Skip to main content
Environmental Sciences Europe
Download PDF

Land use alters cross-ecosystem transfer of high value fatty acids by aquatic insects

Environmental Sciences Europe volume 36, Article number: 10 (2024) Cite this article

Abstract

Background

Many aquatic insects emerge as adults from water bodies to complete parts of their life cycle in terrestrial ecosystems and are potential prey for riparian predators. The benefits of riparian predators from aquatic insects include higher contents of long-chain polyunsaturated fatty acids (PUFA) compared to terrestrial insects. Aquatic insects are therefore considered a high-quality food. Food containing high levels of PUFA can enhance growth and immune response of spiders. However, agricultural stressors like nutrient increase, pesticides and habitat degradation can affect the biomass of aquatic insects and in turn the diet of spiders. Studies quantifying the influence of land use on fatty acid (FA) profiles of emergent aquatic insects and riparian predators are lacking. We quantified differences in exports of FA, saturated FA, monounsaturated FA, and PUFA, FA profiles of aquatic insects and spiders between forested and agricultural sites over the primary emergence period within one year. The FA export to the riparian food web is crucial to understand energy fluxes between ecosystems. Furthermore, we monitored environmental variables to identify associations between agricultural stressors and FA profiles of aquatic insects and spiders.

Results

We found differences in FA export and profiles of aquatic insects between land-use types. The quantity of total FA export via aquatic insects was lower in agricultural sites (95% CI 1147–1313 µg m−2) in comparison to forested sites (95% CI 1555–1845 µg m−2), while the biomass export was higher in agricultural sites. Additionally, in spring the PUFA export was significantly lower (up to 0.06 µg d−1 m−2) in agricultural than forested sites. Agricultural stressors explained only little variation in the FA profiles of aquatic insects, e.g., 4% for caddisflies and 12% for non-biting midges. Percentage of shading and pool habitats were identified as most important variables explaining the variation in FA profiles.

Conclusion

The quality of aquatic insects as food source for riparian spiders was smaller in agricultural than forested sites, which can decrease the fitness of riparian predators. To improve our capacity to predict potential adverse effects in the riparian food web, future studies should identify the mechanisms underlying a lower PUFA content.

Background

Globally, intensive agriculture is a major land-use type [1]. In agricultural areas, stream ecosystems are threatened by enhanced nutrient inputs, pesticides and habitat degradation, which in turn jeopardizes biodiversity and human water security [2,3,4].

Stream ecosystems and adjacent terrestrial ecosystems are closely connected via the exchange of matter and organisms [5]. Many aquatic insects, e.g., Ephemeroptera (mayflies), Plecoptera (stoneflies), Trichoptera (caddisflies) and some Diptera (flies) emerge as adults from water bodies into terrestrial ecosystems. There, they are potential prey for riparian predators like spiders, birds and bats [6,7,8]. Additionally, agriculture is linked to the loss of terrestrial invertebrates [9,10,11]. Therefore, riparian predators can benefit from aquatic insects complementing the food source of terrestrial invertebrates [12,13,14].

Furthermore, aquatic insects are considered a high-quality food source, because they typically contain elevated levels of fatty acids (FA) compared to terrestrial insects [15,16,17]. Especially, levels of polyunsaturated FA (PUFA) of aquatic insects can be ten times higher than in terrestrial insects [15, 16]. These differences originate from the base of the food web propagating to higher trophic levels: aquatic primary producers like diatoms are capable of synthesizing long-chain PUFA [18,19,20], while terrestrial vascular plants cannot [21]. Additionally, many animals are not able to produce PUFA de novo and therefore depend on dietary intake of these compounds [22]. Animals like some bird and spider species that can synthesize PUFA still are constrained by high energetic costs and, thus, may only produce PUFA in the absence of other sources [22,23,24]. Consequently, food containing high levels of long-chain PUFA has been shown to enhance growth and immune response of spiders and birds [25, 26]. Furthermore, FA in general have been linked to the increased growth of spiders [27].

Emergence of aquatic insects is variable over time and reveals seasonal patterns [28, 29] and it has been shown that the timing of emergence can control growth rate, population biomass and maturity rate of terrestrial predators [30, 31]. Therefore, accounting for temporal dynamics is important when aiming to predict effects of total FA export via aquatic insects to terrestrial ecosystems [32].

It is known that agricultural stressors like increased nutrient concentration in stream water, pesticides and habitat degradation affect aquatic insects, e.g., by changing the composition of aquatic insect assemblages [14, 29] and increasing or decreasing their biomass, depending on the aquatic insect order [29, 33]. Furthermore, agricultural stressors can affect riparian spiders by altering the amount of aquatic insects in their diet [12], reducing their richness as well as abundance [34].

Most studies thus far have focused on PUFA profiles of aquatic insects and riparian predators without considering potential effects of agricultural stressors (e.g., [16, 17, 24, 35, 36]). One mesocosm study on Chironomidae (non-biting midges), including nutrient elevation and predation, found that FA export was highest at intermediate phosphate concentrations and that biomass of non-biting midges was the best predictor for FA export [37]. However, under laboratory conditions, with toxicant exposure (copper, pesticides, Bacillus thuringiensis var. israelensis) during larval stages of non-biting midges, no effect on FA profiles of adult non-biting midges and a tendency to decreased FA content in spiders was found [38]. Similarly, one field study on emergent aquatic insects in two streams included stream-bed characteristics and physicochemical variables like nutrients, but did not find an association of these variables with the FA profiles of aquatic insects [39]. In a field study, conducted in agricultural and forested streams focusing on vegetation (herbaceous and woody), the taxonomy of spiders at family level was the best predictor for FA content of spiders [40].

To our knowledge, field studies that quantified the influence of land use and associated stressors on FA profiles of emergent aquatic insects and riparian predators as well as FA export via aquatic insects are lacking. However, this would be important to estimate the effect of changing quality in terms of FA export to the riparian food web, which is crucial to understand energy fluxes between ecosystems and to predict effects on the subsidized food web [41,42,43]. Therefore, we aimed to quantify differences in total FA export and FA profiles of aquatic insects as well as riparian spiders between forested and agricultural sites of ten streams over the primary emergence period within one year (March–September). As spiders can prey on emergent aquatic insects, their FA profiles may be affected by changes in FA profiles of emergent aquatic insects. We collected emergent aquatic insects and riparian spiders and measured their FA profiles. In addition, we monitored a range of environmental variables to identify potential associations between agricultural stressors and the FA profiles of aquatic insects and spiders. We compared (1) total FA export via aquatic insects between both land-use types; (2) FA profiles of aquatic insects and spiders between agricultural and forested sites and (3) examined associations between agricultural stressors and the FA profiles of aquatic insects and spiders.

Methods

Study sites

To cover the primary emergence period [44], our study was conducted from 22nd March to 13th September 2018 in south-western Germany. In 10 parallel, fine substrate-dominated, mostly small, first and second order highland streams, an upstream forested site and a downstream site where agricultural land use dominated were selected (Additional file 1: Figure S1). The mean distance between the upstream and downstream sites within a stream was 5.5 (range: 1.4–14.0) km and the maximum distance between parallel streams was 50 km. All streams originated in the Palatinate Forest, a forested low mountain range. The sites were mostly free from large wastewater treatment plants and industrial facilities. Viticulture was the main agricultural land use. It has been shown that environmental variables were similar across different types of agricultural land use including viticulture, cereals and corn in this region [45]. The stream size and order of all study sites was comparable, for details see Ohler et al. [29].

Agricultural stressors associated with FA profiles

We recorded physicochemical variables every three weeks and hydromorphological structure in March, July, and August to determine land-use-related variables associated with FA profiles of aquatic insects and riparian spiders. For instance: electrical conductivity (EC), nitrate concentration, air and water temperature, oxygen saturation, the percentage of pool habitats and the percentage of shading (Additional file 1: Table S1). Furthermore, in-stream pesticide concentrations were determined from 49 event-driven samples taken during heavy rainfall events and 85 grab samples taken every three weeks. Glass bottle samplers [46] and automated samplers (MAXX TP5, Rangendingen, Germany) took event-driven samples whenever the water level increased more than 5 cm. The samples were filtered (either automatically on site or manually in the lab) to retain particles, which were then analyzed for pesticides bound on particles. More information on pesticide sampling, analysis and exposure are described in Halbach et al. [47] and Liess et al. [48, 49].

The pesticide class of pyrethroids typically enters streams bound on particles [50] and has a high relative toxicity for aquatic insects [51, 52]. Therefore, the concentration of pesticides bound on particles in event samples was used to estimate the bioavailable concentration in water cd following Schäfer et al. [53] and Toro et al. [54] with the equation:

$$ c_{d} = \frac{{c_{{{\text{tot}}}} }}{{f_{{{\text{oc}}}} \cdot k_{{{\text{oc}}}} + 1}}, $$
(1)

where foc is the fraction of organic carbon in the sample, ctot is the total concentration on the suspended particles, and koc is the soil organic carbon–water partitioning coefficient, which was extracted from the Pesticide Property Data Base (PPDB, [55]) and PubChem [56] database (Additional file 1: Table S2).

The logarithmic sum of toxic units (sumTU) was calculated to estimate the toxicity of the pesticide mixture [57]:

$$ {\text{sumTU}} = {\text{log}}\mathop \sum \limits_{i = 1}^{n} \frac{{c_{i} }}{{{\text{EC}}_{50i} }}, $$
(2)

where ci is the concentration of the single pesticide, EC50i the acute effect concentration of the pesticide towards the most sensitive freshwater invertebrate species, and n is the number of pesticides. The R package Standartox (version 0.0.1, Scharmüller et al. [58]) was used to compile the EC50 values from the ECOTOX database [59]. If the EC50 values were missing in Standartox, the values were complemented from the PPDB [55] or Malaj et al. [60] (Additional file 1: Table S3). The maximum pesticide toxicity (maximum sumTU of all samplings per site and season; hereafter pesticide toxicity) was used in the analysis, because it may be responsible for the strongest ecological response.

Spider and aquatic insect sampling

We chose Tetragnatha sp. to determine effects of FA in aquatic–terrestrial food webs, as these spiders frequently colonize riparian areas and prey on aquatic insects [7] with orb webs spanning over streams [61]. Whenever feasible only female and adult spiders of the species T. montana were collected to minimize variation in feeding, because feeding differs between and within spider species [62]. In the subsequent FA analysis 73% of the spiders were adult female T. montana, for more details see Ohler et al. [63]. Up to ten spiders were gathered with a maximum distance of 1 m from the stream by hand in spring (14th–16th May 2018), summer (16th–19th, 23rd, 26th July 2018) and autumn (10th–13th September 2018).

Emergence traps with a basal area of 0.25 m2 and a bottle trap without any solution [64] were used to sample aquatic insects continuously. Two traps were installed at every site covering pool and riffle habitats. This sampling method likely underestimated the fraction of stoneflies that emerge by walking on the banks. As previous studies estimated only < 1% to 3% [65, 66] of aquatic emergent insects returning to water bodies, we assume that most sampled aquatic insects would have reached the riparian area. Twice a week the traps were emptied by replacing the bottle trap.

The spiders as well as aquatic insects were transported on ice until they were euthanized in liquid nitrogen and identified in the laboratory on ice. Under a stereo microscope, spiders were identified to species level using the key by Roberts [67] and aquatic insects to family level with the following keys Bährmann and Müller [68], Nilsson [69, 70], Schäfer and Brohmer [71]. Subsequently, spiders and aquatic insects were lyophilized to complete dryness and weighed to the nearest 0.1 µg.

FA analysis

For FA analysis the major orders of aquatic insects, i.e., mayflies, stoneflies, caddisflies and flies, were chosen. In total 21 FA with 18 or more carbon atoms were included in the analysis. Since non-biting midges (Chironomidae) dominated the emergence of flies (Diptera) [29], only their FA profiles were analyzed. The samples of aquatic insects collected over approximately two weeks (Additional file 1: Table S4) were pooled on order level prior to analysis, which is commonly done in FA analysis (e.g., [35, 39]). An analysis on family level would have exceeded financial and labor capacities, though FA profiles may differ across families [37]. Hence, the FA analysis at order level will reflect the FA profiles of the families present in one site.

After the addition of an internal standard (C17:0 200 μg mL−1; C23:0 250 μg mL−1, Sigma-Aldrich) the FA of all samples were extracted following Folch et al. [72] with chloroform/methanol (GC-grade, 5 mL, v:v; 2:1) at − 20 °C over night. Then the samples were filtered with a syringe filter (PTFE, 13 mm, 0.45 µm, BGB), evaporated until dryness at 40 °C under nitrogen and redissolved in methanol. The volume of methanol depended on the weight of the sample (maximum ratio of weight to volume: 3:10), for details see Ohler et al. [73]. All samples were stored under nitrogen at − 20 °C until derivatization. Methanolic trimethylsulfonium hydroxide (TMSH, 0.2 M, 10 µL, Macherey–Nagel) was used to derivatize FA to fatty acid methyl esters (FAME) in the sample (20 µL) at room temperature for 60 min. A gas chromatograph with a flame ionization detector (Varian CP-3800, Varian Inc) equipped with a DB-225 capillary column (30 m × 0.25 mm × 0.25 µm, Agilent J&W) was used to analyze FAME. The FAME were identified and quantified with external standards (Supelco 37 component FAME mix, 18:1n-7 FAME, ALA FAME, Sigma-Aldrich). OpenChrom [74] was used for identification and R (version 4.2.0 [75] for quantification. Further details are given in Ohler et al. [73].

Data analysis

Comparing FA export between agricultural and forested sites

The export of FA, saturated fatty acids (SFA), monounsaturated fatty acids (MUFA) and PUFA via aquatic insects was assessed with hierarchical generalized additive models (HGAM) following Pedersen et al. [76]. This gives information about the amount of these compounds available for riparian predators. HGAM allow to identify seasonal patterns of FA, SFA, MUFA and PUFA export as well as differences between land-use types in the amount exported. The sum of all FA, SFA, MUFA and PUFA of aquatic insects in total as well as on order level were used in the HGAM. The export of FA, SFA, MUFA and PUFA was normalized for the sampling area and duration. Group-level smoothers without a global smoother for land use and land use crossed with order were applied. That means each group could differ in its shape without restriction. HGAM including one smoothing parameter for all group levels (model S, same wiggliness) yielded lower Bayesian Information Criterion (BIC, Additional file 1: Table S5) than HGAM fitted with one smoothing parameter for every group level (model I, different wiggliness). Additionally, stream was incorporated as random effect smoother. The HGAM were fitted with the R-package mgvc (version 1.8–36 [77]. The effect of land use was quantified with the 95% confidence interval (CI) of the difference between the mean fit (mean per time point) for forest and agriculture. At ɑ = 0.05 non-overlapping CI were considered statistically significant. Furthermore, the mean export of FA, SFA, MUFA and PUFA per area over the whole sampling period was estimated by using the mean fits of the HGAM in forest as well as agriculture. The temporal resolution of agricultural stressors was too low to include them in model selection in HGAM.

Comparing FA profiles between agricultural and forested sites

To identify differences in the FA profiles between land-use types, in every season, FA profiles (FA ≥ 18 carbon atoms) of aquatic insects, i.e., mayflies, stoneflies, caddisflies, non-biting midges, and spiders between forested and agricultural sites were compared with analysis of similarity (ANOSIM; 999 permutations, Euclidean distance, R-package vegan version 2.5–7 [78]). For this purpose, the content of a single FA was calculated as the proportion of the total FA content (proportion of FA) to assess potential effects of land use on the FA profiles. In autumn, a comparison of FA profiles of stoneflies between land-use types was not possible, because no stoneflies were caught in agricultural sites during autumn. The p-values were adjusted with the Benjamini–Hochberg method [79] to decrease the false discovery rate in multiple testing. Similarity percentage (SIMPER) analyses with the R-package vegan version 2.5–7 [78] were conducted whenever ANOSIM resulted in significant differences between land-use types to identify the specific FA contributing to the differences.

Agricultural stressors associated with FA profiles

Redundancy analysis (RDA) was conducted to identify agricultural stressors associated with changes in FA profiles of aquatic insects and spiders. For this purpose, the mean of each FA (expressed as proportion of total FA) and environmental variables per season was calculated for spiders and aquatic insects in total. The proportion of single FA was used to determine, if agricultural stressors were in general associated with FA profiles. Furthermore, the mean of each FA per season was calculated for single orders mayflies, stoneflies, caddisflies and non-biting midges. The latter was done, because data aggregation may hamper the identification and evaluation of associations with stressors [29, 80]. We chose pesticide toxicity, percentage of shading, EC, oxygen saturation, percentage of pool habitats, phosphate and nitrate concentration as well as air and water temperature as variables potentially expressing agricultural influence based on the results of previous studies in the region [45, 81, 82]. Additionally, we included the variables stream and season. Water temperature and EC were only included in RDA for aquatic insects and air temperature only in RDA for spiders. Furthermore, the biomass of aquatic insects was included in RDA for spiders only. The variables were chosen a priori. Temperature is known to affect FA profiles of organisms, because, for example with rising temperature, organisms can modify their PUFA content to decrease fluidity of cell membranes [83, 84]. Shading, phosphate and nitrate concentration can affect primary producers and in turn the trophic transfer of FA by altering the food availability for aquatic insects [14, 85,86,87]. Furthermore, the variables considered in this study can affect the biomass, abundance, and assemblage composition of aquatic insects [12, 14, 29, 33] and the diet, abundance, as well as assemblage composition of spiders [12, 34]. The biomass of aquatic insects determines the potential amount of prey with aquatic origin for spiders [12]. Before the analysis the environmental variables were checked for collinearity. No collinearity was present (highest r = 0.5) and all environmental variables were independent from each other. Additionally, the variables were standardized, which includes mean centering and standardization to unit variance. Variable selection for the agricultural stressors was conducted with automatic forward stepwise model selection using the maximization adjusted R2 (ordiR2step, R-package vegan version 2.5–7 [78]). Stream and season were included in the starting model. After model selection a partial RDA with stream and season as covariates was conducted to identify the variation in FA profiles originating only from the agricultural stressors. All data analysis was conducted with R [75] and figures were generated with the R-package ggplot2 version 3.4.1 [88]. The R code and data are available [63].

Results

Comparing FA export between agricultural and forested sites

Overall, 1555–1845 µg m−2 (95% CI) FA, 425–516 µg m−2 SFA, 178–204 µg m−2 MUFA, and 942–1114 µg m−2 PUFA were exported in forested and 1147–1313 µg m−2 FA, 329–403 µg m−2SFA, 135–151 µg m−2 MUFA, and 670–744 µg m−2 PUFA in agricultural sites over the study period (Additional file 1: Table S6). Differences between land-use types in FA, SFA, and MUFA export were not significant (i.e., non-overlapping 95% CI at alpha = 0.05) over the whole study period for individual time points (Fig. 1a–c). However, during spring the PUFA export was significantly higher (up to 0.06 µg d−1 m−2) in forested than agricultural sites (Fig. 1d). The FA, SFA, MUFA and PUFA export was highest in spring and decreased until autumn in both land-use types and the seasonal patterns of these compounds were similar within land-use types (Fig. 1a–d).

Fig. 1
figure 1

Modeled seasonal patterns of fatty acid (FA), saturated fatty acid (SFA), monounsaturated fatty acid (MUFA) and polyunsaturated fatty acid (PUFA) export from streams of the total emergence including non-biting midges, mayflies, stoneflies, and caddisflies. The values were derived with hierarchical generalized additive models (HGAM) fitted with 354 observations. Solid lines represent the predicted mean fit values of the HGAM, and the ribbon shows ± 2 standard errors around the mean fit. Dots indicate significant differences (non-overlapping 95% confidence intervals at alpha = 0.05) between agricultural and forested sites. Blue shows the seasonal patterns in agriculture and green in forest for a FA export, b SFA export, c MUFA export, and d PUFA export. Seasons: spring: 18th March–16th May, summer: 17th May–26th July, autumn: 27th July–13th September

Full size image

In autumn, the FA, SFA, MUFA, and PUFA export at single time points of mayflies was significantly higher in forested than agricultural sites (Fig. 2a, e, i, m), the latter approximately 0.01 µg d−1 m−2. The FA, SFA, MUFA as well as PUFA export of mayflies peaked during spring in both land-use types (Fig. 2a, e, i, m), but individual time points were not significantly different.

Fig. 2
figure 2

Modeled seasonal patterns of fatty acid (FA), saturated fatty acid (SFA), monounsaturated fatty acid (MUFA) and polyunsaturated fatty acid (PUFA) export from streams of aquatic insect orders. The values were derived with hierarchical generalized additive models (HGAM) fitted with 998 observations. Solid lines represent the predicted mean fit values of the HGAM, and the ribbon shows ± 2 standard errors around the mean fit. Dots indicate significant differences (non-overlapping 95% confidence intervals at alpha = 0.05) between agricultural and forested sites. Blue shows the seasonal patterns in agriculture and green in forest for a, e, i, m mayflies, b, f, j, n non-biting midges, c, g, k, o caddisflies, and d, h, l, p stoneflies. Seasons: spring: 18th March–16th May, summer: 17th May–26th July, autumn: 27th July–13th September. Beware that the y-axis scale varies

Full size image

In contrast, the FA, SFA, MUFA, and PUFA export via non-biting midges was significantly higher in forested than agricultural sites during spring and the beginning of summer (Fig. 2b, f, j, n). The FA export ranged from 0.04 (± 0.02) to 0.08 (± 0.04) µg d−1 m−2 (mean fit HGAM ± 2 standard errors) and from 0.02 (± 0.01) to 0.03 (± 0.02) µg d−1 m−2, respectively. In both land-use types FA, SFA, MUFA, and PUFA export via non-biting midges reached its minimum in summer.

The export of FA, MUFA, and PUFA via caddisflies was significantly higher in agricultural than forested sites during autumn, but no significant differences at individual time points were observed for SFA export via caddisflies (Fig. 2c, g, k, o). In agricultural sites the FA export of caddisflies ranged from 0.05 (± 0.02) to 0.007 (± 0.03) µg d−1 m−2, and the PUFA export from 0.04 (± 0.01) to 0.05 (± 0.02), whereas the FA export of caddisflies ranged from 0.02 (± 0.02) to 0.03 (± 0.01) µg d−1 m−2, and the PUFA export was approximately 0.02 µg d−1 m−2 in forested sites. Additionally, in summer the FA export of caddisflies peaked at 0.07 (± 0.03) µg d−1 m−2 in agricultural sites, while in forested sites a plateau around 0.05 µg d−1 m−2 was observed in summer (Fig. 2c).

Starting at the second half of spring at single time points, the FA, SFA, MUFA, and PUFA export via stoneflies was significantly higher in agricultural than forested sites, though in forested sites the maximum FA export 0.02 (± 0.01) µg d−1 m−2 (beginning of spring) was the same as the maximum FA export 0.02 (± 0.01) µg d−1 m−2 (end of spring) in agricultural sites (Fig. 2d, h, l, p).

Comparing FA profiles between agricultural and forested sites

We did not find differences in FA profiles of spiders and stoneflies between agricultural and forested sites (Table 1, Additional file 1: Figure S2). FA profiles of mayflies (ANOSIM: R = 0.13, p-value = 0.014) exhibited significant differences (i.e., p-value < 0.05) between land-use types in spring (Table 1). The FA eicosapentaenoic acid (20:5n-3, EPA), alpha-linolenic acid (18:3n-3, ALA), linoleic acid (18:2n-6c, LIN), elaidic acid (18:1n-9t, ELA), octadecanoic acid (18:0, ODA) and eicosanoic acid (20:0, EA) contributed most to these differences (Table 2). All of these FA, except EPA, tended to have higher proportions in mayflies in forested than agricultural sites (Table 2, Additional file 1: Figure S2). Additionally, these FA explained between 22% (EPA) and 6% (ELA, LIN) of the differences.

Table 1 Results of the analysis of similarities (ANOSIM) for the fatty acid (FA) profiles
Full size table
Table 2 Results of similarity percentage (SIMPER) analyses conducted when significant differences between forested and agricultural sites in fatty acid (FA) profiles were found with analysis of similarity (ANOSIM)
Full size table

The FA profiles of non-biting midges differed significantly between land-use types in spring (ANOSIM: R = 0.12, p-value = 0.036, Table 1). The FA contributing most to these differences were: gamma-linolenic acid (18:3n-6, GLA), ALA, EPA, ODA and EA, of which GLA explained most of the differences (21%) and EA the least (7%, Table 2). GLA, ODA and, EA tended to reach higher proportions in non-biting midges in forested than agricultural sites, while ALA and EPA tended to have higher proportions in aquatic insects in agricultural than forested sites (Table 2, Additional file 1: Figure S2).

Furthermore, in summer caddisflies revealed significant differences in FA profiles between forested and agricultural sites (ANOSIM: R = 0.1, p-value = 0.026, Table 1). ALA, EPA, LIN, EA, ODA and GLA contributed most to these differences. The former three FA tended to have higher proportions in caddisflies in agricultural sites than forested sites and the latter three FA in forested sites (Table 2, Additional file 1: Figure S2). Overall, these FA explained between 20% (ALA) and 7% (LIN) of the differences in FA profiles of caddisflies between land-use types (Table 2).

Agricultural stressors associated with FA profiles

The partial RDA (first axis: F = 3.7062, p-value = 0.025, second axis: F = 1.9292, p-value = 0.359) of the FA profiles of all analyzed aquatic insects (non-biting midges, mayflies, stoneflies, caddisflies) included water temperature, EC, percentage of pool habitats, oxygen saturation as well as percentage of shading (Fig. 3a) and explained 5% of the variation in FA profiles (Additional file 1: Table S7). For instance, FA profiles of aquatic insects of forested sites were associated with increasing percentage of shading and FA profiles of aquatic insects of agricultural sites with increasing percentage of pool habitats.

Fig. 3
figure 3

Plot of the partial redundancy analysis (RDA) with stream and season as covariates. Colors indicate land-use type: blue = agriculture, green = forest. Asterisks at axes mark significance. NO3 nitrate concentration, PO4 phosphate concentration, oxy oxygen saturation, pool percentage pool habitats, temp temperature (for spiders: air temperature, for emergent aquatic insects: water temperature), tox pesticide toxicity, EC electrical conductivity, shad percentage of shading

Full size image

In the final partial RDA (first axis: F = 7.5766, p-value = 0.007, second axis: F = 3.2750, p-value = 0.084), the variables oxygen saturation, phosphate as well as nitrate concentration, EC, water temperature, percentage of pool habitats and shading, explained 12% of the variation of FA profiles of non-biting midges (Fig. 3b, Additional file 1: Table S7). Increasing percentage of shading and oxygen saturation were associated with FA profiles of non-biting midges in forested sites.

No RDA axes (first axis: F = 2.8377, p-value = 0.152, second axis: F = 1.7128, p-value = 0.431) were significant for mayflies and stoneflies (first axis: F = 1.7548, p-value = 0.249, second axis: F = 1.0624, p-value = 0.419). The agricultural stressors EC, pesticide toxicity, phosphate as well as nitrate concentration were selected for the final partial RDA for mayflies and percentage of shading and phosphate concentration for stoneflies (Fig. 3c, d). The agricultural stressors explained for mayflies and stoneflies 6% of the variation in FA profiles (Additional file 1: Table S7).

For caddisflies, the final partial RDA (first axis: F = 2.9864, p-value = 0.023, second axis: F = 0.9749, p-value = 0.784) contained percentage of pool habitats, pesticide toxicity and shading (Fig. 3e), though only 4% of variation were explained by the agricultural stressors (Additional file 1: Table S7). Pesticide toxicity was associated with the FA profiles of aquatic insects in agricultural sites.

For spiders no RDA axes were significant (first axis: F = 2.4217, p-value = 0.253, second axis: F = 2.1278, p-value = 0.230) and air temperature, percentage of shading, phosphate as well as nitrate concentration explained 3% in the variation of the FA profiles in the partial RDA (Fig. 3f).

Discussion

Comparing FA export between agricultural and forested sites

The export of total FA of aquatic insects was approximately 26–29% higher in forested than agricultural sites, although the biomass of aquatic insects was 61–68% higher in agricultural than forested sites [29]. At individual time points only significant differences of the total PUFA export were observed: in spring more PUFA were exported in forested than agricultural sites. In contrast, at individual time points the biomass of aquatic insects was higher in agricultural than forested sites in spring [29]. Additionally, the biomass of non-biting midges and mayflies was higher in agricultural than forested sites [29], while the FA, SFA, MUFA and PUFA export via non-biting midges and mayflies was higher in forested than agricultural sites. This indicates that the FA, SFA, MUFA, as well as PUFA content in aquatic insects is lower in agricultural than forested sites, and in turn the quality of aquatic insects in terms of FA, SFA, MUFA and PUFA export is decreased in agricultural sites in comparison to forested sites.

The agricultural site was always downstream of the forested site. Hence, the results may partially be influenced by a location effect, where downstream sites are typically larger [91], though the distance between sites within one stream was low and a similar study found that invertebrate populations from the upstream and downstream site were connected [92]. Thus, the spatial location effect is likely negligible compared to the influence of land use. One reason for the lower FA, SFA, MUFA, and PUFA export in agricultural sites may have been energy costs due to agricultural stressors (for details of agricultural stressors see “Agricultural stressors associated with FA profiles”). Typically, in moderate stress conditions the cost for maintenance increases to meet the enhanced energy demand for protection against stressors and the repair of damages [92, 93]. This can lead to a consumption of energy reserves like lipids like neutral lipid FA [93,94,95]. Additionally, ingested FA, including PUFA, may be directly oxidized (β oxidation) to carbon dioxide and water to generate adenosine triphosphate (ATP) [96, 97]. The β oxidation of FA is a very efficient ATP source, which can facilitate ATP-dependent mechanisms like the elimination of toxicants, detoxification and the repair or replacement of damaged molecules [98]. Furthermore, agricultural stressors like pesticides can alter the sex ratio of emergent aquatic insects [99] and FA profiles as well as export can differ between male and female aquatic insects [37, 100, 101]. For example, female non-biting midges were associated with higher SFA levels and a higher total FA content, while male non-biting midges were associated with higher PUFA levels [101]. Future studies, including among others, the sex ratio of aquatic insects and the energy costs to cope with agricultural stressors can help to better understand the decrease in FA, SFA, MUFA, as well as, PUFA export in agricultural sites, despite the higher biomass export in comparison to forested sites.

The smaller PUFA export in agricultural sites may have consequences for riparian predators like decreased growth and impaired immune response [25,26,27]. The extent of the effects on riparian predators depends on their foraging strategy [24] and riparian predators may need to invest more time and energy in foraging to meet their PUFA demand, if the PUFA content in their food sources is decreased [102,103,104]. This may in turn impair their fitness [102, 104, 105].

The difference in FA export via stoneflies should be interpreted with caution, because only four observations (three in spring, one in summer) were available for agricultural sites, while in forested sites 33 observations (17 in spring, 13 in summer, three in autumn) were used in the HGAM. Furthermore, our sampling method missed stoneflies emerging by crawling on land, which may have led to an underestimation of the FA, SFA, MUFA and PUFA export via certain stonefly families. Notwithstanding, previous studies in our study region found only few stoneflies in agricultural streams [82, 106, 107]. Our sampling intervals may have allowed aquatic insects to utilize FA while being trapped for maximum 2–3 days, thereby resulting in a potential underestimation of FA export. Given that the sampling intervals were similar in both land-use types, this very likely does not affect comparisons between land-use types. Furthermore, the consumption of aquatic prey by riparian predators may also occur several days after the day of their emergence. Thus, the sampling interval may provide a realistic estimation of FA available for riparian predators. However, without being trapped the FA profiles of emergent aquatic insects feeding as adults (non-biting midges, stoneflies, some caddisflies) [108,109,110] may also change due to the consumption of terrestrial food sources. How the feeding as adults will affect the FA profiles of emergent aquatic insects will depend, for instance, on the assimilation time of terrestrial-derived FA in the tissue of adult emergent aquatic insects and their ability to synthesize FA [111]. Additionally, we omitted the FA content of other fly families than non-biting midges in the total export of FA, which also lead to an underestimation of the total FA export, though the biomass of non-biting midges peaked at least a factor of ten higher than the biomass of other fly families [29].

In spring, the PUFA export was higher than in the other seasons in both land-use types. Therefore, during spring riparian predators may have benefited most from the nutritional quality in the sense of PUFA of aquatic insects, because PUFA can enhance growth, reproductive success and immune response in riparian predators [25,26,27, 104]. Especially, for riparian birds breeding in spring this is favorable, because PUFA intake via aquatic insects seems to be crucial for their reproductive success [104, 112].

Differences of FA profiles between agricultural and forested sites

We found differences in FA profiles of mayflies, caddisflies and non-biting midges between agricultural and forested sites (Table 1, 2, Additional file 1: Figure S2). In all three orders, ALA, EPA, ODA and EA contributed most to the differences in FA profiles. EPA tended to have higher proportions in agricultural than forested sites, while ODA and EA tended to reach higher proportions in forested sites (Table 2, Additional file 1: Figure S2).

The differences across FA profiles may have originated from direct effects on aquatic insects. For instance, agricultural stressors probably required aquatic insects of agricultural sites to invest more energy into maintenance and repair processes [92, 93] compared to insects of forested sites. Thereby, FA may have been used to meet the increased energy demand [96]. Specific agricultural stressors are discussed in section “Agricultural stressors associated with FA profiles”.

Furthermore, the differences in FA profiles of aquatic insects may have originated from indirect effects in the aquatic food web, because FA are transferred from primary producers to higher trophic levels [19, 20]. In headwater streams, conditioned leaves may be an important food source [91, 113]. Conditioned leaves are colonized by microorganisms like aquatic fungi, which have been shown to alter the FA content of leaves [114]. The FA octadecanoic acid (18:0, ODA) is commonly found in aquatic fungi [114, 115] and tended to be higher in forested than agricultural sites. The percentage of shading tended to be smaller in agricultural than in forested sites (Additional file 1: Figure S3), which can lead to increased primary production in comparison to forested sites [86]. Therefore, the tendency of higher eicosapentaenoic acid (20:5n-3, EPA) levels of aquatic insects in agricultural sites may have originated from the relatively high EPA levels in aquatic primary producers [15, 19, 116]. EPA is an important membrane compound and serves as precursor for many bioactive molecules, e.g., eicosanoids [117, 118], this may affect the quality of emergent aquatic insects as food source for riparian predators. While the lower FA, SFA, MUFA, and PUFA content compromises the quality of emergent aquatic insects, the potential increase in single FA like EPA enhances the quality. Therefore, a higher EPA content may buffer potential negative effects of an overall lower FA content.

In addition, a turnover of aquatic insect families between forested and agricultural sites was shown [29] and may have contributed to the differences between FA profiles, driven by differences in the functional feeding groups and the trophic transfer of FA [37]. For instance, in summer four caddisfly families (Goeridae, Glossosomatidae, Phryganeidae, Philopotamidae) emerged only in forested sites and two caddisfly families (Lepidostomatidae, Limnephilidae) only in agricultural sites [29]. The latter two families are shredders, while the families emerging only in the forested sites belonged to the functional feeding groups grazers, shredders, collectors and predators.

Although we found differences in FA profiles of mayflies, caddisflies and non-biting midges between forested and agricultural sites, we did not find any differences in FA profiles of spiders between forested and agricultural sites. Spiders are capable of extracting nutrients selectively from their prey to avoid nutritional imbalances [119]. Moreover, spiders usually consume aquatic and terrestrial insects [12, 13], thus also terrestrial insects contribute to the spiders’ FA profile. Additionally, spiders are able to synthesize EPA de novo [23], while it is unknown if they can also synthesize other FA. Therefore, the synthesis of EPA by spiders may have masked potential land-use related differences. In previous studies the EPA content in ground dwelling spiders correlated with the biomass of stoneflies [39], PUFA profiles of riparian spiders were more similar to the PUFA profiles of emerging aquatic insects than terrestrial insects [120], and riparian spiders relied more on the PUFA content of aquatic emergent insects than spiders further away from a forested lake [17].

However, it remains unclear how other riparian predators may have been affected by land use in our study, because the amount of aquatic insects in the diet of riparian predators can vary with the foraging strategy. For instance, ground-hunting and web-building spiders differ in their proportion of aquatic insects in their diet and in environmental factors affecting the amount of consumed aquatic insects [12, 13]. Additionally, birds that are aerial insectivores may consume more aquatic insects than gleaners, bark-probers, as well as ground-foragers [121] and therefore may rely more on aquatic insect consumption to meet their PUFA demand [17, 26] than gleaners [24]. Future studies including riparian predators with different foraging strategies are needed to understand the effect of land use on FA profiles in the riparian food web better.

Agricultural stressors associated with FA profiles

Generally, environmental variables associated with impaired habitat quality for aquatic insects and spiders [12, 14, 29, 33, 34] were less favorable in agricultural than forested sites, e.g., higher pesticide toxicity as well as lower percentage of pool habitats and less shading (Additional file 1: Figure S3, Table S1). Nonetheless, little variation in FA profiles of aquatic insects in total and on order level was explained by these variables.

Primary production and nutrient availability in streams depend on light availability and can decrease with the increase of shading [86]. Therefore, shading and nutrients may affect aquatic insects’ FA profiles by the trophic transfer of FA from primary producers to higher trophic levels [15, 19, 73, 116]. The effect of pool habitats on FA profiles may be explained by differences at the base of the food web (algal primary production, conditioned leaves, [122, 123]) and the occurrence of different functional feeding groups in pool and riffle habitats [124], which in turn can result in different FA profiles. EC (commonly used to estimate the salinity of water) can cause osmoregulatory stress and can be associated with ions that are toxic for aquatic insects [125]. Together with pesticide toxicity, EC can increase the energy demand of aquatic insects [92, 93], followed by FA utilization to fulfill the enhanced energy demand [96], and thereby altering the FA profiles of aquatic insects. To our knowledge, it is currently not known if specific FA are utilized or FA in general. Increasing temperature, as observed in agricultural sites, can cause FA profile alterations, as organisms adapt their PUFA content to adjust membrane fluidity to higher temperatures [83, 84]. However, the land-use intensity in the studied agricultural sites was similar. Furthermore, the intensity of agriculture and potentially of agricultural stressors may increase in the future, as for instance globally more pesticides with a higher toxicity towards aquatic insects are used [126,127,128,129].

Most variation in all FA profiles was explained by stream and season (Additional file 1: Table S7). The families of aquatic insects differed across streams and seasons. For instance, the mayfly families Arthropleidae and Siphlonuridae emerged only in summer [29]. Therefore, the composition of aquatic insect assemblages may be more important for the FA profiles than agricultural stressors. This is partly in line with Kowarik et al. [39], who only found an effect of season on the FA profiles of aquatic insects, but not of environmental variables. Furthermore, the FA profiles of species of non-biting midges were shown to differ [37]. Future studies identifying underlying mechanisms of the differences between families are needed to estimate the effect of a turnover of aquatic insect assemblages between land-use types on FA profiles.

Conclusion

The quantity of PUFA export via aquatic insects was decreased in agricultural sites in comparison to forested sites. Additionally, we found differences in FA profiles of aquatic insects between land-use types. We suggest a decreased quality as food source for riparian predators relying on the dietary intake of PUFA. Future studies are needed to identify the mechanisms behind the lower PUFA content in agricultural sites to implement strategies maintaining the PUFA content in aquatic insects. These strategies may focus on the mitigation of stressors that may affect aquatic insects, for example decreasing of pesticide exposure and reforestation to decrease temperature in agricultural streams. Furthermore, our results can be incorporated in modeling food-webs or meta-ecosystems to increase our understanding of effects of timing, food quantity as well as quality in these systems.

Data availability statement

The R code and data are available under: https://doi.org/10.5281/zenodo.8238429.

Abbreviations

ALA:

Alpha-Linolenic acid

ATP:

Adenosine triphosphate

EC:

Electrical conductivity

EA:

Eicosanoic acid

ELA:

Elaidic acid

EPA:

Eicosapentaenoic acid

FA:

Fatty acids

GLA:

Gamma-Linolenic acid

LIN:

Linoleic acid

MUFA:

Mono unsaturated fatty acids

ODA:

Octadecanoic acid

PUFA:

Polyunsaturated fatty acids

SFA:

Saturated fatty acids

References

  1. Václavík T, Lautenbach S, Kuemmerle T, Seppelt R (2013) Mapping global land system archetypes. Glob Environ Change 23:1637–1647. https://doi.org/10.1016/j.gloenvcha.2013.09.004

    Article  Google Scholar 

  2. Collen B, Whitton F, Dyer EE et al (2014) Global patterns of freshwater species diversity, threat and endemism. Glob Ecol Biogeogr 23:40–51. https://doi.org/10.1111/geb.12096

    Article  Google Scholar 

  3. Reid AJ, Carlson AK, Creed IF et al (2019) Emerging threats and persistent conservation challenges for freshwater biodiversity. Biol Rev 94:849–873. https://doi.org/10.1111/brv.12480

    Article  Google Scholar 

  4. Vörösmarty CJ, McIntyre PB, Gessner MO et al (2010) Global threats to human water security and river biodiversity. Nature 467:555–561. https://doi.org/10.1038/nature09440

    Article  CAS  Google Scholar 

  5. Baxter CV, Fausch KD, Saunders WC (2005) Tangled webs: reciprocal flows of invertebrate prey link streams and riparian zones. Freshw Biol 50:201–220. https://doi.org/10.1111/j.1365-2427.2004.01328.x

    Article  Google Scholar 

  6. Gray LJ (1993) Response of insectivorous birds to emerging aquatic insects in riparian habitats of a tallgrass prairie stream. Am Midl Nat 129:288–300. https://doi.org/10.2307/2426510

    Article  Google Scholar 

  7. Kato C, Iwata T, Wada E (2004) Prey use by web-building spiders: stable isotope analyses of trophic flow at a forest-stream ecotone. Ecol Res 19:633–643. https://doi.org/10.1111/j.1440-1703.2004.00678.x

    Article  Google Scholar 

  8. Sullivan CM, Shiel CB, McAney CM, Fairley JS (1993) Analysis of the diets of Leisler’s Nyctalus leisleri, Daubenton’s Myotis daubentoni and pipistrelle Pipistrellus pipistrellus bats in Ireland. J Zool 231:656–663. https://doi.org/10.1111/j.1469-7998.1993.tb01947.x

    Article  Google Scholar 

  9. Ewald JA, Wheatley CJ, Aebischer NJ et al (2015) Influences of extreme weather, climate and pesticide use on invertebrates in cereal fields over 42 years. Glob Change Biol 21:3931–3950. https://doi.org/10.1111/gcb.13026

    Article  Google Scholar 

  10. Hallmann CA, Sorg M, Jongejans E et al (2017) More than 75 percent decline over 27 years in total flying insect biomass in protected areas. PLoS ONE 12:e0185809. https://doi.org/10.1371/journal.pone.0185809

    Article  CAS  Google Scholar 

  11. Seibold S, Gossner MM, Simons NK et al (2019) Arthropod decline in grasslands and forests is associated with landscape-level drivers. Nature 574:671–674. https://doi.org/10.1038/s41586-019-1684-3

    Article  CAS  Google Scholar 

  12. Graf N, Battes KP, Cimpean M et al (2020) Relationship between agricultural pesticides and the diet of riparian spiders in the field. Environ Sci Eur 32:1–12. https://doi.org/10.1186/s12302-019-0282-1

    Article  CAS  Google Scholar 

  13. Krell B, Röder N, Link M et al (2015) Aquatic prey subsidies to riparian spiders in a stream with different land use types. Limnologica 51:1–7. https://doi.org/10.1016/j.limno.2014.10.001

    Article  Google Scholar 

  14. Stenroth K, Polvi LE, Fältström E, Jonsson M (2015) Land-use effects on terrestrial consumers through changed size structure of aquatic insects. Freshw Biol 60:136–149. https://doi.org/10.1111/fwb.12476

    Article  Google Scholar 

  15. Hixson SM, Sharma B et al (2015) Production, distribution, and abundance of long-chain omega-3 polyunsaturated fatty acids: a fundamental dichotomy between freshwater and terrestrial ecosystems. Environ Rev 23:414–424. https://doi.org/10.1139/er-2015-0029

    Article  CAS  Google Scholar 

  16. Parmar TP, Kindinger AL, Mathieu-Resuge M et al (2022) Fatty acid composition differs between emergent aquatic and terrestrial insects—a detailed single system approach. Front Ecol Evol 10:952292

    Article  Google Scholar 

  17. Twining CW, Brenna JT, Lawrence P et al (2019) Aquatic and terrestrial resources are not nutritionally reciprocal for consumers. Funct Ecol 33:2042–2052. https://doi.org/10.1111/1365-2435.13401

    Article  Google Scholar 

  18. Ahlgren G, Gustafsson I-B, Boberg M (1992) Fatty acid content and chemical composition of freshwater microalgae1. J Phycol 28:37–50. https://doi.org/10.1111/j.0022-3646.1992.00037.x

    Article  CAS  Google Scholar 

  19. Kainz M, Arts MT, Mazumder A (2004) Essential fatty acids in the planktonic food web and their ecological role for higher trophic levels. Limnol Oceanogr 49:1784–1793. https://doi.org/10.4319/lo.2004.49.5.1784

    Article  CAS  Google Scholar 

  20. Strandberg U, Hiltunen M, Jelkänen E et al (2015) Selective transfer of polyunsaturated fatty acids from phytoplankton to planktivorous fish in large boreal lakes. Sci Total Environ 536:858–865. https://doi.org/10.1016/j.scitotenv.2015.07.010

    Article  CAS  Google Scholar 

  21. Sayanova OV, Napier JA (2004) Eicosapentaenoic acid: biosynthetic routes and the potential for synthesis in transgenic plants. Phytochemistry 65:147–158. https://doi.org/10.1016/j.phytochem.2003.10.017

    Article  CAS  Google Scholar 

  22. Twining CW, Bernhardt JR, Derry AM et al (2021) The evolutionary ecology of fatty-acid variation: Implications for consumer adaptation and diversification. Ecol Lett 24:1709–1731. https://doi.org/10.1111/ele.13771

    Article  Google Scholar 

  23. Mathieu-Resuge M, Pilecky M, Twining CW et al (2022) Dietary availability determines metabolic conversion of long-chain polyunsaturated fatty acids in spiders: a dual compound-specific stable isotope approach. Oikos. https://doi.org/10.1111/oik.08513

    Article  Google Scholar 

  24. Twining CW, Parmar TP, Mathieu-Resuge M et al (2021) Use of fatty acids from aquatic prey varies with foraging strategy. Front Ecol Evol. https://doi.org/10.3389/fevo.2021.735350

    Article  Google Scholar 

  25. Fritz KA, Kirschman LJ, McCay SD et al (2017) Subsidies of essential nutrients from aquatic environments correlate with immune function in terrestrial consumers. Freshw Sci 36:893–900. https://doi.org/10.1086/694451

    Article  Google Scholar 

  26. Twining CW, Brenna JT, Lawrence P et al (2016) Omega-3 long-chain polyunsaturated fatty acids support aerial insectivore performance more than food quantity. Proc Natl Acad Sci 113:10920–10925

    Article  CAS  Google Scholar 

  27. Mayntz D, Toft S (2001) Nutrient composition of the prey’s diet affects growth and survivorship of a generalist predator. Oecologia 127:207–213. https://doi.org/10.1007/s004420000591

    Article  Google Scholar 

  28. Nakano S, Murakami M (2001) Reciprocal subsidies: dynamic interdependence between terrestrial and aquatic food webs. Proc Natl Acad Sci 98:166–170. https://doi.org/10.1073/pnas.98.1.166

    Article  CAS  Google Scholar 

  29. Ohler K, Schreiner VC, Link M et al (2023) Land use changes biomass and temporal patterns of insect cross-ecosystem flows. Glob Change Biol 29:81–96. https://doi.org/10.1111/gcb.16462

    Article  CAS  Google Scholar 

  30. Sato T, El-Sabaawi RW, Campbell K et al (2016) A test of the effects of timing of a pulsed resource subsidy on stream ecosystems. J Anim Ecol 85:1136–1146. https://doi.org/10.1111/1365-2656.12516

    Article  Google Scholar 

  31. Uno H (2016) Stream thermal heterogeneity prolongs aquatic-terrestrial subsidy and enhances riparian spider growth. Ecology 97:2547–2553. https://doi.org/10.1002/ecy.1552

    Article  Google Scholar 

  32. Gounand I, Harvey E, Little CJ, Altermatt F (2018) Meta-ecosystems 2.0: rooting the theory into the field. Trends Ecol Evol 33:36–46. https://doi.org/10.1016/j.tree.2017.10.006

    Article  Google Scholar 

  33. Raitif J, Plantegenest M, Agator O et al (2018) Seasonal and spatial variations of stream insect emergence in an intensive agricultural landscape. Sci Total Environ 644:594–601. https://doi.org/10.1016/j.scitotenv.2018.07.021

    Article  CAS  Google Scholar 

  34. Graf N, Battes KP, Cimpean M et al (2019) Do agricultural pesticides in streams influence riparian spiders? Sci Total Environ 660:126–135. https://doi.org/10.1016/j.scitotenv.2018.12.370

    Article  CAS  Google Scholar 

  35. Martin-Creuzburg D, Kowarik C, Straile D (2017) Cross-ecosystem fluxes: export of polyunsaturated fatty acids from aquatic to terrestrial ecosystems via emerging insects. Sci Total Environ 577:174–182. https://doi.org/10.1016/j.scitotenv.2016.10.156

    Article  CAS  Google Scholar 

  36. Moyo S, Chari LD, Villet MH, Richoux NB (2017) Decoupled reciprocal subsidies of biomass and fatty acids in fluxes of invertebrates between a temperate river and the adjacent land. Aquat Sci 79:689–703. https://doi.org/10.1007/s00027-017-0529-0

    Article  CAS  Google Scholar 

  37. Scharnweber K, Chaguaceda F, Dalman E et al (2020) The emergence of fatty acids—aquatic insects as vectors along a productivity gradient. Freshw Biol 65:565–578. https://doi.org/10.1111/fwb.13454

    Article  CAS  Google Scholar 

  38. Pietz S, Kolbenschlag S, Röder N et al (2023) Subsidy quality affects common riparian web-building spiders: consequences of aquatic contamination and food resource. Environ Toxicol Chem 42:1346–1358. https://doi.org/10.1002/etc.5614

    Article  CAS  Google Scholar 

  39. Kowarik C, Martin-Creuzburg D, Mathers KL et al (2022) Stream degradation affects aquatic resource subsidies to riparian ground-dwelling spiders. Sci Total Environ 855:158658. https://doi.org/10.1016/j.scitotenv.2022.158658

    Article  CAS  Google Scholar 

  40. Ramberg E, Burdon FJ, Sargac J et al (2020) The structure of riparian vegetation in agricultural landscapes influences spider communities and aquatic-terrestrial linkages. Water 12:2855. https://doi.org/10.3390/w12102855

    Article  CAS  Google Scholar 

  41. Marcarelli AM, Baxter CV, Mineau MM, Hall RO (2011) Quantity and quality: unifying food web and ecosystem perspectives on the role of resource subsidies in freshwaters. Ecology 92:1215–1225. https://doi.org/10.1890/10-2240.1

    Article  Google Scholar 

  42. Pichon B, Thébault E, Lacroix G, Gounand I (2023) Quality matters: stoichiometry of resources modulates spatial feedbacks in aquatic-terrestrial meta-ecosystems. Ecol Lett 26:1700–1713. https://doi.org/10.1111/ele.14284

    Article  Google Scholar 

  43. Osakpolor SE, Manfrin A, Leroux SJ, Schäfer RB (2023) Cascading impacts of changes in subsidy quality on recipient ecosystem functioning. Ecology 104:e4023. https://doi.org/10.1002/ecy.4023

    Article  Google Scholar 

  44. Corbet PS (1964) Temporal patterns of emergence in aquatic insects. Can Entomol 96:264–279. https://doi.org/10.4039/Ent96264-1

    Article  Google Scholar 

  45. Voß K, Fernández D, Schäfer RB (2015) Organic matter breakdown in streams in a region of contrasting anthropogenic land use. Sci Total Environ 527–528:179–184. https://doi.org/10.1016/j.scitotenv.2015.04.071

    Article  CAS  Google Scholar 

  46. Liess M, von der Ohe PC (2005) Analyzing effects of pesticides on invertebrate communities in streams. Environ Toxicol Chem 24:954–965. https://doi.org/10.1897/03-652.1

    Article  CAS  Google Scholar 

  47. Halbach K, Möder M, Schrader S et al (2021) Small streams–large concentrations? Pesticide monitoring in small agricultural streams in Germany during dry weather and rainfall. Water Res 203:117535. https://doi.org/10.1016/j.watres.2021.117535

    Article  CAS  Google Scholar 

  48. Liess M, Liebmann L, Lück M, et al (2022) Umsetzung des Nationalen Aktionsplans zur nachhaltigen Anwendung von Pflanzenschutzmitteln (NAP) – Pilotstudie zur Ermittlung der Belastung von Kleingewässern in der Agrarlandschaft mit Pflanzenschutzmittel-Rückständen. Umweltbundesamt

  49. Liess M, Liebmann L, Vormeier P et al (2021) Pesticides are the dominant stressors for vulnerable insects in lowland streams. Water Res 201:117262. https://doi.org/10.1016/j.watres.2021.117262

    Article  CAS  Google Scholar 

  50. Gan J, Lee SJ, Liu WP et al (2005) Distribution and persistence of pyrethroids in runoff sediments. J Environ Qual 34:836–841. https://doi.org/10.2134/jeq2004.0240

    Article  CAS  Google Scholar 

  51. Rico A, den Brink PJV (2015) Evaluating aquatic invertebrate vulnerability to insecticides based on intrinsic sensitivity, biological traits, and toxic mode of action. Environ Toxicol Chem 34:1907–1917. https://doi.org/10.1002/etc.3008

    Article  CAS  Google Scholar 

  52. Rubach MN, Baird DJ, den Brink PJV (2010) A new method for ranking mode-specific sensitivity of freshwater arthropods to insecticides and its relationship to biological traits. Environ Toxicol Chem 29:476–487. https://doi.org/10.1002/etc.55

    Article  CAS  Google Scholar 

  53. Schäfer RB, Pettigrove V, Rose G et al (2011) Effects of pesticides monitored with three sampling methods in 24 sites on macroinvertebrates and microorganisms. Environ Sci Technol 45:1665–1672. https://doi.org/10.1021/es103227q

    Article  CAS  Google Scholar 

  54. Toro DMD, Zarba CS, Hansen DJ et al (1991) Technical basis for establishing sediment quality criteria for nonionic organic chemicals using equilibrium partitioning. Environ Toxicol Chem 10:1541–1583. https://doi.org/10.1002/etc.5620101203

    Article  Google Scholar 

  55. Lewis KA, Tzilivakis J, Warner DJ, Green A (2016) An international database for pesticide risk assessments and management. Hum Ecol Risk Assess Int J 22:1050–1064. https://doi.org/10.1080/10807039.2015.1133242

    Article  CAS  Google Scholar 

  56. National Center for Biotechnology Information (2021) PubChem Compound Summary for CID 9839306, Prallethrin. https://pubchem.ncbi.nlm.nih.gov/compound/Prallethrin. Accessed 22 Feb 2021

  57. Schäfer RB, Gerner N, Kefford BJ et al (2013) How to characterize chemical exposure to predict ecologic effects on aquatic communities? Environ Sci Technol 47:7996–8004. https://doi.org/10.1021/es4014954

    Article  CAS  Google Scholar 

  58. Scharmüller A, Schreiner VC, Schäfer RB (2020) Standartox: standardizing toxicity data. Data 5:46. https://doi.org/10.3390/data5020046

    Article  Google Scholar 

  59. US EPA (2021) ECOTOX Knowledgebase. US EPA, Washington

    Google Scholar 

  60. Malaj E, von der Ohe PC, Grote M et al (2014) Organic chemicals jeopardize the health of freshwater ecosystems on the continental scale. Proc Natl Acad Sci 111:9549–9554. https://doi.org/10.1073/pnas.1321082111

    Article  CAS  Google Scholar 

  61. Reitze M, Nentwig W (1991) Comparative investigations into the feeding ecology of six Mantodea species. Oecologia 86:568–574. https://doi.org/10.1007/BF00318324

    Article  Google Scholar 

  62. Foelix RF (2011) Biology of spiders, 3rd edn. Oxford University Press, Oxford

    Google Scholar 

  63. Ohler K, Schreiner VC, Link M, et al (2023) ohler_fa

  64. Cadmus P, Pomeranz JPF, Kraus JM (2016) Low-cost floating emergence net and bottle trap: comparison of two designs. J Freshw Ecol 31:653–658. https://doi.org/10.1080/02705060.2016.1217944

    Article  Google Scholar 

  65. Gray LJ (1989) Emergence production and export of aquatic insects from a tallgrass prairie stream. Southwest Nat 34:313–318. https://doi.org/10.2307/3672158

    Article  Google Scholar 

  66. Jackson JK, Fisher SG (1986) Secondary production, emergence, and export of aquatic insects of a sonoran desert stream. Ecology 67:629–638. https://doi.org/10.2307/1937686

    Article  Google Scholar 

  67. Roberts MJ (1995) Spiders of Britain and Northern Europe. HarperCollins Publishers

    Google Scholar 

  68. Bährmann R, Müller HJ (2015) Bestimmung wirbelloser Tiere: Bildtafeln für zoologische Bestimmungsübungen und Exkursionen, 7th edn. Spektrum Akademischer Verlag, Heidelberg

    Google Scholar 

  69. Nilsson A (1996) Aquatic insects of Northern Europe. A taxonomic handbook, vol 2: Odonata. Diptera. Apollo Books, Stenstrup

  70. Nilsson A (1996) Aquatic Insects of Northern Europe. A Taxonomic Handbook, Vol. 1: Ephemeroptera, Plecoptera, Heteroptera, Megaloptera, Neuroptera, Coleoptera, Trichoptera and Lepidoptera. Apollo Books, Stenstrup

  71. Schäfer M, Brohmer P (2010) Fauna von Deutschland. Ein Bestimmungsbuch unserer heimischen Tierwelt, 23rd ed. Quelle & Meyer, Wiebelsheim

  72. Folch J, Lees M, Stanley GHS (1957) A simple method for the isolation and purification of total lipides from animal tissues. J Biol Chem 226:497–509. https://doi.org/10.1016/S0021-9258(18)64849-5

    Article  CAS  Google Scholar 

  73. Ohler K, Schreiner VC, Martin-Creuzburg D, Schäfer RB (2023) Trophic transfer of polyunsaturated fatty acids across the aquatic–terrestrial interface: an experimental tritrophic food chain approach. Ecol Evol 13:e9927. https://doi.org/10.1002/ece3.9927

    Article  Google Scholar 

  74. Wenig P, Odermatt J (2010) OpenChrom: a cross-platform open source software for the mass spectrometric analysis of chromatographic data. BMC Bioinformatics 11:405. https://doi.org/10.1186/1471-2105-11-405

    Article  CAS  Google Scholar 

  75. R Core Team (2022) R: a language and environment for statistical computing. R Core Team

    Google Scholar 

  76. Pedersen EJ, Miller DL, Simpson GL, Ross N (2019) Hierarchical generalized additive models in ecology: an introduction with mgcv. PeerJ 7:e6876. https://doi.org/10.7717/peerj.6876

    Article  Google Scholar 

  77. Wood SN (2011) Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J R Stat Soc Ser B Stat Methodol 73:3–36. https://doi.org/10.1111/j.1467-9868.2010.00749.x

    Article  Google Scholar 

  78. Oksanen J, Blanchet FG, Friendly M, et al (2020) vegan community ecology package version 2.5–7 November 2020

  79. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B Methodol 57:289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x

    Article  Google Scholar 

  80. Jähnig SC, Baranov V, Altermatt F et al (2021) Revisiting global trends in freshwater insect biodiversity. WIREs Water 8:e1506. https://doi.org/10.1002/wat2.1506

    Article  Google Scholar 

  81. Fernández D, Voss K, Bundschuh M et al (2015) Effects of fungicides on decomposer communities and litter decomposition in vineyard streams. Sci Total Environ 533:40–48. https://doi.org/10.1016/j.scitotenv.2015.06.090

    Article  CAS  Google Scholar 

  82. Englert D, Zubrod JP, Schulz R, Bundschuh M (2015) Variability in ecosystem structure and functioning in a low order stream: implications of land use and season. Sci Total Environ 538:341–349. https://doi.org/10.1016/j.scitotenv.2015.08.058

    Article  CAS  Google Scholar 

  83. Arts MT, Kohler CC (2009) Health and condition in fish: the influence of lipids on membrane competency and immune response. In: Kainz M, Brett MT, Arts MT (eds) Lipids in aquatic ecosystems. Springer, New York, pp 237–256

    Chapter  Google Scholar 

  84. Fuschino JR, Guschina IA, Dobson G et al (2011) Rising water temperatures alter lipid dynamics and reduce N-3 essential fatty acid concentrations in scenedesmus obliquus (chlorophyta)1. J Phycol 47:763–774. https://doi.org/10.1111/j.1529-8817.2011.01024.x

    Article  CAS  Google Scholar 

  85. Carlson PE, McKie BG, Sandin L, Johnson RK (2016) Strong land-use effects on the dispersal patterns of adult stream insects: implications for transfers of aquatic subsidies to terrestrial consumers. Freshw Biol 61:848–861. https://doi.org/10.1111/fwb.12745

    Article  Google Scholar 

  86. Griffiths NA, Tank JL, Royer TV et al (2013) Agricultural land use alters the seasonality and magnitude of stream metabolism. Limnol Oceanogr 58:1513–1529. https://doi.org/10.4319/lo.2013.58.4.1513

    Article  CAS  Google Scholar 

  87. Terui A, Negishi JN, Watanabe N, Nakamura F (2018) Stream resource gradients drive consumption rates of supplemental prey in the adjacent riparian zone. Ecosystems 21:772–781. https://doi.org/10.1007/s10021-017-0183-3

    Article  Google Scholar 

  88. Wickham H (2016) ggplot2. Springer International Publishing, Cham

    Book  Google Scholar 

  89. Jaschinski S, Brepohl DC, Sommer U (2011) Seasonal variation in carbon sources of mesograzers and small predators in an eelgrass community: stable isotope and fatty acid analyses. Mar Ecol Prog Ser 431:69–82. https://doi.org/10.3354/meps09143

    Article  Google Scholar 

  90. Chapman MG, Underwood AJ (1999) Ecological patterns in multivariate assemblages: information and interpretation of negative values in ANOSIM tests. Mar Ecol Prog Ser 180:257–265. https://doi.org/10.3354/meps180257

    Article  Google Scholar 

  91. Vannote RL, Minshall GW, Cummins KW et al (1980) The river continuum concept. Can J Fish Aquat Sci 37:130–137. https://doi.org/10.1139/f80-017

    Article  Google Scholar 

  92. Schneeweiss A, Schreiner VC, Liess M et al (2023) Population structure and insecticide response of Gammarus spp. in agricultural and upstream forested sites of small streams. Environ Sci Eur 35:1–16. https://doi.org/10.1186/s12302-023-00747-y

    Article  CAS  Google Scholar 

  93. Calow P, Forbes VE (1998) How do physiological responses to stress translate into ecological and evolutionary processes? Comp Biochem Physiol A Mol Integr Physiol 120:11–16. https://doi.org/10.1016/S1095-6433(98)10003-X

    Article  Google Scholar 

  94. Sokolova IM, Frederich M, Bagwe R et al (2012) Energy homeostasis as an integrative tool for assessing limits of environmental stress tolerance in aquatic invertebrates. Mar Environ Res 79:1–15. https://doi.org/10.1016/j.marenvres.2012.04.003

    Article  CAS  Google Scholar 

  95. Lannig G, Flores JF, Sokolova IM (2006) Temperature-dependent stress response in oysters, Crassostrea virginica: pollution reduces temperature tolerance in oysters. Aquat Toxicol 79:278–287. https://doi.org/10.1016/j.aquatox.2006.06.017

    Article  CAS  Google Scholar 

  96. Azeez OI, Meintjes R, Chamunorwa JP (2014) Fat body, fat pad and adipose tissues in invertebrates and vertebrates: the nexus. Lipids Health Dis 13:1–13. https://doi.org/10.1186/1476-511X-13-71

    Article  CAS  Google Scholar 

  97. Tocher DR (2003) Metabolism and functions of lipids and fatty acids in teleost fish. Rev Fish Sci 11:107–184. https://doi.org/10.1080/713610925

    Article  CAS  Google Scholar 

  98. Gilbert LI (1967) Lipid metabolism and function in insects. In: Beament JWL, Treherne JE, Wigglesworth VB (eds) Advances in insect physiology. Academic Press, pp 69–211

    Google Scholar 

  99. Sokolova I (2021) Bioenergetics in environmental adaptation and stress tolerance of aquatic ectotherms: linking physiology and ecology in a multi-stressor landscape. J Exp Biol 224:236802. https://doi.org/10.1242/jeb.236802

    Article  Google Scholar 

  100. Hahn T, Liess M, Schulz R (2001) Effects of the hormone mimetic insecticide tebufenozide on chironomus riparius larvae in two different exposure setups. Ecotoxicol Environ Saf 49:171–178. https://doi.org/10.1006/eesa.2001.2055

    Article  CAS  Google Scholar 

  101. Gerber R, Cabon L, Piscart C et al (2022) Body stores of emergent aquatic insects are associated with body size, sex, swarming behaviour and dispersal strategies. Freshw Biol 67:2161–2175. https://doi.org/10.1111/fwb.14003

    Article  Google Scholar 

  102. Pietz S, Kainz MJ, Schröder H et al (2023) Metal exposure and sex shape the fatty acid profile of midges and reduce the aquatic subsidy to terrestrial food webs. Environ Sci Technol 57:951–962. https://doi.org/10.1021/acs.est.2c05495

    Article  CAS  Google Scholar 

  103. Schoener TW (1971) Theory of feeding strategies. Annu Rev Ecol Syst 2:369–404

    Article  Google Scholar 

  104. Senécal S, Riva J-C, O’Connor RS et al (2021) Poor prey quality is compensated by higher provisioning effort in passerine birds. Sci Rep 11:11182. https://doi.org/10.1038/s41598-021-90658-w

    Article  CAS  Google Scholar 

  105. Twining CW, Shipley JR, Winkler DW (2018) Aquatic insects rich in omega-3 fatty acids drive breeding success in a widespread bird. Ecol Lett 21:1812–1820. https://doi.org/10.1111/ele.13156

    Article  Google Scholar 

  106. Naef-Daenzer B, Keller LF (1999) The foraging performance of great and blue tits (Parus major and P. caeruleus) in relation to caterpillar development, and its consequences for nestling growth and fledging weight. J Anim Ecol 68:708–718. https://doi.org/10.1046/j.1365-2656.1999.00318.x

    Article  Google Scholar 

  107. Schneeweiss A, Schreiner VC, Reemtsma T et al (2022) Potential propagation of agricultural pesticide exposure and effects to upstream sections in a biosphere reserve. Sci Total Environ 836:155688. https://doi.org/10.1016/j.scitotenv.2022.155688

    Article  CAS  Google Scholar 

  108. Voß K, Schäfer RB (2017) Taxonomic and functional diversity of stream invertebrates along an environmental stress gradient. Ecol Indic 81:235–242. https://doi.org/10.1016/j.ecolind.2017.05.072

    Article  Google Scholar 

  109. Armitage PD, Cranston PS, Pinder LCV (1995) The chironomidae. Springer, Dordrecht

    Book  Google Scholar 

  110. Brittain JE (1990) Life history strategies in Ephemeroptera and Plecoptera. In: Campbell IC (ed) Mayflies and stoneflies: life histories and biology. Springer, Dordrecht, pp 1–12

    Google Scholar 

  111. Petersson E, Hasselrot AT (1994) Mating and nectar feeding in the psychomyiid caddis fly Tinodes waeneri. Aquat Insects 16:177–187. https://doi.org/10.1080/01650429409361553

    Article  Google Scholar 

  112. Galloway AWE, Budge SM (2020) The critical importance of experimentation in biomarker-based trophic ecology. Philos Trans R Soc B Biol Sci 375:20190638. https://doi.org/10.1098/rstb.2019.0638

    Article  CAS  Google Scholar 

  113. Shipley JR, Twining CW, Mathieu-Resuge M et al (2022) Climate change shifts the timing of nutritional flux from aquatic insects. Curr Biol 32:1342-1349.e3. https://doi.org/10.1016/j.cub.2022.01.057

    Article  CAS  Google Scholar 

  114. Graça M, Canhoto C (2006) Leaf litter processing in low order streams. Limnetica ISSN 0213–8409 Vol 25 No 1–2 2006 Ejemplar Dedic Ecol Iber Inland Waters Homage Ramón Margalef Pags 1–10 25:

  115. Zubrod JP, Englert D, Wolfram J et al (2017) Long-term effects of fungicides on leaf-associated microorganisms and shredder populations—an artificial stream study. Environ Toxicol Chem 36:2178–2189. https://doi.org/10.1002/etc.3756

    Article  CAS  Google Scholar 

  116. Arce Funck J, Bec A, Perrière F et al (2015) Aquatic hyphomycetes: a potential source of polyunsaturated fatty acids in detritus-based stream food webs. Fungal Ecol 13:205–210. https://doi.org/10.1016/j.funeco.2014.09.004

    Article  Google Scholar 

  117. Taipale S, Strandberg U, Peltomaa E et al (2013) Fatty acid composition as biomarkers of freshwater microalgae: analysis of 37 strains of microalgae in 22 genera and in seven classes. Aquat Microb Ecol 71:165–178. https://doi.org/10.3354/ame01671

    Article  Google Scholar 

  118. Arts MT, Ackman RG, Holub BJ (2001) “Essential fatty acids” in aquatic ecosystems: a crucial link between diet and human health and evolution. Can J Fish Aquat Sci 58:122–137. https://doi.org/10.1139/f00-224

    Article  CAS  Google Scholar 

  119. Stanley-Samuelson DW, Jurenka RA, Cripps C et al (1988) Fatty acids in insects: Composition, metabolism, and biological significance. Arch Insect Biochem Physiol 9:1–33. https://doi.org/10.1002/arch.940090102

    Article  CAS  Google Scholar 

  120. Mayntz D, Raubenheimer D, Salomon M et al (2005) Nutrient-specific foraging in invertebrate predators. Science 307:111–113. https://doi.org/10.1126/science.1105493

    Article  CAS  Google Scholar 

  121. Kowarik C, Martin-Creuzburg D, Robinson CT (2021) Cross-ecosystem linkages: transfer of polyunsaturated fatty acids from streams to riparian spiders via emergent insects. Front Ecol Evol. https://doi.org/10.3389/fevo.2021.707570

    Article  Google Scholar 

  122. Schilke PR, Bartrons M, Gorzo JM et al (2020) Modeling a cross-ecosystem subsidy: forest songbird response to emergent aquatic insects. Landsc Ecol 35:1587–1604. https://doi.org/10.1007/s10980-020-01038-0

    Article  Google Scholar 

  123. Keithan ED, Lowe RL (1985) Primary productivity and spatial structure of phytolithic growth in streams in the Great Smoky Mountains National Park, Tennessee. Hydrobiologia 123:59–67. https://doi.org/10.1007/BF00006615

    Article  Google Scholar 

  124. Whitledge GW, Rabeni CF (2000) Benthic community metabolism in three habitats in an Ozark stream. Hydrobiologia 437:165–170. https://doi.org/10.1023/A:1026559008308

    Article  Google Scholar 

  125. Angradi TR (1996) Inter-habitat variation in benthic community structure, function, and organic matter storage in 3 Appalachian headwater streams. J North Am Benthol Soc 15:42–63. https://doi.org/10.2307/1467432

    Article  Google Scholar 

  126. Cañedo-Argüelles M, Kefford BJ, Piscart C et al (2013) Salinisation of rivers: an urgent ecological issue. Environ Pollut 173:157–167. https://doi.org/10.1016/j.envpol.2012.10.011

    Article  CAS  Google Scholar 

  127. Bernhardt ES, Rosi EJ, Gessner MO (2017) Synthetic chemicals as agents of global change. Front Ecol Environ 15:84–90. https://doi.org/10.1002/fee.1450

    Article  Google Scholar 

  128. Kattwinkel M, Kühne J-V, Foit K, Liess M (2011) Climate change, agricultural insecticide exposure, and risk for freshwater communities. Ecol Appl 21:2068–2081. https://doi.org/10.1890/10-1993.1

    Article  Google Scholar 

  129. Schulz R, Bub S, Petschick LL et al (2021) Applied pesticide toxicity shifts toward plants and invertebrates, even in GM crops. Science 372:81–84. https://doi.org/10.1126/science.abe1148

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank Moritz Schäfer, Tim Ostertag and Laura Kieffer for their assistance in lyophilizing and weighing aquatic insects as well as spiders, Nadin Graf for support during identification of aquatic insects and Kilian Kenngott for technical support during the FA analysis. We thank the four anonymous reviewers for their helpful comments that improved the quality of the manuscript. The field study was facilitated by the “Pilotstudie zur Ermittlung der Belastung von Kleingewässern in der Agrarlandschaft mit Pflanzenschutzmittel-Rückständen” funded by the German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (FKZ 3717 63 403 0) and the German Helmholtz long-range strategic research funding. The Modular Observation Solutions for Earth Systems (MOSES) and the German Research Foundation (DFG; project number: 216374258) supplied additional funding. The FA analyses were funded by the DFG (Project Number 326210499/GRK 2360). Katharina Ohler was funded by the German Academic Scholarship Foundation as well as the Interdisziplinäres Promotions- und Postdoczentrum (IPZ) completion scholarship.

Author information

Authors and Affiliations

  1. iES Landau, Institute for Environmental Sciences, RPTU Kaiserslautern-Landau, Fortstraße 7, 76829, Landau in der Pfalz, Germany

    Katharina Ohler, Verena C. Schreiner, Lukas Reinhard, Moritz Link & Ralf B. Schäfer

  2. Department System-Ecotoxicology, Helmholtz Centre for Environmental Research, UFZ, 04318, Leipzig, Germany

    Matthias Liess

  3. Institute for Environmental Research, RWTH Aachen University, 52074, Aachen, Germany

    Matthias Liess

  4. Department Effect-Directed Analysis, Helmholtz Centre for Environmental Research, UFZ, 04318, Leipzig, Germany

    Werner Brack

  5. Department of Evolutionary Ecology and Environmental Toxicology, Faculty of Biological Sciences, Goethe University Frankfurt, Max-Von-Laue-Strasse 13, 60438, Frankfurt am Main, Germany

    Werner Brack

Authors
  1. Katharina Ohler
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Verena C. Schreiner
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lukas Reinhard
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Matthias Liess
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Werner Brack
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ralf B. Schäfer
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

KO, VCS, MLiess, RBS designed the study; KO, VCS, MLink selected the sampling sites; KO, VCS, MLink conducted the fieldwork; KO and LR conducted the fatty acid analysis and identified the spiders; KO identified the insects, analyzed the data and drafted the manuscript. All authors revised the manuscript.

Corresponding author

Correspondence to Katharina Ohler.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: Figure S1.

Sampling sites in south-western Germany (European Environment Agency, 2007). Table S1. Mean and standard deviation (sd) of all environmental variables monitored during the field experiment in forested and agricultural sites. Table S2. Name, CAS-number, acute EC50 value, taxon and source of EC50 values of the pesticides used in the calculation of the logarithmic sum of the toxic unit (sumTU). Table S3. Name, CAS-number, koc (soil organic carbon–water partitioning coefficient) value of the pyrethroids used in the calculation of the particle-associated concentration of an estimate of the bioavailable concentration in water. Table S4. Time periods, in which samples of emergent aquatic insects were pooled on order level for fatty acid analysis. Table S5. Results of hierarchical generalized additive models (HGAM) to identify seasonal patterns of fatty acid export via aquatic insects. Figure S2. Mean proportion and standard deviation of fatty acids (FA). Table S6. 95% confidence intervals of the total export of all fatty acids, saturated fatty acids, monounsaturated fatty acids, and polyunsaturated fatty acids during the primary emergence period in forested and agricultural sites. Table S7. Results of the partial redundancy analysis (RDA) and RDA. Figure S3. Differences of environmental variables between forested and agricultural sites visualized with violin plots.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ohler, K., Schreiner, V.C., Reinhard, L. et al. Land use alters cross-ecosystem transfer of high value fatty acids by aquatic insects. Environ Sci Eur 36, 10 (2024). https://doi.org/10.1186/s12302-023-00831-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12302-023-00831-3

Keywords