Plant -soil feedbacks (PSFs) have gained attention for their role in plant community dynamics, but their role in productivity has been overlooked. We developed and tested a biomass-specific, multi-species model to examine the role of PSFs in diversity -productivity relationships. The model predicts a negative relationship between PSFs and overyielding: plants with negative PSFs grow more in communities than in monoculture (i.e. overyield), and plants with positive PSFs grow less in communities than in monoculture (i.e. underyield). This effect is predicted to increase with diversity and saturate at low species richness because the proportion of 'self-cultivated' soils rapidly decreases as species are added to a community. Results in a set of glasshouse experiments supported model predictions. We found that PSFs measured in one experiment were negatively correlated with overyielding in three-species plant communities measured in a separate experiment. Furthermore, when parametrized with our experimental PSF data, our model successfully predicted species-level overyielding and underyielding. The model was less effective at predicting community-level overyielding and underyielding, although this appeared to reflect large differences between communities with or without nitrogen-fixing plants. Results provide conceptual and experimental support for the role of PSFs in diversity -productivity relationships.

Summary 1.A growing number of experiments measure plant growth on soils cultivated by different species. Models show that the resulting plant-soil feedbacks (PSFs) can determine plant abundance and persistence; yet, quantitative tests of their importance in community dynamics are lacking.2. Here, we use the growth of eight plant species on 'self' and 'other' soils to parameterize a threespecies PSF model. Predictions from the parameterized model were compared to plant growth observed in a 3-month glasshouse experiment. Four types of three-species communities were simulated: native, non-native, nitrogen-fixing and non-nitrogen-fixing. Because the PSF model is founded on a competition model, removing PSF effects from the model allowed us to compare PSF model predictions to competition model predictions. 3. Mean plant biomass differed among soil types by 20% and differed among plant species by 101%. 4. The PSF model correctly predicted rank abundance in the four communities tested while the competition model correctly predicted rank abundance in the two communities with nitrogen-fixing plants. Furthermore, PSF model predictions of species abundances were closer to observed values than competition model predictions. Despite consistently improving upon the competition model, predictions from the PSF model were significantly different from observed values for three of four communities. Competition model predictions were different from observed values for all four communities. 5. Our three-species model described the plant and soil conditions that allow coexistence and competitive exclusion, but when parameterized with experimental data, no communities were predicted to result in long-term coexistence. 6. Synthesis. Results suggest that PSFs captured a mechanism of plant community development. However, because improvements in model predictions were consistently small, either PSFs were not a dominant mechanism determining plant community development or PSFs were underestimated by our experimental or modelling approaches. Further testing of PSFs and development of improved methods to measure PSFs are suggested.

We develop a modular landscape model for the mountain pine beetle (Dendroctonus ponderosae Hopkins) infestation of a stage‐structured forest of lodgepole pine (Pinus contorta Douglas). Beetle attack dynamics are modeled using response functions and beetle movement using dispersal kernels. This modeling technique yields four model candidates. These models allow discrimination between four broad possibilities at the landscape scale: whether or not beetles are subject to an Allee effect at the landscape scale and whether or not host selection is random or directed. We fit the models with aerial damage survey data to the Sawtooth National Recreation Area using estimating functions, which allows for more rapid and complete parameter determination. We then introduce a novel model selection procedure based on facial recognition technology to compliment traditional nonspatial selection metrics. Together with these we are able to select a best model and draw inferences regarding the behavior of the beetle in outbreak conditions.

Plant soil feedbacks (PSFs) are thought to be important to plant growth and species coexistence, but most support for these hypotheses is derived from short-term greenhouse experiments. Here we use a seven-year, common garden experiment to measure PSFs for seven native and six nonnative species common to the western United States. We use these long-term, field-based estimates to test correlations between PSF and plant landscape abundance, species origin, functional type, and lifespan. To assess potential PSF mechanisms, we also measured soil microbial community composition, root biomass, nitrogen cycling, bulk density, penetration resistance, and shear strength. Plant abundance on the landscape and plant lifespan were positively correlated with PSFs, though this effect was due to the relationships for native plants. PSFs were correlated with indices of soil microbial community composition. Soil nutrient and physical traits and root biomass differed among species but were not correlated with PSF. While results must be taken with caution because only 13 species were examined, these species represent most of the dominant plant species in the system. Results suggest that native plant abundance is associated with the ability of long-lived plants to create positive plant-soil microbe interactions, while short-lived nonnative plants maintain dominance by avoiding soil-borne antagonists, increasing nitrogen cycling and dedicating resources to aboveground growth and reproduction rather than to belowground growth. Broadly, results suggest that PSFs are correlated with a suite of traits that determine plant abundance.

[1] Advancing our predictive capabilities of heat fluxes in streams and rivers is important because of the effects on ecology and the general use of heat fluxes as analogues for solute transport. Along these lines, we derived a closed-form solution that relates the in-stream temperature spectra to the responding temperature spectra at various depths in the sediment through a physical scaling factor including the effective thermal diffusivity and the Darcy flow velocity. This analysis considers the range of frequencies in temperature fluctuations that occur due to diurnal and meteorological variation both in the long and short term. This approach provides insight regarding the key frequencies for analysing temperature responses at different depths within the sediment and also provides a simple and accurate method that offers quantitative insight into heat transport and surface water interactions with groundwater. We demonstrate for Säva Brook, Sweden, how the values of effective thermal diffusivities can be estimated based on the observed in-stream and sediment temperature time series and explain the temporal scaling of the heat transport as a function of a dimensionless frequency number. We find that the lower limit of periods of significance for the analysis increases with depth, and we recommend further research regarding appropriate frequency windows. Citation: