de Wet, J. M. J. & Harlan, J. R. Weeds and domesticates: Evolution in the man-made habitat. Econ. Bot. 29(2), 99–108. https://doi.org/10.1007/BF02863309 (1975).
Google Scholar
Ceballos, G. et al. Accelerated modern human—Induced species losses: Entering the sixth mass extinction. Sci. Adv. 1(June), 1–6. https://doi.org/10.1126/sciadv.1400253 (2015).
Google Scholar
Wilcove, D. S. Nest predation in forest tracts and the decline of migratory songbirds. Ecology 66(4), 1211–1214 (1985).
Google Scholar
Airoldi, L. & Bulleri, F. Anthropogenic disturbance can determine the magnitude of opportunistic species responses on marine urban infrastructures. PLoS ONE https://doi.org/10.1371/journal.pone.0022985 (2011).
Google Scholar
Baker, H. G. The evolution of weeds. Annu. Rev. Ecol. Syst. 5, 1–24. https://doi.org/10.2307/2096877 (1974).
Google Scholar
Richardson, D. M. et al. Naturalization and invasion of alien plants: Concepts and definitions. Divers. Distrib. 6, 93–107 (2008).
Google Scholar
van Etten, M. L., Conner, J. K., Chang, S. M. & Baucom, R. S. Not all weeds are created equal: A database approach uncovers differences in the sexual system of native and introduced weeds. Ecol. Evol. 7(8), 2636–2642. https://doi.org/10.1002/ece3.2820 (2017).
Google Scholar
Booth, B. D. & Swanton, C. J. Assembly theory applied to weed communities 50th Anniversary—Invited Article Assembly theory applied to weed communities. Weed Sci. 50(3), 2–13. https://doi.org/10.1614/0043-1745(2002)050 (2002).
Google Scholar
Kuester, A., Conner, J. K., Culley, T. & Baucom, R. S. How weeds emerge: A taxonomic and trait-based examination using United States data. New Phytol. 202(3), 1055–1068. https://doi.org/10.1111/nph.12698 (2014).
Google Scholar
van Kleunen, M. et al. The ecology and evolution of alien plants. Annu. Rev. Ecol. Evol. Syst. https://doi.org/10.1146/annurev-ecolsys-110617-062654 (2018).
Google Scholar
de Bona, S. et al. Spatio-temporal dynamics of density-dependent dispersal during a population colonisation. Ecol. Lett. 22, 634–644 (2019).
Google Scholar
Baker, H. G. Self-compatibility and establishment after “long-distance” dispersal. Evolution 9(3), 347. https://doi.org/10.2307/2405656 (1955).
Google Scholar
Razanajatovo, M. et al. Plants capable of selfing are more likely to become naturalized. Nat. Commun. 7, 13313. https://doi.org/10.1038/ncomms13313 (2016).
Google Scholar
Vallejo-Marín, M., Dorken, M. E. & Barrett, S. C. H. The ecological and evolutionary consequences of clonality for plant mating. Annu. Rev. Ecol. Evol. Syst. 41(1), 193–213. https://doi.org/10.1146/annurev.ecolsys.110308.120258 (2010).
Google Scholar
Rodger, J. G., Van Kleunen, M. & Johnson, S. D. Pollinators, mates and Allee effects: The importance of self-pollination for fecundity in an invasive lily. Funct. Ecol. 27(4), 1023–1033. https://doi.org/10.1111/1365-2435.12093 (2013).
Google Scholar
Barrett, S. C. H. & Harder, L. D. The ecology of mating and its evolutionary consequences in seed plants. Annu. Rev. Ecol. Evol. Syst. https://doi.org/10.1146/annurev-ecolsys-110316-023021 (2017).
Google Scholar
Klimeš, L., Klimešová, J., Hendriks, R. & van Groenendael, J. Clonal plant architecture: A comparative analysis of form and function. In The Ecology and Evolution of Clonal Plants (eds De Kroon, H. & Van Groenendael, J. M.) 1–29 (Backhuys, 1997).
Barrett, S. C. H. Influences of clonality on plant sexual reproduction. Proc. Natl. Acad. Sci. 112(29), 8859–8866. https://doi.org/10.1073/pnas.1501712112 (2015).
Google Scholar
Heywood, J. S. Spatial analysis of genetic variation in plant populations. Annu. Rev. Ecol. Syst. 22, 335–355 (1991).
Google Scholar
Barrett, S. C. H. Evolution of mating systems: Outcrossing versus selfing. In The Princeton Guide to Evolution (ed. Losos, J. B.) 356–362 (Princeton University Press, 2013).
Barrett, S. C. H., Arunkumar, R. & Wright, S. I. The demography and population genomics of evolutionary transitions to self-fertilization in plants. Philos. Trans. R. Soc. B Biol. Sci. 369(1648), 20130344 (2014).
Google Scholar
Picó, F. X., Quintana-Ascencio, P. F., Mildén, M., Ehrlén, J. & Pfingsten, I. Modelling the effects of genetics and habitat on the demography of a grassland herb. Basic Appl. Ecol. 10(2), 122–130. https://doi.org/10.1016/j.baae.2008.02.006 (2009).
Google Scholar
Ellstrand, N. C. & Roose, M. L. Patterns of genotypic diversity in clonal plant species. Am. J. Bot. 74, 123–131 (1987).
Google Scholar
Loh, R., Scarano, F. R., Alves-Ferreira, M. & Salgueiro, F. Implications of clonality to population genetic structure of the nurse species Aechmea nuducaulis (L.) Griseb. (Bromeliaceae). Bot. J. Linn. Soc. 178, 329–341 (2015).
Google Scholar
Hedrick, P. W. Purging inbreeding depression and the probability of extinction: Full-sib mating. Heredity 73, 363–372. https://doi.org/10.1038/hdy.1994.183 (1994).
Google Scholar
Arunkumar, R., Ness, R. W., Wright, S. I. & Barrett, S. C. H. The evolution of selfing is accompanied by reduced efficacy of selection and purging of deleterious mutations. Genetics 199(3), 817–829. https://doi.org/10.1534/genetics.114.172809 (2015).
Google Scholar
Pannell, J. R. & Barrett, S. C. H. Baker’s law revisited: Reproductive assurance in a metapopulation. Evolution 52(3), 657–668. https://doi.org/10.2307/2411261 (1998).
Google Scholar
Hamrick, J. L. & Trapnell, D. W. Using population genetic analyses to understand seed dispersal patterns. Acta Oecologica 37, 641–649 (2011).
Google Scholar
Côrtes, M. C. et al. Low plant density enhances gene dispersal in the Amazonian understory herb Heliconia acuminata. Mol. Ecol. 22, 5716–5729 (2013).
Google Scholar
Trapnell, D. W., Hamrick, J. L., Ishibashi, C. D. & Kartzinel, T. R. Genetic inference of epiphytic orchid colonization; it may only take one. Mol. Ecol. 22, 3680–3692. https://doi.org/10.1111/mec.12338 (2013).
Google Scholar
Chung, M. Y. et al. Fine-scale genetic structure in populations of the spring ephemeral herb Megaleranthis saniculifolia (Ranunculaceae). Flora Morphol. Distrib. Funct. Ecol. Plants 240, 16–24 (2018).
Roberts, N. R., Dalton, P. J. & Jordan, G. J. Epiphytic ferns and bryophytes of Tasmanian tree-ferns: A comparison of diversity and composition between two host species. Austral Ecol. 30(2), 146–154. https://doi.org/10.1111/j.1442-9993.2005.01440.x (2005).
Google Scholar
Cardelús, C. L. & Chazdon, R. L. Inner-crown microenvironments of two emergent tree species in a lowland wet forest. Biotropica 37(2), 238–244. https://doi.org/10.1111/j.1744-7429.2005.00032.x (2005).
Google Scholar
Quaresma, A. C., Piedade, M. T. F., Wittmann, F. & ter Steege, H. Species richness, composition, and spatial distribution of vascular epiphytes in Amazonian black-water floodplain forests. Biodivers. Conserv. 27(8), 1981–2002. https://doi.org/10.1007/s10531-018-1520-3 (2018).
Google Scholar
Claver, F. K., Alaniz, J. R. & Caldíz, D. O. Tillandsia spp.: Epiphytic weeds of trees and bushes. For. Ecol. Manag. 6(4), 367–372. https://doi.org/10.1016/0378-1127(83)90044-0 (1983).
Google Scholar
Bartoli, C. G., Beltrano, J., Fernández, L. V. & Caldíz, D. O. Control of the epiphytic weeds Tillandsia recurvata and Tillandsia aeranthos with different herbicides. For. Ecol. Manage. 59, 289–294 (1993).
Google Scholar
Flores-Palacios, A., García-Franco, J. G. & Capistrán-Barradas, A. Biomass, phorophyte specificity and distribution of Tillandsia recurvata in a tropical semi-desert environment (Chihuahuan Desert, Mexico). Plant Ecology and Evolution 148(1), 68–75 (2015).
Google Scholar
Birge, W. I. The anatomy and some biological aspects of the “ball moss”, Tillandsia recurvata, 1–24. L. Bull. Univ. Tex. 194(20) (1911).
Smith, L. B. & Downs, R. J. Tillandsioideae (Bromeliaceae). In Flora Neotropica Monograph 14(2), 663–1492 (1977).
Hewitt, G. M. (1996). Some genetic consequences of ice ages, and their role in speciation. Biological Journal of the Linnaean Society, 58(July), 247–276. Retrieved from papers3://publication/uuid/B9DB7D5E-D6AE-404C-BFFC-9F8133453294
McWilliams, E. Chronology of the Natural Range Expansion of Tillandsia recurvata (Bromeliaceae) in Texas. Contributions to Botany 15(2), 343–346 (1992).
Flores-Palacios, A., Barbosa-Duchateau, C. L., Valencia-Díaz, S., Capistrán-Barradas, A. & García-Franco, J. G. Direct and indirect effects of Tillandsia recurvata on Prosopis laevigata in the Chihuahua desert scrubland of San Luis Potosi, Mexico. J. Arid Environ. 104, 88–95. https://doi.org/10.1016/j.jaridenv.2014.02.010 (2014).
Google Scholar
Benzing, D. H. Bromeliaceae: Profile of an Adaptive Radiation (Cambridge University Press, 2000).
Google Scholar
Benzing, D. H. Air Plants: Epiphytes and Aerial Gardens (Cornell University Press, 2012).
Google Scholar
Foster, M. D. Blueprint of the jungle as depicted by the altitude of growth of the Bromeliadswith notes on the culture of certain tropical epiphytes. Bull. N. Y. Bot. Garden 46, 9–16 (1945).
Soltis, D. E., Gilmartin, A. J., Rieseberg, L. & Gardner, S. Genetic variation in the epiphytes Tillandsia ionantha and T. recurvata (Bromeliaceae). Am. J. Bot. 74(4), 531–537 (1987).
Google Scholar
Orozco-Ibarrola, O. A., Flores-Hernández, P. S., Victoriano-Romero, E., Corona-López, A. M. & Flores-Palacios, A. Are breeding system and florivory associated with the abundance of Tillandsia species (Bromeliaceae)?. Bot. J. Linn. Soc. 177(1), 50–65. https://doi.org/10.1111/boj.12225 (2015).
Google Scholar
Chilpa-Galván, N. et al. Seed traits favouring dispersal and establishment of six epiphytic Tillandsia (Bromeliaceae) species. Seed Sci. Res. https://doi.org/10.1017/S0960258518000247 (2018).
Google Scholar
Southwood, T. & Kennedy, C. Trees as islands. Oikos 41(3), 359–371. https://doi.org/10.2307/3544094 (1983).
Google Scholar
Burns, K. C. Network properties of an epiphyte metacommunity. J. Ecol. 95(5), 1142–1151 (2007).
Google Scholar
Trapnell, D. W., Hamrick, J. L. & Nason, J. D. Three-dimensional fine-scale genetic structure of the neotropical epiphytic orchid, Laelia rubescens. Mol. Ecol. 13, 1111–1118 (2004).
Google Scholar
Torres, E., Riofrío, M.-L. & Iriondo, J. M. Complex fine-scale spatial genetic structure in Epidendrum rhopalostele: an epiphytic orchid. Heredity https://doi.org/10.1038/s41437-018-0139-1 (2018).
Google Scholar
Victoriano-Romero, E., Valencia-Díaz, A., Toledo-Hernández, V. H. & Flores-Palacios, A. Dispersal limitation of Tillandsia species correlates with rain and host structure in a central Mexican tropical dry forest. PLoS ONE 12(2), e0171614 (2017).
Google Scholar
Martins, S. E. (2009). Flora fanerogâmica do estado de São Paulo. FAPESP: Instituto de Botânica.
Chaves, C. J. N., Dyonisio, J. C. J. C. & Rossatto, D. R. D. R. Host trait combinations drive abundance and canopy distribution of atmospheric bromeliad assemblages. AoB Plants 8(October 2015), plw010. https://doi.org/10.1093/aobpla/plw010 (2016).
Google Scholar
Epps, C. W. & Keyghobadi, N. Landscape genetics in a changing world: Disentangling historical and contemporary influences and inferring change. Mol. Ecol. 24(24), 6021–6040. https://doi.org/10.1111/mec.13454 (2015).
Google Scholar
Cushman, S. A., Shirk, A. & Landguth, E. L. Separating the effects of habitat area, fragmentation and matrix resistance on genetic differentiation in complex landscapes. Landsc. Ecol. 27(3), 369–380. https://doi.org/10.1007/s10980-011-9693-0 (2012).
Google Scholar
Jackson, N. D. & Fahrig, L. Habitat amount, not habitat configuration, best predicts population genetic structure in fragmented landscapes. Landsc. Ecol. 31(5), 951–968. https://doi.org/10.1007/s10980-015-0313-2 (2016).
Google Scholar
Grimm, V. & Railsback, S. F. Individual-Based Modelling and Ecology (Princeton University Press, 2005).
Google Scholar
Csilléry, K., Blum, M. G. B., Gaggiotti, O. E. & François, O. Approximate Bayesian Computation (ABC) in practice. Trends Ecol. Evol. 25(7), 410–418. https://doi.org/10.1016/j.tree.2010.04.001 (2010).
Google Scholar
Udupa, S. M. & Baum, M. High mutation rate and mutational bias at (TAA)n microsatellite loci in chickpea (Cicer arietinum L.). Mol. Genet. Genom. 265(6), 1097–1103. https://doi.org/10.1007/s004380100508 (2001).
Google Scholar
Anmarkrud, J. A., Kleven, O., Bachmann, L. & Lifjeld, J. T. Microsatellite evolution: Mutations, sequence variation, and homoplasy in the hypervariable avian microsatellite locus HrU10. BMC Evol. Biol. 8(1), 1–10. https://doi.org/10.1186/1471-2148-8-138 (2008).
Google Scholar
Marriage, T. N. et al. Direct estimation of the mutation rate at dinucleotide microsatellite loci in Arabidopsis thaliana (Brassicaceae). Heredity 103(4), 310–317. https://doi.org/10.1038/hdy.2009.67 (2009).
Google Scholar
Bernal, R., Valverde, T. & Hernández-Rosas, L. Habitat preference of the epiphyte Tillandsia recurvata (Bromeliaceae) in a semi-desert environment in Central Mexico. Can. J. Bot. 83(10), 1238–1247 (2005).
Google Scholar
Chaves, C. J. & Rossatto, D. R. Unravelling intricate interactions among atmospheric bromeliads with highly overlapping niches in seasonal systems. Plant Biol. 22(2), 243–251 (2020).
Google Scholar
Vekemans, X. & Hardy, O. J. New insights from fine-scale spatial genetic structure analyses in plant populations. Mol. Ecol. 13(4), 921–935. https://doi.org/10.1046/j.1365-294X.2004.02076.x (2004).
Google Scholar
Ward, S. Genetic analysis of invasive plant populations at different spatial scales. Biol. Invasions 8(3), 541–552. https://doi.org/10.1007/s10530-005-6443-8 (2006).
Google Scholar
Pettengill, J. B., Briscoe Runquist, R. D. & Moeller, D. A. Mating system divergence affects the distribution of sequence diversity within and among populations of recently diverged subspecies of Clarkia xantiana (Onagraceae). Am. J. Bot. 103(1), 99–109. https://doi.org/10.3732/ajb.1500147 (2016).
Google Scholar
Atwater, D. Z., Fletcher, R. A., Dickinson, C. C., Paterson, A. H. & Barney, J. N. Evidence for fine-scale habitat specialization in an invasive weed. J. Plant Ecol. 11(2), 189–199. https://doi.org/10.1093/jpe/rtw124 (2018).
Google Scholar
Li, J. & Dong, M. Fine-scale clonal structure and diversity of invasive plant Mikania micrantha H.B.K. and its plant parasite Cuscuta campestris Yunker. Biol. Invasions 11(3), 687–695. https://doi.org/10.1007/s10530-008-9283-5 (2009).
Google Scholar
Ren, M. X., Cafasso, D., Cozzolino, S. & Pinheiro, F. Extensive genetic differentiation at a small geographical scale: Reduced seed dispersal in a narrow endemic marsh orchid, Anacamptis robusta. Bot. J. Linn. Soc. 183(3), 429–438. https://doi.org/10.1093/botlinnean/bow017 (2017).
Google Scholar
Barluenga, M. et al. Fine-scale spatial genetic structure and gene dispersal in Silene latifolia. Heredity 106(1), 13–24. https://doi.org/10.1038/hdy.2010.38 (2011).
Google Scholar
Charbonneau, A. et al. Weed evolution: Genetic differentiation among wild, weedy, and crop radish. Evol. Appl. https://doi.org/10.1111/eva.12699 (2018).
Google Scholar
Sagnard, F., Oddou-Muratorio, S., Pichot, C., Vendramin, G. G. & Fady, B. Effects of seed dispersal, adult tree and seedling density on the spatial genetic structure of regeneration at fine temporal and spatial scales. Tree Genet. Genomes 7(1), 37–48. https://doi.org/10.1007/s11295-010-0313-y (2011).
Google Scholar
Counsens, R. & Mortimer, M. Dynamics of Weed Populations (Cambridge University Press, 1995).
Google Scholar
Loreau, M. et al. Unifying sources and sinks in ecology and Earth sciences. Biol. Rev. 88, 365–379 (2013).
Google Scholar
dos Santos, L. S. et al. Generalized Allee effect model. Theory Biosci. 133, 117–124 (2014).
Google Scholar
Spruch, L. et al. Modeling community assembly on growing habitat “islands”: A case study on trees and their vascular epiphyte communities. Theor. Ecol. 12, 1–17 (2019).
Google Scholar
Einzmann, H. J. R. & Zotz, G. “No signs of saturation”: long-term dynamics of vascular epiphyte communities in a human-modified landscape. Biodivers. Conserv. 26, 1393–1410 (2017).
Google Scholar
Belinchón, R., Harrison, P. J., Mair, L., Várkonyi, G. & Snäll, T. Local epiphyte establishment and future metapopulation dynamics in landscapes with different spatiotemporal properties. Ecology 98(3), 741–750. https://doi.org/10.1002/ecy.1686 (2017).
Google Scholar
Vergara-Torres, C. A., Pacheco-Álvarez, M. C. & Flores-Palacios, A. Host preference and host limitation of vascular epiphytes in a tropical dry forest of central Mexico. J. Trop. Ecol. 26(6), 563–570. https://doi.org/10.1017/S0266467410000349 (2010).
Google Scholar
Barrett, S. C. H. & Kohn, J. R. Genetic and evolutionary consequences of small population size in plants: Implications for conservation. In Genetics and Conservation of Rare Plants (eds Falk, D. A. & Holsinge, K. E.) 3–30 (Oxford University Press, 1991).
Nathan, R., Horn, H. S., Chave, J. & Levin, S. A. Mechanistic models for tree seed dispersal by wind in dense forests and open landscapes. In Seed Dispersal and Frugivory-Ecologie, Evolution, Conservation 69–82 (2002). https://doi.org/10.1079/9780851995250.0069
Cousens, R. et al. Dispersal in Plants. A Population Perspective (Oxford University Press, 2008).
Google Scholar
Snäll, T., Ehrlén, J. & Rydin, H. Colonization-extinction dynamics of an epiphyte metapopulation in a dynamic landscape. Ecology 86(1), 106–115 (2005).
Google Scholar
Ruiz-Cordova, J. P., Toledo-Hernández, V. H. & Flores-Palacios, A. The effect of substrate abundance in the vertical stratification of bromeliad epiphytes in a tropical dry forest (Mexico). Flora Morphol. Distrib. Funct. Ecol. Plants 209(8), 375–384. https://doi.org/10.1016/j.flora.2014.06.003 (2014).
Google Scholar
Flores-Palacios, A., Bustamante-Molina, A. B., Corona-López, A. M. & Valencia-Díaz, S. Seed number, germination and longevity in wild dry forest Tillandsia species of horticultural value. Scientia Hortic. 187, 72–79 (2015).
Google Scholar
Goodman, R., & Herold, M. (2014). Why maintaining tropical forests is essential and urgent for a stable climate. Center for Global Development Working Paper, (385).
Seymour, F. & Busch, J. Why Forests? Why Now?: The Science, Economics, and Politics of Tropical Forests and Climate Change (Brookings Institution Press, 2016).
Stephenson, N. L. et al. Rate of tree carbon accumulation increases continuously with tree size. Nature 507(7490), 90–93 (2014).
Google Scholar
Tel-Zur, N., Abbo, S., Myslabodsky, D. & Mizrahi, Y. Modified CTAB procedure for DNA isolation from epiphytic cacti of genera Hylocereus and Selenicereus (Cactaceae). Plant Mol. Biol. Rep. 17, 249–254 (1999).
Google Scholar
Chaves, C. J. N., Aoki-Gonçalves, F., Leal, B. S. S., Rossatto, D. R. & Palma-Silva, C. Transferability of nuclear microsatellite markers to the atmospheric bromeliads Tillandsia recurvata and T. aeranthos (Bromeliaceae). Braz. J. Bot. 41, 931–935. https://doi.org/10.1007/s40415-018-0494-4 (2018).
Google Scholar
Keenan, K., Mcginnity, P., Cross, T. F., Crozier, W. W. & Prodöhl, P. A. DiveRsity: An R package for the estimation and exploration of population genetics parameters and their associated errors. Methods Ecol. Evol. https://doi.org/10.1111/2041-210X.12067 (2013).
Google Scholar
Slatkin, M. A measure of population subdivision based on microsatellite allele frequencies. Genetics 139, 457–462 (1995).
Google Scholar
Nei, M. Genetic distances between populations. Am. Nat. 106, 283–292 (1972).
Google Scholar
Edwards, A. W. F. Distance between populations on the basis of gene frequencies. Biometrics 27, 873–881 (1971).
Google Scholar
Reynolds, J. B., Weir, B. S. & Cockerham, C. C. Estimation of the coancestry coefficient: Basis for a short-term genetic distance. Genetics 105, 767–779 (1983).
Google Scholar
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. PeerJ https://doi.org/10.7717/peerj.281 (2014).
Google Scholar
Paradis, E. pegas: an R package for population genetics with an integrated-modular approach. Bioinformatics 26, 419–420 (2010).
Google Scholar
Excoffier, L., Smouse, P. E. & Quattro, J. M. Analysis of molecular variance inferred from metric distances among DNA haplotypes: Application to human mitochondrial DNA restriction data. Genetics 131, 479–491. https://doi.org/10.1007/s00424-009-0730-7 (1992).
Google Scholar
Loiselle, B. A., Sork, V. L., Nason, J. & Graham, C. Spatial genetic structure of a tropical understory shrub, Psychotria officinalis (Rubiaceae). Am. J. Bot. 82(11), 1420–1425 (1995).
Google Scholar
Bailleul, D., Stoeckel, S. & Arnaud-Haond, S. RClone: A package to identify MultiLocus Clonal Lineages and handle clonal data sets in r. Methods Ecol. Evol. 7(8), 966–970. https://doi.org/10.1111/2041-210X.12550 (2016).
Google Scholar
Harrison, S. et al. Beta diversity on geographic gradients in Britain. J. Anim. Ecol. 61(1), 151–158 (1992).
Google Scholar
Jost, L. Partitioning diversity into independent alpha and beta components. Ecology 88(10), 2427–2439. https://doi.org/10.1890/07-1861.1 (2007).
Google Scholar
Charney, N. & Record, S. Vegetarian: Jost diversity measures for community data. https://cran.r-project.org/web/packages/vegetarian/index.html (2012). Accessed Jul 2018.
Wilensky, U. NetLogo (Northwestern University, Center for Connected Learning and Computer-Based Modeling, 1999).
Grimm, V. et al. A standard protocol for describing individual-based and agent-based models. Ecol. Model. 198(1–2), 115–126. https://doi.org/10.1016/j.ecolmodel.2006.04.023 (2006).
Google Scholar
Grimm, V. et al. The ODD protocol: A review and first update. Ecol. Model. 221(23), 2760–2768. https://doi.org/10.1016/j.ecolmodel.2010.08.019 (2010).
Google Scholar
Kooijman, B. & Kooijman, S. A. L. M. Dynamic Energy Budget Theory for Metabolic Organisation (Cambridge University Press, 2010).
Sibly, R. M. et al. Representing the acquisition and use of energy by individuals in agent-based models of animal populations. Methods Ecol. Evol. 4(2), 151–161 (2013).
Google Scholar
Johnston, A. S. A., Hodson, M. E., Thorbek, P., Alvarez, T. & Sibly, R. M. An energy budget agent-based model of earthworm populations and its application to study the effects of pesticides. Ecol. Model. 280, 5–17 (2014).
Google Scholar
van der Vaart, E., Johnston, A. S. A. & Sibly, R. M. Predicting how many animals will be where: How to build, calibrate and evaluate individual-based models. Ecol. Model. 326, 113–123 (2016).
Google Scholar
Garza, J. C. & Williamson, E. G. Detection of reduction in population size using data from microsatellite loci. Mol. Ecol. 10, 305–318 (2001).
Google Scholar
Excoffier, L., Laval, G. & Schneider, S. Arlequin (version 3.0): An integrated software package for population genetics data analysis. Evol. Bioinform. Online 1, 47–50 (2005).
Google Scholar
Csilléry, K., François, O. & Blum, M. G. abc: An R package for approximate Bayesian computation (ABC). Methods Ecol. Evol. 3(3), 475–479 (2012).
Google Scholar
Pastur, G. M., Lencinas, M. V., Cellini, J. M. & Mundo, I. Diameter growth: Can live trees decrease?. Forestry 80(1), 83–88. https://doi.org/10.1093/forestry/cpl047 (2007).
Google Scholar
Source: Ecology - nature.com