Ecology

1.Gardner, M. R. & Ashby, W. R. Connectance of large dynamic (cybernetic) systems: Critical values for stability. Nature 228, 784 (1970).ADS 
CAS 
PubMed 
Article 

Google Scholar 
2.May, R. M. Will a large complex system be stable?. Nature 238, 413–414 (1972).ADS 
CAS 
PubMed 
Article 

Google Scholar 
3.McCann, K. S. The diversity-stability debate. Nature 405, 228–233 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
4.Dunne, J. A. The network structure of food webs. in Ecological Networks: Linking Structure to Dynamics in Food Webs 27–86 (2006).5.Williams, R. J., Brose, U. & Martinez, N. D. Homage to Yodzis and Innes 1992: Scaling up feeding-based population dynamics to complex ecological networks. in From Energetics to Ecosystems: The Dynamics and Structure of Ecological Systems. 37–51 (Springer, 2007).6.Gross, T., Rudolf, L., Levin, S. A. & Dieckmann, U. Generalized models reveal stabilizing factors in food webs. Science 325, 747–750 (2009).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
7.Fahimipour, A. K., Anderson, K. E. & Williams, R. J. Compensation masks trophic cascades in complex food webs. Theor. Ecol. 10, 245–253 (2017).Article 

Google Scholar 
8.Rooney, N. & McCann, K. S. Integrating food web diversity, structure and stability. Trends Ecol. Evolut. 27, 40–46 (2012).Article 

Google Scholar 
9.Jacquet, C. et al. No complexity-stability relationship in empirical ecosystems. Nat. Commun. 7, 12573 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
10.Brose, U., Williams, R. J. & Martinez, N. D. Allometric scaling enhances stability in complex food webs. Ecol. Lett. 9, 1228–1236 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
11.Martinez, N. D. Allometric trophic networks from individuals to socio-ecosystems: Consumer-resource theory of the ecological elephant in the room. Front. Ecol. Evolut. 8, 92 (2020).Article 

Google Scholar 
12.Segel, L. A. & Levin, S. A. Application of nonlinear stability theory to the study of the effects of diffusion on predator-prey interactions. in AIP Conference Proceedings, Vol. 27, 123–152 (American Institute of Physics, 1976).13.Durrett, R. & Levin, S. The importance of being discrete (and spatial). Theor. Popul. Biol. 46, 363–394 (1994).MATH 
Article 

Google Scholar 
14.McCann, K. S., Rasmussen, J. & Umbanhowar, J. The dynamics of spatially coupled food webs. Ecol. Lett. 8, 513–523 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
15.Fahimipour, A. K. & Hein, A. M. The dynamics of assembling food webs. Ecol. Lett. 17, 606–613 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
16.Brechtel, A., Gramlich, P., Ritterskamp, D., Drossel, B. & Gross, T. Master stability functions reveal diffusion-driven pattern formation in networks. Phys. Rev. E 97, 032307 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
17.Brechtel, A., Gross, T. & Drossel, B. Far-ranging generalist top predators enhance the stability of meta-foodwebs. Sci. Rep. 9, 1–15 (2019).CAS 
Article 

Google Scholar 
18.Gross, T. & et. al. Modern models of trophic meta-communities. Phil. Trans. R. Soc. B (in press).19.Rooney, N., McCann, K., Gellner, G. & Moore, J. C. Structural asymmetry and the stability of diverse food webs. Nature 442, 265–269 (2006).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
20.Otto, S. B., Rall, B. C. & Brose, U. Allometric degree distributions facilitate food-web stability. Nature 450, 1226–1229 (2007).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
21.Williams, R. J. & Martinez, N. D. Simple rules yield complex food webs. Nature 404, 180–183 (2000).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
22.Cohen, J. E., Pimm, S. L., Yodzis, P. & Saldaña, J. Body sizes of animal predators and animal prey in food webs. J. Anim. Ecol. 67–78 (1993).23.Petchey, O. L., Beckerman, A. P., Riede, J. O. & Warren, P. H. Size, foraging, and food web structure. Proc. Natl. Acad. Sci. 105, 4191–4196 (2008).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
24.Cohen, J. E., Briand, F. & Newman, C. M. Community Food Webs: Data and Theory Vol. 20 (Springer, 2012).MATH 

Google Scholar 
25.Elton, C. S. Animal Ecology (University of Chicago Press, 2001).
Google Scholar 
26.Lindeman, R. L. The trophic-dynamic aspect of ecology. Ecology 23, 399–417 (1942).Article 

Google Scholar 
27.Peters, R. H. & Peters, R. H. The Ecological Implications of Body Size Vol. 2 (Cambridge University Press, 1986).
Google Scholar 
28.Riede, J. O. et al. Stepping in Elton’s footprints: A general scaling model for body masses and trophic levels across ecosystems. Ecol. Lett. 14, 169–178 (2011).29.Kalinkat, G. et al. Body masses, functional responses and predator-prey stability. Ecology letters 16, 1126–1134 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
30.Costa-Pereira, R., Araújo, M. S., Olivier, R. d. S., Souza, F. L. & Rudolf, V. H. Prey limitation drives variation in allometric scaling of predator-prey interactions. Am. Nat. 192, E139–E149 (2018).31.Guzman, L. M. & Srivastava, D. S. Prey body mass and richness underlie the persistence of a top predator. Proc. R. Soc. B 286, 20190622 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
32.Brose, U. et al. Consumer-resource body-size relationships in natural food webs. Ecology 87, 2411–2417 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
33.Barnes, C., Maxwell, D., Reuman, D. C. & Jennings, S. Global patterns in predator-prey size relationships reveal size dependency of trophic transfer efficiency. Ecology 91, 222–232 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
34.Potapov, A. M., Brose, U., Scheu, S. & Tiunov, A. V. Trophic position of consumers and size structure of food webs across aquatic and terrestrial ecosystems. Am. Nat. 194, 823–839 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
35.MacArthur, R. H. & Wilson, E. O. The Theory of Island Biogeography Vol. 1 (Princeton University Press, 2001).Book 

Google Scholar 
36.Simberloff, D. S. & Wilson, E. O. Experimental zoogeography of islands: the colonization of empty islands. Ecology 50, 278–296 (1969).Article 

Google Scholar 
37.Brown, J. H. & Kodric-Brown, A. Turnover rates in insular biogeography: effect of immigration on extinction. Ecology 58, 445–449 (1977).Article 

Google Scholar 
38.Levins, R. Some demographic and genetic consequences of environmental heterogeneity for biological control. Am. Entomol. 15, 237–240 (1969).
Google Scholar 
39.Gotelli, N. J. Metapopulation models: The rescue effect, the propagule rain, and the core-satellite hypothesis. Am. Nat. 138, 768–776 (1991).Article 

Google Scholar 
40.Crowley, P. H. Dispersal and the stability of predator-prey interactions. Am. Nat. 118, 673–701 (1981).MathSciNet 
Article 

Google Scholar 
41.Reeve, J. D. Environmental variability, migration, and persistence in host-parasitoid systems. Am. Nat. 132, 810–836 (1988).Article 

Google Scholar 
42.Murdoch, W. W. Population regulation in theory and practice. Ecology 75, 271–287 (1994).Article 

Google Scholar 
43.Briggs, C. J. & Hoopes, M. F. Stabilizing effects in spatial parasitoid-host and predator-prey models: A review. Theor. Popul. Biol. 65, 299–315 (2004).PubMed 
MATH 
Article 
PubMed Central 

Google Scholar 
44.Gravel, D., Massol, F. & Leibold, M. A. Stability and complexity in model meta-ecosystems. Nat. Commun. 7, 1–8 (2016).Article 
CAS 

Google Scholar 
45.Mougi, A. & Kondoh, M. Food-web complexity, meta-community complexity and community stability. Sci. Rep. 6, 24478 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
46.Domenici, P. The scaling of locomotor performance in predator-prey encounters: from fish to killer whales. Comp. Biochem. Physiol. Part A Mol. Integr. Physiol. 131, 169–182 (2001).CAS 
Article 

Google Scholar 
47.Hirt, M. R., Lauermann, T., Brose, U., Noldus, L. P. & Dell, A. I. The little things that run: a general scaling of invertebrate exploratory speed with body mass. Ecology 98, 2751–2757 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
48.Hirt, M. R., Jetz, W., Rall, B. C. & Brose, U. A general scaling law reveals why the largest animals are not the fastest. Nat. Ecol. Evolut. 1, 1116–1122 (2017).Article 

Google Scholar 
49.Cloyed, C. S., Grady, J. M., Savage, V. M., Uyeda, J. C. & Dell, A. I. The allometry of locomotion. Ecology e03369 (2021).50.Reiss, M. Scaling of home range size: Body size, metabolic needs and ecology. Trends Ecol. Evolut. 3, 85–86 (1988).CAS 
Article 

Google Scholar 
51.Minns, C. K. Allometry of home range size in lake and river fishes. Can. J. Fish. Aquat. Sci. 52, 1499–1508 (1995).Article 

Google Scholar 
52.Jetz, W., Carbone, C., Fulford, J. & Brown, J. H. The scaling of animal space use. Science 306, 266–268 (2004).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
53.Hendriks, A. J., Willers, B. J., Lenders, H. R. & Leuven, R. S. Towards a coherent allometric framework for individual home ranges, key population patches and geographic ranges. Ecography 32, 929–942 (2009).Article 

Google Scholar 
54.Hein, A. M., Hou, C. & Gillooly, J. F. Energetic and biomechanical constraints on animal migration distance. Ecol. Lett. 15, 104–110 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
55.Hartfelder, J. et al. The allometry of movement predicts the connectivity of communities. Proc. Natl. Acad. Sci. 117, 22274–22280 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
56.Vander Zanden, M. J. & Vadeboncoeur, Y. Fishes as integrators of benthic and pelagic food webs in lakes. Ecology 83, 2152–2161 (2002).Article 

Google Scholar 
57.Wolkovich, E. M. et al. Linking the green and brown worlds: The prevalence and effect of multichannel feeding in food webs. Ecology 95, 3376–3386 (2014).Article 

Google Scholar 
58.Lomolino, M. V. Immigrant selection, predation, and the distributions of Microtus pennsylvanicus and Blarina brevicauda on islands. Am. Nat. 123, 468–483 (1984).Article 

Google Scholar 
59.Beisner, B. E., Peres-Neto, P. R., Lindström, E. S., Barnett, A. & Longhi, M. L. The role of environmental and spatial processes in structuring lake communities from bacteria to fish. Ecology 87, 2985–2991 (2006).PubMed 
PubMed Central 
Article 

Google Scholar 
60.De Bie, T. et al. Body size and dispersal mode as key traits determining metacommunity structure of aquatic organisms. Ecol. Lett. 15, 740–747 (2012).PubMed 
Article 

Google Scholar 
61.Kareiva, P. Population dynamics in spatially complex environments: Theory and data. Philos. Trans. R. Soc. Lond. Ser. B: Biol. Sci. 330, 175–190 (1990).ADS 
Article 

Google Scholar 
62.Murray, J. Mathematical Biology II: Spatial Models and Biomedical Applications Vol. 3 (Springer, 2001).
Google Scholar 
63.Rietkerk, M. & Van de Koppel, J. Regular pattern formation in real ecosystems. Trends Ecol. Evolut. 23, 169–175 (2008).Article 

Google Scholar 
64.Pedersen, E. J., Marleau, J. N., Granados, M., Moeller, H. V. & Guichard, F. Nonhierarchical dispersal promotes stability and resilience in a tritrophic metacommunity. Am. Nat. 187, E116–E128 (2016).PubMed 
Article 

Google Scholar 
65.Haegeman, B. & Loreau, M. General relationships between consumer dispersal, resource dispersal and metacommunity diversity. Ecol. Lett. 17, 175–184 (2014).PubMed 
Article 

Google Scholar 
66.Amarasekare, P. Spatial dynamics of foodwebs. Annu. Rev. Ecol. Evol. Syst. 39, 479–500 (2008).Article 

Google Scholar 
67.Fronhofer, E. A., Klecka, J., Melián, C. J. & Altermatt, F. Condition-dependent movement and dispersal in experimental metacommunities. Ecol. Lett. 18, 954–963 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
68.Toscano, B. J., Gownaris, N. J., Heerhartz, S. M. & Monaco, C. J. Personality, foraging behavior and specialization: integrating behavioral and food web ecology at the individual level. Oecologia 182, 55–69 (2016).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
69.Fronhofer, E. A. et al. Bottom-up and top-down control of dispersal across major organismal groups. Nat. Ecol. Evolut. 2, 1859–1863 (2018).Article 

Google Scholar 
70.Gross, T. & Feudel, U. Generalized models as a universal approach to the analysis of nonlinear dynamical systems. Phys. Rev. E 73, 016205 (2006).ADS 
Article 
CAS 

Google Scholar 
71.Yeakel, J. D., Stiefs, D., Novak, M. & Gross, T. Generalized modeling of ecological population dynamics. Theor. Ecol. 4, 179–194 (2011).Article 

Google Scholar 
72.Hirt, M. R. et al. Bridging scales: Allometric random walks link movement and biodiversity research. Trends Ecol. Evolut. 33, 701–712 (2018).Article 

Google Scholar 
73.Othmer, H. G. & Scriven, L. Non-linear aspects of dynamic pattern in cellular networks. J. Theor. Biol. 43, 83–112 (1974).ADS 
CAS 
PubMed 
Article 

Google Scholar 
74.Estes, J. A. et al. Trophic downgrading of planet earth. Science 333, 301–306 (2011).ADS 
CAS 
PubMed 
Article 

Google Scholar 
75.Krause, A. E., Frank, K. A., Mason, D. M., Ulanowicz, R. E. & Taylor, W. W. Compartments revealed in food-web structure. Nature 426, 282–285 (2003).ADS 
CAS 
PubMed 
Article 

Google Scholar 
76.Post, D. M., Conners, M. E. & Goldberg, D. S. Prey preference by a top predator and the stability of linked food chains. Ecology 81, 8–14 (2000).Article 

Google Scholar 
77.Neutel, A.-M., Heesterbeek, J. A. & de Ruiter, P. C. Stability in real food webs: Weak links in long loops. Science 296, 1120–1123 (2002).ADS 
CAS 
PubMed 
Article 

Google Scholar 
78.Leibold, M. A. et al. The metacommunity concept: A framework for multi-scale community ecology. Ecol. Lett. 7, 601–613 (2004).Article 

Google Scholar 
79.Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M. & West, G. B. Toward a metabolic theory of ecology. Ecology 85, 1771–1789 (2004).Article 

Google Scholar 
80.Jenkins, D. G. et al. Does size matter for dispersal distance?. Glob. Ecol. Biogeogr. 16, 415–425 (2007).Article 

Google Scholar 
81.Stevens, V. M. et al. A comparative analysis of dispersal syndromes in terrestrial and semi-terrestrial animals. Ecol. Lett. 17, 1039–1052 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
82.Guzman, L. M. & Srivastava, D. S. Genomic variation among populations provides insight into the causes of metacommunity survival. Ecology 101, e03182 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
83.Leitch, K. J., Ponce, F. V., Dickson, W. B., van Breugel, F. & Dickinson, M. H. The long-distance flight behavior of drosophila supports an agent-based model for wind-assisted dispersal in insects. Proc. Natl. Acad. Sci. 118 (2021).84.Bowman, J., Jaeger, J. A. & Fahrig, L. Dispersal distance of mammals is proportional to home range size. Ecology 83, 2049–2055 (2002).Article 

Google Scholar 
85.Shanks, A. L., Grantham, B. A. & Carr, M. H. Propagule dispersal distance and the size and spacing of marine reserves. Ecol. Appl. 13, 159–169 (2003).Article 

Google Scholar 
86.Kartascheff, B., Heckmann, L., Drossel, B. & Guill, C. Why allometric scaling enhances stability in food web models. Theor. Ecol. 3, 195–208 (2010).Article 

Google Scholar 
87.Hudson, L. N. & Reuman, D. C. A cure for the plague of parameters: Constraining models of complex population dynamics with allometries. Proc. R. Soc. B: Biol. Sci. 280, 20131901 (2013).Article 

Google Scholar 
88.Brose, U. et al. Predator traits determine food-web architecture across ecosystems. Nat. Ecol. Evolut. 3, 919–927 (2019).Article 

Google Scholar 
89.Heino, J. et al. Metacommunity organisation, spatial extent and dispersal in aquatic systems: patterns, processes and prospects. Freshw. Biol. 60, 845–869 (2015).Article 

Google Scholar 
90.Siegel, D. et al. The stochastic nature of larval connectivity among nearshore marine populations. Proc. Natl. Acad. Sci. 105, 8974–8979 (2008).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
91.Pillai, P., Loreau, M. & Gonzalez, A. A patch-dynamic framework for food web metacommunities. Theor. Ecol. 3, 223–237 (2010).Article 

Google Scholar 
92.Pillai, P., Gonzalez, A. & Loreau, M. Metacommunity theory explains the emergence of food web complexity. Proc. Natl. Acad. Sci. 108, 19293–19298 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
93.Plitzko, S. J. & Drossel, B. The effect of dispersal between patches on the stability of large trophic food webs. Theor. Ecol. 8, 233–244 (2015).Article 

Google Scholar 
94.Guichard, F. Recent advances in metacommunities and meta-ecosystem theories. F1000Research 6 (2017).95.Hata, S., Nakao, H. & Mikhailov, A. S. Dispersal-induced destabilization of metapopulations and oscillatory turing patterns in ecological networks. Sci. Rep. 4, 3585 (2014).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
96.White, K. & Gilligan, C. Spatial heterogeneity in three species, plant-parasite-hyperparasite, systems. Philos. Trans. R. Soc. Lond. Ser. B: Biol. Sci. 353, 543–557 (1998).Article 

Google Scholar 
97.Gibert, J. P. & Yeakel, J. D. Laplacian matrices and turing bifurcations: Revisiting levin 1974 and the consequences of spatial structure and movement for ecological dynamics. Theor. Ecol. 12, 265–281 (2019).Article 

Google Scholar 
98.Fox, J. W., Vasseur, D., Cotroneo, M., Guan, L. & Simon, F. Population extinctions can increase metapopulation persistence. Nat. Ecol. Evolut. 1, 1271–1278 (2017).Article 

Google Scholar 
99.Hastings, A. Food web theory and stability. Ecology 69, 1665–1668 (1988).Article 

Google Scholar 
100.Anderson, H., Hutson, V. & Law, R. On the conditions for permanence of species in ecological communities. Am. Nat. 139, 663–668 (1992).Article 

Google Scholar 
101.Haydon, D. Pivotal assumptions determining the relationship between stability and complexity: An analytical synthesis of the stability-complexity debate. Am. Nat. 144, 14–29 (1994).Article 

Google Scholar 
102.Chen, X. & Cohen, J. E. Global stability, local stability and permanence in model food webs. J. Theor. Biol. 212, 223–235 (2001).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
103.Bjørnstad, O. N., Ims, R. A. & Lambin, X. Spatial population dynamics: Analyzing patterns and processes of population synchrony. Trends Ecol. Evolut. 14, 427–432 (1999).Article 

Google Scholar 
104.Ims, R. A. & Andreassen, H. P. Spatial synchronization of vole population dynamics by predatory birds. Nature 408, 194–196 (2000).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
105.Sundell, J. et al. Large-scale spatial dynamics of vole populations in Finland revealed by the breeding success of vole-eating avian predators. J. Anim. Ecol. 73, 167–178 (2004).Article 

Google Scholar 
106.Ripple, W. J. et al. Status and ecological effects of the world’s largest carnivores. Science 343 (2014).107.McCauley, D. J. et al. Marine defaunation: Animal loss in the global ocean. Science 347 (2015).108.Parsons, T. The removal of marine predators by fisheries and the impact of trophic structure. Mar. Pollut. Bull. 25, 51–53 (1992).Article 

Google Scholar 
109.Baum, J. K. & Worm, B. Cascading top-down effects of changing oceanic predator abundances. J. Anim. Ecol. 78, 699–714 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
110.Albert, C. H., Rayfield, B., Dumitru, M. & Gonzalez, A. Applying network theory to prioritize multispecies habitat networks that are robust to climate and land-use change. Conserv. Biol. 31, 1383–1396 (2017).PubMed 
Article 

Google Scholar 
111.Schiesari, L. et al. Towards an applied metaecology. Perspect. Ecol. Conserv. 17, 172–181 (2019).
Google Scholar 
112.Vermaat, J. E., Dunne, J. A. & Gilbert, A. J. Major dimensions in food-web structure properties. Ecology 90, 278–282 (2009).PubMed 
Article 

Google Scholar 
113.White, J. W., Rassweiler, A., Samhouri, J. F., Stier, A. C. & White, C. Ecologists should not use statistical significance tests to interpret simulation model results. Oikos 123, 385–388 (2014).Article 

Google Scholar 
114.R Core Team. R: A Language and Environment for Statistical Computing. https://www.R-project.org/. (R Foundation for Statistical Computing, 2020).115.Aufderheide, H., Rudolf, L., Gross, T. & Lafferty, K. D. How to predict community responses to perturbations in the face of imperfect knowledge and network complexity. Proc. R. Soc. B Biol. Sci. 280, 20132355 (2013).Article 

Google Scholar  More

Evaluating indicatorsAmong the selected parameters the ratio of rural to the urban population directly relates to the per capita renewable water, whereas the population density, internet users, and education index exhibit an inverse relation with the per capita renewable water worldwide. It means the per capita renewable water decreases with decreasing rural to urban population and increasing population density, internet users, and education index. The urban population has increased in developing regions, which feature increasing population density. People’s health is threatened by poor urban sanitary infrastructure leading to disease and social decay. Increasing population density and a reduction in per capita renewable water inflict social harm and disrupt society’s economic growth58. Population density also is positively related to the relative number of elderly and social vulnerability because potential casualties increase with population size40. On the other hand, with the increase of Internet users and education index, the per capita renewable water has increased. As long as the knowledge and awareness of communities improved, the consumption algorithm decreased, leading to a reduction of renewable water per capita. Therefore, the level of literacy and knowledge for a community can be the basis for making the right decisions in agriculture, health, natural resource management, and other activities related to water resources for decision-makers. The latter situation calls for better communication among water users through social media and improved education to learn and develop optimal water management.Evaluating models and developing hydro-social equationsThree soft-computing approaches, namely ANN-LM, ANFIS-SC, and GEP, were applied to develop predictive equations with social indicators worldwide. The ANN-Levenberg–Marquardt (LM) backpropagation algorithm with one hidden layer was applied, and the hidden nodes’ number was determined by trial and error. A hybrid algorithm was combined with the ANFIS-SC models. There is no rule for determining the radii values of the ANFIS-SC models. The final radii values were determined by trial-and-error.The numbers of neurons in the ANN-LM models and the radii values of the ANFIS-SC models are listed in Table 4. The activation functions of the output nodes were linear for all the continents. The activation functions of the hidden nodes of the ANN-LM models for the P1 through P4 indicators were respectively the tangent sigmoid, tangent sigmoid, tangent sigmoid, and logarithm sigmoid for Africa; the activation functions of the proportion of rural to urban population was the tangent sigmoid for all the continents. Table 5 lists the results of the soft computing optimal models’ estimates of the proportion of rural to urban population (PRUP), population density (PD), internet users (IU), and education index (EI), denoted respectively by P1 through P4, during the test period in the world’s continents. Figures 4 and 5 display the characteristics of ANN (the number of neurons and activation functions of hidden and output layers) and ANFIS-SC (radii values) models, respectively. The values of R and RMSE for Africa corresponding to the ANN-LM models were respectively (0.921, 0.981, 0.858, 0.862) and (0.193, 0.058, 0.190, 0.172) associated with the PRUP, PD, IU, and EI parameters, respectively. The values of R and RMSE for Africa corresponding to the ANFIS-SC models equaled respectively (0.933, 0.991, 0.868, 0.891) and (0.130, 0.044, 0.186, 0.156) for the P1 through P4 parameters, respectively. Concerning the GEP models, the root relative squared error (RRSE) was selected as the pressure tree’s fitness function. The values of RMSE for GEP models equaled (0.084, 0.029, 0.178, 0.135), (0.197, 0.056, 0.152, 0.163), (0.151, 0.036, 0.123, 0.210), (0.182, 0.039, 0.148, 0.204) and (0.141, 0.030, 0.226, 0.082) for Africa, America, Asia, Europe, and Oceania, respectively. Table 5 results for the R, RMSE, and MAE values establish the GEP model estimates of PRUP, PD, IU, and EI indicators had the highest R values and the lowest RMSE values. The average R values of the best models (GEP) for all selected social parameters equaled 0.942, 0.909, 0.910, 0.889, and 0.947 for Africa, America, Asia, Europe, and Oceania, respectively. These results indicate the climatic characteristics of the continents influence the performance of the models. The models’ performances for Africa and Oceania associated with the type B dominant Koppen climate classification was the best. The models’ performances for Asia and America that have similar climatic classification were nearly equal. The average model performance for Europe in the type D climate classification was the poorest among the continents.Table 4 The characteristics of ANN (the number of neurons) and ANFIS (radii values) models corresponding to social indicators and continents.Full size tableTable 5 The results of soft computing optimal models corresponding to the testing period in the world’s continents.Full size tableFigure 4The characteristics of optimal ANN models; showing the number of neurons and activation functions of hidden and output layers.Full size imageFigure 5The characteristic of optimal ANFIS-SC model showing the radii values.Full size imageFigures 6, 7, 8, 9 and 10 show the observed and estimated social parameters obtained with the soft-computing models during the test period in Africa, America, Asia, Europe, and Oceania, respectively. Figure 11 compares the R, RMSE, and MAE values from the soft-computing models. The R values for soft-computing models are close to 1, with the quality relations being: RGEP  > RANFIS-SC  > RANN-LM for all social indicators. Figure 11 establishes that the ANFIS-SC model exceeded the ANN-LM models’ performance. Also, the GEP models had better performance than the ANFIS-SC and ANN-LM for estimating the proportion of rural to urban population (PRUP), population density (PD), internet users (IU), and education index (EI) parameters in Africa, America, Asia, Europe, and Oceania.Figure 6Observed and estimated social parameters during the testing period in Africa.Full size imageFigure 7Observed and estimated social parameters during the testing period in America.Full size imageFigure 8Observed and estimated social parameters during the testing period in Asia.Full size imageFigure 9Observed and estimated social parameters during the testing period in Europe.Full size imageFigure 10Observed and estimated social indicators during the testing period in Oceania.Full size imageFigure 11Comparison of R, RMSE and MAE values corresponding to the soft computing methods.Full size imageThe main advantage of the GEP over other soft computing methods (e.g., ANFIS and ANN) is in producing predictive equations. The equations obtained with the optimal models for the social indicators (i.e., the proportion of rural to urban population (PRUP), population density (PD), internet users (IU), and education index (EI) in Africa, America, Asia, Europe, and Oceania) are listed in Table 6. The equations that the GEP model discovers as a structure do not necessarily correspond to reality. The equations listed in Table 6 merely show the optimal equations extracted from the model after the evolution, for all indicators and in all basins (considering renewable water per capita as a decision variable).Table 6 Mathematical equations governing hydro-social indicators.Full size tableThe performance of the GEP models in estimating the social indicators in three ranges of values, namely, 20% of the maximum estimated values (20%max), 60% of median estimated values (60%mid or 20%min to 20%max), and 20% of minimum estimated values (20%min), during the test period for the proportion of rural to urban population (PRUP), population density (PD), internet users (IU) and the education index (EI) parameters of Africa, America, Asia, Europe, and Oceania are listed in Table 7. Table 7’s results indicate there is not a regular rule to determine the best-cited ranges performances. The education index and the population density have the lowest and highest R values among the other parameters in the three different ranges (20%max, 60%mid, and 20%min) in Africa, America, Asia, Europe, and Oceania. Therefore, the results indicate a strong pattern of association between the population density parameter and water resources status in all continents of the world.Table 7 The performance of GEP models with respect to selected ranges.Full size tableFigure 12 depicts the distribution of estimated data values of the social parameters (i = 1, 2, 3, 4) and their comparison through the continents. The box plots are a graphic display integrating multiple numerical relations. One approach to understanding the distribution or dispersion of data is through the box diagram, which is based on the “minimum,” “first quartile-Q1(0.25%)”, “median (0.50%)”, “third quartile-Q3(0.75%)” and “maximum” statistical indicators. Figure 12 shows Oceania and Africa exhibit the smallest and largest values of the rural to urban population, respectively. America has the lowest values of the first to the third quartile. The estimated population density value in Europe has the most values in the third quartile (0.75%). The median values of estimated internet users have the smallest and largest values in Africa and Europe, respectively. America has the lowest values of the first quartile, median, third quartile, and maximum values associated with the estimated education index values among the continents.Figure 12Distribution of estimated data values of social indicators (Pi, i = 1, 2, …, 4).Full size imageThe summary of hydro-social equations performance is listed in Table 8, where it is seen the best models’, performances are such that PD  > PRUP  > EI  > IU, PD  > IU  > EI  > PRUP, PD  > IU  > PRUP  > EI, PD  > PRUP  > IU  > EI and PD  > EI  > IU  > PRUP for Africa, America, Asia, Europe, and Oceania, respectively.Table 8 Summary of hydro-social equations performance.Full size tableThis paper’s results indicate the pattern of association between social parameters and water resources is complex. Renewable water per capita was estimated using social indicators PRUP, PD, IU, and EI based on gene expression programming. The results of GEP to estimate RWPC corresponding to the testing period in the world’s continents as listed in Table 9. The values of RMSE for optimal GEP models equaled 0.089, 0.058, 0.042, 0.049, and 0.036 for Africa, America, Asia, Europe, and Oceania, respectively. Figure 13 displays the observed and estimated RWPC parameter during the test period in the world’s continents. The equations obtained with the optimal models for the renewable water per capita in Africa, America, Asia, Europe, and Oceania are listed in Table 10. The fitted equations can be applied at variable spatial and temporal scales. The derived equations imply that water resources in Africa and Oceania are governed by the PRUP, PD, IU, and EI indicators. Also, the PRUP, PD, and IU indicators in Europe and PD and IU indicators in America and Asia have the most influence on their water resources status. The association between social parameters and water resources in all continents is variable. The linking of these social indicators with the per capita renewable water is a function of the countries’ cultural and economic conditions, thus bearing on the future management and policymaking across continents. This study’s results concerning hydro-social indicators are consistent with the findings by Forouzani et al.2, Carey et al.15, Lima et al.25, Pande et al.7, Diep et al.26, and Diaz et al.22.Table 9 The results of GEP estimating RWPC corresponding to the testing period in the world’s continents.Full size tableFigure 13Observed and estimated RWPC parameters during the test period in the world’s continents.Full size imageTable 10 Mathematical equations governing hydro-social indicators.Full size tableThis paper’s results establish the importance of examining the interactions between climate, the status of water resources, and social indicators. The state and social conditions of a country reflect the status of its water resources. Therefore, this study has shown how significant an impact the management and planning of a country can have on its water resources. Each successful water resources project rests on a successful social setting. More

EDITORIAL
31 August 2021

The world’s scientific panel on biodiversity needs a bigger role

IPBES, the international panel of leading biodiversity researchers, should be consulted on how best to measure species loss.

Share on Twitter
Share on Twitter

Share on Facebook
Share on Facebook

Share via E-Mail
Share via E-Mail

A baby green sea turtle in Madagascar, one of the regions where the probability of widespread biodiversity loss is greatest.Credit: Alexis Rosenfeld/Getty

For more than 30 years, the international community has tried and failed to find a path to slow down — and eventually reverse — worldwide declines in the richness of plant and animal species. Next year, it will have another chance. The 15th Conference of the Parties (COP 15) to the United Nations Convention on Biological Diversity, recently delayed for the third time, is now slated to take place in person in Kunming, China, in April and May 2022.Biodiversity is fundamental to Earth’s life-support systems, and humans depend on the services that nature provides. In 2010, countries committed to slowing the overall rate of biodiversity loss by 2020. But just 6 out of the 20 targets that were agreed on that occasion — at COP 10 in Aichi, Japan — have been even partially met, notable among them a commitment to conserve 17% of the world’s land and inland waters.Ahead of the Kunming meeting, policymakers and scientists are discussing a new action plan, called the Global Biodiversity Framework, which they hope to agree next year. The latest draft (published in July; see go.nature.com/3kbvspd) includes a promise to conserve 30% of the world’s land and sea areas by 2030 and reiterates the need to meet earlier targets, including the provision of greater financial support to low-income countries to help them to protect their biodiversity.Missing linkResearchers around the world are advising on the plan, through the UN’s institutions and through universities and various scientific networks. But one piece of the puzzle is missing. In 2012, a host of governments established the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES). It periodically reviews the literature and provides summaries of the latest knowledge. However, the countries organizing the COP are not involving IPBES in the action plan in the way that the UN Intergovernmental Panel on Climate Change has been consulted for advice ahead of climate COPs. It is important that IPBES be asked, because policymakers are being presented with a range of ideas that would benefit from the systematic evaluation that a global scientific advisory body would bring.
The world’s species are playing musical chairs: how will it end?
For example, biodiversity terminology is often unfamiliar, and therefore challenging, for most policymakers. The word itself — defined by the biodiversity convention as the variety and variability of life on Earth, at the level of genes, species and ecosystems — is not commonly used, nor well understood beyond the scientific community. The magnitude of biodiversity’s value to the planet and to people, as well as the risks of losing it, are also not widely appreciated.Over the years, various teams of scientists have researched and offered ideas on how to communicate the state of biodiversity both accurately and in a way that is accessible and engages the wider public. Some are advocating a biodiversity equivalent of the 1.5 °C warming target, or of net-zero emissions. One suggestion, published last year, is for the international community to adopt a target for limiting species extinctions. The goal would be to keep extinctions of known species to below 20 per year globally for the next 100 years — a single headline number to represent biodiversity (M. D. A. Rounsevell et al. Science 368, 1193–1195; 2020).A focus on species extinctions as a proxy for biodiversity is not a new idea, and is controversial. However, the authors say that their intention is not to replace biodiversity’s many facets with only one number, but to communicate biodiversity in a way that would resonate with more people.Another group is proposing a composite index — a single score made up of measures of some of biodiversity’s main components, including the health of species and ecosystems, as well as the services that biodiversity provides to people, such as pollination and clean water (C. A. Soto-Navarro et al. Nature Sustain. https://doi.org/gmjs2f; 2021). This would be biodiversity’s equivalent of the UN Human Development Index — first published in 1990 — which amalgamates information on health, education and income into a single number and has been adopted worldwide as a measure of prosperity and well-being.
Fewer than 20 extinctions a year: does the world need a single target for biodiversity?
A third idea, published by the leaders of some of the world’s most influential conservation and environmental science organizations, is called Nature Positive (see go.nature.com/2ydk89n). Its authors are proposing that the UN’s many global environmental agreements should include three common targets: no net loss of nature from 2020 (meaning that although nature might continue to be degraded in some areas, this would be offset by conservation gains elsewhere); some recovery by 2030; and full recovery by 2050. At present, the UN agreements on biodiversity, stopping climate change and combating desertification all have their own processes, occasionally acting together, but more often operating independently. The goal is to get them to sign up to one set of principles.All of these ideas have advantages and risks, which is why they need to be systematically evaluated by researchers. That’s where IPBES’s role is crucial. IPBES comprises a broad community of researchers, and, importantly, it represents voices from under-represented low- and middle-income countries, as well as the world’s Indigenous peoples. The governments involved in organizing the Kunming COP should ask IPBES to evaluate the ideas being put forward for the next biodiversity action plan, so they can be confident that what they decide has the support of a consensus of researchers, particularly in more-biodiverse regions of the world. Although preparations for the Kunming COP are well under way, this could also happen after the COP.Biodiversity loss could be as serious for the planet — and for humanity — as climate change. World leaders have become skilled at organizing complex international meetings and making promises that they then fail to keep. The upcoming biodiversity COP risks being one more such event, which is why researchers offering solutions are right to feel frustrated. They should work with IPBES to review their ideas. A unified voice is powerful, and if scientists can present a united front, policymakers will have fewer excuses to continue with business as usual.

Nature 597, 7-8 (2021)
doi: https://doi.org/10.1038/d41586-021-02339-3

Related Articles

The world’s species are playing musical chairs: how will it end?

Fewer than 20 extinctions a year: does the world need a single target for biodiversity?

The biodiversity leader who is fighting for nature amid a pandemic

The United Nations must get its new biodiversity targets right

Subjects

Biodiversity

Environmental sciences

Ecology

Climate change

Policy

Latest on:

Biodiversity

Boost for Africa’s research must protect its biodiversity
Correspondence 31 AUG 21

Brazilian road proposal threatens famed biodiversity hotspot
News 17 AUG 21

COVID vaccine inequity, species swaps — the week in infographics
News 06 AUG 21

Environmental sciences

Australian bush fires and fuel loads
Correspondence 31 AUG 21

Boost for Africa’s research must protect its biodiversity
Correspondence 31 AUG 21

African tropical montane forests store more carbon than was thought
News & Views 25 AUG 21

Ecology

Boost for Africa’s research must protect its biodiversity
Correspondence 31 AUG 21

Can artificially altered clouds save the Great Barrier Reef?
News Feature 25 AUG 21

High aboveground carbon stock of African tropical montane forests
Article 25 AUG 21

Jobs

Multiple PhD Positions at the International Max Planck Research School (IMPRS)

IMPRS for Many Particle Systems in Structured Environments
Dresden, Germany

Scientist / Postdoctoral fellow in Proteomics (f/m/d)

Helmholtz Centre for Infection Research (HZI)
Braunschweig, Germany

Professorship (W3) for Energy Conversion Materials endowed by Carl Zeiss Foundation

Karlsruhe Institute of Technology (KIT)
Karlsruhe, Germany

Assistant in Part-time 30 Hours/Week

German Electron Synchrotron (DESY)
Hamburg, Germany

Nature Briefing
An essential round-up of science news, opinion and analysis, delivered to your inbox every weekday.

Email address

Yes! Sign me up to receive the daily Nature Briefing email. I agree my information will be processed in accordance with the Nature and Springer Nature Limited Privacy Policy.

Sign up More

CORRESPONDENCE
31 August 2021

Boost for Africa’s research must protect its biodiversity

Nils Chr. Stenseth

 ORCID: http://orcid.org/0000-0002-1591-5399

0
&

Sebsebe Demissew

 ORCID: http://orcid.org/0000-0002-0123-9596

1

Nils Chr. Stenseth

University of Oslo, Norway.

View author publications

You can also search for this author in PubMed
 Google Scholar

Sebsebe Demissew

Addis Ababa University, Ethiopia.

View author publications

You can also search for this author in PubMed
 Google Scholar

Share on Twitter
Share on Twitter

Share on Facebook
Share on Facebook

Share via E-Mail
Share via E-Mail

Download PDF

We write on behalf of 209 scientists (see go.nature.com/3sa16p9) to endorse a new initiative by the African Research Universities Alliance and the Guild of European Research-Intensive Universities (see go.nature.com/3b364hj). This calls for greater investment by the African Union and the European Union in Africa’s universities, to help them address global challenges such as public health, climate change and good governance. We strongly encourage expansion of the initiative to encompass environmental and biodiversity issues that are crucial to the continent’s future.Safeguarding Africa’s extraordinary natural resources and biodiversity — the backbone of much of its economy and livelihood — demands a new generation of African scientists trained in environmental sciences. Experts are needed in conservation science and environmental economics, as well as in the collection, curation and analysis of biological data.As Julius Nyerere, the former president of Tanzania, put it 60 years ago in a speech now known as the Arusha Manifesto: “The conservation of wildlife and wild places calls for specialist knowledge, trained manpower and money, and we look to other nations to co-operate with us in this important task — the success or failure of which not only affects the continent of Africa but the rest of the world as well.”

Nature 597, 31 (2021)
doi: https://doi.org/10.1038/d41586-021-02356-2

Competing Interests
The authors declare no competing interests.

Related Articles

See more letters to the editor

Subjects

Funding

Biodiversity

Environmental sciences

Latest on:

Funding

Afghanistan’s terrified scientists predict huge research losses
News 27 AUG 21

Preprint ban in grant applications deemed ‘plain ludicrous’
News 25 AUG 21

Gender gap in US patents leads to few inventions that help women
Career News 20 AUG 21

Biodiversity

The world’s scientific panel on biodiversity needs a bigger role
Editorial 31 AUG 21

Brazilian road proposal threatens famed biodiversity hotspot
News 17 AUG 21

COVID vaccine inequity, species swaps — the week in infographics
News 06 AUG 21

Environmental sciences

Australian bush fires and fuel loads
Correspondence 31 AUG 21

The world’s scientific panel on biodiversity needs a bigger role
Editorial 31 AUG 21

African tropical montane forests store more carbon than was thought
News & Views 25 AUG 21

Jobs

Multiple PhD Positions at the International Max Planck Research School (IMPRS)

IMPRS for Many Particle Systems in Structured Environments
Dresden, Germany

Scientist / Postdoctoral fellow in Proteomics (f/m/d)

Helmholtz Centre for Infection Research (HZI)
Braunschweig, Germany

Professorship (W3) for Energy Conversion Materials endowed by Carl Zeiss Foundation

Karlsruhe Institute of Technology (KIT)
Karlsruhe, Germany

Assistant in Part-time 30 Hours/Week

German Electron Synchrotron (DESY)
Hamburg, Germany

Nature Briefing
An essential round-up of science news, opinion and analysis, delivered to your inbox every weekday.

Email address

Yes! Sign me up to receive the daily Nature Briefing email. I agree my information will be processed in accordance with the Nature and Springer Nature Limited Privacy Policy.

Sign up More

1.Hamza MA, Anderson WK. Soil compaction in cropping systems: a review of the nature, causes and possible solutions. Soil and Tillage Res. 2005;82:121–45.Article 

Google Scholar 
2.Etana A, Larsbo M, Keller T, Arvidsson J, Schjønning P, Forkman J, et al. Persistent subsoil compaction and its effects on preferential flow patterns in a loamy till soil. Geoderma. 2013;192:430–6.Article 

Google Scholar 
3.de Andrade Bonetti J, Anghinoni I, de Moraes MT, Fink JR. Resilience of soils with different texture, mineralogy and organic matter under long-term conservation systems. Soil Tillage Res. 2017;174:104–12.Article 

Google Scholar 
4.FAO. Status of the world’s soil resources – main report. Food and agriculture organization of the United Nations and intergovernmental technical panel on soils. 607 (2015). http://www.fao.org/3/i5199e/I5199E.pdf5.Garrigues E, Corson MS, Angers DA, Van Der Werf HMG, Walter C. Development of a soil compaction indicator in life cycle assessment. Int J Life Cycle Assess. 2013;18:1316–24.Article 

Google Scholar 
6.Schäffer B, Stauber M, Mueller TL, Müller R, Schulin R. Soil and macro-pores under uniaxial compression. I. Mechanical stability of repacked soil and deformation of different types of macro-pores. Geoderma. 2008;146:183–91.Article 

Google Scholar 
7.Pagliai M, Marsili A, Servadio P, Vignozzi N, Pellegrini S. Changes in some physical properties of a clay soil in Central Italy following the passage of rubber tracked and wheeled tractors of medium power. Soil Tillage Res. 2003;73:119–29.Article 

Google Scholar 
8.Gysi M, Ott A, Flühler H. Influence of single passes with high wheel load on a structured, unploughed sandy loam soil. Soil Tillage Res. 1999;52:141–51.Article 

Google Scholar 
9.Czyz EA. Effects of traffic on soil aeration, bulk density and growth of spring barley. Soil Tillage Res. 2004;79:153–66.Article 

Google Scholar 
10.Drewry JJ, Paton RJ, Monaghan RM. Soil compaction and recovery cycle on a Southland dairy farm: Implications for soil monitoring. Aust J Soil Res. 2004;42:851–6.Article 

Google Scholar 
11.Keller T, Colombi T, Ruiz S, Manalili MP, Rek J, Stadelmann V, et al. Long-term soil structure observatory for monitoring post-compaction evolution of soil structure. Vadose Zo. J. 2017;16:1–16.
Google Scholar 
12.Wingate-Hill R, Jakobsen BF. Increased mechanisation and soil damage in forests – a review. New Zeal J For Sci. 1982;12:380–93.
Google Scholar 
13.Fierer N, Schimel JP, Holden PA. Variations in microbial community composition through two soil depth profiles. Soil Biol Biochem. 2003;35:167–76.CAS 
Article 

Google Scholar 
14.Jégou D, Brunotte J, Rogasik H, Capowiez Y, Diestel H, Schrader S, et al. Impact of soil compaction on earthworm burrow systems using X-ray computed tomography: preliminary study. Eur J Soil Biol. 2002;38:329–36.Article 

Google Scholar 
15.Larsen T, Schjønning P, Axelsen J. The impact of soil compaction on euedaphic Collembola. Appl Soil Ecol. 2004;26:273–81.Article 

Google Scholar 
16.Rosolem C, Foloni JS, Tiritan C. Root growth and nutrient accumulation in cover crops as affected by soil compaction. Soil Tillage Res. 2002;65:109–15.Article 

Google Scholar 
17.Arvidsson J, Håkansson I. Response of different crops to soil compaction-Short-term effects in Swedish field experiments. Soil Tillage Res. 2014;138:56–63.Article 

Google Scholar 
18.van der Linden AMA, Jeurisson LJJ, Van Veen JA, Schippers G. Turnover of soil microbial biomass as influence by soil compaction. In: Hansen J., Henriksen K. Editors. Nitrogen in Organic Wastes Applied to Soil, London, UK: Academic Press;1989, pp. 25–36.19.Weisskopf P, Reiser R, Rek J, Oberholzer HR. Effect of different compaction impacts and varying subsequent management practices on soil structure, air regime and microbiological parameters. Soil Tillage Res. 2010;111:65–74.Article 

Google Scholar 
20.Li Q, Lee Allen H, Wollum AG. Microbial biomass and bacterial functional diversity in forest soils: effects of organic matter removal, compaction, and vegetation control. Soil Biol Biochem. 2004;36:571–9.CAS 
Article 

Google Scholar 
21.Tan X, Chang SX. Soil compaction and forest litter amendment affect carbon and net nitrogen mineralization in a boreal forest soil. Soil Tillage Res. 2007;93:77–86.Article 

Google Scholar 
22.Dexter AR. Soil physical quality Part I. Theory, effects of soil texture, density, and organic matter, and effects on root growth. Geoderma. 2004;120:201–14.Article 

Google Scholar 
23.Renault P, Sierra J. Modeling oxygen diffusion in aggregated soils: II. Anaerobiosis in topsoil layers. Soil Sci Soc Am J. 1994;58:1017–23.CAS 
Article 

Google Scholar 
24.Schnurr-Pütz S, Bååth E, Guggenberger G, Drake HL, Küsel K. Compaction of forest soil by logging machinery favours occurrence of prokaryotes. FEMS Microbiol Ecol. 2006;58:503–16.PubMed 
Article 
CAS 

Google Scholar 
25.Hartmann M, Niklaus PA, Zimmermann S, Schmutz S, Kremer J, Abarenkov K, et al. Resistance and resilience of the forest soil microbiome to logging-associated compaction. ISME J. 2014;8:226–44.CAS 
PubMed 
Article 

Google Scholar 
26.Marshall VG. Impacts of forest harvesting on biological processes in northern forest soils. For Ecol Manage. 2000;133:43–60.Article 

Google Scholar 
27.Ponder F, Tadros M. Phospholipid fatty acids in forest soil four years after organic matter removal and Soil compaction. Appl Soil Ecol. 2002;19:173–82.Article 

Google Scholar 
28.Frey B, Kremer J, Rüdt A, Sciacca S, Matthies D, Lüscher P. Compaction of forest soils with heavy logging machinery affects soil bacterial community structure. Eur J Soil Biol. 2009;45:312–20.Article 

Google Scholar 
29.Hartmann M, Howes CG, VanInsberghe D, Yu H, Bachar D, Christen R, et al. Significant and persistent impact of timber harvesting on soil microbial communities in Northern coniferous forests. ISME J. 2012;6:2199–218.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
30.Calonego JC, Raphael JPA, Rigon JPG, de Oliveira Neto L, Rosolem CA. Soil compaction management and soybean yields with cover crops under no-till and occasional chiseling. Eur J Agron. 2017;85:31–37.Article 

Google Scholar 
31.Flisch R, Sinaj S, Charles R, Richner W. Grundlagen für die Düngung im Acker- und Futterbau (GRUDAF). Agrar Schweiz. 2009;16:6–31.
Google Scholar 
32.Suter D, Rosenberg E, Mosimann E, Frick R. Mélanges standard pour la production fourragère. Recherche agronomique suisse. 2017;8:1–16.33.Bürgmann H, Pesaro M, Widmer F, Zeyer J. A strategy for optimizing quality and quantity of DNA extracted from soil. J Microbiol Methods. 2001;45:7–20.PubMed 
Article 
PubMed Central 

Google Scholar 
34.Frey B, Rime T, Phillips M, Stierli B, Hajdas I, Widmer F, et al. Microbial diversity in European alpine permafrost and active layers. FEMS Microbiol Ecol. 2016;92:1–17.Article 
CAS 

Google Scholar 
35.Tedersoo L, Lindahl B. Fungal identification biases in microbiome projects. Environ Microbiol Rep. 2016;8:774–9.PubMed 
Article 
PubMed Central 

Google Scholar 
36.Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Lozupone CA, Turnbaugh PJ, et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc Natl Acad Sci USA. 2011;108:4516–22.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
37.Henry S, Baudoin E, López-Gutiérrez JC, Martin-Laurent F, Brauman A, Philippot L. Quantification of denitrifying bacteria in soils by nirK gene targeted real-time PCR. J Microbiol Methods. 2004;59:327–35.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
38.Kandeler E, Deiglmayr K, Tscherko D, Bru D, Philippot L. Abundance of narG, nirS, nirK, and nosZ genes of denitrifying bacteria during primary successions of a glacier foreland. Appl Environ Microbiol. 2006;72:5957–62.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
39.Rognes T, Flouri T, Nichols B, Quince C, Mahé F. VSEARCH: a versatile open source tool for metagenomics. PeerJ. 2016;4:e2584.40.Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 2011;17:10–12.Article 

Google Scholar 
41.Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
42.Edgar RC, Flyvbjerg H. Error filtering, pair assembly and error correction for next-generation sequencing reads. Bioinformatics. 2015;31:3476–82.CAS 
PubMed 
Article 

Google Scholar 
43.Edgar R. UNOISE2: improved error-correction for Illumina 16S and ITS amplicon sequencing. 2016. bioRxiv:081257. Available from: https://doi.org/10.1101/081257.44.Edgar R. UCHIME2: improved chimera prediction for amplicon sequencing. 2016. bioRxiv:074252. Available from: https://doi.org/10.1101/074252.45.Bengtsson-Palme J, Hartmann M, Eriksson KM, Pal C, Thorell K, Larsson DG, et al. metaxa2: improved identification and taxonomic classification of small and large subunit rRNA in metagenomic data. Mol Ecol Resour. 2015;15:1403–14.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
46.Bengtsson-Palme J, Ryberg M, Hartmann M, Branco S, Wang Z, Godhe A, et al. Improved software detection and extraction of ITS1 and ITS2 from ribosomal ITS sequences of fungi and other eukaryotes for analysis of environmental sequencing data. Methods Ecol. Evol. 2013;4:914–9.
Google Scholar 
47.Edgar R. SINTAX: a simple non-Bayesian taxonomy classifier for 16S and ITS sequences. 2016. bioRxiv:074161. Available from: https://doi.org/10.1101/074161.48.Pruesse E, Quast C, Knittel K, Fuchs BM, Ludwig W, Peplies J, et al. SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Res. 2007;35:7188–96.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
49.Abarenkov K, Henrik Nilsson R, Larsson KH, Alexander IJ, Eberhardt U, Erland S, et al. The UNITE database for molecular identification of fungi – recent updates and future perspectives. New Phytol. 2010;186:281–5.PubMed 
Article 
PubMed Central 

Google Scholar 
50.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2017. https://www.R-project.org/.51. Dinno A. dunn.test: Dunn’s Test of Multiple Comparisons Using Rank Sums. R  package version 1.3.5. 2017. https://CRAN.R-project.org/package=dunn.test.52.Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, et al. vegan: Community Ecology Package. R package version 2.5-7. 2020. https://CRAN.R-project.org/package=vegan.53.Martiny JB, Martiny AC, Weihe C, Lu Y, Berlemont R, Brodie EL, et al. Microbial legacies alter decomposition in response to simulated global change. ISME J. 2017;11:490–9.PubMed 
Article 

Google Scholar 
54.Hemkemeyer M, Christensen BT, Tebbe CC, Hartmann M. Taxon-specific fungal preference for distinct soil particle size fractions. Eur J Soil Biol. 2019;94:1–9.Article 

Google Scholar 
55.Anderson MJ. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 2001;26:32–46.
Google Scholar 
56.Maxime Hervé. RVAideMemoire: Testing and Plotting Procedures for Biostatistics. 2020. https://CRAN.R-project.org/package=RVAideMemoire.57.Gower JC. Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika. 1996;53:325–38.Article 

Google Scholar 
58.Anderson MJ, Willis TJ. Canonical analysis of principal coordinates: a useful method of constrained ordination for ecology. Ecology. 2003;84:511–25.Article 

Google Scholar 
59.Kindt R, Coe R. Tree diversity analysis. A manual and software for common statistical methods for ecological and biodiversity studies. World Agroforestry Centre, Nairobi, Kenya (2005) ISBN 92-9059-179-X60.Storey JD, Tibshirani R. Statistical significance for genomewide studies. Proc Natl Acad Sci USA. 2003;100:9440–5.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
61.Storey JD, Bass A, Dabney A, Robinson, D. qvalue: Q-value estimation for false discovery rate control. R package version 2.22.0; 2020. http://qvalue.princeton.edu.62.Letunic I, Bork P. Interactive Tree of Life (iTOL) v4: recent updates and new developments. Nucleic Acids Res. 2019;47:256–9.Article 
CAS 

Google Scholar 
63.Louca S, Parfrey LW, Doebeli M. Faprotax: decoupling function and taxonomy in the global ocean microbiome. Science. 2016;353:1272–7.CAS 
PubMed 
Article 

Google Scholar 
64.Nguyen NH, Song Z, Bates ST, Branco S, Tedersoo L, Menke J, et al. FUNGuild: an open-annotation database for parsing fungal community datasets by ecological guild. Fungal Ecol. 2016;20:241–8.Article 

Google Scholar 
65.Horn R. Stress-strain effects in structured unsaturated soils on coupled mechanical and hydraulic processes. Geoderma. 2003;116:77–88.Article 

Google Scholar 
66.Reiser R, Stadelmann V, Weisskopf P, Grahm L, Keller T. System for quasi-continuous simultaneous measurement of oxygen diffusion rate and redox potential in soil. J Plant Nutr Soil Sci. 2020;183:316–26.CAS 
Article 

Google Scholar 
67.Keller T, Colombi T, Ruiz S, Schymanski SJ, Weisskopf P, Koestel J, et al. Soil structure recovery following compaction – short-term evolution of soil physical properties in a loamy soil. Soil Sci Soc Am J. 2021;18:1–19.
Google Scholar 
68.Yvan C, Stéphane S, Stéphane C, Pierre B, Guy R, Hubert B. Role of earthworms in regenerating soil structure after compaction in reduced tillage systems. Soil Biol Biochem. 2012;55:93–103.CAS 
Article 

Google Scholar 
69.Jabro JD, Allen BL, Rand T, Dangi SR, Campbell JW. Effect of previous crop roots on soil compaction in 2 yr rotations under a no-tillage system. Land. 2021;202:1–10.
Google Scholar 
70.Batey T. Soil compaction and soil management – a review. Soil Use and Manag. 2009;25:335–45.Article 

Google Scholar 
71.Cambi M, Certini G, Neri F, Marchi E. The impact of heavy traffic on forest soils: a review. For Ecol Manag. 2015;338:124–38.Article 

Google Scholar 
72.Hu W, Tabley F, Beare M, Tregurtha C, Gillespie R, Qiu W, et al. Short-term dynamics of soil physical properties as affected by compaction and tillage in a silt loam soil. Vadose Zo J. 2018;17:1–13.
Google Scholar 
73.Manyiwa T, Dikinya O. Impact of tillage types on compaction and physical properties of soils of Sebele farms in Botswana. Soil Environ. 2014;33:124–32.
Google Scholar 
74.Håkansson I, Reeder RC. Subsoil compaction by vehicles with high axle load-extent, persistence and crop response. Soil Tillage Res. 1994;29:277–304.Article 

Google Scholar 
75.Grayston SJ, Wang S, Campbell CD, Edwards AC. Selective influence of plant species on microbial diversity in the rhizosphere. Soil Biol Biochem. 1998;30:369–78.CAS 
Article 

Google Scholar 
76.Degrune F, Theodorakopoulos N, Colinet G, Hiel MP, Bodson B, Taminiau B, et al. Temporal dynamics of soil microbial communities below the seedbed under two contrasting tillage regimes. Front Microbiol. 2017;8:1–15.Article 

Google Scholar 
77.Rousk J, Bååth E, Brookes PC, Lauber CL, Lozupone C, Caporaso JG, et al. Soil bacterial and fungal communities across a pH gradient in an arable soil. ISME J. 2010;4:1340–51.PubMed 
PubMed Central 
Article 

Google Scholar 
78.Bach EM, Baer SG, Meyer CK, Six J. Soil texture affects soil microbial and structural recovery during grassland restoration. Soil Biol Biochem. 2010;42:2182–91.CAS 
Article 

Google Scholar 
79.Evans CA, Coombes PJ, Dunstan RH. Wind, rain and bacteria: the effect of weather on the microbial composition of roof-harvested rainwater. Water Res. 2006;40:37–44.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
80.He M, Zhang J, Shen L, Xu L, Luo W, Li D, et al. High-throughput sequencing analysis of microbial community diversity in response to indica and japonica bar-transgenic rice paddy soils. PLoS ONE. 2019;14:1–26.
Google Scholar 
81.Gschwend F, Aregger K, Gramlich A, Walter T, Widmer F. Periodic waterlogging consistently shapes agricultural soil microbiomes by promoting specific taxa. Appl Soil Ecol. 2020;155:1–9.Article 

Google Scholar 
82.De Neve S, Hofman G. Influence of soil compaction on carbon and nitrogen mineralization of soil organic matter and crop residues. Biol Fertil Soils. 2000;30:544–9.Article 

Google Scholar 
83.Miransari M, Bahrami HA, Rejali F, Malakouti MJ. Effects of soil compaction and arbuscular mycorrhiza on corn (Zea mays L.) nutrient uptake. Soil Tillage Res. 2009;103:282–90.Article 

Google Scholar 
84.Ruser R, Flessa H, Russow R, Schmidt G, Buegger F, Munch JC. Emission of N2O, N2 and CO2 from soil fertilized with nitrate: effect of compaction, soil moisture and rewetting. Soil Biol Biochem. 2006;38:263–74.CAS 
Article 

Google Scholar 
85.Bao Q, Ju X, Qu Z, Christie P, Lu Y. Response of nitrous oxide and corresponding bacteria to managements in an agricultural soil. Soil Biol Biochem. 2012;76:130–41.CAS 

Google Scholar 
86.Ahemad M, Kibret M. Mechanisms and applications of plant growth promoting rhizobacteria: current perspective. J King Saud Univ Sci. 2014;26:1–20.Article 

Google Scholar 
87.Schwarzott D, Walker C, Schüßler A. Glomus, the largest genus of the arbuscular mycorrhizal fungi (Glomales), is nonmonophyletic. Mol Phylogenet Evol. 2001;21:190–7.CAS 
PubMed 
Article 

Google Scholar 
88.Harman GE, Howell CR, Viterbo A, Chet I, Lorito M. Trichoderma species – opportunistic, avirulent plant symbionts. Nat Rev Microbiol. 2004;2:43–56.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
89.Waqas M, Khan AL, Hamayun M, Shahzad R, Kang SM, Kim JG, et al. Endophytic fungi promote plant growth and mitigate the adverse effects of stem rot: an example of Penicillium citrinum and Aspergillus terreus. J. Plant Interact. 2015;10:280–7.CAS 
Article 

Google Scholar 
90.Könneke M, Bernhard AE, de la Torre JR, Walker CB, Waterbury JB, Stahl DA. Isolation of an autotrophic ammonia-oxidizing marine archaeon. Nature. 2005;437:543–6.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
91.Kozlowski TT. Soil compaction and growth of woody plants. Scand J For Res. 1999;14:596–619.Article 

Google Scholar 
92.Haggett KD, Gray PP, Dunn NW. Crystalline cellulose degradation by a strain of Cellulomonas and its mutant derivatives. Eur J Appl Microbiol Biotechnol. 1979;8:183–90.CAS 
Article 

Google Scholar 
93.Rivas R, Trujillo ME, Mateos PF, Martínez-Molina E, Velázquez E. Agromyces ulmi sp. nov., xylanolytic bacterium isolated from Ulmus nigra in Spain. Int J Syst Evol Microbiol. 2004;54:1987–90.CAS 
PubMed 
Article 

Google Scholar 
94.Štursová M, Žifčáková L, Leigh MB, Burgess R, Baldrian P. Cellulose utilization in forest litter and soil: identification of bacterial and fungal decomposers. FEMS Microbiol Ecol. 2012;80:735–46.PubMed 
Article 
CAS 

Google Scholar 
95.Brown AM, Howe DK, Wasala SK, Peetz AB, Zasada IA, Denver DR. Comparative genomics of a plant-parasitic nematode endosymbiont suggest a role in nutritional symbiosis. Genome Biol Evol. 2015;7:2727–46.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
96.Quandt CA, Beaudet D, Corsaro D, Walochnik J, Michel R, Corradi N, et al. The genome of an intranuclear parasite, Paramicrosporidium saccamoebae, reveals alternative adaptations to obligate intracellular parasitism. Elife. 2017;6:1–19.Article 

Google Scholar 
97.Brussaard L, van Faassen, HG. Effects of compaction on soil biota and soil biological processes. In: Soane BD, van Ouwerkerk C. Soil compaction in crop production. Elsevier; 1994. 11, p. 215–35.98.Shestak CJ, Busse MD. Compaction alters physical but not biological indices of soil health. Soil Sci Soc Am J. 2005;69:236.CAS 
Article 

Google Scholar  More

Photosynthetic characteristicsWater and nitrogen coupling treatment had a significant effect on the photosynthetic characteristics (Fig. 1). Generally, the net photosynthetic rates of the treatments were in the following order: CK, W1N1, W1N3, W3N1, W3N3, W2N1, W1N2, W3N2, W2N3, and W2N2. The treatments with low water and low nitrogen had significantly lower net photosynthetic rates than W2N2. The stomatal conductance and transpiration rate changed in similar patterns. The net photosynthetic rate showed a unimodal trend with the increase of nitrogen application at the same irrigation level. Under the same nitrogen application level, the net photosynthetic rate increased first and then decreased slowly with the increase of irrigation amount, with the highest photosynthetic rates in the order of W2  > W3  > W1. The net photosynthetic rate was the highest, with a mean value was 13.87 μmol m−2 s−1, in treatment W2N2. The results showed that severe water stress and excessive nitrogen were not conducive to the absorption and utilization of water and nutrients by crop roots, which led to the decrease of the photosynthetic rate. The effect of water and nitrogen treatment on the intercellular CO2 concentration was significant (Fig. 1). Under the condition of excessive water or nitrogen, the photosynthesis of Isatis indigotica decreased, and the intercellular CO2 concentration showed a trend opposite to that of the net photosynthetic rate.Compared with N, P, and K deficiency treatments, water–N coupling could increase the Pn of crops, which was the same as that of other fruit trees and vegetables13. Accumulated photoassimilates in the third internode of the upper part of the main stems, as well as in the flag leaf sheath, are mobilized in a higher proportion and can contribute to grain filling in rice plants subjected to water stress in the tillering phase14. The Pn, Gs, and Tr of maize leaves at the seedling stage decreased significantly, while the Ci increased significantly when the nitrogen application rate was low15.The experiments with Isatis indigotica demonstrate that the Pn, Gs, and Tr under the same irrigation level first increased and then decreased with the increase of the nitrogen application rate. The net photosynthetic rate, transpiration rate, and stomatal conductance of Isatis indigotica were improved by rational nitrogen application. Studies have reported similar findings in Isatis indigotica; with the decrease of N level, the net photosynthetic rate, transpiration rate, and stomatal conductance of leaves gradually decreased, while the intercellular CO2 concentration increased16,17. Under reasonable water and nitrogen coordination conditions, the synergistic effect of water and nitrogen increased, which effectively promoted the photosynthesis of Isatis indigotica. Under the condition of too much nitrogen or too little water, the antagonism of water and nitrogen was obvious, and the photosynthesis of Isatis indigotica was inhibited to a certain extent.Yield and water use efficiencyThe Isatis indigotica yield values presented are the average of two consecutive years of water–nitrogen trials (Fig. 2). The I. indigotica yields differed significantly between the water–nitrogen treatments; the W2N2 and W2N3 treatments had the highest yields at 7277.5 and 6820.5 kg hm−2, respectively. The lowest yield of 3264.5 kg hm−2 was recorded in the control treatment. The yields of all treatments were significantly higher than that of the control treatment. The yields of the W2N2 and W2N3 treatments were significantly higher than those of the W1N1 and the W3N1 treatments. With the increase of the nitrogen application rate, the yield first increased and then decreased under the same irrigation conditions.The water use efficiency values of Isatis indigotica presented are the average of 2 consecutive years of water–nitrogen trials (Fig. 2). The water use efficiency of Isatis indigotica differed significantly between the water–nitrogen treatments; the W1N2 and W2N2 treatments had the highest yields at 20.78 and 19.63 kg mm−1 hm−2, respectively. The lowest yield of 13.65 kg mm−1 hm−2 was recorded in the W3N1 treatment. The water use efficiency values of the W1N2 and W2N2 treatments were significantly higher than that of the W3N3 treatment, which was the treatment with excess water and nitrogen fertilizer. The water use efficiency decreased with the increase of irrigation under the same nitrogen application conditions. The water use efficiency first increased and then decreased with the increase in nitrogen application rate under the same irrigation conditions. The W2N2 treatment had the highest yield and water use efficiency. Therefore, the water–nitrogen coupling mode of medium water and medium nitrogen application achieved the highest yield and effectively saved water. This was mainly due to the moderate water and nitrogen to promote the photosynthesis of Isatis indigotica and lead to more dry matter accumulation, so as to increase the yield.Generally, appropriate water deficits can improve crop yield and water use efficiency18,19, and rational fertilization can increase crop yield, such as in fruit trees and vegetables20,21,22. The yield increase in the current experiment was probably related to reasonable water stress and reasonable nitrogen application; the W2N2 treatment had the highest yield and water use efficiency. However, excessive water and nitrogen reduced the yield and water use efficiency of Isatis indigotica. This was consistent with recent research reports23,24. Compared with the local flooding irrigation and excessive nitrogen fertilizer mode, the W2N2 treatment with moderate water and nitrogen application not only obtained a high yield but also significantly improved the water use efficiency. This method could reduce the effect of excessive water and fertilizer application on soil productivity and would be a better water and nitrogen management model for local Isatis indigotica production.QualityThe Isatis indigotica quality values presented are the average of two consecutive years of water–nitrogen trials (Fig. 3). These quality indicators mainly include the following content indicators: indigo, indirubin, (R, S)-goitrin, and polysaccharides. The Isatis indigotica quality indicators differed significantly between the water–nitrogen treatments. The CK treatment had the highest values of all quality indicators. Each quality indicator decreased gradually with the increase of water content under the same nitrogen application conditions. Each quality indicator decreased gradually with the increase of nitrogen application under the same water conditions. The (R, S)-goitrin content of the W2N2 treatment decreased by 6.5% compared with CK and by 3.9% compared with the W1N1 treatment.Water is the medium for improving crop quality. Generally, the crop quality was improved by a suitable water deficit25,26,27 and reasonable fertilization28,29,30. The quality of Isatis indigotica in the current experiment increased gradually with the decrease of water. The water deficit treatment increased the content of effective components and improved the quality of Isatis indigotica. The content of the effective components in all treatments reached the pharmacopoeia standard12. The quality indicator values of each treatment in the current experiment were significantly lower than those of the CK treatment, but there was little difference in the quality indicator values between each treatment. Moreover, the yield of the control treatment was much lower than that of other treatments. Therefore, the effective quality content of the control treatment was lower than other treatments. Excessive water and nitrogen inputs were not conducive to quality, which was not consistent with recent research reports31. Generally, the water-nitrogen coupling type of W2N60 was antagonism basing on the average yield of winter wheat in the 10 years32. Some scholars have studied the irrigation of jujube that WUE and ANUE of jujube cannot reach the maximum at the same time. Different ratio of water and nitrogen will produce coupling and antagonism33. The results showed that total N applications over 200 kg ha−1 did not increase yield or quality. Water deficit treatment could be increased the content of effective components and improve the quality of Isatis indigotica. Due to the high evaporation, moderate water stress and effective use of nitrogen fertilizer, the active components of Isatis indigotica were easier to accumulate in its roots. The synergistic effect of water and nitrogen will lead to the accumulation of active components in Isatis indigotica. More

1.Mimura, M. et al. Understanding and monitoring the consequences of human impacts on intraspecific variation. Evol. Appl. 10(2), 121–139 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
2.Leigh, D. M., Hendry, A. P., Vázquez-Domínguez, E. & Friesen, V. L. Estimated six per cent loss of genetic variation in wild populations since the industrial revolution. Evol. Appl. 12(8), 1505–1512 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
3.Habel, J. C., Husemann, M., Finger, A., Danley, P. D. & Zachos, F. E. The relevance of time series in molecular ecology and conservation biology. Biol. Rev. 89(2), 484–492 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
4.Klütsch, C. F. C. et al. Genetic changes caused by restocking and hydroelectric dams in demographically bottlenecked brown trout in a transnational subarctic riverine system. Ecol. Evol. 9(10), 6068–6081 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
5.Hansen, M. M., Fraser, D. J., Meier, K. & Mensberg, K.-L.D. Sixty years of anthropogenic pressure: A spatio-temporal genetic analysis of brown trout populations subject to stocking and population declines. Mol. Ecol. 18(12), 2549–2562 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
6.Savary, R. et al. Stocking activities for the Arctic char in Lake Geneva: Genetic effects in space and time. Ecol. Evol. 7(14), 5201–5211 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
7.Hughes, J. B., Daily, G. C. & Ehrlich, P. R. Population diversity: its extent and extinction. Science 278, 689–692 (1997).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
8.Perrier, C., Guyomard, R., Bagliniere, J.-L., Nikolic, N. & Evanno, G. Changes in the genetic structure of Atlantic salmon populations over four decades reveal substantial impacts of stocking and potential resiliency. Ecol. Evol. 3(7), 2334–2349 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
9.Vøllestad, L. A. & Hesthagen, T. Stocking of freshwater fish in Norway: management goals and effects. Nordic J. Freshwater Res. 75, 143–152 (2001).
Google Scholar 
10.Christie, M. R., Marine, M. L., French, R. A., Waples, R. S. & Blouin, M. S. Effective size of a wild salmonid population is greatly reduced by hatchery supplementation. Heredity 109, 254–260 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
11.Araki, H., Cooper, B. & Blouin, M. S. Carry-over effect of captive breeding reduces reproductive fitness of wild-born descendants in the wild. Biol. Lett. 5, 621–624 (2009).PubMed 
PubMed Central 
Article 

Google Scholar 
12.O’Sullivan, R. J. et al. Captive-bred Atlantic salmon released into the wild have fewer offspring than wild-bred fish and decrease population productivity. Proc. R. Soc. B 287, 20201671 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
13.Amundsen, P.-A. et al. Invasion of vendace Coregonus albula in a subarctic watercourse. Biol. Conserv. 88(3), 405–413 (1999).Article 

Google Scholar 
14.Jensen, H., Bøhn, T., Amundsen, P.-A. & Aspholm, P. E. Feeding ecology of piscivorous brown trout (Salmo trutta L.) in a subarctic watercourse. Ann. Zool. Fenn. 41(1), 319–328 (2004).
Google Scholar 
15.Jensen, H. et al. Predation by brown trout (Salmo trutta) along a diversifying prey community gradient. Can. J. Fish. Aquat. Sci. 65, 1831–1841 (2008).Article 

Google Scholar 
16.Jensen, H. et al. Food consumption rates of piscivorous brown trout (Salmo trutta) foraging on contrasting coregonid prey. Fish. Manag. Ecol. 22, 295–306 (2015).Article 

Google Scholar 
17.Haugland, Ø. Langtidsstudie av næringsøkologi og vekst hos storørret i Pasvikvassdraget. Mastergradsoppgave i biologi (Universitetet i Tromsø, Fakultet for Biovitenskap, fiskeri og økonomi, Institutt for arktisk og marin biologi, 2014).18.Gossieaux, P., Bernatchez, L., Sirois, P. & Garant, D. Impacts of stocking and its intensity on effective population size in Brook Charr (Salvelinus fontinalis) populations. Conserv. Genet. 20(4), 729–742 (2019).Article 

Google Scholar 
19.Pinter, K., Epifanio, J. & Unfer, G. Release of hatchery-reared brown trout (Salmo trutta) as a threat to wild populations? A case study from Austria. Fish. Res. 219, 105296 (2019).Article 

Google Scholar 
20.Wringe, B. F., Purchase, C. F. & Fleming, I. A. In search of a “cultured fish phenotype”: A systematic review, meta-analysis and vote-counting analysis. Rev. Fish Biol. Fish. 26(3), 351–373 (2016).Article 

Google Scholar 
21.Gossieaux, P. et al. Effects of genetic origin on phenotypic divergence in Brook Trout populations stocked with domestic fish. Ecosphere 11(5), e03119 (2020).Article 

Google Scholar 
22.Fleming, I. A., Jonsson, B. & Gross, M. R. Phenotypic divergence of sea-ranched, farmed, and wild salmon. Can. J. Fish. Aquat. Sci. 51, 2808–2824 (1994).Article 

Google Scholar 
23.Heath, D. D., Heath, J. W., Bryden, C. A., Johnson, R. M. & Fox, C. W. Rapid evolution of egg size in captive salmon. Science 299, 1738–1740 (2003).ADS 
CAS 
PubMed 
Article 

Google Scholar 
24.Naish, K. A., Seamons, T. R., Dauer, M. B., Hauser, L. & Quinn, T. P. Relationship between effective population size, inbreeding and adult fitness-related traits in a steelhead (Oncorhynchus mykiss) population released in the wild. Mol. Ecol. 22, 1295–1309 (2013).CAS 
PubMed 
Article 

Google Scholar 
25.Van Oosterhout, C., Weetman, D. & Hutchinson, W. F. Estimation and adjustment of microsatellite null alleles in nonequilibrium populations. Mol. Ecol. Notes 6(1), 255–256 (2006).Article 

Google Scholar 
26.Rousset, F. Genepop’007: a complete reimplementation of the Genepop software for Windows and Linux. Mol. Ecol. Resour. 8(6), 103–106 (2008).PubMed 
Article 

Google Scholar 
27.Peakall, R. & Smouse, P. E. GENALEX 6: Genetic analysis in Excel. Population genetic software for teaching and research. Mol. Ecol. Notes 6, 288–295 (2006).Article 

Google Scholar 
28.Peakall, R. & Smouse, P. E. GenAlEx 6.5: Genetic analysis in Excel. Population genetic software for teaching and research-an update. Bioinformatics 28(19), 2537–2539 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
29.Szpiech, Z. A., Jacobsson, M. & Rosenberg, N. A. ADZE: A rarefaction approach for counting alleles private to combinations of populations. Bioinformatics 24(21), 2498–2504 (2008).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
30.Waples, R. S. & Anderson, E. C. Purging putative siblings from population genetic data sets: A cautionary view. Mol. Ecol. 26(5), 1211–1224 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
31.Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155(2), 945–959 (2000).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
32.Jombart, T., Devillard, S. & Balloux, F. Discriminant analysis of principal components: A new method for the analysis of genetically structured populations. BMC Genet. 11, 94 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
33.Pew, J., Muir, P. H., Wang, J. & Frasier, T. R. Related: An R package for analysing pairwise relatedness from codominant molecular markers. Mol. Ecol. Resour. 15(3), 557–561 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
34.Piry, S., Luikart, G. & Cornuet, J.-M. Bottleneck: A computer program for detecting recent reductions in the effective population size using allele frequency data. J. Heredity 90(4), 502–503 (1999).Article 

Google Scholar 
35.Cornuet, J. M. & Luikart, G. Description and power analysis of two tests for detecting recent population bottlenecks from allele frequency data. Genetics 144(4), 2001–2014 (1996).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
36.Peery, M. Z. et al. Reliability of genetic bottleneck tests for detecting recent population declines. Mol. Ecol. 21(14), 3403–3418 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
37.Luikart, G. Usefulness of molecular markers for detecting population bottlenecks and monitoring genetic change. Ph. D. Thesis. (University of Montana, 1997).38.Do, C. et al. NEESTIMATOR v2: Re-implementation of software for the estimation of contemporary effective population size (Ne) from genetic data. Mol. Ecol. Resour. 14, 209–214 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
39.Waples, R. S. & Do, C. LDNE: A program for estimating effective population size from data on linkage disequilibrium. Mol. Ecol. Resour. 8, 753–756 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
40.Zhdanova, O. L. & Pudovkin, A. I. Nb_HetEx: A program to estimate the effective number of breeders. J. Hered. 99(6), 694–695 (2008).PubMed 
Article 

Google Scholar 
41.Nomura, T. Estimation of effective number of breeders from molecular coancestry of single cohort sample. Evol. Appl. 1, 462–474 (2008).PubMed 
PubMed Central 
Article 

Google Scholar 
42.Jones, O. R. & Wang, J. COLONY: A program for parentage and sibship inference from multilocus genotype data. Mol. Ecol. Resour. 10, 551–555 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
43.Wang, J. A. comparison of single-sample estimators of effective population sizes from genetic data. Mol. Ecol. 25, 4692–4711 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
44.Nei, M. & Chesser, R. K. Estimation of fixation indexes and gene diversities. Ann. Hum. Genet. 47(3), 253–259 (1983).CAS 
PubMed 
MATH 
Article 
PubMed Central 

Google Scholar 
45.Jost, L. Gst and its relatives do not measure differentiation. Mol. Ecol. 17(18), 4015–4026 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
46.Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B (Methodological) 57(1), 289–300 (1995).MathSciNet 
MATH 

Google Scholar 
47.R Core Team. R: A Language and Environment for Statistical Computing. https://www.R-project.org/ (R Foundation for Statistical Computing, 2019).48.White, T., van der Ende, J. & Nichols, T. E. Beyond Bonferroni revisited: Concerns over inflated false positive research findings in the fields of conservation genetics, biology, and medicine. Conserv. Genet. 20, 927–937 (2019).Article 

Google Scholar 
49.Falush, D., Stephens, M. & Pritchard, J. K. Inference of population structure using multilocus genotype data: Linked loci and correlated allele frequencies. Genetics 164(4), 1567–1587 (2003).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
50.Hubisz, M. J., Falush, D., Stephens, M. & Pritchard, J. K. Inferring weak population structure with the assistance of sample group information. Mol. Ecol. Resour. 9(5), 1322–1332 (2009).PubMed 
PubMed Central 
Article 

Google Scholar 
51.Miller, M. A., Pfeiffer, W. & Schwartz, T. Creating the CIPRES science gateway for inference of large phylogenetic trees. in 2010 Gateway Computing Environments Workshop (GCE) 1–8 (2010).52.Besnier, F. & Glover, K. A. ParallelStructure: A R package to distribute parallel runs of the population genetics program STRUCTURE on multi-core computers. PLoS ONE 8(7), e70651 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
53.Li, Y.-L. & Liu, J.-X. StructureSelector: A web-based software to select and visualize the optimal number of clusters using multiple methods. Mol. Ecol. Resour. 18(1), 176–177 (2018).PubMed 
Article 

Google Scholar 
54.Puechmaille, S. J. The program structure does not reliably recover the correct population structure when sampling is uneven: Subsampling and new estimators alleviate the problem. Mol. Ecol. Resour. 16(3), 608–627 (2016).PubMed 
Article 

Google Scholar 
55.Kopelman, N. M., Mayzel, J., Jakobsson, M., Rosenberg, N. A. & Mayrose, I. CLUMPAK: A program for identifying clustering modes and packaging population structure inferences across K. Mol. Ecol. Resour. 15(5), 1179–1191 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
56.Anderson, E. C. & Dunham, K. K. The influence of family groups on inferences made with the program structure. Mol. Ecol. Resour. 8, 1219–1229 (2008).CAS 
PubMed 
Article 

Google Scholar 
57.Dray, S. & Dufour, A. The ade4 package: Implementing the duality diagram for ecologists. J. Stat. Softw. 22(4), 1–20 (2007).Article 

Google Scholar 
58.Levene, H. Robust tests for equality of variances. in Contributions to Probability and Statistics: Essays in Honor of Harold Hotelling (Olkin, I., Hotelling, H. et al. eds.). 278–292 (Stanford University Press, 1960).59.Kassambara, A. rstatix: Pipe-Friendly Framework for Basic Statistical Tests. R package version 0.4.0. https://CRAN.R-project.org/package=rstatix (2020).60.Wang, J. An estimator for pairwise relatedness using molecular markers. Genetics 160, 1203–1215 (2002).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
61.White, S. L., Miller, W. L., Dowell, S. A., Bartron, M. L. & Wagner, T. Limited hatchery introgression into wild brook trout (Salvelinus fontinalis) populations despite reoccurring stocking. Evol. Appl. 11(9), 1567–1581 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
62.Lehnert, S. J. et al. Multiple decades of stocking has resulted in limited hatchery introgression in wild brook trout (Salvelinus fontinalis) populations of Nova Scotia. Evol. Appl. 13(5), 1069–1089 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
63.Knudsen, C. M. et al. Comparison of life history traits between first-generation hatchery and wild upper Yakima River spring Chinook salmon. Trans. Am. Fish. Soc. 135, 1130–1144 (2006).Article 

Google Scholar 
64.Hansen, M. M. & Mensberg, K.-L.D. Admixture analysis of stocked brown trout populations using mapped microsatellite DNA markers: Indigenous trout persist in introgressed populations. Biol. Lett. 5, 656–659 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
65.Christie, M. R., Ford, M. J. & Blouin, M. S. On the reproductive success of early-generation hatchery fish in the wild. Evol. Appl. 7, 883–896 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
66.Fraser, D. J. et al. Population correlates of rapid captive-induced maladaptation in a wild fish. Evol. Appl. 12, 1305–1317 (2019).PubMed 
Article 

Google Scholar 
67.Fischer, J. R. et al. Growth, condition, and trophic relations of stocked trout in southern Appalachian mountain streams. Trans. Am. Fish. Soc. 148, 771–784 (2019).CAS 
Article 

Google Scholar 
68.Hendry, A. P. & Day, T. Population structure attributable to reproductive time: Isolation by time and adaptation by time. Mol. Ecol. 14, 901–916 (2005).CAS 
PubMed 
Article 

Google Scholar 
69.Gauthey, Z. et al. Brown trout spawning habitat selection and its effects on egg survival. Ecol. Freshwater Fish 26, 133–140 (2017).Article 

Google Scholar 
70.Dupont, P.-P., Bourret, V. & Bernatchez, L. Interplay between ecological, behavioural and historical factors in shaping the genetic structure of sympatric walleye populations (Sander vitreus). Mol. Ecol. 16, 937–951 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
71.Sandoval-Castillo, J. et al. SWINGER: A user-friendly computer program to establish captive breeding groups that minimize relatedness without pedigree information. Mol. Ecol. Resour. 17, 278–287 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar  More

1.Chapin, F. S. & Díaz, S. Interactions between changing climate and biodiversity: Shaping humanity’s future. PNAS 2117, 6295–6296 (2020).Article 
CAS 

Google Scholar 
2.Thackeray, S. J. et al. Phenological sensitivity to climate across taxa and trophic levels. Nature 535, 241–245 (2016).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
3.Scranton, K. & Amarasekare, P. Predicting phenological shifts in a changing climate. PNAS 114, 13212–13217 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
4.Madrigal-González, J. et al. Disentangling the relative role of climate change on tree growth in an extreme Mediterranean environment. Sci. Total Environ. 642, 619–628 (2018).ADS 
PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
5.Angilletta, M. J. Thermal adaptation: A Theoretical And Empirical Synthesis (Oxford University Press, 2009).Book 

Google Scholar 
6.Andresen, E. Effects of season and vegetation type on community organization of Dung beetles in a tropical dry forest. Biotropica 37, 291–300 (2005).Article 

Google Scholar 
7.Liberal, C. N., Farias, A. M. I. & Meiado, M. V. How habitat change and rainfall affect dung beetle diversity in Caatinga, a Brazilian semi-arid ecosystem. J. Insect Sci. 11, 114 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
8.Nunes, C. A., Braga, R. F., Figueira, J. E. C., Neves, F. D. S. & Fernandes, G. W. Dung beetles along a tropical altitudinal gradient: Environmental filtering on taxonomic and functional diversity. PLoS One 11, e0157442 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
9.da Silva, P. G. & Cassenote, S. Environmental drivers of species composition and functional diversity of dung beetles along the Atlantic Forest-Pampa transition zone. Austral Ecol. 44, 786–799 (2019).Article 

Google Scholar 
10.Alvarado, F., Salomão, R. P., Hernandez-Rivera, Á. & de Araujo Lira, A. F. Different responses of dung beetle diversity and feeding guilds from natural and disturbed habitats across a subtropical elevational gradient. Acta Oecol. 104, 103533 (2020).Article 

Google Scholar 
11.Barragán, F., Moreno, C. E., Escobar, F., Halffter, G. & Navarrete, D. Negative impacts of human land use on dung beetle functional diversity. PLoS One 6, e17976 (2011).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
12.Costa, C. et al. Variegated tropical landscapes conserve diverse dung beetle communities. PeerJ 5, e3125 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
13.Gómez-Cifuentes, A., Gómez, V. C. G., Moreno, C. E. & Zurita, G. A. Tree retention in cattle ranching systems partially preserves dung beetle diversity and functional groups in the semideciduous Atlantic forest: The role of microclimate and soil conditions. Basic Appl. Ecol. 34, 64–74 (2019).Article 

Google Scholar 
14.Salomão, R. P. et al. Urbanization effects on dung beetle assemblages in a tropical city. Ecol. Indic. 103, 665–675 (2019).Article 

Google Scholar 
15.Correa, C. M., Lara, M. A., Puker, A., Noriega, J. A. & Korasaki, V. Quantifying responses of dung beetle assemblages to cattle grazing removal over a short-term in introduced Brazilian pastures. Acta Oecol. 110, 103681 (2021).Article 

Google Scholar 
16.Romero-Alcaraz, E. & Avila, J. M. Effect of elevation and type of habitat on the abundance and diversity of scarabaeoid dung beetles (Scarabaeoidea) assemblages in a Mediterranean area from Southern Iberian Peninsula. Zool. Stud. 39, 351–359 (2000).
Google Scholar 
17.Halffter, G. & Arellano, L. Response of dung beetle diversity to human-induced changes in a tropical landscape. Biotropica 34, 144–154 (2002).Article 

Google Scholar 
18.Rios-Diaz, C. L. et al. Sheep herding in small grasslands promotes dung beetle diversity in a mountain forest landscape. J. Insect Conserv. 25, 13–26 (2021).Article 

Google Scholar 
19.Krell, F. T., Krell-Westerwalbesloh, S., Weiß, I., Eggleton, P. & Linsenmair, K. E. Spatial separation of Afrotropical dung beetle guilds: A trade-off between competitive superiority and energetic constraints (Coleoptera: Scarabaeidae). Ecography 26, 210–222 (2003).Article 

Google Scholar 
20.Verdú, J. R., Díaz, A. & Galante, E. Thermoregulatory strategies in two closely related sympatric Scarabaeus species (Coleoptera: Scarabaeinae). Physiol. Entomol. 29, 32–38 (2004).Article 

Google Scholar 
21.Verdú, J. R., Arellano, L. & Numa, C. Thermoregulation in endotermic dung beetles (Coleoptera: Scarabaeidae): Effect of body size and ecophysiological constraints in flight. J. Insect Physiol. 52, 854–860 (2006).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
22.Verdú, J. R., Arellano, L., Numa, C. & Micó, E. Roles of endothermy in niche differentiation for ball-rolling dung beetles (Coleoptera: Scarabaeidae) along an altitudinal gradient. Ecol. Entomol. 32, 544–551 (2007).Article 

Google Scholar 
23.Verdú, J. R., Alba-Tercedor, J. & Jiménez-Manrique, M. Evidence of different thermoregulatory mechanisms between two sympatric Scarabaeus species using infrared thermography and microcomputer tomography. PLoS One 7, e33914 (2012).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
24.Giménez-Gómez, V. C., Lomáscolo, S. B., Zurita, G. A. & Ocampo, F. Daily activity patterns and thermal tolerance of three sympatric dung beetle species (Scarabaeidae: Scarabaeinae: Eucraniini) from the Monte Desert, Argentina. Neotrop. Entomol. 47, 821–827 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
25.Gómez, V. C. G., Verdú, J. R. & Zurita, G. A. Thermal niche helps to explain the ability of dung beetles to exploit disturbed habitats. Sci. Rep. 10, 1–14 (2020).ADS 
Article 
CAS 

Google Scholar 
26.Gotcha, N., Machekano, H., Cuthbert, R. N. & Nyamukondiwa, C. Heat tolerance may determine activity time in coprophagic beetle species (Coleoptera: Scarabaeidae). Insect Sci. 28, 1076–2086 (2020).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
27.Gallego, B., Verdú, J. R. & Lobo, J. M. Comparative thermoregulation between different species of dung beetles (Coleoptera: Geotrupinae). J. Therm. Biol. 74, 84–91 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
28.Feer, F. Effects of dung beetles (Scarabaeidae) on seeds dispersed by howler monkeys (Alouatta seniculus) in the French Guianan rainforest. J. Trop. Ecol. 15, 1–14 (1999).Article 

Google Scholar 
29.Feer, F. & Pincebourne, S. Diel flight activity and ecological segregation within an assemblage of tropical forest dung and carrion beetles. J. Trop. Ecol. 21, 21–30 (2005).Article 

Google Scholar 
30.Niino, M. et al. Diel flight activity and habitat preference of dung beetles (Coleoptera: Scarabaeidae) in Peninsular Malaysia. Raffles Bull. Zool. 62, 795–804 (2014).
Google Scholar 
31.da Silva, P. G., Lobo, J. M. & Hernandez, M. I. M. The role of habitat and daily activity patterns in explaining the diversity of mountain Neotropical dung beetle assemblages. Austral Ecol. 44, 300–312 (2019).Article 

Google Scholar 
32.Cambefort, Y. Dung beetles in tropical savannas in Africa. In Dung Beetle Ecology (eds Hanski, I. & Camberfort, Y.) 156–178 (Princeton University Press, 1991).Chapter 

Google Scholar 
33.Hernández, M. I. M. The night and day of dung beetles (Coleoptera, Scarabaeidae) in the Serra do Japi, Brazil: Elytra colour related to daily activity. Rev. Bras. Entomol. 46, 597–600 (2002).Article 

Google Scholar 
34.Krell-Westerwalbesloh, S., Krell, F. T. & Eduard Linsenmair, K. Diel separation of Afrotropical dung beetle guilds—avoiding competition and neglecting resources (Coleoptera: Scarabaeoidea). J. Nat. Hist. 38, 2225–2249 (2004).Article 

Google Scholar 
35.Rajesh, T. P., Prashanth Ballullaya, U., Unni, A. P., Parvathy, S. & Sinu, P. A. Interactive effects of urbanization and year on invasive and native ant diversity of sacred groves of south India. Urban Ecosyst. 23, 1335–1348 (2020).Article 

Google Scholar 
36.Prashanth Ballullaya, U. et al. Stakeholder motivation for the conservation of sacred groves in south India: An analysis of environmental perceptions of rural and urban neighbourhood communities. Land Use Policy 89, 104–213 (2019).Article 

Google Scholar 
37.Manoj, K. et al. Diversity of platygastridae in leaf litter and understory layers of tropical rainforests of the Western Ghats biodiversity hotspot, India. Environ. Entomol. 46, 685–692 (2017).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
38.Rajesh, T., Unni, A., Prashanth Ballullaya, U., Manoj, K. & Sinu, P. A. An insight into the quality of sacred groves—an island habitat—using leaf-litter ants as an indicator in a context of urbanization. J. Trop. Ecol. 37, 82–90. https://doi.org/10.1017/S0266467421000134 (2021).Article 

Google Scholar 
39.Asha, G., Navya, K. K., Rajesh, T. P. & Sinu, P. A. Roller dung beetles of dung piles suggest habitats are alike, but that of guarding pitfall traps suggest habitat are different. J. Trop. Ecol. https://doi.org/10.1017/S0266467421000225 (2021) (Accepted).Article 

Google Scholar 
40.Krell, F. T. Dung beetle sampling protocols. Denver Museum of Nature and Science Technical Report 6, 1–11 (2007).
Google Scholar 
41.Arrow, G. J. The Fauna of British India including Ceylon and Burma, Coleoptera: Lamellicornia (Coprinae) (Taylor and Francis, 1931).
Google Scholar 
42.Sabu, T. K., Vinod, K. V. & Vineesh, P. J. Guild structure, diversity and succession of dung beetles associated with Indian elephant dung in South Western Ghats forests. J. Insect Sci. 6, 6–17 (2006).Article 

Google Scholar 
43.Beiroz, W. et al. Dung beetle community dynamics in undisturbed tropical forests: Implications for ecological evaluations of land-use change. Insect Conserv. Divers. 10, 94–106 (2017).Article 

Google Scholar 
44.De Caceres, M. & Legendre, P. Associations between species and groups of sites: Indices and statistical inference. Ecology 90, 3566–3574 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
45.McGeoch, M. A., van Rensburg, B. J. & Botes, A. The verification and application of bioindicators: A case of study of dung beetles in a savanna ecosystem. J. Appl. Ecol. 39, 661–672 (2002).Article 

Google Scholar 
46.Hsieh, T. C., Ma, K. H. & Chao, A. iNEXT: Interpolation and extrapolation for species diversity. R package version 2.0.12 (2016).47.Oksanen, J. et al. vegan: Community Ecology Package. R package, version 2.5‐3 (2018).48.Caveney, S., Scholtz, C. H. & McIntyre, P. Patterns of daily flight activity in Onitine dung beetles (Scarabaeinae: Onitini). Oecologia 103, 444–452 (1995).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
49.Spector, S. & Ayzama, S. Rapid turnover and edge effects in dung beetle assemblages (Scarabaeidae) at a Bolivian Neotropical forest-savanna ecotone. Biotropica 35, 394–404 (2003).
Google Scholar 
50.Escobar, F. S. Diversity and composition of dung beetle (Scarabaeinae) assemblages in a heterogeneous Andean landscape. Trop. Zool. 17, 123–136 (2004).Article 

Google Scholar 
51.Lobo, J. M., Lumaret, J. P. & Jay-Robert, P. Sampling dung beetles in French Mediterranean area: Effects of abiotic factors and farm practices. Pedobiologia 42, 252–266 (1998).
Google Scholar 
52.Zamora, J., Verdu, J. R. & Galante, E. Species richness in Mediterranean agroecosystems: Spatial and temporal analysis for biodiversity conservation. Biol. Conserv. 134, 113–121 (2007).Article 

Google Scholar 
53.Calatayud, J. et al. Multidimensionality in the thermal niches of dung beetles could limit species’ responses to temperature changes. BioRxiv. https://doi.org/10.1101/2020.11.15.383612(2021) (2020).Article 

Google Scholar 
54.Iannuzzi, L., Salomão, R. P., Costa, F. C. & Liberal, C. N. Environmental patterns and daily activity of dung beetles (Coleoptera: Scarabaeidae) in the Atlantic Rainforest of Brazil. Entomotropica 31, 196–207 (2016).
Google Scholar 
55.Venugopal, K. S., Thomas, S. K. & Flemming, A. T. Diversity and community structure of dung beetles (Coleoptera: Scarabaeinae) associated with semi-urban fragmented agricultural land in the Malabar coast in southern India. JoTT 4, 2685–2692 (2012).
Google Scholar 
56.Price, P. W. Insect Ecology (Wiley, 1984).
Google Scholar 
57.Hanski, I. & Cambefort, Y. Dung Beetle Ecology (Princeton University Press, 1991).Book 

Google Scholar 
58.Finn, J. A. & Gittings, T. A review of competition in north temperate dung beetle communities. Ecol. Entomol. 28, 1–13 (2003).Article 

Google Scholar 
59.Doube, B. Dung beetles of southern Africa. In Dung Beetle Ecology (eds Hanski, I. & Cambefort, Y.) 133–155 (Princeton University Press, 1991).Chapter 

Google Scholar 
60.Gómez-Cifuentes, A., Munevar, A., Gimenez, V. C., Gatti, M. G. & Zurita, G. A. Influence of land use on the taxonomic and functional diversity of dung beetles (Coleoptera: Scarabaeinae) in the southern Atlantic forest of Argentina. J. Insect Conserv. 21, 147–156 (2017).Article 

Google Scholar 
61.Estrada, A. & Coates-Estrada, R. Howler monkeys (Alouatta palliate), dung beetles (Scarabaeidae) and seed dispersal-ecological interactions in the tropical rain-forest of Los-Tuxtlas, Mexico. J. Trop. Ecol. 7, 459–474 (1991).Article 

Google Scholar 
62.Slade, E. M., Mann, D. J., Villanueva, J. F. & Lewis, O. T. Experimental evidence for the effects of dung beetle functional group richness and composition on ecosystem function in a tropical forest. J. Anim. Ecol. 76, 1084–1104 (2007).Article 

Google Scholar 
63.Vinod, K. V. & Sabu, T. K. Species composition and community structure of dung beetles attracted to dung of gaur and elephant in the moist forests of South Western Ghats. J. Insect Sci. 7, 1–14 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
64.Chao, A., Simon-Freeman, R. & Grether, G. Patterns of Niche partitioning and alternative reproductive strategies in an east African dung beetle assemblage. J. Insect Behav. 26, 525–539 (2013).Article 

Google Scholar 
65.Sowig, P. Habitat selection and offspring survival rate in three paracoprid dung beetles: The influence of soil type and soil moisture. Ecography 18, 147–154 (1995).Article 

Google Scholar 
66.Sowig, P. Brood care in the dung beetle Onthophagus vacca (Coleoptera: Scarabaeidae): The effect of soil moisture on time budget, nest structure, and reproductive success. Ecography 19, 254–258 (1996).
Google Scholar 
67.Nichols, E. et al. Ecological functions and ecosystem services provided by Scarabaeinae dung beetles. Biol. Conserv. 141, 1461–1474 (2008).Article 

Google Scholar 
68.Latha, T. & Thomas, S. K. Edge effect on roller dung beetles (Coleoptera: Scarabaeidae: Scarabaeinae) in the moist South Western Ghats. J. Entomol. 8, 1044–1047 (2020).
Google Scholar 
69.Bartholomew, G. A. & Heinrich, B. Endothermy in African dung beetles during flight, ball making, and ball rolling. J. Exp. Biol. 73, 65–83 (1978).Article 

Google Scholar 
70.Boonrotpong, S., Sotthibandhu, S. & Pholpunthin, C. Species composition of dung beetles in the primary and secondary forests at Ton Nga Chang Wildlife Sanctuary. Sci. Asia 30, 59–65 (2004).Article 

Google Scholar 
71.Davis, A. J. Does reduced-impact logging help preserve biodiversity in tropical rainforest? A case study from Borneo using dung beetles (Coleoptera: Scarabaeoidea) as indicators. Environ. Entomol. 29, 467–475 (2000).Article 

Google Scholar 
72.Davis, A. J. et al. Dung beetles as indicators of change in the forests of northern Borneo. J. Appl. Ecol. 38, 593–616 (2001).Article 

Google Scholar 
73.Jayaprakash, S. B. Taxonomy guild structure and dung specificity of dung beetles in a coffee plantation belt in south Wayanad. Ph.D. thesis, University of Calicut. http://hdl.handle.net/10603/222605 (2018). More