richness and productivity (rate of conversion of resources to biomass per unit area gest scale-dependence in the relationship between species richness and The work we report is an extension of research initiated at the National Center for. to disturbance and productivity. The predicted productivity^diversity relationship is unimodal but the productivity level that maximizes species richness increases with increasing disturbance. However, empirical work suggests that the. The current rates of biodiversity loss have exceeded the rates observed Plant species richness-productivity relationship (SRPR) is crucial to the This work was supported by grants from the Fundamental Research Funds.
This may be explained by sampling error causing a low monoculture biomass for that year rather than by a biodiversity effect. What is the relationship between species richness the number of species and productivity within this savannah grassland? These data show, as did the data in Dataset 1, that productivity increases with species richness. In the early years, the percentage of plots that have higher biomass than any of the monoculture plots was constant across species richness, but this percentage increases with species richness in 3 of 4 later years.
Worldwide evidence of a unimodal relationship between productivity and plant species richness
Why might a diverse plot contain more biomass than even the highest monoculture plot? Why might two species be better than one when it comes to biomass production? There are a number of possible answers to this question. The most common explanations for this phenomenon are: Interactions between grasses and legumes. Legumes can access nitrogen from the atmosphere and make it available to other plants.
Grasses have large root systems that are efficient at capturing and retaining soil nitrogen. Differences in the timing and depth of nutrient uptake among species, such that diverse communities have more complete capture of available resources over space and time.
The following material may be useful in presenting some of the issues raised by this and similar studies. You may choose to handout the following critiques and responses and have the students write a one page argument in favor of one of the critiques or responses.
Students can also debate the issue by choosing one side or the other Wardle et al. A number of critiques have been leveled towards biodiversity experiments.Increasing Employee Productivity in the Workplace-Study by Paychex and CFO Research
Three critiques and responses are summarized below. The positive relationship between plant species richness and productivity can be attributed to the higher probability of one or a few high biomass species occurring in diverse plots.
This assumes that these high biomass species are good competitors and dominate the plots such that the total biomass of communities with these species is essentially the same as the biomass of these species when grown alone. In this scenario, there should be an increase in the average biomass across diversity gradients but no increase in the maximum amount of biomass across diversity gradients. Such a pattern was observed initially, but later years show that the maximum biomass increases across the diversity gradient.
Thus, this long-term experiment suggests that the mechanisms behind the biodiversity-productivity relationship may have changed over time.
Because by definition diverse communities contain more species, the higher probability of any given species occurring in diverse communities is a phenomenon that could have effects in many habitats outside of experimentally assembled communities.
Experimentally assembled communities with a design of random species loss cannot predict the effects of real species loss, which will not be random. While real species loss will be biased, it is difficult to predict how it will be biased.
Rare species are vulnerable to extinction, while disease may cause abundant species to become functionally extinct e. Given this uncertainty, it is reasonable to first address the effects of random loss. An interesting question for future research is whether biased species loss will show similar effects as the random species loss simulated in this experiment.
These data were the source of the averages and the standard errors in Dataset 1 and can be used if you want student to calculate averages and errors. This would be a good idea in a more advanced ecology class. As explained above, this richer dataset will allow teachers and students to be creative and the data work-up to be open-ended.
These data would be appropriate for students in a plant ecology course or other advanced ecology class. For undergraduates or beginning graduate students, figure out what you want the students to do with the data and give them clear and specific instructions.
The Student Instructions provide guidance on using this dataset to assess whether an increase in biomass with increasing diversity may be due to presence of particular functional groups. Functional groupings are explained in the student section. As noted above, we have found significant effects of C4 grasses and legumes.
Assessment Possible assessments include the accuracy and clarity of figures students make using the Excel spreadsheet, written description and analysis of these figures, discussion or analysis of one of the papers listed below, and a short essay on one of the discussion questions above or in the "Student Instructions" section.
Cedar Creek Cedar Creek Natural History Area where the research was conducted; includes photos and descriptions of habitat and plant lists Biodiversity. The original studies discussed relationships in experimental communities, especially in fast-growing ecosystems with simple community structure, such as grasslands, meadows, and wetlands [ 7 ]. The ecologists have discovered that increasing plant diversity tends to be correlated with higher community productivity since the s [ 89 ].
Recently, equivocal findings have been obtained from existing studies with respect to the fundamental relationship between plant species diversity and biomass or productivity. Most studies have found that biodiversity could increase community biomass or productivity, whether in simple grassland ecosystems or in complex natural forest ecosystems [ 41011 ].
A few studies found that lower biodiversity levels are associated with higher biomass production [ 1213 ].
Others have found few consistent relationships in natural ecosystems [ 91415 ]. The unimodal curve was the common variation tendency found between biodiversity and biomass in the different natural ecosystems using observation methods [ 16 ], but no findings depicted consistent causal mechanisms. The driving mechanisms of the biodiversity-biomass variations may be explained by the sampling effect and the complementary effect, both highly contingent on our understanding of complex natural communities and spatial variation scales [ 2 ].
Generally speaking, the sampling effect could illustrate that the most productive species will ultimately dominate the proportion of community biomass, while the complementary effect could enhance a functioning process such as productivity through niche partitioning and interspecific facilitation, leading to more utilization of resources [ 1017 ].
The sampling effect and the complementary effect are not mutually exclusive, and both mechanisms will likely affect biomass and productivity. The intensity of responses had larger variation in differing environments and the complementary effect accounted for a large proportion of explanatory ability in large-scale patterns [ 1518 ].
In the case of forest ecosystems, the hypothesis that increasing tree species diversity translates into elevated biomass accumulation is difficult to evaluate through experimental manipulations such as those conducted in grassland ecosystems.
Because of the much slower growth of trees, it is difficult to explore the ecological impact on the biodiversity-biomass relationship. Rather, it is more feasible to explore relationships through meta-analysis of existing datasets. Multivariate analysis techniques have been used to develop understanding of biodiversity-biomass relationships [ 1719 ].
The relationship between plant species diversity and biomass accumulation has been examined in different types of forests using a range of statistical methods. For example, Zhang and Chen found a positive correlation between diversity and aboveground biomass in a natural temperate spruce and pine forest [ 17 ]. In contrast, Jerzy and Anna found a weak negative relationship between species diversity and biomass accumulation in a pine forest of Europe [ 13 ].
One possible explanation for these differences among existing studies is that the most competitive tree species may not always be the most productive and complementary effect on both environmental conditions and species functional characteristics [ 20 ]. Interactions between species and the environment and between different species can shape the nature of the species diversity-biomass relationship [ 21 ]. Climate factors limited the productivity of the community in a larger scale, while hygrothermal index could explain a larger proportion of pine forest productivity [ 22 ].
Simultaneously, plant species diversity usually increases monotonically with the climate variables increase, and climate factors become important driving mediation between biodiversity and forest biomass [ 23 ]. Furthermore, the soil nutrient regime has been demonstrated to alter the strength of the biodiversity-biomass relationship [ 1824 ]. Pinus kesiya Royle ex Gord is an important subtropical mixed pine forest ecosystem in the southern region of Yunnan Province because of its fast growth and high timber production.
The mixed pine forest encompasses an area of The natural mixed pine forests in this region have high species richness associated with immigration from nearby monsoon forests [ 25 ]. The vast majority of mixed pine forests throughout this larger region have been subject to commercial logging and conversion to plantations or agriculture resulting in species loss and reductions in stored carbon [ 26 ].