Ecology

Case study areaThe studied catchment (2 621 km2) is located in the central-western part of Poland, and constitutes a part of the Oder River basin. The Wełna River (118 km) discharges to the Warta River near the town of Oborniki18, with an average flow rate of 8.1 m3s−1 (1980–2019) in this profile19. The natural conditions in this catchment favour the development of intensive agriculture, which covers almost 72% of this area (1888 km2) and contributes to the high consumption of mineral fertilizers20. Forest areas cover another 22% of this catchment (589 km2), while urbanised ones only 4% (93 km2) (Fig. 1). The Wełna River catchment is inhabited by approx. 230,000 people, of which only approx. 74% is served by wastewater treatment facilities21.Figure 1Localisation of the Wełna River catchment with its land use forms and nutrient sources. This figure was created using ArcGIS 10.2.1 for Desktop available at https://www.esri.com/en-us/home. Licence granted to Institute of Meteorology and Water Management.Full size imageInput dataBoth the mass-balance method and the modelling method require a similar amount and type of input data (Supplementary Table S1). Basic information on the Wełna River daily flow rates and nutrient concentrations in the closing profile of the catchment (Oborniki) has been obtained from the state monitoring services (Institute of Meteorology and Water Management—National Research Institute—IMGW-PIB13 and State Environmental Monitoring22—SEM) (Supplementary Table S1). They have formed the basis for the estimation of the share of individual sources in the mass-balance method, as well as for the calibration of the Macromodel DNS/SWAT in the modelling method. Other data, such as maps of elevation, river network and soil maps, as well as meteorological data, necessary for the development of an accurate representation of the studied catchment area on the Macromodel DNS/SWAT digital platform, were also obtained from state repositories. Data on the land use comes from the Corine Land Cover8, while detailed information on nutrient sources has been obtained mostly from the Local Data Bank of statistical information. The utilisation of the collected database has been presented in Fig. 2, and described in the following text. The comparison of the results for nutrient loads from both method was based on the year 2017, which was characterised by the maximum amount of monitoring data for both flows (365 measurements) (IMGW-PIB) and total nitrogen (TN) and total phosphorus (TP) (12 measurements–SEM). The average air temperature in 2017 in Poland was 1.5 °C higher than the long-term average (1971–2000) and was over 10 °C which resulted from the warm autumn and the end of the year. The time of the snow cover presence was shorter than the long-term data, and the rest of the year was classified as thermally normal.Figure 2Methodology diagram with relevant chapters marked in grey ovals (green—steps and data used for Mass Balance method, blue—steps data used for Modelling method, green/blue—steps and data used for both methods).Full size imageIn terms of precipitation, 2017 was assessed as wet, similarly due to rainy autumn and summer. In the Wełna River catchment area, the annual rainfall was about 770 mm, however high variability of precipitation conditions in particular months, from extremely wet to very dry, should be noted23. Therefore hydrologically, 2017 was considered normal with the flows only slightly lower than the long-term average.Mass-balance methodThe first method used for the quantification of sources and loads in the studied area was the static mass-balance method. It is widely used by the Polish administration authorities responsible for water management17. This method is based on the assumption that the sum of the nutrient loads in the river’s closing profile (selected based on access to the monitoring data) and its retention in the catchment equals the emission of nutrients in a given time. Such assumption allows the apportioning of the river loads among identified sources and the estimation of their contribution to the total loads, based on known or assumed values of their retention.River load calculationThe total load of nutrients discharged from the catchment was calculated using the daily flow rate and nutrient concentrations in the closing profile of the catchment area (Oborniki, Fig. 1) from the SEM (Supplementary Table S1). The daily river load was calculated using the following Eq. 5:$${L}_{river}=0.0864sum_{t=1}^{n=365}{({Q}_{t}cdot {C}_{t})}_{t}$$
(1)
where: Lriver is the annual load [kga-1], n is the number of days, t is the consecutive day, Ct is the concentration [mg L-1], Qt is the mean daily flow rate [Ls-1], and 0.0864 is the unit conversion.Due to the fact that the flow rate is measured daily and nutrient concentrations only 12 times a year, the linear interpolation method5 was used to obtain the daily concentration values:$${x}_{k}={x}_{a}+kcdot frac{{x}_{b}-{x}_{a}}{n+1}$$
(2)
where: xk is the interpolated concentration value, xa is the first of the two measured concentration values between which the concentrations are interpolated, xb is the second of the two measured concentration values between which the concentrations are interpolated, k is the consecutive number of missing value and n is number of missing values.Source apportionmentFor the mass-balance method, data on nutrient loads for source apportionment (emission inventory) was collected in order to proceed with further calculations. The calculations were performed for 2017, due to the availability of river monitoring data and the nutrient sources were divided into 7 categories, based on the HELCOM guidelines5: municipal (MWS) and industrial (INS) point sources, municipal diffuse sources (SCS), forestry (MFS), agriculture (AGS), natural background (NBS) and atmospheric deposition (ATS). The category of “unknown sources” (UKS) was taken into account, in order to include possible discrepancies in nutrients load apportionment, and to cover eventual differences between calculated river load and inventoried emission.The MWS loads were calculated on the basis of the number of inhabitants served by the wastewater treatment plants (WWTPs)21. In the Wełna River catchment, 151 771 inhabitants were served by the 12 WWTPs covered by the National Wastewater Treatment Program (NWTP)24, which provides information on the total discharge volume from each facility. For 5 of these plants, information on influent/effluent nutrient concentrations was also available, allowing the direct calculation of discharged loads. For the remaining seven facilities, the loads were calculated on the basis of the mean influent concentration information, available for the WWTPs covered by the NWTP (80 mgL−1 and 12 mgL−1 for TN and TP, respectively), and approximated nutrient reduction level in non-biological WWTPs. This reduction level, based on data from the NWTP, was set at 65% for TN and 35% for TP24. Another 19 350 inhabitants of this catchment were connected to the small WWTPs, not included in the NWTP. This part of the MWS load was calculated using the mean daily wastewater outflow (0.12 m3day−1 per person), the same mean nutrient concentrations and reduction levels as presented above. Additionally, the remaining 25% of the catchment’s population (58,000) is not connected to any WWTP and uses septic tanks and other types of individual wastewater treatment systems. The load from this source was expressed as SCS, and calculated using unit loads set on 11 gday−1 per person and 1.6 gday-1 per person for TN and TP, respectively17. The industrial nutrient input information (INS) was gathered directly from the Statistics Poland office database21.The AGS load was calculated using nitrate and phosphate concentrations in shallow groundwater (90 cm below the ground surface), from 22 sampling points located on agricultural areas in the Wełna River catchment17. Concentrations were recalculated to TN and TP respectively and averaged. Thus, the obtained mean concentrations were 8.25 mgL−1 of TN and 1.92 mgL−1 of TP. Subsequently, load values were calculated by multiplying these concentrations by the outflow from agricultural areas, calculated as a share of the total catchment outflow respective to the agricultural use of the area. The calculated loads were multiplied by coefficients reflecting the share of monitored outflow (groundwaters and tile drainage) from the agricultural runoff (1.11 for TN and 4.17 for TP)25. Subsequently, the natural background (NBS) was subtracted from the AGS load.The load corresponding to NBS was calculated using the total catchment outflow and nutrient concentration values reflecting conditions in undisturbed areas of pre-human activity, set as 0.15 mgL−1 and 0.02 mgL−1, for TN and TP respectively17. The MFS load was also calculated in a similar way, using nutrient concentrations set to represent forest catchment as 0.31 mgL−1 and 0.038 mgL−117 and the outflow calculated as the share of the total catchment area, respective for the catchment part covered by forest. Also in this case, the NBS load has been subtracted. As for the ATS load, data on pollutant deposition into the ground from precipitation was taken from the SEM network26. This data was based on precipitation and its chemistry measurements taken from 22 monitoring stations covering the entire territory of Poland. The total load from the point and diffuse sources was calculated by adding the loads mentioned above. The eventual difference between the river load (“River load calculation” Section) and inventoried emission (“Mass-balance method” Section) accounted for the other sources (UKS).Load apportionmentThe contribution of each source to the calculated river load was calculated based on a simplified equation modified from HELCOM5:$${L}_{river}=DP+LOD-RP-RD$$
(3)
where: Lriver is the river load [kga−1], DP is the load from point sources (MWS and INS) [kga−1], LOD is the load from diffuse sources (SCS, ATS, MFS, AGS and, NBS) [kga−1], RP is the point source retention [kga−1] and RD is the diffuse source retention [kga−1].In the adopted mass-balance method, it is assumed that nutrient load from the point sources (DP) is introduced directly into the river bed phase, while load from the diffuse sources (LOD) is discharged into both phases of the catchment, land and river bed ones. In both phases, self-purification processes are taking place, resulting in the reduction of nutrient loads on the way from the source to the catchment closing profile. However, due to the limited amount of data, the self-purification processes in the river have been omitted, therefore the point source retention (RP) equalled 0 kga−1. Subsequently, the diffuse source retention (RD) has been estimated on the basis of the difference between each nutrient load of the river (Lriver) and the point sources (LOD). The remaining river load has been then attributed proportionally to the contribution of the particular diffuse sources to the total source apportionment (emission inventory).Modelling methodThe digital platform, the Macromodel DNS with the SWAT module27,28,29,30,31,32, was used for comparison for the nutrient balancing method described in “Mass-balance method” Section. This advanced dynamic tool tracks nitrogen and phosphorus migration paths in the river basin taking into account their spatial and temporal variability. For this purpose, it takes into account a very extensive input database, similar to that used in the mass balance method (Supplementary Table S1). Natural and anthropogenic processes that affect the transport and transformation of nutrients, are also part of this platform. The SWAT module (version 2012) is a tool which operates in the spatial information system (GIS) and is fully integrated with it. Using the digital elevation model (DEM), the SWAT module divided the entire analysed Wełna River catchment into a total of 225 sub-catchments of an average area of 11.5 km2. The subsequent use of data on land use (forests, agriculture and urbanised areas) and the types of soils (31 classes) allowed the authors to identify a total of 2824 hydrological response units (HRUs), homogeneous in terms of vegetation, soil and topography33. Afterwards, a simulation of soil water content, evapotranspiration, surface runoff, primary and underground flows was carried out in accordance with the water balance Eq. (4), which represents the basis for the quantitative component and the HRU.$${SW}_{t}={SW}_{0}+sum_{i=1}^{t}({R}_{day}-{Q}_{surf}-{E}_{a}-{W}_{seep}-{Q}_{gw})$$
(4)
where: SWt is the final soil water content (mm H2O), SW0 is the initial soil water content (mm H2O), Rday is the amount of precipitation (mm H2O), Qsurf is amount of surface runoff (mm H2O), Ea is the amount of evapotranspiration (mm H2O), Wseep is the amount of water entering the vadose zone from the soil profile (mm H2O), Qgw is the amount of return flow (mm H2O).The model directs all runoff flows generated by each HRU through the channel network, thus simulating a catchment. The water balance equation also represents a basis for the simulation, transport and transformation of nutrients required for the quantitative component of the model. This tool models forms of nitrogen, organic and inorganic , different forms of phosphorus in soil34, as well as organic nitrogen and phosphorus forms associated with plant residues, microbial biomass and soil humus35,36,37,38. Final results of simulations are produced by the SWAT model as all the forms of nitrogen and phosphorus (in kilograms of N and P per a time unit, respectively) are then summed up to give TN and TP values. To verify that the model properly predicts TN and TP values its results are calibrated with the TN and TP values resulting from SEM, as described in Sect. 2.4.1. Moreover, the particular forms of nitrogen and phosphorus have also been compared with the modelling results (Supplementary Table S4). A detailed overview of the migration and transformation pathways of nitrogen and phosphorus forms in the catchment has been presented in the Supplementary Information (Sect. S1), while mathematical description of these processes is included in the theoretical documentation of the SWAT model39.Similarly, as in the case of the mass-balance method, diffuse sources of nutrients from agriculture (AGS), forestry (MFS) or urban areas (URB) in SWAT were simulated in the land phase of the catchment. In the land phase, the model simulates both the infiltration of nutrients into the soil (fertilization, plant biomass, precipitation) and their removal from it (volatilization, denitrification, erosion, surface runoff). Additionally, changes in the distribution of nutrients in the soil (uptake by plants) and the low mobility of phosphorus itself are also taken into account39,40,41.Pollutants from municipal and industrial point sources (MWS, INS) are introduced directly into the river bed phase. The exception here is the nutrient load from municipal diffuse sources (SCS) which, reduced as a result of the self-purification processes taking place in the land phase, is also treated in the model as point sources. The SCS nutrient load mainly derives from leaking or illegally emptied septic tanks. For this purpose, septic tanks have been divided into three types: leaky, partially illegally emptied, and sealed septic tanks, legally emptied. The loads from the legally emptied tanks are included in the effluents from WWTPs reported in the catchment. While for the remaining types, their loads are calculated using factors depending on their effectiveness in nutrient removal (15 – 50%). The final nutrient load derived from these types of facilities is then calculated, taking into consideration the number of inhabitants using the different types of septic tanks and the average chemical composition of wastewater21.The load of nutrients from the atmospheric deposition (ATS) affects both land and river phases due to the presence of two deposition mechanisms in the SWAT module, i.e., wet and dry deposition. The model also allows for the determination of nutrient loads generated as a result of natural processes of nitrogen and phosphorus transformation and transport in the soil, with the omission of all anthropogenic pressure—natural background (NBS).Calibration, verification and validationThe SWAT module for the Wełna River has been calibrated, verified and validated using the SWAT-CUP software42. For the quantitative component (water circulation in the catchment), the implemented daily flow data (source: IMGW-PIB) for the period of 18 years (2001–2018) came from the water gauge stations on the Wełna River (Pruśce and Kowanówko) and its tributary (the Flinta River-Ryczywół) (Fig. 1). The qualitative component (nitrogen and phosphorus concentration in the catchment) was gathered from the SEM stations localised at the Wełna River (Oborniki and Rogoźno) (Fig. 1) and covered a period of 13 years (2005–2018). Three statistical measures, coefficient of determination (R2)43, percent bias (PBIAS)44, and Kling-Gupta efficiency (KGE)45, have been used to indicate the Wełna River model performance (Supplementary Tables S2 and S3). In terms of the quantitative component, the calibration and verification coefficients R2, KGE and PBIAS classified the model performance generally as good and very good for the main river (Wełna), and satisfactory and good for its tributary (Flinta). During the validation procedure, all coefficient values rated the model performance for daily flow simulations as very good. In terms of qualitative components, the model performance for TN simulations can be considered as very good or good, according to the all-applied coefficients. Lower model performance, mostly satisfactory, was observed for TP mainly due to the variability of phosphorus temporal distribution patterns (Supplementary Table S2). The entire process was described in detail in Orlińska-Woźniak et al46.Variant scenariosIn order to determine the contribution of individual sources to the total load of nutrients in the profile closing the analysed catchment, a final simulation of the model was used and subjected to calibration, verification and validation procedures, and called the baseline scenario (A0). Subsequent so-called variant scenarios (A1–A5), i.e. model simulations, were developed. In variant scenarios the values of selected parameters were changed in relation to the A0 scenario. This was used both in the river bed phase for point sources (A1) and for individual diffuse sources (A2–5), thus imitating surface changes for particular types of land use in the land phase of the catchment (Fig. 3).Figure 3Variant analysis diagram for assessment of nutrient loads (L) for particular modelling scenarios and sources described in the text: MWS, INS, SCS—point sources, URB—urban, AGS—agricultural, MFS—forest.Full size imageIn the A1 scenario, all parameterized and aggregated point sources (MWS, INS, SCS) for each relevant sub-basin (LMWS,INS,SCS), were removed from the model to calculate their contribution to the total nutrient loads in the closing profile of the studied catchment (LA1).In the next two scenarios (A2 and A3), concerning urban and agricultural land use, their surface areas (5 663 ha and 192 917 ha, respectively) were successively replaced by the forest land use. This procedure was based on the assumption that the forest is the primary type of land use for this catchment area47. In order to completely eliminate the influence of these areas, the nutrient loads from the relevant surface area occupied by forest land use were subtracted, in order to estimate the contribution of urban and agricultural land (LURB and LAGS, respectively).The change in land use from urbanised and agricultural areas to forest areas increased their percentage of the catchment area to almost 100%, thus the original image of the catchment area and the nutrient load at its mouth. On this basis, in scenario A4, the nutrient load from forests LMFS, which currently occupy only 20% of the catchment area (A0), flowing to the closing profile, was calculated from the proportion.The A5 scenario is the difference between the nutrient load from the A0 scenario and the sum of the remaining loads from the subsequent variant scenarios (A1–A4). In this way, both the natural background (NBS) and atmospheric deposition (ATS) were taken into account. More

Kilpatrick, A. M. et al. Lyme disease ecology in a changing world: consensus, uncertainty and critical gaps for improving control. Philos. Trans. R. Soc. B-Biol. Sci. 372, 15. https://doi.org/10.1098/rstb.2016.0117 (2017).Article 

Google Scholar 
Adenubi, O. T. et al. Pesticidal plants as a possible alternative to synthetic acaricides in tick control: A systematic review and meta-analysis. Ind. Crop. Prod. 123, 779–806. https://doi.org/10.1016/j.indcrop.2018.06.075 (2018).CAS 
Article 

Google Scholar 
Jordan, R. A. & Schulze, T. L. Availability and nature of commercial tick control services in three Lyme disease endemic states. J. Med. Entomol. 57, 807–814. https://doi.org/10.1093/jme/tjz215 (2019).CAS 
Article 

Google Scholar 
Isman, M. B. Botanical insecticides in the twenty-first century – Fulfilling their promise?. Ann. Rev. Entomol. 65, 233–249 (2020).CAS 
Article 

Google Scholar 
Eisen, L. Control of ixodid ticks and prevention of tick-borne diseases in the United States: The prospect of a new Lyme disease vaccine and the continuing problem with tick exposure on residential properties. Ticks Tick-Borne Dis. https://doi.org/10.1016/j.ttbdis.2021.101649 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Santos, A. C. C. et al. Apis mellifera (Insecta: Hymenoptera) in the target of neonicotinoids: A one-way ticket? Bioinsecticides can be an alternative. Ecotoxicol. Environ. Safe. 163, 28–36. https://doi.org/10.1016/j.ecoenv.2018.07.048 (2018).CAS 
Article 

Google Scholar 
Matos, W. B. et al. Potential source of ecofriendly insecticides: Essential oil induces avoidance and cause lower impairment on the activity of a stingless bee than organosynthetic insecticides, in laboratory. Ecotoxicol. Environ. Safe. 209, 111764. https://doi.org/10.1016/j.ecoenv.2020.111764 (2021).CAS 
Article 

Google Scholar 
Gashout, H. A., Guzman-Novoa, E., Goodwin, P. H. & Correa-Benítez, A. Impact of sublethal exposure to synthetic and natural acaricides on honey bee (Apis mellifera) memory and expression of genes related to memory. J. Insect Physiol. 121, 104014. https://doi.org/10.1016/j.jinsphys.2020.104014 (2020).CAS 
Article 
PubMed 

Google Scholar 
Eisen, L. & Dolan, M. C. Evidence for personal protective measures to reduce human contact with Blacklegged ticks and for environmentally based control methods to suppress host-seeking Blacklegged ticks and reduce infection with Lyme disease spirochetes in tick vectors and rodent reservoirs. J. Med. Entomol. 53, 1063–1092. https://doi.org/10.1093/jme/tjw103 (2016).Article 
PubMed 

Google Scholar 
Dyer, M. C., Requintina, M. D., Berger, K. A., Puggioni, G. & Mather, T. N. Evaluating the effects of minimal risk natural products for control of the tick, Ixodes scapularis (Acari: Ixodidae). J. Med. Entomol. 58, 390–397. https://doi.org/10.1093/jme/tjaa188 (2021).CAS 
Article 
PubMed 

Google Scholar 
Schulze, T. L. & Jordan, R. A. Synthetic pyrethroid, natural product, and entomopathogenic fungal acaricide product formulations for sustained early season suppression of host-seeking Ixodes scapularis (Acari: Ixodidae) and Amblyomma americanum nymphs. J. Med. Entomol. 58, 814–820. https://doi.org/10.1093/jme/tjaa248 (2021).CAS 
Article 
PubMed 

Google Scholar 
Bharadwaj, A., Stafford, K. C. & Behle, R. W. Efficacy and environmental persistence of nootkatone for the control of the Blacklegged tick (Acari: Ixodidae) in residential landscapes. J. Med. Entomol. 49, 1035–1044. https://doi.org/10.1603/me11251 (2012).Article 
PubMed 

Google Scholar 
Pavela, R. & Sedlák, P. Post-application temperature as a factor influencing the insecticidal activity of the essential oil from Thymus vulgaris. Ind. Crop. Prod. 113, 46–49 (2018).CAS 
Article 

Google Scholar 
Brunner, J. L., Killilea, M. & Ostfeld, R. S. Overwintering survival of nymphal Ixodes scapularis (Acari: Ixodidae) under natural conditions. J. Med. Entomol. 49, 981–987. https://doi.org/10.1603/me12060 (2012).Article 
PubMed 

Google Scholar 
Chown, S. L. & Nicolson, S. W. Insect Physiol. Ecol. (Oxford University Press, 2004).Ogden, N. H., Beard, C. B., Ginsberg, H. S. & Tsao, J. I. Possible effects of climate change on Ixodid ticks and the pathogens they transmit: Predictions and observations. J. Med. Entomol. 58, 1536–1545 (2021).Article 

Google Scholar 
Ballard, K. & Bone, C. Exploring spatially varying relationships between Lyme disease and land cover with geographically weighted regression. Appl. Geo. 127, 102383 (2021).Article 

Google Scholar 
Neelakanta, G., Sultana, H., Fish, D., Anderson, J. F. & Fikrig, E. Anaplasma phagocytophilum induces Ixodes scapularis ticks to express an antifreeze glycoprotein gene that enhances their survival in the cold. J. Clin. Invest. 120, 3179–3190. https://doi.org/10.1172/jci42868 (2010).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Adamo, S. A. Animals have a Plan B: how insects deal with the dual challenge of predators and pathogens. J. Comp. Physiol. B-Biochem. Syst. Environ. Physiol. 190, 381–390. https://doi.org/10.1007/s00360-020-01282-5 (2020).Article 

Google Scholar 
Adamo, S. A. How insects protect themselves against combined starvation and pathogen challenges, and the implications for reductionism. Comp. Biochem. Physiol. B-Biochem. Molec. Biol. https://doi.org/10.1016/j.cbpb.2021.110564 (2021).Article 

Google Scholar 
Linske, M. A. et al. Impacts of deciduous leaf litter and snow presence on nymphal Ixodes scapularis (Acari: Ixodidae) overwintering survival in coastal New England, USA. Insects https://doi.org/10.3390/insects10080227 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Burtis, J. C., Fahey, T. J. & Yavitt, J. B. Survival and energy use of Ixodes scapularis nymphs throughout their overwintering period. Parasitol. 146, 781–790. https://doi.org/10.1017/s0031182018002147 (2019).Article 

Google Scholar 
Boehnke, D., Gebhardt, R., Petney, T. & Norra, S. On the complexity of measuring forests microclimate and interpreting its relevance in habitat ecology: the example of Ixodes ricinus ticks. Parasit. Vectors 10, 549. https://doi.org/10.1186/s13071-017-2498-5 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Lindsay, L. R. et al. Survival and development of Ixodes scapularis (Acari, Ixodidae) under various climatic conditions in Ontario, Canada. J. Med. Entomol. 32, 143–152. https://doi.org/10.1093/jmedent/32.2.143 (1995).CAS 
Article 
PubMed 

Google Scholar 
Lindsay, L. R. et al. Survival and development of the different life stages of Ixodes scapularis (Acari: Ixodidae) held within four habitats on Long Point, Ontario, Canada. J. Med. Entomol. 35, 189–199. https://doi.org/10.1093/jmedent/35.3.189 (1998).CAS 
Article 
PubMed 

Google Scholar 
Ginsberg, H. S. et al. Woodland type and spatial distribution of nymphal Ixodes scapularis (Acari: Ixodidae). Environ. Entomol. 33, 1266–1273. https://doi.org/10.1603/0046-225x-33.5.1266 (2004).Article 

Google Scholar 
Clow, K. M. et al. The influence of abiotic and biotic factors on the invasion of Ixodes scapularis in Ontario, Canada. Ticks Tick-Borne Dis. 8, 554–563. https://doi.org/10.1016/j.ttbdis.2017.03.003 (2017).Article 
PubMed 

Google Scholar 
Natural Resources Canada. Balsam fir, (2015).Khatchikian, C. E. et al. Recent and rapid population growth and range expansion of the Lyme disease tick vector, Ixodes scapularis North America. Evolution 69, 1678–1689. https://doi.org/10.1111/evo.12690 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Pichette, A., Larouche, P. L., Lebrun, M. & Legault, J. Composition and antibacterial activity of Abies balsamea essential oil. Phytotherapy Res. 20, 371–373 (2006).CAS 
Article 

Google Scholar 
Poaty, B., Lahlah, J., Porqueres, F. & Bouafif, H. Composition, antimicrobial and antioxidant activities of seven essential oils from the North American boreal forest. World J. Microbiol. Biotechnol. 31, 907–919. https://doi.org/10.1007/s11274-015-1845-y (2015).CAS 
Article 
PubMed 

Google Scholar 
Beasley, T. M. & Schumacker, R. E. Multiple regression approach to analyzing contingency tables: Post hoc and planned comparison procedures. J. Exp. Ed. 64, 79–93. https://doi.org/10.1080/00220973.1995.9943797 (1995).Article 

Google Scholar 
Canon, L., Deslauriers, A., Mshvildadze, V. & Pichette, A. Volatile compounds in the foliage of balsam fir analyzed by static headspace gas chromotography (HS-GS): An example of the spruce budworm defoliation effect in the boreal forest of Quebec, Canada. Microchem. J. 110, 587–590 (2013).Article 

Google Scholar 
Faraone, N., MacPherson, S. & Hillier, N. K. Behavioral responses of Ixodes scapularis tick to natural products: development of novel repellents. Exp. Appl. Acarol. 79, 195–207. https://doi.org/10.1007/s10493-019-00421-0 (2019).CAS 
Article 
PubMed 

Google Scholar 
McMillan, L. E., Miller, D. W. & Adamo, S. A. Eating when ill is risky: immune defense impairs food detoxification in the caterpillar Manduca sexta. J. Exp. Biol. 221, 173336 (2018).
Google Scholar 
Gazave, E., Chevillon, C., Lenormand, T., Marquine, M. & Raymond, M. Dissecting the cost of insecticide resistance genes during the overwintering period of the mosquito Culex pipiens. Heredity 87, 441–448 (2001).CAS 
Article 

Google Scholar 
Lalouette, L., Williams, C. M., Hervant, F., Sinclair, B. J. & Renault, D. Metabolic rate and oxidative stress in insects exposed to low temperature thermal fluctuations. Comp. Biochem. Physiol. A 158, 229–234 (2011).CAS 
Article 

Google Scholar 
Clark, D. D. Lower temperature limits for activity of several Ixodid ticks: Effects of body size and rate of temperature change. J. Med. Entomol. 32, 449–452 (1995).CAS 
Article 

Google Scholar 
Carroll, J. F. & Kramer, M. Winter activity of Ixodes scapularis (Acari : Ixodidae) and the operation of deer-targeted tick control devices in Maryland. J. Med. Entomol. 40, 238–244. https://doi.org/10.1603/0022-2585-40.2.238 (2003).CAS 
Article 
PubMed 

Google Scholar 
Ginsberg, H. S. et al. Environmental factors affecting survival of immature Ixodes scapularis and implications for geographical distribution of Lyme disease: the climate/behavior hypothesis. PLoS ONE 12, e0168723. https://doi.org/10.1371/journal.pone.0168723 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Quadros, D. G., Johnson, T. L., Whitney, T. R., Oliver, J. D. & Chavez, A. S. O. Plant-derived natural compounds for tick pest control in livestock and wildlife: Pragmatism or utopia?. Insects https://doi.org/10.3390/insects11080490 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Ogendo, J. et al. Biocontrol potential of selected plant essential oil constituents as fumigants of insect pests attacking stored food commodities. Health 10, 287–318 (2011).
Google Scholar 
Panella, N. A., Karchesy, J., Maupin, G. O., Malan, J. C. & Piesman, J. Susceptibility of immature Ixodes scapularis (Acari: Ixodidae) to plant-derived acaricides. J. Med. Entomol. 34, 340–345 (1997).CAS 
Article 

Google Scholar 
Rosado-Aguilar, J. A. et al. Plant products and secondary metabolites with acaricide activity against ticks. Vet. Parasitol. 238, 66–76. https://doi.org/10.1016/j.vetpar.2017.03.023 (2017).CAS 
Article 
PubMed 

Google Scholar 
Jaenson, T. G. T., Carboui, S. & Palsson, K. Repellency of oils of lemon eucalyptus, geranium, and lavender and the mosquito repellent MyggA natural to Ixodes ricinus (Acari : Ixodidae) in the laboratory and field. J. Med. Entomol. 43, 731–736. https://doi.org/10.1603/0022-2585(2006)43[731:Rooole]2.0.Co;2 (2006).CAS 
Article 
PubMed 

Google Scholar 
Eigbrett, C. Natural Sourcing Organic Essential Oils Oxford, Connecticut, USA, .praannaturals.com/downloads/msds/SDS_Organic_Essential_Oil_Fir_Balsam_Canada.pdf (2016).Schulze, T. L. et al. Efficacy of granular deltamethrin against Ixodes scapularis and Amblyomma americanum (Acari: Ixodidae) nymphs. J. Med. Entomol. 38, 344–346. https://doi.org/10.1603/0022-2585-38.2.344 (2001).CAS 
Article 
PubMed 

Google Scholar 
Elias, S. P. et al. Effect of a botanical acaricide on Ixodes scapularis (Acari: Ixodidae) and nontarget arthropods. J. Med. Entomol. 50, 126–136. https://doi.org/10.1603/me12124 (2013).Article 
PubMed 

Google Scholar 
Burtis, J. C., Yavitt, J. B., Fahey, T. J. & Ostfeld, R. S. Ticks as soil-dwelling arthropods: an intersection between disease and soil ecology. J. Med. Entomol. 56, 1555–1564. https://doi.org/10.1093/jme/tjz116 (2019).Article 
PubMed 

Google Scholar 
Burtis, J. C., Ostfeld, R. S., Yavitt, J. B. & Fahey, T. J. The relationship between soil arthropods and the overwinter survival of Ixodes scapularis (Acari: Ixodidae) under manipulated snow cover. J. Med. Entomol. 53, 225–229. https://doi.org/10.1093/jme/tjv151 (2016).CAS 
Article 
PubMed 

Google Scholar 
Guerra, M. et al. Predicting the risk of Lyme disease: Habitat suitability for Ixodes scapularis in the north central United States. Emerg. Infect. Dis. 8, 289–297. https://doi.org/10.3201/eid0803.010166 (2002).Article 
PubMed 
PubMed Central 

Google Scholar 
Bunnell, J. E., Price, S. D., Das, A., Shields, T. M. & Glass, G. E. Geographic information systems and spatial analysis of adult Ixodes scapularis (Acari: Ixodidae) in the Middle Atlantic region of the USA. J. Med. Entomol. 40, 570–576. https://doi.org/10.1603/0022-2585-40.4.570 (2003).Article 
PubMed 

Google Scholar 
Lubelczyk, C. B. et al. Habitat associations of Ixodes scapularis (Acari: Ixodidae) in Maine. Environ. Entomol. 33, 900–906. https://doi.org/10.1603/0046-225x-33.4.900 (2004).Article 

Google Scholar 
Killilea, M. E., Swei, A., Lane, R. S., Briggs, C. J. & Ostfeld, R. S. Spatial dynamics of Lyme disease: A review. EcoHealth 5, 167–195. https://doi.org/10.1007/s10393-008-0171-3 (2008).Article 
PubMed 

Google Scholar 
Stafford, K. C. Survival of immature Ixodes scapularis (Acari: Ixodidae) at different relative humidities. J. Med. Entomol. 31, 310–314 (1994).Article 

Google Scholar 
Bertrand, M. R. & Wilson, M. L. Microclimate-dependent survival of unfed adult Ixodes scapularis (Acari: Ixodidae) in Nature: Life cycle and study design implications. J. Med. Entomol. 33, 619–627 (1996).CAS 
Article 

Google Scholar 
Lindsay, L. R. et al. Microclimate and habitat in relation to Ixodes scapularis (Acari: Ixodidae) populations on Long Point, Ontario, Canada. J. Med. Entomol. 36, 255–262. https://doi.org/10.1093/jmedent/36.3.255 (1999).CAS 
Article 
PubMed 

Google Scholar 
Thompson, C., Spielman, A. & Krause, P. J. Coinfecting deer-associated zoonoses: Lyme disease, babesiosis, and ehrlichiosis. Clin. Infect. Dis. 33, 676–685 (2001).CAS 
Article 

Google Scholar 
Hinckley, A. F. et al. effectiveness of residential acaricides to prevent Lyme and other tick-borne diseases in humans. J. Infect. Dis. 214, 182–188. https://doi.org/10.1093/infdis/jiv775 (2016).Article 
PubMed 

Google Scholar 
Keesing, F. et al. Effects of Ttck-control interventions on tick abundance, human encounters with Ttcks, and incidence of tickborne diseases in residential neighborhoods, New York, USA. Emerg. Infect. Dis. 28, 957–966. https://doi.org/10.3201/eid2805.211146 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Hayes, L. E., Scott, J. A. & Stafford, K. C. Influences of weather on Ixodes scapularis nymphal densities at long-term study sites in Connecticut. Ticks Tick-Borne Dis. 6, 258–266. https://doi.org/10.1016/j.ttbdiS.2015.01.006 (2015).Article 
PubMed 

Google Scholar 
Rand, P. W. et al. Trial of a minimal-risk botanical compound to control the vector tick of Lyme disease. J. Med. Entomol. 47, 695–698 (2010).CAS 
Article 

Google Scholar 
United Nations. Convention on Biological Diversity. (1992).Convention on International Trade in Endangered Species of Wild Fauna and Flora. (1973).Burtis, J. C. Method for the efficient deployment and recovery of Ixodes scapularis (Acari: Ixodidae) nymphs and engorged larvae from field microcosms. J. Med. Entomol. 54, 1778–1782. https://doi.org/10.1093/jme/tjx157 (2017).Article 
PubMed 

Google Scholar 
Nova Scotia Department of Natural Resources and Renewables Trees of the Acadian Forest (2021). More

Kyoto University microbiome researcher Hiroyuki Ogata says that the recent work2,3 further connects RNA viruses and the carbon pump, which affects the Earth’s biogeochemical cycles and thus its climate. And it sheds light on the diversity, evolution and ecology of RNA viruses, which has not previously been possible through applying the techniques of traditional DNA-based metagenomics. The team found many new lineages at the phylum-level by using “highly sensitive” computational approaches.It’s possible to assess the ecosystem impact of viruses by inferring auxiliary metabolic genes (AMGs). AMGs hint at the ways RNA viruses manipulate the physiology of their hosts as they seek to maximize production of more virus through the host. As Jian explains, labs have identified a variety of AMGs that are encoded by DNA viruses and, he says, it’s “well-recognized” that AMGs probably play a role in marine ecosystems. It was unknown if AMGs could be found in RNA viruses, which the recent Science paper2 has now established, he says. Jian sees this work as providing “a very important foundational dataset” for exploring questions connected to AMGs. “In my opinion, if more long-sequence or complete marine RNA virus genomes can be obtained in the future, and they can be further connected with specific hosts, it will greatly promote the understanding of the ecological impact of RNA viruses in the oceans.”To tease out AMGs, the scientists used a variety of tools, such as viral identification software for both DNA and RNA viruses, says Wainaina. The ones for DNA viruses are available on Cyverse, and the protocols for the tools from the Sullivan lab are on protocols.io. One method for RNA viruses is in progress and will be soon available on Cyverse, he says. DNA viral identification tools include VirSorter2, a pipeline for identifying viral sequence from metagenomics data, and the protocol for using this and other tools are also on protocols.io. To identify AMGs from viral sequence that had been identified through VirSorter, the team used use DRAM-v, a software tool from the lab of microbiome researcher Kelly Wrighton at Colorado State University. Her group had created Distilled and Refined Annotation of Metabolism (DRAM), a framework to resolve metabolic information from microbial data. The companion tool DRAM-v is for viruses and can be applied to metagenomic data sets for annotating metagenomics-based assembled genomes, for example through the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database, and to contiguous viral sequences identified by VirSorter.The hunt for AMGs is one instance in which the team needed to determine in each case whether a sequence was likely ‘stolen’ from host cells, says Dominguez-Huerta. RNA viral genomes are less than 40 kilobases long and usually have complicated genomic organization, both in a structural genomics sense related to the physical arrangement of genes along the viral genome and in a functional sense in terms of transcription and translation: there are overlapping genes, frameshifts and more, all of which makes this kind of annotation difficult. And sometimes information in the annotation databases is wrong and indicates that a match is cellular when it is in fact viral. Thus, to find AMGs, “we don’t have a defined clean methodology automated in a pipeline yet,” he says. It remains a time-consuming task. Assigning putative function to the protein sequences encoded by AMGs also involves checking the literature and comparing different annotation sources.Dominguez-Huerta says he and the team were glad they could assemble AMG functionalities to suggest the range of ways in which RNA viruses manipulate the metabolisms of their hosts—from photosynthesis to central carbon metabolism to vacuolar digestion and RNA repair. This overview let them see how some AMGs are repeated across different viruses across the oceans. Finding AMGs in long-read sequence is what he calls a “fire test” for the lab. To avoid ‘false AMGs’ from unreliable matches, they use BLASTP, the Basic Alignment Search Tool that compares a protein query sequence to a protein database.“I am fascinated by the ability of viruses to metabolic reprogram not only their hosts but more importantly at the ecosystem level,” says Wainaina. It is probable that the AMGs the team identified “are a central cog in microbial metabolism networks.” Current and future modeling efforts will hopefully provide insights into the ecosystem roles of viruses—both DNA viruses and RNA viruses—and on a global scale both within the ocean ecosystem and beyond.Host inference is challenging, says Dominguez-Huerta, because, for example, viruses with RNA genomes do not share genetic information with their host genomic DNA the way dsDNA viruses do when they infect bacteria. That means there is no clear signal to be derived from the host genome to help one guess the possible host. But sometimes RNA viruses do integrate into host genomes, and those, likely more accidental, events were sufficient for the scientists to capture some signal to infer hosts. “We also performed statistical co-occurrence analytics using abundances to infer the hosts with certain success,” he says.Unlike dsDNA viruses, RNA viruses infect mostly eukaryotes, from protists and fungi to invertebrates and fish larvae; only a minority infect bacteria. Overall, the team has been able to capture “a picture of dsDNA viruses infecting prokaryotes and RNA viruses infecting eukaryotes in the oceans, complementing each other in their marine hosts,” says Dominguez-Huerta. The fact that the scientists can infer “that RNA viruses can steal genes from the host,” in the form of AMGs, to then reprogram host metabolism matters not only as scientists complete the picture of how viruses directly tune the activity of hosts during infection, but also in regard to how this influences biogeochemical cycles, he says. “We think that these AMGs are incorporated into the RNA virus genomes from cellular mRNA transcripts by non-homologous recombination,” he says. This gives, in his view, a new picture of RNA viruses, which, despite their small genome sizes, can squeeze in protein-coding genes. Such proteins could be sufficient to boost the production of virus particles per infected cell, perhaps increasing viral fitness in the difficult conditions of the oligotrophic open ocean and letting the viruses better propagate in the environment.More generally, says Dominguez-Huerta, capturing RNA from ocean samples is difficult, because RNA is physically fragile and degrades rapidly. When digging into metatranscriptomic data, which include the RNA from plankton and RNA from other organisms, less than 1% of this RNA is likely to be viral RNA, he says. Previously, some labs have first purified RNA from samples, enriched it for replicating RNA viruses and then applied a method called dsRNA-seq to recover dsRNA virus sequence and replicate sequences from single-stranded RNA viruses. For future ocean RNA virus projects, he says that the lab is currently working on a wet-lab method to purify RNA virus particles from seawater to solve the challenges of obtaining viral RNA for analysis. More

Microbial biodiversity surveys have often been done in a number of generally better-studied regions3, as with the San Pedro Time Series from the San Pedro Channel off the coast of Southern California. Global surveys have also been emerging, such as the Sorcerer II Global Ocean Sampling Expedition from 2004 to 2006 launched by J. Craig Venter. There are also data and samples from the Malaspina circumnavigation, an expedition devoted to data collection on ocean biodiversity and climate change that was led by the Spanish Ministry of Science and Innovation.As microbiome researcher Shinichi Sunagawa of the ETH Zurich and colleagues point out4, sequencing technologies have advanced such that they now enable systematic and quantitative global ocean surveys. These advances, in turn, made it possible to find and assess marine double-stranded DNA virus populations. This latest work on marine RNA viruses, says Sunagawa, in which he was also involved, embeds new phylum-level findings into a “robust taxonomic framework.” In his view, this research ranks in importance with the reconstruction a few years ago of a group of bacterial genomes representing more than 35 phyla that the researchers call “the candidate phyla radiation”5. If one counts viruses in with other taxonomic groups, the finding might be the largest single expansion of established microbial taxonomy, he says. And he especially likes the definition of a new basal Orthornavirae megataxon, the proposed phylum ‘Taraviricota’. This proposed phylum is one of several findings from recently published analyses of sampling data from Tara Oceans1,2, a global expedition supported by the Tara Ocean Foundation, or Fondation Tara Océan, based in France and with many partner organizations and supporters. The foundation is a major source of global data about the ocean and ocean microbes and, as its president Étienne Bourgois says, it’s a “family project.” The family business is the French fashion house agnès b., founded by his mother Agnès Troublé.Because the family cares about the sea, they bought a 36-meter schooner from Lady Pippa Blake, widow of yachtsman and explorer Sir Peter Blake, after pirates killed him during an environmental expedition in the Amazon delta, and turned it into the expedition vessel and floating science laboratory Tara, devoted to understanding and protecting the world’s marine environment. It’s a way to continue what Peter Blake started, to continue the conversation about the ocean and do research as well, says sailor-scientist Romain Troublé, executive director of the foundation and nephew of Agnès Troublé. The boat had been previously owned by explorer Jean-Louis Étienne. The foundation has supported several expeditions with Tara including the Tara Oceans and Tara Oceans Polar Circle expeditions, as well as Tara Mission Microbiomes, which is currently underway. The equilibrium of the planet “depends on the microbiome of the ocean in the same way we depend on our own microbiome,” says Romain Troublé. Viruses are part of the larger picture of how life is supported on the planet. It’s “a great mystery of the century” to decipher the roles, behaviors and functions of the ocean microbiome, including its beneficial effects. Over the last decade, he says, the expeditions have, for example, collected plankton samples from coastal waters, coral reefs and the high seas around the world for scientists to ask questions of. Microplastics in the ocean concentrate chemical pollutants such as pesticides, and microplastics appear to be substrates for distinct microbiomes. Polystyrene and polypropylene, for example, harbor different microbial communities. “We call it the plastisphere,” he says. All sample collection, not just of microplastics, happens with a view to scientific rigor to assure data quality, says Troublé. Many institutes are part of and support the expeditions through the Tara Ocean Foundation, including AtlantECO, the French Ministry of Research, the Swiss National Science Foundation, the US National Science Foundation, the European Molecular Biology Laboratory and the French National Centre for Scientific Research.Tara Oceans was an expedition initiated by EMBL researcher Eric Karsenti, here in the foreground. He is checking a rosette of Niskin bottles that collect water, and ocean microbe samples, at various depths. Sensors capture parameters such as temperature.
Credit: Fondation Tara OcéanIts expedition Tara Oceans was initiated by cell and marine biologist Eric Karsenti of the European Molecular Biology Laboratory. The expedition ran from 2009 to 2013 and covered 125,000 kilometers of ocean, taking ocean water and samples. It collected nearly 35,000 samples of viruses, algae and plankton and delivered more than 60 terabases of DNA and RNA sequences.The research community strives to follow FAIR data principles, the principles of findability, accessibility, interoperability and reusability, says Sunagawa. Tara Ocean’s data troves can be found, for instance, in the European Nucleotide Archive (ENA), Pangeaea, Cyverse, iVIRUS and on Genoscope. Other data-collection efforts target users with less programming experience and offer various types of data relevant to marine microbial research, he says: for example, the Ocean Gene Atlas, a portal to search for a gene or protein sequence to see, for instance, its abundance on an ocean map. The Ocean Barcode Atlas lets users explore, for example, operational taxonomic units (OTU) data and plankton communities from Tara Oceans and OTUs from Malaspina prokaryote data. Sunagawa also points to the Ocean Microbiomics Database and its high-quality genome-resolved information about the global microbiome, which has sequencing data from 2003 onwards and which includes Tara Oceans data as well as datasets such as the Hawaii Ocean Time-Series (HOT), the Bermuda Atlantic Time-series Study (BATS), with its collection of ocean data dating back to 1988, and BioGeotraces, with hydrographic and marine geochemical data from various expeditions.The recent publications on RNA viruses1,2, in which Sunagawa was also involved, have expanded the known diversity of these viruses, he says. They build on efforts by, for example, the research team that created and applied a cloud-based infrastructure called Serratus6, with which researchers can perform sequence alignment using bowtie2 for nucleotide sequences and DIAMOND2 for protein sequences in ‘ultra-high throughput’ on a petabase scale. Using Serratus, the team identified more than 130,000 previously unknown RNA viruses, both on land and in the oceans. The wealth of resources for microbial and viral data about the oceans is helpful to the research community, but “we could still improve the connectivity between various datasets though,” says Sunagawa. That would help, for example, with searching and finding data products that are derived from primary data, such as identifiers of individual genome assemblies, genes and metagenome assembled genomes, which are all presented in different online locations. But connecting data resources is a project that itself takes resources, and such projects are hard to get funding for.Going forward, it will be challenging, says Sunagawa, to update and keep up to date both past projects and ongoing projects such as the Global Ocean Ship-based Hydrographic Investigations program (GO-SHIP), which is focused on physical oceanography; the Antarctic Circumnavigation Expedition (ACE), on carbon-cycle marine biogeochemistry; Mission Microbiomes; and many more. “And ultimately, we will need to cross boundaries that currently separate biome-focused research to better understand processes at the sea–land–atmosphere interfaces.”Tara Mission Microbiomes has been underway for nearly two years and wraps up in October 2022. At press time, the schooner Tara was off the Angolan Coast. At the end of the expedition, it will have traveled a total of 70,000 km of ocean area around South America, Africa, Europe and Antarctica. Mission Microbiomes is part of the EU-funded AtlantECO and also includes 42 research organizations from 13 countries. The microbiome mission is collecting data on how climate change is affecting the marine microbiome, on how pollution, microplastics pollution in particular, affects the marine environments and on the beneficial impact of the ocean microbiome.Krill are small ocean crustaceans that mainly eat phytoplankton and are a food source for animals such as whales and seals. Krill play a crucial role in biogeochemical cycles.
Credit: F. Aurat, Fondation Tara OcéanChris Bowler, from the Institut de Biologie de l’École Normale Supérieure, is scientific director of the Tara Oceans consortium, was scientific coordinator of the Tara Oceans expedition and was onboard in Antarctica during the Tara Mission Microbiomes expedition to collect data on the impact of icebergs on the Weddell Sea ecosystem. The project’s scientists in Tara Mission Microbiomes, he says, are studying specific processes, including the Amazon plume, the Malvinas confluence, the impact of tabular icebergs in the Weddell Sea, the Benguela upwelling and more. The data from this expedition will be similar to those from Tara Oceans but, he says, “we will have much more contextual data related to the specific processes we have been studying.” The applied techniques are all ones that have undergone much advancement since Tara Oceans, he says. They include long-read sequencing, Hi-C sequencing to capture chromatin organization on a genome-wide basis and various types of microscopy.Data and results from previous and ongoing expeditions are impressive, says Sunagawa but “we are still data-limited in our field of research.” Geographically, sampling stations are usually still separated by hundreds of kilometers, and often they are even further apart than that. This means that what is missing is both temporal and seasonal resolution, “and we keep detecting new organisms,” he says. Tara Mission Microbiomes will help to fill in some of these gaps. The mission is unlike Tara Oceans, with its focus more on coastal areas and environmental pollutants such as microplastics. Sunagawa and his group are not currently involved with Tara Mission Microbiomes, “but we look forward to seeing the first results coming out soon.”Through photosynthesis, phytoplankton deliver oxygen to the planet. They are food for zooplankton, which are food for other marine organisms. This food web and its associated decomposition are part of the ocean’s carbon pump, in which marine viruses play an important role that scientists have only begun exploring.
Credit: M. Bardy, Fondation Tara Océan More

Response of chlorophytes to environmental variables in field vs. forest pondsOur study demonstrated that human-originated transformation in the catchment area surrounding a small water body may influence the water conditions in terms of physical, chemical, and biological parameters as well as the ecological state of the aquatic environment in respect to green algae communities.Chlorophytes inhabiting field ponds were more abundant compared with the forest ponds. This shows that field ponds, due to the higher values of TRP and water conductivity, created favorable conditions for chlorophyte development. The high concentrations of TRP and conductivity in aquatic environments are characteristic in the case of agricultural catchments exposed to anthropogenic pressure because of the inflow from the surrounding fertilized fields42. In this type of pond, we also observed significantly higher water temperatures and pH due to the lack of trees around them compared to the forest ponds, two factors which also positively influenced the growth of chlorophytes. Both the higher light intensity and the smaller size of the field ponds cause earlier warming up than the forest ponds and give an advantage to high light tolerant species. Moreover, it is well known that an increase in temperature stimulates the release of phosphorus from the bottom sediments, so this could be another reason for the higher levels of TRP in the field ponds. Our CCA analysis showed that TRP and conductivity were the strongest determinants of the distribution of chlorophyte species in the examined water bodies. We found a large group of dominant species indicated high values of TRP (e.g. Ankistrodesmus falcatus, A. arcuatus, Monoraphidium griffithii, Pseudopediastrum boryanum, Pediastrum duplex, Scenedesmus obtusus, Scenedesmus arcuatus var. gracilis, Desmodesmus communis, Coelastrum microporum), and another group of species (e.g. Kirchneriella irregularis var. spiralis, Tetraedron minimum, Scenedesmus ecornis) that preferred high levels of conductivity.In the field ponds generally higher mean abundances of filtrators and Rotifera were observed. This could be another important factor stimulating the growth of chlorophytes and increasing their abundances by the resupply of nutrients through excretion43,44. On the other hand, the high densities of algae could be the factor that caused better zooplankton development, and therefore its abundance in field ponds was greater. Filtrating cladocerans and Rotifera also had a significant influence on the distribution of chlorophyte dominating species. However, even though the total abundance of both chlorophytes and filtering zooplankton was greater in the field ponds, CCA analysis revealed a negative relationship existing between filtrators and most dominant species of chlorophytes (e.g. Pandorina morum, Willea rectangularis, Desmodesmus armatus, Nephrochlamys willeana, Cosmarium trilobulatum). Only two chlorophyte species—Lemmermannnia tetrapedia and Tetraedron triangulare—co-occurred with cladoceran zooplankton. These latter species are very small compared to the species above and can therefore be overlooked by filtrators, which have a choice of larger and perhaps more nutritiously satisfying algae of the genus Pandorina, Crucigeniella, Cosmarium or Nephrochlamys, but still of a size suitable for zooplankton. It can also be interpreted in such a way that Crucigenia and Tetraedron are among the r-strategists that reproduce very quickly, so grazing pressure by zooplankton can stimulate their rapid development45 and thus they remain at a stable level.Specific environmental conditions prevailing in the field ponds resulted in a high number of exclusive taxa44, found only in this type of water body. Moreover, a greater diversity of the representatives of different functional groups were found here, compared to the forest ponds.Analyzing the distribution of chlorophytes in terms of phytoplankton functional groups39,40, we found that group W1 was represented by only one species, Gonium pectorale. This was especially noted in the field water bodies. This group is known to prefer small water bodies rich in organic matter from husbandry or sewage40, which suggests that the field catchment in our study migh be a supplier of these substances. It also proves that field surroundings are far more human impacted. In the field ponds we observed a higher abundance of chlorophytes belonging to the groups G (Eudorina elegans, Pandorina morum, Pandorina smithii and Volvox aureus), J (e.g. representatives of the genus Actinastrum, Chlorotetraedron, Coelastrum, Crucigenia, Desmodesmus/Scenedesmus, Golenkinia, Pediastrum, Tetraedron, Tetrastrum, Westella, Willea/Crucigeniella), W0 (genera Chlamydomonas, Chlorangiopsis, Chlamydomonadopsis, Planktococcomyxa/Coccomyxa) and X3 (Chlorella sp.), typical for shallow nutrient-rich waters (G and J), ponds with extremely high organic contents (W0), and for shallow well-mixed layers (X3), according to classification given by Padisak et al.40. Considering that nitrogen compounds had a similar level in both types of ponds it can be stated that the representatives of the above mentioned functional groups of chlorophytes associated with the field ponds were presumably dependent on higher concentrations of TRP and conductivity and not that much on nitrogen concentrations.In the forest ponds significantly higher values of water saturation were recorded compared to the field ponds. Moreover, the lack of inflow of fertilizers from the catchment area resulted in lower TRP concentrations, which along with lower water temperatures, pH and conductivity in the forest ponds may have contributed to the reduced abundance of chlorophytes compared to the field water bodies. RDA analysis showed that some dominant chlorophyte species (e.g. Closterium moniliferum, Closterium tumidulum, Cosmarium trilobulatum and Mougeotia sp.) were associated with this type of small water body. At the same time the abundance of these species was smaller in the field ponds. We also found that chlorophyte diversity (Shannon–Weaver index) was greater in the forest ponds. This suggests that water bodies located within the forested area, usually more natural ponds being less exposed to anthropogenic pressure, are characterized by greater biodiversity. Moreover, in this type of water body we found many exclusive species39, not reported from the field ponds. Interestingly, about the half of these taxa belonged to desmids, which prefer lower pH and conductivity46, conditions typical for forest ponds. This could be also a reason for the dominance of desmid species with the highest abundance/frequency, associated with forest ponds.Taking into consideration the phytoplankton functional groups39,40 our study showed that the chlorophytes associated with forest ponds prefer mesotrophic waters (from the group TD: Cladophora glomerata, Geminella turfosa, Geminella planctonica, Microspora sp., Netrium digitus, Oedogonium sp., Oocystidium ovale, Spirogyra sp. Zygnema sp. and those belonging to the group N: mainly genera Closterium, Cosmarium, Euastrum, Micrasterias, Staurastrum, Staurodesmus, Xanthidium). This explains their greater share in the less fertile forest ponds. Another group associated with the forest ponds – T (Mougeotia sp., Binuclearia lauterbornii) contains species tolerant to light deficiency, so they were able to develop well in the more shaded water bodies located in the forest catchment.Chlorophyte community structure in two types of habitats (open water vs. macrophyte-dominated zone)In our study, the type of habitat (open water and macrophyte-dominated zones) also had a significant structuring effect on chlorophytes. There were a group of species linked to the open water zone (Pandorina morum, Nephrochlamys willeana, Oocystis lacustris, Scenedesmus armatus, Scenedesmus intermedius and Desmodesmus communis), being negatively related to vegetated stations at the same time. Generally, we found here a higher mean abundance of chlorophytes compared to the macrophyte-dominated zones, possibly due to the higher values of nutrients such as NH4 and TRP, the conditions favouring the development of many algae species. The results of the CCA analysis with habitats confirmed the high importance of both nutritional factors in structuring the distribution of chlorophyte species. There was a group of species associated with a rise in the concentration of ammonium (e.g. Scenedesmus arcuatus var. gracilis, Pediastrum duplex, Closterium moniliferum, Closterium tumidulum, Cosmarium trilobulatum, Willea rectangularis) as well as with phosphates (Monoraphidium tortile, Scenedesmus ecornis, Tetradesmus lagerheimii and Tetraedron minimum). Generally, high abundance of chlorophytes in the open water area was accompanied by a small-sized fraction of zooplankton–rotifers. Therefore, rotifers had a lower impact on the distribution of chlorophytes than filtrators. The increasing numbers of cladocerans contributed to the lowering abundance of some chlorophytes, such as Monoraphidium tortile, Scenedesmus ecornis, Tetradesmus lagerheimii or Tetraedron minimum. This shows that filtrators, whose densities were significantly higher among macrophytes, were able to control the development of some chlorophyte species much more efficiently than small-bodied rotifers.The effect of habitat was also visible in the case of phytoplankton functional groups39,40. We found that representatives of the group N (e.g. Closterium, Cosmarium, Euastrum, Micrasterias, Staurastrum) had a significantly higher mean abundance in the open water zones compared to the macrophyte-dominated zones. Interestingly, according to Padisak et al.40 group N prefers less fertile (mesotrophic) conditions, which is inconsistent with our results. However, we think that their association with the open water sites could be connected rather with the place/level where they live in the water column, rather than with the trophic state of water. The above mentioned chlorophytes taxonomically belong to desmids, which are mostly benthic organisms. Their greater quantitative share in the samples from the open water areas could be an effect of the intensive water mixing in the shallow ponds due to the lack of macrophytes. Neustupa et al.47 confirm that desmids are able to form tychoplanktonic communities due to water movements. In the samples collected from the macrophyte-dominated stations the mean abundance of desmids was generally lower, probably because of the macrophyte stabilizing effect. Aquatic plants are known to reduce turbidity and stabilize bottom sediments48, so they can prevent any intensive water mixing in ponds. In the examined open water stations, we also found a higher mean abundance of chlorophytes typical for shallow nutrient-rich waters (group G: Eudorina, Pandorina, Volvox and group K: Radiococcus) and/or for ponds with extremely high organic contents (group W0: e.g. Chlamydomonas), which proves that the sites lacking macrophytes were more fertile. Additionally, clearly more representatives from the codon J and X1 (typical for waters with high trophic levels) and a greater diversity of the representatives of different functional groups were recorded in the open water area compared to the macrophyte-dominated zones.The macrophyte-dominated stations had more abundant communities of filtrators, as aquatic plants are known to provide a profitable shelter for zooplankton49. Cladoceran predominance among macrophytes may have been a force reducing green algae numbers. The chlorophytes of the investigated ponds were mostly small- or medium-size species. Their size distribution makes them a high quality food for zooplankton, particularly for cladoceran filtrators. According to RDA analysis apart from pond size, the presence of filtrators significanly reduced the abundance of several chlorophyte dominating species. The lower algae abundance among macrophytes compared to the open water zone could also be explained by competition between algae and macrophytes for light and nutrients37,50 and/or with the secretion of allelopathic substances e.g. by Ceratophyllum demersum51 inhibiting algal development. Our studies demonstrated that among chemical factors which clearly differentiated the two types of analysed habitat, TRP and NH4 significantly influenced the distribution of chlorophyte dominating species. The lower levels of these parameters in macrophyte-dominated zones suggest that the nutrient uptake by aquatic plants in the investigated water bodies was high. There are many reports on the decrease of nutrient concentrations by macrophytes30,37,52, which are consistent with our observations. Despite lower, compared to the open water zone, chlorophyte densities within the macrophyte-dominated zones there was a group of species (e.g. Mougeotia sp., Pediastrum tetras, Scenedesmus obtusus, Monoraphidium contortum) that selectively chose vegetated stands. Furthermore, we found a great number29 of exclusive chlorophyte species for macrophyte-dominated zones. Half of these taxa belong to desmids, which are often periphytic organisms associated with aquatic macrophytes53,54.Preference towards macrophyte-dominated stations was also documented for two phytoplankton functional groups (T: Mougeotia sp. and Binuclearia lauterbornii and TD: e.g., Spirogyra sp., Zygnema sp., Cladophora glomerata, Oedogonium sp.) and one group which occurred exlusively among vegetated sites (MP—Ulothrix). Interestingly, all the representatives of these groups had a similar filamentous morphological form, which suggests that many of them are of epithytic origin, coexisting within aquatic plants. Two more groups—X2 (Pseudodidymocystis/Didymocystis, Pteromonas) and W1 (Gonium pectorale) were clearly affected by the presence of macrophytes. According to Padisak et al.40, codons TD and X2 indicate mesoeutrophic conditions and their higher abundances in the macrophyte-dominated zones also proves that plants contribute to lowering the trophic levels in the examined ponds. On the other hand, the relatively high abundance of the representative of the group W1 in these habitats suggests that macrophytes could enrich ponds with organic matter during the process of their decomposition.Concluding, our results prove that different types of catchment area (field and forest) as well as different types of habitats (open water zone and macrophyte-dominated zone) create distinct, specific conditions (dependent on some physical–chemical and biological variables) for the occurrence of chlorophytes in small water bodies. We conclude that cosmopolitan chlorophytes undoubtedly respond to the level of habitat heterogeneity, contributing to the ecological assessment of small water bodies. Chlorophytes in particularl react to the level of human transformation in the ponds’ vicinities. This is why we suggest using them for water quality evaluation in ponds. This interdisciplinary research significantly broadens the knowledge, not only about the response of chlorophytes to physical–chemical parameters of water, but also about the food preferences of zooplankton for which green algae are the basic food, and vice versa about the impact of zooplankton on microalgae communities. The analyses provide valuable information on chlorophytes-zooplankton interactions and also about the relationships between chlorophytes and macrophytes. Received data emphasize the high value of field ponds, underestimated habitats particularly vulnerable to destruction in the agricultural landscape. The research will help to better understand the functioning of poorly studied small water bodies, which will contribute to the preservation of their biodiversity and protection against degradation. They will also be useful in the management of small water bodies based on the specificity of chlorophyte occurrence in various habitats and catchment type ponds. Moreover, these results are important in a broader context, as the interactions between the studied organisms and the physico-chemical parameters of water in small bodies of water are to some extent universal, so the analyses will broaden the knowledge about the functioning of larger bodies of water. More

Benelli, G. et al. Sexual communication and related behaviours in Tephritidae: Current knowledge and potential applications for Integrated Pest Management. J. Pest Sci. 87, 385–405 (2014).Article 

Google Scholar 
Kuba, H. & Sokei, Y. The production of pheromone clouds by spraying in the melon fly, Dacus cucurbitae coquillett (Diptera: Tephritidae). J. Ethol. 6, 105–110 (1988).Article 

Google Scholar 
Fletcher, B. S. The structure and function of the sex pheromone glands of the male Queensland fruit fly, Dacus tryoni.. J. Insect Physiol. 15, 1309–1322 (1969).Article 

Google Scholar 
Nation, J. L. Courtship behavior and evidence for a sex attractant in the male Caribbean fruit fly, Anastrepha suspensa. Ann. Entomol. Soc. Am. 65, 1364–1367 (1972).Article 

Google Scholar 
Arita, L. H. & Kaneshiro, K. Y. Sexual selection and lek behavior in the Mediterranean fruit fly, Ceratitis capitata (Diptera: Tephritidae). Pacific Sci. (EUA) 43, 135–143 (1989).
Google Scholar 
Briceño, R.D. & Eberhard, W.G. Male wing positions during courtship by Mediterranean fruit flies (Ceratitis capitata) (Diptera: Tephritidae). J. Kansas Entomol. Soc. 143–47 (2000).Benelli, G. et al. Male wing vibration in the mating behavior of the Olive fruit fly Bactrocera oleae (Rossi) (Diptera: Tephritidae). J. Insect Behav. 25, 590–603 (2012).Article 

Google Scholar 
Feron, M. L’appel sonore du mâle dans le comportement sexuel de Dacus oleae Gmel [Dipt Trypetidae]. Bull. Soc. Entomol. Fr. 65, 139–143 (1960).Article 

Google Scholar 
Feron, M. & Andrieu, A. J. Etude des signaux acoustiques du male dans le comportement sexuel de Dacus Oleae Gmel (Dipt. Trypetidae). Ann. Epiphyt. 13, 269–276 (1962).
Google Scholar 
Rolli, K. Die akustischen Sexualsignale von Ceratitis capitata Wied. Und Dacus oleae Gmel. Z. Angew. Entomol. 81, 219–223 (1976).Article 

Google Scholar 
Webb, J. C., Calkins, C. O., Chambers, D. L., Schwienbacher, W. & Russ, K. Acoustical aspects of behavior of Mediterranean fruit fly, Ceratitis capitata: Analysis and identification of courtship sounds. Entomol. Exp. Appl. 33, 1–8 (1983).Article 

Google Scholar 
Mankin, R. W., Lemon, M., Harmer, A. M. T., Evans, C. S. & Taylor, P. W. Time pattern and frequency analyses of sounds produced by irradiated and untreated male Bactrocera tryoni (Diptera: Tephritidae) during mating behavior. Ann. Entomol. Soc. Am. 101, 664–674 (2008).Article 

Google Scholar 
Miyatake, T. & Kanmiya, K. Male courtship song in circadian rhythm mutants of Bactrocera cucurbitae (Tephritidae: Diptera). J. Insect Physiol. 50, 85–91 (2004).CAS 
PubMed 
Article 

Google Scholar 
Sivinski, J., Burk, T. & Webb, J. Acoustic courtship signals in the Caribbean fruit fly, Anastrepha suspensa (Loew). Anim. Behav. 32, 1011–1016 (1984).Article 

Google Scholar 
Mankin, R. W. et al. Broadcasts of wing-fanning vibrations recorded from calling male Ceratitis capitata (Diptera: Tephritidae) increase captures of females in traps. J. Econ. Entomol. 97, 1299–1309 (2004).CAS 
PubMed 
Article 

Google Scholar 
Mankin, R. W., Petersson, E., Epsky, N. D., Heath, R. R. & Sivinski, J. Exposure to male pheromones enhances Anastrepha suspensa (Diptera: Tephritidae) female response to male calling song. Fla. Entomol. 83, 411 (2000).CAS 
Article 

Google Scholar 
Sivinski, J. & Webb, J. C. Changes in a Caribbean fruit fly acoustic signal with social situation (Diptera: Tephritidae)1. Ann. Entomol. Soc. Am. 79, 146–149 (1986).Article 

Google Scholar 
Canale, A. et al. The courtship song of fanning males in the fruit fly parasitoid Psyttalia concolor (Szépligeti) (Hymenoptera: Braconidae). Bull. Entomol. Res. 103, 303–309 (2013).CAS 
PubMed 
Article 

Google Scholar 
Wicker-Thomas, C. Pheromonal communication involved in courtship behavior in Diptera. J. Insect. Physiol. 53, 1089–1100 (2007).CAS 
PubMed 
Article 

Google Scholar 
Tan, K.H., Nishida, R., Jang, E.B. & Shelly, T.E. Pheromones, male lures, and trapping of tephritid fruit flies. In: Trapping and the Detection, Control, and Regulation of Tephritid Fruit Flies: Lures, Area-Wide Programs, And Trade Implications 15–74 (Springer, 2014).Poramarcom, R. Sexual communication in the Oriental fruit fly, Dacus dorsalis Hendel (Diptera: Tephritidae). Doctoral dissertation. (University of Hawaii at Manoa, 1988).Ekanayake, D. The mating system and courtship behaviour of the Queensland fruit fly, Bactrocera tryoni (Froggatt) (Diptera: Tephritidae). Doctoral dissertation. (Queensland University of Technology, 2017).Suzuki, Y. & Koyama, J. Courtship behavior of the melon fly, Dacus cucurbitae Coquillett (Diptera: Tephritidae). Appl. Entomol. Zool. 16, 164–166 (1981).Article 

Google Scholar 
Scolari, F., Valerio, F., Benelli, G., Papadopoulos, N. T. & Vaníčková, L. Tephritid fruit fly semiochemicals: Current knowledge and future perspectives. Insects 12, 408 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Nieri, R., Anfora, G., Mazzoni, V. & Rossi Stacconi, M. V. Semiochemicals, semiophysicals and their integration for the development of innovative multi-modal systems for agricultural pests’ monitoring and control. Entomol. Gen. 42, 167–183 (2022).Article 

Google Scholar 
Cocroft, R. B. & Rodríguez, R. L. The behavioral ecology of insect vibrational communication. Bioscience 55, 323–334 (2005).Article 

Google Scholar 
Daane, K. M. & Johnson, M. W. Olive fruit fly: Managing an ancient pest in modern times. Annu. Rev. Entomol. 55, 151–169 (2010).CAS 
PubMed 
Article 

Google Scholar 
Rice, R. E., Phillips, P. A., Stewart-Leslie, J. & Sibbett, G. S. Olive fruit fly populations measured in Central and Southern California. Calif. Agric. 57, 122–127 (2003).Article 

Google Scholar 
Wang, X. et al. Exploration for olive fruit fly parasitoids across Africa reveals regional distributions and dominance of closely associated parasitoids. Sci. Rep. 11, 1–14 (2021).Article 
CAS 

Google Scholar 
Loher, W. & Zervas, G. The mating rhythm of the olive fruitfly, Dacus oleae Gmelin. Z. Angew. Entomol. 88, 425–435 (1979).Article 

Google Scholar 
Benelli, G. Aggression in Tephritidae flies: Where, when, why? Future directions for research in integrated pest management. Insects 6, 38–53 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Benelli, G. Aggressive behavior and territoriality in the olive fruit fly, Bactrocera oleae (Rossi) (Diptera: Tephritidae): Role of residence and time of day. J. Insect. Behav. 27, 145–161 (2014).Article 

Google Scholar 
Shelly, T. E. Aggression between wild and laboratory-reared sterile males of the mediterranean fruit fly in a natural habitat (Diptera: Tephritidae). Fla. Entomol. 83, 105–108 (2000).Article 

Google Scholar 
Ekanayake, W. M., Clarke, A. R. & Schutze, M. K. Close-distance courtship of laboratory reared Bactrocera tryoni (Diptera: Tephritidae). Austral. Entomol. 58, 578–588 (2019).Article 

Google Scholar 
Ant, T. et al. Control of the olive fruit fly using genetics-enhanced sterile insect technique. BMC Biol. 10, 1–8 (2012).Article 

Google Scholar 
Estes, A. M. et al. A basis for the renewal of sterile insect technique for the olive fly, Bactrocera oleae (Rossi). J. Appl. Entomol. 136, 1–16 (2012).Article 

Google Scholar 
Zanini, D., Geurten, B., Spalthoff, C. & Göpfert, M. C. Sound communication in Drosophila. In Insect Hearing and Acoustic Communication Animal Signals and Communication, Vol. 1 (ed. Hedwig, B.) (Springer, 2014).
Google Scholar 
Windmill, J. F. C. & Jackson, J. C. Mechanical specializations of insect ears. In Insect Hearing. Springer Handbook of Auditory Research, Vol. 55 (eds Pollack, G. et al.) (Springer, 2016).
Google Scholar 
Talyn, B. C. & Dowse, H. B. The role of courtship song in sexual selection and species recognition by female Drosophila melanogaster. Anim. Behav. 68, 1165–1180 (2004).Article 

Google Scholar 
Kanmiya, K. Acoustic studies on the mechanism of sound production in the mating songs of the melon fly, Dacus cucurbitae Coquillett (Diptera: Tephritidae). J. Ethol. 6, 143–151 (1988).Article 

Google Scholar 
Benelli, G. et al. Wing-fanning frequency as a releaser boosting male mating success—High-speed video analysis of courtship behavior in Campoplex capitator, a parasitoid of Lobesia botrana. Insect Sci. 27, 1298–1310 (2020).PubMed 
Article 

Google Scholar 
Ge, J. et al. Pea leafminer Liriomyza huidobrensis (Diptera: Agromyzidae) uses vibrational duets for efficient sexual communication. Insect Sci. 26, 510–522 (2019).PubMed 
Article 

Google Scholar 
Mazzoni, V., Anfora, G. & Virant-Doberlet, M. Substrate vibrations during courtship in three drosophila species. PLoS ONE 8, e80708 (2013).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
McKelvey, E. G. Z. et al. Drosophila females receive male substrate-borne signals through specific leg neurons during courtship. Curr. Biol. 31, 3894–3904 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Strauß, J., Stritih-Peljhan, N., Nieri, R., Virant-Doberlet, M., & Mazzoni, V. Communication by substrate-borne mechanical waves in insects: From basic to applied biotremology. In: Advances in Insect Physiology, vol. 61, 189–307 (Academic Press, 2021).Mazomenos, B. E. Effect of age and mating on pheromone production in the female olive fruit fly, Dacus oleae (Gmel.). J. Insect Physiol. 30, 765–769 (1984).CAS 
Article 

Google Scholar 
Carpita, A. et al. (Z)-9-tricosene identified in rectal gland extracts of Bactrocera oleae males: First evidence of a male-produced female attractant in in olive fruit fly. Naturwissenschaften 99, 77–81 (2012).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Canale, A. et al. Behavioural and electrophysiological responses of the olive fruit fly, Bactrocera oleae (Rossi) (Diptera: Tephritidae), to male- and female-borne sex attractants. Chemoecology 23, 155–164 (2013).CAS 
Article 

Google Scholar 
Mcdonald, P. T. Intragroup stimulation of pheromone release by male mediterranean fruit flies (Diptera: Tephritidae). Ann. Entomol. Soc. Am. 80, 17–20 (1987).CAS 
Article 

Google Scholar 
Iwahashi, O. & Majima, T. Lek formation and male–male competition in the melon fly, Dacus cucurbitae Coquillett: Diptera: Tephritidae. Appl. Entomol. Zool. 21, 70–75 (1986).Article 

Google Scholar 
Keiser, I., Kobayashi, R. M., Chambers, D. L. & Schneider, E. L. Relation of sexual dimorphism in the wings, potential stridulation, and illumination to mating of oriental fruit flies, melon flies, and Mediterranean fruit flies in Hawaii. Ann. Ent. Soc. Am. 66, 937–941 (1973).Article 

Google Scholar 
Benelli, G. & Canale, A. Aggressive behavior in olive fruit fly females: Oviposition site guarding against parasitic wasps. J. Insect Behav. 29, 680–688 (2016).Article 

Google Scholar 
Rohde, B. B. et al. An acoustic trap to survey and capture two neoscapteriscus species. Fla. Entomol. 102, 654–657 (2019).Article 

Google Scholar 
Shelly, T. E. Lek size and female visitation in two species of tephritid fruit flies. Anim. Behav. 62, 33–40 (2001).Article 

Google Scholar 
Niyazi, N., Shuker, D. M. & Wood, R. J. Male position and calling effort together influence male attractiveness in leks of the medfly, Ceratitis capitata (Diptera: Tephritidae): Male attractiveness in leks of Ceratitis capitata. Biol. J. Linn. Soc. Lond. 95, 479–487 (2008).Article 

Google Scholar 
Greenfield, M. D. Signal interactions and interference in insect choruses: Singing and listening in the social environment. J. Comp. Physiol. A 201, 143–154 (2015).Article 

Google Scholar 
Kouloussis, N. A. et al. Age related assessment of sugar and protein intake of Ceratitis capitata in ad libitum conditions and modeling its relation to reproduction. Front. Physiol. 8, 1–13 (2017).Article 

Google Scholar 
Boersma, P. & Van Heuven, V. Speak and unSpeak with PRAAT. Glot Int. 5, 341–347 (2001).
Google Scholar 
Joyce, A. L. et al. Effect of continuous rearing on courtship acoustics of five braconid parasitoids, candidates for augmentative biological control of Anastrepha species. Biocontrol 55, 573–582 (2010).Article 

Google Scholar 
Sall, J. et al. JMP Start Statistics: A Guide to Statistics and Data Analysis Using JMP (Sas Institute, 2017).
Google Scholar  More

Pienta, K. J., Hammarlund, E. U., Austin, R. H., Axelrod, R., Brown, J. S. & Amend, S. R. Cancer cells employ an evolutionarily conserved polyploidization program to resist therapy. In Seminars in Cancer Biology, 1–15 (2020).Siegel, R. L., Miller, K. D. & Jemal, A. Cancer statistics, 2020. CA A Cancer J. Clin. 70(1), 7–30 (2020).Article 

Google Scholar 
Duesberg, P. & Rasnick, D. Aneuploidy, the somatic mutation that makes cancer a species of its own. Cell Motil. Cytoskelet. 47(2), 81–107 (2000).CAS 
Article 

Google Scholar 
Hanahan, D. & Weinberg, R. A. Leading edge review hallmarks of cancer: The next generation. Cell 144, 646–674 (2011).CAS 
PubMed 
Article 

Google Scholar 
Amend, S. R. et al. Polyploid giant cancer cells: Unrecognized actuators of tumorigenesis, metastasis, and resistance. Prostate 79(13), 1489–1497 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Pienta, K. J. et al. Convergent evolution, evolving evolvability, and the origins of lethal cancer. Mol. Cancer Res. 18(6), 801–810 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pienta, K. J., Hammarlund, E. U., Axelrod, R., Brown, J. S. & Amend, S. R. Poly-aneuploid cancer cells promote evolvability, generating lethal cancer. Evol. Appl. 13(7), 1626–1634 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Roychowdhury, S. et al. Personalized oncology through integrative high-throughput sequencing: A pilot study. Sci. Transl. Med. 3(111), 1–12 (2011).Article 
CAS 

Google Scholar 
Kuczler, M. D., Olseen, A. M., Pienta, K. J. & Amend, S. R. ROS-induced cell cycle arrest as a mechanism of resistance in polyaneuploid cancer cells (PACCs). Prog. Biophys. Mol. Biol. 165, 3–7 (2021).CAS 
PubMed 
Article 

Google Scholar 

Brown, R. L. What evolvability really is. Br. J. Philos. Sci.65(3), 549–572 (2014).MathSciNet 
Article 

Google Scholar 
Crother, B. I. & Murray, C. M. Early usage and meaning of evolvability. Ecol. Evol. 9(7), 3784–3793 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Payne, J. L. & Wagner, A. The causes of evolvability and their evolution. Nat. Rev. Genet. 20, 24–38 (2019).CAS 
PubMed 
Article 

Google Scholar 
Pigliucci, M. Is evolvability evolvable?. Genetics 9, 75–82 (2008).CAS 
PubMed 

Google Scholar 
Sniegowski, P. D. & Murphy, H. A. Evolvability. Curr. Biol. 16, R831–R834 (2006).CAS 
PubMed 
Article 

Google Scholar 
Kostecka, L. G., Pienta, K. J. & Amend, S. R. Polyaneuploid cancer cell dormancy: Lessons from evolutionary phyla. Front. Ecol. Evol. 9, 439 (2021).Article 

Google Scholar 
Rajaraman, R., Rajaraman, M. M., Rajaraman, S. R. & Guernsey, D. L. Neosis–a paradigm of self-renewal in cancer. Cell Biol. Int. 29(12), 1084–1097 (2005).CAS 
PubMed 
Article 

Google Scholar 
Rajaraman, R., Guernsey, D. L., Rajaraman, M. M. & Rajaraman, S. R. Neosis–A parasexual somatic reduction division in cancer. Int. J. Hum. Genet. 7(1), 29–48 (2007).CAS 
Article 

Google Scholar 
Sundaram, M., Guernsey, D. L., Rajaraman, M. M. & Rajaraman, R. Neosis: A novel type of cell division in cancer. Cancer Biol. Ther. 3(2), 207–218 (2004).CAS 
PubMed 
Article 

Google Scholar 
Gatenby, R. A., Cunningham, J. J. & Brown, J. S. Evolutionary triage governs fitness in driver and passenger mutations and suggests targeting never mutations. Nat. Commun. 5(1), 1–9 (2014).Article 

Google Scholar 
Bukkuri, A. & Brown, J. S. Evolutionary game theory: Darwinian dynamics and the G function approach. MDPI Games 12(4), 1–19 (2021).MathSciNet 
MATH 

Google Scholar 
Lopez-Sánchez, L. M. et al. CoCl2, a mimic of hypoxia, induces formation of polyploid giant cells with stem characteristics in colon cancer. PLoS ONE 9(6), e99143 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Mittal, K. et al. Multinucleated polyploidy drives resistance to Docetaxel chemotherapy in prostate cancer. Br. J. Cancer 116(9), 1186–1194 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Niu, N., Mercado-Uribe, I. & Liu, J. Dedifferentiation into blastomere-like cancer stem cells via formation of polyploid giant cancer cells. Oncogene 36(34), 4887–4900 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ogden, A., Rida, P. C. G., Knudsen, B. S., Kucuk, O. & Aneja, R. Docetaxel-induced polyploidization may underlie chemoresistance and disease relapse. Cancer Lett. 367, 89–92 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Puig, P. E. et al. Tumor cells can escape DNA-damaging cisplatin through DNA endoreduplication and reversible polyploidy. Cell Biol. Int. 32(9), 1031–1043 (2008).CAS 
PubMed 
Article 

Google Scholar 
Zhang, S. et al. Generation of cancer stem-like cells through the formation of polyploid giant cancer cells. Oncogene 33(1), 116–128 (2014).CAS 
PubMed 
Article 

Google Scholar 
Lin, K. C. et al. The role of heterogeneous environment and docetaxel gradient in the emergence of polyploid, mesenchymal and resistant prostate cancer cells. Clin. Exp. Metastasis 36(2), 97–108 (2019).PubMed 
Article 

Google Scholar 
Lin, K.-C. et al. Epithelial and mesenchymal prostate cancer cell population dynamics on a complex drug landscape. Converg. Sci. Phys. Oncol. 3(4), 045001 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Boe, L. Mechanism for induction of adaptive mutations in Escherichia coli. Mol. Microbiol. 4(4), 597–601 (1990).CAS 
PubMed 
Article 

Google Scholar 
Cairns, J. Mutation and cancer: The antecedents to our studies of adaptive mutation. Genetics 148(4), 1433–1440 (1998).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hall, B. G. Adaptive mutagenesis: A process that generates almost exclusively beneficial mutations. Genetica 102, 109 (1998).PubMed 
Article 

Google Scholar 
Waddington, C. H. Genetic assimilation of an acquired character. Evolution 7(2), 118–126 (1953).Article 

Google Scholar 
Waddington, C. H. Genetic assimilation. Adv. Genet. 10, 257–293 (1961).CAS 
PubMed 
Article 

Google Scholar 
Jablonka, E. V. A. & Raz, G. A. L. Transgenerational epigenetic inheritance: Prevalence, mechanisms, and implications for the study of heredity and evolution. Q. Rev. Biol. 84(2), 131–176 (2009).PubMed 
Article 

Google Scholar 
Steele, E. J. & Pollard, J. W. Hypothesis: Somatic hypermutation by gene conversion via the error prone DNA(longrightarrow )RNA(longrightarrow )DNA information loop. Mol. Immunol. 24(6), 667–673 (1987).CAS 
PubMed 
Article 

Google Scholar 
Steele, E. J. Somatic hypermutation in immunity and cancer: Critical analysis of strand-biased and codon-context mutation signatures. DNA Repair 45, 1–24 (2016).CAS 
PubMed 
Article 

Google Scholar 
Steele, E. J. Somatic Selection and Adaptive Evolution (Springer, US, 1979).
Google Scholar 
Steele, E. J., Lindley, R. A. & Blanden, R. V. Lamarck’s Signature (Perseus Books, 1998).
Google Scholar 
Foster, P. L. Adaptive mutation: Implications for evolution. Bioessays 22, 1067–1074 (2000).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
McCutcheon, J. P. & Moran, N. A. Extreme genome reduction in symbiotic bacteria. Nat. Rev. Microbiol. 10(1), 13–26 (2012).CAS 
Article 

Google Scholar 
Badyaev, A. V. Stress-induced variation in evolution: From behavioural plasticity to genetic assimilation. Proc. R. Soc. B Biol. Sci. 272, 877–886 (2005).Article 

Google Scholar 
Bateman, K. G. The genetic assimilation of four venation phenocopies. J. Genet. 56(3), 443–474 (1959).Article 

Google Scholar 
Milkman, R. D. The genetic basis of natural variation. VI. Selection of a crossveinless strain of Drosophila by phenocopying at high temperature. Genetics 51(1), 87 (1965).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Waddington, C. H. Genetic assimilation of the bithorax phenotype. Evolution 10(1), 1–13 (1956).Article 

Google Scholar 
Godoy, O., Saldaña, A., Fuentes, N., Valladares, F. & Gianoli, E. Forests are not immune to plant invasions: Phenotypic plasticity and local adaptation allow Prunella vulgaris to colonize a temperate evergreen rainforest. Biol. Invasions 13(7), 1615–1625 (2011).Article 

Google Scholar 
Schlichting, C. D. & Wund, M. A. Phenotypic plasticity and epigenetic marking: An assessment of evidence for genetic accommodation. Evolution 68(3), 656–672 (2014).PubMed 
Article 

Google Scholar 
Otaki, J. M., Hiyama, A., Iwata, M. & Kudo, T. Phenotypic plasticity in the range-margin population of the lycaenid butterfly Zizeeria maha. BMC Evol. Biol. 10(1), 1–13 (2010).Article 

Google Scholar 
Aubret, F. & Shine, R. Genetic assimilation and the postcolonization erosion of phenotypic plasticity in island tiger snakes. Curr. Biol. 19(22), 1932–1936 (2009).CAS 
PubMed 
Article 

Google Scholar 
Losos, J. B., Irschick, D. J. & Schoener, T. W. Adaptation and constraint in the evolution of specialization of Bahamian Anolis lizards. Evolution 48(6), 1786–1798 (1994).PubMed 
Article 

Google Scholar 
Losos, J. B. et al. Evolutionary implications of phenotypic plasticity in the hindlimb of the lizard Anolis sagrei. Evolution 54(1), 301–305 (2000).CAS 
PubMed 

Google Scholar 
Sword, G. A. Density-dependent warning coloration. Nature 397(6716), 217 (1999).ADS 
CAS 
Article 

Google Scholar 
Sword, G. A. A role for phenotypic plasticity in the evolution of aposematism. Proc. R. Soc. B Biol. Sci. 269(1501), 1639–1644 (2002).Article 

Google Scholar 
Clausen, J. & Hiesey, W. M. The balance between coherence and variation in evolution. Proc. Natl. Acad. Sci. 46(4), 494–506 (1960).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gurevitch, J. Variation in leaf dissection and leaf energy budgets among populations of Achillea from an altitudinal gradient. Am. J. Bot. 75(9), 1298–1306 (1988).Article 

Google Scholar 
Gurevitch, J. & Schuepp, P. H. Boundary layer properties of highly dissected leaves: An investigation using an electrochemical fluid tunnel. Plant Cell Environ. 13(8), 783–792 (1990).Article 

Google Scholar 
Gurevitch, J. Sources of variation in leaf shape among two populations of Achillea lanulosa. Genetics 130(2), 385–394 (1992).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Foster, P. L. Stress-induced mutagenesis in bacteria. Crit. Rev. Biochem. Mol. Biol. 42(5), 373–397 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Soppa, J. Polyploidy in archaea and bacteria: About desiccation resistance, giant cell size, long-term survival, enforcement by a eukaryotic host and additional aspects. Microb. Physiol. 24, 409–419 (2014).ADS 
CAS 
Article 

Google Scholar 
Bastide, A. & David, A. The ribosome, (slow) beating heart of cancer (stem) cell. Oncogenesis 7(4), 1–13 (2018).CAS 
Article 

Google Scholar 
Cairns, J., Overbaugh, J. & Miller, S. The origin of mutants. Nature 335, 142–145 (1988).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Foster, P. L. Adaptive mutation: The uses of adversity. Annu. Rev. Microbiol. 47, 467–504. https://doi.org/10.1146/annurev.mi.47.100193.002343 (2003).Article 

Google Scholar 
Lenski, R. E. & Mittler, J. E. The directed mutation controversy and neo-Darwinism. Science 259(5092), 188–194 (1993).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Lenski, R. E. & Sniegowski, P. D. “Adaptive mutation’’: The debate goes on. Science 269, 285–288 (1995).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Noller, H. F., Hoffarth, V. & Zimniak, L. Unusual resistance of peptidyl transferase to protein extraction procedures. Science 256(5062), 1416–1419 (1992).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Pribis, J. P. et al. Gamblers: An antibiotic-induced evolvable cell subpopulation differentiated by reactive-oxygen-induced general stress response. Mol. Cell 74(4), 785–800 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Silvera, D., Formenti, S. C. & Schneider, R. J. Translational control in cancer. Nat. Rev. Cancer 10(4), 254–266 (2010).CAS 
PubMed 
Article 

Google Scholar 
Shcherbakov, D. et al. Ribosomal mistranslation leads to silencing of the unfolded protein response and increased mitochondrial biogenesis. Commun. Biol. 2(1), 1–16 (2019).CAS 
Article 

Google Scholar 
Truitt, M. L. & Ruggero, D. New frontiers in translational control of the cancer genome. Nat. Rev. Cancer 16(5), 288–304 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Alphey, L. S., Crisanti, A., Randazzo, F. & Akbari, O. S. Opinion: Standardizing the definition of gene drive. Proc. Natl. Acad. Sci. USA 117(49), 30864 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Champer, J., Buchman, A. & Akbari, O. S. Cheating evolution: Engineering gene drives to manipulate the fate of wild populations. Nat. Rev. Genet. 17, 146–159 (2016).CAS 
PubMed 
Article 

Google Scholar 
Champer, S. E. et al. Modeling CRISPR gene drives for suppression of invasive rodents using a supervised machine learning framework. PLOS Comput. Biol. 17(12), e1009660 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Deredec, A., Burt, A. & Godfray, H. C. J. The population genetics of using homing endonuclease genes in vector and pest management. Genetics 179(4), 2013–2026 (2008).PubMed 
PubMed Central 
Article 

Google Scholar 
Heffel, M. G. & Finnigan, G. C. Mathematical modeling of self-contained CRISPR gene drive reversal systems. Sci. Rep. 9(1), 1–10 (2019).Article 
CAS 

Google Scholar 
Leftwich, P. T. et al. Recent advances in threshold-dependent gene drives for mosquitoes. Biochem. Soc. Trans. 46, 1203–1212 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Nijhout, H. F., Kudla, A. M. & Hazelwood, C. C. Genetic assimilation and accommodation: Models and mechanisms. Curr. Top. Dev. Biol. 141, 337–369 (2021).PubMed 
Article 

Google Scholar 
Noble, C., Adlam, B., Church, G. M., Esvelt, K. M. & Nowak, M. A. Current CRISPR gene drive systems are likely to be highly invasive in wild populations. eLife 7, e33423 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Novozhilov, A. S., Karev, G. P. & Koonin, E. V. Mathematical modeling of evolution of horizontally transferred genes. Mol. Biol. Evol. 22(8), 1721–1732 (2005).CAS 
PubMed 
Article 

Google Scholar 
Pigliucci, M. & Murren, C. J. Perspective: Genetic assimilation and a possible evolutionary paradox: Can macroevolution sometimes be so fast as to pass us by?. Evolution 57, 1455–1464 (2003).PubMed 
Article 

Google Scholar 
Hammerstein, P. Darwinian adaptation, population genetics and the streetcar theory of evolution. J. Math. Biol. 34(5–6), 511–532 (1996).CAS 
PubMed 
MATH 
Article 

Google Scholar 
Dieckmann, U. Coevolutionary Dynamics of Stochastic Replicator Systems (Central Library of the Research Center Jülich, 1994).
Google Scholar 
Dieckmann, U., Marrow, P. & Law, R. Evolutionary cycling in predator-prey interactions: population dynamics and the red queen. J. Theor. Biol. 176(1), 91–102 (1995).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Dieckmann, U. & Law, R. The dynamical theory of coevolution: a derivation from stochastic ecological processes. J. Math. Biol. 34, 579–612 (1996).MathSciNet 
CAS 
PubMed 
MATH 
Article 

Google Scholar 
Metz, J. A. J., Nisbet, R. M. & Geritz, S. A. H. How should we define ‘fitness’ for general ecological scenarios?. Trends Ecol. Evol. 7(6), 198–202 (1992).CAS 
PubMed 
Article 

Google Scholar 
Goldschmidt, R. Some aspects of evolution. Science 78(2033), 539–547 (1933).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Vincent, T. L., Cohen, Y. & Brown, J. S. Evolution via strategy dynamics. Theor. Popul. Biol. 44(2), 149–176 (1993).MATH 
Article 

Google Scholar 
Bell, G. Evolutionary rescue. Annu. Rev. Ecol. Evol. Syst. 48, 605–627 (2017).Article 

Google Scholar  More

Lombard, F. et al. Consistent quantitative observations of planktonic ecosystems. Front. Mar. Sci. 6, 196. https://doi.org/10.3389/fmars.2019.00196 (2019).Article 

Google Scholar 
Sieracki, M. E., et al. Optical plankton imaging and analysis systems for ocean observation. Proceedings of OceanObs’09: Sustained Ocean Observations and Information for Society, 878–885 (2010). https://doi.org/10.5270/OceanObs09.cwp.81.Irisson, J.-O., Ayata, S.-D., Lindsay, D. J., Karp-Boss, L. & Stemmann, L. Machine learning for the study of plankton and marine snow from images. Ann. Rev. Mar. Sci. 14(1), 277. https://doi.org/10.1146/annurev-marine-041921-013023 (2022).Article 
PubMed 

Google Scholar 
Mars Brisbin, M., Brunner, O. D., Grossmann, M. M. & Mitarai, S. Paired high-throughput, in situ imaging and high-throughput sequencing illuminate acantharian abundance and vertical distribution. Limnol. Oceanogr. 65(12), 2953–2965. https://doi.org/10.1002/lno.11567 (2020).ADS 
CAS 
Article 

Google Scholar 
Benfield, M. et al. RAPID: Research on automated plankton identification. Oceanography 20(2), 172–187. https://doi.org/10.5670/oceanog.2007.63 (2007).Article 

Google Scholar 
Colin, S. et al. Quantitative 3D-imaging for cell biology and ecology of environmental microbial eukaryotes. Elife 6, e26066. https://doi.org/10.7554/eLife.26066 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Kim, M. K. Principles and techniques of digital holographic microscopy. J. Photonics Energy. 1, 018005. https://doi.org/10.1117/6.0000006 (2010).Article 

Google Scholar 
Tahara, T., Quan, X., Otani, R., Takaki, Y. & Matoba, O. Digital holography and its multidimensional imaging applications: A review. Microscopy 67(2), 55–67. https://doi.org/10.1093/jmicro/dfy007 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Jericho, S. K., Garcia-Sucerquia, J. F. W., Jericho, M. H. & Kreuzer, H. J. Submersible digital in-line holographic microscope. Rev. Sci. Instrum. 77(4), 043706. https://doi.org/10.1063/1.2193827 (2006).ADS 
CAS 
Article 

Google Scholar 
Bochdansky, A. B., Jericho, M. H. & Herndl, G. J. Development and deployment of a point-source digital inline holographic microscope for the study of plankton and particlesto a depth of 6000 m. Limnol. Oceanogr: Methods 11, 28–40 (2013).Article 

Google Scholar 
Yourassowsky, C. & Dubois, F. High throughput holographic imaging-in-flow for the analysis of a wide plankton size range. Opt. Express 22(6), 6661. https://doi.org/10.1364/OE.22.006661 (2014).ADS 
Article 
PubMed 

Google Scholar 
Jericho, M. H. & Kreuzer, H. J. Point source digital in-line holographic microscopy. In Coherent Light Microscopy (eds Ferraro, P. et al.) 3–30 (Springer, 2011).Chapter 

Google Scholar 
Kanka, M., Riesenberg, R. & Kreuzer, H. J. Reconstruction of high-resolution holographic microscopic images. Opt. Lett. 34(8), 1162. https://doi.org/10.1364/OL.34.001162 (2009).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Jericho, M. H., Kreuzer, H. J., Kanka, M. & Riesenberg, R. Quantitative phase and refractive index measurements with point-source digital in-line holographic microscopy. Appl. Opt. 51(10), 1503. https://doi.org/10.1364/AO.51.001503 (2012).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Wu, Y. & Ozcan, A. Lensless digital holographic microscopy and its applications in biomedicine and environmental monitoring. Methods 136, 4–16 (2018).CAS 
Article 

Google Scholar 
Sun, H. et al. digital holography for studies of marine plankton. Philos. Trans. R. Soc. A. 366, 1789–1806 (2008).ADS 
CAS 
Article 

Google Scholar 
Bianco, V. et al. microplastic identification via holographic imaging and machine learning. Adv. Intell. Syst. 2(2), 1900153. https://doi.org/10.1002/aisy.201900153 (2020).Article 

Google Scholar 
Guo, B. et al. Automated plankton classification from holographic imagery with deep convolutional neural networks. Limnol. Oceanogr. 19(1), 21–36. https://doi.org/10.1002/lom3.10402 (2021).Article 

Google Scholar 
Nayak, A. R., Malkiel, E., McFarland, M. N., Twardowski, M. S. & Sullivan, J. M. A Review of holography in the aquatic sciences: In situ characterization of particles, plankton, and small scale biophysical interactions. Front. Mar. Sci. 7, 572147. https://doi.org/10.3389/fmars.2020.572147 (2021).Article 

Google Scholar 
Di Bella, J. M., Bao, Y., Gloor, G. B., Burton, J. P. & Reid, G. High throughput sequencing methods and analysis for microbiome research. J. Microbiol. Methods 95(3), 401–414. https://doi.org/10.1016/j.mimet.2013.08.011 (2013).CAS 
Article 
PubMed 

Google Scholar 
Stoeck, T. et al. Multiple marker parallel tag environmental DNA sequencing reveals a highly complex eukaryotic community in marine anoxic water. Mol. Ecol. 19, 21–31. https://doi.org/10.1111/j.1365-294X.2009.04480.x (2010).CAS 
Article 
PubMed 

Google Scholar 
de Vargas, C. et al. Eukaryotic plankton diversity in the sunlit ocean. Science 348(6237), 1261605–1261605. https://doi.org/10.1126/science.1261605 (2015).CAS 
Article 
PubMed 

Google Scholar 
Lima-Mendez, G. et al. Determinants of community structure in the global plankton interactome. Science 348(6237), 1262073–1262073. https://doi.org/10.1126/science.1262073 (2015).CAS 
Article 
PubMed 

Google Scholar 
Santoferrara, L. et al. Perspectives from ten years of protist studies by high-throughput metabarcoding. J. Eukaryot. Microbiol. 67(5), 612–622. https://doi.org/10.1111/jeu.12813 (2020).Article 
PubMed 

Google Scholar 
Eickbush, T. H. & Eickbush, D. G. Finely orchestrated movements: evolution of the ribosomal RNA genes. Genetics 175(2), 477–485. https://doi.org/10.1534/genetics.107.071399 (2007).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kirkham, A. R. et al. Basin-scale distribution patterns of photosynthetic picoeukaryotes along an Atlantic Meridional Transect: Marine photosynthetic picoeukaryote community structure. Environ. Microbiol. 13(4), 975–990. https://doi.org/10.1111/j.1462-2920.2010.02403.x (2011).CAS 
Article 
PubMed 

Google Scholar 
Decelle, J. et al. PhytoREF: A reference database of the plastidial 16S rRNA gene of photosynthetic eukaryotes with curated taxonomy. Mol. Ecol. Resour. 15(6), 1435–1445. https://doi.org/10.1111/1755-0998.12401 (2015).CAS 
Article 
PubMed 

Google Scholar 
Leray, M. & Knowlton, N. Censusing marine eukaryotic diversity in the twenty-first century. Phil. Trans. R. Soc. B. 371(1702), 20150331. https://doi.org/10.1098/rstb.2015.0331 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Cowart, D. A. et al. Metabarcoding is powerful yet still blind: A comparative analysis of morphological and molecular surveys of seagrass communities. PLoS ONE 10(2), e0117562. https://doi.org/10.1371/journal.pone.0117562 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Stefanni, S. et al. Multi-marker metabarcoding approach to study mesozooplankton at basin scale. Sci. Rep. 8(1), 12085. https://doi.org/10.1038/s41598-018-30157-7 (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Pappalardo, P. et al. The role of taxonomic expertise in interpretation of metabarcoding studies. ICES J. Mar. Sci. https://doi.org/10.1093/icesjms/fsab082 (2021).Article 

Google Scholar 
Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V. & Egozcue, J. J. Microbiome datasets are compositional: And this is not optional. Front. Microbiol. 8, 2224. https://doi.org/10.3389/fmicb.2017.02224 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Zhu, F., Massana, R., Not, F., Marie, D. & Vaulot, D. Mapping of picoeucaryotes in marine ecosystems with quantitative PCR of the 18S rRNA gene. FEMS Microbiol. Ecol. 52(1), 79–92. https://doi.org/10.1016/j.femsec.2004.10.006 (2005).CAS 
Article 
PubMed 

Google Scholar 
Sargent, E. C. et al. Evidence for polyploidy in the globally important diazotroph Trichodesmium. FEMS Microbiol. Lett. 363(21), 244. https://doi.org/10.1093/femsle/fnw244 (2016).CAS 
Article 

Google Scholar 
Gong, W. & Marchetti, A. Estimation of 18S gene copy number in marine eukaryotic plankton using a next-generation sequencing approach. Front. Mar. Sci. 6, 219. https://doi.org/10.3389/fmars.2019.00219 (2019).Article 

Google Scholar 
Biard, T. et al. Biogeography and diversity of collodaria (radiolaria) in the global ocean. ISME J. 11, 1331–1344 (2017).Article 

Google Scholar 
Callahan, B. J., McMurdie, P. J. & Holmes, S. P. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J. 11(12), 2639–2643. https://doi.org/10.1038/ismej.2017.119 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Behrenfeld, M. J. et al. The North Atlantic aerosol and marine ecosystem study (NAAMES): Science motive and mission overview. Front. Mar. Sci. 6, 122. https://doi.org/10.3389/fmars.2019.00122 (2019).Article 

Google Scholar 
Bolaños, L. M. et al. Seasonality of the microbial community composition in the North Atlantic. Front. Mar. Sci. 8, 624164. https://doi.org/10.3389/fmars.2021.624164 (2021).Article 

Google Scholar 
Aitchison, J. The statistical analysis of compositional data. J. R. Stat. Soc. B 44(2), 139–160. https://doi.org/10.1111/j.2517-6161.1982.tb01195.x (1982).MathSciNet 
Article 
MATH 

Google Scholar 
Decelle, J. & Not, F. Acantharia. ELS, 1–10 (2015). https://doi.org/10.1002/9780470015902.a0002102.pub2.Yu, L., An, Y. & Cai, L. Numerical reconstruction of digital holograms with variable viewing angles. Opt. Express 10(22), 1250. https://doi.org/10.1364/OE.10.001250 (2002).ADS 
Article 
PubMed 

Google Scholar 
Della Penna, A. & Gaube, P. Overview of (sub)mesoscale Ocean dynamics for the NAAMES field program. Front. Mar. Sci. 6, 384. https://doi.org/10.3389/fmars.2019.00384 (2019).Article 

Google Scholar 
Sverdrup, H. U. Oceanography for Meteorologists (Prentice Hall, 1942).Book 

Google Scholar 
Mahadevan, A. The impact of submesoscale physics on primary productivity of plankton. Annu. Rev. Mar. Sci. 8(1), 161–184. https://doi.org/10.1146/annurev-marine-010814-015912 (2016).ADS 
Article 

Google Scholar 
Fratantoni, P. S. & Pickart, R. S. The Western North Atlantic shelfbreak current system in summer. J. Phys. Oceanogr. 37(10), 2509–2533. https://doi.org/10.1175/JPO3123.1 (2007).ADS 
Article 

Google Scholar 
Bolaños, L. M. et al. Small phytoplankton dominate western North Atlantic biomass. ISME J. 14(7), 1663–1674. https://doi.org/10.1038/s41396-020-0636-0 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kramer, S. J., Siegel, D. A. & Graff, J. R. Phytoplankton community composition determined from co-variability among phytoplankton pigments from the NAAMES field campaign. Front. Mar. Sci. 7, 215. https://doi.org/10.3389/fmars.2020.00215 (2020).Article 

Google Scholar 
Faure, E. et al. Mixotrophic protists display contrasted biogeographies in the global ocean. ISME J. 13(4), 1072–1083. https://doi.org/10.1038/s41396-018-0340-5 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Fratantoni, P. S. & McCartney, M. S. Freshwater export from the labrador current to the North Atlantic Current at the tail of the grand banks of Newfoundland. Deep Sea Res. I. 57(2), 258–283. https://doi.org/10.1016/j.dsr.2009.11.006 (2010).Article 

Google Scholar 
Torti, A., Lever, M. A. & Jørgensen, B. B. Origin, dynamics, and implications of extracellular DNA pools in marine sediments. Mar. Genom. 24, 185–196. https://doi.org/10.1016/j.margen.2015.08.007 (2015).Article 

Google Scholar 
Jian, C., Salonen, A. & Korpela, K. Commentary: How to count our microbes? The effect of different quantitative microbiome profiling approaches. Front. Cell. Infect. Microbiol. 11, 627910. https://doi.org/10.3389/fcimb.2021.627910 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Djurhuus, A. et al. Evaluation of marine zooplankton community structure through environmental DNA metabarcoding: Metabarcoding zooplankton from eDNA. Limnol. Oceanogr. Methods 16(4), 209–221. https://doi.org/10.1002/lom3.10237 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
del Campo, J. et al. The others: Our biased perspective of eukaryotic genomes. Trends Ecol. Evol. 29(5), 252–259. https://doi.org/10.1016/j.tree.2014.03.006 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Karst, S. M. et al. Retrieval of a million high-quality, full-length microbial 16S and 18S rRNA gene sequences without primer bias. Nat. Biotech. 36(2), 190–195. https://doi.org/10.1038/nbt.4045 (2018).CAS 
Article 

Google Scholar 
Johnson, J. S. et al. Evaluation of 16S rRNA gene sequencing for species and strain-level microbiome analysis. Nat. Commun. 10(1), 5029. https://doi.org/10.1038/s41467-019-13036-1 (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Callahan, B. J. et al. High-throughput amplicon sequencing of the full-length 16S rRNA gene with single-nucleotide resolution. Nucleic Acids Res. 47(18), e103–e103. https://doi.org/10.1093/nar/gkz569 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Lin, Y., Gifford, S., Ducklow, H., Schofield, O. & Cassar, N. Towards quantitative microbiome community profiling using internal standards. Appl. Environ. Microbiol. 85(5), 18. https://doi.org/10.1128/AEM.02634-18 (2019).Article 

Google Scholar 
Vogt, M. et al. Global marine plankton functional type biomass distributions: Phaeocystis spp. Earth Syst. Sci. Data 5, 405–443. https://doi.org/10.5194/essdd-5-405-2012 (2012).ADS 
Article 

Google Scholar 
MacNeil, L., Missan, S., Luo, J., Trappenberg, T. & LaRoche, J. Plankton classification with high-throughput submersible holographic microscopy and transfer learning. BMC Ecol. Evol. 21(1), 123. https://doi.org/10.1186/s12862-021-01839-0 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Pan, J., del Campo, J. & Keeling, P. J. Reference tree and environmental sequence diversity of labyrinthulomycetes. J. Eukary. Microbiol. 64(1), 88–96. https://doi.org/10.1111/jeu.12342 (2017).Article 

Google Scholar 
Bochdansky, A. B., Clouse, M. A. & Herndl, G. J. Eukaryotic microbes, principally fungi and labyrinthulomycetes, dominate biomass on bathypelagic marine snow. ISME J. 11(2), 362–373. https://doi.org/10.1038/ismej.2016.113 (2017).Article 
PubMed 

Google Scholar 
Xie, N., Hunt, D. E., Johnson, Z. I., He, Y. & Wang, G. Annual partitioning patterns of Labyrinthulomycetes protists reveal their multifaceted role in marine microbial food webs. Appl. Environ. Microbiol. https://doi.org/10.1128/AEM.01652-20 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Walcutt, N. L. et al. Assessment of holographic microscopy for quantifying marine particle size and concentration. Limnol. Oceanogr. Methods 3, 10379. https://doi.org/10.1002/lom3.10379 (2020).Article 

Google Scholar 
Axler, K. et al. Fine-scale larval fish distributions and predator-prey dynamics in a coastal river-dominated ecosystem. Mar. Ecol. Prog. Ser. 650, 37–61. https://doi.org/10.3354/meps13397 (2020).ADS 
Article 

Google Scholar 
Trudnowska, E. et al. Marine snow morphology illuminates the evolution of phytoplankton blooms and determines their subsequent vertical export. Nat. Commun. 12(1), 2816. https://doi.org/10.1038/s41467-021-22994-4 (2021).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
González, P. et al. Automatic plankton quantification using deep features. J. Plankton Res. 41(4), 449–463. https://doi.org/10.1093/plankt/fbz023 (2019).Article 

Google Scholar 
Briseño-Avena, C. et al. Three-dimensional cross-shelf zooplankton distributions off the Central Oregon Coast during anomalous oceanographic conditions. Prog. Oceanogr. 188, 102436. https://doi.org/10.1016/j.pocean.2020.102436 (2020).Article 

Google Scholar 
Biard, T. et al. In situ imaging reveals the biomass of giant protists in the global ocean. Nature 532, 504–507 (2016).ADS 
CAS 
Article 

Google Scholar 
Orenstein, E. C. et al. The scripps plankton camera system: A framework and platform for in situ microscopy. Limnol. Oceanogr. Methods 18(11), 681–695. https://doi.org/10.1002/lom3.10394 (2020).Article 

Google Scholar 
Fowler, B. L. et al. Dynamics and functional diversity of the smallest phytoplankton on the Northeast US Shelf. PNAS 117(22), 12215–12221. https://doi.org/10.1073/pnas.1918439117 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Tréguer, P. et al. Influence of diatom diversity on the ocean biological carbon pump. Nat. Geosci. 11(1), 27–37. https://doi.org/10.1038/s41561-017-0028-x (2018).ADS 
CAS 
Article 

Google Scholar 
Ryabov, A. et al. Shape matters: The relationship between cell geometry and diversity in phytoplankton. Ecol. Lett. 24(4), 847–861. https://doi.org/10.1111/ele.13680 (2021).MathSciNet 
Article 
PubMed 

Google Scholar 
Keeling, P. J. & del Campo, J. marine protists are not just big bacteria. Curr. Biol. 27(11), R541–R549. https://doi.org/10.1016/j.cub.2017.03.075 (2017).CAS 
Article 
PubMed 

Google Scholar 
Sgubin, G., Swingedouw, D., Drijfhout, S., Mary, Y. & Bennabi, A. Abrupt cooling over the North Atlantic in modern climate models. Nat. Commun. 8(1), 14375. https://doi.org/10.1038/ncomms14375 (2017).CAS 
Article 
PubMed Central 

Google Scholar 
Desbruyères, D., Chafik, L. & Maze, G. A shift in the ocean circulation has warmed the subpolar North Atlantic Ocean since 2016. Commun. Earth Environ. 2(1), 48. https://doi.org/10.1038/s43247-021-00120-y (2021).ADS 
Article 

Google Scholar 
Mitchell, M. R. et al. Atlantic zone monitoring program protocol. Can. Tech. Rep. Hydrogr. Ocean Sci. 223, 1–23 (2002).
Google Scholar 
Li, W. K. W., Glen Harrison, W. & Head, E. J. H. Coherent assembly of phytoplankton communities in diverse temperate ocean ecosystems. Proc. R. Soc. B. 273(1596), 1953–1960. https://doi.org/10.1098/rspb.2006.3529 (2006).Article 
PubMed 
PubMed Central 

Google Scholar 
Richardson, P. L. Florida current, gulf stream, and labrador current. In Encyclopedia of Ocean Sciences (ed. Steele, J. H.) 1054–1064 (Academic Press, 2001). https://doi.org/10.1006/rwos.2001.0357.Chapter 

Google Scholar 
Henson, S. A., Dunne, J. P. & Sarmiento, J. L. Decadal variability in North Atlantic phytoplankton blooms. J. Geophys. Res. 114(C4), C04013. https://doi.org/10.1029/2008JC005139 (2009).ADS 
CAS 
Article 

Google Scholar 
Han, G., Lu, Z., Wang, Z., Helbig, J. & Chen, N. Seasonal variability of the labrador current and shelf circulation off Newfoundland. J. Geophys. Res. 113, 10. https://doi.org/10.1029/2007JC004376 (2008).Article 

Google Scholar 
Pante, E. & Simon-Bouhet, B. marmap: A package for importing, plotting and analyzing bathymetric and topographic data in R. PLoS ONE 8(9), e73051. https://doi.org/10.1371/journal.pone.0073051 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kelley, D. “The Oce Package” In Oceanographic Analysis with R 91–101 (Springer, 2018).Book 

Google Scholar 
Oksanen, J., et al. vegan: Community Ecology Package. R package version 2.5-7 (2020). https://CRAN.R-project.org/package=vegan.Tomas, C. R. Identifying Marine Phytoplankton (Academic Press Inc, 1997).
Google Scholar 
Comeau, A. M., Li, W. K. W., Tremblay, J. -É., Carmack, E. C. & Lovejoy, C. Arctic ocean microbial community structure before and after the 2007 record sea ice minimum. PLoS ONE 6(11), e27492. https://doi.org/10.1371/journal.pone.0027492 (2011).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Parada, A. E., Needham, D. M. & Fuhrman, J. A. Every base matters: Assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples: Primers for marine microbiome studies. Environ. Microbiol. 18(5), 1403–1414. https://doi.org/10.1111/1462-2920.13023 (2016).CAS 
Article 
PubMed 

Google Scholar 
Walters, W. et al. Improved bacterial 16S rRNA gene (V4 and V4–5) and fungal internal transcribed spacer marker gene primers for microbial community surveys. MSystems https://doi.org/10.1128/mSystems.00009-15 (2016).Article 
PubMed 

Google Scholar 
Comeau, A. M., Douglas, G. M. & Langille, M. G. I. Microbiome helper: A custom and streamlined workflow for microbiome research. MSystems 2(1), e00127-e216. https://doi.org/10.1128/mSystems.00127-16 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotech. 37(8), 852–857. https://doi.org/10.1038/s41587-019-0209-9 (2019).CAS 
Article 

Google Scholar 
Amir, A. et al. Deblur rapidly resolves single-nucleotide community sequence patterns. MSystems 2(2), e00191-e216. https://doi.org/10.1128/mSystems.00191-16 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Guillou, L. et al. The protist ribosomal reference database (PR2): A catalog of unicellular eukaryote small sub-unit rRNA sequences with curated taxonomy. Nucleic Acids Res. 41(D1), D597–D604. https://doi.org/10.1093/nar/gks1160 (2013).CAS 
Article 
PubMed 

Google Scholar 
Mohsen, A., Park, J., Chen, Y.-A., Kawashima, H. & Mizuguchi, K. Impact of quality trimming on the efficiency of reads joining and diversity analysis of Illumina paired-end reads in the context of QIIME1 and QIIME2 microbiome analysis frameworks. BMC Bioinform. 20(1), 581. https://doi.org/10.1186/s12859-019-3187-5 (2019).Article 

Google Scholar 
Bokulich, N. A. et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome 6(1), 90. https://doi.org/10.1186/s40168-018-0470-z (2018).MathSciNet 
Article 
PubMed 
PubMed Central 

Google Scholar 
Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41(D1), D590–D596. https://doi.org/10.1093/nar/gks1219 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2021). https://www.R-project.org/.McMurdie, P. J. & Holmes, S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8(4), e61217. https://doi.org/10.1371/journal.pone.0061217 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Willis, A. & Bunge, J. Estimating diversity via frequency ratios: estimating diversity via ratios. Biometrics 71(4), 1042–1049. https://doi.org/10.1111/biom.12332 (2015).MathSciNet 
Article 
PubMed 
MATH 

Google Scholar 
Willis, A. D. Rarefaction, alpha diversity, and statistics. Front. Microbiol. 10, 2407. https://doi.org/10.3389/fmicb.2019.02407 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Quinn, T. P. et al. A field guide for the compositional analysis of any-omics data. GigaScience 8(9), 107. https://doi.org/10.1093/gigascience/giz107 (2019).CAS 
Article 

Google Scholar 
Silverman, J. D., Roche, K., Mukherjee, S. & David, L. A. Naught all zeros in sequence count data are the same. Comput. Struct. Biotech. J. 18, 2789–2798. https://doi.org/10.1016/j.csbj.2020.09.014 (2020).CAS 
Article 

Google Scholar 
Anderson, M. J. A new method for non-parametric multivariate analysis of variance. Austral. Ecol. 26, 32–46 (2001).
Google Scholar  More