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Genotypic heterogeneity in single Rivularia-like colonies
Rivularia-like colonies have a global distribution, occurring in marine or freshwater habitats, where they are usually attached to a rocky substrate; however, many studies have reported that Rivularia spp. are associated with unpolluted environments14. In addition, the relationships between some morphological or physiological features and the environment make these species excellent environmental indicators of changes in running water quality, mainly related to eutrophication processes14,30. Therefore, they have been included in biomonitoring programs21,31,32. On the other hand, because Rivularia colonies sometimes persist for very long periods, avoiding grazing, the toxicity of these colonies is being investigated33. It is undoubted that in all of these approaches, where genera and species must be strictly identified from environmental samples, accurate cyanobacterial characterization is essential.
Traditional identification of cyanobacteria involves assigning a colony to a morphospecies, and conventionally, a bacterial colony is defined as a visible mass of clonal microorganisms, all of which originated from a single cell. However, the results from the present study show that the majority of the analyzed colonies consist of different clones growing together. Among the 28 Rivularia-like colonies, the phylotype corresponding to Rivularia sp. was present in 19 colonies, with abundances ranging from 59.4 to 99.8% depending on the studied colony. Nevertheless, it should also be noted that in most of the colonies, this phylotype dominated, whereby in 14 colonies, it presented an abundance of ≥ 90% (and within 7 of these colonies, the abundance was close to 99%). However, in three colonies, the abundance ranged from 72 to 85%, and in two of them, the abundance decreased to approximately 60%. The other highly abundant phylotypes found in these colonies, which reached abundances up to approximately 21%, corresponded to Calothrix sp. and Oculatella sp., the latter a genus morphologically similar to Leptolyngbya but separated from it because of genetic differences34. These results indicated great variability in the abundance of the phylotype corresponding to Rivularia depending on the analyzed colony, as well as variation in the other phylotypes and their abundances found in these colonies.
One of the surprising findings was that among the twenty-eight analyzed Rivularia-like colonies, seven corresponded to the new, recently described genus Cyanomargarita, which as the authors described, is virtually indistinguishable from Rivularia in field samples15. In these colonies, genotypic heterogeneity was also found, in which the abundance of the phylotype corresponding to Cyanomargarita varied from 57,28% in a colony with clear lamination resembling R. haematites (see Fig. 3b) to 99.2% in a soft colony resembling R. biasolettiana. Interestingly, in these colonies, Phormidium sp. was the dominant nonheterocystous cyanobacterium instead of Oculatella from Rivularia colonies, but the phylotype corresponding to Calothrix was also found.
Furthermore, phylotypes corresponding to Cyanomargarita and Rivularia were never found together in the same colony, although both types of colonies coexisted in the same rivers (e.g., Gordale Beck and Endrinales). Allelopathic effects could explain these results, as previously suggested for other cyanobacteria35. In fact, García-Espín et al.33 showed that extracts obtained from Rivularia colonies affected the photosynthetic activity of several diatoms and a red alga. Further experiments with extracts from both colonies would confirm this possible effect.
Another very surprising finding was that two Rivularia-like colonies did not present any phylotypes corresponding to Rivularia or Cyanomargarita (or contained them at an abundance ≤ 0.7%). In one of these colonies (colony BAT4), five different phylotypes were found at similar abundances (approximately 15–20%), of which three corresponded to different Calothix spp. and the others corresponded to other Nostocaceae and Leptolyngbyaceae. In the other colony (BAT13), the dominant phylotype corresponded to the new genus Macrochaete16. This genus has been described only from cultures, so to the best of our knowledge, this is the first report in which a natural population is morphologically and genetically characterized. Nevertheless, it is noteworthy that the morphological characteristics of filaments and trichomes in this environmental sample were different from those reported in the description of this new genus, in which the phenotypic features resembled those of Calothrix. However, these features corresponded only to isolated strains, which are known to exhibit morphological variability and differences from natural populations7,12,13.
R. biasolettiana vs R. haematites
However, what was very interesting and deserves to be highlighted is that when we tried to differentiate the two typical Rivularia colonies found in calcareous streams, R. biasolettiana and R. haematites, we did not find genetic differences, at least at the studied level, the 16S rRNA gene.
16S rRNA is the most widely used marker gene36,37, which fits the criteria of ubiquity, regions of strong conservation, and regions of hypervariability38,39. This gene is supported by reference databases containing over a million full-length 16S rRNA sequences, therefore spanning a broad phylogenetic spectrum40. The 16S rRNA gene has served as the general framework and as the benchmark for the taxonomy of prokaryotes41. Advances in high-throughput sequencing technologies have enabled almost comprehensive descriptions of bacterial diversity through 16S rRNA gene amplicons, which have been used in surveys of microbial communities to characterize the composition of microorganisms present in environments worldwide42,43,44,45. Although some issues have been raised, such as identification of metabolic or other functional capabilities of microorganisms when studies focus only on this gene, recent studies have shown that the phylogenetic information contained in 16S marker gene sequences is sufficiently well correlated with genomic content to yield accurate predictions when related reference genomes are available46,47,48,49. Therefore, the 16S rRNA gene continues to be the mainstay of sequence-based bacterial analysis, vastly expanding our understanding of the microbial world50.
In particular, in cyanobacteria, as in other prokaryotes, the 16S rDNA gene is currently the most commonly used marker for molecular and phylogenetic studies51,52. The information obtained from 16S rDNA gene phylogenetic reconstructions, together with morphological, ultrastructural, and ecological data, led Komárek et al.53 to propose the current accepted classification of cyanobacteria. There have also been specific studies by this group concerning the problems associated with single-gene phylogenies, in which robust phylogenomic trees of cyanobacteria derived from multiple conserved proteins have also shown congruence between the multilocus and 16S rRNA gene phylogenies, which once again demonstrates the considerable strength of the 16S rRNA gene for phylogenetic inference and evaluation of prokaryote diversity54,55,56,57.
In this study, in contrast to the genetic identity found in R. biasolettiana and R. haematites colonies, showing a dominance of OTU1, the remainder of the studied representatives of Rivulariaceae showed a wide range of variation in the 16S rDNA sequences and with OTU1. Sequence identity between OTU1 and the remaining OTUs belonging to this family was as low as approximately 90%, ranging from 90.73 to 93.41%, and when it was compared with other Rivulariaceae from the databases, in the different clusters of the phylogenetic tree, this value ranged from 87.12 to 93.90%. A large difference between the sequences of this gene was also found in other studies on Rivulariaceae15,16,17,29,58. In fact, several new genera are emerging on the basis of these differences15,16,17. Comparisons of phylogenies using other markers, such as the phycocyanin operon and the intervening intergenic spacer (cpcBA-IGS) with the 16S rRNA gene in previous studies in Rivulariaceae, have shown largely consistent results, with a high level of divergence between the components of this family11.
In addition, the results of the present study showed correlations between morphological characteristics and the analyzed genes in all the cyanobacterial colonies/tufts, except for those of R. biasolettiana and R. haematites. In these two cyanobacteria, only distinct macroscopic phenotypic features were observed due to zonation and different degrees of calcification since no significant differences were found in the size measurements or other microscopic characteristics.
Therefore, although the remainder of the genome has not been studied in these populations, the genetic identity of the studied marker, phenotypic features, together with environmental preferences point out that R. biasolettiana and R. haematites are ecotypes of the same species, as previously suggested59.
R. biasolettiana and R. haematites have very similar morphotypes, and traditional taxonomical classification and studies have distinguished them primarily by their degrees of calcification. R. biasolettiana-type colonies are described as more gelatinous and less calcified, and the crystals are disseminated; however, R. haematites colonies are very hard and exhibit extensive calcification in concentric zones, which leads to clear lamination24,25,60,61. Because of its heavy mineralization, R. haematites is a model for stromatolite-binding organisms25,26.
Microscopic observations from this study showed that some colonies presented typical R. haematites morphology with concentric bands of intense calcification (see Fig. 2a,b), and others were soft and less calcified, such as R. biasolettiana, although all of them presented the same dominant phylotype. Many others with this dominant phylotype have also shown ambiguous morphology with no clear lamination, although some dark/light zones could be observed (see, e.g., Fig. 4b,d, f). Even in Cyanomargarita colonies, whose genotype was clearly separated from that of Rivularia, concentric zones and extensive calcification could be observed (see, e.g., Fig. 3b,d). These results suggested that these phenotypic features are not diagnostic characteristics for further identification.
In a two-year study, Obenlüneschloss and Schneider61 found that not all analyzed R. haematites colonies showed distinct concentric calcification layers. In the stromatolites of both types of Rivularia, the same lamination was observed, and the differences in calcification appeared later60. Pentecost and Franke26 compared populations of R. biasolettiana and R. haematites and argued that although both could be distinguished by their form of calcification and their trichome diameter, some populations of R. biasolettiana were more intensely calcified than others, suggesting that a continuity of forms may exist, even within the same stream, and therefore, a continuum of colony forms probably occurs between these taxa.
Differences in the calcification pattern have been attributed to seasonality and cyanobacterial activity, in particular to photosynthesis24,26,62. The calcification in R. haematites occurred in concentric bands, which varied in thickness and the density of crystals. Since characteristic zonation is formed by filaments of different successive generations, the thickness will vary depending on the growth rate, while crystal density will depend on the rate of calcification. Calcification is the result of photosynthesis (with a maximum of 14%) and evaporation during the warmer seasons, while it is entirely abiogenic during winter as a result of CO2 evasion63. Therefore, dense calcified bands similar to those formed in winter have been described that are caused by a reduction in trichome growth and EPS production, allowing the development of abiotic surface precipitate, and less calcified layers are formed during spring and summer, when calcification is associated with photosynthesis in zones of growth with cell division24,26. Thus, differences in climatic conditions and/or biological activity seem to lead to differences in the degrees of zonation and calcification.
The growth of Rivularia colonies is seasonal and strongly correlated with water temperature24,26. The colony growth rates were 12–14 µm/day in summer and 2 µm/day in winter24. The occurrence of R. biasolettiana was more closely related to high temperatures than that of R. haematites21. Moreover, colonies of R. haematites were generally collected under temperatures below 15 °C in mountain running waters64, and R. haematites stromatolites have been described as preferentially developed in wet periods, particularly in autumn and winter60. Our own field observations during the sampling for this and previous studies were that the gelatinous and weakly calcified R. biasolettiana type was more abundant in warmer locations, and in contrast, R. haematites was dominant in cold locations (data not shown).
One possible explanation for the results found in this study could be related to these differences in the degree of zonation and calcification in relation to climate, which could include microclimatic conditions. In warmer sites or climatic conditions, when growth is rapid, the number of filaments will increase, moving towards the surface in a weakly dense and unaligned arrangement, on which calcite crystals spread, providing a lighter and less calcified structure. Thus, increased growth of Rivularia colonies can lead to the R. biasolettiana type. Under colder conditions, such as in winter, or microclimatic conditions, when growth slows down for other reasons, such as low light, filaments become more densely packed, allowing the development of extensive precipitates and leading to a dark band. When these conditions change, e.g., in the spring and summer, increases in temperatures and/or light will result in increasing and faster growth, leading to a less calcified new layer, and successive seasonal and/or microenvironmental changes will result in the typical lamination of R. haematites. Therefore, warmer places with high temperatures and/or light will allow the occurrence of the R. biasolettiana type, while in colder sites and/or sites with alternating environmental conditions, the R. haematites type will develop. Shaded colonies and colonies that lie in the supratidal spraywater zone often contain small, irregular and more densely packed crystals61.
Cyanobacteria are known to modify EPS production, pigments, and morphology under environmental stimuli6. The production of EPS also varies depending on the cyanobacteria, whereby Rivularia has shown a well-developed exopolymer layer65, which is of great importance for this epilithic cyanobacteria, as it acts as an adhesive that allows cells to stick to the stones in the running waters, and it holds the filaments together, minimizing cell damage during intermittent drying exposure to the air and evaporation in the warmer seasons66. The C/N ratio is an important parameter for the variation in EPS production since high amounts of fixed C compared to N levels drive EPS synthesis to store excess C67,68. Therefore, Rivularia colonies that are exposed, in spring and summer, to high light intensities and temperatures will increase their photosynthetic rates and therefore the amount of EPS, as shown by the R. biasolettiana morphotype. In addition, most of the analyzed populations were dark in color, probably in relation to the accumulation of the yellow–brown scytonemin pigment in the sheaths or EPS, as previously observed in shallow and clear oligotrophic ecosystems, where water clarity allows UV radiation to penetrate well, protecting the cells from the damaging effects of this radiation69,70.
In conclusion, environmental factors can lead to differences in macroscopic phenotypic features, such as those found in the Rivularia colonies studied here. However, further sampling under different climatic conditions and/or microenvironmental conditions or of Rivularia cultures grown under distinct temperature and/or illumination conditions, as well as analysis of other genes, are needed to confirm this hypothesis. More

Experimental design
A multi-layer canopy-root-soil model (MLCan)24,26,27 is used to calculate the energy and entropy fluxes for three climatologically-different ecosystems containing multiple functional groups: water-limited Santa Rita Mesquite (SRM), energy-limited Willow Creek (WCR), and nutrient-limited Tapajos National Forest (TAP)38.
MLCan takes site-specific parameters and weather forcing data and computes the energy and entropy fluxes and temperatures for each of the ecosystem layers. Entropy calculations are based on both the energy fluxes and temperature of soil, air, and leaves (see Entropy Calculations). The model is run for a simulation period of 2 years (2004–2005) at a half-hourly timescale for SRM and WCR and an hourly timescale for TAP due to data availability. Weather forcing data was downloaded from FLUXNET2015: air temperature, air pressure, global radiation, precipitation, wind speed, friction velocity, and relative humidity32,33,34. Additional model input parameters can be found in Table S2 of the Supplementary Information.
The initial soil moisture and temperature profiles for each of the sites—and snow properties for WCR—were produced from a spin-up of the model. The WCR and TAP sites used 2004 LAI with 2003 forcing data for a spin-up of 2 years to provide the initial conditions for the beginning of the 2004 simulation. For the SRM site, the FLUXNET2015 data was not available for 2003, so 2004 data was used instead.
At each site, the model splits up the vegetation into plant functional groups. Domingues et al.35 demonstrates the importance of modeling ecosystems based on functional groups. For WCR and SRM, the vegetation is represented by understory herbaceous species and overstory trees. For TAP, a high biodiversity ecosystem in Amazonia, the vegetation is further divided and represented by four groups: understory tree, mid-canopy tree, upper-canopy tree, and upper-canopy liana35. See Table S1 of the Supplementary Information for functional group abbreviations.
The LAI data for all sites are taken from MODIS39 and calibrated based on site documentation (Fig. S4 of the Supplementary Information). The LAI is then partitioned into two or four components based on the number of functional groups at each site. Additional LAI information can be found in the Supplementary Information.
MLCan has been previously validated for each of the sites considered30,40. Since entropy cannot be directly measured, we provide a comparison of the model outputted latent heat fluxes with the observed fluxes at each site in Fig. S5 of the Supplementary Information for additional validation.
Site descriptions
The SRM site is located on the Santa Rita Experimental Range in southern Arizona ((31.8214^{circ }hbox{N}), (110.8661^{circ }hbox{W})). SRM has a hot semi-arid climate and consists of woody savannas with mesquite trees (Prosopis velutina Woot.) and C4 grasses and subshrubs40,41.
The WCR (Willow Creek) site is located within the Chequamegon-Nicolet National Forest in northern Wisconsin ((45.8059^{circ }hbox{N}), (90.0799^{circ }hbox{W})) with a northern continental climate. It is a deciduous broadleaf forest dominated by sugar maple (Acer saccharum Marsh.) with understory shrubs, including bracken ferns (Pteridium aquilinum), and overstory seedlings and saplings42,43,44.
The TAP (Tapajos National Forest) site data is taken from the Santarem Km 67 Primary Forest site located in Belterra, Pará, Brazil ((2.8567^{circ }hbox{S}), (54.9589^{circ }hbox{W})). This evergreen broadleaf forest in Amazonian Brazil has a tropical monsoon climate with vegetation consisting of dozens of known tree species and lianas30,35.
Entropy calculations
Entropy calculations are based on model-simulated temperature and energy at each of the 20 canopy layers and the soil-surface layer, and results are scaled up to the ecosystem level. No lateral exchange of fluxes are considered. The net sum of energy fluxes from all layers of the ecosystem is equivalent to the total flux of energy across the boundary of the control volume (Fig. S1 of the Supplementary Information). These energy fluxes include shortwave radiation (SW), longwave radiation (LW), latent heat (LE), and sensible heat (H). All results are categorized as the flux of energy at the boundary entering ((SW_{in}), (LW_{in})) or leaving ((SW_{out}), (LW_{out}), LE, H) the ecosystem. Because the total energy flux across the ecosystem boundary is equal to the sum across the canopy layers in the model, the total entropy flux across the boundary can also be taken as the cumulative sum of the entropy fluxes from all layers of the ecosystem.
Entropy flux calculations are summarized in Table 1. All energy variables have units of (hbox{W/m}^2), entropy variables are in (hbox{W/m}^2hbox{K}), and temperatures are in K.
Table 1 Entropy calculations
Full size table

Entropy for LE and H calculations are based on simple heat transfer. The change in entropy is:

$$begin{aligned} dS=frac{dQ}{T} end{aligned}$$
(1)

where dQ is change in heat and T is temperature49. Thus, the flux of entropy for a given energy flux (E) across a boundary is:

$$begin{aligned} J=frac{E}{T} . end{aligned}$$
(2)

However, thermal radiation (SW and LW) cannot be treated this simply. The entropy flux for blackbody radiation is:

$$begin{aligned} J_{BR} = frac{4}{3} sigma T^3 = frac{4}{3} frac{E_{BR}}{T} end{aligned}$$
(3)

where (sigma) is the Stefan–Boltzmann constant, and (E_{BR}) is the blackbody radiation flux defined as (sigma T^4) from the Stefan–Boltzmann Law48,49.
SW is considered blackbody radiation, and entropy fluxes for direct shortwave radiation ((J_{SW,direct})) can be obtained by Eq. 3. However, LW is considered non-blackbody radiation, also called ‘diluted blackbody radiation’, which must include an additional factor (X(epsilon )) to account for the entropy produced during the ‘diluted emission’ of radiation given by an object’s emissivity, (epsilon). This factor is defined as45,46:

$$begin{aligned} X(epsilon ) = 1-Big [frac{45}{4pi ^4}ln {(epsilon )}(2.336-0.26epsilon )Big ]. end{aligned}$$
(4)

Although (SW_{diffuse}) is still a blackbody radiation, it has been demonstrated47 that the entropy flux due to (SW_{diffuse}) can be treated similarly to non-blackbody radiation with a new variable, (xi), in place of emissivity. (xi) is the ‘dilution factor’ of radiation due to scattering, meaning it is the ratio of diffuse solar radiance on Earth’s surface to solar radiance in extraterrestrial space47. Since diluted blackbody radiation ((SW_{diffuse})) is mathematically equivalent to non-blackbody radiation (LW) when the dilution factor is equal to the emissivity, (xi) can also be plugged into Eq. 4 to solve for the amplifying factor of entropy production due to scattering, (X(xi ))37,45,46,48.
Each of the entropy calculations in Table 1 have a temperature value corresponding to the temperature of the energy’s source. For instance, shortwave radiation originates from the sun, so the source temperature in its entropy equations is (T_{sun}). Likewise, longwave radiation is assumed to originate from the atmosphere, leading to a corresponding temperature of (T_{atm}). However, (LW_{out}), LE, and H do not have a single source location, so we must calculate an equivalent temperature ((T_{eq})) for each energy category based on the modeled temperatures and weighted contribution of each layer to the total energy flux at the ecosystem boundary. The equivalent temperatures for these three energy categories are calculated as follows:

$$begin{aligned} T_{eq,j} = sum _{k=1}^{21}[T_{k} times omega _{j, k}] end{aligned}$$
(5)

where (T_{eq,j}) is the equivalent temperature of energy category j such that (j in {LW_{out}, LE, H}). k refers to the layer in the ecosystem such that layers 1-20 are the canopy layers, and layer 21 refers to the ground surface. (T_k) is the temperature of layer k, and (omega _{j,k}) is the weight of energy category j coming from layer k given by:

$$begin{aligned} omega _{j,k} = frac{E_{j,k}}{E_{j,eco}} end{aligned}$$
(6)

where (E_{j,k}) is the energy j leaving layer k, and (E_{j,eco}) is the total energy j leaving the ecosystem.
The total entropy flux of the ecosystem ((J_{eco})) is calculated by summing the energy categories:

$$begin{aligned} J_{eco} = sum J_j + J_{SWout} end{aligned}$$
(7)

where (J_{SWout}) is the entropy flux of diffuse shortwave radiation leaving the ecosystem. The entropy flux per unit energy (EUE) is another way to view the thermodynamic state of ecosystem vegetation. EUE is calculated as:

$$begin{aligned} EUE_{j} = frac{J_{j}}{E_{j}} end{aligned}$$
(8)

where (EUE_{j}) is the entropy per unit energy in 1/K of energy category j. It follows that the corresponding (EUE_{SWout} = J_{SWout}/E_{SWout}), and the total ecosystem EUE is:

$$begin{aligned} EUE_{eco} = frac{sum J_j + J_{SWout}}{sum E_j + E_{SWout}}. end{aligned}$$
(9)

Work calculations
Work in an ecosystem represents the energy required to directly perform motion in the form of heat, effectively decreasing the temperature gradient within the ecosystem. We assume that LE and H are the primary regulators of temperature within a natural ecosystem, and (LW_{out}) is wasted energy. Additionally, we assume that the bottom of the control volume is sufficiently deep such that the temperature at the boundary is consistent and there is no loss of heat (i.e. ground heat flux is ignored). Thus, work is estimated and calculated directly from LE, H, and change in internal energy due to photosynthesis, (Delta Q):

$$begin{aligned} W = LE+H+Delta Q end{aligned}$$
(10)

where (Delta Q) is significantly less than LE and H and can be ignored. So work can be simplified to:

$$begin{aligned} W = LE+H. end{aligned}$$
(11)

Since work represents the ability of an ecosystem’s vegetation to deplete the driving temperature gradient imposed upon the ecosystem, our analysis compares work with temperature gradient. We define temperature gradient as:

$$begin{aligned} frac{Delta T}{Delta z} = frac{T_{surf}-T_{air}}{h_e} end{aligned}$$
(12)

where (T_{surf}) is the temperature of the soil surface, (T_{air}) is the temperature of the air in the top layer of the ecosystem, and (h_e) is the ecosystem height (see Table S2 in the Supplementary Information).
Work efficiency is the work performed for the amount of radiation entering the ecosystem defined as:

$$begin{aligned} WE = frac{LE+H}{E_{SWin} + E_{LWin}} = frac{W}{E_{in}}. end{aligned}$$
(13)

Since each vegetation functional group partitions energy differently among the energy categories, work efficiency is a good way to compare thermodynamic behavior across model scenarios at each site in a normalized way.
Statistical analysis
To determine if the differences of entropy flux and work efficiency among scenarios at each site are statistically significant, we perform two separate tests for entropy flux and work efficiency. Since entropy flux distributions are positively skewed (Fig. 1a), we use the variance as an indicator of the difference between them. To this end we use the distribution-free Miller Jackknife (MJ) significance test50,51 for variance that does not assume that the distributions come from populations with the same median. However, the work efficiency distributions exhibit no such pattern (Fig. 1b), and, therefore, we use the two-sample Kolmogorov–Smirnov (KS) test, which measures the maximum absolute difference between two empirical cumulative distribution functions (CDF)52,53,54.
First, the entropy flux variances are compared with the MJ test. Because functional group scenarios at each site are bounded on the lower end by similar values, if a distribution has a larger variance than another, then the two populations cannot be considered as coming from the same continuous distribution, and the distribution with a larger variance generally consists of larger values. For each site we test the null hypothesis, (H_0), that the distribution of multiple-functional-group entropy fluxes and the distribution for each of its single-functional-groups have the same variance. This is done with each functional group present at each site (Table S1). The alternate hypothesis, (H_{A1}), states that the distribution of entropy fluxes from the multiple-functional-group has a larger variance than that of the corresponding single-functional-group, meaning that the two populations do not belong to the same distribution and the multi-group scenario consists of larger values than the single-group scenario. The results from this test, shown in Table 2, indicate that (H_0) is rejected in favor of (H_{A1}) at the 5% level ((p More

The brightness to the naked eye of giant megaconstellations of satellites could create the greatest alteration to the night sky’s appearance in human history (see Nature https://doi.org/fdz8; 2020). This could have potentially catastrophic effects on celestial navigation by wildlife, and therefore on terrestrial ecology.
Migrating species such as birds, dung beetles and seals use stars as a source of directional information (see J. J. Foster et al. Proc. R. Soc B 285, 20172322; 2018). Some use bright objects as their main cue, and others rely on the starry sky’s centre of rotation, or the fainter band of the Milky Way.
Constellations of satellites can form coherent patterns that could affect night-time migrations in a similar way to the Milky Way, for example. Such errors would have a global effect on migratory populations. Their energy balance could be altered, with long-term repercussions for survival and reproduction, as has been found for excessive or misdirected light (S. A. Cabrera-Cruz et al. Sci. Rep. 8, 3261; 2018).
We call for astronomers and field biologists to identify and quantify the possible ecological effects of satellite megaconstellations. A regulatory framework could then be developed to control their proliferation, based on how species respond to spatio-temporal cues in the field. More

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