Ecology

1.
Ruisi, P. et al. Long-term effects of no tillage treatment on soil N availability, N uptake, and 15N-fertilizer recovery of durum wheat differ in relation to crop sequence. Field Crop. Res. 189, 51–58 (2016).
Article  Google Scholar 
2.
Díaz-Zorita, M., Duarte, G. A. & Grove, J. H. A review of no-till systems and soil management for sustainable crop production in the subhumid and semiarid Pampas of Argentina. Soil Tillage Res. 65, 1–18 (2002).
Article  Google Scholar 

3.
Díaz-Zorita, M. Cambios en el uso de pesticidas y fertilizantes. Cienc. hoy 15, 28–29 (2005).
Google Scholar 

4.
Iturri, L. A. & Buschiazzo, D. E. Light acidification in N-fertilized loess soils along a climosequence affected chemical and mineralogical properties in the short-term. CATENA 139, 92–98 (2016).
CAS  Article  Google Scholar 

5.
Finck, A. Fertilizer and Fertilization. Basics and Instructions for Fertilizing Crops (FAO, Rome, 1979).
Google Scholar 

6.
Smil, V. Nitrogen and food production: proteins for human diets. AMBIO A J. Hum. Environ. 31, 126–131 (2002).
Article  Google Scholar 

7.
Rice, K. C. & Herman, J. S. Acidification of Earth: an assessment across mechanisms and scales. Appl. Geochem. 27, 1–14 (2012).
CAS  Article  Google Scholar 

8.
Zhalnina, K. et al. Soil pH determines microbial diversity and composition in the park grass experiment. Microb. Ecol. 69, 395–406 (2015).
CAS  Article  PubMed  PubMed Central  Google Scholar 

9.
Kemmitt, S. J., Wright, D. & Jones, D. L. Soil acidification used as a management strategy to reduce nitrate losses from agricultural land. Soil Biol. Biochem. 37, 867–875 (2005).
CAS  Article  Google Scholar 

10.
Zeng, J. et al. Nitrogen fertilization directly affects soil bacterial diversity and indirectly affects bacterial community composition. Soil Biol. Biochem. 92, 41–49 (2016).
CAS  Article  Google Scholar 

11.
Tian, D. & Niu, S. A global analysis of soil acidification caused by nitrogen addition. Environ. Res. Lett. 10, 24019 (2015).
Article  CAS  Google Scholar 

12.
Binkley, D. & Richter, D. Nutrient cycles and H+ budgets of forest ecosystems. In Advances in Ecological Research16 (eds MacFayden, A. & Ford, E. D.) 1–51 (Elsevier, Amsterdam, 1987).
Google Scholar 

13.
Bowman, W. D., Cleveland, C. C., Halada, Ĺ, Hreško, J. & Baron, J. S. Negative impact of nitrogen deposition on soil buffering capacity. Nat. Geosci. 1, 767 (2008).
ADS  CAS  Article  Google Scholar 

14.
Chadwick, O. A. & Chorover, J. The chemistry of pedogenic thresholds. Geoderma 100, 767–770 (2001).
Article  Google Scholar 

15.
Dubiková, M., Cambier, P., Šucha, V. & Čaplovic̆ová, M. Experimental soil acidification. Appl. Geochem. 17, 245–257 (2002).
Article  Google Scholar 

16.
Watmough, S. A., Eimers, M. C. & Dillon, P. J. Manganese cycling in central Ontario forests: response to soil acidification. Appl. Geochem. 22, 1241–1247 (2007).
CAS  Article  Google Scholar 

17.
Neves, N. R. et al. Photosynthesis and oxidative stress in the restinga plant species Eugenia uniflora L. exposed to simulated acid rain and iron ore dust deposition: potential use in environmental risk assessment. Sci. Total Environ. 407, 3740–3745 (2009).
ADS  CAS  Article  PubMed  Google Scholar 

18.
Moir, J., Jordan, P., Moot, D. & Lucas, R. Phosphorus response and optimum pH ranges of twelve pasture legumes grown in an acid upland New Zealand soil under glasshouse conditions. J. Soil Sci. Plant Nutr. 16, 438–460 (2016).
CAS  Google Scholar 

19.
Huang, Z. et al. Long-term nitrogen deposition linked to reduced water use efficiency in forests with low phosphorus availability. New Phytol. 210, 431–442 (2016).
CAS  Article  PubMed  Google Scholar 

20.
Tang, X. et al. Effects of inorganic and organic amendments on the uptake of lead and trace elements by Brassica chinensis grown in an acidic red soil. Chemosphere 119, 177–183 (2015).
ADS  CAS  Article  PubMed  Google Scholar 

21.
Redel, Y. et al. Assessment of phosphorus status influenced by Al and Fe compounds in volcanic grassland soils. J. Soil Sci. Plant Nutr. 16, 490–506 (2016).
CAS  Google Scholar 

22.
Mitran, T. & Mani, P. K. Effect of organic amendments on rice yield trend, phosphorus use efficiency, uptake, and apparent balance in soil under long-term rice-wheat rotation. J. Plant Nutr. 40, 1312–1322 (2017).
CAS  Article  Google Scholar 

23.
Xin, X. et al. Yield, phosphorus use efficiency and balance response to substituting long-term chemical fertilizer use with organic manure in a wheat-maize system. Field Crop. Res. 208, 27–33 (2017).
Article  Google Scholar 

24.
Qaswar, M. et al. Partial substitution of chemical fertilizers with organic amendments increased rice yield by changing phosphorus fractions and improving phosphatase activities in fluvo-aquic soil. J. Soils Sediments 20, 1–12 (2019).
Google Scholar 

25.
Ahmed, W. et al. Changes in phosphorus fractions associated with soil chemical properties under long-term organic and inorganic fertilization in paddy soils of southern China. PLoS ONE 14, e0216881 (2019).
Article  PubMed  PubMed Central  Google Scholar 

26.
Simonsson, M. et al. Pools and solubility of soil phosphorus as affected by liming in long-term agricultural field experiments. Geoderma 315, 208–219 (2018).
ADS  CAS  Article  Google Scholar 

27.
Holland, J. E., White, P. J., Glendining, M. J., Goulding, K. W. T. & McGrath, S. P. Yield responses of arable crops to liming—an evaluation of relationships between yields and soil pH from a long-term liming experiment. Eur. J. Agron. 105, 176–188 (2019).
CAS  Article  PubMed  PubMed Central  Google Scholar 

28.
Weng, L., Vega, F. A. & Van Riemsdijk, W. H. Competitive and synergistic effects in pH dependent phosphate adsorption in soils: LCD modeling. Environ. Sci. Technol. 45, 8420–8428 (2011).
ADS  CAS  Article  PubMed  Google Scholar 

29.
Eriksson, A. K., Hesterberg, D., Klysubun, W. & Gustafsson, J. P. Phosphorus dynamics in Swedish agricultural soils as influenced by fertilization and mineralogical properties: insights gained from batch experiments and XANES spectroscopy. Sci. Total Environ. 566, 1410–1419 (2016).
ADS  Article  CAS  PubMed  Google Scholar 

30.
Murrmann, R. P. & Peech, M. Effect of pH on labile and soluble phosphate in soils 1. Soil Sci. Soc. Am. J. 33, 205–210 (1969).
ADS  CAS  Article  Google Scholar 

31.
Veith, J. A. & Sposito, G. Reactions of aluminosilicates, aluminum hydrous oxides, and aluminum oxide with o-phosphate: the formation of X-ray amorphous analogs of variscite and montebrasite 1. Soil Sci. Soc. Am. J. 41, 870–876 (1977).
ADS  CAS  Article  Google Scholar 

32.
Stevens, R. L. & Bayard, E. Clay mineralogy of agricultural soils (Ap horizon) in Västergötland, SW Sweden. GFF 116, 87–91 (1994).
Article  Google Scholar 

33.
Cabrera, F., Madrid, L. & De Arambarri, P. Adsorption of phosphate by various oxides: theoretical treatment of the adsorption envelope. J. Soil Sci. 28, 306–313 (1977).
CAS  Article  Google Scholar 

34.
Zhang, H., Bo-ren, W., Ming-gang, X. U. & Ting-lu, F. A. N. Crop yield and soil responses to long-term fertilization on a red soil in Southern China. Pedosph. Int. J. 19, 199–207 (2009).
CAS  Article  Google Scholar 

35.
Cai, Z. et al. Intensified soil acidification from chemical N fertilization and prevention by manure in an 18-year field experiment in the red soil of southern China. J. Soils Sediments 15, 260–270 (2015).
CAS  Article  Google Scholar 

36.
Qaswar, M. et al. Yield sustainability, soil organic carbon sequestration and nutrients balance under long-term combined application of manure and inorganic fertilizers in acidic paddy soil. Soil Tillage Res. 198, 104569 (2020).
Article  Google Scholar 

37.
Fang, Y. et al. Nitrogen deposition and forest nitrogen cycling along an urban–rural transect in southern China. Glob. Change Biol. 17, 872–885 (2011).
ADS  Article  Google Scholar 

38.
Liu, L., Zhang, X., Wang, S., Zhang, W. & Lu, X. Bulk sulfur (S) deposition in China. Atmos. Environ. 135, 41–49 (2016).
ADS  CAS  Article  Google Scholar 

39.
Jia, Y. et al. Spatial and decadal variations in inorganic nitrogen wet deposition in China induced by human activity. Sci. Rep. 4, 3763 (2014).
Article  CAS  PubMed  PubMed Central  Google Scholar 

40.
FAO. World Reference Base for Soil Resources 2014: International soil classification systems for naming soils and creating legends for soil maps (Updated 2015). World Soil Resources Reports No. 106 (2014).

41.
Baxter, S. World reference base for soil resources. Exp. Agric. 43, 264 (2007).
Article  Google Scholar 

42.
Hurlbert, S. H. Pseudoreplication and the design of ecological field experiments. Ecol. Monogr. 54, 187–211 (1984).
Article  Google Scholar 

43.
Guo, Y. et al. Long-term grazing affects relationships between nitrogen form uptake and biomass of alpine meadow plants. Plant Ecol. 218, 1035–1045 (2017).
Article  Google Scholar 

44.
Zhou, H. et al. Stability of alpine meadow ecosystem on the Qinghai-Tibetan Plateau. Chin. Sci. Bull. 51, 320–327 (2006).
Article  Google Scholar 

45.
Nelson, D. W. & Sommers, L. Total carbon, organic carbon, and organic matter. Methods Soil Anal. Part 2 Chem. Microbiol. Prop. 9, 539–579 (1982).
Google Scholar 

46.
Pages, A. L., Miller, R. H. & Dennis, R. K. Methods of Soil Analysis. Part 2 Chemical Methods (Soil Science Society of America Inc., Madison, 1982).
Google Scholar 

47.
Black, C. A. Methods of Soil Analysis Part II. Chemical and Microbiological Properties (American Society of Agriculture, St. Joseph, 1965).
Google Scholar 

48.
Murphy, J. & Riley, J. P. A modified single solution method for the determination of phosphate in natural waters. Anal. Chim. Acta 27, 31–36 (1964).
Article  Google Scholar 

49.
Knudsen, D., Peterson, G. A. & Pratt, P. F. Lithium, sodium, and potassium. In Methods of Soil Analysis. Part 2. Chemical and microbiological properties (ed. Norman, A. G.) 225–246 (American Society of Agronomy Soil Science Society of America, Madison, 1982).
Google Scholar 

50.
Lu, R. K. Analytical Methods of Soil Agricultural Chemistry (China Agricultural Science and Technology Press, Beijing, 2000).
Google Scholar 

51.
Pavinato, P. S., Rodrigues, M., Soltangheisi, A., Sartor, L. R. & Withers, P. J. A. Effects of cover crops and phosphorus sources on maize yield, phosphorus uptake, and phosphorus use efficiency. Agron. J. 109, 1039–1047 (2017).
CAS  Article  Google Scholar 

52.
Elith, J., Leathwick, J. R. & Hastie, T. A working guide to boosted regression trees. J. Anim. Ecol. 77, 802–813 (2008).
CAS  Article  PubMed  Google Scholar 

53.
Guo, J. H. et al. Significant acidification in major Chinese croplands. Science 327, 1008–1010 (2010).
ADS  CAS  Article  PubMed  Google Scholar 

54.
Schroder, J. L. et al. Soil acidification from long-term use of nitrogen fertilizers on winter wheat. Soil Sci. Soc. Am. J. 75, 957 (2011).
ADS  CAS  Article  Google Scholar 

55.
Chen, D., Lan, Z., Hu, S. & Bai, Y. Effects of nitrogen enrichment on belowground communities in grassland: relative role of soil nitrogen availability vs. soil acidification. Soil Biol. Biochem. 89, 99–108 (2015).
CAS  Article  Google Scholar 

56.
Han, T. et al. The links between potassium availability and soil exchangeable calcium, magnesium, and aluminum are mediated by lime in acidic soil. J. Soils Sediments https://doi.org/10.1007/s11368-018-2145-6 (2018).
Article  Google Scholar 

57.
Bouwman, A. F., Van Vuuren, D. P., Derwent, R. G. & Posch, M. A global analysis of acidification and eutrophication of terrestrial ecosystems. Water. Air Soil Pollut. 141, 349–382 (2002).
ADS  CAS  Article  Google Scholar 

58.
Tang, C. et al. Biological amelioration of subsoil acidity through managing nitrate uptake by wheat crops. Plant Soil 338, 383–397 (2011).
CAS  Article  Google Scholar 

59.
Stevens, C. J., Dise, N. B. & Gowing, D. J. Regional trends in soil acidification and exchangeable metal concentrations in relation to acid deposition rates. Environ. Pollut. 157, 313–319 (2009).
CAS  Article  PubMed  Google Scholar 

60.
Haynes, R. J. Effects of liming on phosphate availability in acid soils. Plant Soil 68, 289–308 (1982).
CAS  Article  Google Scholar 

61.
Nierop, K. G. J. J., Jansen, B. & Verstraten, J. M. Dissolved organic matter, aluminium and iron interactions: precipitation induced by metal/carbon ratio, pH and competition. Sci. Total Environ. 300, 201–211 (2002).
ADS  CAS  Article  PubMed  Google Scholar 

62.
Li, H. et al. Past, present, and future use of phosphorus in Chinese agriculture and its influence on phosphorus losses. Ambio 44, 274–285 (2015).
CAS  Article  PubMed Central  Google Scholar 

63.
Smyth, T. J. & Sanchez, P. A. Effects of lime, silicate, and phosphorus applications to an oxisol on phosphorus sorption and ion retention. Soil Sci. Soc. Am. J. 44, 500–505 (1980).
ADS  CAS  Article  Google Scholar 

64.
Curtin, D. & Syers, J. K. Lime-induced changes in indices of soil phosphate availability. Soil Sci. Soc. Am. J. 65, 147–152 (2001).
ADS  CAS  Article  Google Scholar 

65.
Barth, V. P. et al. Stratification of soil chemical and microbial properties under no-till after liming. Appl. Soil Ecol. 130, 169–177 (2018).
Article  Google Scholar 

66.
Abdi, D. et al. Residual effects of paper mill biosolids and liming materials on soil microbial biomass and community structure. Can. J. Soil Sci. 97, 188–199 (2016).
Google Scholar 

67.
Park, J.-S. & Ro, H.-M. Early-stage changes in chemical phosphorus speciation induced by liming deforested soils. J. Soil Sci. Plant Nutr. 2, 435–447 (2018).
Google Scholar 

68.
Malhi, S. S., Nyborg, M. & Harapiak, J. T. Effects of long-term N fertilizer-induced acidification and liming on micronutrients in soil and in bromegrass hay. Soil Tillage Res. 48, 91–101 (1998).
Article  Google Scholar 

69.
Kostic, L. et al. Liming of anthropogenically acidified soil promotes phosphorus acquisition in the rhizosphere of wheat. Biol. Fertil. Soils 51, 289–298 (2015).
CAS  Article  Google Scholar 

70.
Shahin, M., Esitken, A. & Pirlak, L. The effects of lime does on some morphological and fruit characteristics of some strawberry (Fragaria Xananssa Duch.) cultivars. In IX International Scientific Agriculture Symposium” AGROSYM 2018″, Jahorina, Bosnia and Herzegovina, 4–7 October 2018. Book of Proceedings 575–582 (University of East Sarajevo, Faculty of Agriculture, 2018).

71.
Rheinheimer, D. S., Tiecher, T., Gonzatto, R., Zafar, M. & Brunetto, G. Residual effect of surface-applied lime on soil acidity properties in a long-term experiment under no-till in a Southern Brazilian sandy Ultisol. Geoderma 313, 7–16 (2018).
ADS  CAS  Article  Google Scholar 

72.
Goulding, K. W. T. Soil acidification and the importance of liming agricultural soils with particular reference to the United Kingdom. Soil Use Manag. 32, 390–399 (2016).
CAS  Article  PubMed  PubMed Central  Google Scholar 

73.
Liu, X. Y., Rashti, M. R., Esfandbod, M., Powell, B. & Chen, C. R. Liming improves soil microbial growth, but trash blanket placement increases labile carbon and nitrogen availability in a sugarcane soil of subtropical Australia. Soil Res. 56, 235–243 (2018).
Article  Google Scholar 

74.
Yadvinder-Singh, B.-S. & Timsina, J. Crop residue management for nutrient cycling and improving soil productivity in rice-based cropping systems in the tropics. Adv. Agron. 85, 269–407 (2005).
Article  Google Scholar  More

Research scope and experimental design
The study was conducted on rice production in Tar Pat Village, Maubin, Myanmar (16.617° N, 95.680° E) in the wet season (WS) 2014 and the dry seasons (DS) of 2015 and 2016. Sin Thukha variety with a growing time of 135 days was used for all 3 seasons. Best practices were identified based on the indicators of energy balance, cost-benefits, and GHGE for a functional unit (FU) of 1 ha of rice production. The last factor (GHGE) was estimated using the attributional LCA27,28,29 approach following LCA ISO standard ISO1404:44. Figure 1 shows the system boundary covering all processes of rice production from preharvest (cultivation) to postharvest (until milling). The primary data were collected in harvest and postharvest processes while the secondary data of pre-harvest processes were used in the system analysis. The conversion factors for energy and GHGE of the agronomic inputs, fuel and power consumption, and related transportations were interpreted from the ECOINVENT 3 database (version 3.3)19.
Figure 1

Inputs and outputs of the research system.

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Table 1 shows the research treatments with their major features and applied practices in the different seasons. For the WS2014, a comparative analysis was conducted for two farmer practices (FPs) and one improved practice (IPR). The two farmer practices corresponded to the scenarios of stacking rice plants in the field for 1 week (FP1w) and for 4 weeks (FP4w) after manual cutting. The IPR scenario involved threshing within 12 h after harvest. In addition, the IPR included use of a flatbed dryer for drying the rice, and hermetic bags for storage, instead of sun drying and farmer-granary bags for storage under FP.
Table 1 Scenarios and post-harvest operations covered in the study in Maubin, Ayeyarwady delta, Myanmar during three rice cropping seasons.
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In the dry season of 2015 and 2016, the analysis was conducted for two scenarios, which were farmer practice (FP) and improved post-harvest operations with a combine harvester, flatbed dryer, and hermetic storage (IPRc). Neither of the scenarios had delays or stacking of the rice plants, because farmers were able to thresh the rice immediately after it was manually harvested. The practices involved in FP were manual operations such as cutting of the mature rice plants and sun-drying. The scenarios of DS2015 and DS2016 differed from the WS2014 because the farmers did not stack rice in the DS prior to threshing.
The experiment was set up in fields of farmers on 4110 m2 for WS2014 and 5850 m2 for both DS2015 and DS2016. Each scenario was replicated 5 times in the different plots and were distributed using a completely randomized design (CRD). For the WS2014 experiment, there were 15 plots for three scenarios with each plot 270 m2. The paddy was harvested and processed based on the respective IPR and FP scenarios. The FP scenario included a thresher locally fabricated by the farmers based on the axial threshing principle that was powered by a two-wheel tractor with a 15 HP diesel engine (Fig. 2a), sun drying, and granary bags containing approximately 50 kg of paddy each. The IPR scenario involved a TC-800 axial flow thresher with a 7.5 HP engine (Fig. 2b)30. Compared to the farmer thresher, the imported unit was manufactured and marketed by a branded company in the Philippines and tested by IRRI to ensure good performance. For DS2015 and DS2016, there were 10 plots for two scenarios with each plot 390 m2. Rice was harvested using a Kubota-DC-70G combine harvester with 70 HP (Fig. 2c). A flatbed dryer and hermetic bags for storage of dried grain were used for IPR in all three seasons. The flatbed dryer with 4 t batch−1 capacity (Fig. 2d) was locally made based on published designs6, and was used for the IPR in both the wet and dry seasons. Hermetic bags for storage, also called “Super bags”31 hold 50 kg of paddy per bag. Milling operations were in-situ measured at the local rice mill (two-stage milling system) with 1 t h−1 capacity, located at Maubin, and were applied for both FP and IPR scenarios.
Figure 2

(a) Farmer thresher. (b) Imported thresher, TC-800. (c) Combine harvester Kubota-DC-70G. (d) Flatbed dryer 4 ton batch−1.

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Measurement and quantification of harvest and post-harvest losses
Shattering loss during cutting, stacking, and combine harvesting was determined through sampling of 5 plots using 1 m2 quadrants for each scenario. Shattering losses were calculated based on the ratio between shattered grains and yield at an adjusted moisture content of 14% wet basis (MC). In threshing, the grain losses were quantified in the stacked rice, at the separation process, in the cleaning process, and under the machine during the threshing operation. The sum of these losses comprised the threshing loss. The design did not quantify losses caused in drying and storage during handling, and grain lost to birds and rodents. See Htwe et al.9 for estimate of losses caused by rodents at this study site. The losses associated with discoloration, milling recovery (MR), and head rice recovery (HRR) caused by in-field stacking, delay of drying and storage methods were measured after milling.
MR and HRR were measured on milled rice. Three subsamples of 500 g of paddy were taken randomly from the grain harvested in each plot. The samples were cleaned using a Seedburo Paddy Blower, then 250 g of filled grain were passed twice in a RISE 10″ Rubber Roll Husker, then through a SATAKE Abrasive Whitener, and finally, through a SATAKE laboratory rice grader. MR and HRR were calculated using Eqs. (1) and (2), respectively.

$$MR; (%)=frac{Weight;of;milled ;rice ;left(include ;broken; grainsright)}{Weight; of ;paddy ;samples} times100$$
(1)

$$HRR ;(%) =frac{Weight; of ;whole; grains}{Weight ;of; paddy ;samples} times 100$$
(2)

Discoloration of grain was caused by fungi, bacteria, and environmental conditions such as high humidity and temperature. Milled rice kernels having more than 0.5% grains with a color other than white (usually yellow) or with a spotted surface were considered discolored32. To measure discoloration, three 25 g samples of the product were collected randomly. Discolored grains with spots, streaks, or having more than 0.5% differently colored surface were separated and weighed to calculate the percentage of discoloration based on Eq. (3).

$$Discoloration ;(%)= frac{weight ;of ;discolored ;grains (text{g})}{weight ;of ;sample; (25 ;text{g})} times 100$$
(3)

Energy efficiency and GHG emissions
This study investigated net energy value (NEV) and net energy ratio (NER) which are commonly used to quantify energy efficiency of a production systems33,34,35. NEV accounted for the inputs and outputs of the systems per the FU (ha of rice production) (Eq. 4) while the NER was the ratio between the output and input energy values (Eq. 5).

$$NEV left(frac{GJ}{ha}right)=E{V}_{outputs}-E{V}_{inputs}$$
(4)

where the EVoutputs accounted for rice grain products and by-products such as broken and discolored grains, bran, husks, and straw. The EVinputs includes all the energy consumption of rice production from cultivation to milling; this includes agronomic inputs, machine production, fuel and power consumption, and labor use.

$$NER=frac{E{V}_{outputs}}{E{V}_{inputs}}$$
(5)

Table 2 shows energy values embed in the whole milled rice, broken and discolored rice, bran, husk, and straw. Energy value (EV) of rice product36 is 15.2 MJ kg−1 while that of rice bran, broken rice, and discolored rice is 9.6 MJ kg−1 (Econivent 3 database19) with an assumption that these by-products are used for cattle feed and have a similar economic value. EV of rice husk is 8.7 MJ kg−1 (Ecoinvent 3 database19) and straw is 3.5 MJ kg−120 based on an asumption that partially harvested straw were collected for mushroom production. The collected amount of rice straw was about 50% of the grain yield at harvest34. The EV per kg was then translated to the FU based on the grain yield and post-harvest losses measured in the experiments. In particular, rice husk and bran were assumed to be 20 and 10% of the milled rice produced, respectively. EV of the cultivation (excluding harvesting and transportation) was about 12 and 16 GJ ha−1 for the small-farm irrigated rice production in the WS and DS, respectively, as reported in research in the same region (Ayeyarwaddy delta of Myanmar)37. EV of machine production was accounted for via a depreciation of 5 years. Fuel and power consumption of harvest and post-harvest operations were measured and translated to EV using the coversion factors. The energy of manual labor was calculated based on the metabolic equivalent of tasks (MET) (Table 2). Ainsworth et al.38 described the MET as the ratio of the human metabolic rate when performing an activity to the metabolic rate at rest. This ratio is converted to an energy value as MJ per hour working using the method described by Quilty et al.39 with the assumption of a mean Asian human body weight of 55 kg. For paddy transportation, the tractor-hauled trailor option was used for all scenarios with a distance of 15 km from the field to the station of drying, storage, and milling.
Table 2 Conversion factors for energy and GHGE.
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The GHGE were accounted for the whole production from cultivation to milling. The yield and grain losses were taken into account through a rice product recovery ratio as shown in Eq. (6).

$$GHGE ;(text{kg} ,{text{ha}}^{-1})=frac{text{G}H{G}_{cultivation}+ GH{G}_{harvest} + GH{G}_{postharvest} }{Product; recovery; ratio}$$
(6)

where GHGcultivation for the irrigated rice cultivation in Myanmar was about 2000 and 1200 kgCO2-eq ha−1 in WS and DS, repectively, as reported in recent research at the same site40. GHGE of harvest and post-harvest operations were calculated based on emissions generated during production of harvest and post-harvest equipment, input materials, and fuel consumption during operations. The unit of in-field emissions was per ha while that of off-field emissions was per kg of rice grains. The off-field emission values were therefore translated to attribute for the FU (ha) based on rice yield (kg ha−1). Furthermore, the associated unit (kg of rice grains) was considered as the product at the end of the life-cycle boundary, its carbon footprint therefore accounted for the post-harvest losses or product recovery. The recovered rice product (whole grains) was calculated from the grain yield with a consideration of harvest and postharvest losses. The postharvest losses were brokenness (HRR) and discoloration at harvest, drying, storage, milling, and handling. The rice product recovery ratio was calculated based on Eq. (7).

$$Product ;recovery; ratio =left(1-Los{s}_{harvesting}right)* HRR*(1-Discoloration)$$
(7)

The conversion factors for GHGE of related fuel and power consumption, machine production, and transportation are shown in Table 2. In particular, GHGE from the electric power consumption for drying and milling was translated from Ecoinvent 3 data (version 3.3) for the “rest of the world (ROW)”.
Cost–benefit analysis
Similar to the energy efficiency analysis, cost-benefits were quantified through the net income value (NIV) (Eq. 8) and net income ratio (NIR) (Eq. 9). NIV accounted for the cost of production and income value (IV) of products and co-products while the NIR was the ratio between NIV and the input cost.

$$NIV left(frac{$US}{text{ha}}right)=I{V}_{(whole; rice + broken; rice + discolored; rice+bran+husk+ straw)}-(Cos{t}_{cultivation}+{ Cost}_{left(harvest; and ;post {text{-}}harvestright)}),$$
(8)

$$NIR=frac{NIV}{(Cos{t}_{cultivation}+{ Cost}_{left(harvest ;and ;post{text{-}}harvestright)})}$$
(9)

The price of rice product was $US 400 per t1. Price of discolored rice was assumed to be the same as bran price, which is $US 140 per t1. Cost of the cultivation (excluding harvesting and transportation) was about 650 $US ha−1 for small-farm irrigated rice production in the Ayeyarwaddy delta of Myanmar37. Costs of the post-harvest operations were calculated based on the corresponding depreciation, maintenance, interest, energy consumption, and labor of all related equipment used in the operations from harvesting to milling. The component costs of input materials, labor, and energy included in the analysis were collected based on assessments in Myanmar in 2018 (Table 3).
Table 3 Cost and life span of different component costs of input materials, labor, and energy based on assessments conducted in the Ayeyarwady Delta region of Myanmar in 2018.
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Harvesting loss was used to conduct a sensitivity analysis for NIV and NIR for both the wet and dry seasons. This analysis only applied for the improved post-harvest operations with the flatbed dryer and hermetic storage.
Statistical analysis and software
Analysis of Variance (ANOVA) Single Factor and Two-Factor with replication and F-Test Two-Sample for Variances tools incorporated in Excel were used to evaluate the effects of the contrasting post-harvest management scenarios on the measured post-harvest losses, energy, and GHGE. The ECOINVENT-3 database (version 3.3)19 in association with Cumulative Energy Demand 1.09 method41 and the Global Warming Potential—100 years (GWP100a) presented in IPCC 201342, were used to interpret the conversion factors of energy (MJ) embedded and GHGE (CO2-eq) from the agronomic inputs and fuel consumption. All these databases and methods (Ecoinvent, Cumulatiive Energy Demand, and IPCC) are incorporated in SIMAPRO version 8.5.0.041. More

Data collection
This study uses a compiled data set that includes 12,072 native Lepidoptera species, 2079 native plant genera, and 24,037 different host plant-native Lepidoptera interactions from across the contiguous United States. From these data we extracted the plant information of 83 counties and a Lepidoptera list for the corresponding 25 states. The full dataset will be publicly available in a forthcoming data manuscript.
Lepidoptera range and host plant data
The Lepidoptera species data were compiled from historic citable sources (Supplementary Data 6) of range and host plant records. We originally compiled a similar list for the Mid-Atlantic region29. This dataset was updated to include more states and counties to include on the National Wildlife Federation website48. Non-plant host records (e.g., detriphagous, algae, fungi, lichen, and insect predators) are included, as well as Lepidoptera without known host plant associations, but not considered in this analysis. Plant ranges are to the county, Lepidoptera ranges are to state, and host plant-Lepidoptera records are relationships between a plant genus and Lepidoptera species. Plant genus was included as the unit of interaction because data on Lepidoptera- host plant associations are most accurate and available at the genus level. Although more specific data are occasionally available (e.g., Lepidoptera records to plant species), we limited our analysis to the genus scale in order to make equitable inferences across the geographic and ecological scope of this analysis.
Plant distribution data
The current list uses the Biota of North America Program49 (BONAP) as its major source for plant nomenclature. We used the BONAP database as our source for plant distributions because it specifies North American plant ranges that currently occur beyond their historic native range due to anthropogenic and natural expansion (e.g. Osage orange, Maclura pomifera).
Using the BONAP, a county-level survey was made for each state used in this study. Every county within those states was surveyed and BONAP records include records from adjacent counties. We classified plants into three categories; native, non-native, and adventive. A plant species is classified as adventive if it is native to North America but not in that specified region. Each plant genus was reviewed individually in each state. Genus records that fell entirely in one category resulted in all county records being designated that category. Any plant genus that had species that fell in two or more categories was examined county by county, with adjacent records being noted but not included. The state records for each plant genus are labeled in various combinations of the three categories. County-level data designate a genus either containing native records, or only non-native. For our study, we focused only on native plants, excluding non-native and adventive records (except for parameterizing probabilities of host-plant switching, see below for details).
Eighty-three counties in 25 states (Alabama, Arizona, Arkansas, California, Colorado, Delaware, Florida, Georgia, Idaho, Illinois, Kansas, Maine, Massachusetts, Michigan, Minnesota, Montana, New York, North Dakota, Ohio, Oregon, Pennsylvania, South Carolina, Tennessee, Texas, and Utah) were examined. At least three counties in each state (except Delaware) were used. Two counties from each state were selected from dissimilar ecoregions within each state. Ecoregions were determined using the Commission for Environmental Cooperation’s Ecological Regions of North America map50. For county selection we used the level 2 designation of terrestrial ecoregions (50 separate categories); however, for subsequent analyses, we used the level 1 designation (15 categories). As much as possible, counties that bordered other counties within an ecoregion were used to alleviate the issue of BONAP including records from adjoining states. At least one more county was added to meet the parameters of the latitude study and to fill out ecoregions. Up to five counties were used in some states. While the majority of counties were chosen without criteria beyond ecoregion status, Chase County KS was chosen based on its presence in the ‘South-central Semi-Arid Prairies’ ecoregion, as well as high natural grassland cover relative to other agriculturally dominated Kansas counties.
County data
A series of counties were selected along three primary latitude bands (Latitudes 46, 40, and 34). The latitude and longitude of each county were determined by the county seat using Google Earth. In most cases the county seat was centrally located within the state. A few county seats are not centrally located, most notably Monroe County in Florida where the county seat is Key West. We determined the land area (in km2, excluding inland, coastal, Great Lakes, and territorial sea water) for each county using information from the US Census (https://www.census.gov/quickfacts/fact/note/US/LND110210). Counties varied from 415 to 26,368 km2 with an average of 4519 ± 5598 SD.
Lepidoptera-host plant data
The host plant records for each Lepidoptera species include all known literature records. Not all host plant-Lepidoptera associations occur in every county, state or even the USA due to differences in plant distributions. Thus, we filtered the Lepidoptera list from each state to exclude any Lepidoptera species whose host plant did not occur in the selected county. The final dataset per county includes (1) all plant genera known to occur in the county, (2) all Lepidoptera known to use at least one plant genus that occurs in the county and (3) all host plants used by Lepidoptera that could potentially occur in the county.
Statistical methods
All analyses were conducted using program R, Version 3.5.151.
Distribution analysis
We first determined what distribution best fit our data in order to derive parameters that could be compared among counties. We used the package ‘goft’52 to conduct goodness of fit tests for the Exponential, Gamma, and Pareto distributions on each county separately. Functions in the ‘goft’ package use parametric bootstrap tests for the null hypothesis that a distribution fits a tested distribution. We tested each county (n = 83) and each distribution type (n = 3) separately. Using the distribution that best fit our datasets, we then used the function ‘fitdistr’ from the ‘MASS’ package53 to use maximum likelihood fitting to obtain parameters (e.g., shape α and scale θ) for the Plant-Lepidoptera distributions for each county separately.
We then tested for differences in the distribution of caterpillar richness among plants by county-level diversity and location metrics. For each county, we compared the α and θ of the distribution with county plant richness, Lepidoptera richness, ecoregion, latitude, and county land area. To test whether α or θ varied by ecoregion we used an analysis of variance (ANOVA) with Tukey’s post hoc multiple comparisons of means. To test whether α or θ changed with increasing plant richness, Lepidoptera richness, or land area, we used linear regression. Based on scatter plots of the data, no nonlinear relationships were necessary.
Network analysis
To determine conservation targets, we identified keystone plant species24,25 using methods from Harvey et al.26. To perform this analysis, we used binary networks of host-plant caterpillar interactions. Ecological networks are ideal to determine cascading extinction rates of specialists following host plant loss54. For this analysis and our following simulation, we chose one representative county dataset from each state from our 83 available counties (25 counties total). Our method to identify target keystone species at a national scale consists of three steps.
Species richness
On a per-county basis, we first identified how many Lepidoptera species are recorded in the literature as using each plant genus for growth and reproduction.
Extinction sensitivity
We also determined the ‘extinction sensitivity’ of each plant; in other words, how many Lepidoptera species are at risk of extirpation with the loss of a host plant? In our context, the sensitivity means “specialization”, caterpillars that use only one genus of the plant are considered especially sensitive to the removal of that plant. We modified the “nb.extinct” function from Harvey et al.26 so that we could calculate the number of extinct herbivores following the removal of a plant. This function was repeated for each county to acquire a number of species that were specialists to each host plant.
Network stability
Then we used a network-based approach to assess the effect of each plant genus on network stability. We used a binary matrix where each record of a caterpillar on a host plant indicates the existence of an interaction. The results given from a binary interaction network are correlated with those from a network weighted by abundance55. Here, the community stability index represents the minimum interactions required for the system to be stable where smaller values are the most stable, i.e., the more resilient a network is to disturbance, and large values are the most unstable. We calculated the stability index as the real part of the dominant eigenvalue of a Jacobian matrix following methods from Sauve et al.27 (see Appendix 1 in ref. 27 for definition and details). To acquire baseline stability, we conducted 175 iterations and took the median value. We determined that 175 was the minimum number of iterations needed to acquire a stability value ± 0.001 resolution using simulations on test datasets. For this analysis we only considered woody plants in our analysis because (1) woody plants tend to host the most diverse caterpillar communities29 and (2) computation time to include all plant genera was prohibitive.
To quantify the effect of each plant on total network stability, we reran the analysis, iteratively removing each plant genus and then recalculating the stability26. Then we subtracted the new stability values from baseline stability to find the median change when each plant was removed where negative values indicate reductions in network stability and positive values indicate increases. We then multiplied the stability value by −1 so that increases in this metric indicated an increase in a plant’s importance to stability to make this value comparable in direction to network structure and extinction sensitivity (i.e., increases in Lepidoptera diversity and # of specialist species).
Standardizing results across counties
For each analysis (step 1–3) we scaled our final values from 0–1 using this equation for each plant in each county separately:

$$frac{{{mathrm{x}} + left| {{mathrm{minimum}}}; {{mathrm{x}}} right|}}{{{mathrm{maximum}}; {mathrm{x}} + left| {{mathrm{minimum}}}; {{mathrm{x}}} right|}}.$$
(1)

We then took the mean of our three values to obtain a final ‘score’ for each plant genus per county. Finally, we identified which plant genera had the highest values over all the counties by plotting the means for each plant genera (n = 288) and assessing outliers (values that were 1.5× the interquartile range).
Field-based host plant-caterpillar interaction data
Field sampling
To compare the results from the network analysis on literature-based data collection with results from field-based data collection, we used caterpillar interactions recorded from native host plants from Richard et al.56 (hereafter: Mid-Atlantic dataset). Caterpillar surveys were conducted in 2011 within 8 hedgerows in New Castle County, DE and Cecil County, MD. This dataset contains plants surveyed in both native- ( >95% native plant biomass, n = 4) and nonnative-dominated ( >75% nonnative plant biomass, n = 4) hedgerows. Sites were all located within Mid-atlantic decidous piedmont forest, and were separated by at least 100 m.
In June–July, an observer walked a 100 m transect on days in which foliage was not wet to collect caterpillars in each site. Observers sampled all caterpillars using the total search approach57 to methodically inspect leaves, twigs, and branches of all woody plants within a 2-m3 area along the transect. Each search was conducted for 5 m every 2 m along the 100 m transect. In total, each hedgerow treatment was searched for a total of 1000 min in both June and July. All caterpillars were identified to species or morphospecies using Wagner57, Wagner et al.58, and various web sources. Caterpillars that could not be identified in the field were measured and then brought to the lab to be reared to adulthood for later identification using the literature and the University of Delaware Insect Reference collection.
Data management and analysis
Because our network analysis was based on native woody plant genera, we excluded all non-native plant genera in the Mid-Atlantic dataset. We calculated the number of times each plant was searched and excluded all plant species that were searched 1 chosen host plant) and total interaction richness supported (i.e. all interactions between a plant and a caterpillar consumer).
Host-plant switching
We also included the potential for host plant switching by including a probability of using plants not recorded in the host plant literature. We calculated the probability of random host plant switching as:

$${P}_{{mathrm{ic}}}left( {{mathrm{host plant shift}}} right) = frac{{{E}_{{mathrm{ic}}}}}{{{N}_{mathrm{c}} – {H}_{{mathrm{ic}}}}} * {N},$$
(2)

where Pic is the probability of host plant shifting by Lepidoptera species i in county c, Eic is the proportion of non-native plants used by Lepidoptera species i in county c, Nc is the number of native host plants in county c, Hic is the number of total native host plants used by Lepidoptera species i in county c, and N is the total number of plants included in the simulation (from 1 to 50). This equation gives the probability that Lepidoptera species i in county c shifted to at least one of N plants used in the simulation. Using this probability, we used the sample function in R to predict whether any of the possible Lepidoptera species were included or not in each iteration and added each unique species to those included from known hosts.
Simulation output
For each county, we iterated these scenarios over 100 iterations with random draws of plant genera and keystone genera. We chose 100 iterations for each county to accurately estimate means while maintaining computational efficiency. To standardize across counties, we calculated the percent of Lepidoptera supported out of all potential phytophagous Lepidoptera (for woody plants and herbaceous plants separately) and percent interactions in each iteration. For each county and iteration, we plotted the median value. Simulations were run for woody and herbaceous plants separately.
Reporting summary
Further information on research design is available in the Nature Research Reporting Summary linked to this article. More

1.
Convey, P. & Block, W. Antarctic diptera: Ecology, physiology and distribution. Eur. J. Entomol. 93, 1–14 (1996).
Google Scholar 
2.
Sugg, P., Edwards, J. S. & Baust, J. Phenology and life history of Belgica antarctica, an Antarctic midge (Diptera: Chironomidae). Ecol. Entomol. 8, 105–113 (1983).
Article  Google Scholar 

3.
Usher, M. B. & Edwards, M. A dipteran from south of the Antarctic circle: Belgica antarctica (Chironomidae) with a description of its larva. Biol. J. Lin. Soc. 23, 19–31 (1984).
Article  Google Scholar 

4.
Strong, J. Ecology of terrestrial arthropods at Palmer Station, Antarctic Peninsula. In Entomology of Antarctica (ed. Linsley-Gressitt, J.) 357–371 (American Geophysical Union, Washington DC, 1967).
Google Scholar 

5.
Edwards, J. S. & Baust, J. Sex ratio and adult behaviour of the Antarctic midge Belgica antarctica (Diptera, Chironomklae). Ecol. Entomol. 6, 239–243 (1981).
Article  Google Scholar 

6.
Teets, N. M. et al. Rapid cold-hardening in larvae of the Antarctic midge Belgica antarctica: Cellular cold-sensing and a role for calcium. Am. J. Physiol.-Regulat. Integr. Compar. Physiol. 294, R1938–R1946 (2008).
CAS  Article  Google Scholar 

7.
Benoit, J. B. et al. Mechanisms to reduce dehydration stress in larvae of the Antarctic midge, Belgica antarctica. J. Insect Physiol. 53, 656–667 (2007).
CAS  Article  PubMed  Google Scholar 

8.
Benoit, J. B., Lopez-Martinez, G., Elnitsky, M. A., Lee, R. E. & Denlinger, D. L. Dehydration-induced cross tolerance of Belgica antarctica larvae to cold and heat is facilitated by trehalose accumulation. Compar. Biochem. Physiol. A-Mol. Integr. Physiol. 152, 518–523 (2009).
Article  CAS  Google Scholar 

9.
Lopez-Martinez, G. et al. Dehydration, rehydration, and overhydration alter patterns of gene expression in the Antarctic midge, Belgica antarctica. J. Compar. Physiol. B-Biochem. Syst. Environ. Physiol. 179, 481–491 (2009).
CAS  Article  Google Scholar 

10.
Lopez-Martinez, G., Elnitsky, M. A., Benoit, J. B., Lee, R. E. Jr. & Denlinger, D. L. High resistance to oxidative damage in the Antarctic midge Belgica antarctica, and developmentally linked expression of genes encoding superoxide dismutase, catalase and heat shock proteins. Insect Biochem. Mol. Biol. 38, 796–804 (2008).
CAS  Article  PubMed  Google Scholar 

11.
Harada, E., Lee, R. E., Denlinger, D. L. & Goto, S. G. Life history traits of adults and embryos of the Antarctic midge Belgica antarctica. Polar Biol. 37, 1213–1217 (2014).
Article  Google Scholar 

12.
Convey, P. How are the life history strategies of Antarctic terrestrial invertebrates influenced by extreme environmental conditions? J. Therm. Biol 22, 429–440 (1997).
Article  Google Scholar 

13.
Kennedy, A. D. Water as a limiting factor in the Antarctic terrestrial environment: a biogeographical synthesis. Arct. Alp. Res. 25, 308–315 (1993).
Article  Google Scholar 

14.
Hahn, S. & Reinhardt, K. Habitat preference and reproductive traits in the Antarctic midge Parochlus steinenii (Diptera: Chironomidae). Antarct. Sci. 18, 175–181 (2006).
ADS  Article  Google Scholar 

15.
Wensler, R. J. & Rempel, J. The morphology of the male and female reproductive systems of the midge, Chironomus plumosus L.. Can. J. Zool. 40, 199–229 (1962).
Article  Google Scholar 

16.
Sibley, P. K., Ankley, G. T. & Benoit, D. A. Factors affecting reproduction and the importance of adult size on reproductive output of the midge Chironomus tentans. Environ. Toxicol. Chem. 20, 1296–1303 (2001).
CAS  Article  PubMed  Google Scholar 

17.
Vogt, C. et al. Effects of cadmium and tributyltin on development and reproduction of the non-biting midge Chironomus riparius (Diptera)—Baseline experiments for future multi-generation studies. J. Environ. Sci. Health A 42, 1–9 (2007).
CAS  Article  Google Scholar 

18.
Clark, A. G. et al. Evolution of genes and genomes on the Drosophila phylogeny. Nature 450, 203–218 (2007).
ADS  Article  CAS  PubMed  Google Scholar 

19.
Ravi-Ram, K. & Wolfner, M. F. Seminal influences: Drosophila Acps and the molecular interplay between males and females during reproduction. Integr. Compar. Biol. 47, 427–445 (2007).
CAS  Article  Google Scholar 

20.
McGraw, L. A., Clark, A. G. & Wolfner, M. F. Post-mating gene expression profiles of female Drosophila melanogaster in response to time and to four male accessory gland proteins. Genetics 179, 1395–1408 (2008).
CAS  Article  PubMed  PubMed Central  Google Scholar 

21.
Avila, F. W., Sirot, L. K., LaFlamme, B. A., Rubinstein, C. D. & Wolfner, M. F. Insect seminal fluid proteins: Identification and function. Annu. Rev. Entomol. 56, 21–40 (2011).
CAS  Article  PubMed  PubMed Central  Google Scholar 

22.
Benoit, J. B., Attardo, G. M., Baumann, A. A., Michalkova, V. & Aksoy, S. Adenotrophic viviparity in tsetse flies: Potential for population control and as an insect model for lactation. Annu. Rev. Entomol. 60, 351–371 (2015).
CAS  Article  PubMed  Google Scholar 

23.
Lee, K. P. et al. Lifespan and reproduction in Drosophila: New insights from nutritional geometry. Proc. Natl. Acad. Sci. USA 105, 2498–2503 (2008).
ADS  CAS  Article  PubMed  Google Scholar 

24.
Polak, M. et al. Nutritional geometry of paternal effects on embryo mortality. Proc. R. Soc. B 284, 20171492 (2017).
Article  CAS  PubMed  Google Scholar 

25.
Papa, F. et al. Rapid evolution of female-biased genes among four species of Anopheles malaria mosquitoes. Genome Res. 27, 1536–1548 (2017).
CAS  Article  PubMed  PubMed Central  Google Scholar 

26.
Izquierdo, A. et al. Evolution of gene expression levels in the male reproductive organs of Anopheles mosquitoes. Life Sci. Allian. 2, e201800191 (2019).
Article  Google Scholar 

27.
Dottorini, T. et al. A genome-wide analysis in Anopheles gambiae mosquitoes reveals 46 male accessory gland genes, possible modulators of female behavior. Proc. Natl. Acad. Sci. U.S.A. 104, 16215–16220 (2007).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

28.
Villarreal, S. M. et al. Male contributions during mating increase female survival in the disease vector mosquito Aedes aegypti. J. Insect Physiol. 108, 1–9 (2018).
CAS  Article  PubMed  PubMed Central  Google Scholar 

29.
Alfonso-Parra, C. et al. Mating-induced transcriptome changes in the reproductive tract of female Aedes aegypti. PLoS Negl. Trop. Dis. 10, e0004451 (2016).
Article  CAS  PubMed  PubMed Central  Google Scholar 

30.
Meier, R., Kotrba, M. & Ferrar, P. Ovoviviparity and viviparity in the Diptera. Biol. Rev. Camb. Philos. Soc. 74, 199–258 (1999).
Article  Google Scholar 

31.
Lung, O. & Wolfner, M. F. Identification and characterization of the major Drosophila melanogaster mating plug protein. Insect Biochem. Mol. Biol. 31, 543–551 (2001).
CAS  Article  Google Scholar 

32.
Giglioli, M. & Mason, G. The mating plug in anopheline mosquitoes. Proc. R. Entomol. Soc. Lond. 41, 123–129 (1966).
Google Scholar 

33.
Mitchell, S. N. et al. Evolution of sexual traits influencing vectorial capacity in anopheline mosquitoes. Science 347, 985–988 (2015).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

34.
Scolari, F. et al. The spermatophore in Glossina morsitans morsitans: Insights into male contributions to reproduction. Sci. Rep. https://doi.org/10.1038/srep20334 (2016).
Article  PubMed  PubMed Central  Google Scholar 

35.
Kotrba, M. Sperm transfer by spermatophore in Diptera: New results from the Diopsidae. Zool. J. Linnean Soc. 117, 305–323 (1996).
Article  Google Scholar 

36.
Attardo, G. M. et al. Comparative genomic analysis of six Glossina genomes, vectors of African trypanosomes. Genome Biol. 20, 187 (2019).
CAS  Article  PubMed  PubMed Central  Google Scholar 

37.
Rogers, D. W. et al. Molecular and cellular components of the mating machinery in Anopheles gambiae females. Proc. Natl. Acad. Sci. U.S.A. 105, 19390–19395 (2008).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

38.
Gabrieli, P. et al. Sexual transfer of the steroid hormone 20E induces the postmating switch in Anopheles gambiae. Proc. Natl. Acad. Sci. U.S.A. 111, 16353–16358 (2014).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

39.
Thailayil, J., Magnusson, K., Godfray, H. C. J., Crisanti, A. & Catteruccia, F. Spermless males elicit large-scale female responses to mating in the malaria mosquito Anopheles gambiae. Proc. Natl. Acad. Sci. U.S.A. 108, 13677–13681 (2011).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

40.
Shutt, B., Stables, L., Aboagye-Antwi, F., Moran, J. & Tripet, F. Male accessory gland proteins induce female monogamy in anopheline mosquitoes. Med. Vet. Entomol. 24, 91–94 (2010).
CAS  Article  PubMed  Google Scholar 

41.
Dixon, S. M., Coyne, J. A. & Noor, M. A. The evolution of conspecific sperm precedence in Drosophila. Mol. Ecol. 12, 1179–1184 (2003).
Article  PubMed  Google Scholar 

42.
Price, C. S. Conspecific sperm precedence in Drosophila. Nature 388, 663 (1997).
ADS  CAS  Article  PubMed  Google Scholar 

43.
Gwynne, D. T. Male mating effort, confidence of paternity, and insect sperm competition. In Sperm Competition and the Evolution of Animal Mating Systems (ed. Smith, R.) 117 (Elsevier, Hoboken, 2012).
Google Scholar 

44.
Hopkins, B. R. et al. Divergent allocation of sperm and the seminal proteome along a competition gradient in Drosophila melanogaster. Proc. Natl. Acad. Sci. U.S.A. 116, 17925–17933 (2019).
CAS  Article  PubMed  PubMed Central  Google Scholar 

45.
Hopkins, B. R., Sepil, I. & Wigby, S. Seminal fluid. Curr. Biol. 27, R404–R405 (2017).
CAS  Article  PubMed  Google Scholar 

46.
Degrugillier, M. E. In vitro release of house fly, Musca domestica L. (Diptera: Muscidae), acrosomal material after treatments with secretion of female accessory gland and micropyle cap substance. Int. J. Insect Morphol. Embryol. 14, 381–391 (1985).
Article  Google Scholar 

47.
Leopold, R. A. & Degrugillier, M. E. Sperm penetration of housefly eggs: Evidence for involvement of a female accessory secretion. Science 181, 555–557 (1973).
ADS  CAS  Article  PubMed  Google Scholar 

48.
Lococo, D. & Huebner, E. The ultrastructure of the female accessory gland, the cement gland, in the insect Rhodnius prolixus. Tissue Cell 12, 557–580 (1980).
CAS  Article  PubMed  Google Scholar 

49.
Marchini, D., Bernini, L. F., Marri, L., Giordano, P. C. & Dallai, R. The female reproductive accessory glands of the medfly Ceratitis capitata: antibacterial activity of the secretion fluid. Insect Biochem. 21, 597–605 (1991).
CAS  Article  Google Scholar 

50.
Rosetto, M. et al. A mammalian-like lipase gene is expressed in the female reproductive accessory glands of the sand fly Phlebotomus papatasi (Diptera, Psychodidae). Insect Mol. Biol. 12, 501–508 (2003).
CAS  Article  PubMed  Google Scholar 

51.
Poiani, A. Complexity of seminal fluid: A review. Behav. Ecol. Sociobiol. 60, 289–310 (2006).
Article  Google Scholar 

52.
Lung, O., Kuo, L. & Wolfner, M. F. Drosophila males transfer antibacterial proteins from their accessory gland and ejaculatory duct to their mates. J. Insect Physiol. 47, 617–622 (2001).
CAS  Article  PubMed  Google Scholar 

53.
Kaiwa, N. et al. Symbiont-supplemented maternal investment underpinning host’s ecological adaptation. Curr. Biol. 24, 2465–2470 (2014).
CAS  Article  PubMed  Google Scholar 

54.
Kaulenas, M. Insect Accessory Reproductive Structures: Function, Structure, and Development (Springer, Berlin, 2012).
Google Scholar 

55.
Benoit, J., Kölliker, M. & Attardo, G. Putting invertebrate lactation in context. Science  363, 593–593 (2019).
ADS  CAS  Article  Google Scholar 

56.
Masci, V. L. et al. Reproductive biology in Anophelinae mosquitoes (Diptera, Culicidae): Fine structure of the female accessory gland. Arthropod. Struct. Dev. 44, 378–387 (2015).
Article  Google Scholar 

57.
Kelley, J. L. et al. Compact genome of the Antarctic midge is likely an adaptation to an extreme environment. Nat. Commun. 5, 4611 (2014).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

58.
Rosendale, A. J., Dunlevy, M. E., McCue, M. D. & Benoit, J. B. Progressive behavioural, physiological and transcriptomic shifts over the course of prolonged starvation in ticks. Mol. Ecol. 28, 49–65 (2019).
CAS  Article  PubMed  Google Scholar 

59.
Raudvere, U. et al. g: Profiler: A web server for functional enrichment analysis and conversions of gene lists (2019 update). Nucleic Acids Res. https://doi.org/10.1093/nar/gkz369 (2019).
Article  PubMed  PubMed Central  Google Scholar 

60.
Langfelder, P. & Horvath, S. WGCNA: An R package for weighted correlation network analysis. BMC Bioinform. 9, 559 (2008).
Article  CAS  Google Scholar 

61.
Supek, F., Bošnjak, M., Škunca, N. & Šmuc, T. REVIGO summarizes and visualizes long lists of gene ontology terms. PLoS ONE 6, e21800 (2011).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

62.
Teets, N. M. et al. Gene expression changes governing extreme dehydration tolerance in an Antarctic insect. Proc. Natl. Acad. Sci U.S.A. 109, 20744–20749 (2012).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

63.
Telfer, W. H. & Kunkel, J. G. The function and evolution of insect storage hexamers. Annu. Rev. Entomol. 36, 205–228 (1991).
CAS  Article  PubMed  Google Scholar 

64.
Burmester, T. Evolution and function of the insect hexamerins. Eur. J. Entomol. 96, 213–226 (1999).
CAS  Google Scholar 

65.
Burmester, T., Massey, H. C. Jr., Zakharkin, S. O. & Benes, H. The evolution of hexamerins and the phylogeny of insects. J. Mol. Evol. 47, 93–108 (1998).
ADS  CAS  Article  PubMed  Google Scholar 

66.
Swanson, W. J., Wong, A., Wolfner, M. F. & Aquadro, C. F. Evolutionary expressed sequence tag analysis of Drosophila female reproductive tracts identifies genes subjected to positive selection. Genetics 168, 1457–1465 (2004).
CAS  Article  PubMed  PubMed Central  Google Scholar 

67.
Panfilio, K. A. et al. Molecular evolutionary trends and feeding ecology diversification in the Hemiptera, anchored by the milkweed bug genome. Genome Biol. 20, 64 (2019).
Article  PubMed  PubMed Central  Google Scholar 

68.
Olafson, P. U. et al. Functional genomics of the stable fly, Stomoxys calcitrans, reveals mechanisms underlying reproduction, host interactions, and novel targets for pest control. BioRxiv https://doi.org/10.1101/623009 (2019).
Article  Google Scholar 

69.
Parisi, M. et al. A survey of ovary-, testis-, and soma-biased gene expression in Drosophila melanogaster adults. Genome Biol. 5, R40 (2004).
Article  PubMed  PubMed Central  Google Scholar 

70.
Cao, X. & Jiang, H. An analysis of 67 RNA-seq datasets from various tissues at different stages of a model insect, Manduca sexta. BMC Genomics 18, 796 (2017).
Article  CAS  PubMed  PubMed Central  Google Scholar 

71.
McKenna, D. D. et al. Genome of the Asian longhorned beetle (Anoplophora glabripennis), a globally significant invasive species, reveals key functional and evolutionary innovations at the beetle–plant interface. Genome Biol. 17, 227 (2016).
Article  CAS  PubMed  PubMed Central  Google Scholar 

72.
Pauchet, Y. et al. Pyrosequencing the Manduca sexta larval midgut transcriptome: Messages for digestion, detoxification and defence. Insect Mol. Biol. 19, 61–75 (2010).
CAS  Article  PubMed  Google Scholar 

73.
Venancio, T., Cristofoletti, P., Ferreira, C., Verjovski-Almeida, S. & Terra, W. The Aedes aegypti larval transcriptome: A comparative perspective with emphasis on trypsins and the domain structure of peritrophins. Insect Mol. Biol. 18, 33–44 (2009).
CAS  Article  PubMed  Google Scholar 

74.
Graveley, B. R. et al. The developmental transcriptome of Drosophila melanogaster. Nature 471, 473–479 (2011).
ADS  CAS  Article  PubMed  Google Scholar 

75.
Shim, J., Gururaja-Rao, S. & Banerjee, U. Nutritional regulation of stem and progenitor cells in Drosophila. Development 140, 4647–4656 (2013).
CAS  Article  PubMed  PubMed Central  Google Scholar 

76.
Terashima, J. & Bownes, M. A microarray analysis of genes involved in relating egg production to nutritional intake in Drosophila melanogaster. Cell Death Differ. 12, 429 (2005).
CAS  Article  PubMed  Google Scholar 

77.
Xie, T. & Spradling, A. C. Decapentaplegic is essential for the maintenance and division of germline stem cells in the Drosophila ovary. Cell 94, 251–260 (1998).
CAS  Article  PubMed  Google Scholar 

78.
Newfeld, S. J., Chartoff, E. H., Graff, J. M., Melton, D. A. & Gelbart, W. M. Mothers against dpp encodes a conserved cytoplasmic protein required in DPP/TGF-beta responsive cells. Development 122, 2099–2108 (1996).
CAS  PubMed  Google Scholar 

79.
Soller, M., Bownes, M. & Kubli, E. Control of oocyte maturation in sexually mature Drosophila females. Dev. Biol. 208, 337–351 (1999).
CAS  Article  PubMed  Google Scholar 

80.
Qazi, M. C. B., Heifetz, Y. & Wolfner, M. F. The developments between gametogenesis and fertilization: Ovulation and female sperm storage in Drosophila melanogaster. Dev. Biol. 256, 195–211 (2003).
Article  CAS  Google Scholar 

81.
Lefebvre, F. A. & Lécuyer, É. Flying the RNA nest: Drosophila reveals novel insights into the transcriptome dynamics of early development. J. Dev. Biol. 6, 5 (2018).
Article  CAS  PubMed Central  Google Scholar 

82.
Kim, T. & Kim, Y. Overview of innate immunity in Drosophila. J. Biochem. Mol. Biol. 38, 121 (2005).
CAS  PubMed  Google Scholar 

83.
Gilmore, T. D. Introduction to NF-κB: Players, pathways, perspectives. Oncogene 25, 6680 (2006).
CAS  Article  PubMed  Google Scholar 

84.
Sosic, D. & Olson, E. N. A new twist on twist: Modulation of the NF-KappaB pathway. Cell Cycle 2, 75–77 (2003).
Article  Google Scholar 

85.
Swevers, L., Raikhel, A., Sappington, T., Shirk, P. & Iatrou, K. Vitellogenesis and post-vitellogenic maturation of the insect ovarian follicle. In Comprehensive Molecular Insect Science (eds Gilbert, L. I. et al.) (Elsevier, Amsterdam, 2005).
Google Scholar 

86.
Güiza, J., Barria, I., Saez, J. C. & Vega, J. L. Innexins: Expression, regulation and functions. Front. Physiol. 9, 1414 (2018).
Article  PubMed  PubMed Central  Google Scholar 

87.
Bauer, R. et al. Intercellular communication: The Drosophila innexin multiprotein family of gap junction proteins. Chem. Biol. 12, 515–526 (2005).
CAS  Article  PubMed  Google Scholar 

88.
Richard, M. & Hoch, M. Drosophila eye size is determined by innexin 2-dependent decapentaplegic signalling. Dev. Biol. 408, 26–40 (2015).
CAS  Article  PubMed  Google Scholar 

89.
De Keuckelaere, E., Hulpiau, P., Saeys, Y., Berx, G. & Van Roy, F. Nanos genes and their role in development and beyond. Cell. Mol. Life Sci. 75, 1929–1946 (2018).
Article  CAS  PubMed  Google Scholar 

90.
Quinlan, M. E. Cytoplasmic streaming in the Drosophila oocyte. Annu. Rev. Cell Dev. Biol. 32, 173–195 (2016).
CAS  Article  PubMed  Google Scholar 

91.
Doyen, C. M. et al. A testis-specific chaperone and the chromatin remodeler ISWI mediate repackaging of the paternal genome. Cell Rep. 13, 1310–1318 (2015).
CAS  Article  PubMed  Google Scholar 

92.
Tirmarche, S., Kimura, S., Dubruille, R., Horard, B. & Loppin, B. Unlocking sperm chromatin at fertilization requires a dedicated egg thioredoxin in Drosophila. Nat. Commun. 7, 13539 (2016).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

93.
Loppin, B., Dubruille, R. & Horard, B. The intimate genetics of Drosophila fertilization. Open Biol. 5, 150076 (2015).
Article  CAS  PubMed  PubMed Central  Google Scholar 

94.
Sato, M. & Sato, K. Degradation of paternal mitochondria by fertilization-triggered autophagy in C. elegans embryos. Science 334, 1141–1144 (2011).
ADS  CAS  Article  PubMed  Google Scholar 

95.
DeLuca, S. Z. & O’Farrell, P. H. Barriers to male transmission of mitochondrial DNA in sperm development. Dev. Cell 22, 660–668 (2012).
CAS  Article  PubMed  PubMed Central  Google Scholar 

96.
Bastock, R. & St Johnston, D. Drosophila oogenesis. Curr. Biol. 18, R1082–R1087 (2008).
CAS  Article  PubMed  Google Scholar 

97.
Kaulenas, M. Structure and function of the female accessory reproductive systems. In Insect Accessory Reproductive Structures (ed. Kaulenas, M.) 33–121 (Springer, Berlin, 1992).
Google Scholar 

98.
Orr-Weaver, T. L. When bigger is better: The role of polyploidy in organogenesis. Trends Genet. 31, 307–315 (2015).
CAS  Article  PubMed  PubMed Central  Google Scholar 

99.
Perry, J. C., Harrison, P. W. & Mank, J. E. The ontogeny and evolution of sex-biased gene expression in Drosophila melanogaster. Mol. Biol. Evol. 31, 1206–1219 (2014).
CAS  Article  PubMed  PubMed Central  Google Scholar 

100.
Vanderperre, B. et al. MPC1-like is a placental mammal-specific mitochondrial pyruvate carrier subunit expressed in postmeiotic male germ cells. J. Biol. Chem. 291, 16448–16461 (2016).
CAS  Article  PubMed  PubMed Central  Google Scholar 

101.
Rato, L. et al. Metabolic regulation is important for spermatogenesis. Nat. Rev. Urol. 9, 330 (2012).
CAS  Article  PubMed  Google Scholar 

102.
Ramalho-Santos, J. et al. Mitochondrial functionality in reproduction: From gonads and gametes to embryos and embryonic stem cells. Hum. Reprod. 15, 553–572 (2009).
CAS  Google Scholar 

103.
Silva, J. V. et al. Amyloid precursor protein interaction network in human testis: Sentitnel proteins for male reproduction. BMC Bioinform. 16, 12 (2015).
Article  CAS  Google Scholar 

104.
De Gregorio, E., Spellman, P. T., Rubin, G. M. & Lemaitre, B. Genome-wide analysis of the Drosophila immune response by using oligonucleotide microarrays. Proc. Natl. Acad. Sci. U.S.A. 98, 12590–12595 (2001).
ADS  Article  PubMed  PubMed Central  Google Scholar 

105.
Irving, P. et al. A genome-wide analysis of immune responses in Drosophila. Proc. Natl. Acad. Sci. U.S.A. 98, 15119–15124 (2001).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

106.
Brucker, R. M., Funkhouser, L. J., Setia, S., Pauly, R. & Bordenstein, S. R. Insect Innate Immunity Database (IIID): An annotation tool for identifying immune genes in insect genomes. PLoS ONE 7, e45125 (2012).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

107.
Palmer, W. J. & Jiggins, F. M. Comparative genomics reveals the origins and diversity of arthropod immune systems. Mol. Biol. Evol. 32, 2111–2129 (2015).
CAS  Article  PubMed  PubMed Central  Google Scholar 

108.
Stroumbakis, N. D., Li, Z. & Tolias, P. P. A homolog of human transcription factor NF-X1 encoded by the Drosophila shuttle craft gene is required in the embryonic central nervous system. Mol. Cell. Biol. 16, 192–201 (1996).
CAS  Article  PubMed  PubMed Central  Google Scholar 

109.
Malik, A. et al. Development of resistance mechanism in mosquitoes: Cytochrome P450, the ultimate detoxifier. J. Appl. Emerg. Sci. 4, 100–117 (2016).
Google Scholar 

110.
Rial, D. et al. Toxicity of seabird guano to sea urchin embryos and interaction with Cu and Pb. Chemosphere 145, 384–393 (2016).
ADS  CAS  Article  PubMed  Google Scholar 

111.
Rinehart, J. P. et al. Continuous up-regulation of heat shock proteins in larvae, but not adults, of a polar insect. Proc. Natl. Acad. Sci. U.S.A. 103, 14223–14227 (2006).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

112.
Michaud, M. R. et al. Metabolomics reveals unique and shared metabolic changes in response to heat shock, freezing and desiccation in the Antarctic midge, Belgica antarctica. J. Insect Physiol. 54, 645–655 (2008).
Article  CAS  Google Scholar 

113.
Teets, N. M., Kawarasaki, Y., Lee, R. E. Jr. & Denlinger, D. L. Expression of genes involved in energy mobilization and osmoprotectant synthesis during thermal and dehydration stress in the Antarctic midge, Belgica antarctica. J. Comp. Physiol. B. 183, 189–201 (2013).
CAS  Article  PubMed  Google Scholar 

114.
Lv, D. K. et al. Profiling of cold-stress-responsive miRNAs in rice by microarrays. Gene 459, 39–47 (2010).
CAS  Article  PubMed  Google Scholar 

115.
Zhang, J., Marshall, K. E., Westwood, J. T., Clark, M. S. & Sinclair, B. J. Divergent transcriptomic responses to repeated and single cold exposures in Drosophila melanogaster. J. Exp. Biol. 214, 4021–4029 (2011).
Article  PubMed  Google Scholar 

116.
Ronges, D., Walsh, J. P., Sinclair, B. J. & Stillman, J. H. Changes in extreme cold tolerance, membrane composition and cardiac transcriptome during the first day of thermal acclimation in the porcelain crab Petrolisthes cinctipes. J. Exp. Biol. 215, 1824–1836 (2012).
CAS  Article  PubMed  Google Scholar 

117.
Dunning, L. T. et al. Identification of cold-responsive genes in a New Zealand alpine stick insect using RNA-Seq. Compar. Biochem. Physiol. D Genomics Proteomics 8, 24–31 (2013).
CAS  Article  Google Scholar 

118.
Lee, S.-M., Lee, S.-B., Park, C.-H. & Choi, J. Expression of heat shock protein and hemoglobin genes in Chironomus tentans (Diptera, chironomidae) larvae exposed to various environmental pollutants: A potential biomarker of freshwater monitoring. Chemosphere 65, 1074–1081 (2006).
ADS  CAS  Article  PubMed  Google Scholar 

119.
Gusev, O. et al. Comparative genome sequencing reveals genomic signature of extreme desiccation tolerance in the anhydrobiotic midge. Nat. Commun. 5, 4784 (2014).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

120.
Kaiser, T. S. et al. The genomic basis of circadian and circalunar timing adaptations in a midge. Nature 540, 69 (2016).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

121.
Tran, D. T. & Ten Hagen, K. G. Mucin-type O-glycosylation during development. J. Biol. Chem. 288, 6921–6929 (2013).
CAS  Article  PubMed  PubMed Central  Google Scholar 

122.
Tian, E. & Hagen, K. G. T. O-linked glycan expression during Drosophila development. Glycobiology 17, 820–827 (2007).
CAS  Article  PubMed  Google Scholar 

123.
Zhang, L., Zhang, Y. & Ten Hagen, K. G. A mucin-type O-glycosyltransferase modulates cell adhesion during Drosophila development. J. Biol. Chem. 283, 34076–34086 (2008).
CAS  Article  PubMed  PubMed Central  Google Scholar 

124.
Tran, D. T. et al. Multiple members of the UDP-GalNAc: Polypeptide N-acetylgalactosaminyltransferase family are essential for viability in Drosophila. J. Biol. Chem. 287, 5243–5252 (2012).
CAS  Article  PubMed  Google Scholar 

125.
Sirot, L. K., Wong, A., Chapman, T. & Wolfner, M. F. Sexual conflict and seminal fluid proteins: A dynamic landscape of sexual interactions. Cold Spring Harbor Perspect. Biol. 7, a017533 (2015).
Article  CAS  Google Scholar 

126.
Baldini, F., Gabrieli, P., Rogers, D. W. & Catteruccia, F. Function and composition of male accessory gland secretions in Anopheles gambiae: A comparison with other insect vectors of infectious diseases. Pathog. Glob. Health 106, 82–93 (2012).
Article  PubMed  PubMed Central  Google Scholar 

127.
Tian, C.-B. et al. Comparative transcriptome analysis of three Bactrocera dorsalis (Diptera: Tephritidae) organs to identify functional genes in the male accessory glands and ejaculatory duct. Florida Entomol. 100, 42–51 (2017).
Article  Google Scholar 

128.
Abraham, S. et al. The male ejaculate as inhibitor of female remating in two tephritid flies. J. Insect Physiol. 88, 40–47 (2016).
CAS  Article  PubMed  Google Scholar 

129.
Denis, B. et al. Male accessory gland proteins affect differentially female sexual receptivity and remating in closely related Drosophila species. J. Insect Physiol. 99, 67–77 (2017).
CAS  Article  PubMed  Google Scholar 

130.
Attardo, G. M. et al. Ladybird late homeodomain factor regulates lactation specific expression of milk proteins during pregnancy in the tsetse fly (Glossina morsitans). PLoS Negl. Trop. Dis. 8, e2645 (2014).
Article  CAS  PubMed  PubMed Central  Google Scholar 

131.
Internation Glossina Genome Consortium. Genome sequence of the tsetse fly (Glossina morsitans): Vector of African trypanosomiasis. Science 344, 380–386 (2014).
Article  CAS  Google Scholar 

132.
Larsen, W. J. Cell remodeling in the fat body of an insect. Tissue Cell 8, 73–92 (1976).
CAS  Article  PubMed  Google Scholar 

133.
Keeley, L. Endocrine regulation of fat body development and function. Annu. Rev. Entomol. 23, 329–352 (1978).
CAS  Article  Google Scholar 

134.
Denlinger, D. L. & Ma, W.-C. Dynamics of the pregnancy cycle in the tsetse Glossina morsitans. J. Insect Physiol. 20, 1015–1026 (1974).
CAS  Article  PubMed  Google Scholar 

135.
Ma, W. C., Denlinger, D. L., Jarlfors, U. & Smith, D. S. Structural modulations in the tsetse fly milk gland during a pregnancy cycle. Tissue Cell 7, 319–330 (1975).
CAS  Article  PubMed  Google Scholar 

136.
Otti, O., McTighe, A. P. & Reinhardt, K. In vitro antimicrobial sperm protection by an ejaculate-like substance. Funct. Ecol. 27, 219–226 (2013).
Article  Google Scholar 

137.
Otti, O., Naylor, R. A., Siva-Jothy, M. T. & Reinhardt, K. Bacteriolytic activity in the ejaculate of an insect. Am. Nat. 174, 292–295 (2009).
Article  PubMed  Google Scholar 

138.
Benoit, J. B., Lopez-Martinez, G., Elnitsky, M. A., Lee, R. E. & Denlinger, D. L. Moist habitats are essential for adults of the Antarctic midge, Belgica antarctica (Diptera: Chironomidae), to avoid dehydration. Eur. J. Entomol. 104, 9–14 (2007).
Article  Google Scholar 

139.
Aguila, J. R., Hoshizaki, D. K. & Gibbs, A. G. Contribution of larval nutrition to adult reproduction in Drosophila melanogaster. J. Exp. Biol. 216, 399–406 (2013).
Article  PubMed  Google Scholar 

140.
Aguila, J. R., Suszko, J., Gibbs, A. G. & Hoshizaki, D. K. The role of larval fat cells in adult Drosophila melanogaster. J. Exp. Biol. 210, 956–963 (2007).
Article  PubMed  Google Scholar 

141.
Rosa, E. & Saastamoinen, M. Sex-dependent effects of larval food stress on adult performance under semi-natural conditions: Only a matter of size? Oecologia 184, 633–642 (2017).
ADS  Article  PubMed  PubMed Central  Google Scholar 

142.
Hagan, R. W. et al. Dehydration prompts increased activity and blood feeding by mosquitoes. Sci. Rep. 8, 6804 (2018).
ADS  Article  CAS  PubMed  PubMed Central  Google Scholar 

143.
Grabherr, M. G. et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat. Biotechnol. 29, 644–652 (2011).
CAS  Article  PubMed  PubMed Central  Google Scholar 

144.
Goecks, J., Nekrutenko, A. & Taylor, J. Galaxy: A comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome Biol. 11, R86 (2010).
Article  PubMed  PubMed Central  Google Scholar 

145.
Afgan, E. et al. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update. Nucleic Acids Res. 46, W537–W544 (2018).
CAS  Article  PubMed  PubMed Central  Google Scholar 

146.
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
Article  CAS  PubMed  PubMed Central  Google Scholar 

147.
da Huang, W. et al. Extracting biological meaning from large gene lists with DAVID. Curr. Protoc. Bioinform. https://doi.org/10.1002/0471250953.bi131 (2009).
Article  Google Scholar 

148.
Conesa, A. et al. Blast2GO: A universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics 21, 3674–3676 (2005).
CAS  Article  PubMed  Google Scholar 

149.
Clark, N. & Maayan, A. Introduction to statistical methods for analyzing large data sets: Gene set enrichment analysis (GSEA). Sci. Signal. https://doi.org/10.1126/scisignal.2001966 (2011).
Article  PubMed  PubMed Central  Google Scholar 

150.
Kim, S. et al. Genome sequencing of the winged midge, Parochlus steinenii, from the Antarctic Peninsula. GigaScience 6, 009 (2017).
Google Scholar 

151.
Benoit, J. B. et al. Unique features of a global human ectoparasite identified through sequencing of the bed bug genome. Nat. Commun. 7, 10165 (2016).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

152.
Eddy, S. R. A new generation of homology search tools based on probabilistic inference. In Genome Informatics 2009: Genome Informatics Series (eds Morishita, S. et al.) 205–211 (World Scientific, Yokohama, 2009).
Google Scholar 

153.
Weirauch, M. T. & Hughes, T. A catalogue of eukaryotic transcription factor types, their evolutionary origin, and species distribution. In A Handbook of Transcription Factors (ed. Hughes, T. R.) 25–73 (Springer, Dordrecht, 2011).
Google Scholar 

154.
Weirauch, M. T. et al. Determination and inference of eukaryotic transcription factor sequence specificity. Cell 158, 1431–1443 (2014).
CAS  Article  PubMed  PubMed Central  Google Scholar 

155.
Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).
CAS  Article  PubMed  PubMed Central  Google Scholar 

156.
Rosendale, A. J., Romick-Rosendale, L. E., Watanabe, M., Dunlevy, M. E. & Benoit, J. B. Mechanistic underpinnings of dehydration stress in ticks revealed through RNA-seq and metabolomics. J. Exp. Biol. 219, 1808–1819 (2016).
Article  PubMed  Google Scholar 

157.
Benoit, J. B. et al. Rapid autophagic regression of the milk gland during involution is critical for maximizing tsetse viviparous reproductive output. PLoS Negl. Trop. Dis. 12, e0006204 (2018).
Article  CAS  PubMed  PubMed Central  Google Scholar 

158.
Turnier, J. L. et al. Discovery of SERPINA3 as a candidate urinary biomarker of lupus nephritis activity. Rheumatology 58, 321–330 (2018).
Article  CAS  Google Scholar 

159.
Heaven, M. R. et al. Systematic evaluation of data-independent acquisition for sensitive and reproducible proteomics—A prototype design for a single injection assay. J. Mass Spectrom. 51, 1–11 (2016).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

160.
Convey, P. Aspects of the biology of the midge, Eretmoptera murphyi Schaeffer (Diptera: Chironomidae), introduced to Signy Island, maritime Antarctic. Polar Biol. 12, 653–657 (1992).
Article  Google Scholar 

161.
Lefkovitch, L. The study of population growth in organisms grouped by stages. Biometrics 21, 1–18 (1965).
Article  Google Scholar  More

1.
Myers, N., Mittermeier, R. A., Mittermeier, C. G., Da Fonseca, G. A. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853 (2000).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 
2.
Whittaker, R. J. & Fernández-Palacios, J. M. Island Biogeography: Ecology, Evolution, and Conservation (Oxford University Press, Oxford, 2007).
Google Scholar 

3.
Poulakakis, N. et al. A review of phylogeographic analyses of animal taxa from the Aegean and surrounding regions. J. Zool. Syst. Evol. Res. 53, 18–32 (2015).
Article  Google Scholar 

4.
4Woodward, J. The Physical Geography of the Mediterranean. Vol. 8 (Oxford University Press on Demand, 2009).

5.
De Figueroa, J. M. T., López-Rodríguez, M. J., Fenoglio, S., Sánchez-Castillo, P. & Fochetti, R. Freshwater biodiversity in the rivers of the Mediterranean Basin. Hydrobiologia 719, 137–186 (2013).
Article  Google Scholar 

6.
Triantis, K. A. & Mylonas, M. Greek islands biology. In Encyclopedia of Islands (eds Gillespie, R. & Glague, D. A.) 388–392 (University of California Press, Berkeley, CA, 2009).
Google Scholar 

7.
Meulenkamp, J. The Neogene in the southern Aegean area. Opera Bot. 30, 5–12 (1971).
Google Scholar 

8.
van der Geer, A., Dermitzakis, M. & de Vos, J. Crete before the Cretans: The reign of dwarfs. Pharos 13, 121–132 (2006).
Google Scholar 

9.
9Dermitzakis, M. & Papanikolaou, D. In Annales geologiques des pays helleniques. 245–289.

10.
Perissoratis, C. & Conispoliatis, N. The impacts of sea-level changes during latest Pleistocene and Holocene times on the morphology of the Ionian and Aegean seas (SE Alpine Europe). Mar. Geol. 196, 145–156 (2003).
ADS  Article  Google Scholar 

11.
MacNeil, C., Dick, J. T. & Elwood, R. W. The trophic ecology of freshwater Gammarus spp. (Crustacea: Amphipoda): Problems and perspectives concerning the functional feeding group concept. Biol. Rev. 72, 349–364 (1997).
Article  Google Scholar 

12.
Kelly, D. W., Dick, J. T. & Montgomery, W. I. The functional role of Gammarus (Crustacea, Amphipoda): Shredders, predators, or both?. Hydrobiologia 485, 199–203 (2002).
Article  Google Scholar 

13.
Bilton, D. T., Freeland, J. R. & Okamura, B. Dispersal in freshwater invertebrates. Annu. Rev. Ecol. Syst. 32, 159–181 (2001).
Article  Google Scholar 

14.
Hupało, K., Mamos, T., Wrzesińska, W. & Grabowski, M. First endemic freshwater Gammarus from Crete and its evolutionary history—An integrative taxonomy approach. PeerJ 6, e4457 (2018).
Article  PubMed  PubMed Central  Google Scholar 

15.
Karaman, G. S. & Pinkster, S. Freshwater Gammarus species from Europe, North Africa and Adjacent Regions of Asia (Crustacea-Amphipoda).: Part I. Gammarus pulex-Group and Related Species. Bijdragen tot de Dierkunde 47, 1–97 (1977).
Article  Google Scholar 

16.
Karaman, G. S. & Pinkster, S. Freshwater Gammarus species from Europe, North Africa and adjacent regions of Asia (Crustacea-Amphipoda): Part II. Gammarus roeseli-group and related species. Bijdragen tot de Dierkunde 47, 165–196 (1977).
Article  Google Scholar 

17.
Karaman, G. S. & Pinkster, S. Freshwater Gammarus Species from Europe, North Africa and Adjacent Regions of Asia (Crustacea-Amphipoda).: Part III. Gammarus balcanicus-Group and Related Species. Bijdragen tot de Dierkunde 57, 207–260 (1987).
Article  Google Scholar 

18.
Pinkster, S. A revision of the genus Echinogammarus Stebbing, 1899 with some notes on related genera (Crustacea, Amphipoda). Memorie del Museo Civico di Storia Naturale (IIa serie) Sezione Scienze della Vita (A. Biologia) 10, 1–183 (1993).
Google Scholar 

19.
Copilaş-Ciocianu, D. & Petrusek, A. The southwestern Carpathians as an ancient centre of diversity of freshwater gammarid amphipods: Insights from the Gammarus fossarum species complex. Mol. Ecol. 24, 3980–3992 (2015).
Article  PubMed  Google Scholar 

20.
Copilaş-Ciocianu, D. & Petrusek, A. Phylogeography of a freshwater crustacean species complex reflects a long-gone archipelago. J. Biogeogr. 44, 421–432 (2017).
Article  Google Scholar 

21.
Grabowski, M., Mamos, T., Bącela-Spychalska, K., Rewicz, T. & Wattier, R. A. Neogene paleogeography provides context for understanding the origin and spatial distribution of cryptic diversity in a widespread Balkan freshwater amphipod. PeerJ 5, e3016 (2017).
Article  PubMed  PubMed Central  Google Scholar 

22.
Grabowski, M., Wysocka, A. & Mamos, T. Molecular species delimitation methods provide new insight into taxonomy of the endemic gammarid species flock from the ancient Lake Ohrid. Zool. J. Linn. Soc. 181, 272–285 (2017).
Google Scholar 

23.
Hou, Z., Sket, B. & Li, S. Phylogenetic analyses of Gammaridae crustacean reveal different diversification patterns among sister lineages in the Tethyan region. Cladistics 30, 352–365 (2014).
Article  Google Scholar 

24.
Katouzian, A.-R. et al. Drastic underestimation of amphipod biodiversity in the endangered Irano-Anatolian and Caucasus biodiversity hotspots. Sci. Rep. 6, 22507 (2016).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

25.
Mamos, T., Wattier, R., Burzyński, A. & Grabowski, M. The legacy of a vanished sea: A high level of diversification within a European freshwater amphipod species complex driven by 15 My of Paratethys regression. Mol. Ecol. 25, 795–810 (2016).
Article  PubMed  Google Scholar 

26.
Mamos, T., Wattier, R., Majda, A., Sket, B. & Grabowski, M. Morphological vs molecular delineation of taxa across montane regions in Europe: The case study of Gammarus balcanicus Schäferna, (Crustacea: Amphipoda). J. Zool. Syst. Evol. Res. 52, 237–248 (2014).
Article  Google Scholar 

27.
Hou, Z., Sket, B., Fišer, C. & Li, S. Eocene habitat shift from saline to freshwater promoted Tethyan amphipod diversification. Proc. Natl. Acad. Sci. 108, 14533–14538 (2011).
ADS  CAS  Article  PubMed  Google Scholar 

28.
Özbek, M. & Özkan, N. Gökçeada içsularının Amphipoda (Crustacea: Malacostraca) faunası Amphipoda (Crustacea: Malacostraca) fauna of the inland-waters of Gökçeada Island. Ege J. Fish. Aquat. Sci. 34, 63–67 (2017).
Google Scholar 

29.
Hillis, D. M., Moritz, C., Mable, B. K. & Olmstead, R. G. Molecular systematics Vol. 23 (Sinauer Associates, Sunderland, 1996).
Google Scholar 

30.
Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).
CAS  Article  PubMed  Google Scholar 

31.
Kearse, M. et al. Geneious Basic: An integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28, 1647–1649 (2012).
Article  PubMed  PubMed Central  Google Scholar 

32.
Ratnasingham, S. & Hebert, P. D. BOLD: The Barcode of Life Data System (https://www.barcodinglife.org). Molecular ecology notes 7, 355–364 (2007).

33.
Puillandre, N., Lambert, A., Brouillet, S. & Achaz, G. ABGD, automatic barcode gap discovery for primary species delimitation. Mol. Ecol. 21, 1864–1877 (2012).
CAS  Article  PubMed  Google Scholar 

34.
Monaghan, M. T. et al. Accelerated species inventory on Madagascar using coalescent-based models of species delineation. Syst. Biol. 58, 298–311 (2009).
CAS  Article  PubMed  Google Scholar 

35.
Pons, J. et al. Sequence-based species delimitation for the DNA taxonomy of undescribed insects. Syst. Biol. 55, 595–609 (2006).
Article  PubMed  Google Scholar 

36.
Kapli, P. et al. Multi-rate Poisson tree processes for single-locus species delimitation under maximum likelihood and Markov chain Monte Carlo. Bioinformatics 33, 1630–1638 (2017).
CAS  PubMed  PubMed Central  Google Scholar 

37.
Fourment, M. & Gibbs, M. J. PATRISTIC: A program for calculating patristic distances and graphically comparing the components of genetic change. BMC Evol. Biol. 6, 1 (2006).
Article  CAS  PubMed  PubMed Central  Google Scholar 

38.
Lefébure, T., Douady, C., Gouy, M. & Gibert, J. Relationship between morphological taxonomy and molecular divergence within Crustacea: Proposal of a molecular threshold to help species delimitation. Mol. Phylogenet. Evol. 40, 435–447 (2006).
Article  CAS  Google Scholar 

39.
Kumar, S., Stecher, G. & Tamura, K. MEGA7: Molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol. Biol. Evol. 33, 1870–1874 (2016).
CAS  Article  Google Scholar 

40.
Bouckaert, R. R. & Drummond, A. J. bModelTest: Bayesian phylogenetic site model averaging and model comparison. BMC Evol. Biol. 17, 42 (2017).
Article  PubMed  PubMed Central  Google Scholar 

41.
Xia, X. DAMBE7: New and improved tools for data analysis in molecular biology and evolution. Mol. Biol. Evol. 35, 1550–1552 (2018).
CAS  Article  PubMed  PubMed Central  Google Scholar 

42.
Xia, X., Xie, Z., Salemi, M., Chen, L. & Wang, Y. An index of substitution saturation and its application. Mol. Phylogenet. Evol. 26, 1–7 (2003).
CAS  Article  PubMed  Google Scholar 

43.
Lanfear, R., Calcott, B., Ho, S. Y. & Guindon, S. PartitionFinder: Combined selection of partitioning schemes and substitution models for phylogenetic analyses. Mol. Biol. Evol. 29, 1695–1701 (2012).
CAS  Article  PubMed  Google Scholar 

44.
Bouckaert, R. et al. BEAST 2: A software platform for Bayesian evolutionary analysis. PLoS Comput. Biol. 10, e10003537 (2014).

45.
Baele, G., Li, W. L. S., Drummond, A. J., Suchard, M. A. & Lemey, P. Accurate model selection of relaxed molecular clocks in Bayesian phylogenetics. Mol. Biol. Evol. 30, 239–243 (2012).
Article  CAS  PubMed  PubMed Central  Google Scholar 

46.
Drummond, A. J., Ho, S. Y., Phillips, M. J. & Rambaut, A. Relaxed phylogenetics and dating with confidence. PLoS Biol. 4, e88 (2006).

47.
Wysocka, A. et al. A tale of time and depth: Intralacustrine radiation in endemic Gammarus species flock from the ancient Lake Ohrid. Zool. J. Linn. Soc. 167, 345–359 (2013).
Article  Google Scholar 

48.
Wysocka, A. et al. Origin of the Lake Ohrid gammarid species flock: Ancient local phylogenetic lineage diversification. J. Biogeogr. 41, 1758–1768 (2014).
Article  Google Scholar 

49.
Cristescu, M. E., Hebert, P. D. & Onciu, T. M. Phylogeography of Ponto-Caspian crustaceans: A benthic–planktonic comparison. Mol. Ecol. 12, 985–996 (2003).
CAS  Article  PubMed  Google Scholar 

50.
Nahavandi, N., Ketmaier, V., Plath, M. & Tiedemann, R. Diversification of Ponto-Caspian aquatic fauna: Morphology and molecules retrieve congruent evolutionary relationships in Pontogammarus maeoticus (Amphipoda: Pontogammaridae). Mol. Phylogenet. Evol. 69, 1063–1076 (2013).
Article  PubMed  Google Scholar 

51.
Macdonald Iii, K. S., Yampolsky, L. & Duffy, J. E. Molecular and morphological evolution of the amphipod radiation of Lake Baikal. Mol. Phylogenet. Evol. 35, 323–343 (2005).
Article  CAS  Google Scholar 

52.
Mats, V., Shcherbakov, D. Y. & Efimova, I. Late Cretaceous-Cenozoic history of the Lake Baikal depression and formation of its unique biodiversity. Stratigr. Geol. Correl. 19, 404 (2011).
ADS  Article  Google Scholar 

53.
Sherbakov, D. Y. On the phylogeny of Lake Baikal amphipods in the light of mitochondrial and nuclear DNA sequence data. Crustaceana 72, 911–919 (1999).
Article  Google Scholar 

54.
Copilaş-Ciocianu, D., Sidorov, D. & Gontcharov, A. Adrift across tectonic plates: Molecular phylogenetics supports the ancient Laurasian origin of old limnic crangonyctid amphipods. Organ. Divers. Evol. 19, 191–207 (2019).
Article  Google Scholar 

55.
Sigovini, M., Keppel, E. & Tagliapietra, D. Open Nomenclature in the biodiversity era. Methods Ecol. Evol. 7(10), 1217–1225 (2016).
Article  Google Scholar 

56.
Ketmaier, V., Argano, R. & Caccone, A. Phylogeography and molecular rates of subterranean aquatic stenasellid isopods with a peri-Tyrrhenian distribution. Mol. Ecol. 12, 547–555 (2003).
CAS  Article  PubMed  Google Scholar 

57.
Knowlton, N. & Weigt, L. A. New dates and new rates for divergence across the Isthmus of Panama. Proc. R. Soc. Lond. Ser. B Biol. Sci. 265, 2257–2263 (1998).
Article  Google Scholar 

58.
Papadopoulou, A., Anastasiou, I. & Vogler, A. P. Revisiting the insect mitochondrial molecular clock: The mid-Aegean trench calibration. Mol. Biol. Evol. 27, 1659–1672 (2010).
CAS  Article  PubMed  Google Scholar 

59.
Hamilton, C. A., Hendrixson, B. E., Brewer, M. S. & Bond, J. E. An evaluation of sampling effects on multiple DNA barcoding methods leads to an integrative approach for delimiting species: A case study of the North American tarantula genus Aphonopelma (Araneae, Mygalomorphae, Theraphosidae). Mol. Phylogenet. Evol. 71, 79–93 (2014).
CAS  Article  PubMed  Google Scholar 

60.
Fontaneto, D., Flot, J.-F. & Tang, C. Q. Guidelines for DNA taxonomy, with a focus on the meiofauna. Mar. Biodivers.s 45, 433–451 (2015).
Article  Google Scholar 

61.
Yu, G., Rao, D., Matsui, M. & Yang, J. Coalescent-based delimitation outperforms distance-based methods for delineating less divergent species: The case of Kurixalus odontotarsus species group. Sci. Rep. 7, 1–13 (2017).
Article  CAS  Google Scholar 

62.
De Queiroz, K. Species concepts and species delimitation. Syst. Biol. 56, 879–886 (2007).
Article  PubMed  Google Scholar 

63.
Lagrue, C. et al. Confrontation of cryptic diversity and mate discrimination within G ammarus pulex and G ammarus fossarum species complexes. Freshw. Biol. 59, 2555–2570 (2014).
Article  Google Scholar 

64.
Costa, F., Henzler, C., Lunt, D., Whiteley, N. & Rock, J. Probing marine Gammarus (Amphipoda) taxonomy with DNA barcodes. Syst. Biodivers. 7, 365–379 (2009).
Article  Google Scholar 

65.
Karaman, G. New data on some gammaridean amphipods (Amphipoda, Gammaridea) from Palearctic. Glasnik Sect. Natl. Sci. Monten Acad. Sci. Arts 15, 20–37 (2003).
Google Scholar 

66.
Steininger, F. F. & Rögl, F. Paleogeography and palinspastic reconstruction of the Neogene of the Mediterranean and Paratethys. Geol. Soc. Lond. Spec. Publ. 17, 659–668 (1984).
ADS  Article  Google Scholar 

67.
Lefébure, T. et al. Phylogeography of a subterranean amphipod reveals cryptic diversity and dynamic evolution in extreme environments. Mol. Ecol. 15, 1797–1806 (2006).
Article  CAS  PubMed  Google Scholar 

68.
Sworobowicz, L. et al. Revisiting the phylogeography of Asellus aquaticus in Europe: Insights into cryptic diversity and spatiotemporal diversification. Freshw. Biol. 60, 1824–1840 (2015).
Article  Google Scholar 

69.
Shih, H. T., Yeo, D. C. & Ng, P. K. The collision of the Indian plate with Asia: Molecular evidence for its impact on the phylogeny of freshwater crabs (Brachyura: Potamidae). J. Biogeogr. 36, 703–719 (2009).
Article  Google Scholar 

70.
Jesse, R., Grudinski, M., Klaus, S., Streit, B. & Pfenninger, M. Evolution of freshwater crab diversity in the Aegean region (Crustacea: Brachyura: Potamidae). Mol. Phylogenet. Evol. 59, 23–33 (2011).
Article  PubMed  Google Scholar 

71.
Szarowska, M., Osikowski, A., Hofman, S. & Falniowski, A. Do diversity patterns of the spring-inhabiting snail Bythinella (Gastropoda, Bythinellidae) on the Aegean Islands reflect geological history?. Hydrobiologia 765, 225–243 (2016).
Article  Google Scholar 

72.
Trontelj, P., Machino, Y. & Sket, B. Phylogenetic and phylogeographic relationships in the crayfish genus Austropotamobius inferred from mitochondrial COI gene sequences. Mol. Phylogenet. Evol. 34, 212–226 (2005).
CAS  Article  PubMed  Google Scholar 

73.
Szarowska, M., Hofman, S., Osikowski, A. & Falniowski, A. Divergence preceding island formation among Aegean insular populations of the freshwater snail genus Pseudorientalia (Caenogastropoda: Truncatelloidea). Zoolog. Sci. 31, 680–686 (2014).
Article  PubMed  Google Scholar 

74.
Previšić, A., Walton, C., Kučinić, M., Mitrikeski, P. T. & Kerovec, M. Pleistocene divergence of Dinaric Drusus endemics (Trichoptera, Limnephilidae) in multiple microrefugia within the Balkan Peninsula. Mol. Ecol. 18, 634–647 (2009).
Article  CAS  PubMed  Google Scholar 

75.
Gonçalves, H. et al. Multilocus phylogeography of the common midwife toad, Alytes obstetricans (Anura, Alytidae): Contrasting patterns of lineage diversification and genetic structure in the Iberian refugium. Mol. Phylogenet. Evol. 93, 363–379 (2015).
Article  PubMed  Google Scholar 

76.
Rachalewski, M., Banha, F., Grabowski, M. & Anastácio, P. M. Ectozoochory as a possible vector enhancing the spread of an alien amphipod Crangonyx pseudogracilis. Hydrobiologia 717, 109–117 (2013).
Article  Google Scholar 

77.
Szarowska, M., Hofman, S., Osikowski, A. & Falniowski, A. Daphniola Radoman, 1973 (Caenogastropoda: Truncatelloidea) at east Aegean islands. Folia Malacologica 22 (2014).

78.
Hupało, K. et al. Persistence of phylogeographic footprints helps to understand cryptic diversity detected in two marine amphipods widespread in the Mediterranean basin. Mol. Phylogenet. Evol. 132, 53–66 (2019).
Article  CAS  PubMed  Google Scholar 

79.
Weiss, M., Macher, J. N., Seefeldt, M. A. & Leese, F. Molecular evidence for further overlooked species within the Gammarus fossarum complex (Crustacea: Amphipoda). Hydrobiologia 721, 165–184 (2014).
CAS  Article  Google Scholar 

80.
Hopkins, L. IUCN and the Mediterranean Islands: Opportunities for biodiversity conservation and sustainable use (Gland, Switzerland, International Union for Conservation of Nature, 2002).
Google Scholar 

81.
QGIS Development Team. QGIS Geographic Information System. Open source geospatial foundation project. (2016).

82.
Popov, S. V. et al. Lithological-paleogeographic maps of paratethys: 10 maps late eocene to pliocene. Cour. Forsch Senckenberg 250, 1–46 (2004).
Google Scholar  More

1.
Hutchinson, G. E. Homage to santa rosalia or why are there so many kinds of animals?. Am. Nat. 93, 145–159 (1959).
Article  Google Scholar 
2.
Hubbell, S. P. The Unified Neutral Theory of Biodiversity and Biogeography (Princeton University Press, Princeton, 2001).
Google Scholar 

3.
Leibold, M. A. et al. The metacommunity concept: A framework for multi-scale community ecology. Ecol. Lett. 7, 601–613 (2004).
Article  Google Scholar 

4.
Legendre, P. & Legendre, L. Numerical Ecology (Elsevier, Amsterdam, 2012).
Google Scholar 

5.
Cottenie, K. Integrating environmental and spatial processes in ecological community dynamics. Ecol. Lett. 8, 1175–1182 (2005).
Article  PubMed  Google Scholar 

6.
Dray, S., Legendre, P. & Peres-Neto, P. R. Spatial modelling: a comprehensive framework for principal coordinate analysis of neighbour matrices (PCNM). Ecol. Model. 196, 483–493 (2006).
Article  Google Scholar 

7.
Leibold, M. A. & Chase, J. M. Metacommunity Ecology (Princeton University Press, Princeton, 2018).
Google Scholar 

8.
Grinnell, J. Field tests of theories concerning distributional control. Am. Nat. 51, 115–128 (1917).
Article  Google Scholar 

9.
Elton, C. Competition and the structure of ecological communities. J. Anim. Ecol. 15, 54–68 (1946).
Article  Google Scholar 

10.
Griffith, D. A. & Peres-Neto, P. R. Spatial modeling in ecology: The flexibility of eigenfunction spatial analyses. Ecology 87, 2603–2613 (2006).
Article  PubMed  Google Scholar 

11.
Peres-Neto, P. R., Legendre, P., Dray, S. & Borcard, D. Variation partitioning of species data matrices: Estimation and comparison of fractions. Ecology 87, 2614–2625 (2006).
Article  PubMed  Google Scholar 

12.
Oksanen, J. et al. Vegan: community ecology package. R package version 2.4–5. https://cran.r-project.org/web/packages/vegan/index.html (2017).

13.
Montag, L. F. A. et al. Contrasting associations between habitat conditions and stream aquatic biodiversity in a forest reserve and its surrounding area in the Eastern Amazon. Hydrobiologia 826, 263–277 (2019).
CAS  Article  Google Scholar 

14.
Juen, L. & De Marco, P. Odonate biodiversity in terra-firme forest streamlets in Central Amazonia: On the relative effects of neutral and niche drivers at small geographical extents. Insect Conserv. Divers. 4, 265–274 (2011).
Article  Google Scholar 

15.
Brasil, L. S. et al. Spatial, biogeographic and environmental predictors of diversity in Amazonian Zygoptera. Insect Conserv. Divers. 11, 174–184 (2018).
Article  Google Scholar 

16.
Dambros, C. S., Morais, J. W., Azevedo, R. A. & Gotelli, N. J. Isolation by distance, not rivers, control the distribution of termite species in the Amazonian rain forest. Ecography 40, 1242–1250 (2017).
Article  Google Scholar 

17.
Hepp, L. U., Landeiro, V. L. & Melo, A. S. Experimental assessment of the effects of environmental factors and longitudinal position on alpha and beta diversities of aquatic insects in a neotropical stream. Int. Rev. Hydrobiol. 97, 157–167 (2012).
Article  Google Scholar 

18.
Siqueira, T. et al. Common and rare species respond to similar niche processes in macroinvertebratemetacommunities. Ecography 35, 183–192 (2012).
Article  Google Scholar 

19.
Alahuhta, J. & Heino, J. Spatial extent, regional specificity and metacommunity structuring in lake macrophytes. J. Biogeogr. 40, 1572–1582 (2013).
Article  Google Scholar 

20.
Heino, J. & Alahuhta, J. Elements of regional beetle faunas: Faunal variation and compositional breakpoints along climate, land cover and geographical gradients. J. Anim. Ecol. 84, 427–441 (2015).
Article  PubMed  Google Scholar 

21.
Algarte, V. M., Rodrigues, L., Landeiro, V. L., Siqueira, T. & Bini, L. M. Variance partitioning of deconstructed periphyton communities: Does the use of biological traits matter?. Hydrobiologia 722, 279–290 (2014).
Article  Google Scholar 

22.
Brasil, L. S., Juen, L., Giehl, N. F. S. & Cabette, H. S. R. Effect of environmental and temporal factors on patterns of rarity of ephemeroptera in stream of the braziliancerrado. Neotrop. Entomol. 46, 29–35 (2017).
CAS  Article  PubMed  Google Scholar 

23.
Gaston, K. J. Valuing common species. Science 327, 154–155 (2010).
ADS  CAS  Article  PubMed  Google Scholar 

24.
Gaston, K. J. The importance of being rare. Ecology 487, 46–47 (2012).
CAS  Google Scholar 

25.
Lários, M. C. et al. Evidence of cross-taxon congruence in Neotropical wetlands: Importance of environmental and spatial factors. Glob. Ecol. Conserv. 12, 108–118 (2017).
Article  Google Scholar 

26.
Juen, L. et al. Effects of oil palm plantations on the habitat structure and biota of streams in eastern Amazon. River Res. Appl. 32, 2081–2094 (2016).
Article  Google Scholar 

27.
Legendre, P. & Anderson, M. J. Distance-based redundancy analysis: Testing multispecies responses in multifactorial ecological experiments. Ecol. Monogr. 69, 1–24 (1999).
Article  Google Scholar 

28.
Borcard, D., Gillet, F. & Legendre, P. Numerical Ecology with R (Springer, New York, 2018).
Google Scholar 

29.
Barlow, J. et al. The future of hyperdiverse tropical ecosystems. Nature 559, 517–526 (2018).
ADS  CAS  Article  PubMed  Google Scholar 

30.
Bini, L. M., Diniz-Filho, J. A. F., Rangel, T. F., Bastos, R. P. & Pinto, M. P. Challenging Wallacean and Linnean shortfalls: Knowledge gradients and conservation planning in a biodiversity hotspot. Divers. Distrib. 12, 475–482 (2006).
Article  Google Scholar 

31.
Whittaker, R. J. et al. Conservation biogeography: Assessment and prospect. Divers. Distrib. 11, 3–23 (2005).
Article  Google Scholar 

32.
Crouzeilles, R., Feltran-Barbieri, R., Ferreira, M. S. & Strassburg, B. B. Hard times for the Brazilian environment. Nature ecology & evolution. 1, 1213–1213 (2017).
Article  Google Scholar 

33.
Vieira, T. B. et al. A multiple hypothesis approach to explain species richness patterns in neotropical stream-dweller fish communities. PLoS ONE 13, 1–17 (2018).
Google Scholar 

34.
Brasil, L. S. et al. Net primary productivity and seasonality of temperature and precipitation are predictors of the species richness of the Damselflies in the Amazon. Basic Appl. Ecol. 35, 45–53 (2019).
Article  Google Scholar 

35.
Kéry, M. & Schmid, H. Monitoring programs need to take into account imperfect species detectability. Basic Appl. Ecol. 5, 65–73 (2004).
Article  Google Scholar 

36.
Leitão, R. P. et al. Rare species contribute disproportionately to the functional structure of species assemblages. Proc. R. Soc. B Biol. Sci. 2838, 20160084 (2016).
Article  Google Scholar 

37.
Pereira, D. F. G., Oliveira-Junior, J. M. B. & Juen, L. Environmental changes promote larger species of Odonata (Insecta) in Amazonian streams. Ecol. Ind. 98, 179–192 (2019).
Article  Google Scholar 

38.
Myers, N., Mittermeier, R. A., Mittermeier, C. G., Da Fonseca, G. A. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403(6772), 853 (2000).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

39.
Rodrigues, M. E. et al. Nonlinear responses in damselfly community along a gradient of habitat loss in a savanna landscape. Biol. Conserv. 194, 113–120 (2016).
Article  Google Scholar 

40.
Peel, M. C., Finlayson, B. L. & McMahon, T. A. Updated world map of the Köppen–Geiger climate classification. Hydrol. Earth Syst. Sci. Discuss. 4, 439–473 (2007).
ADS  Article  Google Scholar 

41.
Brasil, L. S. et al. Does the damming of streams in the southern Amazon basin affect dragonfly and damselfly assemblages (Odonata: Insecta)? A preliminary study. Int. J. Odonatol. 17, 187–197 (2014).
Article  Google Scholar 

42.
Lencioni, F. A. A. The Damselflies of Brazil: An Illustrated Guide the Non Coenagrionidae Families (All Print Editora, São Paulo, 2005).
Google Scholar 

43.
Lencioni, F. A. A. The Damselflies of Brazil: An Illustrated Guide—Coenagrionidae (All Print Editora, São Paulo, 2006).
Google Scholar 

44.
Garrison, N. & Ellenrieder, J. A. L. Louton Damselfly Genera of the New World: An Illustrated and Annotated Key to the Zygoptera University Press (Johns Hopkins, Baltimore, 2010).
Google Scholar 

45.
Frissell, C. R., Liss, W. J., Warren, C. E. & Hurley, M. D. A hierarchical framework for stream habitat classification: Viewing streams in a watershed context. Environ. Manag. 10, 199–214 (1986).
ADS  Article  Google Scholar 

46.
Espírito-Santo, H. M. V., Magnusson, W. E., Zuanon, J., Mendonça, F. P. & Landeiro, V. L. Seasonal variation in the composition of fish assemblages in small Amazonian forest streams: Evidence for predictable changes. Freshw. Biol. 54, 536–548 (2009).
Article  Google Scholar 

47.
Uieda, V. S. & Castro, R. M. C. Coleta e fixação de peixes de riachos: Ecologia de peixes de riacho (OecologiaAustralis, Rio de Janeiro, 1999).
Google Scholar 

48.
Planquette, P., Keith, P. & Bail, P. Y. L. Atlas des poissons d’eau douce de Guyane (Service du patrimoine naturel, Paris, 1996).
Google Scholar 

49.
Albert JS. Species Diversity and Phylogenetic Systematics of American Knifefishes (Gymnotiformes, Teleostei). (Miscellaneous Publications of the Museum of Zoology of the University of Michigan, Michigan, 2001).

50.
Kaufmann, P. R., Levine, P., Robison, E. G., Seeliger, C. & Peck, D. V. Quantifying Physical Habitat in Wadeable Streams (U.S. Environmental Protection Agency, Washington, 1999).
Google Scholar 

51.
Peck, D. V. et al. Invironmental Monitoring and Assessment Program-Surface Waters: Western Pilot Study Field Operations Manual for Wadeable Streams (U.S. Environmental ProtectionAgency, Washington, 2006).
Google Scholar 

52.
De Marco, P. & Nobrega, C. C. Evaluating collinearity effects on species distribution models: An approach based on virtual species simulation. PLoS ONE 13, 1–25 (2018).
Google Scholar 

53.
Legendre, P. Spatial autocorrelation: Trouble or new paradigm?. Ecology 74, 1659–1673 (1993).
Article  Google Scholar  More

These bizarre ancient species are rewriting animal evolution – read by Benjamin Thompson
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The Cambrian explosion, around 541 million years ago, has long been regarded as a pivotal point in evolutionary history, as this is when the ancient ancestors of most of today’s animals made their first appearances in the fossil record.
Before this was a period known as the Ediacaran – a time when the world was believed to be populated by strange, simple organisms. But now, modern molecular research techniques, and some newly discovered fossils, are providing evidence that some of these organisms were actually animals, including ones with sophisticated features like legs and guts.
This is an audio version of our feature: These bizarre ancient species are rewriting animal evolution
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Cave-adapted populations of the Mexican tetra, Astyanax mexicanus, have dysregulated blood glucose homeostasis and are insulin-resistant compared to river-adapted (‘surface’) populations. We found that multiple cave populations, including those inhabiting the Tinaja and Pachón caves, carry a mutation in the insulin receptor that leads to decreased insulin binding in vitro and contributes to hyperglycaemia. As part of the analysis that led to this conclusion, we measured fasting blood glucose levels in F2 fish derived from a cross between a surface fish homozygous for the ancestral insulin receptor allele and a cavefish homozygous for the derived allele, allowing us to correlate inheritance of the mutation with inheritance of glucose dysregulation. In this Article, we inadvertently indicated that the cavefish grandparent used in this cross was descended from the Tinaja population. However, subsequent analysis has definitively indicated that this individual actually belongs to the Pachón population. However, as both the Tinaja and Pachón populations carry the same P211L mutation in the insulin receptor, the logic of the experiment, the genotype–phenotype correlation we observed, and the conclusions of the study remain unchanged. Everything in the manuscript is still accurate, other than the name of the cave in the second and third paragraphs on page 649 of the PDF version of the original Article and in Fig. 3b and its legend. This error has not been corrected online. More