Philippa Kaur

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.Kildow, J. T. & McIlgorm, A. The importance of estimating the contribution of the oceans to national economies. Mar. Policy 34, 367–374 (2010).Article 

Google Scholar 
2.Lam, V. W. Y., Cheung, W. W. L., Reygondeau, G. & Sumaila, U. R. Projected change in global fisheries revenues under climate change. Sci. Rep. 6, 32607 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
3.Link, J. S. & Marshak, A. R. Characterizing and comparing marine fisheries ecosystems in the United States: Determinants of success in moving toward ecosystem-based fisheries management. Rev. Fish Biol. Fish. 29, 23–70 (2019).Article 

Google Scholar 
4.National Marine Fisheries Service. Fisheries Economics of the United States, 2016. US Dept. of Commerce, NOAA Tech. Memo. NMFS-F/SPO-187 (2018).5.Jennings, S., Lee, J. & Hiddink, J. G. Assessing fishery footprints and the trade-offs between landings value, habitat sensitivity, and fishing impacts to inform marine spatial planning and an ecosystem approach. ICES J. Mar. Sci. 69, 1053–1063 (2012).Article 

Google Scholar 
6.Link, J. S. & Watson, R. A. Global ecosystem overfishing: Clear delineation within real limits to production. Sci. Adv. 5, eaav0474 (2019).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
7.Ryther, J. H. Photosynthesis and fish production in the sea. Science 166, 72–76 (1969).ADS 
CAS 
PubMed 
Article 

Google Scholar 
8.Stock, C. A. et al. Reconciling fisheries catch and ocean productivity. Proc. Natl. Acad. Sci. USA 114, E1441–E1449 (2017).CAS 
PubMed 
Article 

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

Google Scholar 
10.Free, C. M. et al. Impacts of historical warming on marine fisheries production. Science 363, 979–983 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
11.Hornborg, S. et al. Ecosystem-based fisheries management requires broader performance indicators for the human dimension. Mar. Policy 108, 103639 (2019).Article 

Google Scholar 
12.Marshall, K. N. et al. Ecosystem-based fisheries management for social–ecological systems: Renewing the focus in the United States with next generation fishery ecosystem plans. Conserv. Lett. 11, e12367 (2018).Article 

Google Scholar 
13.Link, J. Ecosystem-Based Fisheries Management: Confronting Tradeoffs (Cambridge University Press, 2010).Book 

Google Scholar 
14.Costanza, R. et al. Changes in the global value of ecosystem services. Glob. Environ. Change 26, 152–158 (2014).Article 

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

Google Scholar 
16.Chassot, E. et al. Global marine primary production constrains fisheries catches. Ecol. Lett. 13, 495–505 (2010).PubMed 
Article 

Google Scholar 
17.Coll, M., Libralato, S., Tudela, S., Palomera, I. & Pranovi, F. Ecosystem overfishing in the ocean. PLoS ONE 3, e3881 (2008).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
18.Murawski, S. A. Definitions of overfishing from an ecosystem perspective. ICES J. Mar. Sci. 57, 649–658 (2000).Article 

Google Scholar 
19.Breitburg, D. L. et al. Nutrient enrichment and fisheries exploitation: Interactive effects on estuarine living resources and their management. Hydrobiologia 629, 31–47 (2009).CAS 
Article 

Google Scholar 
20.Hondorp, D. W., Breitburg, D. L. & Davias, L. A. Eutrophication and fisheries: Separating the effects of nitrogen loads and hypoxia on the pelagic-to-demersal ratio and other measures of landings composition. Mar. Coast. Fish. 2, 339–361 (2010).Article 

Google Scholar 
21.Link, J. S. et al. Emergent properties delineate marine ecosystem perturbation and recovery. Trends Ecol. Evol. 30, 649–661 (2015).PubMed 
Article 

Google Scholar 
22.Tam, J. C. et al. Comparing apples to oranges: Common trends and thresholds in anthropogenic and environmental pressures across multiple marine ecosystems. Front. Mar. Sci. 4, 282 (2017).Article 

Google Scholar 
23.Link, J. S. et al. Marine ecosystem assessment in a fisheries management context. Can. J. Fish. Aquat. Sci. 59, 1429–1440 (2002).Article 

Google Scholar 
24.Garcia, S. M., Rice, J. & Charles, A. Governance of Marine Fisheries and Biodiversity Conservation: Interaction and Co-evolution (Wiley-Blackwell, 2014).Book 

Google Scholar 
25.Colloca, F. et al. Rebuilding Mediterranean fisheries: A new paradigm for ecological sustainability. Fish Fish. 14, 89–109 (2013).Article 

Google Scholar 
26.National Oceanic and Atmospheric Administration (NOAA), Office for Coastal Management (OCM), NOAA Report on the US Marine Economy. NOAA OCM. 23p. https://coast.noaa.gov/digitalcoast/training/econreport.html (2020).27.Teh, L. C. & Sumaila, U. R. Contribution of marine fisheries to worldwide employment. Fish Fish. 14, 77–88 (2013).Article 

Google Scholar 
28.Laterra, P. et al. How are jobs and ecosystem services linked at the local scale?. Ecosyst. Serv. 35, 207–218 (2019).Article 

Google Scholar 
29.Barange, M. et al. Impacts of climate change on marine ecosystem production in societies dependent on fisheries. Nat. Clim. Change 4, 211–216 (2014).ADS 
Article 

Google Scholar 
30.Graham, N. A. et al. Human disruption of coral reef trophic structure. Curr. Biol. 27, 231–236 (2017).CAS 
PubMed 
Article 

Google Scholar 
31.Koslow, J. A. & Davison, P. C. Productivity and biomass of fishes in the California Current Large Marine Ecosystem: Comparison of fishery-dependent and-independent time series. Environ. Dev. 17, 23–32 (2016).Article 

Google Scholar 
32.Kahru, M., Kudela, R., Manzano-Sarabia, M. & Mitchell, B. G. Trends in primary production in the California Current detected with satellite data. J. Geophys. Res. Oceans 114, C02004 (2009).ADS 
Article 
CAS 

Google Scholar 
33.Large, S. I., Fay, G., Friedland, K. D. & Link, J. S. Defining trends and thresholds in responses of ecological indicators to fishing and environmental pressures. ICES J. Mar. Sci. 70, 755–767 (2013).Article 

Google Scholar 
34.Large, S. I., Fay, G., Friedland, K. D. & Link, J. S. Critical points in ecosystem responses to fishing and environmental pressures. Mar. Ecol. Progr. Ser. 521, 1–17 (2015).ADS 
Article 

Google Scholar 
35.Fogarty, M. J. & Murawski, S. A. Large-scale disturbance and the structure of marine systems: Fishery impacts on Georges Bank. Ecol. Appl. 8(sp1), S6–S22 (1998).Article 

Google Scholar 
36.Cheung, W. W. L. et al. Large-scale redistribution of maximum fisheries catch potential in the global ocean under climate change. Glob. Change Biol. 16, 24–35 (2010).ADS 
Article 

Google Scholar 
37.Sumaila, U. R., Cheung, W. W. L., Lam, V. W. Y., Pauly, D. & Herrick, S. Climate change impacts on the biophysics and economics of world fisheries. Nat. Clim. Change 1, 449–456 (2011).ADS 
Article 

Google Scholar 
38.Chavez, F. P., Messié, M. & Pennington, J. T. Marine primary production in relation to climate variability and change. Annu. Rev. Mar. Sci. 3, 227–260 (2010).ADS 
Article 

Google Scholar 
39.Banse, K. Grazing, temporal changes of phytoplankton concentrations, and the microbial loop in the open sea. In Primary Productivity and Biogeochemical Cycles in the Sea (eds Falkowski, P. G. et al.) 409–440 (Springer, 1992).Chapter 

Google Scholar 
40.Murray, C. J. et al. Past, present and future eutrophication status of the Baltic Sea. Front. Mar. Sci. 6, 2 (2019).Article 

Google Scholar 
41.Möllmann, C. Effects of climate change and fisheries on the marine ecosystem of the Baltic Sea. In Oxford Research Encyclopedia of Climate Science (ed. Möllmann, C.) (University Press, 2019). https://doi.org/10.1093/acrefore/9780190228620.013.682.Chapter 

Google Scholar 
42.Intergovernmental Oceanographic Commission (IOC-UNESCO) and United Nations Environmental Program (UNEP). Large Marine Ecosystems: Status and Trends (United Nations Environment Programme UNEP, 2016).
Google Scholar 
43.Sangha, K. K., Stoeckl, N., Crossman, N. & Costanza, R. A state-wide economic assessment of coastal and marine ecosystem services to inform sustainable development policies in the Northern Territory, Australia. Mar. Policy 107, 103595 (2019).Article 

Google Scholar 
44.Hilborn, R. et al. Effective fisheries management instrumental in improving fish stock status. Proc. Natl. Acad. Sci. USA 117, 2218–2224 (2020).CAS 
PubMed 
Article 

Google Scholar 
45.McGowan, J. A., Bograd, S. J., Lynn, R. J. & Miller, A. J. The biological response to the 1977 regime shift in the California Current. Deep Sea Res. Pt. II 50(14–16), 2567–2582 (2003).ADS 
Article 

Google Scholar 
46.Beaugrand, G. The North Sea regime shift: Evidence, causes, mechanisms and consequences. Prog. Oceanogr. 60(2–4), 245–262 (2004).ADS 
Article 

Google Scholar 
47.Kirkman, S. P. et al. Regime shifts in demersal assemblages of the Benguela Current Large Marine Ecosystem: A comparative assessment. Fish. Oceanogr. 24(S1), 15–30 (2015).Article 

Google Scholar 
48.Link, J. S., Watson, R. A., Pranovi, F. & Libralato, S. Comparative production of fisheries yields and ecosystem overfishing in African Large Marine Ecosystems. Environ. Devel. 36, 100529 (2020).Article 

Google Scholar 
49.Ye, Y. et al. Rebuilding global fisheries: The World Summit Goal, costs and benefits. Fish Fish. 14, 174–185 (2013).Article 

Google Scholar 
50.Pinsky, M. L., Worm, B., Fogarty, M. J., Sarmiento, J. L. & Levin, S. A. Marine taxa track local climate velocities. Science 341, 1239–1242 (2013).ADS 
CAS 
PubMed 
Article 

Google Scholar 
51.Ding, Q., Chen, X., Hilborn, R. & Chen, Y. Vulnerability to impacts of climate change on marine fisheries and food security. Mar. Policy 83, 55–61 (2017).Article 

Google Scholar 
52.NOAA Fisheries. NMFS Headquarters Ecosystem Based Fisheries Management Implementation Plan (NOAA Fisheries, 2019).
Google Scholar 
53.Witherell, D., Pautzke, C. & Fluharty, D. An ecosystem-based approach for Alaska groundfish fisheries. ICES J. Mar. Sci. 57, 771–777 (2000).Article 

Google Scholar 
54.National Aeronautics and Space Administration (NASA). NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group. Moderate-resolution Imaging Spectroradiometer (MODIS) Aqua Data. NASA OB.DAAC, Greenbelt, MD, USA. (2014)55.Eppley, R. W. Temperature and phytoplankton growth in the sea. Fish. Bull. 70, 1063–1085 (1972).
Google Scholar 
56.Behrenfeld, M. J. & Falkowski, P. G. Photosynthetic rates derived from satellite-based chlorophyll concentration. Limnol. Oceanogr. 42, 1–20 (1997).ADS 
CAS 
Article 

Google Scholar 
57.Peters, R. et al. Habitat science is a fundamental element in an ecosystem-based fisheries management framework: an update to the Marine Fisheries Habitat Assessment Improvement Plan. US Dept. of Commerce, NOAA. NOAA Tech. Memo. NMFS-F/SPO-181. (2018).58.Cannizzaro, J. P. & Carder, K. L. Estimating chlorophyll a concentrations from remote-sensing reflectance in optically shallow waters. Remote Sens. Environ. 101, 13–24 (2006).ADS 
Article 

Google Scholar 
59.Reid, R. N., Almeida, F. P., & Zetlin, C. A. Essential fish habitat source document: Fishery-independent surveys, data sources, and methods. NOAA Tech. Memo. NMFS NE 122. (1999).60.Stauffer, G. NOAA Protocols for Groundfish Bottom Trawl Surveys of the Nation’s Fishery Resources. US Dep. Commerce, NOAA Tech. Memo. NMFS-F/SPO-65. (2004).61.National Ocean Economics Program. State of the US Ocean and Coastal Economies 2016 Update. Middlebury Institute of International Studies at Monterey, Center for the Blue Economy. (2016).62.Craig, M. T. et al. Status review report of Pacific bluefin tuna (Thunnus orientalis). NOAA Tech. Memo. NMFS-SWFSC-587. (2017).63.National Oceanic and Atmospheric Administration (NOAA). Spatial trends in coastal socioeconomics (STICS): Coastal county definitions. NOAA. 12p. https://coast.noaa.gov/htdata/SocioEconomic/NOAA_CoastalCountyDefinitions.pdf (2013).64.National Oceanic and Atmospheric Administration (NOAA). NOAA Office of Coast Survey maritime zones of the United States. NOAA. (2021). https://nauticalcharts.noaa.gov/data/us-maritime-limits-and-boundaries.html.65.Watson, R. A. A database of global marine commercial, small-scale, illegal and unreported fisheries catch 1950–2014. Sci. Data 4, 1–9 (2017).Article 

Google Scholar 
66.NOAA Fisheries. National marine fisheries service—2nd quarter 2017 update. NOAA Fisheries 53p. (2017). More

1.Haddaway, N. R., Styles, D. & Pullin, A. S. Environmental impacts of farm land abandonment in high altitude/mountain regions: A systematic map of the evidence. Environ. Evid. 2(1), 18. https://doi.org/10.1186/2047-2382-2-18 (2013).Article 

Google Scholar 
2.Bender, O., Boehmer, H. J., Jens, D. & Schumacher, K. P. Analysis of land-use change in a sector of Upper Franconia (Bavaria, Germany) since 1850 using land register records. Landsc. Ecol. 20(2), 149–163. https://doi.org/10.1007/s10980-003-1506-7 (2005).Article 

Google Scholar 
3.Biró, M., Szitár, K., Horváth, F., Bagi, I. & Molnár, Z. Detection of long-term landscape changes and trajectories in a Pannonian sand region: Comparing land-cover and habitat-based approaches at two spatial scales. Community Ecol. 14(2), 219–230. https://doi.org/10.1556/ComEc.14.2013.2.12 (2013).Article 

Google Scholar 
4.Schulp, C. J. E., Levers, C., Kuemmerle, T., Tieskens, K. F. & Verburg, P. H. Mapping and modelling past and future land use change in Europe’s cultural landscapes. Land Use Policy 80, 332–344. https://doi.org/10.1016/j.landusepol.2018.04.030 (2019).Article 

Google Scholar 
5.González Díaz, J. A., Celaya, R., Fernández García, R., Osoro, K. & Rosa García, R. Dynamics of rural landscapes in marginal areas of northern Spain: Past, present, and future. Land Degrad. Dev. 30, 141–150. https://doi.org/10.1002/ldr.3201 (2018).Article 

Google Scholar 
6.Hersperger, A. M., Gennaio, M.-P., Verburg, P. H., & Buergi, M. Linking land change with driving forces and actors: Four conceptual models. Ecol. Soci. 15(4) (2010). Retrieved from http://apps.webofknowledge.com.infozdroje.czu.cz/full_record.do?product=UA&search_mode=GeneralSearch&qid=1&SID=R1PQ9zBCVnF6m2Wcyo8&page=1&doc=2.7.Khromykh, V. & Khromykh, O. Analysis of spatial structure and dynamics of Tom Valley landscapes based on GIS, digital elevation model and remote sensing. Proc. Soc. Behav. Sci. 120, 811–815. https://doi.org/10.1016/j.sbspro.2014.02.165 (2014).Article 

Google Scholar 
8.Kienast, F. Analysis of historic landscape patterns with a geographical information system? A methodological outline. Landsc. Ecol. 8(2), 103–118. https://doi.org/10.1007/BF00141590 (1993).Article 

Google Scholar 
9.Matsushita, B., Xu, M. & Fukushima, T. Characterizing the changes in landscape structure in the Lake Kasumigaura Basin, Japan using a high-quality GIS dataset. Landsc. Urban Plan. 78(3), 241–250. https://doi.org/10.1016/j.landurbplan.2005.08.003 (2006).Article 

Google Scholar 
10.Plieninger, T. et al. Patterns and drivers of scattered tree loss in agricultural landscapes: Orchard meadows in Germany (1968–2009). PLoS ONE https://doi.org/10.1371/journal.pone.0126178 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
11.Swetnam, R. D. Rural land use in England and Wales between 1930 and 1998: Mapping trajectories of change with a high resolution spatio-temporal dataset. Landsc. Urban Plan. 81(1–2), 91–103. https://doi.org/10.1016/j.landurbplan.2006.10.013 (2007).Article 

Google Scholar 
12.Eurostat. Life in Cities. Urban Europe—Statistics on Cities, Towns and Suburbs. (Publications Office of the European Union, 2016). https://doi.org/10.2785/9112013.Fernández Ales, R. Effect of economic development on landscape structure and function in the Province of Seville (SW Spain) and its consequences on conservation. In Land Abandonment and Its Role in Conservation, Options Mediterraneennes, Serie A, Seminaires Mediterraneens Vol. 15 (eds Baudry, J. & Bunce, R. G. H.) 61–69 (Options Méditerranéennes, 1991).
Google Scholar 
14.Falcucci, A., Maiorano, L. & Boitana, L. Changes in land-use/land-cover patterns in Italy and their implications for biodiversity conservation. Landsc. Ecol. 22, 617–631 (2007).Article 

Google Scholar 
15.Antrop, M. Why landscapes of the past are important for the future. Landsc. Urban Plan. https://doi.org/10.1016/j.landurbplan.2003.10.002 (2004).Article 

Google Scholar 
16.Janečková Molnárová, K., Skřivanová, Z., Kalivoda, O. & Sklenička, P. Rural identity and landscape aesthetics in exurbia: Some issues to resolve from a Central European perspective. Morav. Geogr. Rep. 25(1), 2–12. https://doi.org/10.1515/mgr-2017-0001 (2017).Article 

Google Scholar 
17.Bičík, I., Jeleček, L. & Štěpánek, V. Land-use changes and their social driving forces in Czechia in the 19th and 20th centuries. Land Use Policy 18(1), 65–73. https://doi.org/10.1016/S0264-8377(00)00047-8 (2001).Article 

Google Scholar 
18.Lipsky, Z. The changing face of the Czech rural landscape. Landsc. Urban Plan. 31(1–3), 39–45. https://doi.org/10.1016/0169-2046(94)01034-6 (1995).Article 

Google Scholar 
19.Prishchepov, A. V., Radeloff, C. V., Baumann, M., Kemmerle, T. & Müller, D. Effects of institutional changes on land use: Agricultural land abandonment during the transition from state-command to market-driven economies in post-Soviet Eastern Europe. Enivron. Res. Lett. 7, 024021. https://doi.org/10.1088/1748-9326/7/2/024021 (2012).Article 

Google Scholar 
20.Forman, T. T. R. & Godron, M. Landscape Ecology (Wiley, 1986).
Google Scholar 
21.Míchal, I. Ekologická Stabilita (Veronica, 1994).
Google Scholar 
22.Bucala, A. The impact of human activities on land use and land cover changes and environmental processes in the Gorce Mountains (Western Polish Carpathians) in the past 50 years. J. Environ. Manag. 138, 4–14 (2014).Article 

Google Scholar 
23.Kozak, J. Forest cover change in the western Carpathians in the past 180 years. A case study in the Orawa region in Poland. Mt. Res. Dev. 23(4), 369–375 (2003).Article 

Google Scholar 
24.Latocha, A. Land-use changes and longer-term human–environment interactions in a mountain region (Sudetes Mountains, Poland). Geomorphology 108(1), 48–57. https://doi.org/10.1016/j.geomorph.2008.02.019 (2009).ADS 
Article 

Google Scholar 
25.Prauser, S. & Rees, A. The expulsion of the “German” communities from Eastern Europe at the end of the Second World War (Badia Fiesolana, 2004).
Google Scholar 
26.Petráš, R. Reforma správy a národní otázka na počátku první republiky. Historický Obzor: Časopis pro Výuku Dějepisu a Popularizaci Historie 13(5), 133–137 (2002).
Google Scholar 
27.Bičík, I. & Kabrda, J. Land use changes in Czech border regions (1845–2000). AUC Geogr. 42, 23–52 (2007).
Google Scholar 
28.Staněk, T. & von Arburg, A. Vysídlení Němců a proměny českého pohraničí 1945–1951 (I.) (Vysídlení) (Zdeněk Susa, 2010).
Google Scholar 
29.Weber, M. Dědictví krajiny jako výzva pro její současné obyvatele. In Proměny sudetské krajiny, Antikomple (ed. Spurnný, M.) (Nakladatelství Českého lesa, 2006).
Google Scholar 
30.Romportl, D., Chuman, T. & Lipsky, Z. Landscape typology of Czechia. Geografie 118(1), 16–39 (2013).Article 

Google Scholar 
31.Estel, S. et al. Mapping farmland abandonment and recultivation across Europe using MODIS NDVI time series. Remote Sens. Environ. 163, 312–325. https://doi.org/10.1016/J.RSE.2015.03.028 (2015).ADS 
Article 

Google Scholar 
32.Levers, C., Schneider, M., Prishchepov, A. V., Estel, S. & Kuemmerle, T. Spatial variation in determinants of agricultural land abandonment in Europe. Sci. Total Environ. 644, 95–111. https://doi.org/10.1016/J.SCITOTENV.2018.06.326 (2018).ADS 
CAS 
Article 
PubMed 

Google Scholar 
33.Zelinka, V. Continuity and extinction of agricultural land in the sudetes—A case study in the landscape of highlands and mountains. J. Landsc. Ecol. 11(2), 53–66. https://doi.org/10.2478/jlecol-2018-0006 (2018).Article 

Google Scholar 
34.ESRI. ArcGIS 10.6 (2017).35.Statistický lexikon obcí v republice Československé. (I). (Ministerstvo vnitra a Státní úřad statistický.Orbis, 1934).36.CENIA. Historické letecké snímky—50. léta. https://www.cenia.cz (2012).37.Bičík, I. et al. Land use changes in the Czech republic 1845–2010 (Springer, 2015).Book 

Google Scholar 
38.Demková, K. & Lipský, Z. Změny nelesní dřevinné vegetace v jihozápadní části bílých karpat v letech 1949–2011. Geogr. Sb. CGS 120(1), 64–83 (2015).
Google Scholar 
39.Forejt, M., Skalos, J., Pereponova, A., Plieninger, T. & Vojta, J. Changes and continuity of wood-pastures in the lowland landscape in Czechia. Appl. Geogr. 79, 235–244. https://doi.org/10.1016/j.apgeog.2016.12.016 (2017).Article 

Google Scholar 
40.ČÚZK. Geoportál ČÚZK—přístup k mapovým produktům a službám resortu. https://geoportal.cuzk.cz/ (2016).41.Skokanová, H. Application of methodological principles for assessment of land use changes trajectories and processes in South-eastern Moravia for the period 1836–2006 Eastern Moravia for the period 1836–2006. Acta Pruhon. 91, 15–21 (2015).
Google Scholar 
42.Grossmann, E. B. & Mladenoff, D. J. Open woodland and savanna decline in a mixed-disturbance landscape (1938 to 1998) in the Northwest Wisconsin (USA) Sand Plain. Landsc. Ecol. 22, 43–55 (2007).Article 

Google Scholar 
43.Skaloš, J. et al. What are the transitions of woodlands at the landscape level? Change trajectories of forest, non-forest and reclamation woody vegetation elements in a mining landscape in North-western Czech Republic. Appl. Geogr. 58, 206–216. https://doi.org/10.1016/j.apgeog.2015.02.003 (2015).Article 

Google Scholar 
44.Raška, P., Zábranský, V., Brázdil, R. & Lamková, J. The late Little Ice Age landslide calamity in North Bohemia: Triggers, impacts and post-landslide development reconstructed from documentary data (case study of the Kozí vrch Hill landslide). Geomorphology 255, 95–107. https://doi.org/10.1016/j.geomorph.2015.12.009 (2016).ADS 
Article 

Google Scholar 
45.Amici, V. et al. Anthropogenic drivers of plant diversity: Perspective on land use change in a dynamic cultural landscape. Biodivers. Conserv. 24(13), 3185–3199. https://doi.org/10.1007/s10531-015-0949-x (2015).Article 

Google Scholar 
46.Bürgi, M. et al. Processes and driving forces in changing cultural landscapes across Europe. Landsc. Ecol. 32(11), 2097–2112. https://doi.org/10.1007/s10980-017-0513-z (2017).Article 

Google Scholar 
47.Raška, P., Hruška, V. & Kolektiv, A. Adaptabilita a Resilience (Univerzita J.E. Purkyně v Ústí nad Labem, 2014).
Google Scholar 
48.CENIA. CORINE Land Cover 2012 Databáze České Republiky (2014).49.Juřena, J. Soumrak nad českým Podkrkonoším: Německá opkuace Novopacka a Jilemnicka v roce 1938. (FORTprint, 2013).50.Czech Geological Survey. Soil Map of Czech Republic (2018).51.Meduna, P. & Sádlo, J. Bezdězsko—Dokesko. Krajina mezi odolností a stagnací. Hist. Geogr. 35(1), 147–160 (2009).
Google Scholar 
52.Novák, J., Sádlo, J. & Svobodová-svitavská, H. Unusual vegetation stability in a lowland pine forest area (Doksy region, Czech Republic). Holocene 22(8), 947–955. https://doi.org/10.1177/0959683611434219 (2012).ADS 
Article 

Google Scholar 
53.Klápště, J. Proměna českých zemí ve středověku. (Lidové noviny, 2012).54.Engstová, B. & Petříček, V. Landscape and vegetation in a military area—Past and present. J. Landsc. Stud. 1, 91–102 (2008).
Google Scholar 
55.Turner, M. G. & Gardner, R. H. Landscape Ecology in Theory and Practice 2nd edn. (Springer, 2015).
Google Scholar 
56.Chytrý, M. (ed.) Vegetace České republiky 1. Travinná a keříčková vegetace (Academia, 2007).
Google Scholar 
57.Chytrý, M., Kučera, T., Kočí, M., Grulich, V. & Lustyk, P. Katalog biotopů České republiky (Agentura ochrany přírody a krajiny ČR, 2010).
Google Scholar  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

Metacommunity model and asymptotic community assemblyWe built a large set of model metacommunities (detailed in full in “Methods”) describing competitive dynamics within a single guild of species across a landscape. Each metacommunity consisted of a set of patches, or local communities, randomly placed in a square arena and linked by a spatial network. The dynamics of each population are governed by three processes: inter- and intraspecific interactions, heterogeneous responses to the environment and dispersal between adjacent patches (Fig. 1). Competition coefficients between species are drawn at random and the population dynamics within each patch are described by a Lotka-Volterra competition model. We control the level of environmental heterogeneity across the network directly by generating an intrinsic growth rate for each species at each patch from a random, spatially correlated distribution. To ensure any turnover is purely autonomous, we keep the environment fixed throughout simulations. Dispersal between neighbouring patches declines exponentially with distance between sites. This formulation allows precise and independent control of key properties of the metacommunity–the number of patches, the characteristic dispersal length and the heterogeneity of the environment.Fig. 1: Elements of the Lotka-Volterra metacommunity model and the emergence of autonomous population dynamics.Environmental heterogeneity, represented by the intrinsic growth rate matrix R, is modelled using a spatially autocorrelated Gaussian random field. A random spatial network, represented by the dispersal matrix D, defines the spatial connectivity of the landscape. The network of species interactions, represented by the competitive overlap matrix A, is modelled by sampling competition coefficients at random (perpendicular bars indicate recipients of a deleterious competitive impact). The resulting dynamics of local population biomasses, given by the colour-coded equation, are numerically simulated. The Hadamard product ‘∘’ represents element-wise matrix multiplication. For large metacommunities, local populations exhibit persistent dynamics despite the absence of external drivers. In the 3D boxes, typical simulated biomass dynamics of dominating species are plotted on linear axes over 2500 unit times. The graphs illustrate the complexity of the autonomous dynamics and the propensity for compositional change (local extinction and colonisation).Full size imageTo populate the model metacommunities, we iteratively introduced species with randomly generated intrinsic growth rates and interspecific interaction coefficients. Between successive regional invasions we simulated the model dynamics, and removed any species whose abundance fell below a threshold across the whole network. Through this assembly process and the eventual onset of ecological structural instability, both average local diversity, the number of species coexisting in a given patch, and regional diversity, the total number of species in the metacommunity, eventually saturate and then fluctuate around an equilibrium value—any introduction of a new species then leads on average to the extinction of one other species (Supplementary Fig. 1). In these intrinsically regulated metacommunities we then studied the phenomenology of autonomous community turnover in the absence of regional invasions or abiotic change.In our metacommunity model, local community dynamics and therefore local limits on species richness depend on a combination of biotic and abiotic filtering (non-uniform responses of species to local conditions)33,34,35 and immigration from adjacent patches, generating so called mass effects in the local community36,37,38. Biotic filtering via interspecific competition is encoded in the interaction coefficients Aij, while abiotic filtering occurs via the spatial variation of intrinsic growth rates Rix. For simplicity, and since predator-prey dynamics are known to generate oscillations39 through mechanisms distinct from those we report here, we restrict our analysis to competitive communities for which all ecological interactions are antagonistic. The off-diagonal elements of the interaction matrix A describe how one species i affects another species j. These are sampled independently from a discrete distribution, such that the interaction strength Aij is set to a constant value in the range 0 to 1 (in most cases 0.5) with fixed probability (connectance, in most cases 0.5) and otherwise set to zero. Intraspecific competition coefficients Aii are set to 1 for all species. This discrete distribution of the interaction terms was chosen for its relative efficiency. In the Supplementary discussion (and Supplementary Fig. 2) we show that outcomes remain unaffected when more complex distributions are modelled. Intrinsic growth rates Rix are sampled from spatially correlated normal distributions with mean 1, autocorrelation length ϕ and variance σ2 (Supplementary Fig. 3).Dispersal is modelled via a spatial connectivity matrix with elements Dxy. The topology of the model metacommunity, expressed through D, is generated by sampling the spatial coordinates of N patches from a uniform distribution ({mathcal{U}}(0,sqrt{N})times {mathcal{U}}(0,sqrt{N})), i.e., an area of size N. Thus, under variation of the number of patches, the inter-patch distances remain fixed on average. Spatial connectivity is defined by linking these patches through a Gabriel graph40, a planar graph generated by an algorithm that, on average, links each local community to four close neighbours41. Avoidance of direct long-distance dispersal and the sparsity of the resulting dispersal matrix permit the use of efficient numerical methods. The exponential dispersal kernel defining Dxy is tuned by the dispersal length ℓ, which is fixed for all species.The dynamics of local population biomasses Bix = Bix(t) are modelled using a system of spatially coupled Lotka-Volterra (LV) equations that, in matrix notation, takes the form23$$frac{d{bf{B}}}{dt}={bf{B}}circ ({bf{R}}-{bf{A}}{bf{B}})+{bf{B}}{bf{D}},$$
(1)
with ∘ denoting element-wise multiplication. Hereafter this formalism is referred to as the Lotka-Volterra Metacommunity Model (LVMCM). Further technical details are provided in Methods and the Supplementary Discussion.In order to numerically probe the impacts of ℓ, ϕ and σ2 on the emergent temporal dynamics, we initially fixed N = 64 and varied each parameter through multiple orders of magnitude (Supplementary Fig. 4). In order to obtain a full characterisation of autonomous turnover in the computationally accessible spatial range (N ≤ 256), we then selected a parameter combination found to generate substantial fluctuations for further analysis. Thereafter we assembled metacommunities of 8–256 patches (Fig. 2a) until regional diversity limits were reached (with tenfold replication) and generated community time series of 104 unit times from which the phenomenology of autonomous turnover could be explored in detail. We found no evidence to suggest that the phenomenology described below depends on this specific parameter combination. While future results may confirm or refute this, autonomous turnover arises over a wide range of parameters (Supplementary Fig. 4) and as such the phenomenon is robust.Fig. 2: Autonomous turnover in model metacommunities.a Typical model metacommunities: a spatial network with N nodes representing local communities (or patches) and edges, channels of dispersal. Patch colour represents the number of clusters in local community state space detected over 104 unit times t using hierarchical clustering of the Bray-Curtis (BC) dissimilarity matrix, Supplementary Fig. 6. b Colour coded matrices of pairwise temporal BC dissimilarity corresponding to the circled patches in (a). Insets represent 102 unit times. For small networks (N = 8) local compositions converge to static fixed points. As metacommunity extent increases, however, persistent dynamics emerge. Initially this autonomous turnover is oscillatory in nature with communities fluctuating between small numbers of states which can be grouped into clusters (16 ≤ N ≤ 32). Intermediate metacommunities (32 ≤ N ≤ 64) manifest “Clementsian” temporal turnover, characterised by sharp transitions in composition, implying species turn over in cohorts. Large metacommunities (N ≥ 128) turn over continuously, implying “Gleasonian” assembly dynamics in which species’ temporal occupancies are independent. c The mean number of local compositional clusters detected for metacommunities of various numbers of patches N (error bars represent standard deviation across all replicated simulations). While the transition from static to dynamic community composition at the local scale is sharp (see text), non-uniform turnover within metacommunities (a) blurs the transition at the regional scale. Aij = 0.5 with probability 0.5, ϕ = 10, σ2 = 0.01, ℓ = 0.5.Full size imageAutonomous turnover in model metacommunitiesFor small (N ≤ 8) metacommunities assembled to regional diversity limits, populations attain equilibria, i.e., converge to fixed points, implying the absence of autonomous turnover23. With increasing metacommunity size N, however, we observe the emergence of persistent population dynamics (Supplementary Fig. 5 and external video) that can produce substantial turnover in local community composition. This autonomous turnover can be represented through Bray-Curtis42 (BC) dissimilarity matrices comparing local community composition through time (Fig. 2b), and quantified by the number of compositional clusters detected in such matrices using hierarchical cluster analysis (Fig. 2a, c).At intermediate spatial scales (Fig. 2, 16 ≤ N ≤ 32) we often find oscillatory dynamics, which can be perfectly periodic or slightly irregular. With increasing oscillation amplitude, these lead to persistent turnover dynamics where local communities repeatedly transition between a small number of distinct compositional clusters (represented in Fig. 2 by stripes of high pairwise BC dissimilarity spanning large temporal ranges). At even larger scales (N ≥ 64) this compositional coherence begins to break down, and for very large metacommunities (N ≥ 128) autonomous dynamics drive continuous acyclic change in community composition. The number of compositional clusters detected over time typically varies within a given metacommunity (Fig. 2a node colour), however we find a clear increase in the average number of compositional clusters, i.e., an increase in turnover, with increasing total metacommunity size (Fig. 2c).Metacommunities in which the boundaries of species ranges along environmental gradients are clumped are termed Clementsian, while those for which range limits are independently distributed are  referred to as Gleasonian43. We consider the block structure of the temporal dissimilarity matrix at intermediate N to represent a form of Clementsian temporal turnover, characterised by sudden significant shifts in community composition. Metacommunity models similar to ours have been found to generate such patterns along spatial gradients44, potentially via an analogous mechanism45. Large, diverse model metacommunities manifest Gleasonian temporal turnover. In such cases, species colonisations and extirpations are largely independent and temporal occupancies predominantly uncorrelated, such that compositional change is continuous, rarely, if ever, reverting to the same state.Mechanistic explanation of autonomous turnoverSurprisingly, the onset and increasing complexity of autonomous turnover as system size N increases (Fig. 2) can be understood as a consequence of local community dynamics alone. To explain this, we first recall relevant theoretical results for isolated LV communities. Then we demonstrate that, in presence of weak propagule pressure, these results imply local community turnover dynamics, controlled by the richness of potential invaders, that closely mirror the dependence on system size seen in full LV metacommunities.Application of methods from statistical mechanics to models of large isolated LV communities with random interactions revealed that such models exhibit qualitatively distinct phases46,47,48. If the number of modelled species, S, interpreted as species pool size, lies below some threshold value determined by the distribution of interaction strengths (Supplementary Fig. 7), these models exhibit a unique linearly stable equilibrium (Unique Fixed Point phase, UFP). Some species may go extinct, but the majority persists48. When pool size S exceeds this threshold, there appear to be no more linearly stable equilibrium configurations. Any community formed by a selection from the S species is either unfeasible (there is no equilibrium with all species present), intrinsically linearly unstable, or invadable by at least one of the excluded species. This has been called the multiple attractor (MA) phase47. However, the implied notion that this part of the phase space is in fact characterised by multiple stable equilibria may be incorrect.Population dynamical models with many species have been shown to easily exhibit attractors called stable heteroclinic networks49, which are characterised by dynamics in which the system bounces around between several unstable equilibria, each corresponding to a different composition of the extant community, implying indefinite, autonomous community turnover (Fig. 3, red line). As these attractors are approached, models exhibit increasingly long intermittent phases of slow dynamics, which, when numerically simulated, can give the impression that the system eventually reaches one of several ‘stable’ equilibria, suggesting that turnover comes to a halt. We demonstrate in the Supplementary discussion that the MA phase of isolated LV models is in fact characterised by such stable heteroclinic networks (Supplementary Figs. 8 and 9). Note, we retain the MA terminology here because the underlying complete heteroclinic networks, interpreted as a directed graph50,51 (Fig. 3, inset), might have multiple components that are mutually unreachable through dynamic transitions52, each representing a different attractor.Fig. 3: Approximate heteroclinic networks underlie autonomous community turnover.The main panel shows two trajectories in the state space of a community of three hypothetical species (population biomasses B1, B2, B3) that are in non-hierarchical competition with each other, such that no species can competitively exclude both others (a “rock-paper-scissors game”17). Without propagule pressure, the system has three unstable equilibrium points (P1, P2, P3) and cycles between these (red curve), coming increasingly close to the equilibria and spending ever more time in the vicinity of each. The corresponding attractor is called a heteroclinic cycle (dashed arrows). Under weak extrinsic propagule pressure (blue curve), the three equilibria and the heteroclinic cycle disappear, yet the system closely tracks the original cycle in state space. Such a cycle can be represented as a graph linking the dynamically connected equilibria (inset). With more interacting species, these graphs can become complex “heteroclinic networks”49,50,51 with trajectories representing complex sequences of species composition during autonomous community turnover.Full size imageIf one now adds to such isolated LV models terms representing weak propagule pressure for all S species (Supplementary Eq. (2)), dynamically equivalent to mass effects occurring in the full metacommunity model (Eq. (1)), then none of the S species can entirely go extinct. The weak influx of biomass drives community states away from the unstable equilibria representing coexistence of subsets of the S species and the heteroclinic network connecting them (blue line in Fig. 3). Typically, system dynamics then still follow trajectories closely tracking the original heteroclinic networks (Fig. 3), but now without requiring boundless time to transition from the vicinity of one equilibrium to the next.The nature and complexity of the resulting population dynamics depend on the size and complexity of the underlying heteroclinic network, and both increase with pool size S. In simulations (Supplementary Fig. 10) we find that, as S increases, LV models with weak propagule pressure pass through the same sequence of states as we documented for LVMCM metacommunities in Fig. 2: equilibria, oscillatory population dynamics, Clementsian and finally Gleasonian temporal turnover.Above we introduced the number of clusters detected in Bray-Curtis dissimilarity matrices of fixed time series length as a means of quantifying the approximate number of equilibria visited during local community turnover. As shown in Fig. 4a, b, this number increases in LV models with S in a manner strikingly similar to its increase in the LVMCM with the number of species present in the ecological neighbourhood of a given patch. Thus, dynamics within a patch are controlled not by N directly but rather by neighbourhood species richness. For a given neighbourhood, species richness depends on the number of connected patches, the total area and therefore total abiotic heterogeneity encompassed, and the connectivity, all of which can vary substantially within a metacommunity of a given size N. As illustrated in Fig. 4b, there is a tendency for neighbourhood richness to be larger in larger metacommunities, leading indirectly to the dependence of metacommunity dynamics on N seen in Fig. 2.Fig. 4: Ecological mass effects drive autonomous turnover.a The number of compositional clusters detected, plotted against the size of the pool of potential invaders S for an isolated LV community using a propagule pressure ϵ of 10−10 and 10−15, fit by a generalised additive model87. For S 1 compositional cluster) occurs at a pools size of around S = 35 species, consistent with the theoretical prediction47 of the transition between the UFP and MA phases (Supplementary Discussion). Close inspection of this threshold reveals an important and hitherto unreported relationship between the transition into the MA phase and local ecological limits set by the onset of ecological structural instability, which is known to regulate species richness in LV systems subject to external invasion pressure23,24: in the Supplementary Discussion we show that the boundary between the UFP and MA phases47 coincides precisely with the onset of structural instability24 (Supplementary Eqs. (3)–(9)).For LVMCM metacommunities, this relationship (demonstrated analytically in the Supplementary Discussion) is numerically confirmed in Fig. 5. During assembly, local species richness increases until it reaches the limit imposed by local structural instability. Further assembly occurs via the “regionalisation” of the biota53—a collapse in average range sizes23 and associated increase in spatial beta diversity—until regional diversity limits are reached23. The emergence of autonomous turnover coincides with the onset of species saturation at the local scale. Autonomous turnover can therefore serve as an indirect indication of intrinsic biodiversity regulation via local structural instability in complex communities.Fig. 5: The emergence of temporal turnover during metacommunity assembly.a Local species richness, defined by reference to source populations only (({overline{alpha }}_{text{src}}), grey) and regional diversity (γ black) for a single metacommunity of N = 32 coupled communities during iterative regional invasion of random species. We quantify local source diversity ({overline{alpha }}_{text{src}}) as the metacommunity average of the number αsrc of non-zero equilibrium populations persisting when immigration is switched off (off-diagonal elements of D set to zero), since this is the component of a local community subject to strict ecological limits to biodiversity. Note the log scale chosen for easy comparison of local and regional species richness. b Increases in regional diversity beyond local limits arise via corresponding increases in spatial turnover (({overline{beta }}_{text{s}}), black). Autonomous temporal turnover (({overline{beta }}_{text{t}}), grey) sets in (crosses a threshold mean Bray Curtis (BC) dissimilarity of 10−2) precisely when average local species richness ({overline{alpha }}_{text{src}}) has reached its limit, reflecting the equivalence of the transition to the MA phase space and the onset of local structural instability. In both panels, the dashed line marks the point at which autonomous temporal turnover was first detected. Aij = 0.3 with probability 0.3, ϕ = 10, σ2 = 0.01, ℓ = 0.5. Both spatial and temporal turnover computed as the mean BC dissimilarity. In each iteration of the assembly model (regional invasion event), 0.1S + 1 species were introduced. Dynamics were simulated for 2 × 104 unit times, with the second 104 unit times analysed for autonomous turnover, and a total of 104 invasions were modelled.Full size imageThus, we have shown that propagule pressure perturbs local communities away from unstable equilibria and drives compositional change. In order to invade, however, species need to be capable of passing through biotic and abiotic filters33,34,35. We would expect, therefore, that turnover would be suppressed in highly heterogeneous or poorly connected environments where mass effects are weak. Indeed, by manipulating the autocorrelation length ϕ and variance σ2 of the abiotic filter represented by the matrix R and the characteristic dispersal length ℓ, we observe a sharp drop-off in temporal turnover in parameter regimes that maximise between-patch community dissimilarity (short environmental correlation or dispersal lengths, Supplementary Fig. 11). Thus, we conclude that it is not species richness or spatial dissimilarity per se that best predict temporal turnover, but the size of the pool of species with positive invasion fitness, i.e., those not repelled by the combined effects of biotic and abiotic filters.The macroecology of autonomous turnoverWe find good correspondence between temporal and spatio-temporal biodiversity patterns emerging in model metacommunities in the absence of external abiotic change and in empirical data (Fig. 6), with quantitative characteristics lying within the ranges observed in natural ecosystems.Fig. 6: Macroecological signatures of autonomous compositional change.A bimodal distribution in temporal occupancy observed in North American birds54 (a) and in simulations (e N = 64, ϕ = 5, σ2 = 0.01, ℓ = 0.5). Intrisically regulated species richness observed in estuarine fish species59 (b) and in simulations (f N = 64, ϕ = 5, σ2 = 0.01, ℓ = 0.5, 1000 unit times t). The decreasing slopes of the STR with increasing sample area12 (c), and the SAR with increasing sample duration12 (d) for various communities and in simulations (g and h N = 256, ϕ = 10, σ2 = 0.01, ℓ = 0.5, spatial window ΔA, temporal windo ΔT). In (c) and (d) we have rescaled the sample area/duration by the smallest/shortest reported value and coloured by community (see original study for details). In (g) and (h) we study the STAR in metacommunities of various size N, represented by colour. Limited spatio-temporal turnover in the smallest metacommunties (blue colours) greatly reduces the exponents of the STAR relative to large metacommunities (red colours). Aij = 0.5 with probability 0.5 in all cases.Full size imageTemporal occupancyThe proportion of time in which species occupy a community tends to have a bi-modal empirical distribution54,55,56 (Fig. 6a). The distribution we found in simulations (Fig. 6e) closely matches the empirical pattern.Community structureTemporal turnover has been posited to play a stabilising role in the maintenance of community structure57,58. In an estuarine fish community59, for example, species richness (Fig. 6b) and the distribution of abundances were remarkably robust despite changes in population biomasses by multiple orders of magnitude. In model metacommunities with autonomous turnover we found, likewise, that local species richness exhibited only small fluctuations around the steady-state mean (Fig. 6f, three random local communities shown) and that the macroscopic structure of the community was largely time invariant (Supplementary Fig. 12). In the light of our results, we propose the absence of temporal change in community properties such as richness or the abundance distribution despite potentially large fluctuations in population abundances59 as indicative of autonomous compositional turnover.The species-time-area-relation, STARThe species-time-relation (STR), typically fit by a power law of the form S ∝ Tw 12,60,61, describes how observed species richness increases with observation time T. The exponent w of the STR has been found to be consistent across taxonomic groups and ecosystems12,13,62, indicative of some general population dynamical mechanism. However, the exponent of the STR decreases with increasing sampling area12, and the exponent of the empirical Species Area Relation (SAR) (S ∝ Az) consistently decreases with increasing sampling duration12 (Fig. 6c, d). We tested for these patterns in a large simulated metacommunity with N = 256 patches by computing the species-time-area-relation (STAR) for nested subdomains and variable temporal sampling windows (see “Methods”). We observed exponents of the nested SAR in the range z = 0.02–0.44 and for the STR a range w = 0.01–0.44 (Supplementary Fig. 13). We also found a clear decrease in the rate of species accumulation in time as a function of sample area and vice-versa (Fig. 6g, h), consistent with the empirical observations. Meta-analyses of these patterns in nature have reported exponents which are remarkably consistent, with z typically in the range 0.1–0.363, and w typically in the range 0.2–0.413, in both cases largely independent of location or taxonomic group13.Thus, the distribution of temporal occupancy, the time invariance of key macroecological structures and the STAR in our model metacommunities match observed patterns. This evidence suggests that such autonomous dynamics cannot be ruled out as an important driver of temporal compositional change in natural ecosystems.Turnover rate in simulated metacommunitiesHow do the turnover rates that we find in our model compare with those observed? Our current analytic understanding of autonomous turnover is insufficient for estimating the rates directly from parameters, but the simulation results provide some indication of the expected order of magnitude, that can be compared with observations. Key for such a comparison is the fact that, because the elements of R are 1 on average, the time required for an isolated single population to reach carrying capacity is ({mathcal{O}}(1)) unit times. Supplementary Fig. 12b suggests that transitions between community states occur at the scale of around 10–50 unit times. This gives a holistic, rule-of-thumb estimate for the expected rate of autonomous turnover, depending on the typical reproductive rates of the guild of interest. In the case of macroinvertebrates, for example, the time required for populations to saturate in population biomass could be of the order of a month or less. By our rule of thumb, this would mean that autonomous community turnover would occur on a timescale of years. In contrast, for slow growing species like trees, where monoculture stands can take decades to reach maximum population biomass, the predicted timescale for autonomous turnover would be on the order of centuries or more. Indeed, macroinvertebrate communities have been observed switching between community configurations with a period of a few years64,65, while the proportional abundance of tree pollen and tree fern spores fluctuates in rain forest bog deposits with a period of the order of 103 years66—suggesting that the predicted autonomous turnover rates are biologically plausible.ConclusionsCurrent understanding of the mechanisms driving temporal turnover in ecological communities is predominantly built upon phenomenological studies of observed patterns2,67,68,69 and is unquestionably incomplete10,59. That temporal turnover can be driven by external forces—e.g., seasonal or long term climate change, direct anthropogenic pressures—is indisputable. A vitally important question is, however, how much empirically observed compositional change is actually due to such forcing. Recent landmark analyses of temporal patterns in biodiversity have detected no systematic change in species richness or structure in natural communities, despite rates of compositional turnover greater than predicted by stochastic null models1,70,71,72. Here we have shown that empirically realistic turnover in model metacommunities can occur via precisely the same mechanism as that responsible for regulating species richness at the local scale. While the processes regulating diversity in natural communities remain insufficiently understood, our theoretical work suggests local structural instability may explain these empirical observations in a unified and parsimonious way. Therefore, we advocate for the application of null models of metacommunity dynamics that account for natural turnover in ecological status assessments and predictions based on ancestral baselines. Future work will involve fitting the model described here to observations by estimating abiotic and biotic parameters from empirical datasets. In the Supplementary Discussion we show how different combinations of parameters lead to different quantitative outcomes (Supplementary Fig. 4), likely representing different types of empirical metacommunities. Understanding where in this parameter space natural systems exist may provide the foundation for a quantitative null model, a baseline expectation of turnover against which observations can be compared.Our simulations revealed a qualitative transition from “small” metacommunities, where autonomous turnover is absent or minimal, to “large” metacommunities with pronounced autonomous turnover (Fig. 2). The precise location of the transition between these cases depends on details such as dispersal traits, the ecological interaction network, and environmental gradients (Supplementary Fig. 4). Taking, for simplicity, regional species richness as a measure of metacommunity size suggests that both ‘small’ and ‘large’ communities in this sense are realised in nature. In our simulations, the smallest metacommunities sustain 10s of species, while the largest have a regional diversity of the order 103, which is not large comparable to the number of tree species in just 0.25 km2 of tropical rainforest (1100–1200 in Borneo and Ecuador73) or of macroinvertebrates in the UK ( >32,00074). Within the ‘small’ category, where autonomous turnover is absent, we would therefore expect to be, e.g., communities of marine mammals or large fish, where just a few species interact over ranges that can extend across entire climatic niches, implying that the effective number of independent “patches” is small and providing few opportunities for colonisation by species from neighbouring communities. Likely to belong to the ‘large’ category are communities of organisms that occur in high diversity with range sizes that are small compared to climatic niches, such as macroinvertebrates. For these, autonomous turnover of local communities can plausibly be expected based on our findings. Empirically distinguishing between these two cases for different guilds will be an important task for the future.For metacommunities of intermediate spatial extent, autonomous turnover is characterised by sharp transitions between cohesive states at the local scale. To date, few empirical analyses have reported such coherence in temporal turnover, perhaps because the taxonomic and temporal resolution required to detect such patterns is not yet widely available. Developments in biomonitoring technologies75 are likely to reveal a variety of previously undetected ecological dynamics, however and by combining high resolution temporal sampling and metagenetic analysis of community composition, a recent study demonstrated cohesive but short-lived community cohorts in coastal plankton76. Such Clementsian temporal turnover may offer a useful signal of autonomous compositional change in real systems.Thus, overcoming previous computational limits to the study of complex metacommunities11,77, we have discovered the existence of two distinct phases of metacommunity ecology—one characterised by weak or absent autonomous turnover, the other by continuous compositional change even in the absence of external drivers. By synthesising a wide range of established ecological theory11,23,24,47,48,49, we have heuristically explained these phases. Our explanation implies that autonomous turnover requires little more than a diverse neighbourhood of potential invaders, a weak immigration pressure, and a complex network of interactions between co-existing species. More