Philippa Kaur

1.Herrero, M. et al. Livestock and the environment: What have we learned in the past decade?. Annu. Rev. Environ. Resour. 40, 177–202 (2015).
Google Scholar 
2.Robinson, T. P. et al. Mapping the global distribution of livestock. PLoS ONE 9, e96084 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
3.Firbank, L. G., Petit, S., Smart, S., Blain, A. & Fuller, R. J. Assessing the impacts of agricultural intensification on biodiversity: A British perspective. Philos. Trans. R. Soc. B: Biol. Sci. 363, 777–787 (2007).
Google Scholar 
4.Laurance, W. F., Sayer, J. & Cassman, K. G. Agricultural expansion and its impacts on tropical nature. Trends Ecol. Evol. 29, 107–116 (2014).PubMed 

Google Scholar 
5.Steinfeld, H., de Haan, C. & Blackburn, H. Livestock—Environment Interactions 88 (WRENmedia, 1997).
Google Scholar 
6.Eldridge, D. J., Poore, A. G. B., Ruiz-Colmenero, M., Letnic, M. & Soliveres, S. Ecosystem structure, function, and composition in rangelands are negatively affected by livestock grazing. Ecol. Appl. 26, 1273–1283 (2016).PubMed 

Google Scholar 
7.Schieltz, J. M. & Rubenstein, D. I. Evidence based review: Positive versus negative effects of livestock grazing on wildlife. What do we really know?. Environ. Res. Lett. 11, 113003 (2016).ADS 

Google Scholar 
8.Cornwell, W. K. & Ackerly, D. D. Community assembly and shifts in plant trait distributions across an environmental gradient in coastal California. Ecol. Monogr. 79, 109–126 (2009).
Google Scholar 
9.Kraft, N. J. B. et al. Community assembly, coexistence and the environmental filtering metaphor. Funct. Ecol. 29, 592–599 (2015).
Google Scholar 
10.Keddy, P. A. Assembly and response rules: Two goals for predictive community ecology. J. Veg. Sci. 3, 157–164 (1992).
Google Scholar 
11.Pärtel, M., Zobel, M., Zobel, K., van der Maarel, E. & Partel, M. The species pool and its relation to species richness: Evidence from Estonian plant communities. Oikos 75, 111–117 (1996).
Google Scholar 
12.Temperton, V., Hobbs, R. J., Nuttle, T. & Halle, S. Assembly Rules and Restoration Ecology. Bridging the Gap Between Theory and Practice (Island Press, 2004).
Google Scholar 
13.Leibold, M. A. Similarity and local co-existence of species in regional biotas. Evol. Ecol. 12, 95–110 (1998).
Google Scholar 
14.Hortal, J. et al. Ice age climate, evolutionary constraints and diversity patterns of European dung beetles: Ice age determines European scarab diversity. Ecol. Lett. 14, 741–748 (2011).PubMed 

Google Scholar 
15.de Bello, F., Lepš, J. & Sebastià, M.-T. Variations in species and functional plant diversity along climatic and grazing gradients. Ecography 29, 801–810 (2006).
Google Scholar 
16.Reymond, A., Purcell, J., Cherix, D., Guisan, A. & Pellissier, L. Functional diversity decreases with temperature in high elevation ant fauna: Functional diversity in high elevation ant. Ecol. Entomol. 38, 364–373 (2013).
Google Scholar 
17.Safi, K. et al. Understanding global patterns of mammalian functional and phylogenetic diversity. Philos. Trans. R. Soc. B 366, 2536–2544 (2011).
Google Scholar 
18.Mason-Romo, E. D., Farías, A. A. & Ceballos, G. Two decades of climate driving the dynamics of functional and taxonomic diversity of a tropical small mammal community in western Mexico. PLoS ONE 12, e0189104 (2017).PubMed 
PubMed Central 

Google Scholar 
19.Wen, Z. et al. Functional diversity overrides community-weighted mean traits in linking land-use intensity to hydrological ecosystem services. Sci. Total Environ. 682, 583–590 (2019).ADS 
CAS 
PubMed 

Google Scholar 
20Corbelli, J. M. et al. Integrating taxonomic, functional and phylogenetic beta diversities: Interactive effects with the biome and land use across taxa. PLoS ONE 10, e0126854 (2015).PubMed 
PubMed Central 

Google Scholar 
21.Flynn, D. F. B. et al. Loss of functional diversity under land use intensification across multiple taxa. Ecol. Lett. 12, 22–33 (2009).PubMed 

Google Scholar 
22.Spector, S. Scarabaeine dung beetles (Coleoptera: Scarabaeidae: Scarabaeinae): An invertebrate focal taxon for biodiversity research and conservation. Coleopt. Bull. 60, 71–83 (2006).
Google Scholar 
23.Gardner, T. A. et al. The cost-effectiveness of biodiversity surveys in tropical forests: Cost-effectiveness of biodiversity surveys. Ecol. Lett. 11, 139–150 (2008).PubMed 

Google Scholar 
24.Mason, N. W. H., Mouillot, D., Lee, W. G. & Wilson, J. B. Functional richness, functional evenness and functional divergence: The primary components of functional diversity. Oikos 111, 112–118 (2005).
Google Scholar 
25.Villéger, S., Mason, N. W. H. & Mouillot, D. New multidimensional functional diversity indices for a multifaceted framework in functional ecology. Ecology 89, 2290–2301 (2008).PubMed 

Google Scholar 
26.Laliberté, E. & Legendre, P. A distance-based framework for measuring functional diversity from multiple traits. Ecology 91, 299–305 (2010).PubMed 

Google Scholar 
27.Audino, L. D., Louzada, J. & Comita, L. Dung beetles as indicators of tropical forest restoration success: Is it possible to recover species and functional diversity?. Biol. Cons. 169, 248–257 (2014).
Google Scholar 
28.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 

Google Scholar 
29.Correa, C. M. A., Braga, R. F., Puker, A. & Korasaki, V. Patterns of taxonomic and functional diversity of dung beetles in a human-modified variegated landscape in Brazilian Cerrado. J. Insect Conserv. 23, 89–99 (2019).
Google Scholar 
30.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).
Google Scholar 
31.Guerra Alonso, C. B., Zurita, G. A. & Bellocq, M. I. Dung beetles response to livestock management in three different regional contexts. Sci. Rep. 10, 3702 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
32de Siqueira Neves, F. et al. Successional and seasonal changes in a community of dung beetles (Coleoptera: Scarabaeinae) in a Brazilian tropical dry forest. Nat. Conserv. 08, 160–164 (2010).
Google Scholar 
33Kottek, M., Grieser, J., Beck, C., Rudolf, B. & Rubel, F. World Map of the Köppen-Geiger climate classification updated. Meteorol. Z. 15, 259–263 (2006).
Google Scholar 
34.Brown, A. La situación ambiental Argentina 2005 (Fundación Vida Silvestre Argentina, 2006).
Google Scholar 
35.Larsen, T. H., Lopera, A. & Forsyth, A. Extreme trophic and habitat specialization by Peruvian dung beetles (Coleoptera: Scarabaeidae: Scarabaeinae). Coleopt. Bull. 60, 315–324 (2006).
Google Scholar 
36Vaz-de-Mello, F. Z. A Multilingual Key to the Genera and Subgenera of the Subfamily Scarabaeinae of the New World (Coleoptera: Scarabaeidae) (Magnolia Press, 2011).
Google Scholar 
37.Braun-Blanquet, J. Fitosociología [Phytosociology]. Bases para el estudio de las comunidades vegetales [Basis for the study of plant communities] 820 (Editorial H. Blume, 1979).
Google Scholar 
38.Fick, S. E. & Hijmans, R. J. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).
Google Scholar 
39Scholtz, C. H., Davis, A. L. V. & Kryger, U. Evolutionary Biology and Conservation of Dung Beetles (Pensoft, 2009).
Google Scholar 
40.Simmons, L. W. & Ridsdill-Smith, J. Reproductive competition and its impact on the evolution and ecology of dung beetles. In Ecology and Evolution of Dung Beetles (eds Simmons, L. W. & Ridsdill-Smith, T. J.) 1–20 (Wiley, 2011). https://doi.org/10.1002/9781444342000.ch1.Chapter 

Google Scholar 
41.Vaz-de-Mello, F. Scarabaeidae in Catálogo Taxonômico da Fauna do Brasil. Catálogo Taxonômico da Fauna do Brasil. http://fauna.jbrj.gov.br/fauna/faunadobrasil/128171 (2018).42.Zunino, M. Food relocation behaviour: A multivalent strategy of Coleoptera. In Advances in Coleopterology (eds Zunino, M. et al.) 297–314 (AEC, 1991).
Google Scholar 
43.LaBarbera, M. Analyzing body size as a factor in ecology and evolution. Ann. Rev. Ecol. Syst. 20, 97–117 (1989).
Google Scholar 
44Soto, C. S., Giombini, M. I., Giménez Gómez, V. C. & Zurita, G. A. Phenotypic differentiation in a resilient dung beetle species induced by forest conversion into cattle pastures. Evol. Ecol. 33, 385–402 (2019).
Google Scholar 
45.Laliberté, E., Legendre, P. & Shipley, B. Package ‘FD’. Measuring Functional Diversity (FD) from Multiple Traits, and Other Tools for Functional Ecology (2014).46.Gower, J. C. A general coefficient of similarity and some of its properties. Biometrics 27, 857 (1971).
Google Scholar 
47.Pavoine, S., Vallet, J., Dufour, A.-B., Gachet, S. & Daniel, H. On the challenge of treating various types of variables: Application for improving the measurement of functional diversity. Oikos 118, 391–402 (2009).
Google Scholar 
48.Moran, P. A. P. Notes on continuous stochastic phenomena. Biometrika 37, 17–23 (1950).MathSciNet 
CAS 
PubMed 
MATH 

Google Scholar 
49.Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems: Data exploration. Methods Ecol. Evol. 1, 3–14 (2010).
Google Scholar 
50.Lavorel, S. et al. Assessing functional diversity in the field—Methodology matters!. Funct. Ecol. 22, 134–147 (2008).
Google Scholar 
51.Oksanen, J. et al. vegan: Community Ecology Package (2017).52.Clarke, K. R. & Green, R. H. Statistical design and analysis for a ‘biological effects’ study. Mar. Ecol. Prog. Ser. 46, 213–226 (1988).ADS 

Google Scholar 
53.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).
Google Scholar 
54.Giraldo, C., Escobar, F., Chará, J. D. & Calle, Z. The adoption of silvopastoral systems promotes the recovery of ecological processes regulated by dung beetles in the Colombian Andes: Ecological processes regulated by dung beetles. Insect Conserv. Divers. 4, 115–122 (2011).
Google Scholar 
55.Nichols, E. et al. Trait-dependent response of dung beetle populations to tropical forest conversion at local and regional scales. Ecology 94, 180–189 (2013).PubMed 

Google Scholar 
56Gómez-Cifuentes, A., Giménez Gómez, V. C., 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).
Google Scholar 
57.Cerullo, G. R., Edwards, F. A., Mills, S. C. & Edwards, D. P. Tropical forest subjected to intensive post-logging silviculture maintains functionally diverse dung beetle communities. For. Ecol. Manage. 444, 318–326 (2019).
Google Scholar 
58.Filloy, J., Zurita, G. A., Corbelli, J. M. & Bellocq, M. I. On the similarity among bird communities: Testing the influence of distance and land use. Acta Oecol. 36, 333–338 (2010).ADS 

Google Scholar 
59.Chown, S. L., Sørensen, J. G. & Terblanche, J. S. Water loss in insects: An environmental change perspective. J. Insect Physiol. 57, 1070–1084 (2011).CAS 
PubMed 

Google Scholar 
60.Duncan, F. D. & Byrne, M. J. Discontinuous gas exchange in dung beetles: Patterns and ecological implications. Oecologia 122, 452–458 (2000).ADS 
CAS 
PubMed 

Google Scholar 
61.Lobo, J. M., Lumaret, J.-P. & Jay-Robert, P. Sampling dung beetles in the French Mediterranean area: Effects of abiotic factors and farm practices. Pedobiología 42(3), 252–266 (1998).
Google Scholar 
62.Navarrete, D. & Halffter, G. Dung beetle (Coleoptera: Scarabaeidae: Scarabaeinae) diversity in continuous forest, forest fragments and cattle pastures in a landscape of Chiapas, Mexico: The effects of anthropogenic changes. Biodivers. Conserv. 17, 2869–2898 (2008).
Google Scholar 
63.Verdú, J. R., Arellano, L. & Numa, C. Thermoregulation in endothermic dung beetles (Coleoptera: Scarabaeidae): Effect of body size and ecophysiological constraints in flight. J. Insect Physiol. 52, 854–860 (2006).PubMed 

Google Scholar 
64.Davis, A. J., Huijbregts, H. & Krikken, J. The role of local and regional processes in shaping dung beetle communities in tropical forest plantations in Borneo. Glob. Ecol. 9, 281–292 (2000).
Google Scholar 
65.Tuff, K. T., Tuff, T. & Davies, K. F. A framework for integrating thermal biology into fragmentation research. Ecol. Lett. 19, 361–374 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
66.Davis, A. L. V. Habitat fragmentation in southern Africa and distributional response patterns in five specialist or generalist dung beetle families (Coleoptera). Afr. J. Ecol. 32, 192–207 (1994).
Google Scholar 
67.Halffter, G. & Arellano, L. Response of dung beetle diversity to human-induced changes in a tropical landscape. Biotropica 34, 144–154 (2002).
Google Scholar 
68.Hill, C. Habitat specificity and food preferences of an assemblage of tropical Australian dung beetles. J. Trop. Ecol. 12, 449–460 (1996).
Google Scholar 
69.Supp, S. R. & Ernest, S. K. M. Species-level and community-level responses to disturbance: A cross-community analysis. Ecology 95, 1717–1723 (2014).PubMed 

Google Scholar 
70.Davis, A. L. V., Scholtz, C. H. & Deschodt, C. Multi-scale determinants of dung beetle assemblage structure across abiotic gradients of the Kalahari-Nama Karoo ecotone, South Africa. J. Biogeogr. 35, 1465–1480 (2008).
Google Scholar 
71.Nervo, B., Tocco, C., Caprio, E., Palestrini, C. & Rolando, A. The effects of body mass on dung removal efficiency in dung beetles. PLoS ONE 9, e107699 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
72.Bui, V. B., Ziegler, T. & Bonkowski, M. Morphological traits reflect dung beetle response to land use changes in tropical karst ecosystems of Vietnam. Ecol. Ind. 108, 105697 (2020).
Google Scholar 
73.Giménez Gómez, V. C., Verdú, J. R. & Zurita, G. A. Thermal niche helps to explain the ability of dung beetles to exploit disturbed habitats. Sci. Rep. 10, 13364 (2020).ADS 
PubMed 
PubMed Central 

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

Google Scholar 
75.Gómez-Cifuentes, A., Vespa, N., Semmartín, M. & Zurita, G. Canopy cover is a key factor to preserve the ecological functions of dung beetles in the southern Atlantic Forest. Appl. Soil. Ecol. 154, 103652 (2020).
Google Scholar 
76.Fernández, P. D. et al. Understanding the distribution of cattle production systems in the South American Chaco. J. Land Use Sci. 15, 52–68 (2020).
Google Scholar 
77Grau, H. R. & Aide, M. Globalization and land-use transitions in Latin America. Ecol. Soc. 13, 16 (2008).
Google Scholar 
78.Mastrangelo, M. E. & Gavin, M. C. Trade-offs between cattle production and bird conservation in an agricultural frontier of the Gran Chaco of Argentina. Conserv. Biol. 26, 1040–1051 (2012).PubMed 

Google Scholar 
79.Macchi, L. et al. Thresholds in forest bird communities along woody vegetation gradients in the South American Dry Chaco. J. Appl. Ecol. 56, 629–639 (2019).
Google Scholar 
80.Díaz, S. & Cabido, M. Vive la différence: Plant functional diversity matters to ecosystem processes. Trends Ecol. Evol. 16, 646–655 (2001).
Google Scholar 
81.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, 1094–1104 (2007).PubMed 

Google Scholar 
82.Ortega-Martínez, I. J., Moreno, C. E. & Escobar, F. A dirty job: manure removal by dung beetles in both a cattle ranch and laboratory setting. Entomol. Exp. Appl. 161, 70–78 (2016).
Google Scholar  More

1.

2.Li, Q. et al. Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia. N. Engl. J. Med. 382, 1199 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
3.Anderson, R. M., Heesterbeek, H., Klinkenberg, D. & Hollingsworth, T. D. How will country-based mitigation measures influence the course of the COVID-19 epidemic? Lancent 395, 931 (2020).CAS 

Google Scholar 
4.WHO. Coronavirus disease (COVID 2019) situation report-30.5.Linton, N. M. et al. Incubation period and other epidemiological characteristics of 2019 novel coronavirus infections with right truncation: A statistical analysis of publicly available case data. J. Clin. Med. 9, 538 (2020).PubMed Central 

Google Scholar 
6.
https://www.washingtonpost.com/graphics/2020/national/states-reopening-coronavirus-map/
7.Kaufman, G. B. et al. Comparing associations of state reopening strategies with COVID-19 burden. J. Gen. Intern. Med. 35, 3627 (2020).PubMed 
PubMed Central 

Google Scholar 
8.Woolf, S. H. et al. Excess deaths from COVID-19 and other causes, March-July 2020. JAMA 324, 1562 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
9.Faust, J. S. et al. All-cause excess mortality and COVID-19-related mortality among US adults aged 25–44 years, March–July 2020. JAMA 325, 785 (2021).CAS 
PubMed 

Google Scholar 
10.Huppert, A. & Katriel, G. Mathematical modelling and prediction in infectious disease epidemiology. Clin. Microbiol. Infect. 19, 999 (2003).
Google Scholar 
11.Kermack, W. O. & McKendrick, A. G. A contribution to the mathematical theory of epidemics. Proc. R. Soc. Lond. 115, 700 (1927).ADS 
MATH 

Google Scholar 
12.Crokidakis, C. Modeling the early evolution of the COVID-19 in Brazil: Results from a susceptible-infectious-quarantined-recovered (SIQR) model. Int. J. Mod. Phys. C 31, 2050135 (2020).ADS 
MathSciNet 
CAS 

Google Scholar 
13.Bin, M. et al. Post-lockdown abatement of COVID-19 by fast periodic switching. PLoS Comput. Biol. 17, e1008604 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
14.Pedersen, M. G., & Meneghini, M. Quantifying undetected COVID-19 cases and effects of containment measures in Italy: Predicting phase 2 dynamics. https://doi.org/10.13140/RG.2.2.11753.85600 (2020).15.Calafiore, G. C., Novara, C., & Possieri, C. A Modified SIR Model for the COVID-19 Contagion in Italy. arXiv:2003.14391 (2020).16.Bastos, S. B., & Cajueiro, D. O. Modeling and forecasting the early evolution of the Covid-19 pandemic in Brazil. arXiv:2003.14288 (2020).17.Gaeta, G. Chaos, social distancing versus early detection and contacts tracing in epidemic management. Solitons Fractals 140, 110074 (2020).MathSciNet 

Google Scholar 
18.Gaeta, G. Asymptomatic infectives and R0 for COVID. arXiv:2003.14098 (2020).19.te Vrugt, M., Bickmann, J., & Wittkowski, R. Effects of social distancing and isolation on epidemic spreading: a dynamical density functional theory model. arXiv:2003.13967 (2020).20.Schulz, R. A., Coimbra-Araújo, C. H., & Costiche, S. W. S. COVID-19: A model for studying the evolution of contamination in Brazil. arXiv:2003.13932 (2020).21.Zhang, Y., Yu, X., Sun, H., Tick, Geoffrey R., Wei, W., & Jin, B. COVID-19 infection and recovery in various countries: Modeling the dynamics and evaluating the non-pharmaceutical mitigation scenarios. arXiv:2003.13901 (2020).22.Dell’Anna, L. Solvable delay model for epidemic spreading: the case of Covid-19 in Italy. Sci. Rep. 10, 15763 (2020).ADS 
PubMed 
PubMed Central 

Google Scholar 
23.Sonnino, G. & Nardone, P. Annals of clinical and medical case reports dynamics of the COVID-19 comparison between the theoretical predictions and the real data, and predictions about returning to normal life. Ann. Clin. Med. Case Rep. 4, 1 (2020).
Google Scholar 
24.Notari, A. Temperature dependence of COVID-19 transmission. arXiv:2003.12417 (2020).25.Amaro, J. E. The D model for deaths by COVID-19. arXiv:2003:13747 (2020).26.Simha, A., Prasad, R. V., & Narayana, S. A simple stochastic SIR model for COVID 19 infection dynamics for Karnataka: Learning from Europe. arXiv:2003.11920 (2020).27.Acioli, P. H. Diffusion as a first model of spread of viral infection. arXiv:2003.11449 (2020).28.Zullo, F. Some numerical observations about the COVID-19 epidemic in Italy. arXiv:2003.11363 (2020).29.Sameni, R. Mathematical modeling of epidemic diseases; a case study of the COVID-19 coronavirus. arXiv:2003.11371 (2020).30.Radulescu, A., & Cavanagh, K. Management strategies in a SEIR model of COVID 19 community spread. arXiv:2003.11150 (2020).31.Roques, L., Klein, E., Papaix, J. & Soubeyrand, S. Using early data to estimate the actual infection fatality ratio from COVID-19 in France. MDPI Biol. 9, 97 (2020).CAS 

Google Scholar 
32.Teles, P. Predicting the evolution Of SARS-Covid-2 in Portugal using an adapted SIR Model previously used in South Korea for the MERS outbreak. arXiv:2003.10047 (2020).33.Piccolomini, E. L., & Zama, F. Preliminary analysis of COVID-19 spread in Italy with an adaptive SEIRD model. arXiv:2003.09909 (2020).34.Brugnano, L., & Iavernaro, F. A multi-region variant of the SIR model and its extensions. arXiv:2003.09875 (2020).35.Giordano, G. et al. Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy. the COVID19 IRCCS San Matteo Pavia Task Force. Nat. Med. 16, 855 (2020).
Google Scholar 
36.Zlatić, V., Barjašć, I., Kadović, A., Štefančić, H. & Gabrielli, A. Bi-stability of SUDR+ K model of epidemics and test kits applied to COVID-19. Nonlinear Dyn. 101, 1635 (2020).
Google Scholar 
37.Baker, R. Reactive Social distancing in a SIR model of epidemics such as COVID-19. arXiv:2003.08285 (2020).38.Biswas, K., Khaleque, A., & Sen, P. Covid-19 spread: Reproduction of data and prediction using a SIR model on Euclidean network. arXiv:2003.07063 (2020).39.Zhang, J., Wang, L., & Wang, J. SIR model-based prediction of infected population of coronavirus in Hubei Province. arXiv:2003.06419 (2020).40.Chen, Y.-C., Lu, P.-E., Chang, C.-S., & Liu, T.-H. A Time-dependent SIR model for COVID-19 with Undetectable Infected Persons. arXiv:2003.00122 (2020).41.Lloyd, A. L. Realistic distributions of infectious periods in epidemic models: Changing patterns of persistence and dynamics. Theor. Popul. Biol. 60, 59 (2001).CAS 
PubMed 

Google Scholar 
42.Fokas, A. S., Dikaios, N. & Kastis, G. A. Mathematical models and deep learning for predicting the number of individuals reported to be infected with SARS-CoV-2. J. R. Soc. Interface 17, 20200494 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
43.Vadyala, S. R., Betgeri, S. N., Sherer, E. A., & Amritphale, A. Prediction of the number of COVID-19 confirmed cases based on K-means-LSTM. arXiv:2006.14752 (2020).44.Fokas, A. S., Cuevas-Maraver, J. & Kevrekidis, P. G. A quantitative framework for exploring exit strategies from the COVID-19 lockdown. Chaos Solitons Fractals 140, 11024 (2020).MathSciNet 

Google Scholar 
45.Coopera, I., Mondal, A. & Antonopoulos, C. G. A SIR model assumption for the spread of COVID-19 in different communities. Chaos Solitons Fractals 139, 110057 (2020).MathSciNet 

Google Scholar 
46.Bertozzi, A. L., Franco, E., Mohler, G., Short, M. B. & Sledge, D. The challenges of modeling and forecasting the spread of COVID-19. PNAS 117, 16732 (2020).MathSciNet 
CAS 
PubMed 
PubMed Central 

Google Scholar 
47.Prem, K. et al. The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: A modelling study. The Lancet Public Health 5, e261 (2020).PubMed 
PubMed Central 

Google Scholar 
48.He, S., Peng, Y. & Sun, K. SEIR modeling of the COVID-19 and its dynamics. Nonlinear Dyn. 101, 1667 (2020).
Google Scholar 
49.Mwalili, S., Kimathi, M., Ojiambo, V., Gathungu, D. & Mbogo, R. SEIR model for COVID-19 dynamics incorporating the environment and social distancing. BMC Res. Notes 13, 352 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
50.Dehning, J. et al. Inferring change points in the spread of COVID-19 reveals the effectiveness of interventions. Science 369, 160 (2020).
Google Scholar 
51.Atkeson, A., Kopecky, K. A., & Zha, T. A. Estimating and forecasting disease scenarios for COVID-19 with an SIR Model. NBER Working Paper w27335 (2020).52.Wang, N., Fu, Y., Zhang, H. & Shi, H. An evaluation of mathematical models for the outbreak of COVID-19. Precis. Clin. Med. 3, 85 (2020).
Google Scholar 
53.Chang, S. et al. Mobility network models of COVID-19 explain inequities and inform reopening. Nature 589, 82 (2021).ADS 
CAS 
PubMed 

Google Scholar 
54.Alfano, V. & Ercolano, S. The efficacy of lockdown against COVID-19: A cross-country panel analysis. Appl. Health Econ. Health Policy 18, 509 (2020).PubMed 

Google Scholar 
55.Arshed, N., Meo, M. S. & Farooq, F. Empirical assessment of government policies and flattening of the COVID19 curve. J. Public Aff. 20, e2333 (2000).
Google Scholar 
56.Auger, K. A. et al. Association between statewide school closure and COVID-19 incidence and mortality in the US. JAMA 324, 859 (2020).CAS 
PubMed 

Google Scholar 
57.Banerjee, T. & Nayak, A. Coping with being cooped up: Social distancing during COVID-19 among 60+ in the United States. Revista Panamericana de Salud Pública. 44, e81 (2020).
Google Scholar 
58.Castex, G., Dechter, E. & Lorca, M. COVID-19: The impact of social distancing policies, cross-country analysis. Econ. Disasters Clim. Change 5, 135 (2021).
Google Scholar 
59.Bennett, M. All things equal? Heterogeneity in policy effectiveness against COVID-19 spread in Chile. World Dev. 137, 105208 (2021).PubMed 

Google Scholar 
60.Castillo, R. C., Staguhn, E. D. & Weston-Farber, E. The effect of state-level stay-at-home orders on COVID-19 infection rates. Am. J. Infect. Control 48, 958 (2020).PubMed 
PubMed Central 

Google Scholar 
61.Cobb, J. S. & Seale, M. A. Examining the effect of social distancing on the compound growth rate of COVID-19 at the county level (United States) using statistical analyses and a random forest machine learning model. Public Health 185, 27 (2020).CAS 
PubMed 

Google Scholar 
62.Courtemanche, C., Garuccio, J., Le, A., Pinkston, J. & Yelowitz, A. Strong social distancing measures in the United States reduced The COVID-19 growth rate. Health Aff. 39, 1237 (2020).
Google Scholar 
63.Dave, D., Friedson, A. I., Matsuzawa, K. & Sabia, J. J. When do shelter-in-place orders fight COVID-19 best? Policy heterogeneity across states and adoption time. Econ Inq. 59, 29 (2021).
Google Scholar 
64.Dave, D., Friedson, A., Matsuzawa, K., Sabia, J. J. & Safford, S. JUE insight: Were urban cowboys enough to control COVID-19? Local shelter-in-place orders and coronavirus case growth. J. Urban Econ. 2020, 103294 (2020).
Google Scholar 
65.Edelstein, M. et al. SARS-CoV-2 infection in London, England: Changes to community point prevalence around lockdown time, March–May 2020. J. Epidemiol. Commun. Health 75, 185 (2021).
Google Scholar 
66.Gallaway, M. S. et al. Trends in COVID-19 incidence after implementation of mitigation measures–Arizona, January 22–August 7, 2020. MMWR Morb. Mortal. Wkly. Rep. 69, 1460 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
67.Hsiang, S. et al. The effect of large-scale anti-contagion policies on the COVID-19 pandemic. Nature 584, 262 (2020).ADS 
CAS 
PubMed 

Google Scholar 
68.Hyafil, A. & Moriña, D. Analysis of the impact of lockdown on the reproduction number of the SARS-Cov-2 in Spain. Gac. Sanit. 35, 453 (2021).PubMed 

Google Scholar 
69.Islam, N., Sharp, S. J. & Chowell, G. Physical distancing interventions and incidence of coronavirus disease 2019: Natural experiment in 149 countries. BMJ 2020, m2743 (2019).
Google Scholar 
70.Lyu, W. & Wehby, G. L. Comparison of estimated rates of coronavirus disease 2019 (COVID-19) in border counties in Iowa without a stay-at-home order and border counties in Illinois with a stay-at-home order. JAMA Netw. Open 3, e2011102 (2020).PubMed 
PubMed Central 

Google Scholar 
71.Lyu, W. & Wehby, G. L. Community use of face masks and COVID-19: Evidence from a natural experiment of state mandates in the US. Health Aff. 39, 1419 (2020).
Google Scholar 
72.Zhang, R., Li, Y., Zhang, A. L., Wang, Y. & Molina, M. J. Identifying airborne transmission as the dominant route for the spread of COVID-19. PNAS 117, 14857 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
73.Fokas, A. S., Athanassios, S., Jesus, C.-M. & Panayotis, G. K. A quantitative framework for exploring exit strategies from the COVID-19 lockdown. Chaos Solitons Fractals 140, 110244 (2020).MathSciNet 
CAS 
PubMed 
PubMed Central 

Google Scholar 
74.Olumoyin, K. D., Khaliq, A. Q. M., & Furati, F. M. A quantitative framework for exploring exit strategies from the COVID-19 lockdown. arXiv:2104.02603 (2021).75.Tam, K.-M., Walker, N. & Moreno, J. Effect of mitigation measures on the spreading of COVID-19 in hard-hit states in the U.S.. PLoS ONE 15, e0240877 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
76.Tam, K.-M., Walker, N., & Moreno, J. Projected Development of COVID-19 in Louisiana. arXiv:2004.02859 (2020).77.Marchant, R., Samia, N. I., Rosen, O., Tanner, M. A., & Cripps, S. Learning as we go: An examination of the statistical accuracy of COVID19 daily death count predictions. arXiv:2004.04734 (2020).78.Wu, Z. & McGoogan, J. M. Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China. JAMA 323, 1239 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
79.Mizumoto, K., Kagaya, K., Zarebski, A. & Chowell, G. Estimating the asymptomatic proportion of coronavirus disease 2019 (COVID-19) cases on board the Diamond Princess cruise ship, Yokohama, Japan, 2020. Eurosurveill 25, 10 (2020).
Google Scholar 
80.
https://github.com/nytimes/covid-19-data
81.Holmdahl, I. & Buckee, C. Wrong but useful—What covid-19 epidemiologic models can and cannot tell us. N. Engl. J. Med. 383, 303 (2020).CAS 
PubMed 

Google Scholar 
82.
https://www.cdc.gov/coronavirus/2019-ncov/prevent-getting-sick/cloth-face-cover-guidance.html
83.
https://www.cdc.gov/mmwr/volumes/69/wr/mm695152a8.htm
84.Abedi, V. et al. Racial, economic, and health inequality and COVID-19 infection in the United States. J. Racial Ethnic Health Disparities 8, 732 (2021).
Google Scholar 
85.Merow, C. & Urban, M. C. Seasonality and uncertainty in global COVID-19 growth rates. PNAS 117(44), 27456 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
86.Carlson, C. J., Gomez, A. C. R., Bansal, S. & Ryan, S. J. Misconceptions about weather and seasonality must not misguide COVID-19 response. Nat. Comm. 11, 412 (2020).
Google Scholar 
87.Burra, P. et al. Temperature and latitude correlate with SARS-CoV-2 epidemiological variables but not with genomic change worldwide. Evol. Bioinform. https://doi.org/10.1177/1176934321989695 (2021).Article 

Google Scholar  More

Download PDF

This photograph was taken at the Angullong estate in New South Wales, Australia, which hosts some of my field trials. The aim is to study sustainable agriculture in vineyards. You have to dodge the odd brown snake, but, as offices go, this one — among the grapevines of such a picturesque part of the world — makes my job quite a privilege.It’s a November evening, which is springtime here in the Southern Hemisphere, and this time of year is when pests such as the light brown apple moth (Epiphyas postvittana) start to emerge. That means that ecologists such as myself, as well as the commercial winemakers we collaborate with, move into data-capture mode to track the presence of the insects. These moths produce multiple generations every year, so they can be quite numerous by harvest time, and can cause real damage by getting into the grapes.We’re conducting experiments to see whether positioning various plant species between and under grapevines can help to reduce the population of pests by encouraging their predators. Parasitoid wasps, for example, target the eggs of light brown apple moths, injecting them with their own eggs. When the wasp larvae hatch, they eat the moth larvae from the inside out. Although quite gruesome, parasitoid wasps could provide an environmentally friendly way to control moth populations.In my laboratory at Charles Sturt University in Orange, we’re incubating moth eggs that we then put on special cards in the vineyard. Because parasitoids love nectar, we expect to see more attacks on the moth eggs in areas where we’ve planted flowering shrubs than in the control areas, where grass predominates. We collect the cards after about 48 hours in the field, and incubate the moth eggs to measure the level of parasitism. In the next couple of years, with more data, we hope to identify the optimum mix of plant species to manage pests without resorting to chemicals.

Nature 602, 176 (2022)
doi: https://doi.org/10.1038/d41586-022-00218-z

Related Articles

‘For a brown invertebrate’: rescuing native UK oysters

Breeding the sweetest biofuels in the business

An IPCC reviewer shares his thoughts on the climate debate

Collection: Fieldwork

Subjects

Careers

Ecology

Agriculture

Latest on:

Careers

A nutritionist in Kenya shares advice for prospective students
Career Q&A 31 JAN 22

Why early-career researchers should step up to the peer-review plate
Career Feature 31 JAN 22

I’m a lip-reading scientist: here’s how I can discuss science with you
Career Column 28 JAN 22

Ecology

Richard Leakey (1944–2022)
Obituary 28 JAN 22

Emphasizing declining populations in the Living Planet Report
Matters Arising 26 JAN 22

Reply to: Do not downplay biodiversity loss
Matters Arising 26 JAN 22

Agriculture

A hard graft problem solved for key global food crops
News & Views 25 JAN 22

Countries should boycott Brazil over export-driven deforestation
Correspondence 18 JAN 22

From the archive
News & Views 18 JAN 22

Jobs

Postdoctoral Research Assistant

Queen Mary University of London (QMUL)
London, United Kingdom

Faculty Position in Environmental Sensing Technologies Joint Appointement between the Swiss Federal Laboratories for Materials Science and Technology (Empa) and the Ecole polytechnique fédérale de Lausanne (EPFL)

Swiss Federal Institute of Technology in Lausanne (EPFL)
Dübendorf and Lausanne, Switzerland

Private Secretary, Chief Scientist

Alan Turing Institute
London, United Kingdom

Research Fellow/Senior Research Fellow – Mathematical Modeller/Bioinformatician

University College London (UCL)
London, United Kingdom More

Tropospheric ozone is formed through the oxidation of precursor pollutants (nitrogen oxide gases and volatile organic compounds) in the presence of sunlight. This surface ozone — a major pollutant — contributes negatively to air quality, posing risks to human health. Many plant species are also sensitive to ozone, exhibiting reduced growth and seed production, and accelerated ageing. Such impacts translate to high crop yield losses, threatening food security, especially in light of rising ozone concentrations and exposure observed across Asia. More

1.Perrins, C. M. Eggs, egg formation and the timing of breeding. Ibis (Lond. 1859). 138, 2–15. https://doi.org/10.1111/j.1474-919X.1996.tb04308.x (1996).Article 

Google Scholar 
2.Monaghan, P. & Nager, R. G. Why don’t birds lay more eggs? Trends Ecol. Evol. 12, 270–274. https://doi.org/10.1016/S0169-5347(97)01094-X (1997).CAS 
Article 

Google Scholar 
3.Alisauskas, R., DeVink, J.-M. Breeding costs, nutrient reserves, and cross-seasonal effects: dealing with deficits in sea ducks. pp. 125–168 (2015).4.Ebeid, T., Tumova Prague (Czech Republic). Katedra Chovu Prasat a Drubeze) E (Ceska ZU. In press. Physiological aspects of oviposition and its role in egg quality. A review. Sci. Agric. Bohem. (Czech Republic). v. 35.5.Johnson, A. The avian ovary and follicle development: some comparative and practical insights. Turkish J. Vet. Anim. Sci. 38, 660–669 (2014).CAS 
Article 

Google Scholar 
6.Bédécarrats, G. Y., Baxter, M. & Sparling, B. An updated model to describe the neuroendocrine control of reproduction in chickens. Gen. Comp. Endocrinol. 227, 58–63. https://doi.org/10.1016/j.ygcen.2015.09.023 (2016).CAS 
Article 
PubMed 

Google Scholar 
7.Brommer, J. E., Rattiste, K. & Wilson, A. J. Exploring plasticity in the wild: laying date-temperature reaction norms in the common gull Larus canus. Proc. R. Soc. B Biol. Sci. 275, 687–693. https://doi.org/10.1098/rspb.2007.0951 (2008).Article 

Google Scholar 
8.Schaper, S. V. et al. Increasing Temperature, Not Mean Temperature, Is a Cue for Avian Timing of Reproduction. Am. Nat. 179, E55–E69. https://doi.org/10.1086/663675 (2012).Article 
PubMed 

Google Scholar 
9.Shave, A., Garroway, C. J., Siegrist, J. & Fraser, K. C. Timing to temperature: Egg-laying dates respond to temperature and are under stronger selection at northern latitudes. Ecosphere 10, e02974. https://doi.org/10.1002/ecs2.2974 (2019).Article 

Google Scholar 
10.Verhagen, I., Tomotani, B. M., Gienapp, P. & Visser, M. E. Temperature has a causal and plastic effect on timing of breeding in a small songbird. J. Exp. Biol. https://doi.org/10.1242/jeb.218784 (2020).Article 
PubMed 

Google Scholar 
11.Caro, S. P., Schaper, S. V., Hut, R. A., Ball, G. F. & Visser, M. E. The case of the missing mechanism: How does temperature influence seasonal timing in endotherms?. PLoS Biol. 11, e1001517–e1001517. https://doi.org/10.1371/journal.pbio.1001517 (2013).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
12.Bobr, L. W. & Sheldon, B. L. Analysis of ovulation-oviposition patterns in the domestic fowl by telemetry measurement of deep body temperature. Aust. J. Biol. Sci. 30, 243–257. https://doi.org/10.1071/bi9770243 (1977).CAS 
Article 
PubMed 

Google Scholar 
13.Kadono, H., Besch, E. L. & Usami, E. Body temperature, oviposition, and food intake in the hen during continuous light. J. Appl. Physiol. 51, 1145–1149. https://doi.org/10.1152/jappl.1981.51.5.1145 (1981).CAS 
Article 
PubMed 

Google Scholar 
14.Yang, J., Morgan, J. L., Kirby, J. D., Long, D. W. & Bacon a W.,. Circadian rhythm of the preovulatory surge of luteinizing hormone and its relationships to rhythms of body temperature and locomotor activity in turkey hens. Biol. Reprod. 62, 1452–1458. https://doi.org/10.1095/biolreprod62.5.1452 (2000).CAS 
Article 
PubMed 

Google Scholar 
15.Zivkovic, B. D., Underwood, H. & Siopes, T. Circadian ovulatory rhythms in Japanese quail: role of ocular and extraocular pacemakers. J. Biol. Rhythms 15, 172–183. https://doi.org/10.1177/074873040001500211 (2000).CAS 
Article 
PubMed 

Google Scholar 
16.Ward, S. Energy expenditure of female barn swallows Hirundo rustica during egg formation. Physiol. Zool. 69, 930–951. https://doi.org/10.1086/physzool.69.4.30164236 (1996).Article 

Google Scholar 
17.Nilsson, J. -Å. & Råberg, L. The resting metabolic cost of egg laying and nestling feeding in great tits. Oecologia 128, 187–192. https://doi.org/10.1007/s004420100653 (2001).ADS 
Article 
PubMed 

Google Scholar 
18.Vézina, F. & Williams, T. D. Metabolic costs of egg production in the European starling (Sturnus vulgaris). Physiol. Biochem. Zool. 75, 377–385. https://doi.org/10.1086/343137 (2002).Article 
PubMed 

Google Scholar 
19.Götmark, F. The Effects of Investigator Disturbance on Nesting Birds BT – Current Ornithology. In ed. D.M. Power, pp. 63–104. Springer. https://doi.org/10.1007/978-1-4757-9921-7_3 (1992).20.Lyngs, P. Status of the Danish Breeding population of Eiders Somateria mollissima 1988–93. Dansk Ornitol. Foren. Tidsskr. 94, 12–18 (2000).
Google Scholar 
21.Bolduc, F. & Guillemette, M. Human disturbance and nesting success of Common Eiders: interaction between visitors and gulls. Biol. Conserv. 110, 77–83. https://doi.org/10.1016/S0006-3207(02)00178-7 (2003).Article 

Google Scholar 
22.Christensen, T. K. Female pre-nesting foraging and male vigilance in Common Eider Somateria mollissima. Bird Study 47, 311–319. https://doi.org/10.1080/00063650009461191 (2000).Article 

Google Scholar 
23.Guillemette, M. Foraging before spring migration and before breeding in common eiders: Does hyperphagia occur?. Condor 103, 633–638 (2001).Article 

Google Scholar 
24.Guillemette, M. & Ouellet, J. Temporary flightlessness as a potential cost of reproduction in pre-laying Common Eiders Somateria mollissima. Ibis (Lond. 1859). 147, 301–306. https://doi.org/10.1111/j.1474-919x.2005.00402.x (2005).Article 

Google Scholar 
25.Rigou, Y. & Guillemette, M. Foraging effort and pre-laying strategy in breeding common eiders. Waterbirds Int. J. Waterbird Biol. 33, 314–322 (2010).
Google Scholar 
26.Watson, M. D., Robertson, G. J. & Cooke, F. Egg-laying time and laying interval in the common eider. Condor 95, 869–878. https://doi.org/10.2307/1369424 (1993).Article 

Google Scholar 
27.Guillemette, M., Woakes, A. J., Flagstad, A. & Butler, P. J. Effects of data-loggers implanted for a full year in female common eiders. Condor 104, 448–452 (2002).Article 

Google Scholar 
28.Franzmann, N. E. Ederfuglens (Somateria m. mollissima) ynglebiologi of populationsdynamik pa° Christiansø 1973–1977. Ph.D. Diss. Copenhagen 1980.29.Pelletier, D., Guillemette, M., Grandbois, J.-M. & Butler, P. J. It is time to move: linking flight and foraging behaviour in a diving bird. Biol. Lett. 3, 357–359. https://doi.org/10.1098/rsbl.2007.0088 (2007).Article 
PubMed 
PubMed Central 

Google Scholar 
30.Coulson, J. C. The population dynamics of the Eider Duck Somateria mollissima and evidence of extensive non-breeding by adult ducks. Ibis (Lond. 1859). 126, 525–543. https://doi.org/10.1111/j.1474-919X.1984.tb02078.x (2008).Article 

Google Scholar 
31.Sabourin, M. Comportement d’incubation de l’Eider à duvet (Somateria mollissima) et effet du dérangement humain dans deux colonies de l’Estuaire du Saint-Laurentle. Mémoire de maîtrise Université (2003).32.Waltho, C., Coulson, J. Egg laying, parasitism, ‘jumbo clutches’ and egg stealing. In The Common Eider, pp. 7–10. POYSER (2015).33.Guillemette, M., Ydenberg, R. C. & Himmelman, J. H. The role of energy intake rate in prey and habitat selection of common eiders Somateria mollissima in winter: a risk-sensitive interpretation. J. Anim. Ecol. 61, 599. https://doi.org/10.2307/5615 (1992).Article 

Google Scholar 
34.Canty, A. & Ripley, B. boot: Bootstrap R (S-Plus) functions. R Packag Vers 1, 3–20 (2017).
Google Scholar 
35.Carpenter J, Bithell J. Bootstrap confidence intervals: when, which, what? A practical guide for medical statisticians. Stat. Med. 19, 1141–1164 2000. https://doi.org/10.1002/(SICI)1097-0258(20000515)19:93.0.CO;2-F36.Jenssen, B., Ekker, M. & Bech, C. Thermoregulation in winter-acclimatized Common Eiders (Somateria mollissima) in air and water. Can. J. Zool. 67, 669–673. https://doi.org/10.1139/z89-096 (1989).Article 

Google Scholar 
37.Winget, C. M., Averkin, E. G. & Fryer, T. B. Quantitative measurement by telemetry of ovulation and oviposition in the fowl. Am. J. Physiol. 209, 853–858. https://doi.org/10.1152/ajplegacy.1965.209.4.853 (1965).CAS 
Article 
PubMed 

Google Scholar 
38.Cain, J. R. & Wilson, W. O. Multichannel telemetry system for measuring body temperature: circadian rhythms of body temperature, locomotor activity and oviposition in chickens. Poult. Sci. 50, 1437–1443. https://doi.org/10.3382/ps.0501437 (1971).CAS 
Article 
PubMed 

Google Scholar 
39.Khalil, A., Matsui, K. & Takeda, K. Responses to abrupt changes in feeding and illumination in laying hens. Turkish J. Vet. Anim. Sci. https://doi.org/10.3906/vet-0901-25 (2010).Article 

Google Scholar 
40.Kadono, H. & Yamade, T. Changes of body temperature related to oviposition and ovulation induced by LH in the domestic hen. Nihon Juigaku Zasshi. 47, 55–61. https://doi.org/10.1292/jvms1939.47.55 (1985).CAS 
Article 
PubMed 

Google Scholar 
41.Piccione, G. & Refinetti, R. Thermal chronobiology of domestic animals. Front. Biosci. 8, s258–s264. https://doi.org/10.2741/1040 (2003).Article 
PubMed 

Google Scholar 
42.Peters DG, Rose RW. The oestrous cycle and basal body temperature in the common wombat (Vombatus ursinus). Reproduction 57, 453–460 (in press). doi:https://doi.org/10.1530/jrf.0.057045343.Rose, R. W. & Jones, S. M. The association between basal body temperature, plasma progesterone and the oestrous cycle in a marsupial, the Tasmanian bettong (Bettongia gaimardi). J. Reprod. Fertil. 106, 67–71. https://doi.org/10.1530/jrf.0.1060067 (1996).CAS 
Article 
PubMed 

Google Scholar 
44.Graham, C. E., Warner, H., Misener, J., Collins, D. C. & Preedy, J. R. The association between basal body temperature, sexual swelling and urinary gonadal hormone levels in the menstrual cycle of the chimpanzee. J. Reprod. Fertil. 50, 23–28. https://doi.org/10.1530/jrf.0.0500023 (1977).CAS 
Article 
PubMed 

Google Scholar 
45.Nyakudya, T. T., Fuller, A., Meyer, L. C. R., Maloney, S. K. & Mitchell, D. Body temperature and physical activity correlates of the menstrual cycle in Chacma Baboons (Papio hamadryas ursinus). Am. J. Primatol. 74, 1143–1153. https://doi.org/10.1002/ajp.22073 (2012).Article 
PubMed 

Google Scholar 
46.Suthar, V. S., Burfeind, O., Bonk, S., Dhami, A. J. & Heuwieser, W. Endogenous and exogenous progesterone influence body temperature in dairy cows. J. Dairy Sci. 95, 2381–2389. https://doi.org/10.3168/jds.2011-4450 (2012).CAS 
Article 
PubMed 

Google Scholar 
47.Giersch, G. E. W. et al. Menstrual cycle and thermoregulation during exercise in the heat: A systematic review and meta-analysis. J. Sci. Med. Sport 23, 1134–1140. https://doi.org/10.1016/j.jsams.2020.05.014 (2020).Article 
PubMed 

Google Scholar 
48.Farmer, C. G. Parental care: The key to understanding endothermy and other convergent features in birds and mammals. Am. Nat. 155, 326–334. https://doi.org/10.1086/303323 (2000).CAS 
Article 
PubMed 

Google Scholar 
49.Koteja, P. Energy assimilation, parental care and the evolution of endothermy. Proc. R. Soc. Lond. Ser. B Biol. Sci. 267, 479–484. https://doi.org/10.1098/rspb.2000.1025 (2000).CAS 
Article 

Google Scholar 
50.Portugal, S. J. et al. Associations between resting, activity, and daily metabolic rate in free-living endotherms: No universal rule in birds and mammals. Physiol. Biochem. Zool. 89, 251–261. https://doi.org/10.1086/686322 (2016).Article 
PubMed 

Google Scholar 
51.Guillemette, M., Pelletier, D., Grandbois, J.-M. & Butler, P. J. Flightlessnessand the energetic cost of wing molt in a large sea duck. Ecology 88, 2936–2945. https://doi.org/10.1890/06-1751.1 (2007).Article 
PubMed 

Google Scholar 
52.Parker, H. & Holm, H. Patterns of nutrient and energy expenditure in female common eiders nesting in the high arctic. Auk 107, 660–668. https://doi.org/10.2307/4087996 (1990).Article 

Google Scholar 
53.Guillemette, M. & Ouellet, J.-F. Temporary flightlessness in pre-laying Common Eiders Somateria mollissima: Are females constrained by excessive wing-loading or by minimal flight muscle ratio?. Ibis (Lond. 1859). 147, 293–300. https://doi.org/10.1111/j.1474-919x.2005.00401.x (2005).Article 

Google Scholar 
54.Vézina, F., Speakman, J. R. & Williams, T. D. Individually variable energy management strategies in relation to energetic costs of egg production. Ecology 87, 2447–2458. https://doi.org/10.1890/0012-9658(2006)87[2447:ivemsi]2.0.co;2 (2006).Article 
PubMed 

Google Scholar 
55.Bevan, R. M., Butler, P. J., Woakes, A. J. & Prince, P. A. The energy expenditure of free-ranging black-browed albatross. Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci. 350, 119–131. https://doi.org/10.1098/rstb.1995.0146 (1995).ADS 
Article 

Google Scholar 
56.Bevan, R. et al. Heart rates and abdominal temperatures of free-ranging South Georgian shags, Phalacrocorax georgianus. J. Exp. Biol. 200, 661–675 (1997).CAS 
Article 

Google Scholar 
57.Woakes, A. J., Butler, P. J. & Bevan, R. M. Implantable data logging system for heart rate and body temperature: Its application to the estimation of field metabolic rates in Antarctic predators. Med. Biol. Eng. Comput. 33, 145–151. https://doi.org/10.1007/BF02523032 (1995).CAS 
Article 
PubMed 

Google Scholar 
58.Lewden, A. et al. Body surface rewarming in fully and partially hypothermic king penguins. J. Comp. Physiol. B 190, 597–609. https://doi.org/10.1007/s00360-020-01294-1 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
59.Wilson, R. P. & Grémillet, D. Body temperatures of free-living African penguins (Spheniscus demersus) and bank cormorants (Phalacrocorax neglectus). J. Exp. Biol. 199, 2215–2223 (1996).CAS 
Article 

Google Scholar 
60.Schmidt, A., Alard, F. & Handrich, Y. Changes in body temperature in king penguins at sea: The result of fine adjustments in peripheral heat loss?. Am. J. Physiol. Regul. Integr. Comp. Physiol. 291, R608–R618. https://doi.org/10.1152/ajpregu.00826.2005 (2006).CAS 
Article 
PubMed 

Google Scholar 
61.Sherer, J., Wunder, B. A. Thermoregulation of a semi-aquatic mammal, the muskrat, in air and water. 24, 249–256 (1979).62.Dyck, A. P. & MacArthur, R. A. Seasonal patterns of body temperature and activity in free-ranging beaver (Castor canadensis). Can. J. Zool. 70, 1668–1672. https://doi.org/10.1139/z92-232 (1992).Article 

Google Scholar 
63.Kolka, M. A. & Stephenson, L. A. Resetting the thermoregulatory set-point by endogenous estradiol or progesterone in women. Ann. N. Y. Acad. Sci. 813, 204–206. https://doi.org/10.1111/j.1749-6632.1997.tb51694.x (1997).ADS 
CAS 
Article 
PubMed 

Google Scholar 
64.Ubuka, T. & Bentley, G. E. Neuroendocrine control of reproduction in birds. In Hormones and reproduction of Vertebrates (ed Norris DO & Lopez KH), pp. 1–25 (2011).65.van der Klein, S. A. S., Zuidhof, M. J. & Bédécarrats, G. Y. Diurnal and seasonal dynamics affecting egg production in meat chickens: A review of mechanisms associated with reproductive dysregulation. Anim. Reprod. Sci. 213, 106257. https://doi.org/10.1016/j.anireprosci.2019.106257 (2020).Article 
PubMed 

Google Scholar 
66.Tanabe, Y. Production, evolution and reproductive endocrinology of ducks. Asian-Australas. J. Anim. Sci. 5, 173–181. https://doi.org/10.5713/ajas.1992.173 (1992).Article 

Google Scholar 
67.Johnson, A. L. Chapter 3 – Organization and Functional Dynamics of the Avian Ovary. In (eds DO Norris, KHBT-H and R of V Lopez), pp. 71–90. Academic Press (2011). https://doi.org/10.1016/B978-0-12-374929-1.10003-468.Bluhm, C. K., Phillips, R. E. & Burke, W. H. Serum levels of luteinizing hormone (LH), prolactin, estradiol, and progesterone in laying and nonlaying canvasback ducks (Aythya valisineria). Gen. Comp. Endocrinol. 52, 1–16. https://doi.org/10.1016/0016-6480(83)90152-1 (1983).CAS 
Article 
PubMed 

Google Scholar 
69.Bluhm, C. K., Phillips, R. E. & Burke, W. H. Serum levels of luteinizing hormone, prolactin, estradiol and progesterone in laying and nonlaying mallards (Anas platyrhynchos). Biol. Reprod. 28, 295–305. https://doi.org/10.1095/biolreprod28.2.295 (1983).CAS 
Article 
PubMed 

Google Scholar 
70.Sockman, K. W. & Schwabl, H. Daily estradiol and progesterone levels relative to laying and onset of incubation in canaries. Gen. Comp. Endocrinol. 114, 257–268. https://doi.org/10.1006/gcen.1999.7252 (1999).CAS 
Article 
PubMed 

Google Scholar 
71.Proszkowiec, M. & Rzasa, J. Variation in the ovarian and plasma progesterone and estradiol levels of the domestic hen during a pause in laying. Folia Biol. (Praha) 49, 285–289 (2001).CAS 

Google Scholar 
72.Proszkowiec-Weglarz, M., Rzasa, J., Słomczyńska, M. & Paczoska-Eliasiewicz, H. Steroidogenic activity of chicken ovary during pause in egg laying. Reprod. Biol. 5, 205–225 (2005).PubMed 

Google Scholar 
73.Nakayma, T., Suzuki, M. & Ishizuka, N. Action of progesterone on preoptic thermosensitive neurones. Nature 258, 80. https://doi.org/10.1038/258080a0 (1975).ADS 
Article 

Google Scholar 
74.Hampl, R., Stárka, L. & Janský, L. Steroids and thermogenesis. Physiol. Res. 55, 123–131 (2006).CAS 
PubMed 

Google Scholar 
75.Splawinski, J. A., Górka, Z., Zacny, E. & Wojtaszek, B. Hyperthermic effects of arachidonic acid, prostaglandins E2 and F2α in rats. Pflügers Arch. 374, 15–21. https://doi.org/10.1007/BF00585692 (1978).CAS 
Article 
PubMed 

Google Scholar 
76.Gray, D. A., Marais, M. & Maloney, S. K. A review of the physiology of fever in birds. J. Comp. Physiol. B 183, 297–312. https://doi.org/10.1007/s00360-012-0718-z (2013).CAS 
Article 
PubMed 

Google Scholar 
77.Hertelendy, F. & Biellier, H. V. Evidence for a physiological role of prostaglandins in oviposition by the hen. J. Reprod. Fertil. 53, 71–74. https://doi.org/10.1530/jrf.0.0530071 (1978).CAS 
Article 
PubMed 

Google Scholar 
78.Etches, R. J., Kelly, J. D., Anderson-Langmuir, C. E. & Olson, D. M. Prostaglandin production by the largest preovulatory follicles in the domestic hen (Gallus domesticus). Biol. Reprod. 43, 378–384. https://doi.org/10.1095/biolreprod43.3.378 (1990).CAS 
Article 
PubMed 

Google Scholar 
79.Takahashi, T., Tajima, H., Nakagawa-Mizuyachi, K., Nakayama, H. & Kawashima, M. Changes in prostaglandin F2α receptor bindings in the hen oviduct uterus before and after oviposition. Poult. Sci. 90, 1767–1773. https://doi.org/10.3382/ps.2010-01329 (2011).CAS 
Article 
PubMed 

Google Scholar 
80.McNabb, F. M. A. The hypothalamic-pituitary-thyroid (HPT) axis in birds and its role in bird development and reproduction. Crit. Rev. Toxicol. 37, 163–193. https://doi.org/10.1080/10408440601123552 (2007).CAS 
Article 
PubMed 

Google Scholar 
81.Nakao, N., Ono, H. & Yoshimura, T. Thyroid hormones and seasonal reproductive neuroendocrine interactions. Reproduction 136, 1–8. https://doi.org/10.1530/REP-08-0041 (2008).CAS 
Article 
PubMed 

Google Scholar 
82.Sechman, A. The role of thyroid hormones in regulation of chicken ovarian steroidogenesis. Gen. Comp. Endocrinol. 190, 68–75. https://doi.org/10.1016/J.YGCEN.2013.04.012 (2013).CAS 
Article 
PubMed 

Google Scholar 
83.Gabrielsen, G., Mehlum, F., Karlsen, H., Andresen & Parker, H. Energy cost during incubation and thermoregulation in female Common Eider (Somateria mollissima). Nor. Polarinstitutt Skr. 195 (1991).84.Ardia, D. R., Pérez, J. H. & Clotfelter, E. D. Experimental cooling during incubation leads to reduced innate immunity and body condition in nestling tree swallows. Proc. R. Soc. B Biol. Sci. 277, 1881–1888. https://doi.org/10.1098/rspb.2009.2138 (2010).Article 

Google Scholar 
85.Hepp, G. R. & Kennamer, R. A. Warm is better: Incubation temperature influences apparent survival and recruitment of wood ducks (Aix sponsa). PLoS ONE 7, e47777 (2012).ADS 
CAS 
Article 

Google Scholar 
86.Ipek, A., Sahan, U. & Sozcu, A. The effects of different eggshell temperatures between embryonic day 10 and 18 on broiler performance and susceptibility to ascites. Rev. Bras. Ciência Avícola 17, 387–394. https://doi.org/10.1590/1516-635X1703387-394 (2015).Article 

Google Scholar 
87.Haftorn, S. & Reinertsen, R. E. Regulation of body temperature and heat transfer to eggs during incubation. Ornis Scand. Scandinavian J. Ornithol. 13, 1–10. https://doi.org/10.2307/3675966 (1982).Article 

Google Scholar 
88.Vehrencamp, S. Body temperatures of incubating versus non-incubating roadrunners. Condor 84, 203 (1982).Article 

Google Scholar 
89.Evans, S. S., Repasky, E. A. & Fisher, D. T. Fever and the thermal regulation of immunity: The immune system feels the heat. Nat. Rev. Immunol. 15, 335–349. https://doi.org/10.1038/nri3843 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
90.Hupton, G., Portocarrero, S., Newman, M. & Westneat, D. F. Bacteria in the reproductive tracts of red-wingedblackbirds. Condor 105, 453–464. https://doi.org/10.1650/7246 (2003).Article 

Google Scholar 
91.White, J. et al. Sexually transmitted bacteria affect female cloacal assemblages in a wild bird. Ecol. Lett. 13, 1515–1524. https://doi.org/10.1111/j.1461-0248.2010.01542.x (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
92.Hansen, C. M., Meixell, B. W., Van Hemert, C., Hare, R. F. & Hueffer, K. Microbial infections are associated with embryo mortality in arctic-nesting geese. Appl. Environ. Microbiol. 81, 5583–5592. https://doi.org/10.1128/AEM.00706-15 (2015).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
93.Barrow, P. A. & Lovell, M. A. Experimental infection of egg-laying hens with Salmonella enteritidis phage type 4. Avian Pathol. 20, 335–348. https://doi.org/10.1080/03079459108418769 (1991).CAS 
Article 
PubMed 

Google Scholar 
94.Mitchell, D. et al. Revisiting concepts of thermal physiology: Predicting responses of mammals to climate change. J. Anim. Ecol. 87, 956–973. https://doi.org/10.1111/1365-2656.12818 (2018).Article 
PubMed 

Google Scholar 
95.van Heerwaarden, B. & Sgrò, C. M. Male fertility thermal limits predict vulnerability to climate warming. Nat. Commun. 12, 2214. https://doi.org/10.1038/s41467-021-22546-w (2021).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
96.Guillemette, M., Polymeropoulos, E. T., Portugal, S. J. & Pelletier, D. It takes time to be cool: On the relationship between hyperthermia and body cooling in a migrating seaduck. Front. Physiol. 8, 532. https://doi.org/10.3389/fphys.2017.00532 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
97.Stillman, J. H. Heat waves, the new normal: Summertime temperature extremes will impact animals, ecosystems, and human communities. Physiology 34, 86–100. https://doi.org/10.1152/physiol.00040.2018 (2019).CAS 
Article 
PubMed 

Google Scholar 
98.Schou, M. F. et al. Extreme temperatures compromise male and female fertility in a large desert bird. Nat. Commun. 12, 666. https://doi.org/10.1038/s41467-021-20937-7 (2021).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
99.Stevenson, I. R. & Bryant, D. M. Climate change and constraints on breeding. Nature 406, 366–367. https://doi.org/10.1038/35019151 (2000).ADS 
CAS 
Article 
PubMed 

Google Scholar  More

The average water temperature and pH in tanks was 19.29 ± 0.10 °C (SE, range: 17.0–22.5) and 8.59 ± 0.01 (SE, range 8.2–8.9) respectively. There was no significant difference among treatments (water temperature: F = 0.0086, df = 5, p = 1.0000, pH: F = 0.0063, df = 5, p = 1.0000).Intraspecific competition (density = 5, 15, 50 tadpoles per tank)The density of conspecifics did not have any significant effect on survival to metamorphosis of B. j. formosus (treatment: Wald chi-square = 3.468, df = 2, p = 0.1766; block: Wald chi-square = 7.770, df = 4, p = 0.1004; Fig. 1a). However, conspecific density had a significant effect on the combined responses of variables (larval period, metamorph SUL, metamorph mass) of B. j. formosus (MANOVA treatment: Wilks’ Lambda = 0.0181, F = 10.7224, df = 6, 10, p = 0.0007; block: Wilks’ Lambda = 0.2028, F = 0.9326, df = 12, 13.52, p = 0.5441). Higher densities of conspecifics increased the duration of the larval period (treatment: F = 6.678, df = 2, 9.30, p = 0.0159; block: F = 0.817, df = 4, 0.40, p = 0.7574; Fig. 1b), and decreased size at metamorphosis (SUL—treatment: F = 49.729, df = 2, 6.94, p  More

1.Fernández, M. H. & Vrba, E. S. Rapoport effect and biomic specialization in African mammals: revisiting the climatic variability hypothesis. J. Biogeogr. 32, 903–918 (2005).
Google Scholar 
2.Tokeshi, M. & Arakaki, S. Habitat complexity in aquatic systems: fractals and beyond. Hydrobiologia 685, 27–47 (2012).
Google Scholar 
3.Connell, J. H. Diversity in tropical rain forests and coral reefs. Science 199, 1302–1310 (1978).ADS 
CAS 
PubMed 

Google Scholar 
4.Yachi, S. & Loreau, M. Biodiversity and ecosystem productivity in a fluctuating environment: the insurance hypothesis. Proc. Natl Acad. Sci. 96, 1463–1468 (1999).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
5.Tilman, D., Reich, P. B. & Knops, J. M. Biodiversity and ecosystem stability in a decade-long grassland experiment. Nature 441, 629–632 (2006).ADS 
CAS 
PubMed 

Google Scholar 
6.Willig, M. R., Kaufman, D. M. & Stevens, R. D. Latitudinal gradients of biodiversity: pattern, process, scale, and synthesis. Ann. Rev. Ecol. Evol. Syst. 34, 273–309 (2003).
Google Scholar 
7.Stein, A., Gerstner, K. & Kreft, H. Environmental heterogeneity as a universal driver of species richness across taxa, biomes and spatial scales. Ecol. Lett. 17, 866–880 (2014).PubMed 

Google Scholar 
8.Thomsen, M. S. et al. Secondary foundation species enhance biodiversity. Nat. Ecol. Evol. 2, 634–639 (2018).PubMed 

Google Scholar 
9.Mac Arthur, R. H. & Wilson, E. O. The theory of island biogeography. Vol. 1 (Princeton university press, 2001).10.Guégan, J.-F., Lek, S. & Oberdorff, T. Energy availability and habitat heterogeneity predict global riverine fish diversity. Nature 391, 382–384 (1998).ADS 

Google Scholar 
11.Heidrich, L. et al. Heterogeneity–diversity relationships differ between and within trophic levels in temperate forests. Nat. Ecol. Evol. 4, 1204–1212 (2020).PubMed 

Google Scholar 
12.Kerr, J. T. & Packer, L. Habitat heterogeneity as a determinant of mammal species richness in high-energy regions. Nature 385, 252–254 (1997).ADS 
CAS 

Google Scholar 
13.Ranjard, L. et al. Turnover of soil bacterial diversity driven by wide-scale environmental heterogeneity. Nat. Commun. 4, 1–10 (2013).
Google Scholar 
14.Fahrig, L. et al. Functional landscape heterogeneity and animal biodiversity in agricultural landscapes. Ecol. Lett. 14, 101–112 (2011).PubMed 

Google Scholar 
15.Ben‐Hur, E. & Kadmon, R. Heterogeneity–diversity relationships in sessile organisms: a unified framework. Ecol. Lett. 23, 193–207 (2020).PubMed 

Google Scholar 
16.Tews, J. et al. Animal species diversity driven by habitat heterogeneity/diversity: the importance of keystone structures. J. Biogeogr. 31, 79–92 (2004).
Google Scholar 
17.Tuanmu, M. N. & Jetz, W. A global, remote sensing‐based characterization of terrestrial habitat heterogeneity for biodiversity and ecosystem modelling. Global Ecol. Biogeogr. 24, 1329–1339 (2015).
Google Scholar 
18.MacArthur, R. H. & MacArthur, J. W. On bird species diversity. Ecology 42, 594–598 (1961).
Google Scholar 
19.Allouche, O., Kalyuzhny, M., Moreno-Rueda, G., Pizarro, M. & Kadmon, R. Area–heterogeneity tradeoff and the diversity of ecological communities. Proc. Natl Acad. Sci. 109, 17495–17500 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
20.Fahrig, L. Rethinking patch size and isolation effects: the habitat amount hypothesis. J. Biogeogr. 40, 1649–1663 (2013).
Google Scholar 
21.Gómez, J., Valladares, F. & Puerta-Piñero, C. Differences between structural and functional environmental heterogeneity caused by seed dispersal. Funct. Ecol. 18, 787–792 (2004).
Google Scholar 
22.Azevedo, J. C., Jack, S. B., Coulson, R. N. & Wunneburger, D. F. Functional heterogeneity of forest landscapes and the distribution and abundance of the red-cockaded woodpecker. Forest Ecol. Manag. 127, 271–283 (2000).
Google Scholar 
23.Watson, D. M. & Herring, M. Mistletoe as a keystone resource: an experimental test. Proc. Royal Soc. B: Biol. Sci. 279, 3853–3860 (2012).
Google Scholar 
24.Ellison, A. M. et al. Loss of foundation species: consequences for the structure and dynamics of forested ecosystems. Front. Ecol. Environ. 3, 479–486 (2005).
Google Scholar 
25.Altieri, A. H., Silliman, B. R. & Bertness, M. D. Hierarchical organization via a facilitation cascade in intertidal cordgrass bed communities. Am. Natur. 169, 195–206 (2007).PubMed 

Google Scholar 
26.Angelini, C. et al. Foundation species’ overlap enhances biodiversity and multifunctionality from the patch to landscape scale in southeastern US salt marshes. Proc. Royal Soc. B: Biol. Sci. 282, 20150421 (2015).27.Angelini, C. & Silliman, B. R. Secondary foundation species as drivers of trophic and functional diversity: evidence from a tree-epiphyte system. Ecology 95, 185–196 (2014).PubMed 

Google Scholar 
28.Bishop, M. J., Byers, J. E., Marcek, B. J. & Gribben, P. E. Density-dependent facilitation cascades determine epifaunal community structure in temperate Australian mangroves. Ecology 93, 1388–1401 (2012).PubMed 

Google Scholar 
29.Bishop, M. J., Fraser, J. & Gribben, P. E. Morphological traits and density of foundation species modulate a facilitation cascade in Australian mangroves. Ecology 94, 1927–1936 (2013).PubMed 

Google Scholar 
30.Thomsen, M. S., Metcalfe, I., South, P. & Schiel, D. R. A host-specific habitat former controls biodiversity across ecological transitions in a rocky intertidal facilitation cascade. Marine Freshwater Res. 67, 144–152 (2016).
Google Scholar 
31.Gribben, P. E. et al. Positive and negative interactions control a facilitation cascade. Ecosphere 8, e02065 (2017).
Google Scholar 
32.Shurin, J. B. et al. A cross‐ecosystem comparison of the strength of trophic cascades. Ecol. Lett. 5, 785–791 (2002).
Google Scholar 
33.Thomsen, M. S. Experimental evidence for positive effects of invasive seaweed on native invertebrates via habitat-formation in a seagrass bed. Aquat. Invas. 5, 341–346 (2010).
Google Scholar 
34.Gribben, P. E. et al. Facilitation cascades in marine ecosystems: a synthesis and future directions. Oceanogr. Marine Biol. 57, 127–168 (2019).
Google Scholar 
35.Gotelli, N. J. & Colwell, R. K. Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness. Ecol. Lett. 4, 379–391 (2001).
Google Scholar 
36.Thomsen, M. S. et al. Habitat cascades: the conceptual context and global relevance of facilitation cascades via habitat formation and modification. Integrat. Comparat. Biol. 50, 158–175 (2010).
Google Scholar 
37.Thomsen, M. S. et al. Modified kelp seasonality and invertebrate diversity where an invasive kelp co-occurs with native mussels. Marine Biol. 165, 173 (2018).
Google Scholar 
38.Borst, A. C. et al. Food or furniture: separating trophic and non‐trophic effects of Spanish moss to explain its high invertebrate diversity. Ecosphere 10, e02846 (2019).
Google Scholar 
39.Bologna, P. A. & Heck, K. L. Jr. Macrofaunal associations with seagrass epiphytes: relative importance of trophic and structural characteristics. J. Exp. Marine Biol. Ecol. 242, 21–39 (1999).
Google Scholar 
40.Huston, M. A. & Huston, M. A. Biological diversity: the coexistence of species. (Cambridge University Press, 1994).41.Borer, E. T. et al. Finding generality in ecology: a model for globally distributed experiments. Methods Ecol. Evol. 5, 65–73 (2014).
Google Scholar 
42.Fraser, L. H. et al. Coordinated distributed experiments: an emerging tool for testing global hypotheses in ecology and environmental science. Front. Ecol. Environ. 11, 147–155 (2013).
Google Scholar 
43.Thompson, K., Askew, A., Grime, J., Dunnett, N. & Willis, A. Biodiversity, ecosystem function and plant traits in mature and immature plant communities. Funct. Ecol. 19, 355–358 (2005).
Google Scholar 
44.Duffy, J. E. et al. Biodiversity mediates top–down control in eelgrass ecosystems: a global comparative‐experimental approach. Ecol. Lett. 18, 696–705 (2015).PubMed 

Google Scholar 
45.Arft, A. et al. Responses of tundra plants to experimental warming: meta‐analysis of the international tundra experiment. Ecol. Monogr. 69, 491–511 (1999).
Google Scholar 
46.Thomas, M. A. & Klaper, R. Genomics for the ecological toolbox. Trends Ecol. Evol. 19, 439–445 (2004).PubMed 

Google Scholar 
47.Thomsen, M. S. et al. A sixth‐level habitat cascade increases biodiversity in an intertidal estuary. Ecol. Evol. 6, 8291–8303 (2016).PubMed 
PubMed Central 

Google Scholar 
48.Ricklefs, R. E. Environmental heterogeneity and plant species diversity: a hypothesis. Am. Natur. 111, 376–381 (1977).
Google Scholar 
49.Lundholm, J. T. Plant species diversity and environmental heterogeneity: spatial scale and competing hypotheses. J. Vegetation Sci. 20, 377–391 (2009).
Google Scholar 
50.Tamme, R., Hiiesalu, I., Laanisto, L., Szava‐Kovats, R. & Pärtel, M. Environmental heterogeneity, species diversity and co‐existence at different spatial scales. J. Vegetation Sci. 21, 796–801 (2010).
Google Scholar 
51.Hughes, A. R., Gribben, P. E., Kimbro, D. L. & Bishop, M. J. Additive and site-specific effects of two foundation species on invertebrate community structure. Mar. Ecol. Prog. Series 508, 129–138 (2014).ADS 

Google Scholar 
52.Yakovis, E. & Artemieva, A. Cockles, barnacles and ascidians compose a subtidal facilitation cascade with multiple hierarchical levels of foundation species. Sci. Rep. 7, 1–11 (2017).CAS 

Google Scholar 
53.Thomsen, M. S., Stæhr, P. A., Nejrup, L. & Schiel, D. R. Effects of the invasive macroalgae Gracilaria vermiculophylla on two co-occurring foundation species and associated invertebrates. Aquat. Invas. 8, 133–145 (2013).
Google Scholar 
54.Littler, M. M. Morphological form and photosynthetic performances of marine macroalgae: tests of a functional/form hypothesis. Botan. Marina 22, 161–165 (1980).
Google Scholar 
55.Padilla, D. K. & Allen, B. J. Paradigm lost: reconsidering functional form and group hypotheses in marine ecology. J. Exp. Mar. Biol. Ecol. 250, 207–221 (2000).CAS 
PubMed 

Google Scholar 
56.Wainwright, P. C. Functional morphology as a tool in ecological research. Ecol. Morphol.: Int. Organismal Biol. 42, 59 (1994).
Google Scholar 
57.Angelini, C. & Briggs, K. Spillover of secondary foundation species transforms community structure and accelerates decomposition in oak savannas. Ecosystems, 18, 780–791 (2015).
Google Scholar 
58.Gutiérrez, J. L., Bagur, M. & Palomo, M. G. Algal epibionts as co-engineers in mussel beds: effects on abiotic conditions and mobile interstitial invertebrates. Diversity 11, 17 (2019).
Google Scholar 
59.He, Q., Bertness, M. D. & Altieri, A. H. Global shifts towards positive species interactions with increasing environmental stress. Ecol. Lett. 16, 695–706 (2013).PubMed 

Google Scholar 
60.Watson, D. M. Mistletoe—a keystone resource in forests and woodlands worldwide. Ann. Rev. Ecol. Syst. 32, 219–249 (2001).
Google Scholar 
61.Mújica, E., Raventós, J., González, E. & Bonet, A. Long-term hurricane effects on populations of two epiphytic orchid species from Guanahacabibes Peninsula. Cuba. Lankesteriana Int. J. Orchidol. 13, 47–55 (2013).
Google Scholar 
62.Lobelle, D., Kenyon, E. J., Cook, K. J. & Bull, J. C. Local competition and metapopulation processes drive long-term seagrass-epiphyte population dynamics. PLoS ONE 8, e57072 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
63.Svirski, E., Beer, S. & Friedlander, M. Gracilaria conferta and its epiphytes: Interrelationship between the red seaweed and Ulva cf. lactuca. Hydrobiologia 260, 391–396 (1993).
Google Scholar 
64.Cummins, S., Roberts, D. & Zimmerman, K. Effects of the green macroalga Enteromorpha intestinalis on macrobenthic and seagrass assemblages in a shallow coastal estuary. Marine Ecol. Prog. Series 266, 77–87 (2004).ADS 

Google Scholar 
65.Holmquist, J. G. Disturbance and gap formation in a marine benthic mosaic: influence of shifting macroalgal patches on seagrass structure and mobile invertebrates. Marine Ecol. Prog. Series 158, 121–130 (1997).ADS 

Google Scholar 
66.Siciliano, A., Schiel, D. R. & Thomsen, M. S. Effects of local anthropogenic stressors on a habitat cascade in an estuarine seagrass system. Marine Freshwater Res. 70, 1129–1142 (2019).
Google Scholar 
67.Field, R. et al. Spatial species‐richness gradients across scales: a meta‐analysis. J. Biogeogr. 36, 132–147 (2009).
Google Scholar 
68.Šímová, I., Li, Y. M. & Storch, D. Relationship between species richness and productivity in plants: the role of sampling effect, heterogeneity and species pool. J. Ecol. 101, 161–170 (2013).
Google Scholar 
69.Crain, C. M., Kroeker, K. & Halpern, B. S. Interactive and cumulative effects of multiple human stressors in marine systems. Ecol. Lett. 11, 1304–1315 (2008).PubMed 

Google Scholar 
70.Berlow, E. L. Strong effects of weak interactions in ecological communities. Nature 398, 330–334 (1999).ADS 
CAS 

Google Scholar 
71.Darling, E. S. & Côté, I. M. Quantifying the evidence for ecological synergies. Ecol. Lett. 11, 1278–1286 (2008).PubMed 

Google Scholar 
72.Paine, R. T., Tegner, M. J. & Johnson, E. A. Compounded perturbations yield ecological surprises. Ecosystems 1, 535–545 (1998).
Google Scholar 
73.Christensen, M. R. et al. Multiple anthropogenic stressors cause ecological surprises in boreal lakes. Glob. Change Biol. 12, 2316–2322 (2006).ADS 

Google Scholar 
74.Strain, E. M. et al. A global analysis of complexity–biodiversity relationships on marine artificial structures. Glob. Ecol. Biogeogr. 30, 140–153 (2021).
Google Scholar 
75.Richardson, J. T. Eta squared and partial eta squared as measures of effect size in educational research. Educ. Res. Rev. 6, 135–147 (2011).
Google Scholar 
76.Clarke, K. R., Gorley, R., Somerfield, P. J. & Warwick, R. Change in marine communities: an approach to statistical analysis and interpretation. (Primer-E Ltd, 2014).77.Gartner, A., Tuya, F., Lavery, P. S. & McMahon, K. Habitat preferences of macroinvertebrate fauna among seagrasses with varying structural forms. J. Exp. Marine Biol. Ecol. 439, 143–151 (2013).
Google Scholar 
78.Green, D. S. & Crowe, T. P. Context-and density-dependent effects of introduced oysters on biodiversity. Biol. Invasions 16, 1145–1163 (2014).
Google Scholar 
79.Lawton, J. H. Are there general laws in ecology? Oikos 84, 177–192 (1999).
Google Scholar 
80.Borer, E. et al. What determines the strength of a trophic cascade? Ecology 86, 528–537 (2005).
Google Scholar 
81.Vellend, M. Conceptual synthesis in community ecology. Quart. Rev. Biol. 85, 183–206 (2010).PubMed 

Google Scholar 
82.Chase, J. M. & Leibold, M. A. Ecological niches: linking classical and contemporary approaches. (University of Chicago Press, 2003).83.Anderson, M. J. et al. Navigating the multiple meanings of β diversity: a roadmap for the practicing ecologist. Ecol. Lett. 14, 19–28 (2011).ADS 
PubMed 

Google Scholar 
84.Anderson, M. J. A new method for non‐parametric multivariate analysis of variance. Austral Ecol. 26, 32–46 (2001).
Google Scholar 
85.Veech, J. A. & Crist, T. O. Habitat and climate heterogeneity maintain beta‐diversity of birds among landscapes within ecoregions. Glob. Ecol. Biogeogr. 16, 650–656 (2007).
Google Scholar 
86.Turner, M. G. Landscape ecology: the effect of pattern on process. Ann. Rev. Ecol. Syst. 20, 171–197 (1989).
Google Scholar 
87.Wilson, M. V. & Shmida, A. Measuring beta diversity with presence-absence data. J. Ecol. 72, 1055–1064 (1984).
Google Scholar 
88.Jost, L. Partitioning diversity into independent alpha and beta components. Ecology 88, 2427–2439 (2007).PubMed 

Google Scholar 
89.Socolar, J. B., Gilroy, J. J., Kunin, W. E. & Edwards, D. P. How should beta-diversity inform biodiversity conservation? Trends Ecol. Evol. 31, 67–80 (2016).PubMed 

Google Scholar 
90.McAfee, D., Cole, V. J. & Bishop, M. J. Latitudinal gradients in ecosystem engineering by oysters vary across habitats. Ecology 97, 929–939 (2016).PubMed 

Google Scholar 
91.Altieri, A. H. & Irving, A. D. Species coexistence and the superior ability of an invasive species to exploit a facilitation cascade habitat. PeerJ 5, e2848 (2017).PubMed 
PubMed Central 

Google Scholar 
92.Lindenmayer, D., Franklin, J. & Fischer, J. General management principles and a checklist of strategies to guide forest biodiversity conservation. Biol. Conser. 131, 433–445 (2006).
Google Scholar 
93.Le Roux, D. S., Ikin, K., Lindenmayer, D. B., Manning, A. D. & Gibbons, P. Single large or several small? Applying biogeographic principles to tree-level conservation and biodiversity offsets. Biol. Conser. 191, 558–566 (2015).
Google Scholar 
94.Wernberg, T. et al. Genetic diversity and kelp forest vulnerability to climatic stress. Sci. Rep. 8, 1–8 (2018).
Google Scholar 
95.Macintosh, D. J. & Ashton, E. C. A review of mangrove biodiversity conservation and management. Centre for tropical ecosystems research. (University of Aarhus, 2002).96.Grabowski, J. H. et al. Economic valuation of ecosystem services provided by oyster reefs. Bioscience 62, 900–909 (2012).
Google Scholar 
97.Renzi, J. J., He, Q. & Silliman, B. R. Harnessing positive species interactions to enhance coastal wetland restoration. Front. Ecol. Evol. 7, 131 (2019).
Google Scholar 
98.Silliman, B. R. et al. Facilitation shifts paradigms and can amplify coastal restoration efforts. Proc. Natl Acad. Sci. 112, 14295–14300 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
99.Bulleri, F. et al. Harnessing positive species interactions as a tool against climate-driven loss of coastal biodiversity. PLoS Biol. 16, e2006852 (2018).PubMed 
PubMed Central 

Google Scholar 
100.Brancalion, P. H. et al. Global restoration opportunities in tropical rainforest landscapes. Sci. Adv. 5, eaav3223 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
101.Burns, K. Meta-community structure of vascular epiphytes in a temperate rainforest. Botany 86, 1252–1259 (2008).
Google Scholar 
102.Chapman, M. & Blockley, D. Engineering novel habitats on urban infrastructure to increase intertidal biodiversity. Oecologia 161, 625–635 (2009).ADS 
CAS 
PubMed 

Google Scholar 
103.Schneider-Mayerson, M. Some islands will rise: Singapore in the Anthropocene. Resilience: J. Environ. Human. 4, 166–184 (2017).
Google Scholar 
104.Wangpraseurt, D. et al. Bionic 3D printed corals. Nat. Commun. 11, 1–8 (2020).
Google Scholar 
105.de Alvarenga, R. A. F., Galindro, B. M., de Fátima Helpa, C. & Soares, S. R. The recycling of oyster shells: an environmental analysis using Life Cycle Assessment. J. Environ. Manag. 106, 102–109 (2012).CAS 

Google Scholar 
106.Morris, J. P., Backeljau, T. & Chapelle, G. Shells from aquaculture: a valuable biomaterial, not a nuisance waste product. Rev. Aqua. 11, 42–57 (2019).
Google Scholar 
107.Hylander, K. & Nemomissa, S. Home garden coffee as a repository of epiphyte biodiversity in Ethiopia. Front. Ecol. Environ. 6, 524–528 (2008).
Google Scholar 
108.Franken, R. J. et al. Effects of interstitial refugia and current velocity on growth of the amphipod Gammarus pulex Linnaeus. J. North Am. Bentholog. Soc. 25, 656–663 (2006).
Google Scholar 
109.Bishop, M. et al. Facilitation of molluscan assemblages in mangroves by the fucalean alga Hormosira banksii. Marine Ecol. Prog. Series 392, 111–122 (2009).ADS 

Google Scholar 
110.Macreadie, P. I., Kimbro, D. L., Fourgerit, V., Leto, J. & Hughes, A. R. Effects of Pinna clams on benthic macrofauna and the possible implications of their removal from seagrass ecosystems. J. Molluscan Studies 80, 102–106 (2014).
Google Scholar 
111.Thomsen, M. S. et al. Earthquake-driven destruction of an intertidal habitat cascade. Aquat. Botany 164, 103217 (2020).
Google Scholar 
112.Enochs, I. C., Toth, L. T., Brandtneris, V. W., Afflerbach, J. C. & Manzello, D. P. Environmental determinants of motile cryptofauna on an eastern Pacific coral reef. Marine Ecol. Prog. Series 438, 105–118 (2011).ADS 

Google Scholar  More

1.McGill, B. J., Enquist, B. J., Weiher, E. & Westoby, M. Rebuilding community ecology from functional traits. Trends Ecol. Evol. 21, 178–185 (2006).PubMed 

Google Scholar 
2.Urban, M. C. et al. Improving the forecast for biodiversity under climate change. Science 353, 6304 (2016).
Google Scholar 
3.Sheridan, J. A. & Bickford, D. Shrinking body size as an ecological response to climate change. Nat. Clim. Chang. 1, 401–406 (2011).ADS 

Google Scholar 
4.Zeuss, D., Brandl, R., Brändle, M., Rahbek, C. & Brunzel, S. Global warming favours light-coloured insects in Europe. Nat. Commun. 5, 1–10 (2014).
Google Scholar 
5.Senf, C., Sebald, J. & Seidl, R. Increasing canopy mortality affects the future demographic structure of Europe’s forests. One Earth 4, 749–755 (2021).
Google Scholar 
6.Zellweger, F. et al. Forest microclimate dynamics drive plant responses to warming. Science 368, 772–775 (2020).ADS 
CAS 
PubMed 

Google Scholar 
7.Scharenbroch, B. C. & Bockheim, J. G. Impacts of forest gaps on soil properties and processes in old growth northern hardwood-hemlock forests. Plant Soil 294, 219–233 (2007).CAS 

Google Scholar 
8.de Frenne, P. et al. Global buffering of temperatures under forest canopies. Nat. Ecol. Evol. 3, 744–749 (2019).PubMed 

Google Scholar 
9.Kermavnar, J. et al. Effects of various cutting treatments and topographic factors on microclimatic conditions in Dinaric fir-beech forests. Agric. For. Meteorol. 295, 108186 (2020).ADS 

Google Scholar 
10.Brown, M. J., Parker, G. G. & Posner, N. E. A survey of ultraviolet-B radiation in forests. J. Ecol. 82, 843 (1994).
Google Scholar 
11.Thom, D. et al. Effects of disturbance patterns and deadwood on the microclimate in European beech forests. Agric. For. Meteorol. 291, 108066 (2020).ADS 

Google Scholar 
12.Frank, A. et al. Risk of genetic maladaptation due to climate change in three major European tree species. Glob. Change Biol. 23, 5358–5371 (2017).ADS 

Google Scholar 
13.Maxime, C. & Hendrik, D. Effects of climate on diameter growth of co-occurring Fagus sylvatica and Abies alba along an altitudinal gradient. Trees 25, 265–276 (2011).
Google Scholar 
14.Vitasse, Y. et al. Contrasting resistance and resilience to extreme drought and late spring frost in five major European tree species. Glob. Change Biol. 25, 3781–3792 (2019).ADS 

Google Scholar 
15.Seidl, R. et al. Forest disturbances under climate change. Nat. Clim. Chang. 7, 395–402 (2017).ADS 
PubMed 
PubMed Central 

Google Scholar 
16.Penone, C. et al. Specialisation and diversity of multiple trophic groups are promoted by different forest features. Ecol. Lett. 22, 170–180 (2019).PubMed 

Google Scholar 
17.Müller, J. et al. Primary determinants of communities in deadwood vary among taxa but are regionally consistent. Oikos 129, 1579–1588 (2020).
Google Scholar 
18.Krah, F.-S. et al. Independent effects of host and environment on the diversity of wood-inhabiting fungi. J. Ecol. 106, 1428–1442 (2018).
Google Scholar 
19.Nagy, L. G. et al. Six key traits of fungi: Their evolutionary origins and genetic bases. Microbiol. Spect. 5, 4 (2017).
Google Scholar 
20.Baldrian, P. Forest microbiome: Diversity, complexity and dynamics. FEMS Microbiol. Rev. 41, 109–130 (2017).CAS 
PubMed 

Google Scholar 
21.Raudaskoski, M. & Salonen, M. Interrelationships between vegetative development and basidiocarp initiation. in The Ecology and Physiology of the Fungal Mycelium: Symposium of the British Mycological Society, vol. 8, p. 291 (Cambridge University Press, 1984).22.Kües, U. & Liu, Y. Fruiting body production in Basidiomycetes. Appl. Microbiol. Biotechnol. 54, 141–152 (2000).PubMed 

Google Scholar 
23.Sakamoto, Y. Influences of environmental factors on fruiting body induction, development and maturation in mushroom-forming fungi. Fungal Biol. Rev. 32, 236–248 (2018).
Google Scholar 
24.Luo, L., Zhang, S., Wu, J., Sun, X. & Ma, A. Heat stress in macrofungi: Effects and response mechanisms. Appl. Microbiol. Biotechnol. 1, 1–10 (2021).
Google Scholar 
25.Krah, F., Hess, J., Hennicke, F., Kar, R. & Bässler, C. Transcriptional response of mushrooms to artificial sun exposure. Ecol. Evol. 11, 10538–10546 (2021).PubMed 
PubMed Central 

Google Scholar 
26.Krah, F.-S. et al. European mushroom assemblages are darker in cold climates. Nat. Commun. 10, 2890 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
27.Bässler, C. et al. Global analysis reveals an environmentally driven latitudinal pattern in mushroom size across fungal species. Ecol. Lett. https://doi.org/10.1111/ele.13678 (2021).Article 
PubMed 

Google Scholar 
28.Bässler, C. et al. Mean reproductive traits of fungal assemblages are correlated with resource availability. Ecol. Evol. 6, 582–592 (2016).PubMed 
PubMed Central 

Google Scholar 
29.Abrego, N., Norberg, A. & Ovaskainen, O. Measuring and predicting the influence of traits on the assembly processes of wood-inhabiting fungi. J. Ecol. 105, 1070–1081 (2016).
Google Scholar 
30.Sánchez-García, M. et al. Fruiting body form, not nutritional mode, is the major driver of diversification in mushroom-forming fungi. Proc. Natl. Acad. Sci. 117, 32528–32534 (2020).PubMed 
PubMed Central 

Google Scholar 
31.Hibbett, D. S. & Binder, M. Evolution of complex fruiting–body morphologies in homobasidiomycetes. Proc. R. Soc. Lond. B 269, 1963–1969 (2002).CAS 

Google Scholar 
32.Hibbett, D. S., Pine, E. M., Langer, E., Langer, G. & Donoghue, M. J. Evolution of gilled mushrooms and puffballs inferred from ribosomal DNA sequences. Proc. Natl. Acad. Sci. 94, 12002–12006 (1997).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
33.Halbwachs, H., Simmel, J. & Bässler, C. Tales and mysteries of fungal fruiting: How morphological and physiological traits affect a pileate lifestyle. Fungal Biol. Rev. 30, 36–61 (2016).
Google Scholar 
34.Wilson, A. W., Binder, M. & Hibbett, D. S. Effects of gasteroid fruiting body morphology on diversification rates in three independent clades of fungi estimated using binary state speciation and extinction analysis. Evol. Int. J. Org. Evol. 65, 1305–1322 (2011).
Google Scholar 
35.Cordero, R. J. B. & Casadevall, A. Functions of fungal melanin beyond virulence. Fungal Biol. Rev. 31, 99–112 (2017).PubMed 
PubMed Central 

Google Scholar 
36.Zamora-Camacho, F. J., Reguera, S. & Moreno-Rueda, G. Bergmann’s Rule rules body size in an ectotherm: Heat conservation in a lizard along a 2200-metre elevational gradient. J. Evol. Biol. 27, 2820–2828 (2014).CAS 
PubMed 

Google Scholar 
37.Kalmus, H. Physiology and ecology of cuticle colour in insects. Nature 148, 693 (1941).ADS 

Google Scholar 
38.Law, S. J. et al. Darker ants dominate the canopy: Testing macroecological hypotheses for patterns in colour along a microclimatic gradient. J. Anim. Ecol. 89, 347–359 (2020).PubMed 

Google Scholar 
39.Bogert, C. M. Thermoregulation in reptiles, a factor in evolution. Evolution 3, 195–211 (1949).CAS 
PubMed 

Google Scholar 
40.R Core Team. R: A Language and Environment for Statistical Computing. (R Core Team, 2015).41.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).MATH 

Google Scholar 
42.Olou, B. A., Yorou, N. S., Striegel, M., Bässler, C. & Krah, F.-S. Effects of macroclimate and resource on the diversity of tropical wood-inhabiting fungi. For. Ecol. Manage. 436, 79–87 (2019).
Google Scholar 
43.Moser, M. Fungal growth and fructification under stress conditions. Ukrainian Bot. J. 50, 5–11 (1993).
Google Scholar 
44.Walter, H. et al. Vegetation of the Earth in Relation to Climate and the Eco-Physiological Conditions (English Universities Press, 1973).
Google Scholar 
45.Botti, D. A phytoclimatic map of Europe. Cybergeo Eur. J. Geogr. https://doi.org/10.4000/cybergeo.29495 (2018).Article 

Google Scholar 
46.Sofo, A., Manfreda, S., Fiorentino, M., Dichio, B. & Xiloyannis, C. The olive tree: A paradigm for drought tolerance in Mediterranean climates. Hydrol. Earth Syst. Sci. 12, 293–301 (2008).ADS 

Google Scholar 
47.Poorter, H., Niinemets, Ü., Poorter, L., Wright, I. J. & Villar, R. Causes and consequences of variation in leaf mass per area (LMA): A meta-analysis. New Phytol. 182, 565–588 (2009).PubMed 

Google Scholar 
48.Ellenberg, H. H. Spring areas and adjacent swamps. in Vegetation ecology of central Europe 313–313 (Cambridge University Press, 1988).49.Gardner, J. L., Peters, A., Kearney, M. R., Joseph, L. & Heinsohn, R. Declining body size: A third universal response to warming?. New Phytol. 26, 285–291 (2011).
Google Scholar 
50.Stamets, P. Growing Gourmet and Medicinal Mushrooms (Ten Speed Press, 2011).
Google Scholar 
51.Cordero, R. J. B. et al. Impact of yeast pigmentation on heat capture and latitudinal distribution. Curr. Biol. 28, 2657-2664.e3 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
52.Graham, J. H. et al. Species richness, equitability, and abundance of ants in disturbed landscapes. Ecol. Ind. 9, 866–877 (2009).
Google Scholar 
53.Palladini, J. D., Jones, M. G., Sanders, N. J. & Jules, E. S. The recovery of ant communities in regenerating temperate conifer forests. For. Ecol. Manage. 242, 619–624 (2007).
Google Scholar 
54.Punttila, P., Haila, Y., Niemelä, J. & Pajunen, T. Ant communities in fragments of old-growth taiga and managed surroundings. Ann. Zool. Fenn. 31, 131–144 (1994).
Google Scholar 
55.Entling, W., Schmidt-Entling, M. H., Bacher, S., Brandl, R. & Nentwig, W. Body size–climate relationships of European spiders. J. Biogeogr. 37, 477–485 (2010).
Google Scholar 
56.Gotelli, N. J. Null model analysis of species co-occurrence patterns. Ecology 81, 2606–2621 (2000).
Google Scholar 
57.Tucker, C. M., Shoemaker, L. G., Davies, K. F., Nemergut, D. R. & Melbourne, B. A. Differentiating between niche and neutral assembly in metacommunities using null models of beta-diversity. Oikos 125, 778–789 (2015).
Google Scholar 
58.Shipley, B. et al. Reinforcing loose foundation stones in trait-based plant ecology. Oecologia 180, 923–931 (2016).ADS 
PubMed 

Google Scholar 
59.Krah, F.-S. & Bässler, C. What can intraspecific trait variability tell us about fungal communities and adaptations?. Mycol. Prog. 20, 905–910 (2021).
Google Scholar 
60.Norros, V. & Halme, P. Growth sites of polypores from quantitative expert evaluation: Late-stage decayers and saprotrophs fruit closer to ground. Fungal Ecol. 28, 53–65 (2017).
Google Scholar 
61.Senf, C. et al. Canopy mortality has doubled in Europe’s temperate forests over the last three decades. Nat. Commun. 9, 4978 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
62.Bässler, C., Seifert, L. & Müller, J. The BIOKLIM project in the National Park Bavarian Forest: Lessons from a biodiversity survey. Silva Gabreta 21, 81–93 (2015).
Google Scholar 
63.Halme, P. & Kotiaho, J. S. The importance of timing and number of surveys in fungal biodiversity research. Biodivers. Conserv. 21, 205–219 (2012).
Google Scholar 
64.Crous, P. W. et al. MycoBank: An online initiative to launch mycology into the 21st century. Stud. Mycol. 50, 19–22 (2004).
Google Scholar 
65.van den Broek, E. L. & van Rikxoort, E. M. Evaluation of color representation for texture analysis. in Paper presented at 16th Belgium-Dutch Conference on Artificial Intelligence, BNAIC 2004, Groningen, Netherlands 35–42 (2004).66.Bernicchia, A. Fungi Europaei, Volume 10. Polyporaceae sl. (Alassio, Italia: Edizioni Candusso, 2005).67.Kembel, S. Community Phylogenetic Analysis with Picante Installing Picante 1–18 (Springer, 2009).
Google Scholar 
68.Gotelli, N. J. & Graves, G. R. Null Models in Ecology (Springer, 1996).
Google Scholar 
69.Hochberg, Y. & Tamhane, A. C. Multiple Comparison Procedures (Wiley, 1987).MATH 

Google Scholar 
70.Dormann, C. G., Elith, J., Bacher, S., Buchmann, C. & Lautenback, S. Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography 35, 001–020 (2012).
Google Scholar 
71.Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting Linear Mixed-Effects Models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Google Scholar 
72.Purhonen, J. et al. Morphological traits predict host-tree specialization in wood-inhabiting fungal communities. Fungal Ecol. 46, 100863 (2020).
Google Scholar 
73.Heilmann-Clausen, J. & Christensen, M. Does size matter?: On the importance of various dead wood fractions for fungal diversity in Danish beech forests. For. Ecol. Manage. 201, 105–117 (2004).
Google Scholar 
74.Lenth, R. V. Least-squares means: The R package lsmeans. J. Stat. Softw. 69, 1–33 (2016).
Google Scholar  More