Monday, March 18, 2013
Spatial patterns in biodiversity have always been a popular topic in ecology, and understanding these patterns helps us to address the looming threats to biodiversity.
Did you notice how I made the word ‘patterns’ plural? There isn’t a whole separate field of biogeography for nothin’. Biological diversity is difficult (some say impossible) to measure using a single metric. How do you count things? Simply by the number of species? How about rare versus common species? What about species turnover? What geographic scale are you using? How and/or do you define boundaries? Would you like me to keep going or do you get the point?
Lately I’ve noticed a surge in the number of studies that look at diversity gradients that occur along latitude and altitude. We visited the topic of latitude and diversity back in October of last year with the “Dinosaurs, Diversity, Distribution, and the LBG” post. Essentially, latitudinal gradients (LBG or LDG) occur when species richness (a simple count of species) is highest in the tropics and declines polewards (warm areas are more hospitable and produce more food). Altitudinal, or elevational, gradients are nearly as ubiquitous as latitudinal gradients, and they have many of the same characteristics. As you go up in elevation (as on a mountain) the temperature gets colder and habitat areas and the communities they support become smaller and more fragmented. Gradients such as these are overall patterns. Now mix them with diversity indices. In 1960, R.H. Whittaker described the terms alpha diversity (α-diversity), beta diversity (β-diversity), and gamma diversity (γ-diversity). Basically, the total species diversity of a geographic area (γ-diversity) is determined by the mean species diversity at the local, within-site or within-habitat scale (α-diversity) and extent of change in community composition between sites (β-diversity). These definitions have been much argued over (see Tuomisto 2010), and we won’t get in to that. We are going to focus on beta diversity (β-diversity), also called species turnover or differentiation diversity. It is a measure of how different sites are from each other and/or how far apart they are on a gradient of species composition. Factors that drive β-diversity are very important, very poorly understood, and very much argued over. Is it dispersal limitation? Habitat specialization? Environmental heterogeneity? The studies published so far seem to lean toward multiple processes operating at various scales. *sigh* Isn’t that always the way? Observational and experimental studies have shown that impacts on β-diversity vary with productivity. So it would make sense to test this in relation to altitudinal and elevational gradients.
A new study to be published in Global Ecology and Biogeography looks at how local community processes may create an altitudinal pattern of β-diversity. Their study site was the Shiretoko National Park in north-eastern Hokkaido, the northernmost island of Japan. In addition to being one of the richest temperate ecosystems in the world, the area is characterized by sharp altitudinal changes in forest structure and productivity as a result of strong winds on the western side of the mountains. The researchers chose this western side to measure the diversity of woody plants and mites (Oribatida) in mature stands where they set up seven plots (each containing 10 subplots) at different altitudes. For the woody plants they recorded the number of individuals taller than 0.5 m, measured the girth at breast height (GBH), estimated the total basal area (BA), recorded canopy height (CH) of the stand, measured the diameter and length of all coarse woody debris (CWD), measured understory light, took soil cores to extract the mites, removed roots from the soil samples, measured the thickness and dry mass of the soil surface litter (A0 layer), collected leaf litter, measured water content and pH of the litter, and the carbon-to-nitrogen (CN) ratios of the litter and soil. They then calculated the β-diversity of the two organism groups for each altitude. So as to take into account the dependence of β-diversity on gamma diversity (γ-diversity), they used null modeling. This type of modeling “randomly shuffles individuals among subplots while preserving γ-diversity, the relative abundance of each species per plot and the number of individuals per subplot” which enabled them to estimate how much observed β-diversity differed from expected β-diversity. They also calculated a β-deviation value for each altitude. This is equivalent to a standardized effect size, indicating the magnitude of the deviation from what you would expect of a random (stochastic or by chance) assembly process.
First we’ll hit the results for the oribatid mites. These results showed the altitudinal gradient of β-diversity to be less evident than it was for the plants, and β-deviation showed no altitudinal gradient. That said, they found β-deviation to always be greater than expected in all locations for both woody plants and oribatid mites. In woody plants, the magnitude of β-deviation increased with altitude, suggesting that deterministic processes dominate in low-productivity, high-altitude stands, the role of these processes increasing with decreasing productivity in plant communities. The authors conclude that their results support the hypothesis that “the mechanisms underlying community assembly (e.g. niche versus neutral) play an essential role in creating biogeographic patterns of β-diversity.” They found that niche-based processes (species correlate with environmental variables, the conditions in which the species can persist) govern high-altitude stands, particularly when they included the plants growing in lower layers in the analysis. They found similar results when they calculated β-deviation along a stand-structure gradient with basal area, not altitude, particularly when small understory plants were included. This suggests that “given the altitudinal changes in stand structure, the role of understory plants in deterministic assembly becomes more dominant with altitude.” So those little guys really make a difference! Why? Well, considering that light is a precious commodity, low-elevation, structurally well-developed stands have higher competition for available light, especially for those little understory guys. This fierce competition isn’t so fierce in the higher altitude sites where the upper canopy isn't there to usurp all of the light for itself. This favors a greater, more diverse understory in higher elevations. Again, the authors speculate that this greater high altitude diversity is due to fine-scale niche partitioning, which allows more individuals to exist together.
Okay, so that is a pretty good explanation for the high altitude communities. Now what is going on at low altitudes? Well, there you have to take the site’s history a little more into account. What made these stands so “structurally developed” and generated this stochasticity? Consider this: The canopy trees of these forests colonized before the understory individuals, negatively affecting understory species by reducing the availability of space and resources. You then see a relative dominance of these big-ole-bully trees (that’s a technical term, you know) and a one-sided competition scenario.
Did I scramble your brain and make you hate biogeography yet? Can you see any sort of overall conclusion here? Lemme help you out…
β-diversity is dependent on community processes and shaped by local factors within the landscape. The species in a place use what is in a place.
Do you agree?
Mori, A., Shiono, T., Koide, D., Kitagawa, R., Ota, A., & Mizumachi, E. (2013). Community assembly processes shape an altitudinal gradient of forest biodiversity Global Ecology and Biogeography DOI: 10.1111/geb.12058
(image via Japan-Guide.com)