Philippa Kaur

The Aedes aegypti mosquito’s taste for human blood has been linked to exposure to dense settlements dotted with sources of water. Credit: Getty

Ecology
23 July 2020

Population density and water storage created a new ecological niche for the disease-carrying insects.

As humans crowd into cities where standing water abounds, even in the driest months, we become an ever-more-tempting target for bloodsuckers.
Only a few of the more than 3,000 species of mosquito specifically seek out blood from humans — but those few are enough to spread diseases that affect some 100 million people every year. To shed light on the evolutionary origin of insects’ taste for humans, Lindy McBride and Noah Rose at Princeton University in New Jersey and their colleagues captured Aedes aegypti mosquitoes at 27 sites across the insects’ ancestral range in sub-Saharan Africa.
The researchers placed hungry females in boxes with two exits: one offering the smell of a human and the other the smell of either a living guinea pig (Cavia porcellus) or a button quail (Coturnix coturnix). Compared with mosquitoes that were indifferent to human blood, those that sought it out were more likely to hail from areas with dense human populations and long, hot, dry seasons.
The researchers suspect that water stored by humans has become a key breeding location in a landscape with little standing water. Their genetic analysis suggests that the taste for humans evolved just once. More

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Cereal crop yield is determined by three yield components, namely, the number of grains per unit of land area, grain weight, and the ratio of filled grains. In rice, single grain weight is genetically constant, irrespective of growth environments5. This character of rice is largely different from that of other cereal crops. For example, in wheat, single grain weight varies depending on growth conditions23,24, and a negative correlation is frequently observed between grain weight and number23. Therefore, in rice, an important target for achieving a high yield is to increase the number of grains with a high ratio of ripened grains. At the same time, this means that genetic enlargement of grain size has another great impact on increase in yields in rice. However, the relationship between grain size and yield has remained uncertain. Meanwhile, we found that a large-grain cultivar, Akita 63, exhibited a high yield (983 g m−2 of brown rice yield = 1,230 g m−2 of rough rice)10. The single grain weight of Akita 63 was 35% larger and the yield was 20–60% higher than that of the reference cultivars. According to our analysis, in spite of the large grain, the number of grains of Akita 63 did not differ from the common japonica cultivars at any crop-N content10,25,26. Therefore, a large grain size without reduction of the number of grains directly enhances the sink capacity, leading to high yield potential. However, although Oochikara, the maternal cultivar of Akita 63, has 80% larger grains, the yield was not necessarily high (560 g m−2 of brown rice yield9,27). The results in Fig. 2 clearly show that a large grain in Oochikara is associated with reduction of the number of grains and consequently, Oochikara has the same yield as that of reference cultivars with normal grains.
Among the fine-mapped major genes determining grain size, we examined the large-grain alleles of GS3, GW2, TGW6 and qSW5 in the present work. The results show that Akita 63 and Oochikara have the large-grain alleles of GS3 and qSW5 (Fig. 3; Supplementary Fig. S3). Lu et al.28 surveyed natural variation and artificial selection in major genes determining grain size among 127 varieties of rice cultivars, and reported that GS3 and qSW5 are major genes controlling grain size and that japonica cultivars with a nonfunctional GS3 and qSW5 genotype combination show the largest grain weight. Regarding this point, our results clearly coincide with their conclusion. However, as qSW5 with a 1,212-bp deletion was also found for Akita 39 with normal grains, the effects of this allele on single grain weight may be limited. Actually, functional qSW5 mainly originated from indica cultivars and leads to enlarged grain-length. The qSW5 with a 1,212-bp deletion mainly originated from japonica cultivars and has an effect on the enlargement of grain width14,29.
The single grain weight of Oochikara is appreciably greater than that of Akita 63 (Table 1; Fig. 2C). Nevertheless, we did not find a difference in the large-grain alleles between them in our investigation. This indicates that Oochikara has other genes/alleles contributing to large grain size. At the same time, our results also indicate the possibility that Oochikara has other genes/alleles which function as a negative regulator(s) of the number of grains or no genes/alleles which function as a positive regulator(s). Although it is not known whether a trade-off between single grain weight and the number of grains in Oochikara is determined by the same gene(s), the breeding from Oochikara to Akita 63 overcomes such a trade-off trait. This means that the large-grain allele of GS3 and qSW5 combination does not affect the number of grains and can be one of major determinants for a further increase in yield. These two genes are widely observed in Oryza sativa species14,21,28, and GS3 has stronger effects on grain weight in japonica cultivars28. Thus, although there still remains a possibility that other unidentified genes also come into play, it is suggested that the nonfunctional GS3 and qSW5 combination mainly contributes to the large grain size of Akita 63.
The 4-year average yields of Akita 63 were about 20% higher than those of the parents Oochikara and Akita 39, and 40–60% higher than those of the reference cultivar, Akitakomachi (Table 1). These results indicate that when Akita 63 was compared with Akitakomachi, the large grain of Akita 63 is not the sole determinant for high yield. Another factor was N uptake capacity. For the same N application, total crop-N content at the harvest stage tended to be higher in Akita 63 than in Akitakomachi (Table 1). Actually a significant difference in crop N content between them was found at an application of 0 g and 6 g N m−2 (Supplementary Table S2). This indicates that Akita 63 has superior N uptake capacity. This trait may have been inherited from the parental lines, Oochikara and Akita 39.
As already discussed above, to achieve a high yield, it is important to enhance the number of grains with a high ratio of ripened grains. In many cases, however, a negative correlation between the number of grains and the ratio of filled grains is frequently observed, especially when rice is cultivated with heavy N application30. This trend was clearly found for our data in Table 1. The ratio of filled grains to total grains decreased with increasing N application in all cultivars. Among them, the ratio of filled grains of Akita 63 was the lowest at an application of 13 g N m−2 for 3 years (Supplementary Table S2). As we previously pointed out, we think that this is caused by source limitation relative to yield potential26. Although the number of grains was linearly correlated with crop-N content passing through the origin, total biomass was curvilinealy correlated (Fig. 2A,B). Of course, the curvilinear correlation between biomass and crop N content was simply caused by a decline in canopy photosynthesis due to an excessive leaf area that may cause mutual shading at high N application. Grain mass (rough rice) in Akita 63 reached 60% of the total aboveground biomass at harvest, while that of other varieties reached about 45% (Table 1; Fig. 2B,D). This is the highest level of all cereal crops31,32. Thus, yield potential of high-yielding cultivars such as Akita 63 may surpass source capacity, leading to a decline in the ratio of filled grain. This indicates that a further increase in sink capacity is no longer effective and that improvements in source capacity will be essential for maintenance of high ratio of filled grains.
Many recent trials conducted at free-air CO2 enrichment (FACE) facilities have shown a highly positive correlation between enhanced photosynthesis, biomass and yield32,33. These results indicate that enhancement of photosynthesis by elevated [CO2] directly leads to an increase in yield when genetic factors besides photosynthesis are not altered32. Therefore, to examine the effects of source enhancement on yield, we conducted FACE experiments on several rice cultivars, including Akita 6334. The results showed that Akita 63 had the greatest enhancement of yield by CO2 enrichment among all rice cultivars grown at FACE facilities. Furthermore, the absolute yield of Akita 63 was also highest and the ratio of filled grains remained at higher level. These results indicate that enhancement of photosynthesis is of the greatest importance for a further increase in the yield of high-yielding cultivars with a large sink size. While there has been a dispute as to whether photosynthesis improvement leads to an increase in cereal crop yields35, we have actually shown that an increase in photosynthesis by overproducing Rubisco results in increased rice yields under field conditions36,37 Thus, improving photosynthesis is a possible target for realizing a further increase in yield of today’s high-yielding cultivars. More

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A number of studies have assessed meat sourced from wild deer originating from various countries (i.e.11,12 from Poland or13 from South Africa) or from farmed deer (i.e.14 from Czech Republic11,15, from Poland16, from Italy and17from SP). Studies have also examined the main marketed deer meats: meat from wild SP deer1,2,10 and farmed NZ ones6,7,8,18. Despite the number of studies cited below which assess meat quality from different countries or different types of breeding/killing (wild or farmed, stressful or sudden death), they have been undertaken by different research teams, using different scientific equipment and protocols. None of these studies have assessed meat samples from different countries with the same scientific equipment, reagents, and staff. It is therefore likely that some of the anomalies found through comparison of the available literature may actually be caused by methodological rather than regional differences per se.
The quality of wild game meat depends on the hunting method19 and on the hunting season3. Wild deer shot with a projectile (e.g. a bullet) are not exsanguinated immediately after their death and often several hours elapse between death and dressing. Consequently, carcasses are often processed once rigor mortis has set in, which affects meat characteristics. In principle, a low stress death by stalking should result in a better quality of meat and, in fact, meat-processing companies pay better prices for meat from stalked animals than for stress hunted meat, as evidenced by game estate owners and personal interviews. Studies have assessed several types of hunted and farmed venison, but none included stressful pursuit by dogs. Our results show for the first time that the mixed effects of hunting type-season resulted in some differences in meat quality (pH, cooking losses), but surprisingly not in tenderness (shear force).
Recently, Stanisz et al.3 have not observed differences for body weight with hunting season of hunt-harvested does. However, the authors observed a higher technological quality for venison obtained in winter showing compared to that harvested in summer. In fact, it was observed lower purge loss in vacuum, drip loss, free water, free water share in total water, and water loss during roasting. In addition, venison obtained in winter showed a greater brightness and a reduced redness comparing to venison obtained in summer. Colour traits and water retention capacity determine the meat shelf life and its suitability for storage in vacuum packaging.
Values obtained in the current study for pH of meat were similar to those observed previously for meat from NZ farmed deer7,9 and for SP stressed, hunted red deer2,10. However, no data has been found to compare the average values of pH obtained in SP from stalked deer. Our results show that pH values were similar for wild deer in SP (pooled values) and farmed deer in NZ, but meat from stalked deer had the lowest values. This is not surprising as transporting deer to the abattoir also involves stress, and furthermore, recently, Gentsch et al.20 have observed that cortisol levels for stalked deer were much lower than those for deer hunted with dogs in driven hunts (21.8 vs. 66.1 nmol/L, respectively). With regard to the effect of seasons, meat hunted in winter had a higher pH than meat hunted in summer. However, recently, Stanisz et al.3 observed that the pH of the muscles Longissimus lumborum measured 24 h post-mortem was 0.22 units higher in the summer season, compared to the winter season. This is consistent with current results regarding colour and literature reports that pre-harvest stress affects the degree of bleed, leading to an increase in the level of oxymyoglobin21 and confirming the influence of hunting type on meat colour4. According to results obtained by Stanisz et al.3, venison sourced from does shot in summer was redder and had a greater chroma compared to venison obtained in winter. In our study, values obtained for colour traits were similar to those obtained for NZ farmed6,9 and for SP stress hunted10 deer meat.
The IMF content was similar to the values previously reported for NZ farmed deer8,9 and for autumn–winter hunted deer from the same region of SP1,10. However, the most interesting information came from slight differences in IMF between seasons/type of hunting in wild SP: meat from summer stalking had an average IMF content of 0.90%, a value significantly higher than that for winter chased meat (0.11%). This may be only a seasonal difference (unlikely to be attributable to stress at death) because of increased grazing available during spring and summer for the deer. Thus, it is well established that body condition of deer improves, mainly in gaining fat and body weight, during spring and summer. In contrast, loss condition, involving loss of body fat, is higher in autumn and winter22. In addition, deer lose weight in autumn because feed intake decreases considerably during the rut23. Confirming this hypothesis, Serrano et al.2 have found higher contents of IMF and cholesterol in the loin of deer hunted in driven hunts in autumn compared to the loin from deer hunted in driven hunts in winter. The cooking losses of meat were similar to values reported previously for NZ farmed deer7 and for stressed deer hunted in driven hunts in SP10, although there was an effect of stress/season with values higher for stalking-summer. The effect of season on cooking losses has been previously described3. However, the cause of the differences observed in current study is likely to be due to the level of stress at slaughter, corroborated by Cifuni et al.4, who found that meat from culled (selective hunting) deer produced a greater degree of water loss during cooking than meat from deer slaughtered in driven hunts.
In general, values reported for shear force present a high variability2,12,13. Values in the current trial were higher than those reported previously for deer meat1,8,9,10. Differences among authors might be due to a range of interrelated factors, including pH, amount of connective tissue, IMF content, proteolytic enzyme activity and age of the animal13. Differences observed for the shear force between countries of origin (58.7% higher for meat from SP than for NZ meat) are not caused by stress at death: no differences were observed regarding the shear force of SP meat by hunting type/season. In fact, Stanisz et al.3 concluded that a greater impact of season could be evidenced using biting measures (Volodkevich Bite Jaws, test speed 2 mm/s; strain 100%; force 5 g at 24 h and 14 days post-mortem) compared to Warner–Bratzler measures. However, biting measures were not included in the current study. Further studies may be needed to conclude whether there is an effect of season compensating for the apparent effect of stress that yields non-significant results.
In general, the country of origin did not influence the total content of SFA or PUFA and a trend was only detected in the increase of the MUFA content of NZ meat. However, meat from NZ farms had higher content of n-3 FA and long chain n-3 PUFA, less content of n-6 FA and, in consequence, a lower n-6/n-3 ratio than SP wild meat. Values obtained for the n-6/n-3 ratio (ranging from 1.22 to 3.71) correspond with those reported by other authors for deer meat8,14. In any case, the average n-6/n-3 ratio for meat from both countries of origin was lower than 4, as recommended by WHO/FAO24.
The FA profile differed for meat samples obtained by different hunting types/season. The differences observed in the current study between hunting types for the FA profile are likely to be caused by the effects of the diet FA profiles seasonal changes on ruminant products25. Thus, the main FA in winter driven hunt meat were PUFA, followed by SFA and MUFA. In summer stalked deer and farmed venison, the main FA were SFA followed by MUFA and PUFA. No differences were observed for the contents of n-3 FA. Consequently, the fat of animals sourced from driven hunts in winter showed a higher PUFA/SFA ratio, n-6 FA content and n-6/n-3 ratio than the fat of summer stalked deer.
In general, the AA profile obtained in this study corresponds with that reported for meat from red deer, as previously indicated by Lorenzo et al.1. Interesting effects of country of origin and hunting type on AA profile of deer meat were observed in the current study. The SP wild meat had higher contents of total, essential and non-essential AA than NZ farm meat. Moreover, meat collected from summer-stalked deer presented a higher ratio for the essential/non-essential AA than meat collected from deer slaughtered in winter driven hunts. However, authors have not found previous studies comparing the AA profile of meat from SP and NZ deer slaughtered by different methods to compare with current results. It is postulated, therefore, that AA differences are attributable to seasonal effect, not the level of stress at death.
Because mineral composition presented in deer meat is closely related to the natural environment as they graze and browse26, differences found in mineral content in our study seem to depend on season and diet composition rather than on level of stress at death. Therefore, the differences in fodder available in spring and summer as compared to winter, as well as the growth of leaves in deciduous trees and shrubs, may explain some of the differences in the mineral profile observed between meats sourced from winter driven hunts and summer stalking analysed in the current study. Actually, Estévez et al.27 found seasonal differences in the mineral content of plants consumed by deer in SP. It is highly unlikely that mineral differences between both meats are due to the level of stress at death: the only difference that could be expected (Na content) due to increased sweating resulting from the chase and stress, was actually the reverse of what was expected (greater in animals killed in winter driven hunts). These results suggest that the difference was due to a lower level of Na in plants in summer.
One of the most significant effects in the seasonal differences found in the current study may not be related to mineral content of the diet, but to another very interesting and unique physiological characteristic of male deer (the sex examined in this study): cyclic physiologic osteoporosis. This effect is caused by rapid growth of the antlers (more than 1 cm/day), causing a depletion of mineral stores in certain bones in order to transfer the material to antlers28. Because minerals are blood borne, it is not surprising that they may also affect the mineral composition of muscles. This could explain why meat from osteoporotic deer (summer) had less than half the content of Zn, which forms part of alkaline phosphatase, the enzyme needed to deposit Ca in bone tissue29. For the same reason, it may explain the differences found for P and Ca contents (despite Ca is more stable in blood), and even for Mg (which can substitute Ca in the hydroxyapatite forming the antlers and bones). More

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Melita Samoilys

HJR Reefscaping, Boquerón, Puerto Rico
Michelle Schärer-Umpierre

SalvageBlue, Kingstown, Saint Vincent and the Grenadines
Nikola Simpson

School of Natural and Computational Sciences, Massey University, Auckland, New Zealand
Adam N. H. Smith

Indo Ocean Project, PT Nomads Diving Bali, Nusa Penida, Indonesia
Lauren Sparks

Manchester Metropolitan University, Manchester, UK
Akshay Tanna & Alexandra M. Watts

Reef Check Dominican Republic, Santo Domingo, Dominican Republic
Rubén Torres

Institut de Recherche pour le Développement, UMR ENTROPIE (IRD-UR-UNC-CNRS-IFREMER), Nouméa, New Caledonia
Laurent Vigliola

Secretariat of the Pacific Regional, Environment Programme, Apia, Samoa
Juney Ward

Department of Life Science, Tunghai University, Taichung, Taiwan
Colin Wen

School of Environmental and Forest Sciences, University of Washington, Seattle, WA, USA
Aaron J. Wirsing

Corales del Rosario and San Bernardo National Natural Park, GIBEAM Research Group, Universidad del Sinú, Cartagena, Colombia
Esteban Zarza-Gonzâlez

D. Chapman and M. Heithaus conceived the study with assistance from M. Heupel, C.A.S., M.M., E.H. and M.A.M. D. Chapman, M. Heithaus, M. Heupel, C.A.S., M.M. and E.H. directed fieldwork run by J.G., J.K., M.E.B., L.M.C.-R., C.W.S., K.I.F., J.V.-A., G.C. and C.S.S. Database management was by T. Gorham. M.A.M. and D. Chapman drafted the manuscript, with help from M. Heithaus, M. Heupel, C.A.S., J.E.M.C., M.M., E.H., J.G., J.K., M.E.B., L.M.C.-R., C.W.S., C.S.S., M.J.R., V.U. and T. Gorham. All other authors contributed equally, made substantial contributions to data collection, provided input and approved the text in the manuscript. More

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