Philippa Kaur

Study siteThe study was carried out in post-agriculture forests at different growth stages near the forest reserve of Yoko (N00°17′; E25°18′; mean elevation 435 m a.s.l.), situated between 29 and 39 km south east of Kisangani, in the Democratic Republic of the Congo. We set up 15 (40 × 40 m) plots, set out in triplicate along five successional stages (15 plots): agriculture and 5, 12, 20, 60 years old secondary forest (respectively, 5 yrs, 12 yrs, 20 yrs, 60 yrs). Additionally, soils were also characterized in three agricultural plots (Ag). We interviewed owners, farmers, and local experts to determine the time-since-disturbance of all plots. Tree height measurements were recorded at the plot level for 20% of individuals of each diameter class. The climax vegetation in the region is classified as semi-deciduous tropical. Climate falls within the Af-type following the Köppen-Geiger classification33. Annual rainfall ranges from 1418 to 1915 mm with mean monthly temperatures varying from 23.7 to 26.2 °C. Throughout the year, the region is marked by a long and a short rainy season interrupted by two small dry seasons December–January and June–August. Soils in the region are highly weathered Oxisols, being poor in nutrients, with low pH and dominated by sandy texture.Sampling and sample analysisThroughfall and bulk precipitation was collected weekly using polyethylene (PE) funnels supported by a wooden pole of 1.5 m height to which a PE tube was attached and draining into 5 L PE container. A nylon mesh was placed in the neck of the funnel to avoid contamination by large particles. The container was buried in the soil and covered by leaves to avoid the growth of algae and to keep the samples cool. We installed eight throughfall collectors in each plot as two rows of four collectors, with approximately 8 m distance between all collectors. On every sampling occasion, the water volume in each collector was measured in the field, and recipients, funnels and mesh were replaced, rinsed with distilled water. A volume-weighted composite sample of the devices per plot was made. All samples were stored in a freezer immediately and sent in batch to Belgium for chemical analysis. The volume-weighted composite samples were first filtered using a nylon membrane filter of 0.45 µm before freezing. Total phosphorus was measured by inductively coupled plasma atomic emission spectroscopy (ICP AES, IRIS interpid II XSP, Thermo Scientific, USA). Although we acknowledge the potential for microbial activity in the collectors during a 1-week, dark, in situ storage of the samples, the use of total phosphorus concentration and lack of algal growth allow for complete phosphorus recovery.Following analysis, the samples from the replicate field sites per forest stage were pooled into ‘weekly’ forest-type samples, and these were subsequently analyzed for dissolved black carbon (DBC). In short, the pooled water samples were acidified to pH 2 and analyzed for dissolved organic carbon (DOC) concentration via high-temperature catalytic oxidation on Shimadzu TOC-L total organic carbon analyzer following established methodology34. DOC was isolated from the water samples by solid phase extraction (SPE) following Dittmar et al.35. Briefly, SPE cartridges (Varian Bond Elut PPL, 1 g, 6 mL) were conditioned sequentially with methanol, ultrapure water, and ultrapure water acidified to pH 2 using concentrated HCl, then passed through the SPE cartridges by gravity. SPE cartridges were dried under a stream of high-purity N2 gas. DOC was eluted from the SPE cartridge with methanol (SPE-DOC) and stored at −20 °C until further analysis. DBC was quantified using the benzenepolycarboxylic acid (BPCA) method as detailed in Wagner et al.20. The BPCA approach to quantifying DBC involves chemothermal oxidation of condensed aromatic DOC compounds to benzenehexacarboxylic acid (B6CA) and benzenepentacarboxylic acid (B5CA) products. The B6CA and B5CA oxidation products are robustly measured and derive exclusively from pyrogenic sources36. Condensed aromatic DBC, as measured using the BPCA method, is ubiquitous in aquatic environments globally21,37,38,39. DBC has also been quantified in throughfall and stemflow in longleaf pine forests that undergo regular prescribed burning40. Therefore, we use the BPCA method as a proxy for carbon inputs from biomass burning in the current study. To analyze our samples for BPCAs, aliquots of SPE-DOC (~0.5 mg C equivalents) were combined with concentrated HNO3 in flame-sealed glass ampoules and heated to 160 °C for 6 h. The resultant BPCA-containing residue was dried and re-dissolved in mobile phase for subsequent analysis. Individual BPCAs were separated and quantified using an HPLC system (UltiMate 3000, Thermo Fisher, Germany) (CV  More

1.Robert-Gangneux, F. & Darde, M. L. Epidemiology of and diagnostic strategies for Toxoplasmosis. Clin. Microbiol. Rev. 25, 264–296 (2012).CAS 
Article 

Google Scholar 
2.VanWormer, E., Fritz, H., Shapiro, K., Mazet, J. A. K. & Conrad, P. A. Molecules to modeling: Toxoplasma gondii oocysts at the human–animal–environment interface. Comp. Immunol. Microbiol. Infect. Dis. 36, 217–231 (2013).Article 

Google Scholar 
3.Cook, A. J. C. Sources of toxoplasma infection in pregnant women: European multicentre case-control study Commentary: Congenital toxoplasmosis—further thought for food. BMJ 321, 142–147 (2000).CAS 
Article 

Google Scholar 
4.Spalding, S. M., Amendoeira, M. R. R., Klein, C. H. & Ribeiro, L. C. Serological screening and toxoplasmosis exposure factors among pregnant women in South of Brazil. Rev. Soc. Bras. Med. Trop. 38, 173–177 (2005).Article 

Google Scholar 
5.Jones, J. L. et al. Risk factors for Toxoplasma gondii infection in the United States. Clin. Infect. Dis. 49, 878–884 (2009).Article 

Google Scholar 
6.Egorov, A. I. et al. Environmental risk factors for Toxoplasma gondii infections and the impact of latent infections on allostatic load in residents of Central North Carolina. BMC Infect. Dis. 18, 421. https://doi.org/10.1186/s12879-018-3343-y (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
7.Shapiro, K. et al. Environmental transmission of Toxoplasma gondii: Oocysts in water, soil and food. Food Waterborne Parasitol. 15, e00049; https://doi.org/10.1016/j.fawpar. (2019).8.Hill, D. et al. Identification of a sporozoite-specific antigen from Toxoplasma gondii. J. Parasitol. 97, 328–337 (2011).CAS 
Article 

Google Scholar 
9.Ballari, S. A. & Barrios-García, M. N. A review of wild boar Sus scrofa diet and factors affecting food selection in native and introduced ranges: A review of wild boar Sus scrofa diet. Mamm. Rev. 44, 124–134 (2014).Article 

Google Scholar 
10.Kodera, Y., Kanzaki, N., Ishikawa, N. & Minagawa, A. Food habits of wild boar (Sus scrofa) inhabiting Iwami District, Shimane Prefecture, western Japan (In Japanese). Mamm. Sci. 53, 279–287 (2013).
Google Scholar 
11.Chambers, L. K., Singleton, G. R. & Krebs, C. J. Movements and social organization of wild house mice (Mus domesticus) in the wheatlands of northwestern Victoria, Australia. J. Mammal. 81, 59–69 (2000).12.Oka, T. Home range and mating system of two sympatric field mouse species, Apodemus speciosus and Apodemus argenteus. Ecol. Res. 7, 163–169 (1992).Article 

Google Scholar 
13.Yatake, H., Nashimoto, M., Shimano, K., Matuki, R. & Shiraki, S. Present status and subjects of estimation methods of Japanese hare (Lepus brachyurus) density (in Japanese). Mamm. Sci. 42, 23–34 (2002).
Google Scholar 
14.Setoguchi, M. Utilization of holes and home ranges in the Japanese long-tailed mice (Apodemus argenteus) (in Japanese). Jap. J. Ecol. 31, 385–394 (1981).
Google Scholar 
15.Rostami, A. et al. The global seroprevalence of Toxoplasma gondii among wild boars: A systematic review and meta-analysis. Vet. Parasitol. 244, 12–20 (2017).Article 

Google Scholar 
16.Lopez, A. L., Pineda, E., Garakian, A. & Cherry, J. D. Effect of heat inactivation of serum on Bordetella pertussis antibody determination by enzyme-linked immunosorbent assay. Diagn. Microbiol. Infect. Dis. 30, 21–24 (1998).CAS 
Article 

Google Scholar 
17.Taniguchi, Y. et al. A Toxoplasma gondii strain isolated in Okinawa, Japan shows high virulence in Microminipigs. Parasitol. Int. 72, 101935; https://doi.org/10.1016/j.parint.2019.101935 (2019).18.Tadano, R., Nagai, A. & Moribe, J. Local-scale genetic structure in the Japanese wild boar (Sus scrofa leucomystax): insights from autosomal microsatellites. Conserv. Genet. 17, 1125–1135 (2016).Article 

Google Scholar 
19.Ikeda, T., Asano, M., Kuninaga, N. & Suzuki, M. Monitoring relative abundance index and age ratios of wild boar (Sus scrofa) in small scale population in Gifu Prefecture, Japan during classical swine fever outbreak. J. Vet. Med. Sci. 82, 861–865 (2020).Article 

Google Scholar 
20.Matsuo, K., Uetsu, H., Takashima, Y. & Abe, N. High Occurrence of Sarcocystis infection in sika deer Cervus nippon centralis and Japanese wild boar Sus scrofa leucomystax and molecular characterization of Sarcocystis and Hepatozoon isolates from their muscles (in Japanese). Jpn. J. Zoo. Wildl. Med. 21, 35–40 (2016).Article 

Google Scholar 
21.Ogedengbe, M. E. et al. Molecular phylogenetic analyses of tissue coccidia (sarcocystidae; apicomplexa) based on nuclear 18s rDNA and mitochondrial COI sequences confirms the paraphyly of the genus Hammondia. Parasitol. Open 2, e2; https://doi.org/10.1017/pao.2015.7 (2016).22.Moon, M. H. Serological cross-reactivity between Sarcocystis and Toxoplasma in pigs. Kor. J. Parasitol. 25, 188–194 (1987).Article 

Google Scholar 
23.Dubey, J. P. et al. All about Toxoplasma gondii infections in pigs: 2009–2020. Vet. Parasitol. 288, 109185 (2020).24.Puchalska, M. et al. Prevalence of Toxoplasma gondii antibodies in wild boar (Sus scrofa) from Strzałowo Forest Division, Warmia and Mazury Region, Poland. Ann. Agric. Environ. Med. 28, 237–242 (2021).25.Dubey, J. P. et al. Genotyping of viable Toxoplasma gondii from the first national survey of feral swine revealed evidence for sylvatic transmission cycle, and presence of highly virulent parasite genotypes. Parasitology 147, 295–302 (2020).CAS 
Article 

Google Scholar 
26.Kia, E. B., Mirhendi, H., Rezaeian, M., Zahabiun, F. & Sharbatkhori, M. First molecular identification of Sarcocystis miescheriana (Protozoa, Apicomplexa) from wild boar (Sus scrofa) in Iran. Exp. Parasitol. 127, 724–726 (2011).CAS 
Article 

Google Scholar 
27.Coelho, C. et al. Unraveling Sarcocystis miescheriana and Sarcocystis suihominis infections in wild boar. Vet. Parasitol. 212, 100–104 (2015).Article 

Google Scholar 
28.Gazzonis, A. L. et al. Prevalence and molecular characterization of Sarcocystis miescheriana and Sarcocystis suihominis in wild boars (Sus scrofa) in Italy. Parasitol. Res. 118, 1271–1287 (2019).Article 

Google Scholar 
29.Huang, Z. et al. Morphological and molecular characterizations of Sarcocystis miescheriana and Sarcocystis suihominis in domestic pigs (Sus scrofa) in China. Parasitol. Res. 118, 3491–3496 (2019).Article 

Google Scholar 
30.Matsuo, K. et al. Seroprevalence of Toxoplasma gondii infection in cattle, horses, pigs and chickens in Japan. Parasitol. Int. 63, 638–639 (2014).Article 

Google Scholar 
31.Singer, F., Otto, D., Tipton, A. & Hable, C. Home ranges, movements, and habitat use of European wild boar in Tennessee. J. Wildl. Manag. 45, 343–353 (1981).Article 

Google Scholar 
32.Hollings, T., Jones, M., Mooney, N. & McCallum, H. Wildlife disease ecology in changing landscapes: Mesopredator release and toxoplasmosis. Int. J. Parasitol. Parasites Wildl. 2, 110–118 (2013).Article 

Google Scholar 
33.Maeda, T., Nakashita, R., Shionosaki, K., Yamada, F. & Watari, Y. Predation on endangered species by human-subsidized domestic cats on Tokunoshima Island. Sci. Rep. 9, 16200. https://doi.org/10.1038/s41598-019-52472-3 (2019).34.QGIS Development Team. Quantum GIS Geographic Information System. Open Source Geospatial Foundation Project. http://www.qgis.org/en/site/ (2021).35.Verma, S. K., Lindsay, D. S., Grigg, M. E. & Dubey, J. P. Isolation, culture and cryopreservation of Sarcocystis species. Curr. Protoc. Microbiol. https://doi.org/10.1002/cpmc.32 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
36.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2020).37.Robin, X. et al. pROC: an open-source package for R and S + to analyze and compare ROC curves. BMC Bioinformatics 12, 77 (2011).Article 

Google Scholar  More

A diverse and active DNA virosphereWe first leveraged two existing metagenomes that were constructed from the Konza native prairie soil14,15 to screen for viral sequences at the site. Each of the metagenomes was obtained from a composite of all the replicate soils collected at ambient field moisture conditions. One of the metagenomes was de novo assembled from deep sequence data (1.1 Tb)14 and the second was a hybrid assembly of short and long reads (267.0 Gb)16. The combination of the two metagenomes was used to maximize the coverage of viral sequences from the Konza prairie site. To balance between the detection limits of the viral detection tools and the wide range of viral genome size, the viral contigs > 2.5 kb in length were combined with those obtained from screening of the two largest public viral databases (i.e., IMG/VR17 and NCBI Virus16) to further increase the coverage of DNA viral sequences. We acknowledge that the length cutoff of 2.5 kb would preclude detection of some ssDNA viruses with small segmented genome sizes (e.g., Nanoviridae18). As a result, a DNA viral database for the site was curated that included 726,108 de-replicated viral contigs. The DNA viral database then served as a scaffold for mapping of metatranscriptome and metaproteome datasets to determine the activities of soil DNA viruses and their responses to differences in soil moisture. This approach was also recently applied to detect the transcriptional activity of marine prokaryotic and eukaryotic viruses19,20,21,22 and giant viruses in soil5.The metatranscriptome reads from both wet and dry treatments were mapped to a total of 416 unique DNA viral contigs using stringent criteria (% sequence identity > 95% and % sequence coverage > 80%). The 416 DNA viral contigs with an average sequence length of 19 kb were highly diverse and grouped into 139 clusters, with 111 of the clusters being singletons (Supplementary Data 1).We aimed to assign putative host taxa to the viral clusters by combining several approaches: CRISPR spacer matching, and screening for host and viral sequence similarities to respective databases (details in ‘Methods’). As a result, we assigned putative viral host taxa to 160 out of the 416 transcribed DNA viral contigs. Some of these were assigned to more than one host (Supplementary Data 1), resulting in a total of 181 virus–host pairings (Fig. 1a). Of these, 79 host–virus pairs were detected only in the dry soil treatment, 51 were only in the wet soil treatment, and an additional 51 were found in both dry and wet treatments (Fig. 1a). Consistent with previous reports4, the majority of the transcribed DNA viral contigs were annotated as bacteriophage sequences. Different sets of transcribed DNA viral contigs were unique to wet or dry soils and assigned to specific hosts at the phylum level, whereas others were shared (Fig. 1a). However, the dominant soil taxa, i.e., Proteobacteria and Actinobacteria that were previously identified by 16S rRNA gene sequencing in this soil environment, were predicted as hosts under both wet and dry conditions (Supplementary Fig. 1a). Eukaryotic DNA viruses, such as Bracovirus and Ichnovirus belonging to a family of insect viruses within the Polydnaviridae family, were also transcribed in the soils (Fig. 1a and Supplementary Data 1). Most of these insect viruses were only detected in dry soil conditions. These differences in virus–host pairings suggest that some of the respective hosts were impacted differently by the dry and wet incubation conditions.Fig. 1: Transcribed DNA viral communities and their responses to wet and dry soil conditions.a An alluvium plot that illustrates pairings of the transcribed DNA viral contigs to putative host phyla. The transcribed DNA viral community was comprised of viral contigs from the curated DNA viral databases that were mapped by quality-filtered metatranscriptomic reads. The alluvia are colored by host taxa (first x axis of each sub-panel) assigned to respective transcribed DNA viral contigs (second x axis of each sub-panel). b A Venn diagram showing the number of unique transcribed DNA viral contigs detected in both wet and dry soils and ones exclusively detected in one of the soils. c Number of unique DNA viral contigs detected. A t-Test shows significantly more DNA contigs were transcribed in dry soil (p = 0.044). d Number of transcripts that mapped to the DNA viral contigs. For panels (c) and (d), the two independent field sites of Konza Experimental Field Station are indicated as site A (circles) and site C (triangles), with the wet soil in blue and dry soil in red.Full size imageThere were 21 DNA viral contigs that were assigned to hosts across multiple bacterial phyla suggesting the presence of viral generalists1,23 (Supplementary Data 1). We recognize that host assignment based on CRISPR spacer matching, however, is limited to detection of recent or historical virus–host interactions that were captured at the time of sampling24. As bioinformatics assignment of virus–host linkages only suggests possible pairings based on sequence features, there are also chances of introducing false positives. However, we applied the most stringent criteria possible to provide confident host assignments.Increased activity of a subset of DNA viruses in wet soilSoil moisture has a strong influence on the community structures of transcribed DNA viruses. The majority of the transcriptionally active DNA viral contigs were unique to wet or dry conditions, with only 111 viral contigs (~ 26.7%) detected in both wet and dry soils, suggesting that the different soil moisture conditions may shape the activity of the DNA viral community differently (Fig. 1b). Interestingly, although a significantly higher number of transcribed DNA viral contigs were detected in dry soils (Fig. 1b, c), the levels of transcriptional activity were significantly higher (based on the normalized abundance of RNA reads that mapped to the viral contigs) for DNA viruses in wet soils irrespective of sampling site location (Fig. 1d). DNA viral contigs with mapped transcripts could represent either prophages that are passively replicated along with their host genomes, or (lytic) viruses that are actively regulating early/middle/late expression of viral gene clusters25. In soil, a lysogenic lifestyle is considered to be an adaptive strategy for viruses to cope with long periods of low host activity26,27. Therefore, the 1.5-fold increase in the number of transcribed DNA viral contigs representing transcriptionally active DNA viruses, but with lower levels of overall transcription, in dry soil suggests that the increase was due to a higher prevalence of lysogeny in dry conditions. This hypothesis is strengthened by our finding of a 20-fold increase in transcripts for lysogenic markers (i.e., integrase and excisionase) in one of our replicates (A-2) in dry compared to wet conditions (Supplementary Data 2). High number of lysogenic phages were also previously reported in dry Antarctic soils using a cultivation-independent induction assay28. By contrast, under wet soil conditions we found a 2-fold increase in transcription of fewer viral contigs representing a subset of DNA viruses, suggesting that those viruses were more transcriptionally active in response to higher soil moisture. In addition, there was a higher correlation between prokaryotic abundances, as estimated by 16S rRNA gene sequencing, with DNA viral transcript counts in wet soils (R2 = 0.593, Supplementary Fig. 1d) in comparison to dry soils (R2 = 0.069, Supplementary Fig. 1d), supporting this hypothesis.We then identified which soil DNA viruses were most transcriptionally active and how they responded to the differences in soil moisture. As the majority of the transcribed DNA viral contigs (97%) were environmental viruses with unclassified taxonomy assignment, we were not able to calculate the taxonomic abundance of each and instead compared the differential abundances of the transcribed viral contigs. There were four DNA viral contigs with significantly different levels of transcription under wet and dry conditions (VC_1, VC_19, VC_282, VC_412; Fig. 2a). Contigs VC_1 and VC_19 correspond to unclassified viral contigs deposited in IMG/VR (identifiers of ‘REF:2547132004_2547132004’ and ‘3300010038_Ga0126315_10000854’) that were previously detected in metagenomes from the Rifle site29 and from serpentine soil in the UC McLaughlin Reserve30, respectively. Contigs VC_282 and VC_412 were extracted from our Kansas metagenomes. Contigs VC_1 and VC_19 had significantly higher levels of transcriptional activity in wet soils compared to dry soils (p  More

Agrawal AF, Stinchcombe JR (2009) How much do genetic covariances alter the rate of adaptation? Proc Biol Sci 276:1183–1191PubMed 
PubMed Central 

Google Scholar 
Aitken SN, Whitlock MC (2013) Assisted gene flow to facilitate local adaptation to climate change. Annu Rev Ecol Evol S 44:367–388Article 

Google Scholar 
Andersen O (2012) Hemoglobin polymorphisms in Atlantic cod—a review of 50 years of study. Mar Genom 8:59–65Article 

Google Scholar 
Anttila K, Dhillon RS, Boulding EG, Farrell AP, Glebe BD, Elliott JA et al. (2013) Variation in temperature tolerance among families of Atlantic salmon (Salmo salar) is associated with hypoxia tolerance, ventricle size and myoglobin level. J Exp Biol 216:1183–1190CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B 57:289–300
Google Scholar 
Berrigan D, Charnov EL (1994) Reaction norms for age and size at maturity in response to temperature: a puzzle for life historians. Oikos 70:474–478Article 

Google Scholar 
Bontrager M, Angert AL (2019) Gene flow improves fitness at a range edge under climate change. Evol Lett 3:55–68PubMed 
Article 
PubMed Central 

Google Scholar 
Bowen SJ, Washburn KW (1984) Genetics of heat tolerance in Japanese quail. Poult Sci 63:430–435CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Bradshaw AD (1965) Evolutionary significance of phenotypic plasticity in plants. Adv Genet 13:115–155Article 

Google Scholar 
Breau C, Cunjak RA, Bremset G (2007) Age-specific aggregation of wild juvenile Atlantic salmon Salmo salar at cool water sources during high temperature events. J Fish Biol 71:1179–1191Article 

Google Scholar 
Butler DG, Cullis BR, Gilmour AR, Gogel BJ (2009) Mixed models for S language environments ASReml-R reference manual. Queensland Department of Primary Industries and Fisheries, NSW Department of Primary Industries, Brisbane, Australia
Google Scholar 
Catullo RA, Llewelyn J, Phillips BL, Moritz CC (2019) The potential for rapid evolution under anthropogenic climate change. Curr Biol 29:R996–R1007CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Charmantier A, Garant D (2005) Environmental quality and evolutionary potential: lessons from wild populations. Proc R Soc B 272:1415–1425PubMed 
PubMed Central 
Article 

Google Scholar 
Cheung WWL, Sarmiento JL, Dunne J, Frölicher TL, Lam VWY, Deng Palomares ML et al. (2012) Shrinking of fishes exacerbates impacts of global ocean changes on marine ecosystems. Nat Clim Change 3:254–258Article 

Google Scholar 
Clark TD, Sandblom E, Jutfelt F (2013) Aerobic scope measurements of fishes in an era of climate change: respirometry, relevance and recommendations. J Exp Biol 216:2771–2782PubMed 
Article 
PubMed Central 

Google Scholar 
Debes PV, Fraser DJ, McBride MC, Hutchings JA (2013) Multigenerational hybridisation and its consequences for maternal effects in Atlantic salmon. Heredity 111:238–247CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Debes PV, Piavchenko N, Erkinaro J, Primmer CR (2020) Genetic growth potential, rather than phenotypic size, predicts migration phenotype in Atlantic salmon. Proc R Soc B 287:20200867PubMed 
PubMed Central 
Article 

Google Scholar 
Debes PV, Piavchenko N, Ruokolainen A, Ovaskainen O, Moustakas-Verho JE, Parre N et al. (2021) Polygenic and major-locus contributions to sexual maturation timing in Atlantic salmon. Mol Ecol https://doi.org/10.1111/mec.16062Dwyer WP, Piper RG (1987) Atlantic salmon growth efficiency as affected by temperature. Prog Fish Cult 49:57–59Article 

Google Scholar 
Edmands S (2007) Between a rock and a hard place: evaluating the relative risks of inbreeding and outbreeding for conservation and management. Mol Ecol 16:463–475PubMed 
Article 
PubMed Central 

Google Scholar 
Elliott JM, Elliott JA (2010) Temperature requirements of Atlantic salmon Salmo salar, brown trout Salmo trutta and Arctic charr Salvelinus alpinus: predicting the effects of climate change. J Fish Biol 77:1793–1817CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Etterson JR, Shaw RG (2001) Constraint to adaptive evolution in response to global warming. Science 294:151–154CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Falconer DS (1952) The problem of environment and selection. Am Nat 86:293–298Article 

Google Scholar 
Franks SJ, Hoffmann AA (2012) Genetics of climate change adaptation. Annu Rev Genet 46:185–208CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Gallaugher P, Farrell AP (1998) Hematocrit and blood oxygen-carrying capacity. In: Perry SF, Tufts BL (eds) Fish respiration. Academic Press, San Diego, California, p 185–227
Google Scholar 
Gamperl AK, Ajiboye OO, Zanuzzo FS, Sandrelli RM, Peroni EDFC, Beemelmanns A (2020) The impacts of increasing temperature and moderate hypoxia on the production characteristics, cardiac morphology and haematology of Atlantic Salmon (Salmo salar). Aquaculture 519:734874Article 

Google Scholar 
Glover KA, Otterå H, Olsen RE, Slinde E, Taranger GL, Skaala Ø (2009) A comparison of farmed, wild and hybrid Atlantic salmon (Salmo salar L.) reared under farming conditions. Aquaculture 286:203–210Article 

Google Scholar 
Glover KA, Solberg MF, Besnier F, Skaala O (2018) Cryptic introgression: evidence that selection and plasticity mask the full phenotypic potential of domesticated Atlantic salmon in the wild. Sci Rep 8:13966PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Glover KA, Solberg MF, McGinnity P, Hindar K, Verspoor E, Coulson MW et al. (2017) Half a century of genetic interaction between farmed and wild Atlantic salmon: Status of knowledge and unanswered questions. Fish Fish 18:890–927Article 

Google Scholar 
Good C, Davidson J (2016) A review of factors influencing maturation of Atlantic salmon, Salmo salar, with focus on water recirculation aquaculture system environments. J World Aquacult Soc 47:605–632Article 

Google Scholar 
Hartman KJ, Porto MA (2014) Thermal performance of three rainbow trout strains at above-optimal temperatures. Trans Am Fish Soc 143:1445–1454Article 

Google Scholar 
Henderson CR (1950) Estimation of genetic parameters. Ann Math Stat 21:309–310
Google Scholar 
Henderson CR (1973) Sire evaluation and genetic trends. J Anim Sci 1973:10–41Article 

Google Scholar 
Hill WG (2010) Understanding and using quantitative genetic variation. Philos Trans R Soc Lond B Biol Sci 365:73–85PubMed 
PubMed Central 
Article 

Google Scholar 
Hoffmann AA, Merilä J (1999) Heritable variation and evolution under favourable and unfavourable conditions. Trends Ecol Evol 14:96–101CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Hoffmann AA, Sgrò CM (2011) Climate change and evolutionary adaptation. Nature 470:479–485CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Huey RB, Kearney MR, Krockenberger A, Holtum JA, Jess M, Williams SE (2012) Predicting organismal vulnerability to climate warming: roles of behaviour, physiology and adaptation. Philos Trans R Soc Lond B Biol Sci 367:1665–1679PubMed 
PubMed Central 
Article 

Google Scholar 
Huey RB, Kingsolver JG (1989) Evolution of thermal sensitivity of ectotherm performance. Trends Ecol Evol 4:131–135CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Hutchings JA, Myers RA (1994) The evolution of alternative mating strategies in variable environments. Evol Ecol 8:256–268Article 

Google Scholar 
IPCC (2014) Future climate changes, risk and impacts. In: Core Writing Team, Pachauri RK, Meyer LA (eds) Climate change 2014: synthesis report. Contribution of working groups I, II and III to the fifth assessment report of the intergovernmental panel on climate change. IPCC, Geneva, Switzerland, pp 56–74Jones OR, Wang J (2010) COLONY: a program for parentage and sibship inference from multilocus genotype data. Mol Ecol Resour 10:551–555PubMed 
Article 
PubMed Central 

Google Scholar 
Jonsson B, Forseth T, Jensen AJ, Naesje TF (2001) Thermal performance of juvenile Atlantic Salmon, Salmo salar L. Funct Ecol 15:701–711Article 

Google Scholar 
Jonsson B, Jonsson N, Finstad AG (2013) Effects of temperature and food quality on age and size at maturity in ectotherms: an experimental test with Atlantic salmon. J Anim Ecol 82:201–210PubMed 
Article 
PubMed Central 

Google Scholar 
Jutfelt F, Norin T, Ern R, Overgaard J, Wang T, McKenzie DJ et al. (2018) Oxygen- and capacity-limited thermal tolerance: blurring ecology and physiology. J Exp Biol 221:jeb169615PubMed 
Article 
PubMed Central 

Google Scholar 
Kellermann V, van Heerwaarden B, Sgro CM (2017) How important is thermal history? Evidence for lasting effects of developmental temperature on upper thermal limits in Drosophila melanogaster. Proc R Soc B 284:20170447PubMed 
PubMed Central 
Article 

Google Scholar 
Kelly M (2019) Adaptation to climate change through genetic accommodation and assimilation of plastic phenotypes. Philos Trans R Soc Lond B Biol Sci 374:20180176PubMed 
PubMed Central 
Article 

Google Scholar 
Kenward MG, Roger JH (1997) Small sample inference for fixed effects from restricted maximum likelihood. Biometrics 53:983–997CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Kingsolver JG, Buckley LB (2017) Quantifying thermal extremes and biological variation to predict evolutionary responses to changing climate. Philos Trans R Soc Lond B Biol Sci 372:20160147PubMed 
PubMed Central 
Article 

Google Scholar 
Kingsolver JG, Heckman N, Zhang J, Carter PA, Knies JL, Stinchcombe JR et al. (2015) Genetic variation, simplicity, and evolutionary constraints for function-valued traits. Am Nat 185:E166–181PubMed 
Article 
PubMed Central 

Google Scholar 
Kingsolver JG, Izem R, Ragland GJ (2004) Plasticity of size and growth in fluctuating thermal environments: comparing reaction norms and performance curves. Integr Comp Biol 44:450–460PubMed 
Article 
PubMed Central 

Google Scholar 
Klemetsen A, Amundsen PA, Dempson JB, Jonsson B, Jonsson N, O’Connell MF et al. (2003) Atlantic salmon Salmo salar L., brown trout Salmo trutta L. and Arctic charr Salvelinus alpinus (L.): a review of aspects of their life histories. Ecol Freshwat Fish 12:1–59Article 

Google Scholar 
Komender P, Hoeschele I (1989) Use of mixed-model methodology to improve estimation of crossbreeding parameters. Livest Prod Sci 21:101–113Article 

Google Scholar 
Lande R, Arnold SJ (1983) The measurement of selection on correlated characters. Evolution 37:1210–1226PubMed 
Article 
PubMed Central 

Google Scholar 
Lenormand T (2002) Gene flow and the limits to natural selection. Trends Ecol Evol 17:183–189Article 

Google Scholar 
Lutterschmidt WI, Hutchison VH (1997) The critical thermal maximum: history and critique. Can J Zool 75:1561–1574Article 

Google Scholar 
Lynch M, Walsh B (1998) Genetics and analysis of quantitative traits. Sinauer, Sunderland, Massachusetts
Google Scholar 
Mather K, Jinks JL (1982) Biometrical genetics: the study of continuous variation, 3rd edn. Chapman and Hall, LondonBook 

Google Scholar 
McKenzie DJ, Zhang Y, Eliason EJ, Schulte PM, Claireaux G, Blasco FR et al. (2021) Intraspecific variation in tolerance of warming in fishes. J Fish Biol 98:1536–1555PubMed 
Article 
PubMed Central 

Google Scholar 
Merilä J, Hendry AP (2014) Climate change, adaptation, and phenotypic plasticity: the problem and the evidence. Evol Appl 7:1–14PubMed 
PubMed Central 
Article 

Google Scholar 
Messmer V, Pratchett MS, Hoey AS, Tobin AJ, Coker DJ, Cooke SJ et al. (2017) Global warming may disproportionately affect larger adults in a predatory coral reef fish. Glob Change Biol 23:2230–2240Article 

Google Scholar 
Morgan R, Finnoen MH, Jensen H, Pelabon C, Jutfelt F (2020) Low potential for evolutionary rescue from climate change in a tropical fish. Proc Natl Acad Sci USA 117:33365–33372CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Morita K, Tamate T, Kuroki M, Nagasawa T (2014) Temperature-dependent variation in alternative migratory tactics and its implications for fitness and population dynamics in a salmonid fish. J Anim Ecol 83:1268–1278PubMed 
Article 
PubMed Central 

Google Scholar 
Moritz C, Langham G, Kearney M, Krockenberger A, VanDerWal J, Williams S (2012) Integrating phylogeography and physiology reveals divergence of thermal traits between central and peripheral lineages of tropical rainforest lizards. Philos Trans R Soc Lond B Biol Sci 367:1680–1687PubMed 
PubMed Central 
Article 

Google Scholar 
Morrissey MB, Kruuk LE, Wilson AJ (2010) The danger of applying the breeder’s equation in observational studies of natural populations. J Evol Biol 23:2277–2288CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Morrissey MB, Liefting M (2016) Variation in reaction norms: statistical considerations and biological interpretation. Evolution 70:1944–1959PubMed 
Article 
PubMed Central 

Google Scholar 
Muff S, Niskanen AK, Saatoglu D, Keller LF, Jensen H (2019) Animal models with group-specific additive genetic variances: extending genetic group models. Genet Sel Evol 51:7PubMed 
PubMed Central 
Article 

Google Scholar 
Munday PL, Donelson JM, Domingos JA (2017) Potential for adaptation to climate change in a coral reef fish. Glob Change Biol 23:307–317Article 

Google Scholar 
Muñoz NJ, Anttila K, Chen Z, Heath JW, Farrell AP, Neff BD (2014a) Indirect genetic effects underlie oxygen-limited thermal tolerance within a coastal population of chinook salmon. Proc R Soc B 281:20141082PubMed 
PubMed Central 
Article 

Google Scholar 
Muñoz NJ, Farrell AP, Heath JW, Neff BD (2014b) Adaptive potential of a Pacific salmon challenged by climate change. Nat Clim Change 5:163–166Article 

Google Scholar 
Muñoz NJ, Farrell AP, Heath JW, Neff BD (2018) Hematocrit is associated with thermal tolerance and modulated by developmental temperature in juvenile Chinook salmon. Physiol Biochem Zool 91:757–762PubMed 
Article 
PubMed Central 

Google Scholar 
Ørsted M, Hoffmann AA, Rohde PD, Sørensen P, Kristensen TN (2019) Strong impact of thermal environment on the quantitative genetic basis of a key stress tolerance trait. Heredity 122:315–325PubMed 
Article 
PubMed Central 

Google Scholar 
Pörtner HO, Bock C, Mark FC (2017) Oxygen- and capacity-limited thermal tolerance: bridging ecology and physiology. J Exp Biol 220:2685–2696PubMed 
Article 
PubMed Central 

Google Scholar 
Pörtner HO, Farrell AP (2008) Physiology and climate change. Science 322:690–692PubMed 
Article 
PubMed Central 

Google Scholar 
Pörtner HO, Peck MA (2010) Climate change effects on fishes and fisheries: towards a cause-and-effect understanding. J Fish Biol 77:1745–1779PubMed 
Article 
PubMed Central 

Google Scholar 
Robertson A (1959) The sampling variance of the genetic correlation coefficient. Biometrics 15:469–485Article 

Google Scholar 
Robinson ML, Gomez-Raya L, Rauw WM, Peacock MM (2008) Fulton’s body condition factor K correlates with survival time in a thermal challenge experiment in juvenile Lahontan cutthroat trout (Oncorhynchus clarki henshawi). J Therm Biol 33:363–368Article 

Google Scholar 
Rowe DK, Thorpe JE, Shanks AM (1991) Role of fat stores in the maturation of male Atlantic salmon (Salmo salar) parr. Can J Fish Aquat Sci 48:405–413Article 

Google Scholar 
Sheridan JA, Bickford D (2011) Shrinking body size as an ecological response to climate change. Nat Clim Change 1:401–406Article 

Google Scholar 
Siepielski AM, Morrissey MB, Carlson SM, Francis CD, Kingsolver JG, Whitney KD et al. (2019) No evidence that warmer temperatures are associated with selection for smaller body sizes. Proc R Soc B 286:20191332PubMed 
PubMed Central 
Article 

Google Scholar 
Sinclair BJ, Marshall KE, Sewell MA, Levesque DL, Willett CS, Slotsbo S et al. (2016) Can we predict ectotherm responses to climate change using thermal performance curves and body temperatures? Ecol Lett 19:1372–1385PubMed 
Article 
PubMed Central 

Google Scholar 
Solberg MF, Dyrhovden L, Matre IH, Glover KA (2016) Thermal plasticity in farmed, wild and hybrid Atlantic salmon during early development: has domestication caused divergence in low temperature tolerance? BMC Evol Biol 16:38PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Solberg MF, Fjelldal PG, Nilsen F, Glover KA (2014) Hatching time and alevin growth prior to the onset of exogenous feeding in farmed, wild and hybrid Norwegian Atlantic salmon. PLoS ONE 9:e113697PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Stillman JH (2019) Heat waves, the new normal: summertime temperature extremes will impact animals, ecosystems, and human communities. Physiology 34:86–100CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Sutton SG, Bult TP, Haedrich RL (2000) Relationships among fat weight, body weight, water weight, and condition factors in wild Atlantic salmon parr. Trans Am Fish Soc 129:527–538Article 

Google Scholar 
Taggart JB (2006) FAP: an exclusion-based parental assignment program with enhanced predictive functions. Mol Ecol Notes 7:412–415Article 
CAS 

Google Scholar 
Taranger GL, Carrillo M, Schulz RW, Fontaine P, Zanuy S, Felip A et al. (2010) Control of puberty in farmed fish. Gen Comp Endocrinol 165:483–515CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Thompson RM, Beardall J, Beringer J, Grace M, Sardina P (2013) Means and extremes: building variability into community-level climate change experiments. Ecol Lett 16:799–806PubMed 
Article 
PubMed Central 

Google Scholar 
Thorpe JE (1994) Reproductive strategies in Atlantic salmon, Salmo salar L. Aquacult Res 25:77–87Article 

Google Scholar 
Tromp JJ, Jones PL, Brown MS, Donald JA, Biro PA, Afonso LOB (2018) Chronic exposure to increased water temperature reveals few impacts on stress physiology and growth responses in juvenile Atlantic salmon. Aquaculture 495:196–204Article 

Google Scholar 
Underwood ZE, Myrick CA, Rogers KB (2012) Effect of acclimation temperature on the upper thermal tolerance of Colorado River cutthroat trout Oncorhynchus clarkii pleuriticus: thermal limits of a North American salmonid. J Fish Biol 80:2420–2433CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Van Leeuwen TE, McLennan D, McKelvey S, Stewart DC, Adams CE, Metcalfe NB (2016) The association between parental life history and offspring phenotype in Atlantic salmon. J Exp Biol 219:374–382PubMed 
PubMed Central 

Google Scholar 
Walsh B, Blows MW (2009) Abundant genetic variation + strong selection = multivariate genetic constraints: a geometric view of adaptation. Annu Rev Ecol Evol S 40:41–59Article 

Google Scholar 
Whitlock MC, Phillips PC, Wade MJ (1993) Gene interaction affects the additive genetic variance in subdivided populations with migration and extinction. Evolution 47:1758–1769PubMed 
Article 
PubMed Central 

Google Scholar 
Wright S (1932) Proceedings of the Sixth International Congress on Genetics, Vol. 1. Donald FJ (ed.). The Genetics Society of America, pp 356-366Zhang T, Kong J, Liu B, Wang Q, Cao B, Luan S et al. (2014) Genetic parameter estimation for juvenile growth and upper thermal tolerance in turbot (Scophthalmus maximus Linnaeus). Acta Oceano Sin 33:106–110CAS 
Article 

Google Scholar  More

1.Karl D, Letelier R, Tupas L, Dore J, Christian J, Hebel D. The role of nitrogen fixation in biogeochemical cycling in the subtropical North Pacific Ocean. Nature. 1997;388:533–8.CAS 
Article 

Google Scholar 
2.Jickells TD, Buitenhuis E, Altieri K, Baker AR, Capone D, Duce RA, et al. A reevaluation of the magnitude and impacts of anthropogenic atmospheric nitrogen inputs on the ocean. Glob Biogeochem Cycles. 2017;31:289–305.CAS 

Google Scholar 
3.Knapp A. The sensitivity of marine N2 fixation to dissolved inorganic nitrogen. Front Microbiol. 2012;3:374.CAS 
PubMed 
PubMed Central 

Google Scholar 
4.Rees AP, Gilbert JA, Kelly-Gerreyn BA. Nitrogen fixation in the western English Channel (NE Atlantic Ocean). Mar Ecol Prog Ser. 2009;374:7–12.CAS 
Article 

Google Scholar 
5.Shiozaki T, Nagata T, Ijichi M, Furuya K. Nitrogen fixation and the diazotroph community in the temperate coastal region of the northwestern North Pacific. Biogeosciences. 2015;12:4751–64.Article 

Google Scholar 
6.Tang W, Cerdán-García E, Berthelot H, Polyviou D, Wang S, Baylay A, et al. New insights into the distributions of nitrogen fixation and diazotrophs revealed by high-resolution sensing and sampling methods. ISME J. 2020;14:2514–26.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
7.Tang W, Wang S, Fonseca-Batista D, Dehairs F, Gifford S, Gonzalez AG, et al. Revisiting the distribution of oceanic N2 fixation and estimating diazotrophic contribution to marine production. Nat Commun. 2019;10:1–10.Article 
CAS 

Google Scholar 
8.Hamersley M, Turk K, Leinweber A, Gruber N, Zehr J, Gunderson T, Capone D. Nitrogen fixation within the water column associated with two hypoxic basins in the Southern California Bight. Aquat Microb Ecol. 2011;63:193–205.Article 

Google Scholar 
9.Mulholland MR, Bernhardt PW, Blanco-Garcia JL, Mannino A, Hyde K, Mondragon E, et al. Rates of dinitrogen fixation and the abundance of diazotrophs in North American coastal waters between Cape Hatteras and Georges Bank. Limnol Oceanogr. 2012;57:1067–83.CAS 
Article 

Google Scholar 
10.Mulholland MR, Bernhardt PW, Widner BN, Selden CR, Chappell PD, Clayton S, et al. High rates of N2 fixation in temperate, western North Atlantic coastal waters expands the realm of marine diazotrophy. Glob Biogeochem Cycles. 2019;33:826–40.CAS 
Article 

Google Scholar 
11.Bentzon-Tilia M, Traving SJ, Mantikci M, Knudsen-Leerbeck H, Hansen JL, Markager S, et al. Significant N2 fixation by heterotrophs, photoheterotrophs and heterocystous cyanobacteria in two temperate estuaries. ISME J. 2015;9:273–85.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
12.Wen Z, Lin W, Shen R, Hong H, Kao SJ, Shi D. Nitrogen fixation in two coastal upwelling regions of the Taiwan Strait. Sci Rep. 2017;7:1–10.Article 
CAS 

Google Scholar 
13.Voss M, Bombar D, Loick N, Dippner JW. Riverine influence on nitrogen fixation in the upwelling region off Vietnam, South China Sea. Geophys Res Lett. 2006;33:L07604.Article 
CAS 

Google Scholar 
14.Shiozaki T, Furuya K, Kodama T, Kitajima S, Takeda S, Takemura T, et al. New estimation of N2 fixation in the western and central Pacific Ocean and its marginal seas. Glob Biogeochem Cycles. 2010;24:GB1015–n/a.15.Blais M, Tremblay JÉ, Jungblut AD, Gagnon J, Martin J, Thaler M, et al. Nitrogen fixation and identification of potential diazotrophs in the Canadian Arctic. Glob Biogeochem Cycles. 2012;26:GB3022.Article 
CAS 

Google Scholar 
16.Shiozaki T, Fujiwara A, Inomura K, Hirose Y, Hashihama F, Harada N. Biological nitrogen fixation detected under Antarctic sea ice. Nat Geosci. 2020;13:729–32.CAS 
Article 

Google Scholar 
17.Harding K, Turk-Kubo KA, Sipler RE, Mills MM, Bronk DA, Zehr JP. Symbiotic unicellular cyanobacteria fix nitrogen in the Arctic Ocean. Proc Natl Acad Sci USA. 2018;115:13371–5.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
18.Thompson AW, Foster RA, Krupke A, Carter BJ, Musat N, Vaulot D, et al. Unicellular cyanobacterium symbiotic with a single-celled eukaryotic alga. Science. 2012;337:1546–50.CAS 
PubMed 
Article 

Google Scholar 
19.Zehr JP, Shilova IN, Farnelid HM, del Carmen Muñoz-MarínCarmen M, Turk-Kubo KA. Unusual marine unicellular symbiosis with the nitrogen-fixing cyanobacterium UCYN-A. Nat Microbiol. 2016;2:16214.PubMed 
Article 
CAS 

Google Scholar 
20.Zehr JP, Bench SR, Carter BJ, Hewson I, Niazi F, Shi T, et al. Globally distributed uncultivated oceanic N2-fixing cyanobacteria lack oxygenic photosystem II. Science. 2008;322:1110–2.CAS 
PubMed 
Article 

Google Scholar 
21.Tripp HJ, Bench SR, Turk KA, Foster RA, Desany BA, Niazi F, et al. Metabolic streamlining in an open-ocean nitrogen-fixing cyanobacterium. Nature. 2010;464:90–4.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
22.Church MJ, Mahaffey C, Letelier RM, Lukas R, Zehr JP, Karl DM. Physical forcing of nitrogen fixation and diazotroph community structure in the North Pacific subtropical gyre. Glob Biogeochem Cycles. 2009;23:GB2020.23.Langlois RJ, Hummer D, LaRoche J. Abundances and distributions of the dominant nifH phylotypes in the Northern Atlantic Ocean. Appl Environ Microbiol. 2008;74:1922–31.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
24.Moisander PH, Beinart RA, Hewson I, White AE, Johnson KS, Carlson CA, et al. Unicellular cyanobacterial distributions broaden the oceanic N2 fixation domain. Science. 2010;327:1512–4.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
25.Krupke A, Lavik G, Halm H, Fuchs BM, Amann RI, Kuypers MM. Distribution of a consortium between unicellular algae and the N2 fixing cyanobacterium UCYN-A in the North Atlantic Ocean. Environ Microbiol. 2014;16:3153–67.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
26.Shiozaki T, Bombar D, Riemann L, Hashihama F, Takeda S, Yamaguchi T, et al. Basin scale variability of active diazotrophs and nitrogen fixation in the North Pacific, from the tropics to the subarctic Bering Sea. Glob Biogeochem Cycles 2017;31:996–1009.CAS 
Article 

Google Scholar 
27.Krupke A, Musat N, Laroche J, Mohr W, Fuchs BM, Amann RI, et al. In situ identification and N2 and C fixation rates of uncultivated cyanobacteria populations. Syst Appl Microbiol. 2013;36:259–71.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
28.Martínez-Pérez C, Mohr W, Löscher CR, Dekaezemacker J, Littmann S, Yilmaz P, et al. The small unicellular diazotrophic symbiont, UCYN-A, is a key player in the marine nitrogen cycle. Nat Microbiol. 2016;1:16163.PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
29.Mills MM, Turk-Kubo KA, van Dijken GL, Henke BA, Harding K, Wilson ST, et al. Unusual marine cyanobacteria/haptophyte symbiosis relies on N2 fixation even in N-rich environments. ISME J. 2020;14:2395–406.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
30.Scavotto RE, Dziallas C, Bentzon-Tilia M, Riemann L, Moisander PH. Nitrogen-fixing bacteria associated with copepods in coastal waters of the North Atlantic Ocean. Environ. Microbiol. 2015;17:3754–65.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
31.Conroy BJ, Steinberg DK, Song B, Kalmbach A, Carpenter EJ, Foster RA. Mesozooplankton graze on cyanobacteria in the amazon river plume and western tropical North Atlantic. Front Microbiol. 2017;8:1436.PubMed 
PubMed Central 
Article 

Google Scholar 
32.Turk-Kubo KA, Connell P, Caron D, Hogan ME, Farnelid HM, Zehr JP. In situ diazotroph population dynamics under different resource ratios in the North Pacific Subtropical Gyre. Front Microbiol. 2018;9:1616.PubMed 
PubMed Central 
Article 

Google Scholar 
33.Needham DM, Fuhrman JA. Pronounced daily succession of phytoplankton, archaea and bacteria following a spring bloom. Nat Microbiol. 2016;1:16005.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
34.Shiozaki T, Fujiwara A, Ijichi M, Harada N, Nishino S, Nishi S, et al. Diazotroph community structure and the role of nitrogen fixation in the nitrogen cycle in the Chukchi Sea (western Arctic Ocean). Limnol Oceanogr. 2018;63:2191–205.CAS 
Article 

Google Scholar 
35.Sohm JA, Hilton JA, Noble AE, Zehr JP, Saito MA, Webb EA. Nitrogen fixation in the South Atlantic Gyre and the Benguela Upwelling system. Geophys Res Lett. 2011;38:L16608–n/a.Article 
CAS 

Google Scholar 
36.Moreira-Coello V, Mouriño-Carballido B, Marañón E, Fernández-Carrera A, Bode A, Varela MM. Biological N2 fixation in the upwelling region off NW Iberia: magnitude, relevance, and players. Front Mar Sci. 2017;4:303.Article 

Google Scholar 
37.Cabello AM, Turk-Kubo KA, Hayashi K, Jacobs L, Kudela RM, Zehr JP. Unexpected presence of the nitrogen-fixing symbiotic cyanobacterium UCYN-A in Monterey Bay, California. J Phycol. 2020;56:1521–33.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
38.Deutsch C, Frenzel H, McWilliams JC, Renault L, Kessouri F, Howard E, et al. Biogeochemical variability in the California Current System. Prog Oceanogr. 2021;196:102565.Article 

Google Scholar 
39.Grasshoff K, Kremling K, Ehrhardt M, editors. Methods of seawater analysis. 3rd ed. Weinheim: Wiley-VCH; 1999.40.Welschmeyer NA. Fluorometric analysis of chlorophyll a in the presence of chlorophyll b and phaeopigments. Limnol Oceanogr. 1994;39:1985–92.CAS 
Article 

Google Scholar 
41.Moisander PH, Beinart RA, Voss M, Zehr JP. Diversity and abundance of diazotrophic microorganisms in the South China Sea during intermonsoon. ISME J. 2008;2:954–67.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
42.Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7:335–6.CAS 
PubMed 
PubMed Central 

Google Scholar 
43.Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26:2460–1.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
44.Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics. 2011;27:2194–200.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
45.Zehr JP, Jenkins BD, Short SM, Steward GF. Nitrogenase gene diversity and microbial community structure: a cross-system comparison. Environ Microbiol. 2003;5:539–54.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
46.Eren AM, Maignien L, Sul WJ, Murphy LG, Grim SL, Morrison HG, et al. Oligotyping: differentiating between closely related microbial taxa using 16S rRNA gene data. Methods Ecol Evol. 2013;4:1111–9.PubMed Central 
Article 
PubMed 

Google Scholar 
47.Turk-Kubo KA, Farnelid HM, Shilova IN, Henke B, Zehr JP. Distinct ecological niches of marine symbiotic N2-fixing cyanobacterium Candidatus Atelocyanobacterium thalassa sublineages. J Phycol. 2017;53:451–61.CAS 
PubMed 
Article 

Google Scholar 
48.Henke BA, Turk-Kubo KA, Bonnet S, Zehr JP. Distributions and abundances of sublineages of the N2-fixing cyanobacterium Candidatus Atelocyanobacterium thalassa (UCYN-A) in the New Caledonian Coral Lagoon. Front Microbiol. 2018;9:554.PubMed 
PubMed Central 
Article 

Google Scholar 
49.Gradoville MR, Farnelid H, White AE, Turk‐Kubo KA, Stewart B, Ribalet F, et al. Latitudinal constraints on the abundance and activity of the cyanobacterium UCYN‐A and other marine diazotrophs in the North Pacific. Limnol Oceanogr. 2020;65:1858–75.CAS 
Article 

Google Scholar 
50.McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE. 2013;8:e61217.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
51.Church M, Jenkins B, Karl D, Zehr J. Vertical distributions of nitrogen-fixing phylotypes at Stn ALOHA in the oligotrophic North Pacific Ocean. Aquat Microb Ecol. 2005;38:3–14.Article 

Google Scholar 
52.Thompson A, Carter BJ, Turk-Kubo K, Malfatti F, Azam F, Zehr JP. Genetic diversity of the unicellular nitrogen-fixing cyanobacteria UCYN-A and its prymnesiophyte host. Environ Microbiol. 2014;16:3238–49.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
53.Foster RA, Subramaniam A, Mahaffey C, Carpenter EJ, Capone DG, Zehr JP. Influence of the Amazon River plume on distributions of free-living and symbiotic cyanobacteria in the western tropical north Atlantic Ocean. Limnol Oceanogr. 2007;52:517–32.CAS 
Article 

Google Scholar 
54.Goebel NL, Turk KA, Achilles KM, Paerl R, Hewson I, Morrison AE, et al. Abundance and distribution of major groups of diazotrophic cyanobacteria and their potential contribution to N2 fixation in the tropical Atlantic Ocean. Environ Microbiol. 2010;12:3272–89.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
55.Farnelid H, Turk-Kubo K, Munoz-Marin MD, Zehr JP. New insights into the ecology of the globally significant uncultured nitrogen-fixing symbiont UCYN-A. Aquat Microb Ecol. 2016;77:125–38.Article 

Google Scholar 
56.Mohr W, Grosskopf T, Wallace DWR, LaRoche J. Methodological underestimation of oceanic nitrogen fixation rates. PLoS ONE. 2010;5:e12583.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
57.Montoya JP, Voss M, Kahler P, Capone DG. A simple, high-precision, high-sensitivity tracer assay for N2 fixation. Appl Environ Microbiol. 1996;62:986–93.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
58.Gradoville MR, Bombar D, Crump BC, Letelier RM, Zehr JP, White AE. Diversity and activity of nitrogen-fixing communities across ocean basins. Limnol Oceanogr. 2017;62:1895–909.Article 

Google Scholar 
59.White AE, Granger J, Selden C, Gradoville MR, Potts L, Bourbonnais A, et al. A critical review of the 15N2 tracer method to measure diazotrophic production in pelagic ecosystems. Limnol Oceanogr Methods. 2020;18:129–47.Article 

Google Scholar 
60.Cornejo-Castillo FM, Cabello AM, Salazar G, Sánchez-Baracaldo P, Lima-Mendez G, Hingamp P, et al. Cyanobacterial symbionts diverged in the late Cretaceous towards lineage-specific nitrogen fixation factories in single-celled phytoplankton. Nat Commun. 2016;7:11071.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
61.Cabello AM, Cornejo-Castillo FM, Raho N, Blasco D, Vidal M, Audic S, et al. Global distribution and vertical patterns of a prymnesiophyte-cyanobacteria obligate symbiosis. ISME J. 2016;10:693–706.PubMed 
Article 

Google Scholar 
62.Polerecky L, Adam B, Milucka J, Musat N, Vagner T, Kuypers MM. Look@ NanoSIMS–a tool for the analysis of nanoSIMS data in environmental microbiology. Environ Microbiol. 2012;14:1009–23.CAS 
PubMed 
Article 

Google Scholar 
63.Krupke A, Mohr W, LaRoche J, Fuchs BM, Amann RI, Kuypers MM. The effect of nutrients on carbon and nitrogen fixation by the UCYN-A-haptophyte symbiosis. ISME J. 2015;9:1635–47.CAS 
PubMed 
Article 

Google Scholar 
64.Meyer NR, Fortney J, Dekas AE. NanoSIMS sample preparation decreases isotope enrichment: magnitude, variability and implications for single-cell rates of microbial activity. Environ Microbiol. 2020;23:81–98.PubMed 
Article 
CAS 

Google Scholar 
65.Durazo R. Seasonality of the transitional region of the California Current System off Baja California. J Geophys Res Oceans. 2015;120:1173–96.Article 

Google Scholar 
66.Bakun A. Coastal upwelling indices, west coast of North America, 1946–71.67.Redfield AC. On the proportions of organic derivatives in sea water and their relation to the composition of plankton. Vol. 1. Liverpool: University Press of Liverpool; 1934. p. 176–92.68.Bograd SJ, Schroeder ID, Jacox MG. A water mass history of the Southern California current system. Geophys. Res. Lett. 2019;46:6690–8.Article 

Google Scholar 
69.Langlois R, Großkopf T, Mills M, Takeda S, LaRoche J. Widespread distribution and expression of gamma A (UMB), an uncultured, diazotrophic, γ-proteobacterial nifH phylotype. PLoS ONE. 2015;10:e0128912.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
70.Dekaezemacker J, Bonnet S, Grosso O, Moutin T, Bressac M, Capone DG. Evidence of active dinitrogen fixation in surface waters of the eastern tropical South Pacific during El Niño and La Niña events and evaluation of its potential nutrient controls. Glob Biogeochem Cycles 2013;27:768–79.CAS 
Article 

Google Scholar 
71.Chen M, Lu Y, Jiao N, Tian J, Kao SJ, Zhang Y. Biogeographic drivers of diazotrophs in the western Pacific Ocean. Limnol Oceanogr. 2019;64:1403–21.CAS 
Article 

Google Scholar 
72.Turk KA, Rees AP, Zehr JP, Pereira N, Swift P, Shelley R, et al. Nitrogen fixation and nitrogenase (nifH) expression in tropical waters of the eastern North Atlantic. ISME J. 2011;5:1201–12.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
73.White AE, Foster RA, Benitez-Nelson CR, Masqué P, Verdeny E, Popp BN, et al. Nitrogen fixation in the Gulf of California and the Eastern Tropical North Pacific. Prog Oceanogr. 2013;109:1–17.Article 

Google Scholar 
74.Selden CR, Mulholland MR, Bernhardt PW, Widner B, Macías‐Tapia A, Ji Q, et al. Dinitrogen fixation across physico-chemical gradients of the eastern tropical North Pacific oxygen deficient zone. Glob Biogeochem Cycles. 2019;33:1187–202.CAS 
Article 

Google Scholar 
75.Sohm JA, Webb EA, Capone DG. Emerging patterns of marine nitrogen fixation. Nat Rev Microbiol. 2011;9:499–508.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
76.Carlucci A, Bowes PM. Production of vitamin B12, thiamine, and biotin by phytoplankton. J Phycol. 1970;6:351–7.CAS 

Google Scholar 
77.Gledhill M, Buck KN. The organic complexation of iron in the marine environment: a review. Front Microbiol. 2012;3:69.PubMed 
PubMed Central 

Google Scholar 
78.Biddanda B, Benner R. Carbon, nitrogen, and carbohydrate fluxes during the production of particulate and dissolved organic matter by marine phytoplankton. Limnol Oceanogr. 1997;42:506–18.CAS 
Article 

Google Scholar 
79.Hernández de la Torre B, Gaxiola Castro G, Álvarez Borrego S, Gallegos García A, Aguirre Gómez R. New organic carbon in front of the Baja California Peninsula: time series and climatology. Hidrobiológica. 2015;25:74–85.
Google Scholar 
80.Xiu P, Chai F, Curchitser EN, Castruccio FS. Future changes in coastal upwelling ecosystems with global warming: the case of the California Current System. Sci. Rep. 2018;8:2866.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
81.Kimor B, Reid F, Jordan J. An unusual occurrence of Hemiaulus membranaceus Cleve (Bacillariophyceae) with Richelia intracelluaris Schmidt (Cyanophyceae) off the coast of Southern California. Phycologia. 1978;17:162–6.Article 

Google Scholar 
82.White AE, Prahl FG, Letelier RM, Popp BN. Summer surface waters in the Gulf of California: Prime habitat for biological N2 fixation. Glob Biogeochem Cycles. 2007;21:GB2017–n/a.83.Pyle AE, Johnson AM, Villareal TA. Isolation, growth, and nitrogen fixation rates of the Hemiaulus-Richelia (diatom-cyanobacterium) symbiosis in culture. PeerJ. 2020;8:e10115.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
84.Foster RA, Kuypers MM, Vagner T, Paerl RW, Musat N, Zehr JP. Nitrogen fixation and transfer in open ocean diatom–cyanobacterial symbioses. ISME J. 2011;5:1484–93.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
85.Caputo A, Nylander JAA, Foster RA. The genetic diversity and evolution of diatom-diazotroph associations highlights traits favoring symbiont integration. FEMS Microbiol Lett. 2019;366:fny297.CAS 
PubMed Central 
Article 

Google Scholar 
86.Thompson AR. State of the California Current 2017–18: still not quite normal in the north and getting interesting in the south. California cooperative oceanic fisheries investigations, Data report. 2018.87.Larkin AA, Moreno AR, Fagan AJ, Fowlds A, Ruiz A, Martiny AC. Persistent El Nino driven shifts in marine cyanobacteria populations. PLoS ONE. 2020;15:e0238405.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
88.Hagino K, Takano Y, Horiguchi T. Pseudo-cryptic speciation in Braarudosphaera bigelowii (Gran and Braarud) Deflandre. Mar Micropaleontol. 2009;72:210–21.Article 

Google Scholar 
89.Selden CR, Chappell PD, Clayton S, Macías‐Tapia A, Bernhardt PW, Mulholland MR. A coastal N2 fixation hotspot at the Cape Hatteras front: elucidating spatial heterogeneity in diazotroph activity via supervised machine learning. Limnol Oceanogr. 2021;66:1832–49.Article 

Google Scholar 
90.Wang S, Tang W, Delage E, Gifford S, Whitby H, González AG, et al. Investigating the microbial ecology of coastal hotspots of marine nitrogen fixation in the western North Atlantic. Sci Rep. 2021;11:5508.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More

1.FAO—Food and Agriculture Organization of the United Nations. World Crops Production. http://www.wptc.to/releases-wptc.php (2016).2.WPTC—World Processing Tomato Council. World production estimate. http://www.wptc.to/releases-wptc.php. (2016).3.Silva, Y. P. A. et al. Characterization of tomato processing by-product for use as a potential functional food ingredient: Nutritional composition, antioxidant activity and bioactive compounds. Int. J. Food Sci. Nutr. 70, 150–160 (2019).Article 

Google Scholar 
4.Pereira, M. A. B. et al. Postharvest conservation of structural long shelf life tomato fruits and with the mutant rin produced, in edaphoclimatic conditions of the southern state of Tocantins. Ciênc. Agrotec. 39, 225–231 (2015).Article 

Google Scholar 
5.Brummell, D. A. & Harpster, M. H. Cell wall metabolism in fruit softening and quality and its manipulation in transgenic plants. PCW. 47, 311–340 (2001).CAS 

Google Scholar 
6.Meli, V. S. et al. Enhancement of fruit shelf life by suppressing N-glycan processing enzymes. Proc. Natl. Acad. Sci. USA 107, 2413–2418 (2010).ADS 
CAS 
Article 

Google Scholar 
7.Samimi-Akhijahani, H. & Arabhosseini, A. Accelerating drying process of tomato slices in a PV-assisted solar dryer using a sun tracking system. Renew. Energy 123, 428–438 (2018).Article 

Google Scholar 
8.Tripathy, P. P. Investigação da secagem solar da batata: efeito da geometria da amostra na cinética de secagem e na mitigação das emissões de CO2. J. Ciênc. e Tecnol. Alim. 52, 1383–1393 (2015).CAS 

Google Scholar 
9.Badaoui, O., Hanini, S., Djebli, A., Haddad, B. & Benhamou, A. Experimental and modelling study of tomato pomace waste drying in a new solar greenhouse: Evaluation of new drying models. Renew. Energy 133, 144–155 (2019).Article 

Google Scholar 
10.Mohsen, H. A., El-Rahmam, A. A. & Hassan, H. E. Drying of tomato fruits using solar energy. Int. J. Agric. Eng. 21, 204–215 (2019).
Google Scholar 
11.César, L. V. E., Lilia, C. M. A., Octavio, G. V., Isaac, P. F. & Rogelio, B. O. Thermal performance of a passive, mixed-type solar dryer for tomato slices (Solanum lycopersicum). Renew. Energy 147, 845–855 (2020).Article 

Google Scholar 
12.Kingsly, A. R. P., Singh, R., Goyal, R. K. & Singh, D. B. Thin-layer drying behavior of organically produced tomato. Am. J. Food Tech. 2, 71–78 (2007).Article 

Google Scholar 
13.Miller, G. L. Use of dinitrosalicylic acid reagent for determination of reducing sugar. Anal. Chem. 31, 426–428 (1959).CAS 
Article 

Google Scholar 
14.Silva, F. A. S. A. & Azevedo, C. A. V. Versão do programa computacional Assistat para o sistema operacional Windows. Rev. Bras. Prod. Agroindustriais 4, 71–78 (2002).Article 

Google Scholar 
15.Klunklin, W. & Savage, G. Effect on quality characteristics of tomatoes grown under well-watered and drought stress conditions. Foods 6, e56 (2017).Article 

Google Scholar 
16.Azeez, L., Adebisi, S. A., Oyedeji, A. O., Adetoro, R. O. & Tijani, K. O. Bioactive compounds’ contents, drying kinetics and mathematical modelling of tomato slices influenced by drying temperatures and time. J. Saudi Soc. 10, 120–126 (2019).
Google Scholar 
17.Correia, A. F., Loro, K. A. C., Zanatta, S., Spoto, M. H. F. & Vieira, T. M. F. S. Effect of temperature, time, and material thickness on the dehydration process of tomato. Int. J. Food Sci. 1, e970724 (2015).
Google Scholar 
18.Eswara, A. R. & Ramakrishnarao, M. Solar energy in food processing—A critical appraisal. J. Food Sci. Technol. 50, 209–227 (2013).CAS 
Article 

Google Scholar 
19.Castillo, C. P., Silva, F. B. & Lavalle, C. An assessment of the regional potential for solar power generation in EU-28. Energy Policy 88, 86–99 (2016).Article 

Google Scholar 
20.Tampakis, G., Tsantopoulos, G. & Arabatzis, I. R. Citizens’ views on various forms of energy and their contribution to the environment. Renew. Sust. Energ. Rev. 20, 473–482 (2013).Article 

Google Scholar 
21.Tsantopoulos, G. & Arabatzis, T. G. Stilianos Public attitudes towards photovoltaic developments: Case study from Greece. Energy Policy 71, 94–106 (2014).Article 

Google Scholar 
22.Tiwari, R. B. Application of osmo-air dehydration for processing of tropical frepical fruits in rural areas. Indian Food Ind. 24, 62–69 (2005).
Google Scholar 
23.Goula, A. M. & Adamopoulos, K. G. Retention of ascorbic acid during drying of tomato halves and tomato pulp. Drying Technol. 24, 57–64 (2006).CAS 
Article 

Google Scholar 
24.McAlpine, R. D., Cocivera, M. & Chen, H. Photooxidation and reduction of ascorbic acid atudied by E.S.R. Can. J. Chem. 51, 1682–1686 (1973).CAS 
Article 

Google Scholar 
25.Santos, P. H. S. & Silva, M. A. Retention of vitamin C in drying processes of fruits and vegetables—A review. Drying Technol. 26, 1421–1437 (2008).CAS 
Article 

Google Scholar 
26.Santos-Sánchez, N. F., Valadez-Blanco, R., Gómez-Gómez, M. S., Pérez-Herrera, A. & Salas-Coronado, R. Effect of rotating tray drying on antioxidant components, color and rehydration ratio of tomato saladette slices. LWT Food Sci. Technol. 46, 298–304 (2012).Article 

Google Scholar 
27.Yadav, A. K. & Singh, S. V. Y. Osmotic dehydration of fruits and vegetables: A review. J. Food Sci. Technol. 51, 1654–1673 (2014).Article 

Google Scholar 
28.Gunhan, T., Demir, V., Hancioglu, E. & Hepbasli, A. Mathematical modeling of drying of bay leaves. Energy Convers. Manag. 46, 1667–1679 (2005).Article 

Google Scholar 
29.Sacilik, K. & Unal, G. Dehydration characteristics of kastomonu garlic slices. Biosyst. Eng. 92, 207–215 (2005).Article 

Google Scholar 
30.Instituto Adolfo Lutz. Métodos Físico-Químicos Para Análise de Alimentos 1020 (Instituto Adolfo Lutz, 2008).
Google Scholar  More

NEWS AND VIEWS
25 August 2021

African tropical montane forests store more carbon than was thought

The inaccessibility of African montane forests has hindered efforts to quantify the carbon stored by these ecosystems. A remarkable survey fills this knowledge gap, and highlights the need to preserve such forests.

Nicolas Barbier

0

Nicolas Barbier

Nicolas Barbier is at AMAP, Université de Montpellier, IRD, CNRS, INRAE, CIRAD, Montpellier 34980, France.

View author publications

You can also search for this author in PubMed
 Google Scholar

Share on Twitter
Share on Twitter

Share on Facebook
Share on Facebook

Share via E-Mail
Share via E-Mail

Download PDF

In a paper in Nature, Cuni-Sanchez et al.1 report the assembly of a large database of tree inventories for 226 mature montane-forest plots in 12 African countries. The authors analyse the data to determine the amount of aboveground biomass and carbon stored in these highly diverse and threatened ecosystems. Their results suggest that African montane forests store more carbon than was previously thought, and the findings should help to guide efforts to conserve these ecosystems.Cuni-Sanchez and colleagues measured trunk diameters and heights of the trees in plots, and identified the botanical species to deduce wood density — an approach that constitutes the gold standard for estimating the biomass, and thus the amount of carbon, contained per unit of forest area. This method involves the use of general statistical equations for describing tree form, called allometric models, and considers only the aboveground parts of trees. It therefore disregards several other pools of carbon, notably in the roots and soil. The overall approach might seem crude, but recognizing and measuring the many hundreds of tree species found on steep, cloud-shrouded slopes (Fig. 1), let alone the underground carbon, without visiting the sites, will long remain difficult, even with the best drones and satellite systems.

Figure 1 | Montane forest in Boginda, Ethiopia. Cuni-Sanchez et al.1 use data from a survey of montane tropical forests in Africa to quantify the amount of carbon stored above ground in these ecosystems.Credit: Bruno D’Amicis/Nature Picture Library

Anyone who has conducted field inventories in tropical mountains knows that measuring and identifying 72,336 trees, often just a few steps away from the void, is an amazing feat. For comparison, a previously reported study2 based its estimates of the carbon stored in montane African forests on as few as seven plots. The study also brings together contributions from numerous researchers and institutions, including many in Africa, to greatly increase the size of the data set, which is also a remarkable achievement. Even so, the total area of forest studied is less than 150 hectares, whereas African montane forest covers about 100,000 times that area, inevitably raising questions about how representative the inventory is.Statisticians might raise their eyebrows at the sampling design. As is usually the case in meta-analyses, the data set was neither homogeneous (for example, there is a roughly tenfold variation in the plot sizes), nor were the sites selected at random. However, the authors did their best to rule out possible biases induced by sampling artefacts.
Read the paper: High aboveground carbon stock of African tropical montane forests
Cuni-Sanchez et al. chose not to discuss one tricky aspect of surveys of this sort (extensively discussed elsewhere2): how should the land area of a steep slope be measured? The authors followed standard practice, which is to measure the extent of forest plots and of land-cover types in reference to horizontal, planimetric areas (that is, the areas that would be represented on a 2D map, as if seen from the air). This tends to overestimate aboveground carbon because the sloped surface area is greater than that of the planimetric area — which means that the tree density of the planimetric area is higher than it is on the slope. By contrast, the use of planimetric areas underestimates total montane-forest area (by about 40%; see ref. 2). These two biases should roughly cancel each other out when estimating carbon stocks, or changes to stocks, for a region or country. But care should be taken not to combine data acquired using planimetric and non-planimetric areas in future meta-analyses, because the resulting estimates could end up well off the mark.One might expect that trees in mature African montane forests would be, on average, shorter — and therefore store less carbon — than their lowland counterparts, because of their lower environmental temperatures and shallow soils, frequent landslides and strong winds. However, this is not what Cuni-Sanchez et al. report. Instead, they find that average aboveground carbon stocks are not significantly different from those of mature lowland forests. This contrasts with the situation in the neotropics and southeast Asia, where montane forests store, on average, less carbon than do lowland forests.However, the new results fit with the 2016 discovery that the tallest African trees (81.5 metres) grow on Mount Kilimanjaro3, the highest mountain in Africa. African forests, in general, tend to contain fewer but larger-statured tree stands than does, for example, Amazonia4. The current study confirms that this peculiarity applies even at high altitudes.The authors investigate several possible drivers for the variations in biomass observed at different sites in their study, including topography, climate, landslide hazard, and even the presence of elephants or certain conifers (Podocarpaceae), but were unable to identify any clear pattern. Many environmental, historical and biological effects probably interact, with each of these effects varying greatly in ways that are poorly captured by available data sets. These effects must therefore be disentangled before a predictive model of African montane carbon distribution can be developed.
Tropical carbon sinks are saturating at different times on different continents
Nevertheless, Cuni-Sanchez and colleagues’ study underlines a crucial message: African montane forests are immensely valuable, and not only because they host the source of the River Nile, mountain gorillas and ecosystems such as mysterious lichen-covered forests. They also store vast amounts of carbon, and thereby have a key role in tackling climate change. Of course, this immense intrinsic value does not preclude intense human exploitation of these ecosystems, which can lead to rapid degradation and deforestation. For instance, on the basis of satellite monitoring, Cuni-Sanchez and colleagues report that Mozambique lost nearly one-third of its montane forests between 2000 and 2018.There is, however, the faint hope that putting a financial value on carbon, and the establishment of economic incentives to avoid deforestation in tropical countries, might help to check the flood of damage5. The aim is to reward African countries — for which montane forest sometimes constitutes the last remaining forests — for their conservation endeavours, and for renouncing efforts to access the timber and ore in these ecosystems, even when such resources are otherwise desperately lacking. By gathering the best-available data to provide precise, country-level estimates of average aboveground carbon content in African montane forests, Cuni-Sanchez and colleagues’ study will add weight to such efforts — not least because the new estimates are, on average, two-thirds higher than the values reported by the Intergovernmental Panel on Climate Change6.The next step should be to extend measurements in these forests, particularly by continuing to support national forest-inventory efforts. These inventories target all vegetation types, rather than just the most intact forests, and all carbon pools, using standardized protocols and systematic sampling methods. Remote sensors, both in the sky and in space, should also be used to fully map the detailed spatial variation of forest diversity, structure and dynamics. But there is no excuse for delaying policymaking — we already know enough to justify immediate decisive action to preserve yet another of Earth’s threatened treasures.

Nature 596, 488-490 (2021)
doi: https://doi.org/10.1038/d41586-021-02266-3

References1.Cuni-Sanchez, A. et al. Nature 596, 536–542 (2021).Article 

Google Scholar 
2.Spracklen, D. V. & Righelato, R. Biogeosciences 11, 2741–2754 (2014).Article 

Google Scholar 
3.Hemp, A. et al. Biodivers. Conserv. 26, 103–113 (2017).Article 

Google Scholar 
4.Lewis, S. L. et al. Phil. Trans. R. Soc. B 368, 20120295 (2013).PubMed 
Article 

Google Scholar 
5.Venter, O. et al. Science 326, 1368 (2009).PubMed 
Article 

Google Scholar 
6.Domke, G. et al. in 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories Vol. 4 (eds Calvo Buendia, E. et al.) Ch. 4, 4.48 (IPCC, 2019).
Google Scholar 
Download references

Competing Interests
The author declares no competing interests.

Related Articles

Read the paper: High aboveground carbon stock of African tropical montane forests

Southeast Amazonia is no longer a carbon sink

Tropical carbon sinks are saturating at different times on different continents

See all News & Views

Subjects

Biogeochemistry

Environmental sciences

Ecology

Latest on:

Biogeochemistry

High aboveground carbon stock of African tropical montane forests
Article 25 AUG 21

The Montreal Protocol protects the terrestrial carbon sink
Article 18 AUG 21

Amazonia as a carbon source linked to deforestation and climate change
Article 14 JUL 21

Environmental sciences

Brazilian road proposal threatens famed biodiversity hotspot
News 17 AUG 21

‘Polluter pays’ policy could speed up emission reductions and removal of atmospheric CO2
News & Views 16 AUG 21

The world’s species are playing musical chairs: how will it end?
News Feature 04 AUG 21

Ecology

Can artificially altered clouds save the Great Barrier Reef?
News Feature 25 AUG 21

Great Barrier Reef: accept ‘in danger’ status, there’s more to gain than lose
World View 18 AUG 21

Brazilian road proposal threatens famed biodiversity hotspot
News 17 AUG 21

Jobs

Postdoctoral Research Scientist

CRUK Beatson Institute for Cancer Research
Glasgow, United Kingdom

Bioinformatics Software Engineer

CRUK Beatson Institute for Cancer Research
Glasgow, United Kingdom

Computational Biologist

CRUK Beatson Institute for Cancer Research
Glasgow, United Kingdom

Research Fellow – Berbeco Lab

Dana-Farber Cancer Institute (DFCI)
Boston, MA, United States

Nature Briefing
An essential round-up of science news, opinion and analysis, delivered to your inbox every weekday.

Email address

Yes! Sign me up to receive the daily Nature Briefing email. I agree my information will be processed in accordance with the Nature and Springer Nature Limited Privacy Policy.

Sign up More

The Fur-regulated T6SS1 plays an important role in iron acquisition in C. necator
To explore the function of T6SS1 (Reut_A1713 to Reut_A1733) in C. necator (Fig. S1A), we analyzed the T6SS1 promoter and identified a Fur binding site (AGAAATA) upstream of gene reut_A1733. This Fur binding site was highly similar to the Fur-box reported in E. coli [38], with a probability score of 2.25 (out of a maximum score = 2.45) (Fig. S1B), which was calculated by applying the position weight matrix to a sequence [39]. Incubation of the T6SS1 promoter probe with purified Fur protein led to decreased mobility of the probe in the electrophoretic mobility shift assay, suggesting a direct interaction between Fur and the T6SS1 promoter (Fig. 1A). To further determine the function of Fur on the expression of T6SS1, a single-copy PT6SS1::lacZ fusion reporter was introduced into the chromosomes of C. necator wild-type (WT), Δfur deletion mutant, and the Δfur(fur) complementary strain. Compared to WT, the PT6SS1::lacZ promoter activity was significantly increased in the Δfur mutant (about 2.2-fold), and this increase could be restored by introducing the complementary plasmid pBBR1MCS-5-fur (Fig. 1B). Similar results were obtained by analyzing the expression of T6SS1 core component genes (hcp1, clpV1, vgrG1, and tssM1) with qRT-PCR (Fig. S1C). These results demonstrate that the expression of T6SS1 in C. necator is directly repressed by Fur, the master regulator of genes involved in iron homeostasis in many prokaryotes [40, 41].Fig. 1: Regulation of T6SS1 expression by Fur.A The interactions between His6-Fur and the T6SS1 promoter examined by EMSA. Increasing amounts of Fur (0, 0.03, 0.06, 0.13, 0.25, and 1.0 μM) and 10 nM DNA fragments were used in the assay. A 500 bp unrelated DNA fragment (Control A) and 1 µM BSA (Control B) were included in the assay as negative controls. B Fur represses the expression of T6SS1. β-galactosidase activities of T6SS1 promoter from chromosomal lacZ fusions in relevant C. necator strains were measured. C Iron uptake requires T6SS1. Stationary-phase C. necator strains were washed twice with M9 medium. Iron associated with indicated bacterial cells were measured with ICP-MS. The vector corresponds to the plasmid pBBR1MCS-5 (B) and pBBR1MCS-2 (C), respectively. Data are represented as mean values ± SD of three biological replicates, each with three technical replicates. **p  More