Ecology

Our results show that differences in environmental conditions between inland and coastal sinkholes, caused mainly by the inflow of seawater in the latter, influence the microbial community structure of their sediments. Furthermore, the microbial community structure also varied within the sinkholes and according to the sediment zone sampled, suggesting that a connection between the atmosphere in the outermost location of the sediments and sunlight creates an environment distinct from that found in deeper caves. Together with the different environmental factors that were measured (in situ physicochemical composition of water and sediment) these characteristics could drive niche-specific microbial community structures associated with the sediment zones. Additionally, beta-diversity analysis showed separate clustering of the sediment microbial communities from the coastal and inland sinkholes, and of the WM zone from cavern and cave zones at both sinkholes. Microbial community structure associated with karst environments have shown to be significantly influenced by environmental factors as seen in the Bahamian blue holes19, a coastal sinkhole13, a Floridan anchialine sinkhole20, and sediments from Chinese karst caves21.Microbial communities from karst sediments can be limited by nutrients such as carbon, phosphorus, and nitrogen21, therefore, influencing their structure. Differences in the microbial community composition associated with multiple environmental factors (moisture, type of niche, nitrogen) were also reported in karst cave sediments from China21. Previous studies had shown there was no effect on the alpha diversity of water column assemblages in the Yucatan groundwater associated with the type of sinkhole (inland or coastal)6. However, this observation may be limited due to the low sample number used in the study6. For other karst sinkholes, the microbial community dynamics differ between the water column and the sediments6,22,23,24.The karst caves and sinkholes of the underground river in Yucatan are characterized by low phosphorus concentrations and high levels of nitrate, mostly related to Anthropocentric activities (urban developments, farms and agriculture)3. The inland sinkhole at Noh Mozón showed the highest concentration of nitrate detected in the study and, not surprisingly, the area is surrounded by agricultural fields. The presence of organic matter in the sinkholes from the Yucatan peninsula are highly dependent on the connection between the cave systems, on the levels of exposure to light, and on their morphology3. High concentrations of organic carbon (661 ± 132 μM) and methane (6466 ± 659 nM) have been reported in the top layer of the water masses in coastal sinkholes before13. In this study, the highest concentration of organic carbon was observed in the sediments from the coastal sinkhole, likely originating from the surrounding vegetation and from seawater intrusion. We hypothesize that the differences in nutrients found at these two types of sinkholes influence the structure of their microbial communities.Other environmental factors such as pH and dissolved oxygen (DO), may also contribute significantly to the composition and structure of microbial communities, as seen in freshwater lake sediments22. Davis and Garey20 reported distinct microbial communities with unique functions for each water layer from an anchialine sinkhole from the Florida karst aquifer and suggested that this occurred as a result of the influence of the hydrochemistry, including differences in the concentration carbon and other nutrients from the environment20. Analyses of the sinkhole caves from the Yucatan underwater river support observations that physical and chemical parameters create distinct ecological niches which host unique microbes, as a high abundance of exclusive (not shared) ASVs were observed in the three sediment zones at both locations.The taxonomic diversity from the coastal and inland sinkholes included Chloroflexi, Crenarchaeota, Desulfobacterota, Proteobacteria, Nitrospirota, Bacteroidota, and Firmicutes as the most abundant phyla in the sediment samples, however, there were differences in the relative abundance associated with the type of sinkhole and sediment zone. Some of these phyla (Chloroflexi, Proteobacteria, and Bacteroidetes) have been reported in sediments from freshwater karst sinkholes from Lake Huron25 in water and sediments from other sinkholes in the Yucatan peninsula6, and in the karst caves bacteriome from southwest China21. A study that included coastal marine sediments from two sites in the Yucatan peninsula, showed high abundances of Spirochaeta, Desulfococcus, Clostridium, Psychrobacter26, four genera that were abundant in the coastal sinkhole. However, Desulfococcus, Synechococcus were also abundant in the inland sinkhole. Of the most abundant genera reported for sediments from different marine environments in the Yucatan coast23, Acinetobacter, Desulfotignum, Desulfovibrio, Pseudomonas, Sedimenticola, and Sulfurimonas were also present in the coastal sinkhole while only Pseudomonas and Sedimenticola were also present in the inland sinkhole23. The high number of families shared between the coastal sinkhole and marine sediments from the Yucatan coast, together with the salinity levels registered at the bottom layer of the water column in the coastal sinkhole, suggest an interconnection between these two environments which shapes the microbial communities present in the sediments of caverns and caves of this sinkhole. The genus Nitrospira was abundant in the WM from the coastal sinkhole and in all sediment zones from the inland sinkhole. This genus has been reported as one of the most abundant in the surface of speleothems from El Zapote coastal sinkhole2, and is considered a complete ammonia oxidizer (comammox), meaning it converts ammonia to nitrate through nitrite. A negative correlation between abundance of this genus and salinity has been reported before, which could explain the low concentration of Nitrospira in the cavern and cave from the coastal sinkhole, where the highest salinity was observed27. Connectivity between coastal sinkholes and the ocean, as well as the terrestrial input of soil organic matter (OM) has been reported for the underground karst aquifer in the Yucatan peninsula13. As in other sediments, degradation of OM is carried out by several MFGs including acetogenic bacteria, methanogens, and sulfate reducers13,20,28. When these MFGs were analyzed in coastal and inland sinkholes, differences in their relative abundances were clearly marked by the type of sinkhole and by the sediment zone analyzed, supporting the hypothesis that environmental differences drive microbial community distributions in these niches. The high abundance of sulfate-reducing bacteria (SRB) in the three sediment zones from the coastal sinkhole suggests that sulfate reduction is a predominant function. SRB degrade organic matter using sulfate with sulfide as waste or end-product19,30, originating hydrogen sulfide (H2S)29, which could explain the low concentration of sulfate the hydrogen sulfide (H2S) cloud observed and previously reported in the WM zone of El Zapote coastal sinkhole30. In this study, high levels of sulfate (SO−4) were measured in the water samples from the cavern and cave zones from El Zapote sinkhole, which could be associated with sulfate-rich deposits, such as gypsum beds, which have been reported in other sinkholes from the Yucatan peninsula (up to 2400 mg/L of sulfate concentration)3. However, we do not disregard other possible sources of sulfate, associated with seawater intrusion or as a product of sulfide or sulfur oxidation29,31 by sulfur-oxidizing bacteria detected in this study.The inland sinkhole had a low concentration of sulfate and low abundances of SRB. The high abundance of methanogenic bacteria in the WM zone from the coastal sinkhole detected in the MFG analysis supports the previous hypothesis of acetoclastic methanogenesis due to high inputs of organic matter13. Methylotrophic bacteria were most abundant at the inland sinkhole in the WM zone, suggesting the presence of methyl compounds, such as methane or methanol which can be used as a source of carbon and energy32. High methane concentrations have been quantified in shallow water masses from the Yucatan aquifer system13, consistent with observations from this study. ‘Candidatus Methylomirabilis’ was identified in the sediment of the WM zone from Noh-Mozón and has been previously described as being able to perform nitrite-dependent anaerobic methane oxidation, using methane as electron donor and nitrate and nitrite as electron acceptors33, which would be possible in these sediments considering the low levels of oxygen (average of 2.3 mg/L) detected in the water column above them and assuming this would lead to lower levels of oxygen in the sediments. We hypothesize that bacteria from this genus could be using the nitrite produced by ammonia oxidizing bacteria and archaea observed in this zone (Nitrosomonadaceae, Nitrospiraceae, and Nitrosococcaceae). The low abundance of methanotrophic microbes in the coastal sinkhole (mainly the cavern and cave zones) could be derived from the high concentrations of hydrogen sulfide previously reported at this location, which have been suggested to be toxic to methane-oxidizing microbes34. Therefore, a decrease in the anaerobic oxidation of methane, and a poor methane removal capacity is hypothesized in the sediments from this coastal sinkhole. Further research could focus on the influence of the saline intrusion on methanotrophic microbes and methane levels in El Zapote sinkhole sediments. As expected, photosynthetic bacterial abundances differed with the type of sinkhole and sediment zone. Both sinkholes are so the presence of daylight can start the photosynthetic process which would occur most in the WM zone. However, only the inland sinkhole showed a high abundance of photosynthetic bacteria within its WM zone. The coastal sinkhole water column and sediments would be deprived of photosynthetic bacteria since the water source is the underground aquifer, lacks photosynthesizers. Ammonia oxidizing bacteria (AOB) and archaea (AOA), and nitrite-oxidizing bacteria (NOB) were relatively abundant in the three sediment zones from the inland sinkhole and in the WM from the coastal sinkhole, these observation at the WM from El Zapote agree with previous observations2. Anaerobic ammonium oxidation (anammox) uses nitrite (as a product of nitrate reduction), as electron acceptor35. The high levels of nitrate concentration in the water column from the three sediment zones at the inland sinkhole and at the WM from the coastal sinkhole may influence the abundance of AOB, AOA and NOB in the sediments from these zones, while NH4+ and nitrite values were below detection limit ( More

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Relationships between microbial diversity and base flow indexThe number of reads obtained per sample and total number of OTUs obtained after rarefaction for each gene dataset are summarised in Table S2. According to taxonomic analyses of our 16S rRNA gene dataset, archaeal communities in our river sediment samples consisted largely of OTUs assigned to the Woesarchaeota (20.8% of OTUs and 24.7% of reads) and Methanomicrobia (16.9% of OTUs and 31.8% of reads). Of the functional groups analysed here, ten OTUs were assigned to AOA, Nitrososphaera (n = 8) and Nitrosopumilus (n = 2), that together formed 4.8% of all archaeal 16S rRNA reads. A total of 137 OTUs were assigned to orders of methanogenic archaea, with 15.3% and 16% of archaeal reads assigned to the orders Methanomicrobiales and Methanosarcinales, respectively, with other methanogen orders constituting a further 6.7% of reads.Bacterial communities were more diverse and OTUs assigned to taxa within the functional groups analysed here formed a relatively small proportion of our bacterial 16S rRNA gene dataset. Ammonia oxidising bacteria were represented by only five OTUs (all assigned to Nitrosospira) that together constituted 0.02% of the total bacterial community across our sediments. A further 84 OTUs were assigned to methanotrophic genera, and these OTUs contributed a total of 0.88% of all bacterial 16S rRNA sequences. These were Methylobacter (30 OTUs, 0.7% of bacterial sequences), Methylophilus (15 OTUs, 0.1% of bacterial sequences), Methylosoma (7 OTUs, 0.004% of bacterial sequences), Methylomonas and Methylotenera (6 OTUs each, 0.02 and 0.002% of bacterial sequences, respectively), and Methylosarcina (5 OTUs, 0.002% of bacterial sequences), with a further eight genera represented by a total of 15 OTUs. As reported previously, no OTUs were assigned to known anammox genera, which were likely below the limit of detection in our study [8].The OTU richness of archaeal communities (based on 16S rRNA amplicons) was negatively, albeit weakly, related to BFI (coef = 0.52, z = −2.95, adj-D2 = 0.12, P  More

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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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