Is this question really relevant? We have been discussing for at least two generations of scientists on that and mainly going into deep technical problems leading to infinite discussions while losing sight of the ultimate goal of what you do. Now we have PERMANOVA that solve all the problems apparently with permutations! Once you got a measure of "taxonomic or whatever diversity" as you propose to do, what are you going to do then? Can you effectively use that information to guide conservation strategies focused on adaptation and mitigation of climatic change? Is there any reference system to refer to? at what time and at which place and for what if all systems are changing, reference systems included? Species richness, the number of different species in an ecosystem, as well as alpha, beta and gamma diversity was for a long time the predominant measure of global biodiversity. This is now changing rapidly (see, e.g., a recently published paper in Nature http://www.nature.com/nature/journal/v501/n7468/full/nature12529.html) as researchers are realizing that rather than considering the sheer number of species, it is which specific species that are present that is crucial for ecological functions underpinning the provision of ecosystem services (see Millennium Ecosystem Assessment, 2005). If one wants to gain insights into ecosystem change in the context of climate change one has to rely not just on correlations about "diversity" but rather on cause-effect relationships.

I have been slowly but surely moving away from diversity indices. I have been using the multivariate program PRIMER which can create resemblance matrices of communities which can incorporate both presence/absence data as well as density. I have found that PRIMER gives me much more information and an ability to investigate ecosystem, community, population, and individual trends, which you just can not get from diversity indices. Its true strength comes from its ability to work with both biotic and abiotic factors and to investigate correlations. As to how we can use this information, that would depend on the species studied, the conservation challenges, as well as the trends in the data!

I have used PRIMER for doing analysis of similarity and SIMPER. How does primer incorporate presence/absence and density data? Using matrices I assume, but PRIMER and SIMPER are all based on Bray-Curtis dissimilarity matrices.....i.e. a metric measure of beta diversity

Depending on the transformation you use, PRIMER alters the scale of investigation. Specifically, it varies how important rare species are to determining differences. On one extreme you have presence/absence, and on the other extreme you have the log transformation. Regardless of your transformation, the resemblance matrix is going to have components of density as well as presence/absence, however, you can alter the weighting of the rare species by your transformation.

Not all of the matrices are constructed using Bray-Curtis. You can actually select the method in the resemblance matrix menu; the two main ones being the Bray-Curtis and Euclidean. There are even more options in creating resemblance matrices in the "More" tab once you begin crafting the matrix.

Have you used the PERMANOVA add on yet? It is this function which allows you to see how your community varies in time and space. It is similar to ANOVA, where you can have have spatial and temporal variables among your covariates. You can use different transformations to investigate different questions, or to see how the inclusion/exclusion of rare species influences your results. Once you know how the community varies, you can use REALTE and BEST to compare a biotic and abiotic resemblance matrix. This will show any correlation between your biotic and abiotic factors. Or DISTLM to create a multivariate regression like analysis, which incorporates components of multimodel selection. dbRDA is another option, which I do not understand entirely yet!

Is this question really relevant? We have been discussing for at least two generations of scientists on that and mainly going into deep technical problems leading to infinite discussions while losing sight of the ultimate goal of what you do. Now we have PERMANOVA that solve all the problems apparently with permutations! Once you got a measure of "taxonomic or whatever diversity" as you propose to do, what are you going to do then? Can you effectively use that information to guide conservation strategies focused on adaptation and mitigation of climatic change? Is there any reference system to refer to? at what time and at which place and for what if all systems are changing, reference systems included? Species richness, the number of different species in an ecosystem, as well as alpha, beta and gamma diversity was for a long time the predominant measure of global biodiversity. This is now changing rapidly (see, e.g., a recently published paper in Nature http://www.nature.com/nature/journal/v501/n7468/full/nature12529.html) as researchers are realizing that rather than considering the sheer number of species, it is which specific species that are present that is crucial for ecological functions underpinning the provision of ecosystem services (see Millennium Ecosystem Assessment, 2005). If one wants to gain insights into ecosystem change in the context of climate change one has to rely not just on correlations about "diversity" but rather on cause-effect relationships.

Yes. You have a point. I am interested in change in community composition. What I was trying to stir up with this discussion was not that diversity metrics lack utility. Rather, if you can get a barometer on species turnover, and then look at which species are turning over (which is the important element), then you can use it to guide climate adaptation efforts, and other management activities. I'd like to hear more about how people are approaching turnover. I have used PRIMER and mixed models a great deal in this context, but I'd like to know if there are other approaches. I am, of course, also familiar with PERMANOVA.

I still do not see how you can derive the information you are looking for, as you say "to guide climate adaptation efforts", just through correlative information. What are the drivers of species turn-over processes, internal (adaptation, succession) and/or external factors (climate forcing, human impacts), how can you discriminate among those with PRIMER? Even in case you consider environmental factors, most of the controversy over this kind of design is about whether all environmental factors are adequately controlled or if lurking factors may cause rejection when in fact there has been no "treatment" effect. Further on the selection of [unaffected] reference sites in BACI as well as in MBACI for environmental studies is assumed to be a random sample from a population of sites as in design-based approaches (Underwood 1992). However, this assumption is not true, even approximately, and not plausible as a model. It is a subjective guess by the assessor, and thus invalid for inference. It has also been suggested that null hypothesis significance tests approaches can be appropriate in experimental studies but should not be used in observational studies because variance in the data set has not been generated by experimental manipulation but through permutations, leaving inference vulnerable to unconsidered confounding factors. Indeed, data treatments following BACI sampling designs are often suggested involving permutations (e.g. PERMANOVA) between environmental variables and biota assuming that species can be permuted across samples independently of their often strict ecological linkages evolved in time with specific habitats. Also this assumption is not true (with the only exception for ubiquitous species, but are there really any?), and thus invalid for inference. The risk in this case is to destroy the inherent and rich ecological information of temporary or long lasting interdependence of biota with contextual habitats. However, suppose you get an estimate of species turn-over, what do you do with that if you do not know which is the driver of that change? There is no technique that can help you in this respect if you do not have sound real data to indicate which specific species can be crucial for ecological functions "to guide climate adaptation efforts".

If I get it correct, the question is whether we can predict future changes in the community structure from the knowledge of past and ongoing changes? Then statistical approaches are little helpful, because they are descriptive (PERMANOVA, CCA, DCA, or any other multivariate analytical tool). These can rather describe, but not explain or predict future changes unless they are linked to some kind of mechanistic models. To my knowledge, there are not many widely accepted and used such models though, and the existing ones are rather generic, like the Intermediate Disturbance, the Humped-Back model or the Stress-gradient hypotheses.

If we have a target species of a special value (an indicator, or rare and being under protection, flagship, keystone, etc.), then I guess population dynamics models (e.g., Markov) can be employed and linked to environmental variation. Such approach may have a predictive power if we know the responses of a given species to a given set of environmental drivers… this is not my field though, let population biology experts tell us.

My question is more about documenting change in community composition over space and time. It's not about making predictions per se. Yes, temporal shifts are easily measured using permanova, ordination, or mixed models. I am less interested in finding predictors of change, but rather just documenting the change itself using diversity metrics as well as temporal shifts in individual species abundance.

You can document changes in species composition with data from permanent plots, or using "chronosequences". For instance, snowbed communities in the Alps experience changes related to global warming. But you need to understand species characteristics - in case of snowbeds, which were communities of almost exclusively specialized "snowbed" species, now are invaded by more generalist "alpine meadow" species that arrive from nearby meadows. Once you have community matrices constructed, then it is up to you which analytical tool you will use for comparison... I usually look at distributions (proportions) of species with different traits (related to global warming or land use change).

If your question is more about documenting change in community composition over space and time, why are you going to use PERMANOVA that has great pitfalls? What would be a significant change in this respect? Null hypothesis significance tests approaches to document change can be appropriate in experimental studies but should not be used in observational studies when variance in the data set has not been generated by experimental manipulation but through permutations (PERMANOVA), leaving inference vulnerable to unconsidered confounding factors you likely introduce e.g. with your sampling design. How can you control this? And you said "to guide climate adaptation efforts" ...how can you think to guide adaptation efforts if you do not know and even interested in the real drivers of change? So we go back to what I said before.

In my opinion the factors shaping the climate are temperature, pressure and humidity. These variables are to be measured for analysis and comparison because a single value does not indicate anything. These same causes droughts rainfall or melting of the poles is another indicator to measure the levels of rainfall (inches rain / year) or the density of the ice. To do something should reverse the effects of greenhouse gases, which absorb and emit radiation at the earth's surface is increasing the average temperature and generating invernaderoo effect = climate change. The causes of these gases are combustion processes of fossil oil based fuels, refrigerants with a high degree of damage to the ozone layer, etc.. What can be done? Change those habits and develop green technologies

I will recount an experience that might serve to support Giovanni's point. Be aware I have practically no experience using diversity metrics. In 1996 we moved into a new rural neighborhood. I noticed particularly low numbers of songbirds. Then I noticed a large number of domestic house cats that were allowed to roam the area. Later (over a period of years) I noticed birdhouses not used. Boluses of bird feathers in the area, presumably left by cats. Then, I do not know why, numbers of roaming pet cats decreased, numbers of birds increased but with an apparently different species composition. On top of that, the seasonality of bird migratory departures/returns has changed noticeably over the same period. I have noticed particularly that mourning doves arrive earlier each year, and with the earlier arrivals compete with overwintering species for the last of the winter food supplies. Large numbers of water fowl that used to depart for winter, now stay due to ready food supplies in farm fields and apparently warmer winters. The seasonality of rodents (field mice) seeking shelter in human-occupied structures also appears to have changed. And so on for hawks and owls and ... . How one would disentangle the direct influences of human activity(e.g., more or fewer house cats, spilled grain left in farm fields,...) from the indirect (e.g., anthropogenic climate change) with diversity metrics seems hard to fathom. There is no reference condition other than the first observed state, which may or may not have been a stable condition for the area. I have little doubt that diversity metrics could be used to confirm or refute the validity of my observations, but even if that were done, and my informal conclusions were confirmed, what value would it have in terms of understanding the impacts of climate change in such a manner as to provide predictive capabilities or designing adaption efforts?

Bryce, yes, pretty good and exemplary way to proceed in ecological observation, thank you for bringing this example! That is exactly what I meant, with this you contributed to the understanding of what is going on there much more than applying any more or less sophisticated multivariate method! In addition, you are in the pretty good position, now, to really understand any cause-effect relationship to discriminate among impacts and gauge the impacts of climate change for designing consequent adaptation efforts.

From another RG discussion on climate change impacts indicator(s) see:

http://eebweb.arizona.edu/courses/Ecol206/Walther%20et%20al%20Nature%202002.pdf. Seems like should provide at least some direction for those interested in the question in discussion here.

I think you're on the right track in looking at changes in species compositions or assemblages of species.I don't think alpha diversity or individual species (e.g. dominant species, indicator species, etc.) will be terribly useful--except species that are no longer found in an area but were there previously (relatively short term, of course). One of the greatest problems in this area is that good inventories for given points in time are rarely available--you can't see change without baseline data . You might find a paper I wrote interesting--it can be found at http://www.treesearch.fs.fed.us/pubs/44408 . Essentially, I think you need to figure out what species are best adapted to local climate variability and species that are highly responsive to optimal & adverse conditions. The latter group is probably better for detecting change. Such species are likely not common--common species are common because they are well adapted to local climate variability. Once significant change is seen in common species, the system has probably crossed a threshold and the system is on it's way to disintegration or collapse.