Philippa Kaur

Hewitt, G. The genetic legacy of the Quaternary ice ages. Nature 405, 907–913 (2000).CAS 
Article 
PubMed 

Google Scholar 
Muhlfeld, C. C. et al. Invasive hybridization in a threatened species is accelerated by climate change. Nat. Clim. Change 4, 620–624 (2014).Article 

Google Scholar 
Taylor, S. A. et al. Climate-mediated movement of an avian hybrid zone. Curr. Biol. 24, 671–676 (2014).CAS 
Article 
PubMed 

Google Scholar 
Cahill, J. A. et al. Genomic evidence of widespread admixture from polar bears into brown bears during the last ice age. Mol. Biol. Evol. 35, 1120–1129 (2018).CAS 
Article 
PubMed 

Google Scholar 
Mao, Y., Economo, E. P. & Satoh, N. The roles of introgression and climate change in the rise to dominance of Acropora corals. Curr. Biol. 28, 3373–3382.e5 (2018).CAS 
Article 
PubMed 

Google Scholar 
Vianna, J. A. et al. Genome-wide analyses reveal drivers of penguin diversification. Proc. Natl Acad. Sci. USA 117, 22303–22310 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Racimo, F., Sankararaman, S., Nielsen, R. & Huerta-Sánchez, E. Evidence for archaic adaptive introgression in humans. Nat. Rev. Genet. 16, 359–371 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
McKelvey, K. S. et al. Patterns of hybridization among cutthroat trout and rainbow trout in northern Rocky Mountain streams. Ecol. Evol. 6, 688–706 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Kim, B. Y., Huber, C. D. & Lohmueller, K. E. Deleterious variation shapes the genomic landscape of introgression. PLoS Genet. 14, e1007741 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wu, D.-D. et al. Pervasive introgression facilitated domestication and adaptation in the Bos species complex. Nat. Ecol. Evol. 2, 1139–1145 (2018).Article 
PubMed 

Google Scholar 
Wang, M.-S. et al. Ancient hybridization with an unknown population facilitated high-altitude adaptation of canids. Mol. Biol. Evol. 37, 2616–2629 (2020).CAS 
Article 
PubMed 

Google Scholar 
Meier, J. I. et al. Ancient hybridization fuels rapid cichlid fish adaptive radiations. Nat. Commun. 8, 14363 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Haig, S. M., Mullins, T. D., Forsman, E. D., Trail, P. W. & Wennerberg, L. I. V. Genetic identification of spotted owls, barred owls, and their hybrids: legal implications of hybrid identity. Conserv. Biol. 18, 1347–1357 (2004).Article 

Google Scholar 
vonHoldt, B. M. et al. Whole-genome sequence analysis shows that two endemic species of North American wolf are admixtures of the coyote and gray wolf. Sci. Adv. 2, e1501714 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Liu, S. et al. Population genomics reveal recent speciation and rapid evolutionary adaptation in polar bears. Cell 157, 785–794 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kumar, V. et al. The evolutionary history of bears is characterized by gene flow across species. Sci. Rep. 7, 46487 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Preuß, A., Gansloßer, U., Purschke, G. & Magiera, U. Bear-hybrids: behaviour and phenotype. Zool. Gart. 78, 204–220 (2009).Article 

Google Scholar 
Cahill, J. A. et al. Genomic evidence for island population conversion resolves conflicting theories of polar bear evolution. PLoS Genet. 9, e1003345 (2013).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Cahill, J. A. et al. Genomic evidence of geographically widespread effect of gene flow from polar bears into brown bears. Mol. Ecol. 24, 1205–1217 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Pongracz, J. D., Paetkau, D., Branigan, M. & Richardson, E. Recent hybridization between a polar bear and grizzly bears in the Canadian Arctic. Arctic 70, 151–160 (2017).Article 

Google Scholar 
Pugach, I., Matveyev, R., Wollstein, A., Kayser, M. & Stoneking, M. Dating the age of admixture via wavelet transform analysis of genome-wide data. Genome Biol. 12, R19 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
Farquharson, L. et al. Alaskan marine transgressions record out-of-phase Arctic Ocean glaciation during the last interglacial. Geology 46, 783–786 (2018).Article 

Google Scholar 
Kapp, J. D., Green, R. E. & Shapiro, B. A fast and efficient single-stranded genomic library preparation method optimized for ancient DNA. J. Hered. 112, 241–249 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Briggs, A. W. et al. Patterns of damage in genomic DNA sequences from a Neandertal. Proc. Natl Acad. Sci. USA 104, 14616–14621 (2007).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Li, H. & Durbin, R. Inference of human population history from individual whole-genome sequences. Nature 475, 493–496 (2011).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Fu, Q. et al. Genome sequence of a 45,000-year-old modern human from western Siberia. Nature 514, 445–449 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Schiffels, S. & Durbin, R. Inferring human population size and separation history from multiple genome sequences. Nat. Genet. 46, 919–925 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Pease, J. B. & Hahn, M. W. Detection and polarization of introgression in a five-taxon phylogeny. Syst. Biol. 64, 651–662 (2015).CAS 
Article 
PubMed 

Google Scholar 
Barlow, A. et al. Middle Pleistocene genome calibrates a revised evolutionary history of extinct cave bears. Curr. Biol. 31, 1771–1779.e7 (2021).CAS 
Article 
PubMed 

Google Scholar 
Barlow, A. et al. Partial genomic survival of cave bears in living brown bears. Nat. Ecol. Evol. 2, 1563–1570 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Wang, K., Mathieson, I., O’Connell, J. & Schiffels, S. Tracking human population structure through time from whole genome sequences. PLoS Genet. 16, e1008552 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Polyak, L. et al. History of sea ice in the Arctic. Quat. Sci. Rev. 29, 1757–1778 (2010).Article 

Google Scholar 
Dutton, A. et al. Sea-level rise due to polar ice-sheet mass loss during past warm periods. Science 349, aaa4019 (2015).CAS 
Article 
PubMed 

Google Scholar 
Salonen, J. S. et al. Abrupt high-latitude climate events and decoupled seasonal trends during the Eemian. Nat. Commun. 9, 2851 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Guarino, M.-V. et al. Sea-ice-free Arctic during the Last Interglacial supports fast future loss. Nat. Clim. Change 10, 928–932 (2020).Article 

Google Scholar 
Rode, K. D., Robbins, C. T., Nelson, L. & Amstrup, S. C. Can polar bears use terrestrial foods to offset lost ice-based hunting opportunities? Front. Ecol. Environ. 13, 138–145 (2015).Article 

Google Scholar 
Laidre, K. L., Stirling, I., Estes, J. A., Kochnev, A. & Roberts, J. Historical and potential future importance of large whales as food for polar bears. Front. Ecol. Environ. 16, 515–524 (2018).Article 

Google Scholar 
Miller, S., Wilder, J. & Wilson, R. R. Polar bear–grizzly bear interactions during the autumn open-water period in Alaska. J. Mammal. 96, 1317–1325 (2015).Article 

Google Scholar 
Steyaert, S. M. J. G., Endrestøl, A., Hackländer, K., Swenson, J. E. & Zedrosser, A. The mating system of the brown bear Ursus arctos. Mamm. Rev. 42, 12–34 (2012).Article 

Google Scholar 
Stirling, I., Spencer, C. & Andriashek, D. Behavior and activity budgets of wild breeding polar bears (Ursus maritimus). Mar. Mamm. Sci. 32, 13–37 (2016).Article 

Google Scholar 
Méheust, M., Stein, R., Fahl, K. & Gersonde, R. Sea-ice variability in the subarctic North Pacific and adjacent Bering Sea during the past 25 ka: new insights from IP25 and Uk′37 proxy records. Arktos 4, 1–19 (2018).Article 

Google Scholar 
Brigham-Grette, J. & Hopkins, D. M. Emergent marine record and paleoclimate of the last interglaciation along the northwest Alaskan coast. Quat. Res. 43, 159–173 (1995).Article 

Google Scholar 
Boessenkool, S. et al. Combining bleach and mild predigestion improves ancient DNA recovery from bones. Mol. Ecol. Resour. 17, 742–751 (2017).CAS 
Article 
PubMed 

Google Scholar 
Dabney, J. et al. Complete mitochondrial genome sequence of a Middle Pleistocene cave bear reconstructed from ultrashort DNA fragments. Proc. Natl Acad. Sci. USA 110, 15758–15763 (2013).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Meyer, M. & Kircher, M. Illumina sequencing library preparation for highly multiplexed target capture and sequencing. Cold Spring Harb. Protoc. 2010, pdb.prot5448 (2010).Article 
PubMed 

Google Scholar 
Kircher, M., Sawyer, S. & Meyer, M. Double indexing overcomes inaccuracies in multiplex sequencing on the Illumina platform. Nucleic Acids Res. 40, e3 (2012).CAS 
Article 
PubMed 

Google Scholar 
Rohland, N. & Reich, D. Cost-effective, high-throughput DNA sequencing libraries for multiplexed target capture. Genome Res. 22, 939–946 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics 26, 589–595 (2010).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Jónsson, H., Ginolhac, A., Schubert, M., Johnson, P. L. F. & Orlando, L. mapDamage2.0: fast approximate Bayesian estimates of ancient DNA damage parameters. Bioinformatics 29, 1682–1684 (2013).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Prüfer, K. snpAD: an ancient DNA genotype caller. Bioinformatics 34, 4165–4171 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Green, R. E. et al. A complete Neandertal mitochondrial genome sequence determined by high-throughput sequencing. Cell 134, 416–426 (2008).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA–MEM. Preprint at https://doi.org/10.48550/arXiv.1303.3997 (2013).McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kumar, S., Stecher, G., Peterson, D. & Tamura, K. MEGA-CC: computing core of molecular evolutionary genetics analysis program for automated and iterative data analysis. Bioinformatics 28, 2685–2686 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kumar, S., Stecher, G. & Tamura, K. MEGA7: Molecular Evolutionary Genetics Analysis version 7.0 for bigger datasets. Mol. Biol. Evol. 33, 1870–1874 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Vihtakari, M. PlotSvalbard: User Manual. Github https://mikkovihtakari.github.io/PlotSvalbard/articles/PlotSvalbard.html (2020).Yu, G., Smith, D. K., Zhu, H., Guan, Y. & Lam, T. T. ggtree: an R package for visualization and annotation of phylogenetic trees with their covariates and other associated data. Methods Ecol. Evol. 8, 28–36 (2017).Article 

Google Scholar 
Yu, G. Using ggtree to visualize data on tree-like structures. Curr. Protoc. Bioinformatics 69, e96 (2020).Article 
PubMed 

Google Scholar 
Yu, G., Lam, T. T., Zhu, H. & Guan, Y. Two methods for mapping and visualizing associated data on phylogeny using ggtree. Mol. Biol. Evol. 35, 3041–3043 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Wang, L.-G. et al. Treeio: an R package for phylogenetic tree input and output with richly annotated and associated data. Mol. Biol. Evol. 37, 599–603 (2020).CAS 
Article 
PubMed 

Google Scholar 
Lindqvist, C. et al. Complete mitochondrial genome of a Pleistocene jawbone unveils the origin of polar bear. Proc. Natl Acad. Sci. USA 107, 5053–5057 (2010).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Browning, B. L. & Browning, S. R. Genotype imputation with millions of reference samples. Am. J. Hum. Genet. 98, 116–126 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kelleher, J., Etheridge, A. M. & McVean, G. Efficient coalescent simulation and genealogical analysis for large sample sizes. PLoS Comput. Biol. 12, e1004842 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Palkopoulou, E. et al. Complete genomes reveal signatures of demographic and genetic declines in the woolly mammoth. Curr. Biol. 25, 1395–1400 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Green, R. E. et al. A draft sequence of the Neandertal genome. Science 328, 710–722 (2010).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Patterson, N. et al. Ancient admixture in human history. Genetics 192, 1065–1093 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Vershinina, A. O. et al. Ancient horse genomes reveal the timing and extent of dispersals across the Bering Land Bridge. Mol. Ecol. 30, 6144–6161 (2021).Article 
PubMed 

Google Scholar 
Chen, L., Wolf, A. B., Fu, W., Li, L. & Akey, J. M. Identifying and interpreting apparent Neanderthal ancestry in African individuals. Cell 180, 677–687.e16 (2020).CAS 
Article 
PubMed 

Google Scholar 
Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Lisiecki, L. E. & Raymo, M. E. A Pliocene-Pleistocene stack of 57 globally distributed benthic δ18O records. Paleoceanography 20, PA1003 (2005).
Google Scholar  More

Kangaroo Island (~ 4400 km2, KI hereafter) is the third largest island in Australia. It underwent substantial land clearing, and consequent fragmentation of the natural bushland habitat, after World War II1,2. Relatively intact western KI was eventually identified as a key biodiversity hotspot3, home to several endangered and endemic native species including the KI dunnart.Dunnarts (Sminthopsis spp.) are small insectivorous dasyurid marsupials. The KI dunnart is distinguished from the other 17 dunnart species found in Australia by morphological features, including manus, pes, and penis shape4. This endangered species is the only dasyurid found on the island, exclusively resident in ~ 342 km2 before 20205, and found nowhere else in the world2. The species is rarely recorded, with only 28 individuals found during  > 33,000 trap-nights pre-20195. With a low number of individuals restricted to a small geographic area, the KI dunnart is exceptionally vulnerable to stochastic events. Predation by feral cats (Felis catus) is likely to be another source of pressure on the KI dunnart. Cats were introduced to KI during European settlement and quickly became apex predators, reaching higher relative abundance than adjacent mainland6 with an estimated density of 0.37 ± 0.15 cat/km25. Cat predation has been the cause for extinction or near-extinction of several native species around the globe7, with the extinction risk becoming increasingly acute in insular islands like KI. Cat predation on islands has contributed to  > 13% of globally recorded extinction events, accounting for  > 8% of instances within these taxa of species being pushed to critically endangered status8. A recent meta-analysis found evidence of cat predation for three critically endangered species and four endangered species in Australia on the IUCN Red List of Threatened Species7.Australian bushfires in 2019–2020 burnt ~ 97,000 km2 of vegetation9,10, with damage overlapping with habitats of  > 100 threatened species. Dry lightning storms in the remote and vegetated northwest of the Island started the bushfire in the KI. The bushfire eventually spread easterly, burning approximately 98% of the known and predicted habitat of the KI dunnart10.In this study, we have analysed the diet of feral cats humanely euthanized in designated areas of local conservation interest immediately after the 2019 KI bushfire. More

Le Quéré, C. et al. Global carbon budget 2018. Earth Syst. Sci. Data 10, 2141–2194 (2018).ADS 
Article 

Google Scholar 
Friedlingstein, P. et al. Global carbon budget 2020. Earth Syst. Sci. Data 12, 3269–3340 (2020).ADS 
Article 

Google Scholar 
Rayner, P. J. et al. Interannual variability of the global carbon cycle (1992-2005) inferred by inversion of atmospheric CO2 and δ13CO2 measurements. Glob. Biogeochem. Cycles 22, 1–12 (2008).Article 
CAS 

Google Scholar 
Piao, S. et al. Interannual variation of terrestrial carbon cycle: Issues and perspectives. Glob. Chang. Biol. 26, 300–318 (2020).ADS 
PubMed 
Article 

Google Scholar 
Betts, R. A. et al. A successful prediction of the record CO2 rise associated with the 2015/2016 El Niño. Philos. Trans. R. Soc. B Biol. Sci. 373, 20170301 (2018).Article 
CAS 

Google Scholar 
Keeling, C. D., Whorf, T. P., Wahlen, M. & van der Plichtt, J. Interannual extremes in the rate of rise of atmospheric carbon dioxide since 1980. Nature 375, 666–670 (1995).ADS 
CAS 
Article 

Google Scholar 
Cox, P. M. et al. Sensitivity of tropical carbon to climate change constrained by carbon dioxide variability. Nature 494, 341–344 (2013).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Fang, Y. et al. Global land carbon sink response to temperature and precipitation varies with ENSO phase. Environ. Res. Lett. 12, 064007 (2017).ADS 
Article 

Google Scholar 
Humphrey, V. et al. Soil moisture–atmosphere feedback dominates land carbon uptake variability. Nature 592, 65–69 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Jung, M. et al. Compensatory water effects link yearly global land CO2 sink changes to temperature. Nature 541, 516–520 (2017).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Wang, W. et al. Variations in atmospheric CO2 growth rates coupled with tropical temperature. Proc. Natl Acad. Sci. USA 110, 13061–13066 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wang, X. et al. A two-fold increase of carbon cycle sensitivity to tropical temperature variations. Nature 506, 212–215 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Humphrey, V. et al. Sensitivity of atmospheric CO2 growth rate to observed changes in terrestrial water storage. Nature 560, 628–631 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Marcolla, B., Rödenbeck, C. & Cescatti, A. Patterns and controls of inter-annual variability in the terrestrial carbon budget. Biogeosciences 14, 3815–3829 (2017).ADS 
CAS 
Article 

Google Scholar 
Yin, Y. et al. Changes in the response of the northern hemisphere carbon uptake to temperature over the last three decades. Geophys. Res. Lett. 45, 4371–4380 (2018).ADS 
CAS 
Article 

Google Scholar 
Rödenbeck, C., Zaehle, S., Keeling, R. & Heimann, M. How does the terrestrial carbon exchange respond to inter-annual climatic variations? A quantification based on atmospheric CO2 data. Biogeosciences 15, 2481–2498 (2018).ADS 
Article 
CAS 

Google Scholar 
Palmer, P. I. et al. Net carbon emissions from African biosphere dominate pan-tropical atmospheric CO2 signal. Nat. Commun. 10, 1–9 (2019).ADS 
Article 
CAS 

Google Scholar 
Fan, L. et al. Satellite-observed pantropical carbon dynamics. Nat. Plants 5, 944–951 (2019).CAS 
PubMed 
Article 

Google Scholar 
Poulter, B. et al. Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle. Nature 509, 600–603 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Hu, L. et al. Enhanced North American carbon uptake associated with El Niño. Sci. Adv. 5, 1–11 (2019).ADS 

Google Scholar 
Liu, Z. et al. Increased high-latitude photosynthetic carbon gain offset by respiration carbon loss during an anomalous warm winter to spring transition. Glob. Chang. Biol. 26, 682–696 (2020).ADS 
PubMed 
Article 

Google Scholar 
Reichstein, M. et al. Determinants of terrestrial ecosystem carbon balance inferred from European eddy covariance flux sites. Geophys. Res. Lett. 34, 1–5 (2007).Article 

Google Scholar 
Shiga, Y. P. et al. Forests dominate the interannual variability of the North American carbon sink. Environ. Res. Lett. 13, 084015 (2018).ADS 
Article 
CAS 

Google Scholar 
Wang, X., Ciais, P., Wang, Y. & Zhu, D. Divergent response of seasonally dry tropical vegetation to climatic variations in dry and wet seasons. Glob. Chang. Biol. 24, 4709–4717 (2018).ADS 
PubMed 
Article 

Google Scholar 
Wolf, S. et al. Warm spring reduced carbon cycle impact of the 2012 US summer drought. Proc. Natl Acad. Sci. USA 113, 5880–5885 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Liu, J. et al. Detecting drought impact on terrestrial biosphere carbon fluxes over contiguous US with satellite observations. Environ. Res. Lett. 13, 095003 (2018).ADS 
Article 
CAS 

Google Scholar 
Bastos, A. et al. Direct and seasonal legacy effects of the 2018 heat wave and drought on European ecosystem productivity. Sci. Adv. 6, eaba2724 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chevallier, F. et al. Inferring CO2 sources and sinks from satellite observations: Method and application to TOVS data. J. Geophys. Res. 110, D24309 (2005).ADS 
Article 
CAS 

Google Scholar 
Rödenbeck, C., Houweling, S., Gloor, M. & Heimann, M. CO2 flux history 1982-2001 inferred from atmospheric data using a global inversion of atmospheric transport. Atmos. Chem. Phys. 3, 1919–1964 (2003).ADS 
Article 

Google Scholar 
Chevallier, F. et al. Objective evaluation of surface- and satellite-driven carbon dioxide atmospheric inversions. Atmos. Chem. Phys. 19, 14233–14251 (2019).ADS 
CAS 
Article 

Google Scholar 
Rödenbeck, C., Zaehle, S., Keeling, R. & Heimann, M. The European carbon cycle response to heat and drought as seen from atmospheric CO2 data for 1999–2018. Philos. Trans. R. Soc. B Biol. Sci. 375, 20190506 (2020).Article 
CAS 

Google Scholar 
Sitch, S. et al. Recent trends and drivers of regional sources and sinks of carbon dioxide. Biogeosciences 12, 653–679 (2015).ADS 
Article 

Google Scholar 
Jung, M. et al. Scaling carbon fluxes from eddy covariance sites to globe: Synthesis and evaluation of the FLUXCOM approach. Biogeosciences 17, 1343–1365 (2020).ADS 
CAS 
Article 

Google Scholar 
Humphrey, V. & Gudmundsson, L. GRACE-REC: a reconstruction of climate-driven water storage changes over the last century. Earth Syst. Sci. Data 11, 1153–1170 (2019).Harris, I., Osborn, T. J., Jones, P. & Lister, D. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci. Data 7, 109 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Zhou, S., Zhang, Y., Williams, A. P. & Gentine, P. Projected increases in intensity, frequency, and terrestrial carbon costs of compound drought and aridity events. Sci. Adv. 5, 1–9 (2019).CAS 

Google Scholar 
Du, E. et al. Global patterns of terrestrial nitrogen and phosphorus limitation. Nat. Geosci. 13, 221–226 (2020).ADS 
CAS 
Article 

Google Scholar 
Turetsky, M. R. et al. Permafrost collapse is accelerating carbon release. Nature 569, 32–34 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Li, Z. L. et al. Changes in net ecosystem exchange of CO2 in Arctic and their relationships with climate change during 2002–2017. Adv. Clim. Chang. Res. 12, 475–481 (2021).Article 

Google Scholar 
Walker, D. A. et al. The Circumpolar Arctic vegetation map. J. Veg. Sci. 16, 267–282 (2005).Article 

Google Scholar 
Virkkala, A. M. et al. Statistical upscaling of ecosystem CO2 fluxes across the terrestrial tundra and boreal domain: Regional patterns and uncertainties. Glob. Chang. Biol. 27, 4040–4059 (2021).PubMed 
Article 

Google Scholar 
Randazzo, N. A. et al. Higher autumn temperatures lead to contrasting CO2 flux responses in boreal forests versus tundra and shrubland. Geophys. Res. Lett. 48, e2021GL093843 (2021).ADS 
CAS 
Article 

Google Scholar 
Richardson, A. D. et al. Influence of spring and autumn phenological transitions on forest ecosystem productivity. Philos. Trans. R. Soc. B Biol. Sci. 365, 3227–3246 (2010).Article 

Google Scholar 
Piao, S. et al. Weakening temperature control on the interannual variations of spring carbon uptake across northern lands. Nat. Clim. Chang. 7, 359–363 (2017).ADS 
CAS 
Article 

Google Scholar 
Peñuelas, J. et al. Shifting from a fertilization-dominated to a warming-dominated period. Nat. Ecol. Evol. 1, 1438–1445 (2017).PubMed 
Article 

Google Scholar 
Tramontana, G. et al. Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms. Biogeosciences 13, 4291–4313 (2016).ADS 
CAS 
Article 

Google Scholar 
Randerson, J. T., Field, C. B., Fung, I. Y. & Tans, P. P. Increases in early season ecosystem uptake explain recent changes in the seasonal cycle of atmospheric CO2 at high northern latitudes. Geophys. Res. Lett. 26, 2765–2768 (1999).ADS 
CAS 
Article 

Google Scholar 
Black, T. A. et al. Increased carbon sequestration by a boreal deciduous forest in years with a warm spring. Geophys. Res. Lett. 27, 1271–1274 (2000).ADS 
Article 

Google Scholar 
Wang, T. et al. Emerging negative impact of warming on summer carbon uptake in northern ecosystems. Nat. Commun. 9, 1–7 (2018).ADS 
Article 
CAS 

Google Scholar 
Huang, M. et al. Air temperature optima of vegetation productivity across global biomes. Nat. Ecol. Evol. 3, 772–779 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Seneviratne, S. I. et al. Investigating soil moisture-climate interactions in a changing climate: a review. Earth-Sci. Rev. 99, 125–161 (2010).ADS 
CAS 
Article 

Google Scholar 
Buermann, W. et al. Recent shift in Eurasian boreal forest greening response may be associated with warmer and drier summers. Geophys. Res. Lett. 41, 1995–2002 (2014).ADS 
Article 

Google Scholar 
Fu, R. et al. Increased dry-season length over southern Amazonia in recent decades and its implication for future climate projection. Proc. Natl Acad. Sci. USA 110, 18110–18115 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chen, C. et al. Identifying critical climate periods for vegetation growth in the northern hemisphere. J. Geophys. Res. Biogeosci. 123, 2541–2552 (2018).Article 

Google Scholar 
Gloor, E. et al. Tropical land carbon cycle responses to 2015/16 El Niño as recorded by atmospheric greenhouse gas and remote sensing data. Philos. Trans. R. Soc. B Biol. Sci. 373, 20170302 (2018).Article 
CAS 

Google Scholar 
Saatchi, S. et al. Detecting vulnerability of humid tropical forests to multiple stressors. One Earth 4, 988–1003 (2021).ADS 
Article 

Google Scholar 
Peylin, P. et al. Global atmospheric carbon budget: results from an ensemble of atmospheric CO2 inversions. Biogeosciences 10, 6699–6720 (2013).ADS 
CAS 
Article 

Google Scholar 
Liu, J. et al. Carbon monitoring system flux net biosphere exchange 2020 (CMS-Flux NBE 2020). Earth Syst. Sci. Data 13, 299–330 (2021).ADS 
Article 

Google Scholar 
Quetin, G. R., Bloom, A. A., Bowman, K. W. & Konings, A. G. Carbon flux variability from a relatively simple ecosystem model with assimilated data is consistent with terrestrial biosphere model estimates. J. Adv. Model. Earth Syst. 12, e2019MS001889 (2020).ADS 
Article 

Google Scholar 
Schewe, J. et al. State-of-the-art global models underestimate impacts from climate extremes. Nat. Commun. 10, 1005 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Gentine, P. et al. Coupling between the terrestrial carbon and water cycles – A review. Environ. Res. Lett. 14, 83003 (2019).CAS 
Article 

Google Scholar 
Bastos, A. et al. Impacts of extreme summers on European ecosystems: a comparative analysis of 2003, 2010 and 2018. Philos. Trans. R. Soc. B Biol. Sci. 375, 20190507 (2020).CAS 
Article 

Google Scholar 
Fan, Y., Miguez-Macho, G., Jobbágy, E. G., Jackson, R. B. & Otero-Casal, C. Hydrologic regulation of plant rooting depth. Proc. Natl Acad. Sci. USA 114, 10572–10577 (2017).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Haverd, V. et al. A new version of the CABLE land surface model (Subversion revision r4601) incorporating land use and land cover change, woody vegetation demography, and a novel optimisation-based approach to plant coordination of photosynthesis. Geosci. Model Dev. 11, 2995–3026 (2018).ADS 
CAS 
Article 

Google Scholar 
Melton, J. R. & Arora, V. K. Competition between plant functional types in the Canadian Terrestrial Ecosystem Model (CTEM) v. 2.0. Geosci. Model Dev. 9, 323–361 (2016).ADS 
CAS 
Article 

Google Scholar 
Lawrence, D. M. et al. The community land model version 5: description of new features, benchmarking, and impact of forcing uncertainty. J. Adv. Model. Earth Syst. 11, 4245–4287 (2019).ADS 
Article 

Google Scholar 
Tian, H. et al. North American terrestrial CO2 uptake largely offset by CH4 and N2O emissions: toward a full accounting of the greenhouse gas budget. Clim. Change 129, 413–426 (2015).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Meiyappan, P., Jain, A. K. & House, J. I. Increased influence of nitrogen limitation on CO2 emissions from future land use and land use change. Glob. Biogeochem. Cycles 29, 1524–1548 (2015).ADS 
CAS 
Article 

Google Scholar 
Mauritsen, T. et al. Developments in the MPI‐M Earth system model version 1.2 (MPI‐ESM1.2) and its response to increasing CO2. J. Adv. Model. Earth Syst. 11, 998–1038 (2019).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Poulter, B., Frank, D. C., Hodson, E. L. & Zimmermann, N. E. Impacts of land cover and climate data selection on understanding terrestrial carbon dynamics and the CO2 airborne fraction. Biogeosciences 8, 2027–2036 (2011).ADS 
CAS 
Article 

Google Scholar 
Lienert, S. & Joos, F. A Bayesian ensemble data assimilation to constrain model parameters and land-use carbon emissions. Biogeosciences 15, 2909–2930 (2018).ADS 
CAS 
Article 

Google Scholar 
Zaehle, S. & Friend, A. D. Carbon and nitrogen cycle dynamics in the O-CN land surface model: 1. Model description, site-scale evaluation, and sensitivity to parameter estimates. Glob. Biogeochem. Cycles 24, 1–13 (2010).
Google Scholar 
Goll, D. S. et al. A representation of the phosphorus cycle for ORCHIDEE (revision 4520). Geosci. Model Dev. 10, 3745–3770 (2017).ADS 
CAS 
Article 

Google Scholar 
Krinner, G. et al. A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system. Glob. Biogeochem. Cycles 19, 1–33 (2005).Article 
CAS 

Google Scholar 
Walker, A. P. et al. The impact of alternative trait‐scaling hypotheses for the maximum photosynthetic carboxylation rate (Vcmax) on global gross primary production. N. Phytol. 215, 1370–1386 (2017).CAS 
Article 

Google Scholar 
Joetzjer, E. et al. Improving the ISBACC land surface model simulation of water and carbon fluxes and stocks over the Amazon forest. Geosci. Model Dev. 8, 1709–1727 (2015).ADS 
Article 

Google Scholar 
Kato, E., Kinoshita, T., Ito, A., Kawamiya, M. & Yamagata, Y. Evaluation of spatially explicit emission scenario of land-use change and biomass burning using a process-based biogeochemical model. J. Land Use Sci. 8, 104–122 (2013).Article 

Google Scholar 
Wei, Y. et al. The North American carbon program multi-scale synthesis and terrestrial model intercomparison project – Part 2: environmental driver data. Geosci. Model Dev. 7, 2875–2893 (2014).ADS 
Article 

Google Scholar 
Dlugokencky, E. J., Thoning, K. W., Lang, P. M. & Tans, P. P. NOAA greenhouse gas reference from atmospheric carbon dioxide dry air mole fractions from the NOAA ESRL carbon cycle cooperative global air sampling network. ftp://aftp.cmdl.noaa.gov/data/trace_gases/co2/flask/surface/ (2017). More

FAO. Global Forest Resource Assessment 2020—Key Findings (FAO, 2020).
Google Scholar 
Rasmussen, L. V. et al. A combination of methods needed to assess the actual use of provisioning ecosystem services. Ecosyst. Serv. 17, 75–86 (2016).Article 

Google Scholar 
Pan, Y. et al. A large and persistent carbon sink in the World’s Forests. Science 333, 988 (2011).ADS 
CAS 
Article 

Google Scholar 
Gao, J., Tang, X. G., Lin, S. Q. & Bian, H. Y. The influence of land use change on key ecosystem services and their relationships in a mountain region from past to future (1995–2050). Forests 12, 616 (2021).Article 

Google Scholar 
Rodríguez-Echeverry, J., Echeverría, C., Oyarzún, C. & Morales, L. Impact of land-use change on biodiversity and ecosystem services in the Chilean temperate forests. Landsc. Ecol. 33(3), 439–453 (2018).Article 

Google Scholar 
Hoque, M. Z., Islam, I., Ahmed, M., Hasan, S. S. & Prodhan, F. A. Spatio-temporal changes of land use land cover and ecosystem service values in coastal Bangladesh. Egypt. J. Remote Sens. Space Sci. 25(1), 173–180 (2022).
Google Scholar 
Costanza, R. et al. Changes in the global value of ecosystem services. Glob. Environ. Change 26, 152–158 (2014).Article 

Google Scholar 
Sil, Â. et al. Analysing carbon sequestration and storage dynamics in a changing mountain landscape in Portugal: Insights for management and planning. Int. J. Biodivers. Sci. Ecosyst. Serv. Manage. 13(2), 82–104 (2017).Article 

Google Scholar 
Xu, Y., Tang, H., Wang, B. & Chen, J. Effects of land-use intensity on ecosystem services and human well-being: A case study in Huailai County, China. Environ. Earth. Sci. 75(5), 416 (2016).Article 

Google Scholar 
Liang, Y., Liu, L. & Huang, J. Integrating the SD-CLUE-S and InVEST models into assessment of oasis carbon storage in northwestern China. PLoS ONE 12(2), e0172494 (2017).Article 

Google Scholar 
Zhao, M. et al. Assessing the effects of ecological engineering on carbon storage by linking the CA-Markov and InVEST models. Ecol. Indic. 98, 29–38 (2019).Article 

Google Scholar 
Leh, M. D., Matlock, M. D., Cummings, E. C. & Nalley, L. L. Quantifying and mapping multiple ecosystem services change in West Africa. Agric. Ecosyst. Environ. 165, 6–18 (2013).Article 

Google Scholar 
Zhao, Z. et al. Assessment of carbon storage and its influencing factors in Qinghai-Tibet Plateau. Sustainability 10(6), 1864 (2018).Article 

Google Scholar 
Fu, Q. et al. Scenario analysis of ecosystem service changes and interactions in a mountain-oasis-desert system: A case study in Altay Prefecture, China. Sci. Rep. 8(1), 1–13 (2018).ADS 

Google Scholar 
Li, Z., Cheng, X. & Han, H. Future impacts of land use change on ecosystem services under different scenarios in the ecological conservation area, Beijing, China. Forests 11(5), 584 (2020).CAS 
Article 

Google Scholar 
Liu, H., Xiao, W., Li, Q., Tian, Y. & Zhu, J. Spatio-temporal change of multiple ecosystem services and their driving factors: A case study in Beijing, China. Forests 13(2), 260 (2022).CAS 
Article 

Google Scholar 
Nizami, S. M. The inventory of the carbon stocks in sub tropical forests of Pakistan for reporting under Kyoto Protocol. J. For. Res. 23(3), 377–384 (2012).CAS 
Article 

Google Scholar 
Ghafoor, G. Z., Sharif, F., Khan, A. U., Shahzad, L. & Hayyat, M. U. Assessment of tree biomass carbon stock of subtropical scrub forest, Soan valley Pakistan. App. Ecol. Environ. Res. 18(2), 2231–2245 (2020).Article 

Google Scholar 
Siddiq, Z. et al. Models to estimate the above and below ground carbon stocks from a subtropical scrub forest of Pakistan. Glob. Ecol. Conserv. 27, e01539 (2021).Article 

Google Scholar 
Ali, A., Ashraf, M. I., Gulzar, S. & Akmal, M. Estimation of forest carbon stocks in temperate and subtropical mountain systems of Pakistan: Implications for REDD+ and climate change mitigation. Environ. Monit. Assess. 192(3), 1–13 (2020).Article 

Google Scholar 
Mannan, A. et al. Application of land-use/land cover changes in monitoring and projecting forest biomass carbon loss in Pakistan. Glob. Ecol. Conserv. 17, e00535 (2019).Article 

Google Scholar 
Khan, A. U. et al. Piloting restoration initiatives in subtropical scrub forest: Specifying areas asserting adaptive management. Environ. Monit. Assess. 191(11), 675 (2019).Article 

Google Scholar 
Mohajane, M. et al. Land use/land cover (LULC) using Landsat data series (MSS, TM, ETM+ and OLI) in Azrou Forest, in the Central Middle Atlas of Morocco. Environments 5(12), 131 (2018).Article 

Google Scholar 
Brown, J. NDVI, the foundation for remote sensing phenology. In USGS Remote Sensing Phenology: Vegetation Indices (2015).Liping, C., Yujun, S. & Saeed, S. Monitoring and predicting land use and land cover changes using remote sensing and GIS techniques—A case study of a hilly area, Jiangle, China. PLoS ONE 13(7), e0200493 (2018).Article 

Google Scholar 
Ricke, K., Drouet, L., Caldeira, K. & Tavoni, M. Country-level social cost of carbon. Nat. Clim. Change 8(10), 895–900 (2018).ADS 
CAS 
Article 

Google Scholar 
Salehi, M. H., Beni, O. H., Harchegani, H. B., Borujeni, I. E. & Motaghian, H. R. Refining soil organic matter determination by loss-on-ignition. Pedosphere 21(4), 473–482 (2011).Article 

Google Scholar 
Tivet, F. et al. Soil carbon inventory by wet oxidation and dry combustion methods: Effects of land use, soil texture gradients, and sampling depth on the linear model of C-equivalent correction factor. Soil Sci. Soc. Am. J. 76(3), 1048–1059 (2012).ADS 
CAS 
Article 

Google Scholar 
Government of Punjab. The Punjab Forest (Amendment) Act, 2010 (Government of the Punjab, 2010).
Google Scholar 
Kamwi, J. M., Kaetsch, C., Graz, F. P., Chirwa, P. & Manda, S. Trends in land use and land cover change in the protected and communal areas of the Zambezi Region, Namibia. Environ. Monit. Assess. 189(5), 242 (2017).Article 

Google Scholar 
Negassa, M. D., Mallie, D. T. & Gemeda, D. O. Forest cover change detection using Geographic Information Systems and remote sensing techniques: A spatio-temporal study on Komto Protected forest priority area, East Wollega Zone, Ethiopia. Environ. Syst. Res. 9(1), 1 (2020).Article 

Google Scholar 
Government of Punjab. Punjab Development Statistics 2007. Burreau of Statistics (Government of the Punjab, 2007).
Google Scholar 
Government of Punjab. Punjab Development Statistics 2013. Burreau of Statistics (Government of the Punjab, 2013).
Google Scholar 
Government of Punjab. Punjab Development Statistics 2019. Burreau of Statistics (Government of the Punjab, 2019).
Google Scholar 
Dunn, R. J. H., Stanitski, D. M., Gobron, N. & Willett, K. M. State of the climate in 2019: Global climate. Special online supplement to the B. Am. Meteorol. Soc. 101(8), S9. https://doi.org/10.1175/BAMS-D-20-0104.1 (2020).Article 

Google Scholar 
Gray, S. B. & Brady, S. M. Plant developmental responses to climate change. Dev. Biol. 419(1), 64–77 (2016).CAS 
Article 

Google Scholar 
Ghafoor, G. Z. et al. Effect of climate warming on seedling growth and biomass accumulation of Acacia modesta and Olea ferruginea in a subtropical scrub forest of Pakistan. Écoscience 29, 1–14 (2021).
Google Scholar 
Bibi, S., Sultana, J., Sultana, H. & Malik, R. N. Ethnobotanical uses of medicinal plants in the highlands of Soan valley, salt range, Pakistan. J. Ethnopharmacol. 155(1), 352–361 (2014).Article 

Google Scholar 
Chaudhry, Q. U. Z. Climate Change Profile of Pakistan (Asian Development Bank, 2017).
Google Scholar 
Shaheen, H. et al. Carbon stocks assessment in subtropical forest types of Kashmir Himalayas. Pak. J. Bot. 48, 2351–2357 (2016).CAS 

Google Scholar 
Arunyawat, S. & Shrestha, R. P. Assessing land use change and its impact on ecosystem services in Northern Thailand. Sustainability 8(8), 768 (2016).Article 

Google Scholar 
Sing, L., Metzger, M. J., Paterson, J. S. & Ray, D. A review of the effects of forest management intensity on ecosystem services for northern European temperate forests with a focus on the UK. For. Int. J. For. Res. 91(2), 151–164 (2018).
Google Scholar  More

The biotic induction of OAE-2The rapid proliferation of select microbial communities at 427.54 mcd likely represents a pre-OAE biotic perturbation (pre-OAE BP) presaging the protracted period of widespread marine deoxygenation during OAE-2, and progressive deoxygenation predating the +CIE7 (Fig. 4). At the beginning of the pre-OAE BP (427.54 mcd), abruptly elevated tetrapyrroles and crenarchaeol concentrations signify an abrupt increase in primary production by photoautotrophs and chemoautotrophs residing above the chemocline. Increased volumes of precipitating biogenic snow concordantly consumed oxygen, expanding the preexisting OMZ as anaerobic bacteria thrived based on accelerated obGDGTs synthesis. Euxinia did not penetrate the photic zone at the outset of the productivity bloom as isorenieratane was not detected and heightened rates of microbial sulfate reduction were seemingly transient, inferred from the DAGEs profile, and limited to pre-OAE BP initiation. The lack of a well-stratified water column, evinced by absent to low concentrations of halophilic archaeal lipids (i.e., extended archaeols), relatively low rates of microbial sulfate reduction, and a dense oxygenic microbial plate likely precluded the development of PZE initially.Establishing a definitive causal mechanism for the pre-OAE BP is difficult, but the concomitance of LIP activity with the productivity spike is intriguing. Application of a linear sedimentation rate from OAE-2 to the pre-OAE BP interval following previous works6,7 approximated the pre-OAE BP occurring 220 ± 4 kyr before OAE-2, lasting for ~100 kyr (427.54–426.88 mcd; see Estimating the duration of the pre-OAE BP in Supplementary Information for rationale and calculation). Significantly, this was roughly coincident with the onset of LIP activity (~200–300 kyr before OAE-2) inferred from marine osmium isotope stratigraphy27. Similarities in the modern planktonic community response, such as elevated productivity and compositional changes, between the 2018 Kilauea eruption28 and the pre-OAE BP reinforce inference of a potential magmatic trigger for this event (see Evidence for LIP trigger of the pre-OAE biotic perturbation in Supplementary Information for additional details).A constant, yet overall lower, nutrient and trace metal inventory6 (Fig. S4) combined with a redox-driven shift in fixed N species (from NO3− to NH4+)15, potentially leading to a fixed N shortage29 via intensified denitrification and annamox reactions30, were probable culprits in the failure to sustain prolific rates of primary production beyond 100 kyr at the Demerara Rise. The gradual decline in biomass production, indicated by decreasing tetrapyrrole and crenarchaeol profiles (Fig. 4), was accompanied by a notable shift in deep water communities. Sulfate-reducing bacteria exerted increasing predominance over methanogenic archaea, a trend coeval with the primary productivity spike and extending well into the OAE (Fig. 3). A collapse of autotrophic communities to pre-perturbation levels was concordant with the progressive shoaling of H2S-laden waters. Continued vertical migration of the chemocline intruded the photic zone, producing PZE that enabled anoxygenic photosynthesis by Chlorobiaceae (Fig. 4). Unlike the overall oscillatory character of PZE throughout the studied section, this protracted phase of PZE was sustained until the onset of OAE-2 (426.43–426.00 mcd, Figs. 3 and 4) and is approximately contemporaneous with a thallium (Tl) isotope excursion7 (426.40–426.30 mcd).The positive Tl isotope excursion represents the progressive expansion of bottom water anoxia predating OAE-2 by 43 ± 11 kyr6,7. However, evidence for a causal mechanism of pre-OAE deoxygenation remains indeterminate. Our comprehensive biomarker inventory provides an interpreted sequence of events culminating in the regional to global expansion of anoxia predating OAE-2. A protracted phase of enhanced primary productivity began ~220 ± 4 kyr prior to OAE-2, increasing localized production and export of organic carbon at Demerara Rise. Similar productivity spikes likely occurred in settings of comparable paleogeographic configuration (e.g., equatorial, continental margins/shelves), seeding the oceans with fixed carbon. Continued scavenging of marine oxygen via organic carbon remineralization resulted in OMZ expansion locally, and likely initiated oxygen drawdown in much of the proto-North Atlantic Ocean. Stratigraphic records of sulfur isotopes of pyrite (δ34Spyrite) from the proto-North Atlantic and Tethys Oceans11 validate the areal extrapolation of our interpretations. A gradual decline in δ34Spyrite values at Demerara Rise begins at 427.50 mcd, nearly identical to the onset of the pre-OAE BP (427.54 mcd, Fig. 4). Correlation of δ34Spyrite in a global transect (Western Interior Seaway, proto-North Atlantic, Tethys) revealed consistent behavior in δ34Spyrite prior to the +CIE, indicating increasingly expansive marine deoxygenation on a global scale11. Over ~100 kyr, increased regional biomass production induced pervasive marine anoxia, inhibiting Mn-oxide formation, producing the observed positive Tl isotope excursion, and ultimately, the globally observed +CIE reflecting enhanced organic carbon burial signaling the onset of OAE-2. Thus, the local biotic signal recorded at ODP Site 1258 underlines the crucial role the Demerara Rise, and similar undocumented settings, served in initiating deoxygenation of the global ocean.Microbial ecological dynamics during and after OAE-2Changes in microbial community compositions during OAE-2 were apparent, signified by a shift in the normalized total biomarker pool (Fig. 3) and variations in the absolute concentrations of individual biomarkers (Fig. 4). In general, OAE-2 was defined by an expansion and diversification of intermediate and deep water communities (426.00–423.07 mcd), followed by a period of instability leading to the termination of the OAE (423.07–422.00 mcd). Photo- and chemoautotrophs residing above the chemocline were adversely affected, evinced by relatively low, invariant tetrapyrrole and crenarchaeol profiles (Fig. 4). Based on these observations, we divided OAE-2 into two periods defined by contrasting paleoenvironmental conditions modulating the microbial inhabitants of Demerara Rise.The first period of OAE-2 (426.00–423.07 mcd, Fig. 4) was marked by the intrusion of a euxinic OMZ into the photic zone. Elevated, yet fluctuating isorenieratane concentrations suggest relatively persistent PZE of varying vertical extent, in agreement with previous investigations using biomarkers and nitrogen isotopes at nearby sites12,13,31. During this interval, microbial sulfate reduction was likely active as DAGEs continually increased, aligning with estimates of expanded seafloor euxinia32. The co-occurrence of abundant extended archaeols and isorenieratane intimates the role that density stratification served in maintaining the protracted PZE of OAE-2, substantiating concurrent findings based on neodymium33 and oxygen isotopes34. Vertical nutrient advection via upwelling35 led to preferential exposure to expanding intermediate water communities tolerant to sulfidic conditions in the OMZ. Scavenging of a potentially limited fixed N inventory30, depleted in NO3− and predominated by NH4+[ 15,29, and inhibition of efficient nutrient transfer by pronounced density stratification likely induced severe N deficiency in surface water communities, explaining the relatively muted productivity of oxygenic photoautotrophs (i.e., tetrapyrroles) and chemoautotrophs (i.e., crenarchaeol) observed (Fig. 4). The concentration and predominant utilization of fixed N in the OMZ led to the proliferation and diversification of intermediate and deep water microbial taxa, while a shoaling chemocline led to increased nutrient (i.e., fixed N) competition between photoautotrophs and retreating Thaumarchaeota as highlighted by our biomarker inventory and the nitrogen isotopic record31. These findings challenge previous interpretations of highly productive, predominantly eukaryotic primary producers reliant on the upwelling of isotopically depleted NH4+ during OAE-215. Instead, the decline of C30-17-nor-DPEP (Fig. S5; Supplementary Data 3), a source-specific tetrapyrrole diagenetically derived from algal chlorophyll-c36, and reconstructed water column conditions during OAE-2 indirectly support a rise in cyanobacteria, diazotrophs able to fix N2, in oxygenated, nutrient-depleted shallow waters. Increased cyanobacterial contribution is further supported by C and N stable isotopes16,37, as well as the prominence of potentially phylum-specific biomarkers across OAE-2 (e.g., 2-methylhopanoids6,14).Fig. 5: Contrasting biogeochemical conditions between the pre-OAE BP and OAE-2.a, b Microbial ecology and water column conditions during the pre-OAE BP, reflecting high primary production of organic carbon (a) and OAE-2, characterized by relatively lower organic carbon production, but substantially enhanced biomass preservation (b). c, d Averaged fractional abundances of individual biomarkers throughout the pre-OAE BP (c) and OAE-2 (d). Biomarker source organisms are abbreviated as follows: phytoplankton (P), ammonia oxidizing archaea (AOA), sulfur oxidizing bacteria (SOB), unknown anaerobic bacteria (UAB), sulfate reducing bacteria (SRB), halophilic archaea (HA), methanogenic archaea (MA).Full size imageA reversal from the formerly outlined conditions typified the second period of OAE-2 (423.07–421.99 mcd, Fig. 4). Destabilization of the stratified water column and reduced production of H2S led to deepening and contraction of the euxinic OMZ. The observed decline in halophilic archaea, coincident with an overall decline in Chlorobiaceae populations, is roughly coeval with positive neodymium isotopic excursions observed across the proto-North Atlantic33 attributed to the enhanced latitudinal commingling of proto-North Atlantic water masses38. Although detrimental to sustained PZE, the persistence of a well-developed anaerobic bacterial community (i.e., obGDGTs) suggests the lasting presence of a non-euxinic OMZ despite improved bottom water circulation. A premature recovery of the chemoautotrophic Thaumarchaeota, inhabiting the base of the photic zone, relative to the shallower dwelling obligately oxygenic phototrophs (Fig. 3) likely reflects reduced toxicity associated with retreating euxinic waters, lessened resource competition with [primarily] Chlorobiaceae, and a competitive advantage tied to preferential exposure to upwelled nutrients and tolerance to low O2 conditions.The termination of OAE-2 was marked by the temporary re-establishment of microbial community compositions mirroring those observed prior to the pre-OAE BP (Figs. 3 and 4). Contraction of the OMZ led to a deep chemocline, with PZE restricted to the basal photic zone as the production of reduced sulfide species diminished. The Thaumarchaeota continued the recovery initiated towards the latter half of OAE-2, accompanied by the rebounding oxygenic photoautotrophs. However, the recovery of shallow autotrophic communities was halted by an episode of PZE (421.19–421.04 mcd) based on abrupt increases in isorenieratane concentrations (Fig. 4). Temporary development of pronounced density stratification likely facilitated the accumulation of H2S in the lower to intermediate photic zone, producing the short-lived PZE episode. Interestingly, covariant responses observed in additional biomarker profiles (e.g., obGDGTs) to PZE during OAE-2 were not evident across this post-OAE interval, possibly due to the transient nature of PZE at this time. For example, the initial increase in isorenieratane concentrations at the onset of OAE-2 was not immediately accompanied by shifts in other biomarker classes (e.g., obGDGTs; Fig. 4), suggesting frequent recurrences of PZE may be required to illicit a major microbial ecological response as observed later during the OAE. Still, this brief episode of post-OAE PZE (421.19–421.04 mcd) coincides with a positive organic carbon isotope excursion9 (Fig. S5), trace metal drawdown6 (Fig. S4), and minor positive Tl isotope excursion7 at the Demerara Rise. Prior study7 tentatively attributed this interval to enhanced carbon burial during a post-OAE deoxygenation event of smaller magnitude, with subsequent work revealing continued pyrite burial post-OAE 211. Our biomarker inventory revealed some environmental consistencies (e.g., PZE) between this interval and OAE-2, but the overall biotic response to this post-OAE geochemical perturbation was relatively subdued and requires additional sampling and investigation to properly constrain.Broader implicationsThe recognition of the pre-OAE BP and evolving water column conditions at Demerara Rise highlights additional complexities of a dynamic ocean relevant to interpretations of OAE-2 and the +CIE. Enhanced, sustained, and widespread carbon burial is required to produce the +CIE used to define OAE-28,10. Still, the principal forcing, productivity or preservation, remains enigmatic as evidence for the former mounts12,39.Based on the tetrapyrrole profiles (Fig. 4) primary production was greatest during the pre-OAE BP and relatively muted throughout OAE-2 at Demerara Rise, assuming minimal alteration to the genetic tetrapyrrole stratigraphic signal. Biomass preservation was presumedly enhanced during OAE-2 through sulfurization11, as the OMZ transitioned from anoxic to euxinic and penetrated the photic zone, yet low tetrapyrrole concentrations persist. Previous work noted a similar discrepancy between preservation potential and porphyrin abundance, postulating a paucity of trace metals to chelate with the free-base porphyrins induced poor preservation as desulfurization did not reveal additional porphyrin content16. However, both the pre-OAE BP and OAE-2 were characterized by relatively depleted trace metal inventories6 (Fig. S4), yet exhibit contrasting tetrapyrrole profiles, suggesting relative changes in primary production were the predominate control on the stratigraphic distribution of tetrapyrroles across the studied interval at the Demerara Rise. The strong covariance between tetrapyrrole and crenarchaeol concentrations reinforces the interpretation tetrapyrroles faithfully reflect primary production (Fig. S6). Crenarchaeol, a biosynthetic product of chemoautotrophic archaea (Thaumarchaeota) comprising up to 20% of all archaea and bacteria in the modern ocean40, is structurally distinct from the tetrapyrroles making it likely that diagenetic alteration of the two biomarkers is not consistent in rate or form. Thus, the positive correlation between key proxies for major contributors to primary production, the photoautotrophs and chemoautotrophs, minimizes concern for the integrity of the biotic signal at Demerara Rise (see Tetrapyrroles as a record of primary production in Supplementary Information for additional details).These findings provide direct evidence for a causal mechanism resulting in both the Tl isotope excursion and +CIE as previously described. It is highly probable the pre-OAE BP was not exclusive to the Demerara Rise based on the immense and presently unconstrained organic carbon burial required to produce the +CIE10. Further characterization of comparable localities to Demerara Rise may reveal similar high productivity events, as primed, highly productive settings likely capitalized on exogenous nutrient delivery via efficient upwelling to the photic zone prior to stratification during OAE-2. Hence, OAE-2 and the +CIE were not coincident with heightened surface water productivity relative to the pre-OAE BP at the Demerara Rise. Rather, antecedent increases in primary production locally facilitated the initiation of the OAE as a mechanism to consume marine oxygen and subsequently enhance organic carbon preservation globally. This highlights how OAE-2, and perhaps other OAEs in the geologic record, were not instantaneously induced but rather a gradual transition stemming from sustained forcing(s). In addition, the occurrence of the pre-OAE BP well before the established onset of OAE-2 reveals how fluctuations in primary production can be linked to marine deoxygenation but may not necessarily be concurrent. As shown here, OAE-2 at the Demerara Rise was preceded by elevated primary production that progressively attenuated towards event onset. While the hallmark features of an OAE are well-established, further identification and refinement of trends preceding widespread anoxia in the past will improve our understanding of how marine deoxygenation develops, as well as our ability to assess planetary health today.A shift from a productivity- to preservation-dominant system during OAE-2 at Demerara Rise, and possibly similar paleogeographic settings experiencing the pre-OAE BP, facilitated substantial organic carbon burial producing the +CIE. Distinct shifts in water column chemistry and structure from the pre-OAE BP to OAE-2 imparted considerable changes on microbial life, which altered the primary driver governing biomass sequestration (Fig. 5). Yet, both intervals reveal relatively comparable carbonate-corrected total organic carbon values6 (Fig. S5), signifying enhanced preservation as a critical component of organic carbon burial during OAE-2 at Demerara Rise. Consequently, this work suggests that sustained increases in primary production prior to OAE-2 initiated and regulated pre-OAE deoxygenation, resulting in a progressive shift to preservation as the primary control on organic carbon accumulation in sediments. Expanding euxinia and attendant changes to biogeochemical cycling adversely affected primary producers while simultaneously enhancing organic matter preservation via sulfurization11. Flourishment of Thaumarchaeota in oligotrophic settings in the modern open ocean41, and lack thereof during OAE-2 based on diminished crenarchaeol concentrations, underscores the scarcity of bioessential elements (e.g., fixed N) caused by microbial utilization of electron acceptors further down the redox ladder due to intensified marine anoxia, ultimately limiting primary production. The switch from a productivity to preservation model, reconstructed using biomarkers (Fig. 5) and initially suggested based on drawdown of the trace metal inventory6, was also concomitant with relative warming4. Simulated projections of the marine microbial response to continued global warming in the future revealed similar biotic trends (e.g., decreased primary productivity) to warming-induced oceanographic changes42 (e.g., intensified stratification) observed during OAE-2. Thus, an abundance of proxy- and model-based results paired with conceptual evidence suggest relatively low production and enhanced preservation of organic carbon throughout OAE-2 at the equatorial Demerara Rise.The pre-OAE BP may foreshadow greater regional trends observed during OAE-2. Equatorial upwelling centers, like Demerara Rise, are spatially restricted and represent regions of already high primary production before OAE-2. Climatic shifts concurrent with OAE-2 may have produced favorable conditions for elevated primary productivity in regions unable to capitalize on or exposed to allochthonous nutrient delivery prior to the +CIE. While the pre-OAE BP offers a causal mechanism for the Tl isotope excursion and +CIE initiation, areal expansion of organic carbon preservation and production is necessary to sustain enhanced organic carbon burial for the duration of the +CIE.Continued development of preexisting proxies is critical to extract and clarify current understandings of major climatic events in Earth history. Although reliant on excellent preservation of the microbial signal, the analytical and interpretative approach used here enables simultaneous examination of a wide array of biomarkers, producing a more holistic reconstruction of oceanographic changes inferred from microbial ecological variations spanning the surface to the sediment. This is timely, as investigations of the sedimentary archives become increasingly valuable analogs to understand the response of modern oceans to natural and anthropogenic forcings. Similarities between the pre-OAE BP and modern, climate-driven marine deoxygenation are concerning, while particular attention to preexisting highly productive settings may hold the key to forecasting the geologically rapid transition to a global OAE. Even though natural processes are currently beyond our control, stifling anthropogenic catalysts of climate change may decelerate the unfortunate, progressive suitability of OAEs as climate analogs in the future. More

Gattuso, J.-P. et al. Ocean solutions to address climate change and its effects on marine ecosystems. Front. Mar. Sci. 5, 337 (2018).Article 

Google Scholar 
Pauly, D. The gill-oxygen limitation theory (GOLT) and its critics. Sci. Adv. 7, 6050 (2021).Article 
ADS 
CAS 

Google Scholar 
Miller, D. D., Ota, Y., Sumaila, U. R., Cisneros-Montemayor, A. M. & Cheung, W. W. L. Adaptation strategies to climate change in marine systems. Glob. Change Biol. 24, e1–e14 (2018).Article 
ADS 

Google Scholar 
Chan, F. T. et al. Climate change opens new frontiers for marine species in the Arctic: Current trends and future invasion risks. Glob. Change Biol. 25, 25–38 (2019).Article 
ADS 

Google Scholar 
Cheung, W. W. L. et al. Structural uncertainty in projecting global fisheries catches under climate change. Ecol. Model. 325, 57–66 (2016).CAS 
Article 

Google Scholar 
Pita, I., Mouillot, D., Moullec, F. & Shin, Y. Contrasted patterns in climate change risk for Mediterranean fisheries. Glob. Change Biol. 27, 5920–5933 (2021).Article 

Google Scholar 
Tacon, A. G. J. & Metian, M. Fishing for aquaculture: Non-food use of small pelagic forage fish—a global perspective. Rev. Fish. Sci. 17, 305–317 (2009).Article 

Google Scholar 
Coll, M., Pennino, M. G., Steenbeek, J., Sole, J. & Bellido, J. M. Predicting marine species distributions: Complementarity of food-web and Bayesian hierarchical modelling approaches. Ecol. Model. 405, 86–101 (2019).Article 

Google Scholar 
Schickele, A. et al. Improving predictions of invasive fish ranges combining functional and ecological traits with environmental suitability under climate change scenarios. Glob. Change Biol. 27, 6086–6102 (2021).Article 

Google Scholar 
Lejeusne, C., Chevaldonné, P., Pergent-Martini, C., Boudouresque, C. F. & Pérez, T. Climate change effects on a miniature ocean: The highly diverse, highly impacted Mediterranean Sea. Trends Ecol. Evol. 25, 250–260 (2010).PubMed 
Article 

Google Scholar 
Cramer, W. et al. Climate change and interconnected risks to sustainable development in the Mediterranean. Nat. Clim. Change 8, 972–980 (2018).Article 
ADS 

Google Scholar 
FAO. The State of Mediterranean and Black Sea Fisheries 2020—At a glance. 20 (2020).McGinty, N., Barton, A. D., Finkel, Z. V., Johns, D. G. & Irwin, A. J. Niche conservation in copepods between ocean basins. Ecography https://doi.org/10.1111/ecog.05690 (2021).Article 

Google Scholar 
Dormann, C. F. et al. Biotic interactions in species distribution modelling: 10 questions to guide interpretation and avoid false conclusions. Glob. Ecol. Biogeogr. 27, 1004–1016 (2018).Article 

Google Scholar 
Hannemann, H., Willis, K. J. & Macias-Fauria, M. The devil is in the detail: unstable response functions in species distribution models challenge bulk ensemble modelling: Unstable response functions in SDMs. Glob. Ecol. Biogeogr. 25, 26–35 (2016).Article 

Google Scholar 
Beaugrand, G. et al. Prediction of unprecedented biological shifts in the global ocean. Nat. Clim. Chang. 9, 237–243 (2019).Article 
ADS 

Google Scholar 
Lasram, B. R. et al. An open-source framework to model present and future marine species distributions at local scale. Ecol. Inform. 59, 101130 (2020).Article 

Google Scholar 
Araújo, M. B. et al. Standards for distribution models in biodiversity assessments. Sci. Adv. 5, 4858 (2019).Article 
ADS 

Google Scholar 
Schickele, A. et al. European small pelagic fish distribution under global change scenarios. Fish Fish 22, 212–225 (2021).Article 

Google Scholar 
Duarte, R., Azevedo, M., Landa, J. & Pereda, P. Reproduction of angler®sh (Lophius budegassa Spinola and Lophius piscatorius Linnaeus) from the Atlantic Iberian coast. Fish. Res. 13, 2 (2001).
Google Scholar 
Nunes, P., Svensson, L. & Markandya, A. Handbook on the Economics and Management of Sustainable Oceans (Edward Elgar Publishing, 2017).Book 

Google Scholar 
Schickele, A. et al. Modelling European small pelagic fish distribution: Methodological insights. Ecol. Model. 416, 108902 (2020).Article 

Google Scholar 
Cheung, W. W. L., Jones, M. C., Reygondeau, G. & Frölicher, T. L. Opportunities for climate-risk reduction through effective fisheries management. Glob. Change Biol. 24, 5149–5163 (2018).Article 
ADS 

Google Scholar 
Bossier, S. et al. The Baltic Sea Atlantis: An integrated end-to-end modelling framework evaluating ecosystem-wide effects of human-induced pressures. PLoS ONE 13, e0199168 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Dahlke, F. T., Wohlrab, S., Butzin, M. & Pörtner, H.-O. Thermal bottlenecks in the life cycle define climate vulnerability of fish. Science 369, 65–70 (2020).CAS 
PubMed 
Article 
ADS 

Google Scholar 
Valle, C., Bayle-Sempere, J. T., Dempster, T., Sanchez-Jerez, P. & Giménez-Casalduero, F. Temporal variability of wild fish assemblages associated with a sea-cage fish farm in the south-western Mediterranean Sea. Estuar. Coast. Shelf Sci. 72, 299–307 (2007).Article 
ADS 

Google Scholar 
Madurell, T., Cartes, J. E. & Labropoulou, M. Changes in the structure of fish assemblages in a bathyal site of the Ionian Sea (eastern Mediterranean). Fish. Res. 66, 245–260 (2004).Article 

Google Scholar 
Volkoff, H. & Rønnestad, I. Effects of temperature on feeding and digestive processes in fish. Temperature 7, 307–320 (2020).Article 

Google Scholar 
Rutterford, L. A. et al. Future fish distributions constrained by depth in warming seas. Nat. Clim. Change 5, 569–573 (2015).Article 
ADS 

Google Scholar 
Pauly, D. & Christensen, V. Primary production required to sustain global fisheries. Nature 374, 255–257 (1995).CAS 
Article 
ADS 

Google Scholar 
Conti, L. & Scardi, M. Fisheries yield and primary productivity in large marine ecosystems. Mar. Ecol. Prog. Ser. 410, 233–244 (2010).Article 
ADS 

Google Scholar 
Chérif, M. et al. Food and feeding habits of the red mullet, Mullus barbatus (Actinopterygii: Perciformes: Mullidae), off the northern Tunisian coast (central Mediterranean). Acta Icth et Piscat 41, 109–116 (2011).Article 

Google Scholar 
Mellon-Duval, C. et al. Trophic ecology of the European hake in the Gulf of Lions, northwestern Mediterranean Sea. Sci. Mar. 81, 7 (2017).Article 

Google Scholar 
Steingrund, P. & Gaard, E. Relationship between phytoplankton production and cod production on the Faroe Shelf. ICES J. Mar. Sci. 62, 163–176 (2005).Article 

Google Scholar 
Friedland, K. D. et al. Pathways between primary production and fisheries yields of large marine ecosystems. PLoS ONE 7, e28945 (2012).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Frederiksen, M., Edwards, M., Richardson, A. J., Halliday, N. C. & Wanless, S. From plankton to top predators: Bottom-up control of a marine food web across four trophic levels. J. Anim. Ecol. 75, 1259–1268 (2006).PubMed 
Article 

Google Scholar 
Vasilakopoulos, P., Raitsos, D. E., Tzanatos, E. & Maravelias, C. D. Resilience and regime shifts in a marine biodiversity hotspot. Sci. Rep. 7, 13647 (2017).PubMed 
PubMed Central 
Article 
ADS 
CAS 

Google Scholar 
Issifu, I., Alava, J. J., Lam, V. W. Y. & Sumaila, U. R. Impact of ocean warming, overfishing and mercury on European fisheries: A risk assessment and policy solution framework. Front. Mar. Sci. 8, 770805 (2022).Article 

Google Scholar 
Lima, A. R. A. et al. Forecasting shifts in habitat suitability across the distribution range of a temperate small pelagic fish under different scenarios of climate change. Sci. Total Environ. 804, 150167 (2022).CAS 
PubMed 
Article 
ADS 

Google Scholar 
Sumaila, U. R. et al. Benefits of the Paris Agreement to ocean life, economies, and people. Sci. Adv. 5, 3855 (2019).Article 
ADS 

Google Scholar 
Holsman, K. K. et al. Ecosystem-based fisheries management forestalls climate-driven collapse. Nat. Commun. 11, 4579 (2020).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Sumaila, U. R. & Tai, T. C. End overfishing and increase the resilience of the ocean to climate change. Front. Mar. Sci. 7, 523 (2020).Article 

Google Scholar 
Lindegren, M. & Brander, K. Adapting fisheries and their management to climate change: A review of concepts, tools, frameworks, and current progress toward implementation. Rev. Fish. Sci. Aquacult. 26, 400–415 (2018).Article 

Google Scholar 
Demirel, N., Zengin, M. & Ulman, A. First large-scale eastern mediterranean and black sea stock assessment reveals a dramatic decline. Front. Mar. Sci. 7, 103 (2020).Article 

Google Scholar 
Weiss, C. V. C. et al. Climate change effects on marine renewable energy resources and environmental conditions for offshore aquaculture in Europe. ICES J. Mar. Sci. 77, 3168–3182 (2020).Article 

Google Scholar 
Cascarano, M. C. et al. Mediterranean aquaculture in a changing climate: temperature effects on pathogens and diseases of three farmed fish species. Pathogens 10, 1205 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Kleitou, P. et al. Fishery reforms for the management of non-indigenous species. J. Environ. Manag. 280, 111690 (2021).Article 

Google Scholar 
Hamida, B.-B. & O, Ben Hadj Hamida N, Chaouch H, Missaoui H,. Allometry, condition factor and growth of the swimming blue crab Portunus segnis in the Gulf of Gabes, Southeastern Tunisia (Central Mediterranean). Medit. Mar. Sci. 20, 566 (2019).Article 

Google Scholar 
Wisz, M. S. et al. Reply to ‘Sources of uncertainties in cod distribution models’. Nat. Clim. Change 5, 790–791 (2015).Article 
ADS 

Google Scholar 
Kramer-Schadt, S. et al. The importance of correcting for sampling bias in MaxEnt species distribution models. Divers. Distrib. 19, 1366–1379 (2013).Article 

Google Scholar 
Buisson, L., Thuiller, W., Casajus, N., Lek, S. & Grenouillet, G. Uncertainty in ensemble forecasting of species distribution. Glob. Change Biol. 16, 1145–1157 (2010).Article 
ADS 

Google Scholar 
Hao, T., Elith, J., Lahoz-Monfort, J. J. & Guillera-Arroita, G. Testing whether ensemble modelling is advantageous for maximising predictive performance of species distribution models. Ecography 43, 549–558 (2020).Article 

Google Scholar 
Thuiller, W., Damie, G., Robin, E., Frank, F.Biomod2: Ensemble Platform for Species Distribution Modeling (2016).Stolar, J. & Nielsen, S. E. Accounting for spatially biased sampling effort in presence-only species distribution modelling. Divers. Distrib. 21, 595–608 (2015).Article 

Google Scholar 
Stockwell, D. The GARP modelling system: Problems and solutions to automated spatial prediction. Int. J. Geogr. Inf. Sci. 13, 143–158 (1999).Article 

Google Scholar 
Cornwell, W. K., Schwilk, D. W. & Ackerly, D. D. A trait-based test for habitat filtering: Convex hull volume. Ecology 87(6), 1465–1471 (2003).Article 

Google Scholar 
Hengl, T., Sierdsema, H., Radović, A. & Dilo, A. Spatial prediction of species’ distributions from occurrence-only records: Combining point pattern analysis ENFA and regression-kriging. Ecol. Modell. 220, 3499–3511 (2009).Article 

Google Scholar 
Faillettaz, R., Beaugrand, G., Goberville, E. & Kirby, R. R. Atlantic Multidecadal Oscillations drive the basin-scale distribution of Atlantic bluefin tuna. Sci. Adv. 5, eaar6993 (2019).Lavoie, D., Lambert, N. & Gilbert, D. Projections of future trends in biogeochemical conditions in the northwest Atlantic using CMIP5 earth system models. Atmos. Ocean 57, 18–40 (2019).CAS 
Article 

Google Scholar 
Taylor, K. E. Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res. 106, 7183–7192 (2001).Article 
ADS 

Google Scholar 
Cristofari, R. et al. Climate-driven range shifts of the king penguin in a fragmented ecosystem. Nat. Clim. Change 8, 245–251 (2018).Article 
ADS 

Google Scholar 
Zeller, D. et al. Still catching attention: Sea Around Us reconstructed global catch data, their spatial expression and public accessibility. Mar. Policy 70, 145–152 (2016).Article 

Google Scholar 
GBIF.org (27 May 2020) GBIF Occurrence Download https://doi.org/10.15468/dl.2crvdpGBIF.org (7 June 2020) GBIF Occurrence Download https://doi.org/10.15468/dl.y8ujd7GBIF.org (7 June 2020) GBIF Occurrence Download https://doi.org/10.15468/dl.hs8py7GBIF.org (7 June 2020) GBIF Occurrence Download https://doi.org/10.15468/dl.kqwq3aGBIF.org (14 July 2020) GBIF Occurrence Download https://doi.org/10.15468/dl.raka7jGBIF.org (14 July 2020) GBIF Occurrence Download https://doi.org/10.15468/dl.fwbk43GBIF.org (30 July 2020) GBIF Occurrence Download https://doi.org/10.15468/dl.845mcwGBIF.org (30 July 2020) GBIF Occurrence Download https://doi.org/10.15468/dl.wdavbrGBIF.org (11 September 2020) GBIF Occurrence Download https://doi.org/10.15468/dl.ucuavw More

Suding, K. Understanding successes and failures in restoration ecology. Annu. Rev. Ecol. Evol. Syst. 42, (2011).Brudvig, L. A. et al. Interpreting variation to advance predictive restoration science. J. Appl. Ecol. 54, 1018–1027 (2017).Article 

Google Scholar 
Germino, M. J. et al. Thresholds and hotspots for shrub restoration following a heterogeneous megafire. Landsc. Ecol. 33, 1177–1194 (2018).Article 

Google Scholar 
Shriver, R. K. et al. Transient population dynamics impede restoration and may promote ecosystem transformation after disturbance. Ecol. Lett. 22, 1357–1366 (2019).PubMed 
Article 

Google Scholar 
Chambers, J. C. et al. Resilience and resistance of sagebrush ecosystems: implications for state and transition models and management treatments. Rangel. Ecol. Manag. 67, 440–454 (2014).Article 

Google Scholar 
Pilliod, D. S., Welty, J. L. & Toevs, G. R. Seventy-five years of vegetation treatments on public rangelands in the great basin of North America. Rangelands 39, 1–9 (2017).Article 

Google Scholar 
Applestein, C., Germino, M. J., Pilliod, D. S., Fisk, M. R. & Arkle, R. S. Appropriate sample sizes for monitoring burned pastures in sagebrush steppe: how many plots are enough, and can one size fit all? Rangel. Ecol. Manag. 71, 721–726 (2018).Article 

Google Scholar 
Homer, C. et al. Completion of the 2011 National Land Cover Database for the Conterminous United States-Representing a Decade of Land Cover Change Information Landsat-based mapping project. Photogramm. Eng. Remote Sens. 81, 345–354 (2015).
Google Scholar 
Homer, C. G., Aldridge, C. L., Meyer, D. K. & Schell, S. J. Multi-scale remote sensing sagebrush characterization with regression trees over Wyoming, USA: Laying a foundation for monitoring. Int. J. Appl. Earth Obs. Geoinf. 14, 233–244 (2012).ADS 

Google Scholar 
Tredennick, A. T. et al. Forecasting climate change impacts on plant populations over large spatial extents. Ecosphere 7, 1–16 (2016).Article 

Google Scholar 
Rigge, M. et al. Quantifying western U.S. rangelands as fractional components with multi-resolution remote sensing and in situ data. Remote Sens. 12, 1–26 (2020).Article 

Google Scholar 
Shi, H., Homer, C., Rigge, M., Postma, K. & Xian, G. Analyzing vegetation change in a sagebrush ecosystem using long-term field observations and Landsat imagery in Wyoming. Ecosphere 11, 1–20 (2020).Article 

Google Scholar 
Williamson, M. A., Schwartz, M. W. & Lubell, M. N. Spatially explicit analytical models for social–ecological systems. Bioscience 68, 885–895 (2018).
Google Scholar 
Reid, J. L., Fagan, M. E. & Zahawi, R. A. Positive site selection bias in meta-analyses comparing natural regeneration to active forest restoration. Sci. Adv. 4, 1–4 (2018).Article 

Google Scholar 
Joppa, L. N. & Pfaff, A. High and far: biases in the location of protected areas. PLoS One 4, 1–6 (2009).Article 
CAS 

Google Scholar 
Prach, K., Šebelíková, L., Řehounková, K. & del Moral, R. Possibilities and limitations of passive restoration of heavily disturbed sites. Landsc. Res. 45, 247–253 (2020).Article 

Google Scholar 
Andam, K. S., Ferraro, P. J., Pfaff, A., Sanchez-Azofeifa, G. A. & Robalino, J. A. Measuring the effectiveness of protected area networks in reducing deforestation. Proc. Natl Acad. Sci. USA 105, 16089–16094 (2008).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Jones, K. W. & Lewis, D. J. Estimating the counterfactual impact of conservation programs on land cover outcomes: The role of matching and panel regression techniques. PLoS One 10, 1–22 (2015).
Google Scholar 
Christie, A. P. et al. Simple study designs in ecology produce inaccurate estimates of biodiversity responses. J. Appl. Ecol. 56, 2742–2754 (2019).Article 

Google Scholar 
Larsen, A. E., Meng, K. & Kendall, B. E. Causal analysis in control–impact ecological studies with observational data. Methods Ecol. Evol. 10, 924–934 (2019).Article 

Google Scholar 
Parkhurst, T., Prober, S. M., Hobbs, R. J. & Standish, R. J. Global meta-analysis reveals incomplete recovery of soil conditions and invertebrate assemblages after ecological restoration in agricultural landscapes. J. Appl. Ecol. 1–15. https://doi.org/10.1111/1365-2664.13852. (2021)Crouzeilles, R. et al. A global meta-Analysis on the ecological drivers of forest restoration success. Nat. Commun. 7, 1–8 (2016).Article 
CAS 

Google Scholar 
Kettenring, K. M. & Adams, C. R. Lessons learned from invasive plant control experiments: a systematic review and meta-analysis. J. Appl. Ecol. 48, 970–979 (2011).Article 

Google Scholar 
Atkinson, J. & Bonser, S. P. “Active” and “passive” ecological restoration strategies in meta-analysis. Restor. Ecol. 28, 1032–1035 (2020).Article 

Google Scholar 
Rosenbaum, P. R. & Rubin, D. B. The central role of the propensity score in observational studies for causal effects. Biometrika 170–184. https://doi.org/10.1017/CBO9780511810725.016. (1983)Angrist, J. D., & Pischke, J. S. Mostly harmless econometrics. (Princeton University Press, 2009).Bernes, C. et al. How are biodiversity and dispersal of species affected by the management of roadsides? A systematic map. Environ. Evid. 6, 1–16 (2017).Article 

Google Scholar 
França, F. et al. Do space-for-time assessments underestimate the impacts of logging on tropical biodiversity? An Amazonian case study using dung beetles. J. Appl. Ecol. 53, 1098–1105 (2016).Article 

Google Scholar 
Davies, K. W. et al. Saving the sagebrush sea: an ecosystem conservation plan for big sagebrush plant communities. Biol. Conserv. 144, 2573–2584 (2011).Article 

Google Scholar 
Miller, R. F. et al. Characteristics of Sagebrush Habitats and Limitations to Long-term Conservation. Greater sage-grouse: ecology and conservation of a landscape species and its habitats. USGS Adm. Rep. (2011).Pierson, F. B. et al. Hydrologic and erosion responses of sagebrush steppe following juniper encroachment, wildfire, and tree cutting. Rangel. Ecol. Manag. 66, 274–289 (2013).Article 

Google Scholar 
Wijayratne, U. C. & Pyke, D. A. Burial increases seed longevity of two Artemisia tridentata (Asteraceae) subspecies. Am. J. Bot. 99, 438–447 (2012).PubMed 
Article 

Google Scholar 
Pyke, D. A., Wirth, T. A. & Beyers, J. L. Does seeding after wildfires in rangelands reduce erosion or invasive species? Restor. Ecol. 21, 415–421 (2013).Article 

Google Scholar 
Knutson, K. C. et al. Long-term effects of seeding after wildfire on vegetation in Great Basin shrubland ecosystems. J. Appl. Ecol. 51, 1414–1424 (2014).Article 

Google Scholar 
Shriver, R. K. et al. Adapting management to a changing world: Warm temperatures, dry soil, and interannual variability limit restoration success of a dominant woody shrub in temperate drylands. Glob. Chang. Biol. 24, 4972–4982 (2018).PubMed 
Article 
ADS 

Google Scholar 
Eiswerth, M. E., Krauter, K., Swanson, S. R. & Zielinski, M. Post-fire seeding on Wyoming big sagebrush ecological sites: Regression analyses of seeded nonnative and native species densities. J. Environ. Manag. 90, 1320–1325 (2009).Article 

Google Scholar 
Arkle, R. S. et al. Quantifying restoration effectiveness using multi-scale habitat models: Implications for sage-grouse in the Great Basin. Ecosphere 5, 1–32 (2014).Article 

Google Scholar 
Davies, K. W. & Bates, J. D. Restoring big sagebrush after controlling encroaching western juniper with fire: aspect and subspecies effects. Restor. Ecol. 25, 33–41 (2017).Article 

Google Scholar 
Davies, K. W., Bates, J. D. & Boyd, C. S. Postwildfire seeding to restore native vegetation and limit exotic annuals: an evaluation in juniper-dominated sagebrush steppe. Restor. Ecol. 27, 120–127 (2019).Article 

Google Scholar 
Davies, K. W., Boyd, C. S., Madsen, M. D., Kerby, J. & Hulet, A. Evaluating a seed technology for Sagebrush restoration across an elevation gradient: support for Bet Hedging. Rangel. Ecol. Manag. 71, 19–24 (2018).Article 

Google Scholar 
Rinella, M. J. et al. High precipitation and seeded species competition reduce seeded shrub establishment during dryland restoration. Ecol. Appl. 25, 1044–1053 (2015).Davies, K. W., Boyd, C. S. & Nafus, A. M. Restoring the sagebrush component in crested wheatgrass-dominated communities. Rangel. Ecol. Manag. 66, 472–478 (2013).Article 

Google Scholar 
United States General Accounting. WILDLAND FIRES: Better Information Needed on Effectiveness of Emergency Stabilization and Rehabilitation Treatments. Report to Congressional Requesters. https://doi.org/10.1089/blr.2006.9996. (2003)Requena-Mullor, J. M., Maguire, K. C., Shinneman, D. J. & Caughlin, T. T. Integrating anthropogenic factors into regional-scale species distribution models—A novel application in the imperiled sagebrush biome. Glob. Chang. Biol. 00, 1–15 (2019).
Google Scholar 
Pyke, D. A. et al. Restoration handbook for sagebrush steppe ecosystems with emphasis on greater sage-grouse habitat—Part 3. Site level restoration decisions. U.S. Geological Survey Circular 1426 (2017).Chambers, J. C. et al. Science framework for conservation and restoration of the sagebrush biome: Linking the department of the interior’s integrated rangeland fire management strategy to long-term strategic conservation actions. USDA . Serv. – Gen. Tech. Rep. RMRS-GTR 2017, 1–217 (2017).
Google Scholar 
US-BLM. Burned Area Emergency Stabilization and Rehabilitation – BLM Handbook H-1742-1. 2, (2007).Pilliod, D. S. & Welty, J. L. Land Treatment Digital Library. Data Series. https://doi.org/10.3133/ds806. (2013)Bradley, B. A. et al. Cheatgrass (Bromus tectorum) distribution in the intermountain Western United States and its relationship to fire frequency, seasonality, and ignitions. Biol. Invasions 20, 1493–1506 (2018).Article 

Google Scholar 
Fusco, E. J., Finn, J. T., Balch, J. K., Chelsea Nagy, R. & Bradley, B. A. Invasive grasses increase fire occurrence and frequency across US ecoregions. Proc. Natl Acad. Sci. USA 116, 23594–23599 (2019).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
O’Connor, R. C. et al. Small-scale water deficits after wildfires create long-lasting ecological impacts. Environ. Res. Lett. 15, 044001 (2020).Applestein, C., Caughlin, T. T. & Germino, M. J. Weather affects post‐fire recovery of sagebrush‐steppe communities and model transferability among sites. Ecosphere 12, (2021).Cameron, A. C. & Miller, D. L. A. Practitioner’ s Guide to Cluster-Robust Inference. J. Human Resources. 50, 317–372 (2015).Oshchepkov, A. & Shirokanova, A. Bridging the gap between multilevel modeling and economic methods. Soc. Sci. Res. in press, (2022).Aldridge, C. L. & Boyce, M. S. Linking occurrence and fitness to persistence: habitat-based approach for endangered Greater Sage-Grouse. Ecol. Appl. 17, 508–526 (2007).PubMed 
Article 

Google Scholar 
Allen-Diaz, B. & Bartolome, J. W. Sagebrush-grass vegetation dynamics: Comparing Classical and State-Transition models. Ecol. Appl. 8, 795–804 (1998).
Google Scholar 
Schlaepfer, D. R., Lauenroth, W. K. & Bradford, J. B. Natural regeneration processes in big sagebrush (Artemisia tridentata). Rangel. Ecol. Manag. 67, 344–357 (2014).Article 

Google Scholar 
Melgoza, G., Nowak, R. S. & Tausch, R. J. Soil water exploitation after fire: competition between Bromus tectorum (cheatgrass) and two native species. Oecologia 83, 7–13 (1990).PubMed 
Article 
ADS 

Google Scholar 
Williamson, M. A. et al. Fire, livestock grazing, topography, and precipitation affect occurrence and prevalence of cheatgrass (Bromus tectorum) in the central Great Basin, USA. Biol. Invasions 22, 663–680 (2020).Article 

Google Scholar 
Groves, A. M., Bauer, J. T. & Brudvig, L. A. Lasting signature of planting year weather on restored grasslands. Sci. Rep. 10, 1–10 (2020).Article 
CAS 

Google Scholar 
Groves, A. M. & Brudvig, L. A. Interannual variation in precipitation and other planting conditions impacts seedling establishment in sown plant communities. Restor. Ecol. 27, 128–137 (2019).Article 

Google Scholar 
Werner, C. M., Stuble, K. L., Groves, A. M. & Young, T. P. Year effects: Interannual variation as a driver of community assembly dynamics. Ecology 0, 1–8 (2020).
Google Scholar 
Stuble, K. L., Fick, S. E. & Young, T. P. Every restoration is unique: testing year effects and site effects as drivers of initial restoration trajectories. J. Appl. Ecol. 54, 1051–1057 (2017).Article 

Google Scholar 
Stuble, K. L., Zefferman, E. P., Wolf, K. M., Vaughn, K. J. & Young, T. P. Outside the envelope: rare events disrupt the relationshipbetween climate factors and species interactions. Ecology 98, 1623–1630 (2017).PubMed 
Article 

Google Scholar 
Hardegree, S. P. et al. Weather-Centric Rangeland Revegetation Planning. Rangel. Ecol. Manag. 71, 1–11 (2018).Article 

Google Scholar 
Allison, B., Cara, S-W. & Applestein, M. J., Germino Interannual variation in climate contributes to contingency in post‐fire restoration outcomes in seeded sagebrush steppe. Conservation Science and Practice https://doi.org/10.1111/csp2.12737.Callaway, B. & Sant’Anna, P. H. C. Difference-in-Differences with multiple time periods. J. Econom. 225, 200–230 (2021).MathSciNet 
MATH 
Article 

Google Scholar 
Goodman-Bacon, A. Difference-in-differences with variation in treatment timing. J. Econom. 225, 254–277 (2021).MathSciNet 
MATH 
Article 

Google Scholar 
Starrs, C. F., Butsic, V., Stephens, C. & Stewart, W. The impact of land ownership, firefighting, and reserve status on fire probability in California. Environ. Res. Lett. 13, (2018).Ferraro, P. J. & Miranda, J. J. Panel data designs and estimators as substitutes for randomized controlled trials in the evaluation of public programs. J. Assoc. Environ. Resour. Econ. 4, 281–317 (2017).
Google Scholar 
Schlaepfer, D. R., Lauenroth, W. K. & Bradford, J. B. Modeling regeneration responses of big sagebrush (Artemisia tridentata) to abiotic conditions. Ecol. Modell. 286, 66–77 (2014).Article 

Google Scholar 
Kleinhesselink, A. R. & Adler, P. B. The response of big sagebrush (Artemisia tridentata) to interannual climate variation changes across its range. Ecology 99, 1139–1149 (2018).PubMed 
Article 

Google Scholar 
Brabec, M. M., Germino, M. J. & Richardson, B. A. Climate adaption and post-fire restoration of a foundational perennial in cold desert: insights from intraspecific variation in response to weather. J. Appl. Ecol. 54, 293–302 (2017).Article 

Google Scholar 
Eidenshink, J. C. et al. A project for monitoring trends in burn severity. Fire Ecol. 3, 3–21 (2007).Article 

Google Scholar 
Pebesma, E. J. & Bivand, R. S. Classes and methods for spatial data in R. R News 5. http://cran.r-project.org/doc/Rnews/ (2005).Applestein, C. & Germino, M. J. Detecting shrub recovery in sagebrush steppe: comparing Landsat-derived maps with field data on historical wildfires. Fire Ecol. 17, (2021).Rigge, M. et al. Rangeland fractional components across the western United States from 1985 to 2018. Remote Sens. 13, 1–26 (2021).Article 

Google Scholar 
Hijmans, R. J. & van Etten, J. raster: Geographic analysis and modeling with raster data. (2012).U.S. Geological, S. 1/3rd arc-second Digital Elevation Models (DEMs)–USGS National Map 3DEP Downloadable Data Collection. (2017).Walkinshaw, Mike, A. T. O’Geen, D. E. B. Soil Properties. California Soil Resource Lab,McCune, B. & Keon, D. Equations for potential annual direct incident radiation and heat load. J. Veg. Sci. 13, 603–606 (2002).Article 

Google Scholar 
Abatzoglou, J. T. Development of gridded surface meteorological data for ecological applications and modelling. Int. J. Climatol. 33, 121–131 (2013).Article 

Google Scholar 
Ferraro, P. J. & Hanauer, M. M. Advances in measuring the environmental and social impacts of environmental programs. Annu. Rev. Environ. Resour. 39, 495–517 (2014).Article 

Google Scholar 
Butsic, V., Lewis, D. J., Radeloff, V. C., Baumann, M. & Kuemmerle, T. Quasi-experimental methods enable stronger inferences from observational data in ecology. Basic Appl. Ecol. 19, 1–10 (2017).Article 

Google Scholar 
Ho, D., Imai, K., King, G. & Stuart, E. MatchIt: nonparametric preprocessing for parametric causal inference. J. Stat. Softw. 42, 1–28, https://www.jstatsoft.org/v42/i08/ (2011).Article 

Google Scholar 
Guo, S. & Fraser, M. Propensity score analysis: statistical methods and applications. (Sage Publications, 2010).Puhani, P. A. The treatment effect, the cross difference, and the interaction term in nonlinear “difference-in-differences” models. Econ. Lett. 115, 85–87 (2012).MathSciNet 
MATH 
Article 

Google Scholar 
Schlaepfer, D. R., Lauenroth, W. K. & Bradford, J. B. Effects of ecohydrological variables on current and future ranges, local suitability patterns, and model accuracy in big sagebrush. Ecography (Cop.). 35, 374–384 (2012).Article 

Google Scholar 
Stan Development Team. RStan: the R interface to Stan. R package version 2.16.2. http://mc-stan.org (2020).Bürkner, P. C. brms: An R package for Bayesian multilevel models using Stan. J. Stat. Softw. 80, (2017).Mahr, T. & Gabry, J. bayesplot: Plotting for Bayesian Models. https://mc-stan.org/bayesplot/ R package version (2021).Kay, M. tidybayes: Tidy Data and Geoms for Bayesian Models. https://doi.org/10.5281/zenodo.1308151 R package version 3.0.1. (2021).Simler-Williamson, A. & Germino, M. J. Data associated with “Statistical consideration of nonrandom treatment applications reveal region-wide benefits of widespread post-fire restoration action”. https://doi.org/10.25338/B8W63R (2022).Simler‐Williamson, A. B. R code associated with “Statistical consideration of nonrandom treatment applications reveal region-wide benefits of widespread post-fire restoration action”. https://doi.org/10.5281/zenodo.6565074 (2022). More

Panti-May, J. A. et al. A two-year ecological study of Norway rats (Rattus norvegicus) in a Brazilian Urban Slum. PLoS ONE 11(3), 1–12. https://doi.org/10.1371/journal.pone.0152511 (2016).CAS 
Article 

Google Scholar 
Himsworth, C. G. et al. A mixed methods approach to exploring the relationship between Norway rat (Rattus norvegicus) abundance and features of the urban environment in an inner-city neighborhood of Vancouver, Canada. PLoS ONE 9(5), 97776. https://doi.org/10.1371/journal.pone.0097776 (2014).ADS 
CAS 
Article 

Google Scholar 
Lambert, M. S., Quy, R. J., Smith, R. H. & Cowan, D. P. The effect of habitat management on home-range size and survival of rural Norway rat populations. J. Appl. Ecol. 45(6), 1753–1761. https://doi.org/10.1111/j.1365-2664.2008.01543.x (2008).Article 

Google Scholar 
Meerburg, B. G., Singleton, G. R. & Kijlstra, A. Rodent-borne diseases and their risks for public health (Vol. 7828). https://doi.org/10.1080/10408410902989837 (2009)Buckle, A. & Smith, R. Rodent Pests and Their Control 2nd edn. (CABI Press, Wallingford, 2015).Book 

Google Scholar 
Byers, K. A., Lee, M. J., Patrick, D. M. & Himsworth, C. G. Rats about town: A systematic review of rat movement in urban ecosystems. Front. Ecol. Evol. 7, 1–12. https://doi.org/10.3389/fevo.2019.00013 (2019).Article 

Google Scholar 
Carvalho-Pereira, T. et al. The helminth community of a population of Rattus norvegicus from an urban Brazilian slum and the threat of zoonotic diseases. Parasitology 145(6), 797–806. https://doi.org/10.1017/S0031182017001755 (2018).Article 
PubMed 

Google Scholar 
Costa, F. et al. Patterns in Leptospira shedding in Norway rats (Rattus norvegicus) from Brazilian slum communities at high risk of disease transmission. PLoS Negl. Trop. Dis. 9(6), 1–14. https://doi.org/10.1371/journal.pntd.0003819 (2015).CAS 
Article 

Google Scholar 
Parsons, M. H. et al. Rats and the COVID-19 pandemic: Early data on the global emergence of rats in response to social distancing. MedRxiv https://doi.org/10.1101/2020.07.05.20146779 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Awoniyi, A. M. et al. Effect of chemical and sanitary intervention on rat sightings in urban communities of New Providence, the Bahamas. SN Appl. Sci. 3, 495. https://doi.org/10.1007/s42452-021-04459-x (2021).CAS 
Article 

Google Scholar 
Costa, F. et al. Influence of household rat infestation on leptospira transmission in the urban slum environment. PLoS Negl. Trop. Dis. 8(12), 3338. https://doi.org/10.1371/journal.pntd.0003338 (2014).Article 

Google Scholar 
Khalil, H. et al. Poverty, sanitation, and Leptospira transmission pathways in residents from four Brazilian slums. PLoS Negl. Trop. Dis. 15(3), 1–15. https://doi.org/10.1371/journal.pntd.0009256 (2021).Article 

Google Scholar 
Zeppelini, C. G. et al. Demographic drivers of Norway rat populations from urban slums in Brazil. Urban Ecosyst. https://doi.org/10.1007/s11252-020-01075-2 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
United Nations -UN. World Urbanization Prospects: The 2018 Revision. https://www.un.org/development/desa/en/news/population/2018-revision-of-world-urbanization-prospects.html. Accessed 24 Dec 2020 (2018)United Nations UN-SDG. Sustainable Development Goals: Make cities and human settlements inclusive, safe, resilient and sustainable. https://unstats.un.org/sdgs/report/2019/goal-11/#:~:text=The%20absolute%20number%20of%20people,Southern%20Asia%20(227%20million). Accessed 24 Dec 2020 (2018)Russell, J. C., Towns, D. R. & Clout, M. N. Review of rat invasion biology: Implications for island biosecurity. Sci. Conserv. 286, 1–53 (2008).
Google Scholar 
Minter, A. et al. Optimal control of rat-borne leptospirosis in an urban environment. Front. Ecol. Evol. 7, 1–10. https://doi.org/10.3389/fevo.2019.00209 (2019).ADS 
Article 

Google Scholar 
Mathur, R. P. Effectiveness of various rodent control measures in cereal crops and plantations in India. In: Leirs H. and Schockaert E. ed. Proceedings of the International Workshop on Rodent Biology and Integrated Pest Management in Africa, 21-25 October 1996, Morogoro, Tanzania. Belg. J. Zool. 127(supplement 1), 137–144 (1997).
Google Scholar 
Pascal, M., Siorat, F., Lorvelec, O., Yésou, P. & Simberloff, D. A pleasing consequence of Norway rat eradication : Two shrew species recover. Divers. Distrib. 11, 193–198. https://doi.org/10.1111/j.1366-9516.2005.00137.x (2005).Article 

Google Scholar 
Singleton, G. R., Hinds, L. & Leirs, H. Ecologically-based management of rodent pests. Australian Centre for International Agricultural Research, (ACIAR Monograph 59), 494. (1999)Sullivan, L. M. Roof rat control around homes and other structures. Cooper. Extens. Bull. AZ 1280, 1–6 (2002).
Google Scholar 
Childs, J. E. Size-dependent predation on rats (Rattus norvegicus) by house cats (Felis catus) in an urban setting. J. Mammol. 67(1), 196–199 (1986).Article 

Google Scholar 
Davis, D. E. The characteristics of rat populations. Quart. Rev. Biol. 28, 373–401. https://doi.org/10.1086/399860 (1953).CAS 
Article 
PubMed 

Google Scholar 
Glass, G. E. et al. Trophic garnishes: Cat-Rat interactions in an urban environment. PLoS ONE 4(6), e5794. https://doi.org/10.1371/journal.pone.0005794 (2009).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Lenton, G. M. Biological control of rats by owls in oil palm and other plantations. Biotrop Spec. Publ. 12, 87–94 (1980).
Google Scholar 
Smith, R. H. & Meyer, A. N. Rodent controlmethods: Non-chemical and non-lethal chemical, with special reference to food stores. In Rodent Pests and Their Control 2nd edn (eds Buckle, A. & Smith, R.) 81–101 (CABI International, 2015) (ISBN-13: 978-1-84593-817-8).
Google Scholar 
Oyedele, D. T., Sah, S. A. M., Kairuddin, L. & Ibrahim, W. M. M. W. Range measurement and a habitat suitability map for the Norway rat in a highly developed urban environment. Trop. Life Sci. Res. 26(2), 27–44 (2015).PubMed 
PubMed Central 

Google Scholar 
Hansen, N., Hughes, N. K., Bryom, A. E. & Banks, P. B. Population recovery of alien black rats Rattus rattus: A test of reinvasion theory. Austral Ecol. 45, 291–304. https://doi.org/10.1111/aec.12855 (2020).Article 

Google Scholar 
Awoniyi, A. M. et al. Using Rhodamine B to assess the movement of small mammals in an urban slum. Methods Ecol. Evol. 12(11), 2234–2242. https://doi.org/10.1111/2041-210X.13693 (2021).Article 

Google Scholar 
Glass, G. E., Klein, S. L., Norris, D. E. & Gardner, L. C. Multiple paternity in urban Norway rats: Extended ranging for mates. Vector-Borne Zoonotic Dis. 16(5), 342–248. https://doi.org/10.1089/vbz.2015.1816 (2016).Article 
PubMed 

Google Scholar 
Buckle, A. P. & Eason, C. T. Rodent control methods: Chemical. In Rodent Pests and Their Control 2nd edn (eds Buckle, A. & Smith, R.) 81–101 (CABI International, Wallingford, 2015) (ISBN-13: 978-1-84593-817-8).Chapter 

Google Scholar 
de Masi, E., Pedro, J. V. & Maria, T. P. Evaluation on the effectiveness of actions for controlling infestation by rodents in Campo Limpo region, São Paulo Municipality, Brazil Access details: Access Details: [subscription number 913003116]. Int. J. Environ. Health Res. 19(4), 291–304. https://doi.org/10.1080/09603120802592723 (2009).Article 
PubMed 

Google Scholar 
Lambropoulos, A. S. et al. Rodent control in urban areas—An interdisciplinary approach. J. Environ. Health 61, 12–17 (1999).
Google Scholar 
Reis, R. B. et al. Impact of environment and social gradient on Leptospira infection in urban slums. PLoS Negl. Trop. Dis. 2(4), 11–18. https://doi.org/10.1371/journal.pntd.0000228 (2008).MathSciNet 
Article 

Google Scholar 
Instituto Brasileiro de Geografia e Estatistica (IBGE). Accessed 15 November 2019 (2010)CDC. Integrated pest management: conducting urban rodent surveys. Centers for Disease Control and Prevention-Atlanta: US Department of Health and Human Services (2006)Hacker, K. P. et al. A comparative assessment of track plates to quantify fine scale variations in the relative abundance of Norway rats in urban slums. Urban Ecosyst. 19(2), 561–575. https://doi.org/10.1007/s11252-015-0519-8 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Eyre, M. T. et al. A multivariate geostatistical framework for combining multiple indices of abundance for disease vectors and reservoirs: A case study of rattiness in a low-income urban Brazilian community: A multivariate geostatistical framework for combining multiple ind. J. R. Soc. Interface 17(170), 1–21. https://doi.org/10.1098/rsif.2020.0398 (2020).Article 

Google Scholar 
Bursac, Z., Gauss, C. H., Williams, D. K. & Hosmer, D. W. Purposeful selection of variables in logistic regression. Source Code Biol. Med. 8, 1–8. https://doi.org/10.1186/1751-0473-3-17 (2008).Article 

Google Scholar 
Burnham, K. P. & Anderson, D. R. Model selection and multimodel inference: A practical information-theoretic approach (Springer, 2002).MATH 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org (2019).Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67(1), 1–48. https://doi.org/10.18637/jss.v067.i01 (2015).Article 

Google Scholar 
Barton, K. MuMIn: Multi-Model Inference. R package version 1.43.17. https://CRAN.R-project.org/package=MuMIn (2020)Kassambara A. ggpubr: ‘ggplot2’ Based Publication Ready Plots. R package version 0.4.0. https://CRAN.R-project.org/package=ggpubr (2020)Richardson, J. L. et al. Using fine-scale spatial genetics of Norway rats to improve control efforts and reduce leptospirosis risk in urban slum environments. Evol. Appl. 10(4), 323–337. https://doi.org/10.1111/eva.12449 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Santos, N. D. J., Sousa, E., Reis, M. G., Ko, A. I. & Costa, F. Rat infestation associated with environmental deficiencies in an urban slum community with high risk of leptospirosis. Cad. Saúde Pública 33(2), 1–13. https://doi.org/10.1590/0102-311X00132115 (2017).CAS 
Article 

Google Scholar 
Murray, M. H. & Sanchez, C. A. Urban rat exposure to anticoagulant rodenticides and zoonotic infection risk. Biol. Lett. 17, 20210311. https://doi.org/10.1098/rsbl.2021.0311 (2021).CAS 
Article 
PubMed 

Google Scholar 
Parsons, M. H., Banks, P. B., Deutsch, M. A., Corrigan, R. F. & Munshi-South, J. Trends in urban rat ecology: A framework to define the prevailing knowledge gaps and incentives for academia, pest management professionals (PMPs) and public health agencies to participate. J. Urban Ecol. 3(1), 1–8. https://doi.org/10.1093/jue/jux005 (2017).Article 

Google Scholar 
Costa, F. et al. Household rat infestation in urban slum populations: Development and validation of a predictive score for leptospirosis Household rat infestation in urban slum populations: Development and validation of a predictive score for leptospirosis. PLoS Negl. Trop. Dis. 15(3), 9154. https://doi.org/10.1371/journal.pntd.0009154 (2021).Article 

Google Scholar 
Mwanjabe, P. S. & Leirs, H. An early warning system for IPM-based rodent control in smallholder farming systems in Tanzania. In: Leirs, H., & Schockaert, E., ed., Proceedings of the International Workshop on Rodent Biology and Integrated Pest Management in Africa, 21-25 October 1996, Morogoro, Tanzania. Belg. J. Zool. 127(supplement 1), 4–58 (1997).
Google Scholar 
Richards, C. G. J. R. & Buckle, A. P. Towards integrated rodent pest management at the village level. In Control of Mammal Pests (eds Richards, C. G. J. R. & Ku, T. Y.) 293–312 (Taylor and Francis, 1987).
Google Scholar 
Masi, E. Socioeconomic and environmental risk factors for urban rodent infestation in Sao Paulo, Brazil. J. Pest Sci. 83(3), 231–241. https://doi.org/10.1007/s10340-010-0290-9 (2010).Article 

Google Scholar 
Brooks, J. E. Methods of sewer rat control. In Proceedings of the 1st Vertebrate Pest Conference. https://digitalcommons.unl.edu/vpcone/17. Accessed 20 August 2021 (1962) More