Biodiversity and productivity entwined

The plant root economics space in relation to nutrient limitation in Eurasian herbaceous plant communities

Plant species occupy distinct niches along a nitrogen-to-phosphorus (N:P) gradient, yet there is no general framework for belowground nutrient acquisition traits in relation to N or P limitation. We retrieved several belowground traits from databases, placed them in the "root economics space" framework, and linked these to a dataset of 991 plots in Eurasian herbaceous plant communities, containing plant species composition, aboveground community biomass and tissue N and P concentrations. Our results support that under increasing N:P ratio, belowground nutrient acquisition strategies shift from "fast" to "slow" and from "do-it-yourself" to "outsourcing", with alternative "do-it-yourself" to "outsourcing" strategies at both ends of the spectrum. Species' mycorrhizal capacity patterns conflicted with root economics space predictions based on root diameter, suggesting evolutionary development of alternative strategies under P limitation. Further insight into belowground strategies along nutrient stoichiometry is crucial for understanding the high abundance of threatened plant species under P limitation.

Precipitation and temperature regulate species diversity, plant coverage and aboveground biomass through opposing mechanisms in large-scale grasslands

INTRODUCTION

Although the relationships between species diversity and aboveground biomass (AGB) are highly debated in grassland ecosystems, it is not well understood how climatic factors influence AGB directly and indirectly via plant coverage and species diversity in large-scale grasslands along a topographic gradient. In doing so, we hypothesized that climatic factors would regulate plant coverage, species diversity and AGB due to maintaining plant metabolic and ecological processes, but the relationship of plant coverage with AGB would be stronger than species diversity due to covering physical niche space.

METHODS

To test the proposed hypothesis, we collected data for calculations of species richness, evenness, plant coverage and AGB across 123 grassland sites (i.e., the mean of 3 plots in each site) dominated by Leymus chinensis in northern China. We used a structural equation model for linking the direct and indirect effects of topographic slope, mean annual precipitation and temperature on AGB via plant coverage, species richness, and evenness through multiple complex pathways.

RESULTS

We found that plant coverage increased AGB, but species evenness declined AGB better than species richness. Topographic slope influenced AGB directly but not indirectly via plant coverage and species diversity, whereas temperature and precipitation increased with increasing topographic slope. Regarding opposing mechanisms, on the one hand, precipitation increased AGB directly and indirectly via plant coverage as compared to species richness and evenness. On the other hand, temperature declined AGB indirectly via plant coverage but increased via species evenness as compared to species richness, whereas the direct effect was negligible.

DISCUSSION

Our results show that niche complementarity and selection effects are jointly regulating AGB, but these processes are dependent on climatic factors. Plant coverage promoted the coexistence of species but depended greatly on precipitation and temperature. Our results highlight that precipitation and temperature are two key climatic drivers of species richness, evenness, plant coverage and AGB through complex direct and indirect pathways. Our study suggests that grasslands are sensitive to climate change, i.e., a decline in water availability and an increase in atmospheric heat. We argue that temperature and precipitation should be considered in grassland management for higher productivity in the context of both plant coverage and species diversity which underpin animals and human well-being.

Do constrained immigration rates and high β diversity explain contrasting productivity-diversity patterns measured at different scales?

The relationship between productivity and diversity is controversial because of disparity between unimodal and monotonic patterns, especially when occurring simultaneously at different scales. We used stream-side artificial channels to investigate how the availability of a major resource (leaf litter) affected stream invertebrate abundance and diversity at leaf-pack and whole-channel scales. At the larger scale, invertebrate diversity increased monotonically with increasing litter resource density, whereas at the smaller scale the relationship was hump-shaped, in keeping with reports in the literature. This divergence at higher resource levels suggests that multiple mechanisms may be operating. Our results indicate that consistently high species turnover (β diversity) caused the monotonic pattern because of a species-area or "sampling effect" in which new species accumulate with increasing number of samples. The hump-shaped pattern was due to constrained immigration because of a "dilution effect" in which a limited number of immigrants is spread out among the increasing number of available patches. We propose that the relationship between productivity or resource availability and α diversity is generally hump-shaped and the scale-dependent contrast in the relationship only arises where the species pool is large and β diversity is high. Differences in β diversity may, therefore, explain some of the contrasting patterns in the productivity-diversity relationship previously reported.We suggest that continuing immigration by rare taxa is important in sustaining species diversity when productivity is high. The hump-shaped pattern has implications for the impact of anthropogenic ecosystem enrichment on species diversity.

The Ecology of Disturbance Interactions

Global change has been accompanied by recent increases in the frequency and intensity of various ecological disturbances (e.g., fires, floods, cyclones), both natural and anthropogenic in origin. Because these disturbances often interact, their cumulative and synergistic effects can result in unforeseen consequences, such as insect outbreaks, crop failure, and progressive ecosystem degradation. We consider the roles of biological legacies, thresholds, and lag effects responsible for the distinctive impacts of interacting disturbances. We propose a hierarchical classification that distinguishes the patterns and implications associated with random co-occurrences, individual links, and multiple links among disturbances that cascade in chains or networks. Disturbance-promoting interactions apparently prevail over disturbance-inhibiting ones. Complex and exogenous disturbance cascades are less predictable than simple and endogenous links because of their dependency on adjacent or synchronous events. These distinctions help define regional disturbance regimes and can have implications for natural selection, risk assessment, and options for management intervention.

A theory of pulse dynamics and disturbance in ecology

We propose four postulates as the minimum set of logical propositions necessary for a theory of pulse dynamics and disturbance in ecosystems: (1) resource dynamics characterizes the magnitude, rate, and duration of resource change caused by pulse events, including the continuing changes in resources that are the result of abiotic and biotic processes; (2) energy flux characterizes the energy flow that controls the variation in the rates of resource assimilation across ecosystems; (3) patch dynamics characterizes the distribution of resource patches over space and time, and the resulting patterns of biotic diversity, ecosystem structure, and cross-scale feedbacks of pulses processes; and (4) biotic trait diversity characterizes the evolutionary responses to pulse dynamics and, in turn, the way trait diversity affects ecosystem dynamics during and after pulse events. We apply the four postulates to an important class of pulse events, biomass-altering disturbances, and derive seven generalizations that predict disturbance magnitude, resource trajectory, rate of resource change, disturbance probability, biotic trait diversification at evolutionary scales, biotic diversity at ecological scales, and functional resilience. Ultimately, theory must define the variable combinations that result in dynamic stability, comprising resistance, recovery, and adaptation.

Information Needs of Next-Generation Forest Carbon Models: Opportunities for Remote Sensing Science

Forests are integral to the global carbon cycle, and as a result, the accurate estimation of forest structure, biomass, and carbon are key research priorities for remote sensing science. However, estimating and understanding forest carbon and its spatiotemporal variations requires diverse knowledge from multiple research domains, none of which currently offer a complete understanding of forest carbon dynamics. New large-area forest information products derived from remotely sensed data provide unprecedented spatial and temporal information about our forests, which is information that is currently underutilized in forest carbon models. Our goal in this communication is to articulate the information needs of next-generation forest carbon models in order to enable the remote sensing community to realize the best and most useful application of its science, and perhaps also inspire increased collaboration across these research fields. While remote sensing science currently provides important contributions to large-scale forest carbon models, more coordinated efforts to integrate remotely sensed data into carbon models can aid in alleviating some of the main limitations of these models; namely, low sample sizes and poor spatial representation of field data, incomplete population sampling (i.e., managed forests exclusively), and an inadequate understanding of the processes that influence forest carbon accumulation and fluxes across spatiotemporal scales. By articulating the information needs of next-generation forest carbon models, we hope to bridge the knowledge gap between remote sensing experts and forest carbon modelers, and enable advances in large-area forest carbon modeling that will ultimately improve estimates of carbon stocks and fluxes.