Levan,

I'd say that without exploring a number of factors, including sampling, geometric constraints, species area relationships, biogeographical history, and both phylogenetic and functional diversity, you cannot really have an understanding of the mechanisms underlying the richness pattern in your data. Just looking at species richness is insufficient, in my opinion, to say very much about the relative importance of competition, environmental filtering, convergent evolution, or niche conservatism has on the patterns in richness, though there is much research out there that tries to do just that. Christie McCain's meta-analysis "Global analysis of bird elevational diversity" (2009) describes four of the most commonly occurring trends in richness along a gradient (for birds): 1. decreasing, 2. low plateau 3. low plateau with mid peak, and 4. mid peak. Within her paper are numerous citations of papers that have found these different richness patterns. For a paper that addresses species richness, functional diversity, and phylogenetic diversity along a gradient, check out Devictor et al. 2010. Spatial mismatch and congruence between taxonomic, phylogenetic and functional diversity: the need for integrative conservation strategies in a changing world.

Best,

Kevin

You might check Chapter 14 by Michael Kessler in

Climate Change and Biodiversity in the Tropical Andes by Herzog et al. 2011

There he summarize some important potential processes and mechanisms that determine species richness, also along elevational gradients.

Thank you, good reference.

I've looked at data on bird species distributions along gradients of elevation in the Smokey Mountains, and some species have humped shaped distributions, while others are more linear (i.e., increasing abundances at higher (or lower) elevations). Presumably the species with hump shaped distributions are most adapted to mid-peak conditions. Climate is one of the main drivers of species distribution patterns, but cannot describe range limits in isolation (see book by Gaston 2003). There are many other factors to consider as well, including biotic factors (e.g., dispersal, mate availability, competition). On a mountain, space for breeding is increasingly limited as you move up the mountain. So one hypothesis is that competition for space in mid-domain climates coupled with increase in diversity of different habitat conditions leads to mid-peak richness. Species at the base of the mountain have abundant locations to breed. Individuals adapted to base climate are able to breed up the mountain (fundamental niche), but are presumably breeding in suboptimal habitats and subject to competition (realized niche). Individuals adapted to mid-domain climates, out-compete base adapted species, and overlap distribution of mountain-top adapted species. Richness peaks mid-domain because as you move up the mountain, you are adding different ecozones (diversity of habitats increases), but space available for breeding decreases. At mountain top, cooler climates equals harsher conditions, fewer breeding sites, more competition, equals fewer species breeding. That's my best hypothesis.

Not sure if it can be useful for animals (birds) but in plant ecology there are theories predicting humped-back shape of species richness along environmental stress or disturbance gradients: the Humped-Back Model (Grime), the Intermediate Disturbance Hypothesis (Huston) and their combination (Huston).

In addition to Zaal Kikvidze's comment and maybe in interaction with the factors that theses models are describing. mid-elevation as well as transitions from one habitat type to the other are often characterized by a combination of the conditions on either end of the spectrum (e.g. high vs low elevation). These combinations of factors could be in space and in time reciprocally allowing species from either ends of the habitat gradient spectrum to occur sympatrically thus leading to a peak in biodiversity,relative to the more extreme ends of a spectrum of habitat-characterizing factors.

Thank you for your answers. Intermediate disturbance hypothesis seems really strong one. Indeed in one kind of habitat (such as forest) in elevation gradient can be similar enough and it is hard to see any transition zones which potentially can harbor more diversity at middle.

I had a look to many papers and the climate seems main driver of mid peak diversity (either by disturbance, productivity or whatever). However there is a very clear prediction then. If climate is so important in mid peak elevational diversity then moving from equator to pole, the position of elevational peak (on the mountain) of diversity must be monotonically decreasing. I wander if there is any study showing this pattern.

Levan,

I'd say that without exploring a number of factors, including sampling, geometric constraints, species area relationships, biogeographical history, and both phylogenetic and functional diversity, you cannot really have an understanding of the mechanisms underlying the richness pattern in your data. Just looking at species richness is insufficient, in my opinion, to say very much about the relative importance of competition, environmental filtering, convergent evolution, or niche conservatism has on the patterns in richness, though there is much research out there that tries to do just that. Christie McCain's meta-analysis "Global analysis of bird elevational diversity" (2009) describes four of the most commonly occurring trends in richness along a gradient (for birds): 1. decreasing, 2. low plateau 3. low plateau with mid peak, and 4. mid peak. Within her paper are numerous citations of papers that have found these different richness patterns. For a paper that addresses species richness, functional diversity, and phylogenetic diversity along a gradient, check out Devictor et al. 2010. Spatial mismatch and congruence between taxonomic, phylogenetic and functional diversity: the need for integrative conservation strategies in a changing world.

Best,

Kevin

Thanks Kevin for your answer. I agree that observing just a pattern (especially in a single case) will not easily results in a clear picture of mechanisms. However exploring the number of potentially important factors (even only those you have listed) is very difficult, large time and effort consuming and in many case impossible due to lack of relevant information (e.g. climate data more precise than WorldClim). That is why lot of research are looking just for a pattern or uses only few correlates. Thank you for reference of Devictor et. al.

Well, you'd be surprised how much easier things have become of late, when it comes to collecting phylogenetic and trait data for these sorts of analyses. To deal with the lack of poor climate data, as you point out, there are methods that people have used, either using specific weather station data, or, assuming that temperature decreases at a constant rate as one increases elevation (which is a pretty solid assumption) I believe that this is the method used by McCain in many of her papers. For dealing with some issues related to weather data collection for use in elevational gradients, check out: Wu et al. 2013. Explaining the species richness of birds along a subtropical elevational gradient in the Hengduan Mountains. This paper doesn't look at phylogenetic or functional diversity (which is a shame); they do look at many other factors, including SAR, MDE, climate, productivity, and etc. It may be of some interest.

Obviously, addressing all of the potential important factors that influence species distributions is hard, but, nonetheless is there is a way to do it, I'd argue that it is worth the work. I, myself, am working on such manuscript and while all of the analyses were time-consuming, I feel that I have a much better chance of determine the most important causes of the trends found in my data.

Levan,

“The mid-domain effect […] seems not as a driver of that pattern as it has no biological meaning.”

Well: this is just because MDE has no biological meaning that it is a worth-considering null explanation of you data! When randomness correctly describes a pattern, then functional and/or historical ad hoc explanations are (eco)logically useless – unless to call for deterministic, nonrandom processes that mimic randomness, which is not really parcimonious!

Best,

Gilles.

Ok, but it is shown that frequently the prediction of MDE is highly correlated with prediction of other environmental variable(s) (for review: http://www.esajournals.org/doi/abs/10.1890/06-1302.1). I do not think that more parsimonous explanation (i.e. randomness) could be acepted as best way in such cases. Actually I am working on land snails and from praqtical vewpoint it is hard to beleive the importance of random process. Well, I know untested ubservations and impresions is not enough, however since we can not capture and test all potential drivers of species distribution how helpful is to give weight to random process?

This is a good conversation and you ask a great question: "since we cannot capture and test all potential drivers...how helpful is it to give weight to a random process?"

My answer is that deterministic (niche-based processes) and stochastic (random or neutral processes) influences do not have to be found in isolation during community assembly and throughout community dynamics. So finding evidence of a strong stochastic influence on diversity does not mean that the environment does not also play an important role, and vice versa.

Like the Neutral Theory of Biodiversity, The MDE was intended to be a null model that would allow us to test for the influence of random vs deterministic processes on diversity. It is true that they are based on an idea that stochastic processes can be a stronger driver of diversity than the environment, but fundamentally they are both just null models intended to be used to test hypotheses. Unfortunately, they both have suffered from poorly designed tests, and have come to carry the assumption that finding support for random vs deterministic influences excludes the role of one or the other.

Finally, I will say that null models can be useful tools for comparing diversity among groups of samples. They can be used to disentangle the mathematical effects of local and regional diversity on species turnover (beta diversity) which is notoriously difficult to do with many other comparison methods. In this way you can learn how much of the variation you see over elevation, latitude, or other gradients is driven by the gradient itself vs simple changes in local abundance and richness that might be due to sample effort, historical contingency, disturbance, etc.

For endemic plant species (Majella, mid-latitudinal Italy) the mid-elevational peak was locally falsified. This result illustrates that the choice of biodiversity parameter and domain does matter. On the first, the number of all plant species would probably show the mid-elevational peak for reasons described by Kessler, at least on some (but not all) mountains and slopes in this area (not tested). The number of tree species may peak at low elevation here. The domain/elevation gradient in the research area ranges from evergreen forest to arctic deserts.

The (intermediate) disturbance hypothesis is meaningless in my experience; you can always find a man-made or natural ''disturbance" that fits your results.

Article Fine resolution distribution modelling of endemics in Majell...

I am happy to see this thread continued.

I think snails and animals in general are much more difficult to analyze than plants (I refer to biogeographical distributions of various scales). Plant distributions mostly respond to temperature and precipitation changes, while animals are moving, looking for food, need shelter, fight for territory, etc. Anyway, the strategy of such analyses can be (1) identify the pattern of distribution over the space and describe with as much detail as possible; (2) describe the range of distributions in terms of environment (climate, soils, vegetation, fauna); (3) test if the observed pattern of distribution is different from a null-model (random) predictions; (4) if so, try to find links between the environmental variables and possibilities/probabilities of migrations…

There is one trick with the null-models, often left out. If a process is driven by more than one driver of approximately equal relative importance, the outcome is indistinguishable from a random pattern. “Neutral” can be a result of no effects, or because positive and negative effects, even though strong, cancel each other out. Only experiments can say if there is a true neutral pattern or it seems neutral because negative interaction (e.g., seed predation) is counterbalanced by positive interaction (helping dispersal).

I am not a specialist but see this work from my lab (available on RG) :

Aubry, S., Magnin, F., Bonnet, V., & Preece, R. C. (2005). Multi‐scale altitudinal patterns in species richness of land snail communities in south‐eastern France. Journal of Biogeography, 32(6), 985-998.

From first principle plant distributions respond to temperature and precipitation as Zaal commented. In my referred case, legacy effects both at the geological (island theory) and agricultural time scale (de- and reforestation) are relevant. However, In spatial modelling in mountains elevation and vegetation often show to be the better predictors of species from fine (100m) to mid-resolutions (1km). Obviously, elevation and vegetation are proxies of ambient temperature and precipitation. In addition vegetation is often a proxy for land use. But empirical temperature and precipitation information at fine to mid resolutions scale is rarely available,  in contrast to elevation, vegetation and substrate. Instead of empirical data crude, untested interpolations of widely spaced (>100km in cases) meteorological point data are used (e.g. WorldClim).

In my case I did not find any special issue in spatial modelling of plant versus animal in the same area with largely the same set of environmental variables. However, for snails maybe very fine resolutions are needed and the fundamental set of environmental variables data might be more difficult to obtain at corresponding resolutions. 

Article Where the bears roam in Majella National Park, Italy