Ecology

An Nasim consists of deep and narrow interdunal basin in which a sequence of aeolian sands overlain by bedded lacustrine marl is preserved (Figs. 2, S1). In the central part of the An Nasim basin, outcrops of these deposits are exposed extending approximately 800 m north–south and 350 m east–west. The marl outcrops are, however, fragmented and discontinuous, occurring at several distinct altitudes (Fig. S1). The thickest visible exposures of marl are found along the basin’s eastern edge (Figs. 2, 3, S1). At the base of these exposures, the deposits express the morphology of the former interdune depression in which they accumulated, in the form of a concave surface dipping steeply away from the edge of the observable outcrops towards the centre of the basin within which they formed. The stratigraphy of the deposits also dips towards the centre of this palaeobasin, indicative of sediments being deposited in a quiescent water body and draping across the existing topography. The western edge of the deposit is at ~ 930 m above sea level (MASL) and has been deeply eroded, forming a small cliff (maximum of 4 m high) providing a thick exposure of lake sediments. Large ‘boulders’ of sediment at the base of this cliff have been dislodged and transported down-slope towards the centre of the current interdune depression. The marls are thickest at the western edge, which likely lay towards the centre of their contemporary interdune palaeobasin, and thin in an easterly direction towards its edges (0.5 m at their thinnest). The thickness of the marl deposits in the central area is exceptional in comparison to previously excavated comparable late Middle and Late Pleistocene deposits found elsewhere in the western Nefud22,24,25. An additional area of palaeolake deposit exists immediately to the south of the primary exposure at the same altitude, likely a continuation of the same deposit in an area that has experienced differential erosion.Figure 2Stratigraphic sequence of An Nasim and artefact distributions. (a) stratigraphy with the locations of the sediment samples dated by luminescence; (b) Lower Palaeolithic artefacts at An Nasim, mapped through systematic survey of the current interdune and recorded using a differential GPS system. The stratigraphic sequence was drawn from the location of the handaxe in Layer 12. Produced using ArcMap version 10.2. Basemap from Bing Maps Aerial, (c) 2010 Microsoft Corporation and its data suppliers.Full size imageFigure 3Different handaxe forms from An Nasim. Credit: Ian Cartwright.Full size imageThe undulating lower contact and complex bedding geometry of the lake sediments reflect the accumulation of these sediments over a pre-existing aeolian dune topography. In this context, the marl sediment precipitates from the water column, falls out of suspension and, consequently, accumulates in thick beds that drape over the sand dune forms that are preserved on the lake bed. These beds consequently dip into the centre of the basin and undulate throughout the exposure. The dip of the marl beds means that units that occur several meters below the surface at the western section edge are found at the land surface on the eastern basin margin. Of particular relevance to this study is the fact that the marl-rich sand bed that is found near the surface of the marl unit at the outcrop edge, containing lithics in stratigraphic position, can be traced laterally and is found to occur 3 m below the surface towards the centre of the basin (Fig. 3).The massive marl beds at the base of the section (Fig. 2a) indicate deep water conditions, while towards the top of the sequence the interdigitation of beds of marl and sand, with associated desiccation cracks, are typical of a shallower water body that experienced episodic drying (Figs. 2a, S1). The upper layers 11 and 12 are laterally extensive and contain lithics in stratigraphic position within horizontally bedded sands (Layer 11) overlain by a thin bed of marl (Layer 12—Fig. S1). This sequence suggests falling water level and sheet wash deposition of sands from the surrounding landscape, followed by a small subsequent rise in water level. The sedimentology of the upper part of the primary marl sequence, and in particular that of unit 11, within which a stratified lithic was found, is therefore consistent with the occupation of the site during a drier phase featuring low lake levels.In arid environments, where reworking is widespread, it is often difficult to demonstrate that lithic artefacts are contemporaneous with the age of the deposit. However, at An Nasim, three observations are important. Firstly, that diagnostic artefacts have been recovered from within the marls and can therefore be directly related to specific strata. Secondly, the size of the lithics (pebble/cobble) is significantly coarser than the grain size of any of the sediments within the host deposits, which are dominated by sands and silts. This observation demonstrates that the processes responsible for depositing these sediments were incapable of transporting and reworking the artefacts. Finally, the surface of the main marl bed is the highest point at the site, meaning that there are no older, higher deposits from which the lithics can be eroded and redeposited in the marl sequence. When these observations are considered the most likely source of the lithics that are found across the surface of the marl bed is the uppermost layers of this unit where stratified archaeology has been directly recovered.At lower altitudes within the current interdune area, additional marl deposits are visible, all of which are much less distinct and appear more degraded than the primary deposit discussed above. Three small exposures of marl exist on the northern flank of the basin between approximately 930 and 923 MASL, potentially peripheral exposures of the massive marls, whilst at the basin centre two distinct large mounds of eroded marl material are present. Mound 1, the northernmost of these, has a curved upper surface, again suggestive of a lake bed deposited in an interdune basin, this time at around 921 MASL (Fig. 2b). Mound 2 (Fig. 2b), to the south, has an indistinct heavily eroded upper surface at ~ 916 MASL, while its relationship to Mound 1 (Fig. 2b) is unclear. Both are eroded, preserved as inverted relief features above the current interdune floor (which lies at 910 MASL) possessing flanks covered with the deflated remnants of the palaeolake deposits. The stratigraphic relationship of these lower deposits to the primary deposit remains unclear due to deflation having created an unconformity between them. However, the morphology of Mound 1, and the lower altitude of these sediments relative to the primary deposit, strongly indicates that they belong to a lacustrine phase distinct from that of the primary deposit. It is likely that they formed in the floor of a later interdune depression, prior to the more recent deflation that created the present interdune area that they lie within. An Nasim thus preserves several discrete phases of lake basin development separated by episodes of aeolian deflation related to cyclic climate change within the western Nefud.The sedimentological observations at An Nasim are in keeping with the picture observed across the wider western Nefud Desert, where the repeated raising of regional groundwater levels during discrete humid intervals produced lakes and wetlands in the interdune depressions13,24. Previous analyses have indicated the these palaeolakes were widespread across the western Nefud, and that despite an absence of evidence for large-scale fluvial activity within the region, the high density of such interdune lakes facilitated hominin dispersals through it11,13.At An Nasim, two discrete concentrations of Lower Palaeolithic artefacts were discovered distributed across the surfaces of the primary deposit, and the lower mounds (Fig. 3). Systematic collection recovered 354 artefacts, primarily handaxes, together with various flakes that included clearly identifiable bifacial thinning flakes (Table 1). The artefacts were found in two main clusters at the site (Fig. 2b) and appear to be eroding out of the marl deposits. All visible artefacts were systematically collected and their locations recorded using a differential GPS (DGPS). However, it should be noted that ever-shifting sands likely hid other artefacts from view, and were therefore not collected. We acknowledge that the assemblage may therefore be biased towards handaxes, which are larger and thicker and therefore less easily buried than flakes. The results of this survey, mapped in Fig. 3, illustrate the close association between the artefacts and the lake.Table 1 Breakdown of artefact classes from An Nasim. Flake numbers are likely an underestimate from the site, as shifting sands hid smaller artefacts from view.Full size tableThe lithic tools are similar to previously reported Acheulean sites in the Nefud Desert21 and consist of relatively thick and finely flaked bifaces (typically triangular and pointed). The artefacts represent the entire bifacial manufacturing sequence, all of which were constructed by thinning out large tabular blocks of ferruginious quartzitic sandstone26. The presence of minimally flaked pieces of these tabular blocks indicate that the raw material was brought to the site, some of it apparently discarded after having been ‘tested’ by the removal of one or two flakes along an edge. Other flaked pieces were very roughly shaped before being abandoned. Many of the handaxes retained the last vestiges of the flat, tabular cortical surface at their centre, often on both faces. The base of the handaxes also frequently retained the thick, flat cortical edge of the tabular block, perhaps to aid grasping. None of the bifaces were made from flakes and there was no evidence of large flake manufacture, perhaps due to the small, tabular nature of the local raw material. Indeed, broader surveys in the Nefud Desert indicate that this local tabular quartzite was frequently used at other undated Acheulean surface assemblages, all of which lacked evidence for large flake manufacture21. This suggests the local raw material impeded this approach to handaxe manufacture.The surface artefacts exhibited a similar high degree of weathering, while the artefacts from buried or recently exposed contexts were fresh. The handaxes were diverse in form, ranging from ovate to cordiform and triangular forms, as at other Acheulean sites in the Nefud21, and variable in size (Fig. 3). All handaxes with observable flake scars showed fine flaking, regardless of form. 2D Geometric Morphometric (GMM) analysis of a random sub-sample of fifty handaxes showed that this form variation was not continuous (Figs. 4, S1, Tables S2–S4). However, no spatial relationship between discrete forms and findspots was observed in the sample.Figure 4Canonical Variates Analysis of Biface form (n = 50) at An Nasim, showing discrete shape groupings corresponding to triangular, ovate and cordiform forms. See Tables S2–S4 for eigenvalues and distances.Full size imageSurvey revealed one face of a stratified handaxe visible in the section in the top 10 cm of the primary marl deposit (Layer 12—Fig. S1). Small-scale excavation in the form of a shallow 1 × 1 m test trench around this location allowed the subsequent recovery of this firmly embedded handaxe. This handaxe was included in the 2D GMM analysis shown in Fig. 3, where it clustered with the cordiform group found on the surface. The tight, shape-based clustering of the cordiform handaxes, along with the similarity of manufacture and raw material indicates that these forms at least, may be regarded as contemporary with each other in the marl. The similarity of manufacture among all the handaxe forms represented at An Nasim may also indicate broad contemporaneity. Digging for a sediment sample for dating purposes also permitted the recovery of a bifacial thinning flake cemented within the sandy Layer 11.A sample for luminescence dating was collected from Layer 11 (NSM1-2017), where archaeology was also recovered (Fig. 2a, See SI), and additional samples were collected beneath the lithic horizon in Layer 8 (NSM1-OSL4) and Layer 7 (NSM1-OSL3). Dose rates for these samples were determined by thick source alpha and beta counting, while gamma dose rates were measured using a field gamma spectrometer (See Table 2, SI, Table S5).Table 2 IR-RF age results.Full size tableThe K-feldspar grains were isolated and then analysed using the infrared-radiofluorescence protocol at controlled temperature (RF70) (See SI 3)27, using the same parameters as described previously19. IR-RF dose and age estimate are reported in Table 1. The overdispersion values (OD) are less than 20%, which is consistent with our prediction for such sediment. The three samples yield ages of 310 ± 17 (NSM1-OSL3), 243 ± 23 ka (NSM1-OSL4) and 330 ± 23 ka (NSM1-2017). These ages are coherent at 2 sigma, however sample NSM1-OSL4 is much younger than the other two samples, which yield very similar ages. The two older ages also have lower overdispersion values than the younger one, possibly suggesting that they are more reliable.To further contextualize these age determinations, we compared the ages with mean summer insolation at the latitude of the Nefud Desert, (Fig. 5), the driver of ‘Green Arabia’ humid phases14. The buried handaxe is associated with a thick marl sequence overlying the dated sediments. Sedimentological analysis indicates these marls were produced by significant wet conditions. Both the MIS 9 and MIS 7 insolation peaks are modulated by high eccentricity (Fig. 5) and are equal or greater in intensity to that of MIS 5a, which is known to have been wet enough to enable large perennial deep lake formation24. As can be seen, the MIS 9 insolation peaks lie closest to the older age estimates and correspond to a time when other lakes in the An Nasim area are known to have formed23 (Fig. 5). Taken together, this evidence is consistent with a MIS 9 date for the formation of the An Nasim deposits, though the possibility a younger MIS 7 age cannot be completely discounted.Figure 5Luminescence ages from the An Nasim site, displayed above the orbital parameters (derived from44) which produced humid episodes in the Arabian Peninsula (eccentricity [green] modulation of precession [turquoise], with a corresponding influence upon summer [JJA] insolation at the latitude of the Nefud [black], driving monsoon incursion). Marine Isotope Stages of the last 700 ka are displayed for reference. Navy blue bar data are from23 and are displayed as follows. Solid bars indicate lake formation occurred during this range (a direct date or paired bracketing ages). Dashed lines with endcaps and thick bars to the left indicate maximum (underlying, no unconformities) ages for lake formation—which likely occurred either before (i.e. older than) the endcap, or during the period denoted by a thick bar. Dashed lines with endcaps and thick bars to the right indicate minimum (overlying, no unconformities) ages for lake formation—which likely occurred after (i.e. younger than) the endcap, or during the period denoted by a thick bar. The hashed area shows the high concurrence of data suggesting lake formation in MIS 9. Produced using Microsoft Excel.Full size image More

Location of potential larval habitats and probability of occurrenceGenerally, Anopheles arabiensis mosquito takes around 15 days to develop from egg to adult, but the duration can be as short as 10 days due to selection pressures from the stressed environment such as drought, temperature anomaly, or limited food resources48,49. In this regard, we considered areas with WI exceeding 10 and 15 days to be potential larval habitats under critical and normal conditions, respectively.To determine the probability of potential larval habitat occurrence, we computed the probability of ponding occurring longer than 10 and 15 days, P(WI  > T), as shown in Eq. (2). P(WI  > T) is defined as the ratio of D(WI(x,y,t)  > T), the number of cumulated days for which the WI (i.e. persistence of ponding) of a grid cell (x,y) at time t that exceeded T days, to Dperiod, the number of days within a defined period of simulation.$$Pleft( {WI > T} right) = frac{{D(WIleft( {x,y,t} right) > T)}}{{D_{period} }},,T in left{ {10,15} right}$$
(2)
Figure 5 shows the results for the spatial distribution of P(WI  > T) over the three periods of simulation, namely the entire year of 2018, the dry season (i.e. January to April and November to December) and the rainy season (i.e. May to October). It can be observed that ponding was persistent throughout the year around the stream edges and the vicinity. P(WI  > 10) and P(WI  > 15) were consistently close to 1, reflecting a high potential of these areas as larval habitats.Figure 5Spatial distribution for the probability of potential larval habitat occurrence. (a–d) represent the probability of WI exceeding 10 days and 15 days for the baseline scenario and the irrigation scenario for the entire year. Similarly, (e–h) represent the probability of WI exceeding 10 days and 15 days during the dry season, and (i–l) represent the probability of WI exceeding 10 days and 15 days during the rainy season. Areas where the simulated surface water flowrate exceeded 0.01 m3/s for 90% of the time in the simulated year were masked out for all the sub-figures since Anopheles larvae have a lower chance of surviving in fast-moving water61.Full size imageFor the baseline scenario shown in Fig. 5a,b, the P(WI  > T) for the areas outside of the streams was predominantly determined by soil type. The areas characterized by Usterts (see Supplementary Fig S2) with the lowest hydraulic conductivity in the model domain were the next most at risk, with a P(WI  > T) of about 0.4–0.5. In the remaining areas, P(WI  > T) was generally 0.2 or less. Comparing Fig. 5a,b, the differences were minimal except for the steep areas at the watershed upstream boundary where P(WI  > 15) was predominantly zero. The surface water ponding was unable to last more than 15 days due to the terrain gradient.Figure 5c,d show the results for the irrigation scenario. Compared to the baseline scenario, the year-round persistent ponding around the streams and the vicinity was wider in coverage and more noticeable. Irrigation also increased P(WI  > 10) in Fig. 5c and P(WI  > 15) in Fig. 5d from 0.4–0.5 to about 0.7 and 0.6 respectively for Farm #1, Farm #2, and a significant portion of Farm #3 and Farm #4. The P(WI  > T) for the remaining area within the farms remained relatively unchanged at 0.2 and this could be attributed to the Ustoll soil type which drains easily. The increase in the probability of potential larval habitat occurrence from the baseline was more pronounced for P(WI  > 10) than P(WI  > 15) since the interval of irrigation was set at 10 days, after which the farm drained without replenishment until the next irrigation cycle.For the dry season, it can be observed in Fig. 5e,f that the stream edges were the only areas with high potential of larval habitat occurrence. In Fig. 5g,h, P(WI  > T) increased visibly in the farms after irrigation, with a distinct similarity between Farms #1/#3 and Farms #2/#4 that points to the irrigation schedule. While irrigation was alternated evenly between the two groups, Farms#1 and #3 showed a higher P(WI  > T) than Farms #2 and #4, possibly due to the timing of the irrigation relative to the rainfall. Irrigation could either coincide with rainfall or act as a supplement when there was no rainfall to augment soil moisture. Noticeably, there was an area to the northeast straddling both Farm #3 and Farm #4 where P(WI  > 10) was around 0.1 but P(WI  > 15) was almost zero, indicating that irrigation only allowed for larval habitats under critical conditions in that area during the dry season.For the rainy season, it can be observed in the baseline scenario (Fig. 5i,j) that the areas characterized by Ustert exhibited a high potential of larval habitat occurrence, apart from the stream edges. Particularly, there was an area to the north where P(WI  > T) was lower than the other parts which could be due to the relatively steeper terrain. In the irrigation scenario (Fig. 5k,l), there was no visible difference in P(WI  > T) as compared to the baseline scenario, apart from a minor increase around the western part of Farm #4.As a summary, we present the results in boxplots as shown in Fig. 6 to illustrate the effect of irrigation in different seasons for the areas inside and outside farms. The relevant statistics can be found in Table 1. The P(WI  > T) had a highly asymmetrical distribution because it was very low in most of the model domain but could be very high in the remaining areas due to the streams. For the following comparison, we will use the median as it was more representative of the distribution.Figure 6Box plots for the probability of potential larval habitat occurrence for the whole year, dry, and rainy season. Probability of WI exceeding (a) 10 days and 15 days (b) for the area inside farms and the area outside farms. The line within each box is the sample median and the top and bottom of each box are the 25th and 75th percentiles. The whiskers were drawn from the two ends of the box and demarcate the observations which were within 1.5 times the interquartile range from the top and bottom of the box.Full size imageTable 1 Summary statistics of the probability of potential larval habitat occurence for the whole year, dry season, and rainy season. Mean, 25th percentile (P25), median and 75th percentile (P75) of the probability of WI exceeding 10 days and 15 days for the (a) areas inside farms and (b) areas outside farms. The p value was derived from the Wilcoxon Rank-Sum test under the null hypothesis that irrigation did not increase the median probability of exceedance from the baseline scenario.Full size tableIn the baseline scenario, there was a higher potential for larval habitats to form inside the farms, with a median P(WI  > 10) of 0.427 and a median P(WI  > 15) of 0.400, than outside the farms, with a median P(WI  > 10) of 0.06 and a median P(WI  > 15) of 0.019. This is expected because the farms are located in an area with relatively flat terrain and a higher concentration of streams. The difference in the median P(WI  > T) inside and outside the farms was bigger in the rainy season compared to the dry season, as the higher rainfall received intensified ponding.Irrigation increased the median P(WI  > T) inside the farms drastically in the dry season, with the median P(WI  > 10) increasing from 0 to 0.442 and the median P(WI  > 15) increasing from 0 to 0.282. Although irrigation was only applied over the dry season, there was also a statistically significant increase in the median P(WI  > T) during the rainy season (p  10) increased from 0.848 to 0.864 while the median P(WI  > 15) increased from 0.794 to 0.810. This was due to irrigation contributing to the antecedent soil moisture before the onset of the rainy season, which shortened the time for the soil to become saturated and ponding to occur. On the other hand, there was no strong evidence outside the farms of an increase in the median P(WI  > T) due to irrigation (p  > 0.01). This applied to both rainy and dry seasons.Stability of larval habitatsIn the previous section, we showed that irrigation did not have a significant impact on areas outside the farms. Here, we evaluated the stability of the potential larval habitats specifically for the areas inside farms based on the distribution of the maximum duration of ponding for each grid cell within the year as shown in the histogram (Fig. 7a). The total number of cells corresponding to each bin interval of 15 days was expressed as a fraction of the total number of cells in the area inside farms.Figure 7The fraction of area inside the irrigated farms for each potential larval habitat types under the baseline and irrigation scenarios. (a) Shows the histogram of the maximum duration of ponding within the year for the grid cells in each type of habitats expressed as a fraction of the total area of the farms. The bin size was 15 days. Temporary, semi-permanent, and permanent larval habitats were typically characterized by ponding duration of 15–90 days, 90–180 days, and 180 days and above, respectively. The baseline scenario is represented in blue and the irrigation scenario is represented in orange. The darker orange bin is a result of the two overlapping. (b) Shows the comparison of the fractional area occupied by non-habitats (less than 15 days) as well as potential temporary, semi-permanent, and permanent larval habitats inside the farms. Each grid cell within the farm was categorized based on its maximum ponding duration.Full size imageFrom the baseline scenario, 13.2% of the area was not favorable for larval habitats because the maximum duration of ponding in those areas was less than 15 days. The most common maximum ponding duration was between 150 and 165 days, which accounted for more than 20% of the area. This was followed by 15–30 days and 360 days and above which made up 17.6% and 13.8% of the area respectively. With irrigation, there was a general increase in the maximum ponding durations. The most common maximum ponding duration was 360 days and above, accounting for 18% of the area. Noticeably, the area with maximum ponding duration between 210–225 days increased fourfold to 10%. The remaining increase was for 285 days and above. Counter-intuitively, the area that was not conducive as larval habitats (i.e. maximum ponding duration less than 15 days) also increased slightly by 0.6%. This was because irrigation raised the overland flowrate in these areas, mostly near streams, and made them unfavorable for breeding.In Fig. 7b, we grouped the maximum ponding durations into stability periods corresponding to temporary (2 weeks to 3 months), semi-permanent (3–6 months), and permanent (6 months and above) habitats based on field observations from a study at the site35. Temporary habitats such as puddles retain water for a short period while permanent habitats such as stream edges and swamps hold water much longer and are more stable. For the baseline scenario, semi-permanent habitats were the most common, occupying 33.1% of the area, while permanent and temporary habitats also accounted for 29.6% and 24.1% of the area respectively. After irrigation, there was a significant shift from semi-permanent habitats, which reduced to 22.9% of the area, to permanent habitats which increased to 41% of the area. There was also a slight reduction in the extent of temporary habitats to 22.4% of the area.Temporal pattern of potential larval habitatsTo shed light on the temporal patterns, we evaluated F(WI  > T), the fractional coverage of potential larval habitats inside farm, for each day throughout the year. We only focused on the area inside farms since irrigation does not have a significant impact on the area outside farms. As shown in Eq. (3), F(WI  > T) is defined as the ratio of C(WI  > T), the number of cells for which the WI (i.e. persistence of ponding) exceeded T days, to Cfarm, the number of cells within the farm area. T is set as 10 days and 15 days, corresponding to critical and normal conditions respectively.$$Fleft( {WI > T} right) = frac{{Cleft( { WIleft( {x,y,t} right) ge T} right)}}{{C_{farm} }},,T in left{ {10,15} right}$$
(3)
In Fig. 8a, F(WI  > 10) increased steeply on January 10 as WI started increasing from 0 at the beginning of the year. For the baseline scenario, the fractional coverage decreased minimally from 0.18 throughout the dry season despite the sporadic spike in precipitation. At the onset of the rainy season, the peak rainfall event of the year from May 5th to May 11th caused a sharp increase in F(WI  > 10) from 0.15 to 0.61 and thereafter, the relentless rainfall maintained the fractional coverage at about 0.6. Throughout the rainy season, there were four recurring peaks at a frequency of about 2 months. Post-rainy season, F(WI  > 10) dropped gradually to below 0.2 after the last peak at the end of October.Figure 8Daily variations in the extent of the potential larval habitats for the year. Time series of the fractional coverage of areas with Wetness Index (WI) exceeding (a) 10 days and (b) 15 days.Full size imageFor the irrigation scenario, F(WI  > 10) increased during the dry season from January to March with visible cyclical variations between 0.2 and 0.4 due to the rotation of irrigation among the four farms. Subsequently, the spike in rainfall at the end of March combined with the higher antecedent soil moisture from irrigation brought forward the step increase in the fractional coverage to April from May in the baseline scenario. As irrigation stopped at the end of April, F(WI  > 10) gradually dropped back to the same level as the baseline scenario at the end of June. In the dry season from November to December, the fractional coverage started to deviate from the baseline scenario again with cyclical fluctuations, gradually decreasing towards the end of the year.In Fig. 8b, F(WI  > 15) remained largely the same for the dry season but the peaks were moderated in the rainy season, compared to F(WI  > 10). There was one less peak at the end of May in the early rainy season because the watershed did not accumulate enough rainfall for the persistence of the ponded areas to exceed 15 days. Specifically, for the irrigation scenario, the increase in fractional coverage during the dry season was moderated and less sensitive to the spikes in rainfall. Similarly, irrigation resulted in the early onset of the steep increase in F(WI  > 15) in April following the spike in rainfall at the end of March. Also, it took two months after the end of irrigation in April for the fractional coverage to return to the same level as the baseline.From F(WI  > 10) and F(WI  > 15), we calculated the corresponding monthly mean, MF(WI  > 10), and MF(WI  > 15) as well as the 95th confidence interval as shown in Fig. 9. In Fig. 9a, MF(WI  > 10) in the baseline was the highest for the months between June and September, constituting a four-month window in which at least 50% of the area was conducive for larval habitat formation. Of the four months, the highest monthly mean fractional coverage was in July at 79.9%. Irrigation extended the window to include the months of April and May. The monthly mean fractional coverage increased 4.5 times to 64.3% in April and 1.4 times to 63.7% in May. The MF(WI  > 10) for the rest of the months in the window (i.e. June to September) remained one of the highest but the increase due to irrigation was not statistically significant (p  > 0.01). July remained as the month with the highest monthly mean fractional coverage at 80.0%. In Fig. 9b, MF(WI  > 15) was generally slightly lower than MF(WI  > 10) for both the baseline and irrigation scenarios but the general trends were the same.Figure 9Monthly variation in the extent of the potential larval habitats for the year. Monthly mean fractional coverage of areas with a probability of WI exceeding 10 days (a) and 15 days (b). The 95% confidence interval is indicated at the top of each bar chart. The asterisks (*) next to the month on the x-axis indicate that irrigation increased the fractional coverage of the potential larval habitats for the month from the baseline scenario based on a 2-sample t-test (p  More

AffiliationsDepartment of Earth System Science, University of California, Irvine, CA, USAJonathan A. Wang & James T. RandersonDepartment of Earth and Environment, Boston University, Boston, MA, USAJonathan A. Wang, Alessandro Baccini & Mark A. FriedlThe Woodwell Climate Research Center, Falmouth, MA, USAAlessandro Baccini & Mary FarinaDepartment of Land Resources and Environmental Sciences, Montana State University, Bozeman, MT, USAMary FarinaAuthorsJonathan A. WangAlessandro BacciniMary FarinaJames T. RandersonMark A. FriedlCorresponding authorCorrespondence to
Jonathan A. Wang. More

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Characteristics of the single agrometeorological disaster scenariosSAD-f occurred in 49 out of 57 years at different spatial scales, with a maximum IOC of 0.519 in 2013; SAD-d occurred in 33 years with a maximum IOC value of 0.808 in 1995; SAD-s occurred in 5 years, with a maximum IOC value of 0.115 in 1977 (Fig. 4). SAD-d showed a declining trend over the past 57 years, but the SAD-d frequency was higher than SAD-f and SAD-d. Since the mid-1980s, the frequency of SAD-f has increased, while the frequency and scale of SAD-s were relatively small.Figure 4IOC change curve for single agrometeorological disasters (SAD) in different scenarios.Full size imageA large-scale grade SAD-f event occurred in 2013 in Liaoning Province, and regional SAD-f occurred in 17 years. Five years showed a large-scale SAD-d and 11 years had regional SAD-d. There were no large-scale and regional years for SAD-s (Table 5).Table 5 Occurrence years of large-scale and regional single agrometeorological disaster (SAD) in Liaoning Province.Full size tableThe occurrence of SAD-f was recorded at 15 sites with a frequency greater than 20%, 13 sites with frequency in the 10%–20% range, and 24 sites with a low frequency (P ≤ 10%) in Liaoning Province from 1961 to 2017 (Fig. 5). SAD-d occurred at 12 sites with a frequency higher than 20%, 22 sites with frequency between 10% and 20%, and 18 sites with a low frequency (P ≤ 10%). In the three scenarios, the occurrence frequency and distribution of SAD-f was the highest and SAD-s was the lowest.Figure 5Frequency of single agrometeorological disasters (SAD) in different scenarios (a SAD-f, b SAD-d, c SAD-s). Maps generated in ArcGIS 9.3.Full size imageComparison of the characteristics of single agrometeorological disasters and combined agrometeorological disastersThe maximum IOC of SAD was 0.808 in 1995 and the mean value was 0.294 for the 57 years of the study; the maximum IOC of CAD was 0.654 in 1987, and the mean value was 0.180 over the past 57 years; SAD and CAD occurred in all 57 years (Fig. 6). Both SAD and CAD showed declining trends from 1961 to 2017. The IOC was lower for CAD than for SAD for 42 years and higher than SAD for 14 years.Figure 6Change in the IOC for agrometeorological disasters in rice crops.Full size imageThis paper analysed the mean IOC of SAD and CAD over six decades and found that the interdecadal mean value of the IOC in CAD was lower than that of SAD over five of the periods, but the IOC of SAD was lower than that of CAD in 1971–1980 (Fig. 7). The IOC of SAD showed a decreasing trend from the 1970s to the 2010s but showed an increasing trend after 2011. The IOC of CAD showed a decreasing trend from the 1970s to the 2000s, but showed an increasing trend after 2001 (Fig. 7).Figure 7Interdecadal mean value of IOC for agrometeorological disasters in rice crops.Full size imageThere was one site (Fushun) with a SAD frequency of more than 50% in Liaoning Province from 1961 to 2017, 42 sites with a frequency between 20% and 50%, and nine sites with a frequency lower than 20%. There were four sites (Xinfeng, Jianping, Xinbin and Caohekou) with a CAD frequency higher than 50%, 13 sites with a frequency in the range 20%–50%, and 35 sites with a frequency lower than 20% (Fig. 8). The frequency and range of CAD were less than those of SAD.Figure 8Frequency of agrometeorological disaster in rice crops (a combined agrometeorological disaster (CAD), b single agrometeorological disaster (SAD)). Maps generated in ArcGIS 9.3.Full size imageThere has been little research into the temporal or spatial distribution of CAD for rice and its occurrence characteristics: most research has been on SAD. For example, studies have examined the characteristics of SCD, DCD, FD for rice in northeast China26, 27, 29, and the risk of multiple disasters for rice in northeast China30, 31. Han et al.31 analysed the risk of disaster using the reduction rate of rice yield in Liaoning Province from 1980 to 2011, and found that the high-risk areas were distributed in the west and northeast of Liaoning Province; higher rates of yield reduction in lean years were mainly found in western Liaoning and its surrounding areas. In this study, a higher frequency of CAD was mainly distributed in the northwest of Liaoning Province, while that of SAD occurred in the northeast of Liaoning Province. The median frequency of CAD occurred in the northwest and northeast of Liaoning Province, while that of SAD covered most areas in Liaoning Province. The range of medium and higher frequency occurrence in CAD was consistent with the distribution of high-risk and high yield reduction areas in the study of Han et al.31. Therefore, it can be speculated that the CAD scenarios might magnify the effect of each single disaster, and, therefore, CAD would more easily lead to a higher reduction in the rice yield.Comparison of the occurrence of single agrometeorological disasters and combined agrometeorological disastersDuring the rice growing season in Liaoning Province, there were three scenarios of SAD and six scenarios of CAD. Compared with SAD, CAD had more scenarios and more complex processes, and its effect on rice was more difficult to evaluate. In SAD, the occurrence frequency and distribution of SAD-f and SAD-d were both high, when FD and DCD occurred alone in only one rice growth stage. In CAD, the occurrence frequency and distribution of TD-1, when FD and DCD occurred simultaneously, was the highest in the six scenarios. A single or combined occurrence of FD and DCD was most common disaster for rice in Liaoning Province. The occurrence frequency and distribution of OD-1 were both smaller than that of SAD-f, indicating that the occurrence was lower when FD happened at both the seedling and milk stages. SAD-s and OD-2 had the lowest frequency and range in all scenarios, indicating that DSD rarely appeared in SAD and CAD. The occurrence of SCD was not major disaster in the growth and development of rice in Liaoning Province, but the occurrence of DCD or FD, or both, was.In this study, the occurrence frequency and range of SAD and CAD for rice showed declining trends in most sites over the past 57 years, which was consistent with the results of other studies. Studies on rice DCD and SCD concluded that cold damage events of rice in most areas of northeast China showed decreasing trends26, 27. Because of events such as climate warming, earlier warming in spring, delaying first frost dates and fewer low temperature days in summer, the trend of disasters was lower in rice planting areas30. However, although rice disasters showed a decreasing trend, local disasters may increase because of the frequent occurrence of climate anomalies. SAD-f and OD-1 scenarios in this study showed no significant decreasing trend, and even a partial increasing trend. Jiang et al.29 believed that the possibility of frequent SCD in north-east China was still high. According to Xi et al.32, cold periods would still occur in the growing season of rice in northeast China. Hu et al.33 concluded that the increase of SCD in northeast China was mainly because of the increase of climate variability, and most of the sites with increases were located in areas with decreasing temperature or no obvious trend of temperature increase.Rice is a higher temperature-loving crop, which is mainly restricted by temperature conditions during its growing season. Liaoning Province is in the south of the rice planting area of the colder regions in China. Because of the relatively low latitude, heat conditions during the rice growing season were better than those in Jilin and Heilongjiang to the north of Liaoning Province. The climatic risk of cold damage in the rice growing season was lower than other regions in northeast China34. The occurrence of CAD was generally caused by low temperatures, which were the dominant factor. When two or more disasters occur together, there is a coupling or amplifying effect on rice growth compared with a single disaster.A comparison of the rice yield reduction rates in the years when CAD or SAD occurred in more than 50% of stations in Liaoning Province revealed that the former happened in 5 years, 1969, 1974, 1976, 1980 and 1987, whereas the latter happened in 7 years, 1972, 1973, 1985, 1986, 1990, 1995 and 2013. When CAD was the major occurrence, the average yield reduction rate in the five years was 10.6%. The yield reduction rate in 1969 was 34.6%, which was the highest in the past 57 years. When SAD was the major occurrence, the average yield reduction rate in the seven years was 9.8%. The average yield reduction rate in the years when CAD dominated was greater than in the years when SAD dominated. Therefore, it can be speculated that CAD has a greater effect on rice growing than any single disaster within CAD. However, it is difficult to quantify the effect on rice yield of CAD, and further controlled field experiments should be conducted to verify these. It is difficult to control field experiments that are limited by conditions and facilities.Comparison of the occurrence of agrometeorological disasters in years having rice yield reductionsOn the basis of the rice yield reduction rate in calculations Liaoning Province from 1961 to 2017, a total of 10 years (Table 6) were screened. Six years had large-scale disasters (including SAD and CAD) and four years had regional disasters. In 1969, which showed the highest yield reduction rate (34.6%), 30 sites had TD-1 disasters and the other 22 sites had SAD-f disasters. In 1972, the second highest reduction year (29.1%), 11 sites had MD-1 disasters, i.e. three kinds of disasters occurred, seven sites had TD-1 disasters, one site had a TD-2 disaster, 31 sites had SAD-d disasters, one site had a SAD-f disaster, and only one station had no disaster. The TD-1 disaster, i.e. delayed cold damage and frost injury, was the most frequent CAD over the years, and SAD-d, i.e., delayed cold damage, was the most frequent SAD. The occurrence of single and combined agrometeorological disasters in different regions strongly affected the rice yield. Generally, the larger the disaster range, the higher the yield reduction. However, some years were not completely consistent with this conclusion. The yield reduction rate was also related to the type, severity, occurrence period and geographical location of the disasters.Table 6 Comparison of agrometeorological disasters in years having greater than 10% rice yield reduction rates in Liaoning Province.Full size tableIn every year from 1961 to 2017, CAD or SAD occurred in Liaoning Province, and the rice yields declined in 23 of the 57 years owing to meteorological disasters (Fig. 9). Although meteorological disasters occurred in the other 34 years, there was no reduction in rice production, which may be related to the gradient of the disaster or the spatial distribution of the rice planting areas. The rice yield reduction rates in 1969 and 1976 were 34.6% and 15.6%, respectively. In these two years, CAD occurred at 30 stations and SAD occurred at 22 stations, and TD-1 was the main type of CAD, whereas SAD-d was the main type of SAD. Using statistical data, on the rice planting area of each city in Liaoning Province, the provincial area can be divided into four regions. The first region was Shenyang City, which has the largest rice planting area, accounting for 20%–25% of the total rice planting area; the second region was Panjin City, accounting for 15%–20% of the total rice area; the third region encompassed Tieling and other six cities, accounting for nearly 50% of the total rice area, with each city representing 5%–10%; and the fourth region encompassed Jinzhou and five other cities, accounting for 10%–15% of the total, with each city representing 0–5%. As shown in Fig. 10a,b, TD-1 occurred in the first region in both 1969 and 1976 and in the second region in 1969. SAD-d occurred in the second region in 1976. In the third region, TD-1 occurred at more stations of 1969 than in 1976. The rice area in the first three regions accounted for nearly 80% of the total rice area, and CAD occurred more often than SAD in these regions. Thus, there was a greater yield reduction rate in 1969 than in 1976.Figure 9The IOC change curve of all agrometeorological disasters and the rice yield reduction rate from 1961 to 2017.Full size imageFigure 10Distributions of the types of agrometeorological disasters and the percentages of rice planting areas in different regions of Liaoning Province in 1969 and 1976 (a: 1969; b: 1976). Maps generated in ArcGIS 9.3.Full size imageThe occurrence characteristics of single disasters or the risk of yield reduction were analysed in previous studies, but the quantitative effect on rice production was rarely evaluated. Ji et al.26 reported that the delayed cold damage in 1961, 1962, 1969, 1972, 1976, 1989 and 1995 was so severe that there was a large reduction in rice production. In our paper, we examined the occurrence of not just one disaster, i.e. delayed cold damage, over time, but also other types of disasters including SAD and CAD. For example, in 1972 and 1976, the disaster scenario affecting the largest number of stations was TD-1, i.e., both delayed cold damage and frost damage occurred in the growing season of rice. In 1961, the most widespread damage came from a single disaster—frost damage. According to the records35, Liaoning Province experienced frost damage in 1961, 1962, 1969, 1972, 1976 and 1995, and the rice yield was seriously reduced. Most regions of Liaoning Province experienced both delayed cold damage and frost damage in 1976 and 1995. There was a low temperature during the critical period of rice growth (mid-July to mid-August) in 1995. In 1985, the growing season in most areas was characterized by unusually persistent low temperature and little sunshine. These statistics were basically consistent with the conclusion of this study. In the process of rice production, a variety of disasters occurred caused by low temperature, such as delayed cold damage, frost damage and sterile cold damage. More

Fish and experimental protocolThree-hundred-fifty market-size (755–3865 g) common carp Cyprinus carpio were obtained from six sources at various times of year to for effects of variation in rearing conditions. The weight of collected carp corresponded to the weight of carp normally delivered to the market. Fish were obtained from the Faculty of Fisheries and Protection of Waters of the University of South Bohemia in Ceske Budejovice (FFPW USB), Vodnany and the fisheries Chlumec nad Cidlinou, Blatna, Hodonin, Klatovy, Lnare, and Tabor. Ten fish were collected from each fishery at the spring (March/April), summer (June/July), and autumn harvests (October/November) in 2018 and 2019. Carp were transported live to the laboratory of the FFPW, killed by a blow to the head, weighed, measured, and filleted. Two fillets, one with skin removed, from each fish were individually vacuum packed, immediately frozen, and stored at − 32 °C until chemical analysis.Ethics approvalAll the methods used in the present study followed relevant guidelines and regulations. Also, the competent authority (Ethical Committee for the Protection of Animals in Research of the University of South Bohemia, FFPW Vodnany) approved the fish sampling and protocols of the present study and reporting herein follows the recommendations in the ARRIVE guidelines.Chemical analysisSeven-hundred carp fillets were analysed for basic nutritional composition, dry matter, protein, fat, and ash. All samples were homogenized by grinding before analysis.The determination of dry matter followed ISO 1442:1997 Meat and meat products—Determination of moisture content (Reference method)26. The homogenized samples were dried with sand to constant weight at 103 ± 2 °C in a laboratory oven (Memmert UE 500, Memmert GmbH + Co. KG, Germany).The determination of ash was based on the standard ISO 936:1998 Meat and meat products—Determination of total ash27. The homogenized samples were burned in a muffle furnace (Nabertherm A11/HR, Nabertherm GmbH, Germany) at 550 ± 25 °C to a grey-white colour.The determination of total fat was based on the standard ISO 1443:1973 Meat and meat products—Determination of total fat content28. The homogenized samples were hydrolysed by hydrochloric acid, and fat was extracted by light petroleum in SOXTEC 2050 (FOSS Headquarters, Denmark).The determination of nitrogen used the Kjeldahl method based on the standard method ISO 937:1978 Meat and meat products—Determination of nitrogen content (Reference method)29. The homogenized samples were digested by sulphuric acid and a catalyser in a KjelROC Digestor 20 (OPSIS AB, Sweden) digestion unit at 420 ± 10 °C. Organically bound nitrogen was measured on the KJELTEC 8400 with KJELTEC sampler 8420 (FOSS Headquarters, Denmark). Calculation of protein content from nitrogen used the conversion factor for meat of 6.25.All analysis of dry matter, ash, and total fat were performed in duplicate and analysis of nitrogen (protein) was performed in triplicate for each sample.Calculation of fat-free nitrogen (Nff) in g/100 g used the formula24:$$ N_{ff} = frac{{100 times N { }}}{{100 – F { }}}. $$This formula was applied to nitrogen (N) and fat (F) content for all samples, providing a fat-free nitrogen value for each sample.Fish meat content calculated based on nitrogen factor Nf (total fillet) in g/100 g used the formula9:$$ Fish ;content_{Nf} = frac{N times 100}{{N_{f} }}. $$Fish meat content calculated based on fat-free nitrogen factor (Nff) and DCC (defatted carp content) in g/100 g used formulas11:$$ Fishc; content_{Nff} = DCC + F, $$$$ DCC = frac{N times 100}{{N_{ff} }}. $$Statistical analysisKolmogorov–Smirnov and Bartlett’s tests were applied to assess normal distribution data and the homoscedasticity of variance, respectively. A two-way ANOVA and Tukey’s test was conducted to analyse effects of season, weight, fishery, and difference between fillets with and without skin. The significance level was set at p  More

Uganda has been exceptionally successful in scaling-up coverage of LLINs. Following the mass distribution campaigns to deliver free LLINs in 2013–14 and 2017–18, 90 and 83% of households, respectively reported ownership of at least one LLIN7,14. However, despite this success, the burden of malaria remains high in much of the country. Uganda had the third highest number of malaria cases reported in 2019, with reported case incidence increasing since 20142. If Uganda is to achieve the goals established by the World Health Organization’s Global Technical Strategy for malaria including reducing malaria case incidence by at least 90% by 2030 as compared with 201515, additional malaria control measures will be needed. This report highlights the critical role of IRS in substantially reducing the burden of malaria in areas where transmission remains high despite deployment of LLINs. Withdrawing IRS after 5 years of sustained use in three districts in northern Uganda was associated with a more than fivefold increase in malaria cases within 10 months. Restarting IRS with a single round in nine districts in Northern Uganda ~3 years after IRS had been stopped was associated with a transient but important (more than a fivefold) decrease in malaria cases within 8–12 months, returning to pre-IRS levels after 23 months. Initiating and sustaining IRS in five districts in Eastern Uganda was associated with a gradual reduction in malaria cases reaching almost a sevenfold reduction after 4–5 years.Robust evidence supports the widespread use of LLINs for malaria control. In a systematic review of clinical trials conducted between 1987 and 2001, insecticide treated nets reduced all cause child mortality by 17% and the incidence of uncomplicated P. falciparum malaria by almost half16. However, there is concern that the effectiveness of LLINs may be diminishing due to widespread resistance to pyrethroids which until recently were the only class of insecticides approved for LLINs. Similar to many other African countries, high-level resistance to pyrethroids among the principle Anopheles vectors has been reported recently throughout Uganda17,18,19. In addition, behavioral changes in vector biting activity following the introduction of LLINs have been reported which could present new challenges for malaria control20,21,22. Finally, the effectiveness of LLINs may be further compromised by poor adherence and waning coverage in the setting of free distribution campaigns done intermittently. In Uganda, less than 18% of households reported adequate coverage (defined as at least one LLIN per two residents) 3 years after the 2013–14 distribution campaign23 and adequate coverage decreased from 71% to 51% between 6 and 18 months following the 2017–18 distribution campaign24. Although the World Health Organization recommends mass distribution campaigns every 3 years, mounting evidence suggests that LLINs should be distributed more frequently to sustain high coverage25,26,27,28,29,30,31.Given concerns about the current effectiveness of pyrethroid-based LLINs and the persistently high burden of malaria despite aggressive scale-up of LLINs in countries like Uganda, additional malaria control measures are needed. IRS is an attractive option. Historically, IRS programs were used to dramatically reduce and even eliminate malaria in many parts of the world. Thus, while there is some evidence for the impact of IRS in the absence of LLINs32, it is surprising that the evidence base from contemporary controlled trials on the impact of adding IRS to LLINs for vector control is limited. A recent systematic review of cluster randomized controlled trials conducted in sub-Saharan Africa since 2008, reported that adding IRS using a “pyrethroid-like” insecticide to LLINs did not provide any benefits, while adding IRS with a “non-pyrethroid-like” insecticide produced mixed results5. Among the four trials comparing IRS plus LLINs with LLINs alone, three evaluated IRS with a carbamate (bendiocarb) and one evaluated a long-lasting organophosphate, pirimiphos-methyl (Actellic 300CS®)33,34,35,36. Only two trials (both using bendiocarb) assessed malaria incidence; one from Sudan found a 35% reduction when adding IRS to LLINs34, while another from Benin found no benefit of adding IRS33. All four trials assessed parasite prevalence, with an overall non-significant trend towards a lower prevalence when adding IRS to LLINs (RR = 0.67, 95% CI 0.35–1.28)5. However, when the analyses were restricted to include only the two studies with LLIN usage over 50%, adding IRS reduced parasite prevalence by over 50% (RR = 0.47, 95% CI 0.33–0.67)5. Of note, none of the trials that evaluated the impact of adding IRS with a “non-pyrethroid-like” insecticide assessed outcomes beyond 2 years. More recently, a number of observational studies have reported benefits of using IRS with pirimiphos-methyl (Actellic 300CS®). In the Mopti Region of Mali, delivery of a single round of IRS with Actellic 300CS® was associated with a 42% decrease in the peak incidence of laboratory-confirmed malaria cases reported at public health facilities37. In the Koulikoro Region of Mali, villages that received a single round of IRS with Actellic 300CS® combined with LLINs observed a greater than 50% decrease in the incidence of malaria compared to villages that only received LLINs38. In the Northern Region of Ghana, districts that received IRS with Actellic 300CS® reported 26–58% fewer cases of laboratory-confirmed malaria cases reported at public health facilities over a 2-year period, compared to districts that did not receive IRS39. In Northern Zambia, implementation of IRS with Actellic 300CS® targeting only high burden areas over a 3 year period was associated with a 25% decline in parasite prevalence during the rainy season, but no decline during the dry season40. In Western Kenya, the introduction of a single round of IRS with Actellic 300CS® was associated with a 44–65% decrease in district level malaria case counts over a 10 month period compared to pre-IRS levels41. In addition, several recent reports have documented dramatic resurgences of malaria following the withdrawal of IRS with bendiocarb in Benin42, and the withdrawal of IRS with Actellic 300CS® in Mali and Ghana37,39.The results from this study provides additional support for the critical role IRS can play in reducing the burden of malaria in African countries with high LLINs coverage. A strength of the study was its use of a large, rigorously collected dataset. Data were collected over nearly 7 years through an enhanced health facility-based surveillance system covering 14 districts in Uganda where IRS was being withdrawn, re-started, and initiated. This enhanced surveillance system facilitated laboratory testing and provided prospectively collected, individual-level data, allowing for analyses of quantitative changes in laboratory-confirmed cases of malaria over time, controlling for temporal changes in rainfall, seasonal effects, diagnostic practices, and health seeking behavior. Previous work by our group documented a marked decrease in malaria TPRs after 4 years of sustained IRS with bendiocarb in one district of Northern Uganda followed by a rapid resurgence over an 18-month period after IRS was withdrawn11. In this study we expand on these findings by including data from three districts and covering a 31-month period following the withdrawal of IRS. We were able to quantify more than a fivefold increase in malaria cases which was sustained over the 10–31 months following the withdrawal of IRS. This marked resurgence occurred despite the fact the first universal LLIN distribution campaign was timed to occur right after IRS was withdrawn. Given the dramatic nature of the resurgence, the Ugandan government was able to procure funding for a single round of IRS with Actellic 300CS® ~3 years after IRS was withdrawn in 10 districts of Northern Uganda. In this study, we assessed the impact of this single round in nine of these districts. This single round was associated with over a fivefold decrease in malaria cases after 8–12 months, with malaria cases returning to pre-IRS levels after almost 2 years. These data suggest that IRS with longer-acting formulations such as Actellic 300CS® administered every 2 years could be considered as a strategy for mitigating the risk of resurgence following sustained IRS and/or enabling countries to expand coverage when resources are limited, but formal assessment and a cost-effectiveness analyses are needed. This study also evaluated the impact of 5 years of sustained IRS in five districts of Eastern Uganda, starting first with bendiocarb and then switching to Actellic 300CS® after 18 months. Rounds of IRS were initially associated with marked decreases in malaria cases followed by peaks before subsequent rounds until the fourth and fifth years after IRS was initiated when there was a sustained decrease of almost sevenfold compared to pre-IRS level. Given the before-and-after nature of our study design, it is not clear whether the maximum sustained benefits of IRS seen after 4–5 years were due to the cumulative effect of multiple rounds of IRS, the switch from bendiocarb to Actellic 300CS®, improvements in implementation (although campaigns occurred regularly and coverage was universally high across rounds, see Supplementary Table 4), the second universal LLIN distribution campaign which occurred in this area in 2017, and/or other factors.This study had several limitations. First, we used an observational study design, with measures of impact based on comparisons made before-and-after key changes in IRS policy. Although cluster randomized controlled trials are the gold standard study design for estimating the impact of IRS, it could be argued that withholding IRS would be unethical, given what is known about its impact in Uganda. Second, our estimates of impact could have been confounded by secular trends in factors not accounted for in our analyses. However, we feel that our overall conclusions are robust given the large amount of data available from multiple sites over an extended period with multiple complementary objectives providing consistent findings. Third, we could not assess the impact of IRS independent of LLIN use and did not have access to measures of IRS or LLIN coverage from our study populations. It is possible that some of the impacts we observed were from LLIN distributions in combination with IRS campaigns. However, we were able to provide a “real world” assessment of IRS in a setting where LLIN use is strongly supported by repeated universal distribution campaigns that are becoming increasingly common in sub-Saharan Africa. Similarly, we cannot draw conclusions on the impact of different IRS compounds given all sites received the same formulations consecutively. The results from Objective 3 indicate that malaria incidence dropped substantially in the years that districts stopped receiving bendiocarb and began receiving Actellic 300CS®. However, we cannot conclude whether this reduction was a result of this change or rather the cumulative impact of sustained IRS campaigns, as it has been suggested that in very high transmission settings, several years of IRS may be needed to maximize impact on measures of morbidity.43,44 Finally, our study outcome was limited to case counts of laboratory-confirmed malaria captured at health facilities. Thus, we were unable to measure the impact of IRS on other important indicators such as measures of vector distribution, parasite prevalence, or mortality.There is a growing body of evidence that combining LLINs with IRS using “non-pyrethroid-like” insecticides, especially the long acting organophosphate Actellic 300CS®, is highly effective at reducing the burden of malaria in Uganda, and elsewhere in Africa. Despite these encouraging findings, IRS coverage in Africa has been moving in the wrong direction. The proportion of those at risk protected by IRS in Africa peaked at just over 10% in 2010. However, the spread of pyrethroid resistance has led many control programs to switch to more expensive formulations resulting in a 53% decrease in the number of houses sprayed between years of peak coverage and 2015 across 18 countries supported by the US President’s Malaria Initiative45 and an overall reduction in the proportion protected by IRS in Africa to less than 2% in 20192. Given the lack of recent progress in reducing the global burden of malaria coupled with challenges in funding, renewed commitments are needed to address the “high burden to high impact” approach now being advocated by the World Health Organization2. IRS is a widely available tool that could be scaled up, however demands currently exceed the availability of resources. Additional work is needed to optimize the use of IRS, prevent further spread of insecticide resistance, and better evaluate the cost-effectiveness of IRS in the context of other control interventions. More

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