Questions related to Community Ecology
I have raised a question regarding selecting proper statistical methods for the temporal analysis of community composition data. My study site is a river, and it has been implemented with a fishing ban a few years ago. On the other hand, by monitoring water quality parameters, we found the river has also benefited from slightly improved water quality in recent years.
After a long-term fish survey, we found significant changes in fish communities before and after the fishing ban (with one-way PERMANOVA analysis). However, we are not sure what is the main driver for the changes, the fishing ban or improved water quality.
I know that canonical correspondence analysis (RDA/CCA) has been widely used to determine the relationships between biological communities and associated environmental factors. I'm wondering if it's reasonable to consider time series as an environmental factor, by splitting sampling date into before and after, and using it in the CCA analysis with other water quality parameters (please find attached the figure for explanation). However, I didn't find an example for this, which makes me wonder if it's not correct.
Or if there are better statistical method that commonly used in ecology studies to slove this query？
Is it possible to use percentage cover data (e.g. plant cover in sample plots) when creating rarefaction curves with the iNEXT package in R? (that can compute rarefaction curves for the Hill numbers)
Unfortunately, I cannot convert these percentages to count data.
Therefore I can only use the incidence-frequency based data (0/1) as input for creating these curves, but with this limitation only the species richness curves (q=0) make sense to me, and I lose the abundance-based information of my percent cover data in the shannon (q=1) and simpson (q=2) curves.
What is the proper way when I only have percentage cover data?
Any ideas would be appreciated!
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)
During the last decades, researchers with diverse scientific backgrounds have built a common and extremely productive collaborative network around the term ‘meiofauna’.
Please, help us to identify the most crucial questions in #meiofauna research by voting our questionnaire
Your answers are appreciated even if you are not a meiofauna expert!!
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?
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?
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)?
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.
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.
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!
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.
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?
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
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?
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.
Thank you for your time!
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
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)
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.
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).]
See also Habitat, environment and ecological niche (https://www.researchgate.net/post/Habitat_environment_and_ecological_niche).
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?
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.
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?
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.
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 :)
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.
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.
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
-Is it better to consider occurrence (presence-absence) or abundance in RDA analysis? Which give more robust and reliable results?
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.
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
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.
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.
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,
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 !
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.
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.
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.
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.
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.
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?
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?
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.
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)?
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.
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?
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
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?
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?
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!
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?
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.
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.
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).
Thanks in advance.
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.
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.
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.
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.
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.
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!
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.
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!
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.
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?
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.
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.
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?