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Abstract The temporal stability of plant productivity affects species' access to resources, exposure to stressors and strength of interactions with other species in the community, including support to the food web. The magnitude of temporal stability depends on how a species allocates resources among tissues and across phenological stages, such as vegetative growth versus reproduction. Understanding how plant phenological traits correlate with the long‐term stability of plant biomass is particularly important in highly variable ecosystems, such as drylands.We evaluated whether phenological traits predict the temporal stability of plant species productivity by correlating 18 years of monthly phenology observations with biannual estimates of above‐ground plant biomass for 98 plant species from semi‐arid drylands. We then paired these phenological traits with potential climate drivers to identify abiotic contexts that favour specific phenological strategies among plant species.Phenological traits predicted the stability of plant species above‐ground biomass. Plant species with longer vegetative phenophases not only had more stable biomass production over time but also failed to fruit in a greater proportion of years, indicating a growth–reproduction trade‐off. Earlier leaf‐out dates, longer fruiting duration and longer time lags between leaf and fruit production also predicted greater temporal stability.Species with stability‐promoting traits began greening in drier conditions than their unstable counterparts and experienced unexpectedly greater exposure to climate stress, indicated by the wider range of temperatures and precipitation experienced during biologically active periods.Our results suggest that bet‐hedging strategies that spread resource acquisition and reproduction over long time periods help to stabilize plant species productivity in variable environments, such as drylands. Read the freePlain Language Summaryfor this article on the Journal blog. 

We designed novel field experimental infrastructure to resolve the relative importance of changes in the climate mean and variance in regulating the structure and function of dryland populations, communities, and ecosystem processes. The Mean x Variance Experiment (MVE) adds three novel elements to prior designs (Gherardi & Sala 2013) that have manipulated interannual variance in climate in the field by (i) determining interactive effects of mean and variance with a factorial design that crosses a drier mean with increased (more) variance, (ii) studying multiple dryland ecosystem types to compare their susceptibility to transition under interactive climate drivers, and (iii) adding stochasticity to our treatments to permit the antecedent effects that occur under natural climate variability. This new infrastructure enables direct experimental tests of the hypothesis that interactions between the mean and variance of precipitation will have larger ecological impacts than either the mean or variance in precipitation alone. A subset of plots have soil moisture and temperature sensors to evaluate treatment effectiveness by addressing, How do MVE manipulations alter the mean and variance in soil moisture and temperature? And, how does micro-environmental variation among plots influence how much MVE treatments alter soil moisture profiles over three soil depths? This data package includes soil moisture and temperature sensor data from the Mean x Variance Climate experiment in the Desert grassland ecosystem at the Sevilleta National Wildlife Refuge, Socorro, NM. 

We designed novel field experimental infrastructure to resolve the relative importance of changes in the climate mean and variance in regulating the structure and function of dryland populations, communities, and ecosystem processes. The Mean - Variance Experiment (MVE) adds three novel elements to prior designs that have manipulated interannual variance in climate in the field (Gherardi & Sala, 2013) by (i) determining interactive effects of mean and variance with a factorial design that crosses reduced mean with increased variance, (ii) studying multiple dryland biomes to compare their susceptibility to transition under interactive climate drivers, and (iii) adding stochasticity to our treatments to permit the antecedent effects that occur under natural climate variability. This new infrastructure enables direct experimental tests of the hypothesis that interactions between the mean and variance of precipitation will have larger ecological impacts than either the mean or variance in precipitation alone. A subset of plots have soil moisture and temperature sensors to evaluate treatment effectiveness by addressing, How do MVE manipulations alter the mean and variance in soil moisture and temperature? And How does micro-environmental variation among plots influence how treatments alter soil moisture profiles over three soil depths? This data package includes sensor data from the Mean x Variance experiment in the Plains grassland ecosystem at the Sevilleta National Wildlife Refuge, Socorro, NM, which is dominated by the grass species Bouteloua gracilis (blue grama). 

Throughout communities and ecosystems both within and downstream of mountain forests, there is an increasing risk of wildfire. After a wildfire, stakeholder management will vary depending on the rate and spatial heterogeneity of forest re-establishment. However, forest re-establishment and recovery after a wildfire is closely linked to interactions between the temporal evolution of plant-available water (PAW) and spatial patterns in available energy. Therefore, we propose a conceptual model that describes spatial heterogeneity in long-term watershed recovery rate as a function of topographically-mediated interactions between available energy and the movement of water in the subsurface (i.e. subsurface hydrologic redistribution). As vegetation becomes re-established across a burned landscape in response to topographic and subsurface controls on water and energy, canopies shade the ground surface and reduce wind speed creating positive feedbacks that increase PAW. Furthermore, slope aspect differentially impacts the spatial patterns in regrowth and re-establishment. South aspect slopes receive high solar radiation, and consequently are warmer and drier, with lower standing biomass and greater drought stress and mortality compared to north aspect slopes. To date, most assessments of these impacts have taken a bulk approach, or an implicitly one-dimensional conceptual approach that does not include spatial heterogeneity in hydroclimate influenced by topography and vegetation. The presented conceptual model sets a starting point to further our understanding of the spatio-temporal evolution of PAW storage, energy availability, and vegetation re-establishment and survival in forested catchments after a wildfire. The model also provides a template for collaboration with diverse stakeholders to aid the co-production of next generation management tools to mitigate the negative impacts of future wildfires. 

Eddy covariance serves as one the most effective techniques for long-term monitoring of ecosystem fluxes, however long-term data integrations rely on complete timeseries, meaning that any gaps due to missing data must be reliably filled. To date, many gap-filling approaches have been proposed and extensively evaluated for mature and/or less actively managed ecosystems. Random forest regression (RFR) has been shown to be stable and perform better in these systems than alternative approaches, particularly when filling longer gaps. However, the performance of RFR gap filling remains less certain in more challenging ecosystems, e.g., actively managed agri-ecosystems and following recent land-use change due to management disturbances, ecosystems with relatively low fluxes due to low signal to noise ratios, or for trace gases other than carbon dioxide (e.g., methane). In an extension to earlier work on gap filling global carbon dioxide, water, and energy fluxes, we assess the RFR approach for gap filling methane fluxes globally. We then investigate a range of gap-filling methodologies for carbon dioxide, water, energy, and methane fluxes in challenging ecosystems, including European managed pastures, Southeast Asian converted peatlands, and North American drylands. Our findings indicate that RFR is a competent alternative to existing research standard gap-filling algorithms. The marginal distribution sampling (MDS) is still suggested for filling short (< 12 days) gaps in carbon dioxide fluxes, but RFR is better for filling longer (> 30 days) gaps in carbon dioxide fluxes and also for gap filling other fluxes (e.g. sensible heat, latent energy and methane). In addition, using RFR with globally available reanalysis environmental drivers is effective when measured drivers are unavailable. Crucially, RFR was able to reliably fill cumulative fluxes for gaps > 3 moths and, unlike other common approaches, key environment-flux responses were preserved in the gap-filled data. 

Abstract We examined the seasonality of photosynthesis in 46 evergreen needleleaf (evergreen needleleaf forests (ENF)) and deciduous broadleaf (deciduous broadleaf forests (DBF)) forests across North America and Eurasia. We quantified the onset and end (Start_GPPand End_GPP) of photosynthesis in spring and autumn based on the response of net ecosystem exchange of CO₂to sunlight. To test the hypothesis that snowmelt is required for photosynthesis to begin, these were compared with end of snowmelt derived from soil temperature. ENF forests achieved 10% of summer photosynthetic capacity ∼3 weeks before end of snowmelt, while DBF forests achieved that capacity ∼4 weeks afterward. DBF forests increased photosynthetic capacity in spring faster (1.95% d⁻¹) than ENF (1.10% d⁻¹), and their active season length (End_GPP–Start_GPP) was ∼50 days shorter. We hypothesized that warming has influenced timing of the photosynthesis season. We found minimal evidence for long‐term change in Start_GPP, End_GPP, or air temperature, but their interannual anomalies were significantly correlated. Warmer weather was associated with earlier Start_GPP(1.3–2.5 days °C⁻¹) or later End_GPP(1.5–1.8 days °C⁻¹, depending on forest type and month). Finally, we tested whether existing phenological models could predict Start_GPPand End_GPP. For ENF forests, air temperature‐ and daylength‐based models provided best predictions for Start_GPP, while a chilling‐degree‐day model was best for End_GPP. The root mean square errors (RMSE) between predicted and observed Start_GPPand End_GPPwere 11.7 and 11.3 days, respectively. For DBF forests, temperature‐ and daylength‐based models yielded the best results (RMSE 6.3 and 10.5 days).