Ecology

Birkeland, C. Coral Reefs in the Anthropocene (Springer, 2015).Book 

Google Scholar 
Kleypas, J. A., Mcmanus, J. W. & Meñez, L. A. B. Environmental limits to coral reef development: Where do we draw the line?. Am. Zool. 39(1), 146–159. https://doi.org/10.1093/icb/39.1.146 (1999).Article 

Google Scholar 
Perry, C. T. & Larcombe, P. Marginal and non-reef-building coral environments. Coral Reefs 22, 427–432. https://doi.org/10.1007/s00338-003-0330-5 (2003).Article 

Google Scholar 
Wilkinson, C. R. Global and local threats to coral reef functioning and existence: review and predictions. Mar. Freshw. Res. 50, 867–878. https://doi.org/10.1071/mf99121 (1999).Article 

Google Scholar 
Mies, M. et al. South atlantic coral reefs are major global warming refugia and less susceptible to bleaching. Front. Mar. Sci. 7, 514. https://doi.org/10.3389/fmars.2020.00514 (2020).Article 

Google Scholar 
Leão, Z. M. A. N. et al. Brazilian coral reefsin a period of global change: A synthesis. Braz. J. Oceanogr. 64, 97–116. https://doi.org/10.1590/S1679-875920160916064sp2 (2016).Article 

Google Scholar 
Coker, D. J., Wilson, S. K. & Pratchett, M. S. Importance of live coral habitat for reef fishes. Rev. Fish Biol. Fish. 24, 89–126. https://doi.org/10.1007/s11160-013-9319-5 (2014).Article 

Google Scholar 
Alvarez-Filip, L., Gill, J. A. & Dulvy, N. K. Complex reef architecture supports more small-bodied fishes and longer food chains on Caribbean reefs. Ecosphere 2, 118. https://doi.org/10.1890/ES11-00185.1 (2011).Article 

Google Scholar 
Wilson, S. K., Graham, N. A. J., Pratchett, M. S., Jones, G. P. & Polunin, N. V. C. Multiple disturbances and the global degradation of coral reefs: Are reef fishes at risk or resilient?. Glob. Change Biol. 12, 2220–2234. https://doi.org/10.1111/j.1365-2486.2006.01252.x (2006).ADS 
Article 

Google Scholar 
Sully, S., Burkepile, D. E., Donovan, M. K., Hodgson, G. & van Woesik, R. A global analysis of coral bleaching over the past two decades. Nat. Commun. 10, 1264. https://doi.org/10.1038/s41467-019-09238-2 (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Bellwood, D. R., Hughes, T. P., Folke, C. & Nystrom, M. Confronting the coral reef crisis. Nature 429, 827–833. https://doi.org/10.1038/nature02691 (2004).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Hughes, T. P. et al. climate change, human impacts, and the resilience of coral reefs. Science 301, 929–933. https://doi.org/10.1126/science.1085046 (2003).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Holbrook, N. J. et al. Keeping pace with marine heatwaves. Nat. Rev. Earth Environ. 1, 482–493. https://doi.org/10.1038/s43017-020-0068-4 (2020).ADS 
Article 

Google Scholar 
Bleuel, J., Pennino, M. G. & Longo, G. O. Coral distribution and bleaching vulnerability areas in Southwestern Atlantic under ocean warming. Sci. Rep. 11, 12833. https://doi.org/10.1038/s41598-021-92202-2 (2021).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Fontoura, L. et al. The macroecology of reef fish agonistic behaviour. Ecography 43, 1278–1290. https://doi.org/10.1111/ecog.05079 (2020).Article 

Google Scholar 
Inagaki, K. Y., Pennino, M. G., Floeter, S. R., Hay, M. E. & Longo, G. O. Trophic interactions will expand geographically but be less intense as oceans warm. Glob. Change Biol. 26, 6805–6812. https://doi.org/10.1111/gcb.15346 (2020).ADS 
Article 

Google Scholar 
Longo, G. O., Hay, M. E., Ferreira, C. E. L. & Floeter, S. R. Trophic interactions across 61 degrees of latitude in the Western Atlantic. Glob. Ecol. Biogeogr. 28, 107–117. https://doi.org/10.1111/geb.12806 (2019).Article 

Google Scholar 
Pratchett, M. S. et al. Effects of climate-induced coral bleaching on coral-reef fishes: Ecological and economic consequences. Oceanogr. Mar. Biol. Annu. Rev. 46, 251–296. https://doi.org/10.1201/9781420065756.ch6 (2008).Article 

Google Scholar 
Graham, N. A. J. et al. Lag effects in the impacts of mass coral bleaching on coral reef fish, fisheries, and ecosystems. Conserv. Biol. 21, 1291–1300. https://doi.org/10.1111/j.1523-1739.2007.00754.x (2007).Article 
PubMed 

Google Scholar 
Strona, G. et al. Global tropical reef fish richness could decline by around half if corals are lost. Proc. R. Soc. B 288, 20210274. https://doi.org/10.1098/rspb.2021.0274 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
McClenachan, L. Extinction risk in reef fishes 199–207 (Cambridge University Press, 2015).
Google Scholar 
Power, M. E. et al. Challenges in the quest for keystones. Bioscience 46, 609–620. https://doi.org/10.2307/1312990 (1996).Article 

Google Scholar 
Pereira, P. H. C. et al. The influence of multiple factors upon reef fish abundance and species richness in a tropical coral complex. Ichthyol. Res. 61, 375–384. https://doi.org/10.1007/s10228-014-0409-8 (2014).Article 

Google Scholar 
Coni, E. O. C. et al. An evaluation of the use of branching fire-corals (Millepora spp.) as refuge by reef fish in the Abrolhos Bank, eastern Brazil. Environ. Biol. Fish. 96, 45–55. https://doi.org/10.1007/s10641-012-0021-6 (2013).Article 

Google Scholar 
Graham, N. A. J. et al. Extinction vulnerability of coral reef fishes. Ecol. Lett. 14, 341–348. https://doi.org/10.1111/j.1461-0248.2011.01592.x (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
Cornwell, W. K., Schwilk, D. W. & Ackerly, D. D. A trait-based test for habitat filtering: convex hull volume. Ecology 87, 1465–1471. https://doi.org/10.1890/0012-9658(2006)87[1465:ATTFHF]2.0.CO;2 (2006).Article 
PubMed 

Google Scholar 
Mouillot, D., Graham, N. A. J., Villéger, S., Mason, N. W. H. & Bellwood, D. R. A functional approach reveals community responses to disturbances. Trends Ecol. Evol. 28(3), 167–177. https://doi.org/10.1016/j.tree.2012.10.004 (2013).Article 
PubMed 

Google Scholar 
Pimiento, C. et al. Functional diversity of marine megafauna in the Anthropocene. Sci. Adv. 6, 7650. https://doi.org/10.1126/sciadv.aay7650 (2020).ADS 
Article 

Google Scholar 
Loiola, M. et al. Structure of marginal coral reef assemblages under different turbidity regime. Mar. Environ. Res. 147, 138–148. https://doi.org/10.1016/j.marenvres.2019.03.013 (2019).CAS 
Article 
PubMed 

Google Scholar 
Aued, A. W. et al. Large-scale patterns of benthic marine communities in the Brazilian Province. PLoS ONE 13, e0198452. https://doi.org/10.1371/journal.pone.0198452 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Leão, Z. M. A. N., Kikuchi, R. K. P. & Testa, V. Corals and Coral Reefs of Brazil 9–52 (Elsevier Publisher, 2003).
Google Scholar 
Pinheiro, H. T. et al. South-western Atlantic reef fishes: Zoogeographical patterns and ecological drivers reveal a secondary biodiversity centre in the Atlantic Ocean. Divers. Distrib. 24, 951–965. https://doi.org/10.1111/ddi.12729 (2018).Article 

Google Scholar 
Floeter, S. R. et al. Atlantic reef fish biogeography and evolution. J. Biogeogr. 35, 22–47. https://doi.org/10.1111/j.1365-2699.2007.01790.x (2008).Article 

Google Scholar 
Cord, I. et al. Brazilian marine biogeography: A multi-taxa approach for outlining sectorization. Mar. Biol. 169(5), 61. https://doi.org/10.1007/s00227-022-04045-8 (2022).Article 

Google Scholar 
Leal, I. C. S., Araújo, M. E. D., Cunha, S. R. D. & Pereira, P. H. C. The influence of fire-coral colony size and agonistic behaviour of territorial damselfish on associated coral reef fish communities. Mar. Environ. Res. 108, 45–54. https://doi.org/10.1016/j.marenvres.2015.04.009 (2015).CAS 
Article 
PubMed 

Google Scholar 
Kéry, M. & Royle, J. A. Applied hierarchical modeling in ecology: Analysis of distribution abundance and species richness in R and BUGS. In Prelude and Static Models Vol. 1 (eds Kéry, M. & Royle, J. A.) (Academic Press, 2016).MATH 

Google Scholar 
Hadj-Hammou, J., Mouillot, D. & Graham, N. A. J. Response and effect traits of coral reef fish. Front. Mar. Sci. https://doi.org/10.3389/fmars.2021.640619 (2021).Article 

Google Scholar 
McLean, M. et al. Trait similarity in reef fish faunas across the world’s oceans. PNAS 118(12), e2012318118. https://doi.org/10.1073/pnas.2012318118 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Brandl, S. J. et al. Coral reef ecosystem functioning: eight core processes and the role of biodiversity. Front. Ecol. Environ. 17, 445–454. https://doi.org/10.1002/fee.2088 (2019).Article 

Google Scholar 
Eggertsen, L. et al. Seaweed beds support more juvenile reef fish than seagrass beds in a south-western Atlantic tropical seascape. Estuar. Coast. Shelf S. 196, 97–108. https://doi.org/10.1016/j.ecss.2017.06.041 (2017).ADS 
Article 

Google Scholar 
Mouillot, D. et al. Functional over-redundancy and high functional vulnerability in global fish faunas on tropical reefs. PNAS 111, 13757–13762. https://doi.org/10.1073/pnas.1317625111 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Briggs, J. C. Marine Zoogeography (McGraw-Hill, 1974).
Google Scholar 
Garcia, G. S., Dias, M. S. & Longo, G. O. Trade-off between number and length of remote videos for rapid assessments of reef fish assemblages. J. Fish Biol. 99(3), 896–904. https://doi.org/10.1111/jfb.14776 (2021).Article 
PubMed 

Google Scholar 
Quimbayo, J. P. et al. Life-history traits, geographical range, and conservation aspects ofreef fishes from the Atlantic and Eastern Pacific. Ecology 102, e03298. https://doi.org/10.1002/ecy.3298 (2021).Article 
PubMed 

Google Scholar 
Katsanevakis, S. et al. Monitoring marine populations and communities: methods dealing with imperfect detectability. Aquat. Biol. 16, 31–52. https://doi.org/10.3354/ab00426 (2012).Article 

Google Scholar 
Villéger, S., Mason, N. W. H. & Mouillot, D. New multidimensional functional diversity indices for a multifaceted framework in functional ecology. Ecology 89, 2290–2301. https://doi.org/10.1890/07-1206.1 (2008).Article 
PubMed 

Google Scholar 
Maire, E., Grenouillet, G., Brosse, S. & Villéger, S. How many dimensions are needed to accurately assess functional diversity? A pragmatic approach for assessing the quality of functional spaces. Glob. Ecol. Biogeogr. 24, 728–740. https://doi.org/10.1111/geb.12299 (2015).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021)Kellner, K. jagsUI: A Wrapper Around ‘rjags’ to Streamline ‘JAGS’ Analyses. R package version 1.5.2. https://CRAN.R-project.org/package=jagsUI (2021)Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).Book 

Google Scholar 
Ferreira, C. E. L., Gonçalves, J. E. A. & Coutinho, R. Community structure of fishes and habitat complexity on a tropical rocky shore. Environ. Biol. Fish. 61, 353–369 (2001).Article 

Google Scholar 
Fulton, C. J. et al. Macroalgal meadow habitats support fish and fisheries in diverse tropical seascapes. Fish Fish. 21, 700–717. https://doi.org/10.1111/faf.12455 (2020).Article 

Google Scholar 
Ferreira, L. C. L. et al. Different responses of massive and branching corals to a major heatwave at the largest and richest reef complex in South Atlantic. Mar. Biol. 168, 54. https://doi.org/10.1007/s00227-021-03863-6 (2021).CAS 
Article 

Google Scholar 
Lonzetti, B. C., Vieira, E. A. & Longo, G. O. Ocean warming can help zoanthids outcompete branching hydrocorals. Coral Reefs 41, 175–189. https://doi.org/10.1007/s00338-021-02212-9 (2022).Article 

Google Scholar 
Grillo, A. C., Candido, C. F., Giglio, V. J. & Longo, G. O. Unusual high coral cover in a Southwestern Atlantic subtropical reef. Mar. Biodivers. 51, 77. https://doi.org/10.1007/s12526-021-01221-9 (2021).Article 

Google Scholar 
Matheus, Z. et al. Benthic reef assemblages of the Fernando de Noronha Archipelago, tropical South-west Atlantic: Effects of depth, wave exposure and cross-shelf positioning. PLoS ONE 14(1), e0210664. https://doi.org/10.1371/journal.pone.0210664 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Meirelles, P. M. et al. Baseline assessment of mesophotic reefs of the vitória-trindade seamount chain based on water quality, microbial diversity, benthic cover and fish biomass data. PLoS ONE 10(6), e0130084. https://doi.org/10.1371/journal.pone.0130084 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Ferreira, C. E. L., Floeter, S. R., Gasparini, J. L., Ferreira, B. P. & Joyeux, J. C. Trophic structure patterns of Brazilian reef fishes: A latitudinal comparison. J. Biogeogr. 31, 1093–1106. https://doi.org/10.1111/j.1365-2699.2004.01044.x (2004).Article 

Google Scholar 
Fontoura, L. et al. Climate-driven shift in coral morphological structure predicts decline of juvenile reef fishes. Glob. Change Biol. 26, 557–567. https://doi.org/10.1111/gcb.14911 (2020).ADS 
Article 

Google Scholar 
MacNeil, M. A. et al. Accounting for detectability in reef-fish biodiversity estimates. Mar. Ecol.-Prog. Ser. 367, 249–260. https://doi.org/10.3354/meps07580 (2008).ADS 
Article 

Google Scholar 
Capitani, L., de Araujo, J. N., Vieira, E. A., Angelini, R. & Longo, G. O. Ocean warming will reduce standing biomass in a Tropical Western Atlantic reef ecosystem. Ecosystems 25, 843–857. https://doi.org/10.1007/s10021-021-00691-z (2022).Article 

Google Scholar 
Fogliarini, C. O., Longo, G. O., Francini-Filho, R. B., McClenachan, L. & Bender, M. G. Sailing into the past: Nautical charts reveal changes over 160 years in the largest reef complex in the South Atlantic Ocean. PECON 20(3), 231–239. https://doi.org/10.1007/10.1016/j.pecon.2022.05.003 (2022).Article 

Google Scholar 
Gasparini, J. L., Floeter, S. R., Ferreira, C. E. L. & Sazima, I. Marine ornamental trade in Brazil. Biodivers. Conserv. 14, 2883–2899. https://doi.org/10.1007/s10531-004-0222-1 (2005).Article 

Google Scholar 
Francini-Filho, R. B. et al. Brazil 163–198 (Springer, 2019).
Google Scholar 
Bellwood, D. R., Goatley, C. H. R. & Bellwood, O. The evolution of fishes and corals on reefs: Form, function and interdependence. Biol. Rev. 92, 878–901. https://doi.org/10.1111/brv.12259 (2017).Article 
PubMed 

Google Scholar 
Nunes, L. T. et al. Ecology of Prognathodes obliquus, a butterflyfish endemic to mesophotic ecosystems of St. Peter and St. Paul’s Archipelago. Coral Reefs 38, 955–960. https://doi.org/10.1007/s00338-019-01822-8 (2019).ADS 
Article 

Google Scholar 
Liedke, A. et al. Abundance, diet, foraging and nutritional condition of the banded butterflyfish (Chaetodon striatus) along the western Atlantic. Mar. Biol. 163, 6. https://doi.org/10.1007/s00227-015-2788-4 (2016).CAS 
Article 

Google Scholar  More

Vega, F. E., Rosenquist, E. & Collins, W. Global project needed to tackle coffee crisis. Nature 425, 343 (2003).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Craparo, A. C. W., Van Asten, P. J. A., Läderach, P., Jassogne, L. T. P. & Grab, S. W. Coffea arabica yields decline in Tanzania due to climate change: global implications. Agric. For. Meteorol. 207, 1–10 (2015).ADS 
Article 

Google Scholar 
Davis, A. P. et al. High extinction risk for wild coffee species and implications for coffee sector sustainability. Sci. Adv. 5, eaav3473 (2019).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Davis, A. P., Gole, T. W., Baena, S. & Moat, J. The impact of climate change on indigenous arabica coffee (Coffea arabica): predicting future trends and identifying priorities. PLoS ONE 7, e47981 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Davis, A. P., Mieulet, D., Moat, J., Sarmu, D. & Haggar, J. Arabica-like flavour in a heat-tolerant wild coffee species. Nat. Plants 7, 413–418 (2021).CAS 
PubMed 
Article 

Google Scholar 
Moat, J., Gole, T. W. & Davis, A. P. Least concern to endangered: applying climate change projections profoundly influences the extinction risk assessment for wild Arabica coffee. Global Change Biol. 25, 390–403 (2019).ADS 
Article 

Google Scholar 
Moat, J. et al. Resilience potential of the Ethiopian coffee sector under climate change. Nat. Plants 3, 17081 (2017).PubMed 
Article 

Google Scholar 
Kath, J. et al. Not so robust: Robusta coffee production is highly sensitive to temperature. Global Change Biol. 26, 3677–3688 (2020).ADS 
Article 

Google Scholar 
Liu, L. et al. Soil moisture dominates dryness stress on ecosystem production globally. Nat. Commun. 11, 1–9 (2020).ADS 
CAS 

Google Scholar 
Grossiord, C. et al. Plant responses to rising vapor pressure deficit. New Phytol. 226, 1550–1566 (2020).PubMed 
Article 

Google Scholar 
IPCC Climate Change 2022: Impacts, Adaptation, and Vulnerability (eds. Pörtner, H.-O. et al.) (Cambridge Univ. Press, 2022).Burke, M. et al. Higher temperatures increase suicide rates in the United States and Mexico. Nat. Clim. Change 8, 723–729 (2018).ADS 
Article 

Google Scholar 
Burke, M., Hsiang, S. M. & Miguel, E. Global non-linear effect of temperature on economic production. Nature 527, 235–239 (2015).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Duffy, K. A. et al. How close are we to the temperature tipping point of the terrestrial biosphere? Sci. Adv. 7, eaay1052 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Scheffer, M. et al. Early-warning signals for critical transitions. Nature 461, 53–59 (2009).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Schneider, S. H. Abrupt non-linear climate change, irreversibility and surprise. Global Environ. Change 14, 245–258 (2004).Article 

Google Scholar 
Lenton, T. M. Early warning of climate tipping points. Nat. Clim. Change 1, 201–209 (2011).ADS 
Article 

Google Scholar 
Lenton, T. M. et al. Climate tipping points—too risky to bet against. Nature. 575, 592–595 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Lobell, D. B., Bänziger, M., Magorokosho, C. & Vivek, B. Nonlinear heat effects on African maize as evidenced by historical yield trials. Nat. Clim. Change 1, 42–45 (2011).ADS 
Article 

Google Scholar 
Lobell, D. B., Deines, J. M. & Tommaso, S. D. Changes in the drought sensitivity of US maize yields. Nat. Food 1, 729–735 (2020).Article 

Google Scholar 
Lobell, D. B. et al. Greater sensitivity to drought accompanies maize yield increase in the US Midwest. Science 344, 516–519 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Rigden, A., Mueller, N., Holbrook, N., Pillai, N. & Huybers, P. Combined influence of soil moisture and atmospheric evaporative demand is important for accurately predicting US maize yields. Nat. Food 1, 127–133 (2020).Article 

Google Scholar 
Schlenker, W. & Roberts, M. J. Nonlinear temperature effects indicate severe damages to US crop yields under climate change. Proc. Natl Acad. Sci. USA 106, 15594–15598 (2009).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
McDowell, N. G. et al. Mechanisms of woody-plant mortality under rising drought, CO2 and vapour pressure deficit. Nat. Rev. Earth Environ. 3, 294–308 (2022).ADS 
CAS 
Article 

Google Scholar 
Sinclair, T. R. et al. Limited-transpiration response to high vapor pressure deficit in crop species. Plant Sci. 260, 109–118 (2017).CAS 
PubMed 
Article 

Google Scholar 
López, J., Way, D. A. & Sadok, W. Systemic effects of rising atmospheric vapor pressure deficit on plant physiology and productivity. Global Change Biol. 27, 1704–1720 (2021).ADS 
Article 

Google Scholar 
McDowell, N. G. & Allen, C. D. Darcy’s law predicts widespread forest mortality under climate warming. Nat. Clim. Change 5, 669–672 (2015).ADS 
Article 

Google Scholar 
Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A. & Hegewisch, K. C. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci. Data 5, 170191 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
You, L., Wood, S., Wood-Sichra, U. & Wu, W. Generating global crop distribution maps: from census to grid. Agric. Syst. 127, 53–60 (2014).Article 

Google Scholar 
Fong, Y., Huang, Y., Gilbert, P. B. & Permar, S. R. chngpt: threshold regression model estimation and inference. BMC Bioinformatics 18, 1–7 (2017).Article 

Google Scholar 
Qin, Y. et al. Agricultural risks from changing snowmelt. Nat. Clim. Change 10, 459–465 (2020).ADS 
Article 

Google Scholar 
Forster, P. M., Maycock, A. C., McKenna, C. M. & Smith, C. J. Latest climate models confirm need for urgent mitigation. Nat. Clim. Change 10, 7–10 (2020).ADS 
Article 

Google Scholar 
Forster, P. M. et al. Projections of when temperature change will exceed 2 °C above pre-industrial levels. Nat. Clim. Change 10, 407–412 (2011).
Google Scholar 
Joshi, M., Hawkins, E., Sutton, R., Lowe, J. & Frame, D. Projections of when temperature change will exceed 2 °C above pre-industrial levels. Nat. Clim. Change 1, 407–412 (2011).ADS 
Article 

Google Scholar 
IPCC, 2021: Summary for Policymakers. In Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) (Cambridge Univ. Press, in press).Lobell, D. B. et al. The critical role of extreme heat for maize production in the United States. Nat. Clim. Change 3, 497–501 (2013).ADS 

Google Scholar 
Sinclair, T. R., Hammer, G. L. & Van Oosterom, E. J. Potential yield and water-use efficiency benefits in sorghum from limited maximum transpiration rate. Funct. Plant Biol. 32, 945–952 (2005).PubMed 
Article 

Google Scholar 
Martins, M. Q. et al. Protective response mechanisms to heat stress in interaction with high [CO2] conditions in Coffea spp. Front. Plant Sci. 7, 947 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Rodrigues, W. P. et al. Long‐term elevated air [CO2] strengthens photosynthetic functioning and mitigates the impact of supra‐optimal temperatures in tropical Coffea arabica and C. canephora species. Global Change Biol. 22, 415–431 (2016).ADS 
Article 

Google Scholar 
Ghini, R. et al. Coffee growth, pest and yield responses to free-air CO2 enrichment. Clim. Change 132, 307–320 (2015).ADS 
Article 

Google Scholar 
Rakocevic, M. et al. The vegetative growth assists to reproductive responses of Arabic coffee trees in a long-term FACE experiment. Plant Growth Regul. 91, 305–316 (2020).CAS 
Article 

Google Scholar 
Hammer, G. L. et al. Designing crops for adaptation to the drought and high‐temperature risks anticipated in future climates. Crop Sci. 60, 605–621 (2020).Article 

Google Scholar 
Gennari, P., Rosero-Moncayo, J. & Tubiello, F. N. The FAO contribution to monitoring SDGs for food and agriculture. Nat. Plants 5, 1196–1197 (2019).PubMed 
Article 

Google Scholar 
Lesk, C., Rowhani, P. & Ramankutty, N. Influence of extreme weather disasters on global crop production. Nature 529, 84–87 (2016).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Ortiz-Bobea, A., Ault, T. R., Carrillo, C. M., Chambers, R. G. & Lobell, D. B. Anthropogenic climate change has slowed global agricultural productivity growth. Nat. Clim. Change 11, 306–312 (2021).ADS 
Article 

Google Scholar 
Davis, A. P. et al. Hot coffee: the identity, climate profiles, agronomy, and beverage characteristics of Coffea racemosa and C. zanguebariae. Front. Sustain. Food Syst. 5, 740137 (2021).Article 

Google Scholar 
Sarmiento-Soler, A. et al. Disentangling effects of altitude and shade cover on coffee fruit dynamics and vegetative growth in smallholder coffee systems. Agric. Ecosyst. Environ. 326, 107786 (2022).CAS 
Article 

Google Scholar 
Wood, S. N. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. R. Stat. Soc. B 73, 3–36 (2011).MathSciNet 
MATH 
Article 

Google Scholar 
Barton, K. MuMIn: multi-model inference. R-Forge http://r-forge.r-project.org/projects/mumin/ (2009).R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing https://www.r-project.org/ (2021).Harrison, X. A. et al. A brief introduction to mixed effects modelling and multi-model inference in ecology. PeerJ 6, e4794 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Najafi, E., Devineni, N., Khanbilvardi, R. M. & Kogan, F. Understanding the changes in global crop yields through changes in climate and technology. Earths Future 6, 410–427 (2018).ADS 
CAS 
Article 

Google Scholar 
Ovalle-Rivera, O. et al. Assessing the accuracy and robustness of a process-based model for coffee agroforestry systems in Central America. Agrofor. Syst. 94, 2033–2051 (2020).Article 

Google Scholar 
Varma, S. & Simon, R. Bias in error estimation when using cross-validation for model selection. BMC Bioinformatics 7, 1–8 (2006).Article 

Google Scholar 
Yuan, W. et al. Increased atmospheric vapor pressure deficit reduces global vegetation growth. Sci. Adv. 5, eaax1396 (2019).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Son, H. & Fong, Y. Fast grid search and bootstrap-based inference for continuous two-phase polynomial regression models. Environmetrics 32, e2664 (2021).MathSciNet 
Article 

Google Scholar 
Wintgens, J. N. et al. Coffee: Growing, Processing, Sustainable Production. A Guidebook for Growers, Processors, Traders, and Researchers (Wiley, 2004). More

To compare soil viral community composition within and across terrestrial habitats on a regional scale, viromes were generated from 34 near-surface (top 15 cm) soil samples, with a total of 30 viromes included in downstream ecological analyses (see Supplementary Methods). The analyzed viromes were collected from four distinct habitats (wetlands, grasslands, chaparral shrublands, and woodlands, each with 7, 14, 4, and 5 viromes, respectively) across five field sites (Fig. S1 for sampling scheme, Table S1 for soil properties). Following quality filtering, the 30 viromes generated an average of 72,950,833 reads and 416 contigs ≥10 Kbp per virome (Table S2). Wetland viromes yielded more contigs ≥10 Kbp than viromes from other habitats, both in total and on average per virome (Table S2). We used VIBRANT to identify 3490 viral contigs in our assemblies, which were clustered into 3,432 viral operational taxonomic units (vOTUs), defined as ≥10 Kbp viral contigs sharing ≥ 95% average nucleotide identity over 85% contig length [17]. Constrained analysis of principal coordinates (CAP analysis) revealed strong clustering by habitat rather than by site, implying that, where environmental parameters are substantially different, environmental conditions are stronger drivers of viral community composition than geographic distance (Fig. S2).Multiple lines of evidence suggest that wetter soils harbored greater viral diversity than drier soils. We recovered the most vOTUs from wetlands, both in total (56% of all vOTUs were from wetlands) and per virome (on average, 307 vOTUs were recovered per wetland virome, compared to 116 from all habitats) (Fig. 1A). Unsurprisingly, wetlands had significantly greater moisture content than other habitats (Fig. 1B; ANOVA followed by Tukey multiple comparisons of means, p 100 Km distances here. Taken together, we propose that soil viral communities often display high heterogeneity within and among habitats, presumably due to a combination of host adaptations and microdiversity, dispersal limitation, and fluctuating environmental conditions over space and time. More

Sample collection and RNA preparationWe analyzed 6 adult yellow-bellied toad individuals representative of distinct behavioral profiles, i.e. prolonged unken-reflex display vs no unken-reflex display (thereafter referred as “ + ” and “-“, respectively). Behavioral profiles were scored as in Chiocchio et al.12: 3 toads showed prolonged unken-reflex (+), whereas the other 3 did not show unken-reflex (−), as reported in Table 1. Sampling procedures were approved by the Italian Ministry of Ecological Transition and the Italian National Institute for Environmental Protection and Research (ISPRA; permit number: 20824, 18-03-2020). After dissection, brain tissue was immediately stored in RNAprotect Tissue Reagent (Quiagen) until RNA extraction. RNA extractions were performed using the RNeasy Plus Kit (Quiagen), according to the manufacturer’ instructions. RNA quality and concentration were assessed by means of both a spectrophotometer and a Bioanalyzer (Agilent Cary60 UV-vis and Agilent 2100, respectively – Agilent Technologies, Santa Clara, USA).Table 1 Summary of the 6 libraries deposited in the Sequence Read Archive (SRA) of NCBI, in terms of number of raw and trimmed reads per sample.Full size tableLibrary preparation and sequencingLibrary preparation and RNA sequencing were performed by NOVOGENE (UK) COMPANY LIMITED using Illumina NovaSeq platform. Library construction was carried out using the NEBNext® Ultra ™ RNA Library Prep Kit for Illumina®, following manufacturer instructions. Briefly, after the quality control check, the mRNA sample was isolated from the total RNA by using magnetic beads made of oligos d(T)25 (i.e. polyA-tail mRNA enrichment). Subsequently, mRNA was randomly fragmented, and a cDNA synthesis step proceeded using random hexamers and the reverse transcriptase enzyme. Once the synthesis of the first chain has finished, the second chain was synthesized with the addition of the Illumina buffer, dNTPs, RNase H and polymerase I of E.coli, by means of the Nick translation method. Then, the resulting products went through purification, repair, A-tailing and adapter ligation. Fragments of the appropriate size were enriched by PCR, the indexed P5 and P7 primers were introduced, and the final products were purified. Finally, the Illumina Novaseq 6000 sequencing system was used to sequence the libraries, through a paired-end 150 bp (PE150) strategy. We obtained on average 52.7 million reads for each library. The sequencing data are available at the NCBI Sequence Read Archive (project ID PRJNA76401320).Pre-assembly processing stageA total of 316,329,573 pairs of reads was generated by Illumina sequencing. All of them went to a cleaning analytic step. The quality of the raw reads was assessed with the FastQC 0.11.5 tool (http://www.bioinformatics.bbsrc.ac.uk/projects/fastqc), in order to estimate the RNAseq quality profiles. The quality estimators were generated for both the raw and trimmed data. The quality assessment metrics for trimmed data were aggregated across all samples into a single report for a summary visualization with MultiQC software tool21 v.1.9 (see Fig. 1). To remove low quality bases and adapter sequences, raw reads were also analyzed through a quality trimming step with Trimmomatic22, v.0.39 (setting the option SLIDINGWINDOW: 4: 15, MINLEN: 36, and HEADCROP: 13). All the unpaired reads were discarded. After the cleaning step and removal of low-quality reads, 297,354,405 clean reads (i.e. 94% of raw reads) were maintained for building the de novo transcriptome assembly (see Table 1).Fig. 1The cleaned reads from all samples were assessed with FastQC and visualized with MultiQC. (a) Read count distribution for mean sequence quality. (b) Mean quality scores distribution. (c) Read length distribution. (d) Per Sequence GC Content.Full size image
De novo transcriptome assembly and quality assessmentAs there is no reference genome for B. pachypus, we performed a de novo transcriptome assembly procedure. The workflow of the bioinformatic pipelines is shown in Fig. 2. All the described bioinformatics analyses were performed on the high-performance computing systems provided by ELIXIR-IT HPC@CINECA23.Fig. 2Workflow of the bioinformatic pipeline, from raw input data to annotated contigs, for the de novo transcriptome assembly of B. pachypus.Full size imageTo construct an optimized de novo transcriptome, avoiding chimeric transcripts, we employed rnaSPAdes24, a tool for de novo transcriptome assembly from RNA-Seq data implemented in the SPAdes v.3.14.1 package. rnaSPAdes automatically detected two k-mer sizes, approximately one third and half of the maximal read length (the two detected k-mer sizes were 45 and 67 nucleotides, respectively). At this stage, a total of 1,118,671 assembled transcripts were generated by rnaSPAdes runs, with an average length of 689.41 bp and an N50 of 1474 bp (Table 2).Table 2 Similarity rate of newly assembled transcripts versus the de novo transcriptome of B. pachypus.Full size tableResults from the assembly procedures were validated through three independent validator algorithms implemented in BUSCO25 v.4.1.4, DETONATE26 v.1.11 and TransRate27 v.1.0.3. These tools generate several metrics used as a guide to evaluate error sources in the assembly process and provide evidence about the quality of the assembled transcriptome. Busco provides a quantitative measure of transcriptome quality and completeness, based on evolutionarily-informed expectations of gene content from the near-universal, ultra-conserved eukaryotic proteins (eukaryota_odb9) database. Detonate (DE novo TranscriptOme rNa-seq Assembly with or without the Truth Evaluation) is a reference-free evaluation method based on a novel probabilistic model that depends only on the assembly and the RNA-Seq reads used to construct it. Transrate generates standard metrics and remapping statistics. No reference protein sequences were used for the assessment with Transrate. The main metrics resulted from the assembly validators are shown in Table 2 (“Before CD-HIT-est” column). After this triple assessment validation step, the result of the assembly procedure become the input for the CD-HIT-est v.4.8.128 program, a hierarchical clustering tool used to avoid redundant transcripts and fragmented assemblies common in the process of de novo assembly, providing unique genes. CD-HIT-est was run using the default parameters, corresponding to a similarity of 95%. Subsequently, a second validation step was launched on the CD-HIT-est output file. To refine the final transcriptome dataset, a further hierarchical clustering step was performed by running CORSET v1.0629. Then, the output of CORSET was validated by BUSCO, and quality assessment was performed with HISAT230,31 by mapping the trimmed reads to the reference transcriptome (unigenes). Results from all validation steps are shown in Table 2 and discussed in the “Technical Validation” paragraph.Finally, the CORSET output was run on TransDecoder32,33, the current standard tool that identifies long open read frames (ORFs) in assembled transcripts, using default parameters. TransDecoder by default performs ORF prediction on both strands of assembled transcripts regardless of the sequenced library. It also ranks ORFs based on their completeness, and determines if the 5 ‘end is incomplete by looking for any length of AA codons upstream of a start codon (M) without a stop codon. We adopted the “Longest ORF” rule and selected the highest 5 AUG (relative to the inframe stop codon) as the translation start site.Transcriptome annotationWe employed different kinds of annotations for the de novo assembly. We introduced DIAMOND34, an open-source algorithm based on double indexing that is 20,000 times faster than BLASTX on short reads and has a similar degree of sensitivity. Like BLASTX, DIAMOND attempts to determine exhaustively all significant alignments for a given query. Most sequence comparison programs, including BLASTX, follow the seed-and-extend paradigm. In this two-phase approach, users search first for matches of seeds (short stretches of the query sequence) in the reference database, and this is followed by an ‘extend’ phase that aims to compute a full alignment. The following parameter settings were applied: DIAMOND-fast DIAMOND BLASTX-t 48 -k 250 -min-score 40; DIAMOND-sensitive: DIAMOND BLASTX -t 48 -k 250 -sensitive -min-score 40.Contigs were aligned with DIAMOND on Nr, SwissProt and TrEMBL to retrieve the corresponding best annotations. An annotation matrix was then generated by selecting the best hit for each database. Following the analysis of BLASTX against Nr, SwissProt and TremBL, we obtained respectively: 123,086 (64.57%), 77,736 (40.78%), 122,907 (64.48%) contigs. The results obtained following the analysis with BLASTP against Nr, SwissProt and TrEMBL were 96,321 (50.53%), 57,877 (30.36%) and 97,256 (51.02%) contigs respectively. All the information on the resulting datasets is resumed in Table 3.Table 3 Summary of homology annotation hits on the different databases queried in this study.Full size tableThe output obtained by the BLASTX annotation consisted in a total of 77391 sequences simultaneously mapped on the three queried databases (i.e., Nr, SwissProt and TrEMBL). The output obtained following the BLASTP annotation consisted in a total of 57704 sequences simultaneously mapped on the three databases. Venn diagrams are presented in Fig. 3, showing the redundancy of the annotations in the different databases for both DIAMOND BLASTX (Fig. 3a) and DIAMOND BLASTP (Fig. 3b). Furthermore, the ten most represented species and the ten hits of the gene product obtained respectively with BLASTX and BLASTP by mapping the transcripts against the reference database Nr are shown in Figs. 4 and 5. Since BLASTX translated nucleotide sequence searches against protein sequences the BLASTX results are more exhaustive than BLASTP results. Contigs were also processed with InterProScan35 to detect InterProScan signatures. The InterPro database (http://www.ebi.ac.uk/interpro/) integrates together predictive models or ‘signatures’ representing protein domains, families and functional sites from multiple, diverse source databases: Gene3D, PANTHER, Pfam, PIRSF, PRINTS, ProDom, PROSITE, SMART, SUPERFAMILY and TIGRFAMs. The obtained InterProScan results for all the unigenes are available on Figshare in the form of Tab Separated Values (tsv) file format, which includes the GO and KEGG annotated contigs, respectively.Fig. 3Venn diagrams for the number of contigs annotated with DIAMOND (BLASTX (a) and BLASTP (b) functions) against the three databases: Nr, SwissProt, TREMBL.Full size imageFig. 4Most represented species and gene product hits. Top 10 best species (a) and protein (b) hits present in the reference database (Nr, BLASTX).Full size imageFig. 5Most represented species and gene product hits. Top 10 best species (a) and protein (b) hits present in the reference database (Nr, BLASTP).Full size imageComparison with Bombina orientalis brain transcriptomeWe compared the brain de novo transcriptome of B. pachypus with the brain de novo transcriptome of B. orientalis, recently produced in the frame of a prey-catching conditioning experiment17,18. The B. orientalis transcriptome resource was downloaded from GEO archive of NCBI (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE171766). To make the datasets comparable, we first performed ORF prediction on B. orientalis trascriptome using Transdecoder, using default settings. Results from the B. orientalis trascriptome ORF prediction are available in Figshare at the following link https://doi.org/10.6084/m9.figshare.20319633/). We also applied the makedb function implemented in DIAMOND to create the protein database index. Then, we aligned the B. pachypus predicted coding sequences and proteins (query files) against the B. orientalis protein database (reference) using DIAMOND BLASTX and BLASTP, respectively. We obtained 167041 matches from BLASTX and 156248 matches for BLASTP. Results from the BLASTX and BLASTP comparisons, and the most matched proteins, are available on Figshare36 (link available in next paragraph). More

Global climate change combined with regional and local anthropic activities suggest an increase in recurrence and extent of wildfires on ecosystems worldwide31,47,48, affecting in particular regions with higher likelihood of fire occurrences31 and making natural systems more prone to fire occurrences21. Estimates of accumulated burned area in Brazil between 1985–2020 revealed that, among the Brazilian biomes, the Pantanal is the most affected by the fires (with accumulated burned area equivalent to 57.5% of the biome within Brazil)46. But 43% of 2020 burned area (≈13% of the Pantanal) had not burned since 200319. Therefore, it is impressive that nearly 1/3 of the Pantanal burned in a single year17,18,19 (Figs. 1, 2 and S1, S2). The high number of human-induced fires17,18,19,21 combined with the hottest and driest conditions since 198017,22,38,49 led 2020 to record the highest daily severity rating (DSR) index of fires for this time period17,49. With documented increase of 2 °C in the average temperature22 and a 40% shortage in rainfall26,38. But the fire risk got even higher with the simultaneous occurrence of dry and hot spells, between August and November, when the maximum temperature reached, on average, 6 °C above the normal, accounting for 55% of the burned area of 202049.Most fires started close to the agriculture frontiers21, but they predominantly affected the natural vegetation (reaching between 91–95% of it in occurrence of fire50,51 and 96% of it in estimated burned area)31,46, with tragic consequences for jaguars and the Pantanal biota17,19,26. Along with the fires, the severity of the 2020 drought22,52,53 dropped minimum river depths at around 86% below normal25,54 (Fig. 2 and S1, S3, S4). Consequently, resulting in several records of animal starvation, dehydration, and death17,19,26. And late mortality from indirect causes of fires certainly increased these numbers26. Besides, post-fire ecosystem and hydrology changes also had ecological effects with long-term impacts on ecosystem recovery and fire risk31, impacting resource quality, availability, and productivity26,31. Vegetation productivity declined below −1.5 σ over more than 30% of the natural areas and evaporation decreased (by ~ 9%)31. Burned vegetation made the soil more vulnerable to erosion, increasing the runoff (by ~ 5%) over the natural areas31, and the resulting charcoal and ash contaminated rivers17.Many reasons may have contributed to the intensity of the 2020 drought in the Pantanal, from climate8,22,24,49 to direct and indirect human impacts in the Upper Paraguay River Basin (UPRB)21,55,56. In fact, anthropic changes in land use also increased the biome sensitivity to fire-climate extremes)31. The shortage of rain throughout the UPRB, particularly in the summer season, is among the main factors, as the basin water balance controls the hydroclimatological dynamics in the Pantanal (Fig. 2 and S3–S9)22. The shortage of rain may also be a consequence of increased deforestation in the Amazon rainforest57,58, as summer rainfall in the Pantanal is strongly associated with the climate of the Amazon59. Furthermore, the reduction in wetland flooded areas is historically correlated with the spread of fires (Fig. 2 and S1)22,28,29. Low water levels led to the absence of flooding and reduced wetland areas, and the remaining dry vegetation provided flammable material and created favourable conditions for fires to occur22,23,24. In addition, the lack of governmental and human resources and delayed response at federal and local levels58,60,61 amplified the negative effects of water shortage17,19,58.Although historical hydrological series show that extreme drought events occurred in the past22,25,38,62 (e.g., from the late 1960s to early 1970s, Fig. S3), they also show that the recovery of the Pantanal was conditioned to the subsequent 15 years of regular to exceptional floods (1974 to early 1990s, Figs. S1, S3). Savanna-like vegetation, the predominant vegetation type in the Pantanal, usually recovers from the effects of fires in relatively short periods (months to a few years)23, depending on the severity and frequency of fires and climate conditions in the subsequent years23,28,29. But the resilience of many species may decrease with the annual repetition of extreme fire events28,29,30. Thus, human interventions to prevent (instead to promote) sequential fire events in the same area are paramount19,23,62,63.Estimating the effects that uncontrolled extensive fires can cause to the apex predator of the Neotropics in a region considered one of the strongholds for the species can contribute to the conservation of jaguar and other wildlife species, as well as to the debate regarding potential cumulative impact of recurrent wildfires on ecosystems26,31,51,62,63. Our results revealed the drastic impact of fire on estimated numbers of jaguars, home ranges, and priority areas for jaguar conservation in the Pantanal was exceptionally high in 2020 and proportionally more severe than the nominal 31% of burned area across the Pantanal (e.g., fires affected 45% of the jaguars and 79% of their HRs). Moreover, the annual comparison showed that 2019 was the second-worst year regarding fire impacts (only behind 2020) and equally extreme compared to historical means22. Although the Pantanal is well known for its annual and pluri-annual cycles of wet and dry seasons7,64, the unusual levels of droughts22,25,65,66 and fires17,20,21 in subsequent years are alarming. Furthermore, climate assessment and projections of warmer and dryer conditions for the region in the coming years are equally worrying22,24,37,38.We found that 45% of the jaguar population estimated for the Pantanal occupied areas affected by the 2020 fires (Fig. 1). This finding suggests that the fires heavily impacted the jaguars in the Pantanal, even if we assume that the major effects were only temporary displacement. This potential displacement may make it more difficult for jaguars to find new suitable areas, thus increasing territorial disputes and decreasing survival and reproductive success. Furthermore, 2019 ranked as the second-highest year of impact of fire on jaguar population estimates among the 16 years considered (Table 1, Fig. 1). Importantly, we did not consider cumulative impacts on sequential years or fire recurrence in these estimates. Moreover, the available estimates for jaguar abundance we used36 are very conservative and probably underestimated jaguar populations from the Pantanal by a maximum of 3 jaguars/100 km2. However, the reported density of jaguars may reach up to 12.4 jaguars/100 km2 in northern PAs5,67,68 and up to 6.5–7 jaguars/100 km2 in the southern Pantanal farms5,69,70. Considering that PAs in the northern Pantanal were severely damaged by the 2020 fires, our results show conservative figures for the actual number of jaguars affected by fires.We used densities estimated from an ecosystem-wide assessment of impacts as a proxy of the proportion of total population reached by fire each year on a regional scale. Fires affected a substantial proportion of estimated individuals in the Pantanal in 2019–2020. In 2020, for instance, 87% of all jaguars affected by fire were in the Brazilian Pantanal. In contrast, the smaller population in the Paraguayan and Bolivian Pantanal had a higher median percentage of individuals affected by fire between 2005–2019. While 45% of jaguars were affected by fire in a single year (2020) in the Pantanal, a study45 using the same conservative estimates36 for jaguar abundance in the Brazilian Amazon revealed that 1.8% of the population (1422 individuals) was killed or displaced by fire between 2016–2019. Another report estimated that more than 500 individuals were affected by the 2019 fires in the Brazilian and Bolivian Amazon71,72. Based on the same density estimates we found that in the Pantanal — a much smaller biome — more jaguars were affected by fire in single years (n = 513 in 2019 and n = 746 in 2020). This recent increase in the number of jaguars affected by fire raises a red flag to the supposed stability of the species in the Pantanal, which is currently globally and locally classified as Near Threatened1,5. Therefore, we recommend that future assessments by IUCN specialists carefully consider the frequency and intensity of fires as a potentially significant and growing threat to jaguars in the Pantanal, and their effects on long-term populational trends.Quantifying the occurrence of fire on HRs introduced a functional perspective to understanding the impact of fire on individual jaguars. Similarly, our estimates of the number of affected jaguars revealed a vast amount and extent of affected HRs in the last two years (Figs. 2 and 3). Jaguars are apex predators, often considered as a keystone73,74,75,76 and umbrella species45,77, highly dependent on large habitat areas78, dense native vegetation cover35,79,80, and abundance of prey67,81. Considering that jaguars often select areas with high environmental integrity35,68,78,79,80, the higher impact of recent fires on HRs corroborates reports showing the increase of natural areas burned in the Pantanal31,46,50,51. The proportion of burned forests, for instance, was 10 times higher in 2020 than the estimated median between 1985 and 201931. Sadly, it is likely that much of these burned forests in Northern Pantanal included areas pointed as suitable jaguar habitat and of great interest to the creation of additional PAs82.In the Pantanal, HRs are smaller35,83 and population densities are high5,67,68,69,70 because the biome is a highly productive system7,55,67, with an abundance of prey species and quality habitat, thus allowing jaguars to meet their spatial needs using smaller areas35,68,83. Consequently, floodplain jaguars are also usually larger44,84. However, a trend of increasing drought, rising temperatures, and repeated occurrences of exceptional fires would weaken the Pantanal’s resilience22,32. Importantly to note as well that the occurrence and intensity of fires are frequently higher in the dry season, peaking within jaguars HRs in the years with intense fire occurrence in the Pantanal. This apparent higher impact over jaguar habitat agrees with studies pointing out highest damage in PAs17,27 (Fig. S20), natural vegetation and particularly in forested areas in 202031,46,50,51. Recurrent impacts may particularly affect the most sensitive species28,29,30, resulting in a less productive environment32, which ultimately decreases the habitat quality of many species. These effects would likely push jaguars to expand their HRs, which would increase disputes for territories and favour a decrease in body size, consequently decreasing reproductive rates and population size.The extent of protected areas burned is another indicator of how fire can impact biodiversity. Like the HRs, the Pantanal PAs were affected differently in space and time, but the greatest fires occurred in recent years (2019 and 2020). In 2020, fires occurred in 62% of Brazilian PAs — particularly in northern Pantanal — where several portions of PAs overlapping with jaguar HRs were entirely or almost entirely affected by fires (Figs. 1–3). In 2019, however, fires affected the Pantanal PAs in Bolivia, Paraguay and southern Brazil more severely in areas that also overlapped with HRs (Figs. 1–3). Several causes can explain the spread of fires across PAs, including a combination of heat, drought, miscalculated human use of fires, lack of resources and personnel for surveillance and fire control improvement17,18,19,20,21,22,23.The displacement, injuries, and deaths caused by fire to animals within PAs are worrying because these areas are reportedly richer in diversity and biomass85,86 (including higher jaguars densities36,67,87 and are fundamental to safeguarding biodiversity and ensuring the long-term provision of ecosystem services88,89. Protected areas are important to jaguars because they provide larger continuous areas of natural dense vegetation cover (such as forests and shrublands), flooded habitats and limit contact with humans, attributes of great influence in jaguar habitat selection35,78,79,80,82, and particularly important to females90,91. However, although some PAs support up to 12.4 jaguars/100 km2 (e.g., Taiamã Ecological Station – TES)67, the currently availability of Pantanal PAs alone would not support viable jaguar populations for more than 50 years87. Therefore, sustainable management that allows coexistence in private lands is also fundamental for the conservation of jaguars in the Pantanal5,9,10,11. Protected areas of integral protection, such as TES, currently occupy only 5.7% of the Pantanal7 but were the most affected by fires in absolute area (Fig. S20, Table S5)27. The total number of PAs, including the sustainable use ones, corresponds to only 5% of the Brazilian Pantanal (Tables S1–S3)7,92,93,94,95,96 and around 10% of the entire Pantanal7, most of it in Bolivia97. These percentages are much lower than the minimum of 17% recommended in the Aichi goals for terrestrial ecosystems7,56. Furthermore, PAs are also scarce in the Pantanal headwaters (6% of the surrounding Cerrado uplands) (Tables S1–S3, Fig. S19)7,92,93,94,95,96. To make matters worse, PAs were reduced by almost 20% in the Brazilian Pantanal in 2007 and have not been expanded in the Cerrado uplands since 2006 (Tables S1–S3, Fig. S19)93. The relatively small coverage of protected areas in the Pantanal, which serve as refuges, increases the negative effects of fires, as jaguars are likely displaced into sub-optimal habitats. Consequently, jaguars and other species may struggle to find equally resource-rich sites after being displaced from PAs.For the long-term survival of the jaguar, it is essential to implement conservation plans that consider the dispersal and reproduction of the species along the Paraguay River98, increase the network and size of PAs82, and adequately allocate funding and personnel to maintain the PAs. Furthermore, careful implementation of strategies to mitigate the risk of fire18,19,62 and other human impacts outside PAs5,6,7,8,9,10,11,12,13,14,15,16,89,99 are urgent needs for conservation of the Pantanal. In any case, our results highlight that to sustain viable populations of jaguars and other species, conservation plans for the Pantanal must account for fire impact on PAs and other vital areas for biodiversity.Although jaguar HRs often overlap with PAs67,68,87, some individuals may settle in unprotected areas69,70. In our sample, we found that 38 HRs partially overlapped with PAs (Fig. 1) and 10 HRs did not. On the other hand, considering the sum of the HR extents and the total area overlapped with the PAs, we found that 20% of the HR extent matched the PAs. Notably, jaguars coexist with different levels of anthropic pressures outside the PAs4,5,9,10,11,12,13,14,15,16. Jaguar distribution range has been restricted to 63% of the Pantanal5 and even more restricted in the UPRB100. Agriculture expansion, particularly cattle ranching and soybean cultivation (Figs. S17, S18)65, has been identified as the main causes of jaguars’ disappearance or decline due to killing and habitat loss5,9,13.Sustainable use has been advocated as a conservation strategy in the Pantanal, mainly due to the characteristics of the region, where cattle ranching uses as pastures the natural areas restricted by the Pantanal flooding regime since the 17th century7,23. In recent years, ecotourism has also gained great importance55,101,102. However, there are risks in relying on sustainable use as a core strategy for 90% of the biome (95% of Brazilian Pantanal), and exposure to human-induced fires is one of them21,31.Fire is a fundamental factor acting on the dynamics of the Pantanal vegetation23,28,29. However, repeated uncontrolled fires can drastically impact forests and other habitats critical to the jaguars and increase the area for cattle ranching, therefore increasing the risk of livestock depredation and retaliatory hunting11. Thus, the conservation of the jaguar and other animal species in the Pantanal is critically linked to fire management and the use of private lands because the increased fire may extend and aggravate other anthropic impacts (Fig. 4). This work highlights the significant increase in the extent and severity of recent fires in the Pantanal and how these fires have affected jaguars. Further studies that estimate natural habitat recovery and fire recurrence and assess real-time and long-term effects of fire on jaguars and other species are critical to guide fire management and conservation.Fig. 4: Scheme summarizing the main impacts of fires in the Pantanal.The red arrows are intentionally larger and show a feedback loop linking increased negative human impacts, climate change, and drought to increased fires and burned areas, with a consequent negative impact on biodiversity. The blue arrows describe a feedback loop for fire control and impact mitigation. The dashed arrows denote other relevant effects in the biome (e.g., cumulative effects from infrastructure such as hydroelectric power plants, river waterways, water and soil pollution from legal and illegal mining and agriculture, poaching and illegal wildlife trade, opportunistic exploitation of burned areas, as well as natural climate constraints.Full size imageChanges in the climate8,22,24,37,38, landscape and water use in the UPRB over the last four decades7,18,56,65 are cumulative threats that may interfere with water recharge and vegetation resilience in the Pantanal. Global temperatures may increase up to 1.5 °C over the next five years37, in addition to the 2 °C already recorded since 1980. By the end of the 21st century, scientists estimate increases of 5 − 7 °C in the temperature and the frequency of climatic extremes and a 30% reduction in average rainfall8,37,38. Until 2019, pastures covered 15.5% of the Brazilian Pantanal and agriculture about 0.14%25. However, agriculture and pastures occupied 60–65% of the surrounding Cerrado uplands within the UPRB7,55,56, an occupation similar to the adjacent Paraguayan Chaco and Bolivian Chiquitano Forest7,103,104. And future projections estimate a loss of 14,005 km2 of native vegetation from 2018 through 2050105. Consequently, this land occupation impacted the main headwaters of the Pantanal rivers and ultimately the entire Pantanal6,56,106,107. Furthermore, by 2019, 47 hydroelectric power plants were installed or in operation, and another 133 were planned, totalling about 180 potential dam projects in the Brazilian UPRB108. Besides, most of these projected hydropower infrastructures will overlap with the distribution of jaguars, also in the adjacent biomes, impacting negatively the species particularly in Brazil15. These economic and infrastructure activities in the surrounding highlands frequently ignore their cumulative impacts109 and affect the Pantanal in different ways (Fig. 4, S17, S18), including its drainage dynamics and flood pulses, with consequent impacts on drought duration and fire spread17,19,22,23,24(Figs. 1–4, SI). This combination of factors probably intensifies the Pantanal droughts, particularly the periodic sequence of dry years.Therefore, a critical point is how human actions can exacerbate such extreme events7,21,31,55,106,110 and make fire control even more difficult19,23,62 or, on the opposite, contribute to minimize the overall impacts of drought and fires and promote biodiversity conservation19,63 (Fig. 4). Given that the rainfall remained below average in the last wet seasons53 (Figs. S1, S3–S8) and that a severe drought persisted in 2021111, a surveillance protocol for rapid response and programs for fire management, mitigation of human impacts and ecosystem recovery are needed19,23,62,63. If such measures keep lacking, a tragedy similar to the 2020 fires may be repeated in the coming years (Fig. 4). And Pantanal native vegetation may be reduced to only about 62% by 203021. To make matters worse, the government budget allocated for fire control and firefighting for 2021 was reduced to 65.5% of the 2019 budget61 and all funds allocated to the environment were reduced to the lowest level in 20 years61,112, with serious complaints of misuse113, embezzlement114 and wood-smuggling probe115.The extent of the recent wildfire in the Pantanal has signalled that fire is a potential threat to the long-term conservation of the jaguar. Furthermore, fires severely affected other species and human activities17,19,23, demanding an immediate mitigation plan18,19,62. In fact, permanent fire brigades have been established, and an animal rescue centre is under construction in response to the effects of the recent extensive fires in the Pantanal. Although actions are underway at local levels, the warming and drying trend22,24,37,38 is also a combination of global warming8,37 and rapid land-use changes7,18,65 (Figs. S17, S18), with cumulative impacts in the UPRB and Pantanal wetlands (Fig. 4). Therefore, the immediate reduction of deforestation in the Amazon and Pantanal and the establishment of a forest restoration plan in the UPRB are critical. The lack of sufficient mitigatory actions may throw the Pantanal into a perverse vortex (increasing feedback of cumulative negative impacts, (Fig. 4), thus affecting the survival of jaguars and the various species under their umbrella, as well as human welfare. More

Animals, housing, and treatmentsAll procedures involving animals were approved by the University of Florida Institutional Animal Care and Use Committee (Protocol #201709925). All methods were performance in accordance with the relevant guidelines and regulations, and permission and informed consent was obtained from the University of Florida (owners) for the use of the steers in this experiment.The experiment was carried out during July and August of 2017 at the Feed Efficiency Facility of University of Florida, North Florida Research and Education Center, located in Marianna, Florida (30°52′N, 85°11″W, 35 m asl). Both ‘Argentine’ bahiagrass and ‘Florigraze’ rhizoma peanut hays were obtained from commercial producers. The hay bales were stored in enclosed barns throughout the duration of the experiment.Twenty-five Brahman × Angus crossbred steers (Bos sp.) were utilized (average BW = 341 ± 17 kg, approx. 16 months of age). The steers were grazing bermudagrass (Cynodon dactylon) pastures, a C4 grass, prior to the start of the study. The day prior to the start of the experiment (e.g. day-1), steers brought to working facilities, where they remained 16 h off feed and water, in order to obtain shrunk bodyweights. On day 0 of the experiment, steers were weighed, blocked by bodyweight, and allocated to five treatments (5 steers per treatment) and housed in grouped pens. Hay intake was recorded utilizing GrowSafe© systems (GrowSafe Systems Ltd., Calgary, AB, Canada), which utilize radio frequency identification to record feed intake by weight change measured to the nearest gram. Water was available ad libitum. Forage treatments were offered ad libitum by providing sufficient hay to maintain full feed troughs throughout each day of the experiment. Treatments were five proportions of ‘Florigraze’ rhizoma peanut hay in ‘Argentine’ bahiagrass hay: (1) 100% bahiagrass hay (0% RP); (2) 25% rhizoma peanut hay + 75% bahiagrass hay (25% RP); (3) 50% rhizoma peanut hay + 50% bahiagrass hay (50% RP); (4) 75% rhizoma peanut hay + 25% bahiagrass hay (75% RP); (5) 100% rhizoma peanut hay (100% RP). Diet chemical composition is presented in Table 1. All treatment proportions were weighed and mixed on as-fed basis. Mixing of diets was done manually; no hay mixers or choppers were used, to minimize leaf shatter.Sample collectionSteers were housed for 32 days and sampling occurred on 0, 8, 16, 24, and 32 days after initiation of treatment diets; exception was for feces, which were collected on d-1 given steers were fasted on d-0 of the experiment. The hay mixtures offered to the steers were collected (10 samples of each diet) and analyzed for nutritive value (Table 1), at the start of the experiment. All sampling occurred between 0700 and 1000 h on each of the sampling days.Fecal samples were collected directly from the rectum and placed in quart-sized plastic bags to avoid contamination. The feces were frozen at −20 °C. All fecal samples were thawed, dried at 55 °C for 72 h, and ground to pass a 2-mm stainless steel screen using a Wiley Mill (Model 4, Thomas-Wiley Laboratory Mill, Thomas Scientific, Swedesboro, NJ, USA). Samples were then ball milled using a Mixer Mill MM400 (Retsch GmbH, Haan, Germany) at 25 Hz for 9 min.Blood was obtained through jugular venipuncture using 10-mL K2 EDTA vials (Becton Dickinson and Company, Franklin Lakes, NJ, USA), and stored in ice until centrifugation. All tubes were centrifuged at 714 G for 20 min using an Allegra X-22R centrifuge (Beckman Coulter, Brea, CA, USA). A 10-mL sample of plasma was collected and placed in a separate glass vial, the remaining plasma, white blood cell, and platelet fractions were discarded. The remaining RBC pellet was re-suspended with 9 vol. 0.9% NaCl solution and mixed at room temperature for 15 min at 2 Hz orbital shaker. The tubes were then centrifuged at 714 G for 20 min. The saline solution from the centrifuged tubes was discarded after centrifugation. The rinse procedure was repeated two more times for a total of three rinses. After the third rinse procedure, a 500-µL sample was removed, frozen at −20 °C, and subsequently freeze-dried for isotopic analyses.Hair clippings were obtained from an area of 20 × 20 cm on the left hindquarter, utilizing veterinary hair clippers (Sunbeam-Oster Inc., Boca Raton, FL, USA). Hair clippings were collected, placed in nylon bags (Ankom Technology, Macedon, NY, USA), and frozen for subsequent analysis. Clippings were always collected in the same location from each animal in order to ensure new hair growth would be analyzed. All hair samples were cleaned using soapy water and defatted following protocols for other keratin-based tissues 31,34. Each sample was sonicated twice for 30 min in a methanol and chloroform solution (2:1, v/v), rinsed with distilled water, and oven dried overnight at 60 °C. Each hair sample was ball milled using a Mixer Mill MM400 (Retsch GmbH, Haan, Germany) at 25 Hz for 9 min.CalculationsAfter processing, all samples were analyzed for total C and N using a CHNS analyzer through the Dumas dry combustion method (Vario MicroCube, Elementar Americas Inc., Ronkonkoma, NY, USA) coupled to an isotope ratio mass spectrometer (IsoPrime 100, Elementar, Elementar Americas Inc., Ronkonkoma, NY, USA). Certified standards of L-glutamic acid (USGS40, USGS41; United States Geological Survey) were used for isotope ratio mass spectrometer calibration. Isotope ratios were as follows: δ13C of −26.39, + 37.63‰, and δ15N of −4.52, 47.57‰ for USGS40 and USGS41, respectively. Calibration of the IRMS was conducted according to Cook, et al. 35, with an accuracy of ≤ 0.06 ‰ for 15N and ≤ 0.08 ‰ for 13C.The isotope ratio for 13C/12C was calculated as:$$delta^{{{13}}} {text{C}} = , left( {^{{{13}}} {text{C}}/^{{{12}}} {text{C}}_{{{text{sample}}}} {-}^{{{13}}} {text{C}}/^{{{12}}} {text{C}}_{{{text{reference}}}} } right)/ , left( {^{{{13}}} {text{C}}/^{{{12}}} {text{C}}_{{{text{reference}}}} times { 1}000} right)$$
(1)

where δ13C is the C isotope ratio of the sample relative to Pee Dee Belemnite (PDB) standard (‰), 13C/12Csample is the C isotope ratio of the sample, and 13C/12Creference is the C isotope ratio of PDB standard 5. The isotope ratio for 15N/14N was calculated as:$$delta^{{{15}}} {text{N}} = , left( {^{{{15}}} {text{N}}/^{{{14}}} {text{N}}_{{{text{sample}}}} -^{{{15}}} {text{N}}/^{{{14}}} {text{N}}_{{{text{reference}}}} } right)/left( {^{{{15}}} {text{N}}/^{{{14}}} {text{N}}_{{{text{reference}}}} times { 1}000} right)$$
(2)
where δ15N is the N isotope ratio of the sample relative to atmospheric nitrogen (‰), 15N/14Nsample is the N isotope ratio of the sample, and 15N/14Nreference is the N isotope ratio of atmospheric N (standard) 5. The fraction factor (Δ) in this study was considered to be the difference between the diet isotopic composition (δ13C or δ15N) and that of the given sample 5.The dietary proportion of rhizoma peanut hay was back-calculated using δ13C and δ15N of each plant on a DM basis 3. This method is advantageous in that it does not require further tissue processing and facilitates implementation at the field scale. The proportion of rhizoma peanut was estimated using Eq. (3), described by Jones et al. 3:$$%RP=100-left{100 times frac{A-C}{B-C}right}$$
(3)
where %RP is the proportion of RP in the diet, A is the δ13C or δ15N of the sample obtained, B is the δ13C or δ15N of bahiagrass, and C is the δ13C or δ15N of RP.Statistical analysisAll response variables were analyzed using linear mixed model procedures as implemented in SAS PROC GLIMMIX (SAS/STAT 15.1, SAS Institute). Individual animals were considered the experimental unit. Treatment, collection day, and their interaction were considered fixed effects, and block was considered a random effect in the model. The data were analyzed as repeated measures, considering collection day as the repeated measure. The best covariance matrix was selected according to the lowest AICC fit statistic. Least squares treatment means were compared through pairwise t test using the PDIFF option of the LSMEAN statement in the aforementioned procedure. Based on the recommendations by Milliken and Johnson 36 and Saville 37, no adjustment for multiple comparisons was made. Orthogonal polynomial contrasts (linear and quadratic effects) were used to test effects of RP inclusion on isotopic responses. The slice option was used when the treatment × collection day interaction was significant (P ≤ 0.05), using collection day as the factor, to test treatment effects across collection days. Significance was declared at P ≤ 0.05. The contrast statement was used to test for linear or quadratic effects. Regression analyses for the dietary predictions were conducted using PROC REG from SAS.Predictions of dietary proportions based on Eq. (3) were generated for 16 subgroups reflecting combinations of isotope (13C or 15N), day (8 or 32), and sample type (feces, plasm, RBC, or hair). Analyses comparing predicted versus actual diet proportions included several components. First, we computed the concordance correlation coefficient (CCC) following the recommendations from Crawford, et al. 38. The CCC is a measure of agreement that encompasses both precision and accuracy, along with corresponding 95% bias accelerated and corrected (BCa) bootstrap confidence intervals. For comparative purposes we calculated the Pearson correlation coefficient which only reflects precision. Both correlation coefficients range from −1.0 to 1.0 and we interpreted values ≥ 0.80 as indicating strong positive agreement/correlation. Next, we regressed the actual percentages on the predicted percentages using linear regression. Perfect prediction corresponds to the estimated regression line having an intercept of zero and a slope of 1.0. We then partitioned the mean square error (MSE) of the predicted (from Eq. (3), not the above linear regression) versus actual percentages as described in Rice and Cochran 39. This partitioning entails calculating the proportion of MSE attributable to three sources of error: the difference in mean predicted and actual values (mean component, denoted “MC”), the error resulting from the slope of the above linear regression deviating from 1.0 (slope component, denoted “SC”), and random error (random component, denoted “RC”). The results from the above analyses were examined to identify subgroups whose predictions were sufficiently good to warrant hypothesis testing. In this context “good” means that the predicted percentages were strongly correlated with the actual percentages and the magnitudes of the predicted percentages were similar to the actual percentages. The objective of the hypothesis testing was to formally evaluate whether dietary proportions could be directly predicted from Eq. (3) (in contrast to generating predictions using the equation from regressing actual dietary percentages on the predicted percentages from Eq. (3)). Paired two one-sided test (TOST) equivalence tests were conducted for the selected subgroups with α = 0.0540. These tests are formulated such that the null hypothesis is “non-equivalence” and the alternative hypothesis is “equivalence”. An input parameter to the test is the equivalence region, a range for which we consider the mean actual minus predicted difference to be unimportant (“equivalent”) from a practical standpoint. For each equivalence test we also computed the 90% confidence interval for the mean actual minus predicted difference which we refer to as the “minimum equivalence region”. Based on the structure of TOST equivalence tests, to reject the null hypothesis at the 0.05 level, the equivalence region specified for the test must completely contain the minimum equivalence region. For example, if the pre-specified equivalence region is (−15%, 15%) and the computed minimum equivalence region is (−16%, −6%) the null hypothesis would not be rejected. Finally, we re-ran all of the analyses described above for the selected subgroups where, prior to analysis, predicted percentages outside of the valid range were assigned the appropriate boundary value (i.e., predicted percentages  100% were assigned a value of 100%). More

Walas, Ł, Ganatsas, P., Iszkuło, G., Thomas, P. A. & Dering, M. Spatial genetic structure and diversity of natural populations of Aesculus hippocastanum L. in Greece. PLoS ONE 14, e0226225 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Song, Y. G. et al. Past, present and future suitable areas for the relict tree Pterocarya fraxinifolia (Juglandaceae): Integrating fossil records, niche modeling, and phylogeography for conservation. Eur. J. For. Res. 140, 1323–1339 (2021).Article 

Google Scholar 
Dyderski, M. K., Paź, S., Frelich, L. E. & Jagodziński, A. M. How much does climate change threaten European forest tree species distributions?. Glob. Change Biol. 24, 1150–1163 (2018).ADS 
Article 

Google Scholar 
Chakraborty, D., Móricz, N., Rasztovits, E., Dobor, L. & Schueler, S. Provisioning forest and conservation science with high-resolution maps of potential distribution of major European tree species under climate change. Ann. For. Sci. 78, 1–18 (2021).Article 

Google Scholar 
Williams, J. N. et al. Using species distribution models to predict new occurrences for rare plants. Divers. Distrib. 15, 565–576 (2009).Article 

Google Scholar 
Watling, J. I. et al. Performance metrics and variance partitioning reveal sources of uncertainty in species distribution models. Ecol. Modell. 309, 48–59 (2015).ADS 
Article 

Google Scholar 
Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259 (2006).Article 

Google Scholar 
Phillips, S. J., Dudík, M. & Schapire, R. E. [Internet] Maxent software for modeling species niches and distributions. url: http://biodiversityinformatics.amnh.org/open_source/maxent/. Accessed 13 July 2022.Elith, J. et al. A statistical explanation of MaxEnt for ecologists. Divers. Distrib. 17, 43–57 (2011).Article 

Google Scholar 
Marcer, A., Sáez, L., Molowny-Horas, R., Pons, X. & Pino, J. Using species distribution modelling to disentangle realised versus potential distributions for rare species conservation. Biol. Conserv. 166, 221–230 (2013).Article 

Google Scholar 
Rigling, A., Eilmann, B., Koechli, R. & Dobbertin, M. Mistletoe-induced crown degradation in Scots pine in a xeric environment. Tree Physiol. 30, 845–852 (2010).PubMed 
Article 

Google Scholar 
Sangüesa-Barreda, G., Linares, J. C. & Camarero, J. J. Mistletoe effects on Scots pine decline following drought events: Insights from within-tree spatial patterns, growth and carbohydrates. Tree Physiol. 32, 585–598 (2012).PubMed 
Article 

Google Scholar 
Kollas, C., Gutsch, M., Hommel, R., Lasch-Born, P. & Suckow, F. Mistletoe-induced growth reductions at the forest stand scale. Tree Physiol. 38, 735–744 (2018).PubMed 
Article 

Google Scholar 
Schulze, E. D. & Ehleringer, J. R. The effect of nitrogen supply on growth and water-use efficiency of xylem-tapping mistletoes. Planta 162, 268–275 (1984).PubMed 
Article 

Google Scholar 
Escher, P. et al. Transpiration, CO2 assimilation, WUE, and stomatal aperture in leaves of Viscum album L: Effect of abscisic acid (ABA) in the xylem sap of its host (Populus x euamericana). Plant Physiol. Biochem. 46, 64–70 (2008).PubMed 
Article 

Google Scholar 
Zweifel, R., Bangerter, S., Rigling, A. & Sterck, F. J. Pine and mistletoes: How to live with a leak in the water flow and storage system?. J. Exp. Bot. 63, 2565–2578 (2012).PubMed 
Article 

Google Scholar 
Mutlu, S., Osma, E., Ilhan, V., Turkoglu, H. I. & Atici, O. Mistletoe (Viscum album) reduces the growth of the Scots pine by accumulating essential nutrient elements in its structure as a trap. Trees 30, 815–824 (2016).Article 

Google Scholar 
Tsopelas, P., Angelopoulos, A., Economou, A. & Soulioti, N. Mistletoe (Viscum album) in the fir forest of Mount Parnis Greece. For. Ecol. Manag. 202, 59–65 (2004).Article 

Google Scholar 
Dobbertin, M. & Rigling, A. Pine mistletoe (Viscum album ssp. austriacum) contributes to Scots pine (Pinus sylvestris) mortality in the Rhone valley of Switzerland. For. Pathol. 36, 309–322 (2006).Article 

Google Scholar 
Lech, P., Żółciak, A. & Hildebrand, R. Occurrence of European mistletoe (Viscum album L.) on forest trees in Poland and its dynamics of spread in the period 2008–2018. Forests 11, 83 (2020).Article 

Google Scholar 
Iszkuło, G. et al. Jemioła jako zagrożenie dla zdrowotności drzewostanów iglastych. Sylwan 164, 226–236 (2020) ([In Polish]).
Google Scholar 
Mellado, A., Morillas, L., Gallardo, A. & Zamora, R. Temporal dynamic of parasite-mediated linkages between the forest canopy and soil processes and the microbial community. New Phytol. 211, 1382–1392 (2016).PubMed 
Article 

Google Scholar 
Mellado, A. & Zamora, R. Generalist birds govern the seed dispersal of a parasitic plant with strong recruitment constraints. Oecologia 176, 139–147 (2014).ADS 
PubMed 
Article 

Google Scholar 
Hódar, J. A., Lázaro-González, A. & Zamora, R. Beneath the mistletoe: parasitized trees host a more diverse herbaceous vegetation and are more visited by rabbits. Ann. For. Sci. 75, 1–8 (2018).Article 

Google Scholar 
Zuber, D. Biological flora of Central Europe: Viscum album L. Flora Morphol. Distrib Funct. Ecol. Plants 199, 181–203 (2004).Article 

Google Scholar 
Urech, K. & Baumgartner, S. Chemical constituents of Viscum album L.: Implications for the pharmaceutical preparation of mistletoe. In: Mistletoe: From mythology to evidence-based medicine. (eds. Zänker, K.S. & Kaveri, S. V.), 11–23. (S. Karger AG, Basel, Switzerland, 2015).Singh, B. N. et al. European Viscum album: a potent phytotherapeutic agent with multifarious phytochemicals, pharmacological properties and clinical evidence. RSC Adv. 6, 23837–23857 (2016).ADS 
Article 

Google Scholar 
Jeffree, C. E. & Jeffree, E. P. Redistribution of the potential geographical ranges of mistletoe and colorado beetle in Europe in response to the temperature component of climate change. Funct. Ecol. 10, 562–577 (1996).Article 

Google Scholar 
Troels-Smith, J. Ivy, mistletoe and elm climate indicators-fodder plants. A contribution to the interpretation of the pollen zone border VII-VIII. Dan. Geol. Undersøg. IV Række 4, 1–32 (1960).
Google Scholar 
Dobbertin, M. et al. The upward shift in altitude of pine mistletoe (Viscum album ssp. austriacum) in Switzerland—the result of climate warming?. Int. J. Biometeorol. 50, 40–47 (2005).ADS 
PubMed 
Article 

Google Scholar 
Zamora, R. & Mellado, A. Identifying the abiotic and biotic drivers behind the elevational distribution shift of a parasitic plant. Plant Biol. 21, 307–317 (2019).PubMed 
Article 

Google Scholar 
Barney, C. W., Hawksworth, F. G. & Geils, B. W. Hosts of Viscum album. Eur. J. Plant Pathol. 28, 187–208 (1998).
Google Scholar 
Böhling, N. et al. Notes on the Cretan mistletoe, Viscum album subsp. creticum subsp. nova (Loranthaceae/Viscaceae). Isr. J. Plant Sci. 50, 77–84 (2002).
Google Scholar 
Plants of the World Online [Internet] url: https://powo.science.kew.org/taxon/urn:lsid:ipni.org:names:921668-1. Accessed 13 July 2022.Zuber, D. & Widmer, A. Phylogeography and host race differentiation in the European mistletoe (Viscum album L.). Mol. Ecol. 18, 1946–1962 (2009).PubMed 
Article 

Google Scholar 
Schaller, G., Urech, K., Grazi, G. & Giannattasio, M. Viscotoxin composition of the three European subspecies of Viscum album. Planta Med 64, 677–678 (1998).PubMed 
Article 

Google Scholar 
Kahle-Zuber, D. Biology and evolution of the European mistletoe (Viscum album). Doctoral Thesis. ETH Zurich. (2008).Zuber, D. & Widmer, A. Genetic evidence for host specificity in the hemi-parasitic Viscum album L. (Viscaceae). Mol. Ecol. 9, 1069–1073 (2000).PubMed 
Article 

Google Scholar 
Mejnartowicz, L. Relationship and genetic diversity of mistletoe [Viscum album L.] subspecies. Acta Soc. Bot. Pol. Pol. 75, 39–49 (2006).Article 

Google Scholar 
Xie, W., Adolf, J. & Melzig, M. F. Identification of Viscum album L. miRNAs and prediction of their medicinal values. PLoS ONE 12, e0187776 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Valle, A. C. V., de Carvalho, A. C. & Andrade, R. V. Viscum album-literature review. Int. J. Sci. Res 10, 63–71 (2021).
Google Scholar 
Schröder, L. et al. The gene space of European mistletoe (Viscum album). Plant J. 109, 278–294 (2022).PubMed 
Article 

Google Scholar 
Sangüesa-Barreda, G. et al. Delineating limits: Confronting predicted climatic suitability to field performance in mistletoe populations. J. Ecol. 106, 2218–2229 (2018).Article 

Google Scholar 
GBIF.org [Internet] GBIF Occurrence Download Doi: https://doi.org/10.15468/dl.zw6f5q. Accessed 27 July 2021.GBIF.org [Internet] GBIF Occurrence Download Doi: https://doi.org/10.15468/dl.6wmc9d. Accessed 6 August 2021.FloraWeb [Internet] url: https://www.floraweb.de. Accessed 10 December 2021.Pladias – Database of the Czech Flora and Vegetation. [Internet] url: www.pladias.cz. Accessed 14 July 2022.Zając, A., Zając, M., Tertil, R. & Harman, I. Atlas rozmieszczenia roślin naczyniowych w Polsce. 593 (Instytut Botaniki Uniwersytetu Jagiellońskiego, Kraków, 2001) [In Polish].Idžojtić, M., Kogelnik, M., Franjić, J. & Škvorc, Ž. Hosts and distribution of Viscum album L. ssp. album in Croatia and Slovenia. Plant Biosyst. 140, 50–55 (2006).Article 

Google Scholar 
Varga, I. et al. Changes in the Distribution of European Mistletoe (Viscum album) in Hungary During the Last Hundred Years. Folia Geobot 49, 559–577 (2014).Article 

Google Scholar 
Wild, J. et al. Plant distribution data for the Czech Republic integrated in the Pladias database. Preslia 91, 1–24 (2019).Article 

Google Scholar 
Krasylenko, Y. et al. The European mistletoe (Viscum album L.): Distribution, host range, biotic interactions, and management worldwide with special emphasis on Ukraine. Botany 98, 499–516 (2020).Article 

Google Scholar 
Karger, D. N. et al. Climatologies at high resolution for the Earth land surface areas. Sci. Data 4, 170122 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Karger D. N., et al. Data from: Climatologies at high resolution for the earth’s land surface areas. Dryad Digital Repository (2018).Gutjahr, O. et al. Max planck institute earth system model (MPI-ESM1. 2) for the high-resolution model intercomparison project (HighResMIP). Geosci. Model Dev. 12, 3241–3281 (2019).ADS 
Article 

Google Scholar 
Hijmans, R. J., & van Etten, J. raster: Geographic analysis and modeling with raster data. R package version 2.0-12. (2012).R Core Team. The Comprehensive R Archive Network. [Internet] url: https://cran.r-project.org/ Accessed 14 July 2022.Chakraborty, D., Móricz, N., Rasztovits, E., Dobor, L. & Schueler, S. Provisioning forest and conservation science with European tree species distribution models under climate change (Version v1). Zenodo https://doi.org/10.5281/zenodo.3686918 (2020).Wang, Z., Chang, Y. I., Ying, Z., Zhu, L. & Yang, Y. A parsimonious threshold-independent protein feature selection method through the area under receiver operating characteristic curve. Bioinformatics 23, 2788–2794 (2007).PubMed 
Article 

Google Scholar 
Lobo, J. M., Jiménez-Valverde, A. & Hortal, J. The uncertain nature of absences and their importance in species distribution modelling. Ecography 33, 103–114 (2010).Article 

Google Scholar 
QGIS Development Team. QGIS Geographic Information Sys-tem. Open Source Geospatial Foundation Project. [Internet]. url: https://www.qgis.org/en/site/. Accessed 14 July 2022.Fischer, J. T. Water relations of mistletoes and their hosts. In: The biology of mistletoes. (eds. Calder, M., & Bernhard, T.), 163–184 (Academic Press, Sydney, 1983).Skre, O. The regional distribution of vascular plants in Scandinavia with requirements for high summer temperatures. Norweg. J. Bot. 26, 295–318 (1979).
Google Scholar 
Wangerin, B. Loranthaceae. In: Lebensgeschichte der Blütenpflanzen Mitteleuropas (eds. Kirchner, O. V., Loew, E., & Schroeter, C.) 2, 953–1146 (E. Ulmer, Stuttgart, 1937).Rybalka, I. A. Relationship between density of the white mistletoe (Viscum album L.) and some landscape and environmental characteristics of urban areas in the case of Kharkiv. Ekologicheskiy Vestnik 1, 87–97 (2017).
Google Scholar 
Patykowski, J. & Kołodziejek, J. Comparative analysis of antioxidant activity in leaves of different hosts infected by mistletoe (Viscum album L. subsp. album). Arch. Biol. Sci. 65, 851–861 (2013).Article 

Google Scholar 
Skrypnik, L., Maslennikov, P., Feduraev, P., Pungin, A. & Belov, N. Ecological and landscape factors affecting the spread of European mistletoe (Viscum album L.) in urban areas (A Case Study of the Kaliningrad City, Russia). Plants 9, 394 (2020).PubMed Central 
Article 

Google Scholar 
Kunick, W. Veränderungen von Flora und Vegetation einer Grosstadt dargestellt am Beispiel von Berlin (West). PhD Thesis, Technische Universität (1974). [In German].Kołodziejek, J., Patykowski, J. & Kołodziejek, R. Distribution, frequency and host patterns of European mistletoe (Viscum album subsp. album) in the major city of Lodz Poland. Biol. 68, 55–64 (2013).
Google Scholar 
Caudullo, G., Welk, E. & San-Miguel-Ayanz, J. Chorological maps for the main European woody species. Data Brief 12, 662–666 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
O’Donnell, M. S. & Ignizio, D. A. Bioclimatic predictors for supporting ecological applications in the conterminous United States. US Geol. Surv. Data Ser. 691, 4–9 (2012).
Google Scholar 
Luther, P., Becker, H. & Leroi, R. Die Mistel: Botanik, Lektine, medizinische Anwendung. Springer (1987).Gazol, A. et al. Distinct effects of climate warming on populations of silver fir (Abies alba) across Europe. J. Biogeogr. 42, 1150–1162 (2015).Article 

Google Scholar 
Tikkanen, O. P. et al. Freezing tolerance of seeds can explain differences in the distribution of two widespread mistletoe subspecies in Europe. For. Ecol. Manag. 482, 118806 (2021).Article 

Google Scholar 
Pilichowski, S. et al. Wpływ Viscum album ssp. austriacum (Wiesb.) Vollm. na przyrost radialny Pinus sylvestris L. Sylwan 162, 452–459 (2018) ([In Polish]).
Google Scholar 
Szmidla, H., Tkaczyk, M., Plewa, R., Tarwacki, G. & Sierota, Z. Impact of common mistletoe (Viscum album L.) on scots pine forests—A call for action. Forests 10, 847 (2019).Article 

Google Scholar 
Wójcik, R. & Kędziora, W. Abundance of Viscum in central Poland: Results from a large-scale mistletoe inventory. Environ. Sci. Proc. 3, 98 (2020).
Google Scholar 
Sangüesa-Barreda, G., Linares, J. C. & Camarero, J. J. Drought and mistletoe reduce growth and water-use efficiency of Scots pine. For. Ecol. Manag. 296, 64–73 (2013).Article 

Google Scholar 
Mathiasen, R. L., Nickrent, D. L., Shaw, D. C. & Watson, D. M. Mistletoes: Pathology, systematics, ecology, and management. Plant Dis. 92, 988–1006 (2008).PubMed 
Article 

Google Scholar 
Catal, Y. & Carus, S. Effect of pine mistletoe on radial growth of crimean pine (Pinus nigra) in Turkey. J. Environ. Biol. 32, 263 (2011).PubMed 

Google Scholar 
Skre, O. High temperature demands for growth and development in Norway Spruce [Picea abies (L.) Karst.] in Scandinavia. Meld Nor Landbrukshøgsk 51, 1–29 (1971).
Google Scholar 
Utaaker, K. A temperature-growth index—the respiration equivalent—used in climatic studies on the meso-scale in Norway. Agric. Meteorol. 5, 351–359 (1968).Article 

Google Scholar 
Iversen, J. Viscum, Hedera and Ilex as climate indicators: A contribution to the study of the post-glacial temperature climate. Geol. fören. Stockh. förh. 66, 463–483 (1944).Article 

Google Scholar 
Briggs, J. Mistletoe, Viscum album (Santalaceae), in Britain and Ireland; a discussion and review of current status and trends. Brit. Ir. Bot. 3, 419–454 (2021).
Google Scholar  More

Barnosky, A. D. et al. Has the Earth’s sixth mass extinction already arrived? Nature 471, 51–57. https://doi.org/10.1038/nature09678 (2011).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Ceballos, G. et al. Accelerated human-induced species losses: Entering the sixth mass extinction. Sci. Adv. 1, 5. https://doi.org/10.1126/sciadv.1400253 (2015).Article 

Google Scholar 
Purvis, A. et al. IPBES global assessment on biodiversity and ecosystem services chapter 2.2 status and trends. Nature https://doi.org/10.5281/zenodo.5517457.svg (2019).Balvernara, P. et al. IPBES global assessment on biodiversity and ecosystem services chapter 2.2 status and trends. Drivers. Change https://doi.org/10.5281/zenodo.5517423 (2019).Carrol, C. & Noss, R. F. Rewilding in the face of climate change. Conserv. Biol. 35, 155–167. https://doi.org/10.1111/cobi.13531 (2020).Article 

Google Scholar 
Barr, S. L., Larson, B. M. H., Beechey, T. J. & Scott, D. J. Assessing climate change adaptation progress in Canada’s protected areas. Can. Geog. 65, 152–165. https://doi.org/10.1111/cag.12635 (2020).Article 

Google Scholar 
Convention on Biological Diversity. Aichi Target 11, Convention on Biological Diversity. https://www.cbd.int/aichi-targets/target/11. Accessed 14 May 2021.United Nations. Climate Change Pathways. https://unfccc.int/climate-action/marrakech-partnership/reporting-and-tracking/climate_action_pathways. Accessed 12 Sept 2022.Government of Canada. Canada’s nature legacy: Protecting our nature conservation/nature-legacy.html (2021).Coristine, L. E. et al. Informing Canada’s commitment to biodiversity conservation: A science-based framework to help guide protected areas designation through Target 1 and beyond. Facets 3, 531–562. https://doi.org/10.1139/facets-2017-0102 (2017).Article 

Google Scholar 
De Barros, A. E. et al. Identification of areas in Brazil that optimize areas that optimize conservation of forest carbon, Jaguars and Biodiversity. Conserv. Biol. 28, 580–593. https://doi.org/10.1111/cobi.12202 (2013).Article 
PubMed 

Google Scholar 
Jantz, P., Scott, S. & Laporte, N. Carbon stock corridors to mitigate climate change and promote biodiversity in the tropics. Nat. Clim. Change 4, 138–142. https://doi.org/10.1038/nclimate2105 (2014).ADS 
CAS 
Article 

Google Scholar 
Beaudrot, L. et al. Limited carbon and biodiversity co-benefits for tropical mammals and birds. Ecol. Appl. 26, 10998–11111. https://doi.org/10.1890/15-0935 (2016).Article 

Google Scholar 
Morelli, T. L. et al. Climate-change refugia: Biodiversity in a slow lane. Front. Ecol. Environ. 18, 228–234. https://doi.org/10.1002/fee.2189 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Stralberg, et al. Macrorefugia for North American trees ad songbirds: Climatic limiting factors and multi-scale topographic influences. Glob. Ecol. Biogeogr. 27, 690–703. https://doi.org/10.1111/geb.12731 (2018).Article 

Google Scholar 
Caroll, C. & Ray, J. C. Maximizing the effectiveness of national commitments to protected area expansion for conserving biodiversity and ecosystem carbon under climate change. Glob. Chang Biol. 27, 3395–3414. https://doi.org/10.1111/gcb.15645 (2020).Article 

Google Scholar 
Bradshaw, C. J., Warkentin, I. G. & Sodhi, N. S. Urgent preservation of boreal carbon stocks and biodiversity. Trends Ecol. Evol. 24, 541–548. https://doi.org/10.1016/j.tree.2009.03.019 (2009).Article 
PubMed 

Google Scholar 
Harris, L. I. et al. The essential carbon service provided by northern peatlands. Front. Ecol. Environ. 20, 222–230 (2022).Article 

Google Scholar 
Environment and Climate Change Canada. Canadian Environmental Sustainability Indicators: Canada’s conserved areas. environmental-indicators/conserved-areas.html (2020).Office of the Auditor General of Canada. Lessen learnt from 30 years of climate change challenges and opportunities. https://www.oag-bvg.gc.ca/internet/English/att__e_43948.html#hd3l (2020).Shea, T. et al. Canada’s Conservation Vision: A report of the National Advisory Panel. Government of Canada, 43 pp (2018).Environment and Climate Change Canada. Pan-Canadian Approach to transforming species at risk conservation in Canada. species-at-risk-conservation.html (2018).Bergerund, A. T. Caribou, wolves and man. Trends Ecol. Evol. 3, 68–72. https://doi.org/10.1016/0169-5347(88)90019-5 (1988).Article 

Google Scholar 
Vernier, L. A. et al. Effects of natural resource development on the terrestrial biodiversity of Canadian boreal forests. Environ. Rev. 22, 457–490. https://doi.org/10.1139/er-2013-0075 (2014).Article 

Google Scholar 
Wells, J. V., Dawson, N., Culver, N., Reid, F. A. & Slegers, S. M. The state of conservation in North America’s Borel Forest: Issues and opportunities. Front. For. Glob. Change 3, 90. https://doi.org/10.3389/ffgc.2020.00090/full (2020).Article 

Google Scholar 
COSEWIC. COSEWIC assessment and update status report on the woodland caribou Rangifer tarandus caribou in Canada. Committee on the Status of Endangered Wildlife in Canada. Ottawa. xi + 98 pp. (2002).COSEWIC. COSEWIC assessment and status report on the caribou Rangifer tarandus, Newfoundland population, Atlantic-Gaspésie population and Boreal population, in Canada. Committee on the Status of Endangered Wildlifein Canada. Ottawa. xxiii + 128 pp. (2014).Environment and Climate Change Canada. Amended Recovery Strategy for the Woodland Caribou (Rangifer tarandus caribou), Boreal Population, in Canada. Species at Risk Act Recovery Strategy Series. Environment and Climate Change Canada, Ottawa. xiii + 143pp. (2020).Environment and Climate Change Canada. Report on the Progress of Recovery Strategy Implementation for the Woodland Caribou (Rangifer tarandus caribou), Boreal population in Canada for the Period 2012–2017. Species at Risk Act Recovery Strategy Series. Environment and Climate Change Canada, Ottawa. ix + 94 (2017).Hebblewhite, M. Billion dollar boreal woodland caribou and the biodiversity impacts of the global oil and gas industry. Biol. Conserv. 206, 102–111. https://doi.org/10.1016/j.biocon.2016 (2017).Article 

Google Scholar 
Fortin, D., McLoughlin, P. D. & Hebblewhite, M. When the protection of a threatened species depends on the economy of a foreign nation. PLoS ONE 15, e0229555. https://doi.org/10.1371/journal.pone.0229555 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Drever, R. C. et al. Conservation through co-occurrence: Woodland caribou as a focal species for boreal biodiversity. Biol. Conserv. 232, 238–252. https://doi.org/10.1016/j.biocon.2019.01.026 (2019).Article 

Google Scholar 
Government of Canada. Pan-Canadian Framework on clean growth and climate change climatechange/pan-canadian-framework.html.Bradshaw, C. J. & Warkentin, I. G. Global estimates of boreal forest carbon stocks and flux. Glob. Planet Chang 128, 24–30. https://doi.org/10.1016/j.gloplacha.2015.02.004 (2015).ADS 
Article 

Google Scholar 
Jennings, M. D. Gap analysis: Concept, methods, recent results. Land Ecol. 5, 15–20 (2010).
Google Scholar 
Environment and Climate Change Canada. Canadian Protected and Conserved Areas database. national-wildlife-areas/protected-conserved-areas-database (2019).DeLuca, T. H. & Boisvenue, C. Boreal forest soil carbon: Distribution function and modelling. Forestry 85, 161–184. https://doi.org/10.1093/forestry/cps003 (2012).Article 

Google Scholar 
Price, et al. Anticipating the consequences of climate change for Canada’s boreal forest ecosystems. Environ. Rev. 21, 322–365. https://doi.org/10.1139/er-2013-0042 (2013).Article 

Google Scholar 
Southee, F. M., Edwards, B. A., Chetkiewicz, C. B. & O’Connor, C. M. Freshwater conservation planning in the far north of Ontario, Canada: Identifying priority watersheds for conservation of fish biodiversity in an intact boreal landscape. Facets 6, 90–117. https://doi.org/10.1139/facets-2020-0015 (2021).Article 

Google Scholar 
Mitchell, M. G. E. et al. Identifying key ecosystem service providing areas to inform national-scale conservation planning. Environ. Res. Lett. 16, 014038. https://doi.org/10.1088/1748-9326/abc121 (2021).ADS 
CAS 
Article 

Google Scholar 
Labadie, G. P. D., McLoughlin, M. H. & Fortin, D. Insect-mediated apparent competition between mammals in a boreal food web. Proc. Natl. Acad. Sci. U S A. 118, e2022892118. https://doi.org/10.1073/pnas.2022892118 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Cameron, V. & Hargreaves, A. L. Spatial distribution and conservation hotspots of mammals in Canada. Facets 5, 692–703. https://doi.org/10.1139/facets-2020-0018 (2020).Article 

Google Scholar 
Ceballos, G. & Ehrlich, P. R. Global mammal distributions, biodiversity hotspots, and conservation. PNAS 103, 19374–19379. https://doi.org/10.1073/pnas.0609334103 (2016).ADS 
Article 

Google Scholar 
Anielski, M. & Wilson, S. Counting Canada’s natural capital: Assessing the real value of Canada’s boreal ecosystems. Ottawa, On: Canadian Boreal Initiative and Pembina Institute counting-canadas-natural-capital (2009).Kumaraswamy, S. & Udyakumar, M. Biodiversity banking: A strategic conservation mechanism. Biodiver. Conserv. 20, 1155–1165. https://doi.org/10.1007/s10531-011-0020-5 (2011).Article 

Google Scholar 
Garnett, S. T. et al. A spatial overview of the global importance of Indigenous lands for conservation. Nat. Sustain. 1, 369–374. https://doi.org/10.1038/s41893-018-0100-6 (2018).Article 

Google Scholar 
Godden, L. & Cowell, S. Conservation planning and Indigenous governance in Australia’s Indigenous Protected Areas. Restor. Ecol. 24, 692–697. https://doi.org/10.1111/rec.12394 (2016).Article 

Google Scholar 
Greg Brown, B. & Fagerholm, N. Empirical PPGIS/PGIS mapping of ecosystem services: A review and evaluation. Ecol. Ser. 13, 119–133. https://doi.org/10.1016/j.ecoser.2014.10.007 (2021).Article 

Google Scholar 
Martin, A. E., Neave, E., Kirby, P., Drever, C. R. & Johnson, C. A. Multi-objective optimization can balance trade-offs among boreal caribou, biodiversity, and climate change objectives when conservation hotspots do not overlap. Sci. Rep. 12, 11895. https://doi.org/10.1038/s41598-022-15274-8 (2022).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
COSEWIC. Canadian Wildlife Species at Risk. Committee on the Status of Endangered Wildlife in Canada (2018).Alberta Environment and Parks and Alberta Conservation Association. Status of the Arctic Grayling (Thymallus arcticus) in Alberta: Update 2015. Alberta Environment and Parks. Alberta Wildlife Status Report No. 57 (Update 2015). Edmonton, AB. 96 pp. (2015).Environment and Climate Change Canada (ECCC). 2016. Range map extents, species at risk, Canada. Government of Canada. Open Government Dataset. https://open.canada.ca/data/en/dataset/d00f8e8c-40c4-435a-b790-980339ce3121.Magurran, A. E. Measuring Biological Diversity 256 (Blackwell Publishing, 2004).
Google Scholar 
Caissy, P., Klemet-N’Guessan, S., Jackiw, R., Eckert, C. G. & Hargreaves, A. L. High conservation priority of range-edge plant populations not matched by habitat protection or research effort. Biol. Conserv. 249, 108732 (2020).Article 

Google Scholar 
Gaston, K. J. Rarity 201 (Chapman & Hall, 1994).Book 

Google Scholar 
Stralberg, D. Velocity-based macrorefugia for North American ecoregions. Zenodo. https://doi.org/10.5281/zenodo.2579337 (2019).Fuss, S. et al. Betting on negative emissions. Nat. Clim. Change 4, 850–853. https://doi.org/10.1038/nclimate2392 (2014).ADS 
CAS 
Article 

Google Scholar 
Chen, I., Hill, J. K., Ohlemüller, R. D. B. & Thomas, C. D. Rapid range shifts of species associated with high levels of climate warming. Science 333, 1024–1026. https://doi.org/10.1126/science.1206432 (2011).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Woodall, C. W. et al. An indicator of tree migration in forests of the eastern United States. For. Ecol. Manag. 257, 1434–1444 (2009).Article 

Google Scholar 
Iverson, L. R., Schwartz, M. W. & Prasad, A. M. How fast and far might tree species migrate in the eastern United States due to climate change? Glob. Ecol. Biogeogr. 13, 209–219 (2004).Article 

Google Scholar 
McLachlan, J. S., Hellmann, J. J. & Schwartz, M. W. A framework for debate of assisted migration in an era of climate change. Conserv. Biol. 21, 297–302 (2007).Article 

Google Scholar 
Sittaro, F., Paquette, A., Messier, C. & Nock, C. A. Tree range expansion in eastern North America fails to keep pace with climate warming at northern range limits. Glob. Change Biol. 23, 3292–3301. https://doi.org/10.1111/gcb.13622 (2017).ADS 
Article 

Google Scholar 
Ping, C. L. et al. Carbon stores and biogeochemical properties of soils under black spruce forest, Alaska. Soil Sci. Soc. Am. J. 74, 969–978. https://doi.org/10.2136/sssaj2009.0152 (2010).ADS 
CAS 
Article 

Google Scholar 
Hengl, T. et al. SoilGrids250m: Global soil information based on machine learning. PLoS ONE 12, e0169748 (2017).Article 

Google Scholar 
Chung, N. C., Miasojedow, B., Startek, M. & Gambin, A. Jaccard/Tanimoto similarity test and estimation methods for biological presence-absence data. BMC Bioinform. 29, 644. https://doi.org/10.1186/s12859-019-3118-5 (2019).Article 

Google Scholar 
Chung, N. C., Miasojedow, B., Startek, M. & Gambin A. Jaccard: Test Similarity Between Binary Data using Jaccard/Tanimoto Coefficients. R package version 0.1.0. https://CRAN.R-project.org/package=jaccard (2018). More