Chapter 6 Population and Community Ecology Reading Guide Answer Key
Science topic
Community Ecology - Science topic
Community Ecology is an in ecology, a community is an assemblage of two or more populations of different species occupying the same geographical area.
Questions related to Community Ecology
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I'm a community ecologist (for soil microbes), and I find hurdle models are really neat/efficient for modeling the abundance of taxa with many zeros and high degrees of patchiness (separate mechanisms governing likelihood of existing in an environment versus the abundance of the organism once it appears in the environment). However, I'm also very interested in the interaction between organisms, and I've been toying with models that include other taxa as covariates that help explain the abundance of a taxon of interest. But the abundance of these other taxa also behave in a way that might be best understood with a hurdle model. I'm wondering if there's a way of constructing a hurdle model with two gates - one that is defined by the taxon of interest (as in a classic hurdle model); and one that is defined by a covariate such that there is a model that predicts the behavior of taxon 1 given that taxon 2 is absent, and a model that predicts the behavior of taxon 1 given that taxon 2 is present. Thus there would be three models total:
Model 1: Taxon 1 = 0
Model 2: Taxon 1 > 0 ~ Environment, Given Taxon 2 = 0
Model 3: Taxon 1 > 0 ~ Environment, Given Taxon 2 > 0
Is there a statistical framework / method for doing this? If so, what is it called? / where can I find more information about it? Can it be implemented in R? Or is there another similar approach that I should be aware of?
To preempt a comment I expect to receive: I don't think co-occurrence models get at what I'm interested in. These predict the likelihood of taxon 1 existing in a site given the distribution of taxon 2. These models ask the question do taxon 1 and 2 co-occur more than expected given the environment? But I wish to ask a different question: given that taxon 1 does exist, does the presence of taxon 2 change the abundance of taxon 1, or change the relationship of taxon 1 to the environmental parameters?
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Thank you Remal Al-Gounmeein for sharing! I think it's interesting because I have somewhat the opposite problem that this paper addresses; many people in my field use simple correlation to relate the abundance of taxa to one another, but typically those covariances can be explained by an environmental gradient. So including covariates actually vastly decreases the number of "significant" relationships. But still it's a point well-taken because explaining that e.g. taxon1 and taxon2 don't likely interact directly even though they are positively or negatively correlated would in fact require presenting the results of both models. Thanks!
MDE (mid-domain effect) was proposed in 1994 by Collwell and Hurtt, and was subsequently studied deeply. There were also strong criticisms of MDE (Zapata et.al. 2005; Hawkins et al., 2005). In 2009 Collwel et al. wrote: "Like any idea that calls for an entirely new way of looking at an old problem, MDE has, at times, been either too quickly embraced or too quickly dismissed, but has generally met with appropriate scepticism and gradual acceptance".
The statement above does not seem very clear. If one looks at studies of diversity gradients incorporating MDE, there is a rather clear picture. Almost all studies in which explanatory power of MDE was detected it accounted for less than 50% of variability (which is quite low). On the other hand, many studies showed that MDE has no importance (e.g. Aliabadian & Sluys 2008), or that it is correlated with other variables such as climate and says nothing additional (e.g. Kessler et al. 2011). In addition, the relationship between MDE and diversity distribution is inevitable at some degree and this relationship grows in strength as the pattern approaches a perfectly symmetrical hum-shape distribution.
I have an impression that MDE is just a wasted time. Is this true?
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The approach we took in our book 'Big Questions in Ecology and Evolution' (2009 Sherratt, T.N. and Wilkinson, D.M. chapter 5 - Oxford Univerity Press) Was that it may sometime be a useful null model, but seldom (if at all) the real explanation for tropical diversity. However we thought it interesting enougth to be worth spending 3-4 pages on it.
I have a data set of an insect community composition (17 insect species, raw abundance data, sampled 5 times over 60 days within 24 tanks( 4 replicates). There were 3 treatments( free fish, caged fish, absent fish) involved with 2 levels (open and closed tanks) for each treatment.
And my question is does community composition and diversity change over time? And if so, are these changes different under different treatments?
I am considering to use bray-curtis index(BCI) to address this question. However by looking BCI up, it seems that BCI calculates coeffiecients between different sites(spatial distances). If that's true, my dataset doesn't have sites. I was wondering if it's possible that replicates or treatments can be considered at sites (spatial distance)?
Or is PERMANOVA a better way to answer this question?
(a preview of what my dataset looks like in is in the jpeg file)
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Those on this thread might find my paper interesting, from the recent "Ecology in R" conference: "Why I don't use Bray-Curtis in multivariate analysis of ecological data". It's on YouTube at youtube.com/CARMEnetwork, or the direct link to the video is:
I'm having a problem with my bloxplot with Shannon's and Simpson's indexs. The value of Shannon for my Community 1 indicating higher diversity (in relation of the Community 2), but have lower value for Simpson. I used 1-D Simpson's index. I done something wrong? or the Shannon's index is inversely proportional to Simpson's index (1-D)?
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It might have something to do with the fact that both indices give different importance to the evenness and richness of your communities. Simpson index is more affected by the evenness of the community while Shannon is more affected by the richness. Check the evenness on community one, is there a species that is much more common than the others? that will draw the index down even with similar richness between the communities. I found these resources that might be of help https://www.davidzeleny.net/anadat-r/doku.php/en:div-ind
I´ve been trying to compute the data from an habitat on EstimateS, I´ve done this before and I´ve got not one single error for now. The problem is that trying to compute a habitat I get a strange result: Ace and Chao1 start at a high value (33 species in its mean, when I´ve only 4) and then they go down in value for the next sample which is absurd to me. So I´ve been trying to figure out what´s the issue but I have no clue, I made a new copy manually from the beginning to avoid any syntaxis error and I even checked it with another habitat and it seemed okay.
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@Cristian Adrian Martínez Adriano Gracias!
Hi all,
I have a dataset of fish surveys containing counts of 80 or so species seen at 11 different reefs over a several year period. I would like to compare the community composition between each of these reefs, and this kind of analysis is new to me so I'm a little confused.
- Diversity: I plan to use the Shannon Index to calculate the diversity of each reef, but I have read contradicting information on how to interpret this index. Would ANOSIM or PERMANOVA be appropriate for comparing the diversity of the different reefs?
- Composition: My understanding of the Bray-Curtis Dissimilarity Index is that it shows the degree of difference between community composition of each reef. So, for example, it says reef A & reef B are 6% dissimilar in composition; reef B & reef C are 15% dissimilar in composition. Does it tell us specifically where the dissimilarities are? How do we interpret this? Would SIMPER be an appropriate means of doing so? Would SIMPER then tell us the relative abundances of each species? Or would relative abundances be calculated in a different way altogether?
In similar studies I see the same analyses coming up: NMDS, ANOSIM, PERMANOVA, SIMPER, Bray-Curtis, but I'm finding it difficult to figure out which of these, if any of these, is the most appropriate for my data.
I hope my questions are clear enough, but if not please let me know!
Thanks in advance for your help.
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Here Im addressing your questions in the order you posted them:
-Diversity: since you have 11 reefs and I am guessing that each has different number of species, you have to use an index that takes into account the number of species. Shannon and Simpson diversity are helpful but are one step short from accomplishing that goal. Use Hill's numbers instead which is the exponent of Shannon and the inverse of Simpson; I highly recommend you to read this paper so you understand this point
.
anosim and adonis compare the community composition and not diversity. For example, you may have similar richness and diversity indices but totally different communities in terms of species composition, so anosim and adonis come in handy. If you read the "See Also" section of anosim in the vegan R package, they suggest adonis.
-Composition: anosim and adonis address composition. Bray-Curtis is perhaps the most popular (and one of several) distance method to compute communitiy dissimilarity. From my experience, these techniques dont tell you the percentage % by which these communities differ, but will tell you if the community composition of Reefs are different or not. If you want to compare each reef, then do a pairwise adonis comparison https://github.com/pmartinezarbizu/pairwiseAdonis
-simper gives you "the contribution of each species to overall dissimilarities" read "Details" section carefully in vegan package.
In summary, diversity is a combination of richness and evenness but doesnt tell you much. Perform adonis to compare community composition (dont forget to run 'betadisper' which is the assumption prior to adonis), and run pairwiseAdonis if needed. Then plot your nmds for sites, which is helpful to interpret/visualize you adonis (the default distance is Bray-Curtis but you can find the best distance using 'rankindex' in vegan). Then you can use SIMPER to identify what are the species contributing the most to such differences among sites. You could also add environmental variables to your nmds using the 'envfit' function.
Hello,
We are aiming to create a simple randomization algorithm in R program with abundance data. Specifically, we are aiming to create the IT-Algorithm (Ulrich & Gotelli 2010, Ecology), which "assigns individuals randomly to matrix cells with probabilities proportional to observed row and column abundance totals until, for each row and column, total abundances are reached." We haven't come across any code in the R program that has created this algorithm as of yet. This is the code we have so far, and I was curious if any coders out there may be able to give us some guidance on whether our IT-randomization function is legit:
Appreciate any feedback. Thanks!
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I am not a programmer. But I work with R and in principle everything seems to be correct. I have run some line of the code and it was correct. My doubts:
1. Why an as.table? Isn't it better to work with an as.data.frame? Check what is the limit of the column that an ace can have. table.
2. Are you proposing a new R package?
I am working on a new package too and you will need the following:
I am not a programmer. But I work with R and in principle everything seems to be correct. I have run some line of the code and it was correct. My doubts:
1. Why an as.table? Isn't it better to work with an as.data.frame? Check what is the limit of the column that an ace can have. table.
2. Are you proposing a new R package?
I am working on a new package too and you will need the following:
Publish a package
3.1 Itinerary
Check
R CMD check myPackage /
Build
R CMD build myPackage /
Publish (or update) to a repository
3.2 Check
Check a directory (from command line):
R CMD check myPackage /
Check an already built package (from command line):
R CMD check myPackage.tar.gz
This check includes more than 20 test points detailed in the Writing R extensions manual.
Well is to extense....But the code is ok i will check more
I study fish assemblage structures, which observed unimodal response to environmental gradient and relationships between environmental factors.
I would like to use constrained ordination methods (like RDA or CCA), which allow to use bray-curtis dissimilarity matrix.
(If I choose using RDA or CCA , I will choose CCA.)
I think CAP or db-RDA is useful for my study.
When assemblages response unimodal to environmental factors, which should I choose CAP or db-RDA?
Hello, everyone. I am new to R. I want to draw an ordination diagram by just showing the centroid of each group with error bars (similar to example 1 attached). But I only can draw diagram with all plot points (example 2) and it looks too congested. What function in "vegan" should I use? Thanks very much!
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Hello
Anthony Maire
, somehow the link you have provided above is not available anymore. Can you please kindly provide an updated version of that link or some other web pages where ordihull/ ordibar and related functions are explained?
Thank you
Milan
Hello everyone,
I'm having hard time using Generalized Linear Models in hypothesis testing in community ecology. I'm trying to figure out whether a certain treatment has resulted in higher counts of individuals. In the attached file, you will see four treatment results of unequal sample sizes. I want to see whether "Treatment 1a" has attracted more individuals than "Treatment 1b" and, similarly, whether "Treatment 2a" has attracted more individuals than "Treatment 2b" (1a and 1b are compared - 2a and 2b are compared separately). Or I can simply say I want to test the hypothesis "Treatments 1b and 2b have attracted a higher mean number of individuals than their counterparts". Can anybody tell me how to do this hypothesis testing using R and Generalized Linear Models step by step? You can save your answers in the form of a script and just attach it to your answer. Thanks in advance.
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@ Kaan Özgencil ,
This is a problem of analysis of variance of one way classified data having unequal number of observations in the treatment groups.
Accordingly, go for performing the said analysis.
I am analyzing differences in community structure between rocky shore sites. I have 15 samples (quadrats) for each site. I would like to make a nMDS in R with the centroids of each site (not all the samples as I have 14 sites and it would be too messy). Is there a way to do this in vegan with metaMDS?
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Hello dear Meredith Karcz first you run the nmds analysis, then you could use envfit function to test if your variables have an effect on this multivariate distribution. Particularly I use ellipse package to plot the ellipses on your initial NMDS biplot. (I developed all process in R with R-studio).
Regards,
Cristian
I recently moved from distance-based techniques to model-based techniques and I am trying to analyse a dataset I collected during my PhD using the Bayesian method described in Hui 2016 (boral R package). I collected 50 macroinvertebrate samples in a river stretch (approximatively 10x10 m, so in a very small area) according to a two axes grid (x-axis parallel to the shoreline, y-axis transversal to the river stretch). For each point I have several environmental variables, relative coordinates inside the grid and the community matrix (site x species) with abundance data. With these data I would create a correlated response model (e.i. including both environmental covariates and latent variables) using the boral R package (this will allow me to quantify the effect of environmental variable as well as latent variables for each taxon). According to the boral manual there are two different ways to implement site correlation in the model: via random row-effect or by assuming a non-independence correlation structure for the latent variables across sites (in this case the distance matrix for sites has to be added to the model). As specified at page 6, the latter should be used whether one a-priori believes that the spatial correlation cannot be sufficiently well accounted for by row effect. However, moving away from an independence correlation structure for the latent variables massively increases computation time for MCMC sampling. So, my questions are: which is the best solution accounting for spatial correlation? How can be interpreted the random row-effect? Can it be seen as a proxy for spatial correlation?
Any suggestion would be really appreciated
Thank you
Gemma
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Hounyeme Romuald I did not find exactly a solution, I found an alternative. While working with boral I came across another R package, the HMSC package, which allowed me to model my communities explicitly including also spatial correlation.
I am quite satisfied with using this package, so I would recommend also to you to try it. You can find the link below.
Best regards
Gemma
I have been using phylocom to investigate the phylogenetic community structure of saproxylic beetles.
I have been able to use phylocom to obtain the output file that contains the rankHI and rankLOW values for NRI and NTI. How do I use these values to perform a two tailed test, to get a p-value?
Thank You!
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You could do 95 percent confidence limits as well (the '95' is arbitrary). But a high p value may not mean the null hypothesis is correct, because p also depends on sample size and power (you can also get a low p purely by virtue of a large sample). So look for a biological effect too!
Hello,
I have a large data set which consists of ~24,000 identified beetle specimens, which were collected weekly over the course of a few weeks at a dozen sites.
My goal is to quantify how many types of communities were collected at collection week 1, collection week 2, 3, etc. A hypothetical example would be at collection week 1, we identified two types of communities, one dominated by species A, and one dominated by species A & B. And so on for the other time periods. Then I'd like to connect natural history with this. Is there a way to mathematically quantify the number of types of communities, instead of just describing the observed data? I imagine this is something done with plant succession post fires, or something along those lines, but I haven't found anything.
I use R program as well.
Thank you.
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Miguel Angel García-Alvarado Thank you -- you are absolutely right. Then I could see which beetle species have the largest loading score magnitudes on community structure.
Hello,
I'm analyzing bray-curtis values over time. I would like to quantify if 1) there is a rapid or gradual change in bray curtis values, and 2) how long this change occurs. As a hypothetical example, Fig. 1a, a rapid increase until sampling week four. Is there a trend analysis I could run that would allow me to use terminology such as 'gradual' or 'rapid' based on slope? Or something along those lines.
I primarily use R.
Thank you.
Josh
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Bray-Curtis Dissimilarit
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Did you already think about using regression analysis with time as explanatory variable? Than you could use the slope as an indicator of increase (or decrease). Of course normally you would transform your data in order to linearize your relationship (between time and BC). But then you would capture the general trend (over 19 weeks) in your model and not the different parts in your trend (as you describe in fig. 1a
Is it necessary to quantify the changes? Can't you just qualitatively describe the changes?
Hello there!
Im working with a community matrix (abundance data), and i would like to check if there is a taxa indicative of different trophic state of lakes. I would to that with Indval command in R.
The problem is that my data has zeros and some taxa abundance goes up to 10000 individuals per sample.
In this scenario, would it be advisable to transform my data prior to the analisys? Should i use a Hellinger transformation?
I ask about a hellinger transformation because im thinking of doing after a CCA in this same community matrix, with some environmental data.
Any recommendation?
Thank you for your time!
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Lucas, Hellinger transformation is meant to solve the issue of double absences when computing Euclidean-based ordination techniques (PCA, RDA). Thus, it is not appropriate for CCA. Now, in the context of indicator species, you have to ask yourself if transforming your data (Hellinger or otherwise) is meaningful, especially if same sampling effort is put into collecting species from each of the categories you want to find indicators for. Remember that the indicator value doesn't depend only on the relative abundance of the species among categories but also its relative frequency, and the fact that a species has much higher abundances than others in the system must have some ecological implications. Now, the indicator value of one species is independent from the value of the other species in the assemblage, thus transforming for the purpose of accounting for large differences in abundance has little use. If you apply Hellinger (the square root of the relative abundance among species within a sample) and then compute the indicator value (highest value among sites of the product of the relative abundance and relative frequency of a species within a site) you are no longer values independent among species. Hope this helps.
What dissimilarity index would you use (e.g.: Bray-Curtis, Euclidean, Manhattan, etc...) to analyze data from a community with 10 - 12 species, over several years.
I would go for Bray-Curtis but I'm not completely sure that double absences should be disregarded
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You can use beta diversity as a proxy for dissimilarity, which uses presence-absence data and is independent of sample size. But Bray-Curtis is good too because it is based on counts and can include absences because it is bound between 0 and 1and utilises the sum of the lesser values for only those species in common between both sites
Hi,
I had originally thought that distinguishing between fixed and random factors was relatively self explanatory, however, having read an article on this very subject, I am now not so certain.
The author's decision tree (see below), particularly the part stating that any factor with 2-4 levels 'must' be fixed left me especially confused.
"A) Can I talk you out of including it? (solved – drop it from the model)
A) No I can't talk you out of it? too bad. Go to B
B) Is it a continuous variable or has only a few levels (e.g. 2-4) → has to be fixed
B) OK, a choice is possible – go to C.
C) Do you want estimates of s1, s2,…,sn (perhaps because you have lots of data and so lots degrees of freedom to burn and are curious how sites differ)? →Fixed
C) Do you want estimates of σ2, perhaps because it saves you degrees of freedom you really need or perhaps because the variance is more interesting (or useful for variance partitioning) than a bunch of estimates of site effects nobody will ever look at? go to D
D) can you either keep the design really simple or are willing to give up p-values→Random
D) You're kind of out of luck. Change one of your answers and try again"
The article also links to a discussion regarding the recommended number of groups for a factor to be random, which conforms with much of what he has said in his article.
I'm no statistician, so much of this goes straight over my head.
For my particular research question, I'm looking at differences in the composition and abundance of fishes associated with three different coral colony states (live, dead, overgrown by a particular 'coral-killing' sponge species).
I've collected my data from 6 sites, split between two islands. I've also recorded the particular growth form of each coral colony.
To summarise, my factors are as follows:
Colony state (live, dead, overgrown)
Growth form (encrusting, submassive, columnar)
Site (6; nested in Island)
Island (2)
I had originally performed Permanova (in Primer7) using colony state and growth form as fixed factors, with site and island as random. However, as per the advice of the aforementioned articles, I tried again with all four factors as fixed, which produced very different results from my original design. I've tried other combinations of fixed/random, which again, produce very different results.
Basically I'm just looking for any advice as to the correct way to proceed with this, and if anyone could provide a more definitive answer with how to determine the appropriate effect for one's factors.
Thanks in advance.
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Hi Joseph, I'm not sure if this is quite what is meant by Brian McGill. I will review the article again but I felt that his comments were concerned with
- parametric analysis of data and the associated assumptions made about that data; and
- making the distinction between blocking factors which could confound the results.
So from your description of your data and the factors:
- At a superficial level, the categorical values you have mentioned cannot be considered as blocking factors as they are not continuous values.
- The use of non-parametric analysis also removes the imposition of assumptions of distribution.
- The issue with categorical values is they sometimes represent an aggregation of information which may confound the results of analyses (and this is where the article is focused).
- The selection of sites in any experimental design is assumed to be random and therefore they should be considered as random factors in the analysis.
- The State and Growth factors are interesting. If I understand correctly, they are to be considered as potential determinants in fish diversity and abundance. If so, they should be regarded as fixed factors. If they are regarded as random factors, the relationship between fish diversity and abundance and those factors becomes substantially more complicated.
- You probably know this better than anyone, but results of statistical analyses tend to pose more questions than answers. So the iterative process will required you to look at the results and re-analyse.
It really does come down to your understanding of the ecosystems in which you sampled, and the variable you have collected. Investigating the independence of the factor you have collected prior to any construction of models or multivariate analyses is important. If they are independent, the assumption is that they are not confounding (or "blocking") factors and relationships between them can be compared as causal (or not, as the case may be).
From my point of view, I do not think you have done anything untoward in relation to considering all factors as random in the original PERMANOVA analysis. You could play with the Growth and State factors. However, I would suggest that you consider Bayesian analyses to determine the "degree of belief in an event" because (taken straight from Wikipedia but I've never seen expressed more clearly).
Trees don't grow in deserts (e.g., Sahara). Why? – The answer to this question is based on a particular combination of evolutionary history, physiology and ecology.
Do you agree with this statement?
Could you explain your point of view?
[I'm a Brazilian biologist and writer. I write about science (mainly about population biology) and would like to know the opinion of colleagues from any field of scientific knowledge (and from other countries).]
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The relative absence of vegetation in the Sahara is partly due to overgrazing by domestic animals. Have a look at the satellite view of Sidi Toui National Park, Tunisia:
This part of the Sahara is surrounded by a fence, and no domestic animals are let in (but the endemic ungulate Scimitar Oryx is living and grazing here). This part of the Sahara is an arid grassland, scattered with some trees:
Cheers,
Lajos
I'm conducting analysis of bird counts for my Master's thesis on effects of patch size and connectivity on birds of High Andean landscapes. My first goal is to use ordination analysis to figure out which bird species are associated to each of the different kinds of habitat (forest, transitional and open matrix). I have lots of environmental/spatial variables recorded, but I decided to begin with an unconstrained ordination, just labeling the sites with different colours according to habitat and checking which sites and which species seem to group together.
My data is not very good (for many reasons, one of them just not having had enough time in the field) but I'm trying to salvage it the best I can. I've ran a CA and a DCA on my species matrix, using vegan package in R, and the procrustes function shows me large (and quite chaotic) differences between the plots from one method and the other. Is this telling me that arch effects or compression of extreme scores is happening with the CA, and so I should opt for the DCA? Or is it just because the CA explains very little variation in the data (the first two axis amount to around 18% of total inertia), so sites and species will just float around with no real meaning when I do the DCA?
A little extra question - would it help me to get more variation explained if I remove from my dataset some of the rarest species or some of the ones that move around the most between the CA and the DCA?
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Hello Joao. For a multivariate analysis in this case I would recommend you to apply a PCA (Principal Component Analysis). It can show you several correlations at the same time. And I agree with Andrew, don´t take of the analysis the rare species, maybe they are the ones that can provide the most valuable information. But you always can take of outliers from a single group (or species in this case), which are simply data that, for any reason, is totally different from the normal distribution. I mean, if you have, let´s say 30 records for Species 1 corresponding to forest and 1 record of the same species in the transitional habitat, that would be clearly an outlier and, only for statistical purposes, you can remove that record from the analysis because it will influence in the resulting value, but of course you have to do this only when you are totally confident that you are dealing with an outlier. But, if you have just one record of a rare species, that maybe it´s behaving different from the rest of species into the same Family, you must use that data and try to find out the reasons for that difference. Maybe that will help you to explain why that species is so rare.
I built a model that outputs the composition of a coral community (i.e., % cover for each species) at different time steps. The rows of my dataset are consecutive time units and the columns are the different species. I want to compare this output to a real dataset (which has in consequence the same dimensions) in order to measure how close to the real data is my output data.
There are many coefficients out there (e.g., RVcoefficient and its different versions, principal response curves, Mantel test, etc.) and I'm confused about which is the one to pick in my case.
Thanks.
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Hi Bruno,
Have you considered creating distance matrix from your data using e.g. bray-curtis dissimilarities, etc. This could be also visualized through e.g PCA, PCoA. The distance matrix could be then used to test your hypothesis (H0- there is no difference between the real and modeled dataset) through ANOVA or PERMANOVA statistical test.
I think this would be a good way to go.
Best,
Deni
Based on my research, this method is used to assess the adequacy of sampling, but I don't know what the difference is between them.
Can anyone help me regarding this subject?
Which individual based and sample based method is better for determining the adequacy of sampling?
My study was carried out in two regions with different climates and in each region, we are sampling in two different management regimes.
Which scale (management, climate regime or total data) must be used in analysis to assess the adequacy of sampling?
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Species accumulation curves (or collector's curves) are used to estimate the number of species (i.e. species richness) in a particular area. A species accumulation curve records the total number of species observed, during the process of data collection, as additional individuals are added to the pool of all previously observed or collected individuals or samples. Accumulation curves may be either individual-based or sample-based.
A species rarefaction curve answers the question "how many species would a smaller sample include? Say you had two samples, 'A' with 100 individuals total and those 100 individuals distributed among 9 species and sample 'B' with 25 individuals distributed among 4 species. Rarefaction answers the question "How many species would I expect in sample A if I had caught only 25 individuals in all instead of 100?" The species diversity of two samples, containing 100 and 25 individuals respectively, can be compared directly by rarefying the larger sample down to 25 individuals.
I'm trying to calculate rarefied species richness for data set which looks something like https://ibb.co/bZeALk this. I read the .csv file into R and it was converted to a data frame. Now when I try to operate on it, I always end up with error messages like "Error in round(x) : non-numeric argument to mathematical function". What do I do wrong? I'm setting the sample size as the smallest community size. I'm setting MARGIN=2.
By the way, my goal is to compare these different counts from different years in terms of their diversity and I wanted to use rarefied richness too. They are from the same place but from different years.
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When you enter a csv, xlsx aR file, he creates a first column, before performing the analyzes, make sure to delete that column in the edit file and save it. That must be the solution to the problem
I have 17 rocky shore sites. From these sites I have quadrat data with species percent coverage data.
From each of my sites I also have an environmental measure for a gradient I am interested in. I am only really interested in how the community changes over this one gradient.
The problem I have run into is that every method I have read about for constrained ordination/ direct gradient analysis seems to require more than one environmental variable. I only have the one environmental variable that I am interested in. I did measure 2 other variables but they are direct proxys for the one environmental variable I am interested in.
I really want to find a way to do direct gradient analysis on this community data using only the one environmental variable - surely that is possible??
I have already done a nMDS of my data in R and fitted a vector using envfit of my environmental variable. But because the method is unconstrained i feel it is not that informative?
Any help would be really appreciated by this stressed out masters student :)
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Hi Meredith,
Constrained ordination with only one explanatory variable is not the best choice. Yes, you can do it, and there is nothing wrong about it; however, as the main purpose of ordination in general is to reduce dimensionality, in this case you only get one ordination axis (despite the fact that you have several species) and thus you only can assess the gradient in one dimension. The alternative (and not sure how great it is) to 'force' the analysis to by converting your one continuous variable into a categorical variable. Of course, you'd need to have a good and sound rationale to assign categories to it (e.g., natural breaks or something alike). Alternatively, you can double-check if your two other variables are correlated (i.e., colinear) with the one you are interested in, and if not, you can still use them in the analysis. Whether they are significant or not in the model is another story...
I think the actual question here is "how do you identify keystone species from a food web"? Do you always need the biomass data or knowing the whole food web is enough?
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What's changed in our understanding of the keystone species concept? Of course top level predators can be keystone species! Paine's original example was the sea star, Pisaster ochraceus, and sea otters, wolves and jaguars have all, also, been described as keystone species. The latter two are clearly apex predators. I won't comment on jaguars, but the evidence for wolves as keystone predators seems quite strong. So, I'd argue that high level or apex predators CAN be but are not always keystone species.
I wonder if interaction strength could be used as a (partial) measure or indictor of a keystone species. See for example
Ripple et al. Status and Ecological Effects of the World's Largest Carnivores. 2014 Science 343 (6167): 1241484 DOI: 10.1126/science.1241484
Sala, E. & PKD Dayton. 2011. Predicting strong community impacts using experimental estimates of per capita interaction strength: benthic herbivores and giant kelp recruitment. Marine Ecology 32: 300-312 DOI: 10.1111/j.1439-0485.2011.00471.x
Sala, E. and M.H. Graham. 2002. Community-wide distribution of predator-prey interaction strength in kelp forests. Proceedings of the National Academy of Sciences 99: 3678-3683.
Estes, J.A.; Tinker, M.T.; Williams, T.M.; Doak, D.F. (1998-10-16). "Killer whale predation on sea otters linking oceanic and nearshore ecosystems". Science. 282(5388): 473–476. doi:10.1126/science.282.5388.473.
I'm also intrigued by the possibility that the "interaction" need not be a direct, trophic interaction:
Gómez, José M.; González-Megías, Adela (2002). "Asymmetrical interactions between ungulates and phytophagous insects: Being different matters". Ecology. 83(1): 203–11. doi:10.1890/0012-9658(2002)083[0203:AIBUAP]2.0.CO;2.
There seems to be some confusion in the literature and both are commonly used for the same index. However, there must be one reference for the right citation of the index.
Any ideas?
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And finally a short guide to citing the Shannon index: Claude Shannon, Norbert Wiener an Warren Weaver really existed and knew each other as excellent mathematicians. Shannon invented the index 1948 and published in Bell Journal. However the book coauthored by Weaver since 1949 (many reprints) has offered more general implications. Wiener independently published many similar and supportive ideas with impact on Shannon (something like Darwin and Wallace case) .
Shannon index = best alternative
Shannon - Wiener = acceptable alternative
Shannon - Weaver = disputable alternative
Shannon - Weiner = incorrect alternative
Dear researchers,
I am currently working on a project aiming to access the influences of a disturbance on coral reef fish assemblages.
As the title goes, I've encountered a major problem while computing FD indices. I am going to compute Functional Richness, Functional Evenness, Functional Dispersion proposed by Dr. Sébastien Villéger at 2008.
However, the lack of enough species/functional entities in most of our observation makes FD indices computation impossible (The size of the assemblage in every observation is small, usually less than species).
Here are some details of our research method
The field survey method we applied is "modified Stationary Point Count (SPC)", apart from the usual SPC, I select a patch of coral (ranging from 20*20cm2 - 150*150cm2 ) as an object and record down the species either swim by from less than 1m above or crawling on it, as well as the abundance of those species for 6 minutes. And thus we usually encountered less than 3 species. Three treatments are there and for each treatment, we collect 10 data (10 observation).
I appreciate any comment and piece of advice on this topic and thank you in advance.
Best,
Yu-De
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Villeger et al. (2008) Ec
ology .pdf
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I concur with the prior answers. A methodological answer to why you're unable to calculate the metrics is because the FRic (at least in R package "FD") is calculated on a principal coordinates analysis that requires more species than traits in each sample. There are corrections (see ?dbFD or ?calc_metrics in R package "ecospace") you can use to try to get around the limitations. But if some samples really have less than 3 species, then you will not be able to calculate this metric. (Technically, all Villeger's metrics are based on PCoA space, but only FRic requires the "more species than traits" requirement for the convex hull calculation to work correctly. If using dbFD to calculate, you can "turn off" FRic calculation with dbFD(... calc.FRic=FALSE), and you'll still get the others.
In particular, my questions are:
-How to deal with abundances recorded during multiple visits (2 or more) to each sampling unit? I see that a common practice is to consider the maximum over the visits as the abundance in the sampling unit. I wonder whether is it possible to account for species detectability directly in the RDA (as in unmarked for univariate models).
-Is it possible in RDA to account for spatial non-independence of
sampling units?
Finally:
-Is it better to consider occurrence (presence-absence) or abundance in RDA analysis? Which give more robust and reliable results?
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Thank you Elia, the subsequent question is: how to implement random factors in RDA (in R vegan)? By partial-RDA?
Andrew, RDA per se doesn't account for spatial non-independence, I suppose. Probably there are specific techniquest to deal with it in RDA. I am wondering how...
I am investigating the functionality of a lotic river and would like to calculate the exergy to compare with the values of other biotic and abiotic parameters to obtain a quality scale of ecosystem functionality. So I need the values of the βi coefficients in accordance with the Functional Feeding Groups (FFGs) of the macroinvertebrate community of a lotic river.
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thank you very much
the first-one I know already, I will look for the second-one
hi
maurizio
I'm measuring the shrimp diversity using diversity indexes such as Shannon, Simpson, Pielou and Simpson's dominance. I sampled three different sites during three seasons, so I have a total of 9 values for each index. My question is which statistical analysis could I use for testing if there is a significant difference between those values due to the sampling site or season or both?. Or if there is no need to use them and just make my conclusions based on the raw values of the indexes. Thank you for your attention. Best regards
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You should start identifying the nature of the distribution of values of the index, i.e. Shannon index values are stricktly positive and continuos, so you should use some GLM model which allows for Gamma distribution.
Diversity (Shannon values) = a + Beta*Site + Beta*Season,
Diversity(Shannon ~ Gamma(µ, τ)
E(Shannon) = µ_i & Var(Shannon) = µ_i2 / τ
ANOVA approach can ONLY be ok, if you have enough replication, otherwise It will assume normality and homogeneity, in the distribution of values of the Shannon index, and residuals, which we know is not true for small samples. Let me know if you need assistance.
I am trying to identify the subset of non-gelatinous zooplankton species that show correlation with jellyfish species. I have abundance data for
non-gelatinous zooplankton species. While the jellyfish data are presence-absence data.
I am wondering how I can run a forward selection process in Canoco 5 to do so?
Any help would be highly appreciated.
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Thanks Xavi for sharing your paper.
Hello everyone,
I need to analyse the role of spatial vs environmental effect (through variation partitioning) on Notonecta species distribution among fishless ponds. I have been using adespatial package to do that. My question is that after I calculate the MEMs how can I evaluate the scale of each one of them (fine versus large) to form the submodels which can be used in variation partitioning? I have read and tried most of the adespatial functions but I could not find the function which can help me calculate the scale of each MEM.
I really appreciate if anyone can help.
Mitra
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Dear all,
I have posted another question I had on the website, I am copying the same question here too and I really appreciate if anyone has any comments that can help me with it.
I want to analyze the role of spatial vs environmental effect (through variation partitioning) on Notonecta species distribution among fishless ponds. I have been using adespatial package to do that.
After calculating the MEMs, I need to estimate the Moran`s I spatial autocorrelation values, in addition, to the positive and negative part of this index for the ten MEMs I have.
In adespatial package, in the dbMEM example of mite data, Moran`s I is calculated using moran.randtest, without using the listw function as follows:
test<-moran.randtest(mite.dbmem1, nrepet=99)
However, in the explanation of the test itself, listw is been used:
moran.randtest(x, listw, nrepet = 999, ...)
I have analyzed my data with and without using the listw and I get different results. I was not sure which one of the two methods is more appropriate since both examples are given in the same package. I was wondering if anyone has used the function moran.randtest and if yes, do I need to use listw to calculate the Moran`s I index?
I would really appreciate your kind guidance!
Mitra
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I have a species list and their related abundances from various sites.
I have composed a resemblance matrix 'between species' (between variables tab in PRIMER), to investigate similar species groupings, observed in an MDS plot
I am interested in using a PERMANOVA to investigate how certain factors (region, management status (protected/unprotected)), as well as some abiotic variables such as %coral cover, %macroalgae cover are influencing the observed groupings, and which are significant influences.
However, when trying to perform a PERMANOVA based on the resemblance matrix of 'between variables', it does not allow me to input my factors (region, reeftype, and management). If I change the resemblance matrix to 'Analyse between Samples' (comparing sites instead of species), I am now allowed to use the factors in the PERMANOVA design- but I am not interested in this.
My question is: HOW CAN I PERFORM A PERMANOVA BASED ON THE OBSERVED RESEMBLANCE MATRIX OF SPECIES COMPOSITION (ANALYSIS BETWEEN VARIABLES), IN RELATION TO THE FACTORS (REGION, MANAGEMENT) AND ABIOTIC VARIABLES (E.G., % CORAL COVER)?
I hope I have made my issue clear enough? I would appreciate any help/recommendations from anyone.
Thanking you in advance,
Ameer
Dear all,
I'm working on dung beetle assemblages, and I would like to test the hypothesis that the community structure of these insects is different along a gradient of grazing pressure. In two similar sites, dung beetles were sampled into 3 levels of grazing pressure (High, Moderate and Low), with 5 pitfall traps in each level.
After analyzing my data with a Correspondence Analysis (where sampled communities are classified in 3 groups : High, Moderate and Low grazing), I would like to know if the dung beetle community structure is significantly different (or not) between the 3 levels of grazing pressure. An ANOSIM (build under R software) shows that : R = 0.4097, p = 0.000999. That, it's ok ! But I don't understand the other parts of the results... for example, the values of "Dissimilarity ranks between and within classes".
Thanks a lot for your help !
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Hi William
The R-statistic in ANOSIM is a ratio between within-group and between-group dissimilarities. The steps in the analysis are:
1. calculate a matrix of dissimilarity scores for every pair of sites
2. convert the dissimilarities to ranks
3. calculate the R statistic as the ratio between dissimilarities between sites within a group (e.g. high grazing pressure) and the dissimilarities between sites that are in different groups. The closer this value is to 1, the more the sites within a group are similar to each other and dissimilar to sites in other groups.
4. The significance of the R-statistic is determined by permuting the membership of sites in groups.
Most of the research focuses on niche partitioning to find out coexistence mechanism but I am finding difficulties in measuring niche difference in secondary forests where there are lots of species.
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Yes, you can use both. Niche partitioning is used to show how competing species use the environment differently in ways that helps them to coexist. Likewise, phenotypic differences (or functional traits) drive the stabilizing niche differences that promote coexistence.
See:
Plant functional traits and the multidimensional nature of species coexistence, PNAS, vol. 112 no. 3, Nathan J. B. Kraft, 797–802, doi: 10.1073/pnas.1413650112
I hope this helps :)
I want to compare arthropod assemblages (with presence-absence data) found in several plant species belonging to a same genus.
As the sampling effort was not the same in some plant species, the resulting dendrograms are very skewed to the number of localites sampled.
I would like to know if there is a method to weight my data in order to get a more realistic interpretation of the relationship between host plants.
Do you know any statistical method that allows this kind of analysis?
Thanks in advance.
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Hi Daniel
If you have the data on which arthropod species are from which location, you could use rarefaction to rarefy to the smallest number of locations you have for a particular plant species. That is, if I sampled a smaller number of locations for plant species x, how many arthropod species would I expect on average?
This approach is commonly used for alpha-diversity patterns but not beta-diversity. However, in principle, it could be done by repeatedly subsampling the data and recalculating the dissimilarity matrix (I assume you are using dissimilarity because you mentioned dendrograms).
A much simpler alternative is to use Simpson's Dissimilarity. If increased sampling effort leads to more arthropod species per plant species (a very likely assumption), then Simpson's Dissimilarity is designed to correct for this difference in species richness between samples. In practice, I find it an over-correction but it might work fine for your data.
I'm new to this literature base, but I have this notion that plants with the potential to grow big may be more likely to become a dominant in a plant community. When I think of North American ecosystems near me, the dominant is usually one of the larger plants (Ponderosa pine, Creosote, Big Sagebrush, Pinon Pine, etc.). I can imagine that many different traits besides body size could contribute to success in a community, but is there good evidence that (in general), body size is one of these? I'd be grateful if you could suggest a key paper or 2 to review.
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Commonality of functional traits will depend upon monogenic or polygenic controlling ability of domimant species..
I have problems with species tolerance scores or the sensitivity values (ES50) because I'm not plenty sure how to calculate it. I want to evaluate the benthic quality of three sites using caridean shrimps, but I'm a little confused with the estimation of ES50 which is said to be the fifth percentile of the distributions of expected numbers for the samples in which species occurred. So, this means I have to compute the 5% of the abundance of a given species in my sample stations, then using this number in the equation and repeat this for each species found in a that station? or select only a few species (te most abundant) to do so?. Apparently, another problem is the number of samples, which seems they has to be more than 20 and I only have 3 sampling stations.
Thank you very much.
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First, you need to define your sampling terms and be sure of your experimental design. Are your sampling stations distant to each other? If so, then they constitute replicates (you call them sites) and you should obtain 20–30 samples at each site at each visit. If the sites are close to each other, then they are pseudo-replicates, but that's okay so long as you mention this. It's better to obtain too much data than not enough lol.
Second, regarding tolerance scores and sensitivity values, you need to measure all species because ES50 values can include any and all of the species present.
See the following reference, which mentions the minimum 20 samples:
Calculation of species sensitivity values and their precision in marine benthic faunal quality indices
Marine Pollution Bulletin
Volume 93, Issues 1–2, 15 April 2015, Pages 94–102
I hope this helps :)
I have just photographed the spider below (a Salticidae, I guess). It seems that it mimics floral buds to ambush its prey. Can anybody identify it?
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OK! You can use this one, too. Best,
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I have species present and relative abundances from one visit for the historical data to sites that we have been able to resample extensively. We do not know the sampling effort for the historical work - just species lists and numbers of individuals caught/site. There seems to be a great deal of disagreement (and reviewer harshness!) about the most appropriate methods when (historical) data are limited. Our questions are really about how species' ranges have shifted, expanded, contracted, or remained the same over time and across sites.
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You can compare species diversity (using indices) and ranges across sites and years. You need to figure out whether your sites represent true replicates or if they are just subplots within one consistent area. Either is fine, so long as you specify whether you have true replicates or not. Generally, the more widely spaced the plots, the more likely they are to be true replicates :)
Hello everyone, I am trying to apply a PERMANOVA with covariables to a benthic community dataset. I have species density per sample in 4 different distances from a shipwreck and 4 covariables. I am trying to do this using Primer but all the time the results are "no test" and df=0, to pairwise tests for distances. Can anyone help me with that? What am I doing wrong?
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Try using Type 1 sums of squares
The identification of Culicidae by morphological characters takes into account very small structures. Which increase is the ideal to do identification of Culicidae? Does anyone have any suggestion of equipment with a good cost benefit?
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Declan, Thank you for your suggestion.
More occupied niches are characteristics of a, more or less, stable community. At the same time this means that the ecosystem contains a high biodiversity. However, why is the most stable stage of succession, the Climax stage/community, constituted by low biodiversity? This contradicts the first statement of a direct positive correlation between stability and diversity.
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"Ecologist have used quantitative measure to test the assumption that increasing level of diversity lead to increasing community stability and biomass production. In controlled experiment in greenhouse or gardens or grassland plant communities ,increasing the number of species growing together generally lead to greater biomass production and resistance to drought . However the significance of this result to the broader range of natural communities ,such as Forests and coral reefs still need to be convincingly demonstrated" by Richard B Primack, Its seems like relation of diversity and community stability does not apply to all communities. Regarding community climax there several hypothesis trying to explain succession to community climax + there is as well several climax state , and each will have different explanation to your question...please read the book Ecology, Experimental analysis of distribution and abundance by Charles J Krebs chapter 20 on Community change
I know that indirect commensalism and apparent competition are possible, but is indirect mutualism possible? Is there a way for two organisms to benefit one another through the use of a third?
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I think you are referring to facilitation, as Dr Young mentions. By the way; I like a lot these papers, Todd Palmer taught me Community Ecology in my PhD program. The book and papers of Judith Bronstein are very good to get you in this topic, here is one http://userwww.sfsu.edu/parker/bio840/pdfs/2013/Bronstein2009.pdf . In my dissertation, I found out that hummingbirds and flowerpiercers benefit one another through the use of some flowers
I understand that, generally, community stability is defined by having no apparent change in population size over a given period of time. Such that, the level of disturbance occurring in a particular community is just one factor that greatly affects the stability of a community. So what are other significant factors which specifically affect the stability of a marine community, and how are these factors measured or quantified (if possible)?
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Thank you for your responses Mr. Tomascik and Mr. Ben-Yami!
There are four sampling sites on a hillslope (top, upper, lower, bottom), each site has three replications. We have studied soil nutrients and plant biomass in these four treatments, a reviewer suggested that a proper statictical analysis (autocorrelation between topographic positions) was needed.
My question is that how can I do the analysis for autocorrelation for my study? I am familiar with SPSS. Thank you very much.
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Here is a short, relevant, easily understood editorial article which explains pseudo-replication from a journal editor's point-of-view:
Binkley, D. (2008). Three key points in the design of forest experiments. Forest Ecology and Management 255.
Use the article to understand how to draw appropriate conclusions from your present and future studies.
For example, rows are sites and columns are species, data is count data of those species in those sites. Can I use PCA to analyze the species composition in those sites or is it more robust to use NMDS?
I thought since there is no linearity between species abundances then I couldn´t do a PCA because it violates the linearity assumption. However, I read a few articles that use PCA with count data.
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Species counts within a variable can be treated as continuous if the range of count values in a variable is large so that the addition or subtraction of an individual is a small proportion of the total. So, if a species has counts from 20 to 200 then the data for that species is effectively continuous (even though you can't have a value of 34.73 individuals).
If species counts are not like this then there are better alternatives. That is, if the counts are between 20 and 35 (or zero and 10 say), or there are many zeros.
Under these circumstances, if you still want to do PCA, you can: 1, leave out species with many zeros or counts of low range or 2, combine species counts so that the totals meet the criteria outlined above (find a sensible criterion for combining - such as species from the same genus or family or 'all Diptera with aquatic larvae').
Or you can use Categorical PCA (CatOCA) after reducing your counts to categories (discretize the count data). I'd suggest using categories based on the quantiles rather than the standard deviations.
NMDS, however, is a better option if your counts have many zeros.
It may not be the best though. I suggest that you look at Manly's "Introduction to Multivariate Statistics" as a starting point and also, for ideas that may be better for your data, papers like A. Ramette (2007) Multivariate analyses in microbial ecology, FEMS Microbiology Ecology, 62, 142-160.
good luck, Andrew
During my travels to Kenya a little while back, we came across this deer or impala. Our guide explained that they lived in polygynous herds and younger male impalas challenge the leading males. He further explained that once the male loses, he is forever ostracized by his heard and other herds of females. I understand that the new male would exile the losing male as to prevent any chances of being overthrown but I do not understand why the losing male can't start a new herd/challenge other herds. If i recall correctly, our guide said,they just know which male has already lost.
I'm not sure if the beaten males band together when they encounter each other or remain in solitude for the rest of their lives but from what I recall, it would be the latter.
The picture is one I took of a lone male who presumably already lost a battle. We followed him as he grazed near another herd. The herd was very wary of him and was generally not accepting. Do they also lose any drive to challenge new herds once they had lost?
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Dear Laura,
What you are describing is a common behaviour of Impalas. There are two sets of groups: 1. the bachelor herd which consist of males with different ranks-such that the entire herd is controlled by a dominant strong male; 2. a herd with several females and only one dominant male who fight to ensure that his genes are carried to the next generation by mating with all the female in his herd. Over time the dominant male becomes weaker and other strong males from bachelor herds starting him. If the dominant male engage in a strength battle with the other males and lose, then female herd is taken over by the strongest new male. The loser has to find a bachelor herd that will accept him, but on condition that he enters the herd as the lowest member. This means that the loser has to try and climb up the "hierarchy" over time with the hope of taking over the bachelor herd. But since the overthrown male is weak and there are other stronger males in the herd, it might be difficult for it to have another chance to take over the female herds.
Please see the publication by Oliver and others on "territorial behaviour in southern impala rams"
The diseases of focus are malaria, dengue and influenza
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Hi John!
For influenza, since it is a contagious respiratory disease, it should follow the standard notion indicated by the model Rp=NBL, wherein the density of the population (N), transmission rates(B), and average time one stays infectious(L) affects whether or not the the disease spreads as dictated by the number of infected hosts (Rp). I am not familiar with vector borne diseases but surely, it should follow with something similar. Of course as what Muhammad had said, hygiene (as well as vaccines) is very important but there are of course other factors such as these to consider with the spread of diseases.
I just completed the shannon Wiener diversity index and calculated a score of (H)=2.154890613. The values range from 0 to 5, with common ranges usually between 1.5 to 3.5. Can I really say that the population is diverse? What other index should I use when getting a score like this? Thanks
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Hi, Ms. Kayla Tennant!
There are different indices that may be used to measure biodiversity. Based on information-theory, the Shannon-Wiener Diversity Index measures uncertainty in order to determine if a community is diverse. A high degree of uncertainty (H') would indicate high diversity since it would determine how harder it will be to predict the species of a randomly picked individual from a population. It is easy to calculate, however, it does not reflect the dominance or evenness of a population.
Other diversity indices that you may also utilize are the Simpson's Index, Berger-Parker Index, and Brillouin Index.
The Simpson's Index works on the idea that high dominance would indicate low diversity. It measures the probability of dissimilarity of the population, thus, a highly similar population would indicate lower diversity. the index is evenness-sensitive, however, less sensitive to species richness or abundance.
The Berger-Parker Index quantifies the numerical importance of most dominant species, while the Brillouin Index are used in questionable sample randomness.
Biodiversity indices observe changes to richness and evenness between datasets. The type of index to use may vary, depending on the site discrimination, sample size, diversity component in question, and the overall use of the index.
I attached a link that may help you on species diversity measures. I hope this helps!
Could you give me some principles / concepts on ecology of spread of diseases? For example, deforestation causes spread of disease because their niche was destroyed. What concept would best explains this?
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Hello John Vincent!
I agree with Katrina, that among the factors that affect the spread of a disease is the population size, like in the case of parasites. In the simple equation for the number of infected hosts in parasitism (Rp), Rp = NBL (where N is the population, B is the transmission rate of disease, and L is the virulence of the disease), it is inferred that the greater populations have higher susceptibility to the disease since there are more hosts to infect.
On the genetic level, a more genetically diverse population has a higher chance of surviving an epidemic. Genetic diversity could act as a buffer in the spread of a disease. Aside from this, climate could also play a part in an epidemic as pathogens are known to thrive in warmer conditions. Hence, an increasing temperature will also increase disease in several hosts such as plants and humans.
Should you wish to find a more thorough discussion on these, you can find them here: https://www.nceas.ucsb.edu/science/disease#
I'm currently working on the alkaloid composition of the skin secretions of salamanders and am trying to test whether this composition differs between different populations.
In line with previous research on alkaloid profiles in poison frogs, I tested for differences among populations using an ANOSIM. Since I work with relative concentrations (a.k.a. proportions), I thought it was more appropriate to construct an Aitchison dissimilarity matrix for this analysis.
I was further interested in seeing which exact compounds were responsible for differences between the populations. A SIMPER, often associated with an ANOSIM, seemed perfect ... but SIMPER in R uses Bray-Curtis dissimilarities.
I was wondering if there is an alternative for SIMPER that uses other indices of dissimilarity? Could a PCA do the same?
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GIlles, although this doesn't answer your question, just for your information, SIMPER and ANOSIM have serious issues since it is very difficult to determine whether differences are attributed to within-group or between-group variation, which may provide misleading results. You may want to look Warton et al. 2012. Distance-based multivariate analyses confound location and dispersion effects. Methods in Ecology and Evolution, 3, 89--101.
I hope this is a relevant question to ask here since many experts collaborate with this project.
According to my readings, many authors have discussed farmland heterogeneity and its effect on biodiversity, mainly by using 'species richness' and 'functional traits" of some taxa.
My question is what are the novel dimensions that we can use to assess the effects of farmlands heterogeneity on biodiversity??
Thank you very much!
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You might try an ecoacoustic approach as well.
Harris, S.A., Shears, N.T. and Radford, C.A., 2016. Ecoacoustic indices as proxies for biodiversity on temperate reefs. Methods in Ecology and Evolution.
Krause, B. and Farina, A., 2016. Using ecoacoustic methods to survey the impacts of climate change on biodiversity. Biological Conservation, 195, pp.245-254.
Parsons, M., Erbe, C., McCauley, R., McWilliam, J., Marley, S., Gavrilov, A. and Parnum, I., 2016, July. Long-term monitoring of soundscapes and deciphering a usable index: Examples of fish choruses from Australia. In Proceedings of Meetings on Acoustics 4ENAL (Vol. 27, No. 1, p. 010023). ASA.
I read this term in papers discussing functional diversity and phylogenetic diversity; they always mention "ecologically relevant traits". If there are ecologically relevant traits, then there should be ecologically irrelevant traits, which I could not imagine any as I always think that species traits are there as the result of their ecology. Or do I understand this term incorrectly?
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Hi Sabrhina,
It is possible the papers you refer to may have been talking about ecologically relevant traits in the sense of ones that can clearly be related to aspects of ecology and life history (also called 'functional traits'). Examples would include (for plants) traits such as leaf mass per unit area or seed size (cf. the paper by Diaz et al. for how only six such traits were used to explain a 'global spectrum of plant form and function'). 'Ecologically irrelevant traits', as you put it, could be thought of as traits for which either an ecological function cannot easily or clearly be ascribed, or where the relevance of the trait to ecology and life history is indirect (i.e. manifest via one or more other traits) or is simply unknown - there are an enormous number of these (cf. for example the trait table on the TRY Plant Trait Database https://www.try-db.org/de/TabDetails.php; cf. also http://traitnet.ecoinformatics.org/traits-and-protocols). An alternative way of thinking about 'ecologically irrelevant traits' (or 'non-functional traits') is that they are neutral in an evolutionary sense, i.e. have no adaptive value. Hope this helps. Cheers, Matt
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Diaz et al (2015) The global spectrum of plant form and fu
nction.pdf
I want to analyse the biomass, abundance and functional diversity of different flora and fauna in my experimental samples. Please suggests me precise and standard methods for this purpose.
Dear All,
When we like to describe ecology of threatened plants in a forest what are possible parameters must considered?
Is there any significant differences among the parameters in natural forest or planted forest.
Thanks in advance.
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Dear Rishad, - I agree with Judith Okello regarding the importance of the immediate neighborhood of a threatened species. See if this Open-Access paper and the literature cited in it helps: Diversity and Production in an Afromontane Forest. Forest Ecosystems 2016, 3:15 (doi:10.1186/s40663-016-0074-7). regards, KG
Good day
I am currently working on a project attempting to assess the niche overlap of various species using functional traits.
The issue I am running into is that the analysis I had intended to use (link in replies) is individual based and requires multiple individuals of the same species within the data set in the form (Sheet 1) however my data takes the form (Sheet 2) due to my data dedicating a single row to a species and their predominant trait (literature based). My data incorporates categorical and continuous data (reason for using first analysis).
Any suggestions?
Thanks in advance.
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See YouTube: SPSS for newbies: Changing a scale/continuous variable to a categorical variable
I'm looking to see if vegetative structure and plant diversity are linked to increased abundance of polyphagous predators.
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If you are asking whether percent cover (by species) is an appropriate abundance measure, then Yes.
I am analysing the species diversity of some polychaete samples and after performing a dendrogram and an MDS analysis, I got two clearly separated groups with significant differences in biodiversity indices. What analysis/es could I employ in order to know more about the species that account for most of this difference?
Thanks for your help.
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Dear Macarena,
You could use a SIMPER analysis, which is, for instance, provided in the free software PAST (see manual attached, p. 118). It is specifically designed to assess which taxa are primarily responsible for an observed difference between groups of samples. Like nMDS, SIMPER uses similarity measures such as Bray-Curtis or Euclidean distance. The results are given in percent (SIMPER means "similarity percentage").
Diversity indices might give you an idea of differences in diversity among the samples but won't help to identify the species responsible for it.
Best regards,
Thomas
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Hammer_2016_Past_3.13_
manual.pdf
There are various similarity measures and distance metrics used in microbial community analysis. Some examples are Jaccard, Kulczynski, Unifrac, Bray-Curtis, Morisita-Horn and others. I want to know if there are some which are more common than others and for what reason in the context of microbial community. It is justified that Unifrac is phylogenetically "correct" however, the remainders are not but still largely used.
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Thank you Shafagat and David for your clarification. This definitely clears up my doubts regarding the different beta diversity measures. I also see why many papers also use multiple diversity measures.
For David, regarding your comment on the speed of UniFrac, a very recent development was made in the field which accelerated the calculation (see arxiv.org paper). I think researchers will more likely involve UniFrac in their future calculations along with other non-phylogenetic distance/similarity measures.
For Arturo, I believe Renyi entropy is a suitable measure for alpha diversity, rather than beta diversity.
Any methods to rank the conservation status of any plant species, other than diversity indices and Important value Index?
Thanks in advance.
I would suggest restricting the concept of ecological community to an assemblage of organisms living in the same ecosystem and belonging to the same taxonomical group and the same trophic level (e.g. a plant community, a community of orthoptera in a grassland). In this sense, community ecology is dealing with diversity analysis, numerical ecology, etc.
"Vertical" relationships among communities (or populations) across trophic levels in food webs is more related to the concept of biocoenosis.
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what about relation with environmental parameters?
Dear all,
I am working on an ecological community species data matrix (site by species), and I have many species and sites. I want to select sub-communities with different sample sizes randomly, and later compare the similarity of these communities. The idea of doing is that some of my sites have a few specimens, so I want to find a sample size (a threshold) that I can use to compare the communities with each other, and discard certain sites that fall below that threshold. I am trying to decide which sites I want to include in my data analysis.
Two questions:
1- How can I randomly subselect the communities? Along with this line, I tried various options, i.e., rarefy the communities to a certain size or use 'sample' package of R.
2- If I have communities with different sizes, and generate distance matrices using these communities, I am not able to compare them using mantel test in R, due to incompatible dimensions. How would you compare samples with different sizes, regarding their similarity?
Any suggestions on these issues are appreciated.
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It may be better to perform stratified sampling. A stratified sample is a mini-reproduction of the population. Before sampling, the population is divided into characteristics of importance for the research. For example, by main species type. Then the population is randomly sampled within each category or stratum. Random sampling has a very precise meaning in that each community has an equal probability of selection, which it may in fact not have.
Since communities may be of different sizes, perhaps you should compare composition by percentage, for example using Shannon's or Simpson's diversity indices.
See: On sampling procedures in population and community ecology, Vegetatio 83: 195-207, 1989.
I hope this helps :-)
I am using phenetic methods (of morphological traits) to delimit different species of plants. Using PCoA and NMDS analyses, I have been able to get some very nice separation of clusters of "species" in ordination space with my data--but I want to know which of my characters are the most influential in the separation of said clusters.
I am using both discreet and continuous variables, and thus I cannot use CVA/LDA that would typically give biplots showing this information. (Mixed data violate the assumptions of CVA/LDA!)
How can I figure out the character(s) that help to separate out my species? Thank you, everyone!
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In PCA analysis (on correlations), look at the coefficients of the 1st principle component and note the variables that have the largest absolute values. These are the most influential for determining the direction of that component (vector). Note also that the relationship of the positive variable coefficients are inversely related to those with the negatively signed coefficients. For the second principal component (the one that explains the second highest proportion of variance) the interpretation is the same and likely will show a somewhat different set of important variables. And so on...
I have a 3-dimensional NMDS of avian community composition, and I have built predictive models linking site scores to environmental covariates (one for each axis/dimension, so three models) to make spatial predictions across a landscape that estimate a given site's position within the ordination combining predictions for each dimension. Given this, I was hoping to then be able to estimate a given site's avian composition based on it's position (sites scores) within the ordination. I am familiar with OMI, but was hoping to use my existing models to extract or estimate community composition given that I know the site's site scores.
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Thanks Andrew and Susanne for your responses. In reading the papers on CoCA I found this:
Guisan, A., S. B. Weiss, and A. D. Weiss. 1999. GLM versus CCA spatial modeling of plant species distributions. Plant Ecology 143:107–122.
The authors used the euclidean distance from a given site's scores to a given species' centroid in sd units variance of the species scores as a means to estimate a site's habitat suitability for individual species. I am using this as a means to estimate a novel location's species composition based on a suite of environmental covariates that predict its position in ordination space.
Im looking to compare how two communities change over time with each other, but not just their total abundances but also measures of their diversity. This will include techniques such as Bray-Curtis analysis.
What is the best methods to compare variability in each communities diversity over time? regression analysis and correlations? or are there more specific methods?
Many thanks in advance!
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I suggest Non Metric Multidimensional scaling provides a better interpretation
I don't know if the method of Nakagawa & Schielzeth (2013) can be applied to my GLMMs, zero inflated and with a family = negative binomial. Particularly, the method for count data and log link function has properties which suggest that it can be used for my case. Nevertheless, I don't know if the additive dispersion component of the total variance is needed, and if I need other variance elements that Nakagawa & Schielzeth didn't consider.
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Remember that the Hurdle model is a great approach for analysing zero-inflated data and has been showed to perform better than other GLMs and other zero-inflated models. I recently applied it to waterbird communities and their perceived predictor variables. It can be run in R with the 'pscl' package and it actually decides for you whether your dataset is zero-inflated or not so by doing a Chi-square test.
Doing spatial analysis of ecological data, taking environmental and spatial variables and performing partitioning of variance there are obtained fractions that are associated with environment, environment plus space, and only space, among others. It is not clear for me, from literature, the role of this fractions on explaining or suggesting stochastic or neutral processes structuring the community. I know it has to be taken carefully, but I still do not get it completely. Any references for reading?
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Hi Sara,
don´t give too much on this partitioning. Depending on which method you choose, the results can be quite different. See Gilbert & Bennett (2010) for a critique:
Gilbert, B., & Bennett, J. R. (2010). Partitioning variation in ecological communities: do the numbers add up?: Partitioning variation in communities. Journal of Applied Ecology, 47(5), 1071–1082. http://doi.org/10.1111/j.1365-2664.2010.01861.x
cheers
I am currently developing a project aiming to answer if higher trophic levels present higher beta diversity than lower trophic levels in plant-insect metacommunities sampled in fragmented landscapes. Since herbivorous insects present higher alpha diversity than plants, I would like to remove this effect from my analysis. At the landscape level (multiple-site), I intend to use the Whittaker beta diversity (βw), which is independent of alpha (Tuomisto et al 2010). However, for the analysis of beta diversity between pairs of fragments I am not sure about which pairwise index to use. Both quantitative and presence/absence measures may be used for my dataset.
Regards
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Hi,
If you are interested in turnover only, BetaSim (simpson pairwise dissimilarity) is independent from richness (Baselga et al 2010: http://onlinelibrary.wiley.com/doi/10.1111/j.1466-8238.2009.00490.x/full). Check this Baselga & Leprieur, 2015 paper for discussion on abundance metrics independent from richness: http://onlinelibrary.wiley.com/doi/10.1111/2041-210X.12388/full.
"In our view, replacement (and those related ones as turnover, species substitution) should be reserved to those indices that are independent (i.e. not mathematically constrained) from richness difference. These are the Simpson index of dissimilarity (Simpson 1943; Lennon et al. 2001) and the turnover component of Jaccard dissimilarity (Baselga 2012), as well as their abundance-based (Baselga 2013; Legendre 2014), phylogenetic (Leprieur et al. 2012) and functional versions (Villeger, Grenouillet & Brosse 2013). "
Hi,
I have estimated Niche overlap from ENM Tools software for 3 species by 1:1 pair, such as
A:B, B:C, and A:C
However I could not find the overlap between 3, A:B:C
is there a way to do this. I need this a value to plot a venn diagram, where it asks for intersect of 3.
or please suggest me to present this in other way.
many thanks.
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Nick Gotelli maintains a package for this type of analysis
See also the 'spaa' package
Hello to all
The relationship between species diversity and ecosystem stability and its maintenance mechanism has been one of the main topic in ecology research.
In the past, diversity indices frequently used for assessing of community stability. in other words, each community that had more diversity, it was more stable. in the last decade, Functional diversity (FD) that defined as the value, range, distribution and relative abundance of the functional characteristics of organisms in a community, was the most popular methods for evaluation of community stability and functioning. While, in the recent years, Functional Redundancy (FR) that defined as some species perform similar roles in communities and ecosystems, and redundant species can therefore be lost with minimal impact on ecosystem processes. In other words, redundant species are considered necessary to ensure ecosystem resilience (resilience and resistance are two concept of stability). The redundancy hypothesis predicts that the species redundancy in a plant community enhances community stability.
Now, my main question is that: Is there another index that measure plant community stability directly?
Best regards
Reza
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If you are looking at stability then this would imply that you take repeated measures. Assuming that you are working on plant communities I would raise a few questions:
- What system are you considering? For instance - very arid areas are strongly affected by rainfall, and species become visible very quickly (and briefly) in response to only a few mm of rain.
- What time interval would you consider? You might have some short term variation in plant composition around a certain "central" point.
- What part of the plant community would you look at? In arid areas you can expect more variation in ephemeral plants, but perennial composition may remain consistent. So the perennial plants might be considered stable, while the ephemerals would indicate variation.
I agree that remote sensing might help, that you might look at variation around a "mean", but you would probably need more detailed advice from an RS person considering how responsive the various vegetation indexes are
You could use bi-plots based on (N)MDS to get a visual impact of how plant communities "behave".
As I write I am wondering if one number will actually be able to encompass everything that you are looking at. It might, but then again, the index would have to be sufficiently sensitive to pick up on variations, but not over-sensitive so as to make its interpretation impossible.
My idea is much in the line of ... we use a mean and variance to describe a sample / population, no single number can do both. ... we use species richness, a diversity index and an evenness index to describe different aspects of a plant community - the individual measures have little meaning.
Would you split the vegetation up into ephemerals and perennials and consider them separately?
When you find something, could you post it here as well? I'd really like to see what you come up with.
Regards,
Patrick
I have hours of ROV footage that wasn't collected with this in mind so it's not along any kind of video transect. is there some way to standardise how I review the community so i can see changes in the community through time?
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I think you could analyze community structure using 'relative abundances' of species. It does not address the absolute densities of species, but depending on your question, this might be OK. Plant people do that all the time. It does not resolve the issue of what constitutes an individual record when using cameras.
There are many points of dicussion:
What are the key words and concepts that we know first before defining the ecological niche?
Which are the limits of a niche?
There are empty niches (vacancies), or just about habitats?
It is important to note the role of species within the niche?
It is posible talk about ecological niche when is only living one species in a habitat (i.e. Extreme conditions where just one species can get in, or first colonizators)? in this question maybe could exist if there is a viable populations that are having intraespecific relationships.
Is is important take account about viable populations?
Which delimits an ecological niche?
The niche is a feature of the environment or organisms? or we are thinking about chicken or the egg?
Ecological niches change, are mutable?
Exists status (any function or profesion) of species within an ecological niche?
This is the definition that I made, but i'm not sure if is a good one:
The ecological niche is a n dimensional space-time characterized by a set of relationships among limiting factors (biotic and abiotic) and the presence of a one species (with existence of intraspecific relationships) or more, where they evolve through the dynamics of viable populations.
How can we bring together all These questions and others That May be Proposed to define the ecological niche?
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Dear Andrei
A lot of questions!
As a start: a habitat is the combination of environmental characteristics of a species. A material thing.
A niche is the combination of species characteristics enabling it to live in its environment. It is about functional relations with its environment.
You may compare a species' habitat with an address and the niche with its profession.
I hope this may help, eager to read additional reactions!
I mapped FD via morphological traits of fish and made a correlation matrix with the feeding specialist types of the Food-Fish Model from Sibbing and Nagelkere (2001). This was plotted using PCA. Now we would like to compare this diversity index with the species richness of the same African lake systems. Our data on species richness is very basic, only presence/absence of species in the lakes. Preferably we would find a way to create a PCA plot illustrating the variance in species richness between lakes. From there we would hope to analyse the hyperspace or euclidian space overlap (%) of the PC's between the African lakes.
But any other ways to go about this are very welcome as well, any suggestions?
I just wanted to know what are those natural and anthropogenic stresses those are more or less affecting the overall ecology and biodiversity of tropical esturies and coastal lagoon in the last decade.
Have Some body with any review article on this area are requested to provide the relevant soft copies of the publications?
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I am searching for a good method to identify Plant Functional Types from trait and abundance data. I tried to understand the SYNCSA method by Pillar & Sosinski 2003, however to me it is pretty much unclear how they finally identify the PFTs. There are too many unclear descriptions in the method on what is done how and why. Also the recent R-package implementation (Debastiani and Pillar 2012) does not help very much, although it improved my understanding on what is going on.
In fact, I identified the optimal traits, yet what is next? Clustering with k-means like Fyllas et al. (2012) does not seem to be realistic to me.
does anyone here has another workflow ready?
thanks!
- Pillar, Valério DePatta, and Enio E. Sosinski. 2003. "An Improved Method for Searching Plant Functional Types by Numerical Analysis." Journal of Vegetation Science 14 (3): 323.
- Debastiani, Vanderlei J., and Valério D. Pillar. 2012. "SYNCSA—R Tool for Analysis of Metacommunities Based on Functional Traits and Phylogeny of the Community Components." Bioinformatics 28 (15): 2067–68.
- Fyllas, Nikolaos M., Carlos A. Quesada, and Jon Lloyd. 2012. "Deriving Plant Functional Types for Amazonian Forests for Use in Vegetation Dynamics Models." Perspectives in Plant Ecology, Evolution and Systematics 14 (2): 97–110. doi:10.1016/j.ppees.2011.11.001.
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Hi Jens
See if this may help you:
Maire E, Grenouillet G, Brosse S, Villéger S. How many dimensions are needed to accurately assess functional diversity? A pragmatic approach for assessing the quality of functional spaces. Global Ecology and Biogeography [Internet]. Wiley-Blackwell; 2015 Mar 26;24(6):728–40. Available from: http://dx.doi.org/10.1111/geb.12299. This article is well related to some reliable methodologies to build the functional space which may be significant for effective approaches.
LEFCHECK JS, BASTAZINI VAG, GRIFFIN JN. Choosing and using multiple traits in functional diversity research. Environmental Conservation [Internet]. Cambridge University Press (CUP); 2014 Sep 2;42(02):104–7. Available from: http://dx.doi.org/10.1017/s0376892914000307. This may help on how to deal with a large number of traits.
Cheers
Jose
Hi all
I am a PhD student and i would like to request your help for a methodological problem i have with my research.
I need to analyze a community of bats in a cave, one of the objectives is to calculate the abundance of each species of. We use monthly samples during an annual cycle.
Live in the cave several hundred bats, the most represented families are Moormopidae and Phillostomidae.
We want to make recordings with IR video camera at the entrance of the cave, but we do not know if this technique is possible to discriminate very abundant species or to species identification?
We welcome your views and comments on this technique, softwer options for analysis, economic alternative technologies, and literature
greetings to all.
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Hi Fernando, videocameras offer a non-invasive approach to estimation of colony size because you may count bats on emergence without entering the roost, which would disturb the animals. For very large colonies, however, be prepared to work hard and obtain an estimate rather than a fully exact count. You will have to count all bats emerging as well as those moving back to the cave during emergence, which will give you the net number of subjects leaving the site. Since you have more than one bat species in the colony, coupling your video with realtime audiorecording of echolocation calls should help you tell the species apart, but of course this depends on how easily your species can be discriminated acoustically. There are many other site-specific variables to take into account, of course, e.g. how many exits your roost has, how large they are, etc. These might make the task more difficult.
I hope this helps, with best wishes
Danilo
I am trying to put together community data matrices coming from different sampling campaigns and sites. Each campaign provided one data sheet with rows corresponding to observational units, and columns providing both ecological information, and a species list (each variable and species correspond to one column). Ecological variables can be numerical, hierarchical or factors and the species provide the abundance per observation. While the ecological variables and comments follow the same order, and are easy to put together between data sheets, the morphospecies suppose a problem for different reasons:
-In first place, the order and species present for each site are different. So copying one table under the other is not an option. And there are too many to add the gaps and fill the absences.
-Second, the correspondence of the number of morphospecies is not necessarily the same.
I have carried an equivalence data sheet of the morphospecies at each sampling campaign. I'd like to know if there is an easy way to fusion all these matrices, other than adding columns and changing names of the species, which will be extremely tedious. Probably some software can deal with this easily, but I am only used to excel and running R routines, while my programming skills are yet poor. So I am trying to figure out how to solve this as soon as possible.
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Having spent years trying to do this sort of thing in Excel (which you can do, with judicious sorting, summing and so on, tediously) or getting others to help with doing it through Access (or some other database programme), I now use the merge function in Primer (v6 or now v7) which is specifically designed to deal with this problem.
I am trying to compare the species composition between two of my sites, and have read up some similarity/dissimilarity indices. Because my data also contained abundance information, I thought of using the Bray-Curtis measure. I tried reading more about it and have found some sites interchanging 'Bray-Curtis Similarity Index', 'Sorensen Distance' and 'Bray-Curtis Distance' (suggesting that they are the same thing), whereas others have stated that these are different measures. Could anyone explain the difference or suggest a good reading?
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Hello Zu Dienle Tan,
The Bray-Curtis and Sorensen indices are very similar. The difference, as you say, is that the Bray-Curtis index is based on abundance data, while the Sorensen index is based on presence/absence data. Both indices have similarity and dissimilarity (or distance) versions.
Dissimilarity = 1 - Similarity
Both indices take values from zero to one. In a similarity index, a value of 1 means that the two communities you are comparing share all their species, while a value of 0 means they share none. In a dissimilarity index the interpretation is the opposite: 1 means that the communities are totally different.
Distance and dissimilarity are sometimes used interchangeably. However, the Wikipedia page of the Bray-Curtis dissimilarity (https://en.wikipedia.org/wiki/Bray%E2%80%93Curtis_dissimilarity) says that it is incorrect to call it a distance, since it doesn't satisfy the triangle inequality (the sum of the lengths of two sides of a triangle is always greater than the length of the third side). That is, the sum of the dissimilarities between communities A and B and communities A and C is not necessarily greater than between communities B and C.
For more information, you can check:
From many articles & publications I have known that we can identify Species Richness Hotspots using SARs. Now I'm interested to know and if anyone could explain that how exactly SARs models identify these hotspots.
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Hi! The basic idea is that sites with a number of species higher than that predicted by the SAR (i.e. positive residuals) may be considered hotspots.
Im studying the migration in ungulate.
so, I understood why dosen't define the "migration".
however, various researchers are developing new method to quantifying migration recently.
what is the best method?
if I had irigium collar data.
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Regarding methods, you may want to consider some of the track behavioural segmentation algorithms (e.g., Nams Ecology Letters 2014, Blackwell et al. Methods in Ecology and Evolutions 2015). Or, some of the earlier stuff like first passage time or Guarie's behavioral change point analysis.
Alternatively, one may be able to simply define migration as an expert biologist, for example when an animal leaves the boundary of it's territory?
PC ORD and SPSS , CANACO is statistical software mainly used for advanced ecology analysis. Does anyone know which one of these is a good user friendly reasonable cost ecological software for advanced community ecology analysis.
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It really depends on your question, CANOCO is very user friendly. However, R is probably the best tool for advanced community ecology analysis as it has the widest range of analytic methods and is free. That said, it helps to have some programming experience to get the most out of it.
The plant being studied is a small, annual, endangered lupine (wildflower; forb). Although seeds are dehissed from pods they do not travel very far. Would using just one slope with multiple replicates of a treatment on that one slope be considered psuedoreplication if they are distant enough and they have different microhabitat conditions?
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I'm guessing that by 'slope' you are talking about an experimental site that is on the slope of a hill? In that case, what I'm envisioning is that you have an area in which you are running the experiment and in this area you have a number of plants, each of which is assigned to a treatment. What are you measuring? That can make a difference as to the independence of the replicates.
In general though, here's my answer. Having just one experimental site is fine; many field studies do so, and virtually all laboratory studies do (i.e. they are all done in one lab). That means your data has limits; it only absolutely applies to the site at which and time during which the experiment was conducted. However, we generally infer that if it happens on one slope or lab, a similar pattern will occur at the next. So, if I understand the design aright, you will not be pseudoreplicating.
This is provided that 1) the treatments are randomly distributed within the experimental area (this helps to avoid bias) and 2) as you've implied, the replicates are distributed far enough apart that they won't interfere with each other. How far is far enough will depend on the biology of the system and on the measurements you're making. You'll have to make that determination based on what you know about the biology of the plants, what you're measuring, and what other studies have done.
What would be pseudoreplcation would be having sub-blocks on the slope (e.g., high and low elevation), or having multiple slopes (several experimental areas) and then treating each of the plants within each of those blocks/slopes as independent of each other. In that case you'd need another factor in your model; either elevation or slope.
I have two groups of species and four principal components of ecological niche space. I want to find (a single) the pairwise Mahalanobis distance in 4D space between the groups.
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One simple solution for common data files (e.g. copy + paste from xls or txt):
Input: Four Variables as columns and observations as rows (sorted according to your predefined groups which are designated per Edit:Row colour/Symbol).
Menu:Statistics:MANOVA function gives out a table of pairwise comparisons with Mahalanobis distances as one option.
If you want to compare diversity e.g. Hill numbers across species assemblages you need to standardize for sample size or sample completeness. Does the same count for asymptotic species richness estimators (such as Chao indices or ICE and ACE)? You calculate the same, namely 'true' species richness (often based on the number of singletons), so I guess it already corrects for differences in sampling effort?
Second (related) question:
Can anyone explain why I find different values for the same estimators calculated with different programs? Using the same abundance dataset I calculated ACE and Chao1 in EstimateS, R (fossil) and SPADE...and I got three different values for each estimator.
I know that EstimateS uses resampling and that it cannot calculate ACE, but strangely the Chao1 values are lower than the estimated species richness when doubling the reference sample.
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No - it is not necessary to standardize your input data. As per the original rarefaction curve concept of Sanders, the idea is simply to combine all samples into a common pool, then make random draws upon that pool at different sample sizes. The curve is simply a plot of species encountered (expected species) as a function of sample size. Certain data standardizations could actually defeat the elegant simplicity of the underlying concept. That is not to say that sampling methods employed to get the data should not follow a common standardized protocol. Certainly they should. But, equal sample size is not a requirement at all for rarefaction curve analysis. Indeed that is one of the beauties of this method.
In tropical forest inventories, strangler figs are common. How estimate the basal area of a strangler figs and their tree hosts? This issue is very imporatant as it may lead to important mistakes in total basal area estimation.
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Dear Thomas;
Did you find a suitable method? I am facing a similar issue. I am interested determining the Basal area of fig trees. Both free standing, multiple stemmed individuals and/or parasitic stranglers. Is there an effective way to determine basal area short of measuring every stem?
The aim of the study is to compare montane and lowland populations of a plant species with respect to leaf anatomy. The montane populations also include those situated on different altitudes. I collected 25 plant individuals from each population on a defined area: the whole plants as a herbarium material as well as the shoots with leaves (3 vegetative and 3 flower-terminated ones from each individual) as alcohol-fixed material. The plant is small-leaved and each individual contains dozens of leaves on both types of shoots.
Now the question is how many leaves of each individual to analyze anatomically and how many measurements of each particular quantitative trait (e.g., size and density of stomata, hairs, leaf thickness and size, etc.) to take from each leaf? Also, what would be the statistical methods most suitable to use in such a study? Thanks in advance.
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Hi, I can give you some advice coming from my experience. Good sampling is very important; I assume the plant is herbaceous? It is crucial to collect individuals growing in the similar environmental condition and take leaves from the same part of the plant. In our studies we sample 10 leaves per individual and 30 individuals per population (so 25 should be OK). If you need inter-individual variation, you should analize at least 10 leaves - and if you expect differences between vegetative and flowering shoots, you would need 10 leaves od each type of shoots (5 should be enough if you have many samples). This is important to choose leaves from the same part of a shoot - the middle is the best usually, with fully developed leaves. One measurement of each trait from every leaf. You can find examples in the papers (you will see there statistical methods also):
Boratyńska K., Jasińska A.K., Ciepłuch E. 2008. Effect of tree age on needle morphology and anatomy of Pinus uliginosa and Pinus silvestris – species-specific character separation during ontogenesis. Flora - Morphology Distribution Functional Ecology of Plants 11/2008; 203(8-203):617-626. DOI:10.1016/j.flora.2007.10.004
Boratyńska K., Boratyński A. 2007. Taxonomic differences among closely related pines Pinus sylvestris, P. mugo, P. uncinata, P. rotundata and P. uliginosa as revealed in needle sclerenchyma cells. Flora - Morphology Distribution Functional Ecology of Plants 09/2007; 202(7):555-569. DOI:10.1016/j.flora.2006.11.004 ·
Marcysiak K. 2012. Variation of leaf shape of Salix herbacea in Europe. Plant Systematic and Evolution 298: 1597-1607.
Climax communities are said to be in a state of equilibrium because organisms have already adapted to their environment and succession is no longer taking place. Therefore, it can be assumed that it is stable. If climax communities have high levels of stability, why is it low in diversity?
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Hi, Mikee!
Higher stability doesn't necessarily entail higher diversity. In fact, it is those communities with intermediate levels of stability that have the highest diversity.
A community is known to be stable when there is no apparent change in the number of species and population size over a long period of time. It is then important to note that a community's stability is prevented by periodic or stochastic disturbances that give way to recolonization. It is the climax community that is most stable since the species that comprise it, which are the dominant late successional species, are least affected by gradual changes in the physical environment unlike the communities with lower stability. This high stability in climax communities would lead to a low species diversity since the time between disturbances is long, allowing dominance by one or a few number of species that, in turn, competitively exclude other species. It is the communities with intermediate levels of stability that are most diverse since the interval between disturbances are long enough for a wide variety of species to colonize and become established but are disturbed before successional replacements result in dominance and competitive exclusion.
I want to estimate the co-occurrence of species in different scale area and compare with phylogeny of small groups.
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I strongly recommend Löbl & Smetana A (Ed) Catalogue of Palaearctic Coleoptera, Volume 1 (Carabidae) and 4 (Coccinellidae).
For all species the Distribution is written down for Countrys and federal provinces (China),
If you face difficulties in getting the publications write me an email to brunkin@web.de . I will help you with pdf...
Can you describe in more Detail about the extent and aims of your study..
Best wishes
Ingo
Good Day,
I am trying to figure the analysis I need to do for my project. I study shrimp and fish in 3 tropical intermittent streams. I am testing to see if there is a difference in species composition at different elevations. My experimental design includes a low, mid-, and high elevation site in each stream. I have selected 3 streams, so therefore 9 sites total. I chose 3 streams so I can say with confidence that a species distribution changes with elevation. I have been sampling for a year. I have sampled at each site 4 random times in the year; therefore, I have collected 12 samples for each stream (4 at the low site, 4 at the mid-site and 4 at the high elevation site). Altogether 36 samples. We have a dry and wet season here. Seasonality is a covariable.
What is the best way to setup my data? Recommendations for analysis? Help is much appreciated:)
Thank You,
Kayla
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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
I am working with a dataset of six communities that were sampled at two time points. I am looking for a simple way to see whether functional diversity of each community has changed from time point A to time point B.
My functional data is count data [number of species per community with a given functional trait]. As such, my dataset seems too small for most ordination approaches. Overall, I am investigating multiple traits within three different functional categories [growth form, habitat preference, and symbiont status]. At the moment, I am treating each functional category as a separate dataset.
Thank you!
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Dear Klara, it is important to clarify your question:
You could compute functional diversity of each community at time point A and compare to functional diversity of the same communities at time point B. For this I suggest using Rao entropy, for which you will need to compute a dissimilarity matrix between species based on their traits (if the traits are of mixed type, the Gower index may be useful). In this case you can only tell about the temporal change in the overall functional diversity of each community, and nothing about the functional identity of the community components. This is a limitation analogous to when communities are compared by their species diversity (e.g., by using Shannon diversity).
Another option is to compare the communities based on their functional composition, for which you may consider the definition of fuzzy-weighted community composition. This is described in Pillar et al. (2009, http://dx.doi.org/10.1111/j.1654-1103.2009.05666.x). See also Pillar & Duarte (2010, http://dx.doi.org/10.1111/j.1461-0248.2010.01456.x) in the context of phylogenetic analysis. The fuzzy-weighting requires as input a similarity matrix between species based on their traits (which could be the above mentioned Gower index), which after proper standardisation to unit column will define a fuzzy set matrix U of species by species. The fuzzy-weighted community composition is given by matrix X obtained by matrix multiplication (X = UW), where W is the matrix of species composition in the communities.
I have sampled 4 sites for bryophytes using quadrats in and around 4 wetlands. (I recorded abundance data.) Two of the wetlands are embedded in deciduous woodland, two in coniferous woodland.
I'm trying to ask two main questions:
-whether the species compositions of the wetlands are more similar to the other wetlands or their surrounding woodlands
-whether the species compositions of the wetlands in each forest type are more similar to the other wetlands of the same type or to a wetland of the other forest type
Is there a test I can do to answer these questions?
I've done an NMDS ordination of the data. The polygons surround all the quadrats from each of the four sites, the spiders connect the wetland and woodland quadrats from each site. The wetland quadrats are colored blue, woodland green. Conifer is the plus hardwood is the triangle.
It seems to show that the two largest wetlands are most similar to each other (despite being in different forest types). It seems to show that the smaller wetlands each are more similar to the larger wetland of the same forest type. (Although the hardwood wetland is almost as similar to the surrounding woodlands as the other hardwood wetland.)
Is there some way to test if this is actually significant?
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Dear Paul,
One easy solution is to use the dissimilarity in species composition of each sampling. There is a lot of method to calculate the dissimilarity index (e.g. Bray-Curtis… see the function vegdist of the package vegan in R). To test the difference you could use Mantel test (or modified version of it). See the paper of Goslee & Urban 2007 (The ecodist Package for Dissimilarity-based Analysis of Ecological Data) and the package description http://cran.r-project.org/web/packages/ecodist/ecodist.pdf
Best regards,
Simon
Benthic realm and its biotic composition is highly important in glacio-marine fjords, specially when considering the cryosphere dynamics and the resulting phenomena (sedimentation, resuspension, freshwater influx, inter alia).
Also, in Antarctic seasonal bays (i.e. Mackellar Inlet (King George Island, South Shetlands), where I've sampled macrobenthic communities for previous research (see:http://goo.gl/YOy16D)) pelagic realm also plays a key role in terms of primary production and its consequent influence on higher trophic levels.
It's certain that analysing the benthic composition is more predictive when trying to speculate future scenarios. I presumably assume that the Mackellar Inlet is mainly a benthic-controlled system. Nevertheless, in order to be sure of this hypothesis I should go further through an integrated analysis of both realms.
The protocol that my colleagues usually execute is: macrobenthic survey (van Veen grab 0.05m2), collect plankton with plankton nets, and measure abiotic variables like temperature, pH, conductivity, turbidity and marine currents patterns (speed & direction).
I'd like to know if there's any specific protocol pointing straightforward to my question. What other measurements should I consider?
Thanks for the help. Cheers.
∞BSc. Bernabé Moreno
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I agree with the previous comments. Stable Isoptopes can be very useful. Carbon can be affected by methanogenesis, but particularly the sulfur signal can give some indication of benthic energy contributions, but you need to look at external and internal sources, isotope ratio of the various benthic infaunal species and nekton. knowing the ecology of the organisms will help, such as filter feeding vs deposit feeding, benthic vs pelagic foraging by nekton (stomach contents and isotopes). Is the system shallow and oxic enough for benthic photoautotrophy? any chance of looking at benthic oxygen (P/R) and nutrient fluxes?
The location is a corridor from the Florida panhandle, westward to east Texas. Longleaf pine is a significant component of this landscape but riparian, wetland, seepage bogs, and other fragmented ecosystems need to be addressed simultaneously within this landscape. Developing a course tool to prioritize connectivity and where ecological restoration should/can occur is needed.
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Hi Joe - I appreciate the problem you present. There are a number of approaches available to consider. In some ways what you present is a unique case of conservation planning, in that you have specific systems you are interested in as well as restoration as a specified goal. But in the general case, numerous methods are out there to spatially prioritize areas for different conservation and management actions. If you are relatively new to this, I might suggest contacting your local LCC (Landscape Conservation Cooperative) and see what they might offer. I know the SALCC has a conservation blueprint process underway and it includes connectivity. Their products might help. They only cover part of the panhandle, the neighboring LCC is Gulf Coastal Plain and Ozarks. SE Climate Science Center has done some connectivity modeling for target species that might be adapted to your part of the region. But if you already know all this and are looking for more specialized tools to help predict where restoration should occur, I would turn to groups like the Long Leaf Alliance, and maybe contact Tom Hoctor at UF. Tall Timbers might also be able to help. Finally if you need to start from scratch there are an array of spatial planning tools to consider, depending on your goals, data availability, grain size, budget, and time frame. If this is a new field to you you might want to spend some time on the websites related to connectivity and reserve network panning. These might include Marxan, Zonation, Circuitscape.
Pool et al (2014) quantify alpha functional diversity as the volume of the convex hull filled by the fish species of each community in two-dimensional functional space using the values from the first two functional axes.
But I wonder taxonomic alpha diversity is simply the species richness, so the alpha functional diversity can be functional richness...
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Yes, they are. Functional richness is generally measured as the convex hull volume. But when there is only one continuous trait it is measured as the range (or the range of the ranks for an ordinal trait (from dbFD function in FD R package).
Consider reading the following papers:
I am interesting in looking at the relationship of several environmental variables on genetic differentiation across 16 bee populations (Many individuals per population) using dbRDA. Using Legendre and Legendre (2012) as a guide, I have generated a distance matrix for my microsatellite data, and then performed a principal coordinates analysis. I now have two questions:
1. Negative eigenvectors need to be a corrected. Which would be the best method to do so?
2. The PCoA gives me an eigenvalue for each population at each axis, as well as the overall eigenvalue for each axis. I am assuming that it is the eigenvalues for population at each axis that are important. Is there a standard format used by vegan for this type of data?
I am new to analyzing community-type data, so any suggestions of pointers would be greatly appreciated. Thanks!
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Hi Jess,
For dbRDA, there are R packages and separate programs that will perform the dbRDA all in one function. I have used vegan with the function capscale as well as DistLM by Anderson 2003 (PC program). Typically in a dbRDA, all the axes with positive eigenvalues are retained (at least in the calculation methods I have used), and axes with negative eigenvalues are not used in the analyses.
What you will use as your dependent variable are the scores from the PCoA, but any dbRDA function will do that for you. I also suggest that you run a partial dbRDA because you will have issues with spatial autocorrelation if you are running any community or spatial type data in your analyses.
I have code on Dryad from my previous manuscript comparing dbRDA and others in landscape genetics, but briefly here's what I did in vegan:
###gen file must not have headers, and it needs to be either a square or triangular matrix#######
gen<-read.table(file="Your_PairwiseGeneticDistance_File.txt")
xy<-read.table(file="xy_coordinates_for_each_study_site.txt")
land<-read.table(file="landscapedata_for_each_population.txt")
in vegan:
dbRDA<-capscale(gen~land)
p.dbRDA<-capscale(gen~land+Condition(xy))
stats.full<-anova.cca(dbRDA, by="term")
stats.p<-anova.cca(p.dbRDA, by="term")
This will give you your F-stats, inertia, and p-value for each landscape variable you tested. If you want the F-stat, inertia, and p-value for the full model, just leave off the by="term".
Hope this helps,
Liz
I know that both 'Single-linkage' and 'Complete-linkage' clustering are monotonic methods (non-metric), which is great under a theoretical point of view. Nevertheless, 'Group-average' clusters sometimes are easier to describe.
I'm working with Antarctic macrobenthic communities, and I would like to know which of these three methods is the most adequate for an environmental analysis based on your own experience.
Thanks & cheers!
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I would use Ordination analysis altogether instead of cluster analyses. NMDS or a PCA (if your data are linear) for benthic communities (Also can be done on environmental variables) to differentiate sites or communities from one another. RDA or a CCA for correlating environmental variables to species. All can be done in PAST a free software available online.
Dear colleagues, prompt software for calculation of parameters a metacommunity. It is desirable to be able to use tables Excel. How can I adequately describe the effect of spatial scale on the part of the community?
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Вот консольный режим и непривычен. Но, видимо, все равно придется и его освоить. Пока у меня основные программы SPSS, MapInfo, QuantumGIS, Surfer и серия программ для методов SADIE.
I was recently told by a referee that my manuscript about the long term temporal patterns of a fish community in a tropical wetland was irrelevant and useless because it did not include an analysis of the potential factors explaining such pattern, meaning environmental variables. These were not included because information for the years of my study is not available.
I would like to know if this is a widespread opinion among community ecologists, and if the state of the art is such that works limited to describe community patterns are not necessary anymore.
Thank you, I am looking forward to get to know your opinions.
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Gabriela, Sorry to hear your paper was not favorably reviewed, that is always hard to deal with. For the most part I agree with the reviewer that mere descriptive studies of community patterns without reference to the environmental factors causing them is not useful in advancing our understanding of how communities and ecosystems work. On the other hand if the community patterns are truly unique, have not been described before, and potentially provide clues into some general ecological insight, then I would enjoy reading a paper about the subject. However, once those unique patterns had been described in the few initial studies, future studies would need to start linking environmental factors to the community patterns or else we would be getting nowhere in our understanding. That is just my opinion, and I'm curious if others feel differently.
Regarding not having environmental data for your study, there are numerous freely-available GIS data available these days on topography, climate (precip, temperature), geology, elevation, etc. so depending on the nature of your community and the geographic extent, there may still be the opportunity to add that to your study.
I wish you luck in either revising or re-writing the paper!
Chapter 6 Population and Community Ecology Reading Guide Answer Key
Source: https://www.researchgate.net/topic/Community-Ecology
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