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Dissolved oxygen modelThe dissolved oxygen in this model had a number of interactions to consider. Oxygen consumption through the processes of both respiration and nitrification. On the other hand, the water receives oxygen through water agitation as it is pumped through the system and from the oxygen generator. Oxygen is added to the water by oxygen generator and flow aeration (Fig. 1).Figure 1Dissolved oxygen model.Full size imageThe required oxygen supplementation is a sum of the pervious components as follows:$$ DO_{FR} + DO_{B} + DO_{N} = DO_{sup } + DO_{PF} $$
(1)
where DOFR is the dissolved oxygen consumption through fish respiration, g O2 m−3 h−1. DOB is the dissolved oxygen consumption through the biofilter, g O2 m−3 h−1. DON is the dissolved oxygen consumption through nitrification, g O2 m−3 h−1. DOPF is the dissolved oxygen addition through pipe flow, g O2 m−3 h−1. DOsup is the required oxygen supplementation (oxygen generator), g O2 m−3 h−1.The rate of change in DO concentration in fish tank:$$ frac{dDO}{{dt}} = DO_{FR} + DO_{B} + DO_{N} – DO_{PF} $$
(2)
where (frac{dDO}{{dt}}) is the rate of change in DO concentration during the time interval, g O2 m−3 h−1. dt is the rate of change in the time interval, hAfter calculating oxygen concentration for each element at each time step, the net oxygen change is then added to or subtracted from the previous time step`s oxygen concentration. DO concentrations can be calculated at any time (t) as:$$ DO_{t} = DO_{t – 1} + left( {frac{dDO}{{dt}} cdot dt} right) $$
(3)
where DOt is the DO concentration (g m−3) at time t. DOt−1 is the DO concentration (g m−3) at time t−1.The rate of oxygen consumption through fish respiration can be calculated on water temperature and average fish weight. This calculation is shown in the following equation10:$$ FR = 2014.45 + 2.75W – 165.2T + 0.007W^{2} + 3.93T^{2} – 0.21WT $$
(4)
$$ DO_{FR} = frac{FR times SD}{{1000}} $$
(5)
where FR is rate of oxygen consumption through fish respiration, mg O2 kg−1 fish. h−1. W is average of individual fish mass, g. T is water temperature, °C. SD is the stocking density of fish, kg m−3.The correlation coefficient for the equation was 0.99. Data used in preparing the equation ranged from 20 to 200 g for fish weight and from 24 to 32 °C.The rate of oxygen consumption through nitrification is calculated in terms of Total Ammonia Nitrogen (TAN) that is converted from ammonia to nitrate. The rate found in the literature is 4.57 g O2 g−1 TAN6.The oxygen consumption in nitrification process can be calculated as11:$$ DO_{N} = 4.57 times K_{NR} times {{{text{Nr}}} mathord{left/ {vphantom {{{text{Nr}}} {text{V}}}} right. kern-nulldelimiterspace} {text{V}}} $$
(6)
$$ K_{NR} = 0.1left( {1.08} right)^{{left( {T – 20} right)}} $$
(7)
$$ Nr = frac{{0.03 times F_{r} times W times N_{F} }}{24 times 1000} $$
(8)
where KNR is the coefficient of nitrification. Nr is the nitrification rate, g TAN h−1. Fr is the feeding ratio, % of body fish day−1. NF is the number of fish. V is the water volume, m3.The feeding ratio can be calculated as the following equation:$$ F_{r} = 17.02 times e^{{left[ {{raise0.7exhbox{${left( {ln W + 1.14} right)^{2} }$} !mathord{left/ {vphantom {{left( {ln W + 1.14} right)^{2} } { – 19.52}}}right.kern-nulldelimiterspace} !lower0.7exhbox{${ – 19.52}$}}} right]}} $$
(9)
The bacteria in the biofilter are a second source of oxygen consumption. Lawson explains that the biofilter oxygen demand is approximated 2.3 times the BOD5 production rate of fish6. The oxygen consumption of the biofilter is calculated using following equation:$$ DO_{B} = frac{{(2.3)left( {BOD_{5} } right)left( {W_{n} } right)}}{{left( V right)left( {24} right)left( {1000} right)}} $$
(10)
where BOD5 is average unfiltered BOD5 excretion rate, 2160 mg O2 kg−1 fish day−1. Wn is biomass, kg fish.The water pumping cycle was a source of oxygen addition to the system. The amount of oxygen addition through the water pumping cycle was calculated on an hourly basis. The method of calculating aeration from a pipe is detailed by12:$$ DO_{PF} = frac{PC times f times E times OTR}{V} $$
(11)
where PC is pump cycle length, h. f is pumping frequency, h−1. E is efficiency, %. OTR is oxygen transfer rate, g O2 h−1.This model sums the DOFR, DOB, DON, and DOPF to determine the supplemental DO demand in kg h−1. This number can be used to estimate the oxygen consumption if pure oxygen transfers system is used.Fish growth modelFish growth is affected by environmental and physical factors, such as water temperature, dissolved oxygen, unionized ammonia, photoperiod, fish stocking density, food availability, and food quality.In order to calculate the fish growth rate (g day−1) for individual fish, the following model was used13 as it includes the main environmental factors influencing fish growth. These factors are temperature, dissolved oxygen and unionized ammonia.$$ FGR = left( {0.2919 , tau , kappa , delta , varphi , h , f , W^{m} } right) – K.W^{n} $$
(12)
Where FGR is the fish growth rate, g day−1. τ is the temperature factor (0  > τ  к  δ  φ  ƒ  More

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Pesticide residuesInsects were collected in Malaise traps during two-week intervals, where pesticide residues from insect bodies were dissolved in the ethanol that was used to preserve the collected samples. Additionally, particles of plants, pollen, nectar or honeydew adhering to the insect bodies can be carriers of chemical pollution. Detected pesticide residues can therefore come from the insects and potentially attached particles. Under natural conditions of sunlight and warm temperatures, chemical stability of pesticide residues in the ethanol solution may have been affected by hydrolysis, for example, which could have caused the degradation of residues during the two-week collection intervals. Only flying insects that are alive can get into the Malaise traps, and therefore pesticide residues in the collected samples are assumed to represent sublethal levels to all trapped species. Additionally, insect collection was performed over an entire season and did not consider explicit spraying events. Therefore, the sampling we performed did not necessarily record maximum exposure levels that could represent lethal levels for individual species and substances. Hence, the quantification of pesticide amounts cannot be used for risk calculations. Instead we evaluate the presence of residues of CUPs on insects. Since detection is possible at low concentrations (see SOM Table A2) we obtained information on trace concentrations of the pesticide residues that insects were exposed to. It is safe to assume that the pesticide loads of insects were especially high following spraying events, and for individuals that were affected and consequently unable to fly. These insects were then not sampled in the Malaise traps.Of the 92 target common CUPs, 47 were detected in the insect samples from 21 nature conservation areas from two sampling dates in May and August 2020: 13 herbicides, 28 fungicides and 6 insecticides. Additionally, metabolites of fipronil, an insecticide registered for biocidal use in the EU, were recorded at three locations. At the 21 sites, insects in the conservation areas were exposed to 16.7 pesticides on average, ranging from 7 to 27 substances. More fungicides than herbicides were recorded and, on average, insects were exposed to less than two insecticides (Table 1). This may in part reflect the application in arable crops where more fungicides than herbicides are applied and insecticides are used less frequently. On the other hand, as insecticides affect insects directly due to their high acute toxicity, exposure to insecticides results in mortality or sublethal effects that impair mobility, leading to an underestimation of insecticide residues in our samples.Table 1 Number of CUP residues detected at 21 nature conservation areas across Germany and the resulting minimal, maximal and mean number of pesticide substances.Full size tableInsects at all 21 sites were exposed to residues of the herbicides metolachlor-S, prosulfocarb and terbuthylazine, and the fungicides azoxystrobin and fluopyram (Table 2). The presence of the six frequently detected herbicides can be explained by the high volume sold in 2019 (see SOM Table A3). They are among the 25 highest-ranking pesticides in terms of selling volume in Germany34. The same is true for the fungicide azoxystrobin. All other seven regularly detected fungicides were sold at lower volumes and their presence in the insect samples could be related to the high persistence of these fungicides, with soil half-lives reaching 500 d (bixafen), 484 d (boscalid) and 309 d (fluopyram). Only kresoxim-methyl, present in 10 sites, is not highly persistent in soil but has an affinity for the waxy plant cuticle, where it binds and accumulates35,36.Table 2 CUP residues frequently recorded at the 21 sites. Only substances that were recorded in ≥ 10 sites are listed.Full size tableThe neonicotinoid insecticide thiacloprid was recorded on insects in 16 of the 21 nature conservation areas. Thiacloprid was banned in the EU for use in field applications from August 2020 onwards, however, the end of use (grace period) was set to 3rd February, 202137. The high incidence of thiacloprid in our samples at many sites across Germany may therefore also reflect the last opportunity for farmers to use their remaining stocks. A ban could thus result in a greater impact to the ecosystem if parallel applications take place on a large scale. Hence, for potent pesticides which are banned from the market, it seems advisable to stop granting grace periods and instead destroy remaining stock rather than dispersing them into the environment despite knowledge of their high environmental risks.On average, in spring (May) residues of 9.6 and in summer (August) 9.3 CUPs were recorded in individual ethanol samples of the three trapping locations in the conservation areas. The minimum number of pesticide residues of 3 (May, site Mülhauser Halde) and 2 (August, Mittelberg) and the maximum of 16 (May, Bottendorfer Hügel) and 18 (August, Wisseler Dünen) were all from samples closer to the centre of the nature protection area, furthest away from adjacent agricultural fields.Seasonality of CUP exposureThe total number of CUP residues recorded on insects was similar for the two sampling intervals with 32 substances in May and 35 in August. However, a higher number of herbicide residues was recorded in May (13) compared to August (9), whereas for fungicides the reverse was the case [August (23), May (14)]. The number of detected insecticide residues was similar, with three and five substances recorded in May and August, respectively. This resulted in a different set of pesticide residue mixtures, driven by seasonality (Fig. 1). Mixtures in May, dominated by herbicides, were more similar to each other than the August mixtures, which contained more fungicides. The extreme positions of the NMDS analysis in August with Brauselay and Mittelberg are driven by the number of fungicide residues recorded. Brauselay is the only site where vineyards bordered the study area. Wine growing in Germany requires frequent fungicide applications.Figure 1CUP mixtures in May (green) and August (red) analysed with NMDS. The position of each location was determined by the composition of pesticide residues found in the ethanol samples. The closer data points are located in the ordination space, the more similar are their composition of pesticides. For abbreviations see Table 1.Full size imageOn the substance level, residues of the herbicides prosulfocarb, metolachlor-S, dimethenamid-P were recorded in more than half of the sites at both sampling intervals, whereas terbuthylazine was frequently present in May but not in August, and flufenacet was detected more frequently later in the year. Fungicide residues of fluopyram, azoxystrobin and boscalid were common in both sampling intervals, but pyraclostrobin, bixafen and dimoxystrobin were characteristic for May samples and fluazinam and kresoxim-methyl for the August samples (SOM Table A4). Although more residues of fungicides were recorded in August, this did not result in an increase in the number of fungicides that are found at many sites. Thirteen out of the 23 fungicides that were recorded in August were detected comparatively sporadic in samples from one to three sites. For insecticides, only thiacloprid was frequently noted, and the remaining substances acetamiprid, dimethoate, tebufenozide, and indoxacarb were found in May, whereas chlorantraniliprole and indoxacarb were recorded in August. The observed patterns reflect the agricultural practice of using herbicides in spring and early summer to establish crops such as cereals, oilseed rape and maize, and fungicides later in the year to control fungal diseases that increase with warmer temperatures.In addition to pesticide applications, seasonality has a direct effect on insect communities that change in composition from spring to autumn38,39,40. Because of shifts in insect community composition and pesticide application schemes, the mixture of pesticide residues present in insect samples changes throughout the year. Thus, it is likely that a finer time resolution than the selected two sampling intervals could reveal additional pesticide residues for the exposure of insects in conservation areas in the agricultural landscape.Influence of surrounding agricultural production areaOur data demonstrate that insects collected with Malaise traps in the nature conservation areas are exposed to pesticides applied in the surrounding agricultural landscape, where various crops are grown and are treated with a variety of pesticides. As the flight range of aerial insects fluctuates from less than one hundred meters to kilometres (for examples from the literature see SOM Table A5), it is not only the neighbouring arable field that may act as a source of contamination. A correlation analysis of the area of arable fields in the surrounding landscape (buffered from 500 to 3500 m) and number of pesticide residues recorded in the insect-trapping ethanol revealed a best fit for a radius of 2000 m around the center of the trapping positions in the conservation area (Fig. 2, all 21 sites, Pearson correlation coefficient = 0.48, p = 0.029). The site Brauselay differed from all nature conservation areas as vineyards were bordering the nature conservation area. Wine growing is a permanent crop characterised by high fungicide use on a comparable small area. When removing Brauselay from the analysis significance increased further (Pearson correlation coefficient = 0.60, p = 0.005; for further details, see SOM Fig. A2 and Table A6). Hence, pesticide residues on insects collected in the nature conservation areas are not only a result of applications on crops in the direct vicinity, but also from pesticide use in a larger area within the agricultural landscape around the conservation areas.Figure 2The number of CUP residues per site detected in insect/ethanol samples increased with the area of agriculture in a radius of 2000 m around the trapping positions (Pearson correlation coefficient = 0.48, p = 0.029).Full size imageBased on the correlation between pesticides and surrounding arable land, a generalized linear mixed model (GLMM) was applied to model the number of detected pesticide residues as a function of landscape factors (amount of agricultural production area, amount of nature conservation area and amount of FFH area in a 2000 m radius) and biomass of insects collected by the Malaise traps, with the study sites included as random effects (Table 3). Neither the area of the nature conservation area nor the FFH area nor biomass of collected insects was related to the number of pesticides recorded in ethanol samples. Only the agricultural production area in a 2000 m vicinity had a significant (p  More