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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

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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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All methods in this study were approved by the Institutional Animal Care and Use Committee at Texas Tech University (protocol 18032-12). All procedures were performed in accordance with relevant guidelines in the manuscript and the ARRIVE (Animal Research: Reporting of In Vivo Experiments) guidelines (https://arriveguidelines.org/).Experimental design for testing effects of temperature and humidity on Pd infection severity on Perimyotis subflavus
We randomly assigned bats to seven environmental chambers (Caron, Model 7000-33-1, Marietta, Ohio, USA) in a blocked experimental design, controlling temperature and humidity in each chamber (Fig. 1). In each environmental chamber, we divided bats into two cages (23 × 38 × 50 cm) constructed from mesh fabric (Part FMLF, Seattle Fabrics, Inc., Seattle, Washington, USA), PVC pipe, and plastic sheeting. We stratified random assignment to ensure even distribution of initial body mass and sex across microclimate treatments. In addition to the seven treatments with fixed temperature and humidity conditions, we had two treatments that allowed bats to freely move among temperature or humidity conditions (Fig. 1). One group of bats (n = 14) was free to move among three chambers with a common temperature (8 °C) but different humidity (water vapor pressure deficit (VPD) = 0.05 kPa, 0.10 kPa, or 0.15 kPa, corresponding to 95, 90, and 85% relative humidity (RH))36. A second group of bats (n = 14) was free to move among three chambers with a common VPD condition (0.10 kPa, medium humidity) but different temperatures (5, 8, or 11 °C) (Fig. 1). Because our research questions were focused on comparing the effect of temperature and humidity conditions on disease severity, we did not include sham-inoculated control animals in the experiment. We made this decision to reduce the total number of animals used in the experiment and to maximize replication to test the effects of temperature and humidity on disease.Figure 1Schematic of the experimental design and sample sizes with 7 environmental chambers with fixed temperature and humidity conditions and two sets of connected chambers allowing bats to behaviorally select temperature (left) or humidity conditions (bottom) for the infection trial on tri-colored bats (Perimyotis subflavus). Water loss conditions were based on water vapor pressure deficit (VPD) levels set to 0.05 kPA to produce low potential evaporative water loss (pEWL) for high humidity, 0.10 kPa for medium pEWL and humidity, or 0.15 kPA for high pEWL and low humidity. Numbers are sample sizes of bats assigned to separate cages within each chamber. Bats in the low temperature and high humidity chamber were combined into a single cage after a camera failed at the start of the experiment (top right).Full size imageWe inoculated each bat by spreading 20 µL of Pd solution (5 × 105 conidia µL−1) evenly across both wings, following established protocols8,9,32,37; treatments were conducted blind without knowledge of which bat was being assigned to what group and bats were inoculated in no particular order to reduce the confounding influence on the order of treatment. We used a Pd strain collected by Karen J. Vanderwolf at Trent University from naturally infected Myotis lucifugus. We cultured Pd on Sabouraud Dextrose Agar with chloramphenicol and gentamicin (SabDex) (Part L96359, Fisher Scientific, Houston, Texas, USA) and incubated subcultured plates at 10 °C for 60 days to allow the formation of conidia. We then harvested conidia by flooding plates with phosphate buffered saline solution containing 0.5% Tween20 (PBST). Conidia were resuspended in PBST, enumerated, and diluted to the inoculum concentration8.Microclimate treatment conditionsWe used three temperatures 5, 8, or 11 °C to represent a range of roosting temperatures of P. subflavus in natural hibernacula24,29. We set humidity in environmental chambers to achieve specific levels of water vapor pressure deficit (VPD) between the surface of the bat and the environment because relative humidity varies by temperature36. Higher VPD corresponds to drier air resulting in higher potential evaporative water loss (pEWL). We used three levels of VPD: 0.05, 0.10, or 0.15 kPa corresponding to low pEWL (high humidity), medium pEWL (medium humidity), and high pEWL (low humidity) levels (Fig. 1). We verified the ambient temperature and relative humidity in each chamber at 10-min intervals (Hobo Model U23-001, Onset Computer Corporation, Bourne, Massachussetts, USA). For bats in the connected chambers that could behaviorally select their temperature and humidity conditions, we quantified the number of days bats spent in each condition38.Animal handling and data collectionWe used 98 (42 females, 56 males) tricolored bats collected on 10 December 2018 from culverts in Mississippi and transported directly to Texas Tech University39. We took morphometric measurements (body mass ± 0.1 g, forearm length ± 0.1 mm) and used quantitative magnetic resonance (QMR; Echo-MRI-B, Echo Medical Systems, Houston, Texas, USA) to determine pre-hibernation fat at the start of the experiment39,40. As an indicator of pre-hibernation stress, we collected a fur sample from the dorsal intrascapular region to quantify fur cortisol concentration with a commercial ELISA kit, following the manufacturer’s protocol (Arbor Assays, Michigan, USA) (see Supplemental Methods). Fur is moulted once per year in the late summer period41 and therefore fur cortisol reflects the level of circulating cortisol during the period of fur growth prior to hibernation. We attached a uniquely marked, modified datalogger42 (DS1925L iButton, Maxim Integrated, San Jose, California, USA) to the back of each bat using ostomy cement to record skin temperature39. Prior to inoculation, we swabbed bats with a sterile polyester swab (Fisherbrand synthetic tipped applicators 23-400-116) five times on forearm and five times on muzzle to determine if any bats were naturally infected with Pd at time of collection. Swabs were stored in RNAlater at  − 20 °C until testing using quantitative polymerase chain reaction (qPCR) at Northern Arizona University43.During the experiment, we provided ad libitum drinking water in each cage but did not provide food. We secured a motion-activated infrared camera (Model HT5940T, Speco Technologies, New York, New York, USA) above each cage to monitor bats throughout the experiment. Because one camera failed at the start of the experiment, we combined bats in that treatment chamber into a single cage (Fig. 1) and replicated this disturbance among all chambers. We monitored bats without disturbance by reviewing video recordings daily. Three bats died of unknown cause before the end of the experiment and were removed from analyses.After 83 days of hibernation, we terminated the experiment and bats were removed from cages and processed to determine body condition using QMR39. We took respirometry measurements on a subset of animals38, and swabbed for Pd as described above. We photographed the left ventral wing using ultraviolet (UV) transillumination (368-nm wavelength and 2-s exposure) to detect and measure florescence associated with Pd infection37,44. For histology, we removed the wing section from the fifth digit and the body and rolled wing tissue around dental wax dowels and 10% neutral buffered formalin. We collected a 90–110 µL blood sample in lithium-heparin-treated capillary tubes for immediate analysis of blood chemistry with a handheld analyzer (i-STAT1 Vet Scan, Abaxis, Union City, California, USA). Using an EC8+ cartridge, we measured sodium, potassium, chloride, anion gap, glucose, BUN (urea nitrogen), hematocrit, hemoglobin, pH, pCO2, TCO2, HCO3, and base excess (Table S1). We quantified arousals from torpor as reported by McGuire et al.39. All bats were handled and euthanized under Animal Care and Use Committee permit 18032-12 at Texas Tech University.Infection and disease metricsWe used several metrics to determine pathogen and disease presence and severity37: presence and amount of the pathogen, Pd, on a bat were determined by qPCR43, and presence of the disease, WNS, was determined via detection of orange-yellow florescence under UV light characteristic of Pd infection44 and histological presence of characteristic lesions and pustules with fungal hyphae45,46. Three types of cutaneous infection were described histologically, including characteristic cupping erosions with fungal hyphae, neutrophilic pustules with fungal hyphae, and fungal hyphae in the stratum corneum with dermal necrosis. Any bats with any of these three conditions noted were scored as WNS positive by histology. Presence and quantity of DNA of Pd was tested by qPCR at Northern Arizona University. All samples were run in duplicate and considered positive if at least one run was positive below a cycle threshold (Ct) of 40 and quantified using a quantification curve from serial dilutions (nanograms of Pd using the equation load = 10((22.049-Ct value)/3.34789), r2 = 0.986)47. Load values were averaged across multiple runs and then converted to attograms by multiplying loads in nanograms by 109.Statistical analysesWe used three different response variables (Pd prevalence, Pd loads, and WNS prevalence by histology) to determine whether infection status varied by microclimate treatment conditions. Low sample sizes of positive infection status by UV detection (n = 4) precluded use in statistical analyses (Table 1). We used generalized linear models with binomial distribution for analyses of Pd prevalence and WNS prevalence and a linear mixed effects model with Gaussian errors for Pd loads. Although the experiment was designed with replication at the cage level to account for cage effects, we were unable to include cage as a random effect because of the low numbers of bats that had signs of Pd or WNS infection. We analyzed whether infection status (i.e., Pd prevalence, Pd load, or WNS prevalence) varied by sex and cortisol separately from an a priori candidate model set (Table 2) to cope efficiently with small sample sizes. We first asked whether infection response varied by sex to determine if bats could be pooled in subsequent analyses. We analyzed separately whether infection response varied by pre-hibernation cortisol at the start of the experiment on the subset of animals for which we had cortisol measurements (n = 83). We then used an information-theoretic approach comparing a candidate set of models with Akaike Information Criterion (AIC)48 using initial fat mass as an individual covariate and temperature and humidity treatment conditions as categorical treatment groups to assess the effect of microclimate on infection response (Table 2). Bats behaviorally selecting their temperature and humidity conditions were assigned to a temperature or humidity treatment level if a bat spent  > 89% of captive days at that condition or was otherwise placed in an ‘inconstant condition’ treatment group. For WNS prevalence, we used the bias reduction method implemented in package brglm49 to deal with complete separation present in the data (in some treatments all bats were scored as negative for WNS) (Table 1; Fig. 2).Table 1 Signs of Pd infection or WNS disease for tri-colored bats (Perimyotis subflavus) exposed to different temperature and humidity regimes.Full size tableTable 2 Model selection results for model comparisons of humidity and temperature and pre-hibernation fat mass on Pd prevalence, Pd load, and WNS prevalence.Full size tableFigure 2Signs of Pseudogymnoascus destructans (Pd) infection or white-nose syndrome (WNS) disease for tri-colored bats (Perimyotis subflavus) exposed to different temperature and humidity regimes. (A) Fraction of bats with Pd detected by qPCR; (B) Fraction of bats with signs of WNS disease by histology, and (C) Mean quantity of Pd on bats at the end of the experiment. There was no statistical support for differences between temperature or humidity treatments for any response metrics. Points are estimated means and vertical lines show binomial standard error for prevalence and standard errors for Pd load.Full size imageBecause this was the first captive hibernation experiment with P. subflavus, we investigated the effects of temperature and humidity on the hibernation physiology of the species38,39 and how physiological markers (e.g., blood chemistry) may be associated with disease. To determine if physiological indicators were related to infection status at the end of the experiment, we compared total number of torpor arousal bouts during the experiment and 13 different blood chemistry metrics from blood samples taken at the end of the experiment and used t-test comparisons (at α = 0.05) for each metric between Pd/WNS positive and negative bats. We designated bats as Pd/WNS positive if a bat tested positive for either Pd or WNS by qPCR, UV, or histology. We used Program R version 3.6.2 to conduct all analyses.Experimental design for testing effects of temperature and humidity on Pd growth on substratesWe used five environmental chambers (CARON, Model 7000-33-1, Marietta, Ohio, USA) to test for the effects of temperature and humidity on fungal growth on natural and artificial substrates (Fig. S1). Our experimental design comprised a reduced temperature series and humidity gradient than what we used for the experiment on bats. In the humidity gradient, temperature was held constant at 8 °C, with 85%, 90%, and 95% RH representing our low, medium, and high humidity treatments, respectively. In the temperature series, vapor pressure deficit (VPD) was held constant across the low (5 °C), medium (8 °C), and high (11 °C) temperatures (VPD = nominally 0.01 kPa, range (0.105–0.107). The chamber set to 8 °C and 90% humidity (VPD = 0.107 kPa) was common to both series.Media plate inoculation and fungal growth measurementWe constructed modified plate lids to prevent contamination while allowing humidity to equilibrate across the plate lid. We drilled 14 equidistant holes (5.5 mm diameter) into each plate lid and hot glued a piece of circular filter paper to the top of the lid. Lids were then disinfected thoroughly with a hydrogen peroxide wipe before being placed in a disinfected, sealed storage container.We prepared Pd inoculum as described above for the infection trial on bats. We inoculated 30 SabDex plates with 100 µL of inoculum at a concentration of 20 conidia µL−1 by serial dilution with a starting concentration of 2.0 × 104 conidia µL−1 diluted four times by a factor of 10. We used sterile, individually wrapped 1-µL plastic inoculation loops to spread the inoculum evenly across the surface of the plates, added the modified plate lids, and immediately transferred plates into environmental chambers. We included six replicate plates in each of the five microclimate conditions.We took weekly digital photographs (Nikon, Model 26524, Tokyo, Japan) of each plate for the 5-week duration of the experiment (Fig. 3A). Our camera was mounted on a tripod to ensure consistent placement of plates relative to the camera. Each photo included a ruler, which was used to calibrate measurements made in ImageJ (Version 2.0.0-rc-69/1.52p, National Institutes of Health, Bethesda, Maryland, USA). One observer made all measurements for consistency. We used the freehand selection tool to trace the boundary of each fungal colony using a drawing tablet (Wacom, Model CTL-490, Kazo, Saitama, Japan). From these selections, we obtained the total surface area growth as the sum of all area selection (in cm2).Figure 3Examples demonstrate the process of measuring and estimating fungal growth of Pseudogymnoascus destructans (Pd) on media plates in temperature and humidity treatment conditions. (A) Examples of fungal growth on media plates measured at days 7, 14, 21, 28, and 34 from two of the treatment conditions (11 °C, 92% RH and 5 °C, 88% RH). (B) Examples of estimating maximum growth rate and latency variables from fungal growth measurements in panel A. We fit a sigmoidal curve to describe fungal growth (thick solid black line) to estimate the inflection point of the curve (vertical solid line). We calculated the slope (solid red line) at the inflection point of the curve to estimate maximum growth rate, and the days until total growth area reached 2.5 cm2 (dashed red lines) as an estimate of latency.Full size imageWe modelled the growth of Pd on each plate as a sigmoidal curve (Fig. 3B), which we fit using the SSlogis and nls functions in Program R v. 3.6.350. The model fitting function provides an estimate of the inflection point of the curve, and we calculated the slope at the inflection point to estimate the maximum growth rate. We also estimated the latency to rapid fungal growth on the plates by determining the date at which the total area of fungus on the plate reached 2.5 cm2 as an arbitrary threshold.We also quantified growth of individual colonies. To avoid biasing growth rate estimates, we excluded colonies that intercepted another colony by choosing independent colonies at the final time point and tracking them backwards through time. If there were fewer than 10 independent colonies at the final time point, we added additional unimpeded colonies with each earlier time point until the total number of colonies reached 10. We modelled growth of individual colonies following the same procedure as for total area of growth on the plate, with an arbitrary threshold of 0.05 cm2 for latency calculations. We used linear mixed models to test for the effects of temperature and humidity on maximum growth rate or latency, including plate as a random factor to account for measuring multiple colonies per plate.Rock inoculation and fungal growth measurementTo evaluate fungal growth and persistence on a natural substrate, we inoculated pieces of sandstone flagstone. We etched a 4 × 6 sampling grid, composed of 5 × 5 cm squares, onto the surface of each sandstone rock (Texas Rock and Flagstone, Lubbock, Texas, USA), where each square served as a sampling unit (Fig. S2). Each row represented a time series for a single replicate, while each column was composed of replicates for the respective time point. Rocks were then autoclaved at 121 °C for 40 min and stored individually in a disinfected, sealed container until inoculation. At the time of inoculation, we evenly spread 200 µL of inoculum (2.5 × 104 conidia µL−1) across each sampling square and immediately transferred the rock to an environmental chamber.We measured fungal growth at days 0, 14, 28, and 56. We used a sterile cotton swab to collect fungal DNA from each sampling square. Swabs were moistened with RNAlater and rolled horizontally, vertically, and diagonally across the surface of the sampling square to ensure contact with the total surface area. One researcher collected all swabs to maximize consistency among swabs collected throughout the experiment. Swabs were placed in RNAlater and stored at − 20 °C until shipped to Northern Arizona University for qPCR analysis43. We quantified fungal loads for each swab sample from qPCR using the quantification curve provided above and normalized fungal loads to the value at day zero for each rock respectively. We then used linear models to test for effects of temperature and humidity on changes in fungal load (log transformed) over time.To evaluate viability of Pd, we swabbed the entire inoculated surface of each rock at the end of the experiment and vortexed the swabs in RNAlater for one minute to release fungal DNA from the swab. We then applied 100 µL of RNAlater fungal solution from each rock to a respective SabDex media plate, using a sterile inoculation loop. After 2 weeks of incubation at 11 °C and 92% RH, we visually assessed plates for presence of fungal growth to determine viability of Pd collected from rocks at the end of the growth experiment. More