Ecology

1.Bryan, B. A. et al. China’s response to a national land-system sustainability emergency. Nature 559, 193–204 (2018).ADS 
CAS 
Article 

Google Scholar 
2.Zhang, W. J., Xue, X., Peng, F., You, Q. G. & Hao, A. H. Meta-analysis of the effects of grassland degradation on plant and soil properties in the alpine meadows of the Qinghai-Tibetan Plateau. Glob. Ecol. Conserv. 20, e00774 (2019).3.Pan, T., Zou, X. T., Liu, Y. J., Wu, S. H. & He, G. M. Contributions of climatic and non-climatic drivers to grassland variations on the Tibetan Plateau. Ecol. Eng. 108, 307–317 (2017).Article 

Google Scholar 
4.Shen, H. H., Wang, S. P. & Tang, Y. H. Grazing alters warming effects on leaf photosynthesis and respiration in Gentiana straminea, an alpine forb species. J. Plant. Ecol. 6, 418–427 (2013).Article 

Google Scholar 
5.Li, G. Y., Jiang, C. H., Cheng, T. & Bai, J. Grazing alters the phenology of alpine steppe by changing the surface physical environment on the northeast Qinghai-Tibet Plateau, China. J. Environ. Manage. 248, 109257 (2019).6.Li, Y. M. et al. Changes of soil microbial community under different degraded gradients of alpine meadow. Agric. Ecosyst. Environ. 222, 213–222 (2016).Article 

Google Scholar 
7.Guo, N. et al. Changes in vegetation parameters and soil nutrients along degradation and recovery successions on alpine grasslands of the Tibetan plateau. Agric. Ecosyst. Environ. 284, 106593 (2019).8.Lin, L. et al. Predicting parameters of degradation succession processes of Tibetan Kobresia grasslands. Solid Earth 6, 1237–1246 (2015).ADS 
Article 

Google Scholar 
9.Li, H. D. et al. Assessing revegetation effectiveness on an extremely degraded grassland, southern Qinghai-Tibetan Plateau, using terrestrial LiDAR and field data. Agric. Ecosyst. Environ. 282, 13–22 (2019).Article 

Google Scholar 
10.Wang, G. X., Qian, J., Cheng, G. D. & Lai, Y. M. Soil organic carbon pool of grassland soils on the Qinghai-Tibetan Plateau and its global implication. Sci. Total Environ. 291, 207–217. https://doi.org/10.1016/s0048-9697(01)01100-7 (2002).CAS 
Article 

Google Scholar 
11.Yuan, Z. Q. et al. Responses of soil organic carbon and nutrient stocks to human-induced grassland degradation in a Tibetan alpine meadow. CATENA 178, 40–48 (2019).CAS 
Article 

Google Scholar 
12.Askari, M. S. & Holden, N. M. Quantitative soil quality indexing of temperate arable management systems. Soil Till Res. 150, 57–67 (2015).Article 

Google Scholar 
13.Lima, A. C. R., Brussaard, L., Totola, M. R., Hoogmoed, W. B. & de Goede, R. G. M. A functional evaluation of three indicator sets for assessing soil quality. Appl. Soil Ecol. 64, 194–200 (2013).Article 

Google Scholar 
14.Masto, R. E., Chhonkar, P. K., Singh, D. & Patra, A. K. Alternative soil quality indices for evaluating the effect of intensive cropping, fertilisation and manuring for 31 years in the semi-arid soils of India. Environ. Monit. Assess 136, 419–435. https://doi.org/10.1007/s10661-007-9697-z (2008).CAS 
Article 
PubMed 

Google Scholar 
15.Zhou, H. et al. Changes in the soil microbial communities of alpine steppe at Qinghai-Tibetan Plateau under different degradation levels. Sci. Total Environ. 651, 2281–2291 (2019).ADS 
CAS 
Article 

Google Scholar 
16.Yang, C., Zhang, F. G., Liu, N., Hu, J. & Zhang, Y. J. Changes in soil bacterial communities in response to the fairy ring fungus Agaricus gennadii in the temperate steppes of China. Pedobiologia 69, 34–40 (2018).Article 

Google Scholar 
17.Li, J. J. & Yang, C. Inconsistent response of soil bacterial and fungal communities in aggregates to litter decomposition during short-term incubation. Peerj 7, e8078 (2019).18.Yang, C., Li, J. J., Liu, N. & Zhang, Y. J. Effects of fairy ring fungi on plants and soil in the alpine and temperate grasslands of China. Plant Soil 441, 499–510 (2019).CAS 
Article 

Google Scholar 
19.Yang, C., Liu, N. & Zhang, Y. J. Soil aggregates regulate the impact of soil bacterial and fungal communities on soil respiration. Geoderma 337, 444–452 (2019).ADS 
CAS 
Article 

Google Scholar 
20.Wardle, D. A. et al. Ecological linkages between aboveground and belowground biota. Science 304, 1629–1633 (2004).ADS 
CAS 
Article 

Google Scholar 
21.Wu, G.-L., Ren, G.-H., Dong, Q.-M., Shi, J.-J. & Wang, Y.-L. Above- and belowground response along degradation gradient in an alpine grassland of the Qinghai-Tibetan Plateau. Clean-Soil Air Water 42, 319–323. https://doi.org/10.1002/clen.201200084 (2014).CAS 
Article 

Google Scholar 
22.Che, R. X. et al. Degraded patch formation significantly changed microbial community composition in alpine meadow soils. Soil Till Res. 195, 104426 (2019).23.Aßhauer, K. P., Wemheuer, B., Daniel, R. & Meinicke, P. Tax4Fun: predicting functional profiles from metagenomic 16S rRNA data. Bioinformatics 31, 2882–2884 (2015).Article 

Google Scholar 
24.Harris, R. B. Rangeland degradation on the Qinghai-Tibetan plateau: a review of the evidence of its magnitude and causes. J. Arid Environ. 74, 1–12. https://doi.org/10.1016/j.jaridenv.2009.06.014 (2010).ADS 
CAS 
Article 

Google Scholar 
25.Ren, G., Shang, Z., Long, R., Hou, Y. & Deng, B. The relationship of vegetation and soil differentiation during the formation of black-soil-type degraded meadows in the headwater of the Qinghai-Tibetan Plateau China. Environ. Earth Sci. 69, 235–245. https://doi.org/10.1007/s12665-012-1951-1 (2013).Article 

Google Scholar 
26.Zhang, Y. et al. Diversity of nitrogen-fixing, ammonia-oxidizing, and denitrifying bacteria in biological soil crusts of a revegetation area in Horqin Sandy Land Northeast China. Ecol. Eng. 71, 71–79. https://doi.org/10.1016/j.ecoleng.2014.07.032 (2014).Article 

Google Scholar 
27.Wang, Y. et al. Effects of grassland degradation on ecological stoichiometry of soil ecosystems on the Qinghai-Tibet Plateau. Sci. Total Environ. 722, 137910. https://doi.org/10.1016/j.scitotenv.2020.137910 (2020).ADS 
CAS 
Article 
PubMed 

Google Scholar 
28.Zhang, Y. et al. Soil bacterial and fungal diversity differently correlated with soil biochemistry in alpine grassland ecosystems in response to environmental changes. Sci. Rep. 7, 43077. https://doi.org/10.1038/srep43077 (2017).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
29.Hartmann, M. et al. Resistance and resilience of the forest soil microbiome to logging-associated compaction. ISME J. 8, 226–244. https://doi.org/10.1038/ismej.2013.141 (2014).CAS 
Article 
PubMed 

Google Scholar 
30.Liu, S. B., Zamanian, K., Schleuss, P. M., Zarebanadkouki, M. & Kuzyakov, Y. Degradation of tibetan grasslands: consequences for carbon and nutrient cycles. Agric. Ecosyst. Environ. 252, 93–104 (2018).CAS 
Article 

Google Scholar 
31.He, S. Y. & Richards, K. Impact of meadow degradation on soil water status and pasture managementA case study in tibet. Land Degrad. Dev. 26, 468–479. https://doi.org/10.1002/ldr.2358 (2015).Article 

Google Scholar 
32.Yergeau, E., Hogues, H., Whyte, L. G. & Greer, C. W. The functional potential of high Arctic permafrost revealed by metagenomic sequencing, qPCR and microarray analyses. ISME J. 4, 1206–1214. https://doi.org/10.1038/ismej.2010.41 (2010).CAS 
Article 
PubMed 

Google Scholar 
33.Eichorst, S. A. et al. Genomic insights into the Acidobacteria reveal strategies for their success in terrestrial environments. Environ. Microbiol. 20, 1041–1063 (2018).CAS 
Article 

Google Scholar 
34.Fang, D. X. et al. Microbial community structures and functions of wastewater treatment systems in plateau and cold regions. Bioresour. Technol. 249, 684–693 (2018).CAS 
Article 

Google Scholar 
35.Mukhopadhya, I., Hansen, R., El-Omar, E. M. & Hold, G. L. IBD—what role do proteobacteria play?. Nat. Rev. Gastroenterol. Hepatol. 9, 219–230. https://doi.org/10.1038/nrgastro.2012.14 (2012).CAS 
Article 
PubMed 

Google Scholar 
36.Kjoller, A. H. & Struwe, S. Fungal communities, succession, enzymes, and decomposition (2002).37.Poll, C., Brune, T., Begerow, D. & Kandeler, E. Small-scale diversity and succession of fungi in the detritusphere of rye residues. Microbial. Ecol. 59, 130–140. https://doi.org/10.1007/s00248-009-9541-9 (2010).Article 

Google Scholar 
38.Jangid, K. et al. Land-use history has a stronger impact on soil microbial community composition than aboveground vegetation and soil properties. Soil Biol. Biochem. 43, 2184–2193. https://doi.org/10.1016/j.soilbio.2011.06.022 (2011).CAS 
Article 

Google Scholar 
39.Cao, C. et al. Soil bacterial community responses to revegetation of moving sand dune in semi-arid grassland. Appl. Microbiol. Biotechnol. 101, 6217–6228. https://doi.org/10.1007/s00253-017-8336-z (2017).CAS 
Article 
PubMed 

Google Scholar 
40.Tripathi, B. M. et al. Tropical soil bacterial communities in Malaysia: pH dominates in the equatorial tropics too. Microbial. Ecol. 64, 474–484. https://doi.org/10.1007/s00248-012-0028-8 (2012).Article 

Google Scholar 
41.Chu, H. et al. Bacterial community dissimilarity between the surface and subsurface soils equals horizontal differences over several kilometers in the western Tibetan Plateau. Environ. Microbiol. 18, 1523–1533. https://doi.org/10.1111/1462-2920.13236 (2016).CAS 
Article 
PubMed 

Google Scholar 
42.Wu, X. et al. Bacterial communities in the upper soil layers in the permafrost regions on the Qinghai-Tibetan plateau. Appl. Soil Ecol. 120, 81–88. https://doi.org/10.1016/j.apsoil.2017.08.001 (2017).Article 

Google Scholar 
43.Yang, C. et al. Assessing the effect of soil salinization on soil microbial respiration and diversities under incubation conditions. Appl. Soil Ecol. https://doi.org/10.1016/j.apsoil.2020.103671 (2020).Article 

Google Scholar 
44.Langille, M. G. I. et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat. Biotechnol. 31, 814. https://doi.org/10.1038/nbt.2676 (2013).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
45.Mermin, J. et al. Reptiles, amphibians, and human Salmonella infection: a population-based, case-control study. Clin. Infect. Dis. 38, S253–S261. https://doi.org/10.1086/381594 (2004).Article 
PubMed 

Google Scholar 
46.Wang, J. et al. Plant community ecological strategy assembly response to yak grazing in an alpine meadow on the eastern Tibetan Plateau. Land Degrad. Dev. 29, 2920–2931. https://doi.org/10.1002/ldr.3050 (2018).Article 

Google Scholar 
47.Ji, S., Geng, Y., Li, D. & Wang, G. Plant coverage is more important than species richness in enhancing aboveground biomass in a premature grassland, northern China. Agric. Ecosyst. Environ. 129, 491–496. https://doi.org/10.1016/j.agee.2008.11.002 (2009).Article 

Google Scholar 
48.Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336. https://doi.org/10.1038/nmeth.f.303 (2010).CAS 
Article 
PubMed 
PubMed Central 

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

Google Scholar 
50.Chen, W. et al. Consistent responses of surface- and subsurface soil fungal diversity to N enrichment are mediated differently by acidification and plant community in a semi-arid grassland. Soil Biol. Biochem. 127, 110–119. https://doi.org/10.1016/j.soilbio.2018.09.020 (2018).CAS 
Article 

Google Scholar 
51.Kanehisa, M. et al. KEGG for linking genomes to life and the environment. Nucleic Acids Res. 36, D480–D484. https://doi.org/10.1093/nar/gkm882 (2008).CAS 
Article 
PubMed 

Google Scholar  More

Present results showed that Hilsa shad had low nucleotide diversity (0.001809–0.008811) like most of the Clupeiforms, e.g., Elongate ilisha (0.001–0.010), Tapertail anchovy (0.0011–0.0029) in Yangtze river and Japanese anchovy (0.0014–0.0090)44,45,46. Sea fish population had higher genetic diversity than anadromous population within same species or among same group47. Although, Hilsa and Kelee shad belonged to the same subfamily Dorosomantinae but Hilsa shad is anadromous in nature and Kelee shad is exclusively marine48. Because of this habit, nucleotide diversity of Hilsa shad was lower than Kelee shad (Hilsa kelee) (0.010337–0.014690)49. Correspondingly, marine Pacific herring (0.020)50 also had higher nucleotide diversity than Hilsa shad. There were several researchers also reported low nucleotide diversity of Hilsa shad population in the Hoogli, the Ganges and the Brahmaputra river of India10,17,18. Low genetic diversity suggested that only small portion of the total population had the scope of successful spawning. That might be associated with their long anadromous breeding migration journey. At that time huge numbers of individuals were caught in their long migratory routes by the fishermen. Frequent changing of spawning pattern is another reason of unsuccessful spawning51. Therefore, Government of Bangladesh should place some safety and protection actions including, public conscious, restriction on fishing gear, Hilsa fisheries management activities and proper timing of the fishing ban period.Previous studies on genetic population structure of T. ilisha were mostly based on allozymes, allele frequencies, microsatellite DNA markers and mitochondrial DNA regions: Cytochrome b (CytB), ATPase 6&8 (ATPase), 12 s and 16 s rRNA10,15,16,17,18. However, genomic data is more powerful marker than previous markers to present the history, evolution, population status and phylogeny of a fish. Recently, A study discover the population genomics and structure of Hilsa shad in Bangladesh waters based on genomic data at NGS platform by NextRAD sequencing, however they mistakenly assigned samples collected from the confluent of the Meghna River as the north-eastern riverine group19,20. Our study was also based on genomic data at the NGS platform. Conversely, we collected sequence data of 4434 nuclear genes applying a cross-species gene enrichment method22, to examine the genetic diversity and population status of hilsa shad from the Bay of Bengal, its estuaries and all possible lotic and lentic waters and two migratory cohorts.. This study provided a solid estimation of the population status of Hilsa shad using genome-wide data and to infer its genetic diversity.Result of the maximum likelihood IQtree and the population structure suggested that the fresh, estuarine and marine water of Bangladesh have a single population of Hilsa shad. In-addition DAPC, dendrogram and network on SNP loci analysis also represented the same trend. In the phylogenetic tree, samples of all locations were mixed together without making any specific cluster. In the population structure analysis, a single population was present with some admixtured individuals bearing small portion of genes from other group. Pairwise FST value between most locations were poor with non-significant P value (P  > 0.05), that support the deprived local population differences and homogeneity of this fish population throughout our studied locations. The hilsa shad population in Bangladesh might retrieve from a collapsed population. Once upon a time (upto first half of 1990s), this fish was most available and cheap fish in Bangladesh. Because of overexploitation and lack of proper management, the fish population was collapsed more than one decade. After that period, because of fishing ban period and public consciousness (first imposed in 2011), the population started to increase. Hilsa fish production in Bangladesh has doubled in a decade from 2006–2007 (279,189 MT) to 2017–2018 (517,189 MT)4,64. This fact probably caused low genetic diversity and divergence among populations of hilsa shad in the Bangladesh waters.Bangladesh has diversified fresh water habitats for Hilsa shad migration including main river system, coastal and freshwater small rivers, hill stream rivers, haors etc. but anadromous migration of this shad starts from same marine water body, the Bay of Bengal, which is their living ground. Furthermore, this fish has highly migratory nature among marine, estuarine and fresh water bodies. Therefore, it is difficult to draw a conclusion that there is more than one population in this water system. Low variation among groups and among population within groups also did not support more than one population. FST value between most of the locations was poor with non-significant P value, which suggested that the population differences were not significant. Although in some cases, P value was significant but due to their poor FST value that did not provide strong support of local population differences. Here present findings of this study were supported by the findings of some previous researchers who represented the single gene pool or stock of this species in the Bay of Bengal with a substantial gene flow18,52,53.All of the spawning grounds of Hilsa shad were identified in the coastal areas of Bangladesh especially at the lower stretches of the Meghna, the Tetulia, the Ander Manik and the Shahabazpur River e.g., Hatia (Moulavir char) Sandwip (Kalir char) and Bhola (Dhal char and Monpura)6,21. However, migratory plan is mainly initiated during the spawning season, which is activated with follow of fresh water runoff from the inland rivers, and naturally it occurs with the commencement of the south-west monsoon and consequent flooding of all the major rivers draining down to the upper Bay of Bengal and there are no considerable differences in any context. Isolation of spawning ground is an important factor for population differentiation11. Due to presence of un-alienated spawning grounds, it is less feasible to draw population differences of Hilsa shad in the upper streams of different rivers and in their living ground, Bay of Bengal. Therefore, the unique spawning grounds and sole major migratory down-stream route strengthen the presence of single population in all over the Bangladesh water without any significant population clusters. Without specify exact spawning grounds for every cluster, it is unrealistic to draw several clusters in this population.Hilsa population studies in Indian part across the Hoogli, the Bhagirathi, the Ganges and the Brahmaputra Rivers also suggested single and genetically homogeneous population in Indian part10,17,18. Hilsa shad population of the Hoogli-Bhagirathi river system and Hilsa stock of Bangladesh water used same natal habitat, Bay of Bengal. Moreover, the River Ganges is the upstream of the Padma River (Bangladesh) and the Bhagirathi River (India) as well as the Brahmaputra is the upstream of the Jamuna River (Bangladesh). Most of the Hilsa shad of River Ganges comes from the Padma River and as the same way the Brahmaputra river has no other significant source of this fish except the Jamuna River. So genetic homogeneity and unique population across these rivers of Indian part also supported the Hilsa shad’s single population in the Bangladesh water.Nevertheless, Rahman and Naevdal (2000) based on allozymes and muscle proteins as well as Mazumder and Alam (2009) based on mitochondrial D-loop region figured out more than one Hilsa population in Bangladesh waters15,54. Rahman and Naevdal (2000) mentioned two populations: 1. Marine and 2. Estuary and fresh water but they processed without explaining how this highly migratory species was separated into two distinct cohorts. Mazumder and Alam (2009) divided the population into two clusters like previous study but poor pairwise FST value between two groups showed that there were no differences between fresh water and marine-estuarine locations. Recently Asaduzzaman et al. (2020) reported three clusters in the Hilsa population in Bangladesh waters, first one was in marine and estuarine waters and another two belonged to north–western riverine (turbid freshwater) and north-eastern riverine (clear freshwater) ecotypes20. Existing of a single population, the most likely assumption from the present research varied with their findings. Our result suggested that as a highly migratory species, Hilsa shad is incapable to belong to more than one population when sampled at different sections of their migration route. Our postulation is the presence of single cluster in the Bangladesh water because all water bodies are almost connected to each other, raising high rate of gene flow and created large population size. Western and eastern river systems of Bangladesh have immaterial dissimilar water quality (e.g., turbidity) but this is not enough to make population differences of Hilsa shad since they migrate and start their life from same spawning grounds and used almost same route across the lower stream and coastal estuaries during their breeding migration. Asaduzzaman et al. (2020) reported that samples of the Meghna river (MR) was included in the north-eastern riverine (clear freshwater) ecotypes by DAPC and neighbor-joining tree analysis20. However, their sample collection site (MR) was located in the common migratory route for north–western riverine (turbid freshwater) and north-eastern riverine (clear freshwater) ecotypes. Therefore, this site should be representing the samples of both ecotypes rather than specific one.If we draw several specific populations or clusters in the upper streams of Bangladesh that means we had the scope to find this shad in the freshwater all over the year round. However, in the freshwater of Bangladesh, this fish was available in the summer (June–October) and winter season (January-March) only; these were related to their summer and winter migration respectably55. If one or two groups of this fish, continue their complete lifecycle in the freshwater (Western/Eastern part of Bangladesh) that states the assurance of continuous supply of this fish almost year round. However, the original scenario does not support this hypothesis. Finally we can conclude that, only one population of this fish inhabit in the Bangladesh waters without any instance of different populations and clusters (2–4) but in some specific locations, they had some particular characteristics. The Bay of Bengal is their main living ground, at the time of their breeding they come to the freshwater upper streams, spawn in the estuaries and finally return to the sea. Therefore, using all the same ecosystems (sea, estuary and freshwater rivers) in a cyclic fashion is essential to support their life cycle, which certainly pushes all the individuals to belong a unique population.In the population structure analysis, only one population of Hilsa shad was identified with some admixtured individuals (32%) containing partial genes from other population in the water bodies of Bangladesh. The mentioned other population might not represent the Hilsa population of the Hoogly and Bhagirathi river system, India because, the Hilsa shads of both migratory routes of Bangladesh and India showed genetic homogeneity10,17. The Ganges and Brahmaputra rivers of Indian part are the upstream of the Padma and the Jamuna river of Bangladesh and might be belonged to the same population. However, Hilsa population of the Arabian Sea was genetically heterogeneous from the Bay of Bengal18 and those different population genes of admixtured individuals might come from the Arabian Sea by oceanographic dispersion. Once (almost 18,000 years ago) the Arabian Sea had a close connection with the Bay of Bengal through the Laccadive Sea, the Gulf of Mannar and the Palk Bay. Therefore, this likely was an easy way for oceanographic dispersion of Hilsa shad between these two water bodies. After that period, a bridge of limestone shoals, coral reefs and tombolo called as ‘Ram Bridge’ or ‘Adam’s Bridge’ (about 48 km) originated between Pamban Island off the south-eastern coast of Tamil Nadu, India, and Mannar Island, off the north-western coast of Sri Lanka 56,57. Sarker et al. (2020) also mentioned that type of oceanographic dispersion between these two water bodies for another Clupeid fish species, Hilsa kelee49. The Irrawaddy, the Naaf and the Sittang River of Myanmar were also regarded as another important route for Hilsa migration6,58. There is also a possibility of inflowing of these different genes of other population from such population. Still there is no population structure study was conducted in the Myanmar part. Therefore, there is no scope to compare those admixtured individuals with the Hilsa population of Myanmar. However, for completing the full scenario, the Hilsa population of Myanmar also claims research attention in population genomics field.In the present study, Samples of both migration cohorts (summer and winter) were studied. The maximum likelihood IQ tree, population structure and DAPC suggested that samples of both migration cohorts were homogenous. Similarly, Jhingran and Natarajan (1969) and Ramakrishnaiah (1972) also did not find any significant temporal population differences in their previous studies59,60. Dwivedi (2019) found morphometric variations between seasonal migrants of Hilsa shad from Hooghly estuary, India using geometric morphometrics (GM) data61. They explained that these morphotypes might be related to the food availability and temperature fluctuation of summer and winter season but they did not incorporate that to the genetic level of the population. Quddus et al. (1984) reported two seasonal migratory populations of Hilsa shad in Bangladesh water based on spawning, fecundity and sex ratio8. Based on our findings and previous studies we can conclude these mentioned seasonal cohorts might be associated with their food availability and breeding rather than genome level.Hill stream river and haor were two important and unique ecosystems for fish diversity in Bangladesh, they belong to the unique characteristics in the ecological factors as well as fish diversity62,63. Infrequently Hilsa shad use these two water bodies as their migratory routes. Samples were collected from the Shomeswari River and the Dingapota Haor, Mohanganj as the representatives of hill stream river and haor population respectively. However, Hilsa shad of these two exclusive water bodies were similar to the samples of the some other fresh water bodies (i.e., CM, CN and MG) as they were belonging to the Hilsa population without any admixtured individuals. Samples of SS do not have any significant P value with other locations whereas MO samples had significant P value with five other locations but having poor FST value with three locations (i.e., BL, PP, MG). MO samples had only mentionable FST value with MP (estuarine) and MK (marine), which might be the result of differences in water quality of these two water bodies. In DAPC, phylogenetic tree and in network, the samples of hill stream river and haor failed to make any unique cluster or monophyletic clade that represent they are also the part of single unique Hilsa population of Bangladesh waters.Main migration was occurred through the Meghna river estuary, which is connected to the Padma, Meghna and Jamuna river system. However, there are some other alternative routes through some small coastal rivers e.g., the Pashur, the Bishkhali, the Balaswar, the Kocha river, which are connected to the Padma river through the Modhumati and the Gorai river. These coastal rivers passed through or beside the world largest mangrove forest Sundarban. Thus, these two routes are ecologically different from each other. Samples of these two routes have some genetic differences, because most of the locations (MK, CF and BL with PP and KN) of these two estuarine routes had significant P value, but their FST value was not satisfactorily high to make population differences. Ecological differences of these two routes might be played an important role to create this type of slight differences among them. Therefore, these scenarios were not significant enough to describe noteworthy differences in the population level, but may make a sign of upcoming population differences. More

1.Mayaux, P. et al. Tropical forest cover change in the 1990s and options for future monitoring. Philos. Trans. R. Soc. Lond. B Biol. Sci. 60(1454), 373–384. https://doi.org/10.1098/rstb.2004.1590 (2005).Article 

Google Scholar 
2.Lovejoy, T. E. Biodiversity: What is it? In Biodiversity II: Understanding and Protecting Our Biological Resources (eds Reaka-Kudla, M. L. et al.) 7–14 (Joseph Henry Press, 1997).
Google Scholar 
3.Harris, L. D. The Fragmented Forest: Island Biogeographic Theory and the Preservation of Biological Diversity (The University of Chicago Press, 1984).Book 

Google Scholar 
4.Achard, F. et al. Determination of deforestation rates of the world’s humid tropical forests. Science 297(5583), 999–1002. https://doi.org/10.1126/science.1070656 (2002).ADS 
CAS 
Article 
PubMed 

Google Scholar 
5.NRSA – 1983. Mapping of forest cover in India from satellite imagery (1972–75 and 1980–82). Summary Report, National Remote Sensing Agency, Hyderabad, India, pp. 5–6.6.FAO – 2000. Global forest resources assessment. Chapter 23. South Asia. Food and Agriculture Organization, Rome, Italy. http://www.fao.org/3/Y1997E/y1997e0s.htm#bm28. (Accessed on 8 August 2020).7.Datta, D. & Deb, S. Analysis of coastal land use/land cover changes in the Indian Sunderbans using remotely sensed data. Geo-sp. Inf. Sci. 15, 241–250. https://doi.org/10.1080/10095020.2012.714104 (2012).Article 

Google Scholar 
8.Reddy, C. S., Jha, C. S. & Dadhwal, V. K. Assessment and monitoring of long-term forest cover changes in Odisha, India using remote sensing and GIS. Environ. Monit. Assess. 185, 4399–4415. https://doi.org/10.1007/s10661-012-2877-5 (2013).Article 
PubMed 

Google Scholar 
9.Champion, H. G. & Seth, S. K. A revised forest types of India (Manager of Publications, 1968).
Google Scholar 
10.FSI – 2019. State of forest report, Assam. Forest Survey of India, Ministry of Environment and Forests, Dehradun, pp. 23–33.11.Assam Times – 2019. Encroachment killing forest in the state. https://www.assamtimes.org/node/22026. (Accessed on 25 August 2020).12.Saikia, A., Hazarika, R. & Sahariah, D. Land-use/land-cover change and fragmentation in the Nameri Tiger Reserve India. Danish J. Geogr. 113(1), 1–10. https://doi.org/10.1080/00167223.2013.782991 (2013).Article 

Google Scholar 
13.Assam Human Development Report – 2014. Managing diversities, achieving human development. Omeo Kumar Das Institute of Social Change and Development and Institute for Human Development, Planning and Development Department, Government of Assam. https://niti.gov.in/writereaddata/files/human-development/Assam_HDR_30Sep2016.pdf. (Accessed on 26 August 2020).14.Woods, C. H. & Skole, D. Linking satellite, census, and survey data to study deforestation in the Brazilian Amazon. In People and Pixels: Linking Remote Sensing and Social Science (eds Liverman, D. et al.) 70–90 (National Academy Press, 1998). https://doi.org/10.17226/5963.
Google Scholar 
15.Srivastava, S., Singh, T. P., Singh, H., Kushwaha, S. P. S. & Roy, P. S. Assessment of large scale deforestation in Sonitpur district of Assam. Curr. Sci. 82, 1480–1484 (2002).
Google Scholar 
16.Harper, G. J., Steininger, M. K., Tucker, C. J., Juhn, D. & Hawkins, F. Fifty years of deforestation and forest fragmentation in Madagascar. Environ. Conserv. 34(4), 1–9. https://doi.org/10.1017/S0376892907004262 (2007).Article 

Google Scholar 
17.Manjula, K. R., Jyothi, S., Varma, A. K. & Kumar, S. V. Construction of spatial dataset from remote sensing using GIS for deforestation study. Int. J. Comput. Appl. 31(10), 26–32 (2011).
Google Scholar 
18.Phukan, P., Thakuriah, G. & Saikia, R. Land use land cover change detection using remote sensing and GIS techniques: A case study of Golaghat district of Assam, India. Int. Res. J. Earth Sci. 1(1), 11–15 (2013).
Google Scholar 
19.Armenta, S. A. M. et al. Determination and analysis of hot spot areas of deforestation using Remote Sensing and Geographic Information System techniques. Case study: State Sinaloa, Mexico. Open J. For. 6, 295–304. https://doi.org/10.4236/ojf.2016.64024 (2016).Article 

Google Scholar 
20.Sarma, P. K. et al. Land-use and land-cover change and future implication analysis in Manas National Park, India using multi-temporal satellite data. Curr. Sci. 95(2), 223–227 (2008).
Google Scholar 
21.Valožić, L. & Cvitanović, M. Mapping the forest change: using Landsat imagery in forest transition analysis within the Medvednica protected area. Hrvat. Geo. Glas. 73(1), 245–255. https://doi.org/10.21861/hgg.2011.73.01.16 (2011).Article 

Google Scholar 
22.Gambo, J., Mohd Shafri, H. Z., Shaharum, N. S., Abidin, F. A. & Rahman, M. T. Monitoring and predicting land use-land cover (LULC) changes within and around Krau wildlife reserve (KWR) protected area in Malaysia using multi-temporal Landsat data. Geoplanning: J. Geomatics Plan. 5(1), 17–34. https://doi.org/10.14710/geoplanning.5.1.17-34 (2018).‬23.Bapu, T. D. & Nimasow, G. Land cover change assessment of Pakke Tiger Reserve (PTR), East Kameng district of Arunachal Pradesh. J. Remote Sens. & GIS, 9(1), 26–33. http://doi.org/https://doi.org/10.37591/.v9i1.93 (2018).24.Kushwaha, S. P. S. & Hazarika, R. Assessment of habitat loss in Kameng and Sonitpur Elephant reserves. Curr. Sci. 87(10), 1447–1453 (2004).
Google Scholar 
25.Census of India – 2011. Primary Census Abstracts. Registrar General of India, Ministry of Home Affairs, Government of India, Retrieved from https://www.censusindia.gov.in/2011census/PCA/pca_highlights/pe_data.html26.Bose, A. U. Tracking the forest rights act in Nameri National Park & Sonai Rupai Wildlife Sanctuary. A report of Kalpavriksh Environmental Action Group, Pune, Maharashtra. https://kalpavriksh.org/wp-content/uploads/2020/07/Assam-Poster_August14_FINAL1.pdf. (2009). (Accessed on 25 August 2020).27.Das, N. Assessment of ecotourism resources: An applied methodology to Nameri National Park of Assam-India. J. Geogr. Reg. Plan. 6(6), 218–228. https://doi.org/10.5897/JGRP12.057 (2013).Article 

Google Scholar 
28.Dong, J. et al. Mapping deciduous rubber plantations through integration of PALSAR and multitemporal landsat imagery. Remote Sens. Environ. 134, 392–402. https://doi.org/10.3390/rs70101048 (2013).ADS 
Article 

Google Scholar 
29.USGS – 2003. Preliminary Assessment of the Value of Landsat 7 ETM+ Data Following Scan Line Corrector Malfunction. USA: EROS Data Center. United States Geological Survey. https://landsat.usgs.gov/sites/default/files/documents/SLC_off_Scientific_Usability.pdf. (Accessed on 8 August 2020).30.Settle, J. J. & Briggs, S. S. Fast maximum likelihood classification of remotely sensed imagery. Int. J. Remote Sens. 8, 723–734. https://doi.org/10.1080/01431168708948683 (1987).ADS 
Article 

Google Scholar 
31.Richards, J. A. Remote Sensing Digital Image Analysis: An introduction. https://doi.org/10.1007/978-3-642-30062-2_8 (Springer, 2013).Book 

Google Scholar 
32.Stehman, S. V. & Czaplewski, R. L. Design and analysis for thematic map accuracy assessment: Fundamental principles. Remote Sens. Environ. 64, 331–334. https://doi.org/10.1016/S0034-4257(98)00010-8 (1998).ADS 
Article 

Google Scholar 
33.Story, M. & Congalton, R. G. Accuracy assessment: A user’s perspective. Photogram. Eng. Rem. S. 52(3), 397–399 (1986).
Google Scholar 
34.Munoz, S. R. & Bangdiwala, S. I. Interpretation of kappa and B statistics measures of agreement. J. Appl. Stat. 24(1), 105–111. https://doi.org/10.1080/02664769723918 (1997).Article 

Google Scholar 
35.Sim, J. & Wright, C. C. The Kappa statistic in reliability studies: Use, interpretation, and sample size requirements. Phys. Ther. 85(3), 257–268 (2005).Article 

Google Scholar 
36.Landis, J. R. & Koch, G. G. The measurement of observer agreement for categorical data. Biometrics 33, 159–174. https://doi.org/10.2307/2529310 (1977).CAS 
Article 
PubMed 
MATH 

Google Scholar 
37.Balasubramanian, D., Arunachalam, K. & Arunachalam, A. Human-induced land use/land-cover change and bioresource management in Bura Chapori Wildlife Sanctuary in North-East India. Clim. Change Environ. Sustain. 4(1), 28–37. https://doi.org/10.5958/2320-642X.2016.00005.3 (2016).Article 

Google Scholar 
38.Sugden, A. M. Mapping global deforestation patterns. Science 361(6407), 1083. https://doi.org/10.1126/science.361.6407.1083-e (2018).Article 

Google Scholar 
39.Curtis, P. G., Slay, C. M., Harris, N. L., Tyukavina, A. & Hansen, M. C. Classifying drivers of global forest loss. Science 361(6407), 1108–1111. https://doi.org/10.1126/science.aau3445 (2018).ADS 
CAS 
Article 
PubMed 

Google Scholar 
40.Myers, N. Tropical deforestation: rates and patterns. In K. Brown & D. Pearce (Eds.), The causes of tropical deforestation. The economic and statistical analysis of factors giving rise to the loss of the tropical forest (pp. 27–40). London: UCL Press (1994).41.Barraclough, S. & Ghimire, K. B. Agricultural Expansion and Tropical Deforestation (Virginia, 2000).
Google Scholar 
42.Hansen, M. C. et al. High-resolution global maps of the 21st-century forest cover change. Science 342, 850–853. https://doi.org/10.1126/science.1244693 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
43.Reddy, C. S., Rao, P. R. M., Pattanaik, C. & Joshi, P. K. Assessment of large-scale deforestation in Nawarangpur district, Orissa, India: A remote sensing based study. Environ. Monit. Assess. 154, 325–335. https://doi.org/10.1007/s10661-008-0400-9 (2009).Article 

Google Scholar 
44.Saikia, A. Drivers of forest loss. In A. Saikia (Eds.), Over-exploitation of forests. Springer Briefs in Geography. Springer, Cham, Switzerland. https://doi.org/10.1007/978-3-319-01408-1_7 (2014).45.Lele, N. & Joshi, P. K. Analyzing deforestation rates, spatial forest cover changes and identifying critical areas of forest cover changes in North-East India during 1972–1999. Environ. Monit. Assess. 156, 159–170. https://doi.org/10.1007/s10661-008-0472-6 (2009).Article 
PubMed 

Google Scholar 
46.Joppa, L. N., Loarie, S. R. & Pimm, S. L. On the protection of ‘“protected areas”’. Proc. Natl. Acad. Sci. U.S.A. 105(18), 6673–6678 (2008).ADS 
CAS 
Article 

Google Scholar 
47.Bharucha, E. Textbook of Environmental Studies for Undergraduate Courses (Universities Press, 2005).
Google Scholar 
48.Talukdar, N. R. & Choudhury, P. Conserving wildlife wealth of Patharia Hills Reserve Forest, Assam, India: A critical analysis. Glob. Ecol. Conserv. 10, 126–138. https://doi.org/10.1016/j.gecco.2017.02.002 (2017).Article 

Google Scholar 
49.Sonitpur District Judiciary – 2016. In the Court of Additional Sessions Judge, Sonitpur, Tezpur. Sessions Case No. 224 of 2016, U/s. 51 of Wildlife (Protection) Act, 1972. http://sonitpurjudiciary.gov.in/Judgement/09_Sessions%20Case%20No.224%20of%202016.pdf. (Accessed on 2 August 2020.50.Assam Forest Department – 2014. A draft proposal for declaring Eco-Sensitive Zone around Sonai-Rupai Wildlife Sanctuary. Prepared by Divisional Forest Officer Western Assam Wildlife Division, Tezpur, Assam Forest Department, Government of Assam. http://103.8.249.31/assamforest/notificationsOrders/ESZ%20Sonai-Rupai_final.pdf. (Accessed on 25 August 2020.51.ESZ Expert Committee Meeting – 2020. Minutes of 41st ESZ expert committee meeting for the declaration of Eco-Sensitive Zone (ESZ) around protected areas & Zonal Master Plan through video conferencing held on 23rd to 24th June 2020. Ministry of Environment, Forests and Climate Change, Government of India. http://moef.gov.in/wp-content/uploads/2019/10/41-st-ECM_Approved-minutes_.pdf. (Accessed on 25 August 2020). More

1.Møller, A., Fiedler, W. & Berthold, P. Effects of climate change on birds (Oxford University Press, 2010).
Google Scholar 
2.IPCC. Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty. (The Intergovernmental Panel on Climate Change, 2018).3.Lovejoy, T. E., Hannah, L. & Wilson, E. O. Biodiversity and climate change (Yale University Press, 2019).Book 

Google Scholar 
4.Hughes, L. Biological consequences of global warming: is the signal already apparent?. Trends Ecol. Evol. 15, 56–61 (2000).CAS 
PubMed 
Article 

Google Scholar 
5.Moore, N. Precipitation regimes and climate change. In Global Environmental Change (ed. Freedman, B.) 191–197 (Springer, Dordrecht, 2014).
Google Scholar 
6.Knapp, A. K. et al. Characterizing differences in precipitation regimes of extreme wet and dry years: Implications for climate change experiments. Glob. Change Biol. 21, 2624–2633 (2015).ADS 
Article 

Google Scholar 
7.Tobias, A. & Díaz, J. Heat waves, human health, and climate change. In Global Environmental Change (ed. Freedman, B.) 447–453 (Springer, Dordrecht, 2014).
Google Scholar 
8.Freedman, B. Global Environmental Change (Springer, 2014).Book 

Google Scholar 
9.Hannah, L. Climate change biology 2nd edn. (Elsevier, 2014).
Google Scholar 
10.Gibbons, J. W. et al. The global decline of reptiles, Déjà Vu Amphibians: Reptile species are declining on a global scale. Six significant threats to reptile populations are habitat loss and degradation, introduced invasive species, environmental pollution, disease, unsustainable use, and global climate change. BioScience 50, 653–666 (2000).11.Williams, S. E., Shoo, L. P., Isaac, J. L., Hoffmann, A. A. & Langham, G. Towards an integrated framework for assessing the vulnerability of species to climate change. PLoS ONE 6, e325 (2008).Article 
CAS 

Google Scholar 
12.Huey, R. B., Losos, J. B. & Moritz, C. Are Lizards Toast?. Science 328, 832–833 (2010).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
13.Sinervo, B. et al. Erosion of lizard diversity by Climate Change and altered thermal niches. Science 328, 894–899 (2010).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
14.Glick, P., Stein, B. A. & Edelson, N. A. Scanning the conservation horizon: A guide to climate change vulnerability assessment (National Wildlife Federation, 2011).
Google Scholar 
15.Parmesan, C. Climate and species’ range. Nature 382, 765–766 (1996).ADS 
CAS 
Article 

Google Scholar 
16.Parmesan, C. et al. Poleward shifts in geographical ranges of butterfly species associated with regional warming. Nature 399, 579–583 (1999).ADS 
CAS 
Article 

Google Scholar 
17.Kelly, A. E. & Goulden, M. L. Rapid shifts in plant distribution with recent climate change. Proc. Natl. Acad. Sci. USA 105, 11823–11826 (2008).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
18.Zuckerberg, B., Woods, A. M. & Porter, W. F. Poleward shifts in breeding bird distributions in New York State. Glob. Change Biol. 15, 1866–1883 (2009).ADS 
Article 

Google Scholar 
19.Sodhi, N. S. & Ehrlich, P. R. Conservation Biology for all (Oxford University Press, 2010).Book 

Google Scholar 
20.Porter, W. P. & Gates, D. M. Thermodynamic equilibria of animals with environment. Ecol. Monogr. 39, 227–244 (1969).Article 

Google Scholar 
21.Avery, R. A. Field studies of body temperatures and thermoregulation. In Biology of the Reptilia (eds Gans, C. & Pough, F. H.) 93–166 (Academic Press, New York, 1982).
Google Scholar 
22.Angilletta, M. J. Thermal Adaptation: A Theoretical and Empirical Synthesis (Oxford University Press, 2009).Book 

Google Scholar 
23.Huey, R. B. Temperature, physiology, and the ecology of reptiles. In Biology of the Reptilia (eds Gans, C. & Pough, F. H.) 25–91 (Academic Press, New York, 1982).
Google Scholar 
24.Angilletta, M. J. Estimating and comparing thermal performance curves. J. Therm. Biol. 31, 541–545 (2006).Article 

Google Scholar 
25.Shine, R. Incubation regimes of cold-climate reptiles: the thermal consequences of nest-site choice, viviparity and maternal basking. Biol. J. Linn. Soc. 83, 145–155 (2004).Article 

Google Scholar 
26.Shine, R. Life-history evolution in Reptiles. Ann. Rev. Ecol. Evol. Syst. 36, 23–46 (2005).Article 

Google Scholar 
27.Huey, R. B. & Stevenson, R. D. Integrating thermal physiology and ecology of ectotherms: A discussion of approaches. Am. Zool. 19, 357–366 (1979).Article 

Google Scholar 
28.Bennett, A. F. The thermal dependence of lizard behaviour. Anim. Behav. 28, 752–762 (1980).Article 

Google Scholar 
29.Christian, K. A. & Tracy, C. R. The effect of the thermal environment on the ability of hatchling Galapagos land iguanas to avoid predation during dispersal. Oecologia 49, 218–223 (1981).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
30.Snell, H. L., Jennings, R. D., Snell, H. M. & Harcourt, S. Intrapopulation variation in predator-avoidance performance of Galápagos lava lizards: The interaction of sexual and natural selection. Evol. Ecol. 2, 353–369 (1988).Article 

Google Scholar 
31.Robson, M. A. & Miles, D. B. Locomotor performance and dominance in male Tree Lizards, Urosaurus ornatus. Funct. Ecol. 14, 338–344 (2000).Article 

Google Scholar 
32.Perry, G., LeVering, K., Girard, I. & Garland, T. Locomotor performance and social dominance in male Anolis cristatellus. Anim. Behav. 67, 37–47 (2004).Article 

Google Scholar 
33.Cowles, R. B. & Bogert, C. M. A preliminary study of the thermal requirements of desert reptiles. Bull. Am. Mus. Nat. Hist. 83, 265–296 (1944).
Google Scholar 
34.Bartholomew, G. A. Physiological control of body temperature. In Biology of the Reptilia (eds Gans, C. & Pough, F. H.) 167–211 (Academic Press, New York, 1982).
Google Scholar 
35.Beaupre, S. J. Effects of geographically variable thermal environment on bioenergetics of mottled rock rattlesnakes. Ecology 76, 1655–1665 (1995).Article 

Google Scholar 
36.Huey, R. B., Hertz, P. E. & Sinervo, B. Behavioral drive versus behavioral inertia in evolution: a null model approach. Am. Nat. 161, 357–366 (2003).PubMed 
Article 
PubMed Central 

Google Scholar 
37.Huey, R. B. Behavioral thermoregulation in lizards: importance of associated costs. Science 184, 1001 (1974).ADS 
Article 

Google Scholar 
38.Hertz, P. E., Huey, R. B. & Stevenson, R. D. Evaluating temperature regulation by field-active ectotherms: The fallacy of the inappropriate question. Am. Nat. 142, 796–818 (1993).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
39.Vitt, L. & Caldwell, J. Herpetology: An introductory biology of amphibians and reptiles 4th edn. (Elsevier, 2014).
Google Scholar 
40.Ortega, Z., Mencía, A. & Pérez-Mellado, V. Sexual differences in behavioral thermoregulation of the lizard Scelarcis perspicillata. J. Therm. Biol. 61, 44–49 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
41.Rodríguez-Serrano, E., Navas, C. A. & Bozinovic, F. The comparative field body temperature among Liolaemus lizards: Testing the static and the labile hypotheses. J. Therm. Biol. 34, 306–309 (2009).Article 

Google Scholar 
42.Telemeco, R. S., Radder, R. S., Baird, T. A. & Shine, R. Thermal effects on reptile reproduction: Adaptation and phenotypic plasticity in a montane lizard. Biol. J. Linn. Soc. 100, 642–655 (2010).Article 

Google Scholar 
43.Labra, A., Pienaar, J. & Hansen, T. F. Evolution of thermal physiology in Liolaemus Lizards: Adaptation, phylogenetic inertia, and niche tracking. Am. Nat. 174, 204–220 (2009).PubMed 
PubMed Central 
Article 

Google Scholar 
44.Huey, R. B. et al. Why tropical forest lizards are vulnerable to climate warming. Proc. Biol. Sci. 276, 1939–1948 (2009).PubMed 
PubMed Central 

Google Scholar 
45.Deutsch, C. A. et al. Impacts of climate warming on terrestrial ectotherms across latitude. Proc. Natl. Acad. Sci. USA 105, 6668–6672 (2008).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
46.Root, T. L. et al. Fingerprints of global warming on wild animals and plants. Nature 421, 57–60 (2003).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
47.Bestion, E., Teyssier, A., Richard, M., Clobert, J. & Cote, J. Live fast, die young: Experimental evidence of population extinction risk due to climate change. PLoS ONE 13, e1002281 (2015).Article 
CAS 

Google Scholar 
48.Zhang, L., Yang, F. & Zhu, W.-L. Evidence for the ‘rate-of-living’ hypothesis between mammals and lizards, but not in birds, with field metabolic rate. Comp. Biochem. Physiol. Part A 253, 110867 (2021).CAS 
Article 

Google Scholar 
49.Dillon, M. E., Wang, G. & Huey, R. B. Global metabolic impacts of recent climate warming. Nature 467, 704–706 (2010).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
50.Sinervo, B. et al. Climate change, thermal niches, extinction risk and maternal-effect rescue of toad-headed lizards, Phrynocephalus, in thermal extremes of the Arabian Peninsula to the Qinghai—Tibetan Plateau. Integr. Zool. 13, 450–470 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
51.Ibarguengoytía, N. R. et al. Looking at the past to infer into the future: Thermal traits track environmental change in Liolaemidae. Evolution https://doi.org/10.1111/evo.14246 (2021)Article 
PubMed 
PubMed Central 

Google Scholar 
52.Beniston, M. Climate change in mountain regions: A review of possible impacts. Clim. Change 59, 5–31 (2003).Article 

Google Scholar 
53.Thuiller, W. Climate change and the ecologist. Nature 448, 550–552 (2007).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
54.Martínez Carretero, E. La Puna argentina: Delimitación general y división en distritos florísticos. Bol. Soc. Argent. Bot. 31, 27–40 (1995).
Google Scholar 
55.Esquerré, D., Brennan, I. G., Catullo, R. A., Torres-Pérez, F. & Keogh, J. S. How mountains shape biodiversity: The role of the Andes in biogeography, diversification, and reproductive biology in South America’s most species-rich lizard radiation (Squamata: Liolaemidae). Evolution 73, 214–230 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
56.Abdala, C. S., Laspiur, A. & Langstroth, R. Las especies del género Liolaemus (Liolaemidae). Lista de taxones y comentarios sobre los cambios taxonómicos más recientes. Cuad. Herp. 35, 193–223 (2021).
Google Scholar 
57.Abdala, C. S. et al. Unravelling interspecific relationships among highland lizards: First phylogenetic hypothesis using total evidence of the Liolaemus montanus group (Iguania: Liolaemidae). Zool. J. Linn. Soc. 189, 349–377 (2020).Article 

Google Scholar 
58.Cabrera, M. R. & Monguillot, J. C. A new Andean species of Liolaemus of the darwinii complex (Reptilia: Iguanidae). Zootaxa 1106, 35–43 (2006).Article 

Google Scholar 
59.Abdala, C. S. et al. Categorización del estado de conservación de las lagartijas y anfisbenas de la República Argentina. Cuad. Herp. 26, 215–248 (2012).
Google Scholar 
60.Avila, L. J. Liolaemus montanezi. The IUCN Red List of Threatened Species 2016: e.T56077261A56077269. https://doi.org/10.2305/IUCN.UK.2016-1.RLTS.T56077261A56077269.en (2016).61.Barros, V. R. et al. Climate change in Argentina: trends, projections, impacts and adaptation. Wiley Interdiscip. Rev. Clim. Change 6, 151–169 (2015).Article 

Google Scholar 
62.IPCC. Climate change 2014: synthesis report. Contribution of Working Groups I, II and III to the Fifh Assessment Report of the Intergovernmental Panel on Climate Change. (IPCC, Geneva, 2014).63.Bury, R. B. Natural history, field ecology, conservation biology and wildlife management: time to connect the dots. Herp. Con. Biol. 1, 56–61 (2006).
Google Scholar 
64.Fei, T. et al. A body temperature model for lizards as estimated from the thermal environment. J. Therm. Biol. 37, 56–64 (2012).Article 

Google Scholar 
65.Ortega, Z. et al. Disentangling the role of heat sources on microhabitat selection of two Neotropical lizard species. J. Trop. Ecol. 35, 149–156 (2019).Article 

Google Scholar 
66.Bujes, C. S. & Verrastro, L. Thermal biology of Liolaemus occipitalis (Squamata, Tropiduridae) in the coastal sand dunes of Rio Grande do Sul, Brazil. Braz. J. Biol. 66, 945–954 (2006).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
67.Almeida-Santos, P., Militão, C. M., Nogueira-Costa, P., Menezes, V. A. & Rocha, C. F. D. Thermal ecology of five remaining populations of an endangered lizard (Liolaemus lutzae) in different restinga habitats in Brazil. J. Coast. Conserv. 19, 335–343 (2015).Article 

Google Scholar 
68.Liz, A. V., Santos, V., Ribeiro, T., Guimarães, M. & Verrastro, L. Are lizards sensitive to anomalous seasonal temperatures? Long-term thermobiological variability in a subtropical species. PLoS ONE 14, e0226399 (2019).Article 
CAS 

Google Scholar 
69.Martori, R., Bignolo, P. & Cardinale, L. Relaciones térmicas en una población de Liolaemus wiegmannii (Iguania: Tropiduridae). Rev. Esp. Herpetol. 12, 19–26 (1998).
Google Scholar 
70.Martori, R., Aun, L. & Orlandini, S. Relaciones térmicas temporales en una población de Liolaemus koslowskyi. Cuad. Herp. 16, 33–45 (2002).
Google Scholar 
71.Cánovas, M. G., Villavicencio, H. J. & Acosta, J. C. Liolaemus olongasta (NCN) Body temperature. Herp. Rev. 37, 87–88 (2006).
Google Scholar 
72.Villavicencio, H., Acosta, J., Cánovas, M. & Marinero, J. Thermal ecology of a population of the lizard, Liolaemus pseudoanomalus in western Argentina. Amphibia-Reptilia 28, 163–165 (2007).Article 

Google Scholar 
73.Ibargüengoytía, N. R. et al. Thermal biology of the southernmost lizards in the world: Liolaemus sarmientoi and Liolaemus magellanicus from Patagonia, Argentina. J. Therm. Biol. 35, 21–27 (2010).Article 

Google Scholar 
74.Castillo, G. N., Villavicencio, H. J., Acosta, J. C. & Marinero, J. A. Temperatura corporal de campo y actividad temporal de las lagartijas Liolaemus vallecurensis y Liolaemus ruibali en clima riguroso de los Andes centrales de Argentina. Multequina 24, 19–31 (2015).
Google Scholar 
75.Laspiur, A., Villavicencio, H. J. & Acosta, J. C. Liolaemus chacoensis (NCN). Body temperature. Herp. Rev. 38, 458–459 (2007).
Google Scholar 
76.Salva, A. G., Robles, C. I. & Tulli, M. J. Thermal biology of Liolaemus scapularis (Iguania:Liolaemidae) from argentinian northwest. J. Therm. Biol 98, 102924 (2021).PubMed 
Article 

Google Scholar 
77.Mesinger, F., Jovic, D., Chou, S. C., Gomes, J. L. & Bustamante, J. F. Wind forecast around the Andes using the sloping discretization of the eta coordinate. in Proceedings of the 8th International Conference on Southern Hemisphere Meteorology and Oceanography 1837–1848 (INPE, 2006).78.Sannolo, M. & Carretero, M. A. Dehydration constrains thermoregulation and space use in lizards. PLoS ONE 14, e0220384 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
79.Nicholson, K. L. et al. The influence of temperatura and humidity on activity patterns of the lizards Anolis stratulus and Ameiva exsul in the British Virgin Islands. Caribb. J. Sci. 41, 870–873 (2005).
Google Scholar 
80.Adolph, A. S. & Porter, W. P. Temperature, activity, and lizard life histories. Am. Nat. 142, 273–295 (1993).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
81.Bakken, G. S., Santee, W. R. & Erskine, D. Operative and standard operative temperature: Tools for thermal energetics studies. Am. Zool. 25, 933–943 (1985).Article 

Google Scholar 
82.Black, I. R. G., Berman, J. M., Cadena, V. C. & Tattersall, G. J. Behavioral thermoregulation in lizards. Strategies for achieving preferred temperature. In Behavior of lizards. Evolutionary and mechanistic perspectives (eds Bels, V. L. & Russell, A. P.) 13–46 (CRC Press, Florida, 2019).
Google Scholar 
83.Pirtle, E. I., Tracy, C. R. & Kearney, M. R. Hydroregulation. A neglected behavioral response of lizards to climate change? In Behavior of Lizards. Evolutionary and mechanistic perspectives (eds Bels, V. L. & Russell, A. P.) 343–374 (CRC Press, Florida, 2019).
Google Scholar 
84.Medina, M. et al. Thermal biology of genus Liolaemus: A phylogenetic approach reveals advantages of the genus to survive climate change. J. Therm. Biol. 37, 579–586 (2012).Article 

Google Scholar 
85.Blouin-Demers, G. & Weatherhead, P. J. Thermal ecology of black rat snakes (Elaphe obsoleta) in a thermally challenging environment. Ecology 82, 3025–3043 (2001).Article 

Google Scholar 
86.Cabezas-Cartes, F., Fernández, J. B., Duran, F. & Kubisch, E. L. Potential benefits from global warming to the thermal biology and locomotor performance of an endangered Patagonian lizard. PeerJ 7, e7437 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
87.Obregón, R. L., Scolaro, J. A., Ibargüengoytía, N. R. & Medina, M. Thermal biology and locomotor performance in Phymaturus calcogaster: are Patagonian lizards vulnerable to climate change? Integr. Zool. 16, 53–66 (2021).PubMed 
Article 
PubMed Central 

Google Scholar 
88.Litmer, A. R. & Murray, C. M. Critical thermal tolerance of invasion: Comparative niche breadth of two invasive lizards. J. Therm. Biol. 86, 102432 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
89.Bels, V. L. & Russell, A. P. Behavior of lizards. Evoutionary and mechanistic perspectives (CRC Press, Florida, 2019).Book 

Google Scholar 
90.Panda, B. B., Achary, V. M., Mahanty, S. & Panda, K. K. Plant adaptation to abiotic and genotoxic stress: Relevance to climate change and evolution. In Climate Change and plant abiotic stress tolerance (eds Tuteja, N. & Gill, S. S.) 251–294 (Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, 2014).
Google Scholar 
91.Kiesling, R. Flora de San Juan, República Argentina Vol. 1 (Vázquez Mazzini, Buenos Aires, 1994).
Google Scholar 
92.Köeppen, V. P. Climatología. Con un estudio de los climas de la tierra (Fondo de Cultura Económica, Pánuco, México, DF, 1948).
Google Scholar 
93.Bakken, G. S. Measurements and application of operative and standard operative temperatures in ecology. Am. Zool. 32, 194–216 (1992).Article 

Google Scholar 
94.Cecchetto, N. R., Medina, S. M., Taussig, S. & Ibargüengoytía, N. R. The lizard abides: cold hardiness and winter refuges of Liolaemus pictus argentinus in Patagonia, Argentina. Can. J. Zool. 97, 773–782 (2019).Article 

Google Scholar 
95.Cecchetto, N. R., Medina, S. M. & Ibargüengoytía, N. R. Running performance with emphasis on low temperatures in a Patagonian lizard, Liolaemus lineomaculatus. Sci. Rep. 10, 14732 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
96.Mahoney, J. J. & Hutchison, V. H. Photoperiod acclimation and 24-hour variations in the critical thermal maxima of a tropical and a temperate frog. Oecologia 2, 143–161 (1969).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
97.Christian, K. A. & Weavers, B. W. Thermoregulation of monitor lizards in Australia: An evaluation of methods in thermal biology. Ecol. Monogr. 66, 139–157 (1996).Article 

Google Scholar 
98.Camacho, A. et al. Measuring behavioral thermal tolerance to address hot topics in ecology, evolution, and conservation. J. Therm. Biol. 73, 71–79 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
99.Clusella-Trullas, S. & Chown, S. L. Lizard thermal trait variation at multiple scales: a review. J. Comp. Physiol. B 184, 5–21 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
100.Sunday, J. M. et al. Thermal-safety margins and the necessity of thermoregulatory behavior across latitude and elevation. Proc. Natl. Acad. Sci. USA 111, 5610–5615 (2014).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
101.Sokal, R. R. & Rohlf, F. J. Biometry: The Principles and practice of statistics in biological research 3rd edn. (Freeman W.H, 1995).MATH 

Google Scholar 
102.Kovach, W. Oriana ver. 4.0. Software. (Kovach Computing Services, 2001).103.Fitzgerald, L. A., Cruz, F. B. & Perotti, G. Phenology of a lizard assemblage in the dry Chaco of Argentina. J. Herpetol. 33, 526–535 (1999).Article 

Google Scholar 
104.Beasley, T. M. & Schumacker, R. E. Multiple regression approach to analyzing contingency tables: Post hoc and planned comparison procedures. J. Exp. Educ. 64, 79–93 (1995).Article 

Google Scholar 
105.García Pérez, M. A. & Núñez Antón, V. Cellwise residual analysis in two-way contingency tables. Educ. Psychol. Meas. 65, 825–839 (2003).MathSciNet 
Article 

Google Scholar 
106.Peig, J. & Green, A. J. New perspectives for estimating body condition from mass/length data: the scaled mass index as an alternative method. Oikos 118, 1883–1891 (2009).Article 

Google Scholar 
107.Bohonak, A. J. & van Der Linde, K. RMA for JAVA Software for Reduced Major Axis regression. ver. 1.21. (2004).108.Baty, F. et al. A toolbox for nonlinear regression in R: The Package nlstools. J. Stat. Softw. 5, 1–21 (2015).
Google Scholar 
109.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing (2019).110.Thuiller, W., Georges, D., Engler, R. & Breiner, F. biomod2: Ensemble Platform for Species Distribution Modeling. R package ver. 3.4.6. (2020).111.Hijmans, R. J., Phillips, S., Leathwick, J. & Elith, J. dismo: Species Distribution Modeling. R package ver. 1.1-4. (2017).112.Hijmans, R. J. raster: Geographic Data Analysis and Modeling. R package ver. 3.4-5. (2020). More

Fieldwork and rock sample analysisThe primary objective of our fieldwork was to collect sedimentological data that would allow us to interpret the processes responsible for the deposition of the beds of the Greater Phyllopod Bed. These parameters could then be incorporated into our experimental design and recreation of Burgess Shale-type flows. To understand the complex sedimentary deposits of the Burgess Shale Formation, we targeted individual beds (Fig. 3, Supplementary Figs. 2–5) that were logged at outcrop for informative mm-scale and cm-scale sedimentary structures. Grain size analysis was conducted in the field using a grain-size comparator and hand-lens and during petrographic analysis. The Greater Phyllopod Bed has been logged in considerable detail in the field20,33, and so logs produced from our work can be used to compare to previous studies. Detailed descriptions of the intervals sampled included color, bounding surfaces, micro-sedimentary structures, grain size, and textures. Larger-scale field mapping and analysis of sedimentary architecture were not undertaken and so we were not attempting to answers questions on the relationship of the Cathedral Escarpment to the fossil-bearing deposits or the precise provenance of the organisms.We collected whole-rock samples from the Greater Phyllopod Bed of the Walcott Quarry at stratigraphic heights of 111.6, 136, 149.95, 184.83, and 226.68 cm (labeled Bed A to E, respectively) above the top of the Wash Limestone Member. All sedimentological samples for this study were collected in situ from this location under the Parks Canada collection and research permit (YNP-2015-19297). The permit for our fieldwork allowed us to collect and sample sedimentological material exclusively. These were subsequently sampled for laboratory analysis and thin-section preparation.Petrographic analysis was performed on all samples using a Leica DM750P microscope. Each thin section was scanned with an Epson scanner to observe details of the millimeter-scale structures and textures (Fig. 3, Supplementary Figs. 2–5). Plain and cross-polarized light micrographs were taken of areas of particular sedimentological interest from each thin section and documented along with the petrological analysis. These samples were processed for further geochemical and elemental analysis.Sample analysisX-Ray Diffraction (XRD) was used to characterize the mineralogical content of the matrix of Bed A (111.6 cm above the top of the Wash Limestone Member) from the Walcott Quarry. For whole-rock bulk powder analyses, the sample was ground into a powder, and XRD was conducted using a PANalytical X’Pert3 diffractometer. For clay analysis, we applied the fractions to orientated glass slides. Organics were removed from each sample by H2O2 treatment before disaggregating the material using ultrasonic vibration. The suspended material was decanted from the ultrasonic bath in centrifuge bottles, which were topped up with deionized water so that each bottle weighed within the same gram. The bottles were placed in the centrifuge for two treatments, first at 1000 rpm for 4 min, and then again at 4000 rpm for 20 min. After the first treatment, the supernatant was transferred to new centrifuge bottles. The three lightest bottles were topped up with deionized water in order to reach the weight of the heaviest. The resultant concentrated sample yield ( More

Selection of materials for joint repair of chromium-contaminated soilTable 1 shows the results of the orthogonal test. Range analysis was performed according to the results of Table 1. The range-analysis results are shown in Table 2.Table 1 Orthogonal design scheme and results.Full size tableTable 2 Orthogonal test results range analysis calculation table.Full size tableTable 2 shows that, from the perspective of unconfined compressive strength, the primary and secondary order of the 28 day strength, factors affecting the combined repair of chromium-contaminated soil were cement content → fly-ash synthetic zeolite content → CaS5 content. The best test ratio was: CaS5 content 3 times, synthetic zeolite content 15%, and cement content 20%. The unconfined compressive strength of the contaminated soil after remediation increased with the increase in cement content, but the relationship between the content of CaS5 and synthetic zeolite, and the unconfined compressive strength of the specimen was not very obvious. From the perspective of toxicity leaching, the primary and secondary order of factors affecting the total chromium leaching concentration of the combined remediation of chromium-contaminated soil were cement content → fly-ash synthetic zeolite content → CaS5 content. The primary and secondary order of factors affecting the leaching concentration of Cr(VI) in the combined remediation of contaminated soil were CaS5 content → cement content → fly-ash synthetic zeolite content. The best test ratios of the total chromium and Cr(VI) toxicity leaching test were: CaS5 content is 4 times, synthetic zeolite content 15%, and cement content 20%. Total chromium and Cr(VI) leaching concentration of the chromium-contaminated soil after joint remediation was negatively correlated with the content of CaS5, synthetic zeolite, and cement content. The change of total chromium leaching concentration was most significantly affected by cement content and synthetic zeolite. Second, the change of Cr(VI) leaching concentration was most significantly affected by CaS5 content. From the perspective of leaching concentration, when reducing agent CaS5, adsorbent synthetic zeolite, and curing agent cement were all at maximum, the leaching effect of total chromium and Cr(VI) was best. However, considering the actual engineering cost and dosage of the preparation should be reduced as much as possible for meeting the requirements. Therefore, comprehensive balance analysis determined the optimal ratio for joint repair of chromium-contaminated soil to be 3 times the dosage of CaS5, 15% synthetic zeolite, and cement amount 20%.Strength change of combined repair of chromium-contaminated soil under action of dry–wet cycleThe test compared the variation of unconfined compressive strength with the number of dry and wet cycles under different conditions of chromium content, combined to repair standard specimens of chromium-contaminated soil, and test results are shown in Fig. 1.Figure 1The relationship between unconfined compressive strength and the number of dry wet cycles.Full size imageFigure 1 shows that, in the beginning, the unconfined compressive strength of the combined repair of chromium-contaminated soil increased with the increase in the number of wet and dry cycles. After reaching the maximal value, it gradually decreased as the number of dry–wet cycles continued to increase. In the initial stage of the dry–wet cycle, the unconfined compressive strength of the combined repair of chromium-contaminated soil increased to varying degrees. For 1000 and 3000 mg/kg of chromium-contaminated soil, the peak of the unconfined compressive strength appeared at 2 times during the dry–wet cycle, and the peak of the unconfined compressive strength of 5000 mg/kg chromium-contaminated soil appeared at 4 dry–wet cycles. After that, unconfined compressive strength gradually decreased with the progress of dry–wet cycles, and the decrease rate became slower. From strength-loss analysis, the higher the chromium content was, the greater the change in strength loss. After 16 wet and dry cycles, the strength-loss rates of 1000, 3000, and 5000 mg/kg chromium-contaminated soil were 17.95%, 22.27%, and 28.73%, respectively, and strength loss was within 30%, showing better water stability21,22.From analysis of the strength-change process, after 28 days of curing for the joint repair of chromium-contaminated soil, the physical and chemical interaction between cement hydrate and soil in the repair preparation was still occurring, as was the strength increase and dry–wet cycle caused by its hydration products. The weakening effect on strength is a dynamic equilibrium process of mutual decline and growth, and the equilibrium state of the two reaction degrees directly affected the strength of solidified chromium-contaminated soil23. In the initial stage of the dry–wet cycle, the strength increase caused by the interaction between remediation agent and chromium-contaminated soil continued. At that time, the destructive effect of the dry–wet cycle on the joint repair of chromium-contaminated soil was not significant in comparison. As the number of dry–wet cycles increased, hydration products formed and became stable. Dry shrinkage and wet expansion cause internal stress in the joint repair of chromium-contaminated soil, and the soil has cracks due to internal stress changes. A dry–wet cycle has a relatively destructive effect that is gradually noticeable and resulting in a decrease in strength. After many instances of drying and wetting, the strength of repairing chromium-contaminated soil was decreased and stabilized.Figure 1 also shows that, compared with low-content chromium-contaminated soil, the high-content chromium-contaminated-soil solidified body strength peak appeared later, and the peak value was low. This is because the higher the chromium ion content was, the more serious the delay of the hydration reaction of the repair agent was, and the more obvious the weakening effect on the strength of the cured body was, which is not conducive to strength growth. The weakening effect of the dry–wet cycle on strength continued to exist, which led to the repaired contaminated soil with a high content of chromium having lower strength.Toxic-leaching changes of combined remediation of chromium-contaminated soil under dry–wet cycleThe experiment compared the variation of hexavalent chromium and total chromium leaching concentration with the number of dry–wet cycles in standard specimens of the combined repair of chromium-contaminated soil under different chromium-content conditions of the contaminated soil. Test results are shown in Fig. 2.Figure 2Effect of drying–wetting cycle timeson leaching concentration of Cr.Full size imageFigure 2 shows that the leaching concentration of Cr(VI) and total chromium decreased in the initial stage of the dry–wet cycle of the remediation of chromium-contaminated soil. After that, as the number of dry–wet cycles increased, leaching concentration also increased, but the content was low (1000 mg/kg). The medium content (3000 mg/kg) of chromium-contaminated soil Cr(VI) and total chromium leaching concentration fluctuated slightly, and the change was relatively stable, while the high content of chromium-contaminated soil (5000 mg/kg) Cr(VI) leaching the concentration fluctuated greatly, and total chromium increased significantly. Compared with the low-content chromium-contaminated soil, the leaching concentration of the solidified body of high-content chromium-contaminated soil was higher.In the beginning of the dry–wet cycle, the physical and chemical interaction between the cement hydrate and the soil in the repair preparation was still happening. The fly-ash synthetic zeolite had the adsorption effect of metal chromium ions and hydroxide precipitation in the alkaline environment. The formation of chromium ions could meet the requirements of curing/stabilizing chromium ions, and heavy-metal chromium ions are not easy to leach. With the increase in the number of dry–wet cycles, a series of evolutionary processes occurred, such as the expansion of local microcracks, the increase in macropores, the appearance of internal cracks in the contaminated soil, and the appearance of cracks and peeling phenomena on the outside of the contaminated-soil damage. At this time, the contact area between the heavy-metal ions in the contaminated soil and the external environment, especially water, increased, which reduced the ability of the repair agent to adsorb and wrap chromium ions, so that chromium ions were easily leached. In the leaching test, the use of the acidic leaching solution also destroyed the pH balance of the repaired chromium-contaminated soil, the hydrated gel was dissolved and desorbed, and the heavy metals changed, thereby accelerating the leaching of heavy-metal ions24.From analysis of the leaching law shown by the contaminated soil with different chromium content levels, when chromium content in the contaminated soil was low, the remediation agent could effectively solidify/stabilize most of the chromium ions in the soil Cr(VI) and low total chromium leaching. When the chromium content in the contaminated soil was high, the limited content of the repair agent showed an insufficient solidification/stabilization effect of the heavy-metal chromium ions. Because a higher concentration of chromium ions hindered the formation of hydration products of the repair agent, it weakened the adsorption and binding capacity of the hydrated gel. The heavy-metal chromium ions existed in the pores of the contaminated soil in a free state, making the repair agent solidify the chromium ions, the stabilization effect decreased, and the leaching of Cr(VI) and total chromium increased.Overall, the effect of the dry–wet cycle on the joint repair of chromium-contaminated soil was limited, and the joint repair of chromium-contaminated soil had strong resistance to dry–wet cycles, especially the low- and medium-content chromium-polluted soil.Combined repair of quality loss of chromium-contaminated soil under action of dry–wet cyclesThe cumulative mass-loss rate of the sample was calculated from Formula (1), and the result is shown in Fig. 3. With the increase in the number of wet and dry cycles, the cumulative mass-loss rate of the composite preparation to repair chromium-contaminated soil gradually increased; and the higher the chromium content of the contaminated soil was, the greater the cumulative mass-loss rate was. The cumulative mass-loss rate of 16 wet and dry cycles was less than 1%, which shows that the joint repair of chromium-contaminated soil had strong resistance to dry and wet cycles.Figure 3Change of cumulative mass loss rate during dry wet cycle.Full size imageFigure 4 is a photograph of the appearance change of a solidified 5000 mg/kg chromium-contaminated-soil sample after a dry–wet cycle. The soundness-evaluation results of the sample after each dry–wet cycle are shown in Fig. 5.Figure 4Appearance changes of cured chromium contaminated soil samples with dry and wet cycles at (a) 0 times; (b) 2 times; (c) 4 times; (d) 8 times; and (e) 16 times.Full size imageFigure 5Soundness evaluation results of cured chromium contaminated soil samples.Full size imageFigures 4 and 5 show that, after two dry–wet cycles of the joint repair of chromium-contaminated soil, the appearance of the sample did not significantly change, compared with 0 cycles, the surface changed from smooth to rough. Slight cracks appeared from the fourth cycle. Obvious cracks appeared in the sample at the end of the eighth cycle, and a small part of the sample fell off. The sample began to show obvious cracks from the end of the 15th dry–wet cycle, and large pieces of slack simultaneously appeared. The sample was subjected to 16 wet and dry cycles, and soundness was still not at e–h level, indicating that the joint repair of chromium-contaminated soil had strong resistance to dry and wet cycles.Combined repair of chromium-contaminated-soil microstructure changes under action of dry–wet cyclesAfter the joint repair of chromium-contaminated-soil specimens underwent a certain number of wet and dry cycles, the strength, leaching characteristics, and appearance of the specimens significantly changed. From the microstructure, there had to be corresponding changes. Therefore, scanning electron microscope (SEM) and X-ray diffraction (XRD) were used to further analyze the microstructure changes of specimens with different chromium content levels under the action of different wet and dry cycles, as shown in Figs. 6 and 7.Figure 6SEM images of 5000 mg/kg chromium contaminated soil specimens after different dry wet cycles at (a) 0 times; (b) 2 times; (c) 8 times; and (d) 16 times.Full size imageFigure 7XRD pattern of 5000 mg/kg chromium contaminated soil specimen after different dry wet cycles.Full size imageFigure 6 shows that the combined repair of chromium-contaminated soil after 28 days of curing had many pores in the specimen at 0 dry–wet cycles (standard sample), the physical and chemical interaction between the cement hydrate and the soil in the repair preparation still continued, and there were platelike calcium hydroxide crystals on the surface. After two dry–wet cycles, the contaminated soil was denser, and the overall structure was more complete than that in the samples without dry–wet cycles. The plate-shaped calcium hydroxide crystals were reduced, and a large number of fibrous and flocculent hydrated gels could be seen on the surface of the structure. This shows that the reaction between remediation agent and chromium-contaminated soil continued, which is consistent with the law that strength did not drop but rose during the two dry and wet cycles in the unconfined-compressive-strength test. After the test piece had undergone 8 dry–wet cycles, the surface of the test piece not only had a large increase in pores, but also had local cracks, indicating that the structure of the test piece was damaged under the action of the dry–wet cycle, which is consistent with the unconfined compressive strength found in the experiment, coinciding with a sharp drop. After 16 wet and dry cycles, the surface of the specimen not only showed a large number of pores and cracks, but also had obvious roughness. It showed that the dry–wet cycle effect caused the hydration products and cement materials in the soil to be destroyed and dissolved out, and the coupling and supporting forces between soil particles are weakened, and the strength of the soil is reduced accordingly, which was consistent with the macroscopic test results.Figure 7 shows that the main crystal phases of the chromium-contaminated soil were SiO2 and Al2O3 for the samples that did not undergo a dry–wet cycle. A small number of CSH, CAH, Ca(OH)2, and CaCO3 crystals could also be detected from the diffraction peaks. Cr3+ and Cr6+ formed hydroxide precipitates in a highly alkaline environment and wrapped them on the surface of cement, hindering their contact reaction with water. Compared with 0 cycles, SiO2 and Al2O3 in the second cycle were decreased, while the contents of CSH, CAH, Ca(OH)2, and CaCO3 significantly increased. This is because in the process of dry and wet cycles, the sample is fully exposed to moisture and air, so the hydration, depolymerization-cementation, pozzolanic, and carbonation reactions between composite preparation and chromium-contaminated soil continued. After two dry–wet cycles, more hydration products were generated than in the specimens without dry–wet cycles, which filled the pores between the particles of the solidified body, effectively blocking the permeability of the pores, and making the contaminated soil denser, and more structured and complete. At the same time, the full progress of the hydration reaction also delayed the damage rate of the water body to the soil in the dry–wet cycle, so that the soil could maintain a certain strength in the harsh environment, which is consistent with the above-mentioned growth trend of the soil strength. At the same time, the extension of a large amount of fibrous calcium silicate hydrate greatly increased the internal specific surface area of the soil. Free-state Cr3+ and Cr6+ were adsorbed or produced hydroxide precipitation and filled in the pores of the soil, and free ion concentration was also greatly reduced, which is consistent with the above ion-leaching test results. For the specimens with 8 dry and wet cycles, the content of hydration products such as CAH and CSH was reduced. This is due to a series of evolutionary processes such as the expansion of local microcracks, the increase in macropores, the appearance of internal cracks in the contaminated soil, and the appearance of cracks and peeling on the outside of the contaminated soil. Structural integrity was destroyed, and strength was accordingly reduced. By 16 wet and dry cycles, a large amount of fibrous CSH disappeared, which weakened the cementation between soil particles. At this time, the heavy-metal ions originally wrapped in the contaminated soil solidified the body and the external environment, the contact area with the water was increased, the pH value of the environment was decreased, hydrate CSH was decalcified, and Ca/Si ratio was decreased. This reduced the adsorption capacity of the compound formulation to chromium ions, so that chromium ions were dissolved out of the soil. More

Wildlife Conservation Research Unit, Recanati-Kaplan Centre, Zoology, University of Oxford, Oxford, UKHans Bauer & Claudio Sillero-ZubiriEvolutionary Ecology Group, Biology, University of Antwerp, Antwerp, BelgiumHans BauerLaboratory for Applied Ecology, Natural Resource Conservation, University of Abomey-Calavi, Cotonou, BeninAristide Comlan TehouDepartment of HydroSciences and Environment, University Iba Der Thiam, Thiès, SénégalMallé GueyeDirection de la Faune, de la Chasse et des Parcs et Réserves, Ministère de l’Environnement de la Salubrité Urbaine et du Développement Durable, Niamey, NigerHamissou GarbaDirection de la Faune et des Chasses, Ministère de l’Environnement et du Développement Durable, Ouagadougou, Burkina FasoBenoit DoambaNational Parks Directorate, Ministry of Environment and Sustainable Development, Dakar, SenegalDjibril DiouckThe Born Free Foundation, Horsham, UKClaudio Sillero-Zubiri More

1.Wetzel, R. G. In Limnology 3rd edn (ed. Wetzel, R. G.), Ch. 9, 151–168 (Academic Press, 2001).2.Schindler, D. Warmer climate squeezes aquatic predators out of their preferred habitat. Proc. Natl Acad. Sci. USA 114, 9764–9765 (2017).CAS 
Article 
ADS 

Google Scholar 
3.North, R. P., North, R. L., Livingstone, D. M., Köster, O. & Kipfer, R. Long-term changes in hypoxia and soluble reactive phosphorus in the hypolimnion of a large temperate lake: consequences of a climate regime shift. Glob. Change Biol. 20, 811–823 (2014).Article 
ADS 

Google Scholar 
4.Fernández, J. E., Peeters, F. & Hofmann, H. Importance of the autumn overturn and anoxic conditions in the hypolimnion for the annual methane emissions from a temperate lake. Environ. Sci. Technol. 48, 7297–7304 (2014).Article 
ADS 

Google Scholar 
5.Michalak, A. M. et al. Record-setting algal bloom in Lake Erie caused by agricultural and meteorological trends consistent with expected future conditions. Proc. Natl Acad. Sci. USA 110, 6448–6452 (2013).CAS 
Article 
ADS 

Google Scholar 
6.Schmidtko, S., Stramma, L. & Visbeck, M. Decline in global oceanic oxygen content during the past five decades. Nature 542, 335–339 (2017).CAS 
Article 
ADS 

Google Scholar 
7.Breitburg, D. et al. Declining oxygen in the global ocean and coastal waters. Science 359, (2018).8.Jankowski, J., Livingstone, D. M., Bührer, H., Forster, R. & Niederhauser, P. Consequences of the 2003 European heat wave for lake temperature profiles, thermal stability, and hypolimnetic oxygen depletion: implications for a warmer world. Limnol. Oceanogr. 51, 815–819 (2006).Article 
ADS 

Google Scholar 
9.Yvon-Durocher, G., Jones, J. I., Trimmer, M., Woodward, G. & Montoya, J. M. Warming alters the metabolic balance of ecosystems. Phil. Trans. R. Soc. B 365, 2117–2126 (2010).Article 

Google Scholar 
10.Seki, H., Takahashi, Y., Hara, Y. & Ichimura, S. Dynamics of dissolved oxygen during algal bloom in Lake Kasumigaura, Japan. Water Res. 14, 179–183 (1980).CAS 
Article 

Google Scholar 
11.Jacobson, P. C., Stefan, H. G. & Pereira, D. L. Coldwater fish oxythermal habitat in Minnesota lakes: influence of total phosphorus, July air temperature, and relative depth. Can. J. Fish. Aquat. Sci. 67, 2002–2013 (2010).CAS 
Article 

Google Scholar 
12.Harke, M. J. et al. A review of the global ecology, genomics, and biogeography of the toxic cyanobacterium, Microcystis spp. Harmful Algae 54, 4–20 (2016).Article 

Google Scholar 
13.Vaquer-Sunyer, R. & Duarte, C. M. Thresholds of hypoxia for marine biodiversity. Proc. Natl Acad. Sci. USA 105, 15452–15457 (2008).CAS 
Article 
ADS 

Google Scholar 
14.Woolway, R. I. & Merchant, C. J. Worldwide alteration of lake mixing regimes in response to climate change. Nat. Geosci. 12, 271–276 (2019).CAS 
Article 
ADS 

Google Scholar 
15.Livingstone, D. M. Impact of secular climate change on the thermal structure of a large temperate central European lake. Clim. Change 57, 205–225 (2003).Article 

Google Scholar 
16.Zhang, Y. et al. Dissolved oxygen stratification and response to thermal structure and long-term climate change in a large and deep subtropical reservoir (Lake Qiandaohu, China). Water Res. 75, 249–258 (2015).CAS 
Article 

Google Scholar 
17.Bouffard, D., Ackerman, J. D. & Boegman, L. Factors affecting the development and dynamics of hypoxia in a large shallow stratified lake: hourly to seasonal patterns. Wat. Resour. Res. 49, 2380–2394 (2013).CAS 
Article 
ADS 

Google Scholar 
18.O’Reilly, C. M. et al. Rapid and highly variable warming of lake surface waters around the globe. Geophys. Res. Lett. 42, 10773–10781 (2015).ADS 

Google Scholar 
19.Nürnberg, G. K. Trophic state of clear and colored, soft- and hardwater lakes with special consideration of nutrients, anoxia, phytoplankton and fish. Lake Reserv. Manage. 12, 432–447 (1996).Article 

Google Scholar 
20.Ho, J. C., Michalak, A. M. & Pahlevan, N. Widespread global increase in intense lake phytoplankton blooms since the 1980s. Nature 574, 667–670 (2019).CAS 
Article 
ADS 

Google Scholar 
21.Kosten, S. et al. Warmer climates boost cyanobacterial dominance in shallow lakes. Glob. Change Biol. 18, 118–126 (2012).Article 
ADS 

Google Scholar 
22.Müller, B., Bryant, L. D., Matzinger, A. & Wüest, A. Hypolimnetic oxygen depletion in eutrophic lakes. Environ. Sci. Technol. 46, 9964–9971 (2012).PubMed 

Google Scholar 
23.Winslow, L. A., Leach, T. A. & Rose, K. C. Global lake response to the recent warming hiatus. Environ. Res. Lett. 13, 054005 (2018).Article 
ADS 

Google Scholar 
24.Livingstone, D. M. An example of the simultaneous occurrence of climate-driven “sawtooth” deep-water warming/cooling episodes in several Swiss lakes. Verh. Int. Ver. Limnol. 26, 822–828 (1997).
Google Scholar 
25.Williamson, C. E. et al. Ecological consequences of long-term browning in lakes. Sci. Rep. 5, (2015).26.Rose, K. C., Winslow, L. A., Read, J. S. & Hansen, G. J. A. Climate-induced warming of lakes can be either amplified or suppressed by trends in water clarity. Limnol. Oceanogr. Lett. 1, 44–53 (2016).Article 

Google Scholar 
27.Woolway, R. I. et al. Northern hemisphere atmospheric stilling accelerates lake thermal responses to a warming world. Geophys. Res. Lett. 46, 11983–11992 (2019).Article 
ADS 

Google Scholar 
28.Carpenter, S. R., Stanley, E. H. & Vander Zanden, M. J. State of the world’s freshwater ecosystems: physical, chemical, and biological changes. Annu. Rev. Environ. Resour. 36, 75–99 (2011). Article 

Google Scholar 
29.R Core Team. R: A Language and Environment for Statistical Computing. http://www.R-project.org/ (R Foundation for Statistical Computing, Vienna, 2017).30.Borchers, H. W. pracma: Practical Numerical Math Functions. R package version 2.1.5 https://CRAN.R-project.org/package=pracma (2018).31.Winslow, L. A. et al. rLakeAnalyzer: Lake Physics Tools. R package version 1.11.4. https://CRAN.R-project.org/package=rLakeAnalyzer (2017).32.Winslow, L. A. et al. LakeMetabolizer: an R package for estimating lake metabolism from free-water oxygen using diverse statistical models. Inland Waters 6, 622–636 (2016).CAS 
Article 

Google Scholar 
33.Carslaw, D. C. & Ropkins, K. Openair – an R package for air quality data analysis. Environ. Model. Softw. 27-28, 52–61 (2012).Article 

Google Scholar 
34.Moran, P. A. P. The interpretation of statistical maps. J. R. Stat. Soc. B 10, 243–251 (1948).MathSciNet 
MATH 

Google Scholar 
35.Kalogirou, S. lctools: Local Correlation, Spatial Inequalities, Geographically Weighted Regression and Other Tools. R package version 0.2-7. https://CRAN.R-project.org/package=lctools (2019).36.Copernicus Climate Change Service (C3S). ERA5: Climate Data Store (CDS) https://cds.climate.copernicus.eu/cdsapp#!/home (accessed 1 October 2019).37.Gelman, G. & Hill, J. Data Analysis Using Regression and Multilevel/Hierarchical Models (Cambridge Univ. Press, 2007).38.Quinn, G. P. & Keough, M. J. Experimental Design and Data Analysis for Biologists (Cambridge Univ. Press, 2002).39.Lumley, T. leaps: Regression Subset Selection. R package version 3.1. https://CRAN.R-project.org/package=leaps (2020).40.Wood, S. N. Generalized Additive Models: An Introduction with R 2nd edn (CRC Press, 2017).Book 

Google Scholar 
41.Wood, S. & Scheipl, F. gamm4: Generalized Additive Mixed Models using ‘mgcv’ and ‘lme4’. R package version 0.2-5. https://CRAN.R-project.org/package=gamm4 (2017).42.Pinheiro, J. C. & Bates, D. M. Mixed Effects Models in S and S-Plus (Springer, 2000).43.Burnham, K. P., Anderson, D. R. & Huyvaert, K. P. AIC model selection and multimodel inference in behavioral ecology: some background, observations, and comparisons. Behav. Ecol. Sociobiol. 65, 23–35 (2011).Article 

Google Scholar 
44.Hosmer, D. W. & Lemeshow, S. Applied Logistic Regression 2nd edn (John Wiley and Sons, Inc., 2000).45.Homer, C. G. et al. Completion of the 2011 National Land Cover Database for the conterminous United States – Representing a decade of land cover change information. Photogramm. Eng. Remote Sensing 81, 345–354 (2015).
Google Scholar 
46.Lele, S. R., Keim, J. L. & Solymos, P. ResourceSelection: Resource Selection (Probability) Functions for Use-Availability Data. R package version 0.3-2. https://CRAN.R-project.org/package=ResourceSelection (2017).47.Cutler, D. R. et al. Random forests for classification in ecology. Ecology 88, 2783–2792 (2007).Article 

Google Scholar 
48.Liaw, A. & Wiener, M. Classification and regression by randomForest. R News 2, 18–22 (2002).
Google Scholar 
49.Messager, M. L., Lehner, B., Grill, G., Nedeva, I. & Schmitt, O. Estimating the volume and age of water stored in global lakes using a geo-statistical approach. Nat. Commun. 7, 13603 (2016).CAS 
Article 
ADS 

Google Scholar 
50.Stetler, J. T., Jane, S. F., Mincer, J. L., Sanders, M. N. & Rose, K. C. Long-term lake dissolved oxygen and temperature data, 1941–2018 ver 2. Environmental Data Initiative https://doi.org/10.6073/pasta/841f0472e19853b0676729221aedfb56 (2021).51.Adrian, R., Jane, S. F., & Rose, K. C. Widespread deoxygenation of temperate lakes – Müggelsee data. IGB Leibniz-Institute of Freshwater Ecology and Inland Fisheries dataset. https://doi.org/10.18728/568.0 (2021).52.Jenny, J.-P. Time series dataset of dissolved oxygen, water temperature and Secchi depths profiles in Lakes Annecy and Geneva. Portail Data INRAE V1, https://doi.org/10.15454/BUJUSX (2021). More