# Why when measuring turbidity do we use the minimum wavelength?

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As a preface, I read a few other related posts and was able to gather some knowledge, though without any background in physics I am having some trouble here piecing together a coherent view. I looked up videos of Beer-Lambert law on Khan academy and it gave a relationship between transmittance and absorbance as

$$Transmittance (T)=frac{I_1}{I_0}$$ $$Absorbance(A)=-log_{10}(T)$$

Further, this post points out that turbidity is absorbance, and describes to be in relation to diffuse-reflected and scattered light?

Taking a step back, we see okay, turbidity is really just a measure of 'murkiness' and the amount of light that is scattered as a consequence. What about this diffuse-reflected light? And then this post says scattering is really just the sum of refraction and reflection. I don't think that is correct though, as this video shows refraction and reflection and scattering are different concepts, in so much as refraction is the bending of light as it passes through one medium to another, reflection occurs when light hits a smooth or shiny surface and bounces off, and scattering happens when the surface is rough sending the light in different directions. Then this post here. appears to detail out what would be a satisfying explanation but I haven't taken physics. Are there any good videos that might help better explain these topics?

Then, thinking about absorption my chemistry textbook talks about radiation and I had this concept in my head about light being absorbed then admitted at different wavelengths and I feel like that is true and perhaps that is where we are getting the relationship between transmittance and absorbance?

Bringing it back to the question that brought all this up, through other questions we were able to determine the answers to be To measure levels of pigment production it would be best to use the light of 450 nm, and to measure it turbidity would be best to use the light of 560 nm. In other words, for pigment production, we use the maximum value of absorbance and for turbidity the minimum value. The question we have is why this is the case? The most we were able to come up with is that the color of the Chlorella bacteria is green and that things are green because that wavelength of light is reflected, and that the wavelength of green light is 560 nm. I guess we can't use green light to measure the amount of green pigment because the light that would be returned would be a combination of reflected light (how much green pigment) + scattered light(not green pigment but returns green light) + (some combination of absorbed and reradiated light??). So we wouldn't be isolating the information we are after? If we want to know how much "green" is present this would be best measured by measuring the wavelength of light that is the least reflected, or the most absorbed, so the green pigment absorbs most of this light and the light that is not absorbed will tell of how much green stuff was there to absorb it. When it comes to the why for turbidity I am a tad stuck and I think it is because we appear to have a mixture of definitions above. Perhaps some more concise definitions will shed light. It appears the answer to "why can't we use green light" may help to give a possible answer, as the scattered light is the measure of turbidity and that would be with some margin of error, what we have isolated?

Usually when measuring materials in a spectrophotometer we want to use the absorption maxima to minimize the signal-to-noise ratio, that's why for pigment measurement you'll use the maximum point. But when you measure two materials you want to take two measurements with as little correlation as possible, that is to avoid one material influencing your measurements of the other.

Imagine you are trying to breed C. marina for more pigment. Obviously more algae = more pigment so you want to measure how much algae you have and normalize by it. If you'd measure alga density in a wavelength correlated with chlorophyll content you'll get inaccurate results.

I think the question is ill-worded. You should measure at the pigment's absorption minima and not at the alga absorption minima. As the alga can have other peaks unrelated to chlorophyll. When doing this you should remember that chlorophyll and many other pigments are insoluble in water but your measurements of live cells are, usually, in water media.

The best in my opinion and that is the method I'm currently using is to build your own curve by counting cells in some manner (like hemacytometer) and measuring the full spectrum (Your graph stops at 750nm, I got up to 1000nm).

## Light scattering and capillary condensation in porous media

The turbidity of Vycor 7930 porous glass was measured photometrically at 480 nm wavelength, during complete cycles of adsorption and desorption of water, benzene and carbon tetrachloride, all at 28.5°C. The phenomenon described by Zsigmondy as an “opacity point” was observed during desorption in each case. A similar, though much smaller, effect was observed during adsorption. Detailed measurements suggest that the cause is Mie scattering by relatively large groups of vapor-filled pores (although the concentration of scatterers is too great, and their probable shape too irregular, to permit a quantitative interpretation to be given). It is not necessary to postulate the existence of heterogeneities of the same scale in the pore structure itself, since capillary condensation hysteresis enables nonuniform saturation of the porous Vycor to be maintained in metastable equilibrium. The features of the pore system which permit this, together with the role of density fluctuations in the capillary condensate under large negative hydrostatic pressures, are discussed.

## What is Turbidity?

This river owes its muddy appearance to high turbidity levels.

Turbidity is an optical determination of water clarity 1 . Turbid water will appear cloudy, murky, or otherwise colored, affecting the physical look of the water. Suspended solids and dissolved colored material reduce water clarity by creating an opaque, hazy or muddy appearance. Turbidity measurements are often used as an indicator of water quality based on clarity and estimated total suspended solids in water.

The turbidity of water is based on the amount of light scattered by particles in the water column 2 . The more particles that are present, the more light that will be scattered. As such, turbidity and total suspended solids are related. However, turbidity is not a direct measurement of the total suspended materials in water. Instead, as a measure of relative clarity, turbidity is often used to indicate changes in the total suspended solids concentration in water without providing an exact measurement of solids 1 .

Tannins from decomposing vegetation have colored this river red.

Turbidity can come from suspended sediment such as silt or clay, inorganic materials, or organic matter such as algae, plankton and decaying material. In addition to these suspended solids, turbidity can also include colored dissolved organic matter (CDOM), fluorescent dissolved organic matter (FDOM) and other dyes 14 . CDOM is also known as humic stain. Humic stain refers to the tea color produced from decaying plants and leaves underwater due to the release of tannins and other molecules.

This discoloration is often found in bogs, wetlands or other water bodies with high amounts of decaying vegetation in the water. CDOM can cause water to appear red or brown, depending on the type of plants or leaves present. These dissolved substances may be too small to be counted in a suspended solids concentration, but they are still part of a turbidity measurement as they affect water clarity.

## Introduction

Most fish and fish larvae depend on vision in their search of prey (Guthrie, 1986). Factors influencing prey detection in an aquatic environment include predator size (Blaxter and Straines, 1970), the physical conditions of the water such as light and turbidity Vinyard and Oɻrien, 1976, Confer et al., 1978, and prey characteristics such as size (Ware, 1973), mobility, contrast, and colour (Utne-Palm, 1999). Besides the visual detection of a prey, encounter rate is an important factor for feeding rate.

A recent study on spring-spawned herring larvae (15 to 28 mm), conducted using the same experimental setup as this study, showed that both increased light intensity and turbulence had a positive effect on larva attack rate (Utne-Palm and Stiansen, 2002).

Increasing light (when a limiting factor) will increase the predators reaction distance (RD) and ability to detect prey Vinyard and Oɻrien, 1976, Utne, 1997. Many studies have also shown turbulence to have a positive effect on the feeding rate of fish larvae (i.e., Sundby and Fossum, 1990, MacKenzie and Kiørboe, 1995, Gallego et al., 1996) due to an increase in encounter rate. Concurrently, with the increasing encounter rate, increasing turbulence causes a decrease in pursuit success, which means that high turbulence levels tend to have a negative effect on larval feeding rates (MacKenzie and Kiørboe, 2000), forming a doom-shaped relation. Furthermore, as search volume or RD increase with larval size (Breck and Gitter, 1983), turbulence should have a stronger positive effect on the attack rate of larger larvae. Both the doom-shaped relation between increasing turbulence and attack rate and its ontogenetic dependence was supported by the recent study on herring larvae (Utne-Palm and Stiansen, 2002).

However, turbulence and light is not the only physical effects that will influence larva attack rate. Turbidity or water clarity is also expected to have an effect. Atlantic herring (Clupea harengus) spawns in the shallow coastal water, wherein clarity is greatly impaired by freshwater runoffs and algae blooms. The last is particularly frequent during spawning seasons (spring and autumn).

Experiments have shown that increased turbidity has a negative effect on fish that feed visually (e.g., Vinyard and Oɻrien, 1976, Confer et al., 1978, Gregory and Northcote, 1993). However, several studies have also found a higher feeding rate at an intermediate level of turbidity Boehlert and Morgan, 1985, Miner and Stein, 1993, Bristow and Summerfelt, 1994, Bristow et al., 1996, Utne-Palm, 1999. These studies were all done on larvae or small planktivorous. Modelling approaches Giske et al., 1994, Fiksen et al., 2002 have predicted that turbidity will be a beneficial factor for small pelagic fish and fish larvae feeding on microplankton. Suspended particles between predator and prey scatter light and interfere with detection in the same way that fog affects long-distance vision but has little effect on the detection of close objects. Thus, the short reactive distance of planktivorous relative to their piscivorous predators means that the first benefit more from the positive effects of scattering and absorbence within their relative search volume Giske et al., 1994, Fiksen et al., 2002. The results of laboratory and mesocosm studies have thus shown that turbidity has a positive effect on juvenile fish in terms of enabling them to engage in activities that would be risky under clearer conditions, including increasing their feeding rate (Gregory and Northcote, 1993), migratory activity (Ginetz and Larkin, 1976), and increasing their use of open water (Miner and Stein, 1996).

In spite of the many environmental constraints, juveniles of many marine and anadromous species seems to prefer rivers and estuaries with high concentrations of suspended sediments Blaber and Blaber, 1980, Levy and Northcote, 1982, Gregory and Levings, 1998. Boehlert and Morgan (1985) studied Pacific herring (C. harengus pallasi), a species that spawns in estuaries, whose larvae remain in the estuarine nursery grounds throughout the juvenile stages. They found that Pacific herring larvae 8.7 mm reach maximum feeding activity, 90% feeding, in turbidity produced by 500 to 1000 mg l −1 volcanic ash, compared with 50% feeding when no ash was suspended.

The positive effect of increasing turbidity should be greatest for those with a short search volume. One should therefore expect the positive effect of turbidity to be most profound for young larvae, decreasing as the larvae and their search volume grows. To my knowledge, nobody has looked at the effect of turbidity on different size classes (ontogeny) of larvae.

In an experimental study of growth and foraging rate in striped bass larvae (Morone saxatilis) under various light, turbulence, and turbidity conditions, Chesney (1989) found that reduced light or increased turbulence reduced growth or foraging rate, while adding turbidity to turbulence ameliorated some of the negative effects of the latter. Chesney (1989) did not explain this last phenomenon, but the positive effect of an increase in turbidity was most probably due to enhanced prey contrast (Hinshaw, 1985). High prey contrast is known to produce an increase in RD (Utne-Palm, 1999), which in turn is known to make larvae less susceptible to the downside of turbulence MacKenzie et al., 1994, Fiksen and MacKenzie, 2002. To my best knowledge, there are no other studies than the one by Chesney (1989) on the combined effects of turbidity and turbulence on larval feeding.

This study attempts to reveal the individual and combined effects of ontogeny, increasing turbidity, and turbulence on the attack rate and swimming activity of the Atlantic herring larvae. I used three size groups of herring larvae (20, 23, and 29 mm), three turbidity levels [beam attenuation (c) of 0.22, 8.2, and 17.7 m −1 ], and two turbulence levels (1×10 −6 and 8×10 −6 W/ kg). Based on the results from the previous study (Utne-Palm and Stiansen, 2002), a favourable (1×10 −6 W/kg) and an unfavourable (8×10 −6 W/kg) turbulence level was chosen, as well as a sufficient light level, 20 μE m 2 s −1 (above saturation). Larvae of sizes 20, 23, and 29 mm were chosen based on the results of the previous study (Utne-Palm and Stiansen, 2002), which showed that 20 mm larva were the smallest to respond significantly to a change in turbulence, and that there was a significant difference in attack rate between these three size groups.

It would have been interesting to look at the effect of turbidity alone (no turbulence), but turbulence was needed to keep the diatomaceous earth (DE) evenly suspended in the water column. An alternative for future studies would be the use of algae, as these settle slowly. With algae however, there is the disadvantage that the spectral environment in the experimental tank will change with concentration.

## Dissolved Oxygen and Water

Dissolved oxygen (DO) is a measure of how much oxygen is dissolved in the water - the amount of oxygen available to living aquatic organisms. The amount of dissolved oxygen in a stream or lake can tell us a lot about its water quality.

USGS scientist is measuring various water-quality conditions in Holes Creek at Huffman Park in Kettering, Ohio.

The USGS has been measuring water for decades. Some measurements, such as temperature, pH, and specific conductance are taken almost every time water is sampled and investigated, no matter where in the U.S. the water is being studied. Another common measurement often taken is dissolved oxygen (DO), which is a measure of how much oxygen is dissolved in the water - DO can tell us a lot about water quality.

### Dissolved Oxygen and Water

Although water molecules contain an oxygen atom, this oxygen is not what is needed by aquatic organisms living in natural waters. A small amount of oxygen, up to about ten molecules of oxygen per million of water, is actually dissolved in water. Oxygen enters a stream mainly from the atmosphere and, in areas where groundwater discharge into streams is a large portion of streamflow, from groundwater discharge. This dissolved oxygen is breathed by fish and zooplankton and is needed by them to survive.

### Dissolved oxygen and water quality

A eutrophic lake where dissolved-oxygen concentrations are low. Algal blooms can occur under such conditions.

Rapidly moving water, such as in a mountain stream or large river, tends to contain a lot of dissolved oxygen, whereas stagnant water contains less. Bacteria in water can consume oxygen as organic matter decays. Thus, excess organic material in lakes and rivers can cause eutrophic conditions, which is an oxygen-deficient situation that can cause a water body to "die." Aquatic life can have a hard time in stagnant water that has a lot of rotting, organic material in it, especially in summer (the concentration of dissolved oxygen is inversely related to water temperature), when dissolved-oxygen levels are at a seasonal low. Water near the surface of the lake– the epilimnion– is too warm for them, while water near the bottom–the hypolimnion– has too little oxygen. Conditions may become especially serious during a period of hot, calm weather, resulting in the loss of many fish. You may have heard about summertime fish kills in local lakes that likely result from this problem.

### Dissolved oxygen, temperature, and aquatic life

Water temperture affects dissolved-oxygen concentrations in a river or water body.

As the chart shows, the concentration of dissolved oxygen in surface water is affected by temperature and has both a seasonal and a daily cycle. Cold water can hold more dissolved oxygen than warm water. In winter and early spring, when the water temperature is low, the dissolved oxygen concentration is high. In summer and fall, when the water temperature is high, the dissolved-oxygen concentration is often lower.

Dissolved oxygen in surface water is used by all forms of aquatic life therefore, this constituent typically is measured to assess the "health" of lakes and streams. Oxygen enters a stream from the atmosphere and from groundwater discharge. The contribution of oxygen from groundwater discharge is significant, however, only in areas where groundwater is a large component of streamflow, such as in areas of glacial deposits. Photosynthesis is the primary process affecting the dissolved-oxygen/temperature relation water clarity and strength and duration of sunlight, in turn, affect the rate of photosynthesis.

You may have heard about a Gulf of Mexico "dead zone" in areas of the Gulf south of Louisiana, where the Mississippi and Atchafalaya Rivers discharge. A dead zone forms seasonally in the northern Gulf of Mexico when subsurface waters become depleted in dissolved oxygen and cannot support most life. The zone forms west of the Mississippi Delta over the continental shelf off Louisiana and sometimes extends off Texas. The oxygen depletion begins in late spring, increases in summer, and ends in the fall.

Dissolved oxygen in bottom waters, measured from June 8 through July 17, 2009, during the annual summer Gulf of Mexico Southeast Area Monitoring and Assessment Program ( SEAMAP ) cruise in the northern Gulf of Mexico. Orange and red colors indicate lower dissolved oxygen concentrations.

The formation of oxygen-depleted subsurface waters has been associated with nutrient-rich (nitrogen and phosphorus) discharge from the Mississippi and Atchafalaya Rivers. Bio-available nutrients in the discharge can stimulate algal blooms, which die and are eaten by bacteria, depleting the oxygen in the subsurface water. The oxygen content of surface waters of normal salinity in the summer is typically more than 8 milligrams per liter (8 mg/L) when oxygen concentrations are less than 2 mg/L, the water is defined as hypoxic (CENR, 2000). The hypoxia kills many organisms that cannot escape, and thus the hypoxic zone is informally known as the “dead zone.”

The hypoxic zone in the northern Gulf of Mexico is in the center of a productive and valuable fishery. The increased frequency and expansion of hypoxic zones have become an important economic and environmental issue to commercial and recreational users of the fishery.

### Measuring dissolved oxygen

Multi-parameter monitor used to record water-quality measurements.

Field and lab meters to measure dissolved oxygen have been around for a long time. As this picture shows, modern meters are small and highly electronic. They still use a probe, which is located at the end of the cable. Dissolved oxygen is dependent on temperature (an inverse relation), so the meter must be calibrated properly before each use.

Do you want to test your local water quality?

Water test kits are available from World Water Monitoring Challenge (WWMC), an international education and outreach program that builds public awareness and involvement in protecting water resources around the world. Teachers and water-science enthusiasts: Do you want to be able to perform basic water-quality tests on local waters? WWMC offers inexpensive test kits so you can perform your own tests for temperature, pH, turbidity, and dissolved oxygen.

Do you think you know a lot about water properties?
Take our interactive water-properties true/false quiz and test your water knowledge.

## Results

### Evaluation of excitation wavelength combination

Vertical profiles of temperature showed weak stratification at Station 9B on 28th August and at Station 12B on 13th September (Fig 2A and 2D), but not at Station 6B on 18th September (Fig 2G). Chl-a concentration reached 42 μg L −1 at 2 m at Station 9B due to a cyanobacterial bloom (Fig 2C). Vertical profiles of the minimum PSII fluorescence yield (Fo) showed variability among four combinations of excitation wavelengths during cyanobacterial blooms (Fig 2B), but not for communities dominated by diatoms and zygnematophytes (Fig 2E and 2H). For example, Fo values derived from excitation light at 444 nm and 444 + 512 nm were lower than those for excitation combinations of 444 + 633 nm and 444 + 512 + 633 nm when cyanobacteria were dominant at depths of 0 and 2 m at Station 9B (South Basin) in August (Fig 2B and 2C). On the other hand, there were no clear differences in Fo profiles at Station 12B (North Basin) on 13th September and Station 6B (South Basin) on 18th September when diatoms and zygnematophytes dominated (Fig 2E, 2F, 2H and 2I). Further, the relationship between JVf and PAR intensity showed the utility of 633 nm for revealing signatures of cyanobacterial photosynthesis (Fig 3). For example, JVf was clearly lower than JVO using excitation at 444 nm and 444 + 512 nm, but not at 444 + 633 nm and the combination of all three wavelengths at Station 9B on 28th August during a cyanobacterial bloom (Fig 3A). No clear differences in JVf were observed between the combinations of wavelengths at Station 12B on 13th September and Station 6B on 18th September 2018 (Fig 3B and 3C).

Panels showing PAR, water temperature, Chl-a (A, D, G), Fo (B, E, H) estimated by different combinations of excitation wavelength from the FRRf, and phytoplankton biomass (C, F, I) at Station 9B on 28th August (A, B, C), Station 12B on 13th September (D, E, F) and Station 6B on 18th September 2018 (G, H, I). Grey dashed lines denote 0 m.

JVf was estimated by different combinations of excitation wavelength from the FRRf relative to ambient PAR intensity at (A) Station 9B on 28th August, (B) Station 12B on 13th September and (C) Station 6B on 18th September. The JVO estimates from the light-dark bottle method are also shown. For JVO, PAR intensity was calculated by the light intensity of growth chambers and SCF (see Materials and methods). The fitted curve is given for JVO using a two-parameter model [80] to improve visibility.

The data quality of FRRf measurements during the study period showed reasonable stability (Table 4). Upon rejection of low-quality data (e.g. with PSII or PSII′ < 0.03 or > 0.08), the number of successful observations was found to be highest when PSII was excited with a combination of three wavelengths, followed by excitation light at 444 + 633 nm, 444 + 512 nm, and 444 nm. Median values of both PSII and PSII′ were also near the optimal value (0.05) when the three wavelengths were combined.

### Development of Фe,C model

#### Environmental and biological conditions.

Ancillary measurements of water temperature, DO concentration, turbidity, Chl-a, NO2 + NO3, NH4 and PO4 concentrations from each sampling showed clear spatial and seasonal variability (Table 5). Water temperature varied from 7.5 to 30.2°C in the North Basin and 7.5 to 28.5°C in the South Basin throughout the study period (Table 5). NO2 + NO3 and NH4 concentrations were lower in summer and autumn, and higher in winter at both basins throughout the study period. PO4 did not show clear seasonal changes and was always lower than 0.04 μmol L −1 in both basins throughout the study period.

At all sampling dates, diatoms, zygnematophytes, cyanobacteria and cryptophytes were the dominant groups in the phytoplankton biomass (S6 Appendix). Zygnematophytes, mainly composed of Staurastrum dorsidentiferum, S. sebaldi and Micrasterias hardyi, were always found at all stations throughout the study period, except at Station 6B in December. Diatoms were dominant during summer to early autumn and reached 87% in the phytoplankton biomass at Station 6B in September (S6 Appendix). Cryptophytes were present at a relatively low proportion through the study period, except at Station 6B in December. Cyanobacteria were mainly composed of Anabaena (Dolichospermum) affinis and Aphanothece sp. and bloomed at Station 9B on 28th August. Small chlorophytes, crysophytes and dinoflagellates always made up less than 20% of the total phytoplankton biomass. Euglenophytes were very rare and accounted for less than 0.5%of the total biomass throughout the study period.

#### Spatiotemporal variation and GLM development for Фe,C.

To develop an optimal electron requirement for the carbon fixation (Фe,C) model, we used the data set that was obtained by the combination of three excitation wavelengths due to the quality and reliability (Figs 2 and 3). After bootstrap sampling, boxplots of median Фe,C were calculated for each sampling date (Fig 4). Фe,C changed temporally from 1.1 to 31.0 mol e − mol C −1 and was higher in spring and summer in both the North and South basins. The mean annual Фe,C values were 5.6 mol e − mol C −1 for the North Basin, 9.0 mol e − mol C −1 for the South Basin and 7.3 mol e − mol C −1 for all sampling stations.

Spatial and temporal variability of Фe,C of the phytoplankton community in the North Basin (A) and the South Basin (B) throughout the study period. The box plot shows the median (bold line), and 25th (Q1) and 75th (Q3) percentiles. The whiskers indicate 1.5 times the interquartile range (Q3−Q1) below and above Q1 and Q3. Outliers beyond the whiskers were plotted individually. Note: Фe,C values were derived from the data measured by the combination of three excitation wavelengths.

To select and define the explanatory variables for GLM, we examined correlations between Фe,C and all candidate environmental factors (Fig 5). Small chlorophytes, crysophytes and dinoflagellates were excluded because of their low proportions relative to the total phytoplankton biomass. Фe,C correlated positively with PAR, temperature, DO, NPQNSV, Chl-a, and σPSII, and negatively with maximum photochemical efficiency under dark conditions (Fv/Fm) throughout the study period. NPQNSV highly correlated with PAR and Fv/Fm (ρ = 0.70 and −0.96, respectively). RCII concentration highly correlated with Chl-a (ρ = 0.70), and diatoms and zygnematophytes also negatively correlated (ρ = −0.70) with each other. Based on the correlation matrix, we selected temperature, PAR, turbidity, DO, Fv/Fm, σPSII, NH4, NO2 + NO3, PO4, Chl-a, and fractions of diatoms, cyanobacteria, and cryptophytes in the phytoplankton biomass as explanatory variables for the GLM. The explanatory variable ‘PAR’ may include influences of both PAR and NPQNSV in the GLM due to the high correlation between the two. Similarly, the explanatory variable ‘diatoms’ in this analysis may include influences of both diatoms and zygnematophytes.

Coloured panels denote statistical significance (p < 0.05). Abbreviations of variables are as follows: Temp, water temperature NTU, turbidity Chl, Chl-a concentration Diat, diatom Cyano, cyanobacteria Zyg, zygnematophytes Cryp, cryptophytes.

Among all possible models, the best model with the lowest AIC was the full model without PAR (Table 6). All variables in the best model exhibited VIF < 10 and thus collinearity was negligible. The R 2 for the best model was 0.67. Among the explanatory variables, temperature showed the highest significance in the best model (coefficient of 0.51), followed by cyanobacteria (coefficient of −0.20) and σPSII (coefficient of 0.17). The performances of more parsimonious models were examined to evaluate the laborious sampling effort of nutrients and microscopy analysis of phytoplankton assemblages. The lowest AIC models without nutrients (Model 2), and without nutrients and phytoplankton assemblages (Model 3) were employed. Model 2 included six variables (temperature, Fv/Fm, σPSII, cyanobacteria, diatoms and cryptophytes), while Model 3 included three variables (temperature, Fv/Fm and σPSII). The values for R 2 for Model 2 and Model 3 were 0.61 and 0.42, respectively. The results of the other sub-models with and without standardisation of variables are listed in the (S2 and S3 Tables).

## Consequences of Unusual DO Levels

If dissolved oxygen concentrations drop below a certain level, fish mortality rates will rise. Sensitive freshwater fish like salmon can’t even reproduce at levels below 6 mg/L ¹⁹. In the ocean, coastal fish begin to avoid areas where DO is below 3.7 mg/L, with specific species abandoning an area completely when levels fall below 3.5 mg/L ²⁹. Below 2.0 mg/L, invertebrates also leave and below 1 mg/L even benthic organisms show reduced growth and survival rates ²⁹.

### Fish kill / Winterkill

A fishkill occurs when a large number of fish in an area of water die off. It can be species-based or a water-wide mortality. Fish kills can occur for a number of reasons, but low dissolved oxygen is often a factor. A winterkill is a fish kill caused by prolonged reduction in dissolved oxygen due to ice or snow cover on a lake or pond ²⁰.

Dissolved oxygen depletion is the most common cause of fish kills

When a body of water is overproductive, the oxygen in the water may get used up faster than it can be replenished. This occurs when a body of water is overstocked with organisms or if there is a large algal bloom die-off.

Fish kills are more common in eutrophic lakes: lakes with high concentrations of nutrients (particularly phosphorus and nitrogen) ⁴¹. High levels of nutrients fuel algae blooms, which can initially boost dissolved oxygen levels. But more algae means more plant respiration, drawing on DO, and when the algae die, bacterial decomposition spikes, using up most or all of the dissolved oxygen available. This creates an anoxic, or oxygen-depleted, environment where fish and other organisms cannot survive. Such nutrient levels can occur naturally, but are more often caused by pollution from fertilizer runoff or poorly treated wastewater ⁴¹.

Winterkills occur when respiration from fish, plants and other organisms is greater than the oxygen production by photosynthesis ¹. They occur when the water is covered by ice, and so cannot receive oxygen by diffusion from the atmosphere. If the ice is then covered by snow, photosynthesis also cannot occur, and the algae will depend entirely on respiration or die off. In these situations, fish, plants and decomposition are all using up the dissolved oxygen, and it cannot be replenished, resulting in a winter fish kill. The shallower the water, and the more productive (high levels of organisms) the water, the greater the likelihood of a winterkill ²⁰.

### Gas Bubble Disease

Sockeye salmon with gas bubble disease

Just as low dissolved oxygen can cause problems, so too can high concentrations. Supersaturated water can cause gas bubble disease in fish and invertebrates ¹². Significant death rates occur when dissolved oxygen remains above 115%-120% air saturation for a period of time. Total mortality occurs in young salmon and trout in under three days at 120% dissolved oxygen saturation ¹². Invertebrates, while also affected by gas bubble disease, can usually tolerate higher levels of supersaturation than fish ¹².

Extended periods of supersaturation can occur in highly aerated waters, often near hydropower dams and waterfalls, or due to excessive photosynthetic activity. Algae blooms can cause air saturations of over 100% due to large amounts of oxygen as a photosynthetic byproduct. This is often coupled with higher water temperatures, which also affects saturation. ¹² At higher temperatures, water becomes 100% saturated at lower concentrations, so higher dissolved oxygen concentrations mean even higher air saturation levels.

A dead zone is an area of water with little to no dissolved oxygen present. They are so named because aquatic organisms cannot survive there. Dead zones often occur near heavy human populations, such as estuaries and coastal areas off the Gulf of Mexico, the North Sea, the Baltic Sea, and the East China Sea. They can occur in large lakes and rivers as well, but are more well known in the oceanic context.

Hypoxic and anoxic zones around the world (photo credit: NASA)

These zones are usually a result of a fertilizer-fueled algae and phytoplankton growth boom. When the algae and phytoplankton die, the microbes at the seafloor use up the oxygen decomposing the organic matter ³¹. These anoxic conditions are usually stratified, occurring only in the lower layers of the water. While some fish and other organisms can escape, shellfish, young fish and eggs usually die ³².

Naturally occurring hypoxic (low oxygen) conditions are not considered dead zones. The local aquatic life (including benthic organisms) have adjusted to the recurring low-oxygen conditions, so the adverse effects of a dead zone (mass fish kills, sudden disappearance of aquatic organisms, and growth/development problems in fish and invertebrates) do not occur ³¹.

Such naturally occurring zones frequently occur in deep lake basins and lower ocean levels due to water column stratification.

## Physicochemical controls of diffusive methane fluxes in the Okavango Delta, Botswana

Atmospheric methane (CH4) is one of the three key greenhouse gases (GHGs) driving global climate change. The atmospheric concentration of CH4 has increased by about 150 % above pre-industrial levels of 400–700 ppb due to anthropogenic activities. Although tropical wetlands account for 50–60 % of the global wetland CH4 emissions, the biogeochemistry of these wetlands, including controls of CH4 emissions from the systems, is poorly understood compared to temperate wetlands. This has resulted in large inter-model variations of the magnitude and distribution of CH4 emission estimates from these tropical wetlands. A recent study in the Okavango Delta, Botswana, estimated diffusive CH4 flux at 1.8 ± 0.2 Tg year −1 , accounting for 2.8 ± 0.3 % of the total CH4 emission from tropical wetlands. In this paper we present an assessment of relationships between diffusive CH4 flux rates and physicochemical variables in the overlying water column to identify and understand regulatory variables for the diffusive CH4 fluxes in the Delta. The results show that diffusive CH4 flux rates from the Delta seem to be controlled by a combination of physicochemical variables. Although site specific fluxes seem to be controlled by different combinations of factors, temperature was the primary predictor of CH4 flux rates at almost all the sampled habitats and sites in the Delta. Most physicochemical variables, especially in the permanent swamps, were correlated with temperature implying that their regulatory effect on diffusive CH4 fluxes could be modified by climate change feedback as well.

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## Acknowledgements

We thank the Natural Environment Research Council (NERC) for funding the analysis of this data under the Turf2Surf Macronutrients Project (NE/J011967/1) the Engineering and Physical Sciences Research Council for funding the LIMPIDS project (EP/G019967/1) NERC for financially supporting the CEH Thames Initiative monitoring Linda Armstrong, Sarah Harman and Heather Wickham (CEH) for carrying out the laboratory analysis and Colin Roberts (CEH) for the weekly river sampling.

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## Biological interactions mediate context and species-specific sensitivities to salinity

Toxicants have both sub-lethal and lethal effects on aquatic biota, influencing organism fitness and community composition. However, toxicant effects within ecosystems may be altered by interactions with abiotic and biotic ecosystem components, including biological interactions. Collectively, this generates the potential for toxicant sensitivity to be highly context dependent, with significantly different outcomes in ecosystems than laboratory toxicity tests predict. We experimentally manipulated stream macroinvertebrate communities in 32 mesocosms to examine how communities from a low-salinity site were influenced by interactions with those from a high-salinity site along a gradient of salinity. Relative to those from the low-salinity site, organisms from the high-salinity site were expected to have greater tolerance and fitness at higher salinities. This created the potential for both salinity and tolerant-sensitive organism interactions to influence communities. We found that community composition was influenced by both direct toxicity and tolerant-sensitive organism interactions. Taxon and context-dependent responses included: (i) direct toxicity effects, irrespective of biotic interactions (ii) effects that were owing to the addition of tolerant taxa, irrespective of salinity (iii) toxicity dependent on sensitive-tolerant taxa interactions and (iv) toxic effects that were increased by interactions. Our results reinforce that ecological processes require consideration when examining toxicant effects within ecosystems.

### 1. Introduction

At both local and broad spatial and temporal scales, dispersal, stochastic demographic processes, speciation and deterministic niche processes influence patterns of community assembly [1–4]. At local scales, environmental filtering, or the restriction of fundamental niches into realized niche space depend on abiotic gradients and biotic interactions including competition [5,6]. Observational studies further suggest that anthropogenic chemical stressors may influence individuals, populations and communities at concentrations lower than laboratory-based toxicity experiments predict [7,8]. Toxicants often interact with other stressors [9,10], or with chemical [11], physical [12] and biotic ecosystem components, sometimes lessening [11] or strengthening their effects [13]. Among biological interactions, competition [14], predation [15,16] and parasitism [13] modify stressor effects on organisms. In this manner, local environmental gradients and stressors, including toxicants, can cause ‘context sensitivity’ in communities [17]. Therefore, differences between field and laboratory findings may arise from the complexities of a realized niche, including interactions between abiotic gradients, other stressors, biotic niche effects and ecological processes operating at multiple spatial and temporal scales [18]. Where ecological processes modify toxicant impacts [12,19], effect prediction is hindered, especially where species-specific and context-dependent responses occur [20].

Salinization is a globally important stressor in freshwater ecosystems [21]. Salinity is a component in all natural waters and is defined as the total concentration of dissolved inorganic ions in water or soil [22]. The dominant ions in anthropogenic and naturally saline waters include Na + , Cl − , Mg 2+ , Ca 2+ , HCO 3− , and [23]. Natural salinization occurs from catchment weathering, sea-spray and salts transported by seawater evaporation [21]. Anthropogenic salinization can be caused by water harvesting, road de-icing, mining activities and changes to vegetation leading to water-table movement [21]. In freshwaters, salinity is a toxicant and physiological stressor causing direct lethal [24] and sub-lethal organism effects [25], which depend on ion proportions, their concentrations and organism sensitivities. These effects on organisms can reduce biodiversity [26], alter trait and community structure [27] and cause changes to ecosystem processes [28].

While it is well known that biotic interactions shape community composition, there has been little consideration of how interspecific biotic interactions may interact with organism tolerances/sensitivities to toxicants in populations and communities [29]. Increased tolerance to a stressor is expected to influence the outcome of biotic interactions and fitness in relation to that stressor [30]. Despite their expected importance, studies examining the interplay between deterministic abiotic filtering and biotic interactions on communities are rare, but are required to predict the effects of stressors within real ecosystems [29]. Here, we tested whether salinity effects were modified by interspecific biotic interactions between salt-tolerant organisms, collected from a high salinity site, and a community expected to be more salt-sensitive, collected from a low salinity site. Salinity effects were examined between two regimes of biological interaction using mesocosms: (i) interactions among salt-sensitive organisms only and (ii) interactions between salt-sensitive and salt-tolerant organisms. Interactions within salt-sensitive communities were implicit, but could not be considered with the current study design. The design did allow several contrasting predictions to be made at the community and population levels. At the community level, both salinity and biological interactions between sensitive and tolerant organisms may be expected to influence community composition, reduce diversity and potentially homogenize communities. However, population responses are expected to be diverse (figure 1). Salinity effects may: (i) depend on interspecific interactions between salt tolerant and sensitive organisms (e.g. [31]) (ii) dominate through direct toxicity or physiological impairment, irrespective of biotic effects [32] (iii) have effects that are partly related to direct toxicity or physiological impairment but are strengthened by biotic interactions [19,31] by contrast (iv) biotic interactions may be dominant irrespective of salinity [33] (v) salinity could cause positive responses irrespective of biotic interactions [34] or (vi) toxicity and interspecific interactions may cause complex unexpected responses [35,36].

Figure 1. Hypothetical effects and interactions associated without (blue line), and with (green line) interspecific biotic interactions between salt-sensitive and salt-tolerant communities/taxa. Effects have been linearized for simplicity, but could be nonlinear or exhibit threshold responses. Organism and community responses may: (a) depend on biological interactions between salt-sensitive and tolerant taxa (b) be dominated by the toxic effects of the chemical stressor (c) have effects that are partly chemical and partly biotic (d) biotic interactions may have effects that dominate irrespective of the chemical gradient (e) toxicants could cause effects that are beneficial irrespective of biotic interactions or (f) exhibit complex unexpected responses. (Online version in colour.)

### 2. Methods

An outdoor mesocosm experiment was conducted in the austral winter (June–September) of 2017 at the University of Canberra, Canberra, Australia (ca 650 m elevation 35°14′06.5″ S, 149°05′12.0″ E). Thirty-two mesocosms consisting of 1000 l Reln round troughs (1.5 m diameter 0.8 m deep figure 2b) were filled with de-chlorinated Canberra town supply water (ca 900 l per mesocosm). The town water comes from the Cotter River, the source location of the salt-sensitive taxa used in the experiments. Flow was maintained in experiments at ≈0.22 m S −1 using a submersible 1400 l h −1 pump. The experiment was an orthogonal design, with two levels of expected intensity of interspecific interactions: (i) between salt-sensitive taxa only and (ii) between salt-tolerant and salt-sensitive taxa. The first of these treatments included individuals only from the Cotter River, a low-salinity site (mean 30 µS cm −1 35°23′10.5″ S, 148°51′53.6″ E), which had taxa that ranged from salt-sensitive to tolerant (i.e. certain Odonate spp.). The second treatment included individuals both from the low-salinity Cotter River site, and individuals from Cunningham Creek, which had much higher salinity (mean 1600 µS cm −1 Cunningham creek 34°34′26.4″ S, 148°17′04.4″ E), which were assumed to be more salt tolerant compared to those from the Cotter River. Biotic treatments were exposed to a gradient of salinity treatments generated by synthetic marine salt (Ocean Natures). This resulted in electrical conductivity treatments: control (no added salt mean ca 210 µS cm −1 table 1), ca 500 µS cm −1 , ca 1000 µS cm −1 , ca 2500 µS cm −1 and ca 5000 µS cm −1 . This salinity gradient was based on benign-harsh conditions from LC50 values [32].

Figure 2. (a) Salinity treatments across a measured conductivity gradient, crossed with two levels of expected biotic interaction. (b,c) Communities were subjected to treatment regimes in 32 recirculating 1000 l mesocosms. (c1) 1400 l h −1 pumps, re-circulated water at approximately 0.2 m s −1 , along (c2) colonization tray sampling units.

Table 1. Key physico-chemical parameters recorded at regular intervals during the mesocosm experiment (mean ± standard deviation). (For brevity not all comparisons are shown, measured salinity increased predictably within salinity treatments.)

Forty-six days before starting the experiment, colonization trays (garden seedling trays 360 mm long by 300 mm wide and 55 mm deep) were placed in the Cotter River near its confluence with Burkes Creek (−35°23′10.5″ S 148°51′53.6″ E). Trays had natural stream substrata of gravels, pebbles and cobbles translocated directly from the river bed, with existing biofilms and fauna, and were left in place for 44 days for further colonization. This colonization period was designed to minimize transplant and disturbance effects. Salt was added to mesocosms and then allowed to dissolve on the 22 June 2017, 12 h before the addition of colonization trays. Mesocosm colonization was supplemented with kick-net samples from riffle habitat from both the Cunningham and Cotter rivers which occurred from the 23–25 June 2017. Salt-sensitive treatments received two kick-net samples from the Cotter River, while salt-tolerant and sensitive treatments received one kick-net sample from the Cotter River and one from Cunningham Creek. Invertebrate densities differed between the Cunningham and Cotter sites, ascertained three weeks earlier with three replicate Surber samples per site. Mean densities at that time in the Cotter River were 4290 (±2600 s.d.), while Cunningham Creek had 14 900 (±10 200 s.d.). To standardize density differences, Cotter kick-net samples were collected from 3.3 m 2 of the river benthos and from 1 m 2 in Cunningham Creek. All Surber and kick-net samples were collected from undisturbed riffle habitat, moving in an upstream direction to avoid disturbing unsampled habitat. Kick-net samples and trays were randomly allocated to mesocosms.

Colonization leaf packs were placed with colonization trays in the Cotter River and transferred to mesocosms with trays. Leaf packs ensured the addition of shredders and were 15 g of River Red Gum (Eucalyptus camaldulensis) leaves collected from a location near the Yass River (−34°924413″ S, 149°179810″ E). HOBO Pendant temperature/light data loggers (UA-002-08) were deployed at the start of the experiment in a subset of mesocosms to record temperature variability among mesocosms. Weekly measurements were conducted of physico-chemical variables with a Horiba U-52 Water Quality Meter (IC-U52-2 m).

Mesocosms were left for 75 days before sampling on the 6–8 September 2017. One replicate was sampled from each of the 32 mesocosms on the same day, with sampling being consistent with respect to pump location to allow examination of velocity/turbulence effects. Velocities were recorded in a subset of mesocosms in relation to tray position. All invertebrates were sampled by carefully removing trays from the mesocosm using a kick-net working against the flow to collect dislodged invertebrates. All invertebrates were removed from the net, tray substrate and trays and stored in 100% ethanol. Taxa were identified to the lowest taxonomic unit (species where possible).

#### (a) Statistical analysis

All statistical analyses were conducted using R (R Core Development Team, [37]), in R Studio (RStudio Team, [38]). To remove the potential for confounding effects of tolerant taxa additions, all taxa that were known to occur from the Cunningham Creek were excluded prior to all analysis, with the exception of five taxa with less than 0.5% estimated community abundance in Cunningham Creek, typically comprising a single individual. This resulted in response data that was expected to be specific to the Cotter River, and thus were expected to be comparably salt-sensitive. Taxa common to both Cunningham Creek and Cotter River were assumed to be salt-tolerant, thereby allowing abundances of tolerant taxa, tolerant omnivores, tolerant predators and tolerant predator+omnivore densities to be calculated and used as covariates. The removal of Cunningham taxa data was necessary to allow for an unbiased estimate of biological effects on sensitive communities and populations.

#### (b) Community analysis

Hellinger transformation was used on community relative abundance datasets and was examined using non-metric multidimensional scaling (NMDS), partial redundancy analysis (pRDA) and variance partitioning. All abiotic and biotic predictors used in community analysis were standardized (0 mean, unit variance). Permutational multivariate analysis of variance adonis2 from the R package vegan were used to test for community differences among treatments. Mesocosm number was used as a block/random effect or was partialled out in permanova, pRDA, and variance partitioning results, with 200 permutation backwards stepwise used in model reduction. Two hundred permutations were used for model fractions in variance partitioning. NMDS was run for 200 iterations, Bray-Curtis was used as the measure of dissimilarity, and ‘ordisurf’ was used to fit general additive models (GAMs) [39]. Collinearity was set conservatively using Pearson's coefficients at (0.6). For all community analysis, data were transformed to per cent composition where design probably influenced organism densities.

#### (c) Single taxa and metric analysis

We further examined how the addition of salt-tolerant taxa influenced densities of the 20 most abundant salt-sensitive taxa, Ephemeroptera, and Plecoptera and Trichoptera (EPT) and total invertebrate densities. These data were analysed using a mixed-effect model with a lognormal hurdle distribution of salt-sensitive taxa densities. Modelling densities directly allowed comparisons of sensitivities (slopes) independent of differences in density. We fitted separate parameters for each taxon in each type of treatment (salt-sensitive communities and salt-tolerant and sensitive communities):

where: s is species and c for the type of community. A large number of species had 0 abundance for certain concentrations and experimental conditions, which led to our use of the hurdle model. A hurdle model assumes zero and non-zero data come from separate data-generating processes, such that positive densities are first conditional on an initial (Bernoulli) probability. Estimated intercepts and slopes for each taxon in each treatment were conditional on the probability of each taxon being observed. We tested whether salt effects differed among taxa when influenced by the presence of salt-tolerant taxa. The following assumes initial community composition of salt-sensitive taxa to be the same in each mesocosm (ignoring differences in density), therefore attributing reductions in taxonomic abundances to biotic interactions. Taxon intercept differences between treatments can be attributed to either or both of two processes: they had greater initial densities in the control community, or they have been reduced from competition with salt-tolerant taxa. However, the slopes only describe the effect of salinity after controlling for differences in initial densities and can be used to examine how biotic interactions influence salinity sensitivity. If tolerant taxa increase the effects of salinity, greater slopes are expected in the treatments including tolerant taxa, relative to the sensitive only community. An overall variance term ɛ was included to represent observation error. The model was fitted using the R package brms [40]. Sensitive community treatments were encoded 0, with 1 for communities subjected to tolerant taxa interactions. We further estimated biotic interaction coefficients, assessing how they differed between conductivity levels (electronic supplementary material 1). All parameters were given the default brms normal priors with mean 0, uniform Lewandowski, Kurowicka and Joe priors on all possible correlation matrices and Student t-distribution with 3 d.f. for the scale parameter [40]. We ran four Markov chains each of 4000 iterations, discarding half as burn-in. Convergence was assessed using chain traceplots and through calculating Rubin-Gelman statistics for each parameter, which all were less than 1.1 [41].

### 3. Results

#### (a) Physico-chemical differences

Physico-chemical variables changed consistently with salinity treatments and remained stable throughout the experiment. Conductivity (ANOVA, F4,90 = 1423, p < 0.0001) and salinity (ANOVA, F4,90 = 125.7, p < 0.0001) calculated by using electrical conductivity conversion changed consistently with salt addition, with minimal variation within salinity treatments and among the biotic treatments (table 1). Average water temperatures logged at 15 min intervals within 21 of the mesocosms, identified a mean temperature of 12.01°C, a mean minimum of 5.72°C and mean maximum of 23.09°C and were consistent with weekly meter readings (table 1).

#### (b) Community effects

Community analysis using non-metric multidimensional scaling identified patterns that were driven by both conductivity (GAM, p < 0.0001, 27% deviance explained) and the densities of tolerant taxa (GAM, p < 0.0001, 47.7% deviance explained figure 3a). Partial redundancy analysis backwards model reduction of all candidate predictors identified patterns of community assembly were associated with velocity (F1,89 = 2.35, p = 0.03), conductivity (F1,89 = 3.91, p = 0.001), biotic treatments (F1,89 = 2.5, p = 0.002), and the total abundance of salt-tolerant taxa (F1,89 = 4.37, p = 0.001 figure 3b). These predictors collectively explained 12.6% of community variation. Predatory, and predatory + omnivore invertebrate abundances of tolerant taxa (i.e. abundances derived from tolerant taxa common to both the Cunningham and Cotter waterways) explained little variation and were removed in stepwise model reduction procedures. Permutational multivariate analysis of variance (adonis2) identified significant differences in assemblage composition with conductivity (F1,94 = 5.75, p < 0.001) and biotic treatments (F1,94 = 5.87, p < 0.001), and had a close to significant interaction (conductivity: biotic, F1,93 = 1.53, p = 0.97), using mesocosm as a block (strata) effect. Individual multivariate homogeneity of group dispersions (betadisper) tests identified communities that became more variable when subjected to interactions with tolerant taxa (F1,93 = 10.37, p = 0.0018), with no pattern identified by salinity (F4,90 = 1.00, p = 0.41). Linear mixed effects modelling identified richness declined with increasing conductivity (t28 = −2.44, p = 0.021), and was lower in tolerant-sensitive treatments compared to sensitive treatments (t28 = −4.90, p < 0.0001), with no interaction between terms (t28 = 0.102, p= 0.92).

Figure 3. (a) NMDS with surfaces fit using general additive models of conductivity and the abundance of salt-tolerant taxa, (b) partial redundancy analysis (RDA) conditioned by blocks (mesocosms), constrained by conductivity, velocity, and the effects of biotic treatments and salt tolerant taxa abundance. (c) Variance partitioning further explored the combined and individual effects of significant environmental (conductivity, velocity and alkalinity), biotic predictors (salinity treatments and salt tolerant taxa abundance), and within treatment (mesocosm) effects on composition patterns. (Online version in colour.)

Variance partitioning of Hellinger-transformed relative community data identified physical variables explained 5.7% (alkalinity, conductivity and velocity) of community variance (adjusted R 2 ), compared to 4.7% for biotic variables (biotic treatments and total salt-tolerant taxa abundances). Permutation tests (200) of individual fractions were significant for both physical environmental (F3,88 = 2.36, p < 0.001), and biotic partitions (F2,88 = 2.71, p < 0.001), while mesocosm was not significant (F1,88 = 0.94, p = 0.50 figure 3c).

#### (c) Single taxa and metric responses

Taxa and metric specific responses were apparent based on hurdle model results, identifying differences between biotic treatments in relation to salinity as observed by slopes (figure 4) and standardized treatment slope coefficients (electronic supplementary material 1). Taxa on average were absent from the sensitive community 41% of the time, and 57% of the time in the community subject to interactions between tolerant and sensitive taxa. Numerous taxa appeared to show effects dominated by direct toxicity (figure 1b), such as Oecetis spp. (Trichoptera: Leptoceridae) and taxa within the Polycentropodidae family (Trichoptera). Responses of several taxa appeared to depend on interactions between biotic treatments and salinity (e.g. Conoescucidae spp., Trichoptera figures 1a and 4). Some taxa appeared to respond to both direct toxicity and biotic treatment effects (e.g. Notalina fulva Kimmins (Trichoptera: Leptoceridae) figure 1c). Several taxa appeared to exhibit responses suggesting that biotic interactions were important irrespective of salinity (e.g. Agapetus AV 1 (Trichoptera: Glossomatidae), Gomphidae spp (Odonata) Newmanoperla thoreyi (Banks, 1920 Plecoptera) figures 1d and 4). Corynoneura spp. (Diptera: Orthocladiinae) appeared to increase in abundance in treatments mediated by interactions with tolerant taxa along the salinity gradient (figures 1f and 4). When differences in slope coefficients are examined, variable sensitivities are apparent and tolerant taxa appear to be less influenced by biotic treatments (electronic supplementary material 1).

Figure 4. Predicted linear fits and 95% credible intervals for the 20 most common salt-sensitive taxa associated with conductivity and biotic treatments. (Online version in colour.)

Ephemeroptera abundance declined associated with salinity, with both direct salinity effects and in response to interactions with tolerant taxa (figure 5a). Salt-sensitivity appeared to increase in Plecoptera taxa in the presence of tolerant taxa, with little salinity effects in salt-sensitive only treatments (figure 5b). For Trichoptera and total invertebrate densities, direct toxicity effects predominated (figure 5c,d). This pattern was repeated within EPT, probably as a result of the large proportions of Trichoptera in EPT densities.

Figure 5. Posterior coefficient estimates and 95% credible intervals for Ephemeroptera (a), Plecoptera (b), Trichoptera (c) and the total densities of all taxa (d). (Online version in colour.)

### 4. Discussion

Salinity is a growing and globally important threat to freshwater ecosystems [42]. Salinity effects on freshwater communities vary depending on a number of interacting variables, including co-occurring niche gradients, biological interactions and anthropogenic stressors [43,44]. We observed that salinity altered macroinvertebrate community composition, in both salt-sensitive treatments, and where salt-sensitive and salt-tolerant taxa were able to interact. Indeed, similar levels of community variation were explained by physical and biological factors (figure 3a–c). At the population level, however, salinity and tolerant-sensitive taxa interactions caused a range of species-specific and context-dependent responses (figures 4 and 5a–d). Clements & Kotalik [20] similarly identified organism responses to salinity can be species-specific and context-dependent. For example in our study, Austrophlebiodes pusillus (Ephemeroptera) and other Ephemeroptera taxa are sensitive to salinity [45–48], and declined with increasing salinity in the current study, which appeared to be moderately decreased with increasing biotic interaction strength (figure 5a) although competitive exclusion (i.e. a reduced intercept in tolerant-sensitive treatments) may have also caused this result. By contrast, Trichoptera, EPT, and total taxonomic densities declined at similar rates in both treatments, suggesting direct effects were most important across the gradient examined (figure 5a,c,d). At higher salinities, direct effects dominated community responses, resulting in reduced abundance, and altered community composition with almost complete loss of Ephemeroptera and much reduced Trichoptera abundance. We found direct effects of salinity on Plecoptera depended on biotic interactions with salt-tolerant taxa (figure 5b). Similar to the response of Plecoptera taxa that we found, Beklioglu et al. [31] identified low and environmentally relevant concentrations of the organic toxicant 4-nonylphenol had no observable effects on Daphnia magna Straus in single toxicity tests with abundant food, but had strong effects when coupled with effects that might be expected in a real ecosystem (i.e. food limitation and predator cues).

Standard toxicity test methods are structured to minimize control mortality and estimates of toxicity, therefore biological interactions are deliberately avoided [49]. Physical disturbance [50], food limitation [31], and chronic exposure [51], are similarly not typically considered in toxicity tests, although all influence toxicant effects [49]. There are many examples of biotic interactions influencing stressor effects, antagonistically [36], synergistically [35], through delaying recovery [19], with effects in both positive and negative directions [14,19,33,36,49,52,53]. Competitive interactions include exploitative competition for resources, interference competition mediated by aggressive interactions, and apparent competition where effects are indirectly modified through predators [54]. Liess [49] identified both interference and exploitative intraspecific competition caused an increase in pesticide effects in the cased caddis Limnephilus lunatus Curtis (Trichoptera). With limited environmental variability, increased intraspecific competitive effects may be expected given shared niche requirements. Indeed, intraspecific competition can be dominant among biotic processes [55,56]. Within landscapes, the dispersal of locally adapted genotypes is similarly likely to influence the outcomes of intraspecific competition [30]. However, communities subjected to greater variability in abiotic niche gradients (e.g. salinity here), and given organism fitness varies with these gradients, variation in the intensity of interspecific interactions is expected. This may be especially true where continuous dispersal of taxa known to be tolerant to this gradient is likely [4], which was imposed in our study through direct manipulation. In our study Lingora spp. and Agapetus spp. (both Trichoptera) and Newmanoperla thoreyi Banks (Plecoptera), Archichauliodes spp. (Megaloptera) had low densities in tolerant-sensitive treatments regardless of salinity, with effects suggestive of biological exclusion owing to tolerant taxa interactions (figure 1d). This is similar to the findings of Arco et al. [33], who identified that intraspecific competitive effects reduced treatment densities to carrying capacity during a 4 day pre-treatment phase and showed D. magna outcompeted the rotifer Brachionus calyciflorus through exploitative and interference competition. By contrast, Corynoneura spp. declined with increasing salinity in the salt-sensitive communities only, suggesting exposure to tolerant taxa may have caused complex indirect effects such as predatory or competitive release.

Order and family level effects were similar to taxa level responses in Ephemeroptera, but were more variable in Trichoptera and Plecoptera. For example, A. pusillus had similar responses to its order Ephemeroptera. However, there were differences in the two Plecoptera taxa examined (N. thoreyi and Dinotoperla fontana (Kimmins, 1951) figures 4 and 5). Trichoptera exhibited even greater variability in responses, including: densities influenced by salinity irrespective of interactions with tolerant taxa (e.g. total Trichoptera densities, Oecetis spp.) densities affected to a greater extent when exposed to tolerant taxa (e.g. Conoesucidae spp.) densities that may have been dominated by biotic interactions (e.g. Agapetus AV1) or densities that were largely unaffected by salinity or biotic treatments (e.g. Cheumatopsyche AV1).

Stressor effects can propagate throughout ecosystems, altering behavioural and trophic interactions with often unexpected outcomes [9,35,36,57]. Predator abundance was not manipulated in the current study, and top-down pressure was not different among treatments, with no detectable effects on any response examined. However, given variation in sensitivities within the assemblages examined here, and given that many predatory species were salt-tolerant, which is common [57], greater predatory effects may be expected with increasing salinity. Cañedo-Argüelles et al. manipulated the presence of a leech predator (Dina lineata) and salinity in mesocosms. The presence of this predator reduced herbivorous invertebrate abundance, leading to increased primary production, while salinity reduced taxon richness and caused significant changes to community composition within the benthos [58]. Salinity can also provide refugia from negative biological interactions. For example, Rogowski & Stockwell [59] identified both parasites and salinity were observed to have negative effects on pupfish (Cyprinodon tularosa), but high salinity caused a net benefit by reducing parasitism. By contrast, Piscart et al. [36] identified an acanthocephalan parasite (Polymorphus minutus) increased the acute salinity tolerance (LC50) of the Gammarid amphipod Gammarus roeseli.

Within landscapes, population demographic stochasticity, speciation, and dispersal between patches influence local community dynamics [1–3]. At finer scales, coexistence [4], niche [5] and community theories [2] further suggest that patterns of assembly are shaped by dispersal, biological processes and physical gradients. Indeed, Carver et al. [60] used field and mesocosm approaches to show that insects select habitats for oviposition and colonization based on their salinity tolerances and habitat salinity, identifying behaviour influences organism distributions and abundances across salt-affected landscapes. Rico et al. [61] also identified toxicant effects are a single niche component among many, where hydromorphological and habitat parameters were also important in determining community composition. Our results support that biotic processes and abiotic environmental filtering differ among taxonomic groups and are collectively important determinants of community assembly. Furthermore, these local processes, biotic interactions and abiotic niche filtering, coupled with dispersal and stochastic effects can result in multiple stable equilibria [62]. Given this knowledge, we agree with Beketov & Liess [18] that inclusion of ecological theory such as meta-community, coexistence theory, macroecology and multiple stressor research are all necessary to advance understanding in ecotoxicology.

Low temperatures reduce the effects of salinity [63], biotic interactions [64] and probably their combined effects, therefore our results probably underestimate these effects because the current study was conducted during winter. To further explore patterns revealed in this experiment, the research presented here will be coupled with examination of trait-phylogeny-environment relationships to understand how traits, relatedness, and salinity may influence stream invertebrate communities. We expect trait-phylogeny-environment relationships to support that trait related fitness trade-offs occur associated with salinity tolerance [30].

### 5. Conclusion

Interspecific interactions can modify stressor effects and resulting patterns of community assembly. Studies that do not consider ecological processes such as biotic interactions may underestimate and fail to understand the true effect of a stressor in natural settings. Our results reinforced interspecific biological interactions both mediated salinity effects and were important on their own, irrespective of salinity toxicity, influencing taxa and community responses. Across the conductivity gradient examined, direct toxicity had a dominant effect on invertebrate densities. Salinity reduced the abundance and altered community composition, with almost complete losses of Ephemeroptera and other salt-sensitive species. Interspecific interactions between salt-tolerant and salt-sensitive taxa appeared to become more important as sensitivity to the toxicant increased. Several responses reported in other studies were identified here, supporting that species-specific and context-dependent effects may be widespread. In landscapes, ecological processes acting at differing scales are likely to contribute strongly to the effects of toxicants within ecosystems.