Philippa Kaur

Hobbs, R. J. et al. Novel ecosystems: Theoretical and management aspects of the new ecological world order. Glob. Ecol. Biogeogr. 15, 1–7 (2006).
Google Scholar 
Kraft, N. J. B. et al. Community assembly, coexistence and the environmental filtering metaphor. Funct. Ecol. 29, 592–599 (2015).
Google Scholar 
Mayfield, M. M. et al. What does species richness tell us about functional trait diversity? Predictions and evidence for responses of species and functional trait diversity to land-use change. Glob. Ecol. Biogeogr. 19, 423–431 (2010).
Google Scholar 
Zobel, M. The species pool concept as a framework for studying patterns of plant diversity. J. Veg. Sci. 27, 8–18 (2016).
Google Scholar 
Birkhofer, K. et al. Land-use type and intensity differentially filter traits in above- and below-ground arthropod communities. J. Anim. Ecol. 86, 511–520 (2017).PubMed 

Google Scholar 
Temperton, V. M. Assembly Rules and Restoration Ecology: Bridging the Gap Between Theory and Practice (Island Press, 2004).
Google Scholar 
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 
Gascon, C. et al. Matrix habitat and species richness in tropical forest remnants. Biol. Conserv. 91, 223–229 (1999).
Google Scholar 
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 
Vaccaro, A., Filloy, J. & Bellocq, M. What land use better preserves the functional and taxonomic diversity of birds in a grassland biome?. Avian Conserv. Ecol. 14, 1 (2019).
Google Scholar 
Vaccaro, A. S. & Bellocq, M. I. Diversidad taxonómica y funcional de aves: Diferencias entre hábitats antrópicos en un bosque subtropical. Ecol. Austral 29, 391–404 (2019).
Google Scholar 
Sekercioglu, C. H. Bird functional diversity and ecosystem services in tropical forests, agroforests and agricultural areas. J. Ornithol. 153, 153–161 (2012).
Google Scholar 
Zurita, G. A. & Bellocq, M. I. Bird assemblages in anthropogenic habitats: Identifying a suitability gradient for native species in the Atlantic Forest. Biotropica 44, 412–419 (2012).
Google Scholar 
Azpiroz, A. B. et al. Ecology and conservation of grassland birds in southeastern South America: A review. J. Field Ornithol. 83, 217–246 (2012).
Google Scholar 
Devictor, V. et al. Spatial mismatch and congruence between taxonomic, phylogenetic and functional diversity: The need for integrative conservation strategies in a changing world. Ecol. Lett. 13, 1030–1040 (2010).PubMed 

Google Scholar 
Faith, D. P. Conservation evaluation and phylogenetic diversity. Biol. Conserv. 61, 1–10 (1992).
Google Scholar 
Corbelli, J. M. et al. Integrating taxonomic, functional and phylogenetic beta diversities: Interactive effects with the biome and land use across taxa. PLoS ONE 10, 1–17 (2015).
Google Scholar 
Purschke, O. et al. Contrasting changes in taxonomic, phylogenetic and functional diversity during a long-term succession: Insights into assembly processes. J. Ecol. 101, 857–866 (2013).
Google Scholar 
Srivastava, D. S., Cadotte, M. W., Macdonald, A. A. M., Marushia, R. G. & Mirotchnick, N. Phylogenetic diversity and the functioning of ecosystems. Ecol. Lett. 15, 637–648 (2012).PubMed 

Google Scholar 
Cavender-Bares, J., Kozak, K. H., Fine, P. V. A. & Kembel, S. W. The merging of community ecology and phylogenetic biology. Ecol. Lett. 12, 693–715 (2009).PubMed 

Google Scholar 
Mouquet, N. et al. Ecophylogenetics: Advances and perspectives. Biol. Rev. 87, 769–785 (2012).PubMed 

Google Scholar 
Ackerly, D. D., Schwilk, D. W. & Webb, C. O. Niche evolution and adaptive radiation: Testing the order of trait divergence. Ecology 87, S50–S61 (2006).CAS 
PubMed 

Google Scholar 
Cavender-Bares, J., Ackerly, D. D., Baum, D. A. & Bazzaz, F. A. Phylogenetic overdispersion in Floridian oak communities. Am. Nat. 163, 823–843 (2004).CAS 
PubMed 

Google Scholar 
Losos, J. B. et al. Niche lability in the evolution of a Caribbean lizard community. Nature 424, 542–545 (2003).ADS 
CAS 
PubMed 

Google Scholar 
Stevens, R. D., Gavilanez, M. M., Tello, J. S. & Ray, D. A. Phylogenetic structure illuminates the mechanistic role of environmental heterogeneity in community organization. J. Anim. Ecol. 81, 455–462 (2012).PubMed 

Google Scholar 
García-Navas, V. & Thuiller, W. Farmland bird assemblages exhibit higher functional and phylogenetic diversity than forest assemblages in France. J. Biogeogr. 47, 2392–2404 (2020).
Google Scholar 
Henwood, W. D. Toward a strategy for the conservation and protection of the world’s temperate grasslands. Univ. Neb. Press 20, 121–134 (2010).ADS 

Google Scholar 
Myers, N., Mittermeier, R. A., Mittermeier, C. G., da Fonseca, G. A. B. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).ADS 
CAS 
PubMed 

Google Scholar 
Landi, M., Oesterheld, M. & Deregibus, V. A. Manual de especies forrajeras de los pastizales naturales de Entre Ríos (1987).Viglizzo, E. F. et al. Ecological lessons and applications from one century of low external-input farming in the pampas of Argentina. Agric. Ecosyst. Environ. 83, 65–81 (2001).
Google Scholar 
Galindo Leal, C. & de Gusmão Câmara, I. The Atlantic Forest of South America: Biodiversity Status, Threats and Outlook (Island Press, 2003).
Google Scholar 
Oliveira-Filho, A. T. & Fontes, M. A. L. Patterns of floristic differentiation among Atlantic Forests in Southeastern Brazil and the influence of climate. Biotropica 32, 793–810 (2000).
Google Scholar 
DeGraaf, R. M., Geis, A. D. & Healy, P. A. Bird population and habitat surveys in urban areas. Landsc. Urban Plan. 21, 181–188 (1991).
Google Scholar 
Ralph, C. J. et al. Manual de métodos de campo para el monitoreo de aves terrestres. Pacific Southwest Research Station, Forest Service, U.S. Department of Agriculture, Albany, CA 46 http://www.srs.fs.usda.gov/pubs/31462. https://doi.org/10.3145/epi.2006.jan.15 (1996).Bibby, C., Jones, M. & Marsden, S. Expedition field techniques: Bird surveys. in (ed. Society, R. G.) (1998).Zurita, G. A. & Bellocq, M. I. Spatial patterns of bird community similarity: Bird responses to landscape composition and configuration in the Atlantic forest. Landsc. Ecol. 25, 147–158 (2010).
Google Scholar 
Koper, N. & Schmiegelow, F. K. K. A multi-scaled analysis of avian response to habitat amount and fragmentation in the Canadian dry mixed-grass prairie. Landsc. Ecol. 21, 1045 (2006).
Google Scholar 
Xeno-canto-Foundation. Xeno-canto website. https://www.xeno-canto.org (2018).Petchey, O. L. & Gaston, K. J. Functional diversity (FD), species richness and community composition. Ecol. Lett. 5, 402–411 (2002).
Google Scholar 
Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444–448 (2012).ADS 
CAS 
PubMed 

Google Scholar 
Revell, L. J. phytools: An R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).
Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. https://www.r-project.org (2018).Kembel, S. W. et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics 26, 1463–1464 (2010).CAS 
PubMed 

Google Scholar 
Webb, C. O., Ackerly, D. D. & Kembel, S. W. Phylocom: Software for the analysis of phylogenetic community structure and trait evolution. Bioinformatics 24, 2098–2100 (2008).CAS 
PubMed 

Google Scholar 
Pinheiro, J., Bates, D., DebRoy, S. & Sarkar, D. R Core Team. nlme: Linear and Nonlinear mixed effects models. R package version 3.1–117. (2014).Lenth, R. V. Least-Squares Means: The R Package lsmeans. J. Stat. Softw. https://doi.org/10.18637/jss.v069.i01 (2016).Article 

Google Scholar 
Cadotte, M. W. & Tucker, C. M. Should environmental filtering be abandoned?. Trends Ecol. Evol. 32, 429–437 (2017).PubMed 

Google Scholar 
Concepción, E. D. et al. Contrasting trait assembly patterns in plant and bird communities along environmental and human-induced land-use gradients. Ecography 40, 753–763 (2016).
Google Scholar 
Cerezo, A., Conde, M. C. & Poggio, S. L. Pasture area and landscape heterogeneity are key determinants of bird diversity in intensively managed farmland. Biodivers. Conserv. 20, 2649–2667 (2011).
Google Scholar 
Pretelli, M. G., Isacch, J. P. & Cardoni, D. A. Year-round abundance, richness and nesting of the bird assemblage of tall grasslands in the south-east Pampas region, Argentina. Ardeola 60, 327–343 (2013).
Google Scholar 
Molinari, R. L. Biografía de la Pampa: 4 siglos de historia del campo argentino (Fundación Colombina “V Centenario,” 1987).
Google Scholar 
Filloy, J. & Bellocq, M. I. Patterns of bird abundance along the agricultural gradient of the Pampean region. Agric. Ecosyst. Environ. 120, 291–298 (2007).
Google Scholar 
Le Viol, I. et al. More and more generalists: Two decades of changes in the European avifauna. Biol. Lett. 8, 780–782 (2012).PubMed 
PubMed Central 

Google Scholar 
Concepción, E. D., Moretti, M., Altermatt, F., Nobis, M. P. & Obrist, M. K. Impacts of urbanisation on biodiversity: The role of species mobility, degree of specialisation and spatial scale. Oikos 124, 1571–1582 (2015).
Google Scholar 
Emerson, B. C. & Gillespie, R. G. Phylogenetic analysis of community assembly and structure over space and time. Trends Ecol. Evol. 23, 619–630 (2008).PubMed 

Google Scholar 
Morse, N. B. et al. Novel ecosystems in the Anthropocene: A revision of the novel ecosystem concept for pragmatic applications. Ecol. Soc. 19, 12 (2014).
Google Scholar 
Loyn, R. H., McNabb, E. G., Macak, P. & Noble, P. Eucalypt plantations as habitat for birds on previously cleared farmland in south-eastern Australia. Biol. Conserv. 137, 533–548 (2007).
Google Scholar 
Marsden, S., Whiffin, M. & Galetti, M. Bird diversity and abundance in forest fragments and Eucalyptus plantations around an Atlantic forest reserve, Brazil. Biodivers. Conserv. 10, 737–751 (2001).
Google Scholar 
Zurita, G. A., Rey, N., Varela, D. M., Villagra, M. & Bellocq, M. I. Conversion of the Atlantic Forest into native and exotic tree plantations: Effects on bird communities from the local and regional perspectives. For. Ecol. Manag. 235, 164–173 (2006).
Google Scholar 
Flynn, D. F. B., Mirotchnick, N., Jain, M., Palmer, M. I. & Naeem, S. Functional and phylogenetic diversity as predictors of biodiversity ecosystem-function. Ecology 92, 1573–1581 (2011).PubMed 

Google Scholar 
Sol, D. et al. The worldwide impact of urbanisation on avian functional diversity. Ecol. Lett. 23, 962–972 (2020).PubMed 

Google Scholar 
Webb, C. O., Ackerly, D. D., McPeek, M. A. & Donoghue, M. J. Phylogenies and community ecology. Annu. Rev. Ecol. Syst. 33, 475–505 (2002).
Google Scholar 
Palacio, F. X., Ibañez, L. M., Maragliano, R. E. & Montalti, D. Urbanization as a driver of taxonomic, functional, and phylogenetic diversity losses in bird communities. Can. J. Zool. 96, 1114–1121 (2018).
Google Scholar 
Sol, D., Bartomeus, I., González-Lagos, C. & Pavoine, S. Urbanisation and the loss of phylogenetic diversity in birds. Ecol. Lett. 20, 721–729 (2017).PubMed 

Google Scholar 
Luck, G. W., Carter, A. & Smallbone, L. Changes in bird functional diversity across multiple land uses: Interpretations of functional redundancy depend on functional group identity. PLoS ONE 8, e63671 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Coetzee, B. W. T. & Chown, S. L. Land-use change promotes avian diversity at the expense of species with unique traits. Ecol. Evol. 6, 7610–7622 (2016).PubMed 
PubMed Central 

Google Scholar  More

Tsai, C.-W., Lai, C.-F., Chao, H.-C. & Vasilakos, A. V. Big data analytics: a survey. J. Big Data 2, 21 (2015).
Google Scholar 
Lemoine, F. et al. Renewing Felsenstein’s phylogenetic bootstrap in the era of big data. Nature 556, 452–456 (2018).ADS 
CAS 

Google Scholar 
Manzoni, C. et al. Genome, transcriptome and proteome: the rise of omics data and their integration in biomedical sciences. Brief. Bioinform. 19, 286–302 (2018).CAS 

Google Scholar 
Lichtman, J. W., Pfister, H. & Shavit, N. The big data challenges of connectomics. Nat. Neurosci. 17, 1448–1454 (2014).CAS 

Google Scholar 
Altaf-Ul-Amin, M., Afendi, F. M., Kiboi, S. K. & Kanaya, S. Systems biology in the context of big data and networks. Biomed. Res. Int. 2014, 428570 (2014).
Google Scholar 
Xia, J., Wang, J. & Niu, S. Research challenges and opportunities for using big data in global change biology. Glob. Change Biol. 26, 6040–6061 (2020).ADS 

Google Scholar 
Hindell, M. A. et al. Tracking of marine predators to protect Southern Ocean ecosystems. Nature 580, 87–92 (2020).ADS 
CAS 

Google Scholar 
Hussey, N. E. et al. Ecology. Aquatic animal telemetry: A panoramic window into the underwater world. Science 348, 1255642 (2015).
Google Scholar 
Kays, R., Crofoot, M. C., Jetz, W. & Wikelski, M. Terrestrial animal tracking as an eye on life and planet. Science 348, aaa2478 (2015).
Google Scholar 
Sherub, S., Fiedler, W., Duriez, O. & Wikelski, M. Bio-logging, new technologies to study conservation physiology on the move: a case study on annual survival of Himalayan vultures. J. Comp. Physiol. A Neuroethol. Sens. Neural Behav. Physiol. 203, 531–542 (2017).
Google Scholar 
Nathan, R. et al. Big-data approaches lead to an increased understanding of the ecology of animal movement. Science 375, eabg1780 (2022).CAS 

Google Scholar 
Wilson, R. P. et al. Estimates for energy expenditure in free-living animals using acceleration proxies: A reappraisal. J. Anim. Ecol. 89, 161–172 (2020).
Google Scholar 
Patterson, A., Gilchrist, H. G., Chivers, L., Hatch, S. & Elliott, K. A comparison of techniques for classifying behavior from accelerometers for two species of seabird. Ecol. Evol. 9, 3030–3045 (2019).
Google Scholar 
Masello, J. F. et al. How animals distribute themselves in space: energy landscapes of Antarctic avian predators. Mov. Ecol. 9, 24 (2021).
Google Scholar 
Shepard, E. L. C. et al. Energy landscapes shape animal movement ecology. Am. Nat. 182, 298–312 (2013).
Google Scholar 
Elliott, K. H., Le Vaillant, M., Kato, A., Speakman, J. R. & Ropert-Coudert, Y. Accelerometry predicts daily energy expenditure in a bird with high activity levels. Biol. Lett. 9, 20120919 (2013).
Google Scholar 
Nickel, B. A., Suraci, J. P., Nisi, A. C. & Wilmers, C. C. Energetics and fear of humans constrain the spatial ecology of pumas. Proc. Natl. Acad. Sci. USA 118, e2004592118 (2021).
Eisaguirre, J. M., Booms, T. L., Barger, C. P., Lewis, S. B. & Breed, G. A. Novel step selection analyses on energy landscapes reveal how linear features alter migrations of soaring birds. J. Anim. Ecol. 89, 2567–2583 (2020).
Google Scholar 
Wittemyer, G., Northrup, J. M. & Bastille-Rousseau, G. Behavioural valuation of landscapes using movement data. Philos. Trans. R. Soc. Lond. B Biol. Sci. 374, 20180046 (2019).
Google Scholar 
Chimienti, M. et al. The use of an unsupervised learning approach for characterizing latent behaviors in accelerometer data. Ecol. Evol. 6, 727–741 (2016).
Google Scholar 
Hounslow, J. L. et al. Assessing the effects of sampling frequency on behavioural classification of accelerometer data. J. Exp. Mar. Bio. Ecol. 512, 22–30 (2019).
Google Scholar 
Glass, T. W., Breed, G. A., Robards, M. D., Williams, C. T. & Kielland, K. Accounting for unknown behaviors of free-living animals in accelerometer-based classification models: Demonstration on a wide-ranging mesopredator. Ecol. Inf. 60, 101152 (2020).
Google Scholar 
Wang, Y. et al. Movement, resting, and attack behaviors of wild pumas are revealed by tri-axial accelerometer measurements. Mov. Ecol. 3, 2 (2015).
Google Scholar 
Chakravarty, P., Cozzi, G., Ozgul, A. & Aminian, K. A novel biomechanical approach for animal behaviour recognition using accelerometers. Methods Ecol. Evol. https://doi.org/10.1111/2041-210X.13172 (2019).Article 

Google Scholar 
Clarke, T. M. et al. Using tri-axial accelerometer loggers to identify spawning behaviours of large pelagic fish. Mov. Ecol. 9, 26 (2021).
Google Scholar 
Zhang, J., O’Reilly, K. M., Perry, G. L. W., Taylor, G. A. & Dennis, T. E. Extending the functionality of behavioural change-point analysis with k-Means clustering: a case study with the little penguin (Eudyptula minor). PLoS ONE 10, e0122811 (2015).
Google Scholar 
Korpela, J. et al. Machine learning enables improved runtime and precision for bio-loggers on seabirds. Commun. Biol. 3, 633 (2020).
Google Scholar 
Jeantet, L. et al. Behavioural inference from signal processing using animal-borne multi-sensor loggers: a novel solution to extend the knowledge of sea turtle ecology. R. Soc. Open Sci. 7, 200139 (2020).ADS 

Google Scholar 
Wang, G. Machine learning for inferring animal behavior from location and movement data. Ecol. Inf. 49, 69–76 (2019).
Google Scholar 
Dunford, C. E. et al. Surviving in steep terrain: a lab-to-field assessment of locomotor costs for wild mountain lions (Puma concolor). Mov. Ecol. 8, 34 (2020).
Google Scholar 
Jeanniard-du-Dot, T., Guinet, C., Arnould, J. P. Y., Speakman, J. R. & Trites, A. W. Accelerometers can measure total and activity-specific energy expenditures in free-ranging marine mammals only if linked to time-activity budgets. Funct. Ecol. 31, 377–386 (2017).
Google Scholar 
Hicks, O. et al. Acceleration predicts energy expenditure in a fat, flightless, diving bird. Sci. Rep. 10, 21493 (2020).ADS 
CAS 

Google Scholar 
Dentinger, J. E. et al. A probabilistic framework for behavioral identification from animal-borne accelerometers. Ecol. Model. 464, 109818 (2022).
Google Scholar 
Chakravarty, P., Maalberg, M., Cozzi, G., Ozgul, A. & Aminian, K. Behavioural compass: animal behaviour recognition using magnetometers. Mov. Ecol. 7, 28 (2019).
Google Scholar 
Hammond, T. T., Palme, R. & Lacey, E. A. Ecological specialization, variability in activity patterns and response to environmental change. Biol. Lett. 14, 20180115 (2018).
Google Scholar 
Lynch, H. J. & LaRue, M. A. First global census of the Adélie Penguin. Auk 131, 457–466 (2014).
Google Scholar 
Riaz, J., Bestley, S., Wotherspoon, S., Freyer, J. & Emmerson, L. From trips to bouts to dives: temporal patterns in the diving behaviour of chick-rearing Adélie penguins East Antarctica. Mar. Ecol. Prog. Ser. 654, 177–194 (2020).ADS 

Google Scholar 
Cherel, Y. Isotopic niches of emperor and Adélie penguins in Adélie Land, Antarctica. Mar. Biol. 154, 813–821 (2008).
Google Scholar 
Little Penguin (Eudyptula minor) – BirdLife species factsheet. at Carroll, G., Harcourt, R., Pitcher, B. J., Slip, D. & Jonsen, I. Recent prey capture experience and dynamic habitat quality mediate short-term foraging site fidelity in a seabird. Proc. Biol. Sci. 285, 20180788 (2018).
Google Scholar 
Meyer, X. et al. Oceanic thermal structure mediates dive sequences in a foraging seabird. Ecol. Evol. 10, 6610–6622 (2020).
Google Scholar 
Cavallo, C. et al. Quantifying prey availability using the foraging plasticity of a marine predator, the little penguin. Funct. Ecol. https://doi.org/10.1111/1365-2435.13605 (2020).Article 

Google Scholar 
Ropert-Coudert, Y., Chiaradia, A. & Kato, A. An exceptionally deep dive by a Little Penguin Eudyptula minor. Mar. Ornithol 34, 71–74 (2006).
Google Scholar 
Ropert-Coudert, Y., Kato, A., Wilson, R. P. & Cannell, B. Foraging strategies and prey encounter rate of free-ranging Little Penguins. Mar. Biol. 149, 139–148 (2006).
Google Scholar 
Rodríguez, A., Chiaradia, A., Wasiak, P., Renwick, L. & Dann, P. Waddling on the dark side: ambient light affects attendance behavior of little penguins. J. Biol. Rhythms 31, 194–204 (2016).
Google Scholar 
Ropert-Coudert, Y. et al. Happy feet in a hostile world? the future of penguins depends on proactive management of current and expected threats. Front. Mar. Sci. 6, 248 (2019).
Google Scholar 
Shuert, C. R., Pomeroy, P. P. & Twiss, S. D. Assessing the utility and limitations of accelerometers and machine learning approaches in classifying behaviour during lactation in a phocid seal. Anim. Biotelemetry 6, 14 (2018).
Google Scholar 
Dickinson, E. R. et al. Limitations of using surrogates for behaviour classification of accelerometer data: refining methods using random forest models in Caprids. Mov. Ecol. 9, 28 (2021).
Google Scholar 
Conway, A. M., Durbach, I. N., McInnes, A. & Harris, R. N. Frame-by-frame annotation of video recordings using deep neural networks. Ecosphere 12, e03384 (2021).
Google Scholar 
Ravindran, S. Five ways deep learning has transformed image analysis. Nature 609, 864–866 (2022).ADS 
CAS 

Google Scholar 
Del Caño, M. et al. Fine-scale body and head movements allow to determine prey capture events in the Magellanic Penguin (Spheniscus magellanicus). Mar. Biol. 168, 84 (2021).
Google Scholar 
Johnson, J. M. & Khoshgoftaar, T. M. Survey on deep learning with class imbalance. J. Big Data 6, 27 (2019).
Google Scholar 
Hazen, E. L. et al. Marine top predators as climate and ecosystem sentinels. Front. Ecol. Environ. 17, 565–574 (2019).
Google Scholar 
Sánchez, S. et al. Within-colony spatial segregation leads to foraging behaviour variation in a seabird. Mar. Ecol. Prog. Ser. 606, 215–230 (2018).ADS 

Google Scholar 
Patrick, S. C., Martin, J. G. A., Ummenhofer, C. C., Corbeau, A. & Weimerskirch, H. Albatrosses respond adaptively to climate variability by changing variance in a foraging trait. Glob. Change Biol. https://doi.org/10.1111/gcb.15735 (2021).Article 

Google Scholar 
Bonar, M. et al. Geometry of the ideal free distribution: individual behavioural variation and annual reproductive success in aggregations of a social ungulate. Ecol. Lett. 23, 1360–1369 (2020).
Google Scholar 
Michelot, C., Kato, A., Raclot, T. & Ropert-Coudert, Y. Adélie penguins foraging consistency and site fidelity are conditioned by breeding status and environmental conditions. PLoS ONE 16, e0244298 (2021).CAS 

Google Scholar 
Mahoney, P. J. et al. Navigating snowscapes: scale-dependent responses of mountain sheep to snowpack properties. Ecol. Appl. 28, 1715–1729 (2018).
Google Scholar 
Watanabe, Y. Y., Ito, K., Kokubun, N. & Takahashi, A. Foraging behavior links sea ice to breeding success in Antarctic penguins. Sci. Adv. 6, eaba4828 (2020).ADS 

Google Scholar 
Lescroël, A. et al. Working less to gain more: when breeding quality relates to foraging efficiency. Ecology 91, 2044–2055 (2010).
Google Scholar 
Zimmer, I., Ropert-Coudert, Y., Kato, A., Ancel, A. & Chiaradia, A. Does foraging performance change with age in female little penguins (Eudyptula minor)?. PLoS ONE 6, e16098 (2011).ADS 
CAS 

Google Scholar 
Hertel, A. G., Royauté, R., Zedrosser, A. & Mueller, T. Biologging reveals individual variation in behavioural predictability in the wild. J. Anim. Ecol. 90, 723–737 (2021).
Google Scholar 
Dickinson, E. R., Stephens, P. A., Marks, N. J., Wilson, R. P. & Scantlebury, D. M. Best practice for collar deployment of tri-axial accelerometers on a terrestrial quadruped to provide accurate measurement of body acceleration. Anim. Biotelemetry 8, 9 (2020).
Google Scholar 
Garde, B. et al. Ecological inference using data from accelerometers needs careful protocols. Methods Ecol. Evol. https://doi.org/10.1111/2041-210X.13804 (2022).Article 

Google Scholar 
Watanabe, Y. Y., Ito, M. & Takahashi, A. Testing optimal foraging theory in a penguin-krill system. Proc. Biol. Sci. 281, 20132376 (2014).
Google Scholar 
Grémillet, D. et al. Energetic fitness: Field metabolic rates assessed via 3D accelerometry complement conventional fitness metrics. Funct. Ecol. 32, 1203–1213 (2018).
Google Scholar 
Chimienti, M. et al. Quantifying behavior and life-history events of an Arctic ungulate from year-long continuous accelerometer data. Ecosphere 12, e03565 (2021).
Google Scholar 
Sutton, G. J., Botha, J. A., Speakman, J. R. & Arnould, J. P. Y. Validating accelerometry-derived proxies of energy expenditure using the doubly-labelled water method in the smallest penguin species. Biol. Open 10, bio055475 (2021).
Google Scholar 
Pagano, A. M. & Williams, T. M. Estimating the energy expenditure of free-ranging polar bears using tri-axial accelerometers: A validation with doubly labeled water. Ecol. Evol. 9, 4210–4219 (2019).
Google Scholar 
Ballance, L. T., Ainley, D. G., Ballard, G. & Barton, K. An energetic correlate between colony size and foraging effort in seabirds, an example of the Adélie penguin Pygoscelis adeliae. J. Avian Biol. 40, 279–288 (2009).
Google Scholar 
Wilson, R. P. et al. Long-term attachment of transmitting and recording devices to penguins and other seabirds. Wildl. Soc. Bull. 25, 101–106 (1997).
Google Scholar 
Shepard, E. L. C. et al. Identification of animal movement patterns using tri-axial accelerometry. Endanger. Species Res. 10, 47–60 (2008).ADS 

Google Scholar 
Kato, A., Ropert-Coudert, Y., Grémillet, D. & Cannell, B. Locomotion and foraging strategy in foot-propelled and wing-propelled shallow-diving seabirds. Mar. Ecol. Prog. Ser. 308, 293–301 (2006).ADS 

Google Scholar 
Team, R. C. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://url.org/www.R-project.org/ (2021).
Ainley, D. The Adélie Penguin: Bellwether of Climate Change (New York: Columbia University Press) (2006).Langrognet, F. et al. Rmixmod: Classification with Mixture Modelling. (2020).Bishop, C. M. Pattern Recognition and Machine Learning. Springer Science+Business Media, LLC, New
York, NY. (2006).Amélineau, F. et al. Intra- and inter-individual changes in little penguin diving and isotopic composition over the breeding season. Mar. Biol. 168, 62 (2021).
Google Scholar 
Stoffel, M. A., Nakagawa, S. & Schielzeth, H. rptR: repeatability estimation and variance decomposition by generalized linear mixed-effects models. Methods Ecol. Evol. 8, 1639–1644 (2017).
Google Scholar 
Kuhn, M. & Wickham, H. Tidymodels: a collection of packages for modeling and machine learning using tidyverse principles. https://www.tidymodels.org (2020).Wright, M. N. & Ziegler, A. Ranger : A fast implementation of random forests for high dimensional data in C++ andR. J. Stat. Softw. 77, 1–17 (2017).
Google Scholar  More

Description of the study areaThe study covers urban, peri-urban and rural wetlands in the Bamenda Municipality of the North West Region of Cameroon that have evolved concomitantly with different stages of urbanization (Fig. 1). In this study, urbanisation is considered a loose term that is aimed at giving a geographical expression to the variation in the economic, social and cultural practices in the milieu. The central town with many economic activities is termed the urban, the fringe area with sprawls is termed peri-urban while the rural has typical peasant activities and make-shift structures. From the variation of human activities in the three sub-zones, a variety of chemical substances are discharged into drains, playing a substantial role in soil quality, and therefore plant macrophyte diversity. The Plants studied were ubiquitous in the area and verification of their IUCN conservation status in the red data book of plants of Cameroon confirmed their abundance14. Information on protected sites in Cameroon does not place the study area under conservation status. In line with that, permits are not required to undertake academic and research studies as well as do a responsible collection of plants in the study area. The urbanization rate of Bamenda is 42%, and the population grew from 48,111 inhabitants in 1976 to 488,883 inhabitants in 201015, with 150–200 inhabitants/km2.Figure 1Relief Map of Bamenda showing the Bamenda escarpment, topography and the location for quadrat sites.Full size imageThe study area is part of the Bamenda escarpment that is located between latitudes 5° 55″ N and 6° 30″ N and longitudes 10° 25″ E and 10° 67″ E. The town shows an altitudinal range of 1200–1700 m and is divided into two parts by escarpments—a low-lying and gently undulating part with altitudes ranging from 1200 to 1400 m, with many flat areas that are usually inundated for most parts of the year, and an elevated part that range from 1400 to 1700 m altitude. Most of the streams take their rise from this elevated part (Fig. 1).This area experiences two seasons—a rainy season (mid-March to mid-October) and a short dry season (mid-October to mid-March). The thermic and hyperthermic temperature regimes dominate in the area. The mean annual temperature stands at 19.9 °C. January and February are the hottest months with mean monthly temperatures of 29.1 and 29.7 °C, respectively. This area is dominated by the Ustic and Udic moisture regimes with the Udic extending to the south9. Annual rainfall ranges from 1300 to 3000 mm16. The area has a rich hydrographical network with intense human activities and a dense population along different water courses in the watershed. The area is bounded on the West, North and East by the Cameroon Volcanic Line (made up of basalts, trachytes, rhyolites and numerous salt springs). The geologic history of this area originates from the Precambrian era when there was a vast formation of geosynclinal complexes, which became filled with clay-calcareous, and sandstone sediments9. These materials, crossed by intrusions of crystalline rocks, were folded in a generally NE-SW direction and underwent variable metamorphism9. The Rocks in the area are thus of igneous (granitic and volcanic) and metamorphic (migmatites) origin17, which gives rise to ferralitic soils18.Agriculture is the principal human activity in and around this region18. The area equally harbours the commercial center that has factories ranging from soap production, and mechanic workshops to metallurgy, which may be potential sources of pollutants that can influence wetland Geochemistry. Raffia farinifera bush, which is largely limited to the wetlands, is an important vegetation type in this area. R. farinifera provides raffia wine, a vital economic resource to the inhabitants who are fighting against the cultivation of these wetlands by vegetable farmers.Methods of the studyMacrophyte diversity studyThe plant diversity of the wetlands was evaluated using quadrats in the dry season for accessibility reasons. For each of the three wetlands (the urban, peri-urban and rural areas), three transects were established on which representative quadrats, each measuring 10 m × 10 m, were mapped out in uncultivated areas for the determination of plant species cover-abundance and diversity. It is perceived that the different zones receive different mixtures of chemical substances and thus influence macrophyte diversity differently.According to a publication by14 on the vascular plants of Cameroon and a taxonomic checklist with IUCN assessment, the plants of the area are placed under the Least Concern Category(LC), and therefore not in the risky category. Diversity studies involved the identification of a specific area called “relevé” by progressively increasing test quadrat size and sampling for specific diversity until the smallest area with adequate species representation was reached. The relevé size determined here was 1 m2, making a total of 300 sub-quadrats (relevé) in the entire study ie. 100 in each main quadrat). For each site (main quadrat), 10 representative relevés were sampled and all plant species were enumerated. Most plant species in each of them were identified in the field by the Botanist, Dr Ndam Lawrence Monah using visual observation of the morphology of the leaves and flowers, a self-made field guide, the Flora of West Africa and the Flora of Cameroon. 10 unidentified plants were appropriately collected where there were in abundance, placed onto a conventional plant press and dried in the field. Voucher specimens were tagged and transported to the Limbe Botanic Gardens (SCA: Southern Cameroon, the code of the Limbe Botanic Gardens Herbarium) for identification. Mr Elias Ndive, the Taxonomist of the Limbe Botanic Gardens compared unidentified specimens with authentic herbarium specimens and other taxonomic guides and finally identified them. Voucher specimens of the 10 plants were given identification numbers and deposited in the Herbarium of the Limbe Botanic Gardens.The Braun–Banquet method was used19 for the assessment of species cover abundance. Relative abundance and abundance ratings were determined using the Braun–Banquet rating scheme (Table 1).Table 1 Braun-Blanquet rating scheme for vegetation cover-abundance, Source19.Full size tableSimpson’s diversity indexSpecies richness was evaluated using Simpson’s diversity index (D), which takes into account both species richness and the Braun-Blanquet rating scheme for vegetation cover abundance and evenness of abundance among the species present. In essence, D measures the probability that two individuals that are randomly selected from an area will belong to the same species. The formula for calculating D is presented as:$${text{D}} = frac{{sum {{text{n}}_{i} left( {{text{n}}_{i} – 1} right)} }}{{{text{N}}({text{N}} – 1)}}$$where ni = the total number of each species; N = the total number of individuals of all species.The value of D ranges from 0 to 1. With this index, 0 represents infinite diversity and 1 represents no diversity. That is, the larger the value the lower the diversity.Alternatively, Simpson’s Diversity Index, = 1–D,1-D was used as a measure of diversity because it is more logical and less likely to cause confusion. The scale then gives a score from 0 to 1 with higher scores showing higher diversity (instead of being associated with low scores).The Simpson index is a dominance index because it gives more weight to common or dominant species. In this case, a few rare species with only a few representatives will not affect the diversity.
Soil sampling and analysisSoil sampling was done in and around the three quadrats laid in the urban, peri-urban and rural wetlands for macrophytes sampling. Twenty-one (21) composite samples (0–25 cm) were randomly collected (Fig. 2) and taken to the laboratory in black plastic bags. Each composite sample was a collection of 5 dried core soil samples. Due to the observed greater heterogeneity in the urban sector, the sampling density was intensified. The soil samples were air-dried and screened through a 2-mm sieve. They were analyzed in duplicate for their physicochemical properties in the Environmental and Analytical Chemistry Laboratory of the University of Dschang, Cameroon. Particle size distribution, cation exchange capacity (CEC), exchangeable bases, electrical conductivity (EC) and pH were determined by standard procedures20. Soil pH was measured both in water and KCl (1:2.5 soil/water mixture) using a glass electrode pH meter. Part of the soil was ball-milled for organic carbon (Walkley–Black method) and total nitrogen (Macro-Kjeldahl method) as largely described by20. Available phosphorus (P) was determined by Bray I method. Exchangeable cations were extracted using 1 N ammonium acetate at pH 7. Potassium (K) and sodium (Na) in the extract were determined using a flame photometer and magnesium (Mg) and calcium (Ca) were determined by complexiometric titration. Exchange acidity was extracted with 1 M KCl followed by quantification of Al and H by titration20. Effective cation exchange capacity (ECEC) was determined as the sum of bases and exchanged acidity.Figure 2Adapted from the 1980 land use map of the Bamenda City Area showing soil sampling points: Source Bamenda City Council.Map of the study area in freshwater wetlands of Bamenda Municipality.Full size imageApparent CEC (CEC at pH 7) was determined directly as outlined by20. Based on critical values of nutrients established for vegetables, nutrients were declared sufficient or deficient.
Statistical analysisThe data were subjected to statistical analysis using Microsoft Excel 2007 and SPSS statistical package 20.0. Soil properties were assessed for their variability using the coefficient of variation (CV) and compared with variability classes (Table 2).$$CV=frac{Sd}{X}X 100$$where: Sd = standard deviation; = X arithmetic mean of soil properties.Table 2 Grouping coefficient of variation into variability classes.Full size tableThe hierarchical cluster analysis (HCA) was used to group the area under managing units. The main goal of the hierarchical agglomerative cluster analysis is to spontaneously classify the data into groups of similarity (clusters). This is done by searching objects in the n-dimensional space that is located in the closest neighborhood and separating a stable cluster from other clusters. The sampling sites were considered objects for classification. Each object was determined by a set of variables (chemical concentrations of the soils in this case). More

ResultsSoil physiochemical propertiesThe preliminary status of the soil analyzed before the commencement of the field preparatory activities revealed that the soil was subtlety fertile with regard to the physical and chemical properties (Table 1).Table 1 Physicochemical properties of the soil.Full size tableAssessment of disease incidence at sowing dates during each year in the trial studyIn the trial study, very low and statistically significant (p  More

Read, Q. D., Moorhead, L. C., Swenson, N. G., Bailey, J. K. & Sanders, N. J. Convergent effects of elevation on functional leaf traits within and among species. Funct. Ecol. 28, 37–45. https://doi.org/10.1111/1365-2435.12162 (2014).Article 

Google Scholar 
Isbell, F. et al. Biodiversity increases the resistance of ecosystem productivity to climate extremes. Nature 526, 574–577. https://doi.org/10.1038/nature15374 (2015).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Vellend, M. & Geber, M. A. Connections between species diversity and genetic diversity. Ecol. Lett. 8, 767–781. https://doi.org/10.1111/j.1461-0248.2005.00775.x (2005).Article 

Google Scholar 
Hart, S. P., Schreiber, S. J. & Levine, J. M. How variation between individuals affects species coexistence. Ecol. Lett. 19, 825–838. https://doi.org/10.1111/ele.12618 (2016).Article 
PubMed 

Google Scholar 
Paschke, M. C. & Schmid, B. Relationship between population size, allozyme variation, and plant performance in the narrow endemic Cochlearia bavarica. Conserv. Genet. 3, 131–144 (2002).Article 
CAS 

Google Scholar 
Soleimani, V., Baum, B. & Johnson, D. A. AFLP and pedigree-based genetic diversity estimates in modern cultivars of durum wheat [Triticum turgidum L. subsp. durum (Desf.) Husn.]. Theor. Appl. Genet. 104, 350–357. https://doi.org/10.1007/s001220100714 (2002).Article 
CAS 
PubMed 

Google Scholar 
Hughes, A. R., Inouye, B. D., Johnson, M. T., Underwood, N. & Vellend, M. Ecological consequences of genetic diversity. Ecol. Lett. 11, 609–623. https://doi.org/10.1111/j.1461-0248.2008.01179.x (2008).Article 
PubMed 

Google Scholar 
Prati, D., Peintinger, M. & Fischer, M. Genetic composition, genetic diversity and small-scale environmental variation matter for the experimental reintroduction of a rare plant. J. Plant Ecol. https://doi.org/10.1093/jpe/rtv067 (2016).Article 

Google Scholar 
Barrett, R. D. & Schluter, D. Adaptation from standing genetic variation. Trends Ecol. Evol. 23, 38–44. https://doi.org/10.1016/j.tree.2007.09.008 (2008).Article 
PubMed 

Google Scholar 
Atwater, D. Z. & Callaway, R. M. Testing the mechanisms of diversity-dependent overyielding in a grass species. Ecology 96, 3332–3342. https://doi.org/10.1890/15-0889.1 (2015).Article 
PubMed 

Google Scholar 
Cook-Patton, S. C., McArt, S. H., Parachnowitsch, A. L., Thaler, J. S. & Agrawal, A. A. A direct comparison of the consequences of plant genotypic and species diversity on communities and ecosystem function. Ecology 92, 915–923. https://doi.org/10.1890/10-0999.1 (2011).Article 
PubMed 

Google Scholar 
Whitney, K. D. et al. Experimental drought reduces genetic diversity in the grassland foundation species Bouteloua eriopoda. Oecologia 189, 1107–1120. https://doi.org/10.1007/s00442-019-04371-7 (2019).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Violle, C. et al. Let the concept of trait be functional!. Oikos 116, 882–892. https://doi.org/10.1111/j.0030-1299.2007.15559.x (2007).Article 

Google Scholar 
Kattge, J. et al. TRY plant trait database-enhanced coverage and open access. Glob. Change Biol. 26, 119–188. https://doi.org/10.1111/gcb.14904 (2020).Article 
ADS 

Google Scholar 
König, P. et al. Advances in flowering phenology across the Northern Hemisphere are explained by functional traits. Glob. Ecol. Biogeogr. 27, 310–321 (2018).Article 

Google Scholar 
Robinson, K. M., Ingvarsson, P. K., Jansson, S. & Albrectsen, B. R. Genetic variation in functional traits influences arthropod community composition in aspen (Populus tremula L.). PLoS ONE 7, e37679. https://doi.org/10.1371/journal.pone.0037679 (2012).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Karbstein, K., Prinz, K., Hellwig, F. & Römermann, C. Plant intraspecific functional trait variation is related to within-habitat heterogeneity and genetic diversity in Trifolium montanum L. Ecol. Evol. 10, 5015–5033. https://doi.org/10.1002/ece3.6255 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Kaya, S. & Tekin, A. R. The effect of salep content on the rheological characteristics of a typical ice-cream mix. J. Food Eng. 47, 59–62. https://doi.org/10.1016/S0260-8774(00)00093-5 (2001).Article 

Google Scholar 
Ktistis, G. & Georgakopoulos, P. P. Rheology of salep mucilages. Pharmazie 46, 55–56 (1991).CAS 

Google Scholar 
Kayacier, A. & Dogan, M. Rheological properties of some gums-salep mixed solutions. J. Food Eng. 72, 261–265. https://doi.org/10.1016/j.jfoodeng.2004.12.005 (2006).Article 
CAS 

Google Scholar 
Sen, M. A., Palabiyik, I. & Kurultay, S. The effect of saleps obtained from various Orchidacease species on some physical and sensory properties of ice cream. Food Sci. Technol. 39, 82–87. https://doi.org/10.1590/fst.26017 (2019).Article 

Google Scholar 
Farhoosh, R. & Riazi, A. A compositional study on two current types of salep in Iran and their rheological properties as a function of concentration and temperature. Food Hydrocoll. 21, 660–666. https://doi.org/10.1016/j.foodhyd.2006.07.021 (2007).Article 
CAS 

Google Scholar 
Ghorbani, A., Zarre, S., Gravendeel, B. & de Boer, H. J. Illegal wild collection and international trade of CITES-listed terrestrial orchid tubers in Iran. Traffic Bullet. 26, 52–58 (2014).
Google Scholar 
Lenoir, J., Gégout, J.-C., Marquet, P., De Ruffray, P. & Brisse, H. A significant upward shift in plant species optimum elevation during the 20th century. Science 320, 1768–1771. https://doi.org/10.1126/science.1156831 (2008).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Chen, Z.-Q., Algeo, T. J. & Fraiser, M. L. Organism-environment interactions during the Permian-Triassic mass extinction and its aftermath. Palaios 28, 661–663. https://doi.org/10.2110/palo.2012.p12-102r (2013).Article 
ADS 

Google Scholar 
Ebrahimi, A. et al. Evaluation of phenotypic diversity of the endangered orchid (Orchis mascula): Emphasizing on breeding, conservation and development. S. Afr. J. Bot. 132, 304–315. https://doi.org/10.1016/j.sajb.2020.05.013 (2020).Article 

Google Scholar 
Ghorbani, A., Gravendeel, B., Naghibi, F. & de Boer, H. Wild orchid tuber collection in Iran: A wake-up call for conservation. Biodivers. Conserv. 23, 2749–2760. https://doi.org/10.1007/s10531-014-0746-y (2014).Article 

Google Scholar 
Barrett, S. C. & Kohn, J. R. The application of minimum viable population theory to plants. Genetics and conservation of rare plants 3–1 (Oxford University Press, 1991).
Google Scholar 
Yun, S. A., Son, H.-D., Im, H.-T. & Kim, S.-C. Genetic diversity and population structure of the endangered orchid Pelatantheria scolopendrifolia (Orchidaceae) in Korea. PLoS ONE 15, e0237546. https://doi.org/10.1371/journal.pone.0237546 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Gholami, S., Vafaee, Y., Nazari, F. & Ghorbani, A. Exploring genetic variations in threatened medicinal orchids using start codon targeted (SCoT) polymorphism and marker-association with seed morphometric traits. Physiol. Mol. Biol. Plants 27, 769–785. https://doi.org/10.1007/s12298-021-00978-4 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Gholami, S., Vafaee, Y., Nazari, F. & Ghorbani, A. Molecular characterization of endangered Iranian terrestrial orchids using ISSR markers and association with floral and tuber-related phenotypic traits. Physiol. Mol. Biol. Plants 27, 53–68. https://doi.org/10.1007/s12298-020-00920-0 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kaki, A., Vafaee, Y. & Khadivi, A. Genetic variation of Anacamptis coriophora, Dactylorhiza umbrosa, Himantoglossum affine, Orchis mascula, and Ophrys schulzei in the western parts of Iran. Ind. Crops Prod. 156, 112854. https://doi.org/10.1016/j.indcrop.2020.112854 (2020).Article 
CAS 

Google Scholar 
Falk, D., & Holsinger, K. E. Genetic sampling guidelines for conservation collections of endangered plants (1991).Dempewolf, H. et al. Past and future use of wild relatives in crop breeding. Crop Sci. 57, 1070–1082. https://doi.org/10.2135/cropsci2016.10.0885 (2017).Article 

Google Scholar 
Renz, J. Flora Iranica. Part 126: Orchidaceae (1978).Shahsavari, A. Flora of Iran. Part 57: Orchidaceae (2008).Boulila, A., Béjaoui, A., Messaoud, C. & Boussaid, M. Genetic diversity and population structure of Teucrium polium (Lamiaceae) in Tunisia. Biochem. Gen. 48, 57–70. https://doi.org/10.1007/s10528-009-9295-6 (2010).Article 
CAS 

Google Scholar 
Zannou, A., Struik, P., Richards, P., Zoundjih, E. & Yam, J. (Dioscorea spp.) responses to the environmental variability in the Guinea Sudan zone of Benin. Afr. J. Agric. Res. 10, 4913–4925. https://doi.org/10.5897/AJAR2013.8099 (2015).Article 

Google Scholar 
Sujii, P. et al. Morphological and molecular characteristics do not confirm popular classification of the Brazil nut tree in Acre, Brazil. Genet. Mol. Res. https://doi.org/10.4238/2013.september.27.3 (2013).Article 
PubMed 

Google Scholar 
Nadeem, M. A. et al. DNA molecular markers in plant breeding: current status and recent advancements in genomic selection and genome editing. Biotechnol. Biotechnol. Equip. 32, 261–285. https://doi.org/10.1080/13102818.2017.1400401 (2018).Article 
CAS 

Google Scholar 
Jacquemyn, H., Brys, R., Adriaens, D., Honnay, O. & Roldán-Ruiz, I. Effects of population size and forest management on genetic diversity and structure of the tuberous orchid Orchis mascula. Conserv. Genet. 10, 161–168. https://doi.org/10.1007/s10592-008-9543-z (2009).Article 

Google Scholar 
Mitchell, P. & Woodward, F. Responses of three woodland herbs to reduced photosynthetically active radiation and low red to far-red ratio in shade. J. Ecol. https://doi.org/10.2307/2260575 (1988).Article 

Google Scholar 
Likens, G. E., Bormann, F. H., Johnson, N. M., Fisher, D. & Pierce, R. S. Effects of forest cutting and herbicide treatment on nutrient budgets in the Hubbard Brook watershed-ecosystem. Ecol. Monog. 40, 23–47. https://doi.org/10.2307/1942440 (1970).Article 

Google Scholar 
Jacquemyn, H. & Brys, R. Lack of strong selection pressures maintains wide variation in floral traits in a food-deceptive orchid. Ann. Bot. 126, 445–453. https://doi.org/10.1093/aob/mcaa080 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Olaya-Arenas, P., Meléndez-Ackerman, E. J., Pérez, M. E. & Tremblay, R. Demographic response by a small epiphytic orchid. Am. J. Bot. 98, 2040–2048. https://doi.org/10.3732/ajb.1100223 (2011).Article 
PubMed 

Google Scholar 
Primack, R. B., Miao, S. & Becker, K. R. Costs of reproduction in the pink lady’s slipper orchid (Cypripedium acaule): Defoliation, increased fruit production, and fire. Am. J. Bot. 81, 1083–1090. https://doi.org/10.2307/2446500 (1994).Article 

Google Scholar 
Whigham, D. F. & O’Neill, J. P. Dynamics of flowering and fruit production in two eastern North American terrestrial orchids. In Tipularia Discolor and Liparis Lilifolia in Population Ecology of Terrestrial Orchids (eds Wells, T. C. E. & Willems, J. H.) 89–101 (SPB Academic Publishers, 1991).
Google Scholar 
Tekinşen, K. K. & Güner, A. Chemical composition and physicochemical properties of tubera salep produced from some Orchidaceae species. Food Chem. 121, 468–471. https://doi.org/10.1016/j.foodchem.2009.12.066 (2010).Article 
CAS 

Google Scholar 
Whigham, D. F. Biomass and nutrient allocation of Tipularia discolor (Orchidaceae). Oikos https://doi.org/10.2307/3544398 (1984).Article 

Google Scholar 
Mattila, E. & Kuitunen, M. T. Nutrient versus pollination limitation in Platanthera bifolia and Dactylorhiza incarnata (Orchidaceae). Oikos 89, 360–366. https://doi.org/10.1034/j.1600-0706.2000.890217.x (2000).Article 

Google Scholar 
Xu, W. et al. Drought stress condition increases root to shoot ratio via alteration of carbohydrate partitioning and enzymatic activity in rice seedlings. Acta Physiol. Plant. 37, 9. https://doi.org/10.1007/s11738-014-1760-0 (2015).Article 
CAS 

Google Scholar 
March-Salas, M., Fandos, G. & Fitze, P. S. Effects of intrinsic environmental predictability on intra-individual and intra-population variability of plant reproductive traits and eco-evolutionary consequences. Ann. Bot. 127, 413–423. https://doi.org/10.1093/aob/mcaa096 (2021).Article 
CAS 
PubMed 

Google Scholar 
Jump, A. S., Marchant, R. & Peñuelas, J. Environmental change and the option value of genetic diversity. Trends Plant Sci. 14, 51–58. https://doi.org/10.1016/j.tplants.2008.10.002 (2009).Article 
CAS 
PubMed 

Google Scholar 
Huang, J. et al. Global semi-arid climate change over last 60 years. Clim. Dyn. 46, 1131–1150. https://doi.org/10.1007/s00382-015-2636-8 (2016).Article 

Google Scholar 
Crémieux, L., Bischoff, A., Müller-Schärer, H. & Steinger, T. Gene flow from foreign provenances into local plant populations: Fitness consequences and implications for biodiversity restoration. Am. J. Bot. 97, 94–100. https://doi.org/10.3732/ajb.0900103 (2010).Article 
PubMed 

Google Scholar 
Garant, D., Forde, S. E. & Hendry, A. P. The multifarious effects of dispersal and gene flow on contemporary adaptation. Funct. Ecol. 21, 434–443. https://doi.org/10.1111/j.1365-2435.2006.01228.x (2007).Article 

Google Scholar 
Jacquemyn, H. et al. Multigenerational analysis of spatial structure in the terrestrial, food-deceptive orchid Orchis mascula. J. Ecol. 97, 206–216. https://doi.org/10.1111/j.1365-2745.2008.01464.x (2009).Article 

Google Scholar 
Siepielski, A. M. et al. Precipitation drives global variation in natural selection. Science 355, 959–962. https://doi.org/10.1126/science.aag2773 (2017).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Ene, C. O., Ogbonna, P. E., Agbo, C. U. & Chukwudi, U. P. Studies of phenotypic and genotypic variation in sixteen cucumber genotypes. Chilean J. Agric. Res. 76, 307–313. https://doi.org/10.4067/S0718-58392016000300007 (2016).Article 

Google Scholar 
Pradhan, S. K. et al. Population structure, genetic diversity and molecular marker-trait association analysis for high temperature stress tolerance in rice. PLoS ONE 11, e0160027. https://doi.org/10.1371/journal.pone.0160027 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Swarup, S. et al. Genetic diversity is indispensable for plant breeding to improve crops. Crop Sci. 61, 839–852. https://doi.org/10.1002/csc2.20377 (2021).Article 

Google Scholar 
Patzak, A. Plantaginaceae in KH Rechinger Flora Iranica 15: 1–21 (Academische Druck und Verlagsantalt, 1965).
Google Scholar 
Mehrvarz Saeidi, S. Plantaginaceae Family Vol. 14 (Research Institute of Forests and Rangelands, 1995).
Google Scholar 
Limited, M. I. I. Glucomannan assay procedure KGLUM 10/04. Ireland (2004).Limited, M. I. I. Total starch assay procedure (amyloglucosidase/a-Amylase Method) AA/AMG 11/01. AOAC Method 996.11.Ireland (2004).Bradshaw, H., Otto, K. G., Frewen, B. E., McKay, J. K. & Schemske, D. W. Quantitative trait loci affecting differences in floral morphology between two species of monkeyflower (Mimulus). Genetics 149, 367–382. https://doi.org/10.1093/genetics/149.1.367 (1998).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Del Sal, G., Manfioletti, G. & Schneider, C. The CTAB-DNA precipitation method: A common mini-scale preparation of template DNA from phagemids, phages or plasmids suitable for sequencing. Biotechniques 7, 514–520 (1989).PubMed 

Google Scholar 
Vos, P. et al. AFLP: A new technique for DNA fingerprinting. Nucl. Acid Res. 23, 4407–4414. https://doi.org/10.1093/nar/23.21.4407 (1995).Article 
CAS 

Google Scholar 
Bassam, B. J., Caetano-Anollés, G. & Gresshoff, P. M. Fast and sensitive silver staining of DNA in polyacrylamide gels. Anal. Biochem. 196, 80–83. https://doi.org/10.1016/0003-2697(91)90120-I (1991).Article 
CAS 
PubMed 

Google Scholar 
Husson, F., Josse, J., Le, S., Mazet, J. & Husson, M. F. Package ‘FactoMineR’. An R package 96, 698 (2016).
Google Scholar 
Lê, S., Josse, J. & Husson, F. FactoMineR: An R package for multivariate analysis. J. Stat. Softw. 25, 1–18 (2008).Article 

Google Scholar 
Galili, T. in The R User Conference, useR! 2017 July 4–7 2017 Brussels, Belgium. 219.Wei, T. et al. Package ‘corrplot’. 56, e24 (2017).Yeh, F. POPGENE (version 1.3. 1). Microsoft Window-Bases Freeware for Population Genetic Analysis. http://www.ualbertaca/~fyeh/ (1999).Wickham, H. & Chang, W. URL: http://CRAN.R-project.org/package=ggplot2.ggplot2: An implementation of the Grammar of Graphics. 3 (2008).Kolde, R. & Kolde, M. R. Package’ pheatmap’. R package 1, 790 (2015).
Google Scholar 
Landgraf, A. J. & Lee, Y. Dimensionality reduction for binary data through the projection of natural parameters. J. Mult. Anal. 180, 104668. https://doi.org/10.1016/j.jmva.2020.104668 (2020).Article 
MathSciNet 
MATH 

Google Scholar 
Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959. https://doi.org/10.1093/genetics/155.2.945 (2000).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Earl, D. A. & VonHoldt, B. M. STRUCTURE HARVESTER: A website and program for visualizing STRUCTURE output and implementing the Evanno method. Biol. J. Linn. Soc. 4, 359–361. https://doi.org/10.1007/s12686-011-9548-7 (2012).Article 

Google Scholar 
Oksanen, J. et al. Vegan: Community Ecology Package. R package version 2.4-3 (2016).Kamvar, Z. N., Tabima, J. F. & Grünwald, N. J. Poppr: An R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction. Peer. J. 2, e281 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Vavrek, M. J. Fossil: Palaeoecological and palaeogeographical analysis tools. Palaeontol. Electron. 14, 16. https://doi.org/10.7717/peerj.281 (2011).Article 

Google Scholar 
Bradbury, P. J. et al. TASSEL: Software for association mapping of complex traits in diverse samples. Bioinformatics 23, 2633–2635. https://doi.org/10.1093/bioinformatics/btm308 (2007).Article 
CAS 
PubMed 

Google Scholar  More

Phung, T. X., Nascimento, J. C. S., Novarro, A. J. & Wiens, J. J. Correlated and decoupled evolution of adult and larval body size in frogs. Proc. R. Soc. Lond. B 287, 20201474–10 (2020).
Google Scholar 
Hall, B. K. & Wake, M. H. The Origin and Evolution of Larval Forms (Gulf Professional Publishing, 1999).Hime, P. M. et al. Phylogenomics reveals ancient gene tree discordance in the amphibian tree of life. Syst. Biol. 70, 49–66 (2021).CAS 
PubMed 

Google Scholar 
Duellman, W. E. & Trueb, L. Biology of Amphibians (Johns Hopkins University Press, 1994).Nunes-de-Almeida, C. H., Batista Haddad, C. F. & Toledo, L. F. A revised classification of the amphibian reproductive modes. Salamandra 57, 413–427 (2021).
Google Scholar 
Vences, M. & Köhler, J. Global diversity of amphibians (Amphibia) in freshwater. Hydrobiologia 595, 569–580 (2007).
Google Scholar 
Blackburn, D. G. Evolution of vertebrate viviparity and specializations for fetal nutrition: a quantitative and qualitative analysis. J. Morphol. 276, 961–990 (2015).PubMed 

Google Scholar 
AmphibiaWeb. Electronic Database (University of California, Berkeley, CA, USA, 2019). https://amphibiaweb.org.Frost, D. R. Amphibian Species of the World: an Online Reference. Version 6.0 (date of access: 01.08.2019). Electronic Database (American Museum of Natural History, New York, USA, 2019). https://amphibiansoftheworld.amnh.org/.Bonett, R. M., Ledbetter, N. M., Hess, A. J., Herrboldt, M. A. & Denoël, M. Repeated ecological and life cycle transitions make salamanders an ideal model for evolution and development. Developmental Dynamics 251, 957–972 (2022).Salthe, S. N. Reproductive modes and the number and sizes of ova in the urodeles. Am. Midl. Naturalist 81, 467490 (1969).
Google Scholar 
Salthe, S. N. & Duellman, W. E. in Evolutionary Biology of the Anurans (ed. Vial, J. L.) 229–249 (University of Missouri Press Columbia, 1973).Haddad, C. & Prado, C. P. A. Reproductive modes in frogs and their unexpected diversity in the Atlantic Forest of Brazil. BioScience 55, 207–217 (2005).
Google Scholar 
Lutz, B. Ontogenetic evolution in frogs. Evolution 2, 29–39 (1948).CAS 
PubMed 

Google Scholar 
Crump, M. L. Anuran reproductive modes: evolving perspectives. J. Herpetol. 49, 1–16 (2015).
Google Scholar 
Schoch, R. Evolution of life cycles in early amphibians. Annu. Rev. Earth Planet. Sci. 37, 135–162 (2009).ADS 
CAS 

Google Scholar 
Meegaskumbura, M. et al. Patterns of reproductive-mode evolution in Old World tree frogs (Anura, Rhacophoridae). Zool. Scr. 44, 509–522 (2015).
Google Scholar 
Portik, D. M. & Blackburn, D. C. The evolution of reproductive diversity in Afrobatrachia: A phylogenetic comparative analysis of an extensive radiation of African frogs. Evolution 70, 2017–2032 (2016).PubMed 
PubMed Central 

Google Scholar 
San Mauro, D. et al. Life-history evolution and mitogenomic phylogeny of caecilian amphibians. Mol. Phylogenet. Evol. 73, 177–189 (2014).PubMed 

Google Scholar 
Pereira, E. B., Collevatti, R. G., de Carvalho Kokubum, M. N., de Oliveira Miranda, N. E. & Maciel, N. M. Ancestral reconstruction of reproductive traits shows no tendency toward terrestriality in leptodactyline frogs. BMC Evol. Biol. 15, 91 (2015).Gomez-Mestre, I., Pyron, R. A. & Wiens, J. J. Phylogenetic analyses reveal unexpected patterns in the evolution of reproductive modes in frogs. Evolution 66, 3687–3700 (2012).PubMed 

Google Scholar 
Wake, D. B. & Hanken, J. Direct development in the lungless salamanders: what are the consequences for developmental biology, evolution and phylogenesis? Int. J. Dev. Biol. 40, 859–869 (1996).CAS 
PubMed 

Google Scholar 
Dubois, A. Developmental pathway, speciation and supraspecific taxonomy in amphibians: 1. Why are there so many frog species in Sri Lanka? Alytes 22, 19–37 (2004).
Google Scholar 
Hedges, S. B., Duellman, W. E. & Heinicke, M. P. New World direct-developing frogs (Anura: Terrarana): molecular phylogeny, classification, biogeography, and conservation. Zootaxa 1737, 1–182 (2008).
Google Scholar 
Dugo-Cota, Á., Vilà, C., Rodríguez, A. & Gonzalez-Voyer, A. Ecomorphological convergence in Eleutherodactylus frogs: a case of replicate radiations in the Caribbean. Ecol. Lett. 22, 884–893 (2019).PubMed 

Google Scholar 
Simpson, G. G. The Major Features of Evolution (Columbia University Press, 1953).Vági, B., Végvári, Z., Liker, A., Freckleton, R. P. & Szekely, T. Parental care and the evolution of terrestriality in frogs. Proc. R. Soc. Lond. B 286, 20182737–10 (2019).
Google Scholar 
Furness, A. I. & Capellini, I. The evolution of parental care diversity in amphibians. Nat. Commun. 10, 1–12 (2019).CAS 

Google Scholar 
Furness, A. I., Venditti, C. & Capellini, I. Terrestrial reproduction and parental care drive rapid evolution in the trade-off between offspring size and number across amphibians. PLoS Biol. 20, e3001495 (2022).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wollenberg, K. C., Vieites, D. R., Glaw, F. & Vences, M. Speciation in little: the role of range and body size in the diversification of Malagasy mantellid frogs. BMC Evol. Biol. 11, 217 (2011).PubMed 
PubMed Central 

Google Scholar 
Cayuela, H. et al. Determinants and consequences of dispersal in vertebrates with complex life cycles: a review of pond-breeding amphibians. Q. Rev. Biol. 95, 1–36 (2020).
Google Scholar 
Phillimore, A. B., Freckleton, R. P., Orme, C. D. L. & Owens, I. P. F. Ecology predicts large‐scale patterns of phylogenetic diversification in birds. Am. Nat. 168, 220–229 (2006).PubMed 

Google Scholar 
Chen, J.-M. et al. An integrative phylogenomic approach illuminates the evolutionary history of Old World tree frogs (Anura: Rhacophoridae). Mol. Phylogenet. Evol. 145, 106724 (2020).PubMed 

Google Scholar 
Zimkus, B. M., Lawson, L., Loader, S. P. & Hanken, J. Terrestrialization, miniaturization and rates of diversification in African Puddle Frogs (Anura: Phrynobatrachidae). PLoS ONE 7, e35118 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Moen, D. S. Improving inference and avoiding over-interpretation of hidden-state diversification models: specialized plant breeding has no effect on diversification in frogs. Evolution 76, 373–384 (2022).PubMed 

Google Scholar 
Eastman, J. M. & Storfer, A. Correlations of life-history and distributional-range variation with salamander diversification rates: evidence for species selection. Syst. Biol. 60, 503–518 (2011).PubMed 

Google Scholar 
Bonett, R. M., Steffen, M. A., Lambert, S. M., Wiens, J. J. & Chippindale, P. T. Evolution of paedomorphosis in plethodontid salamanders: ecological correlates and re-evolution of metamorphosis. Evolution 68, 466–482 (2014).PubMed 

Google Scholar 
Akaike, H. A new look at the statistical-model identification. IEEE Trans. Autom. Control 19, 716–723 (1974).ADS 
MathSciNet 
MATH 

Google Scholar 
Wagenmakers, E.-J. & Farrell, S. AIC model selection using Akaike weights. Psychon. Bull. Rev. 11, 192–196 (2004).PubMed 

Google Scholar 
Beaulieu, J. M., Oliver, J. C., O’Meara, B. & Boyko, J. R package ‘corHMM’: hidden markov models of character evolution. (2020).Pagel, M. & Meade, A. BayesTraits, version 4. University of Reading, Berkshire, UK. http://www.evolution.rdg.ac.uk (2022).Pagel, M. & Meade, A. Bayesian analysis of correlated evolution of discrete characters by reversible-jump Markov chain Monte Carlo. Am. Nat. 167, 808–825 (2006).PubMed 

Google Scholar 
Maddison, W., Midford, P. & Otto, S. Estimating a binary character’s effect on speciation and extinction. Syst. Biol. 56, 701–710 (2007).PubMed 

Google Scholar 
FitzJohn, R. G., Maddison, W. P. & Otto, S. P. Estimating trait-dependent speciation and extinction rates from incompletely resolved phylogenies. Syst. Biol. 58, 595–611 (2009).PubMed 

Google Scholar 
Beaulieu, J. M. & O’Meara, B. C. Detecting hidden diversification shifts in models of trait-dependent speciation and extinction. Syst. Biol. 65, 583–601 (2016).PubMed 

Google Scholar 
Herrera-Alsina, L., van Els, P. & Etienne, R. S. Detecting the dependence of diversification on multiple traits from phylogenetic trees and trait data. Syst. Biol. 68, 317–328 (2018).
Google Scholar 
Strathmann, R. R. Hypotheses on the origins of marine larvae. Annu. Rev. Ecol. Syst. 24, 89–117 (1993).
Google Scholar 
Wray, G. A. Evolution of larvae and developmental modes. In Ecology of Marine Invertebrate Larvae. (ed. McEdward, L.) 413–447 (CRC Press, 1995).Raff, R. A. Origins of the other metazoan body plans: the evolution of larval forms. Philos. T R. Soc. B 363, 1473–1479 (2008).
Google Scholar 
Collin, R. & Moran, A. in Evolutionary Ecology of Marine Invertebrate Larvae 50–66 (Oxford University Press, 2018).Collin, R. & Miglietta, M. P. Reversing opinions on Dollo’s Law. Trends Ecol. Evol. 23, 602–609 (2008).PubMed 

Google Scholar 
Wiens, J. J. Re-evolution of lost mandibular teeth in frogs after more than 200 million years, and re-evaluating Dollo’s law. Evolution 65, 1283–1296 (2011).PubMed 

Google Scholar 
Wiens, J. J., Kuczynski, C. A., Duellman, W. E. & Reeder, T. W. Loss and re-evolution of complex life cycles in marsupial frogs: does ancestral trait reconstruction mislead? Evolution 61, 1886–1899 (2007).CAS 
PubMed 

Google Scholar 
Chippindale, P. T., Bonett, R. M., Baldwin, A. S. & Wiens, J. J. Phylogenetic evidence for a major reversal of life-history evolution in plethodontid salamanders. Evolution 58, 2809–2815 (2004).CAS 
PubMed 

Google Scholar 
Castroviejo-Fisher, S. et al. Phylogenetic systematics of egg-brooding frogs (Anura: Hemiphractidae) and the evolution of direct development. Zootaxa 4004, 1–75 (2015).PubMed 

Google Scholar 
Naumann, B., Schweiger, S., Hammel, J. U. & Müller, H. Parallel evolution of direct development in frogs—skin and thyroid gland development in African Squeaker Frogs (Anura: Arthroleptidae: Arthroleptis). Dev. Dyn. 250, 584–600 (2021).PubMed 

Google Scholar 
Goldberg, J., Taucce, P. P. G., Quinzio, S. I., Haddad, C. F. B. & Candioti, F. V. Increasing our knowledge on direct-developing frogs: the ontogeny of Ischnocnema henselii (Anura: Brachycephalidae). Zool. Anz. 284, 78–87 (2020).
Google Scholar 
Wassersug, R. J. & Duellman, W. E. Oral structures and their development in egg-brooding hylid frog embryos and larvae: evolutionary and ecological implications. J. Morphol. 182, 1–37 (1984).PubMed 

Google Scholar 
Kerney, R. R., Blackburn, D. C., Müller, H. & Hanken, J. Do larval traits re-evolve? Evidence from the embryogenesis of a direct-developing salamander, Plethodon cinereus. Evolution 66, 252–262 (2011).PubMed 

Google Scholar 
Theska, T. Musculoskeletal development of the Central African caecilian Idiocranium russeli (Amphibia: Gymnophiona: Indotyphlidae) and its bearing on the re-evolution of larvae in caecilian amphibians. Zoomorphology 138, 137–158 (2019).
Google Scholar 
Laslo, M., Denver, R. J. & Hanken, J. Evolutionary conservation of thyroid hormone receptor and deiodinase expression dynamics in ovo in a direct-developing frog, Eleutherodactylus coqui. Front. Endocrinol. 10, 307 (2019).
Google Scholar 
Gao, W. et al. Genomic and transcriptomic investigations of the evolutionary transition from oviparity to viviparity. Proc. Natl Acad. Sci. USA 116, 3646–3655 (2019).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Altig, R. & Crother, B. I. The evolution of three deviations from the biphasic anuran life cycle: alternatives to selection. Herpetol. Rev. 37, 321–356 (2006).
Google Scholar 
Venturelli, D. P., da Silva, W. R. & Giaretta, A. A. Tadpoles’ resistance to desiccation in species of Leptodactylus (Anura, Leptodactylidae). J. Herpetol. 55, 265–270 (2021).
Google Scholar 
Seymour, R. S. Respiration of aquatic and terrestrial amphibian embryos. American Zoologist 39, 261–270 (1999).Blackburn, D. G. Convergent evolution of viviparity, matrotrophy, and specializations for fetal nutrition in reptiles and other vertebrates. Am. Zool. 32, 313–321 (1992).
Google Scholar 
Buckley, D., Alcobendas, M., García-París, M. & Wake, M. H. Heterochrony, cannibalism, and the evolution of viviparity in Salamandra salamandra. Evol. Dev. 9, 105–115 (2007).PubMed 

Google Scholar 
Kusrini, M. D., Rowley, J. J. L., Khairunnisa, L. R., Shea, G. M. & Altig, R. The reproductive biology and larvae of the first tadpole-bearing frog, Limnonectes larvaepartus. PLoS ONE 10, e116154–e116159 (2015).ADS 
PubMed 
PubMed Central 

Google Scholar 
Lanza, B. & Leo, P. Sul primo caso sicuro di riproduzione vivipara nel genere Speleomantes. 1–54 (2000).Lunghi, E. et al. Comparative reproductive biology of European cave salamanders (Genus Hydromantes): nesting selection and multiple annual breeding. Salamandra 54, 101–108 (2018).
Google Scholar 
Liedtke, H. C. et al. Terrestrial reproduction as an adaptation to steep terrain in African toads. Proc. R. Soc. Lond. B 284, 20162598–20162599 (2017).
Google Scholar 
Wake, M. H. The reproductive biology of Eleutherodactylus jasperi (Amphibia, Anura, Leptodactylidae), with comments on the evolution of live-bearing systems. J. Herpetol. 12, 121–133 (1978).
Google Scholar 
Jennings, D. H. & Hanken, J. Mechanistic basis of life history evolution in anuran amphibians: thyroid gland development in the direct-developing frog, Eleutherodactylus coqui. Gen. Comp. Endocr. 111, 225–232 (1998).CAS 
PubMed 

Google Scholar 
Callery, E. M. & Elinson, R. P. Thyroid hormone-dependent metamorphosis in a direct developing frog. Proc. Natl Acad. Sci. USA 97, 2615–2620 (2000).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Callery, E. M., Hung, F. & Elinson, R. P. Frogs without polliwogs: evolution of anuran direct development. BioEssays 23, 233–241 (2001).CAS 
PubMed 

Google Scholar 
Buckley, L. B. & Jetz, W. Environmental and historical constraints on global patterns of amphibian richness. Proc. R. Soc. Lond. B 274, 1167–1173 (2007).
Google Scholar 
Pyron, R. A. & Wiens, J. J. Large-scale phylogenetic analyses reveal the causes of high tropical amphibian diversity. Proc. R. Soc. Lond. B 280, 20131622 (2013).
Google Scholar 
Gómez-Rodríguez, C., Baselga, A. & Wiens, J. J. Is diversification rate related to climatic niche width? Glob. Ecol. Biogeogr. 24, 383–395 (2015).
Google Scholar 
Moen, D. S. & Wiens, J. J. Microhabitat and climatic niche change explain patterns of diversification among frog families. Am. Nat. 190, 29–44 (2017).PubMed 

Google Scholar 
Kozak, K. H. & Wiens, J. J. Accelerated rates of climatic-niche evolution underlie rapid species diversification. Ecol. Lett. 13, 1378–1389 (2010).PubMed 

Google Scholar 
Jaramillo, A. F. et al. Vastly underestimated species richness of Amazonian salamanders (Plethodontidae: Bolitoglossa) and implications about plethodontid diversification. Mol. Phylogenet. Evol. 149, 106841 (2020).PubMed 

Google Scholar 
Jetz, W. & Pyron, R. A. The interplay of past diversification and evolutionary isolation with present imperilment across the amphibian tree of life. Nat. Ecol. Evol. 2, 850–858 (2018).PubMed 

Google Scholar 
Bars-Closel, M., Kohlsdorf, T., Moen, D. S. & Wiens, J. J. Diversification rates are more strongly related to microhabitat than climate in squamate reptiles (lizards and snakes). Evolution 71, 2243–2261 (2017).PubMed 

Google Scholar 
Cyriac, V. P. & Kodandaramaiah, U. Digging their own macroevolutionary grave: fossoriality as an evolutionary dead end in snakes. J. Evolution. Biol. 31, 587–598 (2018).CAS 

Google Scholar 
Zamudio, K. R., Bell, R. C., Nali, R. C., Haddad, C. F. B. & Prado, C. P. A. Polyandry, predation, and the evolution of frog reproductive modes. Am. Nat. 188, S41–S61 (2016).PubMed 

Google Scholar 
Lion, M. B. et al. Global patterns of terrestriality in amphibian reproduction. Glob. Ecol. Biogeogr. 4, 679–13 (2019).
Google Scholar 
Müller, H. et al. Forests as promoters of terrestrial life-history strategies in East African amphibians. Biol. Lett. 9, 20121146–20121146 (2013).PubMed 
PubMed Central 

Google Scholar 
Velo-Antón, G., García-París, M., Galán, P. & Cordero Rivera, A. The evolution of viviparity in Holocene islands: ecological adaptation versus phylogenetic descent along the transition from aquatic to terrestrial environments. J. Zool. Syst. Evol. Res. 45, 345–352 (2007).
Google Scholar 
Liedtke, H. C. AmphiNom: an amphibian systematics tool. Syst. Biodivers. 17, 1–6 (2019).
Google Scholar 
R Core Team. R: a language and environment for statistical computing (R Foundation for Statistical Computing, Vienna, Austria, 2020). http://www.R-project.org/.IUCN. IUCN 2020. The IUCN Red List of Threatened Species. Version 2020-2. https://www.iucnredlist.org (2019).AmphibiaChina. The database of Chinese amphibians. Electronic Database (Kunming Institute of Zoology (CAS), Kunming, Yunnan, China, 2019) http://www.amphibiachina.org/.Ron, S. R., Yanez-Muñoz, M. H., Merino-Viteri, A. & Ortiz, D. A. Anfibios del Ecuador. Version 2019.0 (Museo de Zoología, Pontificia Universidad Católica del Ecuador, 2019). https://bioweb.bio/faunaweb/amphibiaweb.Greven, H. In Reproductive Biology and Phylogeny of Urodela 447–475 (Taylor & Francis, 2003).Marks, S. B. & Collazo, A. Direct development in Desmognathus aeneus (Caudata: Plethodontidae): a staging table. Copeia 1998, 637–648 (1998).
Google Scholar 
Müller, H., Loader, S. P., Ngalason, W., Howell, K. M. & Gower, D. J. Reproduction in brevicipitid frogs (Amphibia: Anura: Brevicipitidae)—evidence from Probreviceps m. macrodactylus. Copeia 2007, 726–733 (2007).
Google Scholar 
Velo-Antón, G., Santos, X., Sanmartín-Villar, I., Cordero-Rivera, A. & Buckley, D. Intraspecific variation in clutch size and maternal investment in pueriparous and larviparous Salamandra salamandra females. Evol. Ecol. 29, 185–204 (2014).
Google Scholar 
Beaulieu, J. M., O’Meara, B. C. & Donoghue, M. J. Identifying hidden rate changes in the evolution of a binary morphological character: the evolution of plant habit in campanulid angiosperms. Systematic biology 62, 725–737 (2013).Pupko, T., Pe’er, I., Shamir, R. & Graur, D. A fast algorithm for joint reconstruction of ancestral amino acid sequences. Mol. Biol. Evol. 17, 890–896 (2000).CAS 
PubMed 

Google Scholar 
Bollback, J. P. SIMMAP: stochastic character mapping of discrete traits on phylogenies. BMC Bioinforma. 7, 88 (2006).
Google Scholar 
Plummer, M., Best, N., Cowles, K. & Vines, K. CODA: convergence diagnosis and output analysis for MCMC. R. News 6, 7–11 (2006).
Google Scholar 
Tuffley, C. & Steel, M. Modeling the covarion hypothesis of nucleotide substitution. Math. Biosci. 147, 63–91 (1998).MathSciNet 
CAS 
PubMed 
MATH 

Google Scholar 
Xie, W., Lewis, P. O., Fan, Y., Kuo, L. & Chen, M. H. Improving marginal likelihood estimation for Bayesian phylogenetic model selection. Syst. Biol. 60, 150–160 (2011).PubMed 

Google Scholar 
Nakov, T., Beaulieu, J. M. & Alverson, A. J. Diatoms diversify and turn over faster in freshwater than marine environments. Evolution 73, 2497–2511 (2019).PubMed 

Google Scholar 
Etienne, R. S. et al. Diversity-dependence brings molecular phylogenies closer to agreement with the fossil record. Proc. R. Soc. Lond. B 279, 1300–1309 (2011).
Google Scholar  More

These authors contributed equally: Laura H. Antão, Benjamin Weigel.These authors jointly supervised this work: Tomas Roslin, Anna-Liisa Laine.Research Centre for Ecological Change, Organismal and Evolutionary Biology Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, FinlandLaura H. Antão, Benjamin Weigel, Giovanni Strona, Maria Hällfors, Elina Kaarlejärvi, Otso Ovaskainen, Marjo Saastamoinen, Jarno Vanhatalo, Tomas Roslin & Anna-Liisa LaineDepartment of Biological Sciences, University of South Carolina, Columbia, SC, USATad DallasDepartment of Biology, Lund University, Lund, SwedenØystein H. OpedalFinnish Environment Institute (SYKE), Helsinki, FinlandJanne Heliölä, Mikko Kuussaari, Juha Pöyry & Kristiina VuorioNatural Resources Institute Finland (Luke), Helsinki, FinlandHeikki Henttonen, Otso Huitu, Andreas Lindén, Päivi Merilä, Maija Salemaa & Tiina TonteriSection of Ecology, Department of Biology, University of Turku, Turku, FinlandErkki KorpimäkiFinnish Museum of Natural History, University of Helsinki, Helsinki, FinlandAleksi LehikoinenKainuu Centre for Economic Development, Transport and the Environment, Kajaani, FinlandReima LeinonenUniversity of Helsinki, Helsinki, FinlandHannu PietiäinenDepartment of Biological and Environmental Science, University of Jyväskylä, Jyväskylä, FinlandOtso OvaskainenCentre for Biodiversity Dynamics, Department of Biology, Norwegian University of Science and Technology, Trondheim, NorwayOtso OvaskainenHelsinki Institute of Life Science, University of Helsinki, Helsinki, FinlandMarjo SaastamoinenDepartment of Mathematics and Statistics, Faculty of Science, University of Helsinki, Helsinki, FinlandJarno VanhataloSpatial Foodweb Ecology Group, Department of Agricultural Sciences, University of Helsinki, Helsinki, FinlandTomas RoslinSpatial Foodweb Ecology Group, Department of Ecology, Swedish University of Agricultural Sciences, Uppsala, SwedenTomas RoslinDepartment of Evolutionary Biology and Environmental Studies, University of Zürich, Zürich, SwitzerlandAnna-Liisa Laine More

G.M., L.O.A., L.V.G. and L.E.O.C.A. thank the São Paulo Research Foundation (FAPESP) for funding (grants 2019/25701-8, 2020/08916-8, 2016/02018-2 and 2020/15230-5, respectively). L.O.A. and L.E.O.C.A. thank the National Council for Scientific and Technological Development (CNPq) for funding (grants 314473/2020-3 and 314416/2020-0, respectively). G.d.O. thanks the University of South Alabama Faculty Development Council Grant for funding (grant 279600-2022). More