these cell numbers). D High reproducibility and strain-specific dynamics of the initial contact between Prochlorococcus and Alteromonas strains (E1). Three biological replicates for each mono-culture and co-culture are shown. Note that the Y axis is linear in panels B, C and logarithmic in panel D. Au: arbitrary units.Full size imageIt has previously been shown that Prochlorococcus MIT9313 is initially inhibited by co-culture with Alteromonas HOT1A3, while Prochlorococcus MED4 is not [12, 32]. This “delayed growth” phenotype was observed here too, was specific to MIT9313 (not observed in other Prochlorococcus strains) and occurred with all Alteromonas strains tested (Fig. 1D). MIT9313 belongs to the low-light adapted clade IV (LLIV), which are relatively distant from other Prochlorococcus strains and differ from them in multiple physiological aspects including the structure of their cell wall [44], the use of different (and nitrogen-containing) compatible solutes [45], and the production of multiple peptide secondary metabolites (lanthipeptides, [46, 47]). LLIV cells also have larger genomes, and are predicted to take up a higher diversity of organic compounds such as sugars and amino acids [48]. It is intriguing that specifically this strain, which has higher predicted metabolic and regulatory flexibilities [49], is the only one initially inhibited in co-culture with Alteromonas.Differences in co-culture phenotype are related to Prochlorococcus and not Alteromonas strains and occur primarily during the decline stageWhile co-culture with all Alteromonas strains had a major effect on Prochlorococcus viability after long-term starvation, there was no significant effect of co-culture on traditional metrics of growth such as maximal growth rate, maximal fluorescence, and lag phase (with the exception of the previously described inhibition of MIT9313; Fig. 2A–C). However, a visual inspection of the growth curves suggested subtle yet consistent differences in the shape of the growth curve, and especially the decline phase, between the different Prochlorococcus strains in the co-cultures (Fig. 1D). To test this, we used the growth curves as input for a principal component analysis (PCA), revealing that the growth curves from each Prochlorococcus strain clustered together, regardless of which Alteromonas strain they were co-cultured with (Fig. 2D). The growth curves of all high-light adapted strains (MED4, MIT9312, and MIT0604) were relatively similar, the low-light I strain NATL2A was somewhat distinct, and the low-light IV strain MIT9313 was a clear outlier (Fig. 2D), consistent with this strain being the only one initially inhibited in all co-cultures. Random forest classification supported the observation that the growth curve shapes were affected more by the Prochlorococcus rather than Alteromonas strains, and also confirmed the visual observation that most of the features differentiating between Prochlorococcus strains occurred during culture decline (random forest is a supervised machine learning algorithm explained in more detail in Supplementary Text S2; see also Supplementary Fig. S2). Thus, co-culture with Alteromonas affects the decline stage of Prochlorococcus in co-culture in a way that differs between Prochlorococcus but not Alteromonas strains.Fig. 2: Growth analysis and principal component analysis (PCA) of the growth curves from all co-cultures during 140 days of E1.A Growth rate, B Maximum fluorescence, and C duration of lag phase during experiment E1. Box-plots represent mean and 75th percentile of co-cultures, circles represent measurements of individual cultures of the axenic controls. The only significant difference between axenic and co-cultures is in the length of the lag phase for MIT9313 (Bonferroni corrected ANOVA, p  More

Chromatographic separation and mass spectrometric optimizationTo obtain the best monitoring conditions for each compound, a 0.5 mg/L mixed standard solution of 11 pesticides was mixed with the mobile phase through a syringe pump and then injected into the mass spectrometer for tuning. The precursor ion of the compound to be tested was determined by the primary mass spectrometry scan under ESI+ and ESI- modes, and then the product ion was scanned by the secondary mass spectrometry. Two groups of ion pairs with the best sensitivity were selected for detection; one group was used for quantification, and another, for qualitative analysis. The optimization results showed high sensitivity of all the 11 pesticides under the ESI+ mode. Among them, abamectin (B1a), β-cypermethrin, deltamethrin, fenpropathrin, and bifenthrin were [M + NH4]+, and other compounds were [M + H]+. MS parameters of 11 pesticides are mentioned in Table S2.Formic acid and ammonium acetate are commonly used reagents to enhance the ionization of target compounds [M + H]+ and [M + NH4]+ under the ESI+ mode, and they can effectively improve the peak pattern, making the peak sharper and more symmetrical; therefore, they need to be added during gradient elution38. To improve work efficiency, it is necessary to separate and complete the monitoring of 11 pesticides in the shortest possible time; therefore, we selected two different types of chromatographic columns (ACQUITY UPLC HSS C18 and ACQUITY UPLC HSS T3) and three different mobile phases (Ι: 0.1% formic acid aqueous solution—ACN, II: 0.05% formic acid aqueous solution—ACN, and III: 0.1% formic acid/5 mmol/L ammonium acetate aqueous solution—ACN) for optimization experiments. We observed that when using the HSS T3 chromatographic column, β-cypermethrin, deltamethrin, fenpropathrin, and bifenthrin did not show a good retention effect under the three mobile phase systems, and there was substantial tailing of the chromatographic peak. The shape of the chromatographic peak and sensitivity of the target compound were used as evaluation indicators. Compared with Ι and II, mobile phase III produced better sensitivity for all target compounds (Fig. 1), with sharper and more symmetrical peaks of β-cypermethrin, deltamethrin, fenpropathrin, and bifenthrin. This may be because the addition of 5 mmol/L ammonium acetate improved the retention performance of the HSS C18 chromatography columns without affecting the ionization efficiency of all target compounds. In summary, we selected the HSS C18 column for chromatographic separation and used 0.1% formic acid/5 mmol/L ammonium acetate aqueous solution—ACN as the mobile phase to further optimize the gradient elution procedure and effectively separate and detect all the target compounds within 8 min.Figure 1When using HSS C18, the peak areas of 11 pesticides in three different mobile phases.Full size imageOptimization of purification materialsThe flesh of apricot contains sugar, protein, calcium, phosphorus, carotene, thiamine, riboflavin, niacin, and vitamin C. Due to these diverse impurities, the analysis of the sample matrix becomes highly complex. Therefore, these impurities need to be removed from the matrix samples before analysis. Currently, PSA, C18, and MWCNTs are widely used to adsorb to the fruit substrate39. PSA has a strong adsorption capacity for metal ions, fatty acids, sugars, and fat-soluble pigments, C18 has a strong adsorption capacity for non-polar impurities (such as fat, sterol, and volatile oil), while MWCNTs have a strong adsorption capacity for pigments, which can effectively remove chlorophyll, lutein, and carotene. However, C18 and MWCNTs can also simultaneously adsorb pesticides, resulting in poor recovery. Nano-ZrO2 has a large specific surface area and good adsorption stability and has recently been used to purify substrates. It can selectively remove fats and pigments from samples compared to conventional C18 fillers.In the current study, different purification materials were combined for the analysis of 11 pesticide residues and to propose the best purification strategy in the pretreatment of apricot samples. As displayed in Fig. 2, the average recovery of 11 pesticides in the apricot was higher using the C18/nano-ZrO2/MWCNTs than other combinations. Nano-ZrO2 showed better adsorption than PSA in purifying fatty acids, organic acids, polar pigments, and sugars in apricot, owing to its larger specific surface area, better adsorption capacity, and stability. To conclude, the combination of 10 mg C18, 30 mg nano-ZrO2, and 5 mg MWCNTs demonstrated the best recovery for 11 pesticides, with recovery in the range of 72% to 114%, at a pesticide spiking level of 0.01 mg/kg. In summary, we finally determined that among the tested combinations, C18/nano-ZrO2/MWCNTs (10 mg/ 30 mg/5 mg) is the best purification combination for the pre-treatment of apricot samples.Figure 2The recoveries of 11 pesticides in apricot matrix under different scavenger combinations (2–1 C18/nano-ZrO2/MWCNTs, 2–2 PSA/C18/MWCNTs, 2–3 nano-ZrO2/PSA/MWCNTs; 0.01 mg/kg, n = 3).Full size imageLinearity, matrix effects, limit of detection and limit of quantificationThe standard curve obtained from the standard working solutions of 11 pesticides and the calibration curve from blank apricot matrix spiked with 11 pesticides showed good linearity (0.001, 0.005, 0.01, 0.05, 0.1, and 0.5 mg/L), with R2 ≥ 0.9959 for all tested samples (Table 1).Table 1 The standard curves, R2 and MEs of 11 pesticides in apricot.Full size tableTo evaluate MEs, the slopes of matching 11 pesticide standards with solvent and apricot matrix were calculated at the same concentration. According to the derived slope of the matrix-matched calibration curve, MEs of 11 pesticides in apricot were between 89 and 113% (Table 1), well within the range of 80% to 120%, indicating that the MEs could be ignored. It also suggests that the current pre-treatment method has a good purification effect and eliminates the matrix effect very well, laying a robust foundation for the subsequent step of quantitative analysis of samples. We next used the standard solution curve to quantify the 11 pesticide residues in apricot.The LOD refers to the minimum concentration or minimum amount of a component to be tested that can be detected from a test sample under a given confidence level by an analytical method. Its physical meaning is the amount of the measured component when the signal is 3 times the standard deviation (S = 3σ) of the reagent blank signal (background signal). Sometimes it also refers to the amount of the measured component corresponding to when the signal is three times the background signal generated by the reagent blank (S = 3 N). The LOQ refers to the minimum amount of the analyte in the sample that can be quantitatively determined, and the determination result should have a certain accuracy40. The LOQ reflects whether the analytical method has the sensitive quantitative detection ability. The LOQ is the lowest validated level with sufficient recovery and precision, which was estimated to be 0.001 mg/L, while the LOD is the lowest calibration level, which was 2 µg/kg, according to SANTE/12,682/2020.Accuracy and precisionIn the matrix, 11 pesticides were spiked at four levels (0.002, 0.02, 0.1, and 1 mg/kg), and for each spiked sample, there were six replicates. The recoveries of 11 pesticides in apricot at all levels ranged between 72 and 119%. The inter- and intra-level relative standard deviations (RSDs, %) of 11 pesticides in apricot were  More

Gibney, E. R. & Nolan, C. M. Epigenetics and gene expression. Heredity 105, 4–13 (2010).CAS 
PubMed 

Google Scholar 
Vogt, G. Facilitation of environmental adaptation and evolution by epigenetic phenotype variation: Insights from clonal, invasive, polyploid, and domesticated animals. Environ. Epigenet. 3, 1–17 (2017).
Google Scholar 
Beal, A., Rodriguez-Casariego, J., Rivera-Casas, C., Suarez-Ulloa, V. & Eirin-Lopez, J. M. Environmental Epigenomics and Its Applications in Marine Organisms 325–359 (Springer, 2018). https://doi.org/10.1007/13836_2018_28.Book 

Google Scholar 
Hofmann, G. E. Ecological epigenetics in marine metazoans. Front. Mar. Sci. 4, 1–7 (2017).CAS 

Google Scholar 
Richards, C. L. et al. Ecological plant epigenetics: Evidence from model and non-model species, and the way forward. Ecol. Lett. 20, 1576–1590 (2017).PubMed 

Google Scholar 
Ryu, T., Veilleux, H. D., Donelson, J. M., Munday, P. L. & Ravasi, T. The epigenetic landscape of transgenerational acclimation to ocean warming. Nat. Clim. Chang. 8, 504–509 (2018).ADS 

Google Scholar 
Liew, Y. J. et al. Epigenome-associated phenotypic acclimatization to ocean acidification in a reef-building coral. Sci. Adv. 4, 6 (2018).
Google Scholar 
Anastasiadi, D., Díaz, N. & Piferrer, F. Small ocean temperature increases elicit stage-dependent changes in DNA methylation and gene expression in a fish, the European sea bass. Sci. Rep. 7, 1–12 (2017).CAS 

Google Scholar 
Strader, M. E., Wong, J. M., Kozal, L. C., Leach, T. S. & Hofmann, G. E. Parental environments alter DNA methylation in offspring of the purple sea urchin, Strongylocentrotus purpuratus. J. Exp. Mar. Bio. Ecol. 517, 54–64 (2019).
Google Scholar 
Rey, O. et al. Linking epigenetics and biological conservation: Towards a conservation epigenetics perspective. Funct. Ecol. 34, 414–427 (2020).
Google Scholar 
Eirin-Lopez, J. M. & Putnam, H. Editorial: Marine environmental epigenetics. Front. Mar. Sci. 8, 5 (2021).
Google Scholar 
Herrera, C. M. & Bazaga, P. Untangling individual variation in natural populations: Ecological, genetic and epigenetic correlates of longterm inequality in herbivory. Mol. Ecol. 20, 1675–1688 (2011).CAS 
PubMed 

Google Scholar 
Varriale, A. DNA methylation, epigenetics, and evolution in vertebrates: Facts and challenges. Int. J. Evol. Biol. 2014, 1–7 (2014).
Google Scholar 
Liebl, A. L., Wesner, J. S., Russell, A. F. & Schrey, A. W. Methylation patterns at fledging predict delayed dispersal in a cooperatively breeding bird. PLoS ONE 16, e0252227 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Metzger, D. C. H. & Schulte, P. M. Persistent and plastic effects of temperature on DNA methylation across the genome of threespine stickleback (Gasterosteus aculeatus). Proc. R. Soc. B Biol. Sci. 284, 5 (2017).
Google Scholar 
Putnam, H. M., Davidson, J. M. & Gates, R. D. Ocean acidification influences host DNA methylation and phenotypic plasticity in environmentally susceptible corals. Evol. Appl. 9, 1165–1178 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Watson, R. G. A., Baldanzi, S., Pérez-Figueroa, A., Gouws, G. & Porri, F. Morphological and epigenetic variation in mussels from contrasting environments. Mar. Biol. 165, 8 (2018).
Google Scholar 
Baldanzi, S., Watson, R., McQuaid, C. D., Gouws, G. & Porri, F. Epigenetic variation among natural populations of the South African sandhopper Talorchestia capensis. Evol. Ecol. 31, 77–91 (2017).
Google Scholar 
Ardura, A., Zaiko, A., Morán, P., Planes, S. & Garcia-Vazquez, E. Epigenetic signatures of invasive status in populations of marine invertebrates. Sci. Rep. 7, 5 (2017).
Google Scholar 
Baldanzi, S., Storch, D., Navarrete, S. A., Graeve, M. & Fernández, M. Latitudinal variation in maternal investment traits of the kelp crab Taliepus dentatus along the coast of Chile. Mar. Biol. 165, 1 (2018).
Google Scholar 
Sobarzo, M., Bravo, L., Donoso, D., Garcés-Vargas, J. & Schneider, W. Coastal upwelling and seasonal cycles that influence the water column over the continental shelf off central Chile. Prog. Oceanogr. 75, 363–382 (2007).ADS 

Google Scholar 
Letelier, J., Pizarro, O. & Nuñez, S. Seasonal variability of coastal upwelling and the upwelling front off central Chile. J. Geophys. Res. Ocean. 114, 12009 (2009).ADS 

Google Scholar 
Vargas, C. A. et al. Species-specific responses to ocean acidification should account for local adaptation and adaptive plasticity. Nat. Ecol. Evol. 1, 1–7 (2017).CAS 

Google Scholar 
Pérez, C. A. et al. Influence of climate and land use in carbon biogeochemistry in lower reaches of rivers in central southern Chile: Implications for the carbonate system in river-influenced rocky shore environments. J. Geophys. Res. Biogeosciences 120, 673–692 (2015).ADS 

Google Scholar 
Saldías, G. S. et al. Satellite-measured interannual variability of turbid river plumes off central-southern Chile: Spatial patterns and the influence of climate variability. Prog. Oceanogr. 146, 212–222 (2016).ADS 

Google Scholar 
Lara, C. et al. Coastal biophysical processes and the biogeography of rocky intertidal species along the south-eastern Pacific. J. Biogeogr. 46, 420–431 (2019).
Google Scholar 
Wieters, E. A. Upwelling control of positive interactions over mesoscales: A new link between bottom-up and top-down processes on rocky shores. Mar. Ecol. Prog. Ser. 301, 43–54 (2005).ADS 

Google Scholar 
Pérez-Matus, A., Carrasco, S. A., Gelcich, S., Fernandez, M. & Wieters, E. A. Exploring the effects of fishing pressure and upwelling intensity over subtidal kelp forest communities in Central Chile. Ecosphere 8, e01808 (2017).
Google Scholar 
Iranon, N. N. & Miller, D. L. Interactions between oxygen homeostasis, food availability, and hydrogen sulfide signaling. Front. Genet. 3, 5 (2012).
Google Scholar 
Ramajo, L., Lagos, N. A. & Duarte, C. M. Seagrass Posidonia oceanica diel pH fluctuations reduce the mortality of epiphytic forams under experimental ocean acidification. Mar. Pollut. Bull. 146, 247–254 (2019).CAS 
PubMed 

Google Scholar 
Aiken, C. & Navarrete, S. Environmental fluctuations and asymmetrical ­dispersal: Generalized stability theory for studying metapopulation persistence and marine protected areas. Mar. Ecol. Prog. Ser. 428, 77–88 (2011).ADS 

Google Scholar 
Baldanzi, S. et al. Combined effects of temperature and hypoxia shape female brooding behaviors and the early ontogeny of the Chilean kelp crab Taliepus dentatus. Mar. Ecol. Prog. Ser. 646, 93–107 (2020).ADS 
CAS 

Google Scholar 
Moran, A. L. & McAlister, J. S. Egg size as a life history character of marine invertebrates: Is it all it’s cracked up to be?. Biol. Bull. 216, 226–242 (2009).PubMed 

Google Scholar 
Doherty-Weason, D. et al. Bioenergetics of parental investment in two polychaete species with contrasting reproductive strategies: The planktotrophic Boccardia chilensis and the poecilogonic Boccardia wellingtonensis (Spionidae). Mar. Ecol. 41, 1 (2020).
Google Scholar 
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B 57, 289–300 (1995).MathSciNet 
MATH 

Google Scholar 
Steneck, R. S. et al. Kelp forest ecosystems: Biodiversity, stability, resilience and future. Environ. Conserv. 29, 436–459 (2002).
Google Scholar 
Sayols-Baixeras, S., Irvin, M. R., Arnett, D. K., Elosua, R. & Aslibekyan, S. W. Epigenetics of lipid phenotypes. Curr. Cardiovasc. Risk Rep. 10, 1–205 (2016).
Google Scholar 
Adam, A. C. et al. Profiling DNA methylation patterns of zebrafish liver associated with parental high dietary arachidonic acid. PLoS ONE 14, e0220934 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
García-Fernández, P., García-Souto, D., Almansa, E., Morán, P. & Gestal, C. Epigenetic DNA methylation mediating Octopus vulgaris early development: Effect of essential fatty acids enriched diet. Front. Physiol. 8, 1–9 (2017).
Google Scholar 
Hearn, J., Pearson, M., Blaxter, M., Wilson, P. J. & Little, T. J. Genome-wide methylation is modified by caloric restriction in Daphnia magna. BMC Genomics 20, 1–11 (2019).
Google Scholar 
Palma, A. T., Henríquez, L. A. & Ojeda, F. P. Phytoplanktonic primary production modulated by coastal geomorphology in a highly dynamic environment of central Chile. Rev. Biol. Mar. Oceanogr. 44, 325–334 (2009).
Google Scholar 
Faúndez-Báez, P., Morales, C. E. & Arcos, D. Variabilidad espacial y temporal en la hidrografía invernal del sistema de bahías frente a la VIII región (Chile centro-sur). Rev. Chil. Hist. Nat. 74, 817–831 (2001).
Google Scholar 
Osma, N. et al. Response of phytoplankton assemblages from naturally acidic coastal ecosystems to elevated pCO2. Front. Mar. Sci. 1, 323 (2020).
Google Scholar 
Rebolledo, L. et al. Siliceous productivity changes in Gulf of Ancud sediments (42°S, 72°W), southern Chile, over the last ∼150 years. Cont. Shelf Res. 31, 356–365 (2011).ADS 

Google Scholar 
Sun, Y. et al. Genome-wide analysis of DNA methylation in five tissues of Zhikong Scallop, Chlamys farreri. PLoS ONE 9, e86232 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Bernhardt, J. R., O’Connor, M. I., Sunday, J. M. & Gonzalez, A. Life in fluctuating environments. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 375, 20190454 (2020).PubMed 
PubMed Central 

Google Scholar 
Feinberg, A. P. & Irizarry, R. A. Colloquium Paper: Stochastic epigenetic variation as a driving force of development, evolutionary adaptation, and disease. Proc. Natl. Acad. Sci. USA 107, 1757 (2010).ADS 
CAS 
PubMed 

Google Scholar 
Tapia, F. J., Largier, J. L., Castillo, M., Wieters, E. A. & Navarrete, S. A. Latitudinal discontinuity in thermal conditions along the nearshore of Central-Northern Chile. PLoS ONE 9, e110841 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Reyna-López, G. E., Simpson, J. & Ruiz-Herrera, J. Differences in DNA methylation patterns are detectable during the dimorphic transition of fungi by amplification of restriction polymorphisms. Mol. Gen. Genet. 253, 703–710 (1997).PubMed 

Google Scholar 
Pérez-Figueroa, A. msap: A tool for the statistical analysis of methylation-sensitive amplified polymorphism data. Mol. Ecol. Resour. 13, 522–527 (2013).PubMed 

Google Scholar 
Gu, Z., Eils, R. & Schlesner, M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32, 2847–2849 (2016).CAS 
PubMed 

Google Scholar 
Valladares, F., Sanches-Gomez, D. & Zavala, M. A. Quantitative estimation of phenotypic plasticity: Bridging the gap between the evolutionary concept and its ecological applications. J. Ecol. 94, 1103–1116 (2006).
Google Scholar 
Excoffier, L., Smouse, P. E. & Quattro, J. M. Analysis of molecular variance inferred from metric distances among DNA haplotypes: Application to human mitochondrial DNA restriction data. Genetics 131, 479–491 (1992).CAS 
PubMed 
PubMed Central 

Google Scholar  More

Lim, C. & Putnam, R. D. Religion, social networks, and life satisfaction. Am. Sociol. Rev. 75, 914–933 (2010).
Google Scholar 
Fox, R. Kinship and marriage: an anthropological perspective/by Robin Fox. (1967).Lévi-Strauss, C. The elementary structures of kinship. (Beacon Press, 1969).Murdock, G. P. Social structure. Macmillan 387 (1949).Chapais, B. Primeval kinship: how pair-bonding gave birth to human society. (Harvard University Press, 2009).Walker, R. S. & Hill, K. R. Causes, consequences, and kin bias of human group fissions. Hum. Nat. 25, 465–475 (2014).PubMed 

Google Scholar 
Shenk, M. K., Towner, M. C., Voss, E. A. & Alam, N. Consanguineous marriage, kinship ecology, and market transition. Curr. Anthropol. 57, S167–S180 (2016).
Google Scholar 
Swann, W. B. Jr., Gómez, A., Seyle, D. C., Morales, J. F. & Huici, C. Identity fusion: The interplay of personal and social identities in extreme group behavior. J. Pers. Soc. Psychol. 96, 995–1011 (2009).PubMed 

Google Scholar 
Richerson, P. J. & Boyd, R. Complex societies. Hum. Nat. 10, 253–289 (1999).CAS 
PubMed 

Google Scholar 
Zelinsky, W. The hypothesis of the mobility transition. Geogr. Rev. 61, 219–249 (1971).
Google Scholar 
Gurven, M., Jaeggi, A. V., von Rueden, C., Hooper, P. L. & Kaplan, H. Does market integration buffer risk, erode traditional sharing practices and increase inequality? A test among Bolivian forager-farmers. Hum. Ecol. Interdiscip. J. 43, 515–530 (2015).PubMed 
PubMed Central 

Google Scholar 
Godoy, R. A. et al. Do markets worsen economic inequalities? Kuznets in the Bush. Hum. Ecol. 32, 339–364 (2004).
Google Scholar 
Kaplan, H. A theory of fertility and parental investment in traditional and modern human societies. Am. J. Phys. Anthropol. 101, 91–135 (1996).
Google Scholar 
Duernecker, G. & Vega-Redondo, F. Social Networks and the Process of Globalization. Rev. Econ. Stud. 85, 1716–1751 (2017).MathSciNet 
MATH 

Google Scholar 
Colleran, H. Market integration reduces kin density in women’s ego-networks in rural Poland. Nat. Commun. 11, 266 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wilding, R. Families, intimacy and globalization. (Macmillan International Higher Education, 2018).Hackman, J. V. & Kramer, K. L. Kin Ties and market integration in a Yucatec Mayan Village. Soc. Sci. 10, 216 (2021).
Google Scholar 
Norenzayan, A. Big gods: How religion transformed cooperation and conflict. (Princeton University Press, 2013).Lauder, W., Mummery, K. & Sharkey, S. Social capital, age and religiosity in people who are lonely. J. Clin. Nurs. 15, 334–340 (2006).PubMed 

Google Scholar 
Agate, S. T., Zabriskie, R. B. & Eggett, D. L. Praying, playing, and successful families. Marriage Fam. Rev. 42, 51–75 (2007).
Google Scholar 
Day, R. D. et al. Family processes and adolescent religiosity and religious practice: View from the NLSY97. Marriage Fam. Rev. 45, 289–309 (2009).
Google Scholar 
Fagan, P. F. Why religion matters even more: The impact of religious practice on social stability. Backgrounder 1992, 1–19 (2006).
Google Scholar 
Ellison, C. G. & George, L. K. Religious involvement, social ties, and social support in a Southeastern Community. J. Sci. Study Relig. 33, 46–61 (1994).
Google Scholar 
Ellison, C. G. & Xu, X. Religion and families. The Wiley Blackwell companion to the sociology of families 277–299 (2014).Ginges, J., Hansen, I. & Norenzayan, A. Religion and support for suicide attacks. Psychol. Sci. 20, 224–230 (2009).PubMed 

Google Scholar 
Lynch, R., Palestis, B. G. & Trivers, R. Religious devotion and extrinsic religiosity affect in-group altruism and out-group hostility oppositely in rural Jamaica. Evol. Psychol. Sci. 3, 335 (2017).
Google Scholar 
Walker, R. S. & Bailey, D. H. Marrying kin in small-scale societies. Am. J. Hum. Biol. 26, 384–388 (2014).PubMed 

Google Scholar 
Putnam, R. D., Leonardi, R. & Nanetti, R. Y. Making Democracy Work: Civic Traditions in Modern Italy. (Princeton University Press, 1994).Coleman, J. Foundations of Social Theory. (Belknap Press of Harvard University Press, Cambridge, Mass, 1990).Wuthnow, R. The Left Behind: Decline and Rage in Rural America. (Princeton University Press, 2018).Sunstein, C. R. # Republic: Divided democracy in the age of social media. (Princeton University Press, 2018).Putnam, R. D. E Pluribus Unum: Diversity and Community in the Twenty-first Century The 2006 Johan Skytte Prize Lecture. Scan. Polit. Stud. 30, (2007).Putnam, R. Bowling alone: The collapse and revival of American community. (Simon and Schuster, 2000).Olson, M. The Logic of Collective Action: Public Goods and the Theory of Groups, Second printing with new preface and appendix (Harvard Economic Studies). Harvard economic studies, v. 124 (Harvard University Press, 1971).Granovetter, M. S. The strength of weak ties. Am. J. Sociol. (1973).Lynch, R., Lummaa, V. & Panchanathan, K. Integration involves a trade-off between fertility and status for World War II evacuees. Nature Human Behaviour (2019).Beyerlein, K. & Hipp, J. R. Social capital, too much of a good thing? American Religious Traditions and Community Crime. Soc. Forces 84, 995–1013 (2005).
Google Scholar 
Lewis, V. A., Macgregor, C. A. & Putnam, R. D. Religion, networks, and neighborliness: The impact of religious social networks on civic engagement. Soc. Sci. Res. 42, 331–346 (2013).PubMed 

Google Scholar 
Yu, M. & Stiffman, A. R. Positive family relationships and religious affiliation as mediators between negative environment and illicit drug symptoms in American Indian adolescents. Addict. Behav. 35, 694–699 (2010).PubMed 

Google Scholar 
Regnerus, M. D. & Burdette, A. Religious change and adolescent family dynamics. Sociol. Q. 47, 175–194 (2006).
Google Scholar 
Marks, L. Religion and family relational health: An overview and conceptual model. J. Relig. Health (2006).Thornton, A. Reciprocal Influences of Family and Religion in a Changing World. J. Marriage Fam. Couns. 47, 381–394 (1985).
Google Scholar 
Mahoney, A., Pargament, K. I., Murray-Swank, A. & Murray-Swank, N. Religion and the Sanctification of Family Relationships. Rev. Relig. Res. 44, 220–236 (2003).
Google Scholar 
Mahoney, A. Religion in families 1999 to 2009: A relational spirituality framework. J. Marriage Fam. 72, 805–827 (2010).PubMed 
PubMed Central 

Google Scholar 
Ebstyne King, P. & Furrow, J. L. Religion as a resource for positive youth development: religion, social capital, and moral outcomes. Dev. Psychol. 40, 703–713 (2004).PubMed 

Google Scholar 
Dudley, M. G. & Kosinski, F. A. Religiosity and marital satisfaction: A research note. Rev. Relig. Res. 32, 78–86 (1990).
Google Scholar 
Milevsky, A., Smoot, K., Leh, M. & Ruppe, A. Familial and contextual variables and the nature of sibling relationships in emerging adulthood. Marriage Fam. Rev. 37, 123–141 (2005).
Google Scholar 
Galbraith, D. & Shaver, J. H. Religion and Fertility Bibliography. evolutionarydemographyofreligion.Shaver, J. H., Sibley, C. G., Sosis, R., Galbraith, D. & Bulbulia, J. Alloparenting and religious fertility: A test of the religious alloparenting hypothesis. Evol. Hum. Behav. 40, 315–324 (2019).
Google Scholar 
Kaufmann, E. Shall the Religious Inherit the Earth?: Demography and Politics in the Twenty-First Century. (Profile Books, 2010).Ebaugh, H. R. & Curry, M. Fictive Kin as social capital in new immigrant communities. Sociol. Perspect. 43, 189–209 (2000).
Google Scholar 
Taylor, R. J., Chatters, L. M., Woodward, A. T. & Brown, E. Racial and ethnic differences in extended family, friendship, fictive kin and congregational informal support networks. Fam. Relat. 62, 609–624 (2013).PubMed 
PubMed Central 

Google Scholar 
Durkheim, E. The elementary forms of the religious life. Preprint at (1915).Rappaport, R. A. Ritual and Religion in the Making of Humanity. vol. 110 (Cambridge University Press, 1999).Hastings, O. P. Not a lonely crowd? Social connectedness, religious service attendance, and the spiritual but not religious. Soc. Sci. Res. 57, 63–79 (2016).PubMed 

Google Scholar 
Putnam, R. & Campbell, D. E. American grace: How religion is reshaping our civic and political lives. Preprint at (2010).Turke, P. W. Evolution and the demand for children. Popul. Dev. Rev. 15, 61–90 (1989).
Google Scholar 
Sear, R. & Coall, D. How much does family matter? Cooperative breeding and the demographic transition. Popul. Dev. Rev. 37, 81–112 (2011).PubMed 

Google Scholar 
Jenkins, P. Fertility and Faith: The Demographic Revolution and the Transformation of World Religions. (Baylor University Press, 2020).Rothstein, B. Corruption and social trust: Why the fish rots from the head down. Soc. Res. 80, 1009–1032 (2013).
Google Scholar 
Lynch, R, Schaffnit, S. and Shenk, M. OSF preregistration – Does religion help to preserve the density of kin networks often disrupted by globalization? Open Science Framework Registries. https://osf.io/xvyqm/registrations (2020).Alam, N. et al. Health and demographic surveillance system (HDSS) in Matlab, Bangladesh. Int. J. Epidemiol. 46, 809–816 (2017).PubMed 

Google Scholar 
Icddr, B. Health and Demographic Surveillance System-Matlab. 2005 Socioeconomic Census (2007).Imf. International Monetary Fund. World Economic Outlook Database. (2016).Razzaque, A., Streatfield, P. K. & Evans, A. Family size and children’s education in Matlab, Bangladesh. J. Biosoc. Sci. 39, 245–256 (2007).PubMed 

Google Scholar 
Afsar, R. Unravelling the vicious cycle of recruitment: Labour migration from Bangladesh to the gulf states. http://ilo.org/wcmsp5/groups/public/—ed_norm/—declaration/documents/publication/wcms_106536.pdf (2009).Kabeer, N. Ideas, economics and ‘the sociology of supply’: Explanations for fertility decline in Bangladesh. J. Dev. Stud. 38, 29–70 (2001).
Google Scholar 
Novak, J. J. Bangladesh: Reflections on the water. (Indiana University Press, 1993).Shenk, M. K., Towner, M. C., Kress, H. C. & Alam, N. A model comparison approach shows stronger support for economic models of fertility decline. Proc. Natl. Acad. Sci. USA 110, 8045–8050 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Devine, J., Hinks, T. & Naveed, A. Happiness in Bangladesh: The role of religion and connectedness. J. Happiness Stud. 20, 351–371 (2019).
Google Scholar 
Henrich, J. Market incorporation, agricultural change, and sustainability among the Machiguenga Indians of the Peruvian Amazon. Hum. Ecol. 25, 319–351 (1997).
Google Scholar 
Lu, F. Integration into the market among indigenous peoples: A cross-cultural perspective from the Ecuadorian Amazon. Curr. Anthropol. 48, 593–602 (2007).
Google Scholar 
Bürkner, P.-C. Advanced Bayesian Multilevel Modeling with the R Package brms. arXiv [stat.CO] (2017).Team, R. C. & Others. R: A language and environment for statistical computing. (2013).Lynch, R. Kin_density_and-religiosity. (2021).McElreath, R. Statistical rethinking. (2017).Clarke, M. New kinship, Islam, and the liberal tradition: sexual morality and new reproductive technology in Lebanon. J. R. Anthropol. Inst. 14, 153–169 (2008).
Google Scholar 
Swann, W. B. et al. What makes a group worth dying for? Identity fusion fosters perception of familial ties, promoting self-sacrifice. J. Pers. Soc. Psychol. 106, 912–926 (2014).PubMed 

Google Scholar 
Benítez, D. M. Bangladesh: Economy Overview and Structural Changes. (2018).Viry, G. Residential mobility and the spatial dispersion of personal networks: Effects on social support. Soc. Networks 34, 59–72 (2012).
Google Scholar 
Mok, D., Wellman, B. & Carrasco, J. Does distance matter in the age of the internet?. Urban Stud. 47, 2747–2783 (2010).
Google Scholar 
Rivera, M. T., Soderstrom, S. B. & Uzzi, B. Dynamics of dyads in social networks: Assortative, relational, and proximity mechanisms. Annu. Rev. Sociol. 36, 91–115 (2010).
Google Scholar 
Pollet, T. V., Roberts, S. G. B. & Dunbar, R. I. M. Going that extra mile: Individuals travel further to maintain face-to-face contact with highly related kin than with less related kin. PLoS ONE 8, e53929 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Madhavan, S., Clark, S., Araos, M. & Beguy, D. Distance or location? How the geographic distribution of kin networks shapes support given to single mothers in urban Kenya. Geogr. J. 184, 75–88 (2018).
Google Scholar 
Curry, O., Roberts, S. G. B. & Dunbar, R. I. M. Altruism in social networks: evidence for a ‘kinship premium’. Br. J. Psychol. 104, 283–295 (2013).PubMed 

Google Scholar 
Sullivan, K. & Sullivan, A. Adolescent–parent separation. Dev. Psychol. 16, 93 (1980).
Google Scholar 
Roberts, S. G. B. & Dunbar, R. I. M. Communication in social networks: Effects of kinship, network size, and emotional closeness. Pers. Relatsh. 18, 439–452 (2011).
Google Scholar 
Shenk, M. K. et al. Social support, nutrition and health among women in rural Bangladesh: complex tradeoffs in allocare, kin proximity and support network size. Philos. Trans. R. Soc. Lond. B Biol. Sci. 376, 207 (2021).Snopkowski, K. & Sear, R. Grandparental help in Indonesia is directed preferentially towards needier descendants: A potential confounder when exploring grandparental influences on child health. Soc. Sci. Med. 128, 105–114 (2015).PubMed 

Google Scholar 
Schaffnit, S. B. & Sear, R. Support for new mothers and fertility in the United Kingdom: Not all support is equal in the decision to have a second child. Popul. Stud. 71, 345–361 (2017).
Google Scholar 
Boyer, P. The Naturalness of Religious Ideas: A Cognitive Theory of Religion. (University of California Press, 1994).Thomas, M. G. et al. Kinship underlies costly cooperation in Mosuo villages. R Soc Open Sci 5, 171535 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Maqsood, A. Love as understanding. Am. Ethnol. https://doi.org/10.1111/amet.13000 (2021).Article 

Google Scholar 
Schurmann, A. T. & Mahmud, S. Civil society, health, and social exclusion in Bangladesh. J. Health Popul. Nutr. 27, 536–544 (2009).PubMed 
PubMed Central 

Google Scholar 
Haque, M. R., Hasan, M. S., Alam, N., Barkat, S. & Others. Fertility preferences in Bangladesh. in Family Demography in Asia (Edward Elgar Publishing, 2018).Mattison, S. M. Economic impacts of tourism and erosion of the visiting system among the Mosuo of Lugu Lake. Asia Pac. J. Anthropol. 11, 159–176 (2010).
Google Scholar 
Mattison, S. M. et al. Context specificity of ‘market integration’ among the matrilineal Mosuo of Southwest China. Curr. Anthropol. 63, 118–124 (2022).
Google Scholar 
Uchida, Y., Kitayama, S., Mesquita, B., Reyes, J. A. S. & Morling, B. Is perceived emotional support beneficial? Well-being and health in independent and interdependent cultures. Pers. Soc. Psychol. Bull. 34, 741–754 (2008).PubMed 

Google Scholar 
Reblin, M. & Uchino, B. N. Social and emotional support and its implication for health. Curr. Opin. Psychiatry 21, 201–205 (2008).PubMed 
PubMed Central 

Google Scholar 
Inglehart, R. Faith and freedom: Traditional and modern ways to happiness. Int. Differ. Well-being 351, 397 (2010).
Google Scholar 
Ferriss, A. L. Religion and the Quality of Life. J. Happiness Stud. 3, 199–215 (2002).
Google Scholar 
Greeley, A. & Hout, M. Happiness and lifestyle among conservative Christians. The truth about conservative Christians 1, 150–161 (2006).
Google Scholar 
Pilisuk, M. Kinship, social networks, social support and health. Soc. Sci. Med. 12, 273–280 (1978).CAS 
PubMed 

Google Scholar 
Schaffnit, S. B. & Sear, R. Supportive families versus support from families: The decision to have a child in the Netherlands. Demogr. Res. 37, 417–454 (2017).
Google Scholar 
Hassan, A., Lawson, D., Schaffnit, S. B., Urassa, M. & Sear, R. Childcare in transition: evidence that patterns of childcare differ by degree of market integration in north-western Tanzania. (2021).https://doi.org/10.31219/osf.io/gtc6kPutnam, R. D. Democracies in Flux: The Evolution of Social Capital in Contemporary Society. (Oxford University Press, 2004). More

Ratcliffe, N. et al. A protocol for the aerial survey of penguin colonies using UAVs. J. Unmanned Veh. Syst. 3, 95–101 (2015).
Google Scholar 
Albores-Barajas, Y. V. et al. A new use of technology to solve an old problem: Estimating the population size of a burrow nesting seabird. PLoS ONE 13, 1–15 (2018).
Google Scholar 
Rush, G. P., Clarke, L. E., Stone, M. & Wood, M. J. Can drones count gulls? Minimal disturbance and semiautomated image processing with an unmanned aerial vehicle for colony-nesting seabirds. Ecol. Evol. 8, 12322–12334 (2018).PubMed 
PubMed Central 

Google Scholar 
Chabot, D., Craik, S. R. & Bird, D. M. Population census of a large Common tern colony with a small unmanned aircraft. PLoS ONE 10, 1–14 (2015).
Google Scholar 
McClelland, G. T. W., Bond, A. L., Sardana, A. & Glass, T. Rapid population estimate of a surface-nesting seabird on a remote island using a low-cost unmanned aerial vehicle. Mar. Ornithol. 44, 215–220 (2016).
Google Scholar 
Lynch, H. J., White, R., Black, A. D. & Naveen, R. Detection, differentiation, and abundance estimation of penguin species by high-resolution satellite imagery. Polar Biol. 35, 963–968 (2012).
Google Scholar 
Fretwell, P. T. et al. An Emperor penguin population estimate: The first global, synoptic survey of a species from space. PLoS ONE 7, e33751 (2012).ADS 
PubMed 
PubMed Central 

Google Scholar 
Xue, Y., Wang, T. & Skidmore, A. K. Automatic counting of large mammals from very high resolution panchromatic satellite imagery. Remote Sens. 9, 1–16 (2017).
Google Scholar 
Laliberte, A. S. & Ripple, W. J. Automated wildlife counts from remotely sensed imagery. Wildl. Soc. Bull. 31, 362–371 (2003).
Google Scholar 
Lyons, M. B. et al. Monitoring large and complex wildlife aggregations with drones. Methods Ecol. Evol. 10, 1024–1035 (2019).
Google Scholar 
LaRue, M. A., Stapleton, S. & Anderson, M. Feasibility of using high-resolution satellite imagery to assess vertebrate wildlife populations. Conserv. Biol. 31, 213–220 (2017).PubMed 

Google Scholar 
Sardà-Palomera, F., Bota, G., Padilla, N., Brotons, L. & Sardà, F. Unmanned aircraft systems to unravel spatial and temporal factors affecting dynamics of colony formation and nesting success in birds. J. Avian Biol. 48, 1273–1280 (2017).
Google Scholar 
Schofield, G., Katselidis, K. A., Lilley, M. K. S., Reina, R. D. & Hays, G. C. Detecting elusive aspects of wildlife ecology using drones: New insights on the mating dynamics and operational sex ratios of sea turtles. Funct. Ecol. 31, 2310–2319 (2017).
Google Scholar 
Lachman, D., Conway, C., Vierling, K. & Matthews, T. Drones provide a better method to find nests and estimate nest survival for colonial waterbirds: A demonstration with Western grebes. Wetl. Ecol. Manag. 28, 837–845 (2020).
Google Scholar 
Torres, L. G., Nieukirk, S. L., Lemos, L. & Chandler, T. E. Drone up! Quantifying whale behavior from a new perspective improves observational capacity. Front. Mar. Sci. 5, 1–14 (2018).
Google Scholar 
Jagielski, P. M., Dey, C. J., Gilchrist, H. G., Richardson, E. S. & Semeniuk, C. A. D. Polar bear foraging on common eider eggs: Estimating the energetic consequences of a climate-mediated behavioural shift. Anim. Behav. 171, 63–75 (2021).
Google Scholar 
Jagielski, P. M. et al. Polar bears are inefficient predators of seabird eggs. R. Soc. Open Sci. 8, 210391 (2021).ADS 
PubMed 
PubMed Central 

Google Scholar 
Callaghan, C. T., Brandis, K. J., Lyons, M. B., Ryall, S. & Kingsford, R. T. A comment on the limitations of UAVS in wildlife research—The example of colonial nesting waterbirds. J. Avian Biol. 49, e01825 (2018).
Google Scholar 
Brisson-Curadeau, É. et al. Seabird species vary in behavioural response to drone census. Sci. Rep. 7, 1–9 (2017).
Google Scholar 
Nowak, M. M., Dziób, K. & Bogawski, P. Unmanned aerial vehicles (UAVs) in environmental biology: A review. Eur. J. Ecol. 4, 56–74 (2019).
Google Scholar 
Watts, A. C. et al. Small unmanned aircraft systems for low-altitude aerial surveys. J. Wildl. Manag. 74, 1614–1619 (2010).
Google Scholar 
Sasse, D. B. Job-related mortality of wildlife workers in the United States, 1937–2000. Wildl. Soc. Bull. 31, 1015–1020 (2003).
Google Scholar 
Carey, M. J. The effects of investigator disturbance on procellariiform seabirds: A review. N. Z. J. Zool. 36, 367–377 (2009).
Google Scholar 
Carney, K. M. & Sydeman, W. J. A review of human disturbance effects on nesting colonial waterbirds. Int. J. Waterbird Biol. 22, 68–79 (1999).
Google Scholar 
Barber-Meyer, S. M., Kooyman, G. L. & Ponganis, P. J. Estimating the relative abundance of Emperor penguins at inaccessible colonies using satellite imagery. Polar Biol. 30, 1565–1570 (2007).
Google Scholar 
Lyons, M. et al. A protocol for using drones to assist monitoring of large breeding bird colonies. EcolEvol https://doi.org/10.32942/osf.io/p9j3f (2019).Article 

Google Scholar 
Hodgson, J. C. et al. Drones count wildlife more accurately and precisely than humans. Methods Ecol. Evol. 9, 1160–1167 (2018).
Google Scholar 
Hodgson, J. C., Baylis, S. M., Mott, R., Herrod, A. & Clarke, R. H. Precision wildlife monitoring using unmanned aerial vehicles. Sci. Rep. 6, 1–7 (2016).
Google Scholar 
Weston, M. A., O’Brien, C., Kostoglou, K. N. & Symonds, M. R. E. Escape responses of terrestrial and aquatic birds to drones: Towards a code of practice to minimize disturbance. J. Appl. Ecol. 57, 777–785 (2020).
Google Scholar 
Korczak-Abshire, M. et al. Preliminary study on nesting Adélie penguins disturbance by unmanned aerial vehicles. CCAMLR Sci. 23, 1–16 (2016).
Google Scholar 
Mesquita, G. P., Rodríguez-Teijeiro, J. D., Wich, S. A. & Mulero-Pázmány, M. Measuring disturbance at a swift breeding colonies due to the visual aspects of a drone: A quasi-experiment study. Curr. Zool. 41, 259–266 (2020).
Google Scholar 
Weimerskirch, H., Prudor, A. & Schull, Q. Flights of drones over sub-Antarctic seabirds show species- and status-specific behavioural and physiological responses. Polar Biol. 41, 259–266 (2018).
Google Scholar 
Mulero-Pázmány, M. et al. Unmanned aircraft systems as a new source of disturbance for wildlife: A systematic review. PLoS ONE 12, 1–14 (2017).
Google Scholar 
Barnas, A. et al. Evaluating behavioral responses of nesting Lesser snow geese to unmanned aircraft surveys. Ecol. Evol. 8, 1328–1338 (2018).PubMed 

Google Scholar 
Ellis-felege, S. N. et al. Nesting Common eiders (Somateria mollissima) show little behavioral response to fixed-wing drone surveys. J. Unmanned Veh. Syst. https://doi.org/10.1139/juvs-2021-0012 (2021).Article 

Google Scholar 
Wilson, R. P., Culik, B., Danfeld, R. & Adelung, D. People in Antarctica—how much do Adélie penguins Pygoscelis adeliae care?. Polar Biol. 11, 363–370 (1991).
Google Scholar 
Ricklefs, R. E. An analysis of nesting mortality in birds. Smithson. Contrib. Zool. 9, 1–48 (1969).
Google Scholar 
Ditmer, M. A. et al. Bears show a physiological but limited behavioral response to unmanned aerial vehicles. Curr. Biol. 25, 2278–2283 (2015).PubMed 

Google Scholar 
Ditmer, M. A. et al. Bears habituate to the repeated exposure of a novel stimulus, unmanned aircraft systems. Conserv. Physiol. 6, 1–7 (2018).
Google Scholar 
Jaatinen, K., Seltmann, M. W. & Öst, M. Context-dependent stress responses and their connections to fitness in a landscape of fear. J. Zool. 294, 147–153 (2014).
Google Scholar 
Seltmann, M. W. et al. Stress responsiveness, age and body condition interactively affect flight initiation distance in breeding female eiders. Anim. Behav. 84, 889–896 (2012).
Google Scholar 
Cockrem, J. F. Stress, corticosterone responses and avian personalities. J. Ornithol. 148, S169–S178 (2007).
Google Scholar 
Criscuolo, F. Does blood sampling during eider incubation induce nest desertion in the female Common eider Somateria mollissima?. Mar. Ornithol. 29, 47–50 (2001).
Google Scholar 
Ellenberg, U., Mattern, T. & Seddon, P. J. Heart rate responses provide an objective evaluation of human disturbance stimuli in breeding birds. Conserv. Physiol. 1, 1–11 (2013).
Google Scholar 
DeRose-Wilson, A., Fraser, J. D., Karpanty, S. M. & Hillman, M. D. Effects of overflights on incubating Wilson’s plover behavior and heart rate. J. Wildl. Manag. 79, 1246–1254 (2015).
Google Scholar 
de Villiers, M., Bause, M., Giese, M. & Fourie, A. Hardly hard-hearted: Heart rate responses of incubating Northern giant petrels (Macronectes halli) to human disturbance on sub-Antarctic Marion Island. Polar Biol. 29, 717–720 (2006).
Google Scholar 
Borneman, T. E., Rose, E. T. & Simons, T. R. Minimal changes in heart rate of incubating American oystercatchers (Haematopus palliatus) in response to human activity. Condor 116, 493–503 (2014).
Google Scholar 
Felton, S. K., Pollock, K. H. & Simons, T. R. Response of beach-nesting American oystercatchers to off-road vehicles: An experimental approach reveals physiological nuances and decreased nest attendance. Condor 120, 47–62 (2018).
Google Scholar 
Bolduc, F. & Guillemette, M. Human disturbance and nesting success of Common eiders: Interaction between visitors and gulls. Biol. Conserv. 110, 77–83 (2003).
Google Scholar 
Hennin, H. L. et al. Plasma mammalian leptin analogue predicts reproductive phenology, but not reproductive output in a capital-income breeding seaduck. Ecol. Evol. 9, 1512–1521 (2019).PubMed 
PubMed Central 

Google Scholar 
Culik, B., Adelung, D. & Woakes, A. J. The effect of disturbance on the heart rate and behaviour of Adélie penguins (Pygoscelis adeliae) during the breeding season. In Antarctic Ecosystems. Ecological Change and Conservation (eds Kerry, K. R. & Hempel, G.) 177–182 (Springer, 1990).
Google Scholar 
Weimerskirch, H. et al. Heart rate and energy expenditure of incubating Wandering albatrosses: Basal levels, natural variation, and the effects of human disturbance. J. Exp. Biol. 205, 475–483 (2002).PubMed 

Google Scholar 
Egan, C. C., Blackwell, B. F., Fernández-Juricic, E. & Klug, P. E. Testing a key assumption of using drones as frightening devices: Do birds perceive drones as risky?. Condor 122, 1–15 (2020).
Google Scholar 
McEvoy, J. F., Hall, G. P. & McDonald, P. G. Evaluation of unmanned aerial vehicle shape, flight path and camera type for waterfowl surveys: Disturbance effects and species recognition. PeerJ 4, e1831 (2016).PubMed 
PubMed Central 

Google Scholar 
Goebel, M. E. et al. A small unmanned aerial system for estimating abundance and size of Antarctic predators. Polar Biol. 38, 619–630 (2015).
Google Scholar 
Bevan, E. et al. Measuring behavioral responses of sea turtles, saltwater crocodiles, and Crested terns to drone disturbance to define ethical operating thresholds. PLoS ONE 13, 4–6 (2018).
Google Scholar 
Rümmler, M. C., Mustafa, O., Maercker, J., Peter, H. U. & Esefeld, J. Measuring the influence of unmanned aerial vehicles on Adélie penguins. Polar Biol. 39, 1329–1334 (2016).
Google Scholar 
Vas, E., Lescroël, A., Duriez, O., Boguszewski, G. & Grémillet, D. Approaching birds with drones: First experiments and ethical guidelines. Biol. Lett. 11, 20140754 (2015).PubMed 
PubMed Central 

Google Scholar 
Frid, A. & Dill, L. Human-caused disturbance stimuli as a form of predation risk. Ecol. Soc. 6, 11 (2002).
Google Scholar 
Forbes, M. R. L., Clark, R. G., Weatherhead, P. J. & Armstrong, T. Risk-taking by female ducks: Intra-and interspecific tests of nest defense theory. Behav. Ecol. Sociobiol. 34, 79–85 (1994).
Google Scholar 
Viblanc, V. A., Smith, A. D., Gineste, B., Kauffmann, M. & Groscolas, R. Modulation of heart rate response to acute stressors throughout the breeding season in the King penguin Aptenodytes patagonicus. J. Exp. Biol. 218, 1686–1692 (2015).PubMed 

Google Scholar 
Montgomerie, R. D. & Weatherhead, P. J. Risks and rewards of nest defence by parent birds. Q. Rev. Biol. 63, 167–187 (1988).
Google Scholar 
Criscuolo, F., Gabrielsen, G. W., Gendner, J.-P. & Maho, Y. L. Body mass regulation during incubation in female Common eiders Somateria mollissima. J. Avian Biol. 33, 83–88 (2002).
Google Scholar 
Cyr, N. E., Wikelski, M. & Romero, L. M. Increased energy expenditure but decreased stress responsiveness during molt. Physiol. Biochem. Zool. Ecol. Evol. Approaches 81, 452–462 (2008).
Google Scholar 
Kralj-Fišer, S., Scheiber, I. B. R., Kotrschal, K., Weiß, B. M. & Wascher, C. A. F. Glucocorticoids enhance and suppress heart rate and behaviour in time dependent manner in Greylag geese (Anser anser). Physiol. Behav. 100, 394–400 (2010).PubMed 

Google Scholar 
Hodgson, J. C. & Koh, L. P. Best practice for minimising unmanned aerial vehicle disturbance to wildlife in biological field research. Curr. Biol. 26, R404–R405 (2016).PubMed 

Google Scholar 
Parker, H. & Holm, H. Patterns of nutrient and energy expenditure in female Common eiders nesting in the high Arctic. Auk 107, 660–668 (1990).
Google Scholar 
Mehlum, F. Eider Studies in Svalbard Vol. 195 (Norsk Polarinstitutt Skrifter, 1991).
Google Scholar 
Markowitz, E. M., Nisbet, M. C., Danylchuk, A. J. & Engelbourg, S. I. What’s that buzzing noise? Public opinion on the use of drones for conservation science. Bioscience 67, 382–385 (2017).
Google Scholar 
Legagneux, P. et al. Unpredictable perturbation reduces breeding propensity regardless of pre-laying reproductive readiness in a partial capital breeder. J. Avian Biol. 47, 880–886 (2016).
Google Scholar 
Love, O. P., Gilchrist, H. G., Descamps, S., Semeniuk, C. A. D. & Bêty, J. Pre-laying climatic cues can time reproduction to optimally match offspring hatching and ice conditions in an Arctic marine bird. Oecologia 164, 277–286 (2010).ADS 
PubMed 

Google Scholar 
Fast, P. L. F., Gilchrist, H. G. & Clark, R. G. Nest-site materials affect nest-bowl use by Common eiders (Somateria mollissima). Can. J. Zool. 88, 214–218 (2010).
Google Scholar 
McKinnon, L., Gilchrist, H. G. & Scribner, K. T. Genetic evidence for kin-based female social structure in Common eiders (Somateria mollissima). Behav. Ecol. 17, 614–621 (2006).
Google Scholar 
Descamps, S., Forbes, M. R., Gilchrist, H. G., Love, O. P. & Bêty, J. Avian cholera, post-hatching survival and selection on hatch characteristics in a long-lived bird, the Common eider Somateria mollissima. J. Avian Biol. 42, 39–48 (2011).
Google Scholar 
Buttler, E. I. Avian Cholera Among Arctic Breeding Common eiders Somateria mollissima: Temporal Dynamics and the Role of Handling Stress in Reproduction and Survival (Carleton University, 2009).
Google Scholar 
Descamps, S., Gilchrist, H. G., Bêty, J., Buttler, E. I. & Forbes, M. R. Costs of reproduction in a long-lived bird: large clutch size is associated with low survival in the presence of a highly virulent disease. Biol. Lett. 5, 278–281 (2009).PubMed 
PubMed Central 

Google Scholar 
Iverson, S. A., Gilchrist, H. G., Smith, P. A., Gaston, A. J. & Forbes, M. R. Longer ice-free seasons increase the risk of nest depredation by Polar bears for colonial breeding birds in the Canadian Arctic. Proc. R. Soc. B Biol. Sci. 281, 20133128 (2014).
Google Scholar 
Dey, C. J. et al. Increasing nest predation will be insufficient to maintain Polar bear body condition in the face of sea ice loss. Glob. Change Biol. 23, 1821–1831 (2017).ADS 

Google Scholar 
Giese, M., Handsworth, R. & Stephenson, R. Measuring resting heart rates in penguins using an artificial egg. J. Field Ornithol. 70, 49–54 (1999).
Google Scholar 
Weller, M. W. A simple field candler for waterfowl eggs. J. Wildl. Manag. 20, 111–113 (1956).
Google Scholar 
Barnas, A. F. et al. A standardized protocol for reporting methods when using drones for wildlife research. J. Unmanned Veh. Syst. 8, 89–98 (2020).
Google Scholar 
Audacity Team. Audacity(R): Free Audio Editor and Recorder [Computer Application]. Version 2.3.2 retrieved Oct 10th 2019 from https://www.audacityteam.org/ (2019).Nimon, A. J., Schroter, R. C. & Oxenham, R. K. C. Artificial eggs: Measuring heart rate and effects of disturbance in nesting penguins. Physiol. Behav. 60, 1019–1022 (1996).PubMed 

Google Scholar 
SAS Institute Inc. SAS® Studio 3.8: User’s Guide (SAS Institute Inc, 2018).
Google Scholar 
Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (Springer, 2002).MATH 

Google Scholar 
Akaike, H. Information theory and an extension of the maximum likelihood principle. In Breakthroughs in Statistics, Volume I, Foundations and Basic Theory (eds Kotz, S. & Johnson, N. L.) 610–624 (Springer, New York, 1998).
Google Scholar 
Wickham, H., François, R., Henry, L. & Müller, K. dplyr: A Grammar of Data Manipulation. R package version 0.8.3. https://CRAN.R-project.org/package=dplyr (2015).Grolemund, G. & Wickham, H. Dates and times made easy with lubridate. J. Stat. Softw. 40, 1–25 (2011).
Google Scholar 
Hijmans, R. J., Williams, E. & Vennes, C. Geosphere: Spherical Trigonometry. https://CRAN.R-project.org/package=geosphere (2017).Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).MATH 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Found. Stat. Comput., Vienna, 2017).
Google Scholar  More

Most records of N-tiger cats were from savanna environments, and it was not surprising that this vegetative formation has a key influence on the N-tiger cat range in the Amazon. The bulk of the L. t. tigrinus distribution lies in the savannas, dry forests and shrublands of the Cerrado and Caatinga biomes. These are also the areas with the vast majority of records for this lowland subspecies (Supplemental Fig. S5). Hence, L. t. tigrinus is more associated with savannas and savanna-like environments than with rainforests. In fact, more than 80% of the records in the Amazon were within 100 km of a savanna patch. Colonization of the northern savanna formations of the Amazon by the N-tiger cat likely occurred during the forest-savanna shifts of the glacial period18, and the cat currently shows a patchy distribution. Strong evidence of established biogeographic corridor connections between the savannas of the Cerrado and those of the Amazon exists, suggesting northward expansion of the former during glacial periods, perhaps predating the Last Glacial Maximum19,20,21. Further corroborating this evidence, tiger cat ‘gene flow’ niche modelling showed prior connectivity between the Guiana population and that of Central Brazil and no connectivity with the Andean population22. Additionally, Guianan tiger cat skin patterns are found in savanna and transitional savanna/Amazon areas and in the semiarid shrub-woodland of Brazil and are very distinct from the patterns of the tiger cats from the Andes of northwestern South America and Central America (Supplementary Information Fig. S6).The bioclimatic variables in the best model also supported the cat’s preference for savanna areas. The best model indicated a positive effect of precipitation in the driest month on the probability of the presence of the N-tiger cat, likely indicating the Aw/As climates of tropical savannas23. These climates are marked by seasonal variation in rainfall, with a pronounced dry season. Higher rainfall during the dry season favors the growth of vegetation, which results in some tree cover within the savannas. Thus, our results agree with previous research suggesting that tiger cats avoid open savanna formations24. Similarly, the species had a significant negative response to net primary productivity. This also supports the species’ avoidance of dense lowland rainforests, which are the most productive habitats. In the Amazon biome, the least productive areas are found in more open landscapes25.The N-tiger cat’s range considered from an ecoregion perspective12 could biogeographically explain its distribution in the Amazon. All records but 2 fell within Guiana savannas, Guiana highland forest, Guiana rainforest, part of the Uatumã-Trombetas rainforest bordering the Guianas or all of it connecting to Gurupá and Monte Alegre varzea forests, as well as Marajó varzeas, the interfluve Tocantins-Araguaia/Maranhão, and the southern block of the interfluve Xingu/Tocantins-Araguaia. There were two records from the Negro-Branco moist forest, which also includes savanna-like “campinarana” formations. The range also reaches the transitional babaçu palm forests of Maranhão and the Mato Grosso seasonal forests (Supplementary Information Fig. S7, Table S3). The N-tiger cat’s range in the Amazon was determined by combining records with species distribution modeling, also matching the ecoregion perspective.Outside the Guiana Shield and likely the savanna patches of the region of the Upper Negro River, in other parts of the Amazon, the N-tiger cat seems to be restricted to the forests of the eastern Amazon, along the arc of deforestation and to transitional areas with savanna formations. The presence and absence points at camera-trapping sites could explain the N-tiger cat’s range in the Amazon and define its distribution range in the biome. Absence points, for instance, were usually located in dense rainforest habitats throughout the Amazon biome.The species may occasionally occupy rainforests, such as those of the Guianas, where it tends to be very rare. At a site in central Suriname, after an enormous trapping effort of  > 20,000 trap days in four years by cat specialists, over an area  > 1100 km2, no records of the N-tiger cat were found (Supplementary Information Table S2), although its presence is expected in that area26. This finding attests to the inherent rarity of this felid in its limited range within the Amazon. However, could its association with the arc of deforestation be related to the replacement of forest by bushy savanna-like vegetation that succeeds abandoned pastures? The other currently recognized subspecies, L. t. pardinoides (the Andean tiger cat) and L. t. oncilla (the oncilla), and the recently split southern tiger cat L. guttulus are all associated with forested areas. Conversely, L. t. tigrinus has higher abundance and is mostly found in the nonforested habitats of the Cerrado and Caatinga domains of Brazil and only rarely in rainforests. Thus, L. t. tigrinus may be an open-habitat (sub)species. However, within savannas, N-tiger cats are restricted to denser savanna formations, with open savannas deemed unsuitable24. In the semiarid Caatinga, the N-tiger cat also prefers denser formations27,28.One of the most interesting findings was the clear relationship between the ranges of the dominant mesopredator and subordinate species. The ranges of ocelots and N-tiger cats in the Amazon were diametrically opposite (Fig. 1), a finding never recorded for felids. The reported ocelot densities and relative abundance indexes (RAIs) in the Amazon range from 0.29 to 0.95 ind/km2 and 0.07–13.2 ind/100 trap-days, respectively7,29. Thus, the expected ocelot density found using modeling that allows for N-tiger cat presence is very low (Fig. 2A). In the Rupununi, the ocelot:N-tiger cat RAI ratio was roughly 10:1, with a very low RAI and expected density for N-tiger cats (see Supplementary Material). The only other relative abundance estimate of tiger cats presented for the Amazon30 was not confirmed as an estimate of tiger cats following inspection of the original records by the authors but as an estimate of margays or ocelots. This antagonistic relationship between ocelots and all other small cat species in their area of sympatry is quite impressive. It is density-dependent, as it seems to take effect only above an ocelot density threshold of 0.12 ind./km231. The influence can range from patterns of density, distribution, and occupancy to spatial and temporal use. Conversely, such an impact was not detected when either the small cats or ocelots were compared to the larger cats31,32,33,34,35.In view of the Red List assessments and applying the limited estimates presented, the expected total population size for N-tiger cats in the Amazon would be approximately 150 and 1622 individuals, considering their AOO or EOO, respectively. Applying the IUCN’s formula for mature individuals8, these numbers would be 45 and 487 individuals for the AOO and EOO, respectively.The ocelot’s preference for very dense rainforests may explain the low probability of N-tiger cat occurrence within the Amazon biome. Notably, most tiger cat records from rainforests and all those from premontane forests came from the Guiana Shield, a region where tropical grasslands and savannas dot more forested landscapes. The Guiana Highlands and Pantepui ecoregions, which make up a considerable portion of the shield, tend to have low ocelot densities (below 0.30 ind/km2), although they do contain some rainforest. Ocelot densities reach some of their lowest values in the Guianan savanna ecoregion (mean ocelot density of 0.029 in the savanna formations), where the N-tiger cat probability of occurrence was highest. At the Karanambu site in the Rupununi, all ocelot records came from either gallery forests or forest patches embedded in the savanna. Although the data did not allow us to test further hypotheses, it is likely that spatial partitioning occurs in the Guiana Shield, with N-tiger cats favoring habitats that are more open. Conversely, areas farther west in the Amazon biome, other than the predicted area, do not have any major savanna patches and are covered mostly by lowland tropical rainforest formations, where ocelots can potentially reach densities in excess of 0.7 ind/km2. Of all Amazonian records of N-tiger cats, only one came from west of the 68th meridian: a preserved specimen from Puerto Leguizamo on the Putumayo River in Colombia. The specimen was identified as L. t. pardinoides by its collector, so it most likely represents an individual that came down from the foothills of the Andes. Alternatively, it could have been caught in the Andean foothills but labeled generally as from Puerto Leguizamo, as museum records do not always present precise locations, like most of those from our dataset; thus, they could represent a broader region, not a single collection location.The records of L. t. tigrinus in the Monte-Alegre Várzea ecoregion and Tapajós-Xingu Moist Forest ecoregion (which shares a border with the Amazon River) are actually from the small savanna patches of Terra Santa and Alter do Chão, respectively, which are imbedded within the forests of these ecoregions. Similarly, the Negro-Branco Moist Forest ecoregion includes open-canopy white sand forests with savanna-like vegetation, known as ‘campinaranas’36.Although our model predicted a high probability of N-tiger cat presence in the Marajó Várzea ecoregion, the records from the island came from savanna patches and not from flooded forests and mangroves. Hence, we did not include such large areas in the AOO for the subspecies. It is likely that the highly predicted probability of presence there is an artifact of low predicted ocelot density. Nevertheless, the environment there is not suitable for either cat. Our ocelot density model was highly significant and explained almost 50% of the variation in ocelot density. The remaining variation was related to either other variables that could not be measured via satellite imagery (such as prey availability) or the sampling design of the different studies. Nonetheless, ocelot densities predicted from our model across the Amazon were within the expected range for the species29.Why are N-tiger cats absent in camera-trapping studies in Amazonian forests throughout the biome? The most straightforward answer seems to be because they simply are not there (central and western Amazon) or, where present, their numbers are extremely low (Guianas and eastern Amazon). The lack of surveys cannot be cited as a potential reason for their apparent absence because the studies that did not detect the species were conducted throughout the Amazon biome, in all nine Amazonian countries. Some of the areas have been surveyed for several years—or decades in some cases—and have failed to record a single individual (Supplementary Information Table S2). Typically, N-tiger cats appear, even prominently, on cameras in other biomes, such as in the savannas of the Cerrado and semiarid scrub of the Caatinga domain in Brazil, including sites where ocelots are present24,27,37. Clouded tiger cats (L. t. pardinoides) have also been frequently recorded on cameras in the Andes, higher than 1500 m above sea level34,38, but not in lowland Amazonian forests. This finding indicates that the N-tiger cat is not camera-shy. In northern Brazilian savannas, its density can reach 0.25 ind/km2 24. Coincidentally, this highest density estimate of the N-tiger cat is the same as the lowest ocelot density estimate for Amazonian forests24,29.Tiger cats and margays show high similarity, making misidentifications relatively common39. However, the evaluation of  > 3000 camera trap images of small-medium felids in the Amazon revealed that only one mildly resembled a tiger cat, a finding that supports the species being absent there and does not represent a case of mistaken identity with margays or even ocelots7.The Amazonian range of L. tigrinus is very limited, and populations are expected to be very small. With the upcoming split of L. t. tigrinus and L. t. pardinoides into two different species40, this situation would have serious implications for the conservation of the former. Thus, L. t. tigrinus conservation lies outside the “Amazonian safe haven” of most other carnivore species found there7. The Brazilian drylands Cerrado and Caatinga represent such places for L. t. tigrinus populations. Unfortunately, these biomes have had  > 50% of their cover completely removed41. Very importantly, besides being extremely rare in the Amazonian savannas, this rather limited vegetative formation is also considered highly threatened and of conservation priority42. Therefore, the tiger cat could become an emblematic flagship species representing the uniqueness of this vegetative formation in dire need of protection.In short, the picture that emerges is that although the N-tiger cat uses both rainforests and deciduous forests in the Amazon, it seems to be mostly associated with savanna formations and that its distribution in the Amazon is highly influenced by the ocelot, the dominant mesopredator. The N-tiger cat’s inherent rarity, expected population size, and restricted range in the Amazon suggest that this biome does not in fact represent a safe haven for the global conservation of this small felid. In addition to shedding light on and refining the N-tiger cat distribution in the Amazon, this paper highlights the importance of including biological variables, such as the potential impacts of competitors and predators on species presence and distribution, in SDMs. More

Davies EK, Peters AD, Keightley PD (1999) High frequency of cryptic deleterious mutations in Caenorhabditis elegans. Science 285:1748–1751Article 
CAS 
PubMed 

Google Scholar 
Eyre-Walker A, Keightley PD (2007) The distribution of fitness effects of new mutations. Nat Rev Genet 8:610–618Article 
CAS 
PubMed 

Google Scholar 
Eyre-Walker A, Keightley PD (2013) A comparison of models to infer the distribution of fitness effects of new mutations. Genetics 193:1197–1208Article 

Google Scholar 
Fry JD, Keightley PD, Heinsohn SL, Nuzhdi SV (1999) New estimates of the rates and effects of mildly deleterious mutation in Drosophila melanogaster. Proc Natl Acad Sci 96:574–579Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
García-Dorado A (2007) Shortcut predictions for fitness properties at the mutation-selection-drift balance and for its buildup after size reduction under different management strategies. Genetics 176:983–997Article 
PubMed 
PubMed Central 

Google Scholar 
García-Dorado A (2012) Understanding and predicting the fitness decline of shrunk populations: inbreeding, purging, mutation, and standard selection. Genetics 190:1461–1476Article 
PubMed 
PubMed Central 

Google Scholar 
García-Dorado A (2015) On the consequences of ignoring purging on genetic recommendations for minimum viable population rules. Heredity 115:185–187Article 
PubMed 
PubMed Central 

Google Scholar 
García-Dorado A, Caballero A (2021) Neutral genetic diversity as a useful tool for conservation biology. Conserv Genet 22:541–545Article 

Google Scholar 
Garner BA, Hoban S, Luikart G (2020) IUCN Red List and the value of integrating genetics. Conserv Genet 21:795–801Article 

Google Scholar 
Hedrick PW, García-Dorado A (2016) Understanding inbreeding depression, purging, and genetic rescue. Trends Ecol Evol 31:940–952Article 
PubMed 

Google Scholar 
Kardos M, Armstrong EE, Fitzpatrick SW, Hauser S, Hedrick PW, Miller J et al. (2021) The crucial role of genome-wide genetic variation in conservation. Proc Natl Acad Sci USA 118:e2104642118Khan A, Patel A, Shukla H, Viswanathan A, van der Valk T, Borthakur U, … & Ramakrishnan U (2021) Genomic evidence for inbreeding depression and purging of deleterious genetic variation in Indian tigers. Proc. Natl. Acad. Sci. 118Kimura M, Maruyama T, Crow JF (1963) The mutation load in small populations. Genetics 48:1303–1312Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kimura M (1980) Average time until fixation of a mutant allele in a finite population under continued mutation pressure: Studies by analytical, numerical, and pseudo-sampling methods. Proc Natl Acad Sci 77:522–526Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Morin PA, Archer FI, Avila CD, Balacco JR, Bukhman YV, Chow, W, … & Jarvis ED (2021) Reference genome and demographic history of the most endangered marine mammal, the vaquita. Mol Ecol Resour 21:1008–1020Mukai T (1964) The genetic structure of natural populations of Drosophila melanogaster. I. Spontaneous mutation rate of polygenes controlling viability. Genetics 50:1–19Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Nietlisbach P, Muff S, Reid JM, Whitlock MC, Keller LF (2019) Nonequivalent lethal equivalents: Models and inbreeding metrics for unbiased estimation of inbreeding load. Evol Applic 12:266–279Article 

Google Scholar 
O’Grady JJ, Brook BW, Reed DH, Ballou JD, Tonkyn DW, Frankham R (2006) Realistic levels of inbreeding depression strongly affect extinction risk in wild populations. Biol Conserv 133:42–51Article 

Google Scholar 
Pérez-Pereira N, Caballero A, García-Dorado A (2021) Reviewing the consequences of genetic purging on the success of rescue programs. Conserv Gen 23:1–17Article 

Google Scholar 
Pérez-Pereira N, Wang J, Quesada H, Caballero A (2022). Prediction of the minimum effective size of a population viable in the long term. Biodivers Conserv https://doi.org/10.1007/s10531-022-02456-zRobinson JA, Kyriazis CC, Nigenda-Morales SF, Beichman AC, Rojas-Bracho L, Robertson KM et al. (2022) The critically endangered vaquita is not doomed to extinction by inbreeding depression. Science 376:635–639Article 
CAS 
PubMed 

Google Scholar 
Teixeira JC, Huber CD (2021) The inflated significance of neutral genetic diversity in conservation genetics. Proc Natl Acad Sci USA 118:e2015096118Wade EE, Kyriazis C, Cavassim MIA, Lohmueller KE (2022) Quantifying the fraction of new mutations that are recessive lethal. bioRxiv 1–24, https://www.biorxiv.org/content/10.1101/2022.04.22.489225v1 More

Each of the 23 key variables can be used for analysis. To validate the dataset, we used five plant-related variables (diversity of order, family, genus, species and species endemic to China) to demonstrate the process of using the dataset for analysis as follows:(1) For the four variables of plant taxa “order”, “family”, “genus” and “species”, the similarity and difference in spatial distribution pattern of diversity of different taxa in the Qinling-Daba Mountains climate transition zone were analyzed. The spatial distribution pattern of the diversity of the four taxa is shown in Fig. 3, which is increasingly lower from south (low latitude) to north (high latitude). This result is consistent with the classical latitudinal gradient model of plant diversity. The boundary between higher diversity in the south and lower diversity in the north is roughly located in the area of Funiu Mountains in the eastern Qinling-Daba Mountains, Taibai Mountains in the central Qinling-Daba Mountains and Baishui River in the western Qinling-Daba Mountains. However, with the reduction in taxon scale, the spatial distribution pattern of diversity tends to be complex. Orders (Fig. 3a) and families (Fig. 3b) can be divided by lines, while genera (Fig. 3c) need thicker lines, and species (Fig. 3d) can only be divided by polygons. Figure 3 shows that the taxonomic groups of families are more clearly divided, while species can only be divided by staggered bands. Therefore, when dividing the north–south boundary, the family taxon scale is appropriate, whereas the species scale is more appropriate when studying the north–south transition zone.Fig. 3Spatial distribution of diversity of orders, families, genera and species. (a) The blue dotted line is basically the dividing line of the order diversity of 50 species. The order diversity to the north of the blue dotted line is lower than 50 species, and the order diversity to the south of the blue dotted line is higher than 50 species. (b) The blue dotted line is basically the dividing line of the family diversity of 150 species. The family diversity to the north of the blue dotted line is lower than 150 species, and the family diversity to the south of the blue dotted line is higher than 150 species. (c) The thicker blue dotted line is basically the dividing line of genus diversity of 578–681 species. The genus diversity to the north of the blue dotted line is lower than 578 species, and the genus diversity to the south of the blue dotted line is higher than 681 species. (d) The blue area is basically the dividing line of species diversity of 1385–1618 species. The species diversity to the north of the blue dotted line is lower than 1385 species, and the species diversity to the south of the blue dotted line is higher than 1618 species.Full size imageThe dataset can also count the orders, families and genera that appear in 58 nature reserves, indicating that these orders, families and genera are widely distributed in this area, while the orders, families and genera that only appear in a single nature reserve indicate that these taxa are unique to this nature reserve in this area, reflecting their locality and uniqueness, which is helpful to understanding the specific distribution of plants in detail. The relevant statistics are as follows:
There are 28 orders present in every nature reserve:
Liliales, Dipsacales, Lamiales, Fabales, Ericales, Poales, Saxifragales, Malpighiales, Malvales, Asterales, Fagales, Gentianales, Geraniales, Ranunculales, Rosales, Solanales, Apiales, Cornales, Brassicales, Caryophyllales, Dioscoreales, Santalales, Myrtales, Asparagales, Celastrales, Sapindales, Alismatales, and Boraginales.The order that only appears in one nature reserve is Petrosaviales, which appears in the Dabashan Nature Reserve in Chongqing.
There are 51 families present in every nature reserve:
Liliaceae, Primulaceae, Plantaginaceae, Lamiaceae, Euphorbiaceae, Cannabaceae, Juncaceae, Fabaceae, Poaceae, Elaeagnaceae, Betulaceae, Apocynaceae, Violaceae, Malvaceae, Crassulaceae, Campanulaceae, Asteraceae, Orchidaceae, Polygonaceae, Orobanchaceae, Onagraceae, Gentianaceae, Geraniaceae, Ranunculaceae, Rubiaceae, Rosaceae, Caprifoliaceae, Thymelaeaceae, Apiaceae, Cyperaceae, Cornaceae, Paeoniaceae, Brassicaceae, Amaryllidaceae, Caryophyllaceae, Rhamnaceae, Santalaceae, Asparagaceae, Celastraceae, Sapindaceae, Adoxaceae, Araliaceae, Berberidaceae, Hydrangeaceae, Scrophulariaceae, Convolvulaceae, Urticaceae, Salicaceae, Papaveraceae, Iridaceae, and Boraginaceae.There are 15 families that only appear in one nature reserve, as shown in Table 2.Table 2 Endemic families of the nature reserves in the Qinling-Daba Mountains and surrounding areas.Full size table
There are 54 genera present in every nature reserve:
Patrinia, Polygonum, Sanicula, Plantago, Allium, Delphinium, Euphorbia, Juncus, Cynanchum, Trigonotis, Artemisia, Sorbus, Polygonatum, Scutellaria, Cirsium, Viburnum, Ajuga, Viola, Galium, Geranium, Salix, Epilobium, Gentiana, Ranunculus, Malus, Acer, Rubia, Rosa, Torilis, Lonicera, Adenophora, Philadelphus, Cornus, Paeonia, Rhamnus, Rumex, Carex, Thalictrum, Asparagus, Carpesium, Clematis, Potentilla, Euonymus, Eleutherococcus, Berberis, Spiraea, Rubus, Populus, Vicia, Silene, Iris, Poa, Aster, and Buddleja.There were 225 genera that only appeared in one nature reserve, as shown in Figshare file 269.(2) For the “species endemic to China” variable of plants, we can see from the diversity distribution pattern of species endemic to China in this region (Fig. 4) that the number of endemic species in the Qinling-Daba Mountains is higher than that of species outside of the region, which reflects the strong transition zone in the Qinling-Daba Mountains. The variables of species endemic to China obtained from the Qinling-Daba Mountains and their surroundings were clustered by the Bray–Curtis dissimilarity measure70 and Ward’s minimum variance (the clustering method recommended for plant cluster analysis). The clustering results are shown in Fig. 5a. At the same time, the clustering results are displayed in space. Figure 5b shows that category 3 extends from the east outside the Qinling-Daba Mountains to the Baishuijiang Nature Reserve inside the western Qinling-Daba Mountains, which is consistent with the fact that the Qinling-Daba Mountains are an important ecogeographical “corridor” connecting the east and the west.Fig. 4Spatial distribution of diversity of species endemic to China in the Qinling-Daba Mountains and adjacent areas.Full size imageFig. 5(a) Clustering results of Ward’s connection aggregation of species endemic to China in 58 nature reserves. (b) Spatial distribution of clustering results of species endemic to China; the larger the dot and the darker the color, the earlier it is merged into this category, and the smaller the dot and the lighter the color, the later it is merged into this category.Full size image More