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    Chemical staining of particulate organic matter for improved contrast in soil X-ray µCT images

    Staining of POM in presence of fine sand and coarse silt
    Compared to the unstained control (unstained sample mean peak grey value ± standard deviation: 3686 ± 1557), all staining treatments caused a slight shift of the central grey value peak to the right, towards 3757 ± 1272 and 4421 ± 1646 for the PMA and PbAc treatments, respectively. This central peak represents X-ray attenuation by coarse silt and POM (Fig. 2a). However, no increased X-ray attenuation was observed in coarse silt in the reconstructed CT-sections (Fig. 3). Instead, the grey value distributions of pin-pointed POM particles (Fig. 3) confirmed that staining did cause a clear shift in POM’s grey values, viz. from 1000–4000 to 6000–18,000. This contrast enhancement of POM was more intense for AgNO3, PbAc and Pb(NO3)2 treatments compared to PMA (Fig. 3) and caused the very clear increase in the histogram’s right tail (grey values  > 7500), which was absent with PMA treatment (Fig. 2a). Staining with Pb2+ or Ag+ also resulted in broad grey value peaks of POM that overlapped those of the coarse silt (grey value range: 2500–5000) and fine sand particles (grey value range: 5000–7500), represented by the second and third peak on the bulk soil histograms (Fig. 2a). However, the histogram peak maxima of the AgNO3 (11,118), PbAc (13,829), Pb(NO3)2 (14,255) and PMA (9678) stained POM particles (Fig. 3) clearly exceeded the grey value range of both the coarse silt and fine sand, which suggests that CT grey value based discrimination of POM from these mineral fractions should be feasible. The smaller peak shift in the POM histograms for PMA (Fig. 3) shows that PMA was less efficient in increasing attenuation of POM and this could be ascribed to the lower atomic mass (AM) of Mo (95.9) compared to Pb (207.2). Likewise, the effect of Ag (AM 107.9) was closer to that of Mo. Next to atomic mass, the affinity for binding to OM likely also differs between the contrast agents. While Ag+ and Pb2+ bind with the organic functional groups of POM via ionic bonds, PMA is known to interact with conjugated unsaturated bonds37.
    Figure 2

    Grey scale histogram (0–16,000) for 16 bit images of (a) the fine sand + coarse silt + POM soil samples, (b) the fine sand + fine silt + POM mixtures and (c) the fine sand + clay + POM mixtures. The unstained control treatment (black) and the stained AgNO3 (red), PbAc (blue), Pb(NO3)2 (purple) and PMA (green) treatments. (d) Grey scale histogram (0–16,000) for 16 bit images of OsO4 stained fine sand + coarse silt + POM (fSa + cSi + POM; red), fine sand + fine silt + POM (fSa + fSi + POM; blue) and fine sand + clay + POM (fSa + C + POM; black) mixtures. The equivalent control treatments are indicated by dotted lines.

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    Figure 3

    Grey scale histograms (0–40,000) of operator-supervised pin-pointed POM particles in 16 bit images of fine sand + coarse silt + POM mixtures: the unstained control treatment (black) and the stained AgNO3 (red), PbAc (blue), Pb(NO3)2 (purple) and PMA (green) treatments. For each treatment, a two dimensional grey scale image representing a segment of a horizontal slice of the fine sand + coarse silt + POM mixtures is included (image contrast was enhanced in this figure). An unstained POM particle in the control treatment and stained POM particles in the AgNO3, PbAc, Pb(NO3)2 and PMA treatments are indicated by the red arrows.

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    There was no shift towards higher grey values in the third peak in the soil core histograms, representing fine sand (Fig. 2a). In addition, we did not find traces of Pb, Ag or Mo on any SEM–EDX scanned fine sand or coarse silt particles (Supplementary Fig. S1). Visual inspection of horizontal reconstructed slices revealed a clear discrimination of the stained POM particles vs. the soil mineral phase. SEM–EDX scans confirmed substantial Mo, Pb or Ag EDX-peaks on randomly chosen POM particles, directly confirming the successful selective staining of POM by all agents (Supplementary Fig. S1). Smaller mineral patches on the surface of POM were visible in the SEM-images (Supplementary Fig. S2), but our analysis demonstrates that these were not responsible for Mo, Pb or Ag-staining of the POM. Stained POM could also be discriminated from several other high density particles such as glauconite, since the latter were not only characterized by higher grey values32 but also a different grey value pattern. The SEM–EDX spectra of some dense mineral particles moreover revealed Zr peaks, suggesting these to be ZrO2 or Zr-silicate. In conclusion, the experiment demonstrates that Pb(NO3)2, AgNO3 and PbAc are able to selectively bind with POM in fine sand + coarse silt mixtures. However the potential to raise POM contrast in such soil mixtures by treatment with PMA is smaller.
    Staining of POM in presence of fine silt and clay
    PMA did not appear to stain fine silt, with no shifts of peaks in the bulk soil histogram (Fig. 2B), no visible impact on contrast of the soil mineral phase (Supplementary Fig. S3) and again no discernible Mo peaks in the SEM–EDX spectra of mineral particles (Supplementary Fig. S4 and Supplementary Fig. S5). In contrast, AgNO3, PbAc and Pb(NO3)2 appeared rather non-selective for POM (Fig. 2B). Indeed, the staining increased the grey values of histogram peaks corresponding to fine silt (Fig. 2b), which can be seen from shifting fine silt histogram peaks from a grey value of 4303 to 4702–5577. The increase of the fine silt grey values following staining even resulted in a partial (Pb(NO3)2 and AgNO3) or complete (PbAc) overlap with the sand fraction histogram peak. The grey value histogram right tail was also much enlarged by Pb(NO3)2, AgNO3 and PbAc treatment and increasingly overlapped with the grey value range of stained POM (6000–18,000) (Fig. 3). This overlap was clearly an effect of staining of fine silt, as was also apparent in the CT-sections (Supplementary Fig. S3). The shift in histogram grey values following staining in the fine sand + clay mixtures was similar to that in the sand + fine silt mixtures, but with a larger shift in the right tail of the Pb2+ and Ag+-histograms to grey values between 7500 and  > 12,000 (Fig. 2c). However, we did not detect Pb or Ag EDX peaks on pinpointed fine silt or clay particles (Supplementary Fig. S4). But as inspection of CT-sections (Fig. 4) demonstrated that large patches of the mixtures were stained by AgNO3, PbAc and Pb(NO3)2, it is well possible that such discrete stained areas were coincidently not sampled in our ancillary unsystematic SEM-analysis of subsamples. Regardless, overlap in grey value-ranges of Pb2+ and Ag+ stained POM and stained clay particles clearly impedes proper segmentation of both phases. As was the case for fine silt, PMA treatment did not cause any shift in histogram peaks of mineral particles (Fig. 2c) and there was no visible impact on mineral phase attenuation in the CT-sections (Fig. 4). This also suggests that Mo has a higher potential than Pb2+ and Ag+ to bind to OM solely and not to mineral surfaces. Indeed, this may be a result of the neutral Mo(VI)O3 in PMA, while the cations Pb2+ and Ag+ may adsorb to negatively charged mineral surfaces. However, Chenu and Plante24 did not detect any sorption of both Pb and Ag on pure clay minerals (vermiculite, illite, kaolinite). Despite minerals identified in our clay fraction being kaolinite and smectite, sorption of Pb (as observed by Chenu and Plante24) or Ag onto Al and Fe oxides and hydroxides in the clay fraction could have occurred.
    Figure 4

    Two dimensional grey scale image representing a horizontal slice of the fine sand + clay + POM mixtures: the control treatment and the stained AgNO3, PbAc, Pb(NO3)2 and PMA treatments (image contrast was enhanced in this figure). Clusters of stained clay particles are clearly observable as brighter structures in the AgNO3, PbAc and Pb(NO3)2 treatment.

    Full size image

    While manual pin-pointing of the PMA stained POM was not impeded, automated POM extraction from the CT volume may still prove to be challenging due to the lack of image contrast between POM and mineral soil particles. Very recent work by Lammel et al.38 applied a machine learning segmentation tool in synchrotron-based soil CT volumes but experienced limited success. Piccoli et al.39 suggested that an operator-based ability for the selection of thresholds may still result in the most accurate segmentation of POM in soil.
    Impact on sample structure
    Bulk sample histogram evaluation (Fig. 2a) of the fine sand + coarse silt samples demonstrated that the grey value peak of the pore space (unstained sample mean peak grey value ± standard deviation: 1765 ± 609) had slightly shifted following staining with Pb(NO3)2 (1930 ± 386), AgNO3 (1923 ± 392) and PbAc (2204 ± 314), and to a lesser extent with PMA (1801 ± 611). There was also a reduction in the pore space peak height for the Pb(NO3)2, AgNO3 and PbAc stained treatments of 16%, 27% and 42%, respectively. In the PbAc treatment, the pore space peak disappeared nearly completely, whereas this drop was smaller for AgNO3 and much smaller for Pb(NO3)2. In contrast, no such decline in pore space peak was observed in the PMA-stained samples. A decrease in pore volume (Fig. 5) for the Pb(NO3)2, AgNO3 and PbAc treatments matched the trend in reduction of pore peak height. In addition, opposite trends of peak height increases existed for the second, coarse silt peak (+ 6%, 13%, 25%, respectively) and for the third, fine sand peak (+ 18%, 19% and 33%, respectively). Combined, these observations demonstrate that the observed changes in pore space and mineral phase peak areas are the result of the compaction of the samples by the Pb(NO3)2, AgNO3 and PbAc treatments.
    Figure 5

    Total X-ray µCT visible porosity in the fine sand + coarse silt + POM mixtures: the unstained control treatment (black) and the stained AgNO3 (red), PbAc (blue), Pb(NO3)2 (purple) and PMA (green) treatments.

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    Given that peak height reductions were very different for Pb(NO3)2 and PbAc, the deterioration of soil structure could not be solely related to the heavy-element applied (both containing Pb2+). In addition, the decreased porosity is unlikely to have been predominantly due to the addition of NO3−, as the addition of Pb(NO3)2 would have impacted structure to a greater extent than AgNO3 which contains less (0.5×) NO3− (solutions were all at 1 M). Deterioration of the sample structure was therefore more likely to be a physical phenomenon. One possible mechanism could be that an increased underpressure, as a result of perfusion of a more viscous agent, could have more severely disrupted the structure of the soil mixtures. However, an increasing degree of structure distortion could not be related to a higher viscosity of the staining solutions (considered at 0.01 or 0.1 M40,41,42). Hence, the results in this study did not allow identification of the exact origin of this artifact. As a consequence, caution has to be taken when perfusing soil samples with liquid staining agents since deterioration of soil structure would alter spatial location of the POM as well. Thus, these results indicate that soil structure validation is still required as long as the specific cause for deterioration is not identified.
    The Pb(NO3)2, AgNO3 and PbAc treatment histograms of both the fine silt (Fig. 2b) and clay (Fig. 2c) mixtures also had a smaller and broader pore space peak. It is likely that these are both derived from increased occurrence of partial volume effects (PVE) from chemical staining. It is thought that compaction following staining resulted in more voxels containing both pore space and mineral particles. With a voxel resolution of 7 µm this increased the number of pore space voxels with an intermediate grey value (2000–4000). The order in grey value increase magnitude for the fine silt and clay samples was similar as for the coarse silt mixtures: PbAc  > Pb(NO3)2  > AgNO3. Chemical staining had an increasingly stronger effect on pore space X-ray attenuation for the finer particle size mixtures, probably because of more intensive binding of Ag+ and Pb2+ on their much larger reactive surfaces.
    In this study the combination of the structural degradation and the overlap in grey value ranges of POM and the mineral fractions render these staining agents unsuitable for staining natural soils. However, further development and testing of the staining method may reduce the impact on soil structure to a minimum. PMA treated silt and clay samples were not structurally degraded and no undesirable shifts of mineral particles’ grey value ranges were observed. However, the artificial soil samples are not fully representative of naturally structured soil and further testing could also rule out degradation of natural soil structure by perfusion with PMA solutions. We expect structural integrity of natural soil samples to exceed that of the ‘loose’ soil mixtures tested in this work.
    Performance of PMA compared to gaseous OsO4 staining
    Inspection of horizontal CT-sections suggested that OsO4 (Supplementary Fig. S6a–c) did not increase X-ray attenuation of mineral material. This is also suggested by the absence of a shift to the left of mineral fraction peaks (grey value 2500–7000), following OsO4 treatment (Fig. 2d).
    The grey value distribution obtained via manual pin-pointing of POM particles demonstrated that the grey value interval of OsO4-stained POM (Fig. 6) corresponded to the right tail of the soil mixture histograms. This effect was larger but still comparable to the POM grey value shift obtained by PMA treatment. This outcome is a likely result of both staining agents targeting unsaturated bonds in e.g. hydrocarbons or proteins, and the higher atomic mass of Os (190.2) compared to Mo (95.9). Because of the significant health risks when using OsO4, PMA appears to be a suitable alternative. The increase in POM grey values following both PMA and OsO4 staining showed a sufficient differentiation of POM particles from the mineral fraction, at least for manual pin-pointing. However, single threshold-based segmentation of PMA- or OsO4-stained POM in the X-ray CT volumes obtained with this lab-scale polychromatic X-ray µCT system did not appear possible. More sophisticated segmentation algorithms are required, as was also very recently suggested by Piccoli et al.39. We propose to develop self-learning algorithms (e.g. incorporate machine learning) that consider the local grey value patterns of POM in combination with morphological characteristics for a more objective and faster segmentation of the stained POM. By using self-learning algorithms, the expert intervention to segment POM particles would be reduced to an absolute minimum and decrease further over time. In addition, technological development will very likely further enhance the X-ray CT resolution, which will strongly improve the morphological characterization of finer grained POM.
    Figure 6

    Grey scale histogram (0–25,000) for pin-pointed POM particles in the 16 bit images of PMA (green) or OsO4 (black) stained fine sand + clay + POM mixtures.

    Full size image

    Alternatively, the occurrence of a K-edge in the X-ray absorption spectrum of molybdenum could be exploited to discriminate POM in CT images. Rawlins et al.33 and Peth et al.31 have previously used the occurrence of a K-edge in the X-ray absorption spectrum of Os by scanning soil with synchrotron µCT at photon energies immediately below and beyond the K-edge successfully. However, the K-edge of Mo is situated at 20 keV, an energy level at which most X-rays may be attenuated by the soil mineral fraction, thereby probably making a dual energy approach similar to that used for Os challenging for non-synchrotron scanners and probably also for synchrotrons. Very recently, Lammel et al.38 identified gaseous iodide (I2) as a plausible candidate for selective staining of OM in soil for use with synchrotron scanners. However, they did not fully demonstrate its selectivity for OM versus silt and clay sized mineral particles, nor in X-ray µCT soil volumes obtained with non-synchrotron scanners.
    The findings presented here demonstrate that laboratory based X-ray µCT scanners may also enable the segmentation of OsO4-stained POM from mineral particles, provided that better segmentation tools are developed. This opens up new possibilities for a more widespread application of the OsO4 staining technique due to much better availability of laboratory based X-ray µCT scanners and throughput time of samples. More

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    Effects of small ridge and furrow mulching degradable film on dry direct seeded rice

    1.
    Huang, H. Study on mechanized production engineering mode for paddy rice in double-cropping areas in south china. Published doctorial dissertation, China Agricultural University, Beijing. https://kns.cnki.net/KCMS/detail/detail.aspx?dbname=CDFD1214&filename=1014223520.nh (2014).
    2.
    Gao, Y. M., Yan, T. & Liu, W. J. Research and progress of direct rice seeding mechanization at home and abroad. Agric. Sci. Technol. Equip. 1, 28–29. https://doi.org/10.16313/j.cnki.nykjyzb.2013.01.020 (2013).
    Article  Google Scholar 

    3.
    Fawzy, S., Osman, A. I., Doran, J. & Rooney, D. W. Strategies for mitigation of climate change: A review. Environ. Chem. Lett. 6, 2069–2094. https://doi.org/10.1007/s10311-020-01059-w (2020).
    CAS  Article  Google Scholar 

    4.
    Hussain, S. et al. Rice production under climate change: Adaptations and mitigating strategies. In Environment, Climate, Plant and Vegetation Growth (eds Fahad, S. et al.) 659–686 (Springer, Berlin, 2020).
    Google Scholar 

    5.
    Vicente-Serrano, S. M., Quiring, S. M., Pena-Gallardo, M., Yuan, S. S. & Dominguez-Castro, F. A review of environmental droughts: Increased risk under global warming?. Earth Sci. Rev. 201, 102953. https://doi.org/10.1016/j.earscirev.2019.102953 (2020).
    Article  Google Scholar 

    6.
    Ault, T. R. On the essentials of drought in a changing climate. Science 6488, 256–260. https://doi.org/10.1126/science.aaz5492 (2020).
    ADS  CAS  Article  Google Scholar 

    7.
    Zhang, L. X. & Zhou, T. J. Drought over east Asia: A review. J. Clim. 8, 3375–3399. https://doi.org/10.1175/JCLI-D-14-00259.1 (2015).
    ADS  Article  Google Scholar 

    8.
    Zhang, X. et al. Urban drought challenge to 2030 sustainable development goals. Sci. Total Environ. 693, 133536. https://doi.org/10.1016/j.scitotenv.2019.07.342 (2019).
    ADS  CAS  Article  PubMed  Google Scholar 

    9.
    Chakraborty, D. et al. A global analysis of alternative tillage and crop establishment practices for economically and environmentally efficient rice production. Sci. Rep. 7, 9342. https://doi.org/10.1038/s41598-017-09742-9 (2017).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    10.
    Peng, S. B., Tang, Q. Y. & Zou, Y. B. Current status and challenges of rice production in China. Plant Prod. Sci. 12, 3–8. https://doi.org/10.1626/pps.12.3 (2009).
    Article  Google Scholar 

    11.
    Sun, L. M. et al. Implications of low sowing rate for hybrid rice varieties under dry direct-seeded rice system in central China. Field Crops Res. 175, 87–95. https://doi.org/10.1016/j.fcr.2015.02.009 (2015).
    Article  Google Scholar 

    12.
    Farooq, M. et al. Rice direct seeding: Experiences, challenges and opportunities. Soil Tillage Res. 111, 87–98. https://doi.org/10.1016/j.still.2010.10.008 (2011).
    Article  Google Scholar 

    13.
    Sandhu, N. et al. Deciphering the genetic basis of root morphology, nutrient uptake, yield, and yield-related traits in rice under dry direct-seeded cultivation systems. Sci. Rep. 9, 9334. https://doi.org/10.1038/s41598-019-45770-3 (2019).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    14.
    Liu, H. Y. et al. Dry direct-seeded rice as an alternative to transplanted-flooded rice in Central China. Agron. Sustain. Dev. 35, 285–294. https://doi.org/10.1007/s13593-014-0239-0 (2015).
    Article  Google Scholar 

    15.
    Muhammad, S. et al. The effect of different weed management strategies on the growth and yield of direct-seeded dry rice (Oryza sativa). Planta Daninha. 34, 57–64. https://doi.org/10.1590/S0100-83582016340100006 (2016).
    Article  Google Scholar 

    16.
    Kakumanu, K. R., Kotapati, G. R., Nagothu, U. S., Kuppanan, P. & Kallam, S. R. Adaptation to climate change and variability: A case of direct seeded rice in Andhra Pradesh, India. J. Water Clim. Change. 10, 419–430. https://doi.org/10.2166/wcc.2018.141 (2019).
    Article  Google Scholar 

    17.
    Yamane, K. et al. Seed vigour contributes to yield improvement in dry direct-seeded rainfed lowland rice. Ann Appl. Biol. 172, 100–110. https://doi.org/10.1111/aab.12405 (2018).
    CAS  Article  Google Scholar 

    18.
    Nakano, H., Hattori, I. & Morita, S. Dry matter yield response to seeding rate and row spacing in direct-seeded and double-harvested forage rice. Jpn. Agric. Res. Q. 53, 255–264. https://doi.org/10.6090/jarq.53.255 (2019).
    CAS  Article  Google Scholar 

    19.
    Sun, C. L. et al. Implications of low sowing rate for hybrid rice varieties under drydirect-seeded rice system in Central China. Field Crops Res. 175, 87–95. https://doi.org/10.1016/j.fcr.2015.02.009 (2015).
    Article  Google Scholar 

    20.
    Jabran, K. et al. Mulching improves water productivity, yield and quality of fine rice under water-saving rice production systems. J. Agron. Crop Sci. 201, 389–400. https://doi.org/10.1111/jac.12099 (2015).
    Article  Google Scholar 

    21.
    Fawibe, O. O., Hiramatsu, M., Taguchi, Y., Wang, J. & Isoda, A. Grain yield, water-use efficiency, and physiological characteristics of rice cultivars under drip irrigation with plastic-film-mulch. J. Crop Improv. 34, 414–436. https://doi.org/10.1080/15427528.2020.1725701 (2020).
    Article  Google Scholar 

    22.
    He, H. B. et al. Rice performance and water use efficiency under plastic mulching with drip irrigation. PLoS ONE 8, e83103. https://doi.org/10.1371/journal.pone.0083103 (2013).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    23.
    Farooqi, Z. U. R., Sabir, M., Zeeshan, N., Naveed, K. & Hussain, M. M. Enhancing carbon sequestration using organic amendments and agricultural practices. In Carbon Capture, Utilization and Sequestration (ed. Agarwal, R. K.) 17–35 (IntechOpen, London, 2018).
    Google Scholar 

    24.
    Fuss, S. et al. Negative emissions-part 2: Costs, potentials and side effects. Environ. Res. Lett. 6, 063002. https://doi.org/10.1088/1748-9326/aabf9f (2018).
    ADS  CAS  Article  Google Scholar 

    25.
    Li, Y. S. et al. Influence of continuous plastic film mulching on yield, water use efficiency and soil properties of rice fields under non-flooding condition. Soil Tillage Res. 93, 370–378. https://doi.org/10.1016/j.still.2006.05.010 (2007).
    Article  Google Scholar 

    26.
    Huang, Y., Liu, Q., Jia, W. Q., Yan, C. R. & Wang, J. Agricultural plastic mulching as a source of microplastics in the terrestrial environment. Environ. Pollut. 260, 114096. https://doi.org/10.1016/j.envpol.2020.114096 (2020).
    CAS  Article  PubMed  Google Scholar 

    27.
    Yu, Q. et al. Distribution, abundance and risks of microplastics in the environment. Chemosphere 249, 126059. https://doi.org/10.1016/j.chemosphere.2020.126059 (2020).
    ADS  CAS  Article  PubMed  Google Scholar 

    28.
    Yan, X. Y. et al. Downward transport of naturally-aged light microplastics in natural loamy sand and the implication to the dissemination of antibiotic resistance genes. Environ. Pollut. 262, 114270. https://doi.org/10.1016/j.envpol.2020.114270 (2020).
    CAS  Article  PubMed  Google Scholar 

    29.
    Geyer, R., Jambeck, J. R. & Law, K. L. Production, use, and fate of all plastics ever made. Sci. Adv. 7, e1700782. https://doi.org/10.1126/sciadv.1700782 (2017).
    ADS  CAS  Article  Google Scholar 

    30.
    Osman, A. I. et al. Pyrolysis kinetic modelling of abundant plastic waste (PET) and in-situ emission monitoring. Environ. Sci. Eur. 1, 112. https://doi.org/10.1186/s12302-020-00390-x (2020).
    CAS  Article  Google Scholar 

    31.
    Kumar, U. S. U. et al. Neem leaves extract based seaweed bio-degradable composite films with excellent antimicrobial activity for sustainable packaging material. BioResources 1, 700–713. https://doi.org/10.15376/biores.14.1.700-713 (2019).
    CAS  Article  Google Scholar 

    32.
    Qasim, U. et al. Renewable cellulosic nanocomposites for food packaging to avoid fossil fuel plastic pollution: A review. Environ. Chem. Lett. https://doi.org/10.1007/s10311-020-01090-x (2020).
    Article  Google Scholar 

    33.
    Wang, Y. J., He, K., Zhang, J. B. & Chang, H. Y. Environmental knowledge, risk attitude, and households’ willingness to accept compensation for the application of degradable agricultural mulch film: Evidence from rural China. Sci. Total Environ. 744, 140616. https://doi.org/10.1016/j.scitotenv.2020.140616 (2020).
    ADS  CAS  Article  PubMed  Google Scholar 

    34.
    Cirujeda, A. et al. Biodegradable mulch instead of polyethylene for weed control of processing tomato production. Agron. Sustain. Dev. 32, 889–897. https://doi.org/10.1007/s13593-012-0084-y (2012).
    CAS  Article  Google Scholar 

    35.
    Yin, M. H., Li, Y. N., Fang, H. & Chen, P. P. Biodegradable mulching film with an optimum degradation rate improves soil environment and enhances maize growth. Agric. Water Manag. 216, 127–137. https://doi.org/10.1016/j.agwat.2019.02.004 (2019).
    Article  Google Scholar 

    36.
    Daryanto, S., Wang, L. X. & Jacinthe, P. A. Can ridge-furrow plastic mulching replace irrigation in dryland wheat and maize cropping systems?. Agric. Water Manag. 190, 1–5. https://doi.org/10.1016/j.agwat.2017.05.005 (2017).
    Article  Google Scholar 

    37.
    Qin, S. H., Zhang, J. L., Dai, H. L., Wang, D. & Li, D. M. Effect of ridge–furrow and plastic-mulching planting patterns on yield formation and water movement of potato in a semi-arid area. Agric. Water Manag. 131, 87–94. https://doi.org/10.1016/j.agwat.2013.09.015 (2014).
    Article  Google Scholar 

    38.
    Fan, Y. L. et al. Effects of ridge and furrow film mulching on soil environment and yield under potato continuous cropping system. Plant Soil Environ. 65, 523–529. https://doi.org/10.17221/481/2019-PSE (2019).
    Article  Google Scholar 

    39.
    Fan, T. L. et al. Film mulched furrow-ridge water harvesting planting improves agronomic productivity and water use efficiency in rainfed areas. Agric. Water Manag. 217, 1–10. https://doi.org/10.1016/j.agwat.2019.02.031 (2019).
    Article  Google Scholar 

    40.
    Diaz-Perez, J. C. Root zone temperature, plant growth and yield of broccoli [Brassica oleracea (plenck) var. italica] as affected by plastic film mulches. Sci. Hortic. 123, 156–163. https://doi.org/10.1016/j.scienta.2009.08.014 (2009).
    Article  Google Scholar 

    41.
    Gholamhoseini, M., Dolatabadian, A. & Habibzadeh, F. Ridge-furrow planting system and wheat straw mulching effects on dryland sunflower yield, soil temperature, and moisture. Agron. J. 111, 3383–3392. https://doi.org/10.2134/agronj2019.02.0097 (2019).
    CAS  Article  Google Scholar 

    42.
    Mo, F. et al. Alternating small and large ridges with full film mulching increase linseed (Linum usitatissimum L.) productivity and economic benefit in a rainfed semiarid environment. Field Crops Res. 219, 120–130. https://doi.org/10.1016/j.fcr.2018.01.036 (2018).
    Article  Google Scholar 

    43.
    Gu, X. B., Li, Y. N., Du, Y. D. & Yin, M. H. Ridge-furrow rainwater harvesting with supplemental irrigation to improve seed yield and water use efficiency of winter oilseed rape (Brassica napus L.). J. Integr. Agric. 16, 1162–1172. https://doi.org/10.1016/S2095-3119(16)61447-8 (2017).
    Article  Google Scholar 

    44.
    Mo, F., Wang, J. Y., Xiong, Y. C., Nguluu, S. N. & Li, F. M. Ridge-furrow mulching system in semiarid Kenya: A promising solution to improve soil water availability and maize productivity. Eur. J. Agron. 80, 124–136. https://doi.org/10.1016/j.eja.2016.07.005 (2016).
    Article  Google Scholar 

    45.
    Zhang, X. D. et al. Ridge-furrow mulching system regulates diurnal temperature amplitude and wetting-drying alternation behavior in soil to promote maize growth and water use in a semiarid region. Field Crops Res. 233, 121–130. https://doi.org/10.1016/j.fcr.2019.01.009 (2019).
    Article  Google Scholar 

    46.
    Li, F. M., Wang, J., Xu, J. Z. & Xu, H. L. Productivity and soil response to plastic film mulching durations for spring wheat on entisols in the semiarid loess plateau of China. Soil Tillage Res. 78, 9–20. https://doi.org/10.1016/j.still.2003.12.009 (2004).
    CAS  Article  Google Scholar 

    47.
    Li, C. J. et al. Towards the highly effective use of precipitation by ridge-furrow with plastic film mulching instead of relying on irrigation resources in a dry semi-humid area. Field Crops Res. 188, 62–73. https://doi.org/10.1016/j.fcr.2016.01.013 (2016).
    ADS  Article  Google Scholar 

    48.
    Li, Y. Z. et al. The effect of tillage on nitrogen use efficiency in maize (Zea mays L.) in a ridge–furrow plastic film mulch system. Soil Tillage Res. 195, 104409. https://doi.org/10.1016/j.still.2019.104409 (2019).
    Article  Google Scholar 

    49.
    Zheng, J., Fan, J. L., Zou, Y. F., Chau, H. W. & Zhang, F. C. Ridge-furrow plastic mulching with a suitable planting density enhances rainwater productivity, grain yield and economic benefit of rainfed maize. J. Arid Land. 12, 181–198. https://doi.org/10.1007/s40333-020-0001-1 (2020).
    CAS  Article  Google Scholar 

    50.
    Zhao, H. et al. Ridge-furrow with full plastic film mulching improves water use efficiency and tuber yields of potato in a semiarid rainfed ecosystem. Field Crops Res. 161, 137–148. https://doi.org/10.1016/j.fcr.2014.02.013 (2014).
    ADS  Article  Google Scholar 

    51.
    Ren, X., Chen, X. & Jia, Z. Effect of rainfall collecting with ridge and furrow on soil moisture and root growth of corn in semiarid northwest China. J. Agron. Crop Sci. 196, 109–122. https://doi.org/10.1111/j.1439-037X.2009.00401.x (2010).
    Article  Google Scholar 

    52.
    Dong, W. L. et al. Ridge and furrow systems with film cover increase maize yields and mitigate climate risks of cold and drought stress in continental climates. Field Crops Res. 207, 71–78. https://doi.org/10.1016/j.fcr.2017.03.003 (2017).
    Article  Google Scholar 

    53.
    Tian, Y., Su, D. R., Li, F. M. & Li, X. L. Effect of rainwater harvesting with ridge and furrow on yield of potato in semiarid areas. Field Crops Res. 84, 385–391. https://doi.org/10.1016/S0378-4290(03)00118-7 (2003).
    Article  Google Scholar 

    54.
    Zhang, X. D. et al. Ridge-furrow mulching system drives the efficient utilization of key production resources and the improvement of maize productivity in the loess plateau of China. Soil Tillage Res. 190, 10–21. https://doi.org/10.1016/j.still.2019.02.015 (2019).
    Article  Google Scholar 

    55.
    Li, R., Hou, X. Q., Jia, Z. K. & Han, Q. F. Soil environment and maize productivity in semi-humid regions prone to drought of Weibei Highland are improved by ridge-and-furrow tillage with mulching. Soil Tillage Res. 196, 104476. https://doi.org/10.1016/j.still.2019.104476 (2020).
    Article  Google Scholar 

    56.
    Gu, X. B., Li, Y. N. & Du, Y. D. Film-mulched continuous ridge-furrow planting improves soil temperature, nutrient content and enzymatic activity in a winter oilseed rape field, northwest China. J. Arid Land. 10, 362–374. https://doi.org/10.1007/s40333-018-0055-5 (2018).
    Article  Google Scholar 

    57.
    Li, M. Study on dynamic of maize (Zea mays L.) yield, soil water and soil carbon under the dry-farming plastic mulching system of ridge and furrow. Published doctorial dissertation, LanZhou University, Lanzhou. https://kns.cnki.net/KCMS/detail/detail.aspx?dbname=CDFDTEMP&filename=1020655864.nh (2020).

    58.
    Liu, X. E. et al. Film-mulched ridge-furrow management increases maize productivity and sustains soil organic carbon in a dryland cropping system. Soil Sci. Soc. Am. J. 4, 1434–1441. https://doi.org/10.2136/sssaj2014.04.0121 (2014).
    CAS  Article  Google Scholar 

    59.
    Wang, Y. P. et al. Multi-site assessment of the effects of plastic-film mulch on the soil organic carbon balance in semiarid areas of China. Agric. For. Meteorol. 228, 42–51. https://doi.org/10.1016/j.agrformet.2016.06.016 (2016).
    ADS  Article  Google Scholar 

    60.
    Li, S. P., Cai, Z. C., Yang, H. & Wang, J. K. Effects of long-term fertilization and plastic film covering on some soil fertility and microbial properties. Acta Ecol. Sin. 5, 2489–2498 (2009).
    Google Scholar  More

  • in

    Degree day-based model predicts pink bollworm phenology across geographical locations of subtropics and semi-arid tropics of India

    1.
    Rahmstorf, S. & Coumou, D. Increase of extreme events in a warming world. Proc. Natl. Acad. Sci. U. S. A. 108(44), 17905–17909 (2011).
    ADS  CAS  Article  Google Scholar 
    2.
    Hodgson, J. A. et al. Predicting insect phenology across space and time. Glob. Change Biol. 17, 1289–1300 (2011).
    ADS  Article  Google Scholar 

    3.
    Lane, J. E., Kruuk, L. E. B., Charmantier, A., Murie, J. O. & Dobson, F. S. Delayed phenology and reduced fitness associated with climate change in a wild hibernator. Nature 489, 554–557 (2012).
    ADS  CAS  Article  Google Scholar 

    4.
    IPCC, 2014: Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, R.K. Pachauri and L.A. Meyer (eds.)]. IPCC, Geneva, Switzerland, 151.

    5.
    Ge, Q. S., Wang, H. J., Rutishauser, T. & Dai, J. H. Phenological response to climate change in China: a meta-analysis. Glob. Chang. Biol. 21, 265–274 (2015).
    ADS  Article  Google Scholar 

    6.
    Thackeray, S. J. et al. Phenological sensitivity to climate across taxa and trophic levels. Nature 535, 241–245 (2016).
    ADS  CAS  Article  Google Scholar 

    7.
    Berzitis, E. A., Minigan, J. N., Hellett, R. H. & Newman, J. A. Climate and host plant availability impact the future distribution of the bean leaf beetle (Cerotoma trifurcata). Glob. Change Biol. 20, 2778–2792 (2014).
    ADS  Article  Google Scholar 

    8.
    Fand, B. B., Tonnang, H. E. Z., Bal, S. K. & Dhawan, A. K. Shift in the Manifestations of Insect Pests Under Predicted Climatic Change Scenarios: Key Challenges and Adaptation Strategies. In Advances in Crop Environment Interaction (eds Bal, S. et al.). (Springer, Singapore, 2018).

    9.
    Cayton, H. L., Haddad, N. M., Gross, K., Diamond, S. E. & Ries, L. Do growing degree days predict phenology across butterfly species?. Ecology 96(6), 1473–1479 (2015).
    Article  Google Scholar 

    10.
    Arnold, C. Y. The determination and significance of the base temperature in a linear heat unit system. Am. Soc. Hort. Sci. 74, 430–445 (1959).
    Google Scholar 

    11.
    Higley, L. G., Pedigo, L. P. & Ostile, K. R. DEGDAY: a program for calculating degree–days, and assumptions behind the degree–day approach. Environ. Entomol. 15, 999–1016 (1986).
    Article  Google Scholar 

    12.
    Campbell, A., Frazer, B. D., Gilbert, N., Gutierrez, A. P. & Mackauer, M. Temperature requirements of some aphids and their parasites. J. Appl. Ecol. 11, 431–438 (1974).
    Article  Google Scholar 

    13.
    Stinner, R. E., Gutierrez, A. P. & Butler, G. D. An algorithm for temperature-dependent growth rate simulation. Can. Entomol. 106, 519–524 (1974).
    Article  Google Scholar 

    14.
    Sharpe, P. J. H. & DeMichele, D. W. Reaction kinetics of poikilotherm development. J. Theor. Biol. 64, 649–670 (1977).
    CAS  Article  Google Scholar 

    15.
    Huber, R. T. Heat units and insect population prediction. In Proceedings of Beltwide cotton Production Mechanization Conference, 6–7 Jan, 1982. Las Vegas (1982).

    16.
    Peddu, H., Fand, B. B., Sawai, H. R. & Lave, N. V. Estimation and validation of developmental thresholds and thermal requirements for cotton pink bollworm Pectinophora gossypiella. Crop Prot. 127, 104984. https://doi.org/10.1016/j.cropro.2019.104984 (2020).
    CAS  Article  Google Scholar 

    17.
    Beasley, C. A. & Adams, C. J. Field–based, degree–day model for pink bollworm (Lepidoptera: Gelechiidae) development. J. Econ. Entomol. 89, 881–890 (1996).
    Article  Google Scholar 

    18.
    Trnka, M. et al. European corn borer life stage model: regional estimates of pest development and spatial distribution under present and future climate. Ecol. Model. 207, 61–84 (2007).
    Article  Google Scholar 

    19.
    Fand, B. B., Tonnang, H. E. Z., Kumar, M., Kamble, A. L. & Bal, S. K. A temperature-based phenology model for predicting development, survival and population growth potential of mealybug, Phenacoccus solenopsis Tinsley (Hemiptera: Pseudococcidae). Crop Prot. 55, 98–108 (2014).
    Article  Google Scholar 

    20.
    Fand, B. B., Sul, N. T., Bal, S. K. & Minhas, P. S. Temperature Impacts the development and survival of common cutworm (Spodoptera litura): Simulation and visualization of potential population growth in India under warmer temperatures through life cycle modelling and spatial mapping. PLoS ONE 10(4), e0124682 (2015).
    Article  Google Scholar 

    21.
    Sporleder, M., Simon, R., Juarez, H. & Kroschel, J. Regional and seasonal forecasting of the potato tuber moth using a temperature-driven phenology model linked with geographic information systems. In Integrated Pest Management for the Potato Tuber Moth, Phthorimaea operculella Zeller – A Potato Pest of Global Importance Zeller – A Potato Pest of Global Importance (eds Kroschel, J. & Lacey, L.) 15–30 (Margraf Publishers, Weikersheim (Germany), 2008).
    Google Scholar 

    22.
    Khadioli, N. et al. Effect of temperature on the phenology of Chilo partellus (Swinhoe) (Lepidoptera, Crambidae): simulation and visualization of the potential future distribution of C. partellus in Africa under warmer temperatures through the development of life-table parameters. Bulle Entomol. Res. 104, 809–822 (2014).
    CAS  Article  Google Scholar 

    23.
    Chimel, S. M. & Wilson, M. C. Estimation of the lower and upper developmental threshold temperatures and duration of the nymphal stages of the meadow spittlebug, Philaenus spumarius. Environ. Entomol. 8, 682–685 (1979).
    Article  Google Scholar 

    24.
    Henneberry, T. J. & Hutchison, W. D. Tobacco budworm (Lepidoptera: Noctuidae): phenology of fall and summer diapausing and degree-day requirements for larval development and adult emergence. Environ. Entomol. 18, 563–569 (1989).
    Article  Google Scholar 

    25.
    Sevacherian, V., Toscano, N. C., Van Steenwyk, R. A., Sharma, R. K. & Sanders, R. R. Forecasting pink bollworm emergence by thermal summation. Environ. Entomol. 6, 545–546 (1977).
    Article  Google Scholar 

    26.
    Zalom, F. G. et al. Degree–days: the calculation and use of heat units in pest management. University of California DANR Leaflet 21373. (1983).

    27.
    Allen, J. C. A modified sine wave method for calculating degree days. Environ. Ent. 5, 388–396 (1976).
    Article  Google Scholar 

    28.
    Fry, K. E. Heat-unit calculations in cotton cropand insect models. USDA-ARS AAT-W-23. Agricultural Research Service (Western Region), U. S. Department of Agriculture, Oakland, CA. (1983).

    29.
    Pruess, K. P. Day-degree methods for pest management. Environ. Entomol. 12, 613–619 (1983).
    Article  Google Scholar 

    30.
    CABI. (2020). Invasive species compendium: Pectinophora gossypiella (pink bollworm). https://www.cabi.org/isc/datasheet/39417#70AF7142-7A8B-4F36-A0BA-4F14FA270EED. Accessed 29 April 2020.

    31.
    Naik, V. C. B. N., Dhara Jothi, B., Dabhade, P. L. & Kranthi, S. Pink Boll worm Pectinophora gossypiella (saunders) infestation on Bt and Non Bt Hybrids in India in 2011–2012. Cotton Res. J. 6, 37–40 (2014).
    Google Scholar 

    32.
    Fand, B. B. et al. Widespread infestation of pink bollworm, Pectinophora gossypiella (Saunders) (Lepidoptera: Gelechidae) on Bt cotton in Central India: a new threat and concerns for cotton production. Phytoparasitica 47, 313–325 (2019).
    CAS  Article  Google Scholar 

    33.
    Huber, R. T., Moore, L. & Hoffman, M. P. Feasibility study of wide–area pheromone trapping of male pink bollworm moths in a cotton insect pest management program. J. Econ. Entomol. 72, 222–227 (1979).
    Article  Google Scholar 

    34.
    Hutchinson, W. D., Butler, G. D. Jr. & Martin, J. M. Age–specific developmental times for pink bollworm (Lepidoptera: Gelechiidae): three age classes of eggs, five larval instars, and pupae. Ann. Entomol. Soc. Am. 79, 482–487 (1986).
    Article  Google Scholar 

    35.
    Bryant, S. R., Thomas, C. D. & Bale, J. S. The influence of thermal ecology on the distribution of three nymphalid butterflies. J. Appl. Ecol. 39, 43–55 (2002).
    Article  Google Scholar 

    36.
    Gergis, M. F., Soliman, M. A., Moftah, E. A. & Naby, A. A. Temperature dependent development and functional responses of pink bollworm Pectinophora gossypiella (Saund.). Assiut. J. Agric. Sci. 21, 119–126 (1990).
    Google Scholar 

    37.
    Yones, M. S., Rahman, H. A., AbouHadid, A. F., Arafat, S. M. & Dahi, H. F. Heat unit requirements for development of the pink bollworm, Pectinophora gossypiella (Saunds.). Egypt Acad. J. Biol. Sci. 4(1), 115–122 (2011).
    Google Scholar 

    38.
    El-Lebody, K. A., Mostafa, H. Z. & Rizk, A. M. Study the biology and thermal requirements of Pectinophora gossypiella (Saunders), infestated cotton bolls var giza 90, under natural conditions. Egypt. Acad. J. Biol. Sci. 8(3), 115–125 (2015).
    Google Scholar 

    39.
    Higley, L. G. & Peterson, R. K. D. Initiating sampling programs. In Handbook of Sampling Methods for Arthropods in Agriculture (eds Pedigo, L. P. & Buntin, G. D.) 119–136 (CRC, Boca Raton, FL, 1994).
    Google Scholar 

    40.
    Nath, V. & Agarwal, R. A. Insect pests of crops and their control. Bharati Publications, Delhi 1:139 (1982).

    41.
    Sevacherian, V. & El-Zik, K. M. A slide rule for cotton crop and insect management. Univ. Calif. Div. Agric. Sci. Coop. Ext. Leaf. 21361 (1983).

    42.
    Klein, Z. & Applebaum, S. W. pink bollworm (Pectinophora gossypiella) lifecycle and diapause induction in Israel 1990. Hassadeh 71(2), 210–213 (1990).
    Google Scholar 

    43.
    Kranthi, K. R. Bt Cotton: Questions and Answers 70 (Indian Society for Cotton Improvement (ISCI), Mumbai, India, 2012).
    Google Scholar 

    44.
    Kranthi, K. R. Pink bollworm strikes Bt cotton. Cotton Stat. News 35, 1–6 (2015).
    Google Scholar 

    45.
    Jha, R. C. & Bisen, R. S. Effect of climatic factors on the seasonal incidence of thepink bollworm on cotton crop. Annu. Plant Prot. Sci. 2, 12–14 (1994).
    Google Scholar 

    46.
    Sarwar, M. Biological parameters of pink bollworm Pectinophora gossypiella (Saunders) (Lepidoptera: Gelechiidae): a looming threat for cotton and its eradication opportunity. Int. J. Res. Agric. For. I7, 25–36 (2017).
    Google Scholar 

    47.
    Ellsworth, P., et al. 2006. Pink Bollworm Management. Newsletter of the Pink Bollworm Action Committee. A Project of The Cotton Foundation Produced by the University of Arizona – Cooperative Extension, 1(2): 1–2.

    48.
    Edwards, M. & Richardson, A. J. Impact of climate change on marine pelagic phenology and trophic mismatch. Nature 430, 881–884 (2004).
    ADS  CAS  Article  Google Scholar 

    49.
    Snyder, R. L. 2002. DegDay.xls, Version 1.01. University of California Department of Land, Air and Water Resources, Atmospheric Science, Davis, California, USA. http://biomet.ucdavis.edu/DegreeDays/DegDay.htm. Accessed 10 Jan 2018.

    50.
    ICAR-CICR. 2006. Approved Package of practices for Cotton: Maharashtra State. ICAR-Central Institute for Cotton Research, Nagpur, Maharashtra, India. 440 010. http://www.cicr.org.in/pop/mh.pdf. Accessed 22 April 2020.

    51.
    Fife, L. C. Factors influencing pink bollworm pupation and emergence from overwintering larvae in central Texas. J. Econ. Entomol. 54, 908–918 (1961).
    Article  Google Scholar 

    52.
    Fand, B. B. et al. A simple and low-cost laboratory rearing technique for cotton pink bollworm, Pectinophora gossypiella (Suanders) (Lepidoptera: Gelechidae) using detached green bolls of cotton. Phytoparasitica https://doi.org/10.1007/s12600-019-00779-2 (2020).
    Article  Google Scholar 

    53.
    Box, G. E. P. & Jenkins, G. M. Time Series Analysis: Forecasting and Control (Holden-Day, San Francisco, 1970).
    Google Scholar  More

  • in

    Wetland hydroperiod predicts community structure, but not the magnitude of cross-community congruence

    1.
    Vellend, M. The Theory of Ecological Communities (MPB-57). The Theory of Ecological Communities (Princeton University Press, Princeton, 2016). https://doi.org/10.1515/9781400883790.
    2.
    Kraft, N. J. B. et al. Community assembly, coexistence and the environmental filtering metaphor. Funct. Ecol. 29, 592–599 (2015).
    Article  Google Scholar 

    3.
    Pearson, D. E., Ortega, Y. K., Eren, Ö. & Hierro, J. L. Community assembly theory as a framework for biological invasions. Trends Ecol. Evol. 33, 313–325 (2018).
    PubMed  Article  Google Scholar 

    4.
    Cadotte, M. W. & Tucker, C. M. Should environmental filtering be abandoned?. Trends Ecol. Evol. 32, 429–437 (2017).
    PubMed  Article  Google Scholar 

    5.
    Duan, M. et al. Disentangling effects of abiotic factors and biotic interactions on cross-taxon congruence in species turnover patterns of plants, moths and beetles. Sci. Rep. 6, 23511 (2016).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    6.
    Uboni, C. et al. Exploring cross-taxon congruence between carabid beetles (Coleoptera: Carabidae) and vascular plants in sites invaded by Ailanthus altissima versus non-invaded sites: The explicative power of biotic and abiotic factors. Ecol. Indic. 103, 145–155 (2019).
    Article  Google Scholar 

    7.
    Robertson, M. & Avilés, L. Rain, predators and vegetation lushness may structure web-building spider communities along precipitation gradients. Ecol. Entomol. 44, 217–226 (2019).
    Article  Google Scholar 

    8.
    Vleminckx, J. et al. Coordinated community structure among trees, fungi and invertebrate groups in Amazonian rainforests. Sci. Rep. 9, 11337 (2019).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    9.
    Maestre, F. T. et al. Do biotic interactions modulate ecosystem functioning along stress gradients? Insights from semi-arid plant and biological soil crust communities. Philos. Trans. R. Soc. B 365, 2057–2070 (2010).
    Article  Google Scholar 

    10.
    He, Q., Bertness, M. D. & Altieri, A. H. Global shifts towards positive species interactions with increasing environmental stress. Ecol. Lett. 16, 695–706 (2013).
    PubMed  Article  Google Scholar 

    11.
    Scherrer, D. et al. Disentangling the processes driving plant assemblages in mountain grasslands across spatial scales and environmental gradients. J. Ecol. 107, 265–278 (2019).
    Article  Google Scholar 

    12.
    Wellborn, G. A., Skelly, D. K. & Werner, E. E. Mechanisms creating community structure across a freshwater habitat gradient. Annu. Rev. Ecol. Syst. 27, 337–363 (1996).
    Article  Google Scholar 

    13.
    Chamberlain, D. E., Cannon, A. R. & Toms, M. P. Associations of garden birds with gradients in garden habitat and local habitat. Ecography 27, 589–600 (2004).
    Article  Google Scholar 

    14.
    Pennings, S. C. & Silliman, B. R. Linking biogeography and community ecology: Latitudinal variation in plant–herbivore interaction strength. Ecology 86, 2310–2319 (2005).
    Article  Google Scholar 

    15.
    Chamberlain, S. A., Bronstein, J. L. & Rudgers, J. A. How context dependent are species interactions?. Ecol. Lett. 17, 881–890 (2014).
    PubMed  Article  Google Scholar 

    16.
    Kissling, W. D. et al. Towards novel approaches to modelling biotic interactions in multispecies assemblages at large spatial extents. J. Biogeogr. 39, 2163–2178 (2012).
    Article  Google Scholar 

    17.
    Rudolf, V. H. W. The role of seasonal timing and phenological shifts for species coexistence. Ecol. Lett. https://doi.org/10.1111/ele.13277 (2019).
    Article  PubMed  Google Scholar 

    18.
    Thompson, J. N. Variation in interspecific interactions. Annu. Rev. Ecol. Syst. 19, 65–87 (1988).
    Article  Google Scholar 

    19.
    Bar-Massada, A. & Belmaker, J. Non-stationarity in the co-occurrence patterns of species across environmental gradients. J. Ecol. 105, 391–399 (2017).
    Article  Google Scholar 

    20.
    Hengeveld, R. Biogeographical ecology. J. Biogeogr. 21, 341–351 (1994).
    Article  Google Scholar 

    21.
    Osborne, P. E., Foody, G. M. & Suárez-Seoane, S. Non-stationarity and local approaches to modelling the distributions of wildlife. Divers. Distrib. 13, 313–323 (2007).
    Article  Google Scholar 

    22.
    Clark, N. J., Wells, K. & Lindberg, O. Unravelling changing interspecific interactions across environmental gradients using Markov random fields. Ecology 99, 1277–1283 (2018).
    PubMed  Article  Google Scholar 

    23.
    Bryant, J. P., Chapin, F. S. & Klein, D. R. Carbon/nutrient balance of boreal plants in relation to vertebrate herbivory. Oikos 40, 357 (1981).
    Article  Google Scholar 

    24.
    Post, D. M., Palkovacs, E. P., Schielke, E. G. & Dodson, S. I. Intraspecific variation in a predator affects community structure and cascading trophic interactions. Ecology 89, 2019–2032 (2008).
    PubMed  Article  Google Scholar 

    25.
    Agrawal, A. A., Lau, J. A. & Hambäck, P. A. Community heterogeneity and the evolution of interactions between plants and insect herbivores. Q. Rev. Biol. 81, 349–376 (2006).
    PubMed  Article  Google Scholar 

    26.
    Lisboa, F. J. G. et al. Much beyond Mantel: Bringing procrustes association metric to the plant and soil ecologist’s toolbox. PLoS ONE 9, e101238 (2014).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    27.
    Kraft, A. J., Robinson, D. T., Evans, I. S. & Rooney, R. C. Concordance in wetland physicochemical conditions, vegetation, and surrounding land cover is robust to data extraction approach. PLoS ONE 14, e0216343 (2019).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    28.
    Toranza, C. & Arim, M. Cross-taxon congruence and environmental conditions. BMC Ecol. 10, 18 (2010).
    PubMed  PubMed Central  Article  Google Scholar 

    29.
    Rooney, R. C. & Bayley, S. E. Community congruence of plants, invertebrates and birds in natural and constructed shallow open-water wetlands: Do we need to monitor multiple assemblages?. Ecol. Indic. 20, 42–50 (2012).
    Article  Google Scholar 

    30.
    Larsen, S., Mancini, L., Pace, G., Scalici, M. & Tancioni, L. Weak concordance between fish and macroinvertebrates in Mediterranean streams. PLoS ONE 7, e51115 (2012).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    31.
    Heino, J., Paavola, R., Virtanen, R. & Muotka, T. Searching for biodiversity indicators in running waters: Do bryophytes, macroinvertebrates, and fish show congruent diversity patterns?. Biodivers. Conserv. 14, 415–428 (2005).
    Article  Google Scholar 

    32.
    Corte, G. N. et al. Cross-taxon congruence in benthic communities: Searching for surrogates in marine sediments. Ecol. Indic. 78, 173–182 (2017).
    Article  Google Scholar 

    33.
    Cracraft, J. & Prum, R. O. Pattern and processes of diversification: Speciation and historical congruence in some Neotropical birds. Evolution 42, 603–620 (1988).
    PubMed  Article  Google Scholar 

    34.
    Moritz, C. et al. Biogeographical concordance and efficiency of taxon indicators for establishing conservation priority in a tropical rainforest biota. Proc. R. Soc. Lond. Ser. B. 268, 1875–1881 (2001).
    CAS  Article  Google Scholar 

    35.
    Rooney, R. C. & Azeria, E. T. The strength of cross-taxon congruence in species composition varies with the size of regional species pools and the intensity of human disturbance. J. Biogeogr. 42, 439–451 (2014).
    Article  Google Scholar 

    36.
    Daniel, J., Gleason, J. E., Cottenie, K. & Rooney, R. C. Stochastic and deterministic processes drive wetland community assembly across a gradient of environmental filtering. Oikos 128, 1158–1169 (2019).
    Article  Google Scholar 

    37.
    Gleason, J. E. & Rooney, R. C. Pond permanence is a key determinant of aquatic macroinvertebrate community structure in wetlands. Freshw. Biol. 63, 264–277 (2018).
    Article  Google Scholar 

    38.
    Clark, J. S., Campbell, J. H., Grizzle, H., Acosta-Martìnez, V. & Zak, J. C. Soil microbial community response to drought and precipitation variability in the chihuahuan desert. Microb. Ecol. 57, 248–260 (2009).
    PubMed  Article  Google Scholar 

    39.
    Brock, M. A., Nielsen, D. L., Shiel, R. J., Green, J. D. & Langley, J. D. Drought and aquatic community resilience: The role of eggs and seeds in sediments of temporary wetlands. Freshw. Biol. https://doi.org/10.1046/j.1365-2427.2003.01083.x (2003).
    Article  Google Scholar 

    40.
    Stewart, R. E. & Kantrud, H. A. Classification of Natural Ponds and Lakes in the Glaciated Prairie Region. Bureau of Sport Fisheries and Wildlife Resource Publication 92, vol. 554 (1971).

    41.
    Euliss, N. H. et al. The wetland continuum: A conceptual framework for interpreting biological studies. Wetlands 24, 448–458 (2004).
    Article  Google Scholar 

    42.
    Wright, H. E. J. Quaternary history of Minnesota. In Geology of Minnesota: A Centennial (eds Sims, P. K. & Morey, G.) 515–546 (Minnesota Geological Survey University of Minnesota, Minnesota, 1972).
    Google Scholar 

    43.
    Sauchyn, D. J., Barrow, E. M., Hopkinson, R. F. & Leavitt, P. R. Aridity on the Canadian plains. Géogr. Phys. Quat. 56, 247–259 (2004).
    Google Scholar 

    44.
    Downing, D. J. & Pettapiece, W. W. Natural Regions and Subregions of Alberta. https://www.albertaparks.ca/media/2942026/nrsrcomplete_may_06.pdf (2006).

    45.
    Government of Alberta. Alberta Merged Wetland Inventory. (2014).

    46.
    Anderson, D. L. & Rooney, R. C. Differences exist in bird communities using restored and natural wetlands in the Parkland region, Alberta, Canada. Restor. Ecol. 27, 1495–1507 (2019).
    Article  Google Scholar 

    47.
    Meyer, M. D., Davis, C. A. & Bidwell, J. R. Assessment of two methods for sampling invertebrates in shallow vegetated wetlands. Wetlands 33, 1063–1073 (2013).
    Article  Google Scholar 

    48.
    Gleason, J. E. & Rooney, R. C. Aquatic macroinvertebrates are poor indicators of agricultural activity in northern prairie pothole wetlands. Ecol. Indic. 81, 333–339 (2017).
    Article  Google Scholar 

    49.
    Clifford, H. F. Aquatic Invertebrates of Alberta (University of Alberta Press, Edmonton, 1991).
    Google Scholar 

    50.
    Merrit, R. W., Cummins, K. W. & Berg, M. B. An Introduction to the Aquatic Insects of North America (Kendall Hunt Publishing Company, Dubuque, 2008).
    Google Scholar 

    51.
    Environment Canada. CABIN Laboratory Methods: Processing, Taxonomy, and Quality Control of Benthic Macroinvertebrate Samples. 36 (2014).

    52.
    Bolding, M. T., Kraft, A. J., Robinson, D. T. & Rooney, R. C. Improvements in multi-metric index development using a whole-index approach. Ecol. Indic. 113, 106191 (2020).
    Article  Google Scholar 

    53.
    Meyers, N. Use of Water Isotope Tracers to Characterize the Hydrology of Prairie Wetlands in Alberta (University of Waterloo, Waterloo, 2018).
    Google Scholar 

    54.
    Peres-Neto, P. R. & Jackson, D. A. How well do multivariate data sets match? The advantages of a procrustean superimposition approach over the Mantel test. Oecologia 129, 169–178 (2001).
    ADS  PubMed  Article  Google Scholar 

    55.
    Nekola, J. C. & White, P. S. The distance decay of similarity in biogeography and ecology. J. Biogeogr. 26, 867–878 (1999).
    Article  Google Scholar 

    56.
    Dijksterhuis, G. B. & Gower, J. C. The interpretation of generalized procrustes analysis and allied methods. Food Qual. Prefer. https://doi.org/10.1016/0950-3293(91)90027-C (1991).
    Article  Google Scholar 

    57.
    Oksanen, J. et al. vegan: Community Ecology Package. R Package Version 2.4–2 (2017).

    58.
    Broadbooks, W. J. & Elmore, P. B. A Monte Carlo study of the sampling distribution of the congruence coefficient. Educ. Psychol. Meas. 47, 1–11 (1987).
    Article  Google Scholar 

    59.
    Fife, D. fifer: A Biostatisticians Toolbox for Various Activities, Including Plotting, Data Cleanup, and Data Analysis. R package version 1.1. https://CRAN.R-project.org/package=fifer. (2017).

    60.
    Levine, J. M. Indirect facilitation: Evidence and predictions from a riparian community. Ecology 80, 1762 (1999).
    Article  Google Scholar 

    61.
    Maestre, F. T., Valladares, F. & Reynolds, J. F. Is the change of plant-plant interactions with abiotic stress predictable? A meta-analysis of field results in arid environments. J. Ecol. 93, 748–757 (2005).
    Article  Google Scholar 

    62.
    Lewis, J. S. et al. Biotic and abiotic factors predicting the global distribution and population density of an invasive large mammal. Sci. Rep. 7, 44152 (2017).
    ADS  PubMed  PubMed Central  Article  Google Scholar 

    63.
    Klanderud, K., Vandvik, V. & Goldberg, D. The importance of biotic vs. abiotic drivers of local plant community composition along regional bioclimatic gradients. PLoS ONE 10, e0130205 (2015).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    64.
    Lários, M. C. et al. Evidence of cross-taxon congruence in Neotropical wetlands: Importance of environmental and spatial factors. Glob. Ecol. Conserv. https://doi.org/10.1016/j.gecco.2017.09.003 (2017).
    Article  Google Scholar 

    65.
    Casanova, M. T. & Brock, M. A. How do depth, duration and frequency of flooding influence the establishment of wetland plant communities?. Plant Ecol. 147, 237–250 (2000).
    Article  Google Scholar 

    66.
    Murkin, H. R., Murkin, E. J. & Ball, J. P. Avian habitat selection and prairie wetland dynamics: A 10-year experiment. Ecol. Appl. 7, 1144–1159 (1997).
    Article  Google Scholar 

    67.
    Naugle, D. E., Johnson, R. R., Estey, M. E. & Higgins, K. F. A landscape approach to conserving wetland bird habitat in the Prairie Pothole Region of eastern South Dakota. Wetlands 20, 588–604 (2001).
    Article  Google Scholar 

    68.
    Mabidi, A., Bird, M. S. & Perissinotto, R. Distribution and diversity of aquatic macroinvertebrate assemblages in a semi-arid region earmarked for shale gas exploration (Eastern Cape Karoo, South Africa). PLoS ONE 12, e0178559 (2017).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    69.
    Panov, V. E. & Caceres, C. Role of diapause in dispersal of aquatic invertebrates. in Diapause in Aquatic Invertebrates Theory and Human Use 187–195 (Springer, New York, 2007). https://doi.org/10.1007/978-1-4020-5680-2_12.

    70.
    Faist, A. M., Ferrenberg, S. & Collinge, S. K. Banking on the past: Seed banks as a reservoir for rare and native species in restored vernal pools. AoB Plants 5, 1–10 (2013).
    Article  Google Scholar 

    71.
    Reynolds, C. & Cumming, G. S. Seed traits and bird species influence the dispersal parameters of wetland plants. Freshw. Biol. 61, 1157–1170 (2016).
    Article  Google Scholar 

    72.
    Klaassen, M. & Nolet, B. A. The role of herbivorous water birds in aquatic systems through interactions with aquatic macrophytes, with special reference to the Bewick’s Swan: Fennel Pondweed system. Hydrobiologia 584, 205–213 (2007).
    Article  Google Scholar 

    73.
    Kleyheeg, E. et al. Movement patterns of a keystone waterbird species are highly predictable from landscape configuration. Mov. Ecol. 5, 2 (2017).
    PubMed  PubMed Central  Article  Google Scholar 

    74.
    DeVlaming, V. & Proctor, V. W. Dispersal of aquatic organisms: viability of seeds recovered from the droppings of captive Killdeer and Mallard Ducks. Am. J. Bot. 55, 20 (2006).
    Article  Google Scholar 

    75.
    Ma, M., Ma, Z. & Du, G. Effects of water level on three wetlands soil seed banks on the Tibetan Plateau. PLoS ONE 9, e101458 (2014).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    76.
    Poiani, K. A. & Johnson, W. C. Effect of hydroperiod on seed-bank composition in semipermanent prairie wetlands. Can. J. Bot. 67, 856–864 (1989).
    Article  Google Scholar 

    77.
    Johnson, W. C. et al. Vulnerability of Northern Prairie wetlands to climate change. Bioscience 55, 863 (2005).
    Article  Google Scholar 

    78.
    Voldseth, R. A., Johnson, W. C., Gilmanov, T., Guntenspergen, G. R. & Millett, B. V. Model estimation of land-use effects on water levels of northern Prairie wetlands. Ecol. Appl. 17, 527–540 (2007).
    PubMed  Article  Google Scholar  More

  • in

    The spreading of the invasive sacred ibis in Italy

    1.
    Wittenberg, R. & Cock, M. J. W. Invasive Alien Species: A Toolkit of Best Prevention and Management Practices (CABI, Wallingford, 2001).
    Google Scholar 
    2.
    Genovesi, P. Eradications of invasive alien species in Europe: a review. Biol. Invas. 7, 127–133 (2005).
    Article  Google Scholar 

    3.
    Butchart, S. H. M. et al. Global biodiversity: indicators of recent declines. Science 328, 1164–1168 (2010).
    ADS  CAS  PubMed  Article  Google Scholar 

    4.
    Pejchar, L. & Mooney, H. A. Invasive species, ecosystem services and human well-being. Trends Ecol. Evol. 24, 497–504 (2009).
    PubMed  Article  Google Scholar 

    5.
    Vilà, M. et al. How well do we understand the impacts of alien species on ecosystem services? A pan-European, cross-taxa assessment. Front. Ecol. Environ. 8, 135–144 (2010).
    Article  Google Scholar 

    6.
    David, P. et al. Impacts of invasive species on food webs: a review of empirical data. In Advances in Ecological Research (eds Bohan, D. A. et al.) 1–60 (Academic Press, Cambridge, 2017).
    Google Scholar 

    7.
    Doherty, T. S., Glen, A. S., Nimmo, D. G., Ritchie, E. G. & Dickman, C. R. Invasive predators and global biodiversity loss. Proc. Natl. Acad. Sci. 113, 11261–11265 (2016).
    CAS  PubMed  Article  Google Scholar 

    8.
    Blackburn, T. M. & Ewen, J. G. Parasites as drivers and passengers of human-mediated biological invasions. EcoHealth 14, 61–73 (2017).
    PubMed  Article  Google Scholar 

    9.
    Volponi, S. & Emiliani, D. Presenza e riproduzione di due specie di Ciconiiformi esotici con formazione di ibridi nelle zone umide ravennati del Parco del Delta del Po. Convegno Inanellatori Ital. 10, 1 (2008).
    Google Scholar 

    10.
    Negri, A. et al. Mitochondrial DNA and microsatellite markers evidence a different pattern of hybridization in red-legged partridge (Alectoris rufa) populations from NW Italy. Eur. J. Wildl. Res. 59, 407–419 (2013).
    Article  Google Scholar 

    11.
    Raffini, F. et al. From nucleotides to satellite imagery: approaches to identify and manage the invasive pathogen Xylella fastidiosa and its insect vectors in Europe. Sustainability 12, 4508 (2020).
    CAS  Article  Google Scholar 

    12.
    Little, M. N. et al. The influence of riparian invasion by the terrestrial shrub Lonicera maackii on aquatic macroinvertebrates in temperate forest headwater streams. Biol. Invas. https://doi.org/10.1007/s10530-020-02349-8 (2020).
    Article  Google Scholar 

    13.
    Falk-Petersen, J., Bøhn, T. & Sandlund, O. T. On the numerous concepts in invasion biology. Biol. Invas. 8, 1409–1424 (2006).
    Article  Google Scholar 

    14.
    Helmstedt, K. J. et al. Prioritizing eradication actions on islands: it’s not all or nothing. J. Appl. Ecol. 53, 733–741 (2016).
    Article  Google Scholar 

    15.
    Moon, K., Blackman, D. A. & Brewer, T. D. Understanding and integrating knowledge to improve invasive species management. Biol. Invas. 17, 2675–2689 (2015).
    Article  Google Scholar 

    16.
    European Commission DG ENV. More rigorous studies needed to evaluate impact of invasive birds. Sci. Environ. Policy 1 (2011).

    17.
    Strubbe, D., Shwartz, A. & Chiron, F. Concerns regarding the scientific evidence informing impact risk assessment and management recommendations for invasive birds. Biol. Conserv. 144, 2112–2118 (2011).
    Article  Google Scholar 

    18.
    Bertolino, S. et al. Spatially explicit models as tools for implementing effective management strategies for invasive alien mammals. Mamm. Rev. 50, 187–199 (2020).
    Article  Google Scholar 

    19.
    Graham, S. et al. Opportunities for better use of collective action theory in research and governance for invasive species management. Conserv. Biol. 33, 275–287 (2019).
    PubMed  PubMed Central  Article  Google Scholar 

    20.
    del Hoyo, J., Elliot, A. & Sargatal, J. Handbook of the Birds of the World (Lynx Edicions, Barcelona, 1992).
    Google Scholar 

    21.
    Kopij, G. Breeding ecology of the Sacred Ibis Threskiornis aethiopicus in the Free State, South Africa. S. Afr. J. Wildl. Res. 29, 25–30 (1999).
    Google Scholar 

    22.
    del Hoyo, J. & Collar, N. HBW and Birdlife International Illustrated Checklist of the Birds of the World. Vol: Non-passerines (2014).

    23.
    Robert, H., Lafontaine, R., Delsinne, T. & Beudels-Jamar, R. Risk analysis of the Sacred ibis Threskiornis aethiopicus (Royal Belgian Institute of Natural Sciences, Brussels, 2013).
    Google Scholar 

    24.
    Yésou, P. & Clergeau, P. Sacred Ibis: a new invasive species in Europe. Bird. World 18, 517–526 (2006).
    Google Scholar 

    25.
    Clergeau, P. & Yésou, P. Behavioural flexibility and numerous potential sources of introduction for the Sacred Ibis: causes of concern in Western Europe?. Biol. Invas. 8, 1381–1388 (2006).
    Article  Google Scholar 

    26.
    Nentwig, W., Bacher, S., Kumschick, S., Pyšek, P. & Vilà, M. More than “100 worst” alien species in Europe. Biol. Invas. 20, 1611–1621 (2018).
    Article  Google Scholar 

    27.
    Carpegna, F., Della Toffola, M., Alessandria, G. & Re, A. L’Ibis sacro Threskiornis aethiopicus nel Parco Naturale Lame del Sesia e sua presenza in Piemonte. Avocetta 23, 82 (1999).
    Google Scholar 

    28.
    Marion, L. Is the Sacred ibis a real threat to biodiversity? Long-term study of its diet in non-native areas compared to native areas. C. R. Biol. 336, 207–220 (2013).
    PubMed  Article  Google Scholar 

    29.
    Novarini, N. & Stival, E. Wading birds predation on Bufotes viridis (Laurenti, 1768) in the Ca’ Vallesina wetland (Ca’ Noghera, Venice, Italy). Boll. Mus. Storia Nat. Venezia 67, 71–75 (2017).
    Google Scholar 

    30.
    Clergeau, P., Reeber, S., Bastian, S. & Yésou, P. L. profil alimentaire de l’Ibis sacré Threskiornis aethiopicus introduit en France métropolitaine: espèce généraliste ou spécialiste. Rev. Ecol. Terre Vie 65, 331–342 (2010).
    Google Scholar 

    31.
    Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).
    Article  Google Scholar 

    32.
    Pavia, M. L. nuova Banca Dati del Gruppo Piemontese Studi Ornitologici. Tichodroma 6, 152 (2017).
    Google Scholar 

    33.
    Saporetti, F. Abundance, phenology and geographical distribution in relation to habitat of Tringa species in N Italy: a summary of data from the Italian online portal www.ornitho.it. Wader Study 122, 60–70 (2015).
    Article  Google Scholar 

    34.
    iNaturalist. iNaturalist (2020) (accessed 29 April 2020). https://www.inaturalist.org.

    35.
    Bibby, C., Burgess, N., Hill, D. & Mustoe, S. Bird Census Techniques 2nd edn. (Academic Press, Cambridge, 2000).
    Google Scholar 

    36.
    Buckland, S. T. et al. Introduction to Distance Sampling: Estimating Abundance of Biological Populations (Oxford University Press, Oxford, 2001).
    Google Scholar 

    37.
    Thomas, L. et al. Distance software: design and analysis of distance sampling surveys for estimating population size. J. Appl. Ecol. 47, 5–14 (2010).
    PubMed  Article  Google Scholar 

    38.
    Fasola, M. & Ruiz, X. The value of rice fields as substitutes for natural wetlands for Waterbirds in the Mediterranean region. Colon. Waterbirds 19, 122–128 (1996).
    Article  Google Scholar 

    39.
    Fasola, M., Rubolini, D., Merli, E., Boncompagni, E. & Bressan, U. Long-term trends of heron and egret populations in Italy, and the effects of climate, human-induced mortality, and habitat on population dynamics. Popul. Ecol. 52, 59–72 (2010).
    Article  Google Scholar 

    40.
    Fasola, M. & Cardarelli, E. Long-term changes in the food resources of a guild of breeding Ardeinae (Aves) in Italy. Ital. J. Zool. 82, 238–250 (2014).
    Google Scholar 

    41.
    Fasola, M., Merli, E., Boncompagni, E. & Rampa, A. Monitoring heron populations in Italy, 1972–2010. J. Heron Biol. Conserv. 1, 1–10 (2011).
    Google Scholar 

    42.
    Ter Braak, C., Van Strien, A., Meijer, R. & Verstrael, T. Analysis of monitorng data with many missing values: which method? In Proc. 12th Int. Conf. IBCC EOAC Noordwijkerhout Neth. 663–673 (1994).

    43.
    Gregory, R. D. et al. Developing indicators for European birds. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 360, 269–288 (2005).
    PubMed  PubMed Central  Article  Google Scholar 

    44.
    Pannekoek, J. & van Strien, A. TRIM 3 Manual. TRends and Indices for Monitoring data. Stat. Neth. Voorbg. Neth. Research Paper No. 0102, 1–57 (2001).

    45.
    Sokal, R. R. & Rohlf, F. J. Biometry: The Principles and Practice of Statistics in Biological Research (W.H. Freeman, New York, 1995).
    Google Scholar 

    46.
    La Sorte, F. A. et al. Opportunities and challenges for big data ornithology. The Condor 120, 414–426 (2018).
    Article  Google Scholar 

    47.
    Camerano, P., Giannetti, F., Terzuolo, P. G. & Guiot, E. La Carta Forestale del Piemonte—Aggiornamento 2016 (IPLA S.p.A.—Regione Piemonte, Turin, 2017).
    Google Scholar 

    48.
    Guisan, A., Thuille, W. & Zimmerman, N. Habitat Suitability and Distribution Models: With Applications in R (Cambridge University Press, Cambridge, 2017).
    Google Scholar 

    49.
    Naimi, B. & Araújo, M. B. sdm: a reproducible and extensible R platform for species distribution modelling. Ecography 39, 368–375 (2016).
    Article  Google Scholar 

    50.
    Naimi, B., Hamm, N. A. S., Groen, T. A., Skidmore, A. K. & Toxopeus, A. G. Where is positional uncertainty a problem for species distribution modelling?. Ecography 37, 191–203 (2014).
    Article  Google Scholar 

    51.
    Chatterjee, S. & Hadi, A. S. Regression Analysis by Example (Wiley, Hoboken, 2006).
    Google Scholar 

    52.
    Elith, J. et al. A statistical explanation of MaxEnt for ecologists: statistical explanation of MaxEnt. Divers. Distrib. 17, 43–57 (2011).
    Article  Google Scholar 

    53.
    Phillips, S. J., Anderson, R. P., Dudík, M., Schapire, R. E. & Blair, M. E. Opening the black box: an open-source release of Maxent. Ecography 40, 887–893 (2017).
    Article  Google Scholar 

    54.
    Treggiari, A. A., Gagliardone, M., Pellegrino, I. & Cucco, M. Habitat selection in a changing environment: the relationship between habitat alteration and Scops Owl (Aves: Strigidae) territory occupancy. Ital. J. Zool. 80, 574–585 (2013).
    Article  Google Scholar 

    55.
    Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS): assessing the accuracy of distribution models. J. Appl. Ecol. 43, 1223–1232 (2006).
    Article  Google Scholar 

    56.
    Heckmann, M. & Burk, L. Gridsampler—a simulation tool to determine the required sample size for repertory grid studies. J. Open Res. Softw. 5, 2 (2017).
    Article  Google Scholar 

    57.
    Guisan, A., Graham, C. H., Elith, J. & Huettmann, F. Sensitivity of predictive species distribution models to change in grain size. Divers. Distrib. 13, 332–340 (2007).
    Article  Google Scholar 

    58.
    Marion, L. & Marion, P. Première installation spontanée d’une colonie d’Ibis Sacré Threskiornis aethiopicus, au Lac de Grand-Lieu. Données préliminaires sur la production en jeunes et sur le régime alimentaire. Alauda 62, 275–280 (1994).
    Google Scholar 

    59.
    ONCFS. L’Ibis sacré (2020). http://www.oncfs.gouv.fr/La-lutte-contre-les-especes-exotiques-envahissantes-ru152/LIbis-sacre-ar282. Accessed 31 July 2020.

    60.
    Yésou, P., Clergeau, P., Bastian, S., Reeber, S. & Maillard, J.-F. The Sacred Ibis in Europe: ecology and management. Br. Birds 110, 197–212 (2017).
    Google Scholar 

    61.
    Herring, G. & Gawlik, D. E. Potential for successful population establishment of the nonindigenous sacred ibis in the Florida Everglades. Biol. Invas. 10, 969–976 (2008).
    Article  Google Scholar 

    62.
    Chen, K. & Hetherington, W. Sacred ibis nest control ineffective: environmentalist. Taipei Times 4 (2018).

    63.
    Bird Ecology Study Group. African Sacred Ibis in Taiwan (2020). https://besgroup.org/2019/01/28/african-sacred-ibis-in-taiwan/. Accessed 31 July 2020.

    64.
    Grussu, M. Checklist of the birds of Sardinia. Aves Ichnusae 4, 3–55 (2001).
    Google Scholar 

    65.
    Chane, M. & Balakrishnan, M. Population structure, feeding habits and activity patterns of the African Sacred ibis (Threskiornis aethiopicus) in Dilla Kera area, southern Ethiopia. Ethiop. J. Biol. Sci. 15, 93–105 (2016).
    Google Scholar 

    66.
    Smeraldo, S. et al. Modelling risks posed by wind turbines and power lines to soaring birds: the black stork (Ciconia nigra) in Italy as a case study. Biodivers. Conserv. 29, 1959–1976 (2020).
    Article  Google Scholar 

    67.
    Grussu, M. Gli uccelli alloctoni in Sardegna: una checklist aggiornata. Mem. Della Soc. Ital. Sci. Nat. E Mus. Civ. Storia Nat. Milano 36, 65 (2008).
    Google Scholar 

    68.
    Ranghetti, L. et al. Testing estimation of water surface in Italian rice district from MODIS satellite data. Int. J. Appl. Earth Observ. Geoinform. 52, 284–295 (2016).
    ADS  Article  Google Scholar 

    69.
    Fasola, M., Cardarelli, E., Pellitteri-Rosa, D. & Ranghetti, L. The recent decline of heron populations in Italy and the changes in rice cultivation practice. In 25th International Ornithological Congress. Ornithological Science (The Ornithological Society of Japan, 2014).

    70.
    Delmastro, G. B. Il gambero della Louisiana Procambarus clarkii (Girard, 1852) in Piemonte: nuove osservazioni su distribuzione, biologia, impatto e utilizzo (Crustacea: Decapoda: Cambaridae). Riv. Piemontese Storia Nat. 38, 61–129 (2017).
    Google Scholar 

    71.
    Bernini, G. et al. Complexity of biogeographic pattern in the endangered crayfish Austropotamobius italicus in northern Italy: molecular insights of conservation concern. Conserv. Genet. 17, 141–154 (2016).
    Article  Google Scholar 

    72.
    Fàbregas, M. C., Guillén-Salazar, F. & Garcés-Narro, C. The risk of zoological parks as potential pathways for the introduction of non-indigenous species. Biol. Invas. 12, 3627–3636 (2010).
    Article  Google Scholar 

    73.
    Brichetti, P. & Fracasso, G. Ornitologia Italiana. 1 Gaviidae-Falconidae (Alberto Perdisa editore, Bologna, 2003).
    Google Scholar 

    74.
    Wasef, S. Mitogenomic diversity in Sacred Ibis Mummies sheds light on early Egyptian practices. PLoS ONE 14, e0223964 (2019).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    75.
    Fasola, M. & Alieri, R. Conservation of heronry Ardeidae sites in North Italian agricultural landscapes. Biol. Conserv. 62, 219–228 (1992).
    Article  Google Scholar 

    76.
    Cocchi, R. & Volponi, S. Information on measures and related costs in relation to species included on the Union list—Threskiornis aethiopicus. Tech. Note Prep. IUCN Eur. Comm. ISPRA Ozzano Emilia Italy (2020).

    77.
    Smits, R., Van Horssen, P. & Van Der Winden, J. A Risk Analysis of the Sacred Ibis in the Netherlands: Including Biology and Management Options of this Invasive Species (Bureau Waardenburg Consultants for Environment & Ecology, Culemborg, 2010).
    Google Scholar 

    78.
    Byers, J. E. et al. Directing research to reduce the impacts of nonindigenous species. Conserv. Biol. 16, 630–640 (2002).
    Article  Google Scholar 

    79.
    Heath, J. A. & Frederick, P. C. Trapping White Ibises with rocket nets and mist nets in the Florida Everglades. J. Field Ornithol. 74, 187–192 (2003).
    Article  Google Scholar 

    80.
    Gurevitch, J. & Padilla, D. Are invasive species a major cause of extinctions?. Trends Ecol. Evol. 19, 470–474 (2004).
    PubMed  Article  Google Scholar 

    81.
    Didham, R. K., Tylianakis, J. M., Hutchison, M. A., Ewers, R. M. & Gemmell, N. J. Are invasive species the drivers of ecological change?. Trends Ecol. Evol. 20, 470–474 (2005).
    PubMed  Article  Google Scholar 

    82.
    Vaslin, M. Predaction de l’Ibis sacré sur des colonies des sternes et des guifettes. Ornithos 12, 106–109 (2005).
    Google Scholar 

    83.
    Evans, T. & Blackburn, T. M. Global variation in the availability of data on the environmental impacts of alien birds. Biol. Invas. 22, 1027–1036 (2020).
    Article  Google Scholar 

    84.
    Clergeau, P., Fourcy, D., Reeber, S. & Yésou, P. New but nice? Do alien sacred ibises Threskiornis aethiopicus stabilize nesting colonies of native spoonbills Platalea leucorodia at Grand-Lieu Lake, France?. Oryx 44, 533–538 (2010).
    Article  Google Scholar 

    85.
    Bremner, A. & Park, K. Public attitudes to the management of invasive non-native species in Scotland. Biol. Conserv. 139, 306–314 (2007).
    Article  Google Scholar  More

  • in

    Description of first nursery area for a pygmy devil ray species (Mobula munkiana) in the Gulf of California, Mexico

    1.
    Castro, J. I. The shark nursery of Bulls Bay, South Carolina, with a review of the shark nurseries of the Southeastern coast of the United States. Environ. Biol. Fishes 38, 37–48 (1993).
    Article  Google Scholar 
    2.
    Heupel, M. R. & Simpfendorfer, C. A. Estuarine nursery areas provide a low mortality environment for young bull sharks Carcharhinus leucas. Mar. Ecol. Prog. Ser. 433, 237–244. https://doi.org/10.3354/meps09191 (2011).
    ADS  Article  Google Scholar 

    3.
    Heupel, M. R., Carlson, J. & Simpfendorfer, C. A. Shark nursery areas: concepts, definition, characterization and assumptions. Mar. Ecol. Prog. Ser. 337, 287–297 (2007).
    ADS  Article  Google Scholar 

    4.
    Martins, A. P. B., Heupel, M. R., Chin, A. & Simpfendorfer, C. A. Batoid nurseries: definition, use and importance. Mar. Ecol. Prog. Ser. 595, 253–267. https://doi.org/10.3354/meps12545 (2018).
    ADS  Article  Google Scholar 

    5.
    Simpfendorfer, C. A. & Milward, N. Utilization of a tropical bay as a nursery area by sharks of the families Carcharhinidae and Sphyrnidae. Environ. Biol. Fishes 37, 337–345. https://doi.org/10.1007/BF00005200 (1993).
    Article  Google Scholar 

    6.
    Heupel, M. R. & Simpfendorfer, C. A. Estimation of mortality of juvenile blacktip sharks, Carcharhinus limbatus, within a nursery area using telemetry data. Can. J. Fish. Aquat. Sci. 59(4), 624–632 (2002).
    Article  Google Scholar 

    7.
    Yokota, L. & Lessa, R. P. A nursery area for sharks and rays in Northeastern Brazil. Environ. Biol. Fishes 75(3), 349–356. https://doi.org/10.1007/s10641-006-0038-9 (2006).
    Article  Google Scholar 

    8.
    Cerutti-Pereyra, F. et al. Restricted movements of juvenile rays in the lagoon of Ningaloo Reef, Western Australia, evidence for the existence of a nursery. Environ. Biol. Fishes 97(4), 371–383. https://doi.org/10.1007/s10641-013-0158-y (2014).
    Article  Google Scholar 

    9.
    Stewart, J. D., Nuttall, M., Hickerson, E. L. & Johnston, M. A. Important juvenile manta ray habitat at flower garden banks national marine sanctuary in the northwestern Gulf of Mexico. Mar. Biol. 165, 111. https://doi.org/10.1007/s00227-018-3409-9 (2018).
    CAS  Article  Google Scholar 

    10.
    Childs, J. N. The Occurrence, Habitat Use, and Behavior of Sharks and Rays Associating with Topographic Highs in the Northwestern Gulf of Mexico (Texas A&M University, College town, 2001).
    Google Scholar 

    11.
    Pate, J. H. & Marshall, A. D. Urban manta rays: potential manta ray nursery habitat along a highly developed Florida coastline. Endang. Species Res. 43, 51–64. https://doi.org/10.3354/esr01054 (2020).
    Article  Google Scholar 

    12.
    Germanov, E. S. et al. Contrasting habitat use and population dynamics of reef manta rays within the Nusa Penida Marine Protected Area Indonesia. Front. Mar. Sci. 6, 215. https://doi.org/10.3389/fmars.2019.00215 (2019).
    Article  Google Scholar 

    13.
    Gaskins, L. C. Pregnant giant devil ray (Mobula mobular) bycatch reveals potential Northern Gulf of California pupping ground. Ecology 100, 7. https://doi.org/10.1002/ecy.2689 (2019).
    Article  Google Scholar 

    14.
    Couturier, L. I. E. et al. Biology, ecology and conservation of the Mobulidae. J. Fish. Biol. 80, 1075–1119. https://doi.org/10.1111/j.1095-8649.2012.03264.x (2012).
    CAS  Article  PubMed  Google Scholar 

    15.
    Stewart, J. D. et al. Research priorities to support effective manta and devil ray conservation. Front. Mar. Sci. 5, 314. https://doi.org/10.3389/fmars.2018.00314 (2018).
    Article  Google Scholar 

    16.
    Stevens, J. D., Bonfil, R., Dulvy, N. K. & Walker, P. A. The effects of fishing on sharks, rays, and chimaeras (chondrichthyans), and the implications for marine ecosystems. ICES J. Mar. Sci. 57, 476–494. https://doi.org/10.1006/jmsc.2000.0724 (2000).
    Article  Google Scholar 

    17.
    Dulvy, N. K., Pardo, S. A., Simpfendorfer, C. A. & Carlson, J. K. Diagnosing the dangerous demography of manta rays using life history theory. PeerJ 2, e400. https://doi.org/10.7717/peerj.400 (2014).
    Article  PubMed  PubMed Central  Google Scholar 

    18.
    Notarbartolo di Sciara, G. Natural history of the rays of the genus Mobula in the Gulf of California. Fish Bull. 86(1), 45–66 (1988).
    Google Scholar 

    19.
    Dulvy, N. K. & Reynolds, J. D. Evolutionary transitions among egg-laying, live-bearing and maternal inputs in sharks and rays. Proc. Phys. Soc. B 264, 1309–1315. https://doi.org/10.1098/rspb.1997.0181 (1997).
    Article  Google Scholar 

    20.
    Marshall, A. D. & Bennett, M. B. Reproductive ecology of the reef manta ray Manta alfredi in southern Mozambique. J. Fish. Biol. 77, 169–190. https://doi.org/10.1111/j.1095-8649.2010.02669.x (2010).
    CAS  Article  PubMed  Google Scholar 

    21.
    Croll, D. A. et al. Vulnerabilities and fisheries impacts: the uncertain future of manta and devil rays. Aquat. Conserv. Mar. Freshw. Ecosyst. 26, 562–657. https://doi.org/10.1002/aqc.2591 (2016).
    Article  Google Scholar 

    22.
    White, W., Giles, J. & Potter, I. Data on the bycatch fishery and reproductive biology of mobulid rays (Myliobatiformes) in Indonesia. Fish. Res. 82, 65–73. https://doi.org/10.1016/j.fishres.2006.08.008 (2006).
    Article  Google Scholar 

    23.
    Rohner, C. et al. Trends in sightings and environmental influences on a coastal aggregation of manta rays and whale sharks. Mar. Ecol. Prog. Ser. 482, 153–168. https://doi.org/10.3354/meps10290 (2013).
    ADS  Article  Google Scholar 

    24.
    Paulin, C. D., Habib, G., Carey, C. L., Swanson, P. M. & Voss, G. J. New records of Mobula japanica and Masturus lanceolatus, and further records of Luvaris imperialis. N. Z. J. Mar Freshw. Res. 16, 11–17 (1982).
    Article  Google Scholar 

    25.
    IUCN. The IUCN Red List of Threatened Species. Version 2020-2. https://www.iucnredlist.org/ (2020). Accessed 20 July 2020.

    26.
    Ward-Paige, C. A., Davis, B. & Worm, B. Global population trends and human use patterns of manta and mobula rays. PLoS ONE 8(9), e74835. https://doi.org/10.1371/journal.pone.0074835 (2013).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    27.
    White, E. R., Myers, M. C., Flemming, J. M. & Baum, J. K. Shifting elasmobranch community assemblage at Cocos Island an isolated marine protected area. Conserv. Biol. 29, 186–1197. https://doi.org/10.1111/cobi.12478 (2015).
    Article  Google Scholar 

    28.
    Stevens, G., Fernando, D., Dando, M. & Sciara, G. Guide to the Manta and Devil Rays of the World (Wild Nature Press, Plymout, 2018).
    Google Scholar 

    29.
    Marshall, A.D. et al. Mobula munkiana. The IUCN Red List of Threatened Species 2019: e.T60198A124450956. (2019). Accessed 8 March 2020.

    30.
    Hobro, F. The Feeding Ecology, Foraging Behavior and Conservation of Manta Rays (Mobulidae) in Baja California, Mexico (University of Wales, Cardiff, 2002).
    Google Scholar 

    31.
    Broadhurst, M. K., Laglbauer, B. J. L. & Bennett, M. B. Gestation and size at parturition for Mobula kuhlii cf. eregoodootenkee. Environ. Biol. Fish. 102, 1009–1014. https://doi.org/10.1007/s10641-019-00886-3 (2019).
    Article  Google Scholar 

    32.
    Lopez, J. N. Estudio comparativo de la reproducción de tres especies del género Mobula (Chondrichthyes: Mobulidae) en el suroeste del Golfo de California, México (Instituto Politécnico Nacional, México). https://www.repositoriodigital.ipn.mx/bitstream/123456789/14110/1/serranol1.pdf (2009). Accessed 15 July 2020.

    33.
    Heinrichs, S., O’Malley, M., Medd, H. B. & Hilton, P. Manta Ray of Hope: The Global Threat to Manta and Mobula Rays. Manta Ray of Hope Project. https://wildaid.org/wp-content/uploads/2017/09/The-Global-Threat-to-Manta-and-Mobula-Rays-WEB.pdf (2011). Accessed 15 July 2020.

    34.
    Notarbartolo di Sciara, G. A revisionary study of the genus Mobula Rafinesque, 1810 (Chondrichthyes: Mobulidae) with the description of a new species. Zool. J. Linn. Soc. 91, 1–91. https://doi.org/10.1111/j.1096-3642.1987.tb01723.x (1987).
    Article  Google Scholar 

    35.
    Smith, W. D., Bizzarro, J. J. & Cailliet, G. M. The artisanal elasmobranch fishery on the east coast of Baja California, Mexico: Characteristics and management considerations. Cienc. Mar. 35(2), 209–236 (2009).
    Article  Google Scholar 

    36.
    Afonso, A. S., Cantareli, C. V., Levy, R. P. & Veras, L. B. Evasive mating behavior by female nurse sharks, Ginglymostoma cirratum (Bonnaterre, 1788), in an equatorial insular breeding ground. Neotrop Ichthyol. 14(4), e160103. https://doi.org/10.1590/1982-0224-20160103 (2016).
    Article  Google Scholar 

    37.
    Hamlett, W. Reproductive biology and phylogeny of chondrichthyes. In Book 3, Reproductive Biology and Phylogeny (ed. Hamlett, W.) (CRC Press, Boca Raton, 2005). https://doi.org/10.1201/9781439856000.
    Google Scholar 

    38.
    Stevens, G. M. W., Hawkins, J. P. & Roberts, C. M. Courtship and mating behaviour of manta rays Mobula alfredi and Mobula birostris in the Maldives. J. Fish. Biol. 93(2), 344–359. https://doi.org/10.1111/jfb.13768 (2018).
    Article  PubMed  Google Scholar 

    39.
    Villavicencio-Garayzar, C. J. Observations on Mobula munkiana (Chondrichthyes: Mobulidae) in the Bahia de la Paz, B.C.S.. Mexico. Rev. Investig. Cient. 2(2), 78–81 (1991).
    Google Scholar 

    40.
    Marshall, A. D., Compagno, L. J. V. & Bennett, M. B. Redescription of the genus Manta with resurrection of Manta alfredi. Zootaxa 28, 1–28. https://doi.org/10.5281/zenodo.191734 (2009).
    Article  Google Scholar 

    41.
    Obeso-Nieblas, M., Gaviño-Rodríguez, J. H., Jiménez-Illescas, A. R. & Shirasago Germán, B. Simulación numérica de la circulación por marea y viento del noroeste y sur en la Bahía de La Paz B.C.S. Oceánides 11, 1–2 (2002).
    Google Scholar 

    42.
    Bass, A. J. Problems in studies of sharks in the Southern Indian Ocean. In Sensory Biology of Sharks, Skates and Rays (ed. Hodgson, E. S.) 545–594 (Tufts University, Medford, 1978).
    Google Scholar 

    43.
    Tenzing, P. The Eco-physiology of Two Species of Tropical Stingrays in an Era of Climate Change (James Cook University, Townsville, 2014).
    Google Scholar 

    44.
    Brinton, E., Fleminger, A. & Siegel-Causey, D. The temperate and tropical planktonic biotas of the Gulf of California. CalCOFI Rep. 27, 228–266 (1986).
    Google Scholar 

    45.
    Deakos, M. H. Paired-laser photogrammetry as a simple and accurate system for measuring the body size of free-ranging manta rays Manta alfredi. Aquat. Biol. 10, 1–10. https://doi.org/10.3354/ab00258 (2010).
    Article  Google Scholar 

    46.
    Salomon-Aguilar, C. A., Villavicencio-Garayzar, C. J. & Reyes-Bonilla, H. Shark breeding grounds and seasons in the Gulf of California: fishery management and conservation strategy. Cienc. Mar. 35(4), 369–388 (2009).
    Article  Google Scholar 

    47.
    Brinton, E. & Townsend, A. W. Euphausiids in the Gulf of California the 1957 cruises. Calif. Coop. Ocean. Fish. Investig. Rep. 21, 211–236 (1980).
    Google Scholar 

    48.
    De Silva-Davila, R. & Palomares-Garcia, R. Unusual larval growth production of Nyctiphanes simplex in Bahia de La Paz, Baja California Mexico. J. Crustac. Biol. 18(3), 490–498. https://doi.org/10.1163/193724098X00313 (1998).
    Article  Google Scholar 

    49.
    Gómez-Gutiérrez, J., Martínez-Gómez, S. & Robinson, C. J. Seasonal growth, molt, and egg production of Nyctiphanes simplex (Crustacea: Euphausiacea) juveniles and adults in the Gulf of California. Mar. Ecol. Prog. Ser. 455, 173–194. https://doi.org/10.3354/meps09631 (2012).
    ADS  Article  Google Scholar 

    50.
    Uchida, S., Toda, M. & Matsumoto, Y. Captive records of manta rays in Okinawa Churaumi Aquarium. In Joint Meeting of Ichthyologists and Herpetologists (Montreal, QC), 23–28 (2008).

    51.
    Hidalgo-Gonzalez, R. M. & Alvarez-Borrego, S. Total and new production in the Gulf of California estimated from ocean color data from the satellite sensor SeaWIFS. Deep Sea Res. II(51), 739–752. https://doi.org/10.1016/j.dsr2.2004.05.006 (2004).
    ADS  CAS  Article  Google Scholar 

    52.
    Santamaria-Del-Angel, E., Alvarez-Borrego, S., Millán-Nuñez, R. & Muller-Karger, F. E. Sobre el efecto de las surgencias de verano en la biomasa fitoplanctónica del Golfo de California. Rev. Soc. Mex. Hist. Nat. 49, 207–212 (1999).
    Google Scholar 

    53.
    VUE Software Manual, Version 2.5 Vemco. Bedford, Nova Scotia, Canada (2014).

    54.
    Daly, R., Smale, M. J., Cowley, P. D. & Froneman, P. W. Residency patterns and migration dynamics of adult bull sharks (Carcharhinus leucas) on the east coast of southern Africa. PLoS ONE 9, e109357. https://doi.org/10.1371/journal.pone.0109357 (2014).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    55.
    Smith, P.E. & Richardson, S.L. Técnicas modelo para prospecciones de huevos y larvas de peces pelágicos. In: FAO Fishing Technical Report. 175, 107 (1979).

    56.
    Beers, J. R. Volumetric methods. In Book 4, Zooplankton Fixation and Preservation. Monographs on Oceanography methods (ed. Steedman, H. F.) (The UNESCO Press, Paris, 1976).
    Google Scholar  More

  • in

    Applying the economic concept of profitability to leaves

    Leaf profitability
    We calculated the economic profitability of leaves and analysed the patterns in relation to leaf size, environment and durability (longevity). There was wide variation in leaf profitability with a mean value (± SD) of 3.4 ± 3.5% day−1 and 5th/95th percentile range of 0.29–10.3% day−1. There are no previous studies reporting leaf profitability values for direct comparison, but they can be re-calculated based on published values for leaf payback time. For example, Williams et al.21 reported payback time values in several Piper species from a Mexican rainforest, corresponding to leaf profitability values between 0.01 and 33% day−1. Poorter et al.23 calculated payback times corresponding to leaf profitability values of 1.25–50% day−1 , depending on species type, light environment, and growth conditions (very high values were observed for seedlings grown under non-limiting, hydroponic conditions). The mean values of our study are lower, but in line with values calculated from payback time of Kikuzawa and Lechowicz25 (2.2 ± 2% day−1), because they consider the mean labour time and the favourable period length, as we also applied in our calculations (see “Methods” and Supplementary File S2 online).
    One factor that could influence profitability is the size of a production unit. For example, a large leaf may imply a higher cost required for structural support; therefore, the changes in profitability will depend on how gains and expenses vary with size. As it turned out, we found that leaf profitability was positively related to leaf size (Fig. 2A), but the percentage of variance explained was not especially high (R2 = 0.07, P  More

  • in

    Asynchronicity of endemic and emerging mosquito-borne disease outbreaks in the Dominican Republic

    1.
    Musso, D., Rodriguez-Morales, A. J., Levi, J. E., Cao-Lormeau, V.-M. & Gubler, D. J. Unexpected outbreaks of arbovirus infections: lessons learned from the Pacific and tropical America. Lancet Infect. Dis. 18, e355–e361 (2018).
    PubMed  Article  Google Scholar 
    2.
    Mavian, C. et al. Islands as hotspots for emerging mosquito-borne viruses: a one-health perspective. Viruses 11, 11 (2018).

    3.
    Cao-Lormeau, V.-M. Tropical islands as new hubs for emerging arboviruses. Emerg. Infect. Dis. 22, 913–915 (2016).
    PubMed  PubMed Central  Article  Google Scholar 

    4.
    Cassadou, S. et al. Emergence of chikungunya fever on the French side of Saint Martin island, October to December 2013. Euro Surveill. 19, 20752 (2014).

    5.
    Dorléans, F. et al. Outbreak of Chikungunya in the French Caribbean Islands of Martinique and Guadeloupe: findings from a Hospital-Based Surveillance System (2013–2015). Am. J. Trop. Med. Hyg. 98, 1819–1825 (2018).

    6.
    Halstead, S. B. Reappearance of chikungunya, formerly called dengue, in the Americas. Emerg. Infect. Dis. 21, 557–561 (2015).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    7.
    Faria, N. R. et al. Zika virus in the Americas: early epidemiological and genetic findings. Science 352, 345–349 (2016).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    8.
    Grubaugh, N. D., Faria, N. R., Andersen, K. G. & Pybus, O. G. Genomic insights into Zika virus emergence and spread. Cell 172, 1160–1162 (2018).
    CAS  PubMed  Article  Google Scholar 

    9.
    Metsky, H. C. et al. Zika virus evolution and spread in the Americas. Nature 546, 411–415 (2017).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    10.
    Hotez, P. J. & Murray, K. O. Dengue, West Nile virus, chikungunya, Zika-and now Mayaro? PLoS Negl. Trop. Dis. 11, e0005462 (2017).
    PubMed  PubMed Central  Article  Google Scholar 

    11.
    Lorenz, C., Freitas Ribeiro, A. & Chiaravalloti-Neto, F. Mayaro virus distribution in South America. Acta Trop. 198, 105093 (2019).
    PubMed  Article  Google Scholar 

    12.
    Ganjian, N. & Riviere-Cinnamond, A. Mayaro virus in Latin America and the Caribbean. Rev. Panam. Salud Publica 44, e14 (2020).
    PubMed  PubMed Central  Article  Google Scholar 

    13.
    Weaver, S. C. & Reisen, W. K. Present and future arboviral threats. Antivir. Res. 85, 328–345 (2010).
    CAS  PubMed  Article  Google Scholar 

    14.
    Long, K. C. et al. Experimental transmission of Mayaro virus by Aedes aegypti. Am. J. Trop. Med. Hyg. 85, 750–757 (2011).
    PubMed  PubMed Central  Article  Google Scholar 

    15.
    Suspected dengue cases by epidemiological week for countries and territories of the America. https://www.paho.org/data/index.php/en/mnu-topics/indicadores-dengue-en/dengue-nacional-en/252-dengue-pais-ano-en.html?start=2.

    16.
    Five-fold increase in dengue cases in the Americas over the past decade. https://www.paho.org/hq/index.php?option=com_content&view=article&id=9657:2014-los-casos-dengue-americas- (2014).

    17.
    Obolski, U. et al. MVSE: an R‐package that estimates a climate‐driven mosquito‐borne viral suitability index. Methods Ecol. Evol. 10, 1357–1370 (2019).
    PubMed  PubMed Central  Article  Google Scholar 

    18.
    Kraemer, M. U. G. et al. The global distribution of the arbovirus vectors Aedes aegypti and Ae. albopictus. Elife 4, e08347 (2015).
    PubMed  PubMed Central  Article  Google Scholar 

    19.
    Kraemer, M. U. G. et al. Past and future spread of the arbovirus vectors Aedes aegypti and Aedes albopictus. Nat. Microbiol. 4, 854–863 (2019).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    20.
    Hamlet, A. et al. The seasonal influence of climate and environment on yellow fever transmission across Africa. PLoS Negl. Trop. Dis. 12, e0006284 (2018).
    PubMed  PubMed Central  Article  Google Scholar 

    21.
    Do, T. T. T., Martens, P., Luu, N. H., Wright, P. & Choisy, M. Climatic-driven seasonality of emerging dengue fever in Hanoi, Vietnam. BMC Public Health 14, 1078 (2014).
    PubMed  PubMed Central  Article  Google Scholar 

    22.
    Perez-Guzman, P. N. et al. Measuring mosquito-borne viral suitability in Myanmar and implications for Local Zika virus transmission. PLoS Curr. 10 (2018).

    23.
    Rodrigues Faria, N. et al. Epidemiology of Chikungunya Virus in Bahia, Brazil, 2014-2015. PLoS Curr. 8 (2016).

    24.
    Lourenço, J. et al. Epidemiological and ecological determinants of Zika virus transmission in an urban setting. eLife 6, e29820 (2017).

    25.
    Dengue serotypes by year for countries and territories of the Americas. https://www.paho.org/data/index.php/es/temas/indicadores-dengue/dengue-nacional/549-dengue-serotypes-es.html.

    26.
    Bowman, L. R., Rocklöv, J., Kroeger, A., Olliaro, P. & Skewes, R. A comparison of Zika and dengue outbreaks using national surveillance data in the Dominican Republic. PLoS Negl. Trop. Dis. 12, e0006876 (2018).
    PubMed  PubMed Central  Article  Google Scholar 

    27.
    Lindsey, N. P., Staples, J. E. & Fischer, M. Chikungunya Virus disease among travelers-United States 2014–2016. Am. J. Trop. Med. Hyg. 98, 192–197 (2018).
    PubMed  Article  Google Scholar 

    28.
    Zingman, M. A., Paulino, A. T. & Payano, M. P. Clinical manifestations of chikungunya among university professors and staff in Santo Domingo, the Dominican Republic. Rev. Panam. Salud Publica 41, e64 (2017).
    PubMed  PubMed Central  Article  Google Scholar 

    29.
    Rosario, V. et al. Chikungunya infection in the general population and in patients with rheumatoid arthritis on biological therapy. Clin. Rheumatol. 34, 1285–1287 (2015).
    CAS  PubMed  Article  Google Scholar 

    30.
    He, A., Brasil, P., Siqueira, A. M., Calvet, G. A. & Kwatra, S. G. The emerging Zika virus threat: a guide for dermatologists. Am. J. Clin. Dermatol. 18, 231–236 (2017).
    PubMed  Article  Google Scholar 

    31.
    Martinez, J. D., Garza, J. A. Cla & Cuellar-Barboza, A. Going viral 2019: Zika, Chikungunya, and Dengue. Dermatol. Clin. 37, 95–105 (2019).
    CAS  PubMed  Article  Google Scholar 

    32.
    Duffy, M. R. et al. Zika virus outbreak on Yap Island, Federated States of Micronesia. N. Engl. J. Med. 360, 2536–2543 (2009).
    CAS  PubMed  Article  Google Scholar 

    33.
    Pineda, C., Muñoz-Louis, R., Caballero-Uribe, C. V. & Viasus, D. Chikungunya in the region of the Americas. A challenge for rheumatologists and health care systems. Clin. Rheumatol. 35, 2381–2385 (2016).
    PubMed  Article  Google Scholar 

    34.
    Langsjoen, R. M. et al. Molecular virologic and clinical characteristics of a chikungunya fever outbreak in La Romana, Dominican Republic, 2014. PLoS Negl. Trop. Dis. 10, e0005189 (2016).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    35.
    Grubaugh, N. D. et al. Tracking virus outbreaks in the twenty-first century. Nat. Microbiol. 4, 10–19 (2019).
    CAS  PubMed  Article  Google Scholar 

    36.
    Kraemer, M. U. G. et al. The global compendium of Aedes aegypti and Ae. albopictus occurrence. Sci. Data 2, 150035 (2015).
    PubMed  PubMed Central  Article  Google Scholar 

    37.
    Liu-Helmersson, J., Stenlund, H., Wilder-Smith, A. & Rocklöv, J. Vectorial capacity of Aedes aegypti: effects of temperature and implications for global dengue epidemic potential. PLoS ONE 9, e89783 (2014).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    38.
    Winokur, O. C., Main, B. J., Nicholson, J. & Barker, C. M. Impact of temperature on the extrinsic incubation period of Zika virus in Aedes aegypti. PLoS Negl. Trop. Dis. 14, e0008047 (2020).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    39.
    Chan, M. & Johansson, M. A. The incubation periods of Dengue viruses. PLoS ONE 7, e50972 (2012).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    40.
    Mordecai, E. A. et al. Detecting the impact of temperature on transmission of Zika, dengue, and chikungunya using mechanistic models. PLoS Negl. Trop. Dis. 11, e0005568 (2017).
    PubMed  PubMed Central  Article  Google Scholar 

    41.
    Faria, N. R. et al. Establishment and cryptic transmission of Zika virus in Brazil and the Americas. Nature 546, 406–410 (2017).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    42.
    Cauchemez, S. et al. Local and regional spread of chikungunya fever in the Americas. Eur. Surveill. 19, 20854 (2014).
    CAS  Article  Google Scholar 

    43.
    Nishiura, H., Kinoshita, R., Mizumoto, K., Yasuda, Y. & Nah, K. Transmission potential of Zika virus infection in the South Pacific. Int. J. Infect. Dis. 45, 95–97 (2016).
    PubMed  Article  Google Scholar 

    44.
    Liu, Y. et al. Reviewing estimates of the basic reproduction number for dengue, Zika and chikungunya across global climate zones. Environ. Res. 182, 109114 (2020).
    CAS  PubMed  Article  Google Scholar 

    45.
    Rodriguez-Barraquer, I., Salje, H. & Cummings, D. A. Opportunities for improved surveillance and control of dengue from age-specific case data. Elife 8, e45474 (2019).

    46.
    Plan de preparación y respuesta frente a brotes de Fiebre Chikungunya. Resolución Ministerial N° 427 – 2014/MINSA Lima, Peu (2014).

    47.
    Low, G. K.-K., Ogston, S. A., Yong, M.-H., Gan, S.-C. & Chee, H.-Y. Global dengue death before and after the new World Health Organization 2009 case classification: a systematic review and meta-regression analysis. Acta Trop. 182, 237–245 (2018).
    PubMed  Article  Google Scholar 

    48.
    Freitas, A. R. R., Alarcón-Elbal, P. M., Paulino-Ramírez, R. & Donalisio, M. R. Excess mortality profile during the Asian genotype chikungunya epidemic in the Dominican Republic. 2014. Trans. R. Soc. Trop. Med. Hyg. 112, 443–449 (2018).
    PubMed  Article  Google Scholar 

    49.
    Imai, N., Dorigatti, I., Cauchemez, S. & Ferguson, N. M. Estimating dengue transmission intensity from sero-prevalence surveys in multiple countries. PLoS Negl. Trop. Dis. 9, e0003719 (2015).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    50.
    Grubaugh, N. D. et al. Xenosurveillance: a novel mosquito-based approach for examining the human-pathogen landscape. PLoS Negl. Trop. Dis. 9, e0003628 (2015).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    51.
    Fauver, J. R. et al. The use of xenosurveillance to detect human bacteria, parasites, and viruses in mosquito bloodmeals. Am. J. Trop. Med. Hyg. 97, 324–329 (2017).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    52.
    Fauver, J. R. et al. Xenosurveillance reflects traditional sampling techniques for the identification of human pathogens: a comparative study in West Africa. PLoS Negl. Trop. Dis. 12, e0006348 (2018).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    53.
    Grubaugh, N. D. et al. Travel surveillance and genomics uncover a hidden zika outbreak during the waning epidemic. Cell 178, 1057–1071.e11 (2019).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    54.
    Vogels, C. B. F. et al. Arbovirus coinfection and co-transmission: a neglected public health concern? PLoS Biol. 17, e3000130 (2019).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    55.
    Bisanzio, D. et al. Spatio-temporal coherence of dengue, chikungunya and Zika outbreaks in Merida, Mexico. PLoS Negl. Trop. Dis. 12, e0006298 (2018).
    PubMed  PubMed Central  Article  Google Scholar 

    56.
    Freitas, L. P., Cruz, O. G., Lowe, R. & Sá Carvalho, M. Space–time dynamics of a triple epidemic: dengue, chikungunya and Zika clusters in the city of Rio de Janeiro. Proc. R. Soc. B: Biol. Sci. 286, 20191867 (2019).
    Article  Google Scholar 

    57.
    Shioda, K. et al. Identifying signatures of the impact of rotavirus vaccines on hospitalizations using sentinel surveillance data from Latin American countries. Vaccine 38, 323–329 (2020).
    PubMed  Article  Google Scholar 

    58.
    Blohm, G. et al. Mayaro as a Caribbean traveler: Evidence for multiple introductions and transmission of the virus into Haiti. Int. J. Infect. Dis. 87, 151–153 (2019).
    CAS  PubMed  Article  Google Scholar 

    59.
    Lednicky, J. et al. Mayaro Virus in Child with Acute Febrile Illness, Haiti, 2015. Emerg. Infect. Dis. 22, 2000–2002 (2016).
    PubMed  PubMed Central  Article  Google Scholar 

    60.
    Weppelmann, T. A. et al. A tale of two flaviviruses: a seroepidemiological study of dengue virus and west nile virus transmission in the ouest and sud-est departments of Haiti. Am. J. Trop. Med. Hyg. 96, 135–140 (2017).
    PubMed  PubMed Central  Article  Google Scholar 

    61.
    Wiggins, K., Eastmond, B. & Alto, B. W. Transmission potential of Mayaro virus in Florida Aedes aegypti and Aedes albopictus mosquitoes. Med. Vet. Entomol. 32, 436–442 (2018).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    62.
    Pereira, T. N., Carvalho, F. D., De Mendonça, S. F., Rocha, M. N. & Moreira, L. A. Vector competence of Aedes aegypti, Aedes albopictus, and Culex quinquefasciatus mosquitoes for Mayaro virus. PLoS Negl. Trop. Dis. 14, e0007518 (2020).
    PubMed  PubMed Central  Article  Google Scholar 

    63.
    Kantor, A. M., Lin, J., Wang, A., Thompson, D. C. & Franz, A. W. E. Infection pattern of Mayaro Virus in Aedes aegypti (Diptera: Culicidae) and transmission potential of the virus in mixed infections with Chikungunya virus. J. Med. Entomol. 56, 832–843 (2019).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    64.
    Komar, O. et al. West Nile virus survey of birds and mosquitoes in the Dominican Republic. Vector-Borne Zoonotic Dis. 5, 120–126 (2005).
    PubMed  Article  PubMed Central  Google Scholar 

    65.
    Requena-Méndez, A. et al. Cases of chikungunya virus infection in travellers returning to Spain from Haiti or Dominican Republic, April-June 2014. Eur. Surveill. 19, 20853 (2014).
    Article  Google Scholar 

    66.
    Millman, A. J. et al. Chikungunya and Dengue virus infections among united states community service volunteers returning from the Dominican Republic, 2014. Am. J. Trop. Med. Hyg. 94, 1336–1341 (2016).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    67.
    Duijster, J. W. et al. Zika virus infection in 18 travellers returning from Surinam and the Dominican Republic, The Netherlands, November 2015–March 2016. Infection 44, 797–802 (2016).
    PubMed  PubMed Central  Article  Google Scholar 

    68.
    Barzon, L. et al. Isolation of infectious Zika virus from saliva and prolonged viral RNA shedding in a traveller returning from the Dominican Republic to Italy, January 2016. Eur. Surveill. 21, 30159 (2016).
    Google Scholar 

    69.
    Goncé, A. et al. Spontaneous abortion associated with Zika virus infection and persistent viremia. Emerg. Infect. Dis. 24, 933–935 (2018).
    PubMed  PubMed Central  Article  Google Scholar 

    70.
    Díaz-Menéndez, M. et al. Initial experience with imported Zika virus infection in Spain. Enfermedades Infecciosas y. Microbiol.ía Cl.ínica 36, 4–8 (2018).
    Google Scholar 

    71.
    Perez, F. et al. The decline of dengue in the Americas in 2017: discussion of multiple hypotheses. Trop. Med. Int. Health 24, 442–453 (2019).
    PubMed  PubMed Central  Article  Google Scholar 

    72.
    Ribeiro, G. S. et al. Does immunity after Zika virus infection cross-protect against dengue?. Lancet Glob. Health 6, e140–e141 (2018).
    PubMed  Article  Google Scholar 

    73.
    Ribeiro, G. S. et al. Influence of herd immunity in the cyclical nature of arboviruses. Curr. Opin. Virol. 40, 1–10 (2020).
    CAS  PubMed  Article  Google Scholar 

    74.
    Gordon, A. et al. Prior dengue virus infection and risk of Zika: a pediatric cohort in Nicaragua. PLoS Med. 16, e1002726 (2019).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    75.
    Rodriguez-Barraquer, I. et al. Impact of preexisting dengue immunity on Zika virus emergence in a dengue endemic region. Science 363, 607–610 (2019).
    ADS  CAS  PubMed  Article  Google Scholar 

    76.
    Tsang, T. K. et al. Effects of infection history on dengue virus infection and pathogenicity. Nat. Commun. 10, 1246 (2019).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    77.
    Katzelnick, L. C., Montoya, M., Gresh, L., Balmaseda, A. & Harris, E. Neutralizing antibody titers against dengue virus correlate with protection from symptomatic infection in a longitudinal cohort. Proc. Natl Acad. Sci. USA 113, 728–733 (2016).
    ADS  CAS  PubMed  Article  Google Scholar 

    78.
    República Dominicana Chikungunya. https://www.paho.org/dor/images/stories/archivos/chikungunya/boletin_chikv_no-13_2014_8_20.pdf?ua=1 (2014).

    79.
    Verdonschot, P. F. M. & Besse-Lototskaya, A. A. Flight distance of mosquitoes (Culicidae): a metadata analysis to support the management of barrier zones around rewetted and newly constructed wetlands. Limnologica 45, 69–79 (2014).
    Article  Google Scholar 

    80.
    Vazeille, M. et al. Two Chikungunya isolates from the outbreak of La Reunion (Indian Ocean) exhibit different patterns of infection in the mosquito, Aedes albopictus. PLoS ONE 2, e1168 (2007).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    81.
    Lamballerie, Xde et al. Chikungunya virus adapts to tiger mosquito via evolutionary convergence: a sign of things to come? Virol. J. 5, 33 (2008).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    82.
    González, M. A. et al. Micro-environmental features associated to container-dwelling mosquitoes (Diptera: Culicidae) in an urban cemetery of the Dominican Republic. Rev. Biol. Trop. 67, 132–145 (2019).

    83.
    González, M. A. et al. A survey of tire-breeding mosquitoes (Diptera: Culicidae) in the Dominican Republic: considerations about a pressing issue. Biomédica 40, 507–515 (2020).

    84.
    IX Censo Nacional De Población Y Vivienda. vol. 1 (2012).

    85.
    PLISA Health Information Platform for the Americas. https://www.paho.org/data/index.php/en/.

    86.
    Dominican Republic: Human Development Indicators. http://hdr.undp.org/en/countries/profiles/DOM.

    87.
    Lipsitch, M. et al. Transmission dynamics and control of severe acute respiratory syndrome. Science 300, 1966–1970 (2003).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    88.
    Petrone, M. E. et al. Asynchronicity of endemic and emerging mosquito-borne disease outbreaks in the Dominican Republic. Repository: Arbovirus_Epi_DR. (2020), https://doi.org/10.5281/zenodo.4287651. More