Philippa Kaur

The absence of the first principles for biological systems in general, and in particular for vegetation populations where phenomena are interconnected makes their mathematical modelling complex. The theory of vegetation pattern formation rests on the self-organisation hypothesis and symmetry-breaking instability that provoke the fragmentation of the uniform cover. The symmetry-breaking instability takes place even if the environment is isotropic31,33,35. This instability may be an advection-induced transition that requires the pre-existence of the environment anisotropy due to the topography of the landscape34,39,40. Generally speaking, this transition requires at least two feedback mechanisms having a short-range activation and a long-range inhibition. In this respect, we consider three different vegetation models that are experimentally relevant systems: (i) the generic interaction redistribution model describing vegetation pattern formation which incorporates explicitly the facilitation, competition and seed dispersion nonlocal interactions (ii) the local nonvariational partial differential model described by a nonvariational Swift–Hohenberg type of model equation, and (iii) the reaction–diffusion system that incorporate explicetely water transport.The interaction-redistribution approachThe integrodifferential modelThis approach consists of considering a well-known logistic equation with nonlocal plant-to-plant interactions. Three types of interactions are considered: the facilitative (M_{f}(mathbf {r},t)), the competitive (M_{c}(mathbf {r},t)), and the seed dispersion (M_{d}(mathbf {r},t)) nonlocal interactions. To simplify further the mathematical modelling, we consider that the seed dispersion obeys a diffusive process (M_{d}(mathbf {r},t)approx nabla ^{2}b(mathbf {r},t)), with D the diffusion coefficient, b the biomass density, and (nabla ^{2}=partial ^2/partial x^2+partial ^2/partial y^2) is the Laplace operator acting in the (x,y) plane. The interaction-redistribution reads$$begin{aligned} M_{i}=expleft{ frac{xi _{i}}{N_{i}}int b(mathbf {r}+mathbf {r}’,t)phi _i(r,t)dmathbf {r}’right} , { text{ with } } phi _i(r,t)= exp(-r/L_{i}) end{aligned}$$
(1)
where (i=f,c). (xi _i) represents the strength of the interaction, (N_i) is a normalisation constant. We assume that their Kernels (phi _i(r,t)) are exponential functions with (L_i) the range of their interactions. The facilitative interaction (M_{f}(mathbf {r},t)) favouring vegetation development. They involve the accumulation of nutrients in the neighbourhood of plants, the reciprocal sheltering of neighbouring plants against climatic harshness which improves the water budget in the soil. The range of the facilitative interaction (L_f) operates on the crown size. The competitive interaction operates over a length (L_c) and involves the below-ground structures, i.e., the rhizosphere. In nutrient-poor or/and in water-limited territories, lateral spreading may extend beyond the radius of the crown. This extension of roots relative to their crown size is necessary for the survival and the development of the plant in order to extract enough nutrients and/or water from the soil. When incorporating these nonlocal interactions in the paradigmatic logistic equation, the spatiotemporal evolution of the normalised biomass density (b(mathbf {r}, t)) in isotropic environmental conditions reads14$$begin{aligned} partial _{t} b(mathbf {r},t)=b(mathbf {r},t)[1-b(mathbf {r},t)]M_{f}(mathbf {r},t)- mu b(mathbf {r},t)M_{c}(mathbf {r},t)+Dnabla ^{2}b(mathbf {r},t). end{aligned}$$
(2)
The normalisation is performed with respect to the total amount of biomass supported by the system. The first two terms in the logistic equation with nonlocal interaction Eq. (2) describe the biomass gains and losses, respectively. The third term models seed dispersion. The aridity parameter (mu) accounts for the biomass loss and gain ratio, which depends on water availability and nutrients soil distribution, topography, etc. The homogeneous cover solutions of Eq. (2) are: (b_{o}=0) which corresponds to the state totally devoid of vegetation, and the homogeneous cover solutions satisfy the equation$$begin{aligned} mu =(1-b)exp (Delta b), end{aligned}$$
(3)
with (Delta =xi _{f}-xi _{c}) measures the community cooperativity if (Delta >0) or anti-cooperativity when (Delta 0). The solution (u_{-}) is always unstable even in the presence of small spatial fluctuations. The linear stability analysis of vegetated cover ((u_{+})) with respect to small spatial fluctuations, yields the dispersion relation$$begin{aligned} sigma (k)=u_{+}(kappa -2u_{+})-(nu -gamma u_{+})k^{2}-alpha u_{+}k^{4}. end{aligned}$$
(8)
Imposing (partial sigma /partial k|_{k_{c}}=0) and (sigma (k_{c})=0), the critical mode can be determined$$begin{aligned} k_{c}=sqrt{frac{gamma -nu /u_{c}}{2alpha }}, end{aligned}$$
(9)
where (u_{c}) satisfies (4alpha u_{c}^2(2u_{c}-kappa )=(2gamma u_{c}-nu )^2). The corresponding aridity parameter (eta _{c}) can be calculated from Eq. (7).The reaction–diffusion approachThe second approach explicitly adds the water transport by below ground diffusion. The coupling between the water dynamics and the plant biomass involves positive feedbacks that tend to enhance water availability. Negative feedbacks allow for an increase in water consumption caused by vegetation growth, which inhibits further biomass growth.The modelling considers the coupled evolution of biomass density (b(mathbf {r},t)) and groundwater density (w(mathbf {r},t)). In its dimensionless form, this model reads33$$begin{aligned} frac{partial b}{partial t}= & {} frac{gamma w}{1+omega w}b-b^{2}-theta b+nabla ^{2}b, end{aligned}$$
(10)
$$begin{aligned} frac{partial w}{partial t}= & {} p-(1-rho b)w-w^{2}b+delta nabla ^{2}(w-beta b). end{aligned}$$
(11)
The first term in the first equation describes plant growth at a constant rate ((gamma /omega)) that grows linearly with w for dry soil. The quadratic nonlinearity (-b^{2}) accounts for saturation imposed by poor nutrients soil. The term proportional to (theta) accounts for mortality, grazing or herbivores. The mechanisms of dispersion are modelled by a simple diffusion process. The groundwater evolves due to a precipitation input p. The term ((1-rho b)w) in the second equation accounts for the evaporation and drainage, that decreases with the presence of vegetation. The term (w^{2}b) models the water uptake by the plants due to the transpiration process. The groundwater movement follows the Darcy’s law in unsaturated conditions; that is, the water flux is proportional to the gradient of the water matric potential41. The matric potential is equal to w, under the assumption that the hydraulic diffusivity is constant41. To model the suction of water by the roots, a correction to the matric potential is included; (-beta b), where (beta) is the strength of the suction. More

1.Frankham, R., Briscoe, D. A. & Ballou, J. D. Introduction to conservation genetics (Cambridge university press, 2002).2.Nadachowska-Brzyska, K., Burri, R., Smeds, L. & Ellegren, H. PSMC analysis of effective population sizes in molecular ecology and its application to black-and-white Ficedula flycatchers. Mol. Ecol. 25, 1058–1072 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
3.Martínez-Freiría, F., Velo-Antón, G. & Brito, J. C. Trapped by climate: Interglacial refuge and recent population expansion in the endemic Iberian adder Vipera seoanei. Divers. Distrib. 21, 331–344 (2015).Article 

Google Scholar 
4.Martínez-Freiría, F. et al. Integrative phylogeographical and ecological analysis reveals multiple pleistocene refugia for Mediterranean Daboia vipers in north-west Africa. Biol. J. Linn. Soc. 122, 366–384 (2017).Article 

Google Scholar 
5.Veríssimo, J. et al. Pleistocene diversification in Morocco and recent demographic expansion in the Mediterranean pond turtle Mauremys leprosa. Biol. J. Linn. Soc. 119, 943–959 (2016).Article 

Google Scholar 
6.Chattopadhyay, B., Garg, K. M., Gwee, C. Y., Edwards, S. V. & Rheindt, F. E. Gene flow during glacial habitat shifts facilitates character displacement in a Neotropical flycatcher radiation. BMC Evol. Biol. 17, 1–15 (2017).Article 

Google Scholar 
7.Garg, K. M., Chattopadhyay, B., Koane, B., Sam, K. & Rheindt, F. E. Last Glacial Maximum led to community-wide population expansion in a montane songbird radiation in highland Papua New Guinea. BMC Evol. Biol. 20, 82 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
8.Vences, M., Wollenberg, K. C., Vieites, D. R. & Lees, D. C. Madagascar as a model region of species diversification. Trends Ecol. Evol. 24, 456–465 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
9.Goodman, S. M., Raherilalao, M. J. & Wohlhauser, S. The Terrestrial Protected Areas of Madagascar: Their History, Description and Biota (Association Vahatra in Antananarivo, The University of Chicago Press, 2018).10.Douglass, K. The diversity of late holocene shellfish exploitation in Velondriake, Southwest Madagascar. J. Island Coast. Archaeol. 12, 333–359 (2016).11.Yoder, A. D., Campbell, C. R., Blanco, M. B., Ganzhorn, J. U. & Goodman, S. M. Geogenetic patterns in mouse lemurs (genus Microcebus) reveal the ghosts of Madagascar’s forests past. PNAS 113, 8049–8056 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
12.Salmona, J., Heller, R., Quéméré, E. & Chikhi, L., Climate change. and human colonization triggered habitat loss and fragmentation in Madagascar. Mol. Ecol. 26, 5203–5222 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
13.Townsend, T. M., Vieites, D. R., Glaw, F. & Vences, M. Testing species-level diversification hypotheses in Madagascar: the case of microendemic Brookesia leaf Chameleons. Syst. Biol. 58, 641–656 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
14.Brown, J. L., Cameron, A., Yoder, A. D. & Vences, M. A necessarily complex model to explain the biogeography of the amphibians and reptiles of Madagascar. Nat. Commun. 5, 5046 (2014).15.Schüßler, D. et al. Ecology and morphology of mouse lemurs (Microcebus spp.) in a hotspot of microendemism in northeastern Madagascar, with the description of a new species. Am. J. Primatol. 82, e23180 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
16.Chikhi, L. & Bruford, M. Mammalian population genetics and genomics. Mamm. Genome https://doi.org/10.1079/9780851999104.0539 (2005).17.Olivieri, G. L., Sousa, V., Chikhi, L. & Radespiel, U. From genetic diversity and structure to conservation: Genetic signature of recent population declines in three mouse lemur species (Microcebus spp.). Biol. Conserv. 141, 1257–1271 (2008).Article 

Google Scholar 
18.Gutenkunst, R. N., Hernandez, R. D., Williamson, S. H. & Bustamante, C. D. Inferring the joint demographic history of multiple populations from multidimensional SNP frequency data. PLoS Genet. 5, e1000695 (2009).19.Li, H. & Durbin, R. Inference of human population history from individual whole-genome sequences. Nature 475, 493–496 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
20.Excoffier, L., Dupanloup, I., Huerta-Sánchez, E., Sousa, V. C. & Foll, M. Robust demographic inference from genomic and SNP data. PLoS Genet. 9, e1003905 (2013).21.Liu, X. & Fu, Y.-X. Exploring population size changes using SNP frequency spectra. Nat Genet. 47, 555–559 (2015).22.Salmona, J., Heller, R., Lascoux, M. & Shafer, A. Inferring demographic history using genomic data. in Population Genomics 511–537 (Springer, 2017).23.Beichman, A. C., Huerta-Sanchez, E. & Lohmueller, K. E. Using genomic data to infer historic population dynamics of nonmodel organisms. Annu. Rev. Ecol. Evol. Syst. 49, 433–456 (2018).Article 

Google Scholar 
24.Sgarlata, G. M. et al. Genetic and morphological diversity of mouse lemurs (Microcebus spp.) in northern Madagascar: The discovery of a putative new species? Am. J. Primatol. 81, e23070 (2019).25.Demenocal, P. et al. Abrupt onset and termination of the African humid period:: rapid climate responses to gradual insolation forcing. Quat. Sci. Rev. 19, 347–361 (2000).Article 

Google Scholar 
26.Tierney, J. E. & DeMenocal, P. B. Abrupt shifts in Horn of Africa hydroclimate since the last glacial maximum. Science 342, 843–846 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
27.Los, S. O. et al. Sensitivity of a tropical montane cloud forest to climate change, present, past and future: Mt. Marsabit, N. Kenya. Quat. Sci. Rev. 218, 34–48 (2019).Article 

Google Scholar 
28.Ivory, S. J. & Russell, J. Climate, herbivory, and fire controls on tropical African forest for the last 60ka. Quat. Sci. Rev. 148, 101–114 (2016).Article 

Google Scholar 
29.Conroy, J. L., Overpeck, J. T., Cole, J. E., Shanahan, T. M. & Steinitz-Kannan, M. Holocene changes in eastern tropical Pacific climate inferred from a Galápagos lake sediment record. Quat. Sci. Rev. 27, 1166–1180 (2008).Article 

Google Scholar 
30.Martin-Puertas, C., Tjallingii, R., Bloemsma, M. & Brauer, A. Varved sediment responses to early Holocene climate and environmental changes in Lake Meerfelder Maar (Germany) obtained from multivariate analyses of micro X-ray fluorescence core scanning data. J. Quat. Sci. 32, 427–436 (2017).Article 

Google Scholar 
31.Flenley, J. R. Tropical forests under the climates of the last 30,000 years. in Potential Impacts of Climate Change on Tropical Forest Ecosystems, 37–57 (Springer, 1998).32.Burrough, S. L. & Thomas, D. S. G. Central southern Africa at the time of the African humid period: a new analysis of Holocene palaeoenvironmental and palaeoclimate data. Quat. Sci. Rev. 80, 29–46 (2013).Article 

Google Scholar 
33.Ivory, S. J. & Russell, J. Lowland forest collapse and early human impacts at the end of the African humid period at Lake Edward, equatorial East. Afr. Quat. Res. 89, 7–20 (2018).Article 

Google Scholar 
34.Anderson, A. et al. New evidence of megafaunal bone damage indicates late colonization of Madagascar. PLoS ONE 13, 1–14 (2018).
Google Scholar 
35.Hansford, J. et al. Early Holocene human presence in Madagascar evidenced by exploitation of avian megafauna. Sci. Adv. 4, eaat6925 (2018).36.Burney, D. A., Robinson, G. S. & Burney, L. P. Sporormiella and the late holocene extinctions in Madagascar. Proc. Natl Acad. Sci. USA 100, 10800–10805 (2003).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
37.Railsback, L. B. et al. Relationships between climate change, human environmental impact, and megafaunal extinction inferred from a 4000-year multi-proxy record from a stalagmite from northwestern Madagascar. Quat. Sci. Rev. 234, 106244 (2020).Article 

Google Scholar 
38.Dewar, R. E. et al. Stone tools and foraging in northern Madagascar challenge Holocene extinction models. PNAS 110, 12583–12588 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
39.Radimilahy, C. Mahilaka: an Archaeological Investigation of an Early Town in Northwestern Madagascar. Acta Universitatis Upsaliensis (University of Uppsala, 1998).40.Liu, X. & Fu, Y.-X. Exploring population size changes using SNP frequency spectra. Nat. Genet 47, 555–559 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
41.Lapierre, M., Lambert, A. & Achaz, G. Accuracy of demographic inferences from the site frequency spectrum: the case of the yoruba population. Genetics 206, 139–449 (2017).Article 

Google Scholar 
42.Terhorst, J., Kamm, J. A. & Song, Y. S. Robust and scalable inference of population history from hundreds of unphased whole genomes. Nat. Genet. 49, 303–309 (2017).CAS 
Article 

Google Scholar 
43.Patton, A. H. et al. Contemporary demographic reconstruction methods are robust to genome assembly quality: a case study in Tasmanian devils. Mol. Biol. Evol. 36, 2906–2921 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
44.Mazet, O., Rodríguez, W., Grusea, S., Boitard, S. & Chikhi, L. On the importance of being structured: Instantaneous coalescence rates and human evolution-lessons for ancestral population size inference? Heredity 116, 362–371 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
45.Orozco-terWengel, P. The devil is in the details: the effect of population structure on demographic inference. Heredity 116, 349–350 (2016).46.Mazet, O., Rodríguez, W. & Chikhi, L. Demographic inference using genetic data from a single individual: separating population size variation from population structure. Theor. Popul. Biol. 104, 46–58 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
47.Chikhi, L. et al. The IICR (inverse instantaneous coalescence rate) as a summary of genomic diversity: Insights into demographic inference and model choice. Heredity 120, 13–24 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
48.Simons, E. L., Godfrey, L. R., Vuillaume-Randriamanantena, M., Chatrath, P. S. & Gagnon, M. Discovery of new giant subfossil lemurs in the Ankarana Mountains of Northern Madagascar. J. Hum. Evol. 19, 311–319 (1990).Article 

Google Scholar 
49.Jungers, W. L., Godfrey, L. R., Simons, E. L. & Chatrath, P. S. Subfossil Indri indri from the Ankarana Massif of northern Madagascar. Am. J. Phys. Anthropol. 97, 357–366 (1995).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
50.Wilson, J. M., Stewart, P. D. & Fowler, S. V. Ankarana — a rediscovered nature reserve in northern Madagascar. Oryx 22, 163–171 (1988).Article 

Google Scholar 
51.Everson, K. M., Jansa, S. A., Goodman, S. M. & Olson, L. E. Montane regions shape patterns of diversification in small mammals and reptiles from Madagascar’s moist evergreen forest. J. Biogeogr. 47, 2059–2072 (2020).Article 

Google Scholar 
52.Douglass, K., Hixon, S., Wright, H. T., Godfrey, L. R. & Crowley, B. E. A critical review of radiocarbon dates clarifies the human settlement of Madagascar. Quat. Sci. Rev. 221, 105878 (2019).53.Orozco-Terwengel, P., Andreone, F., Louis, E. & Vences, M. Mitochondrial introgressive hybridization following a demographic expansion in the tomato frogs of Madagascar, genus. Dyscophus. Mol. Ecol. 22, 6074–6090 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
54.Johnson, J. A. et al. Long-term survival despite low genetic diversity in the critically endangered Madagascar fish-eagle. Mol. Ecol. 18, 54–63 (2009).PubMed 
PubMed Central 

Google Scholar 
55.Sommer, S. Effects of habitat fragmentation and changes of dispersal behaviour after a recent population decline on the genetic variability of noncoding and coding DNA of a monogamous Malagasy rodent. Mol. Ecol. 12, 2845–2851 (2003).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
56.Craul, M. et al. Influence of forest fragmentation on an endangered large-bodied lemur in northwestern Madagascar. Biol. Conserv. 142, 2862–2871 (2009).Article 

Google Scholar 
57.Parga, J. A., Sauther, M. L., Cuozzo, F. P., Jacky, I. A. Y. & Lawler, R. R. Evaluating ring-tailed lemurs (Lemur catta) from southwestern Madagascar for a genetic population bottleneck. Am. J. Phys. Anthropol. 147, 21–29 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
58.Dewar, R. E. et al. Stone tools and foraging in northern Madagascar challenge Holocene extinction models. Proc. Natl Acad. Sci. USA 110, 12583–12588 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
59.Schüler, L. & Hemp, A. Atlas of pollen and spores and their parent taxa of Mt. Kilimanjaro and tropical East Africa. Quat. Int. 425, 301–386 (2016).Article 

Google Scholar 
60.Du Puy, D. J. & Moat, J. Vegetation mapping and classification in Madagascar (using GIS): implications and recommendations for the conservation of biodiversity. in Chorology, Taxonomy and Ecology of the floras of Africa and Madagascar, 97–117 (1998, in press).61.Guillaumet, J.-L., Betsch, J.-M. & Callmander, M. W. Renaud Paulian et le programme du CNRS sur les hautes montagnes à Madagascar: étage vs domaine. Zoosystema 30, 723 (2008).PubMed 
PubMed Central 

Google Scholar 
62.Weisrock, D. W. et al. Delimiting species without nuclear monophyly in Madagascar’s mouse lemurs. PLoS ONE 5, e9883 (2010).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
63.Croudace, I. W., Rindby, A. & Rothwell, R. G. ITRAX: description and evaluation of a new multi-function X-ray core scanner. Geol. Soc. Lond. Spec. Publ. 267, 51–63 (2006).CAS 
Article 

Google Scholar 
64.Blott, S. J. & Pye, K. GRADISTAT: a grain size distribution and statistics package for the analysis of unconsolidated sediments. Earth Surf. Process. Landforms 26, 1237–1248 (2001).Article 

Google Scholar 
65.Hogg, A. G. et al. SHCal13 Southern Hemisphere calibration, 0–50,000 years cal BP. Radiocarbon 55, 1889–1903 (2013).CAS 
Article 

Google Scholar 
66.Rina Evasoa, M. et al. Sources of variation in social tolerance in mouse lemurs (Microcebus spp.). BMC Ecol. 19, 1–16 (2019).CAS 
Article 

Google Scholar 
67.Aleixo-Pais, I. et al. The genetic structure of a mouse lemur living in a fragmented habitat in Northern Madagascar. Conserv. Genet. 20, 229–243 (2019).Article 

Google Scholar 
68.Radespiel, U., Jurić, M. & Zimmermann, E. Sociogenetic structures, dispersal and the risk of inbreeding in a small nocturnal lemur, the golden-brown mouse lemur (Microcebus ravelobensis). Behaviour 146, 607–628 (2009).Article 

Google Scholar 
69.Radespiel, U., Ehresmann, P. & Zimmermann, E. Species-specific usage of sleeping sites in two sympatric mouse lemur species (Microcebus murinus and M. ravelobensis) in northwestern Madagascar. Am. J. Primatol. 59, 139–151 (2003).PubMed 
Article 
PubMed Central 

Google Scholar 
70.Larsen, P. A. et al. Hybrid de novo genome assembly and centromere characterization of the gray mouse lemur (Microcebus murinus). BMC Biol. 15, 1–17 (2017).Article 
CAS 

Google Scholar 
71.Metzker, M. L. Sequencing technologies — the next generation. Nat. Rev. Genet. 11, 31 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
72.Skotte, L., Korneliussen, T. S. & Albrechtsen, A. Estimating individual admixture proportions from next generation sequencing. Data 195, 693–702 (2013).CAS 

Google Scholar 
73.Korneliussen, T. S., Albrechtsen, A. & Nielsen, R. ANGSD: analysis of next generation sequencing data. BMC Bioinforma. 15, 1–13 (2014).Article 

Google Scholar 
74.Korneliussen, T. S. & Moltke, I. Sequence analysis NgsRelate: a software tool for estimating pairwise relatedness from next-generation sequencing data. Bioinformatics 31, 4009–4011 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
75.Soraggi, S., Wiuf, C. & Albrechtsen, A. Powerful inference with the D-Statistic on low-coverage whole-genome data. G3 8, 551–566 (2017).PubMed Central 
Article 

Google Scholar 
76.Chikhi, L., Sousa, V. C., Luisi, P., Goossens, B. & Beaumont, M. A. The confounding effects of population structure, genetic diversity and the sampling scheme on the detection and quantification of population size changes. Genetics 186, 983–995 (2010).PubMed 
PubMed Central 
Article 

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

Google Scholar 
78.Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol. Ecol. 14, 2611–2620 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
79.Salmona, J., Heller, R., Quéméré, E. & Chikhi, L. Climate change and human colonization triggered habitat loss and fragmentation in Madagascar. Mol. Ecol. 26, 5203–5222 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
80.Schneider, N., Chikhi, L., Currat, M. & Radespiel, U. Signals of recent spatial expansions in the grey mouse lemur (Microcebus murinus). BMC Evol. Biol. 10, 105 (2010).81.Radespiel, U., Lutermann, H., Schmelting, B. & Zimmermann, E. An empirical estimate of the generation time of mouse lemurs. Am. J. Primatol. 81, 1–8 (2019).Article 

Google Scholar 
82.Hawkins, M. T. R. et al. Genome sequence and population declines in the critically endangered greater bamboo lemur (Prolemur simus) and implications for conservation. BMC Genomics 19, 1–15 (2018).Article 
CAS 

Google Scholar 
83.Poelstra, J. et al. Cryptic patterns of speciation in cryptic primates: microendemic mouse lemurs and the multispecies coalescent. Syst. Biol. https://doi.org/10.1093/sysbio/syaa053 (2020).84.Campbell, C. R. et al. Pedigree-based and phylogenetic methods support surprising patterns of mutation rate and spectrum in the gray mouse lemur. Heredity 127.2, 233–244 (2021).Article 

Google Scholar 
85.Hudson, R. R. Generating samples under a Wright–Fisher neutral model of genetic variation. Bioinformatics 18, 337–338 (2002).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
86.Fredsted, T., Pertoldi, C., Schierup, M. H. & Kappeler, P. M. Microsatellite analyses reveal fine-scale genetic structure in grey mouse lemurs (Microcebus murinus). Mol. Ecol. 14, 2363–2372 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
87.Radespiel, U., Schulte, J., Burke, R. J. & Lehman, S. M. Molecular edge effects in the endangered golden-brown mouse lemur Microcebus ravelobensis. Oryx 53, 716–726 (2019).Article 

Google Scholar 
88.Radespiel, U., Lutermann, H., Schmelting, B., Bruford, M. W. & Zimmermann, E. Patterns and dynamics of sex-biased dispersal in a nocturnal primate, the grey mouse lemur, Microcebus murinus. Anim. Behav. 65, 709–719 (2003).Article 

Google Scholar 
89.Radespiel, U., Rakotondravony, R. & Chikhi, L. Natural and anthropogenic determinants of genetic structure in the largest remaining population of the endangered golden-brown mouse lemur, Microcebus ravelobensis. Am. J. Primatol. 70, 860–870 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
90.Schliehe-Diecks, S., Eberle, M. & Kappeler, P. M. Walk the line-dispersal movements of gray mouse lemurs (Microcebus murinus). Behav. Ecol. Sociobiol. 66, 1175–1185 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
91.Excoffier, L., Dupanloup, I., Huerta-Sánchez, E., Sousa, V. C. & Foll, M. Robust demographic inference from genomic and SNP data. PLoS Genet. 9, e1003905 (2013).92.Beerli, P. Effect of unsampled populations on the estimation of population sizes and migration rates between sampled populations. Mol. Ecol. 13, 827–836 (2004).PubMed 
Article 
PubMed Central 

Google Scholar 
93.Akaike, H. A new look at the statistical model identification. IEEE Trans. Automat. Control 19, 716–723 (1974).Article 

Google Scholar 
94.Bagley, R. K., Sousa, V. C., Niemiller, M. L. & Linnen, C. R. History, geography and host use shape genomewide patterns of genetic variation in the redheaded pine sawfly (Neodiprion lecontei). Mol. Ecol. 26, 1022–1044 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar  More

CORRESPONDENCE
14 September 2021

Preventing spillover as a key strategy against pandemics

Neil M. Vora

 ORCID: http://orcid.org/0000-0002-4989-3108

0
,

Nigel Sizer

1
&

Aaron Bernstein

2

Neil M. Vora

Conservation International, Arlington, Virginia, USA.

View author publications

You can also search for this author in PubMed
 Google Scholar

Nigel Sizer

Preventing Pandemics at the Source Coalition, Mount Kisco, New York, USA.

View author publications

You can also search for this author in PubMed
 Google Scholar

Aaron Bernstein

Boston Children’s Hospital, Boston, Massachusetts, USA.

View author publications

You can also search for this author in PubMed
 Google Scholar

Share on Twitter
Share on Twitter

Share on Facebook
Share on Facebook

Share via E-Mail
Share via E-Mail

Download PDF

Most new infectious diseases result from the spillover of pathogens from animals, particularly wildlife, to people. Spillover prevention should not be dismissed in discussions on how best to address pandemics (see Nature 596, 332–335; 2021).The belief that we are powerless to prevent spillover is, unfortunately, endorsed by many in public health and government. Improved management of farmed animals, regulations on wildlife trade and conservation of tropical forests have all helped to prevent spillover and subsequent outbreaks, as well as boosting greenhouse-gas mitigation and wildlife conservation (see go.nature.com/2uqwx1u). Moreover, preventing spillover is cheap compared with the costs of a single pandemic (A. P. Dobson et al. Science 369, 379–381; 2020).Outbreak containment measures will always be necessary, especially for the most vulnerable people in resource-limited settings, because spillover can never be completely eliminated. But if prioritized alongside post-spillover initiatives, outcomes will be more cost-effective, scientifically informed and equitable.

Nature 597, 332 (2021)
doi: https://doi.org/10.1038/d41586-021-02427-4

Competing Interests
The authors declare no competing interests.

Related Articles

See more letters to the editor

Subjects

Diseases

Policy

Conservation biology

Latest on:

Diseases

How COVID is derailing the fight against HIV, TB and malaria
News 10 SEP 21

Cells of the human intestinal tract mapped across space and time
Article 08 SEP 21

Burden and characteristics of COVID-19 in the United States during 2020
Article 26 AUG 21

Policy

Afghanistan: conflict risks local and global health
Correspondence 14 SEP 21

Climate science is supporting lawsuits that could help save the world
News Feature 08 SEP 21

Indonesia’s science super-agency must earn researchers’ trust
Editorial 08 SEP 21

Jobs

Post-Doctoral Position

McGuire Research Institute, Inc.
Richmond, VA, United States

Postdoctoral Research Associate

Washington University in St. Louis (WUSTL)
Saint Louis, MO, United States

Division Head, General Internal Medicine and Geriatrics, University Health Network and Sinai Health Systems

University Health Network (UHN), U of T
Toronto, Ontario, Canada

Director Protein Science

Bright Peak Therapeutics
Basel, Switzerland More

The participants of the on-farm trialsThe farmers taking part in the trials own between 0.3 and 40 ha. Most of them were smallholders (less than 2 ha) and used to plant vegetable fields of around 300 m2 per crop. Two out of 233 participating farmers are female, farmers’ age ranges from 24 to 68 years. All farmers learned agriculture from their parents, 70% are literate. Farmers and fields were visited 10–12 times per trial. In 2018, we started with 112 farmer fields, but some farmers did not follow strictly the obligatory agricultural practices (e.g., concerning fertilizer, irrigation, harvest), some lost the entire or parts of fields (e.g., by flood, grazing livestock), therefore all assessments concerning 2018 include 99 farmer fields. In 2019, we started with 136 farmer fields, two farmers did not follow the agreed farming practices, so assessments for 2019 are based on 134 farmer fields.The design of participatory field trialsWe conducted 14 trials in 2018 and 17 in 2019, each trial encompasses five FAP fields and three control fields in neighbouring villages. Minimum distance between FAP fields and between FAP and control fields was two thousand metres for nearly all fields, at least more than one thousand metres. In the mountainous region we used pumpkin, zucchini and faba bean as main crops (two years), in oasis okra and zucchini (two years), faba bean and pumpkin (2019), in the semi-arid region melon, zucchini, pumpkin, eggplant and faba bean (two years) and in the region with adequate rainfall tomato, faba bean, zucchini and eggplant (two years) and pumpkin (2019). The main crops were selected by farmers and agricultural advisors of the respective regions, MHEP by farmers of the respective trials and researchers.Field size was 300 m2 as recommended for smallholders5 with a 75% zone for the main crop in both, FAP and control. Except for okra, the 75% zone had four cultivars with four replications in a randomized system as recommended as enhanced practice by farmers in the pilot project in Morocco27. For okra only two cultivars are available in Morocco and trials used only seeds accessible also for farmers. FAP fields employed the 25% zones for habitat enhancement, whereas control fields had the main crop also in this zone. We used coriander, basil, cumin, dill, anise, celery, sunflower, canola, flax, zucchini, okra, melon, tomato, green pepper, cucumber, Armenian cucumber, eggplant, chia, arugula, watermelon, pumpkin, grass pea, cultivated lupinus, alfalfa, clover, vetch, faba bean and wild lupinus as MHEP, per trial between four and eight different MHEP. As faba bean starts flowering in end of February in Morocco, MHEP were partly forage crops as they flower early. MHEP were seeded in a way that around 2/3 flowered at the same time as the main crop and 1/3 before or after to prolong the foraging season on site for flower visitors. The habitat enhancement zones included also nesting and water support out of local materials, e.g., hollow stems, wood and dry mud with holes.Field managementIn oasis, all fields were irrigated by gravity flow, in the other sites all farmers used drip irrigation. The amount of dung used is based on farmers’ decision and varies per region: semi-arid region 500 kg/300 m2, mountainous region 1000 kg/300 m2, oasis 1500 kg/300 m2 and region with adequate rainfall 3000 kg/300 m2. Soil analysis was conducted for all fields but does not explain the income gaps between FAP and control. Pesticides (mainly neonicotinoids and broad-spectrum insecticides) were prohibited during trials. In some urgent cases with permission of the plant protection specialist, one foliar insecticide application for pest management was accepted when pest density reached the economic threshold.Insect sampling and methods to analyse the dataThe taxa richness of flower visitors was assessed by a combination of transect net samplings and pan trappings. In each field, insects were sampled four times, once before the flowering of the main crop, twice during its flowering and once afterwards. Each sampling took two days for each trial (four fields per day). Two sets of three pan traps (blue, yellow and white) were located in each field at the beginning of the first day of sampling and were collected the second day after 24 h. The samplings in 75% zones consisted of walking along two twenty eight metres transect lines for five min each. In the 25% zones flower visitors were collected once along an 80 m transect line around the 75% zone for ten minutes. The flower visitors were collected and kept separately per MHEP, but the respective time needed was recorded and added to the transect. The insects were collected using both sweep nets and insect vacuums. All flower visitors were collected except Apis mellifera, Bombus terrestris and Xylocopa pubescens that were identified visually on site. The collected insects were first fainted with ethyl acetate and afterwards placed inside killing jars filled with cyanide, afterwards pinned and labelled. Wild bees were identified to the genus level using the most recent key for wild bees in Europe52. The other flower visitors were identified to genus level or to family level. Significance concerning diversity was measured by Wilcoxon test53.In the 75% zones, pest insects, predators and parasitoid wasps were collected four times. Per farmer field, four one-square-metre quadrates were randomly selected, within the quadrates ten randomly selected plants were beaten five times, so in total we used 320 crop samples per trial. In the 25% zones, the beating method was similarly used for each MHEP (five sample plants per MHEP). Specimen were collected in plastic bags and kept in plastic tubes containing 70% ethanol for conservation. Abundance of pests was estimated by counting the number (i) recorded on each sample crop. Pest reduction was calculated by the rate of pest reduction (%) using the following formula: % = (1− AFAP(i) / AControl(i)) × 100, where AFAP (i) is the average of the abundance in the FAP plot; AControl (i) is the average of the abundance in the control plot54.Economic assessmentsThe economic assessments use the same calculation as the pilot projects5,27: the number of fruits was counted and weighed. Investment costs in FAP and control fields are the same in the 75% zones. The income from the 75% zones was assessed by multiplying total weight with market price per kg. The income from the 25% zones of control fields was assessed by total produce weight multiplied by market price per kg; investment costs were deducted. The income of the 25% zone of FAP fields was computed by multiplying total weight with market price per kg of MHEP minus respective investment costs and minus 100 MAD (1.5 person days per FAP field) as calculated labour costs for harvesting MHEP, though in our trials, farmers harvested themselves.SimulationsThe simulation of potential FAP impacts on food security and sparing natural land for pollinator and biodiversity protection is based on following assumptions. Basis is the total production (2016–2017 differentiated per crop; provided by the Moroccan Ministry of Agriculture on request) for faba bean (share of harvested crop with green pods as in the experiments, 105,760 ton in 10,205 ha), zucchini and pumpkin (179,519 ton in 7539 ha), melon (618,588 ton in 20,163 ha), eggplant (52,966 ton in 1885 ha) and tomato (1,293,761 ton in 15,888 ha). We did not include okra due to lack of national production data. For the simulation on potential increase of production through smallholders (≤ 2 ha), we use 13% as share of smallholders in North Africa for vegetable production49. For the simulation of the land-saving potential through smallholders, we used 11% (North Africa, share of smallholders’ land for food crops)55.The formula used for the simulation on the potential FAP impacts on food security (PIFS) is:$${text{PIFS}}, = ,left( {{text{SSP}}*left( {{{1}} – upmu } right)} right), + ,left( {{text{SSP }}*upmu } right){text{ }}*left( {{text{1}}, + ,left( {{text{GFT }}*{text{TE}}} right)} right) – {text{SSP}}$$PIFS: Potential increase in crop production because of FAP (t), SSP: Smallholders’ share of production in (t), GFT: FAP production gain in farm trials (%), µ: the share of smallholder-producers adopting FAP, TE: Technology effectiveness.The GFT employed is 85,2% which represents the average FAP production gain of the vegetables used in the simulation process. For µ we used either 10%, 30% or 50% and for TE we assumed that smallholder-producers gain either 50% or 70% of the total production gain achieved in on-farm trials with smallholder-farmers since farmers will adapt MHEP and their planting to their personal preferences.The formula used for the simulation of potential land saving (PLS):$${text{PLS}} = (({text{SAP}} * {text{PIFS}})/{text{SSP}})-{text{SAP}}$$PLS: Potential land saving in ha, SAP: Smallholders’ area of production in ha. More

1.Barnosky, A. D. et al. Has the Earth’s sixth mass extinction already arrived?. Nature 471(7336), 51–57 (2011).ADS 
CAS 
PubMed 
Article 

Google Scholar 
2.Ripple, W. J. et al. Status and ecological effects of the world’s largest carnivores. Science 343, 151–163 (2014).CAS 
Article 

Google Scholar 
3.Haswell, P. M., Kusak, J. & Hayward, M. W. Large carnivore impacts are context-dependent. Food Webs 12, 3–13. https://doi.org/10.1016/j.fooweb.2016.02.005 (2017).Article 

Google Scholar 
4.Barbosa, P. & Castellanos, I. Ecology of Predator–Prey Interactions (Oxford University Press, 2005).
Google Scholar 
5.Terborgh, J. & Estes, J. A. Trophic Cascades: Predator, Prey, and the Changing Dynamics of Nature (Island Press, 2010).
Google Scholar 
6.Estes, J. A. et al. Trophic downgrading of planet earth. Science 333, 301–306 (2011).ADS 
CAS 
PubMed 
Article 

Google Scholar 
7.Crooks, K. R. & Soulé, M. E. Mesopredator release and avifaunal extinctions in a fragmented system. Nature 400, 563–566 (1999).ADS 
CAS 
Article 

Google Scholar 
8.Ritchie, E. G. & Johnson, C. N. Predator interactions, mesopredator release and biodiversity conservation. Ecol. Lett. 12(9), 982–998. https://doi.org/10.1111/j.1461-0248.2009.01347.x (2009).Article 
PubMed 

Google Scholar 
9.Jachowski, D. S. et al. Identifying mesopredator release in multi-predator systems: A review of evidence from North America. Mamm. Rev. 50, 367–381. https://doi.org/10.1111/mam.12207 (2020).Article 

Google Scholar 
10.Letnic, M., Ritchie, E. G. & Dickman, C. R. Top predators as biodiversity regulators: The dingo Canis lupus dingo as a case study. Biol. Rev. 87(2), 390–413. https://doi.org/10.1111/j.1469-185X.2011.00203.x (2012).Article 
PubMed 

Google Scholar 
11.Glen, A. S. & Dickman, C. R. Complex interactions among mammalian carnivores in Australia, and their implications for wildlife management. Biol. Rev. 80(3), 387–401 (2005).PubMed 
Article 

Google Scholar 
12.Allen, B. L. et al. Can we save large carnivores without losing large carnivore science?. Food Webs. 12, 64–75 (2017).Article 

Google Scholar 
13.Allen, B. L. & Leung, K.-P. The (non)effects of lethal population control on the diet of Australian dingoes. PLoS ONE 9(9), e108251. https://doi.org/10.1371/journal.pone.0108251 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
14.Wallach, A. D. Australia should enlist dingoes to control invasive species. The Conversation 2014. https://theconversation.com/australia-should-enlist-dingoes-to-control-invasive-species-24807. Accessed 26 March, 2014.15.Letnic, M. & Feit, B. Like cats and dogs: dingoes can keep feral cats in check. The Conversation. 2019. https://theconversation.com/like-cats-and-dogs-dingoes-can-keep-feral-cats-in-check-114748. Accessed 4 April 2019.16.Newsome, T. Thinking big gives top predators the competitive edge. The Conversation 2017. https://theconversation.com/thinking-big-gives-top-predators-the-competitive-edge-78106. Accessed 24 May 2017.17.Johnson, C. & VanDerWal, J. Evidence that dingoes limit the abundance of a mesopredator in eastern Australian forests. J Appl Ecol. 46, 641–646 (2009).Article 

Google Scholar 
18.Rolls, E. C. They All Ran Wild: The Animals and Plants that Plague Australia (Angus & Robertson Publishers, 1969).
Google Scholar 
19.Balme, J., O’Connor, S. & Fallon, S. New dates on dingo bones from Madura Cave provide oldest firm evidence for arrival of the species in Australia. Sci. Rep. 8(1), 9933. https://doi.org/10.1038/s41598-018-28324-x (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
20.Fleming, P. J. S., Allen, B. L. & Ballard, G. Seven considerations about dingoes as biodiversity engineers: The socioecological niches of dogs in Australia. Aust. Mammal. 34(1), 119–131 (2012).Article 

Google Scholar 
21.Corbett, L. K. The Dingo in Australia and Asia 2nd edn. (J.B. Books, South Australia, 2001).
Google Scholar 
22.Fleming, P. J. S. et al. Management of wild canids in Australia: Free-ranging dogs and red foxes. In Carnivores of Australia: Past, Present and Future (eds Glen, A. S. & Dickman, C. R.) 105–149 (CSIRO Publishing, 2014).
Google Scholar 
23.Doherty, T. S. et al. Impacts and management of feral cats Felis catus in Australia. Mamm. Rev. 42, 83–97 (2017).Article 

Google Scholar 
24.Brook, L. A., Johnson, C. N. & Ritchie, E. G. Effects of predator control on behaviour of an apex predator and indirect consequences for mesopredator suppression. J. Appl. Ecol. 49(6), 1278–1286. https://doi.org/10.1111/j.1365-2664.2012.02207.x (2012).Article 

Google Scholar 
25.Letnic, M., Koch, F., Gordon, C., Crowther, M. & Dickman, C. Keystone effects of an alien top-predator stem extinctions of native mammals. Proc. R. Soc. B Biol. Sci. 276, 3249–3256 (2009).Article 

Google Scholar 
26.Wallach, A. D., Johnson, C. N., Ritchie, E. G. & O’Neill, A. J. Predator control promotes invasive dominated ecological states. Ecol. Lett. 13, 1008–1018 (2010).PubMed 

Google Scholar 
27.Leo, V., Reading, R. P., Gordon, C. & Letnic, M. Apex predator suppression is linked to restructuring of ecosystems via multiple ecological pathways. Oikos 128, 630–639. https://doi.org/10.1111/oik.05546 (2019).Article 

Google Scholar 
28.Johnson, C. Australia’s Mammal Extinctions: A 50,000 Year History (Cambridge University Press, 2006).
Google Scholar 
29.Read, J. L. & Scoleri, V. Ecological implications of reptile mesopredator release in arid South Australia. J. Herpetol. 49(1), 64–69. https://doi.org/10.1670/13-208 (2015).Article 

Google Scholar 
30.Sutherland, D. R., Glen, A. S. & de Tores, P. J. Could controlling mammalian carnivores lead to mesopredator release of carnivorous reptiles?. Proc. R. Soc. B 278(1706), 641–648. https://doi.org/10.1098/rspb.2010.2103 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
31.Davis, N. E. et al. Interspecific and geographic variation in the diets of sympatric carnivores: Dingoes/wild dogs and red foxes in south-eastern Australia. PLoS ONE 10(3), e0120975. https://doi.org/10.1371/journal.pone.0120975 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
32.Paltridge, R. The diets of cats, foxes and dingoes in relation to prey availability in the Tanami Desert, Northern Territory. Wildl. Res. 29, 389–403 (2002).Article 

Google Scholar 
33.Cupples, J. B., Crowther, M. S., Story, G. & Letnic, M. Dietary overlap and prey selectivity among sympatric carnivores: Could dingoes suppress foxes through competition for prey?. J. Mammal. 92(3), 590–600. https://doi.org/10.1644/10-MAMM-A-164.1 (2011).Article 

Google Scholar 
34.Glen, A. S., Pennay, M., Dickman, C. R., Wintle, B. A. & Firestone, K. B. Diets of sympatric native and introduced carnivores in the Barrington Tops, eastern Australia. Aust. Ecol. 36(3), 290–296. https://doi.org/10.1111/j.1442-9993.2010.02149.x (2011).Article 

Google Scholar 
35.Moseby, K. E., Neilly, H., Read, J. L. & Crisp, H. A. Interactions between a top order predator and exotic mesopredators in the Australian rangelands. Int. J. Ecol. 2012; Article ID 250352.36.Allen, B. L. & Fleming, P. J. S. Reintroducing the dingo: The risk of dingo predation to threatened vertebrates of western New South Wales. Wildl. Res. 39(1), 35–50 (2012).Article 

Google Scholar 
37.Glen, A. S. & Woodman, A. P. What Impact Does Altering Dingo Populations Have on Trophic Structure? (Environmental Evidence Australia, 2013).
Google Scholar 
38.Allen, B. L., Allen, L. R. & Leung, K.-P. Interactions between two naturalised invasive predators in Australia: Are feral cats suppressed by dingoes?. Biol. Invasions 17, 761–776. https://doi.org/10.1007/s10530-014-0767-1 (2015).Article 

Google Scholar 
39.Arthur, A. D., Catling, P. C. & Reid, A. Relative influence of habitat structure, species interactions and rainfall on the post-fire population dynamics of ground-dwelling vertebrates. Aust. Ecol. 37(8), 958–970 (2013).Article 

Google Scholar 
40.Claridge, A. W., Cunningham, R. B., Catling, P. C. & Reid, A. M. Trends in the activity levels of forest-dwelling vertebrate fauna against a background of intensive baiting for foxes. For. Ecol. Manag. 260(5), 822–832. https://doi.org/10.1016/j.foreco.2010.05.041 (2010).Article 

Google Scholar 
41.Stobo-Wilson, A. M. et al. Habitat structural complexity explains patterns of feral cat and dingo occurrence in monsoonal Australia. Divers. Distrib. 247, 108638. https://doi.org/10.1111/ddi.13065 (2020).Article 

Google Scholar 
42.Pavey, C. R., Eldridge, S. R. & Heywood, M. Population dynamics and prey selection of native and introduced predators during a rodent outbreak in arid Australia. J. Mammal. 89(3), 674–683 (2008).Article 

Google Scholar 
43.Greenville, A. C., Wardle, G. M., Tamayo, B. & Dickman, C. R. Bottom-up and top-down processes interact to modify intraguild interactions in resource-pulse environments. Oecologia 175(4), 1349–1358. https://doi.org/10.1007/s00442-014-2977-8 (2014).ADS 
Article 
PubMed 

Google Scholar 
44.Allen, B. L. et al. Does lethal control of top-predators release mesopredators? A re-evaluation of three Australian case studies. Ecol. Manag. Restor. 15(3), 191–195. https://doi.org/10.1111/emr.12118 (2014).Article 

Google Scholar 
45.Allen, B. L. et al. As clear as mud: A critical review of evidence for the ecological roles of Australian dingoes. Biol. Conserv. 159, 158–174 (2013).Article 

Google Scholar 
46.Hayward, M. W. & Marlow, N. Will dingoes really conserve wildlife and can our methods tell?. J. Appl. Ecol. 51(4), 835–838. https://doi.org/10.1111/1365-2664.12250 (2014).Article 

Google Scholar 
47.Newsome, T. M., Greenville, A. C., Letnic, M., Ritchie, E. G. & Dickman, C. R. The case for a dingo reintroduction in Australia remains strong: A reply to Morgan et al., 2016. Food Webs https://doi.org/10.1016/j.fooweb.2017.02.001 (2017).Article 

Google Scholar 
48.Letnic, M., Crowther, M. S., Dickman, C. R. & Ritchie, E. Demonising the dingo: How much wild dogma is enough?. Curr. Zool. 57(5), 668–670 (2011).Article 

Google Scholar 
49.Glen, A. S. Enough dogma: Seeking the middle ground on the role of dingoes. Curr. Zool. 58(6), 856–858 (2012).Article 

Google Scholar 
50.Johnson, C. N. et al. Experiments in no-impact control of dingoes: Comment on Allen et al. 2013. Front. Zool. 11, 17 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
51.Nimmo, D. G., Watson, S. J., Forsyth, D. M. & Bradshaw, C. J. A. Dingoes can help conserve wildlife and our methods can tell. J. Appl. Ecol. 52(2), 281–285. https://doi.org/10.1111/1365-2664.12369 (2015).Article 

Google Scholar 
52.Allen, B. L. et al. Top-predators as biodiversity regulators: Contemporary issues affecting knowledge and management of dingoes in Australia. In Biodiversity Enrichment in a Diverse World. Chapter 4 (ed. Lameed, G. A.) 85–132 (InTech Publishing, 2012).
Google Scholar 
53.Platt, J. R. Strong inference: Certain systematic methods of scientific thinking may produce much more rapid progress than others. Science 146(3642), 347–353 (1964).ADS 
CAS 
PubMed 
Article 

Google Scholar 
54.Caughley, G. Analysis of Vertebrate Populations (Wiley, 1977).
Google Scholar 
55.Krebs, C. J. Ecology: The Experimental Analysis of Distribution and Abundance 6th edn. (Benjamin-Cummings Publishing, 2008).
Google Scholar 
56.Hone, J. Wildlife Damage Control (CSIRO Publishing, 2007).Book 

Google Scholar 
57.Fox, G. A., Negrete-Yankelevich, S. & Sosa, V. J. Ecological Statistics: Contemporary Theory and Application (Oxford University Press, 2015).MATH 
Book 

Google Scholar 
58.Kershaw, K. A. Quantitative and Dynamic Ecology (Edward Arnold Publishers, 1969).
Google Scholar 
59.Li, J. C. R. Introduction to Statistical Inference (Edwards Bos Distributors, 1957).Book 

Google Scholar 
60.Quinn, G. P. & Keough, M. J. Experimental Design and Data Analysis for Biologists (Cambridge University Press, 2002).Book 

Google Scholar 
61.Shadish, W. R., Cook, T. D. & Campbell, D. T. Experimental and Quasi-experimental Designs for Generalized Casual Inference 2nd edn. (Houghton, Mifflin and Company, 2002).
Google Scholar 
62.Underwood, A. J. Experiments in Ecology (Cambridge University Press, 1997).
Google Scholar 
63.Allen, B. L., Allen, L. R., Engeman, R. M. & Leung, L.K.-P. Intraguild relationships between sympatric predators exposed to lethal control: Predator manipulation experiments. Front. Zool. 10, 39 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
64.Allen, B. L., Allen, L. R., Engeman, R. M. & Leung, L.K.-P. Sympatric prey responses to lethal top-predator control: Predator manipulation experiments. Front. Zool. 11, 56 (2014).Article 

Google Scholar 
65.Eldridge, S. R., Shakeshaft, B. J. & Nano, T. J. The impact of wild dog control on cattle, native and introduced herbivores and introduced predators in central Australia. Final report to the Bureau of Rural Sciences. Alice Springs: Parks and Wildlife Commission of the Northern Territory; 2002.66.Kennedy, M., Phillips, B., Legge, S., Murphy, S. & Faulkner, R. Do dingoes suppress the activity of feral cats in northern Australia?. Austral Ecol. 37(1), 134–139 (2012).Article 

Google Scholar 
67.Allen, B. L., Allen, L. R., Engeman, R. M. & Leung, L. K.-P. Reply to the criticism by Johnson et al. (2014) on the report by Allen et al. (2013). Front. Zool. 2014. http://www.frontiersinzoology.com/content/11/1/7/comments#1982699. Accessed 1st June 2014.68.Newsome, T. M. et al. Resolving the value of the dingo in ecological restoration. Restor. Ecol. 23(3), 201–208. https://doi.org/10.1111/rec.12186 (2015).Article 

Google Scholar 
69.Glen, A. S., Dickman, C. R., Soulé, M. E. & Mackey, B. G. Evaluating the role of the dingo as a trophic regulator in Australian ecosystems. Austral Ecol. 32(5), 492–501 (2007).Article 

Google Scholar 
70.Mitchell, B. & Balogh, S. Monitoring techniques for vertebrate pests: wild dogs. Orange: NSW Department of Primary Industries, Bureau of Rural Sciences; 2007.71.Letnic, M. & Koch, F. Are dingoes a trophic regulator in arid Australia? A comparison of mammal communities on either side of the dingo fence. Austral Ecol. 35(2), 267–175 (2010).Article 

Google Scholar 
72.Contos, P. & Letnic, M. Top-down effects of a large mammalian carnivore in arid Australia extend to epigeic arthropod assemblages. J. Arid Environ. (in press). https://doi.org/10.1016/j.jaridenv.2019.03.002.73.Mills, C. H., Wijas, B., Gordon, C. E., Lyons, M., Feit, A., Wilkinson, A., et al. Two alternate states: Shrub, bird and mammal assemblages differ on either side of the Dingo Barrier Fence. Aust Zool. (in press). https://doi.org/10.7882/az.2021.005.74.Engeman, R. M., Allen, L. R. & Allen, B. L. Study design concepts for inferring functional roles of mammalian top predators. Food Webs. 12, 56–63 (2017).Article 

Google Scholar 
75.Kennedy, M. S., Kreplins, T. L., O’Leary, R. A. & Fleming, P. A. Responses of dingo (Canis familiaris) populations to landscape-scale baiting. Food Webs. (in press). https://doi.org/10.1016/j.fooweb.2021.e00195.76.Allen, L. R. Is landscape-scale wild dog control best practice?. Australas. J. Environ. Manag. 24(1), 5–15 (2017).Article 

Google Scholar 
77.Ballard, G., Fleming, P. J. S., Meek, P. D. & Doak, S. Aerial baiting and wild dog mortality in south-eastern Australia. Wildl. Res. 47(2), 99–105. https://doi.org/10.1071/WR18188 (2020).Article 

Google Scholar 
78.Smith, D. & Allen, B. L. Habitat use by yellow-footed rock-wallabies in predator exclusion fences. J. Arid Environ. (in press).79.Smith, D., King, R. & Allen, B. L. Impacts of exclusion fencing on target and non-target fauna: A global review. Biol. Rev. 95(6), 1590–1606 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
80.Smith, D., Waddell, K. & Allen, B. L. Expansion of vertebrate pest exclusion fencing and its potential benefits for threatened fauna recovery in Australia. Animals 10, 1550 (2020).PubMed Central 
Article 
PubMed 

Google Scholar 
81.Clark, P., Clark, E. & Allen, B. L. Sheep, dingoes and kangaroos: New challenges and a change of direction 20 years on. In Advances in Conservation Through Sustainable Use of Wildlife (eds Baxter, G. et al.) 173–178 (University of Queensland, 2018).
Google Scholar 
82.Allen, L. R. The Impact of Wild Dog Predation and Wild Dog Control on Beef Cattle: Large-Scale Manipulative Experiments Examining the Impact of and Response to Lethal Control (LAP Lambert Academic Publishing, 2013).
Google Scholar 
83.Allen, L. R. Demographic and functional responses of wild dogs to poison baiting. Ecol. Manag. Restor. 16(1), 58–66 (2015).Article 

Google Scholar 
84.Eldridge, S. R., Bird, P. L., Brook, A., Campbell, G., Miller, H. A., Read, J. L., et al. The effect of wild dog control on cattle production and biodiversity in the South Australian arid zone: Final report. Port Augusta, South Australia: South Australian Arid Lands Natural Resources Management Board; 2016.85.Fancourt, B. A., Cremasco, P., Wilson, C. & Gentle, M. N. Do introduced apex predators suppress introduced mesopredators? A multiscale spatiotemporal study of dingoes and feral cats in Australia suggests not. J. Appl. Ecol. 56(12), 2584–2595. https://doi.org/10.1111/1365-2664.13514 (2019).Article 

Google Scholar 
86.Allen, B. L., Engeman, R. M. & Allen, L. R. Wild dogma I: An examination of recent “evidence” for dingo regulation of invasive mesopredator release in Australia. Curr. Zool. 57(5), 568–583 (2011).Article 

Google Scholar 
87.Allen, L. R. & Engeman, R. M. Evaluating and validating abundance monitoring methods in the absence of populations of known size: Review and application to a passive tracking index. Environ. Sci. Pollut. Res. 22, 2907–2915. https://doi.org/10.1007/s11356-014-3567-3 (2014).Article 

Google Scholar 
88.Caughley, G. Analysis of Vertebrate Populations, reprinted with corrections. (Wiley, 1980).
Google Scholar 
89.Wysong, M. L. et al. Space use and habitat selection of an invasive mesopredator and sympatric, native apex predator. Mov. Ecol. 8(1), 18. https://doi.org/10.1186/s40462-020-00203-z (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
90.Ritchie, E. G. et al. Ecosystem restoration with teeth: What role for predators?. Trends Ecol. Evol. 27(5), 265–271 (2012).PubMed 
Article 

Google Scholar 
91.Letnic, M. Stop poisoning dingoes to protect native animals. University of New South Wales, Sydney, available at http://newsroom.unsw.edu.au/news/science/stop-poisoning-dingoes-protect-native-mammals. Accessed 1 April 2014: UNSW Newsroom; 2014.92.Ritchie, E. G. The world’s top predators are in decline, and it’s hurting us too. The Conversation. 2014. http://theconversation.com/the-worlds-top-predators-are-in-decline-and-its-hurting-us-too-21830. Accessed 10 January 2014.93.Brown, J. S., Laundre, J. W. & Gurung, M. The ecology of fear: Optimal foraging, game theory, and trophic interactions. J. Mammal. 80, 385–399 (1999).Article 

Google Scholar 
94.Laundré, J. W. et al. The landscape of fear: The missing link to understand top-down and bottom-up controls of prey abundance?. Ecology 95(5), 1141–1152. https://doi.org/10.1890/13-1083.1 (2014).Article 
PubMed 

Google Scholar 
95.Haswell, P. M., Jones, K. A., Kusak, J. & Hayward, M. W. Fear, foraging and olfaction: How mesopredators avoid costly interactions with apex predators. Oecologia https://doi.org/10.1007/s00442-018-4133-3 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
96.Colman, N. J., Gordon, C. E., Crowther, M. S. & Letnic, M. Lethal control of an apex predator has unintended cascading effects on forest mammal assemblages. Proc. R. Soc. B Biol. Sci. 281(1782), 20133094. https://doi.org/10.1098/rspb.2013.3094 (2014).CAS 
Article 

Google Scholar 
97.Sheriff, M. J., Peacor, S., Hawlena, D. & Thaker, M. Non-consumptive predator effects on prey population size: A dearth of evidence. J. Anim. Ecol. 89, 1302–1316. https://doi.org/10.1111/1365-2656.13213 (2020).Article 
PubMed 

Google Scholar 
98.Fleming, P. J. S. et al. Roles for the Canidae in food webs reviewed: Where do they fit?. Food Webs. 12(Supplement C), 14–34. https://doi.org/10.1016/j.fooweb.2017.03.001 (2017).Article 

Google Scholar 
99.Wang, Y. & Fisher, D. Dingoes affect activity of feral cats, but do not exclude them from the habitat of an endangered macropod. Wildl. Res. 39, 611–620 (2012).Article 

Google Scholar 
100.Hayward, M. W. et al. Ecologists need robust survey designs, sampling and analytical methods. J. Appl. Ecol. 52(2), 286–290. https://doi.org/10.1111/1365-2664.12408 (2015).Article 

Google Scholar 
101.Johnson, C. N. & Ritchie, E. The dingo and biodiversity conservation: response to Fleming et al. (2012). Aust. Mammal. 35(1), 8–14 (2013).Article 

Google Scholar 
102.Wallach, A. D. & O’Neill, A. J. Threatened species indicate hot-spots of top-down regulation. Anim. Biodivers. Conserv. 32(2), 127–133 (2009).
Google Scholar 
103.Feit, B., Feit, A. & Letnic, M. Apex predators decouple population dynamics between mesopredators and their prey. Ecosystems. (in press). https://doi.org/10.1007/s10021-019-00360-2.104.Gordon, C. E., Moore, B. D. & Letnic, M. Temporal and spatial trends in the abundances of an apex predator, introduced mesopredator and ground-nesting bird are consistent with the mesopredator release hypothesis. Biodivers. Conserv. https://doi.org/10.1007/s10531-017-1309-9 (2017).Article 

Google Scholar 
105.Letnic, M. et al. Does a top predator suppress the abundance of an invasive mesopredator at a continental scale?. Glob. Ecol. Biogeogr. 20(2), 343–353 (2011).Article 

Google Scholar 
106.Rees, J. D., Kingsford, R. T. & Letnic, M. Changes in desert avifauna associated with the functional extinction of a terrestrial top predator. Ecography 42(1), 67–76. https://doi.org/10.1111/ecog.03661 (2019).Article 

Google Scholar 
107.Allen, B. L. et al. Large carnivore science: Non-experimental studies are useful, but experiments are better. Food Webs 13, 49–50 (2017).Article 

Google Scholar 
108.Allen, B. L., Engeman, R. M. & Allen, L. R. Wild dogma II: The role and implications of wild dogma for wild dog management in Australia. Curr. Zool. 57(6), 737–740 (2011).Article 

Google Scholar 
109.Fleming, P. J. S., Allen, B. L. & Ballard, G. Cautionary considerations for positive dingo management: A response to the Johnson and Ritchie critique of Fleming et al. (2012). Aust Mammal. 35(1), 15–22 (2013).Article 

Google Scholar 
110.Allen, B. L. Did dingo control cause the elimination of kowaris through mesopredator release effects? A response to Wallach and O’Neill (2009). Anim. Biodivers. Conserv. 33(2), 1–4 (2010).
Google Scholar 
111.Woinarski, J. C. Z. et al. Reading the black book: The number, timing, distribution and causes of listed extinctions in Australia. Biol. Conserv. 239, 108261. https://doi.org/10.1016/j.biocon.2019.108261 (2019).Article 

Google Scholar 
112.Kearney, S. G., Cawardine, J., Reside, A. E., Fisher, D., Maron, M., Doherty, T. S., et al. The threats to Australia’s imperilled species and implications for a national conservation response. Pac. Conserv. Biol. (in press). https://doi.org/10.1071/PC18024.113.Burbidge, A. A. & McKenzie, N. L. Patterns in the modern decline of Western Australia’s vertebrate fauna: Causes and conservation implications. Biol. Conserv. 50, 143–198 (1989).Article 

Google Scholar 
114.Lunney, D. Causes of the extinction of native mammals of the western division of New South Wales: An ecological interpretation of the nineteenth century historical record. Rangel. J. 23(1), 44–70 (2001).Article 

Google Scholar 
115.Cremona, T., Crowther, M. S. & Webb, J. K. High mortality and small population size prevents population recovery of a reintroduced mesopredator. Anim. Conserv. 20, 555–563. https://doi.org/10.1111/acv.12358 (2017).Article 

Google Scholar 
116.Bannister, H. L., Lynch, C. E. & Moseby, K. E. Predator swamping and supplementary feeding do not improve reintroduction success for a threatened Australian mammal, Bettongia lesueur. Aust. Mammal. 38, 177–187 (2016).Article 

Google Scholar 
117.Mori, E. et al. Spatiotemporal mechanisms of coexistence in an European mammal community in a protected area of southern Italy. J. Zool. 310(3), 232–245. https://doi.org/10.1111/jzo.12743 (2020).Article 

Google Scholar 
118.Saggiomo, L. Mesopredator Release and Competitive Exclusion: A Global Review and Potential for European Carnivores [Masters] (Alma Mater Studiorum University, 2014).
Google Scholar 
119.Gigliotti, L. C. et al. Context dependency of top-down, bottom-up and density-dependent influences on cheetah demography. J. Anim. Ecol. 89, 449–459. https://doi.org/10.1111/1365-2656.13099 (2020).Article 
PubMed 

Google Scholar 
120.Cozzi, G. et al. Fear of the dark or dinner by moonlight? Reduced temporal partitioning among Africa’s large carnivores. Ecology 93(12), 2590–2599. https://doi.org/10.1890/12-0017.1 (2012).Article 
PubMed 

Google Scholar 
121.Rafiq, K. et al. Spatial and temporal overlaps between leopards (Panthera pardus) and their competitors in the African large predator guild. J. Zool. 311(4), 246–259. https://doi.org/10.1111/jzo.12781 (2020).Article 

Google Scholar 
122.Comley, J., Joubert, C. J., Mgqatsa, N. & Parker, D. M. Lions do not change rivers: Complex African savannas preclude top-down forcing by large predators. J. Nat. Conserv. 56, 125844 (2020).Article 

Google Scholar 
123.Allen, M. L., Peterson, B. & Krofel, M. No respect for apex carnivores: Distribution and activity patterns of honey badgers in the Serengeti. Mamm. Biol. 89, 90–94. https://doi.org/10.1016/j.mambio.2018.01.001 (2018).Article 

Google Scholar 
124.Vitekere, K. et al. Dynamic in species estimates of carnivores (leopard cat, red fox, and north Chinese leopard): A multi-year assessment of occupancy and coexistence in the Tieqiaoshan Nature Reserve, Shanxi Province, China. Animals 10(8), 1333. https://doi.org/10.3390/ani10081333 (2020).Article 
PubMed Central 
PubMed 

Google Scholar 
125.Brodie, J. F. & Giordano, A. Lack of trophic release with large mammal predators and prey in Borneo. Biol. Conserv. 63, 58–67. https://doi.org/10.1016/j.biocon.2013.01.003 (2013).Article 

Google Scholar 
126.Lahkar, D., Ahmed, M. F., Begum, R. H., Das, S. K. & Harihar, A. Inferring patterns of sympatry among large carnivores in Manas National Park—A prey-rich habitat influenced by anthropogenic disturbances. Anim. Conserv. (in press). https://doi.org/10.1111/acv.12662.127.Gehrt, S. D. & Prange, S. Interference competition between coyotes and raccoons: A test of the mesopredator release hypothesis. Behav. Ecol. 18(1), 204–214 (2007).Article 

Google Scholar 
128.Dias, D. M., Massara, R. L., de Campos, C. B. & Rodrigues, F. H. G. Feline predator–prey relationships in a semi-arid biome in Brazil. J. Zool. (in press). https://doi.org/10.1111/jzo.12647.129.Foster, V. C. et al. Jaguar and puma activity patterns and predator–prey interactions in four Brazilian biomes. Biotropica 45(3), 373–379. https://doi.org/10.1111/btp.12021 (2013).Article 

Google Scholar 
130.Allen, L. R. Best practice baiting: Dispersal and seasonal movement of wild dogs (Canis lupus familiaris). Technical highlights: Invasive plant and animal research 2008–09. Brisbane: QLD Department of Employment, Economic Development and Innovation; 2009. 61–62.131.Fleming, P., Corbett, L., Harden, R. & Thomson, P. Managing the impacts of dingoes and other wild dogs. Bomford M, editor. Canberra: Bureau of Rural Sciences; 2001.132.Thomas, L. et al. Distance software: Design and analysis of distance sampling surveys for estimating population size. J. Appl. Ecol. 47, 5–14 (2010).PubMed 
Article 

Google Scholar 
133.Ruette, S., Stahl, P. & Albaret, M. Applying distance-sampling methods to spotlight counts of red foxes. J. Appl. Ecol. 40, 32–43 (2003).Article 

Google Scholar 
134.Engeman, R. Indexing principles and a widely applicable paradigm for indexing animal populations. Wildl. Res. 32(3), 202–210 (2005).Article 

Google Scholar 
135.R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing; 2020. More

Biodiversity assessment through DNA metabarcodingOur analysis detected 160 Operational Taxonomic Units (OTUs) with 12,007,712 sequenced reads, 222,370 ± 41,954 (sd) reads per sample, for a total of 54 sequenced samples. The rarefaction curves showed good sequencing effort for the samples (Supplementary Figure S1) which were rarefied to the least count among samples corresponding to 135,443 reads. Twenty OTUs, (7.2% of the total), were assigned to taxa not relevant to our work (mainly to mosses and ferns during the periods October 2014–March 2015 and July–October 2015). From the remaining OTUs, 108 (88% of the reads) were taxonomically assigned to 32 families of vascular plants (68 identified taxa) (Table 2, Supplementary Table S1) and 32 OTUs (4.8% of the reads) remained unidentified either because of low sequence identity and/or query coverage percentage or the absence of any sequence classification result, even when compared to the complete ‘Nucleotide’ Genbank database. The results of the taxonomic assignment to vascular plants are presented in Supplementary Table S1. The OTU sequences were assigned to plant taxa with at least 95% identity and coverage, from which 70% of the OTUs had ≥ 98% sequence identity with the assigned taxa. The positive control of the DNA extraction, Corylus avellana pollen, was correctly identified after HTS. From the 19 negative controls included in the extraction plate, one negative control was selected for sequencing, the only one with sufficient amplicon concentration (2 ng μl−1). In this sample two OTUs were detected (263,649 reads), both assigned to Quercus spp. and contributing  More

1.Namita, P., Mukesh, R. & Vijay, K. J. Camellia Sinensis (Green Tea): A review. Glob. J. Pharmacol. 6(2), 52–59 (2012).
Google Scholar 
2.Chang, K. World Tea Production and Trade. Current and Future Development (FAO, Rome, 2015).
Google Scholar 
3.Chang, K. & Brattlof, M. World Tea Production and Trade. Current and Future Development (FAO, 2015).
Google Scholar 
4.Kochlamazashvili, I. & Kakulia, N. The Georgian Tea Sector: A Value Chain Study. ISET Policy Institute. Study prepared in the framework of ENPARD project Cooperation for Rural Prosperity in Georgia (2015).5.Lesica, P., McCune, B., Cooper, S. V. & Hong, W. S. Differences in lichen and bryophyte communities between old-growth and managed second-growth forests in the Svan Valley Montana. Can. J. Bot. 69, 1745–1755 (1991).Article 

Google Scholar 
6.Nowak, A., Plášek, V., Nobis, M. & Nowak, S. Epiphytic communities of open habitats in the Western Tian-Shan Mts (Middle Asia: Kyrgyzstan). Cryptog. Bryol. 37(4), 415–433 (2016).Article 

Google Scholar 
7.Rhoades, F. M. Nonvascular epiphytes in forest canopies: Worldwide distribution, abundance and ecological roles. In Forest Canopies (eds. Lowman, M.D. & Nadkarni, N. M.) 353–408 (1995).8.Haines, W. P. & Renwick, J. A. A. Bryophytes as food: Comparative consumption and utilization of mosses by a generalist insect herbivore. Entomol Exp Appl. 133, 296–306. https://doi.org/10.1111/j.1570-7458.2009.00929.x (2009).Article 

Google Scholar 
9.Kuřavová, K. et al. Is feeding on mosses by groundhoppers in the genus Tetrix (Insecta: Orthoptera) opportunistic or selective?. Arthropod-Plant Int. 11, 35–43. https://doi.org/10.1007/s11829-016-9461-9 (2017).Article 

Google Scholar 
10.Matuszkiewicz, W. Przewodnik do Oznaczania Zbiorowisk Roślinnych Polski (Wyd Nauk, PWN, 2001).
Google Scholar 
11.Krestov, P. V. Forest vegetation of easternmost Russia (Russian Far East). In Forest Vegetation of Northeast Asia (eds Kolbek, J. et al.) 93–180 (Springer, 2003).Chapter 

Google Scholar 
12.Kuznetsov, O. Topology-ecological classification of mire vegetation in the Republic of Karelia (Russia). In Biodiversity and Conservation of Boreal Nature. Proceedings of the 10 years anniversary symposium of the Nature Reserve Friendship (eds Heikkilä, R. & Lindholm, T.) 117–123 (Elsevier, 2003).
Google Scholar 
13.Černý, T. Phytosociological Study of Selected Critical Thermophilous Vegetation Complexes in the Czech Republic. A thesis submitted for the degree of Doctor of Philosophy in the Department of Botany Faculty of Sciences, Charles University (2007).14.Chytrý, M. et al. A modern analogue of the Pleistocene steppe-tundra ecosystem in southern Siberia. Boreas 48, 36–56 (2019).Article 

Google Scholar 
15.Wolski, G. J. & Kruk, A. Determination of plant communities based on bryophytes: The combined use of Kohonen artificial neural network and indicator species analysis. Ecol. Indic 113, 106160. https://doi.org/10.1016/j.ecolind.2020.106160 (2020).Article 

Google Scholar 
16.Benzing, D. Vulnerabilities of tropical forests to climate change: The significance of resident epiphytes. Clim. Change 39, 519–540 (1998).Article 

Google Scholar 
17.Gustafsson, L., Fiskesjö, A., Ingelög, T., Petterson, B. & Thor, G. Factors of importance to some lichen species of deciduous broad-leaved woods in southern Sweden. Lichenologist 24, 255–266 (1992).Article 

Google Scholar 
18.Frahm, J. P. Ecology of bryophytes along altitudinal and latitudinal gradients in Chile. Trop. Bryol. 21, 67–79 (2002).
Google Scholar 
19.Číhal, L., Kaláb, O. & Plášek, V. Modeling the distribution of rare and interesting moss species of the family Orthotrichaceae (Bryophyta) in Tajikistan and Kyrgyzstan. Acta Soc. Bot. Pol. 86(2), 3543. https://doi.org/10.5586/asbp.3543 (2017).Article 

Google Scholar 
20.Łubek, A., Kukwa, M., Czortek, P. & Jaroszewicz, B. Impact of Fraxinus excelsior dieback on biota of ash-associated lichen epiphytes at the landscape and community level. Biodivers. Conserv. 29, 431–450. https://doi.org/10.1007/s10531-019-01890-w (2020).Article 

Google Scholar 
21.Łubek, A., Kukwa, M., Jaroszewicz, B. & Czortek, P. Identifying mechanisms shaping lichen functional diversity in a primeval forest. For. Ecol. Manag. 475, 118434. https://doi.org/10.1016/j.foreco.2020.118434 (2020).Article 

Google Scholar 
22.Barkman, J. J. Phytosociology and Ecology of Cryptogamic Epiphytes. Including a Taxonomic Survey and Description of Their Vegetation Units in Europe, Van Gorcum, Comp (N. V Assen, 1958).
Google Scholar 
23.Green, T. G. A. & Lange, O. L. Photosynthesis in poikilohydric plants: A comparison of lichens and bryophytes. In Ecophysiology of Photosynthesis (eds Schulze, E.-D. & Caldwell, M. M.) 319–341 (Springer-Verlag, 1995).Chapter 

Google Scholar 
24.Scheidegger, C., Wolseley, P. A. & Landolt, R. Towards conservation of lichens. Forest. Snow Landsc. Res. 75, 285–433 (2000).
Google Scholar 
25.Tønsberg, T. & Høiland, K. A study of the macrolichen flora on the sand-dune areas on Lista, SW Norway. Nor. J. Bot. 27, 131–134 (1980).
Google Scholar 
26.Thiet, R. K., Doshas, A. & Smith, S. M. Effects of biocrusts and lichen-moss mats on plant productivity in a US sand dune ecosystem. Plant Soil 377(1), 235–244 (2014).CAS 
Article 

Google Scholar 
27.Vaz, A. S., Marques, J. & Honrado, J. P. Patterns of lichen diversity in coastal sand-dunes of northern Portugal. Bot. Complut. 38, 89–96 (2014).Article 

Google Scholar 
28.Antoninka, A., Bowker, M. A., Reed, S. C. & Doherty, K. Production of greenhouse-grown biocrust mosses and associated cyanobacteria to rehabilitate dryland soil function. Restor. Ecol. 24(3), 324–335 (2016).Article 

Google Scholar 
29.Jüriado, I., Kämärä, M.-L. & Oja, E. Environmental factors and ground disturbance affecting the composition of species and functional traits of ground layer lichens on grey dunes and dune heaths of Estonia. Nord. J. Bot. 34(2), 244–255 (2016).Article 

Google Scholar 
30.Balogh, R. et al. Mosses and lichens in dynamics of acidic sandy grasslands: Specific response to grazing exclosure. Acta Biol. Plant. Agriensis 5(1), 30 (2017).
Google Scholar 
31.Concostrina-Zubiri, L., Arenas, J. M., Martínez, I. & Escudero, A. Unassisted establishment of biological soil crusts on dryland road slopes. Web Ecol. 19(1), 39–51 (2019).Article 

Google Scholar 
32.Kubiak, D. & Oszyczka, P. Non-forested vs forest environments: The effect of habitat conditionson host tree parameters and the occurrence of associated epiphytic lichens. Fungal Ecol. 47, 100957 (2020).Article 

Google Scholar 
33.Gradstein, S. R. & Sporn, S. G. Land-use change and epiphytic bryophyte diversity in the Tropics. Nova Hedwigia 138, 311–323 (2010).
Google Scholar 
34.Guevara, S., Purata, S. E. & Van der Maarel, E. The role of remnant forest trees in tropical secondary succession. Vegetatio 66, 77–84 (1986).
Google Scholar 
35.Sillett, S. C., Gradstein, S. R. & Griffin, D. Bryophyte diversity of Ficus tree crowns from cloud forest and pasture in Costa Rica. Bryologist 98(2), 251–260 (1995).Article 

Google Scholar 
36.Werner, F., Homeier, J. & Gradstein, S. R. Diversity of vascular epiphytes on isolated remnant trees in the montane forest belt of southern Ecuador. Ecotropica 11, 21–40 (2005).
Google Scholar 
37.Lara, F., Garilleti, R. & Mazimpaka, V. Orthotrichum karoo (Orthotrichaceae), a new species with hyaline-awned leaves from southwestern Africa. Bryologist 112(1), 194–201 (2009).Article 

Google Scholar 
38.Lara, F. & Mazimpaka, V. Ma´s sobre la presencia de Orthotrichum acuminatum en la Península Ibérica. Cryptog. Bryol. Lichenol. 13(4), 349–354 (1992).
Google Scholar 
39.Garilleti, R., Lara, F. & Mazimpaka, V. Orthotrichum anodon (Orthotrichaceae, Bryopsida), a new species from California, and its relationships with other Orthotricha sharing puckered capsule mouths. Bryologist 109(2), 188–196 (2006).Article 

Google Scholar 
40.Hallingbäck, T. & Hodgetts, N. Mosses Liverworts and Hornworts. Status survey and conservation action plan for bryophytes (Cambridge University Press, 2000).
Google Scholar 
41.Belinchón, R., Martínez, I., Escudero, A., Aragón, G. & Valladares, F. Edge effects on epiphytic communities in a Mediterranean Quercus pyrenaica forest. J. Veg. Sci. 18, 81–90. https://doi.org/10.1111/j.1654-1103.2007.tb02518.x (2007).Article 

Google Scholar 
42.Boudreault, C., Gauthier, S. & Bergeron, Y. Epiphytic lichens and bryophytes on Populus Tremuloides along a chronosequence in the Southwestern Boreal Forest of Quebec, Canada. Bryologist 103, 725–738. https://doi.org/10.1639/0007-2745(2000)103[0725:ELABOP]2.0.CO;2 (2009).Article 

Google Scholar 
43.Rambo, T. Structure and composition of corticolous epiphyte communities in a Sierra Nevada old-growth mixed-conifer forest. Bryologist 113, 55–71. https://doi.org/10.1639/0007-2745-113.1.55 (2010).Article 

Google Scholar 
44.Plášek, V., Nowak, A., Nobis, M., Kusza, G. & Kochanowska, K. Effect of 30 years of road traffic abandonment on epiphytic moss diversity. Environ. Monit. Assess. 186, 8943–8959. https://doi.org/10.1007/s10661-014-4056-3 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
45.Skoupá, Z., Ochyra, R., Guo, S. L., Sulayman, M. & Plášek, V. Distributional novelties for Lewinskya, Nyholmiella and Orthotrichum (Orthotrichaceae) in China. Herzogia 30, 58–73. https://doi.org/10.13158/heia.30.1.2017.58 (2017).Article 

Google Scholar 
46.Skoupá, Z., Ochyra, R., Guo, S.-L., Sulayman, M. & Plášek, V. Three remarkable additions of Orthotrichum species (Orthotrichaceae) to the moss flora of China. Herzogia 31, 88–100. https://doi.org/10.13158/099.031.0105 (2018).Article 

Google Scholar 
47.Gradstein, R. et al. Bryophytes of Mount Patuha, West Java, Indonesia. Reinwardtia 13(2), 107–123 (2010).
Google Scholar 
48.Saat, A., Talib, M. S., Harun, N., Hamzah, Z. & Wood, A. K. Spatial variability of arsenic and heavy metals in a highland tea plantation using lichens and mosses as bio-monitors. Asian J. Nat. Appl. Sci. 5(1), 10–21 (2016).
Google Scholar 
49.Fick, S. E. & Hijmans, R. J. WorldClim 2: New 1km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37(12), 4302–4315 (2017).Article 

Google Scholar 
50.Wirth, V. Ökologische Zeigerwerte von Flechten. Herzogia 23(2), 229–248 (2010).Article 

Google Scholar 
51.Ellenberger, H. et al. Zeigerwerte von Planzen in Mitteleuropa. Scr. Geobot. 18, 1–248 (1991).
Google Scholar 
52.Smith, C. W. et al. The Lichens of Great Britain and Ireland 1046 (British Lichen Society, 2009).
Google Scholar 
53.Hodgetts, N. et al. An annotated checklist of bryophytes of Europe, Macaronesia and Cyprus. J. Bryol. 42(1), 1–116. https://doi.org/10.1080/03736687.2019.1694329 (2020).Article 

Google Scholar 
54.Pancho, J. V. Some bryophytes in tea plantations, Pagilaran Central Java. Biotrop. Bull. 11, 279–282 (1979).
Google Scholar 
55.Tan, B. C. et al. Mosses of Gunung Halimun National Park, West Java, Indonesia. Reinwardtia 12, 205–214 (2006).
Google Scholar 
56.Ohsawa, M. Weeds of tea plantations. In Biology and Ecology of Weeds. Geobotany Vol. 2 (eds Holzner, W. & Numata, M.) (Springer, 1982).
Google Scholar 
57.Gradstein, R. et al. Bryophytes of Mount Patuha, West Java, Indonesia. Reinwardtia 13, 107–123 (2010).
Google Scholar 
58.Whitelaw, M. & Burton, M. A. S. Diversity and distribution of epiphytic bryophytes on Bramley’s Seedling trees in East of England apple orchards. Glob. Ecol. Conserv. 4, 380–387. https://doi.org/10.1016/j.gecco.2015.07.014 (2015).Article 

Google Scholar 
59.Söderström, L. Bryophytes and decaying wood – a comparison between manager and natural forest. Holarc. Ecol. 14, 121–130 (1991).
Google Scholar 
60.Cieśliński, S. et al. Relikty lasu puszczańskiego, In Białowieski Park Narodowy (1921–1996) w badaniach geobotanicznych. Phytocoenosis, 8 (N.S.), Seminarium Geobotanicum (ed. Faliński, J. B.) 4, 47–64 (1996).61.Vanderpoorten, A., Engels, P. & Sotiaux, A. Trends in diversity and abundance of obligate epiphytic bryophytes in a highly managed landscape. Ecography 27, 567–576 (2004).Article 

Google Scholar 
62.Ódor, P., van Dort, K., Aude, E., Heilmann-Clausen, J. & Christensen, M. Diversity and composition of dead wood inhabiting bryophyte communities in European beech forest. Biol. Soc. Esp. Briol. 26–27, 85–102 (2005).
Google Scholar 
63.Friedel, A., Oheimb, G. V., Dengler, J. & Härdtle, W. Species diversity and species composition of epiphytic bryophytes and lichens: A comparison of managed and unmanaged beech forests in NE Germany. Feddes Repert. 117(1–2), 172–185 (2006).Article 

Google Scholar 
64.Wolski, G. J. Siedliskowe Uwarunkowania Występowania Mszaków w Rezerwatach Przyrody Chroniących Jodłę Pospolitą w Polsce Środkowej (Praca doktorska wykonana w Katedrze Geobotaniki i Ekologii Roślin UŁ, 2013).
Google Scholar 
65.Fudali, E. & Wolski, G. J. Ecological diversity of bryophytes on tree trunks in protected forests (a case study from Central Poland). Herzogia 28(1), 91–107 (2015).Article 

Google Scholar 
66.Shi, X.-M. et al. Epiphytic bryophytes as bio-indicators of atmospheric nitrogen deposition in a subtropical montane cloud forest: Response patterns, mechanism, and critical load. Environ. Pollut. 229, 932–941. https://doi.org/10.1016/j.envpol.2017.07.077 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
67.Cornelissen, J. H. C. & Gradstein, S. R. On the occurrence of bryophytes and macrolichens in different lowland rain forest types of Mabura Hill, Guyana. Trop. Bryol. 3, 29–35. https://doi.org/10.11646/bde.3.1.4 (1990).Article 

Google Scholar 
68.Lyons, B., Nadkarni, N. M. & North, M. P. Spatial distribution and succession of epiphytes on Tsuga heterophylla (western hemlock) in an old-growth Douglas-fir forest. Can. J. Bot. 78(7), 957–968. https://doi.org/10.1139/cjb-78-7-957 (2000).Article 

Google Scholar 
69.Cornelissen, J. H. C. & Steege, H. T. Distribution and ecology of epiphytic bryophytes and lichens in dry evergreen forest of Guyana. J. Trop. Ecol. 5, 131–150. https://doi.org/10.1017/S0266467400003400 (1989).Article 

Google Scholar 
70.Woods, C. L., Cardelús, C. L., Dewalt, S. J. & Piper, F. Microhabitat associations of vascular epiphytes in a wet tropical forest canopy. J. Ecol. 103(2), 421–430. https://doi.org/10.1111/1365-2745.12357 (2015).Article 

Google Scholar 
71.Sporn, S. G., Bos, M. M., Kessler, M. & Gradstein, S. R. Vertical distribution of epiphytic bryophytes in an Indonesian rainforest. Biodivers. Conserv. 19(3), 745–760. https://doi.org/10.1007/s10531-009-9731-2 (2010).Article 

Google Scholar 
72.Czerepko, J. et al. How sensitive are epiphytic and epixylic cryptogams as indicators of forest naturalness? Testing bryophyte and lichen predictive power in stands under different management regimes in the Białowieża forest. Ecol. Indic. 125, 107532. https://doi.org/10.1016/j.ecolind.2021.107532 (2021).Article 

Google Scholar 
73.Putna, S. & Mězaka, A. Preferences of epiphytic bryophytes for forest stand and substrate in North-East Latvia. Folia Cryptog. Estonica 51, 75–83 (2014).Article 

Google Scholar 
74.Manakyan, V. A. Results of bryological studies in Armenia. Arctoa 5, 15–33 (1995).Article 

Google Scholar 
75.Redfearn, P. L., Tan, B. C. & He, S. A newly updated and annotated checklist of Chines mosses. J. Hattori Bot. Lab. 79, 163–357 (1996).
Google Scholar 
76.Kürschner, H. Bryophyte Flora of the Arabian Peninsula and Socotra. Bryophytorum Bibliotheca (JCramer in der Gebrüder Borntraeger Verlagsbuchhandlung, 2000).
Google Scholar 
77.Higuchi, M. & Nishimura, N. Mosses of Pakistan. J. Hattori Bot. Lab. 93, 273–291 (2003).
Google Scholar 
78.Ignatov, M. S., Afonina, O. M. & Ignatova, E. A. Check-list of mosses of East Europe and North Asia. Arctoa 15, 1–130. https://doi.org/10.15298/arctoa.15.01 (2006).Article 

Google Scholar 
79.Sabovljević, M. et al. Check-list of the mosses of SE Europe. Phytol. Balcan. 14(2), 207–244 (2008).
Google Scholar 
80.Dandotiya, D., Govindapyari, H., Suman, S. & Uniyal, P. L. Checklist of the bryophytes of India. Arch. Bryol. 88, 71–72 (2011).
Google Scholar 
81.Hodgetts, N. G. Checklist and Country Status of European bryophytes—Towards a New Red List for Europe. Irish Wildlife Manuals, No. 84. (National Parks and Wildlife Service, Department of Arts, Heritage and the Gaeltacht, 2011). https://www.hdl.handle.net/2262/73373.82.Kürschner, H. & Frey, W. Liverworts, Mosses and Hornworts of Southwest Asia (Marchantiophyta, Bryophyta, Anthoceroptophyta). Nova Hedwigia 139, 179–180 (2011).
Google Scholar 
83.Suzuki, T. A revised new catalog of the mosses of Japan. Hattoria 7, 9–223. https://doi.org/10.18968/hattoria.7.0_9 (2016).Article 

Google Scholar 
84.Kürschner, H. & Frey, W. Liverworts, mosses and hornworts of Afghanistan—our present knowledge. Acta Mus. Siles. Sci. Natur. 68, 11–24 (2019).
Google Scholar 
85.Brotherus, V. F. Enumeratio muscorum Caucasi. Acta Soc. Sci. Fenn. 19, 1–170 (1892).
Google Scholar 
86.Chikovani, N. & Svanidze, T. Checklist of bryophyte species of Georgia. Braun-Blanquetia 34, 97–116. https://doi.org/10.13158/heia.26.1.2013.213 (2004).Article 

Google Scholar 
87.Doroshina, G. Y. New moss records from Georgia. 1. Arctoa 19, 281 (2010).
Google Scholar 
88.Sohrabi, M., Ahti, T. & Urbanavichus, G. Parmelioid lichens of Iran and the caucasus Region. Mycol. Balc. 4, 21–30 (2007).
Google Scholar 
89.Hawksworth, D. L., Blanco, O., Divakar, P. K., Ahti, T. & Crespo, A. A first checklist of parmelioid and similar lichens in Europe and some adjacent territories, adopting revised generic circumscriptions and with indications of species distributions. Lichenologist 40(1), 1–21. https://doi.org/10.1017/S0024282908007329 (2008).Article 

Google Scholar 
90.Syrek, M. & Kukwa, M. Taxonomy of the lichen Cladonia rei and its status in Poland. Biologia 63(4), 493–497. https://doi.org/10.2478/s11756-008-0092-1 (2008).Article 

Google Scholar 
91.Burgaz, A. R., Ahti, T., Inashvili, T., Batsatsashvili, K. & Kupradze, I. Study of georgian Cladoniaceae. Bot. Complut. 42, 19–55. https://doi.org/10.5209/BOCM.61367 (2018).Article 

Google Scholar 
92.Fałtynowicz, W. The lichens, lichenicolous and allied fungi of Poland. An annotated checklist. In Biodiversity of Poland (ed. Mirek, A.) 1–435 (W. Szafer Institute of Botany, Polish Academy of Sciences, 2003).
Google Scholar 
93.Plášek, V., Sawicki, J., Ochyra, R., Szczecińska, M. & Kulik, T. New taxonomical arrangement of the traditionally conceived genera Orthotrichum and Ulota (Orthotrichaceae, Bryophyta). Acta Mus. Sil. 64, 169–174. https://doi.org/10.1515/cszma-2015-0024 (2015).Article 

Google Scholar 
94.Lara, F. et al. Lewinskya, a new genus to accommodate the phaneroporous and monoicous taxa of Orthotrichum (Bryophyta, Orthotrichaceae). Cryptog. Bryol. 37, 361–382. https://doi.org/10.7872/cryb/v37.iss4.2016.361 (2016).Article 

Google Scholar 
95.Sawicki, J. et al. Mitogenomic analyses support the recent division of the genus Orthotrichum (Orthotrichaceae, Bryophyta). Sci. Rep. 7, 4408. https://doi.org/10.1038/s41598-017-04833-z (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
96.Kürschner, H., Batsatsashvili, K. & Parolly, G. Noteworthy additions to the bryophyte flora of Georgia. Herzogia 26, 213–216. https://doi.org/10.13158/heia.26.1.2013.213 (2013).Article 

Google Scholar 
97.Ellis, L. T. et al. New national and regional bryophyte records, 49. J. Bryol. 38(4), 327–347 (2016).Article 

Google Scholar 
98.Ellis, L. T. et al. New national and regional bryophyte records, 51. J. Bryol. 39(2), 177–190 (2017).Article 

Google Scholar 
99.Eckstein, J., Garilleti, R. & Lara, F. Lewinskya transcaucasica (Orthotrichaceae, Bryopsida) sp. nov. A contribution to the bryophyte flora of Georgia. J. Bryol. 40(1), 31–38. https://doi.org/10.1080/03736687.2017.1365218 (2018).Article 

Google Scholar 
100.Eckstein, J. & Zündorf, H.-J. Orthotrichaceous mosses (Orthotricheae, Orthotrichaceae) of the Genera Lewinskya, Nyholmiella, Orthotrichum, Pulvigera and Ulota Contributions to the bryophyte flora of Georgia 1. Cryptog. Bryol. 38(4), 365–382. https://doi.org/10.7872/cryb/v38.iss4.2017.365 (2017).Article 

Google Scholar 
101.Schäfer-Verwimp, A. Orthotrichum Hedw. In Die Moose Baden-Württembergs. Band 2: Spezieller Teil (Bryophytina II, Schistostegales bis Hypnobryales) (eds Nebel, M. & Philippi, G.) 170–197 (Eugen Ulmer, 2001).
Google Scholar 
102.Lara, F. & Garilleti, R. Orthotrichum Hedw. In Flora briofítica Ibérica (eds Guerra, J. & Brugués, C. M.) 50–135 (Universidad de Murcia Sociedad Española de Briología, 2014).
Google Scholar 
103.Lewinsky, J. The genus Orthotrichum Hedw. (Orthotrichaceae, Musci) in Southeast Asia. A taxonomic revision. J. Hattori Bot. Lab. 72, 1–88 (1992).
Google Scholar 
104.Schäfer-Verwimp, A. & Gruber, J. P. Orthotrichum (Orthotrichaceae, Bryopsida) in Pakistan. Trop. Bryol. 21, 1–9. https://doi.org/10.11646/bde.21.1.2 (2002).Article 

Google Scholar 
105.Draper, I., Mazimpaka, V., Albertos, B., Garilleti, R. & Lara, F. A survey of the epiphytic bryophyte flora of the Rif and Tazzeka Mountains (northern Morocco). J. Bryol. 27, 23–34. https://doi.org/10.1179/174328205X40554 (2005).Article 

Google Scholar 
106.Brassard, G. R. Orthotrichum stramineum new to North America. Bryologist 87, 168 (1984).Article 

Google Scholar 
107.Lewinsky-Haapasaari, J. & Long, D. G. Orthotrichum stramineum Hornsch. new to China. J. Bryol. 19, 350–352. https://doi.org/10.1179/jbr.1996.19.2.350 (1996).Article 

Google Scholar 
108.Plášek, V. et al. A synopsis of Orthotrichum s. lato (Bryophyta, Orthotrichaceae) in China, with distribution maps and a key to determination. Plants 10, 499. https://doi.org/10.3390/plants10030499 (2021).Article 
PubMed 
PubMed Central 

Google Scholar  More

1.Stein, A., Gerstner, K. & Kreft, H. Environmental heterogeneity as a universal driver of species richness across taxa, biomes and spatial scales. Ecol. Lett. 17, 866–880 (2014).PubMed 
Article 

Google Scholar 
2.Tews, J. et al. Animal species diversity driven by habitat heterogeneity/diversity: The importance of keystone structures—Animal species diversity driven by habitat heterogeneity. J. Biogeogr. 31, 79–92 (2004).Article 

Google Scholar 
3.Graham, N. A. J. & Nash, K. L. The importance of structural complexity in coral reef ecosystems. Coral Reefs 32, 315–326 (2013).ADS 
Article 

Google Scholar 
4.Reimchen, T. E. Substratum heterogeneity, crypsis, and colour polymorphism in an intertidal snail (Littorina mariae). Can. J. Zool. 57, 1070–1085 (1979).Article 

Google Scholar 
5.Petren, K. & Case, T. J. Habitat structure determines competition intensity and invasion success in gecko lizards. Proc. Natl. Acad. Sci. 95, 11739–11744 (1998).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
6.Gratwicke, B. & Speight, M. R. The relationship between fish species richness, abundance and habitat complexity in a range of shallow tropical marine habitats. J. Fish Biol. 66, 650–667 (2005).Article 

Google Scholar 
7.Williams, S. E., Marsh, H. & Winter, J. Spatial scale, species diversity, and habitat structure: Small mammals in Australian tropical rain forest. Ecology 83, 1317–1329 (2002).Article 

Google Scholar 
8.Renoult, J. P., Kelber, A. & Schaefer, H. M. Colour spaces in ecology and evolutionary biology. Biol. Rev. 92, 292–315 (2017).PubMed 
Article 

Google Scholar 
9.Cuthill, I. C. et al. The biology of color. Science 357, eaan0221 (2017).PubMed 
Article 
CAS 

Google Scholar 
10.Guilford, T. & Dawkins, M. S. Receiver psychology and the evolution of animal signals. Anim. Behav. 42, 1–14 (1991).Article 

Google Scholar 
11.Crook, A. C. Colour patterns in a coral reef fish is background complexity important?. J. Exp. Mar. Biol. Ecol. 217, 237–252 (1997).Article 

Google Scholar 
12.Marshall, J. Communication and camouflage with the same ‘bright’ colours in reef fishes. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 355, 1243–1248 (2000).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
13.Seehausen, O. et al. Speciation through sensory drive in cichlid fish. Nature 455, 620–626 (2008).ADS 
CAS 
PubMed 
Article 

Google Scholar 
14.Wilkins, L., Marshall, N. J., Johnsen, S. & Osorio, D. Modelling colour constancy in fish: Implications for vision and signalling in water. J. Exp. Biol. 219, 1884–1892 (2016).PubMed 

Google Scholar 
15.Osorio, D. & Vorobyev, M. A review of the evolution of animal colour vision and visual communication signals. Vis. Res. 48, 2042–2051 (2008).CAS 
PubMed 
Article 

Google Scholar 
16.Caley, J. & St John, J. Refuge availability structures assemblages of tropical reef fishes. J. Anim. Ecol. 45, 414–428 (1996).Article 

Google Scholar 
17.Connolly, S. R., Hughes, T. P., Bellwood, D. R. & Karlson, R. H. Community structure of corals and reef fishes at multiple scales. Science 309, 1363–1365 (2005).ADS 
CAS 
PubMed 
Article 

Google Scholar 
18.Allen, G. R. & Steene, R. Indo-Pacific Coral Reef Field Guide (Tropical Reef Research, 1994).
Google Scholar 
19.Bellwood, D. R. Regional-scale assembly rules and biodiversity of coral reefs. Science 292, 1532–1535 (2001).ADS 
CAS 
PubMed 
Article 

Google Scholar 
20.Humann, P., DeLoach, N., Allen, G. & Steene, G. Reef Fish Identification: Tropical Pacific (New World Publications, 2015).
Google Scholar 
21.Barneche, D. R. et al. Body size, reef area and temperature predict global reef-fish species richness across spatial scales. Glob. Ecol. Biogeogr. 28, 315–327 (2019).Article 

Google Scholar 
22.Brandl, S. J., Goatley, C. H. R., Bellwood, D. R. & Tornabene, L. The hidden half: ecology and evolution of cryptobenthic fishes on coral reefs: Cryptobenthic reef fishes. Biol. Rev. 93, 1846–1873 (2018).PubMed 
Article 

Google Scholar 
23.Carr, M. H., Anderson, T. W. & Hixon, M. A. Biodiversity, population regulation, and the stability of coral-reef fish communities. Proc. Natl. Acad. Sci. 99, 11241–11245 (2002).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
24.Hixon, M. A. 60 years of coral reef fish ecology: Past, present, future. Bull. Mar. Sci. 87, 727–765 (2011).Article 

Google Scholar 
25.Stuart-Smith, R. D. et al. Integrating abundance and functional traits reveals new global hotspots of fish diversity. Nature 501, 539–542 (2013).ADS 
CAS 
PubMed 
Article 

Google Scholar 
26.Froese, R. & Pauly, D. FishBase. World Wide Web electronic publication. http://www.fishbase.org (2019).27.Marshall, N. J., Jennings, K., McFarland, W. N., Loew, E. R. & Losey, G. S. Visual biology of Hawaiian coral reef fishes. II. Colors of Hawaiian coral reef fish. Copeia 2003, 455–466 (2003).Article 

Google Scholar 
28.Merilaita, S. Visual background complexity facilitates the evolution of camouflage. Evolution 57, 1248–1254 (2003).PubMed 
Article 

Google Scholar 
29.Matz, M. V., Lukyanov, K. A. & Lukyanov, S. A. Family of the green fluorescent protein: Journey to the end of the rainbow. BioEssays 24, 953–959 (2002).CAS 
PubMed 
Article 

Google Scholar 
30.Alieva, N. O. et al. Diversity and evolution of coral fluorescent proteins. PLoS ONE 3, e2680 (2008).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
31.Salih, A., Larkum, A., Cox, G., Kühl, M. & Hoegh-Guldberg, O. Fluorescent pigments in corals are photoprotective. Nature 408, 850–853 (2000).ADS 
CAS 
PubMed 
Article 

Google Scholar 
32.Veron, J., Stafford-Smith, M., DeVantier, L. & Turak, E. Overview of distribution patterns of zooxanthellate Scleractinia. Front. Mar. Sci. 1, 81 (2015).Article 

Google Scholar 
33.Matz, M. V., Marshall, N. J. & Vorobyev, M. Are corals colorful?. Photochem. Photobiol. 82, 345 (2006).CAS 
PubMed 
Article 

Google Scholar 
34.Marshall, N. J., Jennings, K., McFarland, W. N., Loew, E. R. & Losey, G. S. Visual biology of Hawaiian coral reef fishes. III. Environmental light and an integrated approach to the ecology of reef fish vision. Copeia 2003, 467–480 (2003).Article 

Google Scholar 
35.Neumeyer, C. Color vision in fishes and its neural basis. In Sensory Processing in Aquatic Environments (eds Collin, S. P. & Marshall, N. J.) 223–235 (Springer, 2003). https://doi.org/10.1007/978-0-387-22628-6_11.Chapter 

Google Scholar 
36.Oswald, F. et al. Contributions of host and symbiont pigments to the coloration of reef corals. FEBS J. 274, 1102–1122 (2007).CAS 
PubMed 
Article 

Google Scholar 
37.Schweikert, L. E., Fitak, R. R., Caves, E. M., Sutton, T. T. & Johnsen, S. Spectral sensitivity in ray-finned fishes: Diversity, ecology, and shared descent. J. Exp. Biol. https://doi.org/10.1242/jeb.189761 (2018).Article 
PubMed 

Google Scholar 
38.Veron, J. E. N., Stafford-Smith., M. G., Turak, E. & DeVantier, L. M. Corals of the World. www.coralsoftheworld.org (2020). Accessed April 2019.39.Weller, H. I. & Westneat, M. W. Quantitative color profiling of digital images with earth mover’s distance using the R package colordistance. PeerJ 7, e6398 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
40.Cox, K., Woods, M. & Reimchen, T. E. Coral species richness, coral hue, and reef fish richness across 74 ecoregions within four oceanic basins. Figshare https://doi.org/10.6084/m9.figshare.12317591 (2020).41.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).
Google Scholar 
42.The Ocean Agency & XL Catlin Seaview Survey. Coral Reef Image Bank. www.coralreefimagebank.org (2019). Accessed April 2019.43.Choat, J. H. & Bellwood, D. R. Reef fishes: Their history and evolution. In The Ecology of Fishes on Coral Reefs (ed. Sale, P. F.) 39–66 (Academic Press, 1991).Chapter 

Google Scholar 
44.Jones, G. P., Barone, G., Sambrook, K. & Bonin, M. C. Isolation promotes abundance and species richness of fishes recruiting to coral reef patches. Mar. Biol. 167, 1–13 (2020).Article 
CAS 

Google Scholar 
45.Lirman, D. et al. Severe 2010 cold-water event caused unprecedented mortality to corals of the florida reef tract and reversed previous survivorship patterns. PLoS ONE 6, e23047 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
46.Habary, A., Johansen, J. L., Nay, T. J., Steffensen, J. F. & Rummer, J. L. Adapt, move or die: How will tropical coral reef fishes cope with ocean warming?. Glob. Change Biol. 23, 566–577 (2017).ADS 
Article 

Google Scholar 
47.Almany, G. R. & Webster, M. S. The predation gauntlet: Early post-settlement mortality in reef fishes. Coral Reefs 25, 19–22 (2006).ADS 
Article 

Google Scholar 
48.Brandl, S. J. et al. Demographic dynamics of the smallest marine vertebrates fuel coral reef ecosystem functioning. Science 364, 1189–1192 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
49.Coker, D. J., Wilson, S. K. & Pratchett, M. S. Importance of live coral habitat for reef fishes. Rev. Fish Biol. Fish. 24, 89–126 (2014).Article 

Google Scholar 
50.Coker, D. J., Pratchett, M. S. & Munday, P. L. Coral bleaching and habitat degradation increase susceptibility to predation for coral-dwelling fishes. Behav. Ecol. 20, 1204–1210 (2009).Article 

Google Scholar 
51.Sale, P. F. Maintenance of high diversity in coral reef fish communities. Am. Nat. 111, 337–359 (1977).Article 

Google Scholar 
52.Munday, P. L. Competitive coexistence of coral-dwelling fishes: The lottery hypothesis revisited. Ecology 85, 623–628 (2004).Article 

Google Scholar 
53.Hixon, M. A. Synergistic predation, density dependence, and population regulation in marine fish. Science 277, 946–949 (1997).CAS 
Article 

Google Scholar 
54.Endler, J. A. & Thery, M. Interacting effects of Lek placement, display behavior, ambient light, and color patterns in three neotropical forest-dwelling birds. Am. Nat. 148, 421–452 (1996).Article 

Google Scholar 
55.Reimchen, T. E. Shell colour ontogeny and tubeworm mimicry in a marine gastropod Littorina mariae. Biol. J. Linn. Soc. 36, 97–109 (1989).Article 

Google Scholar 
56.Sparks, J. S. et al. The covert world of fish biofluorescence: A phylogenetically widespread and phenotypically variable phenomenon. PLoS ONE 9, e83259 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
57.Allen, J. J., Akkaynak, D., Sugden, A. U. & Hanlon, R. T. Adaptive body patterning, three-dimensional skin morphology and camouflage measures of the slender filefish Monacanthus tuckeri on a Caribbean coral reef. Biol. J. Linn. Soc. 116, 377–396 (2015).Article 

Google Scholar 
58.Cheney, K. L., Skogh, C., Hart, N. S. & Marshall, N. J. Mimicry, colour forms and spectral sensitivity of the bluestriped fangblenny, Plagiotremus rhinorhynchos. Proc. R. Soc. B Biol. Sci. 276, 1565–1573 (2009).Article 

Google Scholar 
59.Stevens, M., Lown, A. E. & Denton, A. M. Rockpool gobies change colour for camouflage. PLoS ONE 9, e110325 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
60.Gilby, B. L. et al. Colour change in a filefish (Monacanthus chinensis) faced with the challenge of changing backgrounds. Environ. Biol. Fishes 98, 2021–2029 (2015).Article 

Google Scholar 
61.Barnett, J. B. & Cuthill, I. C. Distance-dependent defensive coloration. Curr. Biol. 24, R1157–R1158 (2014).CAS 
PubMed 
Article 

Google Scholar 
62.Vega Thurber, R. L. et al. Chronic nutrient enrichment increases prevalence and severity of coral disease and bleaching. Glob. Change Biol. 20, 544–554 (2014).ADS 
Article 

Google Scholar 
63.Hughes, T. P. et al. Global warming and recurrent mass bleaching of corals. Nature 543, 373–377 (2017).ADS 
CAS 
PubMed 
Article 

Google Scholar 
64.Ortiz, J.-C. et al. Impaired recovery of the great barrier reef under cumulative stress. Sci. Adv. 4, eaar6127 (2018).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
65.Grottoli, A. G., Rodrigues, L. J. & Palardy, J. E. Heterotrophic plasticity and resilience in bleached corals. Nature 440, 1186–1189 (2006).ADS 
CAS 
PubMed 
Article 

Google Scholar 
66.Roff, G. et al. Porites and the Phoenix effect: Unprecedented recovery after a mass coral bleaching event at Rangiroa Atoll, French Polynesia. Mar. Biol. 161, 1385–1393 (2014).Article 

Google Scholar 
67.Adjeroud, M. et al. Recovery of coral assemblages despite acute and recurrent disturbances on a South Central Pacific reef. Sci. Rep. 8, 1–8 (2018).ADS 
CAS 
Article 

Google Scholar 
68.Tittensor, D. P. et al. Global patterns and predictors of marine biodiversity across taxa. Nature 466, 1098–1101 (2010).ADS 
CAS 
PubMed 
Article 

Google Scholar 
69.Soetaert, K. plot3D: Plotting Multi-Dimensional Data R package version 1.4. https://CRAN.R-project.org/package=plot3D (2021).70.Sarkar, D. Lattice: Multivariate Data Visualization with R (Springer, 2008).MATH 
Book 

Google Scholar 
71.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).MATH 
Book 

Google Scholar 
72.Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
73.Centore, P. sRGB centroids for the ISCC-NBS colour system. Munsell Colour Sci. Paint. 21, 1–21 (2016).
Google Scholar 
74.Kelly, K. L. Central notations for the revised ISCC-NBS color-name blocks. J. Res. Natl. Bur. Stand. 61, 427 (1958).Article 

Google Scholar 
75.Viechtbauer, W. Conducting meta-analyses in R with the metafor package. J. Stat. Softw. 36, 1–48 (2010).Article 

Google Scholar  More