Ecology

Quality control of the full-length transcriptomes
The FL transcriptomes for R. a. hainanus, R. a. himalayanus and Myotis ricketti were constructed based on sequencing data of three separated libraries on the PacBio Sequel platform. Specifically, a total of 3,444,947 subreads with 6,448,987,299 nucleotides, 3,255,638 subreads with 6,504,282,447 nucleotides and 3,403,451 subreads with 7,190,237,257 nucleotides were generated for R. a. hainanus, R. a. himalayanus and Myotis ricketti respectively. After quality control, we obtained 137,159 circular consensus sequencing (CCS) reads for R. a. hainanus, 137,160 CCS reads for R. a. himalayanus and 152,251 CCS reads for Myotis ricketti. With the standard IsoSeq. 3 classification and clustering pipeline, we identified 111,806 FLNC for R. a. hainanus, 105,713 FLNC for R. a. himalayanus and 122,222 FLNC for Myotis ricketti. After isoform-level polishing, 10384, 9984 and 10932 high quality isoforms were retained in R. a. hainanus, R. a. himalayanus and Myotis ricketti respectively. After removing redundancy with CD-HIT-EST and filtering isoforms shorter than 200 bp, the final FL transcriptomes for R. a. hainanus, R. a. himalayanus and Myotis ricketti (FL-CF-Rhai, FL-CF-Rhim and FL-FM-Myo, respectively) contain 10103, 9676 and 10504 FL isoforms with an average length of 2251, 2370 and 2530 bp, respectively (Table 2). Finally, the FL transcriptome from both CF and FM bats (FL-CF-FM) contains 26,342 transcripts with an average length of 2,405 bp (Table 2). BUSCO analysis revealed that a total of 2,354 (57.4%) BUSCOs were included in FL-CF-FM. We also found 39.9%, 38.1% and 41.9% BUSCOs in FL-CF-Rhai, FL-CF-Rhim and FL-FM-Myo, respectively (Table 4). Given the highly specialized function of the cochlea, we should not expect a high level of BUSCO value in FL transcriptome of cochlea. A recent single cell RNA-seq study has identified a similar number of genes expressed in the murine cochlea (a total of 12,944)30.
Table 4 Completeness of each of the four FL transcriptomes assessed by benchmarking universal single-copy ortholog (BUSCO) analysis.
Full size table

Quality control of annotation
Four FL transcriptomes (FL-CF-Rhai, FL-CF-Rhim, FL-FM-Myo, and FL-CF-FM) were functionally annotated by performing DIAMOND and BLASTx searches against the Nr and UniProt databases separately. For FL-CF-FM, 24,793 and 24,198 transcripts were annotated by Nr database and UniProt database, respectively (Table 3). After combining the annotation results from the two databases, a total of 24,833 transcripts were annotated in at least one database. We obtained similar annotation results for FL-CF-Rhai, FL-CF-Rhim and FL-FM-Myo (Table 3). Transcripts without annotations might be novel isoforms of echolocating animals or due to the lack of representative sequences for cochlea in public databases. More

These methods are an expanded version of those in our related work, Boakes et al.15.
The database was compiled over the period 2005–2008. Data collection equates to around 1500 person-days and data were gathered by a team of 21 people. Between them, team members were fluent in English, French, German, Mandarin, Russian, Spanish and Swedish. These languages were extremely helpful in transcribing museum specimen labels and in translating publications. However, the majority of publications were in English and we acknowledge that the database will be biased toward records published in English-language publications.
Our study focuses on the 130 galliform species that occur within the Palaearctic and Indo-Malay biogeographic realms22 (see Online-only Table 1). We have additionally included records of the Imperial Pheasant (Lophura imperialis) although it is now recognised that this is a hybrid and not a species. The geographic range of two of the species in the database, the Red Grouse (Lagopus lagopus) and the Rock Ptarmigan (Lagopus muta), extends to North America. North American data was often included in the information which museums sent us and in these instances we entered those records into the database since we thought they might be of use to researchers studying these species. However, it should be noted that we did not search exhaustively for records of these species in North America, we have merely included those that we came across.
We attempted to gather all species distribution data that could be accessed from five different sources; museum collections, literature records, banding (ringing) data, ornithological atlases and birdwatchers’ trip report websites. For each data source, exhaustive and systematic search strategies were adopted.
Museum collections
Using web-based searches and Roselaar23, 377 natural history collections were identified. We found contact details for 338 of these collections and requested by email or letter a list of the Galliformes in their holdings along with collection localities and dates. Non-respondents were recontacted. 135 museums were able to share data with us (see Online-only Table 2). Museum records were obtained through publicly available online databases e.g. ORNIS, electronic or paper catalogues sent to us by the museums or by visiting the museums and transcribing data directly from specimens or card catalogues. Almost half of the museums we contacted did not respond despite at least one follow-up enquiry, and there was substantial variation in the amount and format of data contributed by those that did reply. Altogether, over 50% of the records came from just six museums (Natural History Museum, London; Zoological Institute of the Russian Academy of Sciences, St Petersburg; Zoological Museum of Lomonosov Moscow State University; Field Museum of Natural History, Chicago; American Museum of Natural History, New York; National Museum of Natural History, Leiden), a single museum (the Natural History Museum, London) contributing nearly 20% of the museum records that could be georeferenced and dated15. Following databasing and/or georeferencing, records were returned to larger collections and to those who had requested the data.
Literature
Data from the literature were added to those previously collected by McGowan24. Entire series of key English-language international and regional ornithological journals such as Ibis, Bird Conservation International, Journal of the Bombay Natural History Society, and Kukila were scanned for relevant information, availability allowing. We began at the library of the Zoological Society of London and followed up missing journal issues at the BirdLife International library, Cambridge UK; the British Library, London, UK; the Edward Grey Institute, University of Oxford, UK. Relevant Chinese literature was also scanned. Additionally, data were obtained from regional reports, personal diaries, letters, newsletters etc stored in the archives of BirdLife International, Cambridge, UK; the World Pheasant Association, Newcastle, UK; the Edward Grey Institute, University of Oxford, UK. Several of the species/regional experts we consulted also contributed their personal records which were recorded in the database as ‘personal communications’. As far as it were possible, records were classed as primary or secondary data within the ‘dynamicProperties’ field of GalliForm14. It is important to note that some primary records or museum specimens will be duplicated within the database in the secondary data.
Banding records
Eighty-three ornithological banding groups were identified using web-based searches and were contacted via email. Thirty of these groups replied and only seven were able to provide us with data (see Table 1). The majority of galliform species tend not to be banded due to their large body sizes and spurs. Additionally, many of the banding groups kept their records on paper and were not able to send them to us. Nevertheless, we were able to access and georeference 15,152 banding records.
Table 1 The ringing groups that shared data with GalliForm.
Full size table

Ornithological atlases
We digitised location data from 20 ornithological atlases (see Table 2). Data from several other atlases were not used since the range of dates for the records was wider than 20 years.
Table 2 The atlases that were digitised to be included in GalliForm.
Full size table

Trip report website data
We used the two trip report websites that were popular with birders during the data recording period (2005–2008), www.travellingbirder.com and www.birdtours.co.uk. At that time, eBird (probably the most relevant current online source today) did not cover the majority of the countries within our study region, and our intention with the deposition of this dataset is to focus on pre-eBird data that are more difficult and time consuming to access. We extracted data from all trip reports of birdwatching visits to European, Asian and North African countries. Care was taken to enter reports that featured on both websites once only.
Criteria for data inclusion
To be included in the database, records had to meet the following criteria:
1.
The record identified the species of the bird concerned.

2.
The record contained either a verbal description of the locality at which the bird concerned was observed or the co-ordinates at which the bird was observed.

Records of captive birds were excluded. Records relating to non-native occurrences were included but were flagged in the ‘establishmentMeans’ field as “introduced”.
Data entry
GalliForm14 was originally compiled in the programme Microsoft Access 2003. To maximise uniformity in data entry, all data recorders were given thorough and consistent training and each was provided with a set of database guidelines. An Access Database form was created to standardise data entry and to enable multiple members of the team to collect data simultaneously.
Each entry in GalliForm14 corresponds to a single record of a single species recorded in a specific location. The data fields of GalliForm14 are described in Online-only Table 3. The taxonomy used has been updated to be consistent with the BirdLife International 2019 taxonomy (datazone.birdlife.org). All information was entered exactly as it was described in the data source, with as much information extracted as possible. Multiple records from different sources which recorded the same information were still included in the interest of completeness. The only exception to this is the trip report data in which we did not enter identical records which occurred on both the Travelling Birder and Bird Tours websites.
The source of the data, i.e. literature, museum, atlas, ringing or website trip report is recorded in the ‘dynamicProperties’ field under the code “dataSource”. For literature data, (where known) the nature of the record, i.e. primary or secondary, is recorded under the code “datatype”.
Taxonomy has of course changed considerably over time. To allow for this we recorded the taxonomy as it was described in the data source in the ‘originalNameUsage’ field. The current taxonomy was then selected from a look-up table. If at the time of data entry, the data compiler was unsure which species the synonym referred to, the species was tagged as “unknown” and the species was designated at a later date following further research on the synonym.
Identical localities can also be described in multiple ways. We recorded the locality as it was given in the data source in the ‘verbatimLocality’ field. If the ‘verbatimLocality’ clearly tallied with a locality already within the database, the record was linked to that locality in order to increase georeferencing efficiency.
It was rare for a source to record absence of evidence, i.e. a survey for a species at a particular locality which failed to find that species. However, in the few cases where we did come across such records, the locality and date of the survey were recorded and “absent” was recorded in the ‘occurrenceStatus’ field.
Each record refers to an independent observation. For museum and ringing records, this means a single individual. For literature, atlas or trip report records this may refer to a group of birds observed in one particular locality, on one particular day. If given, the number of total individuals is recorded in the ‘individualCount’ field. The number of males and females is recorded in the ‘sex’ field and the number of juveniles and adults in the ‘lifeStage’ field. If the ‘lifeStage’ field is blank, it is reasonable to assume the individual(s) is an adult.
Occasionally, additional information about the observation might be included in the data source, for example the habitat the bird was observed in or whether the bird was common or rare in that locality. These data are recorded in the ‘habitat’ and ‘organismQuantity’ fields, respectively. Any additional information which did not fit within the structure of the database was recorded in the ‘occurrenceRemarks’ field, along with any notes found on museum labels.
For the purposes of data deposition, the database was converted to a tab-delimited CSV file with all fields following Darwin Core format. A full summary of these fields is given in Online-only Table 3.
Georeferencing
Locality descriptions were converted to geographic co-ordinates using a wide range of atlases and gazetteers, co-ordinates generally only being assigned if accurate to one degree (although in the majority of cases the locations were accurate to within 30 minutes, Table 3). We would initially search for a locality within the gazetteers available to us at the time. If the locality was not listed within those gazetteers we would search for the locality using atlases. Since this fieldwork was conducted, MaNIS standards have become widely used for studies of this kind, but these weren’t fully developed at the time of data collection25. Named places, e.g. towns or counties, were georeferenced using their geographic centre and georeferencing uncertainty measured from the centre to the edge of the named place. Often localities were given simply as the name of a river, mountain or Protected Area. In these instances we used the midpoint of the river between source and mouth (uncertainty measured as distance from midpoint to source/mouth), the summit of the mountain (uncertainty measured as distance from summit to approximate mountain foot) and the rough centre of the Protected Area (uncertainty measured as distance from centre to Protected Area edge). If a particular locality description matched two or more places their midpoint was taken (uncertainty measured as distance from midpoint to place). Offsets from localities (e.g. “50 km N of Kuala Lumpur”; “8 miles along the road from Sheffield to Chesterfield”) were measured using a digital atlas (uncertainty was approximated at the georeferencer’s discretion in these instances, usually between 3 and 10 arc-minutes, depending on the vagueness of the offset.) For georeferencing done ‘in house’, the gazeteer/atlas used was recorded.
Table 3 Georeference and date completeness of the records.
Full size table

When possible, localities we could not georeference ourselves were sent to regional experts.
92% of our localities are georeferenced to an accuracy of 30 minutes, corresponding to 82% of occurrence records (see Table 3).
We had less success at georeferencing museum records than literature records15, due in part to difficulties in reading hand-writing on specimen labels. Older records were also harder to georeference, presumably due to changes in place names over time, and to some early ornithologists failing to document the collection locality. As might be expected, localities from countries that do not use the Roman alphabet were also harder to georeference.
Some records were excluded from the database based on their locality: records which we thought were trading localities, notably Malacca in Malaysia and Leadenhall Market in the UK; records from captive specimens, e.g. zoological gardens.
Dating
49% of records are dated to within an accuracy of one year. Where possible, we assigned date ranges to undated records. For example, if the name of the collector was given on a museum specimen and we knew when that collector was active in that region, we assigned a date range covering that period. There remain undated records which could perhaps be dated in this way. Undated literature records were designated as occurring before their publication date. We were able to date 89% of records to within 10 years. More

Affiliations

School of Geosciences, University of Edinburgh, Edinburgh, UK
Andrew T. Nottingham & Patrick Meir

Smithsonian Tropical Research Institute, Panama City, Panama
Andrew T. Nottingham, Esther Velasquez & Benjamin L. Turner

Research School of Biology, Australian National University, Canberra, Australian Capital Territory, Australia
Patrick Meir

Authors
Andrew T. Nottingham

Patrick Meir

Esther Velasquez

Benjamin L. Turner

Corresponding author
Correspondence to Andrew T. Nottingham. More

Our starting point is a two-dimensional shallow water model that solves the hydromorphodynamic problem by integrating numerically the depth-averaged shallow water equations coupled with the Exner equation, which describes the time evolution of riverbed elevation41. When paired with a description of the vegetation dynamics and its effect on flow and sediment transport, such a model was demonstrated to reproduce key ecomorphodynamic processes34. In this study, we model feedbacks between hydromorphodynamic processes and vegetation including a description of both above- and below-ground plant traits and their dynamics. In particular, we consider that vegetation impacts flow and sediment transport by modifying the flow resistance, the bed shear stresses, and the threshold for the onset of bed load transport. In turn, morphodynamic processes occurring during flood events are responsible for two main vegetation mortality mechanisms, which are, plant uprooting and sediment burial, while water level fluctuations during low flow periods between floods control the vegetation growth.
Hydromorphodynamic processes
River hydromorphodynamics is simulated using the numerical model BASEMENT (freeware software)41 and the computational domain is discretised using a triangular unstructured grid. First, the model solves the hydrodynamic problem by using the Manning-Strickler approach for evaluating the hydraulic roughness, in which the bottom shear stress, τ, reads as

$$begin{aligned} mathbf{{tau }} = frac{rho g mathbf {u}|mathbf {u}|}{K_s^2h^{1/3}}quad , end{aligned}$$
(1)

where (rho) is the water density, g is the gravitational acceleration, (mathbf {u}) is the flow velocity vector, h the water depth and (K_s) the Strickler coefficient (the inverse of the Manning coefficient n). Second, the sediment continuity equation (Exner equation) is solved to obtain the evolution of the bottom elevation (z_b) of a riverbed composed of a uniform sediment. The bed load transport intensity is evaluated using the standard Meyer-Peter and Müller formula42, as a function of the excess of the Shields shear stress (theta) above a threshold value ((theta _{cr}=0.047)), where

$$begin{aligned} theta = frac{|mathbf{{tau }}|}{(rho _s -rho ) g d_s} end{aligned}$$
(2)

and (rho _s) and (d_s) are the sediment density and diameter, respectively.
Vegetation description
Vegetation is described by a total dimensionless biomass density, B, which is partitioned into two components, above-ground, (B_c) (subscript c stands for canopy) and below-ground, (B_r) (subscript r stands for roots) (Fig. 2a). The above-ground biomass is considered to be evenly distributed along the plant height, H. Conversely, the below-ground biomass is characterised by a dimensionless vertical density distribution (b_r(zeta )) that extends downward in the (zeta)-direction, from the riverbed surface ((zeta =0)) to the rooting depth (zeta _r(t)), i.e. the maximum depth reached by roots at a generic time, t (Fig. 2a). (b_r(zeta )) is calculated via the stochastic model developed by Tron et al.43. The model assumes that fluctuations of the water table level follow the water level in the channel, which produce an alternating sequence of root growth and decay periods at each riverbed depth (zeta). This behaviour is then described as a stochastic process and solved to obtain a steady-state solution for (b_r(zeta )) (see Appendix for the formulation43), which depends on the mean frequency, magnitude, and decay rate of water table fluctuations and represents a key component in our model, controlling plant growth rate and vegetation resistance to uprooting.
Vegetation growth dynamics
The fluctuation of the water table level is one of the main driving factors controlling growth performances of riparian plants22. The ability of species to rapidly grow roots tracking the water table was found to be key for determining the growth rate of plants and their potential establishment success on bars44. Highly variable water table levels during growth periods tend to produce water stress that reduces plant growth because of reduced root respiration, while long free-inundation periods may be similarly harmful for plants that fail to grow roots deep enough to secure a connection with the groundwater. To model this link, we use the function (b_r) as a proxy for the ability of the plant to tolerate inundations, which depends on the riverbed depth reached by the roots a certain time, and relate that to the plant growth rate. We consider that the total biomass density B (above- and below-ground components) grows in time (t) following a logistic function:

$$begin{aligned} frac{dB}{dt} = sigma _B B biggl (1-frac{B}{B_{max}}biggr ) quad , end{aligned}$$
(3)

where (B_{max}) is the maximum biomass value (set to 1 in our model) and (sigma _B) is the growth rate assumed to vary depending on the dimensionless root density distribution, (b_r), as

$$begin{aligned} sigma _B = phi _{B} int _{0}^{1}b_{r}(z)dz end{aligned}$$
(4)

where (phi _{B} [-]) is a scaling factor and (int _{0}^{1}b_{r}(z) dz) represents the steady-state dimensionless root biomass. With Eq. (4), we assume that vegetation grows faster (higher (sigma _B)) when plant roots are more developed (at the steady-state). This assumption is largely used in modelling dryland vegetation where plant species act as phreatophytes45, similarly to riparian plants. Biomass density decay due to waterlogging is included considering that B decreases exponentially with a constant rate of 0.1 when the riverbed level falls below the mean water table level.
We assume that the rooting depth changes over time following an exponential function as

$$begin{aligned} frac{dzeta _r}{dt} = sigma _{r}( zeta _{r,max}-zeta _r) quad , end{aligned}$$
(5)

where (sigma _r) is the root deepening rate, which is constant, and (zeta _{r,max}) represents the distance between the riverbed surface and the minimum water table level (Fig. 2a). Below such level, riverbed matrix is saturated with pore water and roots cannot grow due to the resulting anoxic conditions43. Equation (5) implies that roots grow faster as farther they are from the groundwater and linearly with (zeta _{r,max}). This behaviour is representative of phreatophytes plant species that uses groundwater as main source of water and tend to elongate roots to keep pace with the receding rate of the water table level21,46.
The proportion of the total biomass growth allocated to each plant component is derived using a mass balance model47 by introducing two constant partitioning coefficients, (lambda _i) with (i in (c,r)) (i.e. canopy and roots). These define the fraction of the total biomass growth allocated above- and below-ground and satisfy (lambda _c + lambda _r = 1) for all times. As a consequence, the growth rates of the two plant components can be written as

$$begin{aligned} frac{dB_i}{dt} = lambda _i frac{dB}{dt} ;. end{aligned}$$
(6)

Here we consider (lambda _i) constant, assuming no plasticity in biomass partitioning. The canopy height depends on the above-ground biomass through the function48

$$begin{aligned} H(t)=aB_c^b(t) end{aligned}$$
(7)

where parameters a and b are constant in our model and can be used to modulate the plant height growth.
Feedbacks between vegetation and hydromorphodynamics
We consider that the above-ground biomass changes the bed roughness by modifying the Strickler coefficient (K_s), which is used to calculate the flow resistance [Eq. (1)], such as

$$begin{aligned} K_s = K_{s,g}+(K_{s,v}-K_{s,g})frac{B_{c}(t)}{B_{c,max }} end{aligned}$$
(8)

where (K_{s,g}) represents the roughness of the bare bed, which depends on the sediment grain size, while (K_{s,v}) (( H_{bur}), where (H_{bur}=beta _{bur}H) with (beta _{bur}in [0,1]) (see Fig.  2a) . The parameter (beta _{bur}) accounts for the ability of the plant to withstand sediment burial and the reduction of the canopy height due to bending caused by water flow25. The uprooting is modelled by defining a critical below-ground biomass (B_{r,cr}) that has to be excavated by flow erosion until vegetation is uprooted19. This is defined as32

$$begin{aligned} B_{r,cr}= int _{0}^{hat{zeta }_{upr}}b_r(z)dz = beta _{upr} int _{0}^{1}b_r(z)dz end{aligned}$$
(10)

where (hat{zeta }_{upr}=zeta _{upr}/zeta _r) represents the ratio between the uprooting depth (zeta _{upr}) and the rooting depth (zeta _r). (beta _{upr}) is a constant parameter that defines the strength of the root system to withstand erosion32. Uprooting can occur during the flood event at any time. B and (zeta _r) are set to their initial values when burial or uprooting occurs.
Riverbed erosion and aggradation processes alter the proportion of above and below-ground biomass during floods. According to the mass balance adopted, vegetation is able to re-allocate biomass at a rate that depends on the partitioning coefficients, (lambda _i) [Eq.  (6)]. We consider that buried part of above-ground biomass can convert to roots, while exposed part of below-ground biomass caused by erosion can transform in above-ground biomass. Canopy height is then re-calculated using Eq.  (7) and the rooting depth is adjusted depending on bed level changes. This assumption is justified by the great plasticity observed in riparian species, which are able to easily resprout from buried stems and grow new tissues from exposed roots49. When no morphological changes occur, the proportion of biomass allocated above- and below-ground can be calculated as (B_i=lambda _iB).
Model workflow
The model workflow is shown in Fig. 2b and considers an alternating sequence of floods and low flow periods. During each flood event morphological changes occur as a result of the two-way interaction between riparian vegetation and river morphodynamic processes. Vegetation can be uprooted during the entire flood event and/or can die by burial at the end of the falling limb of the discharge. Low flow periods are comprised between two consecutive flood events and may last from months to years. In this phase the riverbed is inactive ((theta < theta _{cr})) and vegetation is allowed to grow and develop above- and below-ground traits. As a first step, we simulate vegetation growth during a low flow period, given a vegetation cover and a riverbed topography. The biomass growth rate [Eq. (4)] is dynamically computed through the evaluation of the function (b_r) [see Eq.  (11) in the Appendix], which varies in space depending on the local (cell-wise) variability of the water table level during the growth period. Here we assume that the water table level changes locally with the water surface elevation. This assumption holds true for gravel, uniform substrates where hydraulic conductivity is high43. Discharge variability during low flow periods is responsible for changes in water surface elevations, and thus in water table levels. To evaluate the water surface elevation associated with a certain discharge, we derive a water level-discharge h-Q relation for each computational cell by running one hydrodynamic (fixed bed) simulation for a series of discharges. When the water surface elevation is zero, namely when the cell becomes dry at a certain discharge, we assume that the water table level can be calculated by interpolating the water surface elevations of the nearest wet cells. This allow us to transform discharges in water table level time series and to obtain the frequency distribution of water table levels for each cell during low flow periods. The h-Q relation is then used to calibrate the parameters (lambda _w), (eta _w), (gamma _w) required to compute the function ({b}_r) [see Eqs. (12) and (13) in the Appendix for details] and calculate the growth rate, (sigma _B). By numerically integrating Eqs. (3) and (5) for the duration of the specific growth period, we update the vegetation variables and the associated feedbacks. This procedure introduces a link between plant growth rate and low flow regime characteristics, which in turn influences plant traits. As a second step, we simulate the riverbed evolution during floods, where feedback mechanisms between vegetation and hydromorphological processes are activated. In particular, the uprooting mechanism [Eq. (10)], the correction of the bed shear stress and flow resistance [Eq. (8)], and the critical Shield parameter [Eq. (9)] are updated every time step. After the flood, we used the new river bed topography for updating the vegetation cover including mortality by burial. The resulting vegetation cover and plant traits are then used as initial conditions for the subsequent growth period. More

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. More

1.
Smale, M. J. & Cliff, G. Cephalopods in the diets of four shark species (Galeocerdo cuvier, Sphyrna lewini, S. zygaena and S. mokarran) from KwaZulu-Natal, South Africa. Afr. J. Mar. Sci. 20(1), 241–253. https://doi.org/10.2989/025776198784126610 (2010).
Article  Google Scholar 
2.
Smale, M. J. & Cliff, G. White sharks and cephalopod prey. In Global Perspectives on the Biology and Life History of the white shark (ed. Domeier, M. L.) (CRC Press, Boca Raton, 2012).
Google Scholar 

3.
Galván-Magaña, F. et al. Shark predation on cephalopods in the Mexican and Ecuadorian Pacific Ocean. Deep-Sea Res. PT. II 95, 52–62. https://doi.org/10.1016/j.dsr2.2013.04.002 (2013).
Article  Google Scholar 

4.
Ellis, R. & McCosker, J. E. Great White Shark (Stanford University Press, Stanford, 1991).
Google Scholar 

5.
Jaime-Rivera, M., Caraveo-Patiño, J., Hoyos-Padilla, E. M. & Galván-Magaña, F. (2014) Feeding and migration habits of white shark Carcharodon carcharias (Lamniformes: Lamnidae) from Isla Guadalupe inferred by analysis of stable isotopes δ15N and δ13C. Rev. Biol. Trop. 62(2), 637–647. https://doi.org/10.15517/RBT.V62I2.7767.

6.
Boustany, A. M. et al. Expanded niche for white sharks. Nature 415, 35–36. https://doi.org/10.1038/415035b (2002).
ADS  CAS  Article  PubMed  Google Scholar 

7.
Nasby-Lucas, N., Dewar, H., Lam, C. H., Goldman, K. J. & Domeier, M. L. White shark offshore habitat: a behavioral and environmental characterization of the Eastern Pacific shared offshore foraging area. PLoS ONE 4(12), e8163. https://doi.org/10.1371/journal.pone.0008163 (2009).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

8.
Domeier, M. L. & Nasby-Lucas, N. Migration patterns of white sharks Carcharodon carcharias tagged at Guadalupe Island, Mexico, and identification of an eastern Pacific shared offshore foraging area. Mar. Ecol. Prog. Ser. 370, 221–237. https://doi.org/10.3354/meps07628 (2008).
ADS  Article  Google Scholar 

9.
Hoyos-Padilla, E. M., Klimley, A. P., Galván-Magaña, F. & Antoniou, A. Contrasts in the movements and habitat use of juvenile and adult white sharks (Carcharodon carcharias) at Guadalupe Island, Mexico. Anim. Biotelem. 4(1), 14. https://doi.org/10.1186/s40317-016-0106-7 (2016).
Article  Google Scholar 

10.
Galván-Magaña, F., Hoyos-Padilla, E. M., Navarro-Serment, C. J. & Márquez-Farías, F. Records of white shark, Carcharodon carcharias, in the Gulf of California, Mexico. Mar. Biodiver. Rec. https://doi.org/10.1017/S1755267210000977 (2010).
Article  Google Scholar 

11.
Domeier, M. L. A new life-history hypothesis for white sharks, Carcharodon carcharias, in the Northeastern Pacific. In Global Perspectives on the Biology and Life History of the White Shark (ed. Domeier, M. L.) (CRC Press, Boca Raton, 2012).
Google Scholar 

12.
Zeidberg, L. D. & Robinson, B. H. Invasive range expansion by the Humboldt squid, Dosidicus gigas, in the eastern North Pacific. Proc. Natl. Acad. Sci. U.S.A. 104, 12948–12950. https://doi.org/10.1073/pnas.0702043104 (2007).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

13.
Walther-Mendoza, M., Ayala-Bocos, A., Hoyos-Padilla, M. & Reyes-Bonilla, H. New records of fishes from Guadalupe Island, northwest Mexico. Hidrobiologica 23(3), 410–414 (2013).
Google Scholar 

14.
Gálvez, C., Pardo, M. A. & Elorriaga-Verplancken, F. R. Impacts of extreme ocean warming on the early development of a marine top predator: the Guadalupe fur seal. Prog. Oceanogr. 180, 102220. https://doi.org/10.1016/j.pocean.2019.102220 (2020).
Article  Google Scholar 

15.
Gilly, W. F. et al. Vertical and horizontal migrations by the jumbo squid Dosidicus gigas revealed by electronic tagging. Mar. Ecol. Prog. Ser. 324, 1–17. https://doi.org/10.3354/meps324001 (2006).
ADS  Article  Google Scholar 

16.
Medellín-Ortiz, A., Cadena-Cárdenas, L. & Santana-Morales, O. Environmental effects on the jumbo squid fishery along Baja California’s west coast. Fish. Sci. 82(6), 851–861. https://doi.org/10.1007/s12562-016-1026-4 (2016).
CAS  Article  Google Scholar 

17.
Papastamatiou, Y. P., Verbeck, D., Hutchinson, M., Bracken-Grissom, H. D. & Chapman, D. An encounter between a pelagic shark and giant cephalopod. J. Fish. Biol. https://doi.org/10.1111/jfb.14415 (2020).
Article  PubMed  Google Scholar 

18.
Ruiz-Cooley, R. I., Markaida, U., Gendron, D. & Aguíñiga, S. Stable isotopes in jumbo squid (Dosidicus gigas) beaks to estimate its trophic position: comparison between stomach contents and stable isotopes. J. Mar. Biol. Ass. UK 86, 437–445. https://doi.org/10.1017/S0025315406013324 (2006).
Article  Google Scholar 

19.
Skomal, G. B., Hoyos-Padilla, E. M., Kukulya, A. & Stokey, R. Subsurface observations of white shark Carcharodon carcharias predatory behaviour using an autonomous underwater vehicle. J. Fish. Biol. 87(6), 1293–1312. https://doi.org/10.1111/jfb.12828 (2015).
CAS  Article  PubMed  Google Scholar 

20.
Watanabe, H., Kubodera, T., Ichii, T. & Kawahara, S. Feeding habits of neon flying squid Ommastrephes bartramii in the transitional region of the central North Pacific. Mar. Ecol. Prog. Ser. 266, 173–184. https://doi.org/10.3354/meps266173 (2004).
ADS  Article  Google Scholar 

21.
Roe, H. S. J. The food and feeding habits of the sperm whales (Physeter catodon L.) taken off the west coast of Iceland. ICES. J. Mar. Sci. 33(1), 93–102. https://doi.org/10.1093/icesjms/33.1.93 (1969).
Article  Google Scholar 

22.
Evans, K., Morrice, M., Hindell, M. & Thiele, D. Three mass strandings of sperm whales (Physeter macrocephalus) in southern Australian waters. Mar. Mam. Sci. 18(3), 622–643. https://doi.org/10.1111/j.1748-7692.2002.tb01063.x (2002).
Article  Google Scholar 

23.
Smith, A. Cephalopod sucker design and the physical limits to negative pressure. J. Exp. Biol. 199(4), 949–958 (1996).
CAS  PubMed  Google Scholar 

24.
Saito, H., Sakai, M. & Wakabayashi, T. Characteristics of the lipid and fatty acid compositions of the Humboldt squid, Dosidicus gigas: the trophic relationship between the squid and its prey. Euro. J. Lipid Sci. Technol. 116(3), 360–366. https://doi.org/10.1002/ejlt.201300230 (2014).
CAS  Article  Google Scholar 

25.
Pethybridge, H. R., Parrish, C. C., Bruce, B. D., Young, J. W. & Nichols, P. D. Lipid, fatty acid and energy density profiles of white sharks: insights into the feeding ecology and ecophysiology of a complex top predator. PLoS ONE 9(5), e97877. https://doi.org/10.1371/journal.pone.0097877 (2014).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

26.
Galván-Magaña, F. et al. Shark ecology, the role of the apex predator and current conservation status. In Advances in Marine Biology Vol. 83 (eds Lowry, D. & Larson, S. E.) 61–114 (Academic Press, London, 2019). https://doi.org/10.1016/bs.amb.2019.08.005.
Google Scholar 

27.
Rodríguez-Barreto, D. et al. Comparative study of lipid and fatty acid composition in different tissues of wild and cultured female broodstock of greater amberjack (Seriola dumerili). Aquaculture 2012, 360–361. https://doi.org/10.1016/j.aquaculture.2012.07.013 (2012).
CAS  Article  Google Scholar 

28.
Sosa-Nishizaki, O., Morales-Bojórquez, E., Nasby-Lucas, N., Oñate-González, E. & Domeier, M. L. Problems with photo identification as a method of estimating abundance of white sharks, Carcharodon carcharias. In Global Perspectives on the Biology and Life History of the White Shark (ed. Domeier, M. L.) (CRC Press, Boca Raton, 2012).
Google Scholar 

29.
Gallo-Reynoso, J. P., Le Boeuf, B. J., Figueroa-Carranza, A. L. & Maravilla-Chávez, M. O. Los pinnípedos de Isla Guadalupe. In Isla Guadalupe: Hacia su restauración y conservación (eds Santos-del-Prado, K. & Peters, E.) (Instituto Nacional de Ecología, Mexico City, 2005).
Google Scholar 

30.
Lynn, R. J. & Simpson, J. J. The California Current System: the seasonal variability of its physical characteristics. J. Geophys. R. 92, 12947–12966 (1987).
ADS  Article  Google Scholar 

31.
Becerril-García, E. E., Hoyos-Padilla, E. M., Micarelli, P., Galván-Magaña, F. & Sperone, E. Behavioural responses of white sharks to specific baits during cage diving ecotourism. Sci. Rep. 10(1), 1–11. https://doi.org/10.1038/s41598-020-67947-x (2020).
CAS  Article  Google Scholar 

32.
Bruce, B. D. & Bradford, R. W. Habitat use and spatial dynamics of juvenile white sharks, Carcharodon carcharias, in eastern Australia. In Global Perspectives on the Biology and Life History of the White Shark (ed. Domeier, M. L.) (CRC Press, Boca Raton, 2012).
Google Scholar  More

1.
Lovley, D. R. Electromicrobiology. Annu. Rev. Microbiol 66, 391–409 (2012).
CAS  PubMed  Article  Google Scholar 
2.
Nealson, K. H. & Rowe, A. R. Electromicrobiology: realities, grand challenges, goals, and predictions. Micro. Biotechnol. 9, 595–600 (2016).
Article  Google Scholar 

3.
Nealson, K. H. Bioelectricity (electromicrobiology) and sustainability. Micro. Biotechnol. 10, 1114–1119 (2017).
CAS  Article  Google Scholar 

4.
Logan, B. E., Rossi, R., Ragab, A. & Saikaly, P. E. Electroactive microorganisms in bioelectrochemical systems. Nat. Rev. Microbiol 17, 307–319 (2019).
CAS  PubMed  Article  Google Scholar 

5.
Lonergan, D. J. et al. Phylogenetic analysis of dissimilatory Fe(III)-reducing bacteria. J. Bacteriol. 178, 2402–2408 (1996).
CAS  PubMed  PubMed Central  Article  Google Scholar 

6.
Lovley, D. R., Phillips, E. J. P., Caccavo, F., Nealson, K. H. & Myers, C. Acetate oxidation by dissimilatory Fe(III) reducers. Appl. Environ. Microbiol 58, 3205–3208 (1992).
CAS  PubMed  PubMed Central  Article  Google Scholar 

7.
Myers, C. R., Nealson, K. H. & June, I. Bacterial manganese reduction and growth with manganese oxide as the sole electron acceptor. Science 240, 1319–1322 (1988).
CAS  PubMed  Article  Google Scholar 

8.
Rotaru, A. E. et al. A new model for electron flow during anaerobic digestion: direct interspecies electron transfer to Methanosaeta for the reduction of carbon dioxide to methane. Energy Environ. Sci. 7, 408–415 (2014).
CAS  Article  Google Scholar 

9.
Kato, S., Hashimoto, K. & Watanabe, K. Microbial interspecies electron transfer via electric currents through conductive minerals. Proc. Natl Acad Sci. USA 109, 10042–10046 (2012).
CAS  PubMed  Article  Google Scholar 

10.
Katuri, K. P. et al. Dual-function electrocatalytic and macroporous hollow fiber cathode for converting water streams to valuable resources using microbial electrochemical systems. Adv. Mater. 30, 1707072 (2018).
Article  CAS  Google Scholar 

11.
Pandey, P. et al. Recent advances in the use of different substrates in microbial fuel cells toward wastewater treatment and simultaneous energy recovery. Appl. Energ. 168, 706–723 (2016).
CAS  Article  Google Scholar 

12.
Chiranjeevi, P. & Patil, S. A. Strategies for improving the electroactivity and specific metabolic functionality of microorganisms for various microbial electrochemical technologies. Biotechnol. Adv. 39, 107468 (2020).

13.
Kiran, R. & Patil, S. A. in Introduction to Biofilm Engineering, Vol. 1323 (eds Rathinam, N. K. & Sani, R. K.) 159–186 (ACS: Symposium Series, 2019).

14.
Rowe, A. R. et al. In situ electrochemical enrichment and isolation of a magnetite-reducing bacterium from a high pH serpentinizing spring. Environ. Microbiol. 19, 2272–2285 (2017).
CAS  PubMed  Article  Google Scholar 

15.
Shrestha, N. et al. Extremophiles for microbial-electrochemistry applications: a critical review. Bioresour. Technol. 255, 318–330 (2018).
CAS  PubMed  Article  Google Scholar 

16.
Dopson, M., Ni, G. & Sleutels, T. H. J. A. Possibilities for extremophilic microorganisms in microbial electrochemical systems. FEMS Microbiol. Rev. 40, 164–181 (2016).
CAS  PubMed  Article  Google Scholar 

17.
Pierra, M., Carmona-martínez, A. A., Trably, E., Godon, J. & Bernet, N. Bioelectrochemistry specific and efficient electrochemical selection of Geoalkalibacter subterraneus and Desulfuromonas acetoxidans in high current-producing biofilms. Bioelectrochemistry 106, 182–189 (2015).
Article  CAS  Google Scholar 

18.
Alqahtani, M. F. et al. Enrichment of Marinobacter sp. and halophilic homoacetogens at biocathode of microbial electrosynthesis systems inoculated with Red-Sea brine pool. Front. Microbiol. 109, 2563 (2019).
Article  Google Scholar 

19.
Shehab et al. Enrichment of extremophilic exoelectrogens in microbial electrolysis cells using Red Sea brine pools as inocula. Bioresour. Technol. 239, 82–86 (2017).
CAS  PubMed  Article  Google Scholar 

20.
Sulonen, M. L. K., Kokko, M. E., Lakaniemi, A. & Puhakka, J. A. Electricity generation from tetrathionate in microbial fuel cells by acidophiles. J. Hazard Mater. 284, 182–189 (2015).
CAS  PubMed  Article  Google Scholar 

21.
Badalamenti, J. P., Krajmalnik-Brown, R. & Torres, I. Generation of high current densities by pure cultures of anode-respiring Geoalkalibacter spp. under alkaline and saline conditions in microbial electrochemical cells. mBio 4, e00144–13 (2013).
CAS  PubMed  PubMed Central  Article  Google Scholar 

22.
Holmes, D. E., Nicoll, J. S., Bond, D. R. & Lovley, D. R. Potential role of a novel psychrotolerant member of the family Geobacteraceae, Geopsychrobacter electrodiphilus gen. nov., sp. nov., in electricity production by a marine sediment fuel cell. Appl. Environ. Microbiol. 75, 885 (2009).
CAS  PubMed Central  Article  PubMed  Google Scholar 

23.
Parameswaran, P., Bry, T., Popat, S. C., Lusk, B. G. & Rittmann, B. E. Kinetic, electrochemical, and microscopic characterization of the thermophilic, anode-respiring bacterium Thermincola ferriacetica. Environ. Sci. Technol. 47, 4934–4940 (2013).
CAS  PubMed  Article  Google Scholar 

24.
Pillot, G. et al. Specific enrichment of hyperthermophilic electroactive Archaea from a deep-sea hydrothermal vent on electrically conductive support. Bioresour. Technol. 259, 304–311 (2018).
CAS  PubMed  Article  Google Scholar 

25.
Cerqueira, T. et al. Sediment microbial diversity of three deep-sea hydrothermal vents southwest of the azores. Micro. Ecol. 74, 332–349 (2017).
CAS  Article  Google Scholar 

26.
Jangir, Y. et al. In situ electrochemical studies of the terrestrial deep subsurface biosphere at the Sanford Underground Research Facility, South Dakota, USA. Front Energy Res 7, 1–17 (2019).
Article  Google Scholar 

27.
Carmona-Martinez, A. A., Pierra, M., Trably, E. & Bernet, N. High current density via direct electron transfer by the halophilic anode respiring bacterium Geoalkalibacter subterraneus. Phys. Chem. Chem. Phys. 15, 19699–19707 (2013).
CAS  PubMed  Article  Google Scholar 

28.
Abrevaya, X. C., Sacco, N., Mauas, P. J. D. & Cortón, E. Archaea-based microbial fuel cell operating at high ionic strength conditions. Extremophiles 15, 633–642 (2011).
CAS  PubMed  Article  Google Scholar 

29.
Ledezma, P., Lu, Y. & Freguia, S. Electroactive haloalkaliphiles exhibit exceptional tolerance to free ammonia. FEMS Microbiol. Lett. 365, 1–6 (2018).
Article  CAS  Google Scholar 

30.
Kumar, S. K., Feria, S. O., Ramírez, T. J., Seijas, R. N. & Varaldo, P. H. M. Electrochemical, and chemical enrichment methods of a sodic-saline inoculum for microbial fuel cells. Int. J. Hydrog. Energy 38, 12600–12609 (2013).
Article  CAS  Google Scholar 

31.
Borul, S. B. Study of water quality of Lonar Lake. J. Chem. Pharm. Res. 4, 1716–1718 (2012).
CAS  Google Scholar 

32.
Jadhav, R. D. & Mali, H. B. A search for the source of high content of sodium chloride (NaCl) at Crater Lake Lonar, Maharashtra, India. Int. J. Adv. Res. Ideas Innov. Technol. 4, 255–261 (2018).
Google Scholar 

33.
Wani, A. A. et al. Molecular analyses of microbial diversity associated with the Lonar soda lake in India: an impact crater in a basalt area. Res. Microbiol. 157, 928–937 (2006).
CAS  PubMed  Article  Google Scholar 

34.
Joshi, A. A. et al. Cultivable bacterial diversity of alkaline Lonar Lake. India Microbiol. Ecol. 55, 163–172 (2008).
Article  Google Scholar 

35.
Paul, D. et al. Exploration of microbial diversity and community structure of Lonar Lake: the only hypersaline meteorite Crater Lake within basalt rock. Front. Microbiol. 6, 1–12 (2016).
Article  Google Scholar 

36.
Misra, S. et al. Geochemical identification of impactor for Lonar crater, India. Meteorit. Planet Sci. 1018, 1001–1018 (2009).
Article  Google Scholar 

37.
Koshy, N. et al. Characterization of the soil samples from the Lonar crater. India Geotech. Eng. 49, 99–105 (2018).
Google Scholar 

38.
Yee, M. O., Deutzmann, J., Spormann, A. & Rotaru, A. Cultivating electroactive microbes—from field to bench. Nanotechnology 31, 174003 (2020).
CAS  PubMed  Article  Google Scholar 

39.
Korth, B. & Harnisch, F. Spotlight on the energy harvest of electroactive microorganisms: the impact of the applied anode potential. Front. Microbiol. 10, 1–9 (2019).
Article  Google Scholar 

40.
Parot, S., Delia, M. L. & Bergel, A. Forming electrochemically active biofilms from garden compost under chronoamperometry. Bioresour. Technol. 99, 4809–4816 (2008).
CAS  PubMed  Article  Google Scholar 

41.
Torres, C. I. et al. Selecting anode-respiring bacteria based on anode potential: phylogenetic, electrochemical, and microscopic characterization. Environ. Sci. Technol. 43, 9519–9524 (2009).
CAS  PubMed  Article  Google Scholar 

42.
Babu, P., Chandel, A. K. & Singh, O. V. in Extremophiles and Their Applications in Medical Processes (eds Babu, P., Chandel, A. K. & Singh, O.V.) 9–24 (Springer, 2015).

43.
Harnisch, F. & Freguia, S. A basic tutorial on cyclic voltammetry for the investigation of electroactive microbial biofilms. Chem. Asian J. 7, 466–475 (2012).
CAS  PubMed  Article  Google Scholar 

44.
Peng, L. et al. Geobacter sulfurreducens adapts to low electrode potential for extracellular electron transfer. Electrochim. Acta 191, 743–749 (2016).
CAS  Article  Google Scholar 

45.
Marsili, E., Sun, J. & Bond, D. R. Voltammetry and growth physiology of Geobacter sulfurreducens biofilms as a function of the growth stage and imposed electrode potential. Electroanalysis 22, 865–874 (2010).
CAS  Article  Google Scholar 

46.
Yoho, R. A., Popat, S. C., Rago, L. & Guisasola, A. Anode biofilms of Geoalkalibacter ferrihydriticus exhibit electrochemical signatures of multiple electron transport pathways. Langmuir 31, 12552–12559 (2015).
CAS  PubMed  Article  Google Scholar 

47.
Schroder, U. & Harnisch, F. In Encyclopedia of Applied Electrochemistry (eds Kreysa, G., Ota, K. & Savinell, R. F.) 120–126 (Springer, New York, 2014).

48.
Patil, S. A., Harnisch, F., Kapadnis, B. & Schröder, U. Electroactive mixed culture biofilms in microbial bioelectrochemical systems: The role of temperature on the formation and performance. Biosens. Bioelectron. 26, 803–808 (2010).
CAS  PubMed  Article  Google Scholar 

49.
Liu, Y., Climent, V., Berná, A. & Feliu, J. M. Effect of temperature on the catalytic ability of electrochemically active biofilm as anode catalyst in microbial fuel cells. Electroanalysis 23, 387–394 (2011).
CAS  Article  Google Scholar 

50.
Huang, L., Hwang, A. & Phillips, J. Effect of temperature on microbial growth rate-mathematical analysis: the Arrhenius and Eyring-Polanyi connections. J. Food Sci. 76, 553–560 (2011).
Article  CAS  Google Scholar 

51.
Labelle, E. & Bond, D. R. In Bioelectrochemical Systems: From Extracellular Electron Transfer to Biotechnological Applications (eds Rabaey, K., Angenent, I., Schroder, U. & Keller, J.) 137–152 (IWA Publishing, London, 2005).

52.
Patil, S. A., Hägerhäll, C. & Gorton, L. Electron transfer mechanisms between microorganisms and electrodes in bioelectrochemical systems. Bio Anal. Rev. 4, 159–192 (2012).
Article  Google Scholar 

53.
Richter, H. et al. Cyclic voltammetry of biofilms of wild type and mutant Geobacter sulfurreducens on fuel cell anodes indicates possible roles of OmcB, OmcZ, type IV pili, and protons in extracellular electron transfer. Energy Environ. Sci. 2, 506–516 (2009).
CAS  Article  Google Scholar 

54.
Katuri, K. P., Rengaraj, S., Kavanagh, P., O’Flaherty, V. & Leech, D. Charge transport through Geobacter sulfurreducens biofilms grown on graphite rods. Langmuir 28, 7904–7913 (2012).
CAS  PubMed  Article  Google Scholar 

55.
Fricke, K., Harnisch, F. & Schroder, U. On the use of cyclic voltammetry for the study of anodic electron transfer in microbial fuel cells. Energy Environ. Sci. 1, 144–147 (2008).
CAS  Article  Google Scholar 

56.
Harnisch, F. et al. Revealing the electrochemically driven selection in natural community derived microbial biofilms using flow-cytometry. Energy Environ. Sci. 4, 1265–1267 (2011).
CAS  Article  Google Scholar 

57.
Carmona-Martinez, A. A. et al. Cyclic voltammetric analysis of the electron transfer of Shewanella oneidensis MR-1 and nanofilament and cytochrome knock-out mutants. Bioelectrochemistry 81, 74–80 (2011).
CAS  PubMed  Article  Google Scholar 

58.
Firer-Sherwood, M., Pulcu, G. S. & Elliott, S. J. Electrochemical interrogations of the Mtr cytochromes from Shewanella: opening a potential window. J. Biol. Inorg. Chem. 13, 849–854 (2008).
CAS  PubMed  Article  Google Scholar 

59.
Baron, D., LaBelle, E., Coursolle, D., Gralnick, J. A. & Bond, D. R. Electrochemical measurement of electron transfer kinetics by Shewanella oneidensis MR-1. J. Biol. Chem. 284, 28865–28873 (2009).
CAS  PubMed  PubMed Central  Article  Google Scholar 

60.
Holmes, D. E., Nevin, K. P. & Lovley, D. R. Comparison of nifD, recA, gyrB and fusA genes within the family Geobacteraceae Fam. nov. Int. J. Syst. Evol. Microbiol 54, 1591–1599 (2004).
CAS  PubMed  Article  Google Scholar 

61.
Greene, A. C., Patel, B. K. C. & Yacob, S. Anaerobic Fe (III) – and Mn (IV) -reducing bacterium from a petroleum reservoir, and emended descriptions of the family Desulfuromonadaceae and the genus Geoalkalibacter. Int. J. Syst. Evol. Microbiol. 59, 781–785 (2009).
CAS  PubMed  Article  Google Scholar 

62.
Badalamenti, J. P., Summers, Z. M., Chan, C. H., Gralnick, J. A. & Bond, D. R. Isolation and genomic characterization of ‘Desulfuromonas soudanensis WTL’, a metal- and electrode-respiring bacterium from anoxic deep subsurface brine. Front. Microbiol. 7, 1–11 (2016).
Article  Google Scholar 

63.
Jayashree, C., Tamilarasan, K., Rajkumar, M., Arulazhagan, P. & Yogalakshmi, K. N. Treatment of seafood processing wastewater using up-flow microbial fuel cell for power generation and identification of bacterial community in anodic biofilm. J. Environ. Manag. 180, 351–358 (2016).
CAS  Article  Google Scholar 

64.
Monzon, O. et al. Microbial fuel cell fed by Barnett Shale produced water: power production by hypersaline autochthonous bacteria and coupling to a desalination unit. Biochem. Eng. J. 117, 87–91 (2017).
CAS  Article  Google Scholar 

65.
Kevbrin, V. V., Zhilina, T. N., Rainey, F. A. & Zavarzin, G. A. Tindallia magadii gen. nov., sp. nov.: an alkaliphilic anaerobic ammonifier from Soda Lake deposits. Curr. Microbiol. 37, 94–100 (1998).
CAS  PubMed  Article  Google Scholar 

66.
Mei, N. et al. Serpentinicella alkaliphila gen. nov., sp. nov., a novel alkaliphilic anaerobic bacterium isolated from the serpentinite-hosted Prony hydrothermal field, New Caledonia. Int. J. Syst. Evol. Microbiol. 66, 4464–4470 (2016).
CAS  PubMed  Article  Google Scholar 

67.
Pasupuleti, S. B., Srikanth, S., Dominguez-Benetton, X., Mohan, S. V. & Pant, D. Dual gas diffusion cathode design for Microbial Fuel Cell (MFC): optimizing the suitable mode of operation in terms of biochemical & bioelectro-kinetic evaluation. J. Chem. Technol. Biotechnol. 91, 624–639 (2016).
CAS  Article  Google Scholar 

68.
Srikanth, S. et al. Electro-biocatalytic conversion of carbon dioxide to alcohols using gas diffusion electrodes. Bioresour. Technol. 265, 45–51 (2018).
CAS  PubMed  Article  Google Scholar 

69.
Patil, S. A. et al. Electroactive mixed culture derived biofilms in microbial bioelectrochemical systems: the role of pH on biofilm formation, performance, and composition. Bioresour. Technol. 102, 9683–9690 (2011).
CAS  PubMed  Article  Google Scholar 

70.
Feng, Y., Yang, Q., Wang, X. & Logan, B. E. Treatment of carbon fiber brush anodes for improving power generation in air-cathode microbial fuel cells. J. Power Sources 195, 1841–1844 (2010).
CAS  Article  Google Scholar  More

1.
IPBES Summary for Policymakers. In Global Assessment Report on Biodiversity and Ecosystem Services (eds Díaz, S. et al.) (IPBES Secretariat, 2019).
2.
Schaefer, M., Goldman, E., Bartuska, A. M., Sutton-Grier, A. & Lubchenco, J. Nature as capital: advancing and incorporating ecosystem services in United States federal policies and programs. Proc. Natl Acad. Sci. USA 112, 7383–7389 (2015).
CAS  Article  Google Scholar 

3.
Mastrángelo, M. E. et al. Key knowledge gaps to achieve global sustainability goals. Nat. Sustain. https://doi.org/10.1038/s41893-019-0412-1 (2019).

4.
Olander, L. et al. So you want your research to be relevant? Building the bridge between ecosystem services research and practice. Ecosyst. Serv. 26, 170–182 (2017).
Article  Google Scholar 

5.
Polasky, S., Tallis, H. & Reyers, B. Setting the bar: standards for ecosystem services. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.1406490112 (2015).

6.
Rieb, J. et al. When, where and how nature matters for ecosystem services: challenges for the next generation of ecosystem service models. BioScience 67, 820–833 (2017).
Article  Google Scholar 

7.
Natural Capital Protocol (Natural Capital Coalition, 2016).

8.
Mandle, L., Ouyang, Z., Salzman, J. & Daily, G. C. Green Growth that Works: Natural Capital Policy and Finance Mechanisms from the World (Island Press, 2019).

9.
Transforming our World: The 2030 Agenda for Sustainable Development (UN, 2015).

10.
Díaz, S. et al. Assessing nature’s contributions to people: recognizing culture, and diverse sources of knowledge, can improve assessments. Science 359, 270–272 (2018).
Article  Google Scholar 

11.
Arkema, K. K. et al. Embedding ecosystem services in coastal planning leads to better outcomes for people and nature. Proc. Natl Acad. Sci. USA 112, 7390–7395 (2015).
CAS  Article  Google Scholar 

12.
Van Wensem, J. et al. Identifying and assessing the application of ecosystem services approaches in environmental policies and decision making. Integr. Environ. Assess. Manag. 13, 41–51 (2017).
Article  Google Scholar 

13.
Ricketts, T. H. & Lonsdorf, E. Mapping the margin: comparing marginal values of tropical forest remnants for pollination services. Ecol. Appl. 23, 1113–1123 (2013).
Article  Google Scholar 

14.
Mandle, L., Tallis, H., Sotomayor, L. & Vogl, A. L. Who loses? Tracking ecosystem service redistribution from road development and mitigation in the Peruvian Amazon. Front. Ecol. Environ. 13, 309–315 (2015).
Article  Google Scholar 

15.
Wieland, R., Ravensbergen, S., Gregr, E. J., Satterfield, T. & Chan, K. M. A. Debunking trickle-down ecosystem services: the fallacy of omnipotent, homogeneous beneficiaries. Ecol. Econ. 121, 175–180 (2016).
Article  Google Scholar 

16.
Polasky, S. & Segerson, K. Integrating ecology and economics in the study of ecosystem services: some lessons learned. Annu. Rev. Resour. Econ. 1, 409–434 (2009).
Article  Google Scholar 

17.
Keeler, B. L. et al. Linking water quality and well-being for improved assessment and valuation of ecosystem services. Proc. Natl Acad. Sci. USA 109, 18619–18624 (2012).
CAS  Article  Google Scholar 

18.
Vogl, A. L. et al. Valuing investments in sustainable land management in the Upper Tana River basin, Kenya. J. Environ. Manag. 195, 78–91 (2017).
Article  Google Scholar 

19.
Arkema, K., Guannel, G. & Verutes, G. Coastal habitats shield people and property from sea-level rise and storms. Nat. Clim. Change 3, 913–918 (2013).
Article  Google Scholar 

20.
Plummer, M. L. Assessing benefit transfer for the valuation of ecosystem services. Front. Ecol. Environ. 7, 38–45 (2009).
Article  Google Scholar 

21.
Tallis, H., Polasky, S., Lozano, J. S. & Wolny, S. in Inclusive Wealth Report 2012: Measuring Progress Toward Sustainability 195–214 (Cambridge Univ. Press, 2012).

22.
Costanza, R. et al. Changes in the global value of ecosystem services. Glob. Environ. Change https://doi.org/10.1016/j.gloenvcha.2014.04.002 (2014).

23.
Granek, E. F. et al. Ecosystem services as a common language for coastal ecosystem-based management. Conserv. Biol. 24, 207–216 (2010).
Article  Google Scholar 

24.
Ruckelshaus, M. et al. Notes from the field: lessons learned from using ecosystem service approaches to inform real-world decisions. Ecol. Econ. https://doi.org/10.1016/j.ecolecon.2013.07.009 (2013).

25.
Ellis, A. M., Myers, S. S. & Ricketts, T. H. Do pollinators contribute to nutritional health? PLoS ONE 10, e114805 (2015).
Article  Google Scholar 

26.
Olsson, P., Folke, C. & Hughes, T. P. Navigating the Transition to Ecosystem-Based Management of the Great Barrier Reef, Australia. Proc. Natl Acad. Sci. USA 105, 9489–9494 (2008).
CAS  Article  Google Scholar 

27.
Costanza, R. et al. The value of the world’s ecosystem services and natural capital. Nature 387, 253–260 (1997).
CAS  Article  Google Scholar 

28.
SEEA Experimental Ecosystem Accounting Revision (System of Environmental Economic Accounting, 2020); https://go.nature.com/2sqGqFn

29.
Aburto-Oropeza, O. et al. Mangroves in the Gulf of California increase fishery yields. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.0804601105 (2008).

30.
Keeler, B. L. et al. The social costs of nitrogen. Sci. Adv. 2, e1600219 (2016).
Article  Google Scholar 

31.
Kenter, J. O. et al. What are shared and social values of ecosystems? Ecol. Econ. 111, 86–99 (2015).
Article  Google Scholar 

32.
Pascual, U. et al. Valuing nature’s contributions to people: the IPBES approach. Curr. Opin. Environ. Sustain. 26–27, 7–16 (2017).
Article  Google Scholar 

33.
Samberg, L. H., Gerber, J. S., Ramankutty, N., Herrero, M. & West, P. C. Subnational distribution of average farm size and smallholder contributions to global food production. Environ. Res. Lett. 11, 124010 (2016).
Article  Google Scholar 

34.
Jean, N. et al. Combining satellite imagery and machine learning to predict poverty. Science 353, 790–794 (2016).
CAS  Article  Google Scholar 

35.
Wolff, S., Schulp, C. J. E. & Verburg, P. H. Mapping ecosystem services demand: a review of current research and future perspectives. Ecol. Indic. 55, 159–171 (2015).
Article  Google Scholar 

36.
Dawson, N. & Martin, A. Assessing the contribution of ecosystem services to human wellbeing: a disaggregated study in western Rwanda. Ecol. Econ. 117, 62–72 (2015).
Article  Google Scholar 

37.
Daw, T., Brown, K., Rosendo, S. & Pomeroy, R. Applying the ecosystem services concept to poverty alleviation: the need to disaggregate human well-being. Environ. Conserv. 38, 370–379 (2011).
Article  Google Scholar 

38.
Ruhl, J. B. & Salzman, J. The effects of wetland mitigation banking on people. Natl Wetl. Newsl. 28, 7–13 (2006).
Google Scholar 

39.
Kabisch, N. & Haase, D. Green justice or just green? Provision of urban green spaces in Berlin, Germany. Landsc. Urban Plan. 122, 129–139 (2014).
Article  Google Scholar 

40.
Farley, K. A. & Bremer, L. L. ‘Water Is Life’: local perceptions of páramo grasslands and land management strategies associated with payment for ecosystem services. Ann. Am. Assoc. Geogr. 107, 371–381 (2017).
Google Scholar 

41.
Pascual, U. et al. Social equity matters in payments for ecosystem services. BioScience 64, 1027–1036 (2014).
Article  Google Scholar 

42.
Mastrangelo, M. E. & Laterra, P. From biophysical to social-ecological trade-offs: integrating biodiversity conservation and agricultural production in the Argentine Dry Chaco. Ecol. Soc. 20, 20 (2015).
Article  Google Scholar 

43.
Guerry, A. D. et al. Natural capital and ecosystem services informing decisions: from promise to practice. Proc. Natl Acad. Sci. USA 112, 7348–7355 (2015).
CAS  Article  Google Scholar 

44.
Rieb, J. T. et al. When, where, and how nature matters for ecosystem services: challenges for the next generation of ecosystem service models. BioScience 67, 820–833 (2017).
Article  Google Scholar 

45.
Villa, F., Bagstad, K. J., Voigt, B., Johnson, G. W. & Portela, R. A methodology for adaptable and robust ecosystem services assessment. PLoS ONE 9, e91001 (2014).
Article  Google Scholar 

46.
Díaz, S. et al. Assessing nature’s contributions to people. Science 359, 270–272 (2018).
Article  Google Scholar 

47.
Millennium Ecosystem Assessment Ecosystems and Human Well-being: A Framework for Assessment (Island Press, 2003).

48.
Fleiss, J. L. Measuring nominal scale agreement among many raters. Psychol. Bull. 76, 378–382 (1971).
Article  Google Scholar 

49.
Gamer, M., Lemon, J., Fellows, I. & Singh, P. irr: Various Coefficients of Interrater Reliability and Agreement (2012).

50.
Landis, J. R. & Koch, G. G. The measurement of observer agreement for categorical data. Biometrics 33, 159–174 (1977).
CAS  Article  Google Scholar 

51.
Tallis, H. et al. A global system for monitoring ecosystem service change. BioScience 62, 977–986 (2012).
Article  Google Scholar 

52.
Daily, G. C. et al. Ecosystem services in decision making: time to deliver. Front. Ecol. Environ. 7, 21–28 (2009).
Article  Google Scholar  More