Spatiotemporal occurrence of hypoxic events

In 2012, we installed a water quality sonde (Manta2, Eureka Environmental, Austin, TX, USA) in a 6-in diameter, 20-ft long galvanized steel pipe that was affixed to the NMB (Latitude, Longitude decimal degrees, −1.54618, 35.01898, Supplementary Fig. 1). NMB is downstream of 171 hippo pools surveyed in 200640. The pipe had multiple openings to allow the free flow of water around the sonde. Data were collected from December 2012 through February 2015 (Supplementary Fig. 2). The sonde took measurements every 15 min of temperature, DO, and water depth. A barometric pressure logger was used to correct the depth measurements (BaroTROLL, In-Situ Inc., Fort Collins, CO, USA). The DO sensor was calibrated monthly.

Water depth measurements were converted to discharge using the equations in Dutton et al.63. Baseflow was identified using the Lyne–Hollick recursive digital filter64,65. We then identified 55 episodes where the water rose quickly enough to double the calculated base flow (base flow mean index was <0.5, Fig. 1a). We have characterized these 55 episodes as in-channel flushing flows, as they are likely to have enough velocity to mobilize organic material from the benthos of the river. The Mara River is deeply incised and disconnected from the floodplain throughout most of the study area and rarely breaches its banks (Supplementary Fig. 6). During high-flow episodes, the water is constrained within the channel. Out of those 55 flushing flows, 49 caused a decrease of DO in the river ranging from 0.04 to 5.5 mg L−1 (Fig. 1b) from the beginning of the flushing flow to the point of lowest DO during the flushing flow. Nine of the flushing flows caused the DO to drop below 1 mg L−1 for several hours. Increases in discharge ranged from 4 to 180 m3 s−1. Peak discharge ranged from 6 to 197 m3 s−1, and 43 out of 49 of the flushing flows had a peak discharge less than 65 m3 s−1. The average flushing flow increased three-fold over the calculated baseflow.

We conducted a multiple linear regression to determine factors that may contribute to the magnitude in DO drop using the lm function in R v3.4.266. We used total change in DO as the dependent variable and initial DO, initial discharge, peak discharge, time to peak discharge, and number of hours since the last flushing flow as independent variables. We also included the interaction between time since last flushing flow and peak discharge because the effect of time since last flushing flow may be altered by the magnitude of peak discharge during the flushing flow (the degree of flushing). We did not include total storm size in the model; rather, we included initial discharge, peak discharge and time to peak discharge as variables that are components of total storm size but more explicitly linked to the flushing of hippo pools. All variables were log transformed to ensure normality except for beginning DO, which was already normally distributed. Owing to gaps in data coverage (Supplementary Fig. 2), we were only able to determine the timing since prior flushing flow for 44 of the 49 flushing flows.

Documentation of fish kills

We documented nine fish kills in the Mara River around the NMB site from 2009 to 2015 (Supplementary Table 1). These fish kills can be explained by hypoxic episodes we have documented in this study, possibly in combination with concurrent rapid increases in concentrations of hydrogen sulfide and suspended solids (Supplementary Table 3). During one of the fish kills, the Government Chemist of Kenya conducted sampling of fish carcasses and found elevated levels of Karate® Insecticide, suggesting that fish kill may have been caused by pollution from agricultural pesticides. In general, however, the high discharge and suspended sediments that occur during flushing flows in the Mara would be expected to dilute and bind this pesticide, which should limit its influence.

Hippo pool stratification

To investigate the potential for a hippo pool to become stratified, we deployed a remote-controlled boat (Fig. 1d) custom built for this purpose by Platypus LLC (Pittsburgh, PA, USA). The remote-controlled boat was outfitted with a custom-built conductivity/temperature sensor developed by Sodaq (Hilversum, The Netherlands). A pressure sensor was attached to the conductivity sensor to accurately detect the depth at which a reading was taken (MS5803-14BA, Measurement Specialties, Freemont, CA, USA). Both sensors were then integrated into a custom Arduino data logger that we developed at Yale University that also logged GPS coordinates.

The remote-controlled boat was deployed in two pools that did not have hippopotami (Emarti, −1.05788, 35.23111 and Moliband, −1.37388, 35.25827), a pool that had a high density of hippopotami on a tributary of the Mara River (Amani, −1.29539, 35.205) and two pools with low density of hippopotami on the Mara River (Croc, −1.38198, 35.01229 and HPA, −1.3931, 35.02828). Surveyed areas were of a similar size in all five pools.

The conductivity/temperature sensor was successively lowered through the water column at multiple locations within each pool. Average conductivity was calculated for 10 cm at the surface and bottom of all pools (Supplementary Table 2). Data from Emarti and Amani were processed with Surfer and Voxler (Golden Software LLC, Golden, CO, USA) to interpolate between points and generate a three-dimensional representation of pools with and without hippopotami.

The high-density hippo pool exhibited higher conductivity and strong chemical stratification (Supplementary Table 2 and Fig. 1e). In pools with moderate discharge or low densities of hippopotami, bottom water anoxia was not observed. The bottom waters of all the hippo pools (i.e., below the thermocline) had higher conductivity than the surface waters.

Water sampling during flushing flows

We utilized an automated water sampler (6712C Compact Portable Sampler, Teledyne ISCO, Lincoln, NE, USA) to collect water samples during three flushing flow episodes at the NMB site. The Mara River at the NMB site is disconnected from the floodplain in a constrained portion of the river with a bedrock substrate. The sampler was placed on high ground several hours in advance of a predicted flushing flow. A polyvinyl chloride (PVC) hose from the sampler was tied off in the water next to a water level switch. The water level switch was installed so that activation of the switch by rising water levels would trigger the pump to begin filling sample bottles housed within the portable sampler. The water level switch was positioned approximately 1 foot above the water level. Ice was added to the sample bottle reservoir to keep the samples cool. The sampler was programmed to immediately begin taking water samples once the water level switch was submerged and then take an additional sample either 15, 30, or 60 min thereafter to catch the rising and falling limb of the flushing flow. The water samples were retrieved the next morning.

Water samples from three flushing flows were captured on October 27–28, 2012; December 10, 2013; and March 14, 2014. A water quality sonde (Manta2, Eureka Environmental, Austin, TX, USA) was installed approximately 0.8 km downstream of the automated sampler for the October 27–28, 2012 flushing flow and logged data every 5 min. The sonde was installed next to the automated sampler for the December 10, 2013 and March 14, 2014 flushing flows and logged data every 15 min. Data from the sonde are presented at the same resolution as data from the water samples captured by the ISCO water sampler.

Water samples were preserved and analyzed for inorganic nutrients (NO 3 −, SRP, NH 4 +) utilizing the methods described below for the December 10, 2013 and March 14, 2014 flushing flows. For the October 27–28, 2012 flushing flow, NH 4 + was determined fluorometrically in the field67,68, NO 3 − was determined using cadmium reduction on a flow analyzer in the laboratory69, and SRP was determined using the molybdate blue method on a flow analyzer in the laboratory70. Total nitrogen (TN), total phosphorus (TP), dissolved organic carbon (DOC), and ash-free dry mass (AFDM) were measured as described below. BOD was calculated for the flushing flow on December 10, 2013 using methods described below.

A known volume of water from each sample collected during the flushing flow pulses was filtered through a pre-cleaned and pre-weighed Whatman cellulose nitrate membrane filter (47-mm dia., 0.45 µm pore size, GE Healthcare Life Sciences, Pittsburgh, PA, USA). The filter papers were dried in an oven at 60 °C for 24 h and re-weighed, and the mass of the sediment was measured and was corrected for the weight of the filter paper to calculate total suspended solids (TSS). The sediments on the filter were then analyzed for their elemental composition with microwave-assisted multi-acid total digestion on a Perkin-Elmer ICP-MS at Yale University (New Haven, CT, USA) or with a heat-assisted multi-acid total digestion at Bureau Veritas Acme Labs (Vancouver, British Columbia, Canada). A modified sediment fingerprinting statistical method was then used to apportion the total amount of hippopotamus feces-derived sediment within each sample (Supplementary Tables 9-11 and Supplementary Note 3)43.

October 27–28, 2012 flushing flow

The flushing flow began rising at approximately 21:45 h. The automatic sampler took the first sample at 22:38 h after the water level had risen approximately 15 cm. The discharge rose from 6 m3 s−1 at 21:45 h to 33 m3 s−1 by 01:45 h. At the beginning of the flushing flow, DO was 5 mg L−1. By 04:10 h, the DO had reached a minimum concentration of 1.6 mg L−1. At the point of lowest DO, 32% of the suspended sediments were derived from hippopotamus feces (Supplementary Fig. 3, approximately 90,000 kg h−1). The drop in DO coincided with peaks of TSS, TN, TP, DOC, conductivity, and TDS (Supplementary Table 4). TSS rose to >1000 mg L−1 by 01:00 h and stayed there for over 12 h. Concentrations of NO 3 − and NH 4 + declined during the flow pulse. No fish were reported to have died in this flushing flow.

December 10, 2013 flushing flow

The water started rising at 05:30 h and the automatic sampler took its first sample at 06:29 h after the water had risen approximately 30 cm. Discharge increased from 6 m3 s−1 at 05:30 h to a maximum of 36 m3 s−1 at 08:45 h (Supplementary Fig. 4). DO reached its lowest point (0.34 mg L−1) 30 min after the peak of the flushing flow and remained <1 mg L−1 for 210 min. At the DO minimum, over 100,000 kg h−1 of hippopotamus feces-derived sediments were traveling through the system. The drop in DO could partially be explained by the increase in BOD during the peak of the DO crash (Supplementary Fig. 4). The peak drop in DO coincided with the peaks of TSS, NH 4 +, TN, TP, DOC, conductivity, and TDS (Supplementary Table 5). TSS remained above 4000 mg L−1 for over 8 h. Game wardens reported that fish began dying around 8:30 h. By 09:00 h, thousands were dead along the banks including Labeo victorianus, Labeobarbus altianalis, Barbus sp., and Mormyrus kannume. Most of the dead fish washed downstream with the flow pulse.

March 14, 2014 flushing flow

The water started rising at 08:15 h and the automatic sampler took its first sample at 08:57 h after the water had risen approximately 25 cm. Discharge increased from 4 m3 s−1 at 08:15 h to a maximum of 91 m3 s−1 at 18:45 h (Supplementary Fig. 5). DO reached its lowest point (0.4 mg L−1) at 13:15 h. DO remained <1 mg L−1 for 2 h. During the peak of the DO drop, over 300,000 kg h−1 of hippopotamus feces was moving through the system (Supplementary Fig. 5). TSS, TN, and TP all peaked at the same time the DO dropped to its lowest concentration (Supplementary Table 6). DOC, NO 3 −, and NH 4 + all declined during the flow pulse. TSS remained above 5000 mg L−1 for at least 3 h (likely much longer), and concentrations were >1000 mg L−1 in every sample we collected (6 h).

Bottle experiment

Water was collected from a deep borehole and poured into 36 300 mL glass bottles. HPW and hippopotamus feces were collected as described in Supplementary Note 4. Bottles were then spiked with 60 mL of HPW, 1.5 g wet weight of hippopotamus feces, or left as controls and placed in several inches of water inside of a large black, plastic box. Three bottles from each treatment were sacrificed at each sampling time (30, 300, 960, and 1680 min) to measure DO using an optical DO sensor (ProODO, YSI Incorporated, Yellow Springs, OH, USA). For the first and last sample time (30 and 1680 min), water was collected for analysis (H 2 S, Fe(II), NH 4 +, SRP, NO 3 -, CO 2 , CH 4 , and N 2 O) and processed in accordance with the methods described below.

DO decreased in the HPW and hippopotamus feces treatments (Supplementary Table 7). Added HPW caused a rapid drop in DO, whereas added hippopotamus feces caused a slow, linear decline in DO (Fig. 2a). Fe(II) and N 2 O were not detectable in any of the treatments. H 2 S and CH 4 decreased over time in the HPW treatment, presumably due to oxidation. NH 4 + and SRP increased over time in the HPW and hippopotamus feces treatments. CO 2 increased over time in the HPW treatment and decreased in the control.

Experimental streams

We constructed 12 portable, recirculating experimental streams in the field using flexible PVC plastic. Each stream was filled with river water containing DO at concentrations close to atmospheric equilibrium. The final volume of water in each stream after experimental additions was 60 L. A motor-driven paddle wheel provided recirculating flow in each stream. Optical DO loggers (MiniDOT, PME, Inc., Vista, CA, USA) were placed in each stream and configured to log every 5 min for 2.5 days. HPW and hippopotamus feces were collected as described in Supplementary Note 4. Five streams were spiked with varying weights of fresh hippopotamus feces (30, 60, 150, 300, and 600 g wet weights, ~76% water content) while 5 streams were spiked with varying volumes of anoxic HPW (0.5, 1, 2.5, 5, and 10 L). Two streams were used as controls. The depth of water in the experimental streams was approximately 15 cm, which was comparable to depths between hippo pools on tributaries but much shallower than in the river where typical depths ranged from 0.5 to 1.5 m.

For data analysis, the DO concentrations were adjusted so that each stream started at the same value, applying that same correction to all subsequent measurements. This minor adjustment accounted for variability among sensors. Changes in DO in the control channels were subtracted from changes in the treatments. The minimum oxygen concentrations attained during the initial DO drop, expressed relative to the DO in the reference channel, indicated the magnitude of the drop. A mixing model partitioned the oxygen decline between simple mixing of anoxic HPW (0 mg L−1) with oxic river water (8 mg L−1) and oxygen consumption processes.

HPW caused a rapid drop in DO within the experimental streams, with the magnitude of the drop linearly related to the quantity of HPW added (Fig. 2c). Mixing of anoxic water with oxic stream water only accounted for approximately 47% of the drop in DO measured at the peak of the drop (Fig. 2d). The majority of the drop in DO was caused by biochemical processes. By 44 h after the additions, DO concentrations in all of the HPW treatment streams had returned to close to atmospheric equilibrium, although this reaeration rate may be slower in the river where river depths are greater.

Hippopotamus feces also caused a drop in DO within the experimental streams (Fig. 2b). The drop was smaller than the addition with HPW, and DO maintained a downward trend for the two larger treatments (5 and 10 g L−1) throughout the experiment.

Modeling downstream oxygen depletion

We constructed a model to relate the observed DO sags after HPW additions in the experimental streams to DO concentrations in the deeper Mara River channel under the same mixing conditions (Supplementary Note 2). We used the DO and temperature data collected during the experimental stream experiment and then estimated DO consumption rate for each time interval after accounting for reaeration. We then modeled DO concentrations in the deeper river channel assuming the same temperature, DO consumption rate, and reaeration rate as in the experimental stream experiment. The total area and mean depth of hippo pools along the river provided an estimate of the volume of HPW that would be entrained into the river during flushing flows.

Model results for the two highest fractions of HPW are depicted in Supplementary Fig. 7. We estimate that entrained HPW reaching 17% of the river volume (the same proportion in our highest experimental stream treatment) is sufficient to generate hypoxia (< 2 mg L−1) for 16 h, during which the water would be anoxic for 8 h (Supplementary Fig. 7). Note that the river shows greater departures from atmospheric equilibrium as well as a time lag compared to the experimental stream channels; this is directly due to the greater depth of the water column.

We estimated the area of 14 hippo pools in the Mara River and tributaries using Google Earth (Imagery from Digital Globe and CNES/Airbus), confirming apparent pool boundaries with site visits (Supplementary Table 8). Pool volume was conservatively estimated assuming that the average minimum depth of each pool was 1 m (the amount needed for a hippopotamus to stay partially submerged when laying down). With an average pool volume of 3600 m3 and an estimated 171 pools40, there is approximately 616,000 m3 of HPW in the Mara River and tributaries upstream of NMB. The average baseflow discharge prior to flushing flows was 15 m3 s−1, and the average increase in discharge was 32 m3 s−1 reaching a peak of 47 m3 s−1 and returning to baseflow within 8.5 h. Given this average flushing flow and assuming that the rate of the flood rise and fall are approximately equal, the total amount of water moving during these 8.5 h is estimated to be 948,600 m3.

$$948,600\,\mathrm{m}^3 = \left( {15\,{{\mathrm{m}^3\,\mathrm{s}^{-1}}} \ast 8.5\,\mathrm{h}} \right) + \left( {\frac{{32\,{{\mathrm{m}^3\,\mathrm{s}^{-1}}} \ast 8.5\,\mathrm{h}}}{2}} \right)$$ (1)

Thus the proportion of HPW to total water in the river during an average flushing flow is 65%.

Whole-ecosystem manipulation

The Moliband Pool (−1.37388, 35.25827) is located in a private conservancy that allows grazing of cattle by the Maasai tribe. The Moliband Pool has never been occupied by hippopotami (Warden Benson, Naboisho Conservancy, personal communication) likely due to the close proximity of Maasai pastoralists, although the pool appears to be suitable habitat and other pools located nearby are occupied. There are no hippo pools occupied upstream of the Moliband Pool.

A temporary dam was constructed from approximately 6 tons of sandbags 14 m upstream of the Moliband Pool (Fig. 3a, b). A water quality sonde (Manta2, Eureka Environmental, Austin, TX, USA) was placed directly at the pool outlet, approximately 53 m downstream of the temporary dam. Water was allowed to back up behind the dam for 2 days. The dam was then quickly breached, simulating a flushing flow moving through the pool. Water samples were collected at the downstream water quality sonde every few minutes after the flush and analyzed for H 2 S, Fe(II), NH 4 +, SRP, NO 3 −, CO 2 , CH 4 , N 2 O, TN, TP, and DOC.

After the flushing flow moved through the system, the dam upstream of the pool was put back in place. A total of 16,000 L of benthic HPW was loaded into the Moliband pool over 2 days. A small dam was put downstream of the treatment pool to prevent the premature release of the added HPW. RWT sensors (Hydrolabs, Hach Company, Loveland, CO, USA) were placed in the deepest part of the pool, one at the pool surface in the center of the pool, and one next to the downstream water quality sonde. In all, 8.1 g of RWT was then added to the upper pool, the water behind the upstream dam, for an approximate concentration of 300 µg L−1 of RWT. The downstream dam was then removed and the upstream dam was quickly breached, and water samples were taken every few minutes in the same manner as the first flush.

DO dropped further in the treatment flush compared to the reference flush (Fig. 3c). BOD, TN, TP, DOC, NH 4 +, CO 2 , CH 4 , conductivity, and turbidity were higher downstream during the treatment flush.

Water sample analysis

We preserved water samples for analysis of inorganic nutrients (NH 4 +, SRP, NO 3 −) by filtering them through a Supor 0.2 µm polysulfone membrane filter directly into a collection bottle and then freezing them until analysis with a portable flow injection analyzer in the field. Ammonium was analyzed using the gas exchange method69. Nitrate+nitrite was analyzed using zinc reduction71. Soluble reactive phosphate was analyzed using the molybdate blue method70.

Dissolved ferrous iron (Fe(II)) was measured on a field spectrophotometer (DR 1900 Portable Spectrophotometer, Hach Company, Loveland, CO, USA) using a colorimetric method modified from Lovley and Phillips72 and Stookey73. After collection, the sample was immediately filtered through a 0.2 µm Supor polysulfone membrane filter into a solution of 50 mM 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid buffer containing ferrozine (1 g L−1).

Hydrogen sulfide (H 2 S) was also measured colorimetrically on the field spectrophotometer immediately after collection. The sample was filtered through a Whatman GF/F glass fiber filter (GE Healthcare Life Sciences, Pittsburgh, PA, USA) into two scintillation vials and then preserved and analyzed according to the methylene blue method of Golterman74.

DOC samples were filtered through a pre-combusted, pre-weighed Whatman GF/F glass fiber filter. In all, 100 µL of concentrated sulfuric acid was added to 60 mL of sample for preservation before eventual analysis with a Shimadzu high-temperature, platinum-catalyzed total organic carbon analyzer (Shimadzu, Kyoto, Japan). AFDM of suspended particulate material was determined gravimetrically by drying and combusting the filter.

Samples of unfiltered water (60 mL) were collected for analysis of TN and TP and were preserved by adding 100 µL of concentrated sulfuric acid, followed by eventual alkaline potassium persulfate digestion and analysis on an Astoria-Pacific flow analyzer (Astoria-Pacific, Clackamas, OR, USA).

Water samples for analysis of major ions were filtered through a Whatman GF/F 0.45 µm filter and analyzed on a Dionex ion chromatograph system equipped with membrane suppression and conductivity detection (Dionex, Sunnyvale, CA, USA).

Dissolved gases were extracted from water samples utilizing a static headspace equilibration technique75. In short, 115 mL of water was drawn into a 140 mL syringe with a stopcock and 25 mL of ambient air was then drawn into the syringe. The syringe was then gently shaken for 5 min to equilibrate the 25 mL of headspace with the dissolved gases in the sample water. A total of 15 mL of air within the headspace was then injected into evacuated exetainers which were stored in larger vials filled with water. Samples were analyzed by gas chromatography at the Cary Institute for Ecosystem Studies (Millbrook, NY, USA).

BOD was determined for the water samples by incubating a subsample diluted to 300 mL with a dilutant containing phosphate buffer, magnesium sulfate, calcium chloride, and ferric chloride69. Samples were incubated for approximately 24 h within glass bottles. DO was measured at the beginning and end of the incubation with an optical DO sensor (ProODO, YSI Incorporated, Yellow Springs, OH, USA).

Data availability

All data are available in the supplementary materials or from the Dryad Digital Repository: https://doi.org/10.5061/dryad.nh6hn04.