Ecology

1.Bezemer, T. M. & van Dam, N. M. Linking aboveground and belowground interactions via induced plant defenses. Trends Ecol. Evol. 20, 617–624. https://doi.org/10.1016/j.tree.2005.08.006 (2005).Article 
PubMed 

Google Scholar 
2.Kaplan, I. et al. Physiological integration of roots and shoots in plant defense strategies links above-and belowground herbivory. Ecol. Lett. 11, 841–851. https://doi.org/10.1111/j.1461-0248.2008.01200.x (2008).Article 
PubMed 

Google Scholar 
3.Huang, W., Siemann, E., Carrillo, J. & Ding, J. Below-ground herbivory limits induction of extrafloral nectar by above-ground herbivores. Ann. Bot. 115, 841–846. https://doi.org/10.1093/aob/mcv011 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
4.Omer, A. D., Thaler, J. S., Granett, J. & Karban, R. Jasmonic acid induced resistance in grapevines to a root and leaf feeder. J. Econ. Entomol. 93, 840–845. https://doi.org/10.1603/0022-0493-93.3.840 (2000).CAS 
Article 
PubMed 

Google Scholar 
5.Yamawo, A., Ohsaki, H. & Cahill, J. F. Jr. Damage to leaf veins suppresses root foraging precision. Am. J. Bot. 106, 1126–1130. https://doi.org/10.1002/ajb2.1338 (2019).Article 
PubMed 

Google Scholar 
6.Heil, M. & Karban, R. Explaining evolution of plant communication by airborne signals. Trends Ecol. Evol. 25, 137–144. https://doi.org/10.1016/j.tree.2009.09.010 (2010).Article 
PubMed 

Google Scholar 
7.Karban, R., Yang, L. J. & Edwards, K. F. Volatile communication between plants that affects herbivory: A meta-analysis. Ecol. Lett. 17, 44–52. https://doi.org/10.1111/ele.12205 (2014).Article 
PubMed 

Google Scholar 
8.Yoneya, K. & Takabayashi, J. Plant-plant communication mediated by airborne signals: Ecological and plant physiological perspectives. Plant Biotechnol. 31, 409–416. https://doi.org/10.5511/plantbiotechnology.14.0827a (2014).CAS 
Article 

Google Scholar 
9.Morrell, K. & Kessler, A. Plant communication in a widespread goldenrod: Keeping herbivores on the move. Funct. Ecol. 31, 1049–1061. https://doi.org/10.1111/1365-2435.12793 (2017).Article 

Google Scholar 
10.Arimura, G. et al. Herbivory-induced volatiles elicit defence genes in lima bean leaves. Nature 406, 512–515 (2000).ADS 
CAS 
Article 

Google Scholar 
11.Shiojiri, K. et al. Weeding volatiles reduce leaf and seed damage to field-grown soybeans and increase seed isoflavones. Sci. Rep. 7, 1–8. https://doi.org/10.1038/srep41508 (2017).CAS 
Article 

Google Scholar 
12.Karban, R., Shiojiri, K., Huntzinger, M. & McCall, A. C. Damage-induced resistance in sagebrush: Volatiles are key to intra- and interplant communication. Ecology 87, 922–930. https://doi.org/10.1890/0012-9658(2006)87[922:drisva]2.0.co;2 (2006).Article 
PubMed 

Google Scholar 
13.Kikuta, Y. et al. Specific regulation of pyrethrin biosynthesis in Chrysanthemum cinerariaefolium by a blend of volatiles emitted from artificially damaged conspecific plants. Plant Cell Physiol. 52, 588–596. https://doi.org/10.1093/pcp/pcr017 (2011).CAS 
Article 
PubMed 

Google Scholar 
14.Karban, R., Baldwin, I. T., Baxter, K. J., Laue, G. & Felton, G. W. Communication between plants: Induced resistance in wild tobacco plants following clipping of neighboring sagebrush. Oecologia 125, 66–71. https://doi.org/10.1007/PL00008892 (2000).ADS 
CAS 
Article 
PubMed 

Google Scholar 
15.Karban, R., Huntzinger, M. & McCall, A. C. The specificity of eavesdropping on sagebrush by other plants. Ecology 85, 1845–1852. https://doi.org/10.1890/03-0593 (2004).Article 

Google Scholar 
16.Shiojiri, K. et al. Exposure to artificially damaged goldenrod volatiles increases saponins in seeds of field-grown soybean plants. Phytochem. Lett. 36, 7–10. https://doi.org/10.1016/j.phytol.2020.01.014 (2020).CAS 
Article 

Google Scholar 
17.Glinwood, R., Ninkovic, V. & Pettersson, J. Chemical interaction between undamaged plants—Effects on herbivores and natural enemies. Phytochemistry 72, 1683–1689. https://doi.org/10.1016/j.phytochem.2011.02.010 (2011).CAS 
Article 
PubMed 

Google Scholar 
18.Lokesh, R. A. V. I., Manasvi, V. & Lakshmi, B. P. Antibacterial and antioxidant activity of saponin from Abutilon indicum leaves. Asian J. Pharm. Clin. Res. 9, 344–347. https://doi.org/10.22159/ajpcr.2016.v9s3.15064 (2016).CAS 
Article 

Google Scholar 
19.Raji, P., Samrot, A. V., Keerthana, D. & Karishma, S. Antibacterial activity of alkaloids, flavonoids, saponins and tannins mediated green synthesised silver nanoparticles against Pseudomonas aeruginosa and Bacillus subtilis. J. Cluster Sci. 30, 881–895. https://doi.org/10.1007/s10876-019-01547-2 (2019).CAS 
Article 

Google Scholar 
20.Toth, R., Toth, D. & Starke, D. Vesicular-arbuscular mycorrhizal colonization in Zea mays affected by breeding for resistance to fungal pathogens. Can. J. Bot. 68, 1039–1044. https://doi.org/10.1139/b90-131 (1990).Article 

Google Scholar 
21.Matyssek, R. et al. The plant’s capacity in regulating resource demand. Plant Biol. 7, 560–580. https://doi.org/10.1055/s-2005-872981 (2005).CAS 
Article 
PubMed 

Google Scholar 
22.Brundrett, M. C. Coevolution of roots and mycorrhizas of land plants. New Phytol. 154, 275–304. https://doi.org/10.1046/j.1469-8137.2002.00397.x (2002).Article 
PubMed 

Google Scholar 
23.Kiers, E. T., Rousseau, R. A., West, S. A. & Denison, R. F. Host sanctions and the legume-rhizobium mutualism. Nature 425, 78–81. https://doi.org/10.1038/nature01931 (2003).ADS 
CAS 
Article 

Google Scholar 
24.Walters, D. R. Plant Defense: Warding Off Attack by Pathogens, Herbivores, and Parasitic Plants (Blackwell Publishing, 2011).
Google Scholar 
25.Szakiel, A., Pączkowski, C. & Henry, M. Influence of environmental biotic factors on the content of saponins in plants. Phytochem. Rev. 10, 493–502. https://doi.org/10.1007/s11101-010-9164-2 (2011).CAS 
Article 

Google Scholar 
26.Singh, B. & Kaur, A. Control of insect pests in crop plants and stored food grains using plant saponins: A review. LWT 87, 93–101. https://doi.org/10.1016/j.lwt.2017.08.077 (2018).CAS 
Article 

Google Scholar 
27.Barton, K. E. & Koricheva, J. The ontogeny of plant defense and herbivory: Characterizing general patterns using meta-analysis. Am. Nat. 175, 481–493. https://doi.org/10.1086/650722 (2010).Article 
PubMed 

Google Scholar 
28.Feeny PP. Seasonal changes in oak leaf tannins and nutrients as a cause of spring feeding by winter moth caterpillars. Ecology 51, 565–581 https://doi.org/10.2307/1934037 (1970).Article 

Google Scholar 
29.Dudt, J. F. & Shure, D. J. The influence of light and nutrients on foliar phenolics and insect herbivory. Ecology 75, 86–98. https://doi.org/10.2307/1939385 (1994).Article 

Google Scholar 
30.Julkunen-Tiitto, R. Phenolic constituents in the leaves of northern willows: Methods for the analysis of certain phenolics. Asian J. Pharm. Clin. Res. 33, 213–217. https://doi.org/10.1021/jf00062a013 (1985).CAS 
Article 

Google Scholar 
31.Folin, O. & Denis, W. A colorimetric method for the determination of phenols (and phenol derivatives) in urine. J. Biol. Chem. 22, 305–308 (1915).CAS 
Article 

Google Scholar 
32.Huang, D., Ou, B. & Prior, R. L. The chemistry behind antioxidant capacity assays. J. Agric. Food Chem. 53, 1841–1856. https://doi.org/10.1021/jf030723c (2005).CAS 
Article 
PubMed 

Google Scholar 
33.Mukai, T., Horie, H. & Goto, T. A simple method for determining saponin in tea seed. Chagyo Kenkyu Hokoku 75, 29–31 (1992) (Japanese with English abstract).CAS 
Article 

Google Scholar 
34.Dubois, M., Gilles, K. A., Hamilton, J. K., Rebers, P. A. & Smith, F. A colorimetric method for the determination of sugars. Nature 168, 167–167 (1951).ADS 
CAS 
Article 

Google Scholar 
35.Dubois, M., Gilles, K. A., Hamilton, J. K., Rebers, P. T. & Smith, F. Colorimetric method for determination of sugars and related substances. Anal. Chem. 28, 350–356. https://doi.org/10.1021/ac60111a017 (1956).CAS 
Article 

Google Scholar 
36.Harad, Y. Cation and anion exchange capacity of soil background and methods. 302 Jpn. J. Soil Scie. Plant Nutr. 55, 273–283. 303 (1984) (in Japanese).
37.R Development Core Team. R A language and environment for statistical computing (R Foundation for Statistical Computing, 2020).
Google Scholar 
38.De Geyter, E., Lambert, E., Geelen, D. & Smagghe, G. Novel advances with plant saponins as natural insecticides to control pest insects. Pest Technol. 1, 96–105 (2007).
Google Scholar 
39.Pankhurst, C. E. & Sprent, J. I. Effects of water stress on the respiratory and nitrogen-fixing activity of soybean root nodules. J. Exp. Bot. 26, 287–304. https://doi.org/10.1093/jxb/26.2.287 (1975).CAS 
Article 

Google Scholar 
40.Sparg, S., Light, M. E. & Van Staden, J. Biological activities and distribution of plant saponins. J. Ethnopharmacol. 94, 219–243. https://doi.org/10.1016/j.jep.2004.05.016 (2004).CAS 
Article 
PubMed 

Google Scholar 
41.Saha, S., Walia, S., Kumar, J., Dhingra, S. & Parmar, B. S. Screening for feeding deterrent and insect growth regulatory activity of triterpenic saponins from Diploknema butyracea and Sapindus mukorossi. J. Agric. Food Chem. 58, 434–440. https://doi.org/10.1021/jf902439m (2010).CAS 
Article 
PubMed 

Google Scholar 
42.Hoagland, R. E., Zablotowicz, R. M. & Oleszek, W. A. Effects of alfalfa saponins on in vitro physiological activity of soil and rhizosphere bacteria. J. Crop. Prod. 4, 349–361. https://doi.org/10.1300/J144v04n02_16 (2001).CAS 
Article 

Google Scholar 
43.Killeen, G. F. et al. Antimicrobial saponins of Yucca schidigera and the implications of their in vitro properties for their in vivo impact. J. Agric. Food Chem. 46, 3178–3186. https://doi.org/10.1021/jf970928j (1998).CAS 
Article 

Google Scholar 
44.Sugiyama, A. The soybean rhizosphere: Metabolites, microbes, and beyond—A review. J. Adv. Res. 19, 67–73. https://doi.org/10.1016/j.jare.2019.03.005 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
45.Lucas-Barbosa, D. Integration studies on plant-pollinator and plant–herbivore interactions. Trends Plant Sci. 21, 125–133. https://doi.org/10.1016/j.tplants.2015.10.013 (2016).CAS 
Article 
PubMed 

Google Scholar 
46.De Deyn, G. B., Raaijmakers, C. E. & Van der Putten, W. H. Plant community development is affected by nutrients and soil biota. J. Ecol. 92(5), 824–834. https://doi.org/10.1111/j.0022-0477.2004.00924.x (2004).Article 

Google Scholar 
47.Knelman, J. E. et al. Nutrient addition dramatically accelerates microbial community succession. PLoS ONE 9(7), e102609. https://doi.org/10.1371/journal.pone.0102609 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
48.Yoneya, K. & Takeshi, M. Co-evolution of foraging behaviour in herbivores and their natural enemies predicts multifunctionality of herbivore-induced plant volatiles. Funct. Ecol. 29, 451–461. https://doi.org/10.1111/1365-2435.12398 (2015).Article 

Google Scholar 
49.Karban, R. Plant communication increases heterogeneity in plant phenotypes and herbivore movement. Funct. Ecol. 31, 990–991. https://doi.org/10.1111/1365-2435.12806 (2017).Article 

Google Scholar  More

1.May, R. M. Thresholds and breakpoints in ecosystems with a multiplicity of stable states. Nature 269, 471–477 (1977).Article 

Google Scholar 
2.Scheffer, M., Carpenter, S., Foley, J., Folke, C. & Walker, B. Catastrophic shifts in ecosystems. Nature 413, 591–596 (2001).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
3.Archer, S., Schimel, D. S. & Holland, E. A. Mechanisms of shrubland expansion: land use, climate, or carbon dioxide. Clim. Change 29, 91–99 (1995).Article 

Google Scholar 
4.Maher, E. L. & Germino, M. J. Microsite variation among conifer species during seedling establishment at alpine treeline. Ecoscience 13, 334–341 (2006).Article 

Google Scholar 
5.Knapp, A. K. et al. Shrub encroachment in North American grasslands: shifts in growth form dominance alters control of ecosystem carbon inputs. Glob. Change Biol. 14, 615–623 (2008).Article 

Google Scholar 
6.McKee, K. L. & Rooth, J. E. Where temperate meets tropical: multi-factorial effects of elevated CO2, nitrogen enrichment, and competition on a mangrove-salt marsh community. Glob. Change Biol. 14, 971–984 (2008).Article 

Google Scholar 
7.Huang, H., Zinnert, J. C., Wood, L. K., Young, D. R. & D’Odorico, P. Non-linear shift from grassland to shrubland in temperate barrier islands. Ecology 99, 1671–1681 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
8.Huang, H., Anderegg, L. D. L., Dawson, T. E., Mote, S. & D’Odorico, P. Critical transition to woody plant dominance through microclimate feedbacks in North American coastal ecosystems. Ecology 101, e03107 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
9.Huenneke, L. F., Anderson, J. P., Remmenga, M. & Schlesinger, W. H. Desertification alters patterns of aboveground net primary production in Chihuahuan ecosystems. Glob. Change Biol. 8, 247–264 (2002).Article 

Google Scholar 
10.Li, J., Okin, G. S., Hartman, L. J. & Epstein, H. E. Quantitative assessment of wind erosion and soil nutrient loss in desert grasslands of southern New Mexico, USA. Biogeochemistry 85, 317–332 (2007).Article 

Google Scholar 
11.D’Odorico, P., Okin, G. S. & Bestelmeyer, B. T. A synthetic review of feedbacks and drivers of shrub encroachment in arid grasslands. Ecohydrology 5, 520–530 (2012).Article 

Google Scholar 
12.Van Auken, O. Shrub invasions of North American semiarid grasslands. Annu. Rev. Ecol. Evol. Syst. 31, 197–215 (2000).Article 

Google Scholar 
13.Sankaran, M. et al. Determinants of woody cover in African savannas. Nature 438, 846–849 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
14.Gehrig-Fasel, J., Guisan, A. & Zimmermann, N. E. Tree line shifts in the Swiss Alps: climate change or land abandonment? J. Veg. Sci. 18, 571–582 (2007).Article 

Google Scholar 
15.Cavanaugh, K. C. et al. Poleward expansion of mangroves is a threshold response to decreased frequency of extreme cold events. Proc. Natl Acad. Sci. USA 111, 723–727 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
16.D’Odorico, P. et al. Vegetation–microclimate feedbacks in woodland–grassland ecotones. Glob. Ecol. Biogeogr. 22, 364–379 (2013).Article 

Google Scholar 
17.He, Y., D’Odorico, P. & De Wekker, S. F. The relative importance of climate change and shrub encroachment on nocturnal warming in the Southwestern United States. Int. J. Climatol. 35, 475–480 (2014).Article 

Google Scholar 
18.He, Y., D’Odorico, P., De Wekker, S. F., Fuentes, J. D. & Litvak, M. On the impact of shrub encroachment on microclimate conditions in the northern Chihuahuan desert. J. Geophys. Res. Atmos. 115, D21120 (2010).Article 

Google Scholar 
19.Ives, A. R. & Carpenter, S. R. Stability and diversity of ecosystems. Science 317, 58–62 (2007).CAS 
PubMed 
Article 
PubMed Central 

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

Google Scholar 
21.Ratajczak, Z. et al. Abrupt change in ecological systems: inference and diagnosis. Trends Ecol. Evol. 33, 513–526 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
22.Sugihara, G. & May, R. M. Applications of fractals in ecology. Trends Ecol. Evol. 5, 79–86 (1990).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
23.Rodriguez-Iturbe, I. & Rinaldo, A. Fractal River Basins: Chance and Self- Organization. (Cambridge University Press, New York, 1997).24.Majumder, S., Tamma, K., Ramaswamy, S. & Guttal, V. Inferring critical thresholds of ecosystem transitions from spatial data. Ecology 100, e02722 (2019).PubMed 
Article 

Google Scholar 
25.Staver, A. C., Asner, G. P., Rodriguez-Iturbe, I., Levin, S. A. & Smit, I. Spatial patterning among savanna trees in high resolution, spatially extensive data. Proc. Natl Acad. Sci. USA 116, 10685 (2019).Article 
CAS 

Google Scholar 
26.Kéfi, S. et al. Spatial vegetation patterns and imminent desertification in Mediterranean arid ecosystems. Nature 449, 213–217 (2007).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
27.Kéfi, S. et al. Robust scaling in ecosystems and the meltdown of patch size distributions before extinction. Ecol. Lett. 14, 29–35 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
28.Weissmann, H., Kent, R., Michael, Y. & Shnerb, N. M. Empirical analysis of vegetation dynamics and the possibility of a catastrophic desertification transition. PLoS ONE 12, e0189058 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
29.Sankaran, S., Majumder, S., Viswanathan, A. & Guttal, V. Clustering and correlations: inferring resilience from spatial patterns in ecosystems. Methods Ecol. Evol. 10, 2079–2089 (2019).Article 

Google Scholar 
30.Zinnert, J. C. et al. Spatial–temporal dynamics in barrier island upland vegetation: the overlooked coastal landscape. Ecosystems 19, 685–697 (2016).CAS 
Article 

Google Scholar 
31.Thompson, J. A., Zinnert, J. C. & Young, D. R. Immediate effects of microclimate modification enhance native shrub encroachment. Ecosphere 8, e01687 (2017).Article 

Google Scholar 
32.Wood, L. K., Hays, S. & Zinnert, J. C. Decreased temperature variance associated with biotic composition enhances coastal shrub encroachment. Sci. Rep. 10, 8210 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
33.Stauffer, D. & Aharony, A. Introduction to Percolation Theory (Taylor and Francis, London, 1985).34.Taubert, F. et al. Global patterns of tropical forest fragmentation. Nature 554, 519–522 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
35.Scanlon, T. M., Caylor, K. K., Levin, S. A. & Rodriguez-Iturbe, I. Positive feedbacks promote power-law clustering of Kalahari vegetation. Nature 449, 209–212 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
36.Kéfi, S., Rietkerk, M., van Baalen, M. & Loreau, M. Local facilitation, bistability and transitions in arid ecosystems. Theor. Popul. Biol. 71, 367–379 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
37.Berdugo, M., Kéfi, S., Soliveres, S. & Maestre, F. T. Plant spatial patterns identify alternative ecosystem multifunctionality states in global drylands. Nat. Ecol. Evol. 1, 3 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
38.Dakos, V., Kéfi, S., Rietkerk, M., van Nes, E. H. & Scheffer, M. Slowing down in spatially patterned ecosystems at the brink of collapse. Am. Nat. 177, E153–E166 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
39.Weerman, E. J. et al. Changes in diatom patch-size distribution and degradation in a spatially self-organized intertidal mudflat ecosystem. Ecology 93, 608–618 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
40.Kéfi, S., Dakos, V., Scheffer, M., Van Nes, E. H. & Rietkerk, M. Early warning signals also precede non-catastrophic transitions. Oikos 122, 641–648 (2012).Article 

Google Scholar 
41.van Belzen, J. et al. Vegetation recovery in tidal marshes reveals critical slowing down under increased inundation. Nat. Commun. 8, 15811 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
42.USACE-TEC & JALBTCX. Hyperspectral imagery for Hog Island, VA, 2013 ver 7. Environmental Data Initiative. https://doi.org/10.6073/pasta/6a5cc305e93c2baf9283facee688c504 (2018).43.Young, D. R. et al. Cross-scale patterns in shrub thicket dynamics in the Virginia barrier complex. Ecosystems 10, 854–863 (2007).Article 

Google Scholar 
44.Young, D. R., Shao, G. & Porter, J. H. Spatial and temporal growth dynamics of barrier island shrub thickets. Am. J. Bot. 82, 638–645 (1995).Article 

Google Scholar 
45.McGarigal, K., Cushman, S. A. & Ene, E. FRAGSTATS v4: Spatial Pattern Analysis Program for Categorical and Continuous Maps (University of Massachusetts at Amherst, MA, 2012).46.Clauset, A., Shalizi, C. R. & Newman, M. E. J. Power-law distributions in empirical data. SIAM Rev. 51, 661–703 (2009). 2009.Article 

Google Scholar 
47.Hayden, B. P. Ecosystem feedbacks on climate at the landscape scale. Philos. Trans. R. Soc. B, Biol. Sci. 353, 5–18 (1998).Article 

Google Scholar  More

We untangled how the direct and indirect paths of forest cover and water quality variables interact and shape anopheline assemblages in two seasons. Although previous studies determined how environmental variables at different spatial extents affected anopheline distributions in Amazônia, most studies focused on a single effect of an environmental variable or focused on single habitat types (terrestrial or aquatic)22,23,41,42. Our most important finding is that seasonality modulates the direct and indirect effects of forest cover on Amazônian anopheline larval distributions. In particular, we found that forest cover had stronger direct and indirect influence on larval anopheline assemblage composition in the rainy season than the dry season.The different paths and strengths of forest cover influences on anopheline assemblages during the rainy and dry seasons can be associated with the responses of adults and larvae to forest characteristics. Forest cover influences water quality variables of ponds by shading, organic matter inputs and erosion processes43. These effects have consequences for pond water quality44 and favor the establishment of different culicid species45. We showed that during the rainy season, forest cover directly and indirectly influenced site water quality. Greater forest cover in the rainy season directly and indirectly affected A. nimbus and the secondary malaria vectors A. triannulatus and A. braziliensis positively. In the dry season, greater forest cover positively but marginally affected A. peryassui, A. nuneztovari and A. albitarsis, but only indirectly through water quality. Some species like A. triannulatus, A. nuneztovari and A. braziliensis coexist with the malaria vector, A. darlingi, in breeding sites46, and these species have been positively associated with pH, dissolved oxygen and total suspended solids in natural and artificial habitats20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47, which are environmental conditions favored by greater forest cover. The marginal indirect effect of forest cover on anopheline assemblage in the dry season suggests that we need caution in the interpretation of this result and long-term temporal data is required to confirm if this effect is corroborated.Forest conditions influence mosquito vectors and their hosts. For example, some mosquitoes are zoophiles that feed on the blood of birds, reptiles, and mammals48, which are often more abundant in conserved areas. Other species are anthropophilic and prefer to feed on human blood49 and altered environments can force these species to migrate and, consequently, to change hosts48. In our study, A. triannulatus and A. minbus were more abundant in sites with more natural characteristics, whereas A. darlingi and A. nuneztovari were more abundant in altered landscapes. In addition, urbanization and deforestation increase the proximities of humans and domestic animals to mosquito vectors and their hosts, thereby maintaining and increasing transmission cycles50.Forest conditions influence anopheline diversity by different paths, which may alter the strength of their seasonal effects. During the dry season, mosquito survival is also affected by altered microclimate (e.g., lower humidity)51 and lentic habitats contain less water, increased nutrient concentrations and decreased abundance and richness of mosquitoes52,53. We observed that rainfall plays an important role in the larval abundance of Anopheles in artificial larval habitats in Manaus. In addition, climatic factors such as rainfall and river levels are strongly associated with vector abundance and malaria cases in the region54,55. During the rainy season, increased water volume in artificial habitats provides more areas for distribution and development of mosquito species56 and we detected a significant increase in abundance of A. triannulatus, A. darlingi and A. nuneztovari. These observations may partially explain why we found a direct effect of forest cover on mosquitoes only during the rainy season.Our results add more evidence that managing and conserving forest cover is important to control anophelines, thereby decreasing the contact of potential vectors (e.g., A. darlingi) with humans. In general, our results support the idea that mosquitoes are directly affected by the loss of native forest cover57 in the rainy season. Mosquitoes associated with serious human diseases (e.g., malaria, yellow fever, dengue, leishmaniasis) are more abundant in areas with low levels of native forest cover14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58. This is a critically important finding because recent studies have shown that forest cover plays an important role in the vector dynamics of mosquitoes and forest conservation keeps pathogens within the forest, avoiding spillover to human settlements59. On the other hand, deforestation provides favorable conditions for these vectors, thereby increasing malária cases and decreasing scores of the Human Development Index60. In addition, there is a positive correlation between mosquito abundance in fragmented forests and the prevalence of Plasmodium, the protozoan that causes malaria61.Artificial larval habitats promote conditions for malaria vectors in Amazônia62,63. Therefore, the best way to develop control techniques would be to understand larval ecology in these habitats, where they are more sensitive to infections by pathogens, parasites, predation, larvicides and growth regulators64. This information is necessary to minimize failures in programs to control or eradicate the vector and the disease. Under this perspective, our study adds a new piece in the puzzle of mosquito control in Amazônia. For example, during the rainy season when forest cover directly and indirectly influences larval habitats, control programs can strengthen the control of key limnological variables, habitat structure, and entomological aspects, intensifying the environmental filter, particularly in areas with little forest cover and greater human concentrations near those habitats. The limnological study of Anopheles larval habitats is still far from complete, as each case has peculiarities inherent to them. Despite attempts, anophelines demonstrate versatility in relation to abiotic parameters20,21,22,23,24,25,26,65,66. However, we can use approaches that modify the larval environments. For example, more efficient management of water levels in fish farming ponds could decrease larval numbers and anopheline reproduction, Similarly, greater rationing of fish feed would decrease the supply of food resources for mosquito larvae. It is also worth mentioning that some variables are related to the efficiency of others. Regarding biological control via entomopathogenic bacteria, environmental factors (solar radiation) and water quality (amounts of total suspended solids and organic matter), can interfere with the effectiveness of the formulated Bacillus sphaericus applied in habitats for vector control62,63,64,65,66,67. Furthermore, eutrophication decreased the assemblages of aquatic invertebrates predating mosquito larvae.Another alternative is the use of physical control (removal of grasses and macrophytes from the edge of habitats), helping to reduce microhabitats that provide larval refuges. Also, increased light and water temperature at the edges favor natural predation and biological control processes from potential fish and macroinvertebrates. The conservation of natural enemies and the use of biotic agents in the population control of vector mosquitoes have been recommended in small and medium-sized natural and artificial breeding sites19,20,21,22,23,24,25,26,27,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53. A combination of techniques that shape the important environmental variables for the establishment of these species are essential for vector control.The analytical approach used here opens some windows of opportunity for improvements that are important to be recognized. First, our model did not incorporate important complexity of natural systems, such as ecological interactions among vectors and hosts, including human behavior. Agent-based models, including different host behavior, could provide important insights in this way. Second, our study is very limited in terms of temporal climatic variability. Additional information is needed to better understand the effects of long-term changes in land-use, water quality and climate and their interactions with mosquito assemblages in the region, particularly considering an ecological-evolutionary perspective. Third, it is important to highlight that the magnitude of effects of the estimated drivers were not the same in the rainy and dry seasons. Also, they may not remain constant in coming decades, especially considering potential regional process on mosquito assemblages, such as spillover effects, mass effects and host changes. Fourth, our study was carried out in an area of Amazonia that has experienced, a relatively old land use conversion from forest to urban areas (urban expansion rate of around 12% per year for the past 34 years)68. Beginning in the 1970s, human population increased at a rate of around 23% per decade and 25% in Manaus11. Therefore, the region we studied is very relevant in terms of historical interactions among human populations, mosquitoes and land use changes. However, understanding the effect of these changes on mosquito assemblages in areas with different land-use change dynamics, provides us with important information69, particularly those with very rapid urbanization processes, such as in the Arch of Deforestation70. Lastly, we need studies that consider the nexus among climate and land use changes, human and animal population health, economic conditions, and ecosystem services provided by these forest-urban transitional regions. Such information would facilitate including mosquito information in land use planning and climate mitigation programs based on forest management in and around cities.Therefore, identifying ecological factors and paths that affect the composition of species of epidemiological importance are essential because they inform vector integrated management strategies. We emphasize that larval control in lentic habitats requires knowledge about larval ecology and the effects of biotic and abiotic variables on larvae, especially when it comes to biological controls. The application of integrated pest management can be conducted in both dry and rainy seasons. However, we recommend focusing on the dry season when larval habitats are more limited, in smaller volumes and more accessible for entry and application of vector control techniques. These are critically important considerations because over 2 million people live in Amazonas state11 and anophelines transmitted over 59,637 malaria cases in the Amazon region in the first half of 2020, and about 44.4% came from the state of Amazonas71. More

1.Aitken, S. N., Yeaman, S., Holliday, J. A., Wang, T. & Curtis-McLane, S. Adaptation, migration or extirpation: climate change outcomes for tree populations. Evol. Appl. 1, 95–111 (2008).PubMed 
PubMed Central 
Article 

Google Scholar 
2.Renner, S. S. & Zohner, C. M. Climate change and phenological mismatch in trophic interactions among plants, insects, and vertebrates. Annu. Rev. Ecol. Evol. Syst. 49, 165–182 (2018).Article 

Google Scholar 
3.Piao, S. et al. Plant phenology and global climate change: current progresses and challenges. Glob. Change Biol. 25, 1922–1940 (2019).Article 

Google Scholar 
4.Kooyers, N. J., Greenlee, A. B., Coloicchio, J. M., Oh, M. & Blackman, B. K. Replicate altitudinal clines reveal that evolutionary flexibility underlies adaptation to drought stress in annual Mimulus guttatus. New Phytol. 206, 152–165 (2015).5.Evans, L. M. et al. Population genomics of Populus trichocarpa identifies signature of selection and adaptive trait associations. Nat. Genet. 46, 1089–1096 (2016).Article 
CAS 

Google Scholar 
6.Wadgymar, S. M., Daws, S. C. & Anderson, J. T. Integrating viability and fecundity selection to illuminate the adaptive nature of genetic clines. Evol. Lett. 1, 26–39 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
7.Nord, E. A. & Lynch, J. P. Plant phenology: a critical controller of soil resource acquisition. J. Exp. Bot. 60, 1927–1937 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
8.Polgar, C. A. & Primack, R. B. Tansley review: leaf-out phenology of temperate woody plants: from trees to ecosystems. New Phytol. 191, 926–941 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
9.Parmesan, C. & Yohe, G. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421, 37–42 (2003).CAS 
PubMed 
PubMed Central 
Article 

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

Google Scholar 
11.Stephens, P. A. et al. Consistent response of bird population to climate change on two continents. Science 352, 84–87 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
12.Wagner, M. R. et al. Natural soil microbes alter flowering phenology and the intensity of selection on flowering time in a wild Arabidopsis relative. Ecol. Lett. 17, 717–726 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
13.Lu, T. et al. Rhizosphere microorganisms can influence the timing of plant flowering. Microbiome 6, 231 (2018).14.Friesen, M. et al. Microbially mediated plant functional traits. Ann. Rev. Ecol. Evol. Syst. 42, 23–46 (2011).Article 

Google Scholar 
15.Mendes, R., Garbeva, P. & Raaijmakers, J. M. The rhizosphere microbiome: significance of plant beneficial, plant pathogenic, and human pathogenic microorganisms. FEMS Microbiol. Rev. 37, 634–663 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
16.Compant, S., Samad, A., Faist, H. & Sessitsch, A. A review on the plant microbiome: Ecology, functions, and emerging trends in microbial application. J. Adv. Res. 19, 29–37 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
17.Panke-Buisse, K., Poole, A., Goodrich, J., Ley, R. & Kao-Kniffin, J. Selection on soil microbiomes reveals reproducible impacts on plant function. ISME J. 9, 980–989 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
18.Fitzpatrick, C. R., Mustafa, Z. & Viliunas, J. Soil microbes alter plant fitness under competition and drought. J. Evol. Biol. https://doi.org/10.1111/jeb.13426 (2019).19.Lau, J. A. & Lennon, J. T. Rapid responses of soil microorganisms improve plant fitness in novel environments. Proc. Natl Acad. Sci. USA 109, 14058–14062 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
20.Van Nuland, M. E., Ware, I. M., Bailey, J. K. & Schweitzer, J. A. Ecosystem feedbacks contribute to geographic variation in the plant-soil evolutionary dynamics across fertility gradient. Funct. Ecol. 33, 95–106 (2019).21.Zolla, G., Badri, D. V., Bakker, M. G., Manter, D. K. & Vivanco, J. M. Soil microbiome vary in their ability to confer drought tolerance to Arabidopsis. Appl. Soil Ecol. 68, 1–9 (2013).Article 

Google Scholar 
22.Gehring, C. A., Sthultz, C. M., Flores-Renteria, L., Whipple, A. V. & Whitham, T. G. Tree genetics defines fungal partner communities that may confer drought tolerance. Proc. Natl Acad. Sci. USA 114, 11169–11174 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
23.Woolbright, S. A., Whitham, T. G., Gehring, C. A., Allan, G. J. & Bailey, J. K. Climate relicts and their associated communities as natural ecology and evolution laboratories. Trends Ecol. Evol. 29, 406–416 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
24.Lankau, R. A., Zhu, K. & Ordonez, A. Mycorrhizal strategies of tree species correlate with trailing range edge responses to current and past climate change. Ecology 96, 1451–1458 (2015).Article 

Google Scholar 
25.Ware, I. M. et al. Climate-driven reduction of genetic variation in plant phenology alters soil communities and nutrient pools. Glob. Change Biol. 25, 1514–1528 (2019).Article 

Google Scholar 
26.Nguyen, N. H. et al. FUNGuild: an open annotation tool for parsing fungal community datasets by ecological guild. Fung. Ecol. 20, 241–248 (2016).Article 

Google Scholar 
27.Nottingham, A. T. et al. Microbes follow Humboldt: temperature drives plant and soil Microbial diversity patterns from the Amazon to the Andes. Ecology 99, 2455–2466 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
28.Martiny, J. B. et al. Microbial biogeography: putting microorganisms on the map. Nat. Rev. Microbiol. 4, 102–111 (2006).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
29.Fierer, N., Strickland, M. S., Liptzin, D., Bradford, M. A. & Cleveland, C. C. Global patterns of belowground communities. Ecol. Lett. 12, 1238–1249 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
30.Fierer, N. et al. Comparative metagenomic, phylogenetic, and physiological analyses of soil microbial communities across nitrogen gradients. ISME J. 6, 1007–1017 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
31.Waldrop, M. P. et al. The interacting roles of climate, soils, and plant production, on soil microbial communities at a continental scale. Ecology 98, 1957–1967 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
32.Nelson, M. B., Martiny, A. C. & Martiny, J. B. Global biogeography of microbial nitrogen-cycling traits in soil. Proc. Natl Acad. Sci. USA 113, 8033–8040 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
33.Schweitzer, J. A. et al. Plant-soil-microorganism interactions: heritable relationship between plant genotype and associated soil microorganisms. Ecology 89, 773–781 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
34.de Vries, F. T. et al. Abiotic drivers and plant traits explain landscape-scale patterns in soil microbial communities. Ecol. Lett. 15, 1230–1239 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
35.Prober, S. M. et al. Plant diversity predicts beta but not alpha diversity of soil microbes across grasslands worldwide. Ecol. Lett. 18, 85–95 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
36.Delgado-Baquerizo, M. et al. Plant attributes explain the distribution of soil microbial communities in two contrasting regions of the globe. New Phytol. 219, 574–587 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
37.Menzel, A. Trends in phenological phases in Europe between 1951 and 1996. Int. J. Biometeorol. 44, 76–81 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
38.Morin, X. et al. Leaf phenology in 22 North American tree species during the 21st century. Glob. Change Biol. 15, 961–975 (2010).Article 

Google Scholar 
39.Loarie, S. R. et al. The velocity of climate change. Nature 462, 1052–1055 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
40.Renwick, K. M. & Rocca, M. E. Temporal context affects the observed rate of climate-driven range shifts in tree species. Glob. Ecol. Biogeog. 24, 44–51 (2015).Article 

Google Scholar 
41.Elena, S. F. & Lenski, R. E. Evolution experiments with microorganisms: the dynamics and genetic bases of adaptation. Nat. Rev. Genet. 4, 457–469 (2003).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
42.Finlay, B. J. Global dispersal of free-living microbial eukaryote species. Science 296, 1061–1063 (2002).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
43.Kivlin, S. N., Emery, S. M. & Rudgers, J. A. Fungal symbionts alter plant responses to global change. Am. J. Bot. 100, 1445–1457 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
44.Fernandez, C. W. & Kennedy, P. G. Revisiting the ‘Gadgil effect’: do interguild fungal interactions control carbon cycling in forest soils. New Phytol. 209, 1382–1394 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
45.Fisher, D. G. et al. Plant genetic effects on soils under climate change. Plant Soil 379, 1–19 (2014).46.van der Wal, A., Geyden, T. D., Kuyper, T. W. & de Boer, W. A thready affair: linking fungal diversity and community dynamics to terrestrial decomposition processes. FEMS Microbiol. Rev. 37, 477–494 (2013).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
47.Perez-Izquierdo, L. et al. Plant intraspecific variation modulates nutrient cycling through its belowground rhizospheric microbiome. J. Ecol. 107, 1594–1605 (2019).Article 

Google Scholar 
48.Crowther, T. W. et al. The global soil community and its influence on biogeochemistry. Science 365, https://doi.org/10.1126/science.aav0550 (2019).49.Steidinger, B. S. et al. Climatic controls of decomposition drive the global biogeography of forest-tree symbioses. Nature 569, 404–408 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
50.van der Putten, W. H., Bradford, M. A., Brinkman, E. P., van de Voorde, T. F. J. & Veen, G. F. Where, when and how plant-soil feedback matters in a changing world. Funct. Ecol. 30, 1109–1121 (2016).Article 

Google Scholar 
51.Van Nuland, M. E. et al. Plant-soil feedbacks: connecting ecosystem ecology and evolution. Funct. Ecol. 30, 1032–1042 (2016).Article 

Google Scholar 
52.Ware, I. M. et al. Feedbacks link ecosystem ecology and evolution across spatial and temporal scales: empirical evidence and future directions. Funct. Ecol. 33, 31–42 (2019).Article 

Google Scholar 
53.Cooke, J. E. K. & Rood, S. B. Trees of the people: the growing science of poplars in Canada and worldwide. Can. J. Bot. 85, 1103–1110 (2007).Article 

Google Scholar 
54.Evans, L. M., Allan, G. J., Meneses, N., Max, T. L. & Whitham, T. G. Herbivore host-associated genetic differentiation depends on the scale of plant genetic variation examined. Evol. Ecol. 27, 65–81 (2013).Article 

Google Scholar 
55.Hijmans, R. J., Cameron, S. E., Para, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).Article 

Google Scholar 
56.Castro, H. F., Classen, A. T., Austin, E. E., Norby, R. J. & Schadt, C. W. Soil microbial community responses to multiple experimental climate change drivers. Appl. Environ. Microbiol. 76, 999–1007 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
57.Wilson, R. M. et al. Stability of peatland carbon to rising temperatures. Nat. Commun. 7, 13723 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
58.Van Nuland, M. E., Bailey, J. B. & Schweitzer, J. A. Divergent plant-soil feedbacks could alter future elevation ranges and ecosystem dynamics. Nature Ecol. Evol. https://doi.org/10.1038/s41559-017-0150 (2017).59.Richardson, A. D. et al. Influence of spring phenology on seasonal and annual carbon balance in two contrasting New England forests. Tree Phys. 29, 321–331 (2009).CAS 
Article 

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

Google Scholar 
61.Callahan, B. J. et al. DADA2: High resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
62.Tremblay, J. et al. Primer and platform effects on 16S rRNA tag sequencing. Front. Microbiol. 6, 771 (2015).PubMed 
PubMed Central 

Google Scholar 
63.Chao, A., Chiu, C. H. & Jost, L. Unifying species diversity, phylogenetic diversity, functional diversity, and related similarity and differentiation measures through hill numbers. Ann. Rev. Ecol. Evol. Syst. 45, 297–324 (2014).Article 

Google Scholar 
64.Chao, A. et al. Rarefaction and extrapolation with Hill numbers: a framework for sampling and estimation in species diversity studies. Ecol. Monogr. 84, 45–67 (2014).Article 

Google Scholar 
65.Ma, Z. Measuring microbiome diversity and similarity with Hill numbers. Metagenomics https://doi.org/10.1016/B978-0-08-102268-9.00008-2 (2018).66.Ferrier, S., Manion, G., Elith, J. & Richardson, K. Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment. Diversity Distrib. 13, 252–264 (2007).Article 

Google Scholar 
67.Fitzpatrick, M. C. et al. Environmental and historical imprints on beta diversity: insights from variation in rates of species turnover along gradients. Proc. Biol. Sci. 280, 20131201 (2013).PubMed 
PubMed Central 

Google Scholar 
68.R Core Team. R: A Language and Environment for Statistical Computing http://www.R-project.org (R Foundation for Statistical Computing, 2016). More

1.Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).ADS 
CAS 
Article 

Google Scholar 
2.Lotze, H. K. & Worm, B. Historical baselines for large marine animals. Trends Ecol. Evol. 24, 254–262 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
3.Lotze, H. K. et al. Depletion, degradation, and recovery potential of estuaries and coastal seas. Science 312, 1806–1809 (2006).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
4.Worm, B. & Branch, T. A. The future of fish. Trends Ecol. Evol. 27, 594–599 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
5.Erlandson, J. M. & Rick, T. C. Archaeology meets marine ecology: The antiquity of maritime cultures and human impacts on marine fisheries and ecosystems. Ann. Rev. Mar. Sci. 2, 231–251 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
6.McClenachan, L., Ferretti, F. & Baum, J. K. From archives to conservation: Why historical data are needed to set baselines for marine animals and ecosystems. Conserv. Lett. 5, 349–359 (2012).Article 

Google Scholar 
7.Misarti, N., Finney, B. P., Maschner, H. & Wooller, M. J. Changes in northeast Pacific marine ecosystems over the last 4500 years: Evidence from stable isotope analysis of bone collagen from archaeological middens. Holocene 19, 1139–1151 (2009).ADS 
Article 

Google Scholar 
8.Alter, S. E., Newsome, S. D. & Palumbi, S. R. Pre-whaling genetic diversity and population ecology in eastern Pacific gray whales: Insights from ancient DNA and stable isotopes. PLoS One 7, 35–39 (2012).
Google Scholar 
9.Szpak, P., Orchard, T. J., Mckechnie, I. & Gröcke, D. R. Historical ecology of late Holocene sea otters (Enhydra lutris) from northern British Columbia: Isotopic and zooarchaeological perspectives. J. Archaeol. Sci. 39, 1553–1571 (2012).Article 

Google Scholar 
10.McKechnie, I. et al. Archaeological data provide alternative hypotheses on Pacific herring (Clupea pallasii) distribution, abundance, and variability. Proc. Natl. Acad. Sci. USA 111, E807–E816 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
11.Orton, D. C. Archaeology as a tool for understanding past marine resource use and its impact. In Perspectives on Oceans Past (eds Schwerdtner Máñez, K. & Poulsen, B.) 47–69 (Springer, 2016).
Google Scholar 
12.Barrett, J. H., Locker, A. M. & Roberts, C. M. The origins of intensive marine fishing in medieval Europe: The English evidence. Proc. R. Soc. Lond. B. 271, 2417–2421 (2004).Article 

Google Scholar 
13.Edvardsson, R. The Role of Marine Resources in the Medieval Economy of Vestfirðir, Iceland (CUNY, 2019).
Google Scholar 
14.Post, D. M. Using stable isotopes to estimate trophic position: Models, methods, and assumptions. Ecology 83, 703–718 (2002).Article 

Google Scholar 
15.Wada, E., Kabaya, Y. & Kurihara, Y. Stable isotopic structure of aquatic ecosystems. J. Biosci. 18, 483–499 (1993).CAS 
Article 

Google Scholar 
16.Trueman, C. N., MacKenzie, K. M. & Palmer, M. R. Identifying migrations in marine fishes through stable-isotope analysis. J. Fish. Biol. 81, 826–847 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
17.Minagawa, M. & Wada, E. Stepwise enrichment of 15N along food chains: Further evidence and the relation between δ15N and animal age. Geochim. Cosmochim. Acta. 48, 1135–1140 (1984).ADS 
CAS 
Article 

Google Scholar 
18.Newsome, S. D. et al. Historic decline in primary productivity in western Gulf of Alaska and eastern Bering Sea: Isotopic analysis of northern fur seal teeth. Mar. Ecol. Prog. Ser. 332, 211–224 (2007).ADS 
CAS 
Article 

Google Scholar 
19.Guiry, E. J. et al. Lake Ontario salmon (Salmo salar) were not migratory: A long-standing historical debate solved through stable isotope analysis. Sci. Rep. 6, 1–7 (2016).Article 
CAS 

Google Scholar 
20.Emslie, S. D. & Patterson, W. P. Abrupt recent shift in δ13C and δ15N values in Adélie Penguin eggshell in Antarctica. Proc. Natl. Acad. Sci. USA 104, 11666–11669 (2007).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
21.Emslie, S. D., Polito, M. J. & Patterson, W. P. Stable isotope analysis of ancient and modern Gentoo penguin egg membrane and the krill surplus hypothesis in Antarctica. Antarct. Sci. 25, 213–218 (2013).ADS 
Article 

Google Scholar 
22.Drinkwater, K. F. The regime shift of the 1920s and 1930s in the North Atlantic. Prog. Oceanogr. 68, 134–151 (2006).ADS 
Article 

Google Scholar 
23.Ástþórsson, Ó. S., Gíslason, Á. & Jónsson, S. Climate variability and the Icelandic marine ecosystem. Deep-Sea Res. PT II(54), 2456–2477 (2007).ADS 
Article 

Google Scholar 
24.Edvardsson, R., Bárðarson, H., Patterson, W. P., Timsic, S. & Ólafsdóttir, G. Á. Change in Atlantic cod migrations and adaptability of early land-based fishers to severe climate variation in the North Atlantic. Quat. Res. (In press).25.Dahl-Jensen, D. et al. Past temperatures directly from the Greenland ice sheet. Science 282, 268–271 (1998).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
26.Ogilvie, A. E. & Jonsson, T. The Iceberg in the Mist: Northern Research in Pursuit of a Little Age (Kluwer Academic, 2001).Book 

Google Scholar 
27.Jiang, H., Eiríksson, J., Schulz, M., Knudsen, K. L. & Seidenkrantz, M. S. Evidence for solar forcing of sea-surface temperature on the North Icelandic Shelf during the late Holocene. Geology 33, 73–76 (2005).ADS 
Article 

Google Scholar 
28.Vinther, B. M. et al. Climatic signals in multiple highly resolved stable isotope records from Greenland. Quat. Sci. Rev. 29, 522–538 (2010).ADS 
Article 

Google Scholar 
29.Patterson, W. P., Dietrich, K. A., Holmden, C. & Andrews, J. T. Two millennia of North Atlantic seasonality and implications for Norse colonies. Proc. Natl. Acad. Sci. USA 107, 5306–5310 (2010).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
30.Geffen, A. J. et al. High-latitude climate variability and its effect on fisheries resources as revealed by fossil cod otoliths. ICES J. Mar. Sci. 68, 1081–1089 (2011).Article 

Google Scholar 
31.Ólafsdóttir, G. Á., Westfall, K. M., Edvardsson, R. & Pálsson, S. Historical DNA reveals the demographic history of Atlantic cod (Gadus morhua) in medieval and early modern Iceland. Proc. R. Soc. Lond. B. 281, 20132976 (2014).
Google Scholar 
32.Ólafsdóttir, G. Á., Pétursdóttir, G., Bárðarson, H. & Edvardsson, R. A millennium of north-east Atlantic cod juvenile growth trajectories inferred from archaeological otoliths. PLoS One 12, e0187134 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
33.Pinnegar, J. K. & Engelhard, G. H. The ‘shifting baseline’phenomenon: A global perspective. Rev. Fish Biol. Fish. 18, 1–16 (2008).Article 

Google Scholar 
34.Jackson, J. B. et al. Historical overfishing and the recent collapse of coastal ecosystems. Science 293, 629–637 (2001).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
35.Pauly, D., Christensen, V., Dalsgaard, J., Froese, R. & Torres, F. Fishing down marine food webs. Science 279, 860–863 (1998).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
36.Kindsvater, H. K. & Palkovacs, E. P. Predicting eco-evolutionary impacts of fishing on body size and trophic role of Atlantic cod. Copeia 105, 475–482 (2017).Article 

Google Scholar 
37.Persson, A. & Hansson, L. A. Diet shift in fish following competitive release. CJFAS 56, 70–78 (1999).
Google Scholar 
38.Saporiti, F. et al. Longer and less overlapping food webs in anthropogenically disturbed marine ecosystems: Confirmations from the past. PLoS One 9, e103132 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
39.Bas, M. et al. Back to the future? Late Holocene marine food web structure in a warm climatic phase as a predictor of trophodynamics in a warmer South-Western Atlantic Ocean. Glob. Change Biol. 25, 404–419 (2019).ADS 
Article 

Google Scholar 
40.Casey, M. M. & Post, D. M. The problem of isotopic baseline: Reconstructing the diet and trophic position of fossil animals. Earth-Sci. Rev. 106, 131–148 (2011).ADS 
CAS 
Article 

Google Scholar 
41.Bas, M. & Cardona, L. Effects of skeletal element identity, delipidation and demineralization on the analysis of stable isotope ratios of C and N in fish bone. J. Fish. Biol. 92, 420–437 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
42.Harrison, R. The Siglunes 2011/12 Archaeofauna. Interim Report on the Fishing Station’s Sampled Faunal Remains. (http://www.nabohome.org/uploads/ramonah/RH_Siglunes_Faunal_Report_5_30_2014.pdf (2014).43.Lárusdóttir, B., Roberts, H. M., Þorgeirsdóttir, S. S., Harrison, R. & Sigurgeirsson, Á. Siglunes. Archaeological investigations in 2011. http://www.nabohome.org/uploads/ramonah/FS480-11121_Siglunes_2011.pdf (2012).44.Leyden, J. J., Wassenaar, L. I., Hobson, K. A. & Walker, E. G. Stable hydrogen isotopes of bison bone collagen as a proxy for Holocene climate on the Northern Great Plains. Palaeogeogr. Palaeoclimatol. Palaeoecol. 239, 87–99 (2006).Article 

Google Scholar 
45.Craig, H. Standard for reporting concentration of deuterium and oxygen-18 in natural waters. Science 133, 1702–1703 (1961).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
46.DeNiro, M. J. Postmortem preservation and alteration of in vivo bone collagen isotope ratios in relation to palaeodietary reconstruction. Nature 317, 806–809 (1985).ADS 
CAS 
Article 

Google Scholar 
47.Hilton, G. M. et al. A stable isotopic investigation into the causes of decline in a sub-Antarctic predator, the rockhopper penguin Eudyptes chrysocome. Glob. Change Biol. 12, 611–625 (2006).ADS 
Article 

Google Scholar 
48.Szpak, P., Metcalfe, J. Z. & Macdonald, R. A. Best practices for calibrating and reporting stable isotope measurements in archaeology. J. Archaeol. Sci. Rep. 13, 609–616 (2017).
Google Scholar 
49.Gruber, N. et al. Spatiotemporal patterns of carbon-13 in the global surface oceans and the oceanic Suess effect. Glob. Biogeochem. Cycles 13, 307–335 (1999).ADS 
CAS 
Article 

Google Scholar 
50.Quay, P., Sonnerup, R., Westby, T., Stutsman, J. & Mcnichol, A. Changes in the 13C/12C of dissolved inorganic carbon in the ocean as a tracer of anthropogenic CO2 uptake. Glob. Biogeochem. Cycles 17, 1–20 (2003).Article 
CAS 

Google Scholar 
51.Quay, P. D., Tilbrook, B. & Wong, C. S. Oceanic uptake of fossil fuel CO2: Carbon-13 evidence. Science 256, 74–79 (1992).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
52.Hartig, F. DHARMa: Residual diagnostics for hierarchical (multi-level/mixed) regression models. R package version 0.3.1 (2020).53.Jackson, A. L., Inger, R., Parnell, A. C. & Bearhop, S. Comparing isotopic niche widths among and within communities: SIBER–Stable Isotope Bayesian Ellipses in R. J. Anim. Ecol. 80, 595–602 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
54.Layman, C. A. et al. Applying stable isotopes to examine food-web structure: An overview of analytical tools. Biol. Rev. 87, 545–562 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
55.Swanson, H. K. et al. A new probabilistic method for quantifying n-dimensional ecological niches and niche overlap. Ecology 96, 318–324 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
56.Þór, J. Þ. British Trawlers in Icelandic Waters: History of British Steam Trawling off Iceland, 1889–1916, and the Anglo-Icelandic Fisheries Dispute, 1896–1897 (Fjölvi, 1992).
Google Scholar 
57.Þór, J. Þ. Saga Sjávarútvegs á Íslandi. 1902–1939 Vélaöld (Bókaútgáfan Hólar, 2003).
Google Scholar 
58.Gill, A. B. The dynamics of prey choice in fish: The importance of prey size and satiation. J. Fish. Biol. 63, 105–116 (2003).Article 

Google Scholar 
59.Jennings, S. Size-based analyses of aquatic food webs. In Aquatic Food Webs: An Ecosystem Approach (eds Belgrano, A. et al.) 86–97 (Oxford University Press, 2005).Chapter 

Google Scholar 
60.Zenteno, L. et al. Dietary consistency of male South American sea lions (Otaria flavescens) in southern Brazil during three decades inferred from stable isotope analysis. Mar. Biol. 162, 275–289 (2015).CAS 
Article 

Google Scholar 
61.Vales, D. G. et al. Holocene changes in the trophic ecology of an apex marine predator in the South Atlantic Ocean. Oecologia 183, 555–570 (2017).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
62.Bas, M. et al. Predicting habitat use by the Argentine hake Merluccius hubbsi in a warmer world: Inferences from the Middle Holocene. Oecologia 193, 461–474 (2020).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
63.Sharpe, D. M. & Chapman, L. J. Niche expansion in a resilient endemic species following introduction of a novel top predator. Freshw. Biol. 59, 2539–2554 (2014).Article 

Google Scholar 
64.Jaworski, A. & Ragnarsson, S. Á. Feeding habits of demersal fish in Icelandic waters: A multivariate approach. ICES J. Mar. Sci. 63, 1682–1694 (2006).Article 

Google Scholar 
65.Law, R. Fishing, selection, and phenotypic evolution. ICES J. Mar. Sci. 57, 659–668 (2000).Article 

Google Scholar 
66.Romanuk, T. N., Hayward, A. & Hutchings, J. A. Trophic level scales positively with body size in fishes. Glob. Ecol. Biogeogr. 20, 231–240 (2011).Article 

Google Scholar 
67.Jennings, S. & Van Der Molen, J. Trophic levels of marine consumers from nitrogen stable isotope analysis: Estimation and uncertainty. ICES J. Mar. Sci. 72, 2289–2300 (2015).Article 

Google Scholar 
68.MFRI. Atlantic cod Gadus morhua (MFRI Assessment Reports 2020). Marine and Freshwater Research Institute. https://www.hafogvatn.is/static/extras/images/01-cod_tr_isl1232625.pdf (2020).69.Thorsteinsson, V., Pálsson, Ó. K., Tómasson, G. G., Jónsdóttir, I. G. & Pampoulie, C. Consistency in the behaviour types of the Atlantic cod: Repeatability, timing of migration and geo-location. Mar. Ecol. Prog. Ser. 462, 251–260 (2012).ADS 
Article 

Google Scholar  More

1.Goulding, K., Trewavas, A. & Giller, K. E. Feeding the world: a contribution to the debate. World Agric. 2, 32–38 (2011).
Google Scholar 
2.Muller, A. et al. Strategies for feeding the world more sustainably with organic agriculture. Nat. Commun. 8, 1–13 (2017).CAS 
Article 

Google Scholar 
3.Epule, T. E. Organic Farming 1–16 (Elsevier, 2019).Book 

Google Scholar 
4.FAO. FAO Statistical Pocketbook 2015: World Food and Agriculture. Food and Agriculture Organization; ISBN 978-92-5-108802-9. http://www.fao.org/documents/card/en/c/383d384a-28e6-47b3-a1a2-2496a9e017b2/. Accessed 30 November 2016, 2015.5.Willett, W. et al. Food in the Anthropocene: the EAT–Lancet Commission on healthy diets from sustainable food systems. The Lancet 393, 447–492 (2019).Article 

Google Scholar 
6.Hazell, P. & Wood, S. Drivers of change in global agriculture. Philos. Trans. R. Soc. B Biol. Sci. 363, 495–515 (2008).Article 

Google Scholar 
7.Tilman, D., Cassman, K. G., Matson, P. A., Naylor, R. & Polasky, S. Agricultural sustainability and intensive production practices. Nature 418, 671–677 (2002).CAS 
PubMed 
Article 
ADS 

Google Scholar 
8.Ladha, J. et al. Global nitrogen budgets in cereals: A 50-year assessment for maize, rice, and wheat production systems. Sci. Rep. 6, 19355 (2016).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
9.de Ponti, T., Rijk, B. & Van Ittersum, M. K. The crop yield gap between organic and conventional agriculture. Agric. Syst. 108, 1–9 (2012).Article 

Google Scholar 
10.Seufert, V., Ramankutty, N. & Foley, J. A. Comparing the yields of organic and conventional agriculture. Nature 485, 229–232 (2012).CAS 
PubMed 
Article 
ADS 

Google Scholar 
11.Ponisio, L. C. et al. Diversification practices reduce organic to conventional yield gap. Proc. R. Soc. Lond. B Biol. Sci. 282, 20141396 (2015).
Google Scholar 
12.Connor, D. J. Organically grown crops do not a cropping system make and nor can organic agriculture nearly feed the world. Field Crops Res. 144, 145–147. https://doi.org/10.1016/j.fcr.2012.12.013 (2013).Article 

Google Scholar 
13.Berry, P. M. et al. Is the productivity of organic farms restricted by the supply of available nitrogen?. Soil Use Manag. 18, 248–255 (2002).Article 

Google Scholar 
14.Adamtey, N. et al. Productivity, profitability and partial nutrient balance in maize-based conventional and organic farming systems in Kenya. Agric. Ecosyst. Environ. 235, 61–79 (2016).Article 

Google Scholar 
15.Poudel, D., Horwath, W., Lanini, W., Temple, S. & Van Bruggen, A. Comparison of soil N availability and leaching potential, crop yields and weeds in organic, low-input and conventional farming systems in northern California. Agric. Ecosyst. Environ. 90, 125–137 (2002).CAS 
Article 

Google Scholar 
16.Kravchenko, A. N., Snapp, S. S. & Robertson, G. P. Field-scale experiments reveal persistent yield gaps in low-input and organic cropping systems. Proc. Nat. Acad. Sci. 114, 926–931 (2017).CAS 
PubMed 
Article 

Google Scholar 
17.Tittonell, P. & Giller, K. E. When yield gaps are poverty traps: the paradigm of ecological intensification in African smallholder agriculture. Field Crops Res. 143, 76–90 (2013).Article 

Google Scholar 
18.David, C., Jeuffroy, M., Henning, J. & Meynard, J. Yield variation in organic winter wheat: a diagnostic study in the Southeast of France. Agron. Sust. Dev. 25, 213 (2005).Article 

Google Scholar 
19.Köpke, U. Nutrient management in organic farming systems—The Case of Nitrogen. Biol. Agr. Hort. 11, 15–29 (1995).Article 

Google Scholar 
20.Watson, C. A., Atkinson, D., Gosling, P., Jackson, L. R. & Rayns, F. W. Managing soil fertility in organic farming systems. Soil Use Manag. 18, 239–247 (2002).Article 

Google Scholar 
21.Watson, C. et al. A review of farm-scale nutrient budgets for organic farms as a tool for management of soil fertility. Soil Use Manag. 18, 264–273 (2002).Article 

Google Scholar 
22.Nimmo, J., Lynch, D. & Owen, J. Quantification of nitrogen inputs from biological nitrogen fixation to whole farm nitrogen budgets of two dairy farms in Atlantic Canada. Nutr. Cycl. Agroecosyst. 96, 93–105 (2013).Article 

Google Scholar 
23.Van Kessel, C. & Hartley, C. Agricultural management of grain legumes: has it led to an increase in nitrogen fixation?. Field Crops Res. 65, 165–181 (2000).Article 

Google Scholar 
24.Herridge, D. F., Peoples, M. B. & Boddey, R. M. Global inputs of biological nitrogen fixation in agricultural systems. Plant Soil 311, 1–18 (2008).CAS 
Article 

Google Scholar 
25.Smith, O. M. et al. Organic farming provides reliable environmental benefits but increases variability in crop yields: A global meta-analysis. Front. Sustain. Food Syst. 3, 82 (2019).Article 

Google Scholar 
26.Becker, M., Ali, M., Ladha, J. & Ottow, J. Agronomic and economic evaluation of Sesbania rostrata green manure establishment in irrigated rice. Field Crops Res. 40, 135–141 (1995).Article 

Google Scholar 
27.Ventura, W. & Watanabe, I. Green manure production of Azolla microphylla and Sesbania rostrata and their long-term effects on rice yields and soil fertility. Biol. Fert. Soils 15, 241–248 (1993).CAS 
Article 

Google Scholar 
28.Bussink, D. & Oenema, O. Ammonia volatilization from dairy farming systems in temperate areas: a review. Nutr. Cycl. Agroecosyst. 51, 19–33 (1998).Article 

Google Scholar 
29.Fillery, I. The fate of biologically fixed nitrogen in legume-based dryland farming systems: a review. Anim. Prod. Sci. 41, 361–381 (2001).CAS 
Article 

Google Scholar 
30.Oenema, O., Witzke, H., Klimont, Z., Lesschen, J. & Velthof, G. Integrated assessment of promising measures to decrease nitrogen losses from agriculture in EU-27. Agric. Ecosyst. Environ. 133, 280–288 (2009).CAS 
Article 

Google Scholar 
31.Graham, P. H. & Vance, C. P. Legumes: Importance and constraints to greater use. Plant Physiol. 131, 872–877 (2003).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
32.Cassman, K., Whitney, A. & Stockinger, K. Root growth and dry matter distribution of soybean as affected by phosphorus stress, nodulation, and nitrogen source. Crop Sci. 20, 239–244 (1980).CAS 
Article 

Google Scholar 
33.Gunawardena, S., Danso, S. & Zapata, F. Phosphorus requirement and sources of nitrogen in three soybean (Glycine max) genotypes, Bragg, nts 382 and Chippewa. Plant Soil 151, 1–9 (1993).CAS 
Article 

Google Scholar 
34.Scherer, H., Pacyna, S., Manthey, N. & Schulz, M. Sulphur supply to peas (Pisum sativum L.) influences symbiotic N2 fixation. Plant Soil Environ. 52, 72–77 (2006).CAS 
Article 

Google Scholar 
35.Anderson, A. & Spencer, D. Molybdenum in nitrogen metabolism of legumes and non-legumes. Aust. J. Sci. Res. 3, 414–430 (1950).CAS 

Google Scholar 
36.Fuchs, J. G. et al. Evaluation of the causes of legume yield depression syndrome using an improved diagnostic tool. Appl. Soil Ecol. 79, 26–36 (2014).Article 

Google Scholar 
37.Cassmann, K. G., Dobermann, A. & Walters, D. T. Agroecosystems, nitrogen use efficiency and nitrogen management. J. Human Environ. 31, 132–140 (2002).Article 

Google Scholar 
38.Kramer, A. W., Doane, T. A., Horwath, W. R. & van Kessel, C. Combining fertilizer and organic inputs to synchronize N supply in alternative cropping systems in California. Agric. Ecosyst. Envir. 91, 233–243 (2002).Article 

Google Scholar 
39.Oerke, E.-C. Crop losses to pests. J. Agric. Sci. 144, 31–43 (2006).Article 

Google Scholar 
40.van Bruggen, A. H. C. Plant disease severity in high-input compared to reduced-input and organic farming systems. Plant Dis. 79, 976–984 (1995).Article 

Google Scholar 
41.Zehnder, G. et al. Arthropod pest management in organic crops. Annu. Rev. Entomol. 52, 57–80 (2007).CAS 
PubMed 
Article 

Google Scholar 
42.Bond, W. & Grundy, A. C. Non-chemical weed management in organic farming systems. Weed Res. 41, 383–405 (2001).Article 

Google Scholar 
43.Bouwman, A. F., Beusen, A. H. W. & Billen, G. Human alteration of the global nitrogen and phosphorus soil balances for the period 1970–2050. Glob. Biogeochem. Cycl. 23, 99. https://doi.org/10.1029/2009GB003576 (2009).CAS 
Article 

Google Scholar 
44.Lu, C. C. & Tian, H. Global nitrogen and phosphorus fertilizer use for agriculture production in the past half century: shifted hot spots and nutrient imbalance. Earth System Sci. Data 9, 181 (2017).Article 
ADS 

Google Scholar 
45.Smil, V. Nitrogen in crop production: An account of global flows. Glob. Biogeochem. Cycl. 13, 647–662 (1999).CAS 
Article 
ADS 

Google Scholar 
46.Crews, T. & Peoples, M. Legume versus fertilizer sources of nitrogen: ecological tradeoffs and human needs. Agric. Ecosyst. Envir. 102, 279–297 (2004).Article 

Google Scholar 
47.Köpke, U. & Nemecek, T. Ecological services of faba bean. Field Crops Res. 115, 217–233. https://doi.org/10.1016/j.fcr.2009.10.012 (2010).Article 

Google Scholar 
48.Vance, C. P. Symbiotic nitrogen fixation and phosphorus acquisition. Plant nutrition in a world of declining renewable resources. Plant Physiol. 127, 390–397 (2001).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
49.Peterson, T. A. & Russelle, M. P. Alfalfa and the nitrogen cycle in the Corn Belt. J. Soil Water Conserv. 46, 229–235 (1991).
Google Scholar 
50.Peoples, M., Herridge, D. & Ladha, J. Biological nitrogen fixation: an efficient source of nitrogen for sustainable agricultural production?. Plant Soil 174, 3–28 (1995).CAS 
Article 

Google Scholar 
51.Sanchez, P. A. Soil fertility and hunger in Africa. Science 295, 2019–2020 (2002).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
52.Hole, D. G. et al. Does organic farming benefit biodiversity?. Biol. Cons. 122, 113–130 (2005).Article 

Google Scholar 
53.Mäder, P. et al. Soil fertility and biodiversity in organic farming. Science 296, 1694–1697 (2002).PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
54.Banerjee, S. et al. Agricultural intensification reduces microbial network complexity and the abundance of keystone taxa in roots. ISME J. 13, 1722–1736 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
55.Kijlstra, A. & Eijck, I. Animal health in organic livestock production systems: A review. NJAS-Wagen. J. Life Sci. 54, 77–94 (2006).Article 

Google Scholar 
56.Kramer, S. B., Reganold, J. P., Glover, J. D., Bohannan, B. J. M. & Mooney, H. A. Reduced nitrate leaching and enhanced denitrifier activity and efficiency in organically fertilized soils. PNAS 103, 4522–4527 (2006).CAS 
PubMed 
Article 
ADS 

Google Scholar 
57.Foley, J. A. et al. Global consequences of land use. Science 309, 570–574 (2005).CAS 
PubMed 
Article 
ADS 

Google Scholar 
58.Brussaard, L. et al. Reconciling biodiversity conservation and food security: scientific challenges for a new agriculture. Curr. Opin. Environ. Sust. 2, 34–42 (2010).Article 

Google Scholar 
59.Pretty, J. Agricultural sustainability: concepts, principles and evidence. Philos. Trans. R. Soc. Lond. B Biol. Sci. 363, 447–465 (2008).PubMed 
Article 

Google Scholar 
60.Tilman, D. & Clark, M. Global diets link environmental sustainability and human health. Nature 515, 518–522 (2014).CAS 
PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
61.Haas, G., Wetterich, F. & Kopke, U. Comparing intensive, extensified and organic grassland farming in southern Germany by process life cycle assessment. Agric. Ecosyst. Envir. 83, 43–53 (2001).Article 

Google Scholar 
62.Stevens, C. J. et al. Nitrogen deposition threatens species richness of grasslands across Europe. Environ. Pollut. 158, 2940–2945. https://doi.org/10.1016/j.envpol.2010.06.006 (2010).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar  More

1.Polis, G. A. & Strong, D. R. Food web complexity and community dynamics. Am. Nat. 147, 813–846 (1996).Article 

Google Scholar 
2.Lindeman, R. L. The trophic-dynamic aspect of ecology. Ecology 23, 399–417 (1942).Article 

Google Scholar 
3.Coleman, D. C., Andrews, R., Ellis, J. E. & Singh, J. S. Energy flow and partitioning in selected man-managed and natural ecosystems. Agro-Ecosyst. 3, 45–54 (1976).Article 

Google Scholar 
4.Steffan, S. A. et al. Unpacking brown food-webs: Animal trophic identity reflects rampant microbivory. Ecol. Evol. 7, 3532–3541 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
5.Coleman, D. C. Energetics of detritivory and microbivory in soil in theory and practice. In Food Webs (eds. Polis G. A. & Winemiller K. O.) 39–50 (Springer, 1996).Chapter 

Google Scholar 
6.Moore, J. C. et al. Detritus, trophic dynamics and biodiversity. Ecol. Lett. 7, 584–600 (2004).ADS 
Article 

Google Scholar 
7.Hagen, E. M. et al. A meta-analysis of the effects of detritus on primary producers and consumers in marine, freshwater, and terrestrial ecosystems. Oikos 121, 1507–1515 (2012).Article 

Google Scholar 
8.Danovaro, R., Snelgrove, P. V. R. & Tyler, P. Challenging the paradigms of deep-sea ecology. Trends Ecol. Evol. 29, 465–475 (2014).PubMed 
Article 

Google Scholar 
9.Smith, C. R., De Leo, F. C., Bernardino, A. F., Sweetman, A. K. & Arbizu, P. M. Abyssal food limitation, ecosystem structure and climate change. Trends Ecol. Evol. 23, 518–528 (2008).PubMed 
Article 

Google Scholar 
10.Gage, J. D. & Tyler, P. A. Deep-Sea Biology: A Natural History of Organisms at the Deep-Sea Floor. (Cambridge University Press, 1991).Book 

Google Scholar 
11.De La Rocha, C. L. & Passow, U. Factors influencing the sinking of POC and the efficiency of the biological carbon pump. Deep Res. Part II Top. Stud. Oceanogr. 54, 639–658 (2007).Article 

Google Scholar 
12.Smith, K. L., Ruhl, H. A., Huffard, C. L., Messié, M. & Kahru, M. Episodic organic carbon fluxes from surface ocean to abyssal depths during long-term monitoring in NE Pacific. Proc. Natl. Acad. Sci. USA. 115, 12235–12240 (2018).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
13.Ramirez-Llodra, E. et al. Deep, diverse and definitely different: Unique attributes of the world’s largest ecosystem. Biogeosciences 7, 2851–2899 (2010).ADS 
Article 

Google Scholar 
14.Ruhl, H. A. Abundance and size distribution dynamics of abyssal epibenthic megafauna in the northeast Pacific. Ecology 88, 1250–1262 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
15.Billett, D. S. M. Deep-sea holothurians. Oceanogr. Mar. Biol. An Annu. Rev. 29, 259–317 (1991).
Google Scholar 
16.Bett, B. J., Malzone, M. G., Narayanaswamy, B. E. & Wigham, B. D. Temporal variability in phytodetritus and megabenthic activity at the seabed in the deep northeast Atlantic. Prog. Oceanogr. 50, 349–368 (2001).ADS 
Article 

Google Scholar 
17.Durden, J. M. et al. Response of deep-sea deposit-feeders to detrital inputs: A comparison of two abyssal time-series sites. Deep. Res. Part II Top. Stud. Oceanogr. 173, 104677 (2020).18.Khripounoff, A. & Sibuet, M. L. nutrition d’echinodermes abyssaux I. Alimentation des holothuries. Mar. Biol. 60, 17–26 (1980).Article 

Google Scholar 
19.Roberts, D., Gebruka, A., Levin, V. & Manship, B. A. D. Feeding and digestive strategies in deposit-feeding holothurians. Oceanogr. Mar. Biol. Annu. Rev. 38, 257–310 (2000).
Google Scholar 
20.FitzGeorge-Balfour, T., Billett, D. S. M., Wolff, G. A., Thompson, A. & Tyler, P. A. Phytopigments as biomarkers of selectivity in abyssal holothurians; interspecific differences in response to a changing food supply. Deep. Res. Part II Top. Stud. Oceanogr. 57, 1418–1428 (2010).21.Miller, R. J., Smith, C. R., Demaster, D. J. & Fornes, W. L. Feeding selectivity and rapid particle processing by deep-sea megafaunal deposit feeders : A 234 Th tracer approach. J. Mar. Res. 58, 653–673 (2000).Article 

Google Scholar 
22.Witte, U. et al. In situ experimental evidence of the fate of a phytodetritus pulse at the abyssal sea floor. Lett. Nat. 424, 763–766 (2003).CAS 
Article 

Google Scholar 
23.Moore, H., Manship, B. & Roberts, D. Gut structure and digestive strategies in three species of abyssal holothurians. in Echinoderm Research 111–119 (Balkema, 1995).24.Deming, J. W. & Colwell, R. R. Barophilic bacteria associated with digestive tracts of abyssal holothurians. Appl. Environ. Microbiol. 44, 1222–1230 (1982).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
25.Amaro, T., Luna, G. M., Danovaro, R., Billett, D. S. M. & Cunha, M. R. High prokaryotic biodiversity associated with gut contents of the holothurian Molpadia musculus from the Nazaré Canyon (NE Atlantic). Deep. Res. Part I Oceanogr. Res. Pap. 63, 82–90 (2012).26.Roberts, D. et al. Sediment distribution, hydrolytic enzyme profiles and bacterial activities in the guts of Oneirophanta mutabilis, Psychropotes longicauda and Pseudostichopus villosus: What do they tell us about digestive strategies of abyssal holothurians?. Prog. Oceanogr. 50, 443–458 (2001).ADS 
Article 

Google Scholar 
27.Sibuet, M., Khripounoff, A., Deming, J., Colwell, R. & Dinet, A. Modification of the gut contents in the digestive tract of abyssal holothurians. In Proceedings of the International Echinoderm Conference. Tampa Bay. (ed Lawrence, J. M.) 421–428 (Balkema, 1982).
Google Scholar 
28.Bradley, C. J. et al. Trophic position estimates of marine teleosts using amino acid compound specific isotopic analysis. Limnol. Oceanogr. Methods 13, 476–493 (2015).Article 

Google Scholar 
29.Ohkouchi, N. et al. Advances in the application of amino acid nitrogen isotopic analysis in ecological and biogeochemical studies. Org. Geochem. 113, 150–174 (2017).CAS 
Article 

Google Scholar 
30.Popp, B. N. et al. Stable isotopes as indicators of ecological change. Terr. Ecol. 1, 173–190 (2007).
Google Scholar 
31.Chikaraishi, Y. et al. Determination of aquatic food-web structure based on compound-specific nitrogen isotopic composition of amino acids. Limnol. Oceanogr. Methods 7, 740–750 (2009).CAS 
Article 

Google Scholar 
32.McClelland, J. W. & Montoya, J. P. Trophic relationships and the nitrogen isotopic composition of amino acids in plankton. Ecology 83, 2173–2180 (2002).Article 

Google Scholar 
33.Steffan, S. A. et al. Microbes are trophic analogs of animals. Proc. Natl. Acad. Sci. U. S. A. 112, 15119–15124 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
34.Yamaguchi, Y. T. et al. Fractionation of nitrogen isotopes during amino acid metabolism in heterotrophic and chemolithoautotrophic microbes across Eukarya, Bacteria, and Archaea: Effects of nitrogen sources and metabolic pathways. Org. Geochem. 111, 101–112 (2017).CAS 
Article 

Google Scholar 
35.Iken, K., Brey, T., Wand, U., Voigt, J. & Junghans, P. Food web structure of the benthic community at the Porcupine Abyssal Plain (NE Atlantic): A stable isotope analysis. Prog. Oceanogr. 50, 383–405 (2001).ADS 
Article 

Google Scholar 
36.Romero-Romero, S. et al. Seasonal pathways of organic matter within the Avilés submarine canyon: Food web implications. Deep. Res. Part I. Oceanogr. Res. Pap. 117, 1–10 (2016).ADS 
CAS 
Article 

Google Scholar 
37.Reid, W., Wigham, B., McGill, R. & Polunin, N. Elucidating trophic pathways in benthic deep-sea assemblages of the Mid-Atlantic Ridge north and south of the Charlie-Gibbs fracture zone. Mar. Ecol. Prog. Ser. 463, 89–103 (2012).ADS 
Article 

Google Scholar 
38.Drazen, J. et al. Bypassing the abyssal benthic food web: Macrourid diet in the eastern North Pacific inferred from stomach content and stable isotopes analyses. Limnol. Oceanogr. 53, 2644–2654 (2008).ADS 
Article 

Google Scholar 
39.Post, D. Using stable isotopes to estimate trophic position: Models, methods, and assumptions. Ecology 83, 703–718 (2002).Article 

Google Scholar 
40.Witbaard, R., Duineveld, G. C. A., Kok, A., Van Der Weele, J. & Berghuis, E. M. The response of Oneirophanta mutabilis (Holothuroidea) to the seasonal deposition of phytopigments at the Porcupine Abyssal Plain in the Northeast Atlantic. Prog. Oceanogr. 50, 423–441 (2001).ADS 
Article 

Google Scholar 
41.Wigham, B. D., Hudson, I. R., Billett, D. S. M. & Wolff, G. A. Is long-term change in the abyssal Northeast Atlantic driven by qualitative changes in export flux? Evidence from selective feeding in deep-sea holothurians. Prog. Oceanogr. 59, 409–441 (2003).ADS 
Article 

Google Scholar 
42.Hudson, I. R., Wigham, B. D., Billett, D. S. M. & Tyler, P. A. Seasonality and selectivity in the feeding ecology and reproductive biology of deep-sea bathyal holothurians. Prog. Oceanogr. 59, 381–407 (2003).ADS 
Article 

Google Scholar 
43.Lauerman, L. M. L., Smoak, J. M., Shaw, T. J., Moore, W. S. & Smith, K. L. 234Th and 210Pb evidence for rapid ingestion of settling particles by mobile epibenthic megafauna in the abyssal NE Pacific. Limnol. Oceanogr. 42, 589–595 (1997).ADS 
CAS 
Article 

Google Scholar 
44.Roberts, D., Billett, D. S. M., McCartney, G. & Hayes, G. E. Procaryotes on the tentacles of deep-sea holothurians: A novel form of dietary supplementation. Limnol. Oceanogr. 36, 1447–1451 (1991).ADS 
Article 

Google Scholar 
45.Larsen, T., Lee Taylor, D., Leigh, M. B. & O’Brien, D. M. Stable isotope fingerprinting: A novel method for identifying plant, fungal, or bacterial origins of amino acids. Ecology 90, 3526–3535 (2009).46.Plante, C. J., Jumars, P. A. & Baross, J. A. Digestive associations between marine detritivores and bacteria. Annu. Rev. Ecol. Syst. 21, 93–127 (1990).Article 

Google Scholar 
47.Drazen, J. C., Phleger, C. F., Guest, M. A. & Nichols, P. D. Lipid, sterols and fatty acid composition of abyssal holothurians and ophiuroids from the North-East Pacific Ocean: Food web implications. Comp. Biochem. Physiol. – B Biochem. Mol. Biol. 151, 79–87 (2008).48.Amaro, T. et al. Possible links between holothurian lipid compositions and differences in organic matter (OM) supply at the western Pacific abyssal plains. Deep. Res. Part I Oceanogr. Res. Pap. 152, (2019).49.Kharlamenko, V. I., Maiorova, A. S. & Ermolenko, E. V. Fatty acid composition as an indicator of the trophic position of abyssal megabenthic deposit feeders in the Kuril Basin of the Sea of Okhotsk. Deep. Res. Part II Top. Stud. Oceanogr. 154, 374–382 (2018).50.Ginger, M. L. et al. Organic matter assimilation and selective feeding by holothurians in the deep sea: Some observations and comments. Prog. Oceanogr. 50, 407–421 (2001).ADS 
Article 

Google Scholar 
51.McMahon, K. W., Thorrold, S. R., Houghton, L. A. & Berumen, M. L. Tracing carbon flow through coral reef food webs using a compound-specific stable isotope approach. Oecologia 180, 809–821 (2016).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
52.Kaufmann, R. S. & Smith, K. L. Activity patterns of mobile epibenthic megafauna at an abyssal site in the eastern North Pacific: Results from a 17-month time-lapse photographic study. Deep. Res. Part I Oceanogr. Res. Pap. 44, 559–579 (1997).53.Kuhnz, L. A., Ruhl, H. A., Huffard, C. L. & Smith, K. L. Rapid changes and long-term cycles in the benthic megafaunal community observed over 24 years in the abyssal northeast Pacific. Prog. Oceanogr. 124, 1–11 (2014).ADS 
Article 

Google Scholar 
54.Vardaro, M. F., Ruhl, H. A. & Smith, K. L. Climate variation, carbon flux, and bioturbation in the abyssal north pacific. Limnol. Oceanogr. 54, 2081–2088 (2009).ADS 
Article 

Google Scholar 
55.Ziegler, A., Mooi, R., Rolet, G. & De Ridder, C. Origin and evolutionary plasticity of the gastric caecum in sea urchins (Echinodermata: Echinoidea). BMC Evol. Biol. 10, 313 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
56.McCarthy, M. D., Benner, R., Lee, C. & Fogel, M. L. Amino acid nitrogen isotopic fractionation patterns as indicators of heterotrophy in plankton, particulate, and dissolved organic matter. Geochim. Cosmochim. Acta 71, 4727–4744 (2007).ADS 
CAS 
Article 

Google Scholar 
57.Calleja, M. L., Batista, F., Peacock, M., Kudela, R. & McCarthy, M. D. Changes in compound specific δ15N amino acid signatures and d/l ratios in marine dissolved organic matter induced by heterotrophic bacterial reworking. Mar. Chem. 149, 32–44 (2013).CAS 
Article 

Google Scholar 
58.Smith, K. L., Ruhl, H. A., Kaufmann, R. S. & Kahru, M. Tracing abyssal food supply back to upper-ocean processes over a 17-year time series in the northeast Pacific. Limnol. Oceanogr. 53, 2655–2667 (2008).ADS 
Article 

Google Scholar 
59.Huffard, C. L., Kuhnz, L. A., Lemon, L., Sherman, A. D. & Smith, K. L. Demographic indicators of change in a deposit-feeding abyssal holothurian community (Station M, 4000 m). Deep. Res. Part I Oceanogr. Res. Pap. 109, 27–39 (2016).60.Hannides, C. C. S., Popp, B. N., Anela Choy, C. & Drazen, J. C. Midwater zooplankton and suspended particle dynamics in the North Pacific Subtropical Gyre: A stable isotope perspective. Limnol. Oceanogr. 58, 1931–1936 (2013).61.Neto, R. R., Wolff, G. A., Billett, D. S. M., Mackenzie, K. L. & Thompson, A. The influence of changing food supply on the lipid biochemistry of deep-sea holothurians. Deep. Res. Part I Oceanogr. Res. Pap. 53, 516–527 (2006).62.Amaro, T., Witte, H., Herndl, G. J., Cunha, M. R. & Billett, D. S. M. Deep-sea bacterial communities in sediments and guts of deposit-feeding holothurians in Portuguese canyons (NE Atlantic). Deep Res. Part I Oceanogr. Res. Pap. 56, 1834–1843 (2009).ADS 
Article 

Google Scholar 
63.Silfer, J. A., Engel, M. H., Macko, S. A. & Jumeau, E. J. Stable carbon isotope analysis of amino acid enantiomers by conventional isotope ratio mass spectrometry and combined gas chromatography/isotope ratio mass spectrometry. Anal. Chem. 63, 370–374 (1991).CAS 
Article 

Google Scholar 
64.Jarman, C. L. et al. Diet of the prehistoric population of Rapa Nui (Easter Island, Chile) shows environmental adaptation and resilience. Am. J. Phys. Anthropol. 164, 343–361 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
65.Dauwe, B., Middelburg, J. J., Herman, P. M. J. & Heip, C. H. R. Linking diagenetic alteration of amino acids and bulk organic matter reactivity. Limnology 44, 1809–1814 (1999).CAS 

Google Scholar 
66.McCarthy, M. D., Benner, R., Lee, C., Hedges, J. I. & Fogel, M. L. Amino acid carbon isotopic fractionation patterns in oceanic dissolved organic matter: An unaltered photoautotrophic source for dissolved organic nitrogen in the ocean?. Mar. Chem. 92, 123–134 (2004).CAS 
Article 

Google Scholar 
67.R: A Language and Environment for Statistical Computing. https://www.R-project.org (R Core Team. R Foundation for Statistical Computing, 2020). More

1.Pereira, H. M. et al. Scenarios for global biodiversity in the 21st century. Science 330, 1496–1501 (2010).ADS 
CAS 
PubMed 
Article 

Google Scholar 
2.Pereira, H. M. et al. Essential biodiversity variables. Science 339, 277–278 (2013).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
3.Stem, C., Margoluis, R., Salafsky, N. & Brown, M. Monitoring and evaluation in conservation: a review of trends and approaches. Conserv. Biol. 19, 295–309 (2005).Article 

Google Scholar 
4.Atwood, T. B. et al. Herbivores at the highest risk of extinction among mammals, birds, and reptiles. Sci. Adv. 6, eabb8458 (2020).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
5.Cardillo, M. et al. Human population density and extinction risk in the world’s carnivores. PLoS Biol 2, e197 (2004).PubMed 
PubMed Central 
Article 

Google Scholar 
6.Ripple, W. J. et al. Status and ecological effects of the world’s largest carnivores. Science 343, 1241484 (2014).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
7.Wolf, C. & Ripple, W. J. Prey depletion as a threat to the world’s large carnivores. R. Soc. Open Sci. 3, 160252 (2016).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
8.Kissling, W. D. et al. Building essential biodiversity variables (EBV s) of species distribution and abundance at a global scale. Biol. Rev. 93, 600–625 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
9.Nesshöver, C., Livoreil, B., Schindler, S. & Vandewalle, M. Challenges and solutions for networking knowledge holders and better informing decision-making on biodiversity and ecosystem services. Biodivers. Conserv. 25, 1207–1214 (2016).Article 

Google Scholar 
10.Gardner, T. A. et al. The cost-effectiveness of biodiversity surveys in tropical forests. Ecol. Lett. 11, 139–150 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
11.Field, S. A., Tyre, A. J. & Possingham, H. P. Optimizing allocation of monitoring effort under economic and observational constraints. J. Wildl. Manag. 69, 473–482 (2005).Article 

Google Scholar 
12.Braunisch, V. & Suchant, R. Predicting species distributions based on incomplete survey data: The trade-off between precision and scale. Ecography 33, 826–840 (2010).Article 

Google Scholar 
13.Taberlet, P., Coissac, E., Hajibabaei, M. & Rieseberg, L. H. Environmental DNA. Mol. Ecol. 21, 1789–1793 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
14.Deiner, K. et al. Long-range PCR allows sequencing of mitochondrial genomes from environmental DNA. Methods Ecol. Evol. 8, 1888–1898 (2017).Article 

Google Scholar 
15.Deiner, K., Walser, J.-C., Mächler, E. & Altermatt, F. Choice of capture and extraction methods affect detection of freshwater biodiversity from environmental DNA. Biol. Conserv. 183, 53–63 (2015).Article 

Google Scholar 
16.Tsuji, S., Takahara, T., Doi, H., Shibata, N. & Yamanaka, H. The detection of aquatic macroorganisms using environmental DNA analysis—A review of methods for collection, extraction, and detection. Environ. DNA 1, 99–108 (2019).Article 

Google Scholar 
17.Sales, N. G. et al. Fishing for mammals: Landscape-level monitoring of terrestrial and semi-aquatic communities using eDNA from riverine systems. J. Appl. Ecol. 57, 707–716 (2020).CAS 
Article 

Google Scholar 
18.Sales, N. G. et al. Assessing the potential of environmental DNA metabarcoding for monitoring Neotropical mammals: a case study in the Amazon and Atlantic Forest, Brazil. Mammal Rev. 50, 221–225 (2020).Article 

Google Scholar 
19.Barnes, M. A. & Turner, C. R. The ecology of environmental DNA and implications for conservation genetics. Conserv. Genet. 17, 1–17 (2016).CAS 
Article 

Google Scholar 
20.Deiner, K. et al. Environmental DNA metabarcoding: Transforming how we survey animal and plant communities. Mol. Ecol. 26, 5872–5895 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
21.Leempoel, K., Hebert, T. & Hadly, E. A. A comparison of eDNA to camera trapping for assessment of terrestrial mammal diversity. Proc. R. Soc. B Biol. Sci. 287, 20192353 (2020).CAS 
Article 

Google Scholar 
22.Harper, L. R. et al. Environmental DNA (eDNA) metabarcoding of pond water as a tool to survey conservation and management priority mammals. Biol. Conserv. 238, 108225 (2019).Article 

Google Scholar 
23.Rodgers, T. W. & Mock, K. E. Drinking water as a source of environmental DNA for the detection of terrestrial wildlife species. Conserv. Genet. Resour. 7, 693–696 (2015).Article 

Google Scholar 
24.Ushio, M. et al. Environmental DNA enables detection of terrestrial mammals from forest pond water. Mol. Ecol. Resour. 17, e63–e75 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
25.Williams, K. E., Huyvaert, K. P., Vercauteren, K. C., Davis, A. J. & Piaggio, A. J. Detection and persistence of environmental DNA from an invasive, terrestrial mammal. Ecol. Evol. 8, 688–695 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
26.Merkes, C. M., McCalla, S. G., Jensen, N. R., Gaikowski, M. P. & Amberg, J. J. Persistence of DNA in carcasses, slime and avian feces may affect interpretation of environmental DNA data. PLoS ONE 9, e113346 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
27.Pont, D. et al. Environmental DNA reveals quantitative patterns of fish biodiversity in large rivers despite its downstream transportation. Sci. Rep. 8, 1–13 (2018).ADS 
CAS 
Article 

Google Scholar 
28.Zinger, L. et al. Advances and prospects of environmental DNA in neotropical rainforests. Adv. Ecol. Res. 62, 331–373 (2020).Article 

Google Scholar 
29.Withers, P. C., Cooper, C. E., Maloney, S. K., Bozinovic, F. & Cruz-Neto, A. P. Ecological and Environmental Physiology of Mammals Vol. 5 (Oxford University Press, 2016).Book 

Google Scholar 
30.Bicudo, J. E. P., Buttemer, W. A., Chappell, M. A., Pearson, J. T. & Bech, C. Ecological and Environmental Physiology of Birds Vol. 2 (Oxford University Press, 2010).Book 

Google Scholar 
31.Naidoo, R. & Burton, A. C. Relative effects of recreational activities on a temperate terrestrial wildlife assemblage. Conserv. Sci. Pract. 2, e271 (2020).
Google Scholar 
32.Cantera, I. et al. Optimizing environmental DNA sampling effort for fish inventories in tropical streams and rivers. Sci. Rep. 9, 1–11 (2019).CAS 
Article 

Google Scholar 
33.Tarboton, D. G., Bras, R. L. & Rodriguez-Iturbe, I. The fractal nature of river networks. Water Resour. Res. 24, 1317–1322 (1988).ADS 
Article 

Google Scholar 
34.Ishige, T. et al. Tropical-forest mammals as detected by environmental DNA at natural saltlicks in Borneo. Biol. Conserv. 210, 281–285 (2017).Article 

Google Scholar 
35.Joseph, L. N., Field, S. A., Wilcox, C. & Possingham, H. P. Presence–absence versus abundance data for monitoring threatened species. Conserv. Biol. 20, 1679–1687 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
36.Lacy, R. C. Lessons from 30 years of population viability analysis of wildlife populations. Zoo Biol. 38, 67–77 (2019).PubMed 
Article 

Google Scholar 
37.Gärdenfors, U., Hilton-Taylor, C., Mace, G. M. & Rodríguez, J. P. The application of IUCN Red List criteria at regional levels. Conserv. Biol. 15, 1206–1212 (2001).Article 

Google Scholar 
38.Munro, R., Nielsen, S. E., Price, M., Stenhouse, G. & Boyce, M. S. Seasonal and diel patterns of grizzly bear diet and activity in west-central Alberta. J. Mammal. 87, 1112–1121 (2006).Article 

Google Scholar 
39.Deiner, K. & Altermatt, F. Transport distance of invertebrate environmental DNA in a natural river. PLoS ONE 9, e88786 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
40.Hunter, M. E. et al. Detection limits of quantitative and digital PCR assays and their influence in presence–absence surveys of environmental DNA. Mol. Ecol. Resour. 17, 221–229 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
41.Roussel, J.-M., Paillisson, J.-M., Treguier, A. & Petit, E. The downside of eDNA as a survey tool in water bodies. J. Appl. Ecol. 52, 823–826 (2015).CAS 
Article 

Google Scholar 
42.Williams, B. K., Nichols, J. D. & Conroy, M. J. Analysis and Management of Animal Populations (Academic Press, 2002).
Google Scholar 
43.Burton, A. C. et al. Wildlife camera trapping: A review and recommendations for linking surveys to ecological processes. J. Appl. Ecol. 52, 675–685 (2015).Article 

Google Scholar 
44.Morris, W. F. et al. Quantitative Conservation Biology (Sinauer Sunderland, 2002).
Google Scholar 
45.Parks, B. C. South Chilcotin Mountains Park and Big Creek Park Management Plan (2019).46.McLellan, M. L. et al. Divergent population trends following the cessation of legal grizzly bear hunting in southwestern British Columbia, Canada. Biol. Conserv. 233, 247–254 (2019).Article 

Google Scholar 
47.Kays, R. et al. Camera traps as sensor networks for monitoring animal communities. In 2009 IEEE 34th Conference on Local Computer Networks 811–818. https://doi.org/10.1109/LCN.2009.5355046 (IEEE, 2009).48.Hendry, H. & Mann, C. Camelot–intuitive software for camera trap data management. BioRxiv 203216 (2017).49.Valentini, A. et al. Next-generation monitoring of aquatic biodiversity using environmental DNA metabarcoding. Mol. Ecol. 25, 929–942 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
50.Taberlet, P., Bonin, A., Zinger, L. & Coissac, E. Environmental DNA: For biodiversity research and monitoring (Oxford University Press, Oxford, 2018).Book 

Google Scholar 
51.Boyer, F. et al. obitools: A unix-inspired software package for DNA metabarcoding. Mol. Ecol. Resour. 16, 176–182 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
52.De Barba, M. et al. DNA metabarcoding multiplexing and validation of data accuracy for diet assessment: Application to omnivorous diet. Mol. Ecol. Resour. 14, 306–323 (2014).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
53.Schnell, I. B., Bohmann, K. & Gilbert, M. T. P. Tag jumps illuminated–reducing sequence-to-sample misidentifications in metabarcoding studies. Mol. Ecol. Resour. 15, 1289–1303 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
54.Wearn, O. & Glover-Kapfer, P. Camera-trapping for conservation: A guide to best-practices. WWF Conserv. Technol. Ser. 1, 2019–2104 (2017).
Google Scholar 
55.R Core Team. R: A language and environment for statistical computing (R Foundation for Statistical Computing, 2020).
Google Scholar 
56.Plummer, M., et al.. JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling. In Proceedings of the 3rd International Workshop on Distributed Statistical Computing vol. 124, 1–10 (Vienna, Austria, 2003).57.Tuomisto, H. A diversity of beta diversities: Straightening up a concept gone awry. Part 1. Defining beta diversity as a function of alpha and gamma diversity. Ecography 33, 2–22 (2010).Article 

Google Scholar 
58.Ahumada, J. A. et al. Community structure and diversity of tropical forest mammals: data from a global camera trap network. Philos. Trans. R. Soc. B Biol. Sci. 366, 2703–2711 (2011).Article 

Google Scholar 
59.Samejima, H., Ong, R., Lagan, P. & Kitayama, K. Camera-trapping rates of mammals and birds in a Bornean tropical rainforest under sustainable forest management. For. Ecol. Manag. 270, 248–256 (2012).Article 

Google Scholar 
60.Parsons, A. W. et al. Do occupancy or detection rates from camera traps reflect deer density?. J. Mammal. 98, 1547–1557 (2017).Article 

Google Scholar 
61.Sollmann, R., Mohamed, A., Samejima, H. & Wilting, A. Risky business or simple solution–Relative abundance indices from camera-trapping. Biol. Conserv. 159, 405–412 (2013).Article 

Google Scholar 
62.Villette, P., Krebs, C. J., Jung, T. S. & Boonstra, R. Can camera trapping provide accurate estimates of small mammal (Myodes rutilus and Peromyscus maniculatus) density in the boreal forest?. J. Mammal. 97, 32–40 (2016).Article 

Google Scholar 
63.Feldhamer, G. A., Thompson, B. C. & Chapman, J. A. Wild Mammals of North America: Biology, Management, and Conservation (The Johns Hopkins University Press, 2003).
Google Scholar 
64.Burnham, K. P. & Anderson, D. R. A Practical Information-Theoretic Approach. Model Selection Multimodel Inference 2nd edn, Vol. 2 (Springer, Berlin, 2002).MATH 

Google Scholar  More