Ecology

1.Science Advice for Policy by European Academies. A scientific perspective on microplastics in nature and society (SAPEA, 2019). Expert group report summarizing the state of the science regarding microplastics in nature and society.2.Koelmans, A. A. et al. Risks of plastic debris: Unravelling fact, opinion, perception and belief. Environ. Sci. Technol. 51, 11513–11519 (2017).CAS 

Google Scholar 
3.Henderson, L. & Green, C. Making sense of microplastics? Public understandings of plastic pollution. Mar. Pollut. Bull. 152, 110908 (2020).CAS 

Google Scholar 
4.Group of Experts on the Scientific Aspects of Marine Environmental Protection (GESAMP). Sources, fate and effects of microplastics in the marine environment. Part two of a global assessment (eds Kershaw, P. J. & Rochman, C. M.) (IMO/FAO/UNESCO-IOC/UNIDO/WMO/IAEA/UN/UNEP/UNDP, 2016).5.Arthur, C., Baker, J. & Bamford, H. (eds) NOAA technical memorandum NOS-OR&R-30. In Proc. Int. Res. Worksh. Occurrence, Effects and Fate of Microplastic Marine Debris (NOAA, 2009).6.European Chemicals Agency. Annex XV restriction report proposal for a restriction: intentionally added microplastics. Version 1.2. Proposal 1.2. ECA https://echa.europa.eu/documents/10162/05bd96e3-b969-0a7c-c6d0-441182893720 (2019).7.Coffin, S. Proposed definition of ‘microplastics in drinking water’. California Water Boards https://www.waterboards.ca.gov/drinking_water/certlic/drinkingwater/docs/stffrprt_jun3.pdf (2020).8.Andrady, A. L. Microplastics in the marine environment. Mar. Pollut. Bull. 62, 1596–1605 (2011).CAS 

Google Scholar 
9.Kooi, M. & Koelmans, A. A. Simplifying microplastic via continuous probability distributions for size, shape and density. Environ. Sci. Technol. Lett. 6, 551–557 (2019). This paper introduces the concept of describing microplastic characteristics through continuous PDFs, allowing us to capture the diversity of microplastics as a single contaminant in transport, exposure and risk assessment, rather than across many separate categories.CAS 

Google Scholar 
10.Rochman, C. M. et al. Rethinking microplastics as a diverse contaminant suite. Environ. Toxicol. Chem. 38, 703–711 (2019).CAS 

Google Scholar 
11.Kooi, M., Besseling, E., Kroeze, C., van Wezel, A. P. & Koelmans, A. A. Modelling the fate and transport of plastic debris in fresh waters. Review and guidance. In Freshwater Microplastics. The Handbook of Environmental Chemistry Vol. 58 (eds Wagner M. & Lambert S.) 125–152 (Springer, 2017).12.Hardesty, B. D. et al. Using numerical model simulations to improve the understanding of micro-plastic distribution and pathways in the marine environment. Front. Mar. Sci. 4, 1–30 (2017).
Google Scholar 
13.Redondo-Hasselerharm, P. E., Falahudin, D., Peeters, E. T. H. M. & Koelmans, A. A. Microplastic effect thresholds for freshwater benthic macroinvertebrates. Environ. Sci. Technol. 52, 2278–2286 (2018).CAS 

Google Scholar 
14.Adam, V., Yang, T. & Nowack, B. Toward an ecotoxicological risk assessment of microplastics: comparison of available hazard and exposure data in freshwaters. Environ. Toxicol. Chem. 38, 436–447 (2019). This paper introduces probabilistic SSDs for microplastic particles.CAS 

Google Scholar 
15.Koelmans, A. A., Diepens N. J. & Mohamed Nor, N. H. Weight of evidence for the microplastic vector effect in the context of chemical risk assessment. In Microplastic in the Environment: Pattern and Process (ed. Bank, M. S.) (Springer, 2021).16.Besseling, E., Redondo-Hasselerharm, P. E., Foekema, E. M. & Koelmans, A. A. Quantifying ecological risks of aquatic micro- and nanoplastic. Crit. Rev. Environ. Sci. Technol. 49, 32–80 (2019).
Google Scholar 
17.Burns, E. E. & Boxall, A. B. A. Microplastics in the aquatic environment: evidence for or against adverse impacts and major knowledge gaps. Environ. Toxicol. Chem. 37, 2776–2796 (2018).CAS 

Google Scholar 
18.Wright, S. L. & Kelly, F. J. Plastic and human health: a micro issue? Environ. Sci. Technol. 51, 6634–6647 (2017). This is a thorough review and outlook on the implications of plastic for human health.CAS 

Google Scholar 
19.Mohamed Nor, N. H., Kooi, M., Diepens, N. J. & Koelmans, A. A. Lifetime accumulation of nano- and microplastic in children and adults. Environ. Sci. Technol. 55, 5084–5096 (2021). This paper is the first probabilistic and aligned microplastic exposure assessment for humans, using PDFs.CAS 

Google Scholar 
20.Noventa, S. et al. Paradigms to assess the human health risks of nano- and microplastics. Micropl. Nanopl. 1, 9 (2021).
Google Scholar 
21.Ramsperger, A. F. R. M. et al. Environmental exposure enhances the internalization of microplastic particles into cells. Sci. Adv. 6, eabd1211 (2020).CAS 

Google Scholar 
22.Rejman, J., Oberle, V., Zuhorn, I. S. & Hoekstra, D. Size-dependent internalization of particles via the pathways of clathrin- and caveolae-mediated endocytosis. Biochem. J. 377, 159–169 (2004).CAS 

Google Scholar 
23.Ragusa, A. et al. Plasticenta: first evidence of microplastics in human placenta. Environ. Int. 146, 106274 (2021).CAS 

Google Scholar 
24.Schwabl, P. et al. Detection of various microplastics in human stool: a prospective case series. Ann. Intern. Med. 171, 453–457 (2019).
Google Scholar 
25.Connors, K. A., Dyer, S. D. & Belanger, S. E. Advancing the quality of environmental microplastic research. Environ. Toxicol. Chem. 36, 1697–1703 (2017). This paper highlights the need for better quality in microplastic research.CAS 

Google Scholar 
26.Wesch, C., Bredimus, K., Paulus, M. & Klein, R. Towards the suitable monitoring of ingestion of microplastics by marine biota: a review. Environ. Pollut. 218, 1200–1208 (2016).CAS 

Google Scholar 
27.O’Connor, J. et al. Microplastics in freshwater biota: a critical review of isolation, characterization and assessment methods. Glob. Challeng. https://doi.org/10.1002/gch2.201800118 (2019).28.de Ruijter, V. N., Redondo-Hasselerharm, P. E., Gouin, T. & Koelmans, A. A. Quality criteria for microplastic effect studies in the context of risk assessment: a critical review. Environ. Sci. Technol. 54, 11692–11705 (2020).
Google Scholar 
29.Hartmann, N. B. et al. Are we speaking the same language? Recommendations for a definition and categorization framework for plastic debris. Environ. Sci. Technol. 53, 1039–1047 (2019).CAS 

Google Scholar 
30.Kögel, T., Bjorøy, Ø., Toto, B., Bienfait, A. M. & Sanden, M. Micro- and nanoplastic toxicity on aquatic life: determining factors. Sci. Total. Environ. 709, 136050 (2020).
Google Scholar 
31.Bond, T., Ferrandiz-Mas, V., Felipe-Sotelo, M. & van Sebille, E. The occurrence and degradation of aquatic plastic litter based on polymer physicochemical properties: a review. Crit. Rev. Environ. Sci. Technol. 48, 685 (2018).
Google Scholar 
32.Riediker, M. et al. Particle toxicology and health — where are we? Part. Fibre Toxicol. 16, 1–33 (2019).
Google Scholar 
33.Kooi, M. et al. Characterizing the multidimensionality of microplastics across environmental compartments. Water Res. 202, 117429 (2021).CAS 

Google Scholar 
34.Wiesinger, H., Wang, Z. & Hellweg, S. Deep dive into plastic monomers, additives, and processing aids. Environ. Sci. Technol. 55, 9339–9351 (2021).CAS 

Google Scholar 
35.Gouin, T. Addressing the importance of microplastic particles as vectors for long-range transport of chemical contaminants: perspective in relation to prioritizing research and regulatory actions. Micropl. Nanopl. 1, 14 (2021).
Google Scholar 
36.Hermabessiere, L. et al. Occurrence and effects of plastic additives on marine environments and organisms: a review. Chemosphere 182, 781–793 (2017).CAS 

Google Scholar 
37.Gouin, T., Roche, N., Lohmann, R. & Hodges, G. A thermodynamic approach for assessing the environmental exposure of chemicals absorbed to microplastic. Environ. Sci. Technol. 45, 1466–1472 (2011).CAS 

Google Scholar 
38.Lohmann, R. Microplastics are not important for the cycling and bioaccumulation of organic pollutants in the oceans — but should microplastics be considered POPs themselves? Int. Environ. Assess. Manag. 13, 460–465 (2017).CAS 

Google Scholar 
39.Takada, H. & Karapanagioti, H. K. (eds) Hazardous Chemicals Associated with Plastics in the Marine Environment (Springer International Publishing, 2016).40.Hong, S. H., Shim, W. J. & Hong, K. Methods of analysing chemicals associated with microplastics: a review. Anal. Methods 9, 1361–1368 (2017).
Google Scholar 
41.Koelmans, A. A., Bakir, A., Burton, G. A. & Janssen, C. R. Microplastic as a vector for chemicals in the aquatic environment. critical review and model-supported re-interpretation of empirical studies. Environ. Sci. Technol. 50, 3315–3326 (2016).CAS 

Google Scholar 
42.Jahnke, A. et al. Reducing uncertainty and confronting ignorance about the possible impacts of weathering plastic in the marine environment. Environ. Sci. Technol. Lett. 4, 85–90 (2017).CAS 

Google Scholar 
43.Boucher, J. and Friot D. Primary Microplastics in the Oceans: A Global Evaluation of Sources 43 (IUCN, 2017).44.Koelmans, A. A., Kooi, M., Lavender-Law, K. & Van Sebille, E. All is not lost: deriving a top-down mass budget of plastic at sea. Environ. Res. Lett. 12, 114028 (2017).
Google Scholar 
45.Kawecki, D. & Nowack, D. Polymer-specific modeling of the environmental emissions of seven commodity plastics as macro- and microplastics. Environ. Sci. Technol. 53, 9664–9676 (2019).CAS 

Google Scholar 
46.Kooi, M., Van Nes, E. H., Scheffer, M. & Koelmans, A. A. Ups and downs in the ocean: effects of biofouling on vertical transport of microplastics. Environ. Sci. Technol. 51, 7963–7971 (2017).CAS 

Google Scholar 
47.Mateos-Cárdenas, A., O’Halloran, J., van Pelt, F. N. A. M. & Jansen, M. A. K. Rapid fragmentation of microplastics by the freshwater amphipod Gammarus duebeni (Lillj.). Sci. Rep. 10, 12799 (2020).
Google Scholar 
48.Julienne, F., Delorme, N. & Lagarde, F. From macroplastics to microplastics: role of water in the fragmentation of polyethylene. Chemosphere 236, 124409 (2019).CAS 

Google Scholar 
49.Koelmans, A. A., Redondo-Hasselerharm, P. E., Mohamed Nor, N. H. & Kooi, M. Solving the non-alignment of methods and approaches used in microplastic research in order to consistently characterize risk. Environ. Sci. Technol. 54, 12307–12315 (2020).CAS 

Google Scholar 
50.Cózar, A. et al. Plastic debris in the open ocean. Proc. Natl Acad. Sci. USA 111, 10239–10244 (2014).
Google Scholar 
51.Mattsson, K., Björkroth, F., Karlsson, T. & Hassellöv, M. Nanofragmentation of expanded polystyrene under simulated environmental weathering (thermooxidative degradation and hydrodynamic turbulence). Front. Mar. Sci., 7, 1–9 (2021). This paper demonstrates log linear particle size distributions extending to the nanoparticle scale.
Google Scholar 
52.Kaandorp, M. L. A., Dijkstra, H. A. & van Sebille, E. Modelling size distributions of marine plastics under the influence of continuous cascading fragmentation. Environ. Res. Lett. 16, 054075 (2021).
Google Scholar 
53.Koelmans, A. A., Besseling, E. & Shim, W. J. Nanoplastics in the aquatic environment. Critical review. In Marine Anthropogenic Litter (eds Bergmann, M., Gutow, L. & Klages, M.) 325–340 (Springer, 2015).54.Koelmans, A. A. et al. Microplastics in freshwaters and drinking water: critical review and assessment of data quality. Water Res. 155, 410–422 (2019).CAS 

Google Scholar 
55.Chamas, A. et al. Degradation rates of plastics in the environment. ACS Sust. Chem. Engin. 8, 3494–3511 (2020). This paper provides a rare estimate of degradation rates for plastic items in the environment.CAS 

Google Scholar 
56.Unice, K. M. et al. Characterizing export of land-based microplastics to the estuary — Part II: Sensitivity analysis of an integrated geospatial microplastic transport modeling assessment of tire and road wear particles. Sci. Total. Environ. 646, 1650–1659 (2019).CAS 

Google Scholar 
57.Buffle, J. & van Leeuwen, H. P. Environmental Particles Vol. 1 76 (CRC Press, 1992).58.Chamley, H., Clay formation through weathering. In Clay Sedimentology (Springer, 1989).59.Blott, S. J. & Pye, K. Particle size scales and classification of sediment types based on particle size distributions: review and recommended procedures. Sedimentology 59, 2071–2096 (2012).
Google Scholar 
60.Boyd, C. E. Suspended solids, color, turbidity, and light. In Water Quality 119–133 (Springer, 2020).61.Konrad, K. et al. Chemical composition and complex refractive index of Saharan mineral dust at Izaña, Tenerife (Spain) derived by electron microscopy. Atmos. Env. 41, 8058–8074 (2007).
Google Scholar 
62.Mahowald, N. et al. The size distribution of desert dust aerosols and its impact on the Earth system. Aeolian Res. 15, 53–71 (2014).
Google Scholar 
63.De Wit, C. T. & Arens, P. L. Moisture content and density of some clay minerals and some remarks on the hydration pattern of clay. Trans. Int. Congr. Soil Science 2, 59–62 (1951).
Google Scholar 
64.Utembe, W., Potgieter, K., Stefaniak, A. B. & Gulumian, M. Dissolution and biodurability: important parameters needed for risk assessment of nanomaterials. Part. Fiber Toxicol. 12, 11 (2015).
Google Scholar 
65.Köhler, S. J., Bosbach, D. & Oelkers, E. H. Do clay mineral dissolution rates reach steady state? Geochim. Cosmochim. Acta 69, 1997–2006 (2005).
Google Scholar 
66.Torrey, M. L. S. T. Chemistry of Lake Michigan (Argonne National Laboratory, 1976).67.Prestigiacomo, A. R. et al. Turbidity and suspended solids levels and loads in a sediment enriched stream: implications for impacted lotic and lentic ecosystems. Lake Res. Manag. 23, 231–244 (2007).
Google Scholar 
68.Baran, A. et al. The influence of the quantity and quality of sediment organic matter on the potential mobility and toxicity of trace elements in bottom sediment. Environ. Geochem. Health 41, 2893–2910 (2019).CAS 

Google Scholar 
69.Schwarzenbach, R. P., Gschwend, P. M. & Imboden, D. M. Environmental Organic Chemistr 3rd edn 1024 (Wiley, 2016).70.Van Valkenburg, S. D., Jones, J. K. & Heinle, D. R. A comparison by size class and volume of detritus versus phytoplankton in Chesapeake Bay. Estuar. Coast. Mar. Sci. 6, 569–582 (1978).
Google Scholar 
71.Hamilton, S. K., Sippel, S. J. & Bunn, S. E. Separation of algae from detritus for stable isotope or ecological stoichiometry studies using density fractionation in colloidal silica. Limnol. Oceanogr. Methods 3, 149–157 (2005).CAS 

Google Scholar 
72.Zimmer, M. Detritus. Encyclopedia of Ecology 903–911 (Elsevier, 2008).73.Zhao, H.-C., Wang, S.-R., Jiao, L.-X., Yang, S.-W. & Cui, C.-N. Characteristics of composition and spatial distribution of organic matter in the sediment of Erhai Lake. Res. Environ. Sci. 26, 243–249 (2013).CAS 

Google Scholar 
74.Duan, H., Feng, L., Ma, R., Zhang, Y. & Loiselle, S. A. Variability of particulate organic carbon in inland waters observed from MODIS Aqua imagery. Environ. Res. Lett. 9, 084011 (2014).CAS 

Google Scholar 
75.Suaria, G. et al. Microfibers in oceanic surface waters: a global characterization. Sci. Adv. 6, eaay8493 (2020). This paper identifies the relative proportion of microplastic fibres in the oceans.
Google Scholar 
76.Le Guen, C. et al. Microplastic study reveals the presence of natural and synthetic fibres in the diet of King penguins (Aptenodytes patagonicus) foraging from South Georgia. Environ. Intern. 134, 105303 (2020).
Google Scholar 
77.Stanton, T., Johnson, M., Nathanail, P., MacNaughtan, W. & Gomes, R. L. Sci. Total. Environ. 666, 377–389 (2019).CAS 

Google Scholar 
78.Comnea-Stancu, L. R., Wieland, H., Ramer, G., Schwaighofer, A. & Lendl, B. On the identification of rayon/viscose as a major fraction of microplastics in the marine environment: discrimination between natural and manmade cellulosic fibers using Fourier transform infrared spectroscopy. Appl. Spectrosc. 71, 939–950 (2017).CAS 

Google Scholar 
79.Seiler, W. & Crutzen, P. J. Estimates of gross and net fluxes of carbon between the biosphere and the atmosphere from biomass burning. Clim. Change 2, 207–247 (1980).CAS 

Google Scholar 
80.Cornelissen, G. et al. Critical review. Extensive sorption of organic compounds to black carbon, coal, and kerogen in sediments and soils: mechanisms and consequences for distribution, bioaccumulation and biodegradation. Environ. Sci. Technol. 39, 6881–6895 (2005).CAS 

Google Scholar 
81.Jonker, M. T. O., Hawthorne, S. B. & Koelmans, A. A. Extremely slow desorption and limited bioaccumulation of BC-associated PAHs. ACS Div. Environ. Chem. 45, 381–384 (2005).CAS 

Google Scholar 
82.Shrestha, G., Traina, S. J., Swanson & C., W. Black carbons properties and role in the environment: a comprehensive review. Sustainability 2, 294–320 (2010).CAS 

Google Scholar 
83.Bisiaux, M. M. et al. Stormwater and fire as sources of black carbon nanoparticles to Lake Tahoe. Environ. Sci. Technol. 45, 2065–2071 (2011). This paper identifies black carbon abundance in surface waters.CAS 

Google Scholar 
84.World Health Organization. Health effects of black carbon. (WHO, 2012).85.Jonker, M. T. O. & Koelmans, A. A. Sorption of polycyclic aromatic hydrocarbons and polychlorinated biphenyls to soot and soot-like materials in the aqueous environment. Mechanistic considerations. Environ. Sci. Technol. 36, 3725–3734 (2002).CAS 

Google Scholar 
86.Liu, H. et al. Mixing characteristics of refractory black carbon aerosols at an urban site in Beijing. Atmos. Chem. Phys. 20, 5771–5785 (2020).CAS 

Google Scholar 
87.Ouf, F.-X. et al. True density of soot particles: a comparison of results highlighting the influence of the organic contents. J. Aerosol Sci. 134, 1–13 (2019).CAS 

Google Scholar 
88.Wu, Y. et al. A study of the morphology and effective density of externally mixed black carbon aerosols in ambient air using a size-resolved single-particle soot photometer (SP2). Atmos. Meas. Tech. 12, 4347–4359 (2019).CAS 

Google Scholar 
89.Kuzyakov, Y., Subbotina, I., Chen, H., Bogomolova, I. & Xu, X. Black carbon decomposition and incorporation into soil microbial biomass estimated by 14C labeling. Soil. Biol. Biochem. 41, 210–219 (2009).CAS 

Google Scholar 
90.Middelburg, J. J., Nieuwenhuize, J. & Van Breugel, P. Black carbon in marine sediments. Mar. Chem. 65, 245–252 (1999).CAS 

Google Scholar 
91.Murr, L. E., Bang, J. J., Esquivel, E. V., Guerrero, P. A. & Lopez, D. A. Carbon nanotubes, nanocrystal forms and complex nanoparticle aggregates in common fuel gas combustion streams. J. Nanopart. Res. 6, 241–251 (2004).CAS 

Google Scholar 
92.Koelmans, A. A., Nowack, B. & Wiesner, M. Comparison of manufactured and black carbon nanoparticle concentrations in aquatic sediments. Environ. Pollut. 157, 1110–1116 (2009).CAS 

Google Scholar 
93.Dickens, A. F., Gelinas, Y., Masiello, C. A., Wakeham, S. & Hedges, J. I. Reburial of fossil organic carbon in marine sediments. Nature 427, 336–339 (2004).CAS 

Google Scholar 
94.Kharbush, J. J. et al. Particulate organic carbon deconstructed: molecular and chemical composition of particulate organic carbon in the ocean. Front. Mar. Sci. 7, 518 (2020).
Google Scholar 
95.Redondo-Hasselerharm, P. E. Effect assessment of nano- and microplastics in freshwater ecosystems. Thesis, Wageningen Univ. (2020).96.Allen, S. et al. Atmospheric transport and deposition of microplastics in a remote mountain catchment. Nat. Geosci. 12, 339–344 (2019).CAS 

Google Scholar 
97.Evangeliou, N. et al. Atmospheric transport is a major pathway of microplastics to remote regions. Nat. Commun. 11, 3381 (2020).CAS 

Google Scholar 
98.Velzeboer, I., Kwadijk, C. J. A. F. & Koelmans, A. A. Strong sorption of PCBs to nanoplastics, microplastics, carbon nanotubes and fullerenes. Environ. Sci. Technol. 48, 4869–4876 (2014).CAS 

Google Scholar 
99.Beckingham, B. & Ghosh, U. Differential bioavailability of polychlorinated biphenyls associated with environmental particles: microplastic in comparison to wood, coal and biochar. Environ. Pollut. 220, 150–158 (2017).CAS 

Google Scholar 
100.Liping, L. et al. Mechanism of and relation between the sorption and desorption of nonylphenol on black carbon-inclusive sediment. Environ. Pollut. 190, 101–108 (2014).
Google Scholar 
101.Voparil, I. M. et al. Digestive bioavailability to a deposit feeder (Arenicola marina) of polycyclic aromatic hydrocarbons associated with anthropogenic particles. Environ. Toxicol. Chem. 23, 2618–2626 (2004).CAS 

Google Scholar 
102.Birdwell, J., Cook, R. L. & Thibodeaux, L. J. Desorption kinetics of hydrophobic organic chemicals from sediment to water: a review of data and models. Environ. Toxicol. Chem. 26, 424–434 (2007).CAS 

Google Scholar 
103.Koelmans, A. A., Besseling, E. & Foekema, E. M. Leaching of plastic additives to marine organisms. Environ. Pollut. 187, 49–54 (2014).CAS 

Google Scholar 
104.Bundschuh, M. et al. Nanoparticles in the environment: where do we come from, where do we go to? Environ. Sci. Eur. 30, 6 (2018).
Google Scholar 
105.Peijnenburg, W. J. G. M. et al. A review of the properties and processes determining the fate of engineered nanomaterials in the aquatic environment. Crit. Rev. Environ. Sci. Technol. 45, 2084–2134 (2015).CAS 

Google Scholar 
106.Gigault, J. et al. Nanoplastics are neither microplastics nor engineered nanoparticles. Nat. Nanotechnol. 16, 501–507 (2021).CAS 

Google Scholar 
107.Ter Halle, A. et al. Nanoplastic in the North Atlantic Subtropical Gyre. Environ. Sci. Technol. 51, 13689–13697 (2017).
Google Scholar 
108.Sengul, A. B. & Asmatulu, E. Toxicity of metal and metal oxide nanoparticles: a review. Environ. Chem. Lett. 18, 1659–1683 (2020).CAS 

Google Scholar 
109.Botterell, Z. L. R. et al. Bioavailability and effects of microplastics on marine zooplankton: a review. Environ. Pollut. 245, 98–110 (2019).CAS 

Google Scholar 
110.Ribeiro, F., O’Brien, J. W., Galloway, T. & Thomas, K. V. Accumulation and fate of nano- and micro-plastics and associated contaminants in organisms. TrAC 111, 139–147 (2019).CAS 

Google Scholar 
111.da Costa Araújo, A. P. et al. How much are microplastics harmful to the health of amphibians? A study with pristine polyethylene microplastics and Physalaemus cuvieri. J. Hazard. Mater. 382, 121066 (2020).
Google Scholar 
112.Jovanović, B. Ingestion of microplastics by fish and its potential consequences from a physical perspective. Integr. Environ. Assess. Manag. 13, 510–515 (2017).
Google Scholar 
113.Windsor, F. M., Tilley, R. M., Tyler, C. R. & Ormerod, S. J. Microplastic ingestion by riverine macroinvertebrates. Sci. Total. Environ. 646, 68–74 (2018).
Google Scholar 
114.Hu, L., Chernick, M., Hinton, D. E. & Shi, H. Microplastics in small waterbodies and tadpoles from Yangtze River Delta, China. Environ. Sci. Technol. 52, 8885–8893 (2018).CAS 

Google Scholar 
115.McNeish, R. E. et al. Microplastic in riverine fish is connected to species traits. Sci. Rep. 8, 11639 (2018).CAS 

Google Scholar 
116.Duncan, E. M. et al. Microplastic ingestion ubiquitous in marine turtles. Glob. Chang. Biol. 25, 744–752 (2019).
Google Scholar 
117.Kühn, S., Bravo Rebolledo, E. L. & Van Franeker, J. A. Deleterious effects of litter on marine life. In Marine Anthropogenic Litter (eds Bergmann, M., Gutow, L. & Klages, M.) 75–116 (Springer International Publishing, 2015).118.Nelms, S. E. et al. Microplastics in marine mammals stranded around the British coast: ubiquitous but transitory? Sci. Rep. 9, 1–9 (2019).CAS 

Google Scholar 
119.O’Connor, J. D. et al. Microplastics in freshwater biota: a critical review of isolation, characterization, and assessment methods. Glob. Challen. 4, 1800118 (2019).
Google Scholar 
120.Vroom, R. J. E., Koelmans, A. A., Besseling, E. & Halsband, C. Aging of microplastics promotes their ingestion by marine zooplankton. Environ. Pollut. 231, 987–996 (2017).CAS 

Google Scholar 
121.Bour, A., Haarr, A., Keiter, S. & Hylland, K. Environmentally relevant microplastic exposure affects sediment-dwelling bivalves. Environ. Pollut. 236, 652–660 (2018).CAS 

Google Scholar 
122.Kaposi, K. L., Mos, B., Kelaher, B. P. & Dworjanyn, S. A. Ingestion of microplastic has limited impact on a marine larva. Environ. Sci. Technol. 48, 1638–1645 (2014).CAS 

Google Scholar 
123.Lu, Y. et al. Uptake and accumulation of polystyrene microplastics in zebrafish (Danio rerio) and toxic effects in liver. Environ. Sci. Technol. 50, 4054–4060 (2016).CAS 

Google Scholar 
124.Ribeiro, F. et al. Microplastics effects in Scrobicularia plana. Mar. Pollut. Bull. 122, 379–391 (2017).CAS 

Google Scholar 
125.Von Moos, N., Burkhardt-Holm, P. & Köhler, A. Uptake and effects of microplastics on cells and tissue of the blue mussel Mytilus edulis L. after an experimental exposure. Environ. Sci. Technol. 46, 11327–11335 (2012).
Google Scholar 
126.Browne, M. A., Dissanayake, A., Galloway, T. S., Lowe, D. M. & Thompson, R. C. Ingested microscopic plastic translocates to the circulatory system of the mussel, Mytilus edulis (L.). Environ. Sci. Technol. 42, 5026–5031 (2008).CAS 

Google Scholar 
127.Dawson, A. L. et al. Turning microplastics into nanoplastics through digestive fragmentation by Antarctic krill. Nat. Commun. 9, 1001 (2018).
Google Scholar 
128.Zhang, C., Chen, X., Wang, J. & Tan, L. Toxic effects of microplastic on marine microalgae Skeletonema costatum: interactions between microplastic and algae. Environ. Pollut. 220, 1282–1288 (2017).CAS 

Google Scholar 
129.Mateos-Cárdenas, A. et al. Polyethylene microplastics adhere to Lemna minor (L.), yet have no effects on plant growth or feeding by Gammarus duebeni (Lillj.). Sci. Total. Environ. 689, 413–421 (2019).
Google Scholar 
130.Murphy, F. & Quinn, B. The effects of microplastic on freshwater Hydra attenuata feeding, morphology and reproduction. Environ. Pollut. 234, 487–494 (2018).CAS 

Google Scholar 
131.Cole, M. et al. Microplastic ingestion by zooplankton. Environ. Sci. Technol. 47, 6646–6655 (2013).CAS 

Google Scholar 
132.Green, D. S., Boots, B., O’Connor, N. E. & Thompson, R. Microplastics affect the ecological functioning of an important biogenic habitat. Environ. Sci. Technol. 51, 68–77 (2017).CAS 

Google Scholar 
133.Senga Green, D. Effects of microplastics on European flat oysters, Ostrea edulis and their associated benthic communities. Environ. Pollut. 216, 95–103 (2016).
Google Scholar 
134.Ziajahromi, S., Kumar, A., Neale, P. A. & Leusch, F. D. L. Environmentally relevant concentrations of polyethylene microplastics negatively impact the survival, growth and emergence of sediment-dwelling invertebrates. Environ. Pollut. 236, 425–431 (2018).CAS 

Google Scholar 
135.Ogonowski, M., Schür, C., Jarsén, Å. & Gorokhova, E. The effects of natural and anthropogenic microparticles on individual fitness in daphnia magna. PLoS ONE 11, e0155063 (2016). This paper systematically addresses the differences between the biological effects of microplastic and natural particles.
Google Scholar 
136.Mazurais, D. et al. Evaluation of the impact of polyethylene microbeads ingestion in European sea bass (Dicentrarchus labrax) larvae. Mar. Environ. Res. 112, 78–85 (2015).CAS 

Google Scholar 
137.Lee, K.-W., Shim, W. J., Kwon, O. Y. & Kang, J.-H. Size-dependent effects of micro polystyrene particles in the marine copepod Tigriopus japonicus. Env. Sci. Technol. 47, 11278–11283 (2013).CAS 

Google Scholar 
138.Au, S. Y., Bruce, T. F., Bridges, W. C. & Klaine, S. J. Responses of Hyalella azteca to acute and chronic microplastic exposures. Environ. Toxicol. Chem. 34, 2564–2572 (2015).CAS 

Google Scholar 
139.Cole, M., Lindeque, P., Fileman, E., Halsband, C. & Galloway, T. S. The impact of polystyrene microplastics on feeding, function and fecundity in the marine copepod Calanus helgolandicus. Environ. Sci. Technol. 49, 1130–1137 (2015).CAS 

Google Scholar 
140.Sussarellu, R. et al. Oyster reproduction is affected by exposure to polystyrene microplastics. Proc. Natl Acad. Sci. USA 113, 2430–2435 (2016).CAS 

Google Scholar 
141.Jeong, C. B. et al. Microplastic size-dependent toxicity, oxidative stress induction, and p-JNK and p-p38 activation in the monogonont rotifer (Brachionus koreanus). Environ. Sci. Technol. 50, 8849–8857 (2016).CAS 

Google Scholar 
142.Blarer, P. & Burkhardt-Holm, P. Microplastics affect assimilation efficiency in the freshwater amphipod Gammarus fossarum. Environ. Sci. Pollut. Res. 23, 23522–23532 (2016).CAS 

Google Scholar 
143.Wright, S. L., Rowe, D., Thompson, R. C. & Galloway, T. S. Microplastic ingestion decreases energy reserves in marine worms. Curr. Biol. 23, R1031–R1033 (2013).CAS 

Google Scholar 
144.Straub, S., Hirsch, P. E. & Burkhardt-Holm, P. Biodegradable and petroleum-based microplastics do not differ in their ingestion and excretion but in their biological effects in a freshwater invertebrate Gammarus fossarum. Int. J. Environ. Res. Public Health 14, 774 (2017).
Google Scholar 
145.Green, D. S., Boots, B., Sigwart, J., Jiang, S. & Rocha, C. Effects of conventional and biodegradable microplastics on a marine ecosystem engineer (Arenicola marina) and sediment nutrient cycling. Environ. Pollut. 208, 426–434 (2016).CAS 

Google Scholar 
146.Ziajahromi, S., Kumar, A., Neale, P. A. & Leusch, F. D. L. Impact of microplastic beads and fibers on waterflea (Ceriodaphnia dubia) survival, growth, and reproduction: implications of single and mixture exposures. Environ. Sci. Technol. 51, 13397–13406 (2017).CAS 

Google Scholar 
147.Nobre, C. R. et al. Assessment of microplastic toxicity to embryonic development of the sea urchin Lytechinus variegatus (Echinodermata: Echinoidea). Mar. Pollut. Bull. 92, 99–104 (2015).CAS 

Google Scholar 
148.Rehse, S., Kloas, W. & Zarfl, C. Short-term exposure with high concentrations of pristine microplastic particles leads to immobilisation of Daphnia magna. Chemosphere 153, 91–99 (2016).CAS 

Google Scholar 
149.Gambardella, C. et al. Effects of polystyrene microbeads in marine planktonic crustaceans. Ecotoxicol. Environ. Saf. 145, 250–257 (2017).CAS 

Google Scholar 
150.Watts, A. J. R. et al. Effect of microplastic on the gills of the shore crab Carcinus maenas. Environ. Sci. Technol. 50, 5364–5369 (2016).CAS 

Google Scholar 
151.Espinosa, C., Cuesta, A. & Esteban, M. Á. Effects of dietary polyvinylchloride microparticles on general health, immune status and expression of several genes related to stress in gilthead seabream (Sparus aurata L.). Fish. Shellfish. Immunol. 68, 251–259 (2017).CAS 

Google Scholar 
152.Jin, Y., Lu, L., Tu, W., Luo, T. & Fu, Z. Impacts of polystyrene microplastic on the gut barrier, microbiota and metabolism of mice. Sci. Total. Environ. 649, 308–317 (2019).CAS 

Google Scholar 
153.Jin, Y. et al. Polystyrene microplastics induce microbiota dysbiosis and inflammation in the gut of adult zebrafish. Environ. Pollut. 235, 322–329 (2018).CAS 

Google Scholar 
154.Bucci, K., Tulio, M. & Rochman, C. M. What is known and unknown about the effects of plastic pollution: a meta-analysis and systematic review. Ecol. Appl. 30, e02044 (2020). This paper reviews the evidence for effects of plastic pollution across endpoints, organisms and levels of biological organization.CAS 

Google Scholar 
155.Kjelland, M. E., Woodley, C. M., Swannack, T. M. & Smith, D. L. A review of the potential effects of suspended sediment on fishes: potential dredging-related physiological, behavioral, and transgenerational implications. Environ. Syst. Decis. 35, 334–350 (2015).
Google Scholar 
156.Michel, C., Herzog, S., de Capitani, C., Burkhardt-Holm, P. & Pietsch, C. Natural mineral particles are cytotoxic to rainbow trout gill epithelial cells in vitro. PLoS ONE 9, e100856 (2014).
Google Scholar 
157.Gordon, A. K. & Palmer, C. G. Defining an exposure-response relationship for suspended kaolin clay particulates and aquatic organisms: work toward defining a water quality guideline for suspended solids. Environ. Toxicol. Chem. 34, 907–912 (2015).CAS 

Google Scholar 
158.Lu, C., Kania, P. W. & Buchmann, K. Particle effects on fish gills: an immunogenetic approach for rainbow trout and zebrafish. Aquaculture 484, 98–104 (2018).CAS 

Google Scholar 
159.Ogonowski, M., Gerdes, Z. & Gorokhova, E. What we know and what we think we know about microplastic effects — a critical perspective. Curr. Opin. Environ. Sci. Health 1, 41–46 (2018).
Google Scholar 
160.Albarano, L. et al. Comparison of in situ sediment remediation amendments: risk perspectives from species sensitivity distribution. Environ. Pollut. 272, 115995 (2021).CAS 

Google Scholar 
161.Newcombe, C. P. & Macdonald, D. D. Effects of suspended sediments on aquatic ecosystems. North. Am. J. Fish. Manag. 11, 72–82 (1991).
Google Scholar 
162.Yap, V. H. et al. A comparison with natural particles reveals a small specific effect of PVC microplastics on mussel performance. Mar. Pollut. Bull. 160, 111703 (2020).CAS 

Google Scholar 
163.Schür, C., Zipp, S., Thalau, T. & Wagner, M. Microplastics but not natural particles induce multigenerational effects in Daphnia magna. Environ. Pollut. 260, 113904 (2020).
Google Scholar 
164.Gerdes, Z., Hermann, M., Ogonowski, M. & Gorokhova, E. A novel method for assessing microplastic effect in suspension through mixing test and reference materials. Sci. Rep. 9, 1–9 (2019).CAS 

Google Scholar 
165.Niranjan, R. & Thakur, A. K. The toxicological mechanisms of environmental soot (black carbon) and carbon black: focus on oxidative stress and inflammatory pathways. Front. Immunol. 8, 763 (2017).
Google Scholar 
166.Tsuji, J. S. et al. Research strategies for safety evaluation of nanomaterials. Part IV: Risk assessment of nanoparticles. Toxicol. Sci. 89, 42–50 (2006).CAS 

Google Scholar 
167.Schwarze, P. E. et al. Importance of size and composition of particles for effects on cells in vitro. Inhal. Toxicol. 19, 17–22 (2007).CAS 

Google Scholar 
168.Schmid, O. & Stoeger, T. Surface area is the biologically most effective dose metric for acute nanoparticle toxicity in the lung. J. Aerosol Sci. 99, 133–143 (2016). This paper identifies the toxicologically relevant dose metric for particle effects.CAS 

Google Scholar 
169.Fubini, B. Surface reactivity in the pathogenic response to particulates. Environ. Health Perspect. 105, 1013–1020 (1997).
Google Scholar 
170.Poland, C. A., Duffin, R. & Donaldson, K. High aspect ratio nanoparticles and the fibre pathogenicity paradigm. In Nanotoxicity Vivo and In Vitro Models to Health Risks 61–80 (John Wiley and Sons, 2009).171.Gualtieri, A. F. Bridging the gap between toxicity and carcinogenicity of mineral fibres by connecting the fibre crystal-chemical and physical parameters to the key characteristics of cancer. Curr. Res. Toxicol. 2, 42–52 (2021).
Google Scholar 
172.Shao, X. R. et al. Independent effect of polymeric nanoparticle zeta potential/surface charge, on their cytotoxicity and affinity to cells. Cell Prolif. 48, 465–474 (2015).CAS 

Google Scholar 
173.Motskin, M. et al. Hydroxyapatite nano and microparticles: correlation of particle properties with cytotoxicity and biostability. Biomaterials 30, 3307–3317 (2009).CAS 

Google Scholar 
174.Cox, K. D. et al. Human consumption of microplastics. Environ. Sci. Technol. 53, 7068–7074 (2019).CAS 

Google Scholar 
175.Zhang, Q. et al. A review of microplastics in table salt, drinking water, and air: direct human exposure. Environ. Sci. Technol. 54, 3740–3751 (2020).CAS 

Google Scholar 
176.Everaert, G. et al. Risk assessment of microplastics in the ocean: modelling approach and first conclusions. Environ. Pollut. 242, 1930–1938 (2018).CAS 

Google Scholar 
177.Everaert, G. et al. Risks of floating microplastic in the global ocean. Environ. Pollut. 267, 115499 (2020).CAS 

Google Scholar 
178.Zhang, X., Leng, Y., Liu, X., Huang, K. & Wang, J. Microplastics’ pollution and risk assessment in an urban river: a case study in the Yongjiang River, Nanning City, South China. Exposure Health 12, 141–151 (2020).CAS 

Google Scholar 
179.Skåre, J. U. et al. Microplastics, occurrence, levels and implications for environment and human health related to food. Opinion of the steering committee of the Norwegian Scientific Committee for Food and Environment (VKM, 2019).180.Adam, V., von Wyl, A. & Nowack, B. Probabilistic environmental risk assessment of microplastics in marine habitats. Aq. Toxicol. 230, 105689 (2021).CAS 

Google Scholar 
181.Jung, J.-W. et al. Ecological risk assessment of microplastics in coastal, shelf, and deep sea waters with a consideration of environmentally relevant size and shape. Environ. Pollut. 270, 116217 (2021).CAS 

Google Scholar 
182.Posthuma, L., Suter, G. W. & Traas, T. P. Species Sensitivity Distributions In Ecotoxicology (Lewis, 2002).183.Gouin, T. et al. Toward the development and application of an environmental risk assessment framework for microplastic. Environ. Toxicol. Chem. 38, 2087–2100 (2019).CAS 

Google Scholar 
184.Kong, X. & Koelmans, A. A. Effects of microplastic on shallow lake food webs. Environ. Sci. Technol. 53, 13822–13831 (2019).CAS 

Google Scholar 
185.Zimmermann, L., Göttlich, S., Oehlmann, J., Wagner, M. & Völker, C. What are the drivers of microplastic toxicity? Comparing the toxicity of plastic chemicals and particles to Daphnia magna. Environ. Pollut. 267, 115392 (2020).CAS 

Google Scholar 
186.Tian, Z. et al. A ubiquitous tire rubber-derived chemical induces acute mortality in coho salmon. Science 371, 185–189 (2021).CAS 

Google Scholar 
187.Bakir, A., O’Connor, I. A., Rowland, S. J., Hendriks, A. J. & Thompson, R. C. Relative importance of microplastics as a pathway for the transfer of hydrophobic organic chemicals to marine life. Environ. Pollut. 219, 56–65 (2016).CAS 

Google Scholar 
188.Capolupo, M., Sørensen, L., Jayasena, K., Booth, A. M. & Fabbri, E. Chemical composition and ecotoxicity of plastic and car tire rubber leachates to aquatic organisms. Water Res. 169, 115270 (2020).CAS 

Google Scholar 
189.Zimmermann, L. et al. Plastic products leach chemicals that induce in vitro toxicity under realistic use conditions. Environ. Sci. Technol. 55, 11814–11823 (2021).CAS 

Google Scholar 
190.Bucci, K. & Rochman, C. M. A proposed framework for microplastics risk assessment [abstract 07.05.02]. Society of Environmental Toxicology and Chemistry North America 42nd Annual Meeting – SETAC SciCon4 https://scicon4.setac.org/wp-content/uploads/2021/11/SciCon4-abstract-book.pdf (2021).191.Primpke, S., Lorenz, C., Rascher-Friesenhausen, R. & Gerdts, G. An automated approach for microplastics analysis using focal plane array (FPA) FTIR microscopy and image analysis. Anal. Methods 9, 1499–1511 (2017).CAS 

Google Scholar 
192.Rauchschwalbe, M.-T., Fueser, H., Traunspurger, W. & Höss, S. Bacterial consumption by nematodes is disturbed by the presence of polystyrene beads: the roles of food dilution and pharyngeal pumping. Environ. Pollut. 273, 116471 (2021).CAS 

Google Scholar 
193.Donaldson, K. & Seaton, A. A short history of the toxicology of inhaled particles. Part. Fibre Toxicol. 9, 13 (2012).
Google Scholar 
194.Primpke, S., Dias, A. P. & Gerdts, G. Automated identification and quantification of microfibers and microplastics. Anal. Methods 11, 2138–2147 (2019).CAS 

Google Scholar  More

Our results show that differences in environmental conditions between inland and coastal sinkholes, caused mainly by the inflow of seawater in the latter, influence the microbial community structure of their sediments. Furthermore, the microbial community structure also varied within the sinkholes and according to the sediment zone sampled, suggesting that a connection between the atmosphere in the outermost location of the sediments and sunlight creates an environment distinct from that found in deeper caves. Together with the different environmental factors that were measured (in situ physicochemical composition of water and sediment) these characteristics could drive niche-specific microbial community structures associated with the sediment zones. Additionally, beta-diversity analysis showed separate clustering of the sediment microbial communities from the coastal and inland sinkholes, and of the WM zone from cavern and cave zones at both sinkholes. Microbial community structure associated with karst environments have shown to be significantly influenced by environmental factors as seen in the Bahamian blue holes19, a coastal sinkhole13, a Floridan anchialine sinkhole20, and sediments from Chinese karst caves21.Microbial communities from karst sediments can be limited by nutrients such as carbon, phosphorus, and nitrogen21, therefore, influencing their structure. Differences in the microbial community composition associated with multiple environmental factors (moisture, type of niche, nitrogen) were also reported in karst cave sediments from China21. Previous studies had shown there was no effect on the alpha diversity of water column assemblages in the Yucatan groundwater associated with the type of sinkhole (inland or coastal)6. However, this observation may be limited due to the low sample number used in the study6. For other karst sinkholes, the microbial community dynamics differ between the water column and the sediments6,22,23,24.The karst caves and sinkholes of the underground river in Yucatan are characterized by low phosphorus concentrations and high levels of nitrate, mostly related to Anthropocentric activities (urban developments, farms and agriculture)3. The inland sinkhole at Noh Mozón showed the highest concentration of nitrate detected in the study and, not surprisingly, the area is surrounded by agricultural fields. The presence of organic matter in the sinkholes from the Yucatan peninsula are highly dependent on the connection between the cave systems, on the levels of exposure to light, and on their morphology3. High concentrations of organic carbon (661 ± 132 μM) and methane (6466 ± 659 nM) have been reported in the top layer of the water masses in coastal sinkholes before13. In this study, the highest concentration of organic carbon was observed in the sediments from the coastal sinkhole, likely originating from the surrounding vegetation and from seawater intrusion. We hypothesize that the differences in nutrients found at these two types of sinkholes influence the structure of their microbial communities.Other environmental factors such as pH and dissolved oxygen (DO), may also contribute significantly to the composition and structure of microbial communities, as seen in freshwater lake sediments22. Davis and Garey20 reported distinct microbial communities with unique functions for each water layer from an anchialine sinkhole from the Florida karst aquifer and suggested that this occurred as a result of the influence of the hydrochemistry, including differences in the concentration carbon and other nutrients from the environment20. Analyses of the sinkhole caves from the Yucatan underwater river support observations that physical and chemical parameters create distinct ecological niches which host unique microbes, as a high abundance of exclusive (not shared) ASVs were observed in the three sediment zones at both locations.The taxonomic diversity from the coastal and inland sinkholes included Chloroflexi, Crenarchaeota, Desulfobacterota, Proteobacteria, Nitrospirota, Bacteroidota, and Firmicutes as the most abundant phyla in the sediment samples, however, there were differences in the relative abundance associated with the type of sinkhole and sediment zone. Some of these phyla (Chloroflexi, Proteobacteria, and Bacteroidetes) have been reported in sediments from freshwater karst sinkholes from Lake Huron25 in water and sediments from other sinkholes in the Yucatan peninsula6, and in the karst caves bacteriome from southwest China21. A study that included coastal marine sediments from two sites in the Yucatan peninsula, showed high abundances of Spirochaeta, Desulfococcus, Clostridium, Psychrobacter26, four genera that were abundant in the coastal sinkhole. However, Desulfococcus, Synechococcus were also abundant in the inland sinkhole. Of the most abundant genera reported for sediments from different marine environments in the Yucatan coast23, Acinetobacter, Desulfotignum, Desulfovibrio, Pseudomonas, Sedimenticola, and Sulfurimonas were also present in the coastal sinkhole while only Pseudomonas and Sedimenticola were also present in the inland sinkhole23. The high number of families shared between the coastal sinkhole and marine sediments from the Yucatan coast, together with the salinity levels registered at the bottom layer of the water column in the coastal sinkhole, suggest an interconnection between these two environments which shapes the microbial communities present in the sediments of caverns and caves of this sinkhole. The genus Nitrospira was abundant in the WM from the coastal sinkhole and in all sediment zones from the inland sinkhole. This genus has been reported as one of the most abundant in the surface of speleothems from El Zapote coastal sinkhole2, and is considered a complete ammonia oxidizer (comammox), meaning it converts ammonia to nitrate through nitrite. A negative correlation between abundance of this genus and salinity has been reported before, which could explain the low concentration of Nitrospira in the cavern and cave from the coastal sinkhole, where the highest salinity was observed27. Connectivity between coastal sinkholes and the ocean, as well as the terrestrial input of soil organic matter (OM) has been reported for the underground karst aquifer in the Yucatan peninsula13. As in other sediments, degradation of OM is carried out by several MFGs including acetogenic bacteria, methanogens, and sulfate reducers13,20,28. When these MFGs were analyzed in coastal and inland sinkholes, differences in their relative abundances were clearly marked by the type of sinkhole and by the sediment zone analyzed, supporting the hypothesis that environmental differences drive microbial community distributions in these niches. The high abundance of sulfate-reducing bacteria (SRB) in the three sediment zones from the coastal sinkhole suggests that sulfate reduction is a predominant function. SRB degrade organic matter using sulfate with sulfide as waste or end-product19,30, originating hydrogen sulfide (H2S)29, which could explain the low concentration of sulfate the hydrogen sulfide (H2S) cloud observed and previously reported in the WM zone of El Zapote coastal sinkhole30. In this study, high levels of sulfate (SO−4) were measured in the water samples from the cavern and cave zones from El Zapote sinkhole, which could be associated with sulfate-rich deposits, such as gypsum beds, which have been reported in other sinkholes from the Yucatan peninsula (up to 2400 mg/L of sulfate concentration)3. However, we do not disregard other possible sources of sulfate, associated with seawater intrusion or as a product of sulfide or sulfur oxidation29,31 by sulfur-oxidizing bacteria detected in this study.The inland sinkhole had a low concentration of sulfate and low abundances of SRB. The high abundance of methanogenic bacteria in the WM zone from the coastal sinkhole detected in the MFG analysis supports the previous hypothesis of acetoclastic methanogenesis due to high inputs of organic matter13. Methylotrophic bacteria were most abundant at the inland sinkhole in the WM zone, suggesting the presence of methyl compounds, such as methane or methanol which can be used as a source of carbon and energy32. High methane concentrations have been quantified in shallow water masses from the Yucatan aquifer system13, consistent with observations from this study. ‘Candidatus Methylomirabilis’ was identified in the sediment of the WM zone from Noh-Mozón and has been previously described as being able to perform nitrite-dependent anaerobic methane oxidation, using methane as electron donor and nitrate and nitrite as electron acceptors33, which would be possible in these sediments considering the low levels of oxygen (average of 2.3 mg/L) detected in the water column above them and assuming this would lead to lower levels of oxygen in the sediments. We hypothesize that bacteria from this genus could be using the nitrite produced by ammonia oxidizing bacteria and archaea observed in this zone (Nitrosomonadaceae, Nitrospiraceae, and Nitrosococcaceae). The low abundance of methanotrophic microbes in the coastal sinkhole (mainly the cavern and cave zones) could be derived from the high concentrations of hydrogen sulfide previously reported at this location, which have been suggested to be toxic to methane-oxidizing microbes34. Therefore, a decrease in the anaerobic oxidation of methane, and a poor methane removal capacity is hypothesized in the sediments from this coastal sinkhole. Further research could focus on the influence of the saline intrusion on methanotrophic microbes and methane levels in El Zapote sinkhole sediments. As expected, photosynthetic bacterial abundances differed with the type of sinkhole and sediment zone. Both sinkholes are so the presence of daylight can start the photosynthetic process which would occur most in the WM zone. However, only the inland sinkhole showed a high abundance of photosynthetic bacteria within its WM zone. The coastal sinkhole water column and sediments would be deprived of photosynthetic bacteria since the water source is the underground aquifer, lacks photosynthesizers. Ammonia oxidizing bacteria (AOB) and archaea (AOA), and nitrite-oxidizing bacteria (NOB) were relatively abundant in the three sediment zones from the inland sinkhole and in the WM from the coastal sinkhole, these observation at the WM from El Zapote agree with previous observations2. Anaerobic ammonium oxidation (anammox) uses nitrite (as a product of nitrate reduction), as electron acceptor35. The high levels of nitrate concentration in the water column from the three sediment zones at the inland sinkhole and at the WM from the coastal sinkhole may influence the abundance of AOB, AOA and NOB in the sediments from these zones, while NH4+ and nitrite values were below detection limit ( More

1.Clobert, J., Danchin, E., Dhondt, A. A. & Nichols, J. D. Dispersal (Oxford University Press, 2001).
Google Scholar 
2.Ronce, O. How does it feel to be like a rolling stone? Ten questions about dispersal evolution. Annu. Rev. Ecol. Evol. Syst. 38, 231–253 (2007).
Google Scholar 
3.Clobert, J., Baguette, M., Benton, T. G. & Bullock, J. M. Dispersal Ecology and Evolution (Oxford University Press, 2012).
Google Scholar 
4.Cote, J., Fogarty, S., Brodin, T., Weinersmith, K. & Sih, A. Personality-dependent dispersal in the invasive mosquitofish: Group composition matters. Proc. R. Soc. B Biol. Sci. 278, 1670–1678 (2011).
Google Scholar 
5.Quinn, J. L., Cole, E. F., Patrick, S. C. & Sheldon, B. C. Scale and state dependence of the relationship between personality and dispersal in a great tit population. J. Anim. Ecol. 80, 918–928 (2011).PubMed 

Google Scholar 
6.Brodin, T., Lind, M. I., Wiberg, M. K. & Johansson, F. Personality trait differences between mainland and island populations in the common frog (Rana temporaria). Behav. Ecol. Sociobiol. 67, 135–143 (2013).
Google Scholar 
7.Wilson, D. S. Adaptive individual differences within single populations. Philos. Trans. R. Soc. London. Ser. B Biol. Sci. 353, 199–205 (1998).
Google Scholar 
8.Sih, A., Bell, A. & Johnson, J. C. Behavioral syndromes: An ecological and evolutionary overview. Trends Ecol. Evol. 19, 372–378 (2004).PubMed 

Google Scholar 
9.Sih, A., Bell, A. M., Johnson, J. C. & Ziemba, R. E. Behavioral syndromes: An integrative overview. Q. Rev. Biol. 79, 241–277 (2004).PubMed 

Google Scholar 
10.Réale, D., Reader, S. M., Sol, D., McDougall, P. T. & Dingemanse, N. J. Integrating animal temperament within ecology and evolution. Biol. Rev. 82, 291–318 (2007).PubMed 

Google Scholar 
11.Wolf, M. & Weissing, F. J. Animal personalities: Consequences for ecology and evolution. Trends Ecol. Evol. 27, 452–461 (2012).PubMed 

Google Scholar 
12.Juette, T., Cucherousset, J. & Cote, J. Animal personality and the ecological impacts of freshwater non-native species. Curr. Zool. 60, 417–427 (2014).
Google Scholar 
13.Duckworth, R. A. & Badyaev, A. V. Coupling of dispersal and aggression facilitates the rapid range expansion of a passerine bird. Proc. Natl. Acad. Sci. 104, 15017–15022 (2007).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
14.Conrad, J. L., Weinersmith, K. L., Brodin, T., Saltz, J. B. & Sih, A. Behavioural syndromes in fishes: A review with implications for ecology and fisheries management. J. Fish Biol. 78, 395–435 (2011).CAS 
PubMed 

Google Scholar 
15.Cote, J., Fogarty, S., Weinersmith, K., Brodin, T. & Sih, A. Personality traits and dispersal tendency in the invasive mosquitofish (Gambusia affinis). Proc. R. Soc. B Biol. Sci. 277, 1571–1579 (2010).
Google Scholar 
16.Malange, J., Izar, P. & Japyassú, H. Personality and behavioural syndrome in Necromys lasiurus (Rodentia: Cricetidae): Notes on dispersal and invasion processes. Acta Ethol. 19, 189–195 (2016).
Google Scholar 
17.Rees, E. M. A. et al. Socio-economic drivers of specialist anglers targeting the non-native European catfish (Silurus glanis) in the UK. PLoS ONE 12, e0178805 (2017).PubMed 
PubMed Central 

Google Scholar 
18.Bowler, D. E. & Benton, T. G. Causes and consequences of animal dispersal strategies: Relating individual behaviour to spatial dynamics. Biol. Rev. 80, 205–225 (2005).PubMed 

Google Scholar 
19.Clobert, J., Le Galliard, J.-F., Cote, J., Meylan, S. & Massot, M. Informed dispersal, heterogeneity in animal dispersal syndromes and the dynamics of spatially structured populations. Ecol. Lett. 12, 197–209 (2009).PubMed 

Google Scholar 
20.Dukes, J. S. & Mooney, H. A. Does global change increase the success of biological invaders?. Trends Ecol. Evol. 14, 135–139 (1999).CAS 
PubMed 

Google Scholar 
21.Gozlan, R. E., Britton, J. R., Cowx, I. & Copp, G. H. Current knowledge on non-native freshwater fish introductions. J. Fish Biol. 76, 751–786 (2010).
Google Scholar 
22.Pimentel, D. et al. Economic and environmental threats of alien plant, animal, and microbe invasions. Agric. Ecosyst. Environ. 84, 1–20 (2001).
Google Scholar 
23.Dingemanse, N. J., Kazem, A. J. N., Réale, D. & Wright, J. Behavioural reaction norms: Animal personality meets individual plasticity. Trends Ecol. Evol. 25, 81–89 (2010).PubMed 

Google Scholar 
24.Dochtermann, N. A., Schwab, T. & Sih, A. The contribution of additive genetic variation to personality variation: Heritability of personality. Proc. R. Soc. B Biol. Sci. 282, 20142201 (2015).
Google Scholar 
25.Duckworth, R. A. Evolution of personality: Developmental constraints on behavioral flexibility. Auk 127, 752–758 (2010).
Google Scholar 
26.Trillmich, F., Müller, T. & Müller, C. Understanding the evolution of personality requires the study of mechanisms behind the development and life history of personality traits. Biol. Lett. 14, 20170740 (2018).PubMed 
PubMed Central 

Google Scholar 
27.Dingemanse, N. J. & Réale, D. Natural selection and animal personality. Behaviour 142, 1159–1184 (2005).
Google Scholar 
28.Sih, A., Cote, J., Evans, M., Fogarty, S. & Pruitt, J. Ecological implications of behavioural syndromes. Ecol. Lett. 15, 278–289 (2012).PubMed 

Google Scholar 
29.Stamps, J. A. Growth-mortality tradeoffs and ‘personality traits’ in animals. Ecol. Lett. 10, 355–363 (2007).PubMed 

Google Scholar 
30.Chapple, D. G., Simmonds, S. M. & Wong, B. B. M. Can behavioral and personality traits influence the success of unintentional species introductions?. Trends Ecol. Evol. 27, 57–64 (2012).PubMed 

Google Scholar 
31.Hirsch, P. E., Thorlacius, M., Brodin, T. & Burkhardt-Holm, P. An approach to incorporate individual personality in modeling fish dispersal across in-stream barriers. Ecol. Evol. 7, 720–732 (2017).PubMed 

Google Scholar 
32.Groen, M. et al. Is there a role for aggression in round goby invasion fronts?. Behaviour 149, 685–703 (2012).
Google Scholar 
33.Urban, M. C., Phillips, B. L., Skelly, D. K. & Shine, R. A toad more traveled: The heterogeneous invasion dynamics of cane toads in Australia. Am. Nat. 171, E134–E148 (2008).PubMed 

Google Scholar 
34.Lopez, D. P., Jungman, A. A. & Rehage, J. S. Nonnative African jewelfish are more fit but not bolder at the invasion front: A trait comparison across an Everglades range expansion. Biol. Invasions 14, 2159–2174 (2012).
Google Scholar 
35.Dingemanse, N. J. & Wolf, M. Recent models for adaptive personality differences: A review. Philos. Trans. R. Soc. B Biol. Sci. 365, 3947–3958 (2010).
Google Scholar 
36.Dingemanse, N. J. & Réale, D. What is the evidence that natural selection maintains variation in animal personalities? In Animal Personalities: Behavior, Physiology, and Evolution (eds Carere, C. & Maestripieri, D.) 201–220 (University of Chicago Press, 2013).
Google Scholar 
37.Weiss, A. Personality traits: A view from the animal kingdom. J. Pers. 86, 12–22 (2018).PubMed 

Google Scholar 
38.Archard, G. A. & Braithwaite, V. A. The importance of wild populations in studies of animal temperament. J. Zool. 281, 149–160 (2010).
Google Scholar 
39.Holt, R. D., Keitt, T. H., Lewis, M. A., Maurer, B. A. & Taper, M. L. Theoretical models of species’ borders: Single species approaches. Oikos 108, 18–27 (2005).
Google Scholar 
40.Liedvogel, M., Chapman, B. B., Muheim, R. & Åkesson, S. The behavioural ecology of animal movement: Reflections upon potential synergies. Anim. Migr. 1, 39–46 (2013).
Google Scholar 
41.Campos-Candela, A., Palmer, M., Balle, S., Álvarez, A. & Alós, J. A mechanistic theory of personality-dependent movement behaviour based on dynamic energy budgets. Ecol. Lett. 22, 213–232 (2019).PubMed 

Google Scholar 
42.Bubb, D. H., Thom, T. J. & Lucas, M. C. Movement, dispersal and refuge use of co-occurring introduced and native crayfish. Freshw. Biol. 51, 1359–1368 (2006).
Google Scholar 
43.Luque, G. M. et al. The 100th of the world’s worst invasive alien species. Biol. Invasions 16, 981–985 (2014).
Google Scholar 
44.Galib, S. M., Findlay, J. S. & Lucas, M. C. Strong impacts of signal crayfish invasion on upland stream fish and invertebrate communities. Freshw. Biol. 66, 223–240 (2021).
Google Scholar 
45.Lindstrom, T., Brown, G. P., Sisson, S. A., Phillips, B. L. & Shine, R. Rapid shifts in dispersal behavior on an expanding range edge. Proc. Natl. Acad. Sci. 110, 13452–13456 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
46.Bubb, D. H., Thom, T. J. & Lucas, M. C. The within-catchment invasion of the non-indigenous signal crayfish Pacifastacus leniusculus (Dana), in upland rivers. Bull. Fr. Pêche Piscic. 376–377, 665–673 (2005).
Google Scholar 
47.Závorka, L., Lassus, R., Britton, J. R. & Cucherousset, J. Phenotypic responses of invasive species to removals affect ecosystem functioning and restoration. Glob. Chang. Biol. https://doi.org/10.1111/gcb.15271 (2020).Article 
PubMed 

Google Scholar 
48.Sbragaglia, V. & Breithaupt, T. Daily activity rhythms, chronotypes, and risk-taking behavior in the signal crayfish. Curr. Zool. https://doi.org/10.1093/cz/zoab023 (2021).Article 

Google Scholar 
49.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.r-project.org/ (2020).50.Pintor, L. M., Sih, A. & Bauer, M. L. Differences in aggression, activity and boldness between native and introduced populations of an invasive crayfish. Oikos 117, 1629–1636 (2008).
Google Scholar 
51.Rupia, E. J., Binning, S. A., Roche, D. G. & Lu, W. Fight-flight or freeze-hide? Personality and metabolic phenotype mediate physiological defence responses in flatfish. J. Anim. Ecol. 85, 927–937 (2016).PubMed 

Google Scholar 
52.Karavanich, C. & Atema, J. Individual recognition and memory in lobster dominance. Anim. Behav. 56, 1553–1560 (1998).CAS 
PubMed 

Google Scholar 
53.Houlihan, D., Govind, C. & El Haj, A. Energetics of swimming in Callinectes sapidus and walking in Homarus americanus. Comp. Biochem. Physiol. Part A Physiol. 82, 267–279 (1985).
Google Scholar 
54.Vogt, G. Functional anatomy. In Biology of Freshwater Crayfish (ed. Holdich, D. M.) 53–151 (Blackwell Science Ltd., 2002).
Google Scholar 
55.Southwood, T. R. E. & Henderson, P. A. Ecological Methods (Blackwell Science Ltd., 2000).
Google Scholar 
56.Clark, J. & Kershner, M. Size-dependent effects of visible implant elastomer marking on crayfish (Orconectes obscurus) growth, mortality, and tag retention. Crustaceana 79, 275–284 (2006).
Google Scholar 
57.Streissl, F. & Hödl, W. Habitat and shelter requirements of the stone crayfish, Austropotamobius torrentium Schrank. Hydrobiologia 477, 195–199 (2002).
Google Scholar 
58.Chadwick, D. D. A. et al. A novel ‘triple drawdown’ method highlights deficiencies in invasive alien crayfish survey and control techniques. J. Appl. Ecol. 58, 316–326 (2021).
Google Scholar 
59.Stoffel, M. A., Nakagawa, S. & Schielzeth, H. rptR: repeatability estimation and variance decomposition by generalized linear mixed-effects models. Methods Ecol. Evol. 8, 1639–1644 (2017).
Google Scholar 
60.Holm, S. A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6, 65–70 (1979).MathSciNet 
MATH 

Google Scholar 
61.Quinn, G. P. & Keough, M. J. Experimental Design and Data Analysis for Biologists (Cambridge University Press, 2002).
Google Scholar 
62.Jackson, D. A. Stopping rules in principal components analysis: A comparison of heuristical and statistical approaches. Ecology 74, 2204–2214 (1993).
Google Scholar 
63.Budaev, S. V. Using principal components and factor analysis in animal behaviour research: Caveats and guidelines. Ethology 116, 472–480 (2010).
Google Scholar 
64.Robinson, C. A., Thom, T. J. & Lucas, M. C. Ranging behaviour of a large freshwater invertebrate, the white-clawed crayfish Austropotamobius pallipes. Freshw. Biol. 44, 509–521 (2000).
Google Scholar 
65.Bubb, D. H., O’Malley, O. J., Gooderham, A. C. & Lucas, M. C. Relative impacts of native and non-native crayfish on shelter use by an indigenous benthic fish. Aquat. Conserv. Mar. Freshw. Ecosyst. 19, 448–455 (2009).
Google Scholar 
66.Fox, J. & Weisberg, S. An R Companion to Applied Regression (Sage, 2011).
Google Scholar 
67.Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inferences: A Practical Information-Theoretic Approach (Springer, 2002).MATH 

Google Scholar 
68.Bartoń, K. MuMIn: Multi-Model Inference. R Package version 1.43.6. (2019).69.Kleiber, C. & Zeileis, A. Applied Econometrics with R (Springer, 2008).MATH 

Google Scholar 
70.Edwards, D. D., Rapin, K. E. & Moore, P. A. Linking phenotypic correlations from a diverse set of laboratory tests to field behaviors in the crayfish, Orconectes virilis. Ethology 124, 311–330 (2018).
Google Scholar 
71.Teknomo, K. Similarity Measurements. https://people.revoledu.com/kardi/tutorial/Similarity (2015).72.Bell, A. M., Hankison, S. J. & Laskowski, K. L. The repeatability of behaviour: A meta-analysis. Anim. Behav. 77, 771–783 (2009).PubMed 
PubMed Central 

Google Scholar 
73.Vainikka, A., Rantala, M. J., Niemelä, P., Hirvonen, H. & Kortet, R. Boldness as a consistent personality trait in the noble crayfish, Astacus astacus. Acta Ethol. 14, 17–25 (2011).
Google Scholar 
74.Fraser, D. F., Gilliam, J. F., Daley, M. J., Le, A. N. & Skalski, G. T. Explaining leptokurtic movement distributions: Intrapopulation variation in boldness and exploration. Am. Nat. 158, 124–135 (2001).CAS 
PubMed 

Google Scholar 
75.Dingemanse, N. J., Both, C., van Noordwijk, A. J., Rutten, A. L. & Drent, P. J. Natal dispersal and personalities in great tits (Parus major). Proc. R. Soc. London. Ser. B Biol. Sci. 270, 741–747 (2003).
Google Scholar 
76.McMahon, T. E. & Tash, J. C. Experimental analysis of the role of emigration in population regulation of desert pupfish. Ecology 69, 1871–1883 (1988).
Google Scholar 
77.Porter, J. H. & Dooley, J. L. Animal dispersal patterns: A reassessment of simple mathematical models. Ecology 74, 2436–2443 (1993).
Google Scholar 
78.Einum, S., Sundt-Hansen, L. & Nislow, K. H. The partitioning of density-dependent dispersal, growth and survival throughout ontogeny in a highly fecund organism. Oikos 113, 489–496 (2006).
Google Scholar 
79.Lodge, D. M. & Hill, A. M. Factors governing species composition, population size and productivity of coolwater crayfishes. Nord. J. Freshw. Res. 69, 111–136 (1994).
Google Scholar 
80.Berthouly-Salazar, C., van Rensburg, B. J., Le Roux, J. J., van Vuuren, B. J. & Hui, C. Spatial sorting drives morphological variation in the invasive bird, Acridotheris tristis. PLoS ONE 7, e38145 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
81.Juanes, F. & Smith, L. D. The ecological consequences of limb damage and loss in decapod crustaceans: A review and prospectus. J. Exp. Mar. Biol. Ecol. 193, 197–223 (1995).
Google Scholar 
82.Wilshin, S. et al. Limping following limb loss increases locomotor stability. J. Exp. Biol. 221, jeb174268 (2018).PubMed 

Google Scholar 
83.Podgorniak, T., Blanchet, S., De Oliveira, E., Daverat, F. & Pierron, F. To boldly climb: Behavioural and cognitive differences in migrating European glass eels. R. Soc. Open Sci. 3, 150665 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
84.Bubb, D. H., Thom, T. J. & Lucas, M. C. Movement patterns of the invasive signal crayfish determined by PIT telemetry. Can. J. Zool. 84, 1202–1209 (2006).
Google Scholar 
85.Bilton, D. T., Freeland, J. R. & Okamura, B. Dispersal in freshwater invertebrates. Annu. Rev. Ecol. Syst. 32, 159–181 (2001).
Google Scholar 
86.Bubb, D. H., Thom, T. J. & Lucas, M. C. Movement and dispersal of the invasive signal crayfish Pacifastacus leniusculus in upland rivers. Freshw. Biol. 49, 357–368 (2004).
Google Scholar 
87.Hudina, S., Kutleša, P., Trgovčić, K. & Duplić, A. Dynamics of range expansion of the signal crayfish (Pacifastacus leniusculus) in a recently invaded region in Croatia. Aquat. Invasions 12, 67–75 (2017).
Google Scholar 
88.Wutz, S. & Geist, J. Sex- and size-specific migration patterns and habitat preferences of invasive signal crayfish (Pacifastacus leniusculus Dana). Limnologica 43, 59–66 (2013).
Google Scholar 
89.Fraser, H., Barnett, A., Parker, T. H. & Fidler, F. The role of replication studies in ecology. Ecol. Evol. 10, 5197–5207 (2020).PubMed 
PubMed Central 

Google Scholar 
90.Linzmaier, S. M., Goebel, L. S., Ruland, F. & Jeschke, J. M. Behavioral differences in an over-invasion scenario: marbled vs. spiny-cheek crayfish. Ecosphere 9, e02385 (2018).
Google Scholar 
91.Wang, X. et al. Anthropogenic habitat loss accelerates the range expansion of a global invader. Divers. Distrib. https://doi.org/10.1111/ddi.13359 (2021).Article 

Google Scholar  More

Relationships between microbial diversity and base flow indexThe number of reads obtained per sample and total number of OTUs obtained after rarefaction for each gene dataset are summarised in Table S2. According to taxonomic analyses of our 16S rRNA gene dataset, archaeal communities in our river sediment samples consisted largely of OTUs assigned to the Woesarchaeota (20.8% of OTUs and 24.7% of reads) and Methanomicrobia (16.9% of OTUs and 31.8% of reads). Of the functional groups analysed here, ten OTUs were assigned to AOA, Nitrososphaera (n = 8) and Nitrosopumilus (n = 2), that together formed 4.8% of all archaeal 16S rRNA reads. A total of 137 OTUs were assigned to orders of methanogenic archaea, with 15.3% and 16% of archaeal reads assigned to the orders Methanomicrobiales and Methanosarcinales, respectively, with other methanogen orders constituting a further 6.7% of reads.Bacterial communities were more diverse and OTUs assigned to taxa within the functional groups analysed here formed a relatively small proportion of our bacterial 16S rRNA gene dataset. Ammonia oxidising bacteria were represented by only five OTUs (all assigned to Nitrosospira) that together constituted 0.02% of the total bacterial community across our sediments. A further 84 OTUs were assigned to methanotrophic genera, and these OTUs contributed a total of 0.88% of all bacterial 16S rRNA sequences. These were Methylobacter (30 OTUs, 0.7% of bacterial sequences), Methylophilus (15 OTUs, 0.1% of bacterial sequences), Methylosoma (7 OTUs, 0.004% of bacterial sequences), Methylomonas and Methylotenera (6 OTUs each, 0.02 and 0.002% of bacterial sequences, respectively), and Methylosarcina (5 OTUs, 0.002% of bacterial sequences), with a further eight genera represented by a total of 15 OTUs. As reported previously, no OTUs were assigned to known anammox genera, which were likely below the limit of detection in our study [8].The OTU richness of archaeal communities (based on 16S rRNA amplicons) was negatively, albeit weakly, related to BFI (coef = 0.52, z = −2.95, adj-D2 = 0.12, P  More

1.Cherlet, M., et al (Eds.). World Atlas of Desertification. Luxembourg: Publication Office of the European Union (2018).2.Belnap, J. The potential roles of biological soil crusts in dryland hydrologic cycles. Hydrol. Process. 20, 3159–78 (2006).ADS 
CAS 

Google Scholar 
3.Maestre, F. T. et al. Ecology and functional roles of biological soil crusts in semi-arid ecosystems of Spain. J. Arid Environ. 75, 1282–91 (2011).ADS 
PubMed 
PubMed Central 

Google Scholar 
4.Noy-Meir, I. Desert ecosystems: environment and producers. Annu. Rev. Ecol. Evol. Syst. 4(1), 25–51 (1973).
Google Scholar 
5.Puigdefábregas, J., Sole, A., Gutierrez, L., Del Barrio, G. & Boer, M. Scales and processes of water and sediment redistribution in drylands: results from the Rambla Honda field site in Southeast Spain. Earth-Sci. Rev. 48(1–2), 39–70 (1999).ADS 

Google Scholar 
6.Puigdefábregas, J. The role of vegetation patterns in structuring runoff and sediment fluxes in drylands. Earth Surf. Process. Landf. 30(2), 133–147 (2005).ADS 

Google Scholar 
7.Berdugo, M., Soliveres, S. & Maestre, F. T. Vascular plants and biocrusts modulate how abiotic factors affect wetting and drying events in drylands. Ecosystems 17(7), 1242–1256 (2014).CAS 

Google Scholar 
8.Meza, F. J., Montes, C., Bravo-Martínez, F., Serrano-Ortiz, P. & Kowalski, A. S. Soil water content effects on net ecosystem CO2 exchange and actual evapotranspiration in a Mediterranean semiarid savanna of Central Chile. Sci. Rep. 8, 8570 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
9.Austin, A. T. et al. Water pulses and biogeochemical cycles in arid and semiarid ecosystems. Oecologia 141, 221–35 (2004).ADS 
PubMed 

Google Scholar 
10.Safirel, U & Adeel, Z. Ecosystems and human well-being: current state and trends, vol. 1. Washington, DC: Island Press (2005).11.Brocca, L., Melone, F., Moramarco, T. & Morbidelli, R. Spatial-temporal variability of soil moisture and its estimation across scales: Soil Moisture Spatiotemporal Variability. Water Resour. Res. 46, W02516 (2010).ADS 

Google Scholar 
12.Brocca, L. et al. Assimilation of surface-and root-zone ASCAT soil moisture products into rainfall–runoff modeling. IEEE Trans. Geosci. Remote Sens. 50, 2542–2555 (2012).ADS 

Google Scholar 
13.Parinussa, R. et al. Global surface soil moisture from the Microwave Radiation Imager onboard the Fengyun-3B satellite. Int. J. Remote Sens. 35, 7007–7029 (2014).
Google Scholar 
14.Cui, Y. et al. A spatio-temporal continuous soil moisture dataset over the Tibet Plateau from 2002 to 2015. Sci. Data 6, 247 (2019).PubMed 
PubMed Central 

Google Scholar 
15.Solomon, S. et al. (Eds.). Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel in Climate Change. Cambridge and New York: Cambridge University Press (2007).16.Soong, J. L., Phillips, C. L., Ledna, C., Koven, C. D. & Torn, M. S. CMIP5 models predict rapid and deep soil warming over the 21st century. J. Geophys. Res. Biogeosci. 125(2), e2019JG005266 (2020).ADS 

Google Scholar 
17.Zhou, S. et al. Soil moisture–atmosphere feedbacks mitigate declining water availability in drylands. Nat. Clim. Change 11(1), 38–44 (2021).ADS 

Google Scholar 
18.Lian, X. et al. Multifaceted characteristics of dryland aridity changes in a warming world. Nat. Rev. Earth Environ. 1–19 (2021).19.Naz, B. S., Kollet, S., Franssen, H. J. H., Montzka, C. & Kurtz, W. A 3 km spatially and temporally consistent European daily soil moisture reanalysis from 2000 to 2015. Sci. Data 7(1), 1–14 (2020).
Google Scholar 
20.Tietjen, B. et al. Effects of climate change on the coupled dynamics of water and vegetation in drylands. Ecohydrology 3, 226–237 (2010).
Google Scholar 
21.Cui, Y. et al. A spatio-temporal continuous soil moisture dataset over the Tibet Plateau from 2002 to 2015. Sci. Data 6(1), 1–7 (2019).
Google Scholar 
22.Tongway, D.J., Valentin, C., Seghieri, J. (Eds.). Banded vegetation patterning in arid and semiarid environments: ecological processes and consequences for management. Berlin: Springer (2001).23.Maestre, F. T. & Cortina, J. Spatial patterns of surface soil properties and vegetation in a Mediterranean semi-arid steppe. Plant Soil 241(2), 279–291 (2002).CAS 

Google Scholar 
24.Maestre, F.T. et al. Biogeography of global drylands. New Phytol. (2021).25.Bhark, E. W. & Small, E. E. Association between plant canopies and the spatial patterns of infiltration in shrubland and grassland of the Chihuahuan Desert, New Mexico. Ecosystems 6, 0185–96 (2003).
Google Scholar 
26.Yepez, E. A. et al. Dynamics of transpiration and evaporation following a moisture pulse in semiarid grassland: a chamber-based isotope method for partitioning flux components. Agric. For. Meteorol. 132, 359–76 (2005).ADS 

Google Scholar 
27.Eldridge, D. J. et al. Interactive effects of three ecosystem engineers on infiltration in a semi-arid Mediterranean grassland. Ecosystems 13(4), 499–510 (2010).MathSciNet 

Google Scholar 
28.Cerdà, A. The effect of patchy distribution of Stipa tenacissima L. on runoff and erosion. J. Arid Environ. 36(1), 37–51 (1997).ADS 
MathSciNet 

Google Scholar 
29.Weber, B., Büdel, B. & Belnap, J. (Eds.). Biological soil crusts: an organizing principle in drylands. Cham: Springer (2016).30.Eldridge, D. J. et al. The pervasive and multifaceted influence of biocrusts on water in the world’s drylands. Glob. Change Biol. 26(10), 6003–6014 (2020).ADS 

Google Scholar 
31.Castillo-Monroy, A. P., Delgado-Baquerizo, M., Maestre, F. T. & Gallardo, A. Biological soil crusts modulate nitrogen availability in semi-arid ecosystems: Insights from a Mediterranean grassland. Plant Soil 333, 21–34 (2010).CAS 

Google Scholar 
32.Escolar, C., Martínez, I., Bowker, M. A. & Maestre, F. T. Warming reduces the growth and diversity of biological soil crusts in a semi-arid environment: implications for ecosystem structure and functioning. Philos. T. R. Soc. B. 367(1606), 3087–3099 (2012).
Google Scholar 
33.Maestre, F. T. et al. Changes in biocrust cover drive carbon cycle responses to climate change in drylands. Glob. Change Biol. 19, 3835–3847 (2013).ADS 

Google Scholar 
34.Delgado‐Baquerizo, M. et al. Direct and indirect impacts of climate change on microbial and biocrust communities alter the resistance of the N cycle in a semiarid grassland. J. Ecol. 102(6), 1592–1605 (2014).
Google Scholar 
35.Delgado‐Baquerizo, M. et al. Differences in thallus chemistry are related to species‐specific effects of biocrust‐forming lichens on soil nutrients and microbial communities. Funct. Ecol. 29(8), 1087–1098 (2015).
Google Scholar 
36.Lafuente, A., Berdugo, M., Ladron de Guevara, M., Gozalo, B. & Maestre, F. T. Simulated climate change affects how biocrusts modulate water gains and desiccation dynamics after rainfall events. Ecohydrology 11(6), e1935 (2018).PubMed 

Google Scholar 
37.IUSS Working Group WRB. World Reference Base for Soil Resources 2006. World Soil Resources Reports No. 103. Rome, Italy: FAO (2006).38.Chamizo, S., Cantón, Y., Lázaro, R. & Domingo, F. The role of biological soil crusts in soil moisture dynamics in two semiarid ecosystems with contrasting soil textures. J. Hydrol. 489, 74–84 (2013).ADS 

Google Scholar 
39.Chamizo, S., Cantón, Y., Rodríguez‐Caballero, E. & Domingo, F. Biocrusts positively affect the soil water balance in semiarid ecosystems. Ecohydrol. 9(7), 1208–1221 (2016).
Google Scholar 
40.Dalton, M., Buss, P., Treijs, A. & Portmann, M. in Irrigation Australia Limited Regional Conference (Penrith Panthers, 2015).41.Francesca, V., Osvaldo, F., Stefano, P. & Paola, R. P. Soil moisture measurements: Comparison of instrumentation performances. J. Irrig. Drain. Eng. 136(2), 81–89 (2010).
Google Scholar 
42.Payero, J. O., Nafchi, A. M., Davis, R. & Khalilian, A. An Arduino-based wireless sensor network for soil moisture monitoring using Decagon EC-5 sensors. Open J. soil Sci. 7(10), 288 (2017).
Google Scholar 
43.Payero, J. O., Qiao, X., Khalilian, A., Mirzakhani-Nafchi, A. & Davis, R. Evaluating the effect of soil texture on the response of three types of sensors used to monitor soil water status. JWARP 9(06), 566 (2017).
Google Scholar 
44.Sakaki, T., Limsuwat, A., Smits, K.M. & Illangasekare, T.H. Empirical two‐point α‐mixing model for calibrating the ECH2O EC‐5 soil moisture sensor in sands. Water Resour. Res. 44(4) (2008).45.Sharma, H., Shukla, M. K., Bosland, P. W. & Steiner, R. Soil moisture sensor calibration, actual evapotranspiration, and crop coefficients for drip irrigated greenhouse chile peppers. Agric. Water Manag. 179, 81–91 (2017).
Google Scholar 
46.Castillo-Monroy, A. P., Maestre, F. T., Rey, A., Soliveres, S. & García-Palacios, P. Biological soil crust microsites are the main contributor to soil respiration in a semiarid ecosystem. Ecosyst. 14(5), 835–847 (2011).CAS 

Google Scholar 
47.Steven, B., Gallegos-Graves, L. V., Belnap, J. & Kuske, C. R. Dryland soil microbial communities display spatial biogeographic patterns associated with soil depth and soil parent material. FEMS Microbiol. Ecol. 86(1), 101–113 (2013).CAS 
PubMed 

Google Scholar 
48.Ding, J. & Eldridge, D. J. Biotic and abiotic effects on biocrust cover vary with microsite along an extensive aridity gradient. Plant Soil 450(1), 429–441 (2020).CAS 

Google Scholar 
49.Rodríguez-Caballero, E. et al. Ecosystem services provided by biocrusts: from ecosystem functions to social values. J. Arid Envion. 159, 45–53 (2018).ADS 

Google Scholar 
50.Zaady, E., Eldridge, D.J. & Bowker, M.A. in Biological soil crusts: An organizing principle in drylands (Springer, 2016).51.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2019).52.Ushey, K. renv: Project Environments. R package version 0.13.2. https://CRAN.R-project.org/package=renv (2021).53.Dowle, M. & Srinivasan, A. data.table: Extension of ‘data.frame’. R package version 1.14.0. https://CRAN.R-project.org/package=data.table (2021).54.Firke, S. janitor: Simple Tools for Examining and Cleaning Dirty Data. R package version 2.1.0. https://CRAN.R-project.org/package = janitor (2021).55.Wickham, H. et al. Welcome to the tidyverse. J. Open Source Softw. 4(43), 1686 (2019).ADS 

Google Scholar 
56.Zhu, H. kableExtra: Construct Complex Table with ‘kable’ and Pipe Syntax. R package version 1.3.4. https://CRAN.R-project.org/package=kableExtra (2021).57.Microsoft & Weston, S. foreach: Provides Foreach Looping Construct. R package version 1.5.1. https://CRAN.R-project.org/package=foreach (2020).58.Microsoft Corporation & Weston, S. doParallel: Foreach Parallel Adaptor for the ‘parallel’ Package. R package version 1.0.16. https://CRAN.R-project.org/package=doParallel (2020).59.Wickham, H. & Hester, J. readr: Read Rectangular Text Data. R package version 1.4.0. https://CRAN.R-project.org/package=readr (2020).60.Ooms, J. writexl: Export Data Frames to Excel ‘xlsx’ Format. R package version 1.4.0. https://CRAN.R-project.org/package=writexl (2021).61.Müller, K., Wickham, H., James, D.A. & Falcon, S. RSQLite: ‘SQLite’ Interface for R. R package version 2.2.7. https://CRAN.R-project.org/package=RSQLite (2021).62.Csárdi, G., Podgórski, K. & Geldreich, R. zip: Cross-Platform ‘zip’ Compression. R package version 2.1.1. https://CRAN.R-project.org/package=zip (2020).63.Xie, Y. knitr: A General-Purpose Package for Dynamic Report Generation in R. R package version 1.31. https://CRAN.R-project.org/package=knitr (2021).64.R Special Interest Group on Databases (R-SIG-DB), Wickham, H. & Müller, K. DBI: R Database Interface. R package version 1.1.1. https://CRAN.R-project.org/package=DBI (2021).65.Moreno, J. et al. The MOISCRUST dataset. figshare https://doi.org/10.6084/m9.figshare.14748384 (2021).66.Topp, G. C. & Davis, J. L. Measurement of soil water content using time-domain reflectometry (TDR): a field evaluation. Soil Sci. Soc. Am. J. 49, 19–24 (1985).ADS 

Google Scholar 
67.Cantón, Y., Solé-Benet, A. & Domingo, F. Temporal and spatial patterns of soil moisture in semiarid badlands of SE Spain. J. Hydrol. 285, 199–214 (2004).ADS 

Google Scholar 
68.Breshears, D. D. & Barnes, F. J. Interrelationships between plant functional types and soil moisture heterogeneity for semiarid landscapes within the grassland/forest continuum: a unified conceptual model. Landsc. Ecol. 14, 465–78 (1999).
Google Scholar 
69.D’Odorico, P., Caylor, K., Okin, G. S. & Scanlon, T. M. On soil moisture–vegetation feedbacks and their possible effects on the dynamics of dryland ecosystems. J. Geophys. Res. 112, G04010 (2007).ADS 

Google Scholar  More

Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, 91057, Evry, FranceTom O. Delmont, Patrick Wincker & Eric PelletierResearch Federation for the study of Global Ocean systems ecology and evolution, FR2022/Tara GOsee, Paris, FranceTom O. Delmont, Juan José Pierella Karlusich, Chris Bowler, Patrick Wincker & Eric PelletierInstitut de Biologie de l’ENS (IBENS), Département de biologie, École normale supérieure, CNRS, INSERM, Université PSL, 75005, Paris, FranceJuan José Pierella Karlusich & Chris BowlerGraduate Program in Biophysical Sciences, University of Chicago, Chicago, IL, 60637, USAIva VeseliDepartment of Medicine, University of Chicago, Chicago, IL, 60637, USAJessika Fuessel & A. Murat ErenBay Paul Center, Marine Biological Laboratory, Woods Hole, MA, 02543, USAA. Murat ErenDepartment of Ecology, Environment and Plant Sciences, Stockholm University Stockholm, Stockholm, 10691, SwedenRachel A. Foster More

1.Eckehard, G. et al. Forest biodiversity, ecosystem functioning and the provision of ecosystem services. Biodivers. Conserv. 26, 3005–3035. https://doi.org/10.1007/s10531-017-1453-2 (2017).Article 

Google Scholar 
2.Bastrup-Birk, A., Reker, J., Zal, N., Romao, C. & Cugny-Seguin, M. (2016) European Forest Ecosystems: State and Trends Technical Report No 5 (Publications Office of the European Union, European Environment Agency, 2016).
Google Scholar 
3.Aznar-Sánchez, J. A., Belmonte-Ureña, L. J., López-Serrano, M. J. & Velasco-Muñoz, J. F. Forest ecosystem services: An analysis of worldwide research. Forests 9, 453. https://doi.org/10.3390/f9080453 (2018).Article 

Google Scholar 
4.Masiero, M. et al. Valuing Forest Ecosystem Services: A Training Manual for Planners and Project Developers. Forestry Working Paper No. 11 216 (FAO, 2019).
Google Scholar 
5.Maes, J. et al. Mapping and Assessment of Ecosystems and their Services: An Analytical Framework for Ecosystem Condition (Publications Office of the European Union, 2018).
Google Scholar 
6.Pastur, G. M., Perera, A. H., Peterson, U. & Iverson, L. R. Ecosystem services from forested landscapes: An overview. In Ecosystem Services from Forest Landscapes: Broadscale Considerations (eds Perera, A. H. et al.) 1–10 (Springer International, 2018).
Google Scholar 
7.Jenkins, M. & Schaap, B. Background Analytical Study Forest Ecosystem Services, by, Background study prepared for the thirteenth session of the United Nations Forum on Forests (2018).8.Lellia, C. et al. Biodiversity response to forest structure and management: Comparing species richness, conservation relevant species and functional diversity as metrics in forest conservation. For. Ecol. Manage. 432, 707–717. https://doi.org/10.1016/j.foreco.2018.09.057 (2019).Article 

Google Scholar 
9.van der Plas, F. et al. Jack-of-all-trades effects drive biodiversityecosystem multifunctionality relationships in European forests. Nat. Commun. 7, 11109. https://doi.org/10.1038/ncomms11109 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
10.van der Plas, F. et al. Continental mapping of forest ecosystem functions reveals a high but unrealized potential for forest multifunctionality. Ecol. Lett. 21, 32–42. https://doi.org/10.1111/ele.12868 (2017).Article 

Google Scholar 
11.Onyekwelu, J. C. & Olabiwonnu, A. A. Can forest plantations harbour biodiversity similar to natural forest ecosystems over time?. Int. J. Biodivers. Sci. Ecosyst. Serv. Manage. 12, 108–115. https://doi.org/10.1080/21513732.2016.1162199 (2016).Article 

Google Scholar 
12.Saikia, P. et al. Plant diversity patterns and conservation status of eastern Himalayan forests in Arunachal Pradesh, Northeast India. For. Ecosyst. 4, 28. https://doi.org/10.1186/s40663-017-0117-8 (2017).Article 

Google Scholar 
13.Mishra, B. P., Tripathi, O. & Laloo, R. C. Community characteristics of a climax subtropical humid forest of Meghalaya and population structure of ten important tree species. Trop. Ecol. 46, 241–251 (2005).
Google Scholar 
14.de Gouvenain, R. C. & Silander, J. Temperate Forests. Reference Module in Life Sciences (Elsevier, 2017).
Google Scholar 
15.FAO. 2016. Global Forest Resources Assessment 2015: How Are the World’s Forests Changing? Second Edition. Rome, Italy: FAO [www document]. http://www.fao.org/3/a-i4793e.pdf (2015).16.Durigan, M. R. et al. Soil organic matter responses to anthropogenic forest disturbance and land use change in the Eastern Brazilian Amazon. Sustainability 9, 379. https://doi.org/10.3390/su9030379 (2017).CAS 
Article 

Google Scholar 
17.Mukhortova, L., Schepaschenko, D., Shvidenko, A., McCallum, I. & Kraxner, F. Soil contribution to carbon budget of Russian forests. Agric. For. Meteorol. 200, 97–108. https://doi.org/10.1016/j.agrformet.2014.09.017 (2015).ADS 
Article 

Google Scholar 
18.Justine, M. F. Y. et al. Biomass stock and carbon sequestration in a chronosequence of Pinus massoniana plantations in the upper reaches of the Yangtze River. Forests 6, 3665–3682. https://doi.org/10.3390/f6103665 (2015).Article 

Google Scholar 
19.Hansson, K. Impact of tree species on carbon in forest soils. Doctoral Thesis, Swedish University of Agricultural Sciences. Faculty of Natural Resources and Agricultural Sciences (2011).20.Zhang, Y., Duan, B., Xian, J., Korpelainen, H. & Li, C. Links between plant diversity, carbon stocks and environmental factors along a successional gradient in a subalpine coniferous forest in Southwest China. For. Ecol. Manage. 262, 361–369. https://doi.org/10.1016/j.foreco.2011.03.042 (2011).Article 

Google Scholar 
21.Sing, L., Metzger, M. J., Paterson, J. S. & Ray, D. A review of the effects of forest management intensity on ecosystem services for northern European temperate forests with a focus on the UK. Forestry 91, 151–164. https://doi.org/10.1093/forestry/cpx042 (2018).Article 

Google Scholar 
22.Ruiz-Benito, P. et al. Diversity increases carbon storage and tree productivity in Spanish forests. Glob. Ecol. Biogeogr. 23, 311–322. https://doi.org/10.1111/geb.12126 (2014).Article 

Google Scholar 
23.Ricketts, T. H. et al. Disaggregating the evidence linking biodiversity and ecosystem services. Nat. Commun. 7, 13106. https://doi.org/10.1038/ncomms13106 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
24.Jarzyna, M. A. & Jetz, W. Taxonomic and functional diversity change is scale dependent. Nat. Commun. 9, 2565. https://doi.org/10.1038/s41467-018-04889-z (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
25.Madrigal-González, J. et al. Climate reverses directionality in the richness–abundance relationship across the World’s main forest biomes. Nat. Commun. 11, 5635. https://doi.org/10.1038/s41467-020-19460-y (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
26.Kendie, G., Addisu, S. & Abiyu, A. Biomass and soil carbon stocks in different forest types, Northwestern Ethiopia. Int. J. River Basin Manag. 19(1), 123–129. https://doi.org/10.1080/15715124.2019.159318 (2021).Article 

Google Scholar 
27.Omoro, L. M. A., Starr, M. & Pellikka, P. K. E. Tree biomass and soil carbon stocks in indigenous forests in comparison to plantations of exotic species in the Taita Hills of Kenya. Silva Fenn. 47, 935. https://doi.org/10.14214/sf.935 (2013).Article 

Google Scholar 
28.Zhang, G., Zhang, P., Peng, S., Chen, Y. & Cao, Y. The coupling of leaf, litter, and soil nutrients in warm temperate forests in northwestern China. Sci. Rep. 7, 11754. https://doi.org/10.1038/s41598-017-12199-5 (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
29.Kerdraon, D. et al. Litter traits of native and non-native tropical trees influence soil carbon dynamics in timber plantations in panama. Forests 10, 209. https://doi.org/10.3390/f10030209 (2019).Article 

Google Scholar 
30.Novara, A. et al. Litter contribution to soil organic carbon in the processes of agriculture abandon. Solid Earth 6, 425–432. https://doi.org/10.5194/se-6-425-2015 (2015).ADS 
Article 

Google Scholar 
31.Capellesso, E. S. et al. Effects of forest structure on litter production, soil chemical composition and litter–soil interactions. Acta Bot. Bras. 30(3), 329–335. https://doi.org/10.1590/0102-33062016abb0048 (2016).Article 

Google Scholar 
32.Castle, S. C. & Neff, J. C. Plant response to nutrient availability across variable bedrock geologies. Ecosystems 12, 101–113. https://doi.org/10.1007/s10021-008-9210-8 (2009).CAS 
Article 

Google Scholar 
33.Gerdol, R., Marchesini, R. & Iacumin, P. Bedrock geology interacts with altitude in affecting leaf growth and foliar nutrient status of mountain vascular plants. Plant Ecol. 10, 839–850. https://doi.org/10.1093/jpe/rtw092 (2017).Article 

Google Scholar 
34.Sieber, I., Borges, P. & Burkhard, B. Hotspots of biodiversity and ecosystem services: The Outermost Regions and Overseas Countries and Territories of the European Union. One Ecosyst. 3, e24719. https://doi.org/10.3897/oneeco.3.e24719 (2018).Article 

Google Scholar 
35.Iranah, P., Lal, P., Wolde, B. T. & Burli, P. Valuing visitor access to forested areas and exploring willingness to pay for forest conservation and restoration finance: The case of small island developing state of Mauritius. J. Environ. Manage. 223, 868–877. https://doi.org/10.1016/j.jenvman.2018.07.008 (2018).Article 
PubMed 

Google Scholar 
36.Balzan, M. V., Potschin-Young, M. & Haines-Young, R. Island ecosystem services: insights from a literature review on case-study island ecosystem services and future prospects. Int. J. Biodivers. Sci. Ecosyst. Serv. Manage. 14, 71–90. https://doi.org/10.1080/21513732.2018.1439103 (2018).Article 

Google Scholar 
37.Wardle, D. A. Islands as model systems for understanding how species affect ecosystem properties. J. Biogeogr. 29, 583–591. https://doi.org/10.1046/j.1365-2699.2002.00708.x (2002).Article 

Google Scholar 
38.Wardle, D. A., Zackrisson, O., Hornberg, G. & Gallet, C. The influence of island area on ecosystem properties. Science 277, 1296–1299. https://doi.org/10.1126/science.277.5330.1296 (1997).CAS 
Article 

Google Scholar 
39.Santamarta, J. C., Rodríguez-Martín, J. & Neris, J. Water resources management and forest engineering in volcanic islands. IERI Procedia 9, 129–134. https://doi.org/10.1016/j.ieri.2014.09.052 (2014).Article 

Google Scholar 
40.Fontes, J. C., Pereira, L. S. & Smith, R. E. Runoff and erosion in volcanic soils of Azores: Simulation with OPUS. CATENA 56, 199–212. https://doi.org/10.1016/j.catena.2003.10.011 (2004).Article 

Google Scholar 
41.Rodrigues, F. & Rodrigues, A. F. Distribution of environmental isotopes in precipitation on a small oceanic island (Terceira-Azores): Some particularities based on preliminary results. Arquipélago. Agrarian Sci. Environ. 1, 1–6 (2002).
Google Scholar 
42.Dias, E. & Melo, C. Factors influencing the distribution of Azorean mountain vegetation: Implications for nature conservation. Biodivers. Conserv. 19, 3311–3326. https://doi.org/10.1007/s10531-010-9894-x (2010).Article 

Google Scholar 
43.Louvat, P. & Allègre, C. J. Riverine erosion rates on Sao Miguel volcanic island, Azores archipelago. Chem. Geol. 148, 177–200. https://doi.org/10.1016/S0009-2541(98)00028-X (1998).ADS 
CAS 
Article 

Google Scholar 
44.Malheiro, A. Geological hazards in the Azores archipelago: Volcanic terrain instability and human vulnerability. J. Volcanol. Geotherm. Res. 156, 158–171. https://doi.org/10.1016/j.jvolgeores.2006.03.012 (2006).ADS 
CAS 
Article 

Google Scholar 
45.Marques, R., Zêzere, J., Trigo, R., Gaspar, J. & Trigo, I. Rainfall patterns and critical values associated with landslides in Povoação County (São Miguel Island, Azores): Relationships with the North Atlantic Oscillation. Hydrol. Process. https://doi.org/10.1002/hyp.6879 (2008).Article 

Google Scholar 
46.Lopes, F. & Amaral, B. The value of forest recreation in Azorean public parks. Rev. Econ. Sociol. Rural https://doi.org/10.1590/1806-9479.2021.238884 (2021).Article 

Google Scholar 
47.Pavão, D. C. et al. Land cover along hiking trails in a nature tourismdestination: the Azores as a case study. Environ. Dev. Sustain. https://doi.org/10.1007/s10668-021-01356-6 (2021).Article 

Google Scholar 
48.Florestas.pt The Navigator Company Madeira de criptoméria: inovar para reforçar valor (https://florestas.pt/valorizar/madeira-de-criptomeria-inovar-para-reforcar-valor/) 07 de abril 202149.Marcelino, J. A. P., Silva, L., Garcia, P. V., Weber, E. & Soares, A. O. Using species spectra to evaluate plant community conservation value along a gradient of anthropogenic disturbance. Environ. Monit. Assess. 185, 6221–6233. https://doi.org/10.1007/s10661-012-3019-9 (2013).Article 
PubMed 

Google Scholar 
50.Marcelino, J. A. P., Weber, E., Silva, L., Garcia, P. V. & Soares, A. O. Expedient metrics to describe plant community change across gradients of anthropogenic influence. Environ. Manage. 54, 1121–1130. https://doi.org/10.1007/s00267-014-0321-z (2014).ADS 
Article 
PubMed 

Google Scholar 
51.Abreu, P. M. R. Contributo da Criptoméria Para o Sequestro de carbono nos Açores 128 (Tese de Mestrado, Universidade de Aveiro, 2011).
Google Scholar 
52.Vergílio, M., Fjøsneb, K., Nistorab, A. & Calado, H. Carbon stocks and biodiversity conservation on a small island: Pico (the Azores, Portugal). Land Use Policy 58, 196–207. https://doi.org/10.1016/j.landusepol.2016.07.020 (2016).Article 

Google Scholar 
53.Borges Silva, L. et al. Development allometric equations for estimating above-ground biomass of woody plants invaders: The Pittosporum undulatum the Azores archipelago. In Modeling, Dynamics, Optimization and Bioeconomics II. DGS 2014. Springer Proceedings in Mathematics & Statistics Vol. 195 (eds Pinto, A. & Ziberman, D.) 463–484 (Springer, 2017).
Google Scholar 
54.Borges Silva, L., Teixeira, A., Alves, M., Elias, R. B. & Silva, L. Tree age determination in the widespread woody plant invader Pittosporum undulatum. For. Ecol. Manage. 400, 457–467. https://doi.org/10.1016/j.foreco.2017.06.027 (2017).Article 

Google Scholar 
55.Borges Silva, L. et al. Biomass valorization in the management of woody plant invaders: The case of Pittosporum undulatum in the Azores. Biomass Bioenergy 109, 155–165. https://doi.org/10.1016/j.biombioe.2017.12.025 (2018).Article 

Google Scholar 
56.Mendonça, E. F. E. P. Serviços dos Ecossistemas na Ilha Terceira: estudo preliminar com ênfase no sequestro de carbono e na biodiversidade 147 (Tese de Mestrado, Universidade dos Açores, 2012).
Google Scholar 
57.Cruz, A. & Benedicto, J. Assessing socio-economic benefits of Natura 2000: A case study on the ecosystem service provided by SPA Pico da Vara/Ribeira do Guilherme. Output of the project Financing Natura 2000: Cost estimate and benefits of Natura 2000, 43 (2009).58.Cruz, A., Benedicto, J. & Gil, A. Socio-economic benefits of Natura 2000 in Azores Islands – a Case Study approach on ecosystem services provided by a Special Protected Area. J. Coast Res. 64, 1955–1959 (2011).
Google Scholar 
59.Borges, P. A. V. et al. (eds) A List of the Terrestrial and Marine Biota from the Azores 432 (Princípia, 2010).
Google Scholar 
60.Silva, L., Moura, M., Schaefer, H., Rumsey, F. & Dias, E. F. Vascular Plants (Tracheobionta). In A List of the Terrestrial and Marine Biota from the Azores (eds Borges, P. A. V. et al.) 117–146 (Princípia, 2010).
Google Scholar 
61.Elias, R. B. et al. Natural zonal vegetation of the Azores Islands: characterization and potential distribution. Phytocoenologia 46, 107–123. https://doi.org/10.1127/phyto/2016/0132 (2016).Article 

Google Scholar 
62.Borges, P. A. V. et al. Community structure of woody plants on islands along a bioclimatic gradient. Front. Biogeogr. 10, 1–31. https://doi.org/10.21425/F5FBG40295 (2018).Article 

Google Scholar 
63.Fimbel, R. A. & Fimbel, C. A. The role of exotic conifer plantations in rehabilitating degraded tropical forest lands: A case study from the Kibale forest in Uganda. For. Ecol. Manage. 81, 215–226. https://doi.org/10.1016/0378-1127(95)03637-7 (1996).Article 

Google Scholar 
64.Omoro, L. M. A., Pellikka, P. K. E. & Rogers, P. C. Tree species diversity, richness, and similarity between exotic and indigenous forests in the cloud forests of Eastern Arc Mountains, Taita Hills, Kenya. J. For. Res. 21, 255–264. https://doi.org/10.1007/s11676-010-0069-0 (2010).Article 

Google Scholar 
65.Tenzin, J. & Hasenauer, H. Tree species composition and diversity in relation to anthropogenic disturbances in broad-leaved forests of Bhutan. Int. J. Biodivers. Sci. Ecosyst. Serv. Manage. 12, 274–290. https://doi.org/10.1080/21513732.2016.1206038 (2016).Article 

Google Scholar 
66.Braun, A. C. Taxonomic diversity and taxonomic dominance: The example of forest plantations in south-central Chile. Open J. Ecol. 5, 199–212. https://doi.org/10.4236/oje.2015.55017 (2015).Article 

Google Scholar 
67.Cordeiro, N. & Silva, L. Seed production and vegetative growth of Hedychium gardnerianum Ker-Gawler (Zingiberaceae) in São Miguel Island (Azores). Arquipélago. Life Mar. Sci. 20A, 31–36 (2003).
Google Scholar 
68.Ricketts, T. H. Tropical forest fragments enhance pollinator activity in nearby coffee crops. Conserv. Biol. 18, 1262–1271. https://doi.org/10.1111/j.1523-1739.2004.00227.x (2004).Article 

Google Scholar 
69.Bunker, D. E. et al. Species loss and above-ground carbon storage in a tropical forest. Science 310, 1029–1031. https://doi.org/10.1126/science.11176821029-1031 (2005).ADS 
CAS 
Article 
PubMed 

Google Scholar 
70.Phillpott, S. M. et al. Functional richness and ecosystem services: bird predation on arthropods in tropical agroecosystems. Ecol. Appl. 19, 1858–1867. https://doi.org/10.1890/08-1928.1 (2009).Article 

Google Scholar 
71.Ifo, S. A. et al. Tree species diversity, richness, and similarity in intact and degraded forest in the tropical rainforest of the Congo Basin: Case of the Forest of Likouala in the Republic of Congo. Int. J. For. Res. 2016, 1–12. https://doi.org/10.1155/2016/7593681 (2016).Article 

Google Scholar 
72.Borges, P. A. V., Santos, A. M. C., Elias, R. B. & Gabriel, R. The Azores Archipelago: Biodiversity erosion and conservation biogeography. In Encyclopedia of the World’s Biomes-Earth Systems and Environmental Sciences. Reference Module in Earth Systems and Environmental Sciences (eds Scott, E. et al.) 1–13 (Elsevier, 2019).
Google Scholar 
73.Lourenço, P., Medeiros, V., Gil, A. & Silva, L. Distribution, habitat and biomass of Pittosporum undulatum, the most important woody plant invader in the Azores Archipelago. For. Ecol. Manage. 262, 178–187. https://doi.org/10.1016/j.foreco.2011.03.021 (2011).Article 

Google Scholar 
74.Gabriel, R. & Bates, J. W. Bryophyte community composition and habitat specificity in the natural forests of Terçeira, Azores. Plant Ecol. 177, 125–144. https://doi.org/10.1007/s11258-005-2243-6 (2005).Article 

Google Scholar 
75.Elias, R. B., Dias, E. & Pereira, F. Disturbance, regeneration and the spatial pattern of tree species in Azorean mountain forests. Community Ecol. 12, 23–30. https://doi.org/10.1556/ComEc.12.2011.1.4 (2011).Article 

Google Scholar 
76.Elias, R. B. & Dias, E. The effects of landslides on the mountain vegetation of Flores Island, Azores. J. Veg. Sci. 20, 706–717. https://doi.org/10.1111/j.1654-1103.2009.01070.x (2009).Article 

Google Scholar 
77.Gleadow, R. M., Rowan, K. S. & Ashton, D. H. Invasion by Pittosporum undulatum of the forests of Central Victoria IV. Shade tolerance. Aust J. Bot. 31, 151–160. https://doi.org/10.1071/BT9830151 (1983).Article 

Google Scholar 
78.Bradstock, R. A., Tozer, M. G. & Keith, D. A. Effects of high frequency fire on floristic composition and abundance in a fire-prone heathland near Sydney. Aust. J. Bot. 45, 641–655. https://doi.org/10.1071/BT96083 (1997).Article 

Google Scholar 
79.Gleadow, R. M. & Ashton, D. H. Invasion by Pittosporum undulatum of the forests of Central Victoria. I. Invasion patterns and plant morphology. Aust. J. Bot. 29, 705–720. https://doi.org/10.1071/BT9810705 (1981).Article 

Google Scholar 
80.Ramos, J. A. Introduction of exotic tree species as a threat to the azores bullfinch population. J. Appl. Ecol. 33, 710–722 (1996).
Google Scholar 
81.Silva, L., Ojeda-Land, E. & Rodríguez-Luengo, J. L. Invasive terrestrial flora and fauna of Macaronesia. Top 100 in Azores, Madeira and Canaries 546 (ARENA, 2008).
Google Scholar 
82.Castro, S. A. et al. Floristic homogenization as a teleconnected trend in oceanic islands. Divers. Distrib. 16, 902–910. https://doi.org/10.1111/j.1472-4642.2010.00695.x (2010).Article 

Google Scholar 
83.Kueffer, C. et al. Magnitude and form of invasive plant impacts on oceanic islands: A global comparison. Perspect. Plant Ecol. Evol. Syst. 12, 145–161. https://doi.org/10.1016/j.ppees.2009.06.002 (2010).Article 

Google Scholar 
84.Gil, A., Lobo, A., Abadi, M., Silva, L. & Calado, H. Mapping invasive woody plants in Azores Protected Areas by using very high-resolution multispectral imagery. Eur. J. Remote. Sens. 46, 289–304. https://doi.org/10.5721/EuJRS20134616 (2013).Article 

Google Scholar 
85.DRRF. Plano de Gestão Florestal-Perímetro Florestal e Matas Regionais da Ilha de São Miguel. Direção Regional dos Recursos Florestais. Secretaria Regional da Agricultura e Florestas. Região Autónoma dos Açores. (http://drrf.azores.gov.pt/areas/cert/Documents/PGF_do_Perimetro_Florestal_e_Matas_Regionais_da_Ilha_de_Sao_Miguel_2017.pdf) (2017).86.Dutra Silva, L., Azevedo, E. B., Elias, R. B. & Silva, L. Species distribution modeling: Comparison of fixed and mixed effects models using INLA. Int. J. Geogr. Inf. Sci. 6, 1–35. https://doi.org/10.3390/ijgi6120391 (2017).Article 

Google Scholar 
87.Dutra Silva, L., Azevedo, E. B., Reis, F. V., Elias, R. B. & Silva, L. Limitations of species distribution models based on available climate change data: a case study in the Azorean forest. Forests 10, 575. https://doi.org/10.3390/f10070575 (2019).Article 

Google Scholar 
88.Hortal, J., Borges, P. A. V., Jiménez-Valverde, A., Azevedo, E. B. & Silva, L. Assessing the areas under risk of invasion within islands through potential distribution modelling: The case of Pittosporum undulatum in São Miguel, Azores. J. Nat. Conserv. 18, 247–257. https://doi.org/10.1016/j.jnc.2009.11.002 (2010).Article 

Google Scholar 
89.Gil, A., Yu, Q., Abadi, M. & Calado, H. Using ASTER multispectral imagery for mapping woody invasive species in Pico da Vara Natural Reserve (Azores Islands, Portugal). Revista Árvore. 38, 391–401 (2014).
Google Scholar 
90.Magurran, A. E. Ecological Diversity and Its Measurement 178 (Croom Helm, 1988).
Google Scholar 
91.Dias, E., Elias, R. B., Melo, C. & Mendes, C. O elemento insular na estruturação das florestas da Macaronésia. In Árvores e Florestas de Portugal. Volume 6. Açores e Madeira. A Floresta das ilhas 362 (Público, Comunicação Social, SA. Fundação Luso-Americana para o Desenvolvimento, 2007).
Google Scholar 
92.Dias, E., Elias, R. B., Melo, C. & Mendes, C. O elemento insular na estruturação das florestas da Macaronésia. Açores Madeira 6, 15–48 (2007).
Google Scholar 
93.Kacholi, D. S. Analysis of structure and diversity of the Kilengwe forest in the Morogoro Region, Tanzania. Int. J. Biodivers. 2014, 1–8. https://doi.org/10.1155/2014/516840 (2014).Article 

Google Scholar 
94.Jögren, E. Recent changes in the vascular flora and vegetation of the Azores Islands, Memórias da Sociedade Broteriana. Agric. For. 22, 1–113 (1973).
Google Scholar 
95.Silva, L. & Smith, C. W. A quantitative approach to the study of non- indigenous plants: An example from the Azores Archipelago. Biodivers. Conserv. 15, 1661–1679. https://doi.org/10.1007/s10531-004-5015-z (2006).Article 

Google Scholar 
96.Szmyt, J. Structural diversity of selected oak stands (Quercus robur L.) on the Krotoszyn Plateau in Poland. For. Res. Pap. 78, 4–27. https://doi.org/10.1515/frp-2017-0002 (2017).Article 

Google Scholar 
97.Lillo, E. P., Fernando, E. S. & Lillo, M. J. R. Plant diversity and structure of forest habitat types on Dinagat Island, Philippines. J. Asia Pac. Biodivers. 12, 83–105. https://doi.org/10.1016/j.japb.2018.07.003 (2018).Article 

Google Scholar 
98.Morin, X., Fahse, L., Scherer-Lorenzen, M. & Bugmann, H. Tree species richness promotes productivity in temperate forests through strong complementarity between species. Ecol. Lett. 14, 1211–1219. https://doi.org/10.1111/j.1461-0248.2011.01691.x (2011).Article 
PubMed 

Google Scholar 
99.Park, J., Kim, H. S., Jo, H. K. & Jung, B. The influence of tree structural and species diversity on temperate forest productivity and stability in Korea. Forests https://doi.org/10.3390/f10121113 (2019).Article 

Google Scholar 
100.Yang, Y., Luo, Y. & Finzi, A. Carbon and nitrogen dynamics during forest stand development: A global synthesis. New Phytol. 190, 977–989. https://doi.org/10.1111/j.1469-8137.2011.03645.x (2011).CAS 
Article 
PubMed 

Google Scholar 
101.Houghton, R. A., Hall, F. & Goetz, S. J. Importance of biomass in the global carbon cycle. J. Geophys. Res. 114, G00E03. https://doi.org/10.1029/2009JG000935 (2009).ADS 
Article 

Google Scholar 
102.Matos, B. et al. Linking dendrometry and dendrochronology in the Dominant Azorean Tree Laurus azorica (Seub.) Franco. Forests 10, 538. https://doi.org/10.3390/f10070538 (2019).Article 

Google Scholar 
103.Keith, H. et al. Evaluating nature-based solutions for climate mitigation and conservation requires comprehensive carbon accounting. Sci. Total Environ. 769, 144341. https://doi.org/10.1016/j.scitotenv.2020 (2021).ADS 
CAS 
Article 
PubMed 

Google Scholar 
104.Luyssaert, S. et al. Old-growth forests as global carbon sinks. Nature 455, 213–215. https://doi.org/10.1038/nature07276 (2008).ADS 
CAS 
Article 
PubMed 

Google Scholar 
105.Pavão, D. C. et al. Dendrochronological potential of the Azorean endemic gymnosperm Juniperus brevifolia. Dendrochronologica 71, 125901. https://doi.org/10.1016/j.dendro.2021.125901 (2022).Article 

Google Scholar 
106.Fernández-Palácios, J. M., Garcia Esteban, J. J., López, R. J. & Luzardo, M. C. Aproximación a la estima de la biomassa y producción primaria neta aéreas en una estación de la Laurisilva tinerfeña. Vieraea 20, 11–20 (1991).
Google Scholar 
107.Brown, S. & Lugo, A. E. Biomass of tropical forests: A new estimate based on forest volumes. Science 223, 1290–1293. https://doi.org/10.1126/science.223.4642.1290 (1984).ADS 
CAS 
Article 
PubMed 

Google Scholar 
108.Silva, J. Açores e Madeira: A Floresta das Ilhas Vol. 6, 362 (Coleção Árvores e florestas de Portugal,1ª Edição, Fundação Luso-Americana para o Desenvolvimento, 2007).
Google Scholar 
109.Fukuda, M., Iehara, T. & Matsumoto, M. Carbon stock estimates for Sugi and Hinoki forests in Japan. For. Ecol. Manage. 184, 1–16. https://doi.org/10.1016/S0378-1127(03)00146-4 (2003).Article 

Google Scholar 
110.Sasaki, N. & Kim, S. Biomass carbon sinks in Japanese forests: 1966–2012. Forestry 82, 105–115. https://doi.org/10.1093/forestry/cpn049 (2009).Article 

Google Scholar 
111.Dar, J. A. & Sundarapandian, S. M. Soil organic carbon stock assessment in two temperate forest types of western Himalaya of Jammu and Kashmir, India. For. Res. 3, 114. https://doi.org/10.4172/2168-9776.1000114 (2013).Article 

Google Scholar 
112.Gilliam, F. S. Excess nitrogen in temperate forest ecosystems decreases herbaceous layer diversity and shifts control from soil to canopy structure. Forests 10, 66. https://doi.org/10.3390/f10010066 (2019).Article 

Google Scholar 
113.Li, P., Wang, Q., Endo, T., Zhao, X. & Kakubari, Y. Soil organic carbon stock is closely related to vegetation properties in cold-temperate mountainous forests. Geoderma 154, 407–415. https://doi.org/10.1016/j.geoderma.2009.11.023 (2010).ADS 
CAS 
Article 

Google Scholar 
114.Diaz-Pines, E., Rubio, A., Miegroet, H. V., Montes, F. & Benito, M. Does tree species composition control soil organic carbon pools in Mediterranean mountain forests. For. Ecol Manage. 262, 1895–1904. https://doi.org/10.1016/j.foreco.2011.02.004 (2011).Article 

Google Scholar 
115.Berg, B. Litter decomposition and organic matter turnover in northern forest soils. For. Ecol. Manage. 133, 13–22. https://doi.org/10.1016/S0378-1127(99)00294-7 (2000).Article 

Google Scholar 
116.Boring, L. R. & Hendricks, J. J. Litter quality of native herbaceous legumes in a burned pine forest of the Gerogia Piedmont. Can. J. For. Res. 22, 2007–2010. https://doi.org/10.1139/x92-263 (1992).Article 

Google Scholar 
117.Thuille, A. & Schulze, E. D. Carbon dynamics in successional and afforested spruce stands in Thuringia and the Alps. Glob. Chang. Biol. 6, 325–342. https://doi.org/10.1111/j.1365-2486.2005.01078.x (2006).ADS 
Article 

Google Scholar 
118.Jandl, R. et al. How strongly can forest management influence soil carbon sequestration?. Geoderma 137, 253–268. https://doi.org/10.1016/j.geoderma.2006.09.003 (2007).ADS 
CAS 
Article 

Google Scholar 
119.van Wesemael, B. & Veer, M. A. C. Soil organic matter accumulation, litter decomposition and humus forms in Mediterranean forests of southern Tuscany, Italy. J. Soil Sci. 43, 133–144. https://doi.org/10.1111/j.1365-2389.1992.tb00125.x (1992).Article 

Google Scholar 
120.Kavvadias, V. A., Alifragis, D. A., Tsiontsis, A., Brofas, G. & Stamatelos, G. Litterfall, litter accumulation and litter decomposition rates in four forest ecosystems in northern Greece. For. Ecol Manage. 144, 113–127. https://doi.org/10.1016/S0378-1127(00)00365-0 (2001).Article 

Google Scholar 
121.Rahman, M. M., Tsukamoto, J., Tokumoto, Y. & Ashikur, R. S. The role of quantitative traits of leaf litter on decomposition and nutrient cycling of the forest ecosystems. J. For. Sci. 29, 38–48. https://doi.org/10.7747/JFS.2013.29.1.38 (2013).Article 

Google Scholar 
122.Bowden, R. et al. Litter input controls on soil carbon in a temperate deciduous forest. Soil Sci. Soc. Am. J. 78, S66–S75. https://doi.org/10.2136/sssaj2013.09.0413nafsc (2014).Article 

Google Scholar 
123.Madeira, M. et al. (eds) Soils of Volcanic Regions in Europe (Springer, 2007).
Google Scholar 
124.Arnalds, O. et al. (eds) Soils of Volcanic Regions in Europe (Springer, 2007).
Google Scholar 
125.Zheng, X., Wei, X. & Zhang, S. Tree species diversity and identity effects on soil properties in the Huoditang area of the Qinling Mountains, China. Ecosphere 8, e01732. https://doi.org/10.1002/ecs2.1732 (2017).Article 

Google Scholar 
126.Duan, L., Huang, Y., Hao, J., Xie, S. & Hou, M. Vegetation uptake of nitrogen and base cations in China and its role in soil acidification. Sci. Total Environ. 330, 187–198. https://doi.org/10.1016/j.scitotenv.2004.03.035 (2004).ADS 
CAS 
Article 
PubMed 

Google Scholar 
127.Heath, L. S., Kimble, J. M., Birdsey, R. A. & Lal, R. The potential of U.S. forest soils to sequester carbon. In The Potential of U.S. Forest Soils to Sequester Carbon and Mitigate the Greenhouse Effect (eds Kimble, J. M. et al.) 385–394 (CRC Press, 2003).
Google Scholar 
128.D’Amore, D. & Kane, E. Climate Change and Forest Soil Carbon. U.S. Department of Agriculture, Forest Service, Climate Change Resource Center. www.fs.usda.gov/ccrc/topics/forest-soil-carbon (2016).129.Ramade, F. Ecology of Natural Resources (Wiley, 1981).
Google Scholar 
130.Osman, K. T. Physical properties of forest soils. In Forest Soils 19–44 (Springer, 2013).
Google Scholar 
131.Sanchez, P. A. & Logan, T. J. Myths and science about the chemistry and fertility of soils in the tropics. In Myths and Science of Soils of the Tropics Vol. 29 (eds Lal, R. & Sanchez, P. A.) 35–46 (SSSA, 1992).
Google Scholar 
132.Sibrant, A. L. R. et al. Morpho-structural evolution of a volcanic island developed inside an active oceanic rift: S. Miguel Island (Terceira rift, Azores). J. Volcanol. Geotherm. Res. 301, 90–106. https://doi.org/10.1016/j.jvolgeores.2015.04.011 (2015).ADS 
CAS 
Article 

Google Scholar 
133.Hildenbrand, A., Weis, D., Madoreira, P. & Marques, F. O. Recent plate reorganization at the Azores triple junction: Evidence from combined geochemical and geochronological data on Faial, S. Jorge and Terceira volcanic islands. Lithos 210–211, 27–39. https://doi.org/10.1016/j.lithos.2014.09.009 (2014).ADS 
CAS 
Article 

Google Scholar 
134.Demand, J., Fabriol, R., Gerard, F., Lundt, F. & Chovelon, P. Prospection Géothermique, íles de Faial et de Pico (Açores). Rapport géologique, geochimique et gravimétrique. Technical report, BRGM 82 SGN 003 GTH (1982).135.Elias, R. B. & Dias, E. Ecologia das florestas de Juniperus dos Açores Cadernos de Botânica nº5 (Herbário da Universidade dos Açores, 2008).
Google Scholar 
136.DRRF. Avaliação da Biomassa Disponível em Povoamentos Florestais na Região Autonoma dos Açores (Evaluation of Available Biomass in Forestry Stands in the Azores Autonomic Region) 8 (Inventário Florestal da Regiao Autonoma dos Açores Direcção Regional dos Recursos Florestais, Secretaria Regional da Agricultura e Florestas da Região Autonoma dos Açores, 2007).
Google Scholar 
137.Silva, L. & Smith, C. W. A characterization of the non-indigenous flora of the Azores Archipelago. Biol. Invasions 6, 193–204. https://doi.org/10.1023/B:BINV.0000022138.75673.8c (2004).Article 

Google Scholar 
138.Fernandes, A. & Fernandes, R. B. Iconographia Selecta Florae Azoricae Vol. I, 131 (Fasc. 1. Coimbra, 1980).
Google Scholar 
139.Fernandes, A. & Fernandes, R. B. Iconographia Selecta Florae Azoricae Vol. II, 178 (Fasc. 1 Edição da Secretaria Regional da Cultura da Região Autónoma dos Açores, 1983).
Google Scholar 
140.Mengistu, B. & Asfaw, Z. Woody species diversity and structure of agroforestry and adjacent land uses in Dallo Mena District, South-East Ethiopia. Nat. Resour. 7, 515–534. https://doi.org/10.4236/nr.2016.710044 (2016).Article 

Google Scholar 
141.Liu, X. et al. Tree species richness increases ecosystem carbon storage in subtropical forests. Proc. Biol. Sci. 285, 20181240. https://doi.org/10.1098/rspb.2018.1240 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
142.Lou, J. Entropy & diversity. Oikos 113, 363–375. https://doi.org/10.1111/j.2006.0030-1299.14714.x (2006).Article 

Google Scholar 
143.Whittaker, R. H. Communities and Ecosystems 162 (MacMillan, 1970).
Google Scholar 
144.Mori, A. S., Isbell, F. & Seidl, R. β-diversity, community assembly, and ecosystem functioning. Trends Ecol. Evol. 33, 549–564. https://doi.org/10.1016/j.tree.2018.04.012 (2018).Article 
PubMed 

Google Scholar 
145.Oksanen, J. et al. Community Ecology Package. Vegan Tutorial (2018).146.Pavão, D. C., Elias, R. E. & Silva, L. Comparison of discrete and continuum community models: Insights from numerical ecology and Bayesian methods applied to Azorean plant communities. Ecol. Model. 402, 93–106. https://doi.org/10.1016/j.ecolmodel.2019.03.021 (2019).Article 

Google Scholar 
147.Legendre, P. & Legendre, L. Numerical Ecology 2nd edn, 853 (Elsevier, 1998).MATH 

Google Scholar 
148.Oksanen F.G. et al. Vegan: Community Ecology Package. R Package Version 2.4-2 (2017).149.Dufrêne, M. & Legendre, P. Species assemblages and indicator species: The need for a flexible asymmetrical approach. Ecol. Monogr. 67, 345–366. https://doi.org/10.2307/2963459 (1997).Article 

Google Scholar 
150.Silva, L., Le Jean, F., Marcelino, J. & Soares, A. O. Using bayesian inference to validate plant community assemblages and determine indicator species. In Modeling, Dynamics, Optimization and Bioeconomics II. DGS 2014. Springer Proceedings in Mathematics & Statistics Vol. 195 (eds Pinto, A. & Zilberman, D.) (Springer, 2017).
Google Scholar 
151.van Rensburg, B. J., McGeoch, M. A., Chown, S. L. & van Jaarsveld, A. S. Conservation of heterogeneity among dung beetles in the Maputaland Centre of Endemism, South Africa. Biol. Conserv. 88, 145–153. https://doi.org/10.1016/S0006-3207(98)00109-8 (1999).Article 

Google Scholar 
152.Solomou, A. D. & Sfougaris, A. I. Herbaceous plant diversity and identification of indicator species in olive groves in Central Greece. Commun. Soil Sci. Plant Anal. 44, 320–330. https://doi.org/10.1080/00103624.2013.741926 (2013).CAS 
Article 

Google Scholar 
153.De Caceres, M. & Jansen, F. Indicspecies: Relationship Between Species and Groups of Sites. R package version 1.7.5. (2016).154.Aboal, J., Arévalo, J. R. & Fernández, Á. Allometric relationships of different tree species and stand above ground biomass in the Gomera laurel forest (Canary Islands). Flora 200, 264–274. https://doi.org/10.1016/j.flora.2004.11.001 (2005).Article 

Google Scholar 
155.Lim, K. H., Lee, K.-H., Lee, K. H. & Park, I. H. Biomass expansion factors and allometric equations in an age sequence for Japanese cedar (Cryptomeria japonica) in southern. J. For. Res. 18, 316–322. https://doi.org/10.1007/s10310-012-0353-2 (2013).CAS 
Article 

Google Scholar 
156.Paul, K. I. et al. Development and testing of allometric equations for estimating above-ground biomass of mixed-species environmental plantings. For. Ecol. Manage. 310, 483–494. https://doi.org/10.1016/j.foreco.2013.08.054 (2013).Article 

Google Scholar 
157.Acosta-Mireles, M., Vargas-Hernández, J., Velázquez-Martínez, A. & Etchevers-Barra, J. D. Aboveground biomass estimation by means of allometric relationships in six hardwood species in Oaxaca, México. Agrociência 36, 725–736 (2002).
Google Scholar 
158.Zianis, D. & Mencuccini, M. On simplifying allometric analyses of forest biomass. For. Ecol. Manage. 187, 311–332. https://doi.org/10.1016/j.foreco.2003.07.007 (2004).Article 

Google Scholar 
159.IPCC. Guidelines for National Greenhouse Gas Inventories Vol. 4 (Intergovernmental Panel on Climate Change (IPCC), Agriculture, Forestry and Other Land Use (AFLOLU), Institute for Global Environmental Strategies, 2006).
Google Scholar 
160.Mokany, K., Raison, J. R. & Prokushkin, A. S. Critical analysis of root: shoot ratios in terrestrial biomes. Glob. Chang. Biol. 12, 84–96. https://doi.org/10.1111/j.1365-2486.2005.001043.x (2006).ADS 
Article 

Google Scholar 
161.Lamlom, S. & Savidge, R. A. A reassessment of carbon content in wood: Variation within and between 41 North American species. Biomass Bioenergy. 25, 381–388. https://doi.org/10.1016/S0961-9534(03)00033-3 (2003).CAS 
Article 

Google Scholar 
162.Jew, E. K. K., Dougill, A. J., Sallu, S. M., O’Connell, J. & Benton, T. G. Miombo woodland under threat: consequences for tree diversity and carbon storage. For. Ecol. Manage. 361, 144–153. https://doi.org/10.1016/j.foreco.2015.11.0110378-1127 (2016).Article 

Google Scholar 
163.Hetland, J., Yowargana, P., Leduc, S. & Kraxner, F. Carbon-negative emissions: systemic impacts of biomass conversion: A case study on CO2 capture and storage options. Int. J. Greenh. Gas Control. 49, 330–342 (2016).CAS 

Google Scholar 
164.Macías, C. A. S., Orihuela, J. C. A. & Abad, S. I. Estimation of above-ground live biomass and carbon stocks in different plant formations and in the soil of dry forests of the Ecuadorian coast. Food Energy Secur. 6, e115. https://doi.org/10.1002/fes3.115 (2017).Article 

Google Scholar 
165.Yigini, Y. et al. Soil Organic Carbon Mapping Cookbook 2nd edn, 220 (FAO, 2018).
Google Scholar 
166.Azevedo, E. B. & Pereira, L. S. Modelling the local climate in island environments: Water balance applications. Agric. Water Manag. 40, 393–403 (1999).
Google Scholar 
167.Costa, H. et al. Predicting successful replacement of forest invaders by native species using species distribution models: The case of Pittosporum undulatum and Morella faya in the Azores. For. Ecol. Manage. 279, 90–96. https://doi.org/10.1016/j.foreco.2012.05.022 (2012).Article 

Google Scholar 
168.Costa, H., Medeiros, V., Azevedo, E. B. & Silva, L. Evaluating the ecological-niche factor analysis as a modelling tool for environmental weed management in island systems. Weed Res. 53, 221–230. https://doi.org/10.1111/wre.12017 (2013).Article 

Google Scholar  More

1.Eisner, R., Seabrook, L. M. & McAlpine, C. A. Are changes in global oil production influencing the rate of deforestation and biodiversity loss?. Biol. Conserv. 196, 147–155. https://doi.org/10.1016/j.biocon.2016.02.017 (2016).Article 

Google Scholar 
2.Fahrig, L. Effects of habitat fragmentation on biodiversity. Annu. Rev. Ecol. Evol. Syst. 34, 487–515. https://doi.org/10.1146/132419 (2003).Article 

Google Scholar 
3.Pfeifer, M. et al. Creation of forest edges has a global impact on forest vertebrates. Nature 551, 187–191. https://doi.org/10.1038/nature24457 (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
4.Tilman, D., May, R., Lehman, C. & Nowak, M. Habitat destruction and the extinction debt. Nature 371, 65–66. https://doi.org/10.1038/371065a0 (1994).ADS 
Article 

Google Scholar 
5.Fisher, J. T. & Burton, C. A. Wildlife winners and losers in an oil sands landscape. Front Ecol. Environ. https://doi.org/10.1002/fee.1807 (2018).Article 

Google Scholar 
6.Heim, N., Fisher, J. T., Volpe, J., Clevenger, A. P. & Paczkowski, J. Carnivore community response to anthropogenic landscape change: species-specificity foils generalizations. Landscape Ecol. 34, 2493–2507. https://doi.org/10.1007/s10980-019-00882-z (2019).Article 

Google Scholar 
7.Pereira, H. M., Navarro, L. & Martins, I. Global biodiversity change: the bad, the good, and the unknown. Annu. Rev. Environ. Resour. https://doi.org/10.1146/annurev-environ-042911-093511 (2012).Article 

Google Scholar 
8.Northrup, J. M., Anderson, C. R. Jr. & Wittemyer, G. Quantifying spatial habitat loss from hydrocarbon development through assessing habitat selection patterns of mule deer. Glob Change Biol. 21, 3961–3970. https://doi.org/10.1111/gcb.13037 (2015).ADS 
Article 

Google Scholar 
9.Holbrook, S. J. & Schmitt, R. J. The combined effects of predation risk and food reward on patch selection. Ecology 69, 125–134. https://doi.org/10.2307/1943167 (1988).Article 

Google Scholar 
10.Moody, A. L., Houston, A. I. & McNamara, J. M. Ideal free distributions under predation risk. Behav. Ecol. Sociobiol. 38, 131–143 (1996).Article 

Google Scholar 
11.Dietz, H. & Edwards, P. J. Recognition that causal processes change during plant invasion helps explain conflicts in evidence. Ecology 87, 1359–1367 (2006).Article 

Google Scholar 
12.Hobbs, R. J. & Huenneke, L. F. Disturbance, diversity, and invasion: implications for conservation. Conserv. Biol. 6, 324–337 (1992).Article 

Google Scholar 
13.Van der Graaf, S., Stahl, J., Klimkowska, A. & Drent, J. P. B. Surfing on a green wave—How plant growth drives spring migration in the Barnacle Goose Branta leucopsis. Ardea -Wageningen- 94, 567 (2006).
Google Scholar 
14.Parker, I. M. et al. Impact: toward a framework for understanding the ecological effects of invaders. Biol. Invasions 1, 3–19. https://doi.org/10.1023/A:1010034312781 (1999).Article 

Google Scholar 
15.Pimentel, D., Zuniga, R. & Morrison, D. Update on the environmental and economic costs associated with alien-invasive species in the United States. Ecol. Econ. 52, 273–288. https://doi.org/10.1016/j.ecolecon.2004.10.002 (2005).Article 

Google Scholar 
16.Shackelford, N. et al. Primed for change: developing ecological restoration for the 21st Century. Restor. Ecol. 21, 297–304. https://doi.org/10.1111/rec.12012 (2013).Article 

Google Scholar 
17.Pickell, P. D., Pickell, P. D., Andison, D. W., Coops, N. C. & Gergel, S. E. The spatial patterns of anthropogenic disturbance in the western Canadian boreal forest following oil and gas development. Can. J. For. Res. 45, 732–743. https://doi.org/10.1139/cjfr-2014-0546 (2015).Article 

Google Scholar 
18.Fisher, J. T. & Wilkinson, L. The response of mammals to forest fire and timber harvest in the North American boreal forest. Mammal Rev. 35, 51–81 (2005).Article 

Google Scholar 
19.Wittische, J., Heckbert, S., James, P. M. A., Burton, A. C. & Fisher, J. T. Community-level modelling of boreal forest mammal distribution in an oil sands landscape. Sci. Total Environ. 755, 142500. https://doi.org/10.1016/j.scitotenv.2020.142500 (2021).ADS 
CAS 
Article 
PubMed 

Google Scholar 
20.Hewitt, D. G. Biology and management of white-tailed deer (CRC Press, Boca Raton, 2011).Book 

Google Scholar 
21.McCabe, R. E. & McCabe, T. R. in White tailed deer: ecology and management Ch. Chapter 2, 19–72 (Stackpole, A Wildlife Management Institute Book, 1984).22.Webb, R. The range of white-tailed deer in Alberta (Alberta Fish and Wildlife Division Edmonton, Alberta, 1967).
Google Scholar 
23.Dawe, K. L. & Boutin, S. Climate change is the primary driver of white-tailed deer (Odocoileus virginianus) range expansion at the northern extent of its range; land use is secondary. Ecol. Evol. 6, 6435–6451. https://doi.org/10.1002/ece3.2316 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
24.DeCesare, N. J., Hebblewhite, M., Robinson, H. S. & Musiani, M. Endangered, apparently: the role of apparent competition in endangered species conservation. Anim. Conserv. 13, 353–362. https://doi.org/10.1111/j.1469-1795.2009.00328.x (2010).Article 

Google Scholar 
25.Latham, A. D. M., Latham, M. C., McCutchen, N. A. & Boutin, S. Invading white-tailed deer change wolf-caribou dynamics in northeastern Alberta. J. Wildl. Manag. 75, 204–212. https://doi.org/10.1002/jwmg.28 (2011).Article 

Google Scholar 
26.Latham, A. D. M., Latham, M. C., Boyce, M. C. & Boutin, S. Movement responses by wolves to industrial linear features and their effect on woodland caribou in northeastern Alberta. Ecol. Appl. 21, 11 (2011).Article 

Google Scholar 
27.Fisher, J. T., Burton, A. C., Nolan, L. & Roy, L. Influences of landscape change and winter severity on invasive ungulate persistence in the Nearctic boreal forest. Sci. Rep. 10, 8742. https://doi.org/10.1038/s41598-020-65385-3 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
28.Dabros, A., Pyper, M. & Castilla, G. Seismic lines in the boreal and arctic ecosystems of North America: environmental impacts, challenges, and opportunities. Environ. Rev. 26, 214–229. https://doi.org/10.1139/er-2017-0080 (2018).Article 

Google Scholar 
29.Dickie, M., Serrouya, R., McNay, R. S., Boutin, S. & du Toit, J. Faster and farther: wolf movement on linear features and implications for hunting behaviour. J. Appl. Ecol. 54, 253–263. https://doi.org/10.1111/1365-2664.12732 (2017).Article 

Google Scholar 
30.Finnegan, L., MacNearney, D. & Pigeon, K. E. Divergent patterns of understory forage growth after seismic line exploration: implications for caribou habitat restoration. For. Ecol. Manag. 409, 634–652. https://doi.org/10.1016/j.foreco.2017.12.010 (2018).Article 

Google Scholar 
31.Prokopenko, C. M., Boyce, M. S., Avgar, T. & Tulloch, A. Characterizing wildlife behavioural responses to roads using integrated step selection analysis. J. Appl. Ecol. 54, 470–479. https://doi.org/10.1111/1365-2664.12768 (2017).Article 

Google Scholar 
32.Waring, G. H., Griffis, J. L. & Vaughn, M. E. White-tailed deer roadside behavior, wildlife warning reflectors, and highway mortality. Appl. Anim. Behav. Sci. 29, 215–223. https://doi.org/10.1016/0168-1591(91)90249-W (1991).Article 

Google Scholar 
33.Bowman, J., Ray, J. C., Magoun, A. J., Johnson, D. S. & Dawson, F. N. Roads, logging, and the large-mammal community of an eastern Canadian boreal forest. Can. J. Zool. 88, 454–467. https://doi.org/10.1139/z10-019 (2010).Article 

Google Scholar 
34.Munro, K. G., Bowman, J. & Fahrig, L. Effect of paved road density on abundance of white-tailed deer. Wildl. Res. 39, 478. https://doi.org/10.1071/wr11152 (2012).Article 

Google Scholar 
35.Fisher, J. T. & Burton, A. C. Spatial structure of reproductive success infers mechanisms of ungulate invasion in Nearctic boreal landscapes. Ecol. Evol. 11, 900–911. https://doi.org/10.1002/ece3.7103 (2021).Article 
PubMed 

Google Scholar 
36.Kie, J. G. Optimal foraging and risk of predation effects on behavior and social structure in ungulates. J. Mammal. 80, 1114–1129 (1999).Article 

Google Scholar 
37.Brown, J. S., Laundré, J. W. & Gurung, M. The ecology of fear: optimal foraging, game theory, and trophic interactions. J. Mammal. 80, 385–399. https://doi.org/10.2307/1383287 (1999).Article 

Google Scholar 
38.Kittle, A. M., Fryxell, J. M., Desy, G. E. & Hamr, J. The scale-dependent impact of wolf predation risk on resource selection by three sympatric ungulates. Oecologia 157, 163–175. https://doi.org/10.1007/s00442-008-1051-9 (2008).ADS 
Article 
PubMed 

Google Scholar 
39.Moen, A. N. Energy conservation by white-tailed deer in the winter. Ecology 57, 192–198. https://doi.org/10.2307/1936411 (1976).Article 

Google Scholar 
40.Schmidt, K. Winter ecology of nonmigratory Alpine red deer. Oecologia 95, 226–233. https://doi.org/10.1007/BF00323494 (1993).ADS 
Article 
PubMed 

Google Scholar 
41.Kilgo, J. C., Ray, H. S., Vukovich, M., Goode, M. J. & Ruth, C. Predation by coyotes on white-tailed deer neonates in South Carolina. J. Wildl. Manag. https://doi.org/10.1002/jwmg.393 (2012).Article 

Google Scholar 
42.Laurent, M., Dickie, M., Becker, M., Serrouya, R. & Boutin, S. Evaluating the mechanisms of landscape change on white-tailed deer populations. J. Wildl. Manag. 85, 340–353. https://doi.org/10.1002/jwmg.21979 (2020).Article 

Google Scholar 
43.Schneider, R. R., Hauer, G., Adamowicz, W. L. & Boutin, S. Triage for conserving populations of threatened species: the case of woodland caribou in Alberta. Biol. Conserv. 143, 1603–1611. https://doi.org/10.1016/j.biocon.2010.04.002 (2010).Article 

Google Scholar 
44.Kilkenny, C., Browne Wj Fau – Cuthill, I. C., Cuthill Ic Fau – Emerson, M., Emerson M Fau – Altman, D. G. & Altman, D. G. Improving bioscience research reporting: the ARRIVE guidelines for reporting animal research. PLoS biol. 8(6), e1000412 (2010).45.DelGiudice, G. D., Mangipane, B. A., Sampson, B. A. & Kochanny, C. O. Chemical immobilization, body temperature, and post-release mortality of white-tailed deer captured by clover trap and net-gun. Wildl. Soc. Bull. (1973-2006) 29, 1147–1157 (2001).
Google Scholar 
46.Droge, E., Creel, S., Becker, M. S. & M’Soka, J. Risky times and risky places interact to affect prey behaviour. Nat. Ecol. Evol. 1, 1123–1128. https://doi.org/10.1038/s41559-017-0220-9 (2017).Article 
PubMed 

Google Scholar 
47.Kunkel, K. E. & Mech, L. D. Wolf and bear predation on white-tailed deer fawns in northeastern Minnesota. Can. J. Zool. 72, 1557–1565 (1994).Article 

Google Scholar 
48.Latham, A., Latham, M., Knopff, K., Hebblewhite, M. & Boutin, S. Wolves, white-tailed deer, and beaver: Implications of seasonal prey switching for woodland caribou declines. Ecography https://doi.org/10.1111/j.1600-0587.2013.00035.x (2013).Article 

Google Scholar 
49.Alberta Environment and Sustainable Resource Development. Alberta Vegetation Index. Accessed October 2016. https://geodiscover.alberta.ca/50.Manly, B., McDonald, L., Thomas, D., McDonald, T. & Erickson, W.Resource selection by animals: statistical design and analysis for field studies. Vol. 63, pp. 1-10 (Springer Science & Business Media, 2007).51.Boyce, M. S., Vernier, P. R., Nielsen, S. E. & Schmiegelow, F. K. A. Evaluating resource selection functions. Ecol. Model. 157, 281–300. https://doi.org/10.1016/S0304-3800(02)00200-4 (2002).Article 

Google Scholar 
52.Hijmans, R. & van Etten, J. Raster: Geographic data analysis and modeling. CRAN R package 2 (2016).53.R: A language and environment for statistical computing. (Vienna, Austria, 2013).54.Zuur, A., Hilbe, J. & Ieno, E. A Beginner’s Guide to GLM and GLMM with R: a frequentist and Bayesian perspective for ecologists. (Highland Statistics, 2013).55.Gillies, C. S. et al. Application of random effects to the study of resource selection by animals. J. Anim. Ecol. 75, 887–898. https://doi.org/10.1111/j.1365-2656.2006.01106.x (2006).Article 
PubMed 

Google Scholar 
56.Craney, T. A. & Surles, J. G. Model-dependent variance inflation factor cutoff values. Qual. Eng. 14, 391–403. https://doi.org/10.1081/QEN-120001878 (2002).Article 

Google Scholar 
57.Akaike, H. Information theory and an extension of the maximum likelihood principle. Selected papers of hirotugu akaike 199–213 (Springer, New York, 1998).Book 

Google Scholar 
58.Burnham, K. P. & Anderson, D. R. Multimodel inference: understanding AIC and BIC in model selection. Sociol. Methods Res. 33, 261–304. https://doi.org/10.1177/0049124104268644 (2004).MathSciNet 
Article 

Google Scholar 
59.Boulanger, Y. et al. Climate change impacts on forest landscapes along the Canadian southern boreal forest transition zone. Landscape Ecol. 32, 1415–1431. https://doi.org/10.1007/s10980-016-0421-7 (2017).Article 

Google Scholar 
60.Sulla-Menashe, D., Woodcock, C. E. & Friedl, M. A. Canadian boreal forest greening and browning trends: an analysis of biogeographic patterns and the relative roles of disturbance versus climate drivers. Environ. Res. Lett. 13, 014007. https://doi.org/10.1088/1748-9326/aa9b88 (2018).ADS 
Article 

Google Scholar 
61.St-Pierre, F., Drapeau, P. & St-Laurent, M.-H. Drivers of vegetation regrowth on logging roads in the boreal forest: Implications for restoration of woodland caribou habitat. For. Ecol. Manag. 482, 118846. https://doi.org/10.1016/j.foreco.2020.118846 (2021).Article 

Google Scholar 
62.Berger, J. Fear, human shields and the redistribution of prey and predators in protected areas. Biol. Let. 3, 620–623. https://doi.org/10.1098/rsbl.2007.0415 (2007).Article 

Google Scholar 
63.Heyes, A., Leach, A. & Mason, C. F. The economics of Canadian oil sands. Rev. Environ. Econ. Policy 12, 242–263. https://doi.org/10.1093/reep/rey006 (2018).Article 

Google Scholar 
64.Komers, P. E. & Stanojevic, Z. Rates of disturbance vary by data resolution: implications for conservation schedules using the Alberta boreal forest as a case study. Global Change Biol. 19, 2916–2928 (2013).ADS 
CAS 
Article 

Google Scholar 
65.Hebblewhite, M. & Merrill, E. H. Trade-offs between predation risk and forage differ between migrant strategies in a migratory ungulate. Ecology 90, 3445–3454. https://doi.org/10.1890/08-2090.1 (2009).Article 
PubMed 

Google Scholar 
66.Mech, D. L. & Boitani, L. Wolves: behavior, ecology, and conservation Vol. 57 (University of Chicago Press, Chicago, 2004).
Google Scholar 
67.Creel, S., Winnie, J. A., Christianson, D. & Liley, S. Time and space in general models of antipredator response: tests with wolves and elk. Anim. Behav. 76, 1139–1146. https://doi.org/10.1016/j.anbehav.2008.07.006 (2008).Article 

Google Scholar 
68.Steenweg, R. et al. Scaling-up camera traps: monitoring the planet’s biodiversity with networks of remote sensors. Front. Ecol. Environ. 15, 26–34. https://doi.org/10.1002/fee.1448 (2017).Article 

Google Scholar 
69.Hebblewhite, M. Billion dollar boreal woodland caribou and the biodiversity impacts of the global oil and gas industry. Biol. Cons. 206, 102–111. https://doi.org/10.1016/j.biocon.2016.12.014 (2017).Article 

Google Scholar 
70.Côté, S. D., Rooney, T. P., Tremblay, J.-P., Dussault, C. & Waller, D. M. Ecological impacts of deer overabundance. Annu. Rev. Ecol. Evol. Syst. 35, 113–147 (2004).Article 

Google Scholar 
71.McCullough, D. R. Evaluation of night spotlighting as a deer study technique. J. Wildl. Manag. 46, 963–973. https://doi.org/10.2307/3808229 (1982).Article 

Google Scholar 
72.Preston, T., Wildhaber, M., Green, N., Albers, J. & Debenedetto, G. Enumerating white-tailed deer using unmanned aerial vehicles. Wildlife Soc. Bull. https://doi.org/10.1002/wsb.1149 (2021).Article 

Google Scholar 
73.Parks, A. E. Provincial woodland caribou range plan. 212 (Edmonton, Alberta, 2017).74.Tattersall, E. R., Burgar, J. M., Fisher, J. T. & Burton, A. C. Boreal predator co-occurrences reveal shared use of seismic lines in a working landscape. Ecol. Evol. 10, 1678–1691. https://doi.org/10.1002/ece3.6028 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
75.Diaz, S. et al. Pervasive human-driven decline of life on Earth points to the need for transformative change. Science (New York N.Y.) https://doi.org/10.1126/science.aax3100 (2019).Article 
PubMed Central 

Google Scholar 
76.Bayoumi, T. & Muhleisen, M. Energy, the exchange rate, and the economy: macroeconomic benefits of Canada’s oil sands production (International Monetary Fund, Washington, 2006).
Google Scholar 
77.Zhu, K., Song, Y. & Qin, C. Forest age improves understanding of the global carbon sink. Proc. Natl. Acad. Sci. 116, 3962. https://doi.org/10.1073/pnas.1900797116 (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar  More