A mid-century ecological forecast with partitioned uncertainty predicts increases in loblolly pine forest productivity

Climate change could negate U.S. forest ecosystem service benefits gained through reductions in nitrogen and sulfur deposition

Climate change and atmospheric deposition of nitrogen (N) and sulfur (S) impact the health and productivity of forests. Here, we explored the potential impacts of these environmental stressors on ecosystem services provided by future forests in the contiguous U.S. We found that all stand-level services benefitted (+ 2.6 to 8.1%) from reductions in N+S deposition, largely attributable to positive responses to reduced S that offset the net negative effects of lower N levels. Sawtimber responded positively (+ 0.5 to 0.6%) to some climate change, but negatively (- 2.4 to - 3.8%) to the most extreme scenarios. Aboveground carbon (C) sequestration and forest diversity were negatively impacted by all modelled changes in climate. Notably, the most extreme climate scenario eliminated gains in all three services achieved through reduced deposition. As individual tree species responded differently to climate change and atmospheric deposition, associated services unique to each species increased or decreased under future scenarios. Our results suggest that climate change should be considered when evaluating the benefits of N and S air pollution policies on the services provided by U.S. forests.

Using near-term forecasts and uncertainty partitioning to inform prediction of oligotrophic lake cyanobacterial density

Near-term ecological forecasts provide resource managers advance notice of changes in ecosystem services, such as fisheries stocks, timber yields, or water quality. Importantly, ecological forecasts can identify where there is uncertainty in the forecasting system, which is necessary to improve forecast skill and guide interpretation of forecast results. Uncertainty partitioning identifies the relative contributions to total forecast variance introduced by different sources, including specification of the model structure, errors in driver data, and estimation of current states (initial conditions). Uncertainty partitioning could be particularly useful in improving forecasts of highly variable cyanobacterial densities, which are difficult to predict and present a persistent challenge for lake managers. As cyanobacteria can produce toxic and unsightly surface scums, advance warning when cyanobacterial densities are increasing could help managers mitigate water quality issues. Here, we fit 13 Bayesian state-space models to evaluate different hypotheses about cyanobacterial densities in a low nutrient lake that experiences sporadic surface scums of the toxin-producing cyanobacterium, Gloeotrichia echinulata. We used data from several summers of weekly cyanobacteria samples to identify dominant sources of uncertainty for near-term (1- to 4-week) forecasts of G. echinulata densities. Water temperature was an important predictor of cyanobacterial densities during model fitting and at the 4-week forecast horizon. However, no physical covariates improved model performance over a simple model including the previous week's densities in 1-week-ahead forecasts. Even the best fit models exhibited large variance in forecasted cyanobacterial densities and did not capture rare peak occurrences, indicating that significant explanatory variables when fitting models to historical data are not always effective for forecasting. Uncertainty partitioning revealed that model process specification and initial conditions dominated forecast uncertainty. These findings indicate that long-term studies of different cyanobacterial life stages and movement in the water column as well as measurements of drivers relevant to different life stages could improve model process representation of cyanobacteria abundance. In addition, improved observation protocols could better define initial conditions and reduce spatial misalignment of environmental data and cyanobacteria observations. Our results emphasize the importance of ecological forecasting principles and uncertainty partitioning to refine and understand predictive capacity across ecosystems.

Regional Assessment of Carbon Pool Response to Intensive Silvicultural Practices in Loblolly Pine Plantations

Tree plantations represent an important component of the global carbon (C) cycle and are expected to increase in prevalence during the 21st century. We examined how silvicultural approaches that optimize economic returns in loblolly pine (Pinus taeda L.) plantations affected the accumulation of C in pools of vegetation, detritus, and mineral soil up to 100 cm across the loblolly pine’s natural range in the southeastern United States. Comparisons of silvicultural treatments included competing vegetation or ‘weed’ control, fertilization, thinning, and varying intensities of silvicultural treatment for 106 experimental plantations and 322 plots. The average age of the sampled plantations was 17 years, and the C stored in vegetation (pine and understory) averaged 82.1 ± 3.0 (±std. error) Mg C ha−1, and 14.3 ± 0.6 Mg C ha−1 in detrital pools (soil organic layers, coarse-woody debris, and soil detritus). Mineral soil C (0–100 cm) averaged 79.8 ± 4.6 Mg C ha−1 across sites. For management effects, thinning reduced vegetation by 35.5 ± 1.2 Mg C ha−1 for all treatment combinations. Weed control and fertilization increased vegetation between 2.3 and 5.7 Mg C ha−1 across treatment combinations, with high intensity silvicultural applications producing greater vegetation C than low intensity (increase of 21.4 ± 1.7 Mg C ha−1). Detrital C pools were negatively affected by thinning where either fertilization or weed control were also applied, and were increased with management intensity. Mineral soil C did not respond to any silvicultural treatments. From these data, we constructed regression models that summarized the C accumulation in detritus and detritus + vegetation in response to independent variables commonly monitored by plantation managers (site index (SI), trees per hectare (TPH) and plantation age (AGE)). The C stored in detritus and vegetation increased on average with AGE and both models included SI and TPH. The detritus model explained less variance (adj. R2 = 0.29) than the detritus + vegetation model (adj. R2 = 0.87). A general recommendation for managers looking to maximize C storage would be to maintain a high TPH and increase SI, with SI manipulation having a greater relative effect. From the model, we predict that a plantation managed to achieve the average upper third SI (26.8) within our observations, and planted at 1500 TPH, could accumulate ~85 Mg C ha−1 by 12 years of age in detritus and vegetation, an amount greater than the region’s average mineral soil C pool. Notably, SI can be increased using both genetic and silviculture technologies.

Towards Sustainable Forest Management in Central America: Review of Southern Pine Beetle (Dendroctonus frontalis Zimmermann) Outbreaks, Their Causes, and Solutions

Outbreaks of the southern pine beetle (SPB) Dendroctonus frontalis Zimmermann in Central America have had a devastating impact on pine forests. It remains unclear to what extent these outbreaks are caused by the beetle’s ecology, climate, and historical management practices. Using research data and experience accumulated in northern latitudes to guide management of forests in Central America is associated with great uncertainty, given the many unique features of the conifer forests in this region. The main recommendation from this review is that Central American bark beetle outbreaks need to be studied locally, and the local climate, biotic elements, and silvicultural history need to be considered. The key to reducing the impacts of SPB in Central America are local research and implementation of proactive management, in order to facilitate the establishment of forests more resilient to increasing environmental and anthropogenic pressures.