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    Spatial–temporal dynamics and driving factor analysis of urban ecological land in Zhuhai city, China

    Land use mapping and accuracy assessment
    According to the land use planning map of Zhuhai city, the characteristics of the city, the status of human activities and land use, and the types of natural ecosystems, we identified and categorized land use into 10 types: woodland, grassland, rainfed cropland, paddy fields, aquaculture areas, reservoirs and pit ponds, tidal flats, rivers and shallow water, built-up land and unutilized land (Supplemental Materials S1: Land use types and descriptions). The ecological land types include woodland, grassland, reservoirs and pit ponds, tidal flats, and rivers and shallow water. Rainfed cropland, paddy fields and aquaculture areas were not included as ecological land types because they are agricultural land mainly used for agricultural production. These land use types are greatly disturbed by humans, their ecological functions are very fragile, and they are affected by economic interests and have low ecological value. Unutilized land provides few ecological benefits and may be converted into built-up land in the short term; thus, its ecological benefits are unsustainable.
    After the preprocessing and splicing of multiperiod satellite RS images, we completed object-based multiscale automatic segmentation and land use classification of the images using eCognition Developer software. Specifically, the Estimation of Scale Parameters (ESP) tool was first used to obtain the local variance parameter, which reflects the internal homogeneity of the segmentation object; then, the rate of change (ROC) of the local variance (LV) parameter was calculated37,38. When the ROC reaches its peak, the corresponding segmentation scale can be used as the optimal segmentation scale37. At the optimal segmentation scale, classification is based on the object unit using the nearest neighbor method of eCognition Developer. The nearest neighbor method is a commonly used supervised classification method that is simple and easy to understand, and it is suitable for multiclassification problems39.
    Finally, based on the preliminary results data of the four stages automatically classified by eCognition Developer, obvious errors and omissions in the data of the preliminary results were revised and improved through manual visual interpretation. The final revised result data were used for the subsequent analysis of the land pattern and its changes.
    This study first drew land use maps for four years: 1991, 2000, 2010, and 2018. We extracted no less than 200 regions of interest (ROIs) in each study year and compared high-resolution Google Earth images to perform a land mapping accuracy assessment. To ensure that the accuracy of each land type was reliably estimated, we confirmed that each land type had at least 10 ROIs when laying out the ROI area. Table 1 shows the land use classification accuracy for the 1991–2018 period. The overall accuracy of the land mapping for 1991, 2000, 2010, and 2018 was 93.4%, 94.1%, 91.1%, and 94.5%, respectively, and the Kappa coefficients were 0.925, 0.933, 0.890, and 0.938, respectively, meeting the research requirements.
    Table 1 Classification accuracy of land use types in Zhuhai city.
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    Spatial patterns and dynamics of ecological land
    From 1991 to 2018, the ecological land in Zhuhai was dominated by woodland and rivers and shallow water, and the overall area of ecological land continuously decreased (Fig. 1). In 1991, the total area of ecological land was 849.4 km2, accounting for 53.7% of Zhuhai’s urban area. In 2018, the area was reduced to 574.6 km2, accounting for only 36.3% of Zhuhai’s urban area.
    Figure 1

    The net change in ecological land in Zhuhai city, 1991–2018. The area of woodland is the largest, followed by the area of rivers and shallow water. The proportions of woodland and grassland in the total area of ecological land increased by 7.6% and 1.3%, respectively. Rivers and shallow water and tidal flats showed downward trends, decreasing by 8.7% and 1.8%, respectively. Reservoirs and pit ponds increased slightly and showed dynamic changes.

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    In 28 years, the amount of ecological land decreased by 32.3%, of which woodland decreased by 24.2% (129.6 km2), tidal flats decreased by 67.2% (19.3 km2), and rivers and shallow water decreased by as much as 51.8% (132.3 km2). The reduction in rivers and shallow water represented the bulk of the reduction in ecological land area (48.1%). In contrast, the area of reservoirs and pit ponds grew slightly while maintaining a steady state, increasing by 1.1 km2. Compared with 1991, the grassland area grew slightly, increasing by 5.3 km2, mainly due to the construction of golf courses and parks. Clearly, there is an order of magnitude difference between the increase and decrease in ecological land.
    From the temporal perspective (Fig. 2), the change in ecological land mainly occurred in the 1991–2000 period. During this period, the reduction in ecological land was the largest (212.3 km2), mainly distributed in the contiguous area of woodland and built-up land in the central and western areas of the Doumen District and in the coastal areas of the Jinwan District and Xiangzhou District. At the same time, there was a small increase in ecological land, mainly due to the restoration and regulation of tidal flats and reservoirs and pit ponds.
    Figure 2

    Ecological land gains and losses in Zhuhai city, 1991–2018. (a,c,e) show an increase in ecological land; (b,d,f) show a decrease in ecological land. The decrease in ecological land is obviously higher than the increase, and there is an increase in the degree of patch fragmentation. The reduced patches are mostly marginal woodland and river and shallow water areas. The boundaries of the map come from the Zhuhai Natural Resources Bureau. The drawing of the map was completed with the support of ArcGIS 10.7 software.

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    Since 2000, ecological environmental protection and construction work have gradually been taken more seriously, and the State Council of China promulgated the “National Ecological Environmental Protection Program”. Local governments at all levels have gradually strengthened their awareness of ecological environmental protection. The occupation of ecological land by urban development has rapidly decreased, while the area of new ecological land formed by ecological protection and ecological restoration has gradually and steadily increased. From 2000 to 2010, the ecological land in Zhuhai decreased by 130.1 km2 and increased by 53.6 km2, with a net reduction of 76.5 km2. From 2010 to 2018, the decrease and increase in ecological land were similar, and the net reduction in area was only 18.6 km2; thus, the spatial distribution and quantity of ecological land in Zhuhai city was approximately stable (Fig. 3).
    Figure 3

    Losses and gains in ecological land area in Zhuhai city, 1991–2018. Green indicates an increase in ecological land, and red indicates a decrease in ecological land. From 1991 to 2000, the net reduction in ecological land was 177.9 km2. From 2000 to 2010, the net reduction in ecological land was 76.5 km2. From 2010 to 2018, the net reduction in ecological land was 18.6 km2.

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    In the 28-year monitoring period of this paper, the reduction in ecological land in the first 10 years (1991–2000) was 0.99 times that in the subsequent 18 years (2000–2018). The total amount of ecological land added in the subsequent 18 years (2000–2018) was 3.5 times that of the first 10 years (1991–2000).
    Landscape characteristics
    At the landscape level (Table 2), the edge density (ED) of ecological land in the study area is significantly lower than that of nonecological land. The ED exhibited a pattern of first increasing, then decreasing, and subsequently slightly increasing (with values of 33.6 in 1991, 37.7 in 2000, 31.8 in 2010, and 34.7 in 2018). The patch density (PD), landscape shape index (LSI), and largest patch index (LPI) had the same trend as that of the ED. These changes indicate that over time, the landscape of ecological land began to experience an increase in fragmentation and a decrease in regularity and continuity; then, the landscape was reintegrated into a more regular and continuous pattern.
    Table 2 Changes in landscape-level indexes in Zhuhai city, 1991–2018.
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    In addition, from 1991 to 2018, the contagion index (CONTAG) of all land in Zhuhai city fluctuated slightly at approximately 55%, and the degree of landscape pattern aggregation did not change much. However, the CONTAG of ecological land was approximately 70%, which was significantly higher than that of nonecological land; this result indicates that the CONTAG and connectivity of ecological land were higher than those of nonecological land. Shannon’s diversity index (SHDI) and Shannon’s evenness index (SHEI) did not change much in the time series, indicating that the landscape diversity of Zhuhai city has basically been stable over the past 28 years. However, compared with 1991, the SHDI and SHEI decreased slightly, indicating that the ecological landscape diversity and uniformity decreased in the study area, while the landscape heterogeneity increased.
    At the class level (Table 3), the PD and the area-weighted mean contiguity index (CONTIG_AM) of woodland remained basically unchanged, the LSI increased from 19.99 to 21.7, and the LPI decreased from 9.6 to 3.9. These changes were caused by the following processes: the expansion of built-up land, the preferential occupation of marginal forestland by built-up land, the reduction in the dominance of the landscape type, and the increasing complexity of the original geometry. However, woodland mainly exists in a continuous form, and these encroachment behaviors have little effect on the number, spatial connectivity or proximity of woodland patches.
    Table 3 Changes in class-level indexes in Zhuhai city, 1991–2018.
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    The PD and LSI of grassland showed downward trends, while the LPI and CONTIG_AM showed upward trends. This result is closely related to the increase in grassland in the study area. The increased grassland caused the number of patches to increase slightly, improving the superiority of the landscape. The construction of artificial grassland is more regular in the shape of grass patches, and the connectivity is enhanced between landscape units.
    In addition, the PD, LSI and LPI of tidal flats showed downward trends, indicating that the development and utilization of tidal flat reclamation were strengthened, the number decreased, and the shape tended to be regular. The landscape characteristics of reservoirs and pit ponds and rivers and shallow water were basically the same: the LSI showed an upward trend, indicating that the patches were seriously disturbed by human activities, the large patches experienced continuous fragmentation, and the landscape type shapes were complicated. In contrast, the LPI showed a downward trend, indicating that activities such as sea filling led to a continuous decrease in sea area.
    Ecological quality evaluation
    Ecological quality is used to characterize the conditions of the ecosystem; the ecosystem is disturbed by human activities and land use change, and the ability to provide services is also affected40. The value of ecosystem services is an important comprehensive indicator reflecting ecological quality, and the ecological service value of ecological land is higher than that of nonecological land41. Based on the ecosystem service value coefficient proposed by Xie et al.28, we normalized the coefficient value to 0–1 and used the equivalent area and the average equivalent area, which were used to evaluate the ecological service quality of ecological land.
    The transformation matrix of ecological land and nonecological land shows the following (Table 4): the probability of ecological land being transformed into nonecological land in the periods 1991–2000, 2000–2010 and 2010–2018 was 25.0%, 19.4% and 14.3%, respectively. The contributions of ecological land to nonecological land were 23.3%, 13.2% and 8.4%, respectively. The transformation of ecological land to nonecological land showed a weakening trend after 2000, and the ecological quality showed improvement.
    Table 4 Probability of ecological land being transformed into nonecological land in Zhuhai city, 1991–2018.
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    From 1991 to 2018, the equivalent area of ecological land continued to decrease, but the downward trend gradually stabilized after 2000 (Fig. 4). In 1991, the equivalent area of regional ecological land was 849.4 km2, and in 2000, it was 673.2 km2, indicating a significant decrease in the equivalent area, with a reduction of 20.7%. In 2010, the equivalent area of ecological land further dropped to 600.2 km2, a reduction of 10.8%, although the decrease was significantly smaller than that in the previous period. In 2018, the equivalent area was 574.6 km2, representing a reduction of only 4.3%.
    Figure 4

    Dynamic changes in ecological land quality in Zhuhai city, 1991–2018. From 1991 to 2018, the equivalent area of ecological land in Zhuhai city showed a downward trend, with a decrease of 274.8 km2, i.e., 32.3%. The average equivalent area index showed an upward trend, with an increase of 0.11, i.e., 9.3%.

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    As shown in Fig. 4, the average equivalent area of ecological land showed a continuous upward trend. Specifically, the average equivalent area was 1.14 in 1991, 1.22 in 2000, 1.24 in 2010, and 1.25 in 2018. This result shows that although the ecological land area decreased, the quality of the ecological land gradually improved. In reality, this pattern was manifested as follows: the area of grasslands and reservoirs and pit ponds gradually increased, the degree of landscape fragmentation weakened, and the landscape dominance became more obvious. In addition, these land types have relatively high ecosystem service values among all land types.
    Changes in the center of gravity of ecological land
    From 1991 to 2018, the center of gravity of ecological land shifted to the northeast, and the center of gravity of built-up land shifted to the southwest (Fig. 5).
    Figure 5

    Changes in the center of gravity of ecological land and built-up land in Zhuhai city, 1991–2018. From 1991 to 2018, the center of gravity of ecological land in Zhuhai moved to the northeast by 1346 m. The center of gravity of built-up land moved in the opposite direction, moving 7254 m to the southwest. The boundaries of the map come from the Zhuhai Natural Resources Bureau, and the base map in the main map is the China Online Community Basemap in ArcGIS. The drawing of this map was completed with the support of ArcGIS 10.7 software.

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    From 1991 to 2000, the center of gravity of ecological land moved 404 m to the east and 409 m to the north, and the overall movement was 578 m to the northeast. From 2000 to 2010, the center of gravity of ecological land moved 24 m to the east and 355 m to the north, and the overall movement trend was northward. From 2010 to 2018, the center of gravity of ecological land moved 273 m to the east and 236 m to the north, and the overall movement was 473 m to the northeast. In these three periods, the center of gravity of built-up land moved to the southwest by 2871 m, 3983 m and 424 m. The urban expansion and internal construction mainly experienced a rapid and then slow evolution from the northeast to the southwest.
    From the spatial distribution of all ecological land types, the center of gravity of woodland moved to the southeast (0.68 km) from 1991 to 2018. This movement occurred because human construction activities such as deforestation, urban expansion, and infrastructure construction were prominent in the western and northern parts of Zhuhai during the 1991–2000 period. The movement of the center of gravity of grassland to the east and south was highly related to the construction of golf courses, such as the Zhuxiandong Golf Club in the Xiangzhou District, the Dananshan Cuihu Golf Course in Jinding Town, a golf club in the Jinwan District, and Zhuhai Stadium in the Xiangzhou District. The center of gravity of reservoirs and pit ponds moved southward (2.9 km); the center of gravity of tidal flats moved eastward (5.8 km); and the center of gravity of rivers and shallow water moved northward (3.5 km). These changes were closely related to the reclamation engineering carried out by Zhuhai city in recent years.
    Modeling the ecological land change process
    Changes in urban ecological land are mainly due to the expansion of the outer edge of cities and the oppression of urban internal land development. Therefore, we selected four indicators of natural geography and regional development that might reflect changes in urban expansion and urban construction: elevation, slope, distance from built-up land, and growth rate of built-up land.
    With the support of SPSS software, the equation of the transformation probability of ecological land to nonecological land in Zhuhai can be obtained through the binary logistic regression analysis module. Specifically, this equation is expressed as follows (see Supplemental Materials S2: Parameter of the driving factors for modeling):

    $$P = 1 – frac{1}{{{1 + }e^{{{ – }left( {{0}{text{.069}} times {text{A } + text{ 0}}{.033} times {text{B } + text{ 0}}{.473} times {text{C } – text{ 1}}{.079} times {text{D } – text{ 0}}{.963} times {text{E } – text{ 0}}{.853}} right)}} }}$$
    (1)

    where A is the slope; B is the elevation; C is the distance from built-up land; and D and E are the built-up land growth rates of categories 4 and 5, respectively. The squared maximum likelihood of the numerical values (− 2 log-likelihood) of the model was 18,155.4, and the value of the χ2(5) comprehensive test statistic was 7871.2 (p  More

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    Different organic manure sources and NPK fertilizer on soil chemical properties, growth, yield and quality of okra

    1.
    Gemede, H. F., Ratta, N., Haki, G. D., Woldegiorgis, A. Z. & Beyen, F. Nutritional quality and health benefits of “okra” (Abelmoschus esculentus): A review. Int. J. Nutr. Food Sci. 4(2), 208–215 (2015).
    CAS  Google Scholar 
    2.
    Adekiya, A. O., Aboyeji, C. M., Dunsin, O., Adebiyi, O. V. & Oyinlola, O. T. Effect of urea fertilizer and maize cob ash on soil chemical properties, growth, yield, and mineral composition of okra, Abelmoschus esculentus (L.) Moench. J. Hortic. Res. 26(1), 67–76. https://doi.org/10.2478/johr-2018-0008 (2018).
    CAS  Article  Google Scholar 

    3.
    Oyolu, C. Okra seeds: Potential source of high quality vegetable oil. in Proceedings of 5th Annual Conference Horticulture Society,Nigeria, Nsukka (1983).

    4.
    Adekiya, A. O., Agbede, T. M., Aboyeji, C. M., Dunsin, O. & Ugbe, J. O. Green manures and NPK fertilizer effects on soil properties, growth, yield, mineral and vitamin C composition of okra (Abelmoschus esculentus(L.) Moench). J. Saudi Soc. Agric. Sci. 18, 218–223. https://doi.org/10.1016/j.jssas.2017.05.005 (2019).
    Article  Google Scholar 

    5.
    Oladipo, O. G., Olayinka, A. & Aduayi, E. A. Effects of organic amendments on microbial activity, N and P mineralization in an Alfisol. Environ. Manage. J. 2, 30–40 (2005).
    Google Scholar 

    6.
    Maheswarappa, H. P., Nanjappa, H. V., Hegde, M. R. & Prabhu, S. R. Influence of planting material, plant population and organic manures on yield of East Indian galangal (Kaempferia galanga), soil physico-chemical and biological properties. Indian J. Agron. 44(3), 651–657 (1999).
    Google Scholar 

    7.
    Olowoake, A. A. Influence of organic, mineral and organomineral fertilizers on growth, yield, and soil properties in grain amaranth (Amaranthus cruentus L.). J. Org. 1(1), 39–47 (2014).
    Google Scholar 

    8.
    Agbede, T. M. & Adekiya, A. O. Effect of wood ash, poultry manure and NPK fertilizer on soil and leaf nutrient composition, growth and yield of okra (Abelmoschus esculentus). Emirate J. Food Agric. 24(4), 314–321 (2012).
    Google Scholar 

    9.
    Khandaker, M. M., Jusoh, N., Ralmi, N. H. A. & Ismail, S. Z. The effect of different types of organic fertilizers on growth and yield of Abelmoschus esculentus L. Moench (okra). Bulg. J. Agric. Sci. 23(1), 119–125 (2017).
    Google Scholar 

    10.
    Tiamiyu, R. A., Ahmed, H. G. & Muhammad, A. S. Effect of sources of organic manure on growth and yields of okra (Abelmoschus esculentus L.) in Sokoto, Nigeria. Niger. J. Basic Appl. Sci. 20(3), 213–216 (2012).
    Google Scholar 

    11.
    Fagwalawa, L. D. & Yahaya, S. M. Effect organic manure on the growth and yield of okra. Imperial J. Interdiscipl. Res. 2(3), 130–133 (2016).
    Google Scholar 

    12.
    Akinrinde, E. A. & Obigbesan, G. O. Evaluation of the fertility status of selected soils for crop production in five ecological zones of Nigeria. In Proceedings of the 26th Annual Conference of Soil Science Society of Nigeria (ed. Babalola, O.) 279–288 (University of Agriculture, Ibadan, 2000).
    Google Scholar 

    13.
    Mbah, C. N. & Mbagwu, J. S. C. Effect of organic wastes on physiochemical properties up a dystrice leptosol and maize yield in southeastern—Nigeria. Niger. J. Soil Sci. 16, 96–103 (2006).
    Google Scholar 

    14.
    Adekiya, A. O. Legume mulch materials and poultry manure affect soil properties, and growth and fruit yield of tomato. Agric. Conspect. Sci. 83(2), 161–167 (2018).
    Google Scholar 

    15.
    Agbede, T. M., Adekiya, A. O. & Ogeh, J. S. Response of soil properties and yam yield to Chromolaena odorata (Asteraceae) and Tithonia diversifolia (Asteraceae) mulches. Arch. Agron. Soil Sci. 60(2), 209–224 (2014).
    Google Scholar 

    16.
    Wolf, B. & Snyder, G. H. Sustainable Soils: The Place of Organic Matter in Sustaining Soils and Their Productivity (The Haworth Press Inc., New York, 2003).
    Google Scholar 

    17.
    Togun, A. O., Akanbi, W. B. & Adediran, J. A. Growth, nutrient uptake and yield of tomato in response to different plant residue composts. J. Food Agric. Environ. 2(1), 310–316 (2004).
    Google Scholar 

    18.
    Olaniyi, J. O., Akanbi, W. B., Oladiran, O. A. & Ilupeju, O. T. The effect of organo-mineral and inorganic fertilizers on the growth, fruit yield, quality and chemical compositions of Okra. J. Anim. Plant Sci. 1, 1135–1140 (2010).
    Google Scholar 

    19.
    Ajari, O., Tsado, L. E. K., Oladiran, J. A. & Salako, E. A. Plant height and fruit yield of okra as affected by field application of fertilizer and organic matter in Bida, Nigeria. Niger. Agric. J. 34, 74–80 (2003).
    Google Scholar 

    20.
    Adekiya, A. O., Agbede, T. M., Aboyeji, C. M. & Dunsin, O. Response of okra (Abelmoschus esculentus (L.) Moench) and soil properties to different mulch materials in different cropping seasons. Sci. Hortic. 217, 209–216 (2017).
    CAS  Google Scholar 

    21.
    Adekiya, A. O., Agbede, T. M. & Ojeniyi, S. O. The effect of three years of tillage and poultry manure application on soil and plant nutrient composition, growth and yield of cocoyam. Exp. Agric. 52, 466–476 (2016).
    Google Scholar 

    22.
    Agbede, O. O. Understanding Soil and Plant Nutrition (Petra Digital Press, Abuja, 2009).
    Google Scholar 

    23.
    Oyenuga, V.A. & Fetuga, B.I. Dietary importance of fruits and vegetables. in Proceeding First National Seminar on Fruits and Vegetables, 122–131. University of Ibadan. October 13–17 (1975).

    24.
    Rubatizky, V. E. & Yamaguchi, M. World Vegetables: Principles, Production and Nutritive Values 2nd edn. (International Thomas Publishing, Chapman and Hall, New York, 1997).
    Google Scholar 

    25.
    Blumenthal, J., Battenspenrger, D., Cassman, K. G., Mason, K. G. & Pavlista, A. Importance of nitrogen on crop quality and health. In Nitrogen in the Environment: Sources, Problems and Management 2nd edn (eds Hatfield, J. L. & Folett, R. F.) (Elsevier, Amsterdam, 2008).
    Google Scholar 

    26.
    Mani, S. & Ramanathan, K. M. Effect of nitrogen and potassium on the crude fibre content of bhendi fruit on successive stage of picking. South Indian Hortic. 29(2), 100–104 (1981).
    Google Scholar 

    27.
    Ahmad, E., Moaveni, P. & Farahani, H. A. Effects of planting dates and compost on mucilage variations in borage (Borago officinalis L.) under different chemical fertilization systems. Int. J. Biotechnol. Mol. Biol. Res. 1(5), 58–61 (2010).
    Google Scholar 

    28.
    Leroy, B. M. M., Bommele, L., Reheul, D., Moen, M. & de Neve, S. The application of vegetable, fruit and garden waste (VFG) compost in addition to cattle slurry in a silage maize monoculture: Effects on soil fauna and yield. Eur. J. Soil Biol. 43, 91–100 (2007).
    Google Scholar 

    29.
    Lumpkin, H. Organic vegetable production: A theme for international agricultural research. in Proceedings of the seminar on the production and export of organic fruit and vegetables in Asia. https://www.fao.org/DOCREP/006/AD429E/ad429e13.htm. Accessed 8 Dec 2019 (2003).

    30.
    Weston, L. A. & Barth, M. M. Pre-harvest factors affecting post-harvest quality of vegetables. HortScience 32(5), 812–816 (1997).
    Google Scholar 

    31.
    Lefsrud, M. G., Kopsell, D. A., Kopsell, D. E. & Curran-Celentano, J. Air temperature affect biomass and carotenoid pigment accumulation in kale and spinach grown in a controlled environment. HortScience 40(7), 2026–2030 (2005).
    CAS  Google Scholar 

    32.
    Cardoso, M. O. & Berni, R. F. Nitrogen applied in okra under non-tightness grown and residual fertilization. Hortic. Bras. 30, 645–652 (2012).
    Google Scholar 

    33.
    Gee, G. W. Particle-size analysis. In Methods of Soil Analysis, Part 4. Physical Methods (eds Dane, J. H. & Topp, G. C.) 255–293 (Wiley, Hoboken, 2002).
    Google Scholar 

    34.
    Nelson, D. W. & Sommers, L. E. Total carbon, organic carbon, and organic matter. In Methods of Soil Analysis, Part 3. Chemical Methods (ed. Sparks, D. L.) 961–1010 (Wiley, Hoboken, 1996).
    Google Scholar 

    35.
    Bremner, J. M. Nitrgen-total. In Methods of Soil Analysis, Part 3. Chemical Methods (ed. Sparks, D. L.) 1085–1121 (Wiley, Hoboken, 1996).
    Google Scholar 

    36.
    Frank, K., Beegle, D. & Denning, J. Phosphorus. In Recommended chemical soil test procedures for the North Central Region, North Central Regional Research Publication No. 221 (re-vised) (ed. Brown, J. R.) 21–26 (Missouri Agriculture Experiment Station, Columbia, 1998).
    Google Scholar 

    37.
    Hendershot, W. H. & Lalande, H. Ion exchange and exchangeable cations. In Soil Sampling and Methods of Analysis (ed. Carter, M. R.) (Lewis Publishers, CRC Press, Cambridge, 1993).
    Google Scholar 

    38.
    Omolaiye, J. A. et al. Development of leaf area prediction model of okra (Abelmoschus spp.). Product. Agric. Technol. J. 11(1), 130–136 (2015).
    Google Scholar 

    39.
    AOAC. Official Methods of Analysis of AOAC International (AOAC, Arlington, 2003).
    Google Scholar 

    40.
    Williams, P., El-Baramen, F. J., Nakkow, B. & Rihawi, S. Crop Quality Evaluation Methods and Guidelines (International Centre for Agricultural research in the Dry Area, Aleppo, 1986).
    Google Scholar 

    41.
    Thanatcha, R. & Pranee, A. Extraction and characterization of mucilage in Ziziphus mauritiana Lam. Int. Food Res. J. 18, 201–212 (2011).
    CAS  Google Scholar 

    42.
    Tel, D.A. & Hagarty, M. Soil and Plant Analysis. Study Guide for Agricultural Laboratory Directors and Technologists Working in Tropical Regions. International Institute of Tropical Agriculture, Ibadan, Nigeria, in conjunction with the University of Guelph, Canada (1984).

    43.
    Horwitz, W. & Latimer, G. W. (eds) Official Methods of Analysis of AOAC International (AOAC, Arlington, 2005).
    Google Scholar 

    44.
    Genstat. Genstat 5 Release 3.2 Reference Manual (Oxford University Press, Oxford, 1993).
    Google Scholar  More

  • in

    Thermal melanism explains macroevolutionary variation of dorsal pigmentation in Eurasian vipers

    1.
    Caro, T., Merilaita, S. & Stevens, M. The Colours of animals: from Wallace to the present day I. Cryptic coloration. In Natural Selection and Beyond, pp 125–143 (eds Smith, C. H. & Beccaloni, G.) (Oxford University Press, Oxford, 2008).
    Google Scholar 
    2.
    Ruxton, G. D., Sherratt, T. N. & Speed, M. P. Avoiding Attack (Oxford University Press, Oxford, 2004).
    Google Scholar 

    3.
    Protas, M. E. & Patel, N. H. Evolution of coloration patterns. Annu. Rev. Cell Dev. Biol. 24, 425–446 (2008).
    CAS  PubMed  Article  Google Scholar 

    4.
    Stevens, M. & Ruxton, G. D. Linking the evolution and form of warning coloration in nature. Proc. R. Soc. B 279, 417–426 (2011).
    PubMed  Article  Google Scholar 

    5.
    Clusella-Trullas, S., van Wyk, J. H. & Spotila, J. R. Thermal melanism in ectotherms. J. Therm. Biol. 32, 235–245 (2007).
    Article  Google Scholar 

    6.
    Clusella-Trullas, S., Terblanche, J. S., Blackburn, T. M. & Chown, S. L. Testing the thermal melanism hypothesis: a macrophysiological approach. Funct. Ecol. 22, 232–238 (2008).
    Article  Google Scholar 

    7.
    Clusella-Trullas, S., Wyk, J. H. & Spotila, J. R. Thermal benefits of melanism in cordylid lizards: a theoretical and field test. Ecology 90, 2297–2312 (2009).
    PubMed  Article  Google Scholar 

    8.
    Reguera, S., Zamora-Camacho, F. J. & Moreno-Rueda, G. The lizard Psammodromus algirus (Squamata: Lacertidae) is darker at high altitudes. Biol. J. Linn. Soc. 112, 132–141 (2014).
    Article  Google Scholar 

    9.
    King, R. B. Polymorphic populations of the garter snake Thamnophis sirtalis near Lake Erie. Herpetologica 44, 451–458 (1988).
    Google Scholar 

    10.
    Luiselli, L. Reproductive success in melanistic adders: a new hypothesis and some considerations on Andren and Nilson’s (1981) suggestions. Oikos 1992, 601–604 (1992).
    Article  Google Scholar 

    11.
    Capula, M. & Luiselli, L. Reproductive strategies in alpine adders, Vipera berus. The black females bear more often. Acta Oecol. 15, 207–214 (1994).
    Google Scholar 

    12.
    Castella, B. et al. Melanism, body condition and elevational distribution in the asp viper. J. Zool. 290, 273–280 (2013).
    Article  Google Scholar 

    13.
    Andrén, C. & Nilson, G. Reproductive success and risk of predation in normal and melanistic colour morphs of the adder, Vipera berus. Biol. J. Linn. Soc. 15, 235–246 (1981).
    Article  Google Scholar 

    14.
    Forsman, A. Heating rates and body temperature variation in melanistic and zigzag Vipera berus: does colour make a difference? In Annales Zoologici Fennici 365–374 (Finnish Zoological and Botanical Publishing Board, 1995)

    15.
    Martínez-Freiría, F., de Lanuza, G. P., Pimenta, A. A., Pinto, T. & Santos, X. Aposematism and crypsis are not enough to explain dorsal polymorphism in the Iberian adder. Acta Oecol. 85, 165–173 (2017).
    ADS  Article  Google Scholar 

    16.
    Valkonen, J. K., Niskanen, M., Björklund, M. & Mappes, J. Disruption or aposematism? Significance of dorsal zigzag pattern of European vipers. Evol. Ecol. 25, 1047–1063 (2011).
    Article  Google Scholar 

    17.
    Pizzigalli, C. et al. Eco-geographical determinants of the evolution of ornamentation in vipers. Biol. J. Linn. Soc. 130, 345–358 (2020).
    Article  Google Scholar 

    18.
    Valkonen, J. K., Nokelainen, O. & Mappes, J. Antipredatory function of head shape for vipers and their mimics. PLoS ONE 6, e22272 (2011).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    19.
    Santos, X. et al. Phylogeographic and environmental correlates support the cryptic function of the zigzag pattern in a European viper. Evol. Ecol. 28, 611–626 (2014).
    Article  Google Scholar 

    20.
    Phelps, T. Old World Vipers, a Natural History of the Azemiopinae and Viperinae (Edition Chimaira, Frankfurt, 2010).
    Google Scholar 

    21.
    Freitas, I. et al. Evaluating taxonomic inflation: towards evidence-based species delimitation in Eurasian vipers (Serpentes: Viperinae). Amphibia-Reptilia 41, 285–311 (2020).
    Article  Google Scholar 

    22.
    Hansen, T. F. & Orzack, S. H. Assessing current adaptation and phylogenetic inertia as explanations of trait evolution: the need for controlled comparisons. Evolution 59, 2063–2972 (2005).
    PubMed  Google Scholar 

    23.
    Blomberg, S. P., Garland, T. J. & Ives, A. R. Testing for phylogenetic signal in comparative data: behavioural traits are more labile. Evolution 57, 717–745 (2003).
    PubMed  Article  Google Scholar 

    24.
    Herrmann, H. W., Joger, U. & Nilson, G. Phylogeny and systematics of viperine snakes. III: resurrection of the genus Macrovipera (Reuss, 1927) as suggested by biochemical evidence. Amphibia-Reptilia 13, 375–392 (1992).
    Article  Google Scholar 

    25.
    Nilson, G. & Andrén, C. The meadow and steppe vipers of Europe and Asia—the Vipera (Acridophaga) ursinii complex. Acta Zool. Acad. Sci. Hung. 47, 87–267 (2001).
    Google Scholar 

    26.
    Brito, J. C., Santos, X., Pleguezuelos, J. M. & Sillero, N. Inferring evolutionary scenarios with geostatistics and geographical information systems for the viperid snakes Vipera latastei and Vipera monticola. Biol. J. Linn. Soc. 95, 790–806 (2008).
    Article  Google Scholar 

    27.
    Martínez-Freiría, F. & Brito, J. C. Integrating classical and spatial multivariate analyses for assessing morphological variability in the endemic Iberian viper Vipera seoanei. J. Zool. Syst. Evol. Res. 51, 122–131 (2013).
    Article  Google Scholar 

    28.
    Porter, W. P. & Gates, D. M. Thermodynamic equilibria of animals with environment. Ecol. Monogr. 39, 245–270 (1969).
    Article  Google Scholar 

    29.
    Porter, W. P. & Norris, K. S. Lizard reflectivity change and its effect on light transmission through body wall. Science 163, 482–484 (1969).
    ADS  CAS  PubMed  Article  Google Scholar 

    30.
    Byers, J. A. Analysis of insect and plant colors in digital images using java software on the internet. Ann. Entomol. Soc. Am. 99, 865–874 (2006).
    Article  Google Scholar 

    31.
    Badiane, A., Pérez i de Lanuza, G., Carmen García-Custodio, M. D., Carazo, P. & Font, E. Colour patch size and measurement error using reflectance spectrophotometry. Methods Ecol. Evol. 8, 1585–1593 (2017).
    Article  Google Scholar 

    32.
    Olsson, M., Stuart-Fox, D. & Ballen, C. Genetics and evolution of colour patterns in reptiles. In Seminars in Cell and Developmental Biology (Vol. 24, No. 6–7) 529–541 (Academic Press, 2013)

    33.
    Ducrest, A. L. et al. Pro-opiomelanocortin gene and melanin-based colour polymorphism in a reptile. Biol. J. Linn. Soc. 111, 160–168 (2014).
    Article  Google Scholar 

    34.
    King, R. B. Mendelian inheritance of melanism in the garter snake Thamnophis sirtalis. Herpetologica 59, 484–489 (2003).
    Article  Google Scholar 

    35.
    Westphal, M. F. & Morgan, T. J. Quantitative genetics of pigmentation development in two populations of the common garter snake, Thamnophis sirtalis. J. Hered. 101, 573–580 (2010).
    CAS  PubMed  Article  Google Scholar 

    36.
    Martínez-Freiría, F. & Santos, X. Assessing the heritability of dorsal pattern shape in Vipera latastei. Amphibia-Reptilia 36, 313–317 (2015).
    Article  Google Scholar 

    37.
    Lorioux, S. et al. Stage dependence of phenotypical and phenological maternal effects: insight into squamate reptile reproductive strategies. Am. Nat. 182, 223–233 (2013).
    PubMed  Article  Google Scholar 

    38.
    Bonnet, X., Lorioux, S., Brischoux, F. & De Crignis, M. Is melanism adaptive in sea kraits?. Amphibia-Reptilia 29, 1–5 (2008).
    Article  Google Scholar 

    39.
    Martínez-Freiría, F., Santos, X., Pleguezuelos, J. M., Lizana, M. & Brito, J. C. Geographical patterns of morphological variation and environmental correlates in contact zones: a multi-scale approach using two Mediterranean vipers (Serpentes). J. Zool. Syst. Evol. Res. 47, 357–367 (2009).
    Article  Google Scholar 

    40.
    Drummond, A. J., Suchard, M. A., Xie, D. & Rambaut, A. Bayesian phylogenetics with BEAUti and the BEAST 1.7. Mol. Biol. Evol. 29, 1969–1973 (2012).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    41.
    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  Google Scholar 

    42.
    Rambaut, A. & Drummond, A. J. Tracer v1.6: MCMC Trace Analyses Tool (University of Edinburgh, Edinburgh, 2007).
    Google Scholar 

    43.
    Stadler, T. TreeSim: Simulating Phylogenetic Trees. R package version 2.4. https://CRAN.R-project.org/package=TreeSim (2019)

    44.
    Stümpel, N. & Joger, U. Recent advances in phylogeny and taxonomy of Near and Middle Eastern Vipers—an update. ZooKeys 31, 179 (2009).
    Article  Google Scholar 

    45.
    Göçmen, B., Mebert, K., Karış, M., Oğuz, M. A. & Ursenbacher, S. A new population and subspecies of the critically endangered Anatolian meadow viper Vipera anatolica Eiselt and Baran, 1970 in eastern Antalya province. Amphibia-Reptilia 38, 289–305 (2017).
    Article  Google Scholar 

    46.
    Mebert, K. et al. Mountain vipers in central-eastern Turkey: huge range extensions for four taxa reshape decades of misleading perspectives. Herpetol. Conserv. Biol. 15, 169–187 (2020).
    Google Scholar 

    47.
    Martínez-Freiría, F. et al. Integrative phylogeographical and ecological analysis reveals multiple Pleistocene refugia for Mediterranean Daboia vipers in north-west Africa. Biol. J. Linn. Soc. 122, 366–384 (2017).
    Article  Google Scholar 

    48.
    Freitas, I., Fahd, S., Velo-Antón, G. & Martínez-Freiría, F. Chasing the phantom: biogeography and conservation of Vipera latastei-monticola in the Maghreb (North Africa). Amphibia-Reptilia 39, 145–161 (2018).
    Article  Google Scholar 

    49.
    Ursenbacher, S. et al. Molecular phylogeography of the nose-horned viper (Vipera ammodytes, Linnaeus (1758)): evidence for high genetic diversity and multiple refugia in the Balkan peninsula. Mol. Phylogenet. Evol. 46, 1116–1128 (2008).
    CAS  PubMed  Article  Google Scholar 

    50.
    Stümpel, N., Rajabizadeh, M., Avcı, A., Wüster, W. & Joger, U. Phylogeny and diversification of mountain vipers (Montivipera, Nilson et al., 2001) triggered by multiple Plio-Pleistocene refugia and high-mountain topography in the Near and Middle East. Mol. Phylogenet. Evol. 101, 336–351 (2016).
    PubMed  Article  Google Scholar 

    51.
    Martínez-Freiría, F. et al. Climatic refugia boosted allopatric diversification in western Mediterranean vipers. J. Biogeogr. https://doi.org/10.1111/jbi.13861 (2020).
    Article  Google Scholar 

    52.
    ESRI. ArcGIS 10.5.1, ArcGIS Pro 2.0, and ArcGIS Earth 1.5 Enterprise Deployment (ESRI, California, 2017).
    Google Scholar 

    53.
    R Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. https://www.R-project.org/ (2019)

    54.
    Revell, L. J. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).
    Article  Google Scholar 

    55.
    Collyer, M. L. & Adams, D. C. RRPP: RRPP: an R package for fitting linear models to high-dimensional data using residual randomization. Methods Ecol. Evol. 9, 1772–1779 (2018).
    Article  Google Scholar 

    56.
    Collyer, M. L. & Adams, D. C. RRPP: linear model evaluation with randomized residuals in a permutation procedure. Version 0.4.2.9000. https://CRAN.R-project.org/package=RRPP (2019).

    57.
    Adams, D. C. & Felice, R. N. Assessing trait covariation and morphological integration on phylogenies using evolutionary covariance matrices. PLoS ONE 9, e94335 (2014).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    58.
    Adams, D. C., Collyer, M. L. & Kaliontzopoulou, A. geomorph: software for geometric morphometric analyses. R package version 3.1.2. https://cran.r-project.org/package=geomorph (2019).

    59.
    Paradis, E. & Schliep, K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35, 526–528 (2018).
    Article  CAS  Google Scholar 

    60.
    Harmon, L. J., Weir, J. T., Brock, C. D., Glor, R. E. & Challenger, W. GEIGER: investigating evolutionary radiations. Bioinformatics 24, 129–131 (2008).
    CAS  PubMed  Article  Google Scholar  More

  • in

    The grim truth behind eyewitness accounts of sea serpents

    Hundreds of people in the nineteenth-century United States reported seeing the Gloucester Sea Serpent (above), which was probably a marine creature bedecked with fishing debris. Credit: Museum of Fine Arts, Boston

    Fisheries
    30 September 2020

    Centuries-old ‘unidentified marine objects’ hint that sea creatures have been getting entangled in fishing lines since before the invention of plastic.

    ‘Sea serpents’ spotted around Great Britain and Ireland in the nineteenth century were probably whales and other marine animals ensnared in fishing gear — long before the advent of the plastic equipment usually blamed for such entanglements.
    The snaring of sea creatures in fishing equipment is often considered a modern phenomenon, because the hemp and cotton ropes used in the past degraded more quickly than their plastic counterparts. But Robert France at Dalhousie University in Truro, Canada, identified 51 probable entanglements near Great Britain and Ireland dating as far back as 1809.
    France analysed 214 accounts of ‘unidentified marine objects’ from the early nineteenth century to 2000, looking for observations of a monster that had impressive length, a series of humps protruding above the sea surface and a fast, undulating movement through the water. France says that such accounts describe not sea serpents but whales, basking sharks (Cetorhinus maximus) or other marine animals trailing fishing gear such as buoys or other floats.
    Such first-hand accounts could help researchers to construct a better picture of historical populations of marine species and the pressures they faced, France says. More

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    Gene loss through pseudogenization contributes to the ecological diversification of a generalist Roseobacter lineage

    1.
    Nowell RW, Green S, Laue BE, Sharp PM. The extent of genome flux and its role in the differentiation of bacterial lineages. Genome Biol Evol. 2014;6:1514–29.
    PubMed  PubMed Central  Article  CAS  Google Scholar 
    2.
    Ochman H, Lawrence JG, Groisman EA. Lateral gene transfer and the nature of bacterial innovation. Nature. 2000;405:299–304.
    CAS  PubMed  Article  Google Scholar 

    3.
    Wiedenbeck J, Cohan FM. Origins of bacterial diversity through horizontal genetic transfer and adaptation to new ecological niches. FEMS Microbiol Rev. 2011;35:957–76.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    4.
    Albalat R, Cañestro C. Evolution by gene loss. Nat Rev Genet. 2016;17:379–91.
    CAS  PubMed  Article  Google Scholar 

    5.
    Jacq C, Miller JR, Brownlee GG. A pseudogene structure in 5S DNA of Xenopus laevis. Cell. 1977;12:109–20.
    CAS  PubMed  Article  Google Scholar 

    6.
    Li W-H, Gojobori T, Nei M. Pseudogenes as a paradigm of neutral evolution. Nature. 1981;292:237–9.
    CAS  PubMed  Article  Google Scholar 

    7.
    Ohta T. The nearly neutral theory of molecular evolution. Annu Rev Ecol Syst. 1992;23:263–86.
    Article  Google Scholar 

    8.
    Bolotin E, Hershberg R. Gene loss dominates as a source of genetic variation within clonal pathogenic bacterial species. Genome Biol Evol. 2015;7:2173–87.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    9.
    Hottes AK, Freddolino PL, Khare A, Donnell ZN, Liu JC, Tavazoie S. Bacterial adaptation through loss of function. PLoS Genet. 2013;9:e1003617.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    10.
    Sharma V, Hecker N, Roscito JG, Foerster L, Langer BE, Hiller M. A genomics approach reveals insights into the importance of gene losses for mammalian adaptations. Nat Commun. 2018;9:1–9.
    Article  CAS  Google Scholar 

    11.
    Sokurenko EV, Hasty DL, Dykhuizen DE. Pathoadaptive mutations: gene loss and variation in bacterial pathogens. Trends Microbiol. 1999;7:191–5.
    CAS  PubMed  Article  Google Scholar 

    12.
    Ortega AP, Villagra NA, Urrutia IM, Valenzuela LM, Talamilla-Espinoza A, Hidalgo AA, et al. Lose to win: marT pseudogenization in Salmonella enterica serovar Typhi contributed to the surV-dependent survival to H2O2, and inside human macrophage-like cells. Infect Genet Evol. 2016;45:111–21.
    CAS  PubMed  Article  Google Scholar 

    13.
    Goodhead I, Darby AC. Taking the pseudo out of pseudogenes. Curr Opin Microbiol. 2015;23:102–9.
    CAS  PubMed  Article  Google Scholar 

    14.
    Johnson LJ. Pseudogene rescue: an adaptive mechanism of codon reassignment. J Evol Biol. 2010;23:1623–30.
    CAS  PubMed  Article  Google Scholar 

    15.
    Librado P, Vieira FG, Rozas J. BadiRate: estimating family turnover rates by likelihood-based methods. Bioinformatics. 2012;28:279–81.
    CAS  PubMed  Article  Google Scholar 

    16.
    David LA, Alm EJ. Rapid evolutionary innovation during an Archaean genetic expansion. Nature. 2011;469:93–96.
    CAS  PubMed  Article  Google Scholar 

    17.
    Avni E, Montoya D, Lopez D, Modlin R, Pellegrini M, Snir S. A phylogenomic study quantifies competing mechanisms for pseudogenization in prokaryotes—the Mycobacterium leprae case. PLoS One. 2017;13:e0204322.
    Article  CAS  Google Scholar 

    18.
    Ochman H. The nature and dynamics of bacterial genomes. Science. 2006;311:1730–3.
    CAS  PubMed  Article  Google Scholar 

    19.
    Grote J, Thrash JC, Huggett MJ, Landry ZC, Carini P, Giovannoni SJ, et al. Streamlining and core genome conservation among highly divergent members of the SAR11 clade. mBio. 2012;3:e00252–12.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    20.
    Giovannoni SJ, Cameron Thrash J, Temperton B. Implications of streamlining theory for microbial ecology. ISME J. 2014;8:1553–65.
    PubMed  PubMed Central  Article  Google Scholar 

    21.
    Buchan A, González JM, Moran MA. Overview of the marine Roseobacter lineage. Appl Environ Microbiol. 2005;71:5665–77.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    22.
    Luo H, Moran MA. How do divergent ecological strategies emerge among marine bacterioplankton lineages? Trends Microbiol. 2015;23:577–84.
    CAS  PubMed  Article  Google Scholar 

    23.
    Luo H, Moran MA. Evolutionary ecology of the marine Roseobacter clade. Microbiol Mol Biol Rev. 2014;78:573–87.
    PubMed  PubMed Central  Article  Google Scholar 

    24.
    Tujula NA, Crocetti GR, Burke C, Thomas T, Holmström C, Kjelleberg S. Variability and abundance of the epiphytic bacterial community associated with a green marine Ulvacean alga. ISME J. 2010;4:301–11.
    PubMed  Article  Google Scholar 

    25.
    Littman RA, Willis BL, Pfeffer C, Bourne DG. Diversities of coral-associated bacteria differ with location, but not species, for three acroporid corals on the Great Barrier Reef. FEMS Microbiol Ecol. 2009;68:152–63.
    CAS  PubMed  Article  Google Scholar 

    26.
    Rosenberg E, Koren O, Reshef L, Efrony R, Zilber-Rosenberg I. The role of microorganisms in coral health, disease and evolution. Nat Rev Microbiol. 2007;5:355–62.
    CAS  PubMed  Article  Google Scholar 

    27.
    Sweet MJ, Croquer A, Bythell JC. Bacterial assemblages differ between compartments within the coral holobiont. Coral Reefs. 2011;30:39–52.
    Article  Google Scholar 

    28.
    Crossland CJ, Barnes DJ, Borowitzka MA. Diurnal lipid and mucus production in the staghorn coral Acropora acuminata. Mar Biol. 1980;60:81–90.
    CAS  Article  Google Scholar 

    29.
    Shashar N, Stambler N. Endolithic algae within corals—life in an extreme environment. J Exp Mar Biol Ecol. 1992;163:277–86.
    CAS  Article  Google Scholar 

    30.
    Highsmith RC. Lime-boring algae in hermatypic coral skeletons. J Exp Mar Biol Ecol. 1981;55:267–81.
    Article  Google Scholar 

    31.
    Kühl M, Holst G, Larkum AWD, Ralph PJ. Imaging of oxygen dynamics within the endolithic algal community of the massive coral Porites lobata. J Phycol. 2008;44:541–50.
    PubMed  Article  CAS  Google Scholar 

    32.
    Kalhoefer D, Thole S, Voget S, Lehmann R, Liesegang H, Wollher A, et al. Comparative genome analysis and genome-guided physiological analysis of Roseobacter litoralis. BMC Genomics. 2011;12:324.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    33.
    Lachnit T, Fischer M, Künzel S, Baines JF, Harder T. Compounds associated with algal surfaces mediate epiphytic colonization of the marine macroalga Fucus vesiculosus. FEMS Microbiol Ecol. 2013;84:411–20.
    CAS  PubMed  Article  Google Scholar 

    34.
    Singh RP, Reddy CRK. Seaweed–microbial interactions: key functions of seaweed-associated bacteria. FEMS Microbiol Ecol. 2014;88:213–30.
    CAS  PubMed  Article  Google Scholar 

    35.
    Khailov KM, Burlakova ZP. Release of dissolved organic matter by marine seaweeds and distribution of their total organic production to inshore communities. Limnol Oceanogr. 1969;14:521–7.
    Article  Google Scholar 

    36.
    Wai TC, Ng JSS, Leung KMY, Dudgeon D, Williams GA. The source and fate of organic matter and the significance of detrital pathways in a tropical coastal ecosystem. Limnol Oceanogr. 2008;53:1479–92.
    CAS  Article  Google Scholar 

    37.
    Braeckman U, Pasotti F, Vázquez S, Zacher K, Hoffmann R, Elvert M, et al. Degradation of macroalgal detritus in shallow coastal Antarctic sediments. Limnol Oceanogr. 2019;64:1423–41.
    CAS  PubMed  PubMed Central  Google Scholar 

    38.
    Moran MA, Belas R, Schell MA, Gonzalez JM, Sun F, Sun S, et al. Ecological genomics of marine roseobacters. Appl Environ Microbiol. 2007;73:4559–69.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    39.
    Sonnenschein EC, Nielsen KF, D’Alvise P, Porsby CH, Melchiorsen J, Heilmann J, et al. Global occurrence and heterogeneity of the Roseobacter clade species Ruegeria mobilis. ISME J. 2017;11:569–83.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    40.
    Slightom RN, Buchan A. Surface colonization by marine roseobacters: integrating genotype and phenotype. Appl Environ Microbiol. 2009;75:6027–37.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    41.
    Thole S, Kalhoefer D, Voget S, Berger M, Engelhardt T, Liesegang H, et al. Phaeobacter gallaeciensis genomes from globally opposite locations reveal high similarity of adaptation to surface life. ISME J. 2012;6:2229–44.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    42.
    Newton RJ, Griffin LE, Bowles KM, Meile C, Gifford S, Givens CE, et al. Genome characteristics of a generalist marine bacterial lineage. ISME J. 2010;4:784–98.
    CAS  PubMed  Article  Google Scholar 

    43.
    Brinkhoff T, Giebel H-A, Simon M. Diversity, ecology, and genomics of the Roseobacter clade: a short overview. Arch Microbiol. 2008;189:531–9.
    CAS  PubMed  Article  Google Scholar 

    44.
    Luo H, Löytynoja A, Moran MA. Genome content of uncultivated marine Roseobacters in the surface ocean. Environ Microbiol. 2012;14:41–51.
    CAS  PubMed  Article  Google Scholar 

    45.
    Lerat E, Ochman H. Ψ-Φ: Exploring the outer limits of bacterial pseudogenes. Genome Res. 2004;14:2273–8.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    46.
    Lerat E, Ochman H. Recognizing the pseudogenes in bacterial genomes. Nucleic Acids Res. 2005;33:3125–32.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    47.
    Kuo C-H, Ochman H. The extinction dynamics of bacterial pseudogenes. PLoS Genet. 2010;6:e1001050.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    48.
    Umezaki I. Ecological studies of Sargassum hemiphyllum C. AGARDH in Obama Bay, Japan Sea. Nippon Suisan Gakkaishi. 1984;50:1677–83.
    Article  Google Scholar 

    49.
    Tam TW, Ang PO. Repeated physical disturbances and the stability of sub-tropical coral communities in Hong Kong. China Aquat Conserv Mar Freshw Ecosyst. 2008;18:1005–24.
    Article  Google Scholar 

    50.
    Cheang CC, Chu KH, Ang PO. Phylogeography of the marine macroalga Sargassum hemiphyllum (Phaeophyceae, Heterokontophyta) in northwestern Pacific. Mol Ecol. 2010;19:2933–48.
    CAS  PubMed  Article  Google Scholar 

    51.
    Raghunathan C, Venkataraman K. Diversity and distribution of corals and their associated fauna of Rani Jhansi Marine National Park, Andaman and Nicobar Islands. In: Venkataraman K, Raghunathan C, Sivaperuman C, (eds). Ecology of Faunal Communities on the Andaman and Nicobar Islands. Berlin, Heidelberg: Springer; 2012. p. 177–208.
    Google Scholar 

    52.
    Ang PO. Phenology of Sargassum spp. in Tung Ping Chau Marine Park, Hong Kong SAR, China. J Appl Phycol. 2006;18:629–36.
    Article  Google Scholar 

    53.
    Huggett MJ, Apprill A. Coral microbiome database: Integration of sequences reveals high diversity and relatedness of coral-associated microbes. Environ Microbiol Rep. 2019;11:372–85.
    PubMed  Article  Google Scholar 

    54.
    Passel MWJ, van, Marri PR, Ochman H. The emergence and fate of horizontally acquired genes in Escherichia coli. PLoS Comput Biol. 2008;4:e1000059.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    55.
    Ochman H. Distinguishing the ORFs from the ELFs: short bacterial genes and the annotation of genomes. Trends Genet. 2002;18:335–7.
    CAS  PubMed  Article  Google Scholar 

    56.
    Emms DM, Kelly S. OrthoFinder: phylogenetic orthology inference for comparative genomics. Genome Biol. 2019;20:238.
    PubMed  PubMed Central  Article  Google Scholar 

    57.
    Nguyen LT, Schmidt HA, von Haeseler A, Minh BQ. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol Biol Evol. 2015;32:268–74.
    CAS  Article  Google Scholar 

    58.
    Schliep KP. Phangorn: phylogenetic analysis in R. Bioinformatics. 2011;27:592–3.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    59.
    Liu Y, Harrison PM, Kunin V, Gerstein M. Comprehensive analysis of pseudogenes in prokaryotes: widespread gene decay and failure of putative horizontally transferred genes. Genome Biol. 2004;5:R64.
    PubMed  PubMed Central  Article  Google Scholar 

    60.
    Halldal P. Photosynthetic capacities and photosynthetic action spectra of endozoic algae of the massive coral Favia. Biol Bull. 1968;134:411–24.
    CAS  Article  Google Scholar 

    61.
    Shibata K, Haxo FT. Light transmission and spectral distribution through epi- and endozoic algal layers in the brain coral, Favia. Biol Bull. 1969;136:461–8.
    CAS  Article  Google Scholar 

    62.
    Park JT, Uehara T. How bacteria consume their own exoskeletons (turnover and recycling of cell wall peptidoglycan). Microbiol Mol Biol Rev. 2008;72:211–27.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    63.
    Mauck J, Chan L, Glaser L. Turnover of the cell wall of gram-positive bacteria. J Biol Chem. 1971;246:1820–7.
    CAS  PubMed  Google Scholar 

    64.
    Goodell E. Recycling of murein by Escherichia coli. J Bacteriol. 1985;163:305–10.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    65.
    Uehara T, Suefuji K, Jaeger T, Mayer C, Park JT. MurQ etherase is required by Escherichia coli in order to metabolize Anhydro-N-Acetylmuramic acid obtained either from the environment or from its own cell wall. J Bacteriol. 2006;188:1660–2.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    66.
    Dik DA, Marous DR, Fisher JF, Mobashery S. Lytic transglycosylases: concinnity in concision of the bacterial cell wall. Crit Rev Biochem Mol Biol. 2017;52:503–42.
    PubMed  PubMed Central  Article  Google Scholar 

    67.
    Jiang H, Kong R, Xu X. The N-acetylmuramic acid 6-phosphate etherase gene promotes growth and cell differentiation of cyanobacteria under light-limiting conditions. J Bacteriol. 2010;192:2239–45.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    68.
    Ferrer LM, Szmant AM. Nutrient regeneration by the endolithic community in coral skeletons. In: Proceedings of the 6th International Coral Reef Symposium 1988. pp 1–4.

    69.
    Risk MJ, Muller HR. Porewater in coral heads: evidence for nutrient regeneration. Limnol Oceanogr. 1983;28:1004–8.
    Article  Google Scholar 

    70.
    Yu LJ, Wu JR, Zheng ZZ, Lin CC, Zhan XB. Changes in gene transcription and protein expression involved in the response of Agrobacterium sp. ATCC 31749 to nitrogen availability during curdlan production. Appl Biochem Microbiol. 2011;47:487–93.
    CAS  Article  Google Scholar 

    71.
    Wada S, Aoki M, Mikami A, Komatsu T, Tsuchiya Y, Sato T, et al. Bioavailability of macroalgal dissolved organic matter in seawater. Mar Ecol Prog Ser. 2008;370:33–44.
    CAS  Article  Google Scholar 

    72.
    Essenberg MK, Cooper RA. Two ribose-5-phosphate isomerases from Escherichia coli K12: partial characterisation of the enzymes and consideration of their possible physiological roles. Eur J Biochem. 1975;55:323–32.
    CAS  PubMed  Article  Google Scholar 

    73.
    Nelson CE, Goldberg SJ, Wegley Kelly L, Haas AF, Smith JE, Rohwer F, et al. Coral and macroalgal exudates vary in neutral sugar composition and differentially enrich reef bacterioplankton lineages. ISME J. 2013;7:962–79.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    74.
    Mulligan C, Fischer M, Thomas GH. Tripartite ATP-independent periplasmic (TRAP) transporters in bacteria and archaea. FEMS Microbiol Rev. 2011;35:68–86.
    CAS  PubMed  Article  Google Scholar 

    75.
    Beyenbach KW, Wieczorek H. The V-type H+ ATPase: molecular structure and function, physiological roles and regulation. J Exp Biol. 2006;209:577–89.
    CAS  PubMed  Article  Google Scholar 

    76.
    Guadayol Ò, Silbiger NJ, Donahue MJ, Thomas FIM. Patterns in temporal variability of temperature, oxygen and pH along an environmental gradient in a coral reef. PLoS One. 2014;9:e85213.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    77.
    Bodenmiller DM, Spiro S. The yjeB(nsrR) gene of Escherichia coli encodes a nitric oxide-sensitive transcriptional regulator. J Bacteriol. 2006;188:874–81.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    78.
    Gilberthorpe NJ, Lee ME, Stevanin TM, Read RC, Poole RK. NsrR: a key regulator circumventing Salmonella enterica serovar Typhimurium oxidative and nitrosative stress in vitro and in IFN-γ-stimulated J774.2 macrophages. Microbiology. 2007;153:1756–71.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    79.
    da Fonseca RR, Johnson WE, O’Brien SJ, Vasconcelos V, Antunes A. Molecular evolution and the role of oxidative stress in the expansion and functional diversification of cytosolic glutathione transferases. BMC Evol Biol. 2010;10:281.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    80.
    Green ER, Mecsas J. Bacterial secretion systems—an overview. Microbiol Spectr. 2016;4:1–32.
    CAS  Article  Google Scholar 

    81.
    Ansari MI, Schiwon K, Malik A, Grohmann E. Biofilm formation by environmental bacteria. In: Malik A, Grohmann E (eds). Environmental protection strategies for sustainable development. 2012. Springer Netherlands, Dordrecht, pp 341–77.

    82.
    Meron D, Efrony R, Johnson WR, Schaefer AL, Morris PJ, Rosenberg E, et al. Role of flagella in virulence of the coral pathogen Vibrio coralliilyticus. Appl Environ Microbiol. 2009;75:5704–7.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    83.
    Attmannspacher U, Scharf BE, Harshey RM. FliL is essential for swarming: motor rotation in absence of FliL fractures the flagellar rod in swarmer cells of Salmonella enterica. Mol Microbiol. 2008;68:328–41.
    CAS  PubMed  Article  Google Scholar 

    84.
    Fernando SC, Wang J, Sparling K, Garcia GD, Francini-Filho RB, de Moura RL, et al. Microbiota of the major south atlantic reef building coral Mussismilia. Micro Ecol. 2015;69:267–80.
    Article  Google Scholar 

    85.
    Pollock FJ, McMinds R, Smith S, Bourne DG, Willis BL, Medina M, et al. Coral-associated bacteria demonstrate phylosymbiosis and cophylogeny. Nat Commun. 2018;9:4921.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    86.
    Marcelino VR, van Oppen MJ, Verbruggen H. Highly structured prokaryote communities exist within the skeleton of coral colonies. ISME J. 2018;12:300–3.
    PubMed  Article  Google Scholar 

    87.
    Hill C. Virulence or niche factors: what’s in a name? J Bacteriol. 2012;194:5725–7.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    88.
    Egan S, Harder T, Burke C, Steinberg P, Kjelleberg S, Thomas T. The seaweed holobiont: understanding seaweed–bacteria interactions. FEMS Microbiol Rev. 2013;37:462–76.
    CAS  PubMed  Article  Google Scholar 

    89.
    Levy A, Salas Gonzalez I, Mittelviefhaus M, Clingenpeel S, Herrera Paredes S, Miao J, et al. Genomic features of bacterial adaptation to plants. Nat Genet. 2018;50:138–50.
    CAS  Article  Google Scholar 

    90.
    Koren O, Rosenberg E. Bacteria associated with mucus and tissues of the coral Oculina patagonica in summer and winter. Appl Environ Microbiol. 2006;72:5254–9.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    91.
    Wang X, Grus WE, Zhang J. Gene losses during human origins. PLoS Biol. 2006;4:e52.
    PubMed  PubMed Central  Article  CAS  Google Scholar  More

  • in

    Azure-winged magpies’ decisions to share food are contingent on the presence or absence of food for the recipient

    1.
    Fehr, E. & Fischbacher, U. The nature of human altruism. Nature 425(6960), 785–791. https://doi.org/10.1038/nature02043 (2003).
    ADS  CAS  Article  PubMed  Google Scholar 
    2.
    Hamann, K., Warneken, F., Greenberg, J. R. & Tomasello, M. Collaboration encourages equal sharing in children but not in chimpanzees. Nature 476(7360), 328–331. https://doi.org/10.1038/nature10278 (2011).
    ADS  CAS  Article  PubMed  Google Scholar 

    3.
    Marshall-Pescini, S., Dale, R., Quervel-Chaumette, M. & Range, F. Critical issues in experimental studies of prosociality in non-human species. Anim. Cogn. 19, 679–705 (2016).
    CAS  Article  Google Scholar 

    4.
    Tan, J., Ariely, D. & Hare, B. Bonobos respond prosocially toward members of other groups. Sci. Rep. https://doi.org/10.1038/s41598-017-15320-w (2017).
    Article  PubMed  PubMed Central  Google Scholar 

    5.
    Warneken, F. & Tomasello, M. Altruistic helping in human infants and young chimpanzees. Science 311(5765), 1301–1303. https://doi.org/10.1126/science.1121448 (2006).
    ADS  CAS  Article  PubMed  Google Scholar 

    6.
    Tennie, C., Jensen, K. & Call, J. The nature of prosociality in chimpanzees. Nat. Commun. 7, 13915 (2016).
    ADS  CAS  Article  Google Scholar 

    7.
    Melis, A. P., Engelmann, J. M. & Warneken, F. Chimpanzee helping is real, not a byproduct. Nat. Commun. 9, 615 (2018).
    ADS  Article  Google Scholar 

    8.
    Cronin, K. A. Prosocial behaviour in animals: the influence of social relationships, communication and rewards. Anim. Behav. 84(5), 1085–1093. https://doi.org/10.1016/j.anbehav.2012.08.009 (2012).
    Article  Google Scholar 

    9.
    Hernandez-Lallement, J., van Wingerden, M., Marx, C., Srejic, M. & Kalenscher, T. Rats prefer mutual rewards in a prosocial choice task. Front. Neurosci. https://doi.org/10.3389/fnins.2014.00443 (2015).
    Article  PubMed  PubMed Central  Google Scholar 

    10.
    Quervel-Chaumette, M., Dale, R., Marshall-Pescini, S. & Range, F. Familiarity affects other-regarding preferences in pet dogs. Sci. Rep. https://doi.org/10.1038/srep18102 (2016).
    Article  Google Scholar 

    11.
    Duque, J. F., Leichner, W., Ahmann, H. & Stevens, J. R. Mesotocin influences pinyon jay prosociality. Biol. Lett. 14(4), 20180105. https://doi.org/10.1098/rsbl.2018.0105 (2018).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    12.
    Horn, L., Scheer, C., Bugnyar, T. & Massen, J. J. M. Proactive prosociality in a cooperatively breeding corvid, the azure-winged magpie (Cyanopica cyana). Biol. Lett. 12(10), 20160649. https://doi.org/10.1098/rsbl.2016.0649 (2016).
    Article  PubMed  PubMed Central  Google Scholar 

    13.
    Brucks, D. & von Bayern, A. M. Parrots voluntarily help each other to obtain food rewards. Curr. Biol. 30, 292–297. https://doi.org/10.1016/j.cub.2019.11.030 (2020).
    CAS  Article  PubMed  Google Scholar 

    14.
    Burkart, J. M. et al. The evolutionary origin of human hyper-cooperation. Nat. Commun. 5, 4747. https://doi.org/10.1038/ncomms5747 (2014).
    ADS  CAS  Article  PubMed  Google Scholar 

    15.
    Feistner, A. T. C. & McGrew, W. C. Food-sharing in primates: a critical review. In Perspectives in Primate Biology (eds Seth, P. K. & Seth, S.) 21–36 (Today & Tomorrow’s Printers and Publishers, New Delhi, 1989).
    Google Scholar 

    16.
    Hames, R. & McCabe, C. Meal sharing among the Ye’kwana. Hum. Nat. 18(1), 1–21. https://doi.org/10.1007/BF02820843 (2007).
    Article  PubMed  Google Scholar 

    17.
    Kaplan, H. et al. Food sharing among ache foragers: tests of explanatory hypotheses [and Comments and Reply]. Curr. Anthropol. 26(2), 223–246. https://doi.org/10.1086/203251 (1985).
    Article  Google Scholar 

    18.
    Isaac, G. L. I. The Harvey lecture series, 1977–1978. Food sharing and human evolution: archaeological evidence from the Plio-Pleistocene of East Africa. J. Anthropol. Res. 34(3), 311–325. https://doi.org/10.1086/jar.34.3.3629782 (1978).
    Article  Google Scholar 

    19.
    Clutton-Brock, T. H. The Evolution of Parental Care (Princeton University Press, Princeton, 1991).
    Google Scholar 

    20.
    Hamilton, W. D. The genetical evolution of social behaviour. I. J. Theor. Biol. 7(1), 1–16. https://doi.org/10.1016/0022-5193(64)90038-4 (1964).
    MathSciNet  CAS  Article  PubMed  Google Scholar 

    21.
    Fruth, B. & Hohmann, G. Food sharing across borders. Hum. Nat. 29, 91–103. https://doi.org/10.1007/s12110-018-9311-9 (2018).
    Article  PubMed  PubMed Central  Google Scholar 

    22.
    Jaeggi, A. V., Stevens, J. M. G. & Van Schaik, C. P. Tolerant food sharing and reciprocity is precluded by despotism among bonobos but not chimpanzees. Am. J. Phys. Anthropol. 143(1), 41–51. https://doi.org/10.1002/ajpa.21288 (2010).
    Article  PubMed  Google Scholar 

    23.
    Wittig, R. M. et al. Food sharing is linked to urinary oxytocin levels and bonding in related and unrelated wild chimpanzees. Proc. R. Soc. B Biol. Sci. 281(1778), 20133096–20133096. https://doi.org/10.1098/rspb.2013.3096 (2014).
    Article  Google Scholar 

    24.
    John, M., Duguid, S., Tomasello, M. & Melis, A. P. How chimpanzees (Pan troglodytes) share the spoils with collaborators and bystanders. PLoS ONE 14(9), e0222795 (2019).
    CAS  Article  Google Scholar 

    25.
    Jaeggi, A. V. & Van Schaik, C. P. The evolution of food sharing in primates. Behav. Ecol. Sociobiol. 65(11), 2125–2140. https://doi.org/10.1007/s00265-011-1221-3 (2011).
    Article  Google Scholar 

    26.
    Carter, G. G. & Wilkinson, G. S. Food sharing in vampire bats: Reciprocal help predicts donations more than relatedness or harassment. Proc. R. Soc. B 280(1753), 20122573. https://doi.org/10.1098/rspb.2012.2573 (2013).
    Article  PubMed  Google Scholar 

    27.
    Wright, B. M., Stredulinsky, E. H., Ellis, G. M. & Ford, J. K. B. Kin-directed food sharing promotes lifetime natal philopatry of both sexes in a population of fish-eating killer whales, Orcinus orca. Anim. Behav. 115, 81–95. https://doi.org/10.1016/j.anbehav.2016.02.025 (2016).
    Article  Google Scholar 

    28.
    Boucherie, P. H., Poulin, N. & Dufour, V. Not much ado about something: behavioural mechanisms of pair bond separation and formation in long-term pairing rooks. Ecoscience 25(1), 71–83. https://doi.org/10.1080/11956860.2017.1414671 (2018).
    Article  Google Scholar 

    29.
    de Kort, S. R., Emery, N. J. & Clayton, N. S. Food sharing in jackdaws, Corvus monedula: what, why and with whom?. Anim. Behav. 72(2), 297–304. https://doi.org/10.1016/j.anbehav.2005.10.016 (2006).
    Article  Google Scholar 

    30.
    Duque, J. F. & Stevens, J. R. Voluntary food sharing in pinyon jays: the role of reciprocity and dominance. Anim. Behav. 122, 135–144. https://doi.org/10.1016/j.anbehav.2016.09.020 (2016).
    Article  Google Scholar 

    31.
    Ostojić, L., Shaw, R. C., Cheke, L. G. & Clayton, N. S. Evidence suggesting that desire-state attribution may govern food sharing in Eurasian jays. Proc. Nat. Ac. Sci. USA 110(10), 4123–4128. https://doi.org/10.1073/pnas.1209926110 (2013).
    ADS  Article  Google Scholar 

    32.
    von Bayern, A., de Kort, S., Clayton, N. & Emery, N. The role of food- and object-sharing in the development of social bonds in juvenile jackdaws (Corvus monedula). Behaviour 144(6), 711–733. https://doi.org/10.1163/156853907781347826 (2007).
    Article  Google Scholar 

    33.
    Shaw, R. C., MacKinlay, R. D., Clayton, N. S. & Burns, K. C. Male New Zealand robins (Petroica longipes) cater to their mate’s desire when sharing food in the wild. Sci. Rep. https://doi.org/10.1038/s41598-017-00879-1 (2017).
    Article  PubMed  PubMed Central  Google Scholar 

    34.
    Scheid, C., Schmidt, J. & Noë, R. Distinct patterns of food offering and co-feeding in rooks. Anim. Behav. 76(5), 1701–1707. https://doi.org/10.1016/j.anbehav.2008.07.023 (2008).
    Article  Google Scholar 

    35.
    Legg, E. W., Ostojić, L. & Clayton, N. S. Food sharing and social cognition. Wiley Interdiscip. Rev. Cogn. Sci. 6(2), 119–129. https://doi.org/10.1002/wcs.1329 (2015).
    Article  PubMed  Google Scholar 

    36.
    Jaeggi, A. V. & Gurven, M. Reciprocity explains food sharing in humans and other primates independent of kin selection and tolerated scrounging: a phylogenetic meta-analysis. Proc. R. Soc. B Biol. Sci. 280(1768), 20131615–20131615. https://doi.org/10.1098/rspb.2013.1615 (2013).
    Article  Google Scholar 

    37.
    Samuni, L. et al. Social bonds facilitate cooperative resource sharing in wild chimpanzees. Proc. R. Soc. B 285(1888), 20181643. https://doi.org/10.1098/rspb.2018.1643 (2018).
    Article  PubMed  Google Scholar 

    38.
    Brosnan, S. F. & de Waal, F. B. M. A proximate perspective on reciprocal altruism. Hum. Nat. 13(1), 129–152. https://doi.org/10.1007/s12110-002-1017-2 (2002).
    Article  PubMed  Google Scholar 

    39.
    de Waal, F. B. M. & Luttrell, L. M. Mechanisms of social reciprocity in three primate species: symmetrical relationship characteristics or cognition?. Ethol. Sociobiol. 9(2–4), 101–118. https://doi.org/10.1016/0162-3095(88)90016-7 (1988).
    Article  Google Scholar 

    40.
    Schino, G. & Aureli, F. Primate reciprocity and its cognitive requirements. Evol. Anthropol. 19(4), 130–135. https://doi.org/10.1002/evan.20270 (2010).
    Article  Google Scholar 

    41.
    Stevens, J. R. & Hauser, M. D. Why be nice? Psychological constraints on the evolution of cooperation. Trends Cogn. Sci. 8(2), 60–65. https://doi.org/10.1016/j.tics.2003.12.003 (2004).
    Article  PubMed  Google Scholar 

    42.
    Massen, J. J. M., Behrens, F., Martin, J. S., Stocker, M. & Brosnan, S. F. A comparative approach to affect and cooperative decision-making. Neurosci. Biobehav. Rev. 107, 370–387 (2019).
    Article  Google Scholar 

    43.
    Melis, A. P. et al. Chimpanzees help conspecifics obtain food and non-food items. Proc. R. Soc. B Biol. Sci. 278(1710), 1405–1413. https://doi.org/10.1098/rspb.2010.1735 (2011).
    Article  Google Scholar 

    44.
    Yamamoto, S., Humle, T. & Tanaka, M. Chimpanzees’ flexible targeted helping based on an understanding of conspecifics’ goals. Proc. Nat. Ac. Sci. USA 109(9), 3588–3592. https://doi.org/10.1073/pnas.1108517109 (2012).
    ADS  Article  Google Scholar 

    45.
    Yamamoto, S., Humle, T. & Tanaka, M. Chimpanzees help each other upon request. PLoS ONE 4(10), e7416. https://doi.org/10.1371/journal.pone.0007416 (2009).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    46.
    Horner, V., Carter, J. D., Suchak, M. & de Waal, F. B. M. Spontaneous prosocial choice by chimpanzees. Proc. Nat. Ac. Sci. USA 108(33), 13847–13851. https://doi.org/10.1073/pnas.1111088108 (2011).
    ADS  CAS  Article  Google Scholar 

    47.
    Liebal, K. & Rossano, F. The give and take of food sharing in Sumatran orang-utans, Pongo abelii, and chimpanzees, Pan troglodytes. Anim. Behav. 133, 91–100. https://doi.org/10.1016/j.anbehav.2017.09.006 (2017).
    Article  Google Scholar 

    48.
    Liebal, K., Vaish, A., Haun, D. & Tomasello, M. Does sympathy motivate prosocial behaviour in great apes?. PLoS ONE 9(1), e84299 (2014).
    ADS  Article  Google Scholar 

    49.
    Vaish, A., Carpenter, M. & Tomasello, M. Sympathy through affective perspective taking and its relation to prosocial behavior in toddlers. Dev. Psychol. 45(2), 534–543. https://doi.org/10.1037/a0014322 (2009).
    Article  PubMed  Google Scholar 

    50.
    Kopp, K. S. & Liebal, K. Here you are!—Selective and active food sharing within and between groups in captive Sumatran orangutans (Pongo abelii). Behav. Ecol. Sociobiol. 70(8), 1219–1233. https://doi.org/10.1007/s00265-016-2130-2 (2016).
    Article  Google Scholar 

    51.
    Dufour, V., Pelé, M., Neumann, M., Thierry, B. & Call, J. Calculated reciprocity after all: computation behind token transfer in orang-utans. Biol. Lett. 5, 172–175 (2009).
    CAS  Article  Google Scholar 

    52.
    Dennett, D. C. Intentional systems in cognitive ethology: The “Panglossian paradigm” defended. Behav. Brain Sci. 6(3), 343–355. https://doi.org/10.1017/S0140525X00016393 (1983).
    Article  Google Scholar 

    53.
    Wrangham, R. W. Behavioural ecology of chimpanzees in Gombe National Park, Tanzania(Doctoral thesis)https://doi.org/https://doi.org/10.17863/CAM.16415 (1975).

    54.
    Stevens, J. R. & Stephens, D. W. Food sharing: A model of manipulation by harassment. Behav. Ecol. 13(3), 393–400 (2002).
    Article  Google Scholar 

    55.
    Komeda, S., Yamagishi, S. & Fujioka, M. Cooperative breeding in azure-winged magpies, Cyanopica cyana, living in a region of heavy snowfall. Condor 89(4), 835. https://doi.org/10.2307/1368532 (1987).
    Article  Google Scholar 

    56.
    Bayandonoi, G. Cooperative breeding and anti-predator strategies of the azure-winged magpie (Cyanopica cyanus Pallas, 1776) in northern Mongolia. PhD-thesis, Georg-August_university Göttingen, Germany (2016).

    57.
    Ren, Q.-M. et al. Helper effects in the azure-winged magpie Cyanopica cyana in relation to highly-clumped nesting pattern and high frequency of conspecific nest-raiding. J. Avian Biol. 47(4), 449–456. https://doi.org/10.1111/jav.00783 (2016).
    Article  Google Scholar 

    58.
    Wang, L. et al. Azure-winged magpies solve string-pulling tasks by partial understanding of the physical cognition. Curr. Zool. 65, 385–392. https://doi.org/10.1093/cz/zoy070 (2019).
    Article  PubMed  Google Scholar 

    59.
    Bond, A. B., Wei, C. A. & Kamil, A. C. Cognitive representation in transitive inference: a comparison of four corvid species. Behav. Process. 85(3), 283–292. https://doi.org/10.1016/j.beproc.2010.08.003 (2010).
    Article  Google Scholar 

    60.
    de Buchanan, K. L. et al. Guidelines for the treatment of animals in behavioural research and teaching. Anim. Behav. 83, 301–309 (2012).
    Article  Google Scholar 

    61.
    Madge, S. & Burn, H. Crows and Jays. A Guide to the Crows, Jays and Magpies of the World (Bloomsburry Publishing Plc, London, 2013).
    Google Scholar 

    62.
    Massen, J. J. M., Sterck, E. & de Vos, H. Close social associations in animals and humans: functions and mechanisms of friendship. Behaviour 147(11), 1379–1412. https://doi.org/10.1163/000579510X528224 (2010).
    Article  Google Scholar 

    63.
    Massen, J. J. M., Ritter, C. & Bugnyar, T. Tolerance and reward equity predict cooperation in ravens (Corvus corax). Sci. Rep. https://doi.org/10.1038/srep15021 (2015).
    Article  PubMed  PubMed Central  Google Scholar 

    64.
    Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67(1), 1–48 (2015).
    Article  Google Scholar 

    65.
    R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ (2013).

    66.
    Massen, J. J. M., Bauer, L., Spurny, B., Bugnyar, T. & Kret, M. E. Sharing of science is most likely among male scientists. Sci. Rep. 7, 12927. https://doi.org/10.1038/s41598-017-13491-0 (2017).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    67.
    Guerreiro Martins, E. M., Moura, A. C. d. A., Finkenwirth, C., Griesser, M., & Burkart, J. M. Food sharing patterns in three species of callitrichid monkeys (Callithrix jacchus, Leontopithecus chrysomelas, Saguinus midas): Individual and species differences.J. Comp. Psychol. Advance online publication (2019).

    68.
    Fraser, O. N. & Bugnyar, T. The quality of social relationships in ravens. Anim. Behav. 79(4), 927–933 (2010).
    Article  Google Scholar 

    69.
    Miyazawa, E., Seguchi, A., Takahashi, N., Motai, A. & Izawa, E. I. Different patterns of allopreening in the same-sex and opposite-sex interactions of juvenile large-billed crows (Corvus macrorhynchos). Ethology 126(2), 195–206 (2020).
    Article  Google Scholar 

    70.
    Morales Picard, A. et al. Why preen others? Predictors of allopreening in parrots and corvids and comparisons to grooming in great apes. Ethology 126(2), 207–228 (2020).
    Article  Google Scholar 

    71.
    Hattori, Y., Leimgruber, K., Fujita, K. & De Waal, F. B. M. Food-related tolerance in capuchin monkeys (Cebus apella) varies with knowledge of the partner’s previous food-consumption. Behaviour 149, 171–185 (2012).
    Article  Google Scholar 

    72.
    Massen, J. J. M., van den Berg, L. M., Spruijt, B. M. & Sterck, E. H. M. Generous leaders and selfish underdogs: pro-sociality in despotic macaques. PLoS ONE 5(3), e9734 (2010).
    ADS  Article  Google Scholar 

    73.
    Rilling, J. K. et al. Sex differences in the neural and behavioral response to intranasal oxytocin and vasopressin during human social interaction. Psychoneuroendocrinology 39, 237–248 (2014).
    CAS  Article  Google Scholar  More

  • in

    Extensive new Anopheles cryptic species involved in human malaria transmission in western Kenya

    Overview of molecular determination of Anopheles species
    Out of the 3556 Anopheles mosquitoes, 87.1% (3099/3556) were determined by species-specific PCRs or multiplex-PCRs and sequencing as major species An. gambiae sensu stricto (hereafter referred to as An. gambiae) (1440), An. arabiensis (718), and An. funestus sensu stricto (hereafter referred to as An. funestus) (941) in the five study sites (Fig. 1, Table 1, Supplementary Fig. S1). A subset of 21 randomly selected individuals from each major species identified by PCRs were confirmed by ITS2 sequencing based on similarity ( > 98%) to the sequences of anopheline voucher species retrieved from NCBI GenBank database (Supplementary Fig. S2).
    Figure 1

    Maps of sampling sites and Anopheles species distribution in western Kenya. (a) distribution of Anopheles major species; (b) distribution of Anopheles rare species. Pie-chart showed the abundance of Anopheles specimens for each site. The maps were generated using ArcGIS Pro 2.6 software. Map source: ESRI, CGIAR, and USGS (available at: www.esri.com).

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    Table 1 Species composition of Anopheles mosquitoes determined by molecular approaches in western Kenya.
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    The remaining 457 collected anophelines (12.9%) were classified into 18 rare species groups based on ITS2 sequence homology. Except for two species groups (An. sp.18 and An. sp.19), the ITS2 sequences of all the species were identified as different species based on their similarity ( > 98%) to the sequences of Anopheles voucher species retrieved from NCBI GenBank database (Supplementary Fig. S2). The ITS2 sequences of two species could not match with similarity  > 98% threshold to reference anopheline sequences or known vector species in GenBank databases, suggesting the existence of novel cryptic species.
    Pairwise comparison of ITS2 sequence similarities of the 21 Anopheles species indicated that except for one pair with 98.5% identity between An. gambiae and An. arabiensis, all pairs showed a similarity of 90% or less with confirmed species classifications (Supplementary Table S1). Phylogenetic tree analysis indicated that the 21 species belong to two different Subgenus (Subgenus Cellia Theobald and Subgenus Anopheles Meigen) in five species series groups, including Myzomyia, Neocellia, Pyretophorus, Cellia, and Myzorhynchus series (Fig. 2, Supplementary Table S2). The two new species An. sp.18 and An. sp.19 as well as An. sp.17 (a recently reported species13) belong to two different series groups, and An. sp.18 belongs to a different Subgenus (Subgenus Anopheles Meigen). The ITS2 sequence of An. sp.9 is homogenous with that of An. theileri (GenBank acc. JN994151) and An. sp. 9 BSL-2014 (GenBank acc. KJ522821)14. The ITS2 sequences obtained in the study are available in GenBank with accession numbers: MT408564-MT408584.
    Figure 2

    Molecular phylogenetic analysis of ITS2 sequences by Maximum Likelihood method. The phylogenetic tree was constructed using MEGA 7.0 software based on the Kimura 2-parameter model with 1000 bootstrap replicates. Pink filled diamonds showed the major species; red filled circles indicated the novel cryptic species tested positive for Plasmodium infections.

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    Comparison of morphological and molecular identifications
    Of the 3556 mosquitoes tested, 3226 (90.7%) samples with available morphological data were used to evaluate the accuracy of morphological identification as compared to molecular identification. Among the 3226 mosquito samples, 2192 (67.9%) individuals were morphologically identified as An. gambiae s.l., 938 (29.1%) as An. funestus, 94 (2.9%) as An. coustani, and the remaining 2 (0.1%) as An. pharoensis (Table 2). The An. gambiae s.l. complex and An. funestus group (An. funestus, An. cf.rivulorum, and An. leesoni) had a similar percentage of matches (gambiae complex: 85.8%, 1881/2192, funestus group: 85.2%, 799/938) between molecular assay and morphological identification, while only 53.2% of specimens morphologically identified as An. coustani were confirmed by the molecular assay (50/94). Based on molecular assays, An. gambiae s.l. complex had the lowest misidentification (4.3%), followed by An. funestus group (6.8%), while 18.0% (11/61) An. coustani specimens were morphologically misidentified as An. gambiae complex (9) or An. funestus (2). Based on morphological identification, less than 15% of specimens morphologically assigned to An. gambiae complex (14.2%) or An. funestus group (14.8%) were identified as other species, while there were 44 specimens morphologically assigned to An. coustani (46.8%) that were classified by molecular assay into 9 anopheline species, including An. rufipes (17), An. funestus (7), and An. gambiae complex (6). Overall, more than 60% (264/427) of the rare species were morphologically misidentified as An. gambiae s.l. Specifically, nearly 20% (81/427) and 7.2% (31/427) of rare species were misidentified as An. funestus and An. coustani, respectively, whereas only 11.7% (50/427) of the rare species were correctly identified as An. coustani. Altogether, 84.0% (2710/3226) identification alignment was observed between the morphological and molecular analysis (Table 2).
    Table 2 Comparison of morphological and molecular identifications in Anopheles mosquitoes from western Kenya.
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    Comparison of Anopheles species distributions and diversity
    Overall, the three major species were found in all five study sites but at varying proportions. Anopheles funestus accounted for a large proportion (44.7–98.2%) of species observed throughout the five study sites (Fig. 1A, Table 1). Anopheles gambiae was the predominant species in two highland sites (56.7% in Emutete and 61.3% in Iguhu) and one lowland site (47.73% in Kombewa), whereas An. gambiae was nearly absent in Homa Bay (0.4%), a lowland site, and Kisii (0.6%), a highland site. An. arabiensis, was observed in high proportion in lowland areas (Homa Bay: 70.8% and Kombewa: 24.9%) than in highland areas, which ranged from 1.8% (Emutete) to 5.2% (Iguhu).
    Seventeen of 18 rare species were identified in the highland areas, whereas only six rare species were detected in the lowland areas, suggesting that cryptic species might be more related to the sympatric An. gambiae than An. arabiensis. In lowland sites, the most abundant rare anopheline species was An. sp.15 (n = 17), followed by An. rufipes (n = 14) and An. cf.rivulorum (n = 14), whereas multiple rare species (such as An. christyi, An. sp.1, and An. sp.17) were identified in the highlands (Fig. 1B, Table 1).
    A significantly higher species diversity was observed in the highland areas than in the lowland areas (Shannon index H, t-test, t =  − 6.59, df = 3419, p  More

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    Three questions to ask before using model outputs for decision support

    Models are developed for a specific purpose and by the need to address certain questions about real systems. Models therefore focus on aspects of the real system that are considered important in answering these questions. Consequently, different models exist for the same system. Without knowing its purpose, it is impossible to assess whether a model’s outputs can be used to support decisions affecting the real world.
    Model purposes fall into three main categories: demonstration, understanding, and prediction. Given these different purposes, models also reflect different scopes. Models for demonstration are designed to explore ideas, demonstrate the consequences of certain assumptions, and thereby help communicate key concepts and mechanisms. For example, at the onset of the Covid-19 pandemic simple mathematical models were used to demonstrate how lowering the basic reproduction value, R0, would lead to “flattening the curve” of infections over time. This is an important logical prediction that helped to make key decisions, but it does not, and cannot, say anything about how effective interventions like social distancing are in reducing R0.
    Models for understanding are aimed at exploring how different components of a system interact to shape observed behavior of real systems. For example, a model can mechanistically represent movement and contact rates of individuals. The model can be run to let R0 emerge and then explore how R0 changes with interventions such as social distancing. Such models are not necessarily numerically precise, but they provide mechanistic understanding that helps to evaluate the consequences of alternative management measures.
    Finally, models for prediction focus on numerical precision. They tend to be more detailed and complex and rely heavily on data for calibration. Their ability to make future projections therefore depends on the quality of data used for model calibration. Such models still do not predict the future with precision, as this is impossible11, but they provide important estimates of alternative future scenarios12.
    Decision makers can benefit from all three types of models if they use them according to their given purpose. Modelers should therefore state a model’s purpose clearly and upfront. By asking this first screening question, one of the most common misuses of models can be prevented: using them for purposes for which they were not designed13. More