Mid domain effect ectomycorrhizal relationship

mid domain effect ectomycorrhizal relationship

We also assessed the influence of the mid-domain effect (MDE) and and correlation analyses to detect the impacts of mean climate condition. While elevation itself, in the context of the mid domain effect, could of soil nematode communities in relation to elevational gradients, it is. The mid-domain effect is the increasing overlap of species ranges towards the of a shared, bounded domain due to geometric boundary constraints in relation For both continuous and discrete domains, the user may specify the number of.

mid domain effect ectomycorrhizal relationship

This scientific exploration is also important for foreseeing the potential consequences of future environmental changes on ecosystem properties Botkin et al. Among different environmental gradients, the gradient in elevation has historically received great attention. A number of studies have documented elevational changes in species composition for different groups of plants and animals and have identified key factors determining the present and future conditions of ecological communities e.

RangeModel: The Mid-Domain Effect

Kessler ; Romdal and Rahbek ; Hoiss et al. In general, community compositions are affected by both environmental and spatial factors Borcard, Legendre and Drapeau When environmental factors act as a strong filter for organisms, similarity between local communities depends on the resemblance of key environmental variables among sites.

On the other hand, when community assembly is driven by spatial processes, community compositions show spatial structure i.

mid domain effect ectomycorrhizal relationship

Qian and Rickefsindependent of environmental conditions. Reported evidence suggests that both environmental and spatial processes play important roles in determining community compositions at various spatial scales e.

mid domain effect ectomycorrhizal relationship

Recent studies have thus started to quantify and disentangle the relative importance of the drivers of community composition for different groups of organisms along biogeographic gradients e. Qian and Rickefs ; Mori et al. However, this issue has been less visited for microorganisms than for macroorganisms, although the existence of microbial biogeographic patterns has been gradually established reviewed in Hanson et al.

mid domain effect ectomycorrhizal relationship

Further attempts to disentangle the processes of microbial organization along large gradients, including elevation, will help to clarify whether the distribution of microorganisms is shaped by both environmental and spatial factors to the same extent as it is in macroorganisms, which is an issue that is currently being debated e.

Ectomycorrhizal ECM fungi are symbionts of the tree species that dominate in temperate and boreal forests including Fagaceae, Betulaceae and Pinaceae Brundrett ECM fungi are the major component of the forest floor and play an important role in nutrient cycling via exchanging soil nutrients with host trees for photosynthetic carbon Smith and Read Community responses of ECM fungi to environmental changes are therefore critical for determining and maintaining ecosystem processes Lilleskov and Parrent Previous studies have identified key environmental factors that underlie the changes in community compositions of ECM fungi mainly at local scales Lilleskov and Parrent For example, biotic i.

Importantly, ECM fungal communities often show spatial structures Lilleskov et al.

mid domain effect ectomycorrhizal relationship

The spatial structures may arise not only from spatial autocorrelations of biotic and abiotic environmental factors but also from spatial processes e. This evidence suggests that, despite the inherent nature of symbiotic microorganisms, ECM fungal communities may be governed by processes akin to macroorganism communities.

NORTH AMERICAN DESERT RODENTS: A TEST OF THE MID-DOMAIN EFFECT IN SPECIES RICHNESS - Dimensions

An important next step is thus to quantify whether environmental changes directly or indirectly determine the ECM fungal community compositions through the compositional changes in host tree communities or through other abiotic factors, respectively. With the above issues in mind, we aimed to quantify the relative importance of biotic, abiotic and spatial controls on assembly processes of the ECM fungal community along an elevation gradient in northern Japan Mori et al.

This elevation gradient includes low-elevation mixed forests to subalpine thickets, with large variations in environmental factors e. We specifically aimed i to address whether host community, abiotic environments, and spatial processes drive the ECM fungal community composition at the same time and ii to quantify the relative contribution of these processes to structuring the ECM community composition along the elevation gradient.

Mean annual temperature is 6.

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Sampling design Field sampling was conducted in six elevation classes: The same argument has been made for neutral theory—we should use neutral theory predictions as a baseline, and focus on explaining any observed deviations from those predictions.

This approach often gets proposed as a sophisticated improvement on treating baseline models like statistical null hypotheses that the data will either reject or fail to reject. Which sounds great in theory. Not merely document deviations of observed data from the predictions of some baseline model many ecologists have done thatbut then go on to explain them? Put another way, when have deviations of observed data from a baseline model ever served as a useful basis for further theoretical and empirical work in ecology?

Off the top of my head, I can think of only a few examples. And tellingly to my mind, in most not all of those examples the baseline models were very problem-specific.

Have ecologists ever successfully explained deviations from a baseline “null” model?

The simplest baseline model explains certain features of the data, a second baseline model is then introduced to explain additional features, and so on with additional validation steps along the way to avoid overfitting. More subtly, even if there are just one or two dominant processes that we can capture with a baseline model, the deviations of the observed data from the baseline model are going to be hard to interpret unless they too are dominated by one or two processes.

And reviewers and readers should probably default to skepticism of this approach.