Philippa Kaur

The GCBD is stored at figshare23. Below we describe 20 Tables (also see Fig. 3 schematic) that comprise the GCBD: (1) Site_Info_tbl, (2) Sample_Event_tbl, (3) R_Scripts_tbl, (4) Cover_tbl, (5) Bleaching_tbl, (6) Environmental_tbl, (7) Authors_LUT, (8) Bleaching_Level_LUT, (9) City_Town_Name_LUT, (10) Country_Name_LUT, (11) Data_Source_LUT, (12) Ecoregion_Name_LUT, (13) Exposure_LUT, (14) Ocean_Name_LUT, (15) Realm_Name_LUT, (16) State_Island_Province_Name_LUT, (17) Substrate_Type_LUT, (18) Relevant_Papers_tbl, (19) Severity_Code_LUT, and (20) Bleaching_Prevalence_Score_LUT, where LUT stands for look-up table.

1)

Site Information (Site_Info_tbl)
Latitude_Degrees: latitude coordinates in decimal degrees.
Longitude_Degrees: longitude coordinates in decimal degrees.
Ocean_Name: the ocean in which the sampling took place.
Realm_Name: identification of realm as defined by the Marine Ecoregions of the World (MEOW)12.
Ecoregion_Name: identification of the Ecoregions (150) as defined by Veron et al.13.
Country_Name: the country where sampling took place.
State_Island_Province_Name: the state, territory (e.g., Guam) or island group (e.g., Hawaiian Islands) where sampling took place.
City_Town_Name: the region, city, or nearest town, where sampling took place.
Site_Name: the accepted name of the site or the name given by the team that sampled the reef.
Distance_to_Shore: the distance (m) of the sampling site from the nearest land.
Exposure: a site was considered exposed if it had >20 km of fetch, if there were strong seasonal winds, or if the site faced the prevailing winds. Otherwise, the site was considered sheltered or ‘sometimes’. ‘Sometimes’ refers to a few sites with a >20 km fetch through a narrow geographic window, and therefore we considered that the site was potentially exposed during cyclone seasons. We left the category ‘sometimes’ in the database because those sites were not clearly exposed sites, nor were they clearly sheltered sites, and future researchers may be interested in temporary exposure.
Turbidity: kd490 with a 100-km buffer.
Cyclone_Frequency: number of cyclone events from 1964 to 2014.
Comments: comments of any issues with the site or additional information.

2)

Sample Event Information (Sample_Event_tbl)
Site_ID: site ID field from Site_Info_tbl.
Reef_ID: name of reef site that was adopted by sampling group (from ReefCheck).
Quadrat_No: quadrat number (from McClanahan et al.)20.
Date_Day: the date of the sampling event.
Date_Month: the month of sampling event.
Date_Year: the year of sampling event.
Depth: depth (m) of sampling site. Comments: comments of any issue or additional information of sampling event.

3)

R Code (R_Scripts_tbl)
Relevant_Papers_ID: relevant papers ID field from Relevant_Papers_tbl.
Project name: name of project associated with R code.
Paper_Title: title of paper where R code was published.
Code_Name: name of R code file.
Description: description of the R code.
Data_Source: data source ID field from Data_Source_LUT.
R_Code: attachment of R code file.
URL: hyperlink to R code or link to github.

4)

Coral Cover Information (Cover_tbl)
Sample_ID: sampled ID field from Sample_Event_tbl.
Substrate_Type: substrate type ID field from Substrate_LUT.
S1: Reef Check breaks down transects into four 20 m × 5 m segments, point data from segment one of transect.
S2: Reef Check breaks down transects into four 20 m × 5 m segments, point data from segment two of transect.
S3: Reef Check breaks down transects into four 20 m × 5 m segments, point data from segment three of transect.
S4: Reef Check breaks down transects into four 20 m × 5 m segments, point data from segment four of transect.
Perc_hardcoral: percent hard coral cover from McClanahan et al.20 data source.
Perc_macroalgae: percent macroalgae cover from McClanahan et al.20 data source.
Average_Ellipse_Transect: calculated percent hard coral cover per 10 m × 1 m transect using ellipse equation.
Average_Ellipse_Site: calculated percent hard coral cover per site using ellipse equation.
Comments: comments of any issue or additional information of sampling event

5)

Bleaching Information (Bleaching_tbl)
Sample_ID: sample ID field from Sample_Event_tbl.
Bleaching_Level: Reef Check data, coral population or coral colony.
S1: Reef Check breaks down transects into four 20 m × 5 m segments, percent bleaching from segment one of transect.
S2: Reef Check breaks down transects into four 20 m × 5 m segments, percent bleaching from segment two of transect.
S3: Reef Check breaks down transects into four 20 m × 5 m segments, percent bleaching from segment three of transect.
S4: Reef Check breaks down transects into four 20 m × 5 m segments, percent bleaching from segment four of transect.
Percent_Bleaching_RC_Old_Method: old method of determining percent bleaching from Reef_Check.
Severity_Code: coded range of bleaching severity from Donner et al.10.
Percent_Bleached: percent of coral bleaching.
Number_Bleached_colonies: number of bleached corals from McClanahan et al.20 data source.
Bleaching_intensity: from McClanahan et al.20 data source.
Bleaching_Prevalence_Score: coded range of bleaching prevalence from Safaie et al.21.

6)

Environmental Parameter Information (Environmental_tbl)
Sample_ID: sample ID field from Sample_Event_tbl.
ClimSST: CoRTAD. [Climatological Sea-Surface Temperature (SST)] based on weekly SSTs for the study time frame, created using a harmonics approach.
Temperature_ Kelvin: CoRTAD. SST in Kelvin.
Temperature_Mean: CoRTAD. Mean SST in degrees Celsius.
Temperature_Minimum: CoRTAD. Minimum SST in degrees Celsius.
Temperature_Maximum: CoRTAD. Maximum SST in degrees Celsius.
Temperature_Kelvin_Standard_Deviation: CoRTAD. Standard deviation of SST in Kelvin.
Windspeed: CoRTAD. meters per hour.
SSTA: CoRTAD. (Sea-Surface Temperature Anomaly) weekly SST minus weekly climatological SST.
SSTA_Standard_Deviation: CoRTAD. The Standard Deviation of weekly SSTA in degrees Celsius over the entire period.
SSTA_Mean: CoRTAD. The mean SSTA in degrees Celsius over the entire period.
SSTA_Minimum: CoRTAD. The minimum SSTA in degrees Celsius over the entire period.
SSTA_Maximum: CoRTAD. The maximum SSTA in degrees Celsius over the entire period.
SSTA_Frequency: CoRTAD. (Sea Surface Temperature Anomaly Frequency) number of times over the previous 52 weeks that SSTA  >  = 1 degree Celsius.
SSTA_Frequency_Standard_Deviation: CoRTAD. The standard deviation of SSTA Frequency in degrees Celsius over the entire time period of 40 years.
SSTA_FrequencyMax: CoRTAD. The maximum SSTA Frequency in degrees Celsius over the entire time period.
SSTA_FrequencyMean: CoRTAD. The mean SSTA Frequency in degrees Celsius over the entire time period of 40 years.
SSTA_DHW: CoRTAD. (Sea Surface Temperature Degree Heating Weeks) sum of previous 12 weeks when SSTA  >  = 1 degree Celsius.
SSTA_DHW_Standard_Deviation: CoRTAD. The standard deviation SSTA DHW in degrees Celsius over the entire period.
SSTA_DHWMax: CoRTAD. The maximum SSTA DHW in degrees Celsius over the entire time period of 40 years.
SSTA_DHWMean: CoRTAD. The mean SSTA DHW in degrees Celsius over the entire time period of 40 years.
TSA: CoRTAD. (Thermal Stress Anomaly) weekly SSTs minus the maximum of weekly climatological SSTs in degrees Celsius.
TSA_Standard_Deviation: CoRTAD. The standard deviation of TSA in degrees Celsius over the entire time period of 40 years.
TSA_Minimum: CoRTAD. The minimum TSA in degrees Celsius over the entire time period of 40 years.
TSA_Maximum: CoRTAD. The maximum TSA in degrees Celsius over the entire time period of 40 years.
TSA_Mean: CoRTAD. The mean TSA in degrees Celsius over the entire time period of 40 years.
TSA_Frequency: CoRTAD. The number of times over previous 52 weeks that TSA  >  = 1 degree Celsius.
TSA_Frequency_Standard_Deviation: CoRTAD. The standard deviation of frequency of TSA in degrees Celsius over the entire time period of 40 years.
TSA_FrequencyMax: CoRTAD. The maximum TSA frequency in degrees Celsius over the entire time period of 40 years.
TSA_FrequencyMean: CoRTAD. The mean TSA frequency in degrees Celsius over the entire time period of 40 years.
TSA_DHW: CoRTAD. (Thermal Stress Anomaly Degree Heating Weeks) sum of previous 12 weeks when TSA  >  = 1 degree Celsius.
TSA_DHW_Standard_Deviation: CoRTAD. The standard deviation of TSA DHW in degrees Celsius over the entire time period of 40 years.
TSA_DHWMax: CoRTAD. The maximum TSA DHW in degrees Celsius over the entire time period of 40 years.
TSA_DHWMean: CoRTAD. The mean TSA DHW in degrees Celsius over the entire time period of 40 years.

7)

Author Names (Authors_LUT)
Last_Name: author’s last name.
First_Name: author’s first name.
Middle_Initial: author’s middle initial.

8)

Bleaching Level Information (Bleaching_Level_LUT)
Bleaching_Level: Reef Check data, coral population or coral colony.

9)

City, Town Names (City_Town_Name_LUT)
City_Town_Name: the region, city, or town, where sampling took place.

10)

Country names (Country_Name_LUT)
Country_Name: name of the country where sampling took place.

11)

Data Source Information (Data_Source_LUT)
Data_Source: name of source of original data set.
Sample_Method: Description of the sampling methods used to collect the data. If more than one method was used then we stated that an amalgamation of methods were used to collect the data, and the original papers are found in “Relevant_Papers_tbl”, and can be referenced therein.

12)

Ecoregion Names (Ecoregion_Name_LUT)
Ecoregion_Name: name of Ecoregion from Veron et al.13.

13)

Exposure Type (Exposure_LUT)
Exposure_Type: site exposure to fetch.

14)

Ocean Name Information (Ocean_Name_LUT)
Ocean_Name: name of ocean where sampling took place.

15)

Name of Realm (Realm_Name_LUT)
Realm_Name: name of realm as identified by the Marine Ecoregions of the World (MEOW)12.

16)

State, Island, Province Name (State_Island_Province_Name_LUT)
State_Island_Province_Name, Name of the state, territory (e.g. Guam) or island group (e.g. Hawaiian Islands) where sampling took place.

17)

Substrate Type (Substrate_Type_LUT)
Substrate_Type: type of substrate from Reef Check data.

18)

Relevant Publications (Relevant_Papers_tbl)
Data_Source: source associated with publication.
Author_ID: author ID field from Authors_LUT.
Title: title of published work.
Journal_Name: name of publication journal.
Year_Published: year of publication.
Volume: volume number of journal.
Issue: issue number of journal.
Pages: page range of publication.
URL: hyperlink to publication.
DOI: DOI number of publication.
pdf: pdf attachment of publication.

19)

Severity Index Code (Severity_Code_LUT)
Severity_Code: coded range of bleaching severity from Donner et al.10.

20)

Bleaching Prevalence Code (Bleaching_Prevalence_Score_LUT)

Bleaching_Prevalence_Score: coded range of bleaching prevalence from Safaie et al. 21. More

Here we describe methods for the GAEZ+ 2015 Annual Crop Data, and the GAEZ+ 2015 Monthly Cropland Data. The Annual Crop Data was generated first, then the Monthly Cropland Data was calculated based on the Harvest Area results of the Annual Data (Fig. 1).Fig. 1Schematic overview of annual and monthly data production methods. The GAEZ+ 2015 products described in this paper are in dark blue boxes; publicly available data used are in light blue. Dark blue arrows indicate which data are used in each processing step, and grey arrows from steps to data show which steps result in final GAEZ+ 2015 data products. The processing steps listed here are referred to in the Methods section text.Full size imageGAEZ+ 2015 Annual Crop Data MethodsThe GEAZ+ 2015 Annual Crop Data updates the 2010 GAEZ v4 crop harvest area, yield, and production maps6,7 (identified as Theme 5 in ref. 7) using national-scale data on the change in crop harvested area and livestock numbers from 2010 to 2015, based on statistics for 160 crop groups, and cattle and buffalo, from FAOSTAT5.Three datasets were used to produce GAEZ+ 2015 Annual Crop Data:

1.

FAOSTAT crop production domain: annual, country-level data on crop harvested area (H) and crop production (P) for each crop from the FAOSTAT database (Table 1)Table 1 GAEZ and FAOSTAT crop harmonization.Full size table

2.

GAEZ v46,7 gridded global annual harvested area, yield, and production by crop for the 26 FAOSTAT crops and crop categories at 5-minute resolution

3.

Global Administrative Unit Layer (GAUL 2012)13 data. GAUL 2012 reports the fraction of each global 5-minute grid cell that falls within a given country or disputed territory. There are 275 unique global administrative units.

Step 1. Calculate crop changes from 2010 to 2015 by country:
For each country, we extracted the harvested area (H) and crop production (P) for each of the 160 FAOSTAT crop categories, c, from the FAOSTAT database. We averaged three years (2009–2011) of annual national crop harvested area data to represent 2010 national crop harvest area, H2010, and three years (2014–2016) of annual crop harvested area data to represent 2015 national crop harvest area, H2015, then calculated a ratio, rHc, of 2015 to 2010 harvested areas for each crop c in each country, and equivalently, for crop production:$$r{H}_{c}={H}_{2015}/{H}_{2010}$$
(1)
$$r{P}_{c}={P}_{2015}/{P}_{2010}$$
(2)
This results in 160 rH and rP values per country. If harvest area and production values for a particular crop are zero or unreported in the FAOSTAT data, then rHc and rPc are both set to 1.0 (i.e., no change from 2010 to 2015). Three years of data are averaged (2009 – 2011 and 2014 – 2016) to account for missing data for some country/year combinations and to avoid emphasizing reported outliers.
Step 2. Aggregate FAOSTAT-based ratios to the GAEZ crop categories:
We followed the crop aggregation methods of the GAEZ model to aggregate the FAOSTAT crop list (160 unique crops as of 2019) to 26 crops (see Table 1). For each of the 26 GAEZ crop categories, if there is more than one matching FAOSTAT crop (see Table 1) then we applied an area-weighted average (based on FAOSTAT year 2015 harvested area) of the FAOSTAT crops within each country to the rH and rP values for that crop and country. This results in 26 rH and rP values per country. There was one exception to this: the GAEZ_2010 crop category ‘fodder crops’ was an aggregate of 17 FAOSTAT crops (see Table 1) for which harvest area data are no longer reported on FAOSTAT; i.e., GAEZ_2010 had obtained FAOSTAT data on fodder crops circa 2010, but FAOSTAT no longer provides any data on fodder crops for any year. We assumed that the 2010 to 2015 fractional change in fodder crop harvest area in each country was proportional to the change in the FAOSTAT reported national herd sizes for cattle and buffalo livestock data5 for that country, following the same methodology as for crop harvested area change (see Step 2 below). This method assumes a negligible international trade of fodder crops as indicated by bilateral trade matrices available from FAOSTAT.
Step 3. Apply country-level ratios to grid cells:
Calculated country-level ratios were then applied to each grid cell k, using the GAUL_201213 definitions for which grid cells fall within which countries. Some grid cells are split between two or more countries. In this case, all model output variables for the grid cell are divided between the countries based on the fraction of grid cell area falling within the country i:$${H}_{c,2015}^{k}={H}_{c,2010}^{k}{sum }_{i},{f}_{i}^{k}r{H}_{c,i}$$
(3)
$${P}_{c,2015}^{k}={P}_{c,2010}^{k}{sum }_{i},{f}_{i}^{k}r{P}_{c,i}$$
(4)
where ({H}_{c,2015}^{k}) is the year 2015 harvested area (or production) for crop c in grid cell k; ({f}_{i}^{k}) is the fraction of country i in grid cell k, and rHc,i and rPc,i are the ratios for crop c in country i as calculated in Eqs. 1 and 2. This results in 26 H and P values per grid cell. If the sum of all crop harvest areas exceeds 99% of the grid cell area, all crop harvest areas are reduced equally to fit within 99% of the area.
Special Case: Sudan
FAOSTAT data for years before 2011 report data for Sudan, and for South Sudan and Sudan after 2011. To compute the ratios for these grid cells, we split the 2010 data for Sudan into a virtual ‘North’ Sudan and ‘South_Sudan’, using the data for the year 2012, which was reported for both countries. We then used these generated 2010 data and applied the same methodology as described above to calculate changes in harvested areas and production in all grid cells in both countries.
Special Case: Small regions and islands
Forty-nine countries – generally small regions or islands – had no data reported for crop harvested area by FAOSTAT. We assumed that there was no change in crop harvested area for the grid cells in these countries. Note that many may have had zero ha as previously-reported crop area in GAEZ v4. These countries are (the number following each region is the region’s number in ADM0_CODE in the GAUL_2012 data13):Anguilla (9), Aruba (14), Ashmore_and_Cartier_Islands (16), Azores_Islands (74578), Baker_Island (22), Bassas_da_India (25), Bird_Island (32), Bouvet_Island (36), British_Indian_Ocean_Territory (38), Christmas_Island (54), Clipperton_Island (55), Cocos (Keeling)_Islands (56), Europa_Island (80), French_Southern_and_Antarctic_Territories (88), Glorioso_Island (96), Greenland (98), Guernsey (104), Heard_Island_and_McDonald_Islands (109), Howland_Island (112), Isle_of_Man (120), Jarvis_Island (127), Jersey (128), Johnston_Atoll (129), Juan_de_Nova_Island (131), Kingman_Reef (134), Kuril_islands (136), Madeira_Islands (151), Mayotte (161), Midway_Island (164), Navassa_Island (174), Netherlands_Antilles (176), Norfolk_Island (184), Northern_Mariana_Islands (185), Palmyra_Atoll (190), Paracel_Islands (193), Pitcairn (197), Saint_Helena (207), Scarborough_Reef (216), Senkaku_Islands (218), South_Georgia_and_the_South_Sandwich_Islands (228), Spratly_Islands (230), Svalbard_and_Jan_Mayen_Islands (234), Tromelin_Island (247), Turks_and_Caicos_Islands (251), United_States_Virgin_Islands (258), Wake_Island (265), Gibraltar (95), Holy_See (110), Liechtenstein (146).
Special Case: Disputed Areas
Some grid cells in the GAUL_201213 cell-table database are assigned to nine disputed areas, rather than to specific countries. We assumed that there was no change in crop harvested area or production from 2010 to 2015 for grid cells these disputed areas. These areas are (the number following each region is the region’s number of the ADM0_CODE in the GAUL_201213 data):Abyei (102), Aksai_Chin (2), Arunachal_Pradesh (15), China/India (52), Hala’ib_Triangle (40760), Ilemi_Triangle (61013), Jammu_and_Kashmir (40781), Ma’tan_al-Sarra (40762), Falkland_Islands_(Malvinas) (81).
Step 4. Compute 2015 crop yields:
Crop yields were computed for each crop, c, and grid cell, k, as the ratio of crop production to crop harvest area (if harvest area, Hc,k,2015, is zero, then yield, Yc,k,2015, is set to zero):$${Y}_{c,k,2015}={P}_{c,k,2015}/{H}_{c,k,2015}$$
(5)
The resulting gridded global data are:

A.

GAEZ+ 2015 Crop Harvest Area14

B.

GAEZ+ 2015 Crop Yield15

C.

GAEZ+ 2015 Crop Production16

This new data product consists of 156 data files in geotiff format, one rainfed harvested area file and one irrigated harvested area file for each crop harvest area (1000 ha (107 m2) per 5-minute grid cell), crop production (1000 tonnes (106 kg) per 5-minute grid cell), and crop yield (tonnes per ha (10−1 kg m−2) per 5-minute grid cell), for each of the 26 GAEZ crops or crop categories in Table 1.GAEZ+ 2015 monthly cropland area methodsTwo datasets were used to produce monthly cropland area by crop and by irrigated vs rainfed management. These are:

1.

GAEZ+ 2015 Annual Harvested Area14 (as developed above)

2.

MIRCA2000 cropland area4

Step 5. Harmonize the GAEZ+ 2015 and MIRCA2000 crop lists
The MIRCA20004 cropland product provides monthly growing area grids (gridded physical cropland area) for 26 irrigated and rainfed crops and crop categories, as well as cropping calendars that identify the planting month and harvesting month for each crop (via ‘subcrops’ – see below). However, the MIRCA2000 crop list is not the same as the GAEZ+ 2015 crop list; we matched each crop type in the GAEZ+ 2015 crop list to a crop type in the MIRCA2000 crop list to enable the application of MIRCA2000 crop calendars to GAEZ+ 2015 crops (Table 2). Out of the 26 GAEZ+ 2015 crops, 18 had clear 1:1 matching crop categories within MIRCA2000. The remaining 8 crops were matched based on general crop characteristics, i.e., annual vs. perennial, or to unmatched MIRCA2000 cereals.Table 2 List of GAEZ crop categories used in all GAEZ+ 2015 products, as well as the matching between GAEZ+ 2015 crops and MIRCA20004 crop categories for the purposes of producing GAEZ+ 2015 monthly cropland data.Full size tableAn essential component of the MIRCA2000 cropland dataset is the identification of subcrop categories within each crop category to split crops into areas grown in different seasons, or crops with different planting and harvesting dates within the same season. Up to 5 subcrops can be defined to represent such multi-cropping practices. Below, we use the following notation:HG = annual harvested area from the GAEZ+ 2015 product for a given cropHM = annual harvested area calculated from the MIRCA2000 data for a given cropAM,n = cropland area of MIRCA2000 crop, subcrop n, by monthAG,n = cropland area of GAEZ+ 2015 crop, subcrop n, by monthAG = cropland area of GAEZ+ 2015 crop, by month
Step 6. Apply MIRCA2000 monthly crop calendars to GAEZ+ 2015 annual data
To generate the monthly cropland physical area of GAEZ+ 2015 crops, we followed these steps for each GAEZ crop in each grid cell:

1.

For a given GAEZ crop in a given grid cell, is the area reported >0 for the matching MIRCA2000 crop?

a.

If YES, then use the MIRCA2000 data for the grid cell and crop considered.

b.

If NO, then find the closest grid cell with the matching MIRCA2000 crop category, and apply the MIRCA2000 crop rotation from that grid cell to the given crop/grid cell combination for the following steps.

2.

Does the matching MIRCA2000 crop category (Table 1) have more than 1 subcrop?

a.

If NO, then AG = HG for all months of the cropping season, as defined by the MIRCA2000 crop calendar.

b.

If YES, then for each subcrop category n, apply the ratio of AM,n/HM to HG, then sum the subcrop areas within each month such that:

$${A}_{G}=sum _{n}frac{{A}_{M,n}}{{H}_{M}}{H}_{G}$$

3.

For each month and each grid cell, check if the sum of all crops (irrigated and rainfed) is greater than the 99% of area of the grid cell. We assume that at least 1% of land must be retained as non-cropland for agricultural infrastructure such as roads, buildings, irrigation infrastructure, and other landcovers (e.g. rivers, wetlands).

a.

If NO, then no further processing is done.

b.

If YES, then reduce crop area by the excess value based on a removal order (Table 2). Rainfed crops have higher removal order numbers for the excess truncation (starting with 1) before removing irrigated crops, until the cell area is not exceeded. A large removal number (e.g., 20) indicates that the crop’s land is unlikely to be removed. Large priority numbers are given to the staple crops to ensure these important food producing lands are consistent with FAOSTAT country data.

The maximum monthly amount of physical cropland that was removed by step 3 is 711,543 ha, which is 0.05% of total global cropland physical area.The resulting global gridded data from Step 6 are monthly time series of cropland physical area by crop, subcrop, and production system, called GAEZ+_2015 Monthly Cropland Data17. Combining the MIRCA2000 crop calendar and subcrop rotation information with the GAEZ+ 2015 annual data allows for the representation of crop seasonality; e.g., Fig. 2 shows the aggregate monthly cropland physical area for Rice 1 and Rice 2 (two sub-crops of rice) over the northern hemisphere, clearly illustrating the two main rice-growing seasons.Fig. 2Aggregate monthly cropland physical area for Rice 1 and Rice 2 subcrops from monthly GAEZ+ 2015 over the northern hemisphere shows the two main rice-growing seasons. This seasonality is the result of combining GAEZ+ 2015 annual data with the MIRCA20004 crop calendars and subcrop divisions.Full size image More

1.Ottesen, E. A. et al. Pattern and synchrony of gene expression among sympatric marine microbial populations. Proc. Natl Acad. Sci. USA 110, E488–E497 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
2.Muñoz-Marín, M. D. C. et al. The transcriptional cycle is suited to daytime N2 fixation in the unicellular cyanobacterium “Candidatus Atelocyanobacterium thalassa” (UCYN-A). mBio 10, e02495-18 (2019).PubMed 
PubMed Central 

Google Scholar 
3.Vislova, A., Sosa, O. A., Eppley, J. M., Romano, A. E. & DeLong, E. F. Diel oscillation of microbial gene transcripts declines with depth in oligotrophic ocean waters. Front. Microbiol. 10, 2191 (2019).PubMed 
PubMed Central 

Google Scholar 
4.Harke, M. J. et al. Periodic and coordinated gene expression between a diazotroph and its diatom host. ISME J. 13, 118–131 (2019).CAS 
PubMed 

Google Scholar 
5.Hernández Limón, M. D. et al. Transcriptional patterns of Emiliania huxleyi in the North Pacific Subtropical Gyre reveal the daily rhythms of its metabolic potential.Environ. Microbiol. 22, 381–396 (2020).PubMed 

Google Scholar 
6.Becker, K. W. et al. Daily changes in phytoplankton lipidomes reveal mechanisms of energy storage in the open ocean. Nat. Commun. 9, 5179 (2018).PubMed 
PubMed Central 

Google Scholar 
7.Frischkorn, K. R., Haley, S. T. & Dyhrman, S. T. Coordinated gene expression between Trichodesmium and its microbiome over day–night cycles in the North Pacific Subtropical Gyre. ISME J. 12, 997–1007 (2018).PubMed 
PubMed Central 

Google Scholar 
8.Ottesen, E. A. et al. Ocean microbes. Multispecies diel transcriptional oscillations in open ocean heterotrophic bacterial assemblages. Science 345, 207–212 (2014).CAS 
PubMed 

Google Scholar 
9.Wilson, S. T. et al. Coordinated regulation of growth, activity and transcription in natural populations of the unicellular nitrogen-fixing cyanobacterium Crocosphaera. Nat. Microbiol. 2, 17118 (2017).CAS 
PubMed 

Google Scholar 
10.Saito, M. A. et al. Iron conservation by reduction of metalloenzyme inventories in the marine diazotroph Crocosphaera watsonii. Proc. Natl Acad. Sci. USA 108, 2184–2189 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
11.Strenkert, D. et al. Multiomics resolution of molecular events during a day in the life of Chlamydomonas. Proc. Natl Acad. Sci. USA 116, 2374–2383 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
12.Boysen, A. K. et al. Particulate metabolites and transcripts reflect diel oscillations of microbial activity in the surface ocean. mSystems 6, e00896-20 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
13.White, A. E., Barone, B., Letelier, R. M. & Karl, D. M. Productivity diagnosed from the diel cycle of particulate carbon in the North Pacific Subtropical Gyre: optically derived productivity. Geophys. Res. Lett. 44, 3752–3760 (2017).CAS 

Google Scholar 
14.DeLong, E. F. et al. Community genomics among stratified microbial assemblages in the ocean’s interior. Science 311, 496–503 (2006).CAS 
PubMed 

Google Scholar 
15.Sunagawa, S. et al. Ocean plankton. Structure and function of the global ocean microbiome. Science 348, 1261359 (2015).PubMed 

Google Scholar 
16.Coles, V. J. et al. Ocean biogeochemistry modeled with emergent trait-based genomics. Science 358, 1149–1154 (2017).CAS 
PubMed 

Google Scholar 
17.Walbauer, J. R., Rodrigue, S., Coleman, M. L. & Chisholm, S. W. Transcriptome and proteome dynamics of a light–dark synchronized bacterial cell cycle.PLoS ONE 7, e43432 (2012).
Google Scholar 
18.Steiner, P. A. et al. Highly variable mRNA half-life time within marine bacterial taxa and functional genes. Environ. Microbiol. 21, 3873–3884 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
19.Moran, M. A. et al. Sizing up metatranscriptomics. ISME J. 7, 237–243 (2013).CAS 
PubMed 

Google Scholar 
20.Tamames, J., Cobo-Simón, M. & Puente-Sánchez, F. Assessing the performance of different approaches for functional and taxonomic annotation of metagenomes. BMC Genomics 20, 960 (2019).21.DiTullio, G. R. & Laws, E. A. Diel periodicity of nitrogen and carbon assimilation in five species of marine phytoplankton: accuracy of methodology for predicting N-assimilation rates and N/C composition ratios. Mar. Ecol. Prog. Ser. 32, 123–132 (1986).CAS 

Google Scholar 
22.Granum, E., Kirkvold, S. & Myklestad, S. M. Cellular and extracellular production of carbohydrates and amino acids by the marine diatom Skeletonema costatum: diel variations and effects of N depletion. Mar. Ecol. Prog. Ser. 242, 83–94 (2002).CAS 

Google Scholar 
23.Lacour, T., Sciandra, A., Talec, A., Mayzaud, P. & Bernard, O. Diel variations of carbohydrates and neutral lipids in nitrogen-sufficient and nitrogen-starved cyclostat cultures of Isochrysis sp. J. Phycol. 48, 966–975 (2012).PubMed 

Google Scholar 
24.Follett, C. L., Dutkiewicz, S., Karl, D. M., Inomura, K. & Follows, M. J. Seasonal resource conditions favor a summertime increase in North Pacific diatom–diazotroph associations. ISME J. 12, 1543–1557 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
25.Chen, W.-N. U. et al. Diel rhythmicity of lipid-body formation in a coral-Symbiodinium endosymbiosis. Coral Reefs 31, 521–534 (2012).
Google Scholar 
26.Zhou, X. & Mopper, K. Photochemical production of low-molecular-weight carbonyl compounds in seawater and surface microlayer and their air-sea exchange. Mar. Chem. 56, 201–213 (1997).CAS 

Google Scholar 
27.Durham, B. P. et al. Sulfonate-based networks between eukaryotic phytoplankton and heterotrophic bacteria in the surface ocean.Nat. Microbiol. 4, 1706–1715 (2019).CAS 
PubMed 

Google Scholar 
28.Lambert, S. et al. Rhythmicity of coastal marine picoeukaryotes, bacteria and archaea despite irregular environmental perturbations. ISME J. 13, 388–401 (2019).PubMed 

Google Scholar 
29.Kolody, B. C. et al. Diel transcriptional response of a California Current plankton microbiome to light, low iron, and enduring viral infection. ISME J. 13, 2817–2833 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
30.Aylward, F. O. et al. Microbial community transcriptional networks are conserved in three domains at ocean basin scales. Proc. Natl Acad. Sci. USA 112, 5443–5448 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
31.Rusch, D. B. et al. The Sorcerer II Global Ocean Sampling expedition: northwest Atlantic through eastern tropical Pacific. PLoS Biol. 5, e77 (2007).PubMed 
PubMed Central 

Google Scholar 
32.Bork, P. et al. Tara Oceans studies plankton at planetary scale. Science 348, 873 (2015).CAS 
PubMed 

Google Scholar 
33.Delmont, T. O. et al. Nitrogen-fixing populations of Planctomycetes and Proteobacteria are abundant in surface ocean metagenomes. Nat. Microbiol. 3, 804–813 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
34.Fuhrman, J. A. et al. Annually reoccurring bacterial communities are predictable from ocean conditions. Proc. Natl Acad. Sci. USA 103, 13104–13109 (2006).CAS 
PubMed 
PubMed Central 

Google Scholar 
35.Morris, R. M. et al. Temporal and spatial response of bacterioplankton lineages to annual convective overturn at the Bermuda Atlantic Time‐series Study site. Limnol. Oceanogr. 50, 1687–1696 (2005).CAS 

Google Scholar 
36.Mende, D. R. et al. Environmental drivers of a microbial genomic transition zone in the ocean’s interior. Nat. Microbiol. 2, 1367–1373 (2017).CAS 
PubMed 

Google Scholar 
37.Keeling, P. J. et al. The Marine Microbial Eukaryote Transcriptome Sequencing Project (MMETSP): illuminating the functional diversity of eukaryotic life in the oceans through transcriptome sequencing. PLoS Biol. 12, e1001889 (2014).PubMed 
PubMed Central 

Google Scholar 
38.Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M. & Tanabe, M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 44, D457–D462 (2016).CAS 

Google Scholar 
39.Thaben, P. F. & Westermark, P. O. Detecting rhythms in time series with RAIN. J. Biol. Rhythms 29, 391–400 (2014).PubMed 
PubMed Central 

Google Scholar 
40.Cuhel, R. L., Ortner, P. B. & Lean, D. R. S. Night synthesis of protein by algae. Limnol. Oceanogr. 29, 731–744 (1984).CAS 

Google Scholar 
41.Coesel, S. N. et al. Diel transcriptional oscillations of light-sensitive regulatory elements in open-ocean eukaryotic plankton communities. Proc. Natl Acad. Sci. USA 118, e2011038118 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
42.Bolay, P., Muro-Pastor, M. I., Florencio, F. J. & Klähn, S. The distinctive regulation of cyanobacterial glutamine synthetase. Life (Basel) 8, 52 (2018).CAS 

Google Scholar 
43.Karl, D. M., Church, M. J., Dore, J. E., Letelier, R. M. & Mahaffey, C. Predictable and efficient carbon sequestration in the North Pacific Ocean supported by symbiotic nitrogen fixation. Proc. Natl Acad. Sci. USA 109, 1842–1849 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
44.Berman, T. & Bronk, D. A. Dissolved organic nitrogen: a dynamic participant in aquatic ecosystems. Aquat. Microb. Ecol. 31, 279–305 (2003).
Google Scholar 
45.Lee, C. & Bada, J. L. Amino acids in equatorial Pacific Ocean water. Earth Planet. Sci. Lett. 26, 61–68 (1975).CAS 

Google Scholar 
46.Bada, J. L. & Lee, C. Decomposition and alteration of organic compounds dissolved in seawater. Mar. Chem. 5, 523–534 (1977).CAS 

Google Scholar 
47.Poretsky, R. S., Sun, S., Mou, X. & Moran, M. A. Transporter genes expressed by coastal bacterioplankton in response to dissolved organic carbon. Environ. Microbiol. 12, 616–627 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
48.Berthelot, H. et al. NanoSIMS single cell analyses reveal the contrasting nitrogen sources for small phytoplankton. ISME J. 13, 651–662 (2019).CAS 
PubMed 

Google Scholar 
49.Moore, L. R., Post, A. F., Rocap, G. & Chisholm, S. W. Utilization of different nitrogen sources by the marine cyanobacteria Prochlorococcus and Synechococcus. Limnol. Oceanogr. 47, 989–996 (2002).CAS 

Google Scholar 
50.Hu, S. K., Connell, P. E., Mesrop, L. Y. & Caron, D. A. A hard day’s night: diel shifts in microbial eukaryotic activity in the North Pacific Subtropical Gyre. Front. Mar. Sci. https://doi.org/10.3389/fmars.2018.00351 (2018).51.Hannides, C. C. S., Popp, B. N., Choy, C. A. & Drazen, J. C. Midwater zooplankton and suspended particle dynamics in the North Pacific Subtropical Gyre: a stable isotope perspective. Limnol. Oceanogr. 58, 1931–1946 (2013).CAS 

Google Scholar 
52.Becker, K. W. et al. Combined pigment and metatranscriptomic analysis reveals highly synchronized diel patterns of phenotypic light response across domains in the open oligotrophic ocean.ISME J. 15, 520–533 (2021).CAS 
PubMed 

Google Scholar 
53.Mruwat, N. et al. A single-cell polony method reveals low levels of infected Prochlorococcus in oligotrophic waters despite high cyanophage abundances. ISME J. 15, 41–54 (2021).CAS 
PubMed 

Google Scholar 
54.Chesson, P. L. & Warner, R. R. Environmental variability promotes coexistence in lottery competitive systems. Am. Nat. 117, 923–943 (1981).
Google Scholar 
55.Shmida, A. & Ellner, S. Coexistence of plant species with similar niches. Vegetatio 58, 29–55 (1984).
Google Scholar 
56.Ellner, S. P., Snyder, R. E. & Adler, P. B. How to quantify the temporal storage effect using simulations instead of math. Ecol. Lett. 19, 1333–1342 (2016).PubMed 

Google Scholar 
57.Adler, P. B., Fajardo, A., Kleinhesselink, A. R. & Kraft, N. J. B. Trait-based tests of coexistence mechanisms. Ecol. Lett. 16, 1294–1306 (2013).PubMed 

Google Scholar 
58.Adler, P. B., HilleRisLambers, J., Kyriakidis, P. C., Guan, Q. & Levine, J. M. Climate variability has a stabilizing effect on the coexistence of prairie grasses. Proc. Natl Acad. Sci. USA 103, 12793–12798 (2006).CAS 
PubMed 
PubMed Central 

Google Scholar 
59.Cáceres, C. E. Temporal variation, dormancy, and coexistence: a field test of the storage effect. Proc. Natl Acad. Sci. USA 94, 9171–9175 (1997).PubMed 
PubMed Central 

Google Scholar 
60.Padisák, J. Identification of relevant time-scales in non-equilibrium community dynamics: conclusions from phytoplankton surveys. N. Z. J. Ecol. 18, 169–176 (1994).
Google Scholar 
61.Anderies, J. M. & Beisner, B. E. Fluctuating environments and phytoplankton community structure: a stochastic model. Am. Nat.155, 556–569 (2000).PubMed 

Google Scholar 
62.Wagg, C. et al. Functional trait dissimilarity drives both species complementarity and competitive disparity. Funct. Ecol. 31, 2320–2329 (2017).
Google Scholar 
63.Bligh, E.G. & Dyer, W. J. A rapid method of total lipid extraction and purification. Can. J. Biochem. Physiol. 37, 911–917 (1959).CAS 
PubMed 

Google Scholar 
64.Boysen, A. K., Heal, K. R., Carlson, L. T. & Ingalls, A. E. Best-matched internal standard normalization in liquid chromatography–mass spectrometry metabolomics applied to environmental samples. Anal. Chem. 90, 1363–1369 (2018).CAS 
PubMed 

Google Scholar 
65.MacLean, B. et al. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26, 966–968 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
66.Fountoulakis, M. & Lahm, H. W. Hydrolysis and amino acid composition analysis of proteins. J. Chromatogr. A 826, 109–134 (1998).CAS 
PubMed 

Google Scholar 
67.Popendorf, K. J., Fredricks, H. F. & Van Mooy, B. A. S. Molecular ion-independent quantification of polar glycerolipid classes in marine plankton using triple quadrupole MS. Lipids 48, 185–195 (2013).CAS 
PubMed 

Google Scholar 
68.Collins, J. R., Edwards, B. R., Fredricks, H. F. & Van Mooy, B. A. S. LOBSTAHS: an adduct-based lipidomics strategy for discovery and identification of oxidative stress biomarkers. Anal. Chem. 88, 7154–7162 (2016).CAS 
PubMed 

Google Scholar 
69.Hummel, J. et al. Ultra performance liquid chromatography and high resolution mass spectrometry for the analysis of plant lipids. Front. Plant Sci. 2, 54 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
70.Smith, C. A., Want, E. J., O’Maille, G., Abagyan, R. & Siuzdak, G. XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal. Chem. 78, 779–787 (2006).CAS 
PubMed 

Google Scholar 
71.Kuhl, C., Tautenhahn, R., Böttcher, C., Larson, T. R. & Neumann, S. CAMERA: an integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets. Anal. Chem. 84, 283–289 (2012).CAS 
PubMed 

Google Scholar 
72.Biller, S. J. et al. Prochlorococcus extracellular vesicles: molecular composition and adsorption to diverse microbes.Environ. Microbiol. https://doi.org/10.1111/1462-2920.15834 (2021).Article 
PubMed 

Google Scholar 
73.Aylward, F. O. et al. Diel cycling and long-term persistence of viruses in the ocean’s euphotic zone. Proc. Natl Acad. Sci. USA 114, 11446–11451 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
74.Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
75.Masella, A. P., Bartram, A. K., Truszkowski, J. M., Brown, D. G. & Neufeld, J. D. PANDAseq: paired-end assembler for illumina sequences. BMC Bioinformatics 13, 31 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
76.Joshi, N. & Fass, J. Sickle: A sliding-window, adaptive, quality-based trimming tool for FastQ files. Version 1.33. GitHub https://github.com/najoshi/sickle (2015).77.Kopylova, E., Noé, L. & Touzet, H. SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics 28, 3211–3217 (2012).CAS 

Google Scholar 
78.Kiełbasa, S. M., Wan, R., Sato, K., Horton, P. & Frith, M. C. Adaptive seeds tame genomic sequence comparison. Genome Res. 21, 487–493 (2011).PubMed 
PubMed Central 

Google Scholar 
79.Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics 26, 589–595 (2010).PubMed 
PubMed Central 

Google Scholar 
80.Alexander, H. et al. Functional group-specific traits drive phytoplankton dynamics in the oligotrophic ocean. Proc. Natl Acad. Sci. USA 112, E5972–E5979 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
81.Anders, S., Pyl, P. T. & Huber, W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).CAS 

Google Scholar 
82.Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).PubMed 
PubMed Central 

Google Scholar 
83.Meinicke, P. UProC: tools for ultra-fast protein domain classification. Bioinformatics 31, 1382–1388 (2015).CAS 
PubMed 

Google Scholar 
84.Mende, D. R., Boeuf, D. & DeLong, E. F. Persistent core populations shape the microbiome throughout the water column in the North Pacific Subtropical Gyre. Front. Microbiol. 10, 2273 (2019).PubMed 
PubMed Central 

Google Scholar 
85.White, A. E. et al. Phenology of particle size distributions and primary productivity in the North Pacific subtropical gyre (Station ALOHA). J. Geophys. Res. Oceans 120, 7381–7399 (2015).PubMed 
PubMed Central 

Google Scholar 
86.Borchers, H. W. pracma: Practical numerical math functions. R package version 2 https://cran.r-project.org/web/packages/pracma/index.html (2019).87.Maechler, M., Rousseeuw, P., Struyf, A., Hubert, M. & Hornik, K. cluster: Cluster analysis basics and extensions. R package version 1.56 (2012).88.Wehrens, R. & Buydens, L. M. C. Self- and super-organizing maps in R: the Kohonen package. J. Stat. Softw. 21, 1–19 (2007).
Google Scholar 
89.Hennig, C. fpc: Flexible procedures for clustering. R package version 2.2-9 (2010).90.Muratore, D. Code for complex marine microbial communities partition metabolism of scarce resources over the diel cycle. Zenodo https://doi.org/10.5281/zenodo.3817416 (2020). More

EDITORIAL
19 January 2022

Biodiversity faces its make-or-break year, and research will be key

A new action plan to halt biodiversity loss needs scientific specialists to work with those who study how governments function.

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Targeted measures can help to stop extinctions, including of Père David’s deer (Elaphurus davidianus), but conserving biodiversity will also require combating climate change, cutting pollution and enhancing sustainable food systems.Credit: Staffan Widstrand/Wild Wonders of China/Nature Picture Library

Biodiversity is being lost at a rate not seen since the last mass extinction. But the United Nations decade-old plan to slow down and eventually stop the decline of species and ecosystems by 2020 has failed. Most of the plan’s 20 targets — known as the Aichi Biodiversity Targets — have not been met.The Aichi targets are part of an international agreement called the UN Convention on Biological Diversity, and member states are now finalizing replacements for them. Currently referred to as the post-2020 global biodiversity framework (GBF), the new targets are expected to be agreed this summer at the second part of the convention’s Conference of the Parties (COP15) in Kunming, China. The meeting was due to be held in May, but is likely to be delayed by a few months. Finalizing the framework will be down to government representatives working with the world’s leading biodiversity specialists. But input from social-science researchers, especially those who study how organizations and governments work, would improve its chances of success.A draft of the GBF was published last July. It aims to slow down the rate of biodiversity loss by 2030. And by 2050, biodiversity will be “valued, conserved, restored and wisely used, maintaining ecosystem services, sustaining a healthy planet and delivering benefits essential for all people”. The plan comprises 4 broad goals and 21 associated targets. The headline targets include conserving 30% of land and sea areas by 2030, and reducing government subsidies that harm biodiversity by US$500 billion per year. Overall, the goals and targets are designed to tackle each of the main contributors to biodiversity loss, which include agriculture and food systems, climate change, invasive species, pollution and unsustainable production and consumption.
Fewer than 20 extinctions a year: does the world need a single target for biodiversity?
The biodiversity convention’s science advisory body is reviewing the GBF and helping governments to decide how the targets are to be monitored. But researchers and policymakers have been writing biodiversity action plans since the 1990s, and most of these strategies have failed to make a lasting impact on two of the three key demands: that global biodiversity be conserved and that natural resources be used sustainably.Some of these failures are to do with governance, which is why it is important to involve not just researchers in the biological sciences, but also people who study organizations and how governments work. This knowledge, when allied to conservation science, will help policymakers to obtain a fuller picture of both the science gaps and the organizational challenges in implementing biodiversity plans.The GBF is a comprehensive plan. But success will require systemic change across public policy. That is both a strength and a weakness. If systemic change can be implemented, it will lead to real change. But if it cannot, there’s no plan B. This has led some researchers to argue that one target or number should be prioritized, and defined in a way that is clear to the public and to policymakers. It would be biodiversity’s equivalent of the 2 °C climate target. The researchers’ “rallying point for policy action and agreements” is to keep species extinction to well below 20 per year across all major groups (M. D. A. Rounsevell et al. Science 368, 1193–1195; 2020). Such focus does yield results. A study published in Conservation Letters found a high probability that targeted action has prevented 21–32 bird and 7–16 mammal extinctions since 1993 (F. C. Bolam et al. Conserv. Lett. 14, e12762; 2021). Extinction rates would have been around three to four times greater without conservation action, the researchers found.But not all agree that just one target should be given priority. A group of more than 50 biodiversity researchers from 23 countries point out in a policy report this week (see go.nature.com/3fv8oiv) that data on species are distributed unequally: 10, mostly high-income, countries account for 82% of records.
The United Nations must get its new biodiversity targets right
The researchers also modelled how different scenarios would affect the GBF’s 21 targets. They found that achieving the targets would require action in all of the target areas — not just a few. Focusing strongly on just one or two targets — such as expanding protected areas — will have, at best, a modest impact on achieving the UN convention’s goals and targets.The difficulty in getting governments to adopt such an integrated approach is that they (as well as non-governmental organizations and businesses) tend to tackle sustainability challenges piecemeal. Actions from last November’s climate COP in Glasgow, UK, will be implemented separately from those decided at the biodiversity COP because, in most countries, different government departments deal with climate change and biodiversity.The science advisers for the biodiversity convention will meet in Geneva, Switzerland, in March to finalize their advice. They are not advocating reform of how governments organize themselves to implement policies in sustainable development — partly (and rightly) because this is generally beyond their fields of expertise. But it’s not too late to consult those with the relevant knowledge.In the past, the UN has commissioned social scientists, for example in the UN Intellectual History Project, a series of 17 studies summarizing the experience of UN agencies spanning gender equality, diplomacy, development, trade and official statistics. However, this work, which ended in 2010, did not assess what has and hasn’t worked in science and environmental policy. Unless these perspectives are incorporated into biodiversity-research advice, any future plans risk going the way of their predecessors.

Nature 601, 298 (2022)
doi: https://doi.org/10.1038/d41586-022-00110-w

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1.Mordecai, E. A., Ryan, S. J., Caldwell, J. M., Shah, M. M. & LaBeaud, A. D. Climate change could shift disease burden from malaria to arboviruses in Africa. Lancet Planet. Health 4, e416–e423 (2020).PubMed 
PubMed Central 

Google Scholar 
2.W. H. O. Multisectoral approach to the prevention and control of vector-borne diseases (2020).3.Ryan, S. J. et al. Warming temperatures could expose more than 1.3 billion new people to Zika virus risk by 2050. Glob. Change Biol. 27, 84–93 (2021).
Google Scholar 
4.Iwamura, T., Guzman-Holst, A. & Murray, K. A. Accelerating invasion potential of disease vector Aedes aegypti under climate change. Nat. Commun. 11, 2130 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
5.Savage, V. M., Gillooly, J. F., Brown, J. H., West, G. B. & Charnov, E. L. Effects of body size and temperature on population growth. Am. Nat. 163, 429–441 (2004).PubMed 

Google Scholar 
6.Shocket, M. S. et al. Transmission of West Nile and five other temperate mosquito-borne viruses peaks at temperatures between 23 °C and 26 °C. eLife 9, 1–67 (2020).
Google Scholar 
7.Couret, J., Dotson, E. & Benedict, M. Q. Temperature, larval diet, and density effects on development rate and survival of Aedes aegypti (Diptera: Culicidae). PLoS ONE 9, 1–9 (2014).
Google Scholar 
8.Barreaux, A. M. G., Stone, C. M., Barreaux, P. & Koella, J. C. The relationship between size and longevity of the malaria vector Anopheles gambiae (s.s.) depends on the larval environment. Parasites Vectors 11, 485 (2018).PubMed 
PubMed Central 

Google Scholar 
9.Huxley, P. J., Murray, K. A., Pawar, S. & Cator, L. J. The effect of resource limitation on the temperature dependence of mosquito population fitness. Proc. R. Soc. B: Biol. Sci. 288, rspb.2020.3217 (2021).10.Ostfeld, R. S. & Keesing, F. Pulsed resources and community dynamics of consumers in terrestrial ecosystems. Trends Ecol. Evol. 15, 232–237 (2000).CAS 
PubMed 

Google Scholar 
11.Beltran, R. S. et al. Seasonal resource pulses and the foraging depth of a Southern Ocean top predator. Proc. R. Soc. B: Biol. Sci. 288, rspb.2020.2817 (2021).12.Yang, L. H., Bastow, J. L., Spence, K. O. & Wright, A. N. What can we learn from resource pulses? Ecology 89, 621–634 (2008).PubMed 

Google Scholar 
13.Dye, C. Models for the population dynamics of the yellow fever mosquito, Aedes aegypti. J. Animal Ecol. 53, 247 (1984).
Google Scholar 
14.Southwood, T. R., Murdie, G., Yasuno, M., Tonn, R. J. & Reader, P. M. Studies on the life budget of Aedes aegypti in Wat Samphaya, Bangkok, Thailand. Bull. World Health Organ. 46, 211–226 (1972).CAS 
PubMed 
PubMed Central 

Google Scholar 
15.Arrivillaga, J. & Barrera, R. Food as a limiting factor for Aedes aegypti in water-storage containers. J. Vector Ecol. 29, 11–20 (2004).PubMed 

Google Scholar 
16.Barrera, R., Amador, M. & Clark, G. G. Ecological factors influencing Aedes aegypti (Diptera: Culicidae) productivity in artificial containers in Salinas, Puerto Rico. J. Med. Entomol. 43, 484–492 (2006).PubMed 

Google Scholar 
17.Yee, D. A. & Juliano, S. A. Concurrent effects of resource pulse amount, type, and frequency on community and population properties of consumers in detritus-based systems. Oecologia 169, 511–522 (2012).PubMed 

Google Scholar 
18.Subra, R. & Mouchet, J. The regulation of preimaginal populations of Aedes aegypti (L.) (Diptera: Culicidae) on the Kenya coast. Ann. Trop. Med. Parasitol. 78, 63–70 (1984).CAS 
PubMed 

Google Scholar 
19.Amarasekare, P. & Savage, V. A framework for elucidating the temperature dependence of fitness. Am. Nat. 179, 178–191 (2012).PubMed 

Google Scholar 
20.Huey, R. B. & Kingsolver, J. G. Climate warming, resource availability, and the metabolic meltdown of ectotherms. Am. Nat. 194, 6 (2019).21.García-Carreras, B. et al. Role of carbon allocation efficiency in the temperature dependence of autotroph growth rates. Proc. Natl Acad. Sci. USA 115, E7361–E7368 (2018).PubMed 
PubMed Central 

Google Scholar 
22.Smith, T. P., Clegg, T., Bell, T. & Pawar, S. Systematic variation in the temperature dependence of bacterial carbon use efficiency. Ecol. Lett. 24, 2123–2133 (2021).PubMed 

Google Scholar 
23.Lehmann, P. et al. Complex responses of global insect pests to climate warming. Front. Ecol. Environ. 18, 141–150 (2020).
Google Scholar 
24.Amarasekare, P. Effects of climate warming on consumer-resource interactions: a latitudinal perspective. Front. Ecol. Evol. 7, 1–15 (2019).25.Amarasekare, P. & Simon, M. W. Latitudinal directionality in ectotherm invasion success. Proc. R. Soc. B: Biol. Sci. 287, 20191411 (2020).
Google Scholar 
26.Diagne, C. et al. High and rising economic costs of biological invasions worldwide. Nature 592, 571–576 (2021).CAS 
PubMed 

Google Scholar 
27.Cross, W. F., Hood, J. M., Benstead, J. P., Huryn, A. D. & Nelson, D. Interactions between temperature and nutrients across levels of ecological organization. Glob. Change Biol. 21, 1025–1040 (2015).
Google Scholar 
28.Mordecai, E. A. et al. Thermal biology of mosquito‐borne disease. Ecol. Lett. 22, 1690–1708 (2019).PubMed 
PubMed Central 

Google Scholar 
29.Thomas, M. K. et al. Temperature-nutrient interactions exacerbate sensitivity to warming in phytoplankton. Glob. Change Biol. 23, 3269–3280 (2017).
Google Scholar 
30.Siegel, P., Baker, K. G., Low‐Décarie, E. & Geider, R. J. High predictability of direct competition between marine diatoms under different temperatures and nutrient states. Ecol. Evol. 10, 7276–7290 (2020).PubMed 
PubMed Central 

Google Scholar 
31.Bestion, E., García-Carreras, B., Schaum, C.-E., Pawar, S. & Yvon-Durocher, G. Metabolic traits predict the effects of warming on phytoplankton competition. Ecol. Lett. 21, 655–664 (2018).PubMed 
PubMed Central 

Google Scholar 
32.Jackson, C. flexsurv: A Platform for Parametric Survival Modeling in R. J. Stat. Softw. 70, 1–33 (2016).
Google Scholar 
33.Bellows, T. S. The descriptive properties of some models for density dependence. J. Animal Ecol. 50, 139–156 (1981).
Google Scholar 
34.Orcutt, J. D. & Porter, K. G. The synergistic effects of temperature and food concentration of life history parameters of Daphnia. Oecologia 63, 300–306 (1984).PubMed 

Google Scholar 
35.Huey, R. B. & Berrigan, D. Temperature, demography, and ectotherm fitness. Am. Nat. 158, 204–210 (2001).CAS 
PubMed 

Google Scholar 
36.Caswell, H. A general formula for the sensitivity of population growth rate to changes in life history parameters. Theor. Popul. Biol. 14, 215–230 (1978).CAS 
PubMed 

Google Scholar 
37.Kammenga, J. E., Busschers, M., Straalen, N. M., Van, Jepson, P. C. & Bakker, J. Stress induced fitness reduction is not determined by the most sensitive life-cycle trait. Funct. Ecol. 10, 106 (1996).
Google Scholar 
38.Cator, L. J. et al. The role of vector trait variation in vector-borne disease dynamics. Front. Ecol. Evol. 8, 1–25 (2020).
Google Scholar 
39.Juliano, S. A. Species introduction and replacement among mosquitoes: interspecific resource competition or apparent competition? Ecology 79, 255 (1998).
Google Scholar 
40.Shapiro, L. L. M., Murdock, C. C., Jacobs, G. R., Thomas, R. J. & Thomas, M. B. Larval food quantity affects the capacity of adult mosquitoes to transmit human malaria. Proc. R. Soc. B: Biol. Sci. 283, 20160298 (2016).
Google Scholar 
41.Carvajal-Lago, L., Ruiz-López, M. J., Figuerola, J. & Martínez-de la Puente, J. Implications of diet on mosquito life history traits and pathogen transmission. Environ. Res. 195, 110893 (2021).CAS 
PubMed 

Google Scholar 
42.Reiner, R. C. et al. A systematic review of mathematical models of mosquito-borne pathogen transmission: 1970-2010. J. R. Soc. Interface 10, 20120921–20120921 (2013).PubMed 
PubMed Central 

Google Scholar 
43.Farjana, T., Tuno, N. & Higa, Y. Effects of temperature and diet on development and interspecies competition in Aedes aegypti and Aedes albopictus. Med.Vet. Entomol. 26, 210–217 (2012).CAS 
PubMed 

Google Scholar 
44.Kooijman, S. A. L. M. Dynamic energy and mass budgets in biological systems. (Cambridge University Press, 2000).45.Merritt, R. W., Dadd, R. H. & Walker, E. D. Feeding behaviour, natural food, and nutritional relationships and larval mosquitoes. Annu. Rev. Entomol. 37, 349–376 (1992).46.Craine, J. M., Fierer, N. & McLauchlan, K. K. Widespread coupling between the rate and temperature sensitivity of organic matter decay. Nat. Geosci. 3, 854–857 (2010).CAS 

Google Scholar 
47.Smith, T. P. et al. Community-level respiration of prokaryotic microbes may rise with global warming. Nat. Commun. 10, 5124 (2019).PubMed 
PubMed Central 

Google Scholar 
48.Yee, D. A., Kaufman, M. G. & Juliano, S. A. The significance of ratios of detritus types and micro-organism productivity to competitive interactions between aquatic insect detritivores. J. Animal Ecol. 76, 1105–1115 (2007).
Google Scholar 
49.Chouaia, B. et al. Delayed larval development in Anopheles mosquitoes deprived of Asaia bacterial symbionts. BMC Microbiol. 12, S2 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
50.Souza, R. S. et al. Microorganism-based larval diets affect mosquito development, size and nutritional reserves in the yellow fever mosquito Aedes aegypti (Diptera: Culicidae). Front. Physiol. 10, 1–24 (2019).
Google Scholar 
51.Dickson, L. B. et al. Carryover effects of larval exposure to different environmental bacteria drive adult trait variation in a mosquito vector. Sci. Adv. 3, e1700585 (2017).PubMed 
PubMed Central 

Google Scholar 
52.Hery, L. et al. Natural variation in physicochemical profiles and bacterial communities associated with Aedes aegypti breeding sites and larvae on Guadeloupe and French Guiana. Microbial Ecol. 81, 93–109 (2021).CAS 

Google Scholar 
53.Liikanen, A., Murtoniemi, T., Tanskanen, H., Väisänen, T. & Martikainen, P. J. Effects of temperature and oxygen availability on greenhouse gas and nutrient dynamics in sediment of a eutrophic mid-boreal lake. Biogeochemistry 59, 269–286 (2002).CAS 

Google Scholar 
54.Lister, B. C. & Garcia, A. Climate-driven declines in arthropod abundance restructure a rainforest food web. Proc. Natl Acad. Sci. USA 115, E10397–E10406 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
55.Du, E. et al. Global patterns of terrestrial nitrogen and phosphorus limitation. Nat. Geosci. 13, 221–226 (2020).CAS 

Google Scholar 
56.Briegel, H. Metabolic relationship between female body size, reserves, and fecundity of Aedes aegypti. J. Insect Physiol. 36, 165–172 (1990).
Google Scholar 
57.Steinwascher, K. Relationship between pupal mass and adult survivorship and fecundity for Aedes aegypti. Environ. Entomol. 11, 150–153 (1982).
Google Scholar 
58.Trisos, C. H., Merow, C. & Pigot, A. L. The projected timing of abrupt ecological disruption from climate change. Nature 580, 496–501 (2020).CAS 
PubMed 

Google Scholar 
59.Parmesan, C. Ecological and evolutionary responses to recent climate change. Annu. Rev. Ecol., Evol. Syst. 37, 637–669 (2006).
Google Scholar 
60.Taheri, S., Naimi, B., Rahbek, C. & Araújo, M. B. Improvements in reports of species redistribution under climate change are required. Sci. Adv. 7, eabe1110 (2021).PubMed 
PubMed Central 

Google Scholar 
61.Bargielowski, I. E., Lounibos, L. P. & Carrasquilla, M. C. Evolution of resistance to satyrization through reproductive character displacement in populations of invasive dengue vectors. Proc. Natl Acad. Sci. USA 110, 2888–2892 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
62.Arguez, A. et al. NOAA’s 1981–2010 U.S. climate normals: an overview. Bull. Am. Meteorol. Soc. 93, 1687–1697 (2012).
Google Scholar 
63.Caswell, H. Matrix population models construction, analysis, and interpretation. Nat. Resource Model. (Sinauer Associates, 1989).64.Birch, L. C. The intrinsic rate of natural increase of an insect population. J. Animal Ecol. 17, 15 (1948).
Google Scholar 
65.Cole, L. C. The population consequences of life history phenomena. Q. Rev. Biol. 29, 103–137 (1954).CAS 
PubMed 

Google Scholar 
66.R. Core Team. R: A language and environment for statistical computing. (2018).67.Stubben, C. & Milligan, B. Estimating and analyzing demographic models using the popbio Package in R. J. Stat. Softw. 22, 1–23 (2007).
Google Scholar 
68.Therneau, T. A Package for Survival Analysis in R. (2021).69.Agnew, P., Hide, M., Sidobre, C. & Michalakis, Y. A minimalist approach to the effects of density-dependent competition on insect life-history traits. Ecol. Entomol. 27, 396–402 (2002).
Google Scholar 
70.Honěk, A. Intraspecific variation in body size and fecundity in insects: a general relationship. Oikos 66, 483 (1993).
Google Scholar 
71.Livdahl, T. P. & Sugihara, G. Non-linear interactions of populations and the importance of estimating per capita rates of change. J. Animal Ecol. 53, 573 (1984).
Google Scholar 
72.Juliano, S. A. & Lounibos, L. P. Ecology of invasive mosquitoes: effects on resident species and on human health. Ecol. Lett. 8, 558–574 (2005).PubMed 
PubMed Central 

Google Scholar 
73.van den Heuvel, M. J. The effect of rearing temperature on the wing length, thorax length, leg length and ovariole number of the adult mosquito, Aedes aegypti (L.). Trans. R. Entomol. Soc. Lond. 115, 197–216 (1963).
Google Scholar 
74.Farjana, T. & Tuno, N. Effect of body size on multiple blood feeding and egg retention of Aedes aegypti (L.) and Aedes albopictus (Skuse) (Diptera: Culicidae). Med. Entomol. Zool. 63, 123–131 (2012).
Google Scholar 
75.Skalski, J. R., Millspaugh, J. J., Dillingham, P. & Buchanan, R. A. Calculating the variance of the finite rate of population change from a matrix model in Mathematica. Environ. Model. Softw. 22, 359–364 (2007).
Google Scholar 
76.Hope, R. M. Rmisc: Rmisc: Ryan Miscellaneous. (2013).77.Caswell, H., Naiman, R. J. & Morin, R. Evaluating the consequences of reproduction in complex salmonid life cycles. Aquaculture 43, 123–134 (1984).
Google Scholar 
78.de Kroon, H., Plaisier, A., van Groenendael, J. & Caswell, H. Elasticity: the relative contribution of demographic parameters to population growth rate. Ecology 67, 1427–1431 (1986).
Google Scholar 
79.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Statist. Softw. 67, (2015).80.Padfield, D., O’Sullivan, H. & Pawar, S. rTPC and nls.multstart: a new pipeline to fit thermal performance curves in R. Methods Ecol. Evol. 12, 1138–1143 (2021).
Google Scholar 
81.Lactin, D. J., Holliday, N. J., Johnson, D. L. & Craigen, R. Improved rate model of temperature-dependent development by arthropods. Environ. Entomol. 24, 68–75 (1995).
Google Scholar 
82.Kamykowski, D. & McCollum, S. A. The temperature acclimatized swimming speed of selected marine dinoflagellates. J. Plankton Res. 8, 275–287 (1986).
Google Scholar  More

1.Cummings, M. E., Rosenthal, G. G. & Ryan, M. J. A private ultraviolet channel in visual communication. Proc. R. Soc. B-Biol. Sci. 270, 897–904. https://doi.org/10.1098/rspb.2003.2334 (2003).Article 

Google Scholar 
2.Tedore, C. & Nilsson, D. E. Avian UV vision enhances leaf surface contrasts in forest environments. Nat. Commun. https://doi.org/10.1038/s41467-018-08142-5 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
3.Qi, Y. D., Bai, S. J. & Heisler, G. M. Changes in ultraviolet-B and visible optical properties and absorbing pigment concentrations in pecan leaves during a growing season. Agric. For. Meteorol. 120, 229–240. https://doi.org/10.1016/j.agrformet.2003.08.018 (2003).ADS 
Article 

Google Scholar 
4.Mollon, J. D. “Tho’ she kneel’d in that place where they grew…” The uses and origins of primate colour vision. J. Exp. Biol. 146, 21–38. https://doi.org/10.1242/jeb.146.1.21 (1989).ADS 
CAS 
Article 
PubMed 

Google Scholar 
5.Osorio, D. & Vorobyev, M. Photoreceptor sectral sensitivities in terrestrial animals: Adaptations for luminance and colour vision. Proc. R. Soc. B Biol. Sci. 272, 1745–1752. https://doi.org/10.1098/rspb.2005.3156 (2005).CAS 
Article 

Google Scholar 
6.Osorio, D. & Vorobyev, M. A review of the evolution of animal colour vision and visual communication signals. Vis. Res. 48, 2042–2051. https://doi.org/10.1016/j.visres.2008.06.018 (2008).CAS 
Article 
PubMed 

Google Scholar 
7.Bowmaker, J. K. & Dartnall, H. J. A. Visual pigments of rods and cones in a human retina. J. Physiol. Lond. 298, 501–511. https://doi.org/10.1113/jphysiol.1980.sp013097 (1980).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
8.Bowmaker, J. K. & Hunt, D. M. Evolution of vertebrate visual pigments. Curr. Biol. 16, R484–R489. https://doi.org/10.1016/j.cub.2006.06.016 (2006).CAS 
Article 
PubMed 

Google Scholar 
9.van der Kooi, C. J., Stavenga, D. G., Arikawa, K., Belušič, G. & Kelber, A. Evolution of insect color vision: From spectral sensitivity to visual ecology. Annu. Rev. Entomol. 66, 435–461 (2021).Article 

Google Scholar 
10.Briscoe, A. D. & Chittka, L. The evolution of color vision in insects. Annu. Rev. Entomol. 46, 471–510. https://doi.org/10.1146/annurev.ento.46.1.471 (2001).CAS 
Article 
PubMed 

Google Scholar 
11.Ogawa, Y., Kinoshita, M., Stavenga, D. G. & Arikawa, K. Sex-specific retinal pigmentation results in sexually dimorphic long-wavelength-sensitive photoreceptors in the eastern pale clouded yellow butterfly, Colias erate. J. Exp. Biol. 216, 1916–1923. https://doi.org/10.1242/jeb.083485 (2013).CAS 
Article 
PubMed 

Google Scholar 
12.Kelber, A. Ovipositing butterflies use a red receptor to see green. J. Exp. Biol. 202, 2619–2630 (1999).Article 

Google Scholar 
13.Osorio, D. & Vorobyev, M. Colour vision as an adaptation to frugivory in primates. Proc. R. Soc. B Biol. Sci. 263, 593–599. https://doi.org/10.1098/rspb.1996.0089 (1996).ADS 
CAS 
Article 

Google Scholar 
14.Zaccardi, G., Kelber, A., Sison-Mangus, M. P. & Briscoe, A. D. Color discrimination in the red range with only one long-wavelength sensitive opsin. J. Exp. Biol. 209, 1944–1955. https://doi.org/10.1242/jeb.02207 (2006).Article 
PubMed 

Google Scholar 
15.Wakakuwa, M., Stavenga, D. G., Kurasawa, M. & Arikawa, K. A unique visual pigment expressed in green, red and deep-red receptors in the eye of the small white butterfly, Pieris rapae crucivora. J. Exp. Biol. 207, 2803–2810. https://doi.org/10.1242/jeb.01078 (2004).CAS 
Article 
PubMed 

Google Scholar 
16.Satoh, A. et al. Red-shift of spectral sensitivity due to screening pigment migration in the eyes of a moth, Adoxophyes orana. Zool. Lett. https://doi.org/10.1186/s40851-017-0075-6 (2017).Article 

Google Scholar 
17.Pirih, P. et al. The giant butterfly-moth Paysandisia archon has spectrally rich apposition eyes with unique light-dependent photoreceptor dynamics. J. Comp. Physiol. A Neuroethol. Sens. Neural Behav. Physiol. 204, 639–651. https://doi.org/10.1007/s00359-018-1267-z (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
18.Cronin, T. W., Jarvilehto, M., Weckstrom, M. & Lall, A. B. Tuning of photoreceptor spectral sensitivity in fireflies (Coleoptera: Lampyridae). J. Comp. Physiol. A Sens. Neural Behav. Physiol. 186, 1–12. https://doi.org/10.1007/s003590050001 (2000).CAS 
Article 

Google Scholar 
19.Lall, A. B. et al. Vision in click beetles (Coleoptera: Elateridae): pigments and spectral correspondence between visual sensitivity and species bioluminescence emission. J. Comp. Physiol. A Neuroethol. Sens. Neural Behav. Physiol. 196, 629–638. https://doi.org/10.1007/s00359-010-0549-x (2010).Article 
PubMed 

Google Scholar 
20.Frentiu, F. D. et al. Adaptive evolution of color vision as seen through the eyes of butterflies. Proc. Natl. Acad. Sci. U.S.A. 104, 8634–8640. https://doi.org/10.1073/pnas.0701447104 (2007).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
21.Liénard, M. A. et al. The evolution of red color vision is linked to coordinated rhodopsin tuning in lycaenid butterflies. Proc. Natl. Acad. Sci. U. S. A. https://doi.org/10.1073/pnas.2008986118 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
22.Saito, T. et al. Spectral tuning mediated by helix III in butterfly long wavelength-sensitive visual opsins revealed by heterologous action spectroscopy. Zool. Lett. https://doi.org/10.1186/s40851-019-0150-2 (2019).Article 

Google Scholar 
23.Enright, J. M. et al. Cyp27c1 red-shifts the spectral sensitivity of photoreceptors by converting vitamin A1 into A2. Curr. Biol. 25, 3048–3057. https://doi.org/10.1016/j.cub.2015.10.018 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
24.Martin, M., Le Galliard, J. F., Meylan, S. & Loew, E. R. The importance of ultraviolet and near-infrared sensitivity for visual discrimination in two species of lacertid lizards. J. Exp. Biol. 218, 458–465. https://doi.org/10.1242/jeb.115923 (2015).Article 
PubMed 

Google Scholar 
25.Ala-Laurila, P., Donner, K. & Koskelainen, A. Thermal activation and photoactivation of visual pigments. Biophys. J. 86, 3653–3662. https://doi.org/10.1529/biophysj.103.035626 (2004).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
26.Ala-Laurila, P., Pahlberg, J., Koskelainen, A. & Donner, K. On the relation between the photoactivation energy and the absorbance spectrum of visual pigments. Vis. Res. 44, 2153–2158. https://doi.org/10.1016/j.visres.2004.03.031 (2004).Article 
PubMed 

Google Scholar 
27.Barlow, H. B. Purkinje shift and retinal noise. Nature 179, 255–256. https://doi.org/10.1038/179255b0 (1957).ADS 
CAS 
Article 
PubMed 

Google Scholar 
28.Koskelainen, A., Ala-Laurila, P., Fyhrquist, N. & Donner, K. Measurement of thermal contribution to photoreceptor sensitivity. Nature 403, 220–223. https://doi.org/10.1038/35003242 (2000).ADS 
CAS 
Article 
PubMed 

Google Scholar 
29.Luo, D. G., Yue, W. W. S., Ala-Laurila, P. & Yau, K. W. Activation of visual pigments by light and heat. Science 332, 1307–1312. https://doi.org/10.1126/science.1200172 (2011).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
30.Rieke, F. & Baylor, D. A. Origin and functional impact of dark noise in retinal cones. Neuron 26, 181–186. https://doi.org/10.1016/s0896-6273(00)81148-4 (2000).CAS 
Article 
PubMed 

Google Scholar 
31.Cronin, T. W., Johnsen, S., Marshall, N. J. & Warrant, E. J. Visual pigments and photoreceptors. In Visual Ecology, pp. 37–65: Princeton University Press.32.Kelber, A., Yovanovich, C. & Olsson, P. Thresholds and noise limitations of colour vision in dim light. Philos. Trans. R. Soc. B-Biol. Sci. https://doi.org/10.1098/rstb.2016.0065 (2017).Article 

Google Scholar 
33.Kemp, D. J. et al. An integrative framework for the appraisal of coloration in nature. Am. Nat. 185, 705–724. https://doi.org/10.1086/681021 (2015).Article 
PubMed 

Google Scholar 
34.Hawkeswood, T. Observations on some Buprestidae (Coleoptera) from the Blue Mountains, N.S.W.. Aust. Zool. 19, 257–275 (1978).
Google Scholar 
35.Hawkeswood, T. Observations on two sympatric species of Buprestidae (Coleoptera) from sand dunes on the north coast of New South Wales. Victorian Naturalist 98, 146–151 (1981).
Google Scholar 
36.Hawkeswood, T. Observations on some jewel beetles (Coleoptera Buprestidae) from the Armidale district, North-eastern New South Wales. Vic. Nat. 98, 152–155 (1981).
Google Scholar 
37.Poland, T. M., Chen, Y. G., Koch, J. & Pureswaran, D. Review of the emerald ash borer (Coleoptera: Buprestidae), life history, mating behaviours, host plant selection, and host resistance. Can. Entomol. 147, 252–262. https://doi.org/10.4039/tce.2015.4 (2015).Article 

Google Scholar 
38.Bellamy, C. L., Williams, G., Hasenpusch, J. & Sundholm, A. A summary of the published data on host plants and morphology of immature stages of Australian jewel beetles (Coleoptera: Buprestidae), with additional new records. Insecta Mundi, 1–172 (2013).39.Domingue, M. J. et al. Field observations of visual attraction of three European oak buprestid beetles toward conspecific and heterospecific models. Entomol. Exp. Appl. 140, 112–121. https://doi.org/10.1111/j.1570-7458.2011.01139.x (2011).Article 

Google Scholar 
40.Domingue, M. J. et al. Differences in spectral selectivity between stages of visually guided mating approaches in a buprestid beetle. J. Exp. Biol. 219, 2837–2843 (2016).PubMed 

Google Scholar 
41.Pureswaran, D. S. & Poland, T. M. Effects of visual silhouette, leaf size and host species on feeding preference by adult emerald ash borer, Agrilus planipennis Fairmaire (Coleoptera: Buprestidae). Great Lakes Entomol. 42, 4 (2018).
Google Scholar 
42.Crook, D. J. et al. Laboratory and field response of the emerald ash borer (Coleoptera: Buprestidae), to selected regions of the electromagnetic spectrum. J. Econ. Entomol. 102, 2160–2169 (2009).Article 

Google Scholar 
43.Lord, N. P. et al. A cure for the blues: Opsin duplication and subfunctionalization for short-wavelength sensitivity in jewel beetles (Coleoptera: Buprestidae). BMC Evol. Biol. 16, 107 (2016).Article 

Google Scholar 
44.Meglič, A., Ilić, M., Quero, C., Arikawa, K. & Belušič, G. Two chiral types of randomly rotated ommatidia are distributed across the retina of the flathead oak borer Coraebus undatus (Coleoptera: Buprestidae). J. Exp. Biol. 223, jeb225920. https://doi.org/10.1242/jeb.225920 (2020).Article 
PubMed 

Google Scholar 
45.Chen, Y. G. & Poland, T. M. Biotic and abiotic factors affect green ash volatile production and emerald Ash borer adult feeding preference. Environ. Entomol. 38, 1756–1764. https://doi.org/10.1603/022.038.0629 (2009).CAS 
Article 
PubMed 

Google Scholar 
46.Govardovskii, V. I., Fyhrquist, N., Reuter, T., Kuzmin, D. G. & Donner, K. In search of the visual pigment template. Vis. Neurosci. 17, 509–528. https://doi.org/10.1017/s0952523800174036 (2000).CAS 
Article 
PubMed 

Google Scholar 
47.Dartnall, H. J. A. Visual pigment. Trans. Zool. Soc. Lond. 33, 147–152. https://doi.org/10.1111/j.1096-3642.1976.tb00047.x (1976).Article 

Google Scholar 
48.Arikawa, K., Scholten, D. G. W., Kinoshita, M. & Stavenga, D. G. Tuning of photoreceptor spectral sensitivities by red and yellow pigments in the butterfly Papilio xuthus. Zool. Sci. 16, 17–24. https://doi.org/10.2108/zsj.16.17 (1999).Article 

Google Scholar 
49.Das, D., Wilkie, S. E., Hunt, D. M. & Bowmaker, J. K. Visual pigments and oil droplets in the retina of a passerine bird, the canary Serinus canaria: microspectrophotometry and opsin sequences. Vision. Res. 39, 2801–2815. https://doi.org/10.1016/s0042-6989(99)00023-1 (1999).CAS 
Article 
PubMed 

Google Scholar 
50.Sison-Mangus, M. P., Bernard, G. D., Lampel, J. & Briscoe, A. D. Beauty in the eye of the beholder: The two blue opsins of lycaenid butterflies and the opsin gene-driven evolution of sexually dimorphic eyes. J. Exp. Biol. 209, 3079–3090. https://doi.org/10.1242/jeb.02360 (2006).CAS 
Article 
PubMed 

Google Scholar 
51.Bernard, G. D. Red-absorbing visual pigment of butterflies. Science 203, 1125. https://doi.org/10.1126/science.203.4385.1125 (1979).ADS 
CAS 
Article 
PubMed 

Google Scholar 
52.Martínez-Harms, J. et al. Evidence of red sensitive photoreceptors in Pygopleurus israelitus (Glaphyridae: Coleoptera) and its implications for beetle pollination in the southeast Mediterranean. J. Comp. Physiol. A Neuroethol. Sens. Neural Behav. Physiol. 198, 451–463. https://doi.org/10.1007/s00359-012-0722-5 (2012).CAS 
Article 
PubMed 

Google Scholar 
53.Stavenga, D. G. & Arikawa, K. Photoreceptor spectral sensitivities of the Small White butterfly Pieris rapae crucivora interpreted with optical modeling. J. Comp. Physiol. A Neuroethol. Sens. Neural Behav. Physiol. 197, 373–385. https://doi.org/10.1007/s00359-010-0622-5 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
54.Vorobyev, M., Osorio, D., Bennett, A. T. D., Marshall, N. J. & Cuthill, I. C. Tetrachromacy, oil droplets and bird plumage colours. J. Comp. Physiol. A Neuroethol. Sens. Neural Behav. Physiol. 183, 621–633. https://doi.org/10.1007/s003590050286 (1998).CAS 
Article 

Google Scholar 
55.Vorobyev, M. & Osorio, D. Receptor noise as a determinant of colour thresholds. Proc. R. Soc. Lond. Ser. B Biol. Sci. 265, 351–358. https://doi.org/10.1098/rspb.1998.0302 (1998).CAS 
Article 

Google Scholar 
56.Vorobyev, M., Brandt, R., Peitsch, D., Laughlin, S. B. & Menzel, R. Colour thresholds and receptor noise: Behaviour and physiology compared. Vis. Res. 41, 639–653. https://doi.org/10.1016/s0042-6989(00)00288-1 (2001).CAS 
Article 
PubMed 

Google Scholar 
57.Maia, R., Gruson, H., Endler, J. A. & White, T. E. pavo 2: new tools for the spectral and spatial analysis of colour in R. Methods in Ecology and Evolution 10, 1097–1107 (2019).58.Matsushita, A., Awata, H., Wakakuwa, M., Takemura, S. Y. & Arikawa, K. Rhabdom evolution in butterflies: insights from the uniquely tiered and heterogeneous ommatidia of the Glacial Apollo butterfly, Parnassius glacialis. Proc. R. Soc. B Biol. Sci. 279, 3482–3490. https://doi.org/10.1098/rspb.2012.0475 (2012).Article 

Google Scholar 
59.McCulloch, K. J. et al. Sexual dimorphism and retinal mosaic diversification following the evolution of a violet receptor in butterflies. Mol. Biol. Evol. 34, 2271–2284. https://doi.org/10.1093/molbev/msx163 (2017).CAS 
Article 
PubMed 

Google Scholar 
60.Bates, D., Mächler, M., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48. https://doi.org/10.18637/jss.v067.i01 (2015).Article 

Google Scholar 
61.R: A Language and environment for statistical computing (R Foundation for Statistical Computing, 2018).62.van der Kooi, C. J., Elzenga, J. T. M., Staal, M. & Stavenga, D. G. How to colour a flower: On the optical principles of flower coloration. Proc. R. Soc. B Biol. Sci. 283, 20160429. https://doi.org/10.1098/rspb.2016.0429 (2016).CAS 
Article 

Google Scholar 
63.Horler, D. N. H., Dockray, M. & Barber, J. The red edge of plant leaf reflectance. Int. J. Remote Sens. 4, 273–288. https://doi.org/10.1080/01431168308948546 (1983).Article 

Google Scholar 
64.Silberglied, R. E. Communication in the Ultraviolet. Annu. Rev. Ecol. Syst. 10, 373–398. https://doi.org/10.1146/annurev.es.10.110179.002105 (1979).Article 

Google Scholar 
65.Lind, O. Colour vision and background adaptation in a passerine bird, the zebra finch (Taeniopygia guttata). R. Soc. Open Sci. 3, 160383. https://doi.org/10.1098/rsos.160383 (2016).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
66.Santiago, C. et al. Does conspicuousness scale linearly with colour distance? A test using reef fish. Proc. R. Soc. B Biol. Sci. 287, 20201456. https://doi.org/10.1098/rspb.2020.1456 (2020).Article 

Google Scholar 
67.Giurfa, M., Vorobyev, M., Brandt, R., Posner, B. & Menzel, R. Discrimination of coloured stimuli by honeybees: Alternative use of achromatic and chromatic signals. J. Comp. Physiol. A. 180, 235–243. https://doi.org/10.1007/s003590050044 (1997).Article 

Google Scholar 
68.Garcia, J. E., Spaethe, J. & Dyer, A. G. The path to colour discrimination is S-shaped: Behaviour determines the interpretation of colour models. J. Comp. Physiol. A Neuroethol. Sens. Neural Behav. Physiol. 203, 983–997. https://doi.org/10.1007/s00359-017-1208-2 (2017).Article 
PubMed 

Google Scholar 
69.Hart, N. S., Bailes, H. J., Vorobyev, M., Marshall, N. J. & Collin, S. P. Visual ecology of the Australian lungfish (Neoceratodus forsteri). BMC Ecol. 8, 21. https://doi.org/10.1186/1472-6785-8-21 (2008).Article 
PubMed 
PubMed Central 

Google Scholar 
70.Vorobyev, M. Coloured oil droplets enhance colour discrimination. Proc. R. Soc. B Biol. Sci. 270, 1255–1261. https://doi.org/10.1098/rspb.2003.2381 (2003).Article 

Google Scholar 
71.Carleton, K. L. et al. Visual sensitivities tuned by heterochronic shifts in opsin gene expression. Bmc Biol. https://doi.org/10.1186/1741-7007-6-22 (2008).Article 
PubMed 
PubMed Central 

Google Scholar 
72.Seki, T. & Vogt, K. Evolutionary aspects of the diversity of visual pigment chromophores in the class Insecta. Comp. Biochem. Physiol. B Biochem. Mol. Biol. 119, 53–64. https://doi.org/10.1016/s0305-0491(97)00322-2 (1998).Article 

Google Scholar 
73.Stavenga, D. G., Smits, R. P. & Hoenders, B. J. Simple exponential functions describing the absorbance bands of visual pigment spectra. Vis. Res. 33, 1011–1017. https://doi.org/10.1016/0042-6989(93)90237-q (1993).CAS 
Article 
PubMed 

Google Scholar 
74.Kinoshita, M. & Arikawa, K. Color and polarization vision in foraging Papilio. J. Comp. Physiol. A Neuroethol. Sens. Neural Behav. Physiol. 200, 513–526. https://doi.org/10.1007/s00359-014-0903-5 (2014).Article 
PubMed 

Google Scholar 
75.Vorobyev, M. & Menzel, R. Flower advertisement for insects: Bees, a case study. In Adaptive Mechanisms in the Ecology of Vision (eds S. N. Archer et al.) 537–553 (Springer Netherlands, 1999).76.Bernard, G. D. & Remington, C. L. Color vision in Lycaena butterflies: Spectral tuning of receptor arrays in relation to behavioral ecology. Proc. Natl. Acad. Sci. U.S.A. 88, 2783–2787. https://doi.org/10.1073/pnas.88.7.2783 (1991).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
77.McCulloch, K. J., Osorio, D. & Briscoe, A. D. Sexual dimorphism in the compound eye of Heliconius erato: A nymphalid butterfly with at least five spectral classes of photoreceptor. J. Exp. Biol. 219, 2377–2387. https://doi.org/10.1242/jeb.136523 (2016).Article 
PubMed 

Google Scholar  More

1.Hughes TP, Kerry J, Álvarez-Noriega M, Álvarez-Romero J, Anderson K, Baird A, et al. Global warming and recurrent mass bleaching of corals. Nature. 2017;543:373–7.CAS 

Google Scholar 
2.Hoegh-Guldberg O. Climate change, coral bleaching and the future of the world’s coral reefs. Mar Freshw Res. 1999;50:839–66.
Google Scholar 
3.Patten NL, Harrison PL, Mitchell JG. Prevalence of virus-like particles within a staghorn scleractinian coral (Acropora muricata) from the Great Barrier Reef. Coral Reefs. 2008;27:569–80.
Google Scholar 
4.Leruste A, Bouvier T, Bettarel Y. Enumerating viruses in coral mucus. Appl Environ Microbiol. 2012;78:6377–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
5.Nguyen-kim H, Bouvier T, Bouvier C, Bui VN, Le-lan H, Bettarel Y. Viral and bacterial epibionts in thermally-stressed corals. J Mar Sci Eng. 2015;3:1272–86.
Google Scholar 
6.Vega Thurber R, Payet JP, Thurber AR, Correa AMS. Virus–host interactions and their roles in coral reef health and disease. Nat Rev Microbiol. 2017;15:205–16.
Google Scholar 
7.Sweet M, Bythell J. The role of viruses in coral health and disease. J Invertebr Pathol. 2017;147:136–44.PubMed 

Google Scholar 
8.Wilson WH, Francis I, Ryan K, Davy SK. Temperature induction of viruses in symbiotic dinoflagellates. Aquat Micro Ecol. 2001;25:99–102.
Google Scholar 
9.Lohr J, Munn CB, Wilson WH. Characterization of a latent virus-like infection of symbiotic zooxanthellae. Appl Environ Microbiol. 2007;73:2976–81.CAS 
PubMed 
PubMed Central 

Google Scholar 
10.Lawrence SA, Wilson WH, Davy JE, Davy SK. Latent virus-like infections are present in a diverse range of Symbiodinium spp. (Dinophyta). J Phycol. 2014;50:984–97.PubMed 

Google Scholar 
11.Messyasz A, Rosales SM, Mueller RS, Sawyer T, Correa AMS, Thurber AR, et al. Coral bleaching phenotypes associated with differential abundances of nucleocytoplasmic large DNA viruses. Front Mar Sci. 2020;7:555474.
Google Scholar 
12.Marhaver KL, Edwards RA, Rohwer F. Viral communities associated with healthy and bleaching corals. Environ Microbiol. 2008;10:2277–86.CAS 
PubMed 
PubMed Central 

Google Scholar 
13.Correa AMS, Ainsworth TD, Rosales SM, Thurber AR, Butler CR, Vega Thurber RL. Viral outbreak in corals associated with an in situ bleaching event: atypical herpes-like viruses and a new megavirus infecting Symbiodinium. Front Microbiol. 2016;7:127.PubMed 
PubMed Central 

Google Scholar 
14.Bettarel Y, Thuy NT, Huy TQ, Hoang PK, Bouvier T. Observation of virus-like particles in thin sections of the bleaching scleractinian coral Acropora cytherea. J Mar Biol Assoc U K. 2013;93:909–12.
Google Scholar 
15.Lesser MP, Bythell JC, Gates RD, Johnstone RW, Hoegh-Guldberg O. Are infectious diseases really killing corals? Alternative interpretations of the experimental and ecological data. J Exp Mar Biol Ecol. 2007;346:36–44.
Google Scholar 
16.Soffer N, Brandt ME, Correa AMS, Smith TB, Thurber RV. Potential role of viruses in white plague coral disease. ISME J. 2014;8:271–83.CAS 
PubMed 

Google Scholar 
17.Lawrence SA, Davy JE, Aeby GS, Wilson WH, Davy SK. Quantification of virus-like particles suggests viral infection in corals affected by Porites tissue loss. Coral Reefs. 2014;33:687–91.
Google Scholar 
18.Lawrence SA, Davy JE, Wilson WH, Hoegh-Guldberg O, Davy SK. Porites white patch syndrome: associated viruses and disease physiology. Coral Reefs. 2015;34:249–57.
Google Scholar 
19.Pollock FJ, M. Wood-Charlson E, Van Oppen MJH, Bourne DG, Willis BL, Weynberg KD. Abundance and morphology of virus-like particles associated with the coral Acropora hyacinthus differ between healthy and white syndrome-infected states. Mar Ecol Prog Ser. 2014;510:39–43.
Google Scholar 
20.Vega Thurber RL, Correa AMS. Viruses of reef-building scleractinian corals. J Exp Mar Biol Ecol. 2011;408:102–13.
Google Scholar 
21.Weynberg KD, Voolstra CR, Neave MJ, Buerger P, Van Oppen MJH. From cholera to corals: viruses as drivers of virulence in a major coral bacterial pathogen. Sci Rep. 2015;5:17889.CAS 
PubMed 
PubMed Central 

Google Scholar 
22.Quistad SD, Grasis JA, Barr JJ, Rohwer FL. Viruses and the origin of microbiome selection and immunity. ISME J. 2017;11:835–40.CAS 
PubMed 

Google Scholar 
23.Oppen MJHV, Leong J, Gates RD. Coral-virus interactions: a double-edged sword? SYMBIOSIS. 2009;47:1–8.
Google Scholar 
24.Correa AMS, Howard-Varona C, Coy SR, Buchan A, Sullvian MB, Weitz JS. The virus-microbe infection continuum: revisiting the viral rules of life. Nat Rev Microbiol. 2021;19:501–13.CAS 
PubMed 

Google Scholar 
25.Correa AMS, Welsh RM, Vega Thurber RL. Unique nucleocytoplasmic dsDNA and +ssRNA viruses are associated with the dinoflagellate endosymbionts of corals. ISME J. 2013;7:13–27.CAS 
PubMed 

Google Scholar 
26.Lawrence SA, Floge SA, Davy JE, Davy SK, Wilson WH. Exploratory analysis of Symbiodinium transcriptomes reveals potential latent infection by large dsDNA viruses. Environ Microbiol. 2017;19:3909–19.CAS 
PubMed 

Google Scholar 
27.Levin RA, Voolstra CR, Weynberg KD, Van Oppen M. Evidence for a role of viruses in the thermal sensitivity of coral photosymbionts. ISME J. 2017;11:808–12.CAS 
PubMed 

Google Scholar 
28.Knowles B, Bonachela JA, Behrenfeld MJ, Bondoc KG, Cael BB, Carlson CA, et al. Temperate infection in a virus–host system previously known for virulent dynamics. Nat Commun. 2020;11:1–13.
Google Scholar 
29.Montalvo-Proaño J, Buerger P, Weynberg KD, Van Oppen MJH. A PCR-based assay targeting the major capsid protein gene of a dinorna-like ssRNA virus that infects coral photosymbionts. Front Microbiol. 2017;8:1665.PubMed 
PubMed Central 

Google Scholar 
30.Tomaru Y, Katanozaka N, Nishida K, Shirai Y, Tarutani K, Yamaguchi M, et al. Isolation and characterization of two distinct types of HcRNAV, a single-stranded RNA virus infecting the bivalve-killing microalga Heterocapsa circularisquama. Aquat Micro Ecol. 2004;34:207–18.
Google Scholar 
31.Miller JL, Chen S, Nagasaki K, Roseman A, Wepf R, Sewell T, et al. Three-dimensional reconstruction of Heterocapsa circularisquama RNA virus by electron cryo-microscopy. J Gen Virol. 2011;92:1960–70.CAS 
PubMed 

Google Scholar 
32.Shi M, Lin XD, Tian J-H, Chen L-J, Chen X, Li C-IU, et al. Redefining the invertebrate RNA virosphere. Nature. 2016;540:539–43.CAS 
PubMed 

Google Scholar 
33.Domingo E, Sheldon J, Perales C. Viral quasispecies evolution. Microbiol Mol Biol Rev. 2012;76:159–216.CAS 
PubMed 
PubMed Central 

Google Scholar 
34.Sanjuán R, Domingo-Calap P. Mechanisms of viral mutation. Cell Mol Life Sci. 2016;73:4433–48.PubMed 
PubMed Central 

Google Scholar 
35.Vlok M, Lang AS, Suttle CA. Marine RNA virus quasispecies are distributed throughout the oceans. mSphere. 2019;4:1–18.
Google Scholar 
36.Domingo E, Martinez-salas E, Sobrino F, de la Torre JC, Portela A, Ortin J, et al. The quasispecies (extremely heterogeneous) nature of viral RNA genome populations: biological relevance—a review. Gene. 1985;40:1–8.CAS 
PubMed 

Google Scholar 
37.Vignuzzi M, Stone JK, Arnold JJ, Cameron CE, Andino R. Quasispecies diversity determines pathogenesis through cooperative interactions in a viral population. Nature. 2006;439:344–8.CAS 
PubMed 

Google Scholar 
38.Holland J, Spindler K, Horodyski F, Grabau E, Nichol S, VandePol S. Rapid evolution of RNA genomes. Science. 1982;215:1577–85.CAS 
PubMed 

Google Scholar 
39.Gélin P, Postaire B, Fauvelot C, Magalon H. Molecular phylogenetics and evolution reevaluating species number, distribution and endemism of the coral genus Pocillopora Lamarck, 1816 using species delimitation methods and microsatellites. Mol Phylogenet Evol. 2017;109:430–46.PubMed 

Google Scholar 
40.Pratchett MS, McCowan D, Maynard JA, Heron SF. Changes in bleaching susceptibility among corals subject to ocean warming and recurrent bleaching in Moorea, French Polynesia. PLoS ONE. 2013;8:1–10.
Google Scholar 
41.Donovan MK, Adam TC, Shantz AA, Speare KE, Munsterman KS, Rice MM, et al. Nitrogen pollution interacts with heat stress to increase coral bleaching across the seascape. Proc Natl Acad Sci USA. 2020;117:5351–7.CAS 
PubMed 
PubMed Central 

Google Scholar 
42.Siebeck UE, Marshall NJ, Kluter A, Hoegh-Guldberg O. Monitoring coral bleaching using a colour reference card. Coral Reefs. 2006;25:453–60.
Google Scholar 
43.Winters G, Holzman R, Blekhman A, Beer S, Loya Y. Photographic assessment of coral chlorophyll contents: Implications for ecophysiological studies and coral monitoring. J Exp Mar Biol Ecol. 2009;380:25–35.CAS 

Google Scholar 
44.Turnham KE, Wham DC, Sampayo E, LaJeunesse TC. Mutualistic microalgae co-diversify with reef corals that acquire symbionts during egg development. ISME J. 2021;15:3271–85.PubMed 

Google Scholar 
45.Pinzón JH, Lajeunesse TC. Species delimitation of common reef corals in the genus Pocillopora using nucleotide sequence phylogenies, population genetics and symbiosis ecology. Mol Ecol. 2011;20:311–25.PubMed 

Google Scholar 
46.Flot JF, Tillier S. The mitochondrial genome of Pocillopora (Cnidaria: Scleractinia) contains two variable regions: the putative D-loop and a novel ORF of unknown function. Gene. 2007;401:80–87.CAS 
PubMed 

Google Scholar 
47.Pinzón JH, Sampayo E, Cox E, Chauka LJ, Chen CA, Voolstra CR, et al. Blind to morphology: genetics identifies several widespread ecologically common species and few endemics among Indo-Pacific cauliflower corals (Pocillopora, Scleractinia). J Biogeogr. 2013;40:1595–608.
Google Scholar 
48.Johnston EC, Forsman ZH, Flot JF, Schmidt-Roach S, Pinzón JH, Knapp ISS, et al. A genomic glance through the fog of plasticity and diversification in Pocillopora. Sci Rep. 2017;7:5991.PubMed 
PubMed Central 

Google Scholar 
49.Wham DC, Carmichael M, LaJeunesse TC. Microsatellite loci for Symbiodinium goreaui and other Clade C Symbiodinium. Coservation Genet Resour. 2014;6:127–9.
Google Scholar 
50.Bay LK, Ulstrup KE, Nielsen HB, Jarmer H, Goffard N, Willis BL, et al. Microarray analysis reveals transcriptional plasticity in the reef building coral Acropora millepora. Mol Ecol. 2009;18:3062–75.CAS 
PubMed 

Google Scholar 
51.Veglia AJ, Vicéns RER, Grupstra CGB, Howe-Kerr LI, Correa AMS. vAMPirus: an automated, comprehensive virus amplicon sequencing analysis program. 2021: available at https://zenodo.org/record/4549851 (accessed February 17, 2021).52.Edgar RC. UNOISE2: improved error-correction for Illumina 16S and ITS amplicon sequencing. bioRxiv 2016;81257: available at https://doi.org/10.1101/081257.53.Rognes T, Flouri T, Nichols B, Quince C, Mahé F. VSEARCH: a versatile open source tool for metagenomics. PeerJ. 2016;2016:1–22.
Google Scholar 
54.Edgar RC. MUSCLE: a multiple sequence alignment method with reduced time and space complexity. BMC Bioinforma. 2004;5:1–19.
Google Scholar 
55.Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2014;12:59–60.PubMed 

Google Scholar 
56.Darriba D, Posada D, Kozlov AM, Stamatakis A, Morel B, Flouri T. ModelTest-NG: a new and scalable tool for the selection of DNA and protein evolutionary models. Mol Biol Evol. 2020;37:291–4.CAS 
PubMed 

Google Scholar 
57.Oksanen J, Blanchet G, Friendly M, Kindt R, Legendre P, McGlinn D, et al. vegan: Community Ecology Package. R package version 2.4-6. R Package Version 25-6 2019.58.Anderson MJ, Walsh DCI. PERMANOVA, ANOSIM, and the Mantel test in the face of heterogeneous dispersions: what null hypothesis are you testing? Ecol Monogr. 2013;83:557–74.
Google Scholar 
59.Bates DM, Maechler M, Bolker B, Walker S. lme4: Mixed-effects modeling with R. R Package Version 11-7 HttpCRANR-Proj 2014.60.Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550–550.PubMed 
PubMed Central 

Google Scholar 
61.Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012;19:455–77.CAS 
PubMed 
PubMed Central 

Google Scholar 
62.Goodacre N, Aljanahi A, Nandakumar S, Mikailov M, Khan AS. A reference viral database (RVDB) to enhance bioinformatics analysis of high-throughput sequencing for novel virus detection. mSphere. 2018;3:e00069–18.PubMed 
PubMed Central 

Google Scholar 
63.Rädecker N, Pogoreutz C, Gegner HM, Cárdenas A, Roth F. Heat stress destabilizes symbiotic nutrient cycling in corals. Proc Natl Acad Sci USA. 2021;118:e2022653118.PubMed 
PubMed Central 

Google Scholar 
64.Biebricher CK, Eigen M. What Is a Quasispecies? In: Domingo E (ed). Quasispecies: concept and implications for virology. 2006. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 1–31.65.Gelbart M, Harari S, Ben-ari Y, Kustin T, Wolf D, Mandelboim M, et al. Drivers of within-host genetic diversity in acute infections of viruses. PLoS Pathog. 2020;16:e1009029.CAS 
PubMed 
PubMed Central 

Google Scholar 
66.Breitbart M, Bonnain C, Malki K, Sawaya NA. Phage puppet masters of the marine microbial realm. Nat Microbiol. 2018;3:754–66.CAS 
PubMed 

Google Scholar 
67.Mann NH, Cook A, Millard A, Bailey S, Clokie M. Bacterial photosynthesis genes in a virus. Nature. 2003;424:741.CAS 
PubMed 

Google Scholar 
68.Marquez LM, Redman RS, Rodriguez RJ, Roossinck MJ. A virus in a fungus in a plant: three-way symbiosis required for thermal tolerance. Science. 2007;315:513–5.CAS 
PubMed 

Google Scholar 
69.Holmes EC. The RNA virus quasispecies: fact or fiction? J Mol Biol. 2010;400:271–3.CAS 
PubMed 

Google Scholar 
70.Pybus OG, Rambaut A, Belshaw R, Freckleton RP, Drummond AJ, Holmes EC. Phylogenetic evidence for deleterious mutation load in RNA viruses and its contribution to viral evolution. Mol Biol Evol. 2007;24:845–52.CAS 
PubMed 

Google Scholar 
71.Holmes EC. Patterns of intra- and interhost nonsynonymous variation reveal strong purifying selection in dengue virus. J Virol. 2003;77:11296–8.CAS 
PubMed 
PubMed Central 

Google Scholar 
72.Edwards CTT, Holmes EC, Pybus OG, Wilson DJ, Viscidi RP, Abrams EJ, et al. Evolution of the human immunodeficiency virus envelope gene is dominated by purifying selection. Genetics. 2006;174:1441–53.CAS 
PubMed 
PubMed Central 

Google Scholar 
73.Roux S, Hawley AK, Beltran MT, Scofield M, Schwientek P, Stepanauskas R, et al. Ecology and evolution of viruses infecting uncultivated SUP05 bacteria as revealed by single-cell- and meta-genomics. eLIFE. 2014;3:e03125.PubMed 
PubMed Central 

Google Scholar 
74.Labonté JM, Swan BK, Poulos B, Luo H, Koren S, Hallam SJ, et al. Single-cell genomics-based analysis of virus-host interactions in marine surface bacterioplankton. ISME J. 2015;9:2386–99.PubMed 
PubMed Central 

Google Scholar 
75.Munson-mcgee JH, Peng S, Dewerff S, Stepanauskas R, Whitaker RJ, Weitz JS, et al. A virus or more in (nearly) every cell: ubiquitous networks of virus–host interactions in extreme environments. ISME J. 2018;12:1706–14.CAS 
PubMed 
PubMed Central 

Google Scholar 
76.Díaz-Muñoz SL. Viral coinfection is shaped by host ecology and virus-virus interactions across diverse microbial taxa and environments. Virus Evol. 2017;3:1–14.
Google Scholar 
77.Wang L, Wu S, Liu T, Sun J, Chi S, Liu C, et al. Endogenous viral elements in algal genomes. Acta Oceano Sin. 2014;33:102–7.CAS 

Google Scholar 
78.Moniruzzaman M, Weinheimer AR, Martinez-Gutierrez CA, Aylward FO. Widespread endogenization of giant viruses shapes genomes of green algae. Nature. 2020;588:141–5.CAS 
PubMed 

Google Scholar 
79.Koonin EV, Dolja VV, Krupovic M, Varsani A, Wolf YI, Yutin N, et al. Global organization and proposed megataxonomy of the virus world. Microbiol Mol Biol Rev. 2020;84:e00061–19.CAS 
PubMed 
PubMed Central 

Google Scholar 
80.Holmes EC. The evolution of endogenous viral elements. Cell Host Microbe. 2011;10:368–77.CAS 
PubMed 
PubMed Central 

Google Scholar 
81.Ripp S, Miller RV. The role of pseudolysogeny in bacteriophage-host interactions in a natural freshwater environment. Microbiology. 1997;143:2065–70.CAS 
PubMed 

Google Scholar 
82.Onodera S, Olkkonen VM, Gottlieb P, Strassman J, Qiao XY, Bamford DH, et al. Construction of a transducing virus from double-stranded RNA bacteriophage phi6: establishment of carrier states in host cells. J Virol. 1992;66:190–6.CAS 
PubMed 
PubMed Central 

Google Scholar 
83.de la Higuera I, Kasun GW, Torrance EL, Pratt AA, Maluenda A, Colombet J, et al. Unveiling crucivirus diversity by mining metagenomic data. mBio. 2020;11:e01410–20.PubMed 
PubMed Central 

Google Scholar 
84.Ku C, Sheyn U, Sebé-Pedrós A, Ben-Dor S, Schatz D, Tanay A, et al. A single-cell view on alga-virus interactions reveals sequential transcriptional programs and infection states. Sci Adv. 2020;6:eaba4137.85.Deng L, Ignacio-espinoza JC, Gregory AC, Poulos BT, Weitz JS, Hugenholtz P, et al. Viral tagging reveals discrete populations in Synechococcus viral genome sequence space. Nature. 2014;513:242–6.CAS 

Google Scholar 
86.Jonge PAD, Costa AR, Franklin L, Brouns SJJ, Jonge PAD, Meijenfeldt FABV, et al. Adsorption sequencing as a rapid method to link environmental bacteriophages to hosts. ISCIENCE. 2020;23:101439.PubMed 
PubMed Central 

Google Scholar 
87.Jiang SC, Paul JH. Seasonal and diel abundance of viruses and occurrence of lysogeny/bacteriocinogeny in the marine environment. Mar Ecol Prog Ser. 1994;104:163–72.
Google Scholar 
88.Brum JR, Hurwitz BL, Schofield O, Ducklow HW, Sullivan MB. Seasonal time bombs: dominant temperate viruses affect Southern Ocean microbial dynamics. ISME J. 2016;10:437–49.CAS 
PubMed 

Google Scholar 
89.Winter C, Bouvier T, Weinbauer MG, Thingstad TF. Trade-offs between competition and defense specialists among unicellular planktonic organisms: the “killing the winner” hypothesis revisited. Microbiol Mol Biol Rev. 2010;74:42–57.CAS 
PubMed 
PubMed Central 

Google Scholar 
90.Thingstad TF, Våge S, Storesund JE, Sandaa R, Giske J. A theoretical analysis of how strain-specific viruses can control microbial species diversity. PNAS. 2014;111:7813–8.CAS 
PubMed 
PubMed Central 

Google Scholar 
91.Thingstad TF. Elements of a theory for the mechanisms controlling abundance, diversity, and biogeochemical role of lytic bacterial viruses in aquatic systems. Limnol Oceanogr. 2000;45:1320–8.
Google Scholar 
92.Tomaru Y, Hata N, Masuda T, Tsuji M, Igata K, Masuda Y, et al. Ecological dynamics of the bivalve-killing dinoflagellate Heterocapsa circularisquama and its infectious viruses in different locations of western Japan. Environ Microbiol. 2007;9:1376–83.PubMed 

Google Scholar 
93.Sadeghi M, Tomaru Y, Ahola T. RNA viruses in aquatic unicellular eukaryotes. Viruses 2021.94.Randall RE, Griffin DE. Within host RNA virus persistence: mechanisms and consequences. Curr Opin Virol. 2017;23:35–42.CAS 
PubMed 
PubMed Central 

Google Scholar 
95.Roossinck MJ. Lifestyles of plant viruses. Philos Trans R Soc B Biol Sci. 2010;365:1899–905.
Google Scholar 
96.Honjo MN, Emura N, Kawagoe T, Sugisaka J, Kamitani M, Nagano AJ, et al. Seasonality of interactions between a plant virus and its host during persistent infection in a natural environment. ISME J. 2020;14:506–18.CAS 
PubMed 

Google Scholar 
97.Kim Y, Kim YJ, Paek K-H. Temperature-specific vsiRNA confers RNAi-mediated viral resistance at elevated temperature in Capsicum annuum. J Exp Bot. 2020;72:1432–48.
Google Scholar 
98.Jones RAC. Chapter three – future scenarios for plant virus pathogens as climate change progresses. In: Kielian M, Maramorosch K, Mettenleiter TC (eds).2016. Academic Press, pp 87–147.99.Brüwer JD, Agrawal S, Liew YJ, Aranda M, Voolstra CR. Association of coral algal symbionts with a diverse viral community responsive to heat shock. BMC Microbiol. 2017;17:1–11.
Google Scholar 
100.Cevallos RC, Sarnow P. Temperature protects insect cells from infection by cricket paralysis virus. J Virol. 2010;84:1652–5.CAS 
PubMed 

Google Scholar 
101.Edgar RS, Lielausis I. Temperature-sensitive mutants of bacteriophage T4D: their isolation and genetic characterization. Genetics. 1964;49:649–62.CAS 
PubMed 
PubMed Central 

Google Scholar 
102.Vega Thurber RL, Barott KL, Hall D, Liu H, Rodriguez-Mueller B, Desnues C, et al. Metagenomic analysis indicates that stressors induce production of herpes-like viruses in the coral Porites compressa. Proc Natl Acad Sci USA. 2008;105:18413–8.CAS 
PubMed 
PubMed Central 

Google Scholar 
103.Seifert M, van Nies P, Papini FS, Arnold JJ, Poranen MM, Cameron CE, et al. Temperature controlled high-throughput magnetic tweezers show striking difference in activation energies of replicating viral RNA-dependent RNA polymerases. Nucleic Acids Res. 2020;48:5591–602.CAS 
PubMed 
PubMed Central 

Google Scholar 
104.Wooldridge SA. Differential thermal bleaching susceptibilities amongst coral taxa: re-posing the role of the host. Coral Reefs. 2014;33:15–27.
Google Scholar 
105.Hédouin L, Rouzé H, Berthe C, Perez-Rosales G, Martinez E, Chancerelle Y, et al. Contrasting patterns of mortality in Polynesian coral reefs following the third global coral bleaching event in 2016. Coral Reefs. 2020;39:939–52.
Google Scholar 
106.Tonk L, Sampayo EM, Weeks S, Magno-Canto M, Hoegh-Guldberg O. Host-Specific interactions with environmental factors shape the distribution of Symbiodinium across the Great Barrier Reef. PLoS ONE. 2013;8:e68533–e68533.CAS 
PubMed 
PubMed Central 

Google Scholar 
107.Serrano X, Baums IB, O’Reilly K, Smith TB, Jones RJ, Shearer TL, et al. Geographic differences in vertical connectivity in the Caribbean coral Montastraea cavernosa despite high levels of horizontal connectivity at shallow depths. Mol Ecol. 2014;23:4226–40.CAS 
PubMed 

Google Scholar  More

1.McKenney EA, Koelle K, Dunn RR, Yoder AD. The ecosystem services of animal microbiomes. Mol Ecol. 2018;27:2164–72.CAS 
PubMed 

Google Scholar 
2.Miller ET, Svanbäck R, Bohannan BJM. Microbiomes as metacommunities: understanding host-associated microbes through metacommunity ecology. Trends Ecol Evol. 2018;33:926–35.PubMed 

Google Scholar 
3.Johnson AJ, Vangay P, Al-Ghalith GA, Hillmann BM, Ward TL, Shields-Cutler RR, et al. Daily sampling reveals personalized diet-microbiome associations in humans. Cell Host Microbe. 2019;25:789–802.CAS 
PubMed 

Google Scholar 
4.Björk JR, Dasari M, Grieneisen L, Archie EA. Primate microbiomes over time: Longitudinal answers to standing questions in microbiome research. Am J Primatol. 2019;81:1–23.
Google Scholar 
5.Sun B, Wang X, Bernstein S, Huffman MA, Xia DP, Gu Z, et al. Marked variation between winter and spring gut microbiota in free-ranging Tibetan Macaques (Macaca thibetana). Sci Rep. 2016;6:1–8.
Google Scholar 
6.Wu Q, Wang X, Ding Y, Hu Y, Nie Y, Wei W, et al. Seasonal variation in nutrient utilization shapes gut microbiome structure and function in wild giant pandas. Proc R Soc B Biol Sci. 2017;284:20170955.7.Maurice CF, Knowles SCL, Ladau J, Pollard KS, Fenton A, Pedersen AB, et al. Marked seasonal variation in the wild mouse gut microbiota. ISME J. 2015;9:2423–34.CAS 
PubMed 
PubMed Central 

Google Scholar 
8.Amato KR, Leigh SR, Kent A, Mackie RI, Yeoman CJ, Stumpf RM, et al. The gut microbiota appears to compensate for seasonal diet variation in the wild black howler monkey (Alouatta pigra). Microb Ecol. 2014;69:434–43.PubMed 

Google Scholar 
9.Orkin JD, Campos FA, Myers MS, Cheves Hernandez SE, Guadamuz A, Melin AD. Seasonality of the gut microbiota of free-ranging white-faced capuchins in a tropical dry forest. ISME J. 2019;13:183–96. 22.CAS 
PubMed 

Google Scholar 
10.Springer A, Fichtel C, Al-Ghalith GA, Koch F, Amato KR, Clayton JB, et al. Patterns of seasonality and group membership characterize the gut microbiota in a longitudinal study of wild Verreaux’s sifakas (Propithecus verreauxi). Ecol Evol. 2017;7:5732–45.PubMed 
PubMed Central 

Google Scholar 
11.Jagsi R, Jiang J, Momoh AO, Alderman A, Giordano SH, Buchholz TA, et al. Seasonal cycling in the gut microbiome of the Hadza Hunter-Gatherers of Tanzania. Science. 2017;357:802–6.
Google Scholar 
12.Hicks AL, Lee KJ, Couto-Rodriguez M, Patel J, Sinha R, Guo C, et al. Gut microbiomes of wild great apes fluctuate seasonally in response to diet. Nat Commun. 2018;9:1786.PubMed 
PubMed Central 

Google Scholar 
13.Baniel A, Amato KR, Beehner JC, Bergman TJ, Mercer A, Perlman RF, et al. Seasonal shifts in the gut microbiome indicate plastic responses to diet in wild geladas. Microbiome. 2021;9:1–20.
Google Scholar 
14.Ren T, Grieneisen LE, Alberts SC, Archie EA, Wu M. Development, diet and dynamism: longitudinal and cross-sectional predictors of gut microbial communities in wild baboons. Environ Microbiol. 2016;18:1312–25.PubMed 

Google Scholar 
15.Větrovský T, Baldrian P. The variability of the 16S rRNA gene in bacterial genomes and its consequences for bacterial community analyses. PLoS One. 2013;8:e57923.PubMed 
PubMed Central 

Google Scholar 
16.De Vrieze J, Pinto AJ, Sloan WT, Ijaz UZ. The active microbial community more accurately reflects the anaerobic digestion process: 16S rRNA (gene) sequencing as a predictive tool. Microbiome. 2018;6:63.PubMed 
PubMed Central 

Google Scholar 
17.Kappeler PM, Fichtel C. A 15-year perspective on the social organization and life history of sifaka in Kirindy Forest. In: Long-term field studies of primates. Springer Berlin Heidelberg; 2012. p. 101–21.18.Peckre LR, Defolie C, Kappeler PM, Fichtel C. Potential self-medication using millipede secretions in red-fronted lemurs: combining anointment and ingestion for a joint action against gastrointestinal parasites? Primates. 2018;59:483–94.PubMed 

Google Scholar 
19.Ostner J. Social thermoregulation in redfronted lemurs (Eulemur fulvus rufus). Folia Primatol. 2002;73:175–80.
Google Scholar 
20.Amoroso CR, Kappeler PM, Fichtel C, Nunn CL. Water availability impacts habitat use by red-fronted lemurs (Eulemur rufifrons): An experimental and observational study. Int J Primatol. 2020;41:61–80.
Google Scholar 
21.Koch F, Ganzhorn JU, Rothman JM, Chapman CA, Fichtel C. Sex and seasonal differences in diet and nutrient intake in Verreaux’s sifakas (Propithecus verreauxi). Am J Primatol. 2017;79:1–10.CAS 
PubMed 

Google Scholar 
22.Clough D. Gastro-intestinal parasites of red-fronted lemurs in Kirindy Forest, western Madagascar. J Parasitol. 2010;96:245–51.PubMed 

Google Scholar 
23.Gogarten JF, Calvignac-Spencer S, Nunn CL, Ulrich M, Saiepour N, Nielsen HV, et al. Metabarcoding of eukaryotic parasite communities describes diverse parasite assemblages spanning the primate phylogeny. Mol Ecol Resour. 2020;20:204–15.PubMed 

Google Scholar 
24.Lai GC, Tan TG, Pavelka N. The mammalian mycobiome: A complex system in a dynamic relationship with the host. Wiley Interdiscip Rev Syst Biol Med. 2019;11:e1438.PubMed 

Google Scholar 
25.Mann AE, Mazel F, Lemay MA, Morien E, Billy V, Kowalewski M, et al. Biodiversity of protists and nematodes in the wild nonhuman primate gut. ISME J. 2020;14:609–22.CAS 
PubMed 

Google Scholar 
26.Grieneisen LE, Livermore J, Alberts S, Tung J, Archie EA. Group living and male dispersal predict the core gut microbiome in wild baboons. Integr Comp Biol. 2017;57:770–85.PubMed 
PubMed Central 

Google Scholar 
27.Klindworth A, Pruesse E, Schweer T, Peplies J, Quast C, Horn M, et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 2013;41:1–11.
Google Scholar 
28.Toju H, Tanabe AS, Yamamoto S, Sato H. High-coverage ITS primers for the DNA-based identification of ascomycetes and basidiomycetes in environmental samples. PLoS One. 2012;7:e40863.29.Porat I, Vishnivetskaya TA, Mosher JJ, Brandt CC, Yang ZK, Brooks SC, et al. Characterization of archaeal community in contaminated and uncontaminated surface stream sediments. Microb Ecol. 2010;60:784–95.PubMed 
PubMed Central 

Google Scholar 
30.Gantner S, Andersson AF, Alonso-Sáez L, Bertilsson S. Novel primers for 16S rRNA-based archaeal community analyses in environmental samples. J Microbiol Methods. 2011;84:12–8.CAS 
PubMed 

Google Scholar 
31.Stoeck T, Bass D, Nebel M, Christen R, Jones MDM, Breiner H-W, et al. Multiple marker parallel tag environmental DNA sequencing reveals a highly complex eukaryotic community in marine anoxic water. Mol Ecol. 2010;19:21–31.CAS 
PubMed 

Google Scholar 
32.Bahram M, Anslan S, Hildebrand F, Bork P, Tedersoo L. Newly designed 16S rRNA metabarcoding primers amplify diverse and novel archaeal taxa from the environment. Environ Microbiol Rep. 2019;11:487–94.PubMed 

Google Scholar 
33.Berkelmann D, Schneider D, Hennings N, Meryandini A, Daniel R. Soil bacterial community structures in relation to different oil palm management practices. Sci Data. 2020;7:1–7.
Google Scholar 
34.Chen S, Zhou Y, Chen Y, Gu J. Fastp: An ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018;34:i884–90.PubMed 
PubMed Central 

Google Scholar 
35.Zhang J, Kobert K, Flouri T, Stamatakis A. PEAR: A fast and accurate Illumina paired-end read mergeR. Bioinformatics. 2014;30:614–20.CAS 
PubMed 

Google Scholar 
36.Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 2011;17:10–2.
Google Scholar 
37.Rognes T, Flouri T, Nichols B, Quince C, Mahé F. VSEARCH: A versatile open source tool for metagenomics. PeerJ. 2016;2016:1–22.
Google Scholar 
38.Edgar RC UNOISE2: improved error-correction for Illumina 16S and ITS amplicon sequencing. bioRxiv. 2016;081257.39.Edgar RC UCHIME2: improved chimera prediction for amplicon sequencing. bioRxiv. 2016;074252.40.Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215:403–10.CAS 

Google Scholar 
41.Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 2013;41:590–6.
Google Scholar 
42.Guillou L, Bachar D, Audic S, Bass D, Berney C, Bittner L, et al. The protist ribosomal reference database (PR2): A catalog of unicellular eukaryote small sub-unit rRNA sequences with curated taxonomy. Nucleic Acids Res. 2013;41:597–604.
Google Scholar 
43.Nilsson RH, Larsson KH, Taylor AFS, Bengtsson-Palme J, Jeppesen TS, Schigel D, et al. The UNITE database for molecular identification of fungi: Handling dark taxa and parallel taxonomic classifications. Nucleic Acids Res. 2019;47:D259–64.CAS 

Google Scholar 
44.Yarza P, Yilmaz P, Pruesse E, Glöckner FO, Ludwig W, Schleifer KH, et al. Uniting the classification of cultured and uncultured bacteria and archaea using 16S rRNA gene sequences. Nat Rev Microbiol. 2014;12:635–45.CAS 
PubMed 

Google Scholar 
45.Louca S, Parfrey LW, Doebeli M. Decoupling function and taxonomy in the global ocean microbiome. Science. 2016;353:1272–7.CAS 
PubMed 

Google Scholar 
46.Team Rc. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2020. Available from: https://www.r-project.org.47.Team Rs. RStudio: Integrated Development for R. Boston, MA: RStudio; 2019.48.Andersen KS, Kirkegaard RH, Albertsen M ampvis2: an R package to analyse and visualise 16S rRNA amplicon data. bioRxiv. 2018;10–1.49.Paradis E, Claude J, Strimmer K. APE: Analyses of phylogenetics and evolution in R language. Bioinformatics. 2004;20:289–90.CAS 
PubMed 

Google Scholar 
50.Wickham H, Rstudio. stringr: Simple, consistent wrappers for common string operations. 2019. Available from: https://cran.r-project.org/package=stringr.51.Wickham H. Reshaping data with the reshape package. J Stat Softw. 2007;21:1–20.
Google Scholar 
52.Dowle M, Srinivasan A, Gorecki J, Chirico M, Stetsenko P, Short T, et al. data.table: Extension of “data.frame”. 2019. Available from: https://cran.r-project.org/package=data.table.53.Wickham H, Averick M, Bryan J, Chang W, McGowan L, François R, et al. Welcome to the Tidyverse. J Open Source Softw. 2019;4:1686.
Google Scholar 
54.Wickham H ggplot2: Elegant graphics for data analysis. New York: Spinger-Verlag New York; 2016.55.Chen L, Reeve J, Zhang L, Huang S, Wang X, Chen J. GMPR: A robust normalization method for zero-inflated count data with application to microbiome sequencing data. PeerJ. 2018;2018:1–20.
Google Scholar 
56.Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Mol Biol Evol. 2013;30:772–80.CAS 
PubMed 
PubMed Central 

Google Scholar 
57.Price MN, Dehal PS, Arkin AP. FastTree 2—Approximately maximum-likelihood trees for large alignments. PLoS One. 2010;5:e9490.58.Rambaut A. FigTree—tree figure drawing tool. Institute of Evolutionary Biology, University of Edinburg; 2020. Available from: http://tree.bio.ed.ac.uk/software/figtree.59.Kembel SW, Cowan PD, Helmus MR, Cornwell WK, Morlon H, Ackerly DD, et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics. 2010;26:1463–4.CAS 
PubMed 

Google Scholar 
60.Marzano V, Mancinelli L, Bracaglia G, Del Chierico F, Vernocchi P, Di Girolamo F, et al. “Omic” investigations of protozoa and worms for a deeper understanding of the human gut “parasitome.”. PLoS Negl Trop Dis. 2017;11:e0005916.PubMed 
PubMed Central 

Google Scholar 
61.Mandal S, Van Treuren W, White RA, Eggesbø M, Knight R, Peddada SD. Analysis of composition of microbiomes: a novel method for studying microbial composition. Microb Ecol Heal Dis. 2015;26:27663.62.Hothorn T, Hornik K exactRankTests: exact distributions for rank and permutation tests. 2019. Available from: https://cran.r-project.org/package=exactRankTests.63.Pinheiro J, Bates D, DebRoy S, Sarkar D, Core Team R nlme: Linear and nonlinear mixed effects models. 2020. Available from: https://cran.r-project.org/package=nlme.64.van den Boogaart G, Tolosana R compositions: an R package for compositional data analysis. 2020. Available from: http://www.stat.boogaart.de/compositions/.65.Wickham H, Francois R, Core Team R, Rstudio, Jylänki J, Jorgensen M readr: Read rectangular text data. 2018. Available from: http://readr.tidyverse.org.66.Oksanen J, Blanchet G, Friendly M, Kindt R, Legendre P, McGlinn D, et al. vegan: Community ecology package. 2019. Available from: https://cran.r-project.org/package=vegan.67.Lozupone C, Lladser ME, Knights D, Stombaugh J, Knight R. UniFrac: An effective distance metric for microbial community comparison. ISME J. 2011;5:169–72.PubMed 

Google Scholar 
68.Bates D, Mächler M, Bolker BM, Walker SC. Fitting linear mixed-effects models using lme4. J Stat Softw. 2015;67:1–48.
Google Scholar 
69.Reitmeier S, Hitch TCA, Treichel N, Fikas N, Hausmann B, Ramer-Tait AE, et al. Handling of spurious sequences affects the outcome of high-throughput 16S rRNA gene amplicon profiling. ISME COMMUN. 2021;1:31.
Google Scholar 
70.Sweeny AR, Lemon H, Ibrahim A, Nussey DH, Free A, McNally L. A mixed model approach for estimating drivers of microbiota community composition and differential taxonomic abundance (preprint). bioRxiv. 2020; https://doi.org/10.1101/2020.11.24.395715.71.Barr DJ, Levy R, Scheepers C, Tily HJ. Random effects structure for confirmatory hypothesis testing: Keep it maximal. J Mem Lang. 2013;68:255–78.
Google Scholar 
72.Matuschek H, Kliegl R, Vasishth S, Baayen H, Bates D. Balancing type I error and power in linear mixed models. J Mem Lang. 2017;94:305–15.
Google Scholar 
73.Fox J, Weisberg S. An R companion to applied regression. 3rd ed. Thousand Oaks, California: Sage Publishing; 2019.74.Bolker BM. Ecological models and data in R. New Jersey, USA: Princeton University Press; 2008.75.Adams DC, Anthony CD. Using randomization techniques to analyse behavioural data. Anim Behav. 1996;51:733–8.
Google Scholar 
76.Baayen RH. Analyzing linguistic data. Cambridge: Cambridge University Press; 2008.77.Bartón K MuMIn: Multi-Model Inference. 2020. Available from: https://cran.r-project.org/package=MuMIn.78.Lüdecke D. sjPlot: Data Visualization for Statistics in Social Science. 2021. Available from: https://cran.r-project.org/package=sjPlot.79.Edgar RC. MUSCLE: Multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32:1792–7.CAS 
PubMed 
PubMed Central 

Google Scholar 
80.Kumar S, Stecher G, Li M, Knyaz C, Tamura K. MEGA X: Molecular evolutionary genetics analysis across computing platforms. Mol Biol Evol. 2018;35:1547–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
81.NCBI Resource Coordinators. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 2018;46:D8–13.
Google Scholar 
82.Nilsson RH, Anslan S, Bahram M, Wurzbacher C, Baldrian P, Tedersoo L. Mycobiome diversity: high-throughput sequencing and identification of fungi. Nat Rev Microbiol. 2019;17:95–109. 15.CAS 
PubMed 
PubMed Central 

Google Scholar 
83.Koskinen K, Pausan MR, Perras AK, Beck M, Bang C, Mora M, et al. First insights into the diverse human archaeome: specific detection of archaea in the gastrointestinal tract, lung, and nose and on skin. MBio. 2017;8:1–17.
Google Scholar 
84.Raymann K, Moeller AH, Goodman AL, Ochman H. Unexplored archaeal diversity in the great ape gut microbiome. mSphere. 2017;2:1–12.
Google Scholar 
85.Ley RE. Prevotella in the gut: choose carefully. Nat Rev Gastroenterol Hepatol. 2016;13:69–70.CAS 
PubMed 

Google Scholar 
86.Manara S, Asnicar F, Beghini F, Bazzani D, Cumbo F, Zolfo M, et al. Microbial genomes from non-human primate gut metagenomes expand the primate-associated bacterial tree of life with over 1000 novel species. Genome Biol. 2019;20:299.CAS 
PubMed 
PubMed Central 

Google Scholar 
87.Umanets A, de Winter I, IJdema F, Ramiro-Garcia J, van Hooft P, Heitkönig IMA, et al. Occupancy strongly influences faecal microbial composition of wild lemurs. FEMS Microbiol Ecol. 2018;94:1–13.
Google Scholar 
88.Greene LK, Clayton JB, Rothman RS, Semel BP, Semel MA, Gillespie TR, et al. Local habitat, not phylogenetic relatedness, predicts gut microbiota better within folivorous than frugivorous lemur lineages. Biol Lett. 2019;15:5–11.
Google Scholar 
89.Daniel H, Gholami AM, Berry D, Desmarchelier C, Hahne H, Loh G, et al. High-fat diet alters gut microbiota physiology in mice. ISME J. 2014;8:295–308.CAS 
PubMed 

Google Scholar 
90.Hiippala K, Kainulainen V, Kalliomäki M, Arkkila P, Satokari R. Mucosal prevalence and interactions with the epithelium indicate commensalism of Sutterella spp. Front Microbiol. 2016;7:1–13.
Google Scholar 
91.Heintz-Buschart A, Wilmes P. Human gut microbiome: Function matters. Trends Microbiol. 2018;26:563–74.CAS 
PubMed 

Google Scholar 
92.Ricaboni D, Mailhe M, Cadoret F, Vitton V, Fournier PE, Raoult D. ‘Colidextribacter massiliensis’ gen. nov., sp. nov., isolated from human right colon. New Microbes New Infect. 2017;17:27–9.CAS 
PubMed 

Google Scholar 
93.Qin P, Zou Y, Dai Y, Luo G, Zhang X, Xiao L. Characterization a novel butyric acid-producing bacterium Collinsella aerofaciens subsp. shenzhenensis subsp. nov. Microorganisms. 2019;7:78.94.Sizova MV, Muller PA, Stancyk D, Panikov NS, Mandalakis M, Hazen A, et al. Oribacterium parvum sp. nov. and Oribacterium asaccharolyticum sp. nov., obligately anaerobic bacteria from the human oral cavity, and emended description of the genus Oribacterium. Int J Syst Evol Microbiol. 2014;64:2642–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
95.Amato KR, Garber PA. Nutrition and foraging strategies of the black howler monkey (Alouatta pigra) in Palenque National Park, Mexico. Am J Primatol. 2014;76:774–87.CAS 
PubMed 

Google Scholar 
96.White TCR. The significance of unripe seeds and animal tissues in the protein nutrition of herbivores. Biol Rev. 2011;86:217–24.PubMed 

Google Scholar 
97.Ortmann S, Bradley BJ, Stolter C, Ganzhorn JU. Estimating the quality and composition of wild animal diets—a critical survey of methods. In: Hohmann G, Robbins M, Boesch C, editors. Feeding ecology in apes and other primates ecological, physical, and behavioral aspects. Cambridge: Cambridge University Press; 2006. p. 395–418.98.Hippe H, Hagelstein A, Kramer I, Swiderski J, Stackebrandt E. Phylogenetic analysis of Formivibrio citricus, Propionivibrio dicarboxylicus, Anaerobiospirillum thomasii, Succinirnonas amylolytica and Succinivibrio dextrinosolvens and proposal of Succinivibrionaceae fam. nov. Int J Syst Evol Microbiol. 1999;49:779–82.
Google Scholar 
99.Privé F, Kaderbhai NN, Girdwood S, Worgan HJ, Pinloche E, Scollan ND, et al. Identification and characterization of three novel lipases belonging to families II and V from Anaerovibrio lipolyticus 5ST. PLoS One. 2013;8:e69076.100.Flaiz M, Baur T, Brahner S, Poehlein A, Daniel R, Bengelsdorf FR. Caproicibacter fermentans gen. nov., sp. nov., a new caproate-producing bacterium and emended description of the genus Caproiciproducens. Int J Syst Evol Microbiol. 2020;70:4269–79.CAS 
PubMed 

Google Scholar 
101.Watanabe Y, Nagai F, Morotomi M. Characterization of Phascolarctobacterium succinatutens sp. Nov., an asaccharolytic, succinate-utilizing bacterium isolated from human feces. Appl Environ Microbiol. 2012;78:511–8.PubMed 
PubMed Central 

Google Scholar 
102.Clough D, Heistermann M, Kappeler PM. Host intrinsic determinants and potential consequences of parasite infection in free-ranging red-fronted lemurs (Eulemur fulvus rufus). Am J Phys Anthropol. 2010;142:441–52.PubMed 

Google Scholar 
103.Sarkar A, Harty S, Johnson KVA, Moeller AH, Archie EA, Schell LD, et al. Microbial transmission in animal social networks and the social microbiome. Nat Ecol Evol. 2020;4:1020–35.PubMed 

Google Scholar 
104.van Vliet DM, Lin Y, Bale NJ, Koenen M, Villanueva L, Stams AJM, et al. Pontiella desulfatans gen. nov., sp. nov., and Pontiella sulfatireligans sp. nov., two marine anaerobes of the Pontiellaceae fam. nov. producing sulfated glycosaminoglycan-like exopolymers. Microorganisms. 2020;8:920. 18.PubMed Central 

Google Scholar  More