Stream Ecology - Science topic

Parner is found. I have already got a letter of agreement.

I found this pdf, hope to help you and it said based on individual numbers. Page 13 and 14

Hi,

To account for the apparent pseudoreplication on your river, you could just include it in your analysis via an asymmetric eigenvector map (AEM). This technique allows you to create directional eigenvector that correspond to your unidirectional phenomenon (here the river's flow). You'll only need your geographic coordinate if you use R. You can, then include those vector into the analysis of choise, as a new explanatory value and include the link between your site without ignoring the potential pseudoreplication.

Hi Libourn,

The Indval Package is used in R plataform. Then you will dowloading this package in R and just follow the instructions described by Pierre Legendre.

Dear Megan,

the importance of the seasonal amplitude of streamflow is enormous from an ecological standpoint in many systems, e.g. in non-perennial rivers and streams (NPRS) (see e.g. Skoulikidis et al. 2017 and references therein). This was even one of the main points determining the evolution of the concepts of ecologically sustainable flows from a minimum vital flow to environmental or ecological flows since the 1990s.

Related to this is the ecological relevance of the spatial and SEASONAL extent of Water-Level Fluctuations (WLF) in lakes and reservoirs (e.g. Leira & Cantonati 2008, attached) and of level drawdowns in rivers.

Another example is the large relevance of seasonal variability of discharge in special habitats such as springs that are often biodiversity hotspots in spite of typically small size (see e.g. Cantonati et al. 2012 attached).

Best wishes.

Marco

Dear Reza, in the two middle sentences I am afraid there's something missing, a verb? Probably can end more easily readable if you reformulate them. For example,

Urban storm sewer had a warming effect on rivers water during storm events.

Riparian vegetation had a cooling effect.....

Hi,

Daniel is right, it is Tabellaria flocculosa. The "zig-zag" bands are the result of divisions - one pole is still attached to the next cell via extracellular poysaccharides, the other not. This is the so.called side- or girdleband-view.

Regards

Michael

other papers attached. I hope they can be useful or you can contact corresponding authors to have more information.

I agree with Timur and Kevin (and others).  You'll pick it up rapidly.  Your first couple of samples may seem challenging, but it will get incrementally better from there.  You'll rapidly build up a list of the "usual suspects". 

Some considerations:  1. Chironomidae is THE common dipteran family that will probably stay at family unless you invest significant time and effort mounting heads (or find a consultant if you can afford one).  2. Getting the EPT families down to genus is well worth it; they respond to impacts. 3. If your samples are quite large then consider sub sampling with a goal of around 300 macroinvertebrates per sample (Look up Vinson and Hawkins work supporting that number).  4.  Stratify your ID work....ID your sampling sites and sampling times in rotation building up your replication in each treatment as you go.  If differences are large then you will detect them with few samples and you can then decide how many replicates you truly need to ID (it's usually fewer than you think). 5. The functional feeding group indices may not be the best way to detect differences....diversity indices that include the EPT taxa may be more effective.  6.  Contact your Utah DNR folks and get a list of what they find....especially if they work your river...this will help with your taxonomy.  7.  If you are permitted to hire undergrad workstudy kids....do it....and be prepared to work with them.  They can pick, sort, and if you get reliable ones they can ID. 8.  Have fun with it!

Here's a paper I did with undergrads.....generally to genus except the midges.  We evaluated several indices by building up replication level until we were able to detect differences: http://academics.smcvt.edu/dmccabe/research/PubsPDFs/Standardized%20effect%20size11-080.pdf

Cheers

Declan

Are you intend to measure each individual CWD? How about measuring the size (diameter at both ends and length) and estimate the density (using penetrometer or decay class)

Be careful if sampling, some of the blue green algae can be hazardous or toxic.  Personal protective gear may be needed and best to avoid contact until you know what it is.  Blue green algae can fix their own oxygen and CO2, so if the temperature and alkalinity/pH systems are out of balance, green algae cannot live and blue green develop.  I dont remember explanation for specific color, but you can look that up on the internet.  Also I dont know if they are using mercury or other chemicals to concentrate the gold, but if they are using hazardous materials, they may have altered the conditions for algae.  If looks like this is settling ponds, so the water is not being circulated or diluted, so the temperatures and nutrients are elevated, and open to sunlight also encourages algae growth.  Please dont let them dump this in the river, it will probably eventually go away.  If you test water quality for alkalinity and pH, if your alkalinity is low and/or pH high, blue green algae may invade.  It has been a long time since I studied this, so best to consult an expert and make sure you have the proper permissions if treating.  Hopefully this will help.  

Hi,

as you my know the MDS is a graphical representation of the similarity matrix, closer points means that these are more similar than some that are further apart. If I get your description right the points that clearly deviate from major clusters are below the threshold of 60% similarity.

There are various ways to see how variation is distributed in your data such as Principal Component Analysis (PCA), ANOSIM, Principal component Analysis (PCO) which may help you to interpret the reasons for the underlying grouping.

Cheers

Did rose bengal kill the mayflies? Or was it simply not taken up by them?

You are doing sort of a repeated measures analysis of variance, or, as mentioned above, repeated measures analysis of covariance. In JMP, the organization of the dataset has to be the way JMP wants it (which requires a little manipulation from the most sensible way to input the data) and you have to have sample ALL moments in ALL treatments. If you have empty cells, it makes it more complicated, although there are ways to get around it. It is beyond the limits of this medium of discussion to explain how to rearrange your data - although the help in JMP is pretty good at explaining it. Also, your sample size is very small for such a complex analysis, as Maurizio suggested. In general, a multivariate scheme requires an N of 25 plus 3 times the number of treatments as a minimum. If it is very complex, add 25 per level.

I hope this helps!  Jim

SWAT and/or Hydrological models are different from ARIMA. ARIMA needs only observed data............. but SWAT requires too much info including observed data for calibration and validation. The challenge is to obtain all info for physical-based Hydrologic models

Hi Paola,

This featured collection on riparian ecosystems and buffers might have some useful references - see Mayer et al (2014) for intro: http://onlinelibrary.wiley.com/doi/10.1111/jawr.12212/abstract

Also for a review on the beneficial effects of riparian vegetation on water quality see Dosskey et al (2010): http://onlinelibrary.wiley.com/doi/10.1111/j.1752-1688.2010.00419.x/abstract

Hope this helps.

Marian

Otto, in Tanzania also there is a development in the use of SASS in a modified version called TARISS (Tanzania River Scoring System).

Thanks to Francis for linking in Hand et al.'s essay. Very interesting, and one of those concepts that's obvious...after it's pointed out. As Edward Tufte reminds us "It's More Complicated Than That!". The research implications? As usual, it's a case of a big idea that needs to be translated into testable hypotheses on scales that can be studied on practical levels. In riparian corridors, you might expect -for example- genetic variation to be more highly correlated along the corridor, where conditions are similar, than across a stream-to-upland gradient. Or maybe the opposite, depending on the organism.

First of all, I measured the DOM from stream to lake ecosystem, but not the POM. Of course, they play important role in organic matter bugdet in lakes. You can read my paper from the reference (Mostofa KMG, Yoshioka T, Konohira E, Tanoue E, Hayakawa K, Takahashi M (2005b) Three-dimensional fluorescence as a tool for investigating the dynamics of dissolved organic matter in the Lake Biwa watershed Limnology 6: 101-115).

POM play an important, specifically depending on the contents of different organic matters (wood, leaves, algae, etc) and subsequent decomposition in lake bed and surface. For example, alage can release DOM more quickly, but wood may release decompose for weeks to months in lake bed. This should be considered clearly. You can also read book "Photobiogeochemistry of Organic Matter: Principles and Practices in Water Environments" that publsihed by Springer in 2013.

Dear Fransis John,

I know that in the laboratory of Dr. Juergen Giest several project on stream  restoration are carried out. You could contact them http://fisch.wzw.tum.de/index.php?id=37

Best wishes,

Tatiana

@Irene: I reiterate that the formula does not work.

When you use B to estimate P (the P/B method), predator consumption is already accounted for (they ate some of the B of the non-predators). So the production available to fish is the sum of the production of predators and non-predators.

We contract annual redd surveys of spring Chinook salmon through Oregon Department of Fish and Wildlife. I PM'd you the POC as well as a DRAFT report. Typically to estimate the escapement for a reach the redd count is multiplied by 2.5. This conforms to the negative bias you are seeing. However, we don't have DIDSON monitoring, and instead use the video counts at barrier dams.

What is interesting is that the productivity and effective population size of fish transported above our projects can vary widely by watershed . We are monitoring this through parental based genetic studies. In one basin we transport over 500 adults yet the number of individuals contributing to the cohort for that brood year number lass than 100. This is likely due to a combination of PSM and losses during incubation. It is likely that the interbasin variation is likely due to the hydrological and geological differences. Our lowest performing subbasin has the combination of large floods during incubation, combined with a river channel that has a lot of bedrock.

We also have done a lot of investigations into pre-spawn mortality which I can provide if it would be useful.

v/r,

Griff

This is more a matter of terminology than a discussion on the role of those vegetated gravel bars within the river landscape. The riparian vegetation is the herbaceous or woody vegetation located at the interface of two ecosystems - the terrestrial and the aquatic - and it is not relevant for this that the river is chanelised or not. However, if those gravel bars are inside the riverbed and subjected to submersion caused by floods, that vegetation should not be considered riparian. So, you have to figure out if that vegetation is located in the in-stream channel, inner banks or outer banks, to name it correctly.

Check out the publications in the Researchgate profile of Jan J. Verspoor. He looked at salmon interactions with stream periphyton at  watershed scales - might be some useful resources in his work.

Different approaches have been found to be effective for identifying different types of changes so the answer will partly depend on the nature of changes that you suspect may be an issue for your data set and the type of changes that exist in the data set.  Hence, applying many of the techniques suggested so far will be useful to help learn more about your data.  One additional technique to consider is Bayesian Change Point Analysis, which is available through the BCP package in R.  For any changes that are found, it is important to examine the metadata for your time series to look for any changes in measurement protocols or equipment that may explain the changes as well as any known physical changes that could explain the change (i.e., a forest fire on a watershed can result in changes to the streamflow time series).

Good points - thank you.

I have estimates of photosynthesis (g/m3/d at 20C). Reaeration calculated from time lag between solar noon and DOmax. Respiration and photosynthesis calculated using delta method. Is it reasonable to treat that photosynthetic rate as net prim. productivity?

This gives a similar result with max photosynthesis preceding max plant abundance by about two weeks. I get a similar lag with depth-corrected photosynthesis (g/m2/d at 20C, using a time-series of depth), and with P/R and P+R. Within a few days of each other. Probably worth mentioniong that the end of season decline in plant abundance had the most pronounced lag after the decline in photosynthesis (closer to 4 weeks).

Of course I don't have a true measure of plant abundance, and I can write the lag off as an artefact of the methods I used to estimate abundance (%bed cover). If I could measure true population biomass, would that determine net primary productivity given that not all of the plant biomass is actively photosyntheisizing (e.g. shaded, structural or sensecing material)?

Hi Seth!

Some kind of 'reference condition approach' could maybe work. This is usually used for fish or invert assemblages, where you set a reference condition based on undisturbed streams and then calibrate your metric based on that reference condition. You will always get a score between 0 and 1, which can then be compared to a a different measurement system. There's a few papers on this flying around, including one I was involved in where we modeled pre-european habitat conditions in Australia,

Cheers

Simon

These link will be useful for you.

 

In this, create a vector data after that you may try to analysis it, the following link may useful for you. http://resources.arcgis.com/en/help/main/10.1/index.html#//002r0000002m000000

Cost -effective makes it tough. Silver 0.45 micron filters and a 100 ml syringe would be best e.g. from Millipore. Clean the filters by ashing, use then to filter your water, measure TC on the filter by combustion.

You could potentially use over the stream to induce a greenhouse, causing warming. However, you would probably need a pretty long reach of this warming setup as the water is (obviously) continually renewed. Many terrestrial climate change studies use plexiglass roofs to artificially increase temperature, but they don't have to deal with running water.

Hi Rosanna,

Sure thing will email the information to you tomorrow. I assume you are aware of the BMWP scoring system primarily used for detecting organic pollution events. One current shortfall in this method is that it fails to utilise abundances, although this soon will be superseded by the WHPT index which will have pressure sensitivity scores for each scoring family linked to abundance categories.

Kind regards,

Drew.

I can suggest you to investigate your sediments collecting them directly (it is already an approximation, without taking into account the variability of the operators).

In our lab we evaluate the dry weight of about 10 grams of sediments (48h 60° C)

The dry sediment is collected in crucibles (ours are very small, less than 1 gram capacity) and treated for 8h at 450 ° C to obtain the amount of organic matter (for difference in weight). With larger crucibles (up to 10 grams) the time lengthens to 12h.

For the inorganic matter we process samples for other 8h (or 12h) to 900 ° C.

For the particle size analysis you need more material, 200 grams dry weight is a good amount for our tools. If you are sediments of freshwater environments must first be treated with H2O2. I uploaded two jobs on my profile, unfortunately, are in Italian (Protocollo di campionamento ed analisi chimica di sedimenti lacustri Protocollo di campionamento ed analisi granulometrica di sedimenti lacustri). If you need more information contact me .. I hope to be helpful

I generally agree with the above mentioned theoretical consideration that BOD is the part of COD that can be oxidized biologically by microorganisms.

However in practice the two parameters are defined by analytic procedures. If one uses standard methods with an inocloum for the BOD test of activated sludge and the COD is determined by the oxidation of chromate it happens in both wastewater and polluted streams that the measured BOD is higher than the COD because chromic acid does not oxidise ammonia while a bacteria community with nitrifiers does this quite efficiently.